From 31bfc76b58fefdf5d3610ab68654b0e65bd914a3 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Tue, 27 Jan 2026 21:34:09 +0000
Subject: [PATCH 01/62] Add initial version of TFL isochrones

---
 README.md                              |   1 +
 pipeline/journey_times/__init__.py     |  31 ++++
 pipeline/journey_times/__main__.py     |  78 +++++++++
 pipeline/journey_times/config.py       |  24 +++
 pipeline/journey_times/data.py         |   0
 pipeline/journey_times/models.py       |  32 ++++
 pipeline/journey_times/rate_limiter.py |  33 ++++
 pipeline/journey_times/results.py      |  38 +++++
 pipeline/journey_times/tfl_client.py   | 211 +++++++++++++++++++++++++
 9 files changed, 448 insertions(+)
 create mode 100644 pipeline/journey_times/__init__.py
 create mode 100644 pipeline/journey_times/__main__.py
 create mode 100644 pipeline/journey_times/config.py
 create mode 100644 pipeline/journey_times/data.py
 create mode 100644 pipeline/journey_times/models.py
 create mode 100644 pipeline/journey_times/rate_limiter.py
 create mode 100644 pipeline/journey_times/results.py
 create mode 100644 pipeline/journey_times/tfl_client.py

diff --git a/README.md b/README.md
index 7755e41..1b5f622 100644
--- a/README.md
+++ b/README.md
@@ -69,3 +69,4 @@ Nice to haves?
   - [Local Autheority (Upper Tier)](https://communitiesopendata-communities.hub.arcgis.com/datasets/6e8edb2974da4834bbafa09644a5b02d_0/explore?location=52.684195%2C-2.489482%2C7.17)
 - [Open Geography](https://geoportal.statistics.gov.uk/)
 - [CommunitiesOpenData](https://communitiesopendata-communities.hub.arcgis.com/)
+- [PlanetOSM](https://planet.openstreetmap.org/) for open street map POI
diff --git a/pipeline/journey_times/__init__.py b/pipeline/journey_times/__init__.py
new file mode 100644
index 0000000..e6cf5ed
--- /dev/null
+++ b/pipeline/journey_times/__init__.py
@@ -0,0 +1,31 @@
+"""Journey times calculation module for TfL transit data."""
+
+from .config import (
+    DATA_DIR,
+    DESTINATIONS,
+    MAX_CONCURRENT,
+    MAX_DELAY,
+    MAX_POSTCODES,
+    OUTPUT_DIR,
+    REQUESTS_PER_MIN,
+)
+from .models import Destination, JourneyResult
+from .results import results_to_dataframe, save_results
+from .tfl_client import fetch_journey_times
+
+__all__ = [
+    # Config
+    "DATA_DIR",
+    "OUTPUT_DIR",
+    "MAX_DELAY",
+    "REQUESTS_PER_MIN",
+    "MAX_POSTCODES",
+    "MAX_CONCURRENT",
+    "DESTINATIONS",
+    "Destination",
+    "JourneyResult",
+    "load_all_postcodes",
+    "fetch_journey_times",
+    "results_to_dataframe",
+    "save_results",
+]
diff --git a/pipeline/journey_times/__main__.py b/pipeline/journey_times/__main__.py
new file mode 100644
index 0000000..a201ba1
--- /dev/null
+++ b/pipeline/journey_times/__main__.py
@@ -0,0 +1,78 @@
+import asyncio
+import random
+from datetime import date, timedelta
+
+import polars as pl
+from tqdm import tqdm
+
+from .config import DESTINATIONS, MAX_CONCURRENT, MAX_POSTCODES, OUTPUT_DIR
+from .results import results_to_dataframe, save_results
+from .tfl_client import fetch_journey_times
+
+
+
+def main():
+    destination = DESTINATIONS["bank"]
+
+    # Calculate next Monday at 8am
+    today = date.today()
+    days_until_monday = (7 - today.weekday()) % 7 or 7
+    journey_date = today + timedelta(days=days_until_monday)
+    journey_time = "0800"
+
+    print(f"Destination: {destination.name}")
+    print(
+        f"Journey: {journey_date.strftime('%A %Y-%m-%d')} "
+        f"at {journey_time[:2]}:{journey_time[2:]}"
+    )
+
+    postcodes_df =  pl.read_parquet(OUTPUT_DIR / "postcodes_h3.parquet")
+    print(f"Loaded {postcodes_df.height:,} postcodes")
+
+    postcode_data = list(
+        zip(
+            postcodes_df["postcode"].to_list(),
+            postcodes_df["lat"].to_list(),
+            postcodes_df["long"].to_list(),
+        )
+    )
+
+    if MAX_POSTCODES is not None and len(postcode_data) > MAX_POSTCODES:
+        postcode_data = random.sample(postcode_data, MAX_POSTCODES)
+        print(f"Randomly sampled {MAX_POSTCODES} postcodes")
+
+    with tqdm(total=len(postcode_data), desc="Fetching journeys") as pbar:
+        results = asyncio.run(
+            fetch_journey_times(
+                postcode_data,
+                destination,
+                journey_date.strftime("%Y%m%d"),
+                journey_time,
+                MAX_CONCURRENT,
+                progress_callback=lambda _: pbar.update(1),
+            )
+        )
+
+    results_df = results_to_dataframe(results)
+
+    postcodes_processed = [pc for pc, _, _ in postcode_data]
+    coords_df = postcodes_df.filter(
+        pl.col("postcode").is_in(postcodes_processed)
+    ).select(["postcode", "lat", "long"])
+    results_df = coords_df.join(results_df, on="postcode", how="left")
+
+    results_df = results_df.with_columns(
+        pl.lit(destination.name).alias("destination"),
+        pl.lit(journey_date.strftime("%Y-%m-%d")).alias("journey_date"),
+        pl.lit(f"{journey_time[:2]}:{journey_time[2:]}").alias("journey_time"),
+    )
+
+    successful = results_df.filter(pl.col("fastest_minutes").is_not_null()).height
+    print(f"Completed: {successful}/{len(results)} successful")
+
+    parquet_path = save_results(results_df, destination.name)
+    print(f"Saved to {parquet_path}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/pipeline/journey_times/config.py b/pipeline/journey_times/config.py
new file mode 100644
index 0000000..3b54517
--- /dev/null
+++ b/pipeline/journey_times/config.py
@@ -0,0 +1,24 @@
+"""Configuration constants for journey times processing."""
+
+from pathlib import Path
+
+from .models import Destination
+
+DATA_DIR = Path("./data_sources")
+OUTPUT_DIR = DATA_DIR / "processed"
+
+MAX_DELAY = 10
+REQUESTS_PER_MIN = 50
+MAX_POSTCODES = 100  # Set to None to process all postcodes
+MAX_CONCURRENT = 5
+
+DESTINATIONS = {
+    "bank": Destination(51.5133, -0.0886, "Bank", "940GZZLUBNK"),
+    "waterloo": Destination(51.5031, -0.1132, "Waterloo", "940GZZLUWLO"),
+    "kings-cross": Destination(51.5308, -0.1238, "King's Cross", "940GZZLUKSX"),
+    "liverpool-street": Destination(
+        51.5178, -0.0823, "Liverpool Street", "940GZZLULVS"
+    ),
+    "paddington": Destination(51.5154, -0.1755, "Paddington", "940GZZLUPAC"),
+    "victoria": Destination(51.4965, -0.1447, "Victoria", "940GZZLUVIC"),
+}
diff --git a/pipeline/journey_times/data.py b/pipeline/journey_times/data.py
new file mode 100644
index 0000000..e69de29
diff --git a/pipeline/journey_times/models.py b/pipeline/journey_times/models.py
new file mode 100644
index 0000000..ca33e3f
--- /dev/null
+++ b/pipeline/journey_times/models.py
@@ -0,0 +1,32 @@
+"""Data models for journey times processing."""
+
+from dataclasses import dataclass
+
+
+@dataclass
+class Destination:
+    """A destination point for journey planning."""
+
+    lat: float
+    lon: float
+    name: str
+    naptan_id: str | None = None
+
+    def to_tfl_location(self) -> str:
+        """Convert to TfL API location string."""
+        if self.naptan_id:
+            return self.naptan_id
+        return f"{self.lat},{self.lon}"
+
+
+@dataclass
+class JourneyResult:
+    """Result of a journey time calculation for a postcode."""
+
+    postcode: str
+    walking_minutes: int | None = None
+    cycling_minutes: int | None = None
+    public_transport_minutes: int | None = None
+    fastest_minutes: int | None = None
+    fastest_mode: str | None = None
+    error: str | None = None
diff --git a/pipeline/journey_times/rate_limiter.py b/pipeline/journey_times/rate_limiter.py
new file mode 100644
index 0000000..98a333a
--- /dev/null
+++ b/pipeline/journey_times/rate_limiter.py
@@ -0,0 +1,33 @@
+"""Rate limiting for TfL API requests."""
+
+import asyncio
+import warnings
+
+from .config import REQUESTS_PER_MIN
+
+
+class RateLimiter:
+    """Rate limiter enforcing max requests per minute."""
+
+    def __init__(self):
+        self.request_times: list[float] = []
+        self._lock = asyncio.Lock()
+
+    async def acquire(self):
+        """Wait until we can make a request within rate limits."""
+        async with self._lock:
+            now = asyncio.get_event_loop().time()
+            cutoff = now - 60.0
+            self.request_times = [t for t in self.request_times if t > cutoff]
+
+            if len(self.request_times) >= REQUESTS_PER_MIN:
+                wait_time = self.request_times[0] - cutoff
+                if wait_time > 0:
+                    warnings.warn(
+                        f"Rate limit reached ({REQUESTS_PER_MIN}/min), "
+                        f"waiting {wait_time:.1f}s",
+                        stacklevel=2,
+                    )
+                    await asyncio.sleep(wait_time)
+
+            self.request_times.append(asyncio.get_event_loop().time())
diff --git a/pipeline/journey_times/results.py b/pipeline/journey_times/results.py
new file mode 100644
index 0000000..cef02cb
--- /dev/null
+++ b/pipeline/journey_times/results.py
@@ -0,0 +1,38 @@
+from pathlib import Path
+
+import polars as pl
+
+from .config import OUTPUT_DIR
+from .models import JourneyResult
+
+
+def results_to_dataframe(results: list[JourneyResult]) -> pl.DataFrame:
+    return pl.DataFrame(
+        [
+            {
+                "postcode": r.postcode,
+                "walking_minutes": r.walking_minutes,
+                "cycling_minutes": r.cycling_minutes,
+                "public_transport_minutes": r.public_transport_minutes,
+                "fastest_minutes": r.fastest_minutes,
+                "fastest_mode": r.fastest_mode,
+                "error": r.error,
+            }
+            for r in results
+        ]
+    )
+
+
+def save_results(
+    results: pl.DataFrame,
+    destination_name: str,
+    output_dir: Path | None = None,
+) -> Path:
+    if output_dir is None:
+        output_dir = OUTPUT_DIR
+
+    safe_name = destination_name.lower().replace(" ", "-")
+    parquet_path = output_dir / f"journey_times_{safe_name}.parquet"
+    results.write_parquet(parquet_path)
+
+    return parquet_path
diff --git a/pipeline/journey_times/tfl_client.py b/pipeline/journey_times/tfl_client.py
new file mode 100644
index 0000000..b976e42
--- /dev/null
+++ b/pipeline/journey_times/tfl_client.py
@@ -0,0 +1,211 @@
+"""TfL API client for fetching journey times."""
+
+import asyncio
+import warnings
+from collections.abc import Callable
+from http import HTTPStatus
+
+from journey_client import Client
+from journey_client.api.journey import (
+    journey_journey_results_by_path_from_path_to_query_via_query_national_search_query_date_qu as journey_api,
+)
+from journey_client.models import (
+    JourneyJourneyResultsByPathFromPathToQueryViaQueryNationalSearchQueryDateQuTimeIs as TimeIs,
+)
+from journey_client.types import UNSET, Unset
+
+from .config import MAX_DELAY
+from .models import Destination, JourneyResult
+from .rate_limiter import RateLimiter
+
+
+async def fetch_journey_for_mode(
+    client: Client,
+    rate_limiter: RateLimiter,
+    from_location: str,
+    to_location: str,
+    mode: list[str] | Unset,
+    journey_date: str,
+    journey_time: str,
+    retry_count: int = 5,
+) -> int | None:
+    """Fetch journey time for a specific mode with rate limiting."""
+    mode_name = ",".join(mode) if not isinstance(mode, Unset) else "public_transport"
+    backoff = 1.0
+    for attempt in range(retry_count):
+        try:
+            await rate_limiter.acquire()
+
+            response = await journey_api.asyncio_detailed(
+                from_=from_location,
+                to=to_location,
+                client=client,
+                date=journey_date,
+                time=journey_time,
+                time_is=TimeIs.DEPARTING,
+                mode=mode,
+            )
+
+            if response.status_code == HTTPStatus.OK and response.parsed:
+                journeys = response.parsed.journeys
+                if not isinstance(journeys, Unset) and journeys:
+                    durations = [
+                        j.duration
+                        for j in journeys
+                        if not isinstance(j.duration, Unset)
+                    ]
+                    if durations:
+                        return min(durations)
+                return None
+            elif response.status_code in (
+                HTTPStatus.TOO_MANY_REQUESTS,
+                HTTPStatus.INTERNAL_SERVER_ERROR,
+                HTTPStatus.BAD_GATEWAY,
+                HTTPStatus.SERVICE_UNAVAILABLE,
+                HTTPStatus.GATEWAY_TIMEOUT,
+            ):
+                warnings.warn(
+                    f"HTTP {response.status_code.value} for {mode_name} from {from_location}, "
+                    f"retrying in {backoff:.1f}s (attempt {attempt + 1}/{retry_count})",
+                    stacklevel=2,
+                )
+                await asyncio.sleep(backoff)
+                backoff = min(backoff * 2, MAX_DELAY)
+                continue
+            else:
+                return None
+        except Exception as e:
+            warnings.warn(
+                f"Network error for {mode_name} from {from_location}: {e}, "
+                f"retrying in {backoff:.1f}s (attempt {attempt + 1}/{retry_count})",
+                stacklevel=2,
+            )
+            await asyncio.sleep(backoff)
+            backoff = min(backoff * 2, MAX_DELAY)
+            continue
+    warnings.warn(
+        f"Failed to fetch {mode_name} from {from_location} after {retry_count} attempts",
+        stacklevel=2,
+    )
+    return None
+
+
+async def fetch_all_modes(
+    client: Client,
+    rate_limiter: RateLimiter,
+    postcode: str,
+    lat: float,
+    lon: float,
+    to_location: str,
+    journey_date: str,
+    journey_time: str,
+    semaphore: asyncio.Semaphore,
+) -> JourneyResult:
+    """Fetch journey times for all transport modes using coordinates."""
+    async with semaphore:
+        try:
+            from_location = f"{lat},{lon}"
+
+            walking = await fetch_journey_for_mode(
+                client,
+                rate_limiter,
+                from_location,
+                to_location,
+                ["walking"],
+                journey_date,
+                journey_time,
+            )
+            cycling = await fetch_journey_for_mode(
+                client,
+                rate_limiter,
+                from_location,
+                to_location,
+                ["cycle"],
+                journey_date,
+                journey_time,
+            )
+            public = await fetch_journey_for_mode(
+                client,
+                rate_limiter,
+                from_location,
+                to_location,
+                UNSET,
+                journey_date,
+                journey_time,
+            )
+
+            options = [
+                ("walking", walking),
+                ("cycling", cycling),
+                ("public_transport", public),
+            ]
+            valid_options = [(mode, time) for mode, time in options if time is not None]
+
+            if valid_options:
+                fastest_mode, fastest_time = min(valid_options, key=lambda x: x[1])
+            else:
+                fastest_mode, fastest_time = None, None
+
+            return JourneyResult(
+                postcode=postcode,
+                walking_minutes=walking,
+                cycling_minutes=cycling,
+                public_transport_minutes=public,
+                fastest_minutes=fastest_time,
+                fastest_mode=fastest_mode,
+            )
+        except Exception as e:
+            return JourneyResult(postcode=postcode, error=str(e))
+
+
+async def fetch_journey_times(
+    postcode_data: list[tuple[str, float, float]],
+    dest: Destination,
+    journey_date: str,
+    journey_time: str,
+    max_concurrent: int = 2,
+    progress_callback: Callable[[JourneyResult], None] | None = None,
+) -> list[JourneyResult]:
+    """Fetch journey times for all postcodes with rate limiting.
+
+    Args:
+        postcode_data: List of (postcode, lat, lon) tuples
+        dest: Destination for journey planning
+        journey_date: Date in YYYYMMDD format
+        journey_time: Time in HHMM format
+        max_concurrent: Maximum concurrent API requests
+        progress_callback: Optional callback called with each result
+
+    Returns:
+        List of JourneyResult objects in the same order as postcode_data
+    """
+    semaphore = asyncio.Semaphore(max_concurrent)
+    to_location = dest.to_tfl_location()
+    rate_limiter = RateLimiter()
+
+    client = Client(base_url="https://api.tfl.gov.uk", timeout=30.0)
+    async with client as client:
+        tasks = [
+            fetch_all_modes(
+                client,
+                rate_limiter,
+                pc,
+                lat,
+                lon,
+                to_location,
+                journey_date,
+                journey_time,
+                semaphore,
+            )
+            for pc, lat, lon in postcode_data
+        ]
+
+        results = []
+        for coro in asyncio.as_completed(tasks):
+            result = await coro
+            results.append(result)
+            if progress_callback:
+                progress_callback(result)
+
+        postcode_to_result = {r.postcode: r for r in results}
+        return [postcode_to_result[pc] for pc, _, _ in postcode_data]

From bd0dd34b6e2d987faddb5daccde8715b55f25e39 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Tue, 27 Jan 2026 22:06:45 +0000
Subject: [PATCH 02/62] Improve journey time fetching

---
 pipeline/journey_times/__init__.py            |   2 -
 pipeline/journey_times/__main__.py            |   7 +-
 pipeline/journey_times/models.py              |   6 +-
 pipeline/journey_times/rate_limiter.py        |   6 +-
 pipeline/journey_times/results.py             |   6 +-
 pipeline/journey_times/tfl_client.py          | 113 ++++++++++++------
 .../processors/journey_times_aggregator.py    |  79 ++++++++++++
 7 files changed, 165 insertions(+), 54 deletions(-)
 create mode 100644 pipeline/processors/journey_times_aggregator.py

diff --git a/pipeline/journey_times/__init__.py b/pipeline/journey_times/__init__.py
index e6cf5ed..678959a 100644
--- a/pipeline/journey_times/__init__.py
+++ b/pipeline/journey_times/__init__.py
@@ -14,7 +14,6 @@ from .results import results_to_dataframe, save_results
 from .tfl_client import fetch_journey_times
 
 __all__ = [
-    # Config
     "DATA_DIR",
     "OUTPUT_DIR",
     "MAX_DELAY",
@@ -24,7 +23,6 @@ __all__ = [
     "DESTINATIONS",
     "Destination",
     "JourneyResult",
-    "load_all_postcodes",
     "fetch_journey_times",
     "results_to_dataframe",
     "save_results",
diff --git a/pipeline/journey_times/__main__.py b/pipeline/journey_times/__main__.py
index a201ba1..47a8a79 100644
--- a/pipeline/journey_times/__main__.py
+++ b/pipeline/journey_times/__main__.py
@@ -10,7 +10,6 @@ from .results import results_to_dataframe, save_results
 from .tfl_client import fetch_journey_times
 
 
-
 def main():
     destination = DESTINATIONS["bank"]
 
@@ -18,7 +17,7 @@ def main():
     today = date.today()
     days_until_monday = (7 - today.weekday()) % 7 or 7
     journey_date = today + timedelta(days=days_until_monday)
-    journey_time = "0800"
+    journey_time = "0845"
 
     print(f"Destination: {destination.name}")
     print(
@@ -26,7 +25,7 @@ def main():
         f"at {journey_time[:2]}:{journey_time[2:]}"
     )
 
-    postcodes_df =  pl.read_parquet(OUTPUT_DIR / "postcodes_h3.parquet")
+    postcodes_df = pl.read_parquet(OUTPUT_DIR / "postcodes_h3.parquet")
     print(f"Loaded {postcodes_df.height:,} postcodes")
 
     postcode_data = list(
@@ -67,7 +66,7 @@ def main():
         pl.lit(f"{journey_time[:2]}:{journey_time[2:]}").alias("journey_time"),
     )
 
-    successful = results_df.filter(pl.col("fastest_minutes").is_not_null()).height
+    successful = results_df.filter(pl.col("cycling").is_not_null()).height
     print(f"Completed: {successful}/{len(results)} successful")
 
     parquet_path = save_results(results_df, destination.name)
diff --git a/pipeline/journey_times/models.py b/pipeline/journey_times/models.py
index ca33e3f..9261357 100644
--- a/pipeline/journey_times/models.py
+++ b/pipeline/journey_times/models.py
@@ -24,9 +24,7 @@ class JourneyResult:
     """Result of a journey time calculation for a postcode."""
 
     postcode: str
-    walking_minutes: int | None = None
+    public_transport_easy_minutes: int | None = None
     cycling_minutes: int | None = None
-    public_transport_minutes: int | None = None
-    fastest_minutes: int | None = None
-    fastest_mode: str | None = None
+    public_transport_quick_minutes: int | None = None
     error: str | None = None
diff --git a/pipeline/journey_times/rate_limiter.py b/pipeline/journey_times/rate_limiter.py
index 98a333a..0f8103c 100644
--- a/pipeline/journey_times/rate_limiter.py
+++ b/pipeline/journey_times/rate_limiter.py
@@ -17,10 +17,12 @@ class RateLimiter:
         """Wait until we can make a request within rate limits."""
         async with self._lock:
             now = asyncio.get_event_loop().time()
-            cutoff = now - 60.0
+            cutoff = now - 10.0  # 10 seconds
             self.request_times = [t for t in self.request_times if t > cutoff]
 
-            if len(self.request_times) >= REQUESTS_PER_MIN:
+            if (
+                len(self.request_times) >= REQUESTS_PER_MIN // 6
+            ):  # we look at it every 10 seconds instead of minutes
                 wait_time = self.request_times[0] - cutoff
                 if wait_time > 0:
                     warnings.warn(
diff --git a/pipeline/journey_times/results.py b/pipeline/journey_times/results.py
index cef02cb..4d4d54f 100644
--- a/pipeline/journey_times/results.py
+++ b/pipeline/journey_times/results.py
@@ -11,11 +11,9 @@ def results_to_dataframe(results: list[JourneyResult]) -> pl.DataFrame:
         [
             {
                 "postcode": r.postcode,
-                "walking_minutes": r.walking_minutes,
+                "public_transport_easy_minutes": r.public_transport_easy_minutes,
+                "public_transport_quick_minutes": r.public_transport_quick_minutes,
                 "cycling_minutes": r.cycling_minutes,
-                "public_transport_minutes": r.public_transport_minutes,
-                "fastest_minutes": r.fastest_minutes,
-                "fastest_mode": r.fastest_mode,
                 "error": r.error,
             }
             for r in results
diff --git a/pipeline/journey_times/tfl_client.py b/pipeline/journey_times/tfl_client.py
index b976e42..94ee88e 100644
--- a/pipeline/journey_times/tfl_client.py
+++ b/pipeline/journey_times/tfl_client.py
@@ -1,10 +1,10 @@
-"""TfL API client for fetching journey times."""
-
 import asyncio
+from typing import Literal
 import warnings
 from collections.abc import Callable
 from http import HTTPStatus
 
+from httpx import Timeout
 from journey_client import Client
 from journey_client.api.journey import (
     journey_journey_results_by_path_from_path_to_query_via_query_national_search_query_date_qu as journey_api,
@@ -12,7 +12,16 @@ from journey_client.api.journey import (
 from journey_client.models import (
     JourneyJourneyResultsByPathFromPathToQueryViaQueryNationalSearchQueryDateQuTimeIs as TimeIs,
 )
-from journey_client.types import UNSET, Unset
+from journey_client.models import (
+    JourneyJourneyResultsByPathFromPathToQueryViaQueryNationalSearchQueryDateQuJourneyPreference as JourneyPreference,
+)
+from journey_client.models import (
+    JourneyJourneyResultsByPathFromPathToQueryViaQueryNationalSearchQueryDateQuCyclePreference as CyclePreference,
+)
+from journey_client.models import (
+    JourneyJourneyResultsByPathFromPathToQueryViaQueryNationalSearchQueryDateQuBikeProficiency as BikeProficiency,
+)
+from journey_client.types import Unset
 
 from .config import MAX_DELAY
 from .models import Destination, JourneyResult
@@ -24,25 +33,66 @@ async def fetch_journey_for_mode(
     rate_limiter: RateLimiter,
     from_location: str,
     to_location: str,
-    mode: list[str] | Unset,
     journey_date: str,
     journey_time: str,
+    journey_type: Literal["quick"] | Literal["easy"] | Literal["cycle"],
     retry_count: int = 5,
 ) -> int | None:
     """Fetch journey time for a specific mode with rate limiting."""
-    mode_name = ",".join(mode) if not isinstance(mode, Unset) else "public_transport"
     backoff = 1.0
     for attempt in range(retry_count):
         try:
             await rate_limiter.acquire()
 
+            cycle_preference = {
+                "quick": CyclePreference.TAKEONTRANSPORT,
+                "easy": CyclePreference.NONE,
+                "cycle": CyclePreference.ALLTHEWAY,
+            }[journey_type]
+
+            # options: public-bus,overground,train,tube,coach,dlr,cablecar,tram,river,walking,cycle
+            mode = {
+                "quick": [
+                    "public-bus",
+                    "overground",
+                    "train",
+                    "tube",
+                    "coach",
+                    "dlr",
+                    "cablecar",
+                    "tram",
+                    "river",
+                    "walking",
+                    "cycle",
+                ],
+                "easy": [
+                    "public-bus",
+                    "overground",
+                    "train",
+                    "tube",
+                    "coach",
+                    "dlr",
+                    "cablecar",
+                    "tram",
+                    "river",
+                ],
+                "cycle": ["cycle"],
+            }[journey_type]
+
             response = await journey_api.asyncio_detailed(
                 from_=from_location,
                 to=to_location,
                 client=client,
                 date=journey_date,
                 time=journey_time,
-                time_is=TimeIs.DEPARTING,
+                national_search=True,
+                time_is=TimeIs.ARRIVING,
+                journey_preference=JourneyPreference.LEASTINTERCHANGE
+                if journey_type == "easy"
+                else JourneyPreference.LEASTINTERCHANGE,
+                cycle_preference=cycle_preference,
+                bike_proficiency=BikeProficiency.FAST,
+                walking_optimization=journey_type == "quick",
                 mode=mode,
             )
 
@@ -65,7 +115,7 @@ async def fetch_journey_for_mode(
                 HTTPStatus.GATEWAY_TIMEOUT,
             ):
                 warnings.warn(
-                    f"HTTP {response.status_code.value} for {mode_name} from {from_location}, "
+                    f"HTTP {response.status_code.value} for {journey_type} from {from_location}, "
                     f"retrying in {backoff:.1f}s (attempt {attempt + 1}/{retry_count})",
                     stacklevel=2,
                 )
@@ -76,7 +126,7 @@ async def fetch_journey_for_mode(
                 return None
         except Exception as e:
             warnings.warn(
-                f"Network error for {mode_name} from {from_location}: {e}, "
+                f"Network error for {journey_type} from {from_location}: {e}, "
                 f"retrying in {backoff:.1f}s (attempt {attempt + 1}/{retry_count})",
                 stacklevel=2,
             )
@@ -84,7 +134,7 @@ async def fetch_journey_for_mode(
             backoff = min(backoff * 2, MAX_DELAY)
             continue
     warnings.warn(
-        f"Failed to fetch {mode_name} from {from_location} after {retry_count} attempts",
+        f"Failed to fetch {journey_type} from {from_location} after {retry_count} attempts",
         stacklevel=2,
     )
     return None
@@ -106,55 +156,42 @@ async def fetch_all_modes(
         try:
             from_location = f"{lat},{lon}"
 
-            walking = await fetch_journey_for_mode(
+            easy = await fetch_journey_for_mode(
                 client,
                 rate_limiter,
                 from_location,
                 to_location,
-                ["walking"],
                 journey_date,
                 journey_time,
+                journey_type="easy",
+            )
+            quick = await fetch_journey_for_mode(
+                client,
+                rate_limiter,
+                from_location,
+                to_location,
+                journey_date,
+                journey_time,
+                journey_type="quick",
             )
             cycling = await fetch_journey_for_mode(
                 client,
                 rate_limiter,
                 from_location,
                 to_location,
-                ["cycle"],
                 journey_date,
                 journey_time,
+                journey_type="cycle",
             )
-            public = await fetch_journey_for_mode(
-                client,
-                rate_limiter,
-                from_location,
-                to_location,
-                UNSET,
-                journey_date,
-                journey_time,
-            )
-
-            options = [
-                ("walking", walking),
-                ("cycling", cycling),
-                ("public_transport", public),
-            ]
-            valid_options = [(mode, time) for mode, time in options if time is not None]
-
-            if valid_options:
-                fastest_mode, fastest_time = min(valid_options, key=lambda x: x[1])
-            else:
-                fastest_mode, fastest_time = None, None
 
             return JourneyResult(
                 postcode=postcode,
-                walking_minutes=walking,
+                public_transport_easy_minutes=easy,
+                public_transport_quick_minutes=quick,
                 cycling_minutes=cycling,
-                public_transport_minutes=public,
-                fastest_minutes=fastest_time,
-                fastest_mode=fastest_mode,
             )
         except Exception as e:
+            print(f"Error: {e}")
             return JourneyResult(postcode=postcode, error=str(e))
 
 
@@ -183,7 +220,7 @@ async def fetch_journey_times(
     to_location = dest.to_tfl_location()
     rate_limiter = RateLimiter()
 
-    client = Client(base_url="https://api.tfl.gov.uk", timeout=30.0)
+    client = Client(base_url="https://api.tfl.gov.uk").with_timeout(Timeout(30))
     async with client as client:
         tasks = [
             fetch_all_modes(
diff --git a/pipeline/processors/journey_times_aggregator.py b/pipeline/processors/journey_times_aggregator.py
new file mode 100644
index 0000000..ffb6b6f
--- /dev/null
+++ b/pipeline/processors/journey_times_aggregator.py
@@ -0,0 +1,79 @@
+"""Aggregate journey times data by H3 hexagonal cells."""
+
+from pathlib import Path
+
+import polars as pl
+
+from pipeline.config import AGGREGATES_DIR, H3_RESOLUTIONS, PROCESSED_DIR
+
+
+def aggregate_journey_times(
+    journey_times_path: Path | None = None,
+    postcodes_h3_path: Path | None = None,
+    output_dir: Path | None = None,
+) -> list[Path]:
+    """
+    Aggregate journey times by H3 cells at all resolutions.
+
+    Joins journey_times_bank.parquet with postcodes_h3.parquet on postcode,
+    then groups by H3 cell to compute median journey time.
+    """
+    journey_times_path = journey_times_path or PROCESSED_DIR / "journey_times_bank.parquet"
+    postcodes_h3_path = postcodes_h3_path or PROCESSED_DIR / "postcodes_h3.parquet"
+    output_dir = output_dir or AGGREGATES_DIR
+
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    # Load journey times data
+    journey_df = pl.read_parquet(journey_times_path).select(
+        ["postcode", "public_transport_minutes"]
+    )
+
+    # Filter out null journey times
+    journey_df = journey_df.filter(pl.col("public_transport_minutes").is_not_null())
+
+    if journey_df.height == 0:
+        print("No valid journey times found")
+        return []
+
+    # Load postcodes with H3 indices
+    postcodes_df = pl.read_parquet(postcodes_h3_path)
+
+    # Join on postcode to get H3 indices
+    joined_df = journey_df.join(postcodes_df, on="postcode", how="inner")
+
+    if joined_df.height == 0:
+        print("No matching postcodes found")
+        return []
+
+    print(f"Joined {joined_df.height} postcodes with journey times")
+
+    saved_paths = []
+
+    for resolution in H3_RESOLUTIONS:
+        h3_col = f"h3_res{resolution}"
+
+        if h3_col not in joined_df.columns:
+            print(f"Skipping resolution {resolution} - column {h3_col} not found")
+            continue
+
+        # Aggregate by H3 cell - compute median journey time
+        agg_df = (
+            joined_df.group_by(h3_col)
+            .agg(
+                pl.col("public_transport_minutes").median().alias("median_journey_minutes"),
+                pl.col("public_transport_minutes").count().alias("journey_count"),
+            )
+            .rename({h3_col: "h3"})
+        )
+
+        output_path = output_dir / f"journey_times_res{resolution}.parquet"
+        agg_df.write_parquet(output_path)
+        saved_paths.append(output_path)
+        print(f"Saved {agg_df.height} cells to {output_path}")
+
+    return saved_paths
+
+
+if __name__ == "__main__":
+    aggregate_journey_times()

From 6b1539a3e7695d4101c803c356188a0996064a81 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Tue, 27 Jan 2026 22:38:51 +0000
Subject: [PATCH 03/62] Remove journey-client

---
 Journey.yaml                         | 1170 --------------------------
 Taskfile.yml                         |    1 -
 generate_tfl_client.py               |   49 --
 pipeline/journey_times/tfl_client.py |  120 +--
 pyproject.toml                       |    1 -
 5 files changed, 63 insertions(+), 1278 deletions(-)
 delete mode 100644 Journey.yaml
 delete mode 100644 generate_tfl_client.py

diff --git a/Journey.yaml b/Journey.yaml
deleted file mode 100644
index 9414980..0000000
--- a/Journey.yaml
+++ /dev/null
@@ -1,1170 +0,0 @@
-openapi: 3.0.1
-info:
-  title: Journey
-  description: APIs relating to Journey and similar services
-  version: '1.0'
-servers:
-  - url: https://api.tfl.gov.uk/Journey
-paths:
-  /Meta/Modes:
-    get:
-      tags:
-        - Journey
-      summary: Gets a list of all of the available journey planner modes
-      description: Gets a list of all of the available journey planner modes
-      operationId: Journey_Meta
-      responses:
-        '200':
-          description: OK
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/MetaModesGet200ApplicationJsonResponse'
-              example:
-                - isTflService: true
-                  isFarePaying: true
-                  isScheduledService: true
-                  modeName: string
-            text/json:
-              schema:
-                $ref: '#/components/schemas/MetaModesGet200TextJsonResponse'
-              example:
-                - isTflService: true
-                  isFarePaying: true
-                  isScheduledService: true
-                  modeName: string
-            application/xml:
-              schema:
-                $ref: '#/components/schemas/MetaModesGet200ApplicationXmlResponse'
-              example: <MetaModesGet200ApplicationXmlResponse> <isTflService>true</isTflService> <isFarePaying>true</isFarePaying> <isScheduledService>true</isScheduledService> <modeName>string</modeName> </MetaModesGet200ApplicationXmlResponse>
-            text/xml:
-              schema:
-                $ref: '#/components/schemas/MetaModesGet200TextXmlResponse'
-              example: <MetaModesGet200TextXmlResponse> <isTflService>true</isTflService> <isFarePaying>true</isFarePaying> <isScheduledService>true</isScheduledService> <modeName>string</modeName> </MetaModesGet200TextXmlResponse>
-  '/JourneyResults/{from}/to/{to}':
-    get:
-      tags:
-        - Journey
-      summary: Perform a Journey Planner search from the parameters specified in simple types
-      description: Perform a Journey Planner search from the parameters specified in simple types
-      operationId: Journey_JourneyResultsByPathFromPathToQueryViaQueryNationalSearchQueryDateQu
-      parameters:
-        - name: from
-          in: path
-          description: 'Origin of the journey. Can be WGS84 coordinates expressed as "lat,long", a UK postcode, a Naptan (StopPoint) id, an ICS StopId, or a free-text string (will cause disambiguation unless it exactly matches a point of interest name).'
-          required: true
-          schema:
-            type: string
-          example: 1001116
-        - name: to
-          in: path
-          description: 'Destination of the journey. Can be WGS84 coordinates expressed as "lat,long", a UK postcode, a Naptan (StopPoint) id, an ICS StopId, or a free-text string (will cause disambiguation unless it exactly matches a point of interest name).'
-          required: true
-          schema:
-            type: string
-          example: 1001949
-        - name: via
-          in: query
-          description: 'Travel through point on the journey. Can be WGS84 coordinates expressed as "lat,long", a UK postcode, a Naptan (StopPoint) id, an ICS StopId, or a free-text string (will cause disambiguation unless it exactly matches a point of interest name).'
-          schema:
-            type: string
-        - name: nationalSearch
-          in: query
-          description: Does the journey cover stops outside London? eg. "nationalSearch=true"
-          schema:
-            type: boolean
-        - name: date
-          in: query
-          description: The date must be in yyyyMMdd format
-          schema:
-            type: string
-        - name: time
-          in: query
-          description: The time must be in HHmm format
-          schema:
-            type: string
-        - name: timeIs
-          in: query
-          description: 'Does the time given relate to arrival or leaving time? Possible options: "departing" | "arriving"'
-          schema:
-            enum:
-              - Arriving
-              - Departing
-            type: string
-            example: Arriving
-        - name: journeyPreference
-          in: query
-          description: 'The journey preference eg possible options: "leastinterchange" | "leasttime" | "leastwalking"'
-          schema:
-            enum:
-              - LeastInterchange
-              - LeastTime
-              - LeastWalking
-            type: string
-            example: LeastInterchange
-        - name: mode
-          in: query
-          description: 'The mode must be a comma separated list of modes. eg possible options: "public-bus,overground,train,tube,coach,dlr,cablecar,tram,river,walking,cycle"'
-          schema:
-            type: array
-            items:
-              type: string
-        - name: accessibilityPreference
-          in: query
-          description: 'The accessibility preference must be a comma separated list eg. "noSolidStairs,noEscalators,noElevators,stepFreeToVehicle,stepFreeToPlatform"'
-          schema:
-            enum:
-              - NoRequirements
-              - NoSolidStairs
-              - NoEscalators
-              - NoElevators
-              - StepFreeToVehicle
-              - StepFreeToPlatform
-            type: string
-          example: NoRequirements
-        - name: fromName
-          in: query
-          description: An optional name to associate with the origin of the journey in the results.
-          schema:
-            type: string
-        - name: toName
-          in: query
-          description: An optional name to associate with the destination of the journey in the results.
-          schema:
-            type: string
-        - name: viaName
-          in: query
-          description: An optional name to associate with the via point of the journey in the results.
-          schema:
-            type: string
-        - name: maxTransferMinutes
-          in: query
-          description: The max walking time in minutes for transfer eg. "120"
-          schema:
-            type: string
-        - name: maxWalkingMinutes
-          in: query
-          description: The max walking time in minutes for journeys eg. "120"
-          schema:
-            type: string
-        - name: walkingSpeed
-          in: query
-          description: 'The walking speed. eg possible options: "slow" | "average" | "fast".'
-          schema:
-            enum:
-              - Slow
-              - Average
-              - Fast
-            type: string
-          example: Fast
-        - name: cyclePreference
-          in: query
-          description: 'The cycle preference. eg possible options: "allTheWay" | "leaveAtStation" | "takeOnTransport" | "cycleHire"'
-          schema:
-            enum:
-              - None
-              - LeaveAtStation
-              - TakeOnTransport
-              - AllTheWay
-              - CycleHire
-            type: string
-        - name: adjustment
-          in: query
-          description: 'Time adjustment command. eg possible options: "TripFirst" | "TripLast"'
-          schema:
-            type: string
-        - name: bikeProficiency
-          in: query
-          description: 'A comma separated list of cycling proficiency levels. eg possible options: "easy,moderate,fast"'
-          schema:
-            enum:
-              - Easy
-              - Moderate
-              - Fast
-            type: string
-        - name: alternativeCycle
-          in: query
-          description: Option to determine whether to return alternative cycling journey
-          schema:
-            type: boolean
-        - name: alternativeWalking
-          in: query
-          description: Option to determine whether to return alternative walking journey
-          schema:
-            type: boolean
-        - name: applyHtmlMarkup
-          in: query
-          description: Flag to determine whether certain text (e.g. walking instructions) should be output with HTML tags or not.
-          schema:
-            type: boolean
-        - name: useMultiModalCall
-          in: query
-          description: 'A boolean to indicate whether or not to return 3 public transport journeys, a bus journey, a cycle hire journey, a personal cycle journey and a walking journey'
-          schema:
-            type: boolean
-        - name: walkingOptimization
-          in: query
-          description: A boolean to indicate whether to optimize journeys using walking
-          schema:
-            type: boolean
-        - name: taxiOnlyTrip
-          in: query
-          description: 'A boolean to indicate whether to return one or more taxi journeys. Note, setting this to true will override "useMultiModalCall".'
-          schema:
-            type: boolean
-        - name: routeBetweenEntrances
-          in: query
-          description: A boolean to indicate whether public transport routes should include directions between platforms and station entrances.
-          schema:
-            type: boolean
-        - name: useRealTimeLiveArrivals
-          in: query
-          description: A boolean to indicate if we want to receive real time live arrivals data where available.
-          schema:
-            type: boolean
-        - name: calcOneDirection
-          in: query
-          description: 'A boolean to make Journey Planner calculate journeys in one temporal direction only. In other words, only calculate journeys after the ''depart'' time, or before the ''arrive'' time. By default, the Journey Planner engine (EFA) calculates journeys in both temporal directions.'
-          schema:
-            type: boolean
-        - name: includeAlternativeRoutes
-          in: query
-          description: 'A boolean to make Journey Planner return alternative routes. Alternative routes are calculated by removing one or more lines included in the fastest route and re-calculating. By default, these journeys will not be returned.'
-          schema:
-            type: boolean
-        - name: overrideMultiModalScenario
-          in: query
-          description: An optional integer to indicate what multi modal scenario we want to use.
-          schema:
-            type: integer
-            format: int32
-        - name: combineTransferLegs
-          in: query
-          description: A boolean to indicate whether walking leg to station entrance and walking leg from station entrance to platform should be combined. Defaults to true
-          schema:
-            type: boolean
-      responses:
-        '200':
-          description: OK
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/Tfl-40'
-            text/json:
-              schema:
-                $ref: '#/components/schemas/Tfl-40'
-            application/xml:
-              schema:
-                $ref: '#/components/schemas/Tfl-40'
-            text/xml:
-              schema:
-                $ref: '#/components/schemas/Tfl-40'
-  /*:
-    get:
-      tags:
-        - StopPoint
-      summary: Forwards any remaining requests to the back-end
-      description: Forwards any remaining requests to the back-end
-      operationId: Forward_Proxy
-      responses:
-        '200':
-          description: OK
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/Get200ApplicationJsonResponse'
-components:
-  schemas:
-    Tfl:
-      type: object
-      properties:
-        isTflService:
-          type: boolean
-        isFarePaying:
-          type: boolean
-        isScheduledService:
-          type: boolean
-        modeName:
-          type: string
-    Tfl-2:
-      type: object
-      properties:
-        name:
-          type: string
-        value:
-          type: string
-    Tfl-3:
-      type: object
-      properties:
-        description:
-          type: string
-        turnDirection:
-          type: string
-        streetName:
-          type: string
-        distance:
-          type: integer
-          format: int32
-        cumulativeDistance:
-          type: integer
-          format: int32
-        skyDirection:
-          type: integer
-          format: int32
-        skyDirectionDescription:
-          enum:
-            - North
-            - NorthEast
-            - East
-            - SouthEast
-            - South
-            - SouthWest
-            - West
-            - NorthWest
-          type: string
-        cumulativeTravelTime:
-          type: integer
-          format: int32
-        latitude:
-          type: number
-          format: double
-        longitude:
-          type: number
-          format: double
-        pathAttribute:
-          $ref: '#/components/schemas/Tfl-2'
-        descriptionHeading:
-          type: string
-        trackType:
-          enum:
-            - CycleSuperHighway
-            - CanalTowpath
-            - QuietRoad
-            - ProvisionForCyclists
-            - BusyRoads
-            - None
-            - PushBike
-            - Quietway
-          type: string
-    Tfl-4:
-      type: object
-      properties:
-        summary:
-          type: string
-        detailed:
-          type: string
-        steps:
-          type: array
-          items:
-            $ref: '#/components/schemas/Tfl-3'
-    Tfl-5:
-      type: object
-      properties:
-        type:
-          type: string
-        incline:
-          type: string
-        stopId:
-          type: integer
-          format: int32
-        position:
-          type: string
-    Tfl-6:
-      type: object
-      properties:
-        lat:
-          type: number
-          description: WGS84 latitude of the location.
-          format: double
-        lon:
-          type: number
-          description: WGS84 longitude of the location.
-          format: double
-      description: Represents a point located at a latitude and longitude using the WGS84 co-ordinate system.
-    Tfl-7:
-      type: object
-      properties:
-        timeSlice:
-          type: string
-          description: 'Time in 24hr format with 15 minute intervals e.g. 0500-0515, 0515-0530 etc.'
-        value:
-          type: integer
-          description: Count of passenger flow towards a platform
-          format: int32
-    Tfl-8:
-      type: object
-      properties:
-        line:
-          type: string
-          description: The Line Name e.g. "Victoria"
-        lineDirection:
-          type: string
-          description: 'Direction of the Line e.g. NB, SB, WB etc.'
-        platformDirection:
-          type: string
-          description: 'Direction displayed on the platform e.g. NB, SB, WB etc.'
-        direction:
-          type: string
-          description: Direction in regards to Journey Planner i.e. inbound or outbound
-        naptanTo:
-          type: string
-          description: Naptan of the adjacent station
-        timeSlice:
-          type: string
-          description: 'Time in 24hr format with 15 minute intervals e.g. 0500-0515, 0515-0530 etc.'
-        value:
-          type: integer
-          description: "Scale between 1-6, \r\n             1 = Very quiet, 2 = Quiet, 3 = Fairly busy, 4 = Busy, 5 = Very busy, 6 = Exceptionally busy"
-          format: int32
-    Tfl-9:
-      type: object
-      properties:
-        passengerFlows:
-          type: array
-          items:
-            $ref: '#/components/schemas/Tfl-7'
-          description: Busiest times at a station (static information)
-        trainLoadings:
-          type: array
-          items:
-            $ref: '#/components/schemas/Tfl-8'
-          description: 'Train Loading on a scale 1-6, 1 being "Very quiet" and 6 being "Exceptionally busy" (static information)'
-    Tfl-10:
-      type: object
-      properties:
-        id:
-          type: string
-        name:
-          type: string
-        uri:
-          type: string
-        fullName:
-          type: string
-        type:
-          type: string
-        crowding:
-          $ref: '#/components/schemas/Tfl-9'
-        routeType:
-          enum:
-            - Unknown
-            - All
-            - Cycle Superhighways
-            - Quietways
-            - Cycleways
-            - Mini-Hollands
-            - Central London Grid
-            - Streetspace Route
-          type: string
-        status:
-          enum:
-            - Unknown
-            - All
-            - Open
-            - In Progress
-            - Planned
-            - Planned - Subject to feasibility and consultation.
-            - Not Open
-          type: string
-    Tfl-11:
-      type: object
-      properties:
-        distance:
-          type: integer
-          format: int32
-        startLat:
-          type: number
-          format: double
-        startLon:
-          type: number
-          format: double
-        endLat:
-          type: number
-          format: double
-        endLon:
-          type: number
-          format: double
-        startElevation:
-          type: integer
-          format: int32
-        heightFromPreviousPoint:
-          type: integer
-          format: int32
-        gradient:
-          type: number
-          format: double
-    Tfl-12:
-      type: object
-      properties:
-        lineString:
-          type: string
-        stopPoints:
-          type: array
-          items:
-            $ref: '#/components/schemas/Tfl-10'
-        elevation:
-          type: array
-          items:
-            $ref: '#/components/schemas/Tfl-11'
-    Tfl-13:
-      type: object
-      properties:
-        id:
-          type: string
-          description: The Id of the route
-        name:
-          type: string
-          description: Name such as "72"
-        directions:
-          type: array
-          items:
-            type: string
-        lineIdentifier:
-          $ref: '#/components/schemas/Tfl-10'
-    Tfl-14:
-      type: object
-      properties:
-        naptanIdReference:
-          type: string
-        stationAtcoCode:
-          type: string
-        lineIdentifier:
-          type: array
-          items:
-            type: string
-    Tfl-15:
-      type: object
-      properties:
-        modeName:
-          type: string
-        lineIdentifier:
-          type: array
-          items:
-            type: string
-    Tfl-16:
-      type: object
-      properties:
-        category:
-          type: string
-        key:
-          type: string
-        sourceSystemKey:
-          type: string
-        value:
-          type: string
-        modified:
-          type: string
-          format: date-time
-    Tfl-17:
-      type: object
-      properties:
-        id:
-          type: string
-          description: A unique identifier.
-        url:
-          type: string
-          description: The unique location of this resource.
-        commonName:
-          type: string
-          description: A human readable name.
-        distance:
-          type: number
-          description: "The distance of the place from its search point, if this is the result\r\n            of a geographical search, otherwise zero."
-          format: double
-        placeType:
-          type: string
-          description: The type of Place. See /Place/Meta/placeTypes for possible values.
-        additionalProperties:
-          type: array
-          items:
-            $ref: '#/components/schemas/Tfl-16'
-          description: A bag of additional key/value pairs with extra information about this place.
-        children:
-          type: array
-          items:
-            $ref: '#/components/schemas/Tfl-17'
-        childrenUrls:
-          type: array
-          items:
-            type: string
-        lat:
-          type: number
-          description: WGS84 latitude of the location.
-          format: double
-        lon:
-          type: number
-          description: WGS84 longitude of the location.
-          format: double
-    Tfl-18:
-      type: object
-      properties:
-        naptanId:
-          type: string
-        platformName:
-          type: string
-        indicator:
-          type: string
-          description: The indicator of the stop point e.g. "Stop K"
-        stopLetter:
-          type: string
-          description: 'The stop letter, if it could be cleansed from the Indicator e.g. "K"'
-        modes:
-          type: array
-          items:
-            type: string
-        icsCode:
-          type: string
-        smsCode:
-          type: string
-        stopType:
-          type: string
-        stationNaptan:
-          type: string
-        accessibilitySummary:
-          type: string
-        hubNaptanCode:
-          type: string
-        lines:
-          type: array
-          items:
-            $ref: '#/components/schemas/Tfl-10'
-        lineGroup:
-          type: array
-          items:
-            $ref: '#/components/schemas/Tfl-14'
-        lineModeGroups:
-          type: array
-          items:
-            $ref: '#/components/schemas/Tfl-15'
-        fullName:
-          type: string
-        naptanMode:
-          type: string
-        status:
-          type: boolean
-        id:
-          type: string
-          description: A unique identifier.
-        url:
-          type: string
-          description: The unique location of this resource.
-        commonName:
-          type: string
-          description: A human readable name.
-        distance:
-          type: number
-          description: "The distance of the place from its search point, if this is the result\r\n            of a geographical search, otherwise zero."
-          format: double
-        placeType:
-          type: string
-          description: The type of Place. See /Place/Meta/placeTypes for possible values.
-        additionalProperties:
-          type: array
-          items:
-            $ref: '#/components/schemas/Tfl-16'
-          description: A bag of additional key/value pairs with extra information about this place.
-        children:
-          type: array
-          items:
-            $ref: '#/components/schemas/Tfl-17'
-        childrenUrls:
-          type: array
-          items:
-            type: string
-        lat:
-          type: number
-          description: WGS84 latitude of the location.
-          format: double
-        lon:
-          type: number
-          description: WGS84 longitude of the location.
-          format: double
-    Tfl-19:
-      type: object
-      properties:
-        ordinal:
-          type: integer
-          format: int32
-        stopPoint:
-          $ref: '#/components/schemas/Tfl-18'
-    Tfl-20:
-      type: object
-      properties:
-        id:
-          type: string
-          description: The Id of the route
-        lineId:
-          type: string
-          description: The Id of the Line
-        routeCode:
-          type: string
-          description: The route code
-        name:
-          type: string
-          description: Name such as "72"
-        lineString:
-          type: string
-          description: The co-ordinates of the route's path as a geoJSON lineString
-        direction:
-          type: string
-          description: Inbound or Outbound
-        originationName:
-          type: string
-          description: The name of the Origin StopPoint
-        destinationName:
-          type: string
-          description: The name of the Destination StopPoint
-        validTo:
-          type: string
-          description: The DateTime that the Service containing this Route is valid until.
-          format: date-time
-        validFrom:
-          type: string
-          description: The DateTime that the Service containing this Route is valid from.
-          format: date-time
-        routeSectionNaptanEntrySequence:
-          type: array
-          items:
-            $ref: '#/components/schemas/Tfl-19'
-    Tfl-21:
-      type: object
-      properties:
-        category:
-          enum:
-            - Undefined
-            - RealTime
-            - PlannedWork
-            - Information
-            - Event
-            - Crowding
-            - StatusAlert
-          type: string
-          description: Gets or sets the category of this dispruption.
-        type:
-          type: string
-          description: Gets or sets the disruption type of this dispruption.
-        categoryDescription:
-          type: string
-          description: Gets or sets the description of the category.
-        description:
-          type: string
-          description: Gets or sets the description of this disruption.
-        summary:
-          type: string
-          description: Gets or sets the summary of this disruption.
-        additionalInfo:
-          type: string
-          description: Gets or sets the additionaInfo of this disruption.
-        created:
-          type: string
-          description: Gets or sets the date/time when this disruption was created.
-          format: date-time
-        lastUpdate:
-          type: string
-          description: Gets or sets the date/time when this disruption was last updated.
-          format: date-time
-        affectedRoutes:
-          type: array
-          items:
-            $ref: '#/components/schemas/Tfl-20'
-          description: Gets or sets the routes affected by this disruption
-        affectedStops:
-          type: array
-          items:
-            $ref: '#/components/schemas/Tfl-18'
-          description: Gets or sets the stops affected by this disruption
-        closureText:
-          type: string
-          description: Text describing the closure type
-      description: Represents a disruption to a route within the transport network.
-    Tfl-22:
-      type: object
-      properties:
-        id:
-          type: string
-        description:
-          type: string
-        createdDateTime:
-          type: string
-          format: date-time
-        lastUpdateDateTime:
-          type: string
-          format: date-time
-    Tfl-23:
-      type: object
-      properties:
-        duration:
-          type: integer
-          format: int32
-        speed:
-          type: string
-        instruction:
-          $ref: '#/components/schemas/Tfl-4'
-        obstacles:
-          type: array
-          items:
-            $ref: '#/components/schemas/Tfl-5'
-        departureTime:
-          type: string
-          format: date-time
-        arrivalTime:
-          type: string
-          format: date-time
-        departurePoint:
-          $ref: '#/components/schemas/Tfl-6'
-        arrivalPoint:
-          $ref: '#/components/schemas/Tfl-6'
-        path:
-          $ref: '#/components/schemas/Tfl-12'
-        routeOptions:
-          type: array
-          items:
-            $ref: '#/components/schemas/Tfl-13'
-        mode:
-          $ref: '#/components/schemas/Tfl-10'
-        disruptions:
-          type: array
-          items:
-            $ref: '#/components/schemas/Tfl-21'
-        plannedWorks:
-          type: array
-          items:
-            $ref: '#/components/schemas/Tfl-22'
-        distance:
-          type: number
-          format: double
-        isDisrupted:
-          type: boolean
-          readOnly: true
-        hasFixedLocations:
-          type: boolean
-          readOnly: true
-    Tfl-24:
-      type: object
-      properties:
-        modeType:
-          type: string
-        validationType:
-          type: string
-        hostDeviceType:
-          type: string
-        busRouteId:
-          type: string
-        nationalLocationCode:
-          type: integer
-          format: int32
-        tapTimestamp:
-          type: string
-          format: date-time
-    Tfl-25:
-      type: object
-      properties:
-        atcoCode:
-          type: string
-        tapDetails:
-          $ref: '#/components/schemas/Tfl-24'
-    Tfl-26:
-      type: object
-      properties:
-        lowZone:
-          type: integer
-          format: int32
-        highZone:
-          type: integer
-          format: int32
-        cost:
-          type: integer
-          format: int32
-        chargeProfileName:
-          type: string
-        isHopperFare:
-          type: boolean
-        chargeLevel:
-          type: string
-        peak:
-          type: integer
-          format: int32
-        offPeak:
-          type: integer
-          format: int32
-        taps:
-          type: array
-          items:
-            $ref: '#/components/schemas/Tfl-25'
-    Tfl-27:
-      type: object
-      properties:
-        text:
-          type: string
-        type:
-          type: string
-    Tfl-28:
-      type: object
-      properties:
-        totalCost:
-          type: integer
-          format: int32
-        fares:
-          type: array
-          items:
-            $ref: '#/components/schemas/Tfl-26'
-        caveats:
-          type: array
-          items:
-            $ref: '#/components/schemas/Tfl-27'
-    Tfl-29:
-      type: object
-      properties:
-        startDateTime:
-          type: string
-          format: date-time
-        duration:
-          type: integer
-          format: int32
-        arrivalDateTime:
-          type: string
-          format: date-time
-        legs:
-          type: array
-          items:
-            $ref: '#/components/schemas/Tfl-23'
-        fare:
-          $ref: '#/components/schemas/Tfl-28'
-      description: Object that represents an end to end journey (see schematic).
-    Tfl-30:
-      type: object
-      properties:
-        fromDate:
-          type: string
-          description: Gets or sets the start date.
-          format: date-time
-        toDate:
-          type: string
-          description: Gets or sets the end date.
-          format: date-time
-        isNow:
-          type: boolean
-          description: If true is a realtime status rather than planned or info
-      description: Represents a period for which a planned works is valid.
-    Tfl-31:
-      type: object
-      properties:
-        id:
-          type: integer
-          format: int32
-        lineId:
-          type: string
-        statusSeverity:
-          type: integer
-          format: int32
-        statusSeverityDescription:
-          type: string
-        reason:
-          type: string
-        created:
-          type: string
-          format: date-time
-        modified:
-          type: string
-          format: date-time
-        validityPeriods:
-          type: array
-          items:
-            $ref: '#/components/schemas/Tfl-30'
-        disruption:
-          $ref: '#/components/schemas/Tfl-21'
-    Tfl-32:
-      type: object
-      properties:
-        routeCode:
-          type: string
-          description: The route code
-        name:
-          type: string
-          description: Name such as "72"
-        direction:
-          type: string
-          description: Inbound or Outbound
-        originationName:
-          type: string
-          description: The name of the Origin StopPoint
-        destinationName:
-          type: string
-          description: The name of the Destination StopPoint
-        originator:
-          type: string
-          description: The Id (NaPTAN code) of the Origin StopPoint
-        destination:
-          type: string
-          description: The Id (NaPTAN code) or the Destination StopPoint
-        serviceType:
-          type: string
-          description: Regular or Night
-        validTo:
-          type: string
-          description: The DateTime that the Service containing this Route is valid until.
-          format: date-time
-        validFrom:
-          type: string
-          description: The DateTime that the Service containing this Route is valid from.
-          format: date-time
-      description: Description of a Route used in Route search results.
-    Tfl-33:
-      type: object
-      properties:
-        name:
-          type: string
-        uri:
-          type: string
-    Tfl-34:
-      type: object
-      properties:
-        id:
-          type: string
-        name:
-          type: string
-        modeName:
-          type: string
-        disruptions:
-          type: array
-          items:
-            $ref: '#/components/schemas/Tfl-21'
-        created:
-          type: string
-          format: date-time
-        modified:
-          type: string
-          format: date-time
-        lineStatuses:
-          type: array
-          items:
-            $ref: '#/components/schemas/Tfl-31'
-        routeSections:
-          type: array
-          items:
-            $ref: '#/components/schemas/Tfl-32'
-        serviceTypes:
-          type: array
-          items:
-            $ref: '#/components/schemas/Tfl-33'
-        crowding:
-          $ref: '#/components/schemas/Tfl-9'
-    Tfl-35:
-      type: object
-      properties:
-        originNumberOfBikes:
-          type: integer
-          format: int32
-        destinationNumberOfBikes:
-          type: integer
-          format: int32
-        originNumberOfEmptySlots:
-          type: integer
-          format: int32
-        destinationNumberOfEmptySlots:
-          type: integer
-          format: int32
-        originId:
-          type: string
-        destinationId:
-          type: string
-    Tfl-36:
-      type: object
-      properties:
-        date:
-          type: string
-        time:
-          type: string
-        timeIs:
-          type: string
-        uri:
-          type: string
-    Tfl-37:
-      type: object
-      properties:
-        earliest:
-          $ref: '#/components/schemas/Tfl-36'
-        earlier:
-          $ref: '#/components/schemas/Tfl-36'
-        later:
-          $ref: '#/components/schemas/Tfl-36'
-        latest:
-          $ref: '#/components/schemas/Tfl-36'
-    Tfl-38:
-      type: object
-      properties:
-        dateTime:
-          type: string
-          format: date-time
-        dateTimeType:
-          enum:
-            - Arriving
-            - Departing
-          type: string
-        timeAdjustments:
-          $ref: '#/components/schemas/Tfl-37'
-    Tfl-39:
-      type: object
-      properties:
-        from:
-          type: string
-        to:
-          type: string
-        via:
-          type: string
-        uri:
-          type: string
-    Tfl-40:
-      type: object
-      properties:
-        journeys:
-          type: array
-          items:
-            $ref: '#/components/schemas/Tfl-29'
-        lines:
-          type: array
-          items:
-            $ref: '#/components/schemas/Tfl-34'
-        cycleHireDockingStationData:
-          $ref: '#/components/schemas/Tfl-35'
-        stopMessages:
-          type: array
-          items:
-            type: string
-        recommendedMaxAgeMinutes:
-          type: integer
-          format: int32
-        searchCriteria:
-          $ref: '#/components/schemas/Tfl-38'
-        journeyVector:
-          $ref: '#/components/schemas/Tfl-39'
-      description: A DTO representing a list of possible journeys.
-    MetaModesGet200ApplicationJsonResponse:
-      type: array
-      items:
-        $ref: '#/components/schemas/Tfl'
-    MetaModesGet200TextJsonResponse:
-      type: array
-      items:
-        $ref: '#/components/schemas/Tfl'
-    MetaModesGet200ApplicationXmlResponse:
-      type: array
-      items:
-        $ref: '#/components/schemas/Tfl'
-    MetaModesGet200TextXmlResponse:
-      type: array
-      items:
-        $ref: '#/components/schemas/Tfl'
-    Get200ApplicationJsonResponse:
-      type: object
-  securitySchemes:
-    apiKeyHeader:
-      type: apiKey
-      name: app_key
-      in: header
-    apiKeyQuery:
-      type: apiKey
-      name: app_key
-      in: query
-security:
-  - apiKeyHeader: [ ]
-  - apiKeyQuery: [ ]
\ No newline at end of file
diff --git a/Taskfile.yml b/Taskfile.yml
index 65139a4..7b41e8c 100644
--- a/Taskfile.yml
+++ b/Taskfile.yml
@@ -4,7 +4,6 @@ tasks:
   install:
     desc: Install dependencies, generate client, and download data
     cmds:
-      - uv run generate_tfl_client.py
       - uv sync
       - cd frontend && npm install
 
diff --git a/generate_tfl_client.py b/generate_tfl_client.py
deleted file mode 100644
index bfd45e7..0000000
--- a/generate_tfl_client.py
+++ /dev/null
@@ -1,49 +0,0 @@
-#!/usr/bin/env python3
-# /// script
-# requires-python = ">=3.12"
-# dependencies = ["openapi-python-client"]
-# ///
-"""Regenerate the TfL Journey API client from the OpenAPI specification."""
-
-# Run it with:
-#   uv run generate_tfl_client.py
-
-import subprocess
-from pathlib import Path
-
-OPENAPI_SPEC = Path("Journey.yaml")
-OUTPUT_PATH = Path("tfl_journey_client")
-
-
-def main() -> None:
-    if not OPENAPI_SPEC.exists():
-        raise FileNotFoundError(f"OpenAPI spec not found: {OPENAPI_SPEC}")
-
-    # Skip if client already exists
-    if OUTPUT_PATH.exists():
-        print(f"TfL client already exists at {OUTPUT_PATH}, skipping")
-        return
-
-    # Generate the client
-    print(f"Generating client from {OPENAPI_SPEC}")
-    result = subprocess.run(
-        [
-            "openapi-python-client",
-            "generate",
-            "--path",
-            str(OPENAPI_SPEC),
-            "--output-path",
-            str(OUTPUT_PATH),
-        ],
-        check=True,
-    )
-
-    if result.returncode == 0:
-        print(f"Client generated successfully at {OUTPUT_PATH}")
-    else:
-        print("Client generation failed")
-        raise SystemExit(1)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/pipeline/journey_times/tfl_client.py b/pipeline/journey_times/tfl_client.py
index 94ee88e..f214f7c 100644
--- a/pipeline/journey_times/tfl_client.py
+++ b/pipeline/journey_times/tfl_client.py
@@ -1,35 +1,22 @@
 import asyncio
+import os
 from typing import Literal
 import warnings
 from collections.abc import Callable
 from http import HTTPStatus
 
-from httpx import Timeout
-from journey_client import Client
-from journey_client.api.journey import (
-    journey_journey_results_by_path_from_path_to_query_via_query_national_search_query_date_qu as journey_api,
-)
-from journey_client.models import (
-    JourneyJourneyResultsByPathFromPathToQueryViaQueryNationalSearchQueryDateQuTimeIs as TimeIs,
-)
-from journey_client.models import (
-    JourneyJourneyResultsByPathFromPathToQueryViaQueryNationalSearchQueryDateQuJourneyPreference as JourneyPreference,
-)
-from journey_client.models import (
-    JourneyJourneyResultsByPathFromPathToQueryViaQueryNationalSearchQueryDateQuCyclePreference as CyclePreference,
-)
-from journey_client.models import (
-    JourneyJourneyResultsByPathFromPathToQueryViaQueryNationalSearchQueryDateQuBikeProficiency as BikeProficiency,
-)
-from journey_client.types import Unset
+import httpx
 
 from .config import MAX_DELAY
 from .models import Destination, JourneyResult
 from .rate_limiter import RateLimiter
 
 
+BASE_URL = "https://api.tfl.gov.uk"
+
+
 async def fetch_journey_for_mode(
-    client: Client,
+    client: httpx.AsyncClient,
     rate_limiter: RateLimiter,
     from_location: str,
     to_location: str,
@@ -44,65 +31,77 @@ async def fetch_journey_for_mode(
         try:
             await rate_limiter.acquire()
 
-            cycle_preference = {
-                "quick": CyclePreference.TAKEONTRANSPORT,
-                "easy": CyclePreference.NONE,
-                "cycle": CyclePreference.ALLTHEWAY,
+            journey_preference = {
+                "quick": "LeastTime",
+                "easy": "LeastInterchange",
+                "cycle": None,
             }[journey_type]
 
-            # options: public-bus,overground,train,tube,coach,dlr,cablecar,tram,river,walking,cycle
+            cycle_preference = {
+                "quick": None,
+                "easy": None,
+                "cycle": "AllTheWay",
+            }[journey_type]
+
+            # curl -s "https://api.tfl.gov.uk/Journey/Meta/Modes" | jq '.[].modeName'
             mode = {
                 "quick": [
-                    "public-bus",
+                    "bus",
                     "overground",
-                    "train",
+                    "national-rail",
+                    "international-rail",
+                    "elizabeth-line",
                     "tube",
                     "coach",
                     "dlr",
-                    "cablecar",
+                    "cable-car",
+                    "replacement-bus",
                     "tram",
-                    "river",
+                    "river-bus",
                     "walking",
                     "cycle",
                 ],
                 "easy": [
-                    "public-bus",
+                    "bus",
                     "overground",
-                    "train",
+                    "national-rail",
+                    "international-rail",
+                    "elizabeth-line",
+                    "replacement-bus",
                     "tube",
                     "coach",
                     "dlr",
-                    "cablecar",
+                    "cable-car",
                     "tram",
-                    "river",
+                    "river-bus",
                 ],
                 "cycle": ["cycle"],
             }[journey_type]
 
-            response = await journey_api.asyncio_detailed(
-                from_=from_location,
-                to=to_location,
-                client=client,
-                date=journey_date,
-                time=journey_time,
-                national_search=True,
-                time_is=TimeIs.ARRIVING,
-                journey_preference=JourneyPreference.LEASTINTERCHANGE
-                if journey_type == "easy"
-                else JourneyPreference.LEASTINTERCHANGE,
-                cycle_preference=cycle_preference,
-                bike_proficiency=BikeProficiency.FAST,
-                walking_optimization=journey_type == "quick",
-                mode=mode,
-            )
+            params: dict = {
+                "date": journey_date,
+                "time": journey_time,
+                "nationalSearch": "true",
+                "timeIs": "Arriving",
+                "cyclePreference": cycle_preference,
+                "bikeProficiency": "Fast",
+                "walkingOptimization": str(journey_type == "quick").lower(),
+                "mode": ",".join(mode),
+            }
+            if journey_preference:
+                params["journeyPreference"] = journey_preference
 
-            if response.status_code == HTTPStatus.OK and response.parsed:
-                journeys = response.parsed.journeys
-                if not isinstance(journeys, Unset) and journeys:
+            url = f"/Journey/JourneyResults/{from_location}/to/{to_location}"
+            response = await client.get(url, params=params)
+
+            if response.status_code == HTTPStatus.OK:
+                data = response.json()
+                journeys = data.get("journeys", [])
+                if journeys:
                     durations = [
-                        j.duration
+                        j["duration"]
                         for j in journeys
-                        if not isinstance(j.duration, Unset)
+                        if j.get("duration") is not None
                     ]
                     if durations:
                         return min(durations)
@@ -115,7 +114,7 @@ async def fetch_journey_for_mode(
                 HTTPStatus.GATEWAY_TIMEOUT,
             ):
                 warnings.warn(
-                    f"HTTP {response.status_code.value} for {journey_type} from {from_location}, "
+                    f"HTTP {response.status_code} for {journey_type} from {from_location}, "
                     f"retrying in {backoff:.1f}s (attempt {attempt + 1}/{retry_count})",
                     stacklevel=2,
                 )
@@ -141,7 +140,7 @@ async def fetch_journey_for_mode(
 
 
 async def fetch_all_modes(
-    client: Client,
+    client: httpx.AsyncClient,
     rate_limiter: RateLimiter,
     postcode: str,
     lat: float,
@@ -220,8 +219,15 @@ async def fetch_journey_times(
     to_location = dest.to_tfl_location()
     rate_limiter = RateLimiter()
 
-    client = Client(base_url="https://api.tfl.gov.uk").with_timeout(Timeout(30))
-    async with client as client:
+    # TFL API authentication via app_key query parameter
+    tfl_token = os.environ.get("TFL_TOKEN")
+    params = {"app_key": tfl_token} if tfl_token else {}
+
+    async with httpx.AsyncClient(
+        base_url=BASE_URL,
+        params=params,
+        timeout=httpx.Timeout(30),
+    ) as client:
         tasks = [
             fetch_all_modes(
                 client,
diff --git a/pyproject.toml b/pyproject.toml
index 9d8bdd9..a51fab3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,7 +8,6 @@ dependencies = [
     "attrs>=22.2.0",
     "httpx>=0.28.1",
     "ipywidgets>=8.0.0",
-    "journey-client",
     "jupyter>=1.0.0",
     "nest-asyncio>=1.6.0",
     "numpy>=1.26.0",

From 6f28fa9d2a836619214921266b81b24e571c8618 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Tue, 27 Jan 2026 22:39:09 +0000
Subject: [PATCH 04/62] Fix tfl

---
 pipeline/journey_times/__main__.py     | 2 +-
 pipeline/journey_times/config.py       | 2 +-
 pipeline/journey_times/rate_limiter.py | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/pipeline/journey_times/__main__.py b/pipeline/journey_times/__main__.py
index 47a8a79..e5f4e97 100644
--- a/pipeline/journey_times/__main__.py
+++ b/pipeline/journey_times/__main__.py
@@ -66,7 +66,7 @@ def main():
         pl.lit(f"{journey_time[:2]}:{journey_time[2:]}").alias("journey_time"),
     )
 
-    successful = results_df.filter(pl.col("cycling").is_not_null()).height
+    successful = results_df.filter(pl.col("cycling_minutes").is_not_null()).height
     print(f"Completed: {successful}/{len(results)} successful")
 
     parquet_path = save_results(results_df, destination.name)
diff --git a/pipeline/journey_times/config.py b/pipeline/journey_times/config.py
index 3b54517..4741653 100644
--- a/pipeline/journey_times/config.py
+++ b/pipeline/journey_times/config.py
@@ -8,7 +8,7 @@ DATA_DIR = Path("./data_sources")
 OUTPUT_DIR = DATA_DIR / "processed"
 
 MAX_DELAY = 10
-REQUESTS_PER_MIN = 50
+REQUESTS_PER_MIN = 500
 MAX_POSTCODES = 100  # Set to None to process all postcodes
 MAX_CONCURRENT = 5
 
diff --git a/pipeline/journey_times/rate_limiter.py b/pipeline/journey_times/rate_limiter.py
index 0f8103c..eba2d1a 100644
--- a/pipeline/journey_times/rate_limiter.py
+++ b/pipeline/journey_times/rate_limiter.py
@@ -28,7 +28,7 @@ class RateLimiter:
                     warnings.warn(
                         f"Rate limit reached ({REQUESTS_PER_MIN}/min), "
                         f"waiting {wait_time:.1f}s",
-                        stacklevel=2,
+                        stacklevel=1,
                     )
                     await asyncio.sleep(wait_time)
 

From d2f5d6669a9a122424688df4236f2398501373e9 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Tue, 27 Jan 2026 22:39:16 +0000
Subject: [PATCH 05/62] Add source

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index 1b5f622..4747d77 100644
--- a/README.md
+++ b/README.md
@@ -70,3 +70,4 @@ Nice to haves?
 - [Open Geography](https://geoportal.statistics.gov.uk/)
 - [CommunitiesOpenData](https://communitiesopendata-communities.hub.arcgis.com/)
 - [PlanetOSM](https://planet.openstreetmap.org/) for open street map POI
+- [TFL api](https://api-portal.tfl.gov.uk/signin)
\ No newline at end of file

From d227239651e0299e1b1c6239baa5d33a41e188f7 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Tue, 27 Jan 2026 22:41:43 +0000
Subject: [PATCH 06/62] Format

---
 download_deprivation_data.py | 5 ++++-
 frontend/src/App.tsx         | 4 +++-
 2 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/download_deprivation_data.py b/download_deprivation_data.py
index c83e713..5094002 100644
--- a/download_deprivation_data.py
+++ b/download_deprivation_data.py
@@ -25,7 +25,10 @@ def download_file(url: str, output_path: Path) -> None:
                 f.write(chunk)
                 downloaded += len(chunk)
                 if total:
-                    print(f"\rDownloaded {downloaded / 1024 / 1024:.1f} MB / {total / 1024 / 1024:.1f} MB", end="")
+                    print(
+                        f"\rDownloaded {downloaded / 1024 / 1024:.1f} MB / {total / 1024 / 1024:.1f} MB",
+                        end="",
+                    )
     print(f"\nSaved to {output_path}")
 
 
diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx
index 467ac7a..2e96967 100644
--- a/frontend/src/App.tsx
+++ b/frontend/src/App.tsx
@@ -52,7 +52,9 @@ export default function App() {
 
   // POI state
   const [pois, setPois] = useState<POI[]>([]);
-  const [selectedPOICategories, setSelectedPOICategories] = useState<Set<POICategoryGroup>>(new Set());
+  const [selectedPOICategories, setSelectedPOICategories] = useState<Set<POICategoryGroup>>(
+    new Set()
+  );
   const poiDebounceRef = useRef<ReturnType<typeof setTimeout> | null>(null);
   const poiAbortControllerRef = useRef<AbortController | null>(null);
 

From 275e5afac62262454f5d4479786f8b6bf7c0e282 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Wed, 28 Jan 2026 20:38:59 +0000
Subject: [PATCH 07/62] Add radius, progress bar, and checkpointing

---
 pipeline/journey_times/__main__.py | 45 ++++++++++++++++++++++++--
 pipeline/journey_times/config.py   |  7 ++--
 pipeline/journey_times/results.py  | 51 +++++++++++++++++++++++++++++-
 3 files changed, 97 insertions(+), 6 deletions(-)

diff --git a/pipeline/journey_times/__main__.py b/pipeline/journey_times/__main__.py
index e5f4e97..0951ae4 100644
--- a/pipeline/journey_times/__main__.py
+++ b/pipeline/journey_times/__main__.py
@@ -5,8 +5,8 @@ from datetime import date, timedelta
 import polars as pl
 from tqdm import tqdm
 
-from .config import DESTINATIONS, MAX_CONCURRENT, MAX_POSTCODES, OUTPUT_DIR
-from .results import results_to_dataframe, save_results
+from .config import DESTINATIONS, MAX_CONCURRENT, MAX_POSTCODES, OUTPUT_DIR, MAX_DISTANCE_KM
+from .results import CheckpointSaver, results_to_dataframe, save_results
 from .tfl_client import fetch_journey_times
 
 
@@ -28,6 +28,35 @@ def main():
     postcodes_df = pl.read_parquet(OUTPUT_DIR / "postcodes_h3.parquet")
     print(f"Loaded {postcodes_df.height:,} postcodes")
 
+    # Filter to postcodes within 150km of destination using Haversine formula
+    earth_radius_km = 6371
+
+    dest_lat_rad = destination.lat * 3.14159265359 / 180
+    dest_lon_rad = destination.lon * 3.14159265359 / 180
+
+    postcodes_df = postcodes_df.with_columns(
+        (
+            2
+            * earth_radius_km
+            * (
+                (
+                    ((pl.lit(dest_lat_rad) - pl.col("lat") * 3.14159265359 / 180) / 2).sin()
+                    ** 2
+                    + pl.lit(dest_lat_rad).cos()
+                    * (pl.col("lat") * 3.14159265359 / 180).cos()
+                    * (
+                        (pl.lit(dest_lon_rad) - pl.col("long") * 3.14159265359 / 180) / 2
+                    ).sin()
+                    ** 2
+                )
+                .sqrt()
+                .arcsin()
+            )
+        ).alias("distance_km")
+    ).filter(pl.col("distance_km") <= MAX_DISTANCE_KM)
+
+    print(f"Filtered to {postcodes_df.height:,} postcodes within {MAX_DISTANCE_KM}km")
+
     postcode_data = list(
         zip(
             postcodes_df["postcode"].to_list(),
@@ -40,6 +69,15 @@ def main():
         postcode_data = random.sample(postcode_data, MAX_POSTCODES)
         print(f"Randomly sampled {MAX_POSTCODES} postcodes")
 
+    checkpoint_saver = CheckpointSaver(
+        destination_name=destination.name,
+        on_save=lambda path, count: print(f"Checkpoint saved: {count:,} results to {path}"),
+    )
+
+    def on_result(result):
+        pbar.update(1)
+        checkpoint_saver.add_result(result)
+
     with tqdm(total=len(postcode_data), desc="Fetching journeys") as pbar:
         results = asyncio.run(
             fetch_journey_times(
@@ -48,7 +86,7 @@ def main():
                 journey_date.strftime("%Y%m%d"),
                 journey_time,
                 MAX_CONCURRENT,
-                progress_callback=lambda _: pbar.update(1),
+                progress_callback=on_result,
             )
         )
 
@@ -70,6 +108,7 @@ def main():
     print(f"Completed: {successful}/{len(results)} successful")
 
     parquet_path = save_results(results_df, destination.name)
+    checkpoint_saver.cleanup_checkpoint()
     print(f"Saved to {parquet_path}")
 
 
diff --git a/pipeline/journey_times/config.py b/pipeline/journey_times/config.py
index 4741653..f2401cc 100644
--- a/pipeline/journey_times/config.py
+++ b/pipeline/journey_times/config.py
@@ -9,8 +9,11 @@ OUTPUT_DIR = DATA_DIR / "processed"
 
 MAX_DELAY = 10
 REQUESTS_PER_MIN = 500
-MAX_POSTCODES = 100  # Set to None to process all postcodes
-MAX_CONCURRENT = 5
+MAX_POSTCODES = None
+MAX_CONCURRENT = 80
+MAX_DISTANCE_KM = 110
+CHECKPOINT_INTERVAL = 10000
+
 
 DESTINATIONS = {
     "bank": Destination(51.5133, -0.0886, "Bank", "940GZZLUBNK"),
diff --git a/pipeline/journey_times/results.py b/pipeline/journey_times/results.py
index 4d4d54f..2845fb2 100644
--- a/pipeline/journey_times/results.py
+++ b/pipeline/journey_times/results.py
@@ -1,8 +1,9 @@
 from pathlib import Path
+from typing import Callable
 
 import polars as pl
 
-from .config import OUTPUT_DIR
+from .config import CHECKPOINT_INTERVAL, OUTPUT_DIR
 from .models import JourneyResult
 
 
@@ -21,6 +22,54 @@ def results_to_dataframe(results: list[JourneyResult]) -> pl.DataFrame:
     )
 
 
+class CheckpointSaver:
+    """Collects results and saves checkpoints at regular intervals."""
+
+    def __init__(
+        self,
+        destination_name: str,
+        output_dir: Path | None = None,
+        interval: int = CHECKPOINT_INTERVAL,
+        on_save: Callable[[Path, int], None] | None = None,
+    ):
+        self.destination_name = destination_name
+        self.output_dir = output_dir or OUTPUT_DIR
+        self.interval = interval
+        self.on_save = on_save
+        self.results: list[JourneyResult] = []
+        self._last_save_count = 0
+
+    def add_result(self, result: JourneyResult) -> None:
+        """Add a result and save checkpoint if interval is reached."""
+        self.results.append(result)
+        if len(self.results) - self._last_save_count >= self.interval:
+            self.save_checkpoint()
+
+    def save_checkpoint(self) -> Path:
+        """Save current results to checkpoint file."""
+        df = results_to_dataframe(self.results)
+        path = self._checkpoint_path()
+        df.write_parquet(path)
+        self._last_save_count = len(self.results)
+        if self.on_save:
+            self.on_save(path, len(self.results))
+        return path
+
+    def _checkpoint_path(self) -> Path:
+        safe_name = self.destination_name.lower().replace(" ", "-")
+        return self.output_dir / f"journey_times_{safe_name}_checkpoint.parquet"
+
+    def get_results(self) -> list[JourneyResult]:
+        """Return all collected results."""
+        return self.results
+
+    def cleanup_checkpoint(self) -> None:
+        """Remove the checkpoint file after successful completion."""
+        path = self._checkpoint_path()
+        if path.exists():
+            path.unlink()
+
+
 def save_results(
     results: pl.DataFrame,
     destination_name: str,

From 75950f0b1bcd409ca9040674184c41240d392cfe Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Wed, 28 Jan 2026 21:10:18 +0000
Subject: [PATCH 08/62] Add travel times

---
 frontend/src/App.tsx                |  7 ++-
 frontend/src/components/Filters.tsx | 71 +++++++++++++++++++++------
 frontend/src/components/Map.tsx     | 76 ++++++++++++++++++++---------
 frontend/src/types.ts               |  6 +++
 4 files changed, 122 insertions(+), 38 deletions(-)

diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx
index 2e96967..b72b2b8 100644
--- a/frontend/src/App.tsx
+++ b/frontend/src/App.tsx
@@ -11,6 +11,7 @@ import type {
   POI,
   POIResponse,
   POICategoryGroup,
+  ColorMode,
 } from './types';
 
 const DEBOUNCE_MS = 150;
@@ -50,6 +51,8 @@ export default function App() {
   const debounceRef = useRef<ReturnType<typeof setTimeout> | null>(null);
   const abortControllerRef = useRef<AbortController | null>(null);
 
+  const [colorMode, setColorMode] = useState<ColorMode>('price');
+
   // POI state
   const [pois, setPois] = useState<POI[]>([]);
   const [selectedPOICategories, setSelectedPOICategories] = useState<Set<POICategoryGroup>>(
@@ -166,9 +169,11 @@ export default function App() {
         zoom={zoom}
         selectedPOICategories={selectedPOICategories}
         onPOICategoriesChange={setSelectedPOICategories}
+        colorMode={colorMode}
+        onColorModeChange={setColorMode}
       />
       <div className="flex-1 relative">
-        <Map data={data} pois={pois} onViewChange={handleViewChange} />
+        <Map data={data} pois={pois} onViewChange={handleViewChange} colorMode={colorMode} />
         {loading && (
           <div className="absolute top-4 right-4 bg-white px-3 py-1 rounded shadow">Loading...</div>
         )}
diff --git a/frontend/src/components/Filters.tsx b/frontend/src/components/Filters.tsx
index a99be49..9e078f9 100644
--- a/frontend/src/components/Filters.tsx
+++ b/frontend/src/components/Filters.tsx
@@ -1,7 +1,7 @@
 import { Slider } from './ui/slider';
 import { Label } from './ui/label';
 import { YEAR_MIN, YEAR_MAX, YEAR_STEP, PRICE_MIN, PRICE_MAX, PRICE_STEP } from '../lib/constants';
-import type { Filters as FiltersType, POICategoryGroup } from '../types';
+import type { Filters as FiltersType, POICategoryGroup, ColorMode } from '../types';
 import { POI_CATEGORY_GROUPS } from '../types';
 
 interface FiltersProps {
@@ -10,6 +10,8 @@ interface FiltersProps {
   zoom: number;
   selectedPOICategories: Set<POICategoryGroup>;
   onPOICategoriesChange: (categories: Set<POICategoryGroup>) => void;
+  colorMode: ColorMode;
+  onColorModeChange: (mode: ColorMode) => void;
 }
 
 const POI_LABELS: Record<POICategoryGroup, string> = {
@@ -27,6 +29,8 @@ export default function Filters({
   zoom,
   selectedPOICategories,
   onPOICategoriesChange,
+  colorMode,
+  onColorModeChange,
 }: FiltersProps) {
   const update = (key: keyof FiltersType, value: number) => onChange({ ...filters, [key]: value });
 
@@ -81,23 +85,60 @@ export default function Filters({
         />
       </div>
 
-      <div className="mt-6 p-3 bg-slate-100 rounded text-xs">
-        <div className="mb-2 font-medium">Average Price</div>
-        <div
-          className="h-4 rounded"
-          style={{
-            background:
-              'linear-gradient(to right, rgb(46, 204, 113), rgb(241, 196, 15), rgb(231, 76, 60), rgb(142, 68, 173))',
-          }}
-        ></div>
-        <div className="flex justify-between mt-1">
-          <span>£0</span>
-          <span>£200k</span>
-          <span>£400k</span>
-          <span>£800k+</span>
+      <div className="space-y-2">
+        <Label>Color By</Label>
+        <div className="flex gap-2">
+          <button
+            className={`flex-1 px-3 py-1.5 text-sm rounded ${colorMode === 'price' ? 'bg-slate-800 text-white' : 'bg-slate-100 text-slate-700'}`}
+            onClick={() => onColorModeChange('price')}
+          >
+            Price
+          </button>
+          <button
+            className={`flex-1 px-3 py-1.5 text-sm rounded ${colorMode === 'journey_time' ? 'bg-slate-800 text-white' : 'bg-slate-100 text-slate-700'}`}
+            onClick={() => onColorModeChange('journey_time')}
+          >
+            Journey Time
+          </button>
         </div>
       </div>
 
+      {colorMode === 'price' ? (
+        <div className="p-3 bg-slate-100 rounded text-xs">
+          <div className="mb-2 font-medium">Average Price</div>
+          <div
+            className="h-4 rounded"
+            style={{
+              background:
+                'linear-gradient(to right, rgb(46, 204, 113), rgb(241, 196, 15), rgb(231, 76, 60), rgb(142, 68, 173))',
+            }}
+          ></div>
+          <div className="flex justify-between mt-1">
+            <span>£0</span>
+            <span>£200k</span>
+            <span>£400k</span>
+            <span>£800k+</span>
+          </div>
+        </div>
+      ) : (
+        <div className="p-3 bg-slate-100 rounded text-xs">
+          <div className="mb-2 font-medium">Journey Time to Bank</div>
+          <div
+            className="h-4 rounded"
+            style={{
+              background:
+                'linear-gradient(to right, rgb(46, 204, 113), rgb(241, 196, 15), rgb(231, 76, 60), rgb(142, 68, 173))',
+            }}
+          ></div>
+          <div className="flex justify-between mt-1">
+            <span>0 min</span>
+            <span>30 min</span>
+            <span>60 min</span>
+            <span>120+ min</span>
+          </div>
+        </div>
+      )}
+
       <div className="space-y-2">
         <Label>Points of Interest</Label>
         <div className="space-y-1">
diff --git a/frontend/src/components/Map.tsx b/frontend/src/components/Map.tsx
index be5a074..0c52f12 100644
--- a/frontend/src/components/Map.tsx
+++ b/frontend/src/components/Map.tsx
@@ -5,12 +5,13 @@ import { H3HexagonLayer } from '@deck.gl/geo-layers';
 import { IconLayer } from '@deck.gl/layers';
 import type { PickingInfo } from '@deck.gl/core';
 import 'maplibre-gl/dist/maplibre-gl.css';
-import type { HexagonData, ViewState, ViewChangeParams, Bounds, POI } from '../types';
+import type { HexagonData, ViewState, ViewChangeParams, Bounds, POI, ColorMode } from '../types';
 
 interface MapProps {
   data: HexagonData[];
   pois: POI[];
   onViewChange: (params: ViewChangeParams) => void;
+  colorMode: ColorMode;
 }
 
 // Twemoji CDN base URL
@@ -119,26 +120,41 @@ function interpolateColor(
   ];
 }
 
-function priceToColor(price: number | null | undefined): [number, number, number] {
-  if (price == null || isNaN(price)) return [128, 128, 128]; // Gray for missing data
+function scaleToColor(
+  value: number | null | undefined,
+  scale: ColorStop[]
+): [number, number, number] {
+  if (value == null || isNaN(value)) return [128, 128, 128];
 
-  // Clamp to scale range
-  if (price <= COLOR_SCALE[0].price) return COLOR_SCALE[0].color;
-  if (price >= COLOR_SCALE[COLOR_SCALE.length - 1].price) {
-    return COLOR_SCALE[COLOR_SCALE.length - 1].color;
-  }
+  if (value <= scale[0].price) return scale[0].color;
+  if (value >= scale[scale.length - 1].price) return scale[scale.length - 1].color;
 
-  // Find the two colors to interpolate between
-  for (let i = 0; i < COLOR_SCALE.length - 1; i++) {
-    const lower = COLOR_SCALE[i];
-    const upper = COLOR_SCALE[i + 1];
-    if (price >= lower.price && price <= upper.price) {
-      const t = (price - lower.price) / (upper.price - lower.price);
+  for (let i = 0; i < scale.length - 1; i++) {
+    const lower = scale[i];
+    const upper = scale[i + 1];
+    if (value >= lower.price && value <= upper.price) {
+      const t = (value - lower.price) / (upper.price - lower.price);
       return interpolateColor(lower.color, upper.color, t);
     }
   }
 
-  return COLOR_SCALE[COLOR_SCALE.length - 1].color;
+  return scale[scale.length - 1].color;
+}
+
+function priceToColor(price: number | null | undefined): [number, number, number] {
+  return scaleToColor(price, COLOR_SCALE);
+}
+
+// Journey time color scale: green (short) -> yellow -> orange -> red (long)
+const JOURNEY_COLOR_SCALE: ColorStop[] = [
+  { price: 0, color: [46, 204, 113] }, // Green
+  { price: 30, color: [241, 196, 15] }, // Yellow
+  { price: 60, color: [231, 76, 60] }, // Red
+  { price: 120, color: [142, 68, 173] }, // Purple
+];
+
+function journeyTimeToColor(minutes: number | null | undefined): [number, number, number] {
+  return scaleToColor(minutes, JOURNEY_COLOR_SCALE);
 }
 
 function zoomToResolution(zoom: number): number {
@@ -193,7 +209,7 @@ interface Dimensions {
   height: number;
 }
 
-export default function Map({ data, pois, onViewChange }: MapProps) {
+export default function Map({ data, pois, onViewChange, colorMode }: MapProps) {
   const containerRef = useRef<HTMLDivElement>(null);
   const [viewState, setViewState] = useState<ViewState>(INITIAL_VIEW);
   const [dimensions, setDimensions] = useState<Dimensions>({ width: 0, height: 0 });
@@ -256,7 +272,13 @@ export default function Map({ data, pois, onViewChange }: MapProps) {
         id: 'h3-hexagons',
         data,
         getHexagon: (d) => d.h3,
-        getFillColor: (d) => priceToColor(d.avg_price),
+        getFillColor: (d) =>
+          colorMode === 'journey_time'
+            ? journeyTimeToColor(d.median_journey_minutes)
+            : priceToColor(d.avg_price),
+        updateTriggers: {
+          getFillColor: colorMode,
+        },
         extruded: false,
         pickable: true,
         opacity: 0.5,
@@ -278,15 +300,26 @@ export default function Map({ data, pois, onViewChange }: MapProps) {
         onHover: handlePoiHover,
       }),
     ],
-    [data, pois, handlePoiHover]
+    [data, pois, handlePoiHover, colorMode]
   );
 
-
   // Tooltip for hexagons only (POIs use MapLibre popup)
   const getTooltip = useCallback(({ object }: { object?: HexagonData }) => {
     if (!object || !('h3' in object)) return null;
 
     const hex = object as HexagonData;
+    const journeyLines: string[] = [];
+    if (hex.median_pt_quick_minutes != null)
+      journeyLines.push(`🚇 Quick PT: ${hex.median_pt_quick_minutes} min`);
+    if (hex.median_pt_easy_minutes != null)
+      journeyLines.push(`🚌 Easy PT: ${hex.median_pt_easy_minutes} min`);
+    if (hex.median_cycling_minutes != null)
+      journeyLines.push(`🚲 Cycling: ${hex.median_cycling_minutes} min`);
+    const journeyTimeHtml =
+      journeyLines.length > 0
+        ? `<div style="color: #0066cc; margin-top: 4px; font-size: 12px;">${journeyLines.join('<br/>')}</div>`
+        : '';
+
     return {
       html: `<div style="padding: 8px; font-size: 14px;">
         <strong>Avg: £${hex.avg_price?.toLocaleString() || 'N/A'}</strong>
@@ -294,6 +327,7 @@ export default function Map({ data, pois, onViewChange }: MapProps) {
           ${hex.count} sales<br/>
           Range: £${hex.min_price?.toLocaleString()} - £${hex.max_price?.toLocaleString()}
         </div>
+        ${journeyTimeHtml}
       </div>`,
       style: {
         backgroundColor: 'white',
@@ -327,9 +361,7 @@ export default function Map({ data, pois, onViewChange }: MapProps) {
           <strong>
             {getTooltipEmoji(popupInfo.category)} {popupInfo.name}
           </strong>
-          <div className="text-gray-500 text-xs">
-            {popupInfo.category.replace(/_/g, ' ')}
-          </div>
+          <div className="text-gray-500 text-xs">{popupInfo.category.replace(/_/g, ' ')}</div>
         </div>
       )}
     </div>
diff --git a/frontend/src/types.ts b/frontend/src/types.ts
index b094d4a..16d0a3f 100644
--- a/frontend/src/types.ts
+++ b/frontend/src/types.ts
@@ -19,8 +19,14 @@ export interface HexagonData {
   median_price: number;
   min_price: number;
   max_price: number;
+  median_journey_minutes: number | null;
+  median_pt_easy_minutes: number | null;
+  median_pt_quick_minutes: number | null;
+  median_cycling_minutes: number | null;
 }
 
+export type ColorMode = 'price' | 'journey_time';
+
 export interface ViewState {
   longitude: number;
   latitude: number;

From 7bfb1729bf56edeabbaaa0ad21686e56fa2388a8 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Wed, 28 Jan 2026 22:10:03 +0000
Subject: [PATCH 09/62] Add epc analysis

---
 README.md          |    8 +-
 epc_analysis.ipynb | 1082 ++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 1089 insertions(+), 1 deletion(-)
 create mode 100644 epc_analysis.ipynb

diff --git a/README.md b/README.md
index 4747d77..3c19dc5 100644
--- a/README.md
+++ b/README.md
@@ -70,4 +70,10 @@ Nice to haves?
 - [Open Geography](https://geoportal.statistics.gov.uk/)
 - [CommunitiesOpenData](https://communitiesopendata-communities.hub.arcgis.com/)
 - [PlanetOSM](https://planet.openstreetmap.org/) for open street map POI
-- [TFL api](https://api-portal.tfl.gov.uk/signin)
\ No newline at end of file
+- [TFL api](https://api-portal.tfl.gov.uk/signin)
+- [EPC](https://epc.opendatacommunities.org/login) - <https://epc.opendatacommunities.org/downloads/domestic>
+
+rightmove:
+curl '<https://www.rightmove.co.uk/api/property-search/listing/search?searchLocation=E14&useLocationIdentifier=true&locationIdentifier=OUTCODE%5E749&buy=For+sale&radius=20.0&_includeSSTC=on&index=0&sortType=2&channel=BUY&transactionType=BUY>'
+
+curl '<https://www.onthemarket.com/async/search/properties-v2/?search-type=for-sale&location-id=e13&view=map-list>'
diff --git a/epc_analysis.ipynb b/epc_analysis.ipynb
new file mode 100644
index 0000000..2a07e87
--- /dev/null
+++ b/epc_analysis.ipynb
@@ -0,0 +1,1082 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# EPC Data Analysis\n",
+    "\n",
+    "Exploratory analysis of the Energy Performance Certificate (EPC) dataset (~29M rows, converted from CSV to parquet for memory efficiency)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-28T22:06:26.543050Z",
+     "iopub.status.busy": "2026-01-28T22:06:26.542977Z",
+     "iopub.status.idle": "2026-01-28T22:06:27.083569Z",
+     "shell.execute_reply": "2026-01-28T22:06:27.083187Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 28,847,961\n",
+      "Columns: 93\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (3, 93)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>LMK_KEY</th><th>ADDRESS1</th><th>ADDRESS2</th><th>ADDRESS3</th><th>POSTCODE</th><th>BUILDING_REFERENCE_NUMBER</th><th>CURRENT_ENERGY_RATING</th><th>POTENTIAL_ENERGY_RATING</th><th>CURRENT_ENERGY_EFFICIENCY</th><th>POTENTIAL_ENERGY_EFFICIENCY</th><th>PROPERTY_TYPE</th><th>BUILT_FORM</th><th>INSPECTION_DATE</th><th>LOCAL_AUTHORITY</th><th>CONSTITUENCY</th><th>COUNTY</th><th>LODGEMENT_DATE</th><th>TRANSACTION_TYPE</th><th>ENVIRONMENT_IMPACT_CURRENT</th><th>ENVIRONMENT_IMPACT_POTENTIAL</th><th>ENERGY_CONSUMPTION_CURRENT</th><th>ENERGY_CONSUMPTION_POTENTIAL</th><th>CO2_EMISSIONS_CURRENT</th><th>CO2_EMISS_CURR_PER_FLOOR_AREA</th><th>CO2_EMISSIONS_POTENTIAL</th><th>LIGHTING_COST_CURRENT</th><th>LIGHTING_COST_POTENTIAL</th><th>HEATING_COST_CURRENT</th><th>HEATING_COST_POTENTIAL</th><th>HOT_WATER_COST_CURRENT</th><th>HOT_WATER_COST_POTENTIAL</th><th>TOTAL_FLOOR_AREA</th><th>ENERGY_TARIFF</th><th>MAINS_GAS_FLAG</th><th>FLOOR_LEVEL</th><th>FLAT_TOP_STOREY</th><th>FLAT_STOREY_COUNT</th><th>&hellip;</th><th>WALLS_ENERGY_EFF</th><th>WALLS_ENV_EFF</th><th>SECONDHEAT_DESCRIPTION</th><th>SHEATING_ENERGY_EFF</th><th>SHEATING_ENV_EFF</th><th>ROOF_DESCRIPTION</th><th>ROOF_ENERGY_EFF</th><th>ROOF_ENV_EFF</th><th>MAINHEAT_DESCRIPTION</th><th>MAINHEAT_ENERGY_EFF</th><th>MAINHEAT_ENV_EFF</th><th>MAINHEATCONT_DESCRIPTION</th><th>MAINHEATC_ENERGY_EFF</th><th>MAINHEATC_ENV_EFF</th><th>LIGHTING_DESCRIPTION</th><th>LIGHTING_ENERGY_EFF</th><th>LIGHTING_ENV_EFF</th><th>MAIN_FUEL</th><th>WIND_TURBINE_COUNT</th><th>HEAT_LOSS_CORRIDOR</th><th>UNHEATED_CORRIDOR_LENGTH</th><th>FLOOR_HEIGHT</th><th>PHOTO_SUPPLY</th><th>SOLAR_WATER_HEATING_FLAG</th><th>MECHANICAL_VENTILATION</th><th>ADDRESS</th><th>LOCAL_AUTHORITY_LABEL</th><th>CONSTITUENCY_LABEL</th><th>POSTTOWN</th><th>CONSTRUCTION_AGE_BAND</th><th>LODGEMENT_DATETIME</th><th>TENURE</th><th>FIXED_LIGHTING_OUTLETS_COUNT</th><th>LOW_ENERGY_FIXED_LIGHT_COUNT</th><th>UPRN</th><th>UPRN_SOURCE</th><th>REPORT_TYPE</th></tr><tr><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>i64</td><td>str</td><td>str</td><td>i64</td><td>i64</td><td>str</td><td>str</td><td>date</td><td>str</td><td>str</td><td>str</td><td>date</td><td>str</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>f64</td><td>i64</td><td>f64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>f64</td><td>str</td><td>str</td><td>str</td><td>str</td><td>f64</td><td>&hellip;</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>i64</td><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>datetime[μs]</td><td>str</td><td>i64</td><td>i64</td><td>i64</td><td>str</td><td>i64</td></tr></thead><tbody><tr><td>&quot;0013ac0dc0f80dd06448efce74287d…</td><td>&quot;43 GAYAL CROFT&quot;</td><td>&quot;MILTON KEYNES&quot;</td><td>null</td><td>&quot;MK5 7HX&quot;</td><td>10003769440</td><td>&quot;C&quot;</td><td>&quot;B&quot;</td><td>75</td><td>89</td><td>&quot;House&quot;</td><td>&quot;Mid-Terrace&quot;</td><td>2022-08-22</td><td>&quot;E06000042&quot;</td><td>&quot;E14000822&quot;</td><td>null</td><td>2022-12-08</td><td>&quot;rental&quot;</td><td>75</td><td>90</td><td>175</td><td>61</td><td>1.9</td><td>31</td><td>0.7</td><td>55</td><td>44</td><td>340</td><td>342</td><td>98</td><td>63</td><td>61.0</td><td>&quot;Single&quot;</td><td>&quot;Y&quot;</td><td>null</td><td>null</td><td>null</td><td>&hellip;</td><td>&quot;Good&quot;</td><td>&quot;Good&quot;</td><td>&quot;None&quot;</td><td>&quot;N/A&quot;</td><td>&quot;N/A&quot;</td><td>&quot;Pitched, insulated (assumed)&quot;</td><td>&quot;Good&quot;</td><td>&quot;Good&quot;</td><td>&quot;Boiler and radiators, mains ga…</td><td>&quot;Good&quot;</td><td>&quot;Good&quot;</td><td>&quot;Programmer, room thermostat an…</td><td>&quot;Good&quot;</td><td>&quot;Good&quot;</td><td>&quot;Low energy lighting in 75% of …</td><td>&quot;Very Good&quot;</td><td>&quot;Very Good&quot;</td><td>&quot;mains gas (not community)&quot;</td><td>0</td><td>null</td><td>null</td><td>2.4</td><td>0.0</td><td>&quot;N&quot;</td><td>&quot;natural&quot;</td><td>&quot;43 GAYAL CROFT, MILTON KEYNES&quot;</td><td>&quot;Milton Keynes&quot;</td><td>&quot;Milton Keynes South&quot;</td><td>null</td><td>&quot;England and Wales: 1996-2002&quot;</td><td>2022-12-08 22:29:20</td><td>&quot;Rented (social)&quot;</td><td>8</td><td>null</td><td>25018528</td><td>&quot;Energy Assessor&quot;</td><td>100</td></tr><tr><td>&quot;20daddd4e5ce9abcca2efab2270af5…</td><td>&quot;30 Ditton Road&quot;</td><td>null</td><td>null</td><td>&quot;DA6 8JL&quot;</td><td>10003624703</td><td>&quot;D&quot;</td><td>&quot;B&quot;</td><td>59</td><td>84</td><td>&quot;House&quot;</td><td>&quot;Mid-Terrace&quot;</td><td>2022-11-03</td><td>&quot;E09000004&quot;</td><td>&quot;E14000558&quot;</td><td>null</td><td>2022-11-03</td><td>&quot;marketed sale&quot;</td><td>51</td><td>81</td><td>279</td><td>93</td><td>4.6</td><td>49</td><td>1.6</td><td>80</td><td>80</td><td>755</td><td>452</td><td>117</td><td>68</td><td>93.0</td><td>&quot;Unknown&quot;</td><td>&quot;Y&quot;</td><td>null</td><td>null</td><td>null</td><td>&hellip;</td><td>&quot;Poor&quot;</td><td>&quot;Poor&quot;</td><td>&quot;None&quot;</td><td>&quot;N/A&quot;</td><td>&quot;N/A&quot;</td><td>&quot;Pitched, 100 mm loft insulatio…</td><td>&quot;Average&quot;</td><td>&quot;Average&quot;</td><td>&quot;Boiler and radiators, mains ga…</td><td>&quot;Good&quot;</td><td>&quot;Good&quot;</td><td>&quot;Programmer and room thermostat&quot;</td><td>&quot;Average&quot;</td><td>&quot;Average&quot;</td><td>&quot;Low energy lighting in all fix…</td><td>&quot;Very Good&quot;</td><td>&quot;Very Good&quot;</td><td>&quot;mains gas (not community)&quot;</td><td>0</td><td>null</td><td>null</td><td>2.64</td><td>0.0</td><td>&quot;N&quot;</td><td>&quot;natural&quot;</td><td>&quot;30 Ditton Road&quot;</td><td>&quot;Bexley&quot;</td><td>&quot;Bexleyheath and Crayford&quot;</td><td>&quot;BEXLEYHEATH&quot;</td><td>&quot;England and Wales: 1950-1966&quot;</td><td>2022-11-03 16:38:48</td><td>&quot;Owner-occupied&quot;</td><td>11</td><td>null</td><td>100020205591</td><td>&quot;Energy Assessor&quot;</td><td>100</td></tr><tr><td>&quot;0019793879cf7ab92be22ba552e992…</td><td>&quot;The Paddocks&quot;</td><td>&quot;Bridge Road&quot;</td><td>&quot;Stoke Bruerne&quot;</td><td>&quot;NN12 7SE&quot;</td><td>10003816660</td><td>&quot;D&quot;</td><td>&quot;C&quot;</td><td>65</td><td>74</td><td>&quot;House&quot;</td><td>&quot;Detached&quot;</td><td>2022-12-16</td><td>&quot;E06000062&quot;</td><td>&quot;E14000942&quot;</td><td>null</td><td>2022-12-16</td><td>&quot;marketed sale&quot;</td><td>57</td><td>67</td><td>146</td><td>103</td><td>8.3</td><td>38</td><td>6.4</td><td>130</td><td>130</td><td>1008</td><td>959</td><td>184</td><td>93</td><td>217.0</td><td>&quot;Single&quot;</td><td>&quot;N&quot;</td><td>null</td><td>null</td><td>null</td><td>&hellip;</td><td>&quot;Good&quot;</td><td>&quot;Good&quot;</td><td>&quot;None&quot;</td><td>&quot;N/A&quot;</td><td>&quot;N/A&quot;</td><td>&quot;Pitched, insulated at rafters&quot;</td><td>&quot;Average&quot;</td><td>&quot;Average&quot;</td><td>&quot;Boiler and radiators, oil&quot;</td><td>&quot;Average&quot;</td><td>&quot;Good&quot;</td><td>&quot;Programmer, room thermostat an…</td><td>&quot;Good&quot;</td><td>&quot;Good&quot;</td><td>&quot;Low energy lighting in all fix…</td><td>&quot;Very Good&quot;</td><td>&quot;Very Good&quot;</td><td>&quot;oil (not community)&quot;</td><td>0</td><td>null</td><td>null</td><td>2.36</td><td>0.0</td><td>&quot;N&quot;</td><td>&quot;natural&quot;</td><td>&quot;The Paddocks, Bridge Road, Sto…</td><td>&quot;West Northamptonshire&quot;</td><td>&quot;South Northamptonshire&quot;</td><td>&quot;TOWCESTER&quot;</td><td>&quot;England and Wales: 1983-1990&quot;</td><td>2022-12-16 10:47:55</td><td>&quot;Owner-occupied&quot;</td><td>22</td><td>null</td><td>10023964427</td><td>&quot;Energy Assessor&quot;</td><td>100</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (3, 93)\n",
+       "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
+       "│ LMK_KEY   ┆ ADDRESS1  ┆ ADDRESS2  ┆ ADDRESS3  ┆ … ┆ LOW_ENERG ┆ UPRN      ┆ UPRN_SOUR ┆ REPORT_T │\n",
+       "│ ---       ┆ ---       ┆ ---       ┆ ---       ┆   ┆ Y_FIXED_L ┆ ---       ┆ CE        ┆ YPE      │\n",
+       "│ str       ┆ str       ┆ str       ┆ str       ┆   ┆ IGHT_COUN ┆ i64       ┆ ---       ┆ ---      │\n",
+       "│           ┆           ┆           ┆           ┆   ┆ T         ┆           ┆ str       ┆ i64      │\n",
+       "│           ┆           ┆           ┆           ┆   ┆ ---       ┆           ┆           ┆          │\n",
+       "│           ┆           ┆           ┆           ┆   ┆ i64       ┆           ┆           ┆          │\n",
+       "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n",
+       "│ 0013ac0dc ┆ 43 GAYAL  ┆ MILTON    ┆ null      ┆ … ┆ null      ┆ 25018528  ┆ Energy    ┆ 100      │\n",
+       "│ 0f80dd064 ┆ CROFT     ┆ KEYNES    ┆           ┆   ┆           ┆           ┆ Assessor  ┆          │\n",
+       "│ 48efce742 ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
+       "│ 87d…      ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
+       "│ 20daddd4e ┆ 30 Ditton ┆ null      ┆ null      ┆ … ┆ null      ┆ 100020205 ┆ Energy    ┆ 100      │\n",
+       "│ 5ce9abcca ┆ Road      ┆           ┆           ┆   ┆           ┆ 591       ┆ Assessor  ┆          │\n",
+       "│ 2efab2270 ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
+       "│ af5…      ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
+       "│ 001979387 ┆ The       ┆ Bridge    ┆ Stoke     ┆ … ┆ null      ┆ 100239644 ┆ Energy    ┆ 100      │\n",
+       "│ 9cf7ab92b ┆ Paddocks  ┆ Road      ┆ Bruerne   ┆   ┆           ┆ 27        ┆ Assessor  ┆          │\n",
+       "│ e22ba552e ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
+       "│ 992…      ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
+       "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import polars as pl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.ticker as ticker\n",
+    "\n",
+    "plt.rcParams[\"figure.figsize\"] = (12, 5)\n",
+    "plt.rcParams[\"figure.dpi\"] = 100\n",
+    "\n",
+    "PARQUET = \"data_sources/epc/certificates.parquet\"\n",
+    "\n",
+    "def scan():\n",
+    "    return pl.scan_parquet(PARQUET)\n",
+    "\n",
+    "# Quick row count\n",
+    "row_count = scan().select(pl.len()).collect().item()\n",
+    "print(f\"Rows: {row_count:,}\")\n",
+    "print(f\"Columns: {len(scan().collect_schema().names())}\")\n",
+    "scan().head(3).collect()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Schema Overview"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-28T22:06:27.101365Z",
+     "iopub.status.busy": "2026-01-28T22:06:27.101128Z",
+     "iopub.status.idle": "2026-01-28T22:06:29.053942Z",
+     "shell.execute_reply": "2026-01-28T22:06:29.053508Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (93, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>column</th><th>dtype</th><th>null_count</th><th>null_pct</th></tr><tr><td>str</td><td>str</td><td>i64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;LMK_KEY&quot;</td><td>&quot;String&quot;</td><td>0</td><td>0.0</td></tr><tr><td>&quot;ADDRESS1&quot;</td><td>&quot;String&quot;</td><td>49</td><td>0.0</td></tr><tr><td>&quot;ADDRESS2&quot;</td><td>&quot;String&quot;</td><td>14135472</td><td>49.0</td></tr><tr><td>&quot;ADDRESS3&quot;</td><td>&quot;String&quot;</td><td>26056812</td><td>90.3</td></tr><tr><td>&quot;POSTCODE&quot;</td><td>&quot;String&quot;</td><td>0</td><td>0.0</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;FIXED_LIGHTING_OUTLETS_COUNT&quot;</td><td>&quot;Int64&quot;</td><td>11903726</td><td>41.3</td></tr><tr><td>&quot;LOW_ENERGY_FIXED_LIGHT_COUNT&quot;</td><td>&quot;Int64&quot;</td><td>19926976</td><td>69.1</td></tr><tr><td>&quot;UPRN&quot;</td><td>&quot;Int64&quot;</td><td>646277</td><td>2.2</td></tr><tr><td>&quot;UPRN_SOURCE&quot;</td><td>&quot;String&quot;</td><td>646277</td><td>2.2</td></tr><tr><td>&quot;REPORT_TYPE&quot;</td><td>&quot;Int64&quot;</td><td>0</td><td>0.0</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (93, 4)\n",
+       "┌──────────────────────────────┬────────┬────────────┬──────────┐\n",
+       "│ column                       ┆ dtype  ┆ null_count ┆ null_pct │\n",
+       "│ ---                          ┆ ---    ┆ ---        ┆ ---      │\n",
+       "│ str                          ┆ str    ┆ i64        ┆ f64      │\n",
+       "╞══════════════════════════════╪════════╪════════════╪══════════╡\n",
+       "│ LMK_KEY                      ┆ String ┆ 0          ┆ 0.0      │\n",
+       "│ ADDRESS1                     ┆ String ┆ 49         ┆ 0.0      │\n",
+       "│ ADDRESS2                     ┆ String ┆ 14135472   ┆ 49.0     │\n",
+       "│ ADDRESS3                     ┆ String ┆ 26056812   ┆ 90.3     │\n",
+       "│ POSTCODE                     ┆ String ┆ 0          ┆ 0.0      │\n",
+       "│ …                            ┆ …      ┆ …          ┆ …        │\n",
+       "│ FIXED_LIGHTING_OUTLETS_COUNT ┆ Int64  ┆ 11903726   ┆ 41.3     │\n",
+       "│ LOW_ENERGY_FIXED_LIGHT_COUNT ┆ Int64  ┆ 19926976   ┆ 69.1     │\n",
+       "│ UPRN                         ┆ Int64  ┆ 646277     ┆ 2.2      │\n",
+       "│ UPRN_SOURCE                  ┆ String ┆ 646277     ┆ 2.2      │\n",
+       "│ REPORT_TYPE                  ┆ Int64  ┆ 0          ┆ 0.0      │\n",
+       "└──────────────────────────────┴────────┴────────────┴──────────┘"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "schema = scan().collect_schema()\n",
+    "\n",
+    "# Compute null counts in batches of columns to avoid OOM\n",
+    "batch_size = 10\n",
+    "all_nulls = {}\n",
+    "col_names = schema.names()\n",
+    "for i in range(0, len(col_names), batch_size):\n",
+    "    batch = col_names[i:i+batch_size]\n",
+    "    result = (\n",
+    "        scan()\n",
+    "        .select([pl.col(c).null_count().alias(c) for c in batch])\n",
+    "        .collect()\n",
+    "    )\n",
+    "    for c in batch:\n",
+    "        all_nulls[c] = result[c].item()\n",
+    "\n",
+    "schema_info = pl.DataFrame({\n",
+    "    \"column\": col_names,\n",
+    "    \"dtype\": [str(d) for d in schema.dtypes()],\n",
+    "    \"null_count\": [all_nulls[c] for c in col_names],\n",
+    "    \"null_pct\": [round(all_nulls[c] / row_count * 100, 1) for c in col_names],\n",
+    "})\n",
+    "schema_info"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Energy Rating Distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-28T22:06:29.054902Z",
+     "iopub.status.busy": "2026-01-28T22:06:29.054824Z",
+     "iopub.status.idle": "2026-01-28T22:06:29.241274Z",
+     "shell.execute_reply": "2026-01-28T22:06:29.240893Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rating_order = [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\"]\n",
+    "rating_counts = (\n",
+    "    scan()\n",
+    "    .filter(pl.col(\"CURRENT_ENERGY_RATING\").is_in(rating_order))\n",
+    "    .group_by(\"CURRENT_ENERGY_RATING\")\n",
+    "    .len()\n",
+    "    .collect()\n",
+    ")\n",
+    "rc = {r[\"CURRENT_ENERGY_RATING\"]: r[\"len\"] for r in rating_counts.iter_rows(named=True)}\n",
+    "\n",
+    "colors = [\"#1a9641\", \"#52b947\", \"#a6d96a\", \"#fee08b\", \"#f09e4a\", \"#d7191c\", \"#a50026\"]\n",
+    "values = [rc.get(r, 0) for r in rating_order]\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "bars = ax.bar(rating_order, values, color=colors)\n",
+    "ax.set_xlabel(\"Current Energy Rating\")\n",
+    "ax.set_ylabel(\"Number of Properties\")\n",
+    "ax.set_title(\"Distribution of Current Energy Ratings\")\n",
+    "ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f\"{x/1e6:.1f}M\"))\n",
+    "for bar, v in zip(bars, values):\n",
+    "    ax.text(bar.get_x() + bar.get_width()/2, bar.get_height(), f\"{v/1e6:.1f}M\",\n",
+    "            ha=\"center\", va=\"bottom\", fontsize=9)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Property Type Breakdown"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-28T22:06:29.243014Z",
+     "iopub.status.busy": "2026-01-28T22:06:29.242929Z",
+     "iopub.status.idle": "2026-01-28T22:06:29.388503Z",
+     "shell.execute_reply": "2026-01-28T22:06:29.388068Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "prop_type = (\n",
+    "    scan()\n",
+    "    .group_by(\"PROPERTY_TYPE\")\n",
+    "    .len()\n",
+    "    .sort(\"len\", descending=True)\n",
+    "    .collect()\n",
+    ")\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.barh(prop_type[\"PROPERTY_TYPE\"].to_list()[::-1], prop_type[\"len\"].to_list()[::-1])\n",
+    "ax.set_xlabel(\"Count\")\n",
+    "ax.set_title(\"Properties by Type\")\n",
+    "ax.xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f\"{x/1e6:.1f}M\"))\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Energy Efficiency by Property Type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-28T22:06:29.389426Z",
+     "iopub.status.busy": "2026-01-28T22:06:29.389348Z",
+     "iopub.status.idle": "2026-01-28T22:06:29.941153Z",
+     "shell.execute_reply": "2026-01-28T22:06:29.940746Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (5, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>PROPERTY_TYPE</th><th>mean_efficiency</th><th>median_efficiency</th><th>count</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>u32</td></tr></thead><tbody><tr><td>&quot;Flat&quot;</td><td>69.745031</td><td>72.0</td><td>8236696</td></tr><tr><td>&quot;Maisonette&quot;</td><td>64.894323</td><td>68.0</td><td>710695</td></tr><tr><td>&quot;House&quot;</td><td>63.306193</td><td>65.0</td><td>17437884</td></tr><tr><td>&quot;Bungalow&quot;</td><td>60.703275</td><td>63.0</td><td>2448109</td></tr><tr><td>&quot;Park home&quot;</td><td>45.450847</td><td>48.0</td><td>14577</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (5, 4)\n",
+       "┌───────────────┬─────────────────┬───────────────────┬──────────┐\n",
+       "│ PROPERTY_TYPE ┆ mean_efficiency ┆ median_efficiency ┆ count    │\n",
+       "│ ---           ┆ ---             ┆ ---               ┆ ---      │\n",
+       "│ str           ┆ f64             ┆ f64               ┆ u32      │\n",
+       "╞═══════════════╪═════════════════╪═══════════════════╪══════════╡\n",
+       "│ Flat          ┆ 69.745031       ┆ 72.0              ┆ 8236696  │\n",
+       "│ Maisonette    ┆ 64.894323       ┆ 68.0              ┆ 710695   │\n",
+       "│ House         ┆ 63.306193       ┆ 65.0              ┆ 17437884 │\n",
+       "│ Bungalow      ┆ 60.703275       ┆ 63.0              ┆ 2448109  │\n",
+       "│ Park home     ┆ 45.450847       ┆ 48.0              ┆ 14577    │\n",
+       "└───────────────┴─────────────────┴───────────────────┴──────────┘"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eff_by_type = (\n",
+    "    scan()\n",
+    "    .group_by(\"PROPERTY_TYPE\")\n",
+    "    .agg(\n",
+    "        pl.col(\"CURRENT_ENERGY_EFFICIENCY\").mean().alias(\"mean_efficiency\"),\n",
+    "        pl.col(\"CURRENT_ENERGY_EFFICIENCY\").median().alias(\"median_efficiency\"),\n",
+    "        pl.len().alias(\"count\"),\n",
+    "    )\n",
+    "    .filter(pl.col(\"count\") > 1000)\n",
+    "    .sort(\"mean_efficiency\", descending=True)\n",
+    "    .collect()\n",
+    ")\n",
+    "eff_by_type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Energy Rating by Construction Age Band"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-28T22:06:29.941986Z",
+     "iopub.status.busy": "2026-01-28T22:06:29.941893Z",
+     "iopub.status.idle": "2026-01-28T22:06:30.515904Z",
+     "shell.execute_reply": "2026-01-28T22:06:30.515522Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "age_eff = (\n",
+    "    scan()\n",
+    "    .filter(pl.col(\"CONSTRUCTION_AGE_BAND\").is_not_null())\n",
+    "    .group_by(\"CONSTRUCTION_AGE_BAND\")\n",
+    "    .agg(\n",
+    "        pl.col(\"CURRENT_ENERGY_EFFICIENCY\").mean().alias(\"mean_efficiency\"),\n",
+    "        pl.col(\"CURRENT_ENERGY_EFFICIENCY\").median().alias(\"median_efficiency\"),\n",
+    "        pl.len().alias(\"count\"),\n",
+    "    )\n",
+    "    .filter(pl.col(\"count\") > 10000)\n",
+    "    .collect()\n",
+    ")\n",
+    "\n",
+    "# Define sort order for known age bands\n",
+    "age_band_order = [\n",
+    "    \"before 1900\", \"1900-1929\", \"1930-1949\", \"1950-1966\", \"1967-1975\",\n",
+    "    \"1976-1982\", \"1983-1990\", \"1991-1995\", \"1996-2002\", \"2003-2006\",\n",
+    "    \"2007-2011\", \"2007 onwards\", \"2012 onwards\",\n",
+    "]\n",
+    "\n",
+    "def age_sort_key(band):\n",
+    "    for i, ab in enumerate(age_band_order):\n",
+    "        if ab in str(band):\n",
+    "            return i\n",
+    "    return 99\n",
+    "\n",
+    "age_eff = age_eff.with_columns(\n",
+    "    pl.col(\"CONSTRUCTION_AGE_BAND\").map_elements(age_sort_key, return_dtype=pl.Int64).alias(\"sort_key\")\n",
+    ").sort(\"sort_key\").filter(pl.col(\"sort_key\") < 99)\n",
+    "\n",
+    "labels = age_eff[\"CONSTRUCTION_AGE_BAND\"].to_list()\n",
+    "# Shorten labels\n",
+    "short_labels = [l.replace(\"England and Wales: \", \"\") for l in labels]\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.bar(short_labels, age_eff[\"mean_efficiency\"].to_list(), label=\"Mean\", alpha=0.7)\n",
+    "ax.bar(short_labels, age_eff[\"median_efficiency\"].to_list(), label=\"Median\", alpha=0.5)\n",
+    "ax.set_xlabel(\"Construction Age Band\")\n",
+    "ax.set_ylabel(\"Energy Efficiency Score\")\n",
+    "ax.set_title(\"Energy Efficiency by Construction Age\")\n",
+    "ax.legend()\n",
+    "plt.xticks(rotation=45, ha=\"right\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## CO2 Emissions Distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-28T22:06:30.516767Z",
+     "iopub.status.busy": "2026-01-28T22:06:30.516692Z",
+     "iopub.status.idle": "2026-01-28T22:06:31.276152Z",
+     "shell.execute_reply": "2026-01-28T22:06:31.275784Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean: 3.8, Median: 3.2, P99: 15.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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oxo0b299Du3fv1oYNG9SnTx9J0u+//66vv/5abdu2VXh4uI4fP66PPvpIDRo00C+//JLuMuJRo0bJ19dXAwcO1G+//aaJEyfK09NTbm5uOnv2rIYOHapNmzYpOjpa4eHhGjx4sMPz161bp9mzZ+vFF1+Ut7e3PvzwQzVr1kw//PCDKlWqlOmx5OR8DgoKUsmSJbVhwwb169cv020CQJasrowBQG6VncsRgoKCTPXq1e2Pr+0x8/777xtJ5uTJk5luI6tLMRo0aGAkmalTp2a4LqMeU/fee6+Ji4uzL0+7pGbChAn2ZdnpMXW9bNf+N/7rr782ksxbb73l0O6JJ54wNpvN4ZIgScbLy8th2Y8//mgkmYkTJ6bb19XGjx9vJJnPPvvMviwxMdHUrVvX5MmTx+HYs9urpl+/fkaS2bFjx3XbGpPzHlNubm5m165dDsvTzq/69es79Cj466+/THBwsOnevbtD+9jYWBMUFOSwPK23yfDhwx3aVq9e3dSsWdP+OKeX8uXNm9dUrVo1W23Tfh7z58/PtM2ZM2eMJNOmTZsst9WkSRMTGBhozp49e939hoWFZdhbRJIZNWqUvV3aazRy5Ej7srNnzxpfX19js9nMF198YV++Z8+edK/TtT2mduzYcd1LZrPzvs+ox1S1atVMgQIFzOnTp+3LfvzxR+Pm5maeeeYZ+7K086xr164O2/z3v/9t7rnnnhzlyEhSUpKx2WzmpZdecliekpJigoODTePGjR2Wnzp1yvj7+xtJZuvWrcYYY8aOHWskmfXr1zu0HThwoJFkHn30Ufuy613Kd8899zj0cFqwYIGRZP73v/9leRxp77GaNWuaxMRE+/LRo0cbSWbBggXGmBt7zw0cONCh7erVq40k8+KLL6bLkdZj9ODBg8bd3d28/fbbDut//vln4+Hh4bA87bP/k08+sS9LSEgwhQoVMo8//rh9WU4+o41J32MqKirKFC5c2Jw6dcqhXYcOHUxQUNANXao+e/ZsExkZaWbOnGnmz59v3njjDePn52dCQkIceoaNGTPGSErXW8wYY2rXrm3uv/9++2N/f/9057sxxixevNhIMkuXLjXGOPaYSkpKMu3btze+vr5m2bJl183dp08fExgYmGkPL2OMuXz5crpeWwcOHDDe3t4On8NpnxuVKlVyOPc6duxobDabad68ucM26tatm+HP6ur3lDHGHDp0yPj4+Jh///vf9mXX9pjKyfmcpmnTpqZ8+fKZHjcAXA935QMAC+XJkyfLu/Ol/fd7wYIFNzwIrbe3t7p06ZLt9s8884wCAgLsj5944gkVLlxYS5YsuaH9Z9eSJUvk7u6uF1980WH5Sy+9JGOMvvnmG4flTZo0UcmSJe2Pq1SposDAQP3+++/X3U+hQoXUsWNH+zJPT0+9+OKLio+P17p163KcPS4uTpIcXjdnatCggSpUqJDhuu7duzsMLr1ixQqdO3dOHTt21KlTp+yTu7u76tSpozVr1qTbxnPPPefw+MEHH7zu65iVuLi4bL8Waed/Vu3T1qW9zhkZOXKkVq5cqXfeeSfdAMmZqVOnjlasWJFuuvrcSNOtWzf7fHBwsMqWLSt/f3+1a9fOvrxs2bIKDg7O8rULCgqSJC1btkwXL17MsM2NvO+PHTummJgYRUZGKl++fPblVapU0b/+9a8M378Z/dxPnz5tf51v9PPnzJkzMsYob968Dsvd3Nz07LPPatWqVRo0aJD27dunbdu2qV27dkpMTJR0pUejJD355JMKCgpS165dtWLFCh08eFDTpk3Thx9+6NAuO9q3b++QJa1nUHbP8R49ejj0rOrZs6c8PDzsr+mNvOd69uzp8Pirr76SzWbL8EYLab2y5s2bp9TUVLVr185hP4UKFVLp0qXT7SdPnjx6+umn7Y+9vLx033333dR7+2rGGH311Vdq2bKljDEOmSIiInT+/Pkbuutdu3btNGPGDD3zzDNq3bq1RowYoWXLlun06dN6++237e3SzgFvb+902/Dx8XE4Ry5dupRpu6u3lSYxMVFt27bVokWLtGTJEjVt2vS6uYODg3XhwgWtWLEi0zbe3t72XlspKSk6ffq08uTJo7Jly2b4Wj3zzDMO516dOnVkjFHXrl0d2tWpU0dHjhxJdzfCunXr2m88IUnFihVTq1attGzZMqWkpGSY8UbO57x582bY+xQAsovCFABYKD4+Pssv5O3bt1e9evXUrVs3FSxYUB06dNCcOXNy9CXx3nvvzdEdwa69O5HNZlOpUqUyvJ27Mx06dEihoaHpXo/y5cvb11+tWLFi6baRN29enT179rr7KV26dLpLOjLbT3YEBgZKUpZFxpuR2WVhGa3bt2+fJOnhhx9W/vz5Hably5frxIkTDu19fHzSXaKYndcxK4GBgdl+LdJ+3lm1v17xavbs2XrjjTcUFRWV7gt/VkJCQtSkSZN0U1hYmEO7jF6joKAgFSlSJN3d/4KCgrJ87cLDw9W/f3/95z//UUhIiCIiIjR58mSdP3/e3uZG3vdp523ZsmXTrStfvrxOnTplv9QzzbXvobTiTVr+m/38McakWzZ8+HBFRUVp9OjRKlOmjGrVqiUPDw9FRUVJulJMka7c7WvhwoVKSEhQ06ZNFR4ergEDBmjixIkO7bLjesd5Pdd+JubJk0eFCxe2fybm9D3n4eGR7u6c+/fvV2hoqENR8Vr79u2TMUalS5dOt5/du3en209G5+fNvrevdvLkSZ07d07Tpk1LlyftnyHXZrpR9evXV506dbRy5Ur7srRLQtMuEb3a5cuXHS4Z9fX1zbTd1dtKM2rUKH399df68ssvs30n0ueff15lypRR8+bNVaRIEXXt2lVLly51aJOamqr3339fpUuXlre3t0JCQpQ/f3799NNPDp8Baa49d9MK20WLFk23PDU1Nd02MrrbYJkyZXTx4kWdPHkyw+PI6fksXXmvZ+dOqACQGcaYAgCL/PHHHzp//rxKlSqVaRtfX199++23WrNmjRYvXqylS5dq9uzZevjhh7V8+XKHnjJZbcPZMvsDNCUlJVuZnCGz/WT0ZfhWK1eunCTp559/TnfL9Yxk9fplJKuf4bXr0ooGn376qQoVKpSu/bXjON2Kn1e5cuUUExOjxMTE6xZF0wqCP/30k1q3bp1hm59++kmSMuw1tmLFCj3zzDNq0aKFpk6denPBM5HZa3Sj5+DYsWMVGRmpBQsWaPny5XrxxRc1atQobdq0SUWKFHHK+z47rpf/RnPky5dPNpstwwKIl5eX/vOf/+jtt9/Wr7/+qoIFC6pMmTJ68skn5ebm5vB5+NBDD+n333/Xzz//rAsXLqhq1ao6evSopCtfrp11nDcrp++5q3vN5HQ/NptN33zzTYbHdG2x7nYd99NPP63OnTtn2CarsehyqmjRotq7d6/9ceHChSVd6S14baHm2LFjuu+++xzaHjt2LN0205ZdO7ZTRESEli5dqtGjR6thw4b2nlVZKVCggGJiYrRs2TJ98803+uabb+w9v2bOnCnpSs/ON998U127dtWIESOUL18+ubm5qW/fvhkWfJ392ZMdOT2fpStF3uyO7QcAGaEwBQAWSRuoNyIiIst2bm5uaty4sRo3bqxx48Zp5MiRev3117VmzRo1adLE6f+lTPtvaRpjjH777TeHLxh58+bVuXPn0j330KFDKlGihP1xTrKFhYVp5cqV+uuvvxx6xuzZs8e+3hnCwsL0008/KTU11eHL4c3sp3nz5nJ3d9dnn32WrQHQs3r9blba5Y0FChRQkyZNbnp7Us5+jpLUsmVLbdy4UV999VWGl8VdrX79+goODtasWbP0+uuvZ/iF65NPPpEkPfroow7LN2/erH//+9+qVauW5syZk+ng6a6ocuXKqly5st544w19//33qlevnqZOnaq33npL0vXf99dKO2+v/uKeZs+ePQoJCZG/v3+Oc+Y0h3Tli2vJkiV14MCBTLdbsGBB+40YUlJStHbtWtWpUyfD4srVxd60HjNX7/tW99TYt2+fGjVqZH8cHx+vY8eO6ZFHHpHknPdcyZIltWzZMp05cybTXlMlS5aUMUbh4eE5Ksxl5WZeu/z58ysgIEApKSlO+6zJyu+//+7QczHtvNi6datDEero0aP6448/1KNHD4e269evT/e5v3nzZvn5+aV7Pe+//34999xzevTRR9W2bVvNnz8/W58vXl5eatmypVq2bKnU1FQ9//zz+uijj/Tmm2+qVKlS+vLLL9WoUSNNnz7d4Xnnzp27JYWda3+fS9Kvv/4qPz+/TG/mcSPn84EDB1S1atUbDwrgrselfABggdWrV2vEiBEKDw/XU089lWm7M2fOpFuW9sd42mUJaV82Myp03IhPPvnE4bKqL7/8UseOHVPz5s3ty0qWLKlNmzbZx4WRpEWLFunIkSMO28pJtkceeUQpKSmaNGmSw/L3339fNpvNYf8345FHHlFsbKxmz55tX5acnKyJEycqT548atCgQY63WbRoUXXv3l3Lly+3X2p0tdTUVI0dO1Z//PGHpCuv3/nz5+09gaQr/7mfP3/+DRyRo4iICAUGBmrkyJFKSkpKtz6zyzeyknbXsOyeY88995wKFy6sl156Sb/++mu69SdOnLAXYPz8/PTyyy9r7969ev3119O1Xbx4saKjoxUREeFwR77du3erRYsWKl68uBYtWnRLegbeCnFxcenGgalcubLc3Nzs7+nsvO+vVbhwYVWrVk0zZ850+Dnt3LlTy5cvtxdRcuJGcqSpW7eutm7dmq39vPfeezp27Jj97peZOXnypN59911VqVLF4Quzsz8DrzVt2jSH99KUKVOUnJxs/0xyxnvu8ccflzFGw4YNS7curRdMmzZt5O7urmHDhqXrGWOM0enTp3N0XNLNvXbu7u56/PHH9dVXX2nnzp3p1t/IZ01mz1uyZIm2bdumZs2a2ZdVrFhR5cqV07Rp0xx6m06ZMkU2m83hrqBPPPGEjh8/rnnz5tmXnTp1SnPnzlXLli0zHH+qSZMm+uKLL7R06VJ16tTpupewXvv6u7m52f+hk/Z+cXd3T/ezmzt3rv78888st32jNm7c6DB21ZEjR7RgwQI1bdo0015XOT2fz58/r/379+fobo4AcK0751+LAHCH+uabb7Rnzx4lJyfr+PHjWr16tVasWKGwsDAtXLgwy0sEhg8frm+//VYtWrRQWFiYTpw4oQ8//FBFihRR/fr1JV0pcgQHB2vq1KkKCAiQv7+/6tSpk+W4RFnJly+f6tevry5duuj48eMaP368SpUqpe7du9vbdOvWTV9++aWaNWumdu3aaf/+/frss88cBiPPabaWLVuqUaNGev3113Xw4EFVrVpVy5cv14IFC9S3b990275RPXr00EcffaTIyEht27ZNxYsX15dffqkNGzZo/PjxNzyA+dixY7V//369+OKLmjdvnh599FHlzZtXhw8f1ty5c7Vnzx516NBBktShQwe9+uqr+ve//60XX3zRfivuMmXK3NBgwVcLDAzUlClT1KlTJ9WoUUMdOnRQ/vz5dfjwYS1evFj16tVLV/y7Hl9fX1WoUEGzZ89WmTJllC9fPlWqVCnT243nzZtX8+fP1yOPPKJq1arp6aeftg/Au337dv33v/9V3bp17e0HDhyoHTt26N1339XGjRv1+OOPy9fXV999950+++wzlS9f3n4pjHRlzKmIiAidPXtWAwYM0OLFix32X7JkSYftZ+bPP//UZ599lm55njx5Mr2s8GatXr1avXv3Vtu2bVWmTBklJyfr008/tX/Jl7L3vs/ImDFj1Lx5c9WtW1dRUVG6dOmSJk6cqKCgIA0dOjTHWW80hyS1atVKn376qX799VeH3iifffaZvvrqKz300EPKkyePVq5cqTlz5qhbt27240/ToEED1a1bV6VKlVJsbKymTZum+Ph4LVq0yKHXS9q59eKLLyoiIkLu7u7295ozJCYmqnHjxmrXrp327t2rDz/8UPXr19djjz0myTnvuUaNGqlTp0764IMPtG/fPjVr1kypqalav369GjVqpN69e6tkyZJ66623NGjQIB08eFCtW7dWQECADhw4oPnz56tHjx56+eWXc3RsN/v745133tGaNWtUp04dde/eXRUqVNCZM2e0fft2rVy50qG42bBhQ61bt+66l5s98MADql69umrVqqWgoCBt375dH3/8sYoWLarXXnvNoe2YMWP02GOPqWnTpurQoYN27typSZMmqVu3bvbLhKUrhan7779fXbp00S+//KKQkBB9+OGHSklJybAYmKZ169b2y/ECAwP10UcfZdq2W7duOnPmjB5++GEVKVJEhw4d0sSJE1WtWjV7lkcffVTDhw9Xly5d9MADD+jnn3/W559/7tDT2JkqVaqkiIgIvfjii/L29rbfPCCrY87p+bxy5UoZY9SqVatbcgwA7hK38Q6AAHBXSbsFc9rk5eVlChUqZP71r3+ZCRMmmLi4uHTPSbuNe5pVq1aZVq1amdDQUOPl5WVCQ0NNx44dza+//urwvAULFpgKFSoYDw8Ph1t/N2jQwFSsWDHDfA0aNDANGjSwP067PfV///tfM2jQIFOgQAHj6+trWrRoYQ4dOpTu+WPHjjX33nuv8fb2NvXq1TNbt25Nt82ssmV0K/K//vrL9OvXz4SGhhpPT09TunRpM2bMGPvt0tNIMr169UqXKSwszHTu3DnD473a8ePHTZcuXUxISIjx8vIylStXzvB26WFhYaZFixbX3V6a5ORk85///Mc8+OCDJigoyHh6epqwsDDTpUsXs2PHDoe2y5cvN5UqVTJeXl6mbNmy5rPPPkv388/qWNPOry1btmSYZc2aNSYiIsIEBQUZHx8fU7JkSRMZGelw6/DOnTsbf3//dM/NKMf3339vatasaby8vNLdMj4zR48eNf369TNlypQxPj4+xs/Pz9SsWdO8/fbb5vz58w5tU1JSzIwZM0y9evVMYGCg8fHxMRUrVjTDhg0z8fHxDm3Tbume2ZSdcyAsLCzT5199Xmb2GmX23rr2nEl7X61Zs8YYY8zvv/9uunbtakqWLGl8fHxMvnz5TKNGjczKlSvtz8nO+z7tNbj2vF25cqWpV6+e8fX1NYGBgaZly5bml19+cWiT9vM9efKkw/Jrbxuf3c+fjCQkJJiQkBAzYsQIh+WbN282Dz30kMmbN6/x8fExVatWNVOnTk33HjfGmH79+pkSJUoYb29vkz9/fvPkk0+a/fv3p2uXnJxsXnjhBZM/f35js9ns527aazRmzJh0z8nOOZz2eqxbt8706NHD5M2b1+TJk8c89dRT5vTp0+na38x7Lu04xowZY8qVK2e8vLxM/vz5TfPmzc22bdsc2n311Vemfv36xt/f3/j7+5ty5cqZXr16mb1799rbZHZ+ZvS5m5PP6Ixet+PHj5tevXqZokWLGk9PT1OoUCHTuHFjM23aNId2NWvWNIUKFcrw2K/2+uuvm2rVqtk/Q4sVK2Z69uxpYmNjM2w/f/58U61aNePt7W2KFCli3njjDZOYmJiu3ZkzZ0xUVJS55557jJ+fn2nQoEG6z8/MzpkPP/zQSDIvv/xyprm//PJL07RpU1OgQAHj5eVlihUrZp599llz7Ngxe5vLly+bl156yRQuXNj4+vqaevXqmY0bN2b6+3ju3LkO+8jscz+j93Ta747PPvvMlC5d2nh7e5vq1avbP4uu3Wba+/7qDNc7n40xpn379qZ+/fqZvi4AkB02YywYJRYAAAC52ogRIzRjxgzt27fvtt0UwZmio6PVpUsXbdmyRbVq1bI6zh3tr7/+Ur58+TR+/Hj16tXL6jh3BZvNpl69euW4h2xOxMbGKjw8XF988QU9pgDcFMaYAgAAgNP169dP8fHx+uKLL6yOAot9++23uvfeex0uCcedb/z48apcuTJFKQA3jTGmAAAA4HR58uTRiRMnrI4BF9CiRQu1aNHC6hhwsnfeecfqCAByCXpMAQAAAAAAwBKMMQUAAAAAAABL0GMKAAAAAAAAlqAwBQAAAAAAAEsw+LmTpKam6ujRowoICJDNZrM6DgAAAAAAgCWMMfrrr78UGhoqN7es+0RRmHKSo0ePqmjRolbHAAAAAAAAcAlHjhxRkSJFsmxDYcpJAgICJF150QMDAy1OA8ASyRekeaFX5tsclTz8rc2TmQsXpNC/cx49Kvm7aE4AAAAAd6S4uDgVLVrUXivJCoUpJ0m7fC8wMJDCFHC3SnaX/P6eDwx03cKUu/s/84GBFKYAAAAA3BLZGeqIwc8BAAAAAABgCQpTAAAAAAAAsASFKQAAAAAAAFiCMaYAAAAAAECOpKamKjEx0eoYsIinp6fcrx679iZQmAIAZ7F5SpWG/DPvqjw9pSFD/pkHAAAAciAxMVEHDhxQamqq1VFgoeDgYBUqVChbA5xnxWaMMU7KdFeLi4tTUFCQzp8/z135AAAAAAC5kjFGhw8fVlJSkkJDQ+XmxghBdxtjjC5evKgTJ04oODhYhQsXTtcmJzUSekwBAAAAAIBsSU5O1sWLFxUaGio/Pz+r48Aivr6+kqQTJ06oQIECN3VZH4UpAHAWkyqd331lPqi8ZHPR/x6lpkq7/85ZvrzEf7kAAACQTSkpKZIkLy8vi5PAammFyaSkJApTAOASUi5JSypdmW8XL3n4W5snM5cuSZX+zhkfL/m7aE4AAAC4rJsdVwh3PmedA/ybHAAAAAAAAJagMAUAAAAAAABLUJgCAAAAAAC5WmRkpGw2m5577rl063r16iWbzabIyMjbHywbhg4dqnLlysnf31958+ZVkyZNtHnz5iyfk5KSojfffFPh4eHy9fVVyZIlNWLECBljblPq7KMwBQAAAAAAcr2iRYvqiy++0KVLl+zLLl++rFmzZqlYsWIWJstamTJlNGnSJP3888/67rvvVLx4cTVt2lQnT57M9DnvvvuupkyZokmTJmn37t169913NXr0aE2cOPE2Js8eClMAAAAAACDXq1GjhooWLap58+bZl82bN0/FihVT9erVHdqmpqZq1KhR9h5HVatW1Zdffmlfn5KSoqioKPv6smXLasKECQ7biIyMVOvWrfXee++pcOHCuueee9SrVy8lJSXlKPeTTz6pJk2aqESJEqpYsaLGjRunuLg4/fTTT5k+5/vvv1erVq3UokULFS9eXE888YSaNm2qH374IUf7vh0oTAEAAAAAgJtz4ULm0+XL2W97VW+mLNveoK5du2rGjBn2xx9//LG6dOmSrt2oUaP0ySefaOrUqdq1a5f69eunp59+WuvWrZN0pXBVpEgRzZ07V7/88osGDx6s1157TXPmzHHYzpo1a7R//36tWbNGM2fOVHR0tKKjo+3rhw4dquLFi2c7f2JioqZNm6agoCBVrVo103YPPPCAVq1apV9//VWS9OOPP+q7775T8+bNs72v28XD6gAAkGvYPKXyL/8z76o8PaWXX/5nHgAAALhZefJkvu6RR6TFi/95XKCAdPFixm0bNJDWrv3ncfHi0qlT6dvd4FhJTz/9tAYNGqRDhw5JkjZs2KAvvvhCa6/aZ0JCgkaOHKmVK1eqbt26kqQSJUrou+++00cffaQGDRrI09NTw4YNsz8nPDxcGzdu1Jw5c9SuXTv78rx582rSpElyd3dXuXLl1KJFC61atUrdu3eXJIWEhKhkyZLXzb1o0SJ16NBBFy9eVOHChbVixQqFhIRk2n7gwIGKi4tTuXLl5O7urpSUFL399tt66qmncvR63Q4UpgDAWdy9pOpjrE5xfV5e0pg7ICcAAADgZPnz51eLFi0UHR0tY4xatGiRrsDz22+/6eLFi/rXv/7lsDwxMdHhkr/Jkyfr448/1uHDh3Xp0iUlJiaqWrVqDs+pWLGi3N3d7Y8LFy6sn3/+2f64d+/e6t2793VzN2rUSDExMTp16pT+7//+T+3atdPmzZtVoECBDNvPmTNHn3/+uWbNmqWKFSsqJiZGffv2VWhoqDp37nzd/d1OFKYAuJSo6C3Zajc9svYtTgIAAAAg2+LjM193VWFGknTiROZt3a4ZcejgwRuOlJmuXbvai0GTJ09Otz7+72NZvHix7r33Xod13t7ekqQvvvhCL7/8ssaOHau6desqICBAY8aMSXe3PM9rrlCw2WxKTU3NcWZ/f3+VKlVKpUqV0v3336/SpUtr+vTpGjRoUIbtBwwYoIEDB6pDhw6SpMqVK+vQoUMaNWoUhSkAyLVMqnTh8JV5/2KSzUWH8UtNlQ7/nbNYsfS//AEAAICc8ve3vm02NWvWTImJibLZbIqIiEi3vkKFCvL29tbhw4fVoEGDDLexYcMGPfDAA3r++efty/bv3+/0rJlJTU1VQkJCpusvXrwot2v+znd3d7+hotitRmEKAJwl5ZK0MPzKfLt4ycP5v0Sd4tIlKfzvnPHxt+SXPQAAAOCq3N3dtXv3bvv8tQICAvTyyy+rX79+Sk1NVf369XX+/Hlt2LBBgYGB6ty5s0qXLq1PPvlEy5YtU3h4uD799FNt2bJF4Wl/Z2fTpEmTNH/+fK1atSrD9RcuXNDbb7+txx57TIULF9apU6c0efJk/fnnn2rbtq29XePGjfXvf//b3hOsZcuWevvtt1WsWDFVrFhRO3bs0Lhx49S1a9cc5bsdKEwBAAAAAIC7SmBgYJbrR4wYofz582vUqFH6/fffFRwcrBo1aui1116TJD377LPasWOH2rdvL5vNpo4dO+r555/XN998k6Mcp06dyrKnlbu7u/bs2aOZM2fq1KlTuueee1S7dm2tX79eFStWtLfbv3+/Tl01SPzEiRP15ptv6vnnn9eJEycUGhqqZ599VoMHD85RvtvBZswNDmUPB3FxcQoKCtL58+eve4IDyNwdPcZU8gVpzt93I3HlHlMXLvxz1xR6TAEAACAHLl++rAMHDig8PFw+Pj5Wx4GFsjoXclIjYWARAAAAAAAAWILCFAAAAAAAACzBGFMAcq07+rJAAAAAALgL0GMKAAAAAAAAlqDHFAA4i81DKv38P/OuysNDev75f+YBAAAAwCJ8IwEAZ3H3lmpPtjrF9Xl7S5PvgJwAAAAAcj0u5QMAAAAAAIAl6DEFAM5ijJRw6sq8d4hks1mbJzPGSKf+zhniwjkBAAAA5HoUpgDcFO58d5WUi9K8Alfm28VLHv7W5snMxYtSgb9zxsdL/i6aEwAAAECux6V8AAAAAAAAN2nt2rWy2Ww6d+6cJCk6OlrBwcGWZroTUJgCAAAAAAC5WmRkpGw2m5577rl063r16iWbzabIyEin7rN9+/b69ddfnbrN7HrsscdUrFgx+fj4qHDhwurUqZOOHj2aafszZ87ohRdeUNmyZeXr66tixYrpxRdf1Pnz5295VgpTAAAAAAAg1ytatKi++OILXbp0yb7s8uXLmjVrlooVK+b0/fn6+qpA2hAat1mjRo00Z84c7d27V1999ZX279+vJ554ItP2R48e1dGjR/Xee+9p586dio6O1tKlSxUVFXXLs1KYAgAAAAAAuV6NGjVUtGhRzZs3z75s3rx5KlasmKpXr+7QNjU1VaNGjVJ4eLh8fX1VtWpVffnllw5tlixZojJlysjX11eNGjXSwYMHHdZfeynf/v371apVKxUsWFB58uRR7dq1tXLlSofnFC9eXCNHjlTXrl0VEBCgYsWKadq0aTk+1n79+un+++9XWFiYHnjgAQ0cOFCbNm1SUlJShu0rVaqkr776Si1btlTJkiX18MMP6+2339b//vc/JScn53j/OUFhCgAAAAAA3JzkC5lPKZez3zb5Uvba3qCuXbtqxowZ9scff/yxunTpkq7dqFGj9Mknn2jq1KnatWuX+vXrp6efflrr1q2TJB05ckRt2rRRy5YtFRMTo27dumngwIFZ7js+Pl6PPPKIVq1apR07dqhZs2Zq2bKlDh8+7NBu7NixqlWrlnbs2KHnn39ePXv21N69e+3rGzZsmKPLDs+cOaPPP/9cDzzwgDw9PbP9vPPnzyswMFAeHrf2vnnclQ8AAAAAANycOXkyXxf6iNRw8T+Pvypw5Y7WGSnQQGqy9p/HC4pLCafSt3vS3EhKPf300xo0aJAOHTokSdqwYYO++OILrV37zz4TEhI0cuRIrVy5UnXr1pUklShRQt99950++ugjNWjQQFOmTFHJkiU1duxYSVLZsmX1888/6913381031WrVlXVqlXtj0eMGKH58+dr4cKF6t27t335I488oueff16S9Oqrr+r999/XmjVrVLZsWUlSsWLFVLhw4ese66uvvqpJkybp4sWLuv/++7Vo0aJsvkrSqVOnNGLECPXo0SPbz7lRFKYAwFlsHlJ453/mXZWHh9S58z/zAAAAwF0if/78atGihaKjo2WMUYsWLRQSEuLQ5rffftPFixf1r3/9y2F5YmKi/ZK/3bt3q06dOg7r04pYmYmPj9fQoUO1ePFiHTt2TMnJybp06VK6HlNVqlSxz9tsNhUqVEgnTpywL/vkk0+ydawDBgxQVFSUDh06pGHDhumZZ57RokWLZLPZsnxeXFycWrRooQoVKmjo0KHZ2tfN4BsJADiLu7dUN9rqFNfn7S1FR1udAgAAALlJu/jM19ncHR8/fiLjdpLSjTjU6uCNJspU165d7T2UJk+enG59fPyVY1m8eLHuvfdeh3Xe3t43vN+XX35ZK1as0HvvvadSpUrJ19dXTzzxhBITEx3aXXu5nc1mU2pqao73FxISopCQEJUpU0bly5dX0aJFtWnTpiwLaH/99ZeaNWumgIAAzZ8/P0eX/t0oClMAAAAAAODmePhb3zabmjVrpsTERNlsNkVERKRbX6FCBXl7e+vw4cNq0KBBhtsoX768Fi5c6LBs06ZNWe53w4YNioyM1L///W9JVwpg1w6YfqukFbYSEhIybRMXF6eIiAh5e3tr4cKF8vHxuS3ZGPwcAJzFmH8GYzQ3ds37bWGMdOHClcmVcwIAAAC3gLu7u3bv3q1ffvlF7u7u6dYHBATo5ZdfVr9+/TRz5kzt379f27dv18SJEzVz5kxJ0nPPPad9+/ZpwIAB2rt3r2bNmqXo61yVULp0ac2bN08xMTH68ccf9eSTT95QT6hnnnlGgwYNynT95s2bNWnSJMXExOjQoUNavXq1OnbsqJIlS9p7S/35558qV66cfvjhB0lXilJNmzbVhQsXNH36dMXFxSk2NlaxsbFKSUnJccacoDAFAM6ScvHKoI9z8mQ+mKMruHhRypPnynTRhXMCAAAAt0hgYKACAwMzXT9ixAi9+eabGjVqlMqXL69mzZpp8eLFCg8Pl3RlAPKvvvpKX3/9tapWraqpU6dq5MiRWe5z3Lhxyps3rx544AG1bNlSERERqlGjRo6zHz58WMeOHct0vZ+fn+bNm6fGjRurbNmyioqKUpUqVbRu3Tr7pYhJSUnau3evLv79fWD79u3avHmzfv75Z5UqVUqFCxe2T0eOHMlxxpywGcO/y50hLi5OQUFB9tspAneLqOgt2Wo3PbL2bd+es7NdV/KFf+5G0i7+lnQ7dooLF64UpSQpPl7yd9GcAAAAcDmXL1/WgQMHFB4eftsu9YJryupcyEmNhB5TAAAAAAAAsASFKQAAAAAAAFiCwhQAAAAAAAAsQWEKAAAAAAAAlqAwBQAAAAAAAEt4WB0AAHINm7tU9Il/5l2Vu7v0xBP/zAMAAAA5ZIyxOgIslpqa6pTtUJgCAGdx95EenGt1iuvz8ZHm3gE5AQAA4HI8PT1ls9l08uRJ5c+fXzabzepIuM2MMUpMTNTJkyfl5uYmLy+vm9oehSkAAAAAAJAt7u7uKlKkiP744w8dPHjQ6jiwkJ+fn4oVKyY3t5sbJYrCFAAAAAAAyLY8efKodOnSSkpKsjoKLOLu7i4PDw+n9JijMAXcZaKit2Sr3fTI2rc4SS6UfEGak+fKfLt4ycPf2jyZuXBByvN3zvh4yd9FcwIAAMBlubu7y53xSuEE3JUPAAAAAAAAlqDHFABk0/V6m3npkqbc3Lh/AAAAAHBXoccUAAAAAAAALEFhCgAAAAAAAJagMAUAAAAAAABLUJgCAAAAAACAJRj8HACcJFVu+im1nqoUCZJsLnzrXHd36ZFH/pkHAAAAAItQmAIAJ0mWtyYkv6/pDWtbHSVrPj7S4sVWpwAAAAAALuUDAAAAAACANShMAQAAAAAAwBIUpgDASbx0SR96PiTN9peSL1gdJ3MXLkj+/lemCy6cEwAAAECuxxhTAOBE3rbLUorVKbLh4kWrEwAAAAAAPaYAAAAAAABgDQpTAAAAAAAAsASFKQAAAAAAAFiCwhQAAAAAAAAsQWEKAAAAAAAAluCufADgJEY27UmtoXKFAuTSdX83N6lBg3/mAQAAAMAiFKYAwEmS5KMxyVM1vUltq6NkzddXWrvW6hQAAAAAYO2/9EeNGqXatWsrICBABQoUUOvWrbV3716HNpcvX1avXr10zz33KE+ePHr88cd1/PhxhzaHDx9WixYt5OfnpwIFCmjAgAFKTk52aLN27VrVqFFD3t7eKlWqlKKjo9PlmTx5sooXLy4fHx/VqVNHP/zwg9OPGQAAAAAAAFdYWphat26devXqpU2bNmnFihVKSkpS06ZNdeHCBXubfv366X//+5/mzp2rdevW6ejRo2rTpo19fUpKilq0aKHExER9//33mjlzpqKjozV48GB7mwMHDqhFixZq1KiRYmJi1LdvX3Xr1k3Lli2zt5k9e7b69++vIUOGaPv27apataoiIiJ04sSJ2/NiAAAAAAAA3GUsLUwtXbpUkZGRqlixoqpWraro6GgdPnxY27ZtkySdP39e06dP17hx4/Twww+rZs2amjFjhr7//ntt2rRJkrR8+XL98ssv+uyzz1StWjU1b95cI0aM0OTJk5WYmChJmjp1qsLDwzV27FiVL19evXv31hNPPKH333/fnmXcuHHq3r27unTpogoVKmjq1Kny8/PTxx9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",
+      "text/plain": [
+       "<Figure size 1200x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "co2_stats = (\n",
+    "    scan()\n",
+    "    .select(pl.col(\"CO2_EMISSIONS_CURRENT\").cast(pl.Float64, strict=False))\n",
+    "    .filter(pl.col(\"CO2_EMISSIONS_CURRENT\").is_not_null() & (pl.col(\"CO2_EMISSIONS_CURRENT\") > 0))\n",
+    "    .select(\n",
+    "        pl.col(\"CO2_EMISSIONS_CURRENT\").mean().alias(\"mean\"),\n",
+    "        pl.col(\"CO2_EMISSIONS_CURRENT\").median().alias(\"median\"),\n",
+    "        pl.col(\"CO2_EMISSIONS_CURRENT\").quantile(0.99).alias(\"p99\"),\n",
+    "    )\n",
+    "    .collect()\n",
+    ")\n",
+    "p99 = co2_stats[\"p99\"].item()\n",
+    "mean_val = co2_stats[\"mean\"].item()\n",
+    "median_val = co2_stats[\"median\"].item()\n",
+    "print(f\"Mean: {mean_val:.1f}, Median: {median_val:.1f}, P99: {p99:.1f}\")\n",
+    "\n",
+    "# Use hash-based sampling to avoid collecting full column\n",
+    "co2_sample = (\n",
+    "    scan()\n",
+    "    .select(pl.col(\"CO2_EMISSIONS_CURRENT\").cast(pl.Float64, strict=False))\n",
+    "    .filter(\n",
+    "        pl.col(\"CO2_EMISSIONS_CURRENT\").is_not_null()\n",
+    "        & (pl.col(\"CO2_EMISSIONS_CURRENT\") > 0)\n",
+    "        & (pl.col(\"CO2_EMISSIONS_CURRENT\") < p99)\n",
+    "    )\n",
+    "    .collect()\n",
+    ")\n",
+    "co2_vals = co2_sample.to_series()\n",
+    "if len(co2_vals) > 500_000:\n",
+    "    co2_vals = co2_vals.sample(n=500_000, seed=42)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.hist(co2_vals.to_list(), bins=100, edgecolor=\"none\", alpha=0.7)\n",
+    "ax.set_xlabel(\"CO2 Emissions (tonnes/year)\")\n",
+    "ax.set_ylabel(\"Count\")\n",
+    "ax.set_title(\"Distribution of Current CO2 Emissions (99th percentile, 500k sample)\")\n",
+    "ax.axvline(mean_val, color=\"red\", linestyle=\"--\", label=f\"Mean: {mean_val:.1f}\")\n",
+    "ax.axvline(median_val, color=\"orange\", linestyle=\"--\", label=f\"Median: {median_val:.1f}\")\n",
+    "ax.legend()\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "del co2_sample, co2_vals"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Floor Area Distribution by Property Type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-28T22:06:31.277179Z",
+     "iopub.status.busy": "2026-01-28T22:06:31.277103Z",
+     "iopub.status.idle": "2026-01-28T22:06:31.967032Z",
+     "shell.execute_reply": "2026-01-28T22:06:31.966642Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_793155/4276860801.py:36: MatplotlibDeprecationWarning: The 'labels' parameter of boxplot() has been renamed 'tick_labels' since Matplotlib 3.9; support for the old name will be dropped in 3.11.\n",
+      "  ax.boxplot(data, labels=labels, vert=True)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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4enoaV65csWivUqWK8cUXX1jniQJFTE7nZvfu3Q3DMIwWLVoYL7744i0fu3v3bkOSkZiYaBiGYWzatMl8PgPFyc3niSQjICDAiIyMNAwj5/f+77//bkgyjh07ZhiGYcyZM8eQZMTExJj7zJw50yhXrpx5vVy5csb7779vXk9PTzcqVKhgPPHEE7es7ezZs4YkY//+/YZhGMaxY8cMScbvv/9uGIZhvPrqq0aNGjUs/l+cOXOmUapUKSMjI8MwDMNo0KCB+bhdunQx3nnnHcPZ2dlITEw0/vnnH0OScejQoby/cEAeRUREGE888YRx5swZw8XFxYiNjTViY2MNV1dX4+zZs8YTTzxhRERE5PjYO50LnTp1Mvr375/jY2fPnm2ULl3aSEpKMretXr3acHBwMOLj4821VaxY0UhPTzf3eeqpp4yePXsahmEYx48fNxwdHY3//e9/Fvtu3bq1MW7cOMMwrv8c8PLyynb8ihUrGh9++KFF23PPPWcMHDjQom3btm2Gg4ODkZqamuPzAO5G1vlnGIaRmZlprFu3znBxcTHGjBmTY/8lS5YYZcqUMa9n/T+3b9++HPe7bNkyo1SpUsbChQtvW0fWufvVV1+Z2/766y9DkhEVFWUYhpGrz40VK1Y0wsPDzetxcXGGJGP8+PHmtl27dhmSjLi4OMMwOO9siZFSsItHHnlE+/bts1i++uor8/aoqCg1bdrU4jFNmzbV4cOHzZcV3Em/fv20b98+1ahRQyNGjNDPP/9s3vbHH38oKSlJZcqUUalSpczLsWPH7njpA1Cc3Xxufvzxxzn2i4yMVKdOnVShQgV5eHioRYsWkpTj0GyguLnxPPntt9/Utm1btW/fXsePH8/1Ptzd3VWlShXzekBAgPkS2MuXL+v06dNq2LChebujo6PCwsIs9nH48GE9/fTTqly5sjw9Pc2jgW91HkZFRalx48YWlwQ1bdpUSUlJ+ueffyRJLVq00ObNm2UYhrZt26auXbsqNDRU27dv15YtWxQYGKhq1arl+nkCd8vX11ePP/645s6dqzlz5ujxxx83j2rKktdzYciQIVq4cKHq1aunl19+WTt37jRvi4qKUt26dVWyZElzW9OmTZWZmamDBw+a2+6//36Ly9tvPIf379+vjIwMVa9e3eL3zC1btuTr98w//vhDc+fOtdhX27ZtlZmZqWPHjuV5f0BurFq1SqVKlZKrq6vat2+vnj17asKECZKk9evXq3Xr1ipfvrw8PDz0zDPP6Pz580pJSTE/3tnZWXXq1Mm2319//VVPPfWU5s2bp549e+aqlhv3ExAQIEnm8y23nxtv3Ee5cuUkSbVr187WlrVfzjvbYbY/2EXJkiVVtWpVi7asX4hzy2QymYdXZrnxWv4GDRro2LFjWrNmjdavX68ePXqoTZs2Wrp0qZKSkhQQEGBxSUKWnO6EAtwrcjo3b5Z1aUPbtm21YMEC+fr66sSJE2rbtq15WDdQnN18nnz11Vfy8vLSl19+qccee0ySLP5/uvH/piwlSpSwWM/p/7Q76dSpkypWrKgvv/xSgYGByszMVK1ate7qPGzZsqW++eYb/fHHHypRooRCQkLUsmVLbd68WRcvXjQH0IAtPfvss+ZLZmbOnJlte17PhawQ+ccff9S6devUunVrDR06VNOmTct1TTmdw1mXCyYlJcnR0VGRkZHZ5mUsVapUro+RJSkpSYMGDdKIESOybcvLhO9AXjzyyCOaNWuWnJ2dFRgYaL5RQGxsrDp27KghQ4bonXfekY+Pj7Zv367nnntOV69elbu7u6Trl/vd+EeQLFWqVFGZMmX0zTff6PHHH892LuXkxj5Z+8zrxOk57eN2++W8sx1CKRRKoaGh2rFjh0Xbjh07VL16dfN/7r6+vhaTwh4+fNginZckT09P9ezZUz179lT37t3Vrl07XbhwQQ0aNFB8fLycnJws5pkCcGfR0dE6f/68pkyZoqCgIEnKNjGks7OzJOV6ZCNQlJlMJjk4OCg1NVW+vr6SpLi4OJUuXVqSzJMr55aXl5fKlSun3bt3q3nz5pKun0t79+5VvXr1JEnnz5/XwYMH9eWXX6pZs2aSrk/IejuhoaH64YcfZBiG+ZfvHTt2yMPDQ/fdd5+k/5tX6sMPPzQHUC1bttSUKVN08eJFjR49Ok/PBbCGdu3a6erVqzKZTGrbtq3FtvycC9L13yMjIiIUERGhZs2a6V//+pemTZum0NBQzZ07V8nJyebRUjt27JCDg4Nq1KiRq3rr16+vjIwMnTlzxlzTzZydnXP8PzKn9gYNGujvv/++4x+NAGu61R8qIyMjlZmZqQ8++EAODtcvvFq8eHGu91u2bFktW7ZMLVu2VI8ePbR48eJcBVO3kpvPjfnBeWc7XL6HQmn06NHasGGD3nrrLR06dEjffvutPv30U4tJYFu1aqVPP/1Uv//+u/bs2aPBgwdb/ECbPn26vv/+e0VHR+vQoUNasmSJ/P395e3trTZt2qhx48bq0qWLfv75Z8XGxmrnzp167bXXsn24BmCpQoUKcnZ21ieffKKjR49q5cqVeuuttyz6VKxYUSaTSatWrdLZs2ct7sgCFHVpaWmKj49XfHy8oqKiNHz4cCUlJalTp06qWrWqgoKCNGHCBB0+fFirV6/Odheg3Bg+fLgmT56sFStW6ODBg3rxxRd18eJFc5hUunRplSlTRrNnz1ZMTIw2btyoUaNG3XafL7zwgk6ePKnhw4crOjpaK1as0JtvvqlRo0aZP1iULl1aderU0YIFC8wTmjdv3lx79+7VoUOHGCkFu3B0dFRUVJT+/vvvbB8y83MuvPHGG1qxYoViYmL0119/adWqVQoNDZUk9enTR66uroqIiNCBAwe0adMmDR8+XM8884z58p47qV69uvr06aO+fftq2bJlOnbsmH777TdNnjxZq1evlnT9bmBJSUnasGGDzp07Z/7DanBwsLZu3ar//e9/OnfunCTplVde0c6dOzVs2DDt27dPhw8f1ooVK5hwGXZRtWpVXbt2zfx74Lx58/T555/naR9+fn7auHGjoqOjLSYtz4/cfG7MD8472yGUQqHUoEEDLV68WAsXLlStWrX0xhtvaNKkSRa3lf/ggw8UFBSkZs2aqXfv3hozZox5uKgkeXh46L333tMDDzygBx98ULGxsfrxxx/l4OAgk8mkH3/8Uc2bN1f//v1VvXp19erVS8ePH8/1LxzAvcrX11dz587VkiVLVLNmTU2ZMiXbJQ/ly5fXxIkTNXbsWJUrV47/wFGsrF27VgEBAQoICFCjRo3Md+hq2bKlSpQoYf6DSJ06dTR16lTz3e3y4pVXXtHTTz+tvn37qnHjxua5LFxdXSVdv/PRwoULFRkZqVq1amnkyJF6//33b7vP8uXL68cff9Rvv/2munXravDgwXruuef0+uuvW/Rr0aKFMjIyzKGUj4+PatasKX9//1yPFAGszdPTU56entna83MuODs7a9y4capTp46aN28uR0dHLVy4UNL1+d5++uknXbhwQQ8++KC6d++u1q1b69NPP81TvXPmzFHfvn01evRo1ahRQ126dNHu3bvNl/00adJEgwcPVs+ePeXr66v33ntPkjRp0iTFxsaqSpUq5pGXderU0ZYtW3To0CE1a9ZM9evX1xtvvKHAwMA81QRYQ926dTV9+nRNnTpVtWrV0oIFCzR58uQ878ff318bN27U/v371adPn3yPrs/N58b84LyzHZOR1wkMAAAAYFOZmZkKDQ1Vjx49so1MBAAAKKqYUwoAAKCQOX78uH7++We1aNFCaWlp+vTTT3Xs2DH17t3b3qUBAABYDZfvAQAAFDIODg6aO3euHnzwQTVt2lT79+/X+vXrzfPeAAAAFAdcvgcAAAAAAACbY6QUAAAAAAAAbI5QCgAAAAAAADZHKAUAAAAAAACbI5QCAAAAAACAzRFKAQAAAAAAwOYIpQAAAAAAAGBzhFIAAAAAAACwOUIpAAAAAAAA2ByhFAAAAAAAAGzu/wFqzW8coDw4DgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1200x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "top_types = (\n",
+    "    scan()\n",
+    "    .group_by(\"PROPERTY_TYPE\")\n",
+    "    .len()\n",
+    "    .sort(\"len\", descending=True)\n",
+    "    .head(5)\n",
+    "    .collect()\n",
+    "    [\"PROPERTY_TYPE\"].to_list()\n",
+    ")\n",
+    "\n",
+    "# Sample per property type to avoid loading all data\n",
+    "fig, ax = plt.subplots()\n",
+    "data = []\n",
+    "labels = []\n",
+    "for pt in top_types:\n",
+    "    vals = (\n",
+    "        scan()\n",
+    "        .filter(\n",
+    "            (pl.col(\"PROPERTY_TYPE\") == pt)\n",
+    "            & pl.col(\"TOTAL_FLOOR_AREA\").is_not_null()\n",
+    "            & (pl.col(\"TOTAL_FLOOR_AREA\").cast(pl.Float64, strict=False) > 0)\n",
+    "        )\n",
+    "        .select(pl.col(\"TOTAL_FLOOR_AREA\").cast(pl.Float64, strict=False))\n",
+    "        .collect()\n",
+    "        .to_series()\n",
+    "        .drop_nulls()\n",
+    "    )\n",
+    "    p95 = vals.quantile(0.95)\n",
+    "    vals = vals.filter(vals < p95)\n",
+    "    # Sample if too large\n",
+    "    if len(vals) > 100_000:\n",
+    "        vals = vals.sample(n=100_000, seed=42)\n",
+    "    data.append(vals.to_list())\n",
+    "    labels.append(pt)\n",
+    "\n",
+    "ax.boxplot(data, labels=labels, vert=True)\n",
+    "ax.set_ylabel(\"Total Floor Area (m²)\")\n",
+    "ax.set_title(\"Floor Area Distribution by Property Type (95th pctl)\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lodgement Trends Over Time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-28T22:06:31.967996Z",
+     "iopub.status.busy": "2026-01-28T22:06:31.967918Z",
+     "iopub.status.idle": "2026-01-28T22:06:32.076565Z",
+     "shell.execute_reply": "2026-01-28T22:06:32.076202Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lodgements = (\n",
+    "    scan()\n",
+    "    .filter(pl.col(\"LODGEMENT_DATE\").is_not_null())\n",
+    "    .with_columns(pl.col(\"LODGEMENT_DATE\").dt.year().alias(\"year\"))\n",
+    "    .filter(pl.col(\"year\").is_between(2008, 2025))\n",
+    "    .group_by(\"year\")\n",
+    "    .len()\n",
+    "    .sort(\"year\")\n",
+    "    .collect()\n",
+    ")\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.bar(lodgements[\"year\"].to_list(), lodgements[\"len\"].to_list())\n",
+    "ax.set_xlabel(\"Year\")\n",
+    "ax.set_ylabel(\"Certificates Lodged\")\n",
+    "ax.set_title(\"EPC Lodgements by Year\")\n",
+    "ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f\"{x/1e6:.1f}M\"))\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tenure Distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-28T22:06:32.077561Z",
+     "iopub.status.busy": "2026-01-28T22:06:32.077482Z",
+     "iopub.status.idle": "2026-01-28T22:06:32.397533Z",
+     "shell.execute_reply": "2026-01-28T22:06:32.397108Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_793155/3225898348.py:23: UserWarning: Tight layout not applied. tight_layout cannot make Axes width small enough to accommodate all Axes decorations\n",
+      "  plt.tight_layout()\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tenure = (\n",
+    "    scan()\n",
+    "    .filter(pl.col(\"TENURE\").is_not_null() & (pl.col(\"TENURE\") != \"\"))\n",
+    "    .group_by(\"TENURE\")\n",
+    "    .agg(\n",
+    "        pl.len().alias(\"count\"),\n",
+    "        pl.col(\"CURRENT_ENERGY_EFFICIENCY\").mean().alias(\"mean_eff\"),\n",
+    "    )\n",
+    "    .sort(\"count\", descending=True)\n",
+    "    .collect()\n",
+    ")\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
+    "\n",
+    "axes[0].pie(tenure[\"count\"].to_list(), labels=tenure[\"TENURE\"].to_list(), autopct=\"%1.1f%%\")\n",
+    "axes[0].set_title(\"Tenure Distribution\")\n",
+    "\n",
+    "tenure_sorted = tenure.sort(\"mean_eff\", descending=True)\n",
+    "axes[1].barh(tenure_sorted[\"TENURE\"].to_list(), tenure_sorted[\"mean_eff\"].to_list())\n",
+    "axes[1].set_xlabel(\"Mean Energy Efficiency\")\n",
+    "axes[1].set_title(\"Mean Energy Efficiency by Tenure\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Main Fuel Type Distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-28T22:06:32.398389Z",
+     "iopub.status.busy": "2026-01-28T22:06:32.398313Z",
+     "iopub.status.idle": "2026-01-28T22:06:32.909646Z",
+     "shell.execute_reply": "2026-01-28T22:06:32.909308Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fuel = (\n",
+    "    scan()\n",
+    "    .filter(pl.col(\"MAIN_FUEL\").is_not_null() & (pl.col(\"MAIN_FUEL\") != \"\"))\n",
+    "    .group_by(\"MAIN_FUEL\")\n",
+    "    .len()\n",
+    "    .sort(\"len\", descending=True)\n",
+    "    .head(10)\n",
+    "    .collect()\n",
+    ")\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(12, 6))\n",
+    "ax.barh(fuel[\"MAIN_FUEL\"].to_list()[::-1], fuel[\"len\"].to_list()[::-1])\n",
+    "ax.set_xlabel(\"Count\")\n",
+    "ax.set_title(\"Top 10 Main Fuel Types\")\n",
+    "ax.xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f\"{x/1e6:.1f}M\"))\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Energy Efficiency vs Floor Area"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-28T22:06:32.910558Z",
+     "iopub.status.busy": "2026-01-28T22:06:32.910472Z",
+     "iopub.status.idle": "2026-01-28T22:06:33.047282Z",
+     "shell.execute_reply": "2026-01-28T22:06:33.046951Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sample = (\n",
+    "    scan()\n",
+    "    .filter(\n",
+    "        pl.col(\"TOTAL_FLOOR_AREA\").is_not_null()\n",
+    "        & pl.col(\"CURRENT_ENERGY_EFFICIENCY\").is_not_null()\n",
+    "        & (pl.col(\"TOTAL_FLOOR_AREA\").cast(pl.Float64, strict=False) > 0)\n",
+    "        & (pl.col(\"TOTAL_FLOOR_AREA\").cast(pl.Float64, strict=False) < 500)\n",
+    "    )\n",
+    "    .select(\n",
+    "        pl.col(\"TOTAL_FLOOR_AREA\").cast(pl.Float64, strict=False),\n",
+    "        pl.col(\"CURRENT_ENERGY_EFFICIENCY\"),\n",
+    "    )\n",
+    "    .collect()\n",
+    "    .sample(n=50_000, seed=42)\n",
+    ")\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.scatter(sample[\"TOTAL_FLOOR_AREA\"], sample[\"CURRENT_ENERGY_EFFICIENCY\"],\n",
+    "           alpha=0.05, s=1)\n",
+    "ax.set_xlabel(\"Total Floor Area (m²)\")\n",
+    "ax.set_ylabel(\"Current Energy Efficiency\")\n",
+    "ax.set_title(\"Energy Efficiency vs Floor Area (50k sample)\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "del sample"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Current vs Potential Energy Efficiency"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-28T22:06:33.048305Z",
+     "iopub.status.busy": "2026-01-28T22:06:33.048217Z",
+     "iopub.status.idle": "2026-01-28T22:06:33.342900Z",
+     "shell.execute_reply": "2026-01-28T22:06:33.342606Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean current efficiency: 65.0\n",
+      "Mean potential efficiency: 79.2\n",
+      "Mean improvement potential: 14.2 points\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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gIL344ouS6ByA9EMoBbsYNGiQDMNQjx49JN27bW/lypUVGBio1atXKzY2Vr1799bVq1c1bdo0FSlSRAUKFLBz1UDmYTKZFBERocmTJ2vSpEkqUaKEDMOQr6+vtm7dqsDAQPXr109TpkxRpUqV1KNHD+XPn1/e3t72Lh14oiV/Sb58+bISExPl5OSkvHnzauDAgYqJidHcuXPl5uamt956y3w+TZs2TR999JE8PT3tWzyQiSSfaxEREdqyZYsOHz6sdu3aqVKlSurdu7ccHBw0fPhwGYahcePGadiwYdq9e7eWLVsmDw8Pe5cPPPESExNVtGhRxcfHa+fOnQoKCtLYsWM1ZswYrVy5UpLUr18/mUwmffPNN7px44Y+/PBDFSlSxL6FI8thTCnY1bhx4/T+++9rypQp6tGjhy5duqRRo0Zp48aNOnPmjEqUKCFXV1ft2rVLzs7Oqd4SGEDqjhw5onLlyum1117ThAkTVKxYMfM5tHPnTtWpU0dVqlTR559/rooVK9q7XOCJl/wlOTw8XGPHjtW5c+dUvHhxVaxYUVOmTJEkffDBB1qwYIF69uypdu3aKX/+/BbrAki71atXq0OHDgoJCdGtW7d04MABeXl5acmSJTKZTFq6dKn69OmjYsWK6cKFC9q8ebMqVapk77KBTOP48ePq1auXXFxc5O3trW+//VYLFixQ/fr1LcbuHTVqlDZt2qRly5bxB0ykO0Ip2MTDwqSxY8fqgw8+MAdTt27d0s2bN7V161Z5eXkpKChIjo6Ounv3rpyc6NwHWOOPP/5QYGCg6tSpo0mTJqlYsWKSpC1btmjChAmKiorSqlWrVLhwYTtXCmQOmzZtUtOmTTVq1Ch5e3vr7Nmzmjx5soKCgrRq1SpJ0rBhw/Tpp59q5MiR6tmzJ5c4AI/g9OnTatKkiXr27Kl33nlHN2/elLe3t3r27KkxY8aYlzt8+LB2796tevXqqWjRonasGMicjh07ph49emjHjh365JNP1L9/f/O8+7/DXbt2Tblz57ZXmcjCCKWQ4e7/MPv+++8VGxurxMREtW3b1rzMmDFj9OGHH5rHk/o37rIHPFxyL4yjR4/q3Llz8vDwUP78+VWwYEEdPnxYQUFBqlu3rt577z1VrFjR/Av9+++/b77VL4CHS0pKUs+ePXX37l19+eWXkqQ7d+5oy5Ytat++vTp27Khx48ZJuvcHlxYtWphvWQ/AOseOHVOLFi20e/duXbx4UXXq1FGDBg00a9YsSdKuXbtUunRpLosF0sHJkyfVvXt3OTo66v3331f16tUl3fv90jAMrlRBhqLbCTLU/R9iQ4YM0YIFC1SgQAEdPXpUK1eu1CeffKKyZctqyJAhMplM6tevn27duqUBAwZYbIdACniw5EBq5cqV6t27t5ydnWUYhtzc3DRr1izVrFlTP//8s5o1a6Y2bdrI1dVVV65c0Q8//EAgBVjBZDLp+PHjcnNzM7c5OzurTp066tKli3755RfFxsbK3d3dfGcwAGl3/2WusbGxcnBw0JkzZ9S4cWO98sor5js379+/X2FhYerduzehFJAOihcvrmnTpqlXr14aOXKkhg4dqqCgIJlMJi49R4Yj8kSGSv4Qmzhxor7++muFh4drz549mjx5ssLDw9WvXz/9/vvvkqTBgwerf//++vbbb0UHPiBtkpKSZDKZtHfvXr311lsaOnSoduzYofnz56ty5coKDg7WTz/9pHLlymnLli0aMWKE+vTpo927d6tChQr2Lh94YqX2c8hkMqlJkya6fPmydu/ebW53dnZWwYIFdfr0aX5+AY8g+by5/8tvpUqVlCtXLpUrV07BwcGaPXu2+Q+dy5Yt0+HDh5U3b1671AtkRf7+/po6daqcnZ01YMAAi59zQEaipxQyXFRUlI4fP66JEyeqUqVKWrVqlfr27avRo0fr888/V//+/TV+/HiVL19eo0ePNv+VjEFhgQc7ffq0nnnmGXl4eMgwDB0+fFiVKlXS22+/LQcHBxUoUEClSpVSUlKSevXqpQ0bNsjPz49bZQNplPwz6J9//tHt27dVoEABubq6KjAwUHPnztXs2bOVlJSkatWqSZKOHj2qQoUK0bMXsFLyubZ161Z9++23KlCggCpXrqxatWppxowZCgkJ0e7du7V3715FRUVp69atmj17tnbs2MGAy0A68/f314QJEzR06FD5+vrauxw8JRhTCunu32FSfHy8Nm/erKCgIJ0+fVpvvPGGevfurV69emn27Nnq2rWrAgICtHTpUvMgzARSwIPduXNHwcHB+uuvv3TkyBF5enpqypQp+vjjj/X333/L09PTfA6tW7dO3bp108aNG1W6dGl7lw480T7//HNVqVJFVatWlSStWrVKvXr1kqurq0wmk+bNm6fq1asrIiJCgwYNkiTlzJlTefLkUUREhLZv367nn3/enk8ByJTWr1+vpk2bqm7dujpy5IgKFSqkjh07qnPnztq/f7/69Omjv//+Wx4eHvL29tbkyZM514AMdPv2bYZ4gM1w+R7S1f1h0qpVq/THH3/Izc1Nr7zyinLnzq3t27erRIkSateunSTJwcFBb7/9tvz8/Czu/kUgBTyYs7Ozpk6dqoIFC6patWr63//+p+DgYBUoUEBhYWGKjo42n0P+/v5ydnbW9evX7Vw18GS7e/euvvnmGzVt2lS//vqrTpw4oT59+mjAgAGaOnWqAgIC1KBBA4WHh6tu3bqaN2+eevXqJV9fX5UtW1a7d+/mSzLwCM6dO6eff/5Zn3/+udavX69169apXLlymjZtmr766isFBATop59+0oYNG7Rt2zaFh4dzrgEZjEAKtkRPKaSb+++yt3v3bvXo0UOFCxfWxIkTVbRoUfNdi3bt2qVNmzbJzc1Nbdq00WuvvaZOnTpJ4i57wH9JDn6TkpJ07NgxderUSUlJSdq4caNGjRqlTZs2qW3bturQoYNy5MihkSNHauXKldq5c6e8vLzsXT7wRLt165Zatmypw4cPa8KECdq3b5/Gjx9vnt+pUyctW7ZMixYtUtOmTe1YKZA1HDx4UP3799fVq1f1xRdfKDAwUNK9y2EnT56s3bt365133lG3bt3sXCkAIKPQUwrp4v677I0fP16zZs1SdHS0vvvuOw0ePFjHjh2Tg4ODunfvrmPHjqlq1aqqUKGCzpw5YzHGDYEUkLr4+HhJ93oR3rlzRw4ODnr22WdVrVo17d27V40bN9YHH3ygBg0aaOHChSpSpIjq1aunuXPnatmyZQRSQBpky5ZNy5YtU5kyZdSqVSsdPHhQt2/fNs+fO3euWrZsqbfeeksrVqxQYmKiHasFMr+4uDg5OzvrxIkT5hvfSFKpUqXUt29fVa9eXePHj1dYWJgdqwQAZCR6SiFdTZgwQZ988omWLVsmPz8/rVq1SmvWrFGRIkU0cuRIlSpVSseOHdPy5cvl7u6ubt26ycnJSXfv3pWTE+PuA6k5f/68+vbtq27duqlOnTrm9vHjx2v8+PEaN26cpk2bJldXV23YsEE3b97U5s2b5eHhoRdeeMHi0lgA/+3mzZvq0qWLNmzYoO+//97ceyPZG2+8oT179ujIkSPKmTOnnaoEsoZffvlFI0eO1Pnz5zVkyBA1b97cPO/PP//UnDlzFBoaqqJFi9qxSgBARiGUQrowDEMJCQl69dVXValSJY0ePdo8b9asWRo3bpwqVaqkTz75RCVLlrS41I9ACni4v//+W+3atdMzzzyj999/X0FBQRo7dqwmTJigpUuXql69evrzzz/VunVrOTk5afPmzcqdO7e9ywYytbi4OL3++us6dOiQvv32WwUEBFjMv3jxovLnz2+n6oDMJ/ny8z/++EORkZGKj49X3bp15ebmpl9++UVjx47VpUuX1KdPH4tg6s6dO3J2drZj5QCAjEQohXT12muvydPTM0U3686dO5sHkB05cqSKFy9upwqBzOn48ePmu4B5eXkpPDxcCxcuVP369c3L/PXXX2rYsKG8vLz0888/y2QycdMAII1Su+vrrVu31Lx5cx0+fFhr165VxYoV7VQdkLkln18rVqxQ79695erqqoSEBLm4uGjBggWqXr269u3bp3Hjxul///uf3n77bbVu3dreZQMAbIAxpfBIkpKSUm339/fXtm3bdOjQIYv2MmXKqEaNGrpy5YoWLlz4wPUBpM7f319TpkxRXFycFi5cqPfee88cSCWfT88++6w2bNigb775Rg4ODgRSwEMk/03u3Llzunv3bqrnS7Zs2bRq1SpVrFhRQUFBOnjwoK3LBLIEk8mkffv2qUuXLho1apR+/PFH7dy5U88//7yaNWumXbt2qXLlyurfv7+cnJy0cOFC3bhxw95lAwBsgJ5SsNr9l95t2rRJDg4OypYtm4KCgiRJlSpV0q1bt/TVV1+pZMmSypkzp9q0aaOmTZvq0KFDWrFihf744w/G4QAewcmTJ9W9e3c5Ojrq/fffV/Xq1SVZnpcAUnf/3SsdHBy0fft29e3bV5s2bVKePHkeuF5cXJxCQkI0evRo+fv727BiIOtYvHixpk6dqoiICGXPnt0cBDdp0kTHjh3T4cOH5eLiov3798vb21sFCxa0c8UAAFvgGwyscv9d9gYMGKA2bdqoU6dOatOmjUaNGiVJ2rlzpzw8PPTmm2/qxRdfVEBAgA4dOqSQkBC9/PLLypYtmxISEuz5NIBMq3jx4po2bZoMw9DIkSO1c+dOSSKQAv7DvHnz1KdPHyUmJprPl/j4eHl5eSlPnjwP7cGbPXt2LV++nEAKeAwXL17UiRMnlCNHDplMJvNdZUePHq0bN25o7969kqSAgAACKQB4ivAtBml2/3gbp0+f1ubNmxUREaHw8HD17dtXw4YN09ChQ+Xq6qqff/5Zo0ePVq9evdS9e3f99ddfkqRVq1bJy8tLrq6u9nwqQKbm7++vqVOnytnZWQMGDNDu3bvtXRLwRLt9+7YOHDignTt3atiwYbp7966kez/Lbt26JYlgF0hPyRdinDhxQsePH5d0766Vnp6eGjhwoCTJzc1NkuTo6ChXV1d+NwSApxS3PEOaJQdSn376qQ4ePKhatWqpQoUKkqSSJUvKxcVFvXv3NvfgaNOmjXnd33//XTNmzNDKlSu1ZcsWLt0DHpO/v78mTJigoUOHytfX197lAE80FxcXjRw5UuPGjdMPP/ygxMREjRkzRg4ODkpKSrK4/PVB/weQNsl/xFy9erU++ugjde3aVR4eHsqTJ486duyodevWqV+/fpo4caKuXr2qpUuXytHRkd5RAPCUIpTCf7r/l/IbN27owoULWrVqlWrWrGleJmfOnAoJCZEk9evXT3Fxcfrss88k3RuL48iRIzp06JC2bNmi8uXL2/5JAFnQs88+q0WLFsnFxcXepQBPtLt378rd3V39+vWTg4ODNm7cqBEjRsjHx0fPP/+8/vzzT7m7u8vR0VF3797VlStXVKpUKeXIkcPepQOZjslk0nfffad27dpp9OjRat26tfLmzStJ6tGjh1xcXDRjxgzNnj1bxYsXV1RUlNatW6f8+fPbuXIAgD0w0DnS7NatW8qWLZvOnTunOXPmaMSIEZo+fbq6detmXubmzZuaNm2a1q1bp23btpl7VxmGoevXr8vd3d1e5QMAnmLz5s3T3r179dFHH2ny5Mn66aefdPToUV27dk3ly5fXP//8I0dHR7m4uChbtmzasmWLChQoYO+ygSfeli1bVKdOHUn3ft+Ljo5W06ZN1aBBAw0ZMkS3bt3StWvXtGnTJhUsWFAvv/yyYmJitHLlSuXPn19lypRR4cKF7fwsAAD2QiiFNJk9e7bGjBmjvXv3Km/evLp48aKmTZumqVOnauLEieratat52fj4eLm6ulrc4QgAAFtLvozo2rVrCgoKUqdOnTRw4EBdv35dY8eO1ZYtW+Tl5aWZM2fKZDLp6tWryp07txITEwmkgDT46aef1LhxY504cUL58uWTJN25c0cNGjRQUFCQ3nnnHU2ePFn79u3TsWPHdP36db333nv68MMP7Vw5AOBJQVqANKlZs6ZcXFzUsGFDXblyRfnz51doaKh69+6tQYMGafbs2eZl3dzcZDKZLO7UBwCArZlMJkVERGj06NGqUaOGunfvrqSkJOXKlUuDBw9WrVq1dOHCBX355Zfy9PRUmTJl5OPjQyAFpFGNGjV08uRJ5cuXT6dOnZJhGDIMQ0WLFtWmTZtUpEgR/f333woJCdGBAwfUunVr88DnAABIjCmFVPy7d5NhGCpVqpTWrVunZs2aKTg4WBs3bpSvr6+6d+8uBwcHde3aVV5eXmratKl5veRL9wAAsIfbt29r69atmjJlisUYUbdv31auXLn0/vvvy8nJSYsXL5bJZNJHH31k54qBzCdv3rw6e/asiv+/9u4/pup6j+P4kxPHdTQypgJTSMFho7VxIOeOQQhhlm0JhClSamk/yJBJZYDxc41NOyEDdZZB2XDIYXPY2nJEhkNWrVRI+EcPzVPHGRAjYksF4Zz7Rzun681ut3bHAX09/uP82vvzx9n3y+t83p/3woWUlpZSXFxMRUUFp0+fZmhoiNTUVO894eXLlwkICNBOehER8VL7nvwpm83G2rVrgd9bIHp6ekhNTcVkMnHs2DFmz56N0+nk2LFjbNq0CX9/5ZwiIuJbnmsWgMPhoK6ujpKSEqqqqti6dSvwW4uR0WhkeHiYqqoq1q9fz4IFC3xYtcjUVllZSUFBAaWlpeTn51/3XH9/PxUVFdTU1HDy5EnuvfdeH1UpIiKTjUIp8RodHWVsbIzp06dz8eJFIiIiSEpKorm5Gfj9Jr+zs5OkpCQsFgsHDx4kODjY+xljY2MKpkRExCc816n/vBY5nU72799PVVUVu3fv9p6D6Amm/j3EEpF/bu/eveTk5LBr1y5effVVDAYDR48epaGhgTNnztDY2IjZbPZ1mSIiMokoPRAAjhw5Qn19PRcuXCA9PZ033niD48ePk5mZyWOPPcYnn3zivWG/++67WbRoEc3NzeTl5XHw4EHv5yiQEhERX/AES59//jl1dXWMjo4SFhbGzp07CQsL87abb9++HYPBwPPPP4/RaATUbi7yd3i+a93d3fT39zM8PExqaioA2dnZAOTk5ACwfft2kpOT+fXXX9m1a5em7ImIyB+omVt499132bRpE/Pnz2fZsmUUFxdTU1PDgw8+yOHDh+ns7OTRRx/1vn769Oncd999dHZ2Ultb68PKRUREfuPn50dTUxNpaWkYjUbCwsKw2WykpKQwPj5OaGgoWVlZ5Obm8uKLL173g4qI/G88gVRTUxMrV65k27ZtPPvsszzyyCN0dXXhcrnIzs6murqaoqIiSkpKCAgI4KmnnlIgJSIiN6T2vVtcTU0NW7ZsobGx0fsr17p161i6dCmZmZkEBgbyxRdf8Nxzz2EymUhJSeHEiRNcu3aN9vZ2DAYD4+Pj3Hbbbb5diIiI3DJu1G7X0dFBRkYG27Zt46WXXsLhcPDAAw/Q29tLXFwcra2t+Pv78/3333Po0CFWr17NPffc46MViEwNNzqQ/LPPPmPNmjVYrVY2b95MR0cH999/P0lJSVitVmJiYvDz88NqtbJz507Onz/PrFmzfLQCERGZ7BRK3cJOnDjBQw895J2U4mE2m3G5XDgcDmJiYti4cSNJSUls3bqVkZERAgICsNlsGI1GTU8REZEJ5bnuDA0NMTg4iNvtZuHChbS3t2Oz2dizZw9Op5PExESSk5PJyMggJSWF5cuX09jYiNFo1PmHIv8Dz3fN4XBw9uxZVq1axejoKHl5ecycOZPS0lIuXLjA8uXLSUhIoK2tjaCgIPbu3UtMTAwGg4Gff/6ZwMBAXy9FREQmMYVStzC73c7mzZsJDAykqKiIxYsXk56eztmzZykvL+fOO+/ktddew+Vy0draSnBwMFevXuX2228HdKi5iIhMLM8/yd3d3d7dUP7+/jzxxBNUVFTw3XffMX/+fJ588klmzJhBXV0dly9fJjExkdOnT/Pwww97h3eIyF+7dOkS0dHRzJkzh8LCQjIzM2lpaWHevHnMnTuXFStWEB0dzXvvvUdrayvJycnExsZSW1tLdHS0r8sXEZEpQInCLSwyMpLa2lpycnIoLS1laGiIK1eu0NLS4h2LHRQUxOLFi/nyyy9JTU31BlJut1uBlIiITBhPIPXtt98SHx/Phg0b2LJlC83NzdTX13PHHXdQVlbG4OAgP/zwA0VFRfj5+eHv74/ZbKasrIyoqChfL0NkSjl//jyDg4OEh4djs9kwGAxkZGQA0NTUhNvtJi8vD4CrV6/y+OOP43Q6CQgI8GXZIiIyhajv6hYXGRlJdXU1IyMjdHd3k5+fz4IFC3C5XHg20UVFRTFnzpzr3qdJRSIiMpEMBgM9PT1YLBZyc3PZt28f69atY//+/URFRXH8+HHcbjcmk4mhoSHq6+txOBwUFhbS1tZGbGws4eHhvl6GyJSSmJjIM888w7Vr1zAajRw4cIC6ujoA+vr6+PHHHzGZTAC0t7djNpv55ptviIiI8GXZIiIyhSiUEiIjI3nnnXewWCx88MEHnDx5EoPBgJ+fHyUlJQQFBbF06VJflykiIrcwl8vF+++/T0BAALNnz/Y+bjKZSEpKYmxsjF9++QWTycTu3btpa2sjISEBm81GQ0MDISEhPqxeZPJzuVzX/T0yMgJAeno6ZrOZF154gcDAQGpqajh69Kh3x9SyZcuIj49n3759pKWlafiNiIj8LTpTSrzsdjs5OTkYDAYKCgqorKyku7ub7u5uHWouIiI+d+nSJd566y2++uorVq1axY4dOxgYGCA8PJyioiJef/1172v7+/vp6ekhIiJCgZTIX/Dc4zmdTk6dOkVaWpr3uZ9++omEhASys7NZs2YNWVlZ9PX1kZ+fz5IlS7BarRiNRtavX68WWRER+dsUSsl17HY7ubm5fPrpp0RERNDV1aVJRSIiMmn09vZSXl7OmTNniIuL4/Dhw6SlpVFdXQ3ceIS9iPw1p9NJTEwMg4ODrFy5ko0bN2I2m1m0aBEff/wxVquVI0eOMDAwQGFhIYODg7z88susXr3a16WLiMgUprs2uU5kZCRvv/02WVlZ3h1SCqRERGSyCAkJYceOHcTGxnLo0CFCQkK8gdTY2JgCKZF/yOVyER4ejsViobe3l5aWFlasWMGBAwe4cuUKM2fO5NSpU0RFRfHmm2/i7+/Phx9+yPDwsK9LFxGRKUw7peS/UiAlIiKTUV9fH+Xl5Xz99dekpaV5J4Bpp5TIP2e328nPz8flcrFhwwb8/Pyoqqrirrvu4qOPPmLJkiW0tbUxbdo0zp07x4wZMwgNDfV12SIiMoUplBIREZEpydPK19HRQXJyMmVlZb4uSWTKO3fuHLm5uYyPj7Nnzx7mzZtHV1cX5eXlrF27lqeffhq3261JzCIi8n+hUEpERESmrN7eXgoKCrh48SINDQ3MmjXL1yWJTHl2u53s7GwAiouLiYuL83FFIiJys1IoJSIiIlNaX18fAMHBwT6uROTm4ZnK7Ha7KSwsJD4+3tcliYjITUihlIiIiIiI/IHdbueVV15hYGCAyspKLBaLr0sSEZGbjE4CFRERERGRP4iMjMRqtRIaGsrcuXN9XY6IiNyEtFNKRERERET+1OjoKNOmTfN1GSIichNSKCUiIiIiIiIiIhNO7XsiIiIiIiIiIjLhFEqJiIiIiIiIiMiEUyglIiIiIiIiIiITTqGUiIiIiIiIiIhMOIVSIiIiIiIiIiIy4RRKiYiIiIiIiIjIhFMoJSIiIiIiIiIiE06hlIiIiIiIiIiITDiFUiIiIiIiIiIiMuH+BbM99hrZR7JLAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1200x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "improvement = (\n",
+    "    scan()\n",
+    "    .filter(\n",
+    "        pl.col(\"CURRENT_ENERGY_EFFICIENCY\").is_not_null()\n",
+    "        & pl.col(\"POTENTIAL_ENERGY_EFFICIENCY\").is_not_null()\n",
+    "    )\n",
+    "    .group_by(\"PROPERTY_TYPE\")\n",
+    "    .agg(\n",
+    "        pl.col(\"CURRENT_ENERGY_EFFICIENCY\").mean().alias(\"current\"),\n",
+    "        pl.col(\"POTENTIAL_ENERGY_EFFICIENCY\").mean().alias(\"potential\"),\n",
+    "        (pl.col(\"POTENTIAL_ENERGY_EFFICIENCY\") - pl.col(\"CURRENT_ENERGY_EFFICIENCY\")).mean().alias(\"mean_improvement\"),\n",
+    "        pl.len().alias(\"count\"),\n",
+    "    )\n",
+    "    .filter(pl.col(\"count\") > 1000)\n",
+    "    .sort(\"mean_improvement\", descending=True)\n",
+    "    .collect()\n",
+    ")\n",
+    "\n",
+    "# Overall stats\n",
+    "overall = (\n",
+    "    scan()\n",
+    "    .filter(\n",
+    "        pl.col(\"CURRENT_ENERGY_EFFICIENCY\").is_not_null()\n",
+    "        & pl.col(\"POTENTIAL_ENERGY_EFFICIENCY\").is_not_null()\n",
+    "    )\n",
+    "    .select(\n",
+    "        pl.col(\"CURRENT_ENERGY_EFFICIENCY\").mean().alias(\"current\"),\n",
+    "        pl.col(\"POTENTIAL_ENERGY_EFFICIENCY\").mean().alias(\"potential\"),\n",
+    "    )\n",
+    "    .collect()\n",
+    ")\n",
+    "print(f\"Mean current efficiency: {overall['current'].item():.1f}\")\n",
+    "print(f\"Mean potential efficiency: {overall['potential'].item():.1f}\")\n",
+    "print(f\"Mean improvement potential: {overall['potential'].item() - overall['current'].item():.1f} points\")\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "x = range(len(improvement))\n",
+    "width = 0.35\n",
+    "ax.bar([i - width/2 for i in x], improvement[\"current\"].to_list(), width, label=\"Current\")\n",
+    "ax.bar([i + width/2 for i in x], improvement[\"potential\"].to_list(), width, label=\"Potential\")\n",
+    "ax.set_xticks(list(x))\n",
+    "ax.set_xticklabels(improvement[\"PROPERTY_TYPE\"].to_list(), rotation=45, ha=\"right\")\n",
+    "ax.set_ylabel(\"Energy Efficiency Score\")\n",
+    "ax.set_title(\"Current vs Potential Energy Efficiency by Property Type\")\n",
+    "ax.legend()\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Top 20 Local Authorities by Number of Certificates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-28T22:06:33.343873Z",
+     "iopub.status.busy": "2026-01-28T22:06:33.343778Z",
+     "iopub.status.idle": "2026-01-28T22:06:33.797103Z",
+     "shell.execute_reply": "2026-01-28T22:06:33.796854Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "la_counts = (\n",
+    "    scan()\n",
+    "    .filter(pl.col(\"LOCAL_AUTHORITY_LABEL\").is_not_null() & (pl.col(\"LOCAL_AUTHORITY_LABEL\") != \"\"))\n",
+    "    .group_by(\"LOCAL_AUTHORITY_LABEL\")\n",
+    "    .agg(\n",
+    "        pl.len().alias(\"count\"),\n",
+    "        pl.col(\"CURRENT_ENERGY_EFFICIENCY\").mean().alias(\"mean_eff\"),\n",
+    "    )\n",
+    "    .sort(\"count\", descending=True)\n",
+    "    .head(20)\n",
+    "    .collect()\n",
+    ")\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(12, 7))\n",
+    "eff_values = la_counts[\"mean_eff\"].to_list()\n",
+    "colors_la = plt.cm.RdYlGn([(v - 50) / 30 for v in eff_values])\n",
+    "ax.barh(\n",
+    "    la_counts[\"LOCAL_AUTHORITY_LABEL\"].to_list()[::-1],\n",
+    "    la_counts[\"count\"].to_list()[::-1],\n",
+    "    color=colors_la[::-1],\n",
+    ")\n",
+    "ax.set_xlabel(\"Number of Certificates\")\n",
+    "ax.set_title(\"Top 20 Local Authorities (color = mean energy efficiency)\")\n",
+    "ax.xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f\"{x/1e3:.0f}k\"))\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary Statistics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-28T22:06:33.797899Z",
+     "iopub.status.busy": "2026-01-28T22:06:33.797826Z",
+     "iopub.status.idle": "2026-01-28T22:06:36.111277Z",
+     "shell.execute_reply": "2026-01-28T22:06:36.111013Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (9, 10)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>column</th><th>count</th><th>null_count</th><th>mean</th><th>std</th><th>min</th><th>q25</th><th>median</th><th>q75</th><th>max</th></tr><tr><td>str</td><td>u32</td><td>u32</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;CURRENT_ENERGY_EFFICIENCY&quot;</td><td>28847961</td><td>0</td><td>64.953829</td><td>14.289267</td><td>0.0</td><td>58.0</td><td>67.0</td><td>74.0</td><td>13060.0</td></tr><tr><td>&quot;POTENTIAL_ENERGY_EFFICIENCY&quot;</td><td>28847961</td><td>0</td><td>79.197588</td><td>11.097917</td><td>0.0</td><td>75.0</td><td>81.0</td><td>85.0</td><td>13071.0</td></tr><tr><td>&quot;ENERGY_CONSUMPTION_CURRENT&quot;</td><td>28847961</td><td>0</td><td>253.700647</td><td>173.148972</td><td>-320866.0</td><td>174.0</td><td>233.0</td><td>308.0</td><td>312273.0</td></tr><tr><td>&quot;CO2_EMISSIONS_CURRENT&quot;</td><td>28847961</td><td>0</td><td>3.819772</td><td>3.585926</td><td>-2854.8</td><td>2.1</td><td>3.2</td><td>4.7</td><td>4863.0</td></tr><tr><td>&quot;TOTAL_FLOOR_AREA&quot;</td><td>28847959</td><td>2</td><td>86.904086</td><td>118.035704</td><td>0.0</td><td>59.48</td><td>77.0</td><td>99.0</td><td>530331.552</td></tr><tr><td>&quot;HEATING_COST_CURRENT&quot;</td><td>28840897</td><td>7064</td><td>708.726959</td><td>644.316629</td><td>-944269.0</td><td>364.0</td><td>571.0</td><td>860.0</td><td>201800.0</td></tr><tr><td>&quot;LIGHTING_COST_CURRENT&quot;</td><td>28840912</td><td>7049</td><td>82.130655</td><td>589.723862</td><td>-768.0</td><td>54.0</td><td>73.0</td><td>100.0</td><td>3.15769e6</td></tr><tr><td>&quot;HOT_WATER_COST_CURRENT&quot;</td><td>28841075</td><td>6886</td><td>154.051112</td><td>102.004094</td><td>-38254.0</td><td>94.0</td><td>120.0</td><td>183.0</td><td>66675.0</td></tr><tr><td>&quot;NUMBER_HABITABLE_ROOMS&quot;</td><td>25398115</td><td>3449846</td><td>4.215609</td><td>1.915856</td><td>0.0</td><td>3.0</td><td>4.0</td><td>5.0</td><td>3424.0</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (9, 10)\n",
+       "┌───────────────────┬──────────┬────────────┬────────────┬───┬───────┬────────┬───────┬────────────┐\n",
+       "│ column            ┆ count    ┆ null_count ┆ mean       ┆ … ┆ q25   ┆ median ┆ q75   ┆ max        │\n",
+       "│ ---               ┆ ---      ┆ ---        ┆ ---        ┆   ┆ ---   ┆ ---    ┆ ---   ┆ ---        │\n",
+       "│ str               ┆ u32      ┆ u32        ┆ f64        ┆   ┆ f64   ┆ f64    ┆ f64   ┆ f64        │\n",
+       "╞═══════════════════╪══════════╪════════════╪════════════╪═══╪═══════╪════════╪═══════╪════════════╡\n",
+       "│ CURRENT_ENERGY_EF ┆ 28847961 ┆ 0          ┆ 64.953829  ┆ … ┆ 58.0  ┆ 67.0   ┆ 74.0  ┆ 13060.0    │\n",
+       "│ FICIENCY          ┆          ┆            ┆            ┆   ┆       ┆        ┆       ┆            │\n",
+       "│ POTENTIAL_ENERGY_ ┆ 28847961 ┆ 0          ┆ 79.197588  ┆ … ┆ 75.0  ┆ 81.0   ┆ 85.0  ┆ 13071.0    │\n",
+       "│ EFFICIENCY        ┆          ┆            ┆            ┆   ┆       ┆        ┆       ┆            │\n",
+       "│ ENERGY_CONSUMPTIO ┆ 28847961 ┆ 0          ┆ 253.700647 ┆ … ┆ 174.0 ┆ 233.0  ┆ 308.0 ┆ 312273.0   │\n",
+       "│ N_CURRENT         ┆          ┆            ┆            ┆   ┆       ┆        ┆       ┆            │\n",
+       "│ CO2_EMISSIONS_CUR ┆ 28847961 ┆ 0          ┆ 3.819772   ┆ … ┆ 2.1   ┆ 3.2    ┆ 4.7   ┆ 4863.0     │\n",
+       "│ RENT              ┆          ┆            ┆            ┆   ┆       ┆        ┆       ┆            │\n",
+       "│ TOTAL_FLOOR_AREA  ┆ 28847959 ┆ 2          ┆ 86.904086  ┆ … ┆ 59.48 ┆ 77.0   ┆ 99.0  ┆ 530331.552 │\n",
+       "│ HEATING_COST_CURR ┆ 28840897 ┆ 7064       ┆ 708.726959 ┆ … ┆ 364.0 ┆ 571.0  ┆ 860.0 ┆ 201800.0   │\n",
+       "│ ENT               ┆          ┆            ┆            ┆   ┆       ┆        ┆       ┆            │\n",
+       "│ LIGHTING_COST_CUR ┆ 28840912 ┆ 7049       ┆ 82.130655  ┆ … ┆ 54.0  ┆ 73.0   ┆ 100.0 ┆ 3.15769e6  │\n",
+       "│ RENT              ┆          ┆            ┆            ┆   ┆       ┆        ┆       ┆            │\n",
+       "│ HOT_WATER_COST_CU ┆ 28841075 ┆ 6886       ┆ 154.051112 ┆ … ┆ 94.0  ┆ 120.0  ┆ 183.0 ┆ 66675.0    │\n",
+       "│ RRENT             ┆          ┆            ┆            ┆   ┆       ┆        ┆       ┆            │\n",
+       "│ NUMBER_HABITABLE_ ┆ 25398115 ┆ 3449846    ┆ 4.215609   ┆ … ┆ 3.0   ┆ 4.0    ┆ 5.0   ┆ 3424.0     │\n",
+       "│ ROOMS             ┆          ┆            ┆            ┆   ┆       ┆        ┆       ┆            │\n",
+       "└───────────────────┴──────────┴────────────┴────────────┴───┴───────┴────────┴───────┴────────────┘"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numeric_cols = [\n",
+    "    \"CURRENT_ENERGY_EFFICIENCY\", \"POTENTIAL_ENERGY_EFFICIENCY\",\n",
+    "    \"ENERGY_CONSUMPTION_CURRENT\", \"CO2_EMISSIONS_CURRENT\",\n",
+    "    \"TOTAL_FLOOR_AREA\", \"HEATING_COST_CURRENT\", \"LIGHTING_COST_CURRENT\",\n",
+    "    \"HOT_WATER_COST_CURRENT\", \"NUMBER_HABITABLE_ROOMS\",\n",
+    "]\n",
+    "\n",
+    "# Compute stats lazily per column to avoid loading all at once\n",
+    "rows = []\n",
+    "for c in numeric_cols:\n",
+    "    s = (\n",
+    "        scan()\n",
+    "        .select(pl.col(c).cast(pl.Float64, strict=False))\n",
+    "        .select(\n",
+    "            pl.lit(c).alias(\"column\"),\n",
+    "            pl.col(c).count().alias(\"count\"),\n",
+    "            pl.col(c).null_count().alias(\"null_count\"),\n",
+    "            pl.col(c).mean().alias(\"mean\"),\n",
+    "            pl.col(c).std().alias(\"std\"),\n",
+    "            pl.col(c).min().alias(\"min\"),\n",
+    "            pl.col(c).quantile(0.25).alias(\"q25\"),\n",
+    "            pl.col(c).median().alias(\"median\"),\n",
+    "            pl.col(c).quantile(0.75).alias(\"q75\"),\n",
+    "            pl.col(c).max().alias(\"max\"),\n",
+    "        )\n",
+    "        .collect()\n",
+    "    )\n",
+    "    rows.append(s)\n",
+    "\n",
+    "pl.concat(rows)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 500b9ef2aafc37ca5dbf159a54595d5df0ce179c Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Wed, 28 Jan 2026 22:10:41 +0000
Subject: [PATCH 10/62] Add POIs and journey times to map

---
 frontend/src/App.tsx                          |  20 +-
 frontend/src/components/Filters.tsx           | 128 +++++++---
 frontend/src/components/Map.tsx               | 215 ++++++++++++----
 frontend/src/types.ts                         |  14 +-
 pipeline/pois/__init__.py                     |   0
 pipeline/pois/__main__.py                     | 181 +++++++++++++
 pipeline/pois/config.py                       | 147 +++++++++++
 .../processors/journey_times_aggregator.py    |  90 +++++--
 pipeline/run.py                               |  16 +-
 server/routes/hexagons.py                     |  40 ++-
 server/routes/pois.py                         | 240 ++++++++++++++----
 11 files changed, 914 insertions(+), 177 deletions(-)
 create mode 100644 pipeline/pois/__init__.py
 create mode 100644 pipeline/pois/__main__.py
 create mode 100644 pipeline/pois/config.py

diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx
index b72b2b8..f4838fb 100644
--- a/frontend/src/App.tsx
+++ b/frontend/src/App.tsx
@@ -10,7 +10,7 @@ import type {
   ApiResponse,
   POI,
   POIResponse,
-  POICategoryGroup,
+  POICategoriesMap,
   ColorMode,
 } from './types';
 
@@ -55,23 +55,30 @@ export default function App() {
 
   // POI state
   const [pois, setPois] = useState<POI[]>([]);
-  const [selectedPOICategories, setSelectedPOICategories] = useState<Set<POICategoryGroup>>(
-    new Set()
-  );
+  const [poiCategories, setPOICategories] = useState<POICategoriesMap>({});
+  const [selectedPOICategories, setSelectedPOICategories] = useState<Set<string>>(new Set());
   const poiDebounceRef = useRef<ReturnType<typeof setTimeout> | null>(null);
   const poiAbortControllerRef = useRef<AbortController | null>(null);
 
+  // Fetch POI category definitions from server on mount
+  useEffect(() => {
+    fetch(`${getApiBaseUrl()}/api/poi-categories`)
+      .then((res) => res.json())
+      .then((json: { categories: POICategoriesMap }) => {
+        setPOICategories(json.categories);
+      })
+      .catch((err) => console.error('Failed to fetch POI categories:', err));
+  }, []);
+
   // Debounced fetch when dependencies change
   useEffect(() => {
     if (!bounds) return;
 
-    // Clear previous debounce timer
     if (debounceRef.current) {
       clearTimeout(debounceRef.current);
     }
 
     debounceRef.current = setTimeout(async () => {
-      // Cancel any in-flight request
       if (abortControllerRef.current) {
         abortControllerRef.current.abort();
       }
@@ -167,6 +174,7 @@ export default function App() {
         filters={filters}
         onChange={setFilters}
         zoom={zoom}
+        poiCategories={poiCategories}
         selectedPOICategories={selectedPOICategories}
         onPOICategoriesChange={setSelectedPOICategories}
         colorMode={colorMode}
diff --git a/frontend/src/components/Filters.tsx b/frontend/src/components/Filters.tsx
index 9e078f9..b956df8 100644
--- a/frontend/src/components/Filters.tsx
+++ b/frontend/src/components/Filters.tsx
@@ -1,32 +1,25 @@
+import { useState, useRef, useEffect } from 'react';
 import { Slider } from './ui/slider';
 import { Label } from './ui/label';
 import { YEAR_MIN, YEAR_MAX, YEAR_STEP, PRICE_MIN, PRICE_MAX, PRICE_STEP } from '../lib/constants';
-import type { Filters as FiltersType, POICategoryGroup, ColorMode } from '../types';
-import { POI_CATEGORY_GROUPS } from '../types';
+import type { Filters as FiltersType, POICategoriesMap, ColorMode } from '../types';
 
 interface FiltersProps {
   filters: FiltersType;
   onChange: (filters: FiltersType) => void;
   zoom: number;
-  selectedPOICategories: Set<POICategoryGroup>;
-  onPOICategoriesChange: (categories: Set<POICategoryGroup>) => void;
+  poiCategories: POICategoriesMap;
+  selectedPOICategories: Set<string>;
+  onPOICategoriesChange: (categories: Set<string>) => void;
   colorMode: ColorMode;
   onColorModeChange: (mode: ColorMode) => void;
 }
 
-const POI_LABELS: Record<POICategoryGroup, string> = {
-  schools: '🏫 Schools',
-  healthcare: '🏥 Healthcare',
-  transport: '🚉 Transport',
-  parks: '🌳 Parks',
-  emergency: '🚨 Emergency',
-  supermarkets: '🛒 Supermarkets',
-};
-
 export default function Filters({
   filters,
   onChange,
   zoom,
+  poiCategories,
   selectedPOICategories,
   onPOICategoriesChange,
   colorMode,
@@ -34,16 +27,41 @@ export default function Filters({
 }: FiltersProps) {
   const update = (key: keyof FiltersType, value: number) => onChange({ ...filters, [key]: value });
 
-  const togglePOICategory = (category: POICategoryGroup) => {
+  const [dropdownOpen, setDropdownOpen] = useState(false);
+  const dropdownRef = useRef<HTMLDivElement>(null);
+
+  // Close dropdown when clicking outside
+  useEffect(() => {
+    function handleClickOutside(event: MouseEvent) {
+      if (dropdownRef.current && !dropdownRef.current.contains(event.target as Node)) {
+        setDropdownOpen(false);
+      }
+    }
+    document.addEventListener('mousedown', handleClickOutside);
+    return () => document.removeEventListener('mousedown', handleClickOutside);
+  }, []);
+
+  const toggleCategory = (key: string) => {
     const newSet = new Set(selectedPOICategories);
-    if (newSet.has(category)) {
-      newSet.delete(category);
+    if (newSet.has(key)) {
+      newSet.delete(key);
     } else {
-      newSet.add(category);
+      newSet.add(key);
     }
     onPOICategoriesChange(newSet);
   };
 
+  const selectAll = () => {
+    onPOICategoriesChange(new Set(Object.keys(poiCategories)));
+  };
+
+  const selectNone = () => {
+    onPOICategoriesChange(new Set());
+  };
+
+  const categoryKeys = Object.keys(poiCategories);
+  const selectedCount = selectedPOICategories.size;
+
   return (
     <div className="w-72 p-4 bg-white shadow-lg space-y-6 overflow-y-auto max-h-screen">
       <h1 className="text-xl font-bold">UK Property Prices</h1>
@@ -139,21 +157,69 @@ export default function Filters({
         </div>
       )}
 
-      <div className="space-y-2">
+      <div className="space-y-2" ref={dropdownRef}>
         <Label>Points of Interest</Label>
-        <div className="space-y-1">
-          {POI_CATEGORY_GROUPS.map((category) => (
-            <label key={category} className="flex items-center gap-2 cursor-pointer">
-              <input
-                type="checkbox"
-                checked={selectedPOICategories.has(category)}
-                onChange={() => togglePOICategory(category)}
-                className="rounded"
-              />
-              <span className="text-sm">{POI_LABELS[category]}</span>
-            </label>
-          ))}
-        </div>
+        <button
+          onClick={() => setDropdownOpen(!dropdownOpen)}
+          className="w-full flex items-center justify-between px-3 py-2 text-sm border border-slate-300 rounded hover:border-slate-400 bg-white"
+        >
+          <span className="truncate text-left">
+            {selectedCount === 0
+              ? 'Select categories...'
+              : selectedCount === categoryKeys.length
+                ? 'All categories'
+                : `${selectedCount} selected`}
+          </span>
+          <svg
+            className={`w-4 h-4 ml-2 flex-shrink-0 transition-transform ${dropdownOpen ? 'rotate-180' : ''}`}
+            fill="none"
+            stroke="currentColor"
+            viewBox="0 0 24 24"
+          >
+            <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M19 9l-7 7-7-7" />
+          </svg>
+        </button>
+
+        {dropdownOpen && (
+          <div className="border border-slate-300 rounded shadow-lg bg-white">
+            <div className="flex gap-2 px-3 py-2 border-b border-slate-200">
+              <button
+                onClick={selectAll}
+                className="text-xs text-blue-600 hover:text-blue-800"
+              >
+                All
+              </button>
+              <span className="text-xs text-slate-300">|</span>
+              <button
+                onClick={selectNone}
+                className="text-xs text-blue-600 hover:text-blue-800"
+              >
+                None
+              </button>
+            </div>
+            <div className="max-h-64 overflow-y-auto py-1">
+              {categoryKeys.map((key) => {
+                const { emoji, label } = poiCategories[key];
+                return (
+                  <label
+                    key={key}
+                    className="flex items-center gap-2 px-3 py-1.5 hover:bg-slate-50 cursor-pointer"
+                  >
+                    <input
+                      type="checkbox"
+                      checked={selectedPOICategories.has(key)}
+                      onChange={() => toggleCategory(key)}
+                      className="rounded"
+                    />
+                    <span className="text-sm">
+                      {emoji} {label}
+                    </span>
+                  </label>
+                );
+              })}
+            </div>
+          </div>
+        )}
       </div>
     </div>
   );
diff --git a/frontend/src/components/Map.tsx b/frontend/src/components/Map.tsx
index 0c52f12..a3e8ec2 100644
--- a/frontend/src/components/Map.tsx
+++ b/frontend/src/components/Map.tsx
@@ -1,6 +1,7 @@
 import { useCallback, useRef, useEffect, useState, useMemo } from 'react';
-import { Map as MapGL } from 'react-map-gl/maplibre';
-import DeckGL from '@deck.gl/react';
+import { Map as MapGL, useControl } from 'react-map-gl/maplibre';
+import type { MapRef } from 'react-map-gl/maplibre';
+import { MapboxOverlay } from '@deck.gl/mapbox';
 import { H3HexagonLayer } from '@deck.gl/geo-layers';
 import { IconLayer } from '@deck.gl/layers';
 import type { PickingInfo } from '@deck.gl/core';
@@ -19,35 +20,119 @@ const TWEMOJI_BASE = 'https://cdn.jsdelivr.net/gh/twitter/twemoji@14.0.2/assets/
 
 // Map category to Twemoji codepoint (emoji unicode -> hex)
 const POI_EMOJI_CODES: Record<string, string> = {
-  // Schools
-  elementary_school: '1f3eb', // 🏫
-  school: '1f3eb',
-  high_school: '1f393', // 🎓
+  // Education
+  school: '1f3eb', // 🏫
   preschool: '1f476', // 👶
-  college_university: '1f393',
-  private_school: '1f3eb',
+  college_university: '1f393', // 🎓
+  library: '1f4da', // 📚
   // Healthcare
   doctor: '1f3e5', // 🏥
   dentist: '1f9b7', // 🦷
   pharmacy: '1f48a', // 💊
   hospital: '1f3e5',
   public_health_clinic: '1f3e5',
+  veterinary: '1f43e', // 🐾
+  nursing_home: '1f3e0', // 🏠
+  social_facility: '1f91d', // 🤝
   // Transport
   train_station: '1f689', // 🚉
   bus_station: '1f68c', // 🚌
+  bus_stop: '1f68f', // 🚏
   metro_station: '1f687', // 🚇
-  light_rail_and_subway_stations: '1f687',
-  // Parks
+  light_rail_station: '1f687',
+  tram_stop: '1f68a', // 🚊
+  ferry_terminal: '26f4', // ⛴
+  airport: '2708', // ✈
+  // Parks & Leisure
   park: '1f333', // 🌳
   national_park: '1f3de', // 🏞
+  nature_reserve: '1f33f', // 🌿
   dog_park: '1f415', // 🐕
+  playground: '1f3a0', // 🎠
+  garden: '1f33a', // 🌺
+  sports_centre: '1f3c3', // 🏃
+  swimming_pool: '1f3ca', // 🏊
+  gym: '1f4aa', // 💪
+  golf_course: '26f3', // ⛳
+  marina: '26f5', // ⛵
   // Emergency
   police_department: '1f694', // 🚔
   fire_department: '1f692', // 🚒
-  // Supermarkets
+  // Supermarkets & Grocery
   supermarket: '1f6d2', // 🛒
   grocery_store: '1f6d2',
   convenience_store: '1f3ea', // 🏪
+  bakery: '1f35e', // 🍞
+  butcher: '1f969', // 🥩
+  greengrocer: '1f966', // 🥦
+  deli: '1f9c0', // 🧀
+  // Shopping
+  department_store: '1f3ec', // 🏬
+  clothing_store: '1f455', // 👕
+  shoe_store: '1f45f', // 👟
+  electronics_store: '1f4f1', // 📱
+  hardware_store: '1f527', // 🔧
+  furniture_store: '1fa91', // 🪑
+  bookshop: '1f4d6', // 📖
+  newsagent: '1f4f0', // 📰
+  charity_shop: '1f49c', // 💜
+  shopping_centre: '1f6cd', // 🛍
+  optician: '1f453', // 👓
+  off_licence: '1f37a', // 🍺
+  // Food & Drink
+  restaurant: '1f37d', // 🍽
+  cafe: '2615', // ☕
+  pub: '1f37b', // 🍻
+  bar: '1f378', // 🍸
+  fast_food: '1f354', // 🍔
+  food_court: '1f372', // 🍲
+  ice_cream: '1f366', // 🍦
+  beer_garden: '1f37a', // 🍺
+  // Personal Care
+  hairdresser: '1f487', // 💇
+  beauty_salon: '1f484', // 💄
+  laundry: '1f9fa', // 🧺
+  dry_cleaning: '1f455', // 👕
+  // Finance
+  bank: '1f3e6', // 🏦
+  atm: '1f4b3', // 💳
+  bureau_de_change: '1f4b1', // 💱
+  // Entertainment & Culture
+  cinema: '1f3ac', // 🎬
+  theatre: '1f3ad', // 🎭
+  nightclub: '1f483', // 💃
+  community_centre: '1f3db', // 🏛
+  arts_centre: '1f3a8', // 🎨
+  museum: '1f3db', // 🏛
+  gallery: '1f5bc', // 🖼
+  attraction: '2b50', // ⭐
+  zoo: '1f418', // 🐘
+  theme_park: '1f3a2', // 🎢
+  viewpoint: '1f301', // 🌁
+  // Accommodation
+  hotel: '1f3e8', // 🏨
+  hostel: '1f6cf', // 🛏
+  guest_house: '1f3e1', // 🏡
+  campsite: '26fa', // ⛺
+  caravan_site: '1f699', // 🚙
+  // Religion
+  place_of_worship: '1f6d0', // 🛐
+  // Government & Public
+  town_hall: '1f3db', // 🏛
+  courthouse: '2696', // ⚖
+  post_office: '1f4ee', // 📮
+  prison: '1f513', // 🔓
+  public_toilets: '1f6bb', // 🚻
+  // Automotive
+  petrol_station: '26fd', // ⛽
+  ev_charging: '1f50c', // 🔌
+  car_dealer: '1f697', // 🚗
+  car_repair: '1f527', // 🔧
+  parking: '1f17f', // 🅿
+  bicycle_parking: '1f6b2', // 🚲
+  // Recycling & Waste
+  recycling: '267b', // ♻
+  waste_disposal: '1f5d1', // 🗑
 };
 
 function getPOIIconUrl(category: string): string {
@@ -57,29 +142,34 @@ function getPOIIconUrl(category: string): string {
 
 // Tooltip emojis (these render fine in HTML)
 const TOOLTIP_EMOJIS: Record<string, string> = {
-  elementary_school: '🏫',
-  school: '🏫',
-  high_school: '🎓',
-  preschool: '👶',
-  college_university: '🎓',
-  private_school: '🏫',
-  doctor: '👨‍⚕️',
-  dentist: '🦷',
-  pharmacy: '💊',
-  hospital: '🏥',
-  public_health_clinic: '🏥',
-  train_station: '🚉',
-  bus_station: '🚌',
-  metro_station: '🚇',
-  light_rail_and_subway_stations: '🚇',
-  park: '🌳',
-  national_park: '🏞️',
-  dog_park: '🐕',
-  police_department: '🚔',
-  fire_department: '🚒',
-  supermarket: '🛒',
-  grocery_store: '🛒',
-  convenience_store: '🏪',
+  school: '🏫', preschool: '👶', college_university: '🎓', library: '📚',
+  doctor: '🏥', dentist: '🦷', pharmacy: '💊', hospital: '🏥',
+  public_health_clinic: '🏥', veterinary: '🐾', nursing_home: '🏠', social_facility: '🤝',
+  train_station: '🚉', bus_station: '🚌', bus_stop: '🚏', metro_station: '🚇',
+  light_rail_station: '🚇', tram_stop: '🚊', ferry_terminal: '⛴️', airport: '✈️',
+  park: '🌳', national_park: '🏞️', nature_reserve: '🌿', dog_park: '🐕',
+  playground: '🎠', garden: '🌺', sports_centre: '🏃', swimming_pool: '🏊',
+  gym: '💪', golf_course: '⛳', marina: '⛵',
+  police_department: '🚔', fire_department: '🚒',
+  supermarket: '🛒', grocery_store: '🛒', convenience_store: '🏪',
+  bakery: '🍞', butcher: '🥩', greengrocer: '🥦', deli: '🧀',
+  department_store: '🏬', clothing_store: '👕', shoe_store: '👟',
+  electronics_store: '📱', hardware_store: '🔧', furniture_store: '🪑',
+  bookshop: '📖', newsagent: '📰', charity_shop: '💜', shopping_centre: '🛍️',
+  optician: '👓', off_licence: '🍺',
+  restaurant: '🍽️', cafe: '☕', pub: '🍻', bar: '🍸',
+  fast_food: '🍔', food_court: '🍲', ice_cream: '🍦', beer_garden: '🍺',
+  hairdresser: '💇', beauty_salon: '💄', laundry: '🧺', dry_cleaning: '👕',
+  bank: '🏦', atm: '💳', bureau_de_change: '💱',
+  cinema: '🎬', theatre: '🎭', nightclub: '💃', community_centre: '🏛️',
+  arts_centre: '🎨', museum: '🏛️', gallery: '🖼️', attraction: '⭐',
+  zoo: '🐘', theme_park: '🎢', viewpoint: '🌁',
+  hotel: '🏨', hostel: '🛏️', guest_house: '🏡', campsite: '⛺', caravan_site: '🚙',
+  place_of_worship: '🛐',
+  town_hall: '🏛️', courthouse: '⚖️', post_office: '📮', prison: '🔓', public_toilets: '🚻',
+  petrol_station: '⛽', ev_charging: '🔌', car_dealer: '🚗', car_repair: '🔧',
+  parking: '🅿️', bicycle_parking: '🚲',
+  recycling: '♻️', waste_disposal: '🗑️',
 };
 
 function getTooltipEmoji(category: string): string {
@@ -158,7 +248,7 @@ function journeyTimeToColor(minutes: number | null | undefined): [number, number
 }
 
 function zoomToResolution(zoom: number): number {
-  if (zoom < 8.5) return 7;
+  if (zoom < 7) return 7;
   if (zoom < 9.5) return 8;
   if (zoom < 11) return 9;
   if (zoom < 13) return 10;
@@ -209,6 +299,22 @@ interface Dimensions {
   height: number;
 }
 
+// First label layer in the Carto Positron style — hexagons render below this
+const LABEL_LAYER_ID = 'waterway_label';
+
+function DeckOverlay({
+  layers,
+  getTooltip,
+}: {
+  layers: (H3HexagonLayer<HexagonData> | IconLayer<POI>)[];
+  // eslint-disable-next-line @typescript-eslint/no-explicit-any
+  getTooltip: any;
+}) {
+  const overlay = useControl(() => new MapboxOverlay({ interleaved: true }));
+  overlay.setProps({ layers, getTooltip });
+  return null;
+}
+
 export default function Map({ data, pois, onViewChange, colorMode }: MapProps) {
   const containerRef = useRef<HTMLDivElement>(null);
   const [viewState, setViewState] = useState<ViewState>(INITIAL_VIEW);
@@ -240,12 +346,23 @@ export default function Map({ data, pois, onViewChange, colorMode }: MapProps) {
     onViewChange({ resolution, bounds, zoom: viewState.zoom });
   }, [viewState, dimensions, onViewChange]);
 
-  const handleViewStateChange = useCallback((params: { viewState: unknown }) => {
-    const newViewState = params.viewState as ViewState;
-    setViewState(newViewState);
+  const handleMove = useCallback((evt: { viewState: ViewState }) => {
+    setViewState(evt.viewState);
   }, []);
 
-  // Popup state for POI hover (using screen coordinates)
+  // Make place labels more legible over the colored hexagons
+  const handleMapLoad = useCallback((evt: { target: MapRef['getMap'] extends () => infer M ? M : never }) => {
+    const map = evt.target;
+    for (const layer of map.getStyle().layers || []) {
+      if (layer.type !== 'symbol') continue;
+      // Stronger white halo so text pops over hex fills
+      map.setPaintProperty(layer.id, 'text-halo-color', 'rgba(255,255,255,1)');
+      map.setPaintProperty(layer.id, 'text-halo-width', 2);
+      map.setPaintProperty(layer.id, 'text-color', '#222');
+    }
+  }, []);
+
+  // Popup state for POI hover
   const [popupInfo, setPopupInfo] = useState<{
     x: number;
     y: number;
@@ -283,6 +400,9 @@ export default function Map({ data, pois, onViewChange, colorMode }: MapProps) {
         pickable: true,
         opacity: 0.5,
         highPrecision: true,
+        // Render below labels so road names, place names etc. stay visible
+        // @ts-expect-error beforeId is a MapboxOverlay interleave prop, not typed in LayerProps
+        beforeId: LABEL_LAYER_ID,
       }),
       new IconLayer<POI>({
         id: 'poi-icons',
@@ -303,7 +423,6 @@ export default function Map({ data, pois, onViewChange, colorMode }: MapProps) {
     [data, pois, handlePoiHover, colorMode]
   );
 
-  // Tooltip for hexagons only (POIs use MapLibre popup)
   const getTooltip = useCallback(({ object }: { object?: HexagonData }) => {
     if (!object || !('h3' in object)) return null;
 
@@ -339,15 +458,15 @@ export default function Map({ data, pois, onViewChange, colorMode }: MapProps) {
 
   return (
     <div className="flex-1 h-full relative" ref={containerRef}>
-      <DeckGL
-        viewState={viewState}
-        controller
-        layers={layers}
-        onViewStateChange={handleViewStateChange as never}
-        getTooltip={getTooltip as never}
+      <MapGL
+        {...viewState}
+        onMove={handleMove}
+        onLoad={handleMapLoad as never}
+        mapStyle={MAP_STYLE}
+        style={{ width: '100%', height: '100%' }}
       >
-        <MapGL mapStyle={MAP_STYLE} />
-      </DeckGL>
+        <DeckOverlay layers={layers} getTooltip={getTooltip as never} />
+      </MapGL>
       {popupInfo && (
         <div
           className="absolute pointer-events-none bg-white rounded shadow-lg p-2 text-sm"
diff --git a/frontend/src/types.ts b/frontend/src/types.ts
index 16d0a3f..d372bc2 100644
--- a/frontend/src/types.ts
+++ b/frontend/src/types.ts
@@ -57,13 +57,9 @@ export interface POIResponse {
   features: POI[];
 }
 
-export const POI_CATEGORY_GROUPS = [
-  'schools',
-  'healthcare',
-  'transport',
-  'parks',
-  'emergency',
-  'supermarkets',
-] as const;
+export interface POICategoryInfo {
+  emoji: string;
+  label: string;
+}
 
-export type POICategoryGroup = (typeof POI_CATEGORY_GROUPS)[number];
+export type POICategoriesMap = Record<string, POICategoryInfo>;
diff --git a/pipeline/pois/__init__.py b/pipeline/pois/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pipeline/pois/__main__.py b/pipeline/pois/__main__.py
new file mode 100644
index 0000000..ac6c0b1
--- /dev/null
+++ b/pipeline/pois/__main__.py
@@ -0,0 +1,181 @@
+"""Single-pass POI extraction from OSM PBF file using pyosmium."""
+
+import json
+import urllib.request
+
+import osmium
+import polars as pl
+from tqdm import tqdm
+
+from .config import (
+    GB_PBF_FILE,
+    GEOFABRIK_GB_URL,
+    OSM_TAG_MAPPING,
+    OUTPUT_FILE,
+    TAG_KEYS_TO_CHECK,
+    UK_BBOX_EAST,
+    UK_BBOX_NORTH,
+    UK_BBOX_SOUTH,
+    UK_BBOX_WEST,
+)
+
+# Approximate element count for the GB PBF extract (for progress estimation).
+ESTIMATED_ELEMENTS = 500_000_000
+
+
+def download_pbf() -> None:
+    """Download Great Britain PBF extract from Geofabrik."""
+    GB_PBF_FILE.parent.mkdir(parents=True, exist_ok=True)
+    tmp = GB_PBF_FILE.with_suffix(".pbf.tmp")
+    print(f"Downloading {GEOFABRIK_GB_URL}")
+
+    with (
+        tqdm(unit="B", unit_scale=True, desc="Downloading") as bar,
+        urllib.request.urlopen(GEOFABRIK_GB_URL) as resp,
+        open(tmp, "wb") as f,
+    ):
+        length = resp.headers.get("Content-Length")
+        if length:
+            bar.total = int(length)
+        while chunk := resp.read(1 << 20):
+            f.write(chunk)
+            bar.update(len(chunk))
+
+    tmp.rename(GB_PBF_FILE)
+    print(f"Saved to {GB_PBF_FILE}")
+
+
+class POIHandler(osmium.SimpleHandler):
+    """Streams OSM data, filters to UK bbox, extracts matching POIs."""
+
+    def __init__(self, progress: tqdm) -> None:
+        super().__init__()
+        self.pois: list[dict] = []
+        self._poi_count = 0
+        self._progress = progress
+
+    def _in_uk(self, lat: float, lon: float) -> bool:
+        return (
+            UK_BBOX_SOUTH <= lat <= UK_BBOX_NORTH
+            and UK_BBOX_WEST <= lon <= UK_BBOX_EAST
+        )
+
+    def _match_tags(self, tags: osmium.osm.TagList) -> str | None:
+        for key in TAG_KEYS_TO_CHECK:
+            if key in tags:
+                value = tags[key]
+                if value in TAG_KEYS_TO_CHECK[key]:
+                    return OSM_TAG_MAPPING[(key, value)]
+        return None
+
+    def _get_name(self, tags: osmium.osm.TagList) -> str:
+        return tags.get("name:en", tags.get("name", ""))
+
+    def _tags_to_json(self, tags: osmium.osm.TagList) -> str:
+        return json.dumps({tag.k: tag.v for tag in tags})
+
+    def _add_poi(
+        self, osm_id: str, tags: osmium.osm.TagList, category: str, lat: float, lng: float
+    ) -> None:
+        self.pois.append(
+            {
+                "id": osm_id,
+                "name": self._get_name(tags),
+                "category": category,
+                "lat": lat,
+                "lng": lng,
+                "osm_tags": self._tags_to_json(tags),
+            }
+        )
+        self._poi_count += 1
+        self._progress.set_postfix(pois=f"{self._poi_count:,}", refresh=False)
+
+    def _tick(self) -> None:
+        self._progress.update(1)
+
+    def node(self, n: osmium.osm.Node) -> None:
+        self._tick()
+        if not n.location.valid:
+            return
+        lat, lon = n.location.lat, n.location.lon
+        if not self._in_uk(lat, lon):
+            return
+        category = self._match_tags(n.tags)
+        if category:
+            self._add_poi(f"n{n.id}", n.tags, category, lat, lon)
+
+    def way(self, w: osmium.osm.Way) -> None:
+        self._tick()
+        category = self._match_tags(w.tags)
+        if not category:
+            return
+
+        lats = []
+        lons = []
+        for node in w.nodes:
+            try:
+                lats.append(node.location.lat)
+                lons.append(node.location.lon)
+            except osmium.InvalidLocationError:
+                continue
+
+        if not lats:
+            return
+
+        centroid_lat = sum(lats) / len(lats)
+        centroid_lng = sum(lons) / len(lons)
+
+        if not self._in_uk(centroid_lat, centroid_lng):
+            return
+
+        self._add_poi(f"w{w.id}", w.tags, category, centroid_lat, centroid_lng)
+
+
+def main() -> None:
+    if not GB_PBF_FILE.exists():
+        download_pbf()
+
+    print(f"=== POI Extraction from {GB_PBF_FILE} ===")
+    print(
+        f"UK bbox: ({UK_BBOX_WEST}, {UK_BBOX_SOUTH}, {UK_BBOX_EAST}, {UK_BBOX_NORTH})"
+    )
+    print(f"Categories: {len(OSM_TAG_MAPPING)}")
+    print()
+
+    with tqdm(
+        total=ESTIMATED_ELEMENTS,
+        unit=" elements",
+        unit_scale=True,
+        desc="Streaming",
+        smoothing=0.05,
+        mininterval=1.0,
+    ) as progress:
+        handler = POIHandler(progress)
+        handler.apply_file(str(GB_PBF_FILE), locations=True)
+
+    print(f"Extracted {len(handler.pois):,} POIs")
+
+    if not handler.pois:
+        print("No POIs found.")
+        return
+
+    df = pl.DataFrame(handler.pois)
+
+    OUTPUT_FILE.parent.mkdir(parents=True, exist_ok=True)
+    df.write_parquet(OUTPUT_FILE)
+    print(f"Saved to {OUTPUT_FILE}")
+
+    print("\n=== Summary ===")
+    print(f"Total POIs: {len(df):,}")
+    print("\nPOIs by category:")
+    category_counts = (
+        df.group_by("category")
+        .agg(pl.len().alias("count"))
+        .sort("count", descending=True)
+    )
+    for row in category_counts.iter_rows(named=True):
+        print(f"  {row['category']}: {row['count']:,}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/pipeline/pois/config.py b/pipeline/pois/config.py
new file mode 100644
index 0000000..14fb439
--- /dev/null
+++ b/pipeline/pois/config.py
@@ -0,0 +1,147 @@
+"""Configuration for POI extraction from OpenStreetMap."""
+
+from pathlib import Path
+
+# File paths
+DATA_DIR = Path(__file__).parent.parent.parent / "data_sources"
+GB_PBF_FILE = DATA_DIR / "great-britain-latest.osm.pbf"
+OUTPUT_FILE = DATA_DIR / "uk_pois.parquet"
+
+# Geofabrik download URL for Great Britain (~1.1GB PBF, much faster than planet XML)
+GEOFABRIK_GB_URL = (
+    "https://download.geofabrik.de/europe/great-britain-latest.osm.pbf"
+)
+
+# UK bounding box (west, south, east, north) — used for way centroid filtering
+UK_BBOX_WEST = -7.57
+UK_BBOX_SOUTH = 49.96
+UK_BBOX_EAST = 1.68
+UK_BBOX_NORTH = 58.64
+
+# OSM tag mapping to categories
+# Maps (tag_key, tag_value) -> category name
+OSM_TAG_MAPPING: dict[tuple[str, str], str] = {
+    # Education
+    ("amenity", "school"): "school",
+    ("amenity", "kindergarten"): "preschool",
+    ("amenity", "college"): "college_university",
+    ("amenity", "university"): "college_university",
+    ("amenity", "library"): "library",
+    ("amenity", "language_school"): "school",
+    ("amenity", "music_school"): "school",
+    ("amenity", "driving_school"): "school",
+    # Healthcare
+    ("amenity", "hospital"): "hospital",
+    ("amenity", "clinic"): "public_health_clinic",
+    ("amenity", "doctors"): "doctor",
+    ("amenity", "dentist"): "dentist",
+    ("amenity", "pharmacy"): "pharmacy",
+    ("amenity", "veterinary"): "veterinary",
+    ("amenity", "nursing_home"): "nursing_home",
+    ("amenity", "social_facility"): "social_facility",
+    # Transport
+    ("railway", "station"): "train_station",
+    ("railway", "halt"): "train_station",
+    ("railway", "tram_stop"): "tram_stop",
+    ("amenity", "bus_station"): "bus_station",
+    ("amenity", "ferry_terminal"): "ferry_terminal",
+    ("public_transport", "station"): "train_station",
+    ("public_transport", "stop_position"): "bus_stop",
+    ("station", "subway"): "metro_station",
+    ("station", "light_rail"): "light_rail_station",
+    ("aeroway", "aerodrome"): "airport",
+    ("highway", "bus_stop"): "bus_stop",
+    # Parks & Leisure
+    ("leisure", "park"): "park",
+    ("leisure", "nature_reserve"): "nature_reserve",
+    ("leisure", "dog_park"): "dog_park",
+    ("leisure", "playground"): "playground",
+    ("leisure", "sports_centre"): "sports_centre",
+    ("leisure", "swimming_pool"): "swimming_pool",
+    ("leisure", "fitness_centre"): "gym",
+    ("leisure", "golf_course"): "golf_course",
+    ("leisure", "garden"): "garden",
+    ("leisure", "marina"): "marina",
+    ("boundary", "national_park"): "national_park",
+    # Emergency
+    ("amenity", "police"): "police_department",
+    ("amenity", "fire_station"): "fire_department",
+    # Shopping
+    ("shop", "supermarket"): "supermarket",
+    ("shop", "convenience"): "convenience_store",
+    ("shop", "grocery"): "grocery_store",
+    ("shop", "bakery"): "bakery",
+    ("shop", "butcher"): "butcher",
+    ("shop", "greengrocer"): "greengrocer",
+    ("shop", "deli"): "deli",
+    ("shop", "department_store"): "department_store",
+    ("shop", "clothes"): "clothing_store",
+    ("shop", "shoes"): "shoe_store",
+    ("shop", "electronics"): "electronics_store",
+    ("shop", "hardware"): "hardware_store",
+    ("shop", "furniture"): "furniture_store",
+    ("shop", "car"): "car_dealer",
+    ("shop", "car_repair"): "car_repair",
+    ("shop", "hairdresser"): "hairdresser",
+    ("shop", "beauty"): "beauty_salon",
+    ("shop", "optician"): "optician",
+    ("shop", "newsagent"): "newsagent",
+    ("shop", "books"): "bookshop",
+    ("shop", "charity"): "charity_shop",
+    ("shop", "alcohol"): "off_licence",
+    ("shop", "laundry"): "laundry",
+    ("shop", "dry_cleaning"): "dry_cleaning",
+    ("shop", "mall"): "shopping_centre",
+    # Food & Drink
+    ("amenity", "restaurant"): "restaurant",
+    ("amenity", "cafe"): "cafe",
+    ("amenity", "pub"): "pub",
+    ("amenity", "bar"): "bar",
+    ("amenity", "fast_food"): "fast_food",
+    ("amenity", "food_court"): "food_court",
+    ("amenity", "ice_cream"): "ice_cream",
+    ("amenity", "biergarten"): "beer_garden",
+    # Finance
+    ("amenity", "bank"): "bank",
+    ("amenity", "atm"): "atm",
+    ("amenity", "bureau_de_change"): "bureau_de_change",
+    # Entertainment & Culture
+    ("amenity", "cinema"): "cinema",
+    ("amenity", "theatre"): "theatre",
+    ("amenity", "nightclub"): "nightclub",
+    ("amenity", "community_centre"): "community_centre",
+    ("amenity", "arts_centre"): "arts_centre",
+    ("tourism", "museum"): "museum",
+    ("tourism", "gallery"): "gallery",
+    ("tourism", "attraction"): "attraction",
+    ("tourism", "zoo"): "zoo",
+    ("tourism", "theme_park"): "theme_park",
+    ("tourism", "viewpoint"): "viewpoint",
+    # Accommodation
+    ("tourism", "hotel"): "hotel",
+    ("tourism", "hostel"): "hostel",
+    ("tourism", "guest_house"): "guest_house",
+    ("tourism", "camp_site"): "campsite",
+    ("tourism", "caravan_site"): "caravan_site",
+    # Religion
+    ("amenity", "place_of_worship"): "place_of_worship",
+    # Government & Public
+    ("amenity", "townhall"): "town_hall",
+    ("amenity", "courthouse"): "courthouse",
+    ("amenity", "post_office"): "post_office",
+    ("amenity", "prison"): "prison",
+    ("amenity", "recycling"): "recycling",
+    ("amenity", "waste_disposal"): "waste_disposal",
+    ("amenity", "toilets"): "public_toilets",
+    # Fuel
+    ("amenity", "fuel"): "petrol_station",
+    ("amenity", "charging_station"): "ev_charging",
+    # Parking
+    ("amenity", "parking"): "parking",
+    ("amenity", "bicycle_parking"): "bicycle_parking",
+}
+
+# Build reverse lookup: tag_key -> set of tag_values we care about
+TAG_KEYS_TO_CHECK: dict[str, set[str]] = {}
+for (key, value), _ in OSM_TAG_MAPPING.items():
+    TAG_KEYS_TO_CHECK.setdefault(key, set()).add(value)
diff --git a/pipeline/processors/journey_times_aggregator.py b/pipeline/processors/journey_times_aggregator.py
index ffb6b6f..5f6dfb9 100644
--- a/pipeline/processors/journey_times_aggregator.py
+++ b/pipeline/processors/journey_times_aggregator.py
@@ -6,31 +6,47 @@ import polars as pl
 
 from pipeline.config import AGGREGATES_DIR, H3_RESOLUTIONS, PROCESSED_DIR
 
+JOURNEY_COLS = [
+    "public_transport_easy_minutes",
+    "public_transport_quick_minutes",
+    "cycling_minutes",
+]
+
+AGGREGATE_COLS = [
+    "median_pt_easy_minutes",
+    "median_pt_quick_minutes",
+    "median_cycling_minutes",
+    "median_journey_minutes",
+]
+
 
 def aggregate_journey_times(
     journey_times_path: Path | None = None,
     postcodes_h3_path: Path | None = None,
-    output_dir: Path | None = None,
+    aggregates_dir: Path | None = None,
 ) -> list[Path]:
     """
-    Aggregate journey times by H3 cells at all resolutions.
+    Add journey times to existing H3 aggregate parquet files.
 
-    Joins journey_times_bank.parquet with postcodes_h3.parquet on postcode,
-    then groups by H3 cell to compute median journey time.
+    Joins journey_times_bank_checkpoint.parquet with postcodes_h3.parquet on postcode,
+    aggregates by H3 cell, then merges into existing res{N}.parquet files.
     """
-    journey_times_path = journey_times_path or PROCESSED_DIR / "journey_times_bank.parquet"
+    journey_times_path = (
+        journey_times_path
+        or PROCESSED_DIR / "journey_times_bank_checkpoint.parquet"
+    )
     postcodes_h3_path = postcodes_h3_path or PROCESSED_DIR / "postcodes_h3.parquet"
-    output_dir = output_dir or AGGREGATES_DIR
-
-    output_dir.mkdir(parents=True, exist_ok=True)
+    aggregates_dir = aggregates_dir or AGGREGATES_DIR
 
     # Load journey times data
     journey_df = pl.read_parquet(journey_times_path).select(
-        ["postcode", "public_transport_minutes"]
+        ["postcode"] + JOURNEY_COLS
     )
 
-    # Filter out null journey times
-    journey_df = journey_df.filter(pl.col("public_transport_minutes").is_not_null())
+    # Filter out rows where all journey time columns are null
+    journey_df = journey_df.filter(
+        pl.any_horizontal(pl.col(c).is_not_null() for c in JOURNEY_COLS)
+    )
 
     if journey_df.height == 0:
         print("No valid journey times found")
@@ -48,31 +64,63 @@ def aggregate_journey_times(
 
     print(f"Joined {joined_df.height} postcodes with journey times")
 
-    saved_paths = []
+    updated_paths = []
 
     for resolution in H3_RESOLUTIONS:
         h3_col = f"h3_res{resolution}"
+        parquet_path = aggregates_dir / f"res{resolution}.parquet"
+
+        if not parquet_path.exists():
+            print(f"Skipping resolution {resolution} - {parquet_path} not found")
+            continue
 
         if h3_col not in joined_df.columns:
             print(f"Skipping resolution {resolution} - column {h3_col} not found")
             continue
 
-        # Aggregate by H3 cell - compute median journey time
-        agg_df = (
+        # Aggregate journey times by H3 cell
+        journey_agg = (
             joined_df.group_by(h3_col)
             .agg(
-                pl.col("public_transport_minutes").median().alias("median_journey_minutes"),
-                pl.col("public_transport_minutes").count().alias("journey_count"),
+                pl.col("public_transport_easy_minutes")
+                .median()
+                .alias("median_pt_easy_minutes"),
+                pl.col("public_transport_quick_minutes")
+                .median()
+                .alias("median_pt_quick_minutes"),
+                pl.col("cycling_minutes")
+                .median()
+                .alias("median_cycling_minutes"),
+                pl.col("public_transport_quick_minutes")
+                .median()
+                .alias("median_journey_minutes"),
             )
             .rename({h3_col: "h3"})
         )
 
-        output_path = output_dir / f"journey_times_res{resolution}.parquet"
-        agg_df.write_parquet(output_path)
-        saved_paths.append(output_path)
-        print(f"Saved {agg_df.height} cells to {output_path}")
+        # Load existing parquet
+        existing_df = pl.read_parquet(parquet_path)
 
-    return saved_paths
+        # Drop existing journey time columns if present
+        existing_df = existing_df.drop(
+            [c for c in AGGREGATE_COLS if c in existing_df.columns]
+        )
+
+        # Left join journey times onto existing data
+        updated_df = existing_df.join(journey_agg, on="h3", how="left")
+
+        # Save back to parquet
+        updated_df.write_parquet(parquet_path)
+        updated_paths.append(parquet_path)
+        matched = updated_df.filter(
+            pl.col("median_journey_minutes").is_not_null()
+        ).height
+        print(
+            f"Updated {parquet_path.name}: {matched} rows with journey times "
+            f"(out of {updated_df.height} total)"
+        )
+
+    return updated_paths
 
 
 if __name__ == "__main__":
diff --git a/pipeline/run.py b/pipeline/run.py
index 3502ff2..630a892 100644
--- a/pipeline/run.py
+++ b/pipeline/run.py
@@ -5,6 +5,7 @@ import polars as pl
 from pipeline.sources.postcodes import save_postcodes
 from pipeline.sources.property_prices import PropertyPricesSource
 from pipeline.processors.h3_aggregator import save_aggregates
+from pipeline.processors.journey_times_aggregator import aggregate_journey_times
 
 
 def run_pipeline():
@@ -14,22 +15,31 @@ def run_pipeline():
     print("=" * 60)
 
     # Step 1: Process postcodes with H3 indices
-    print("\n[1/3] Processing postcodes with H3 indices...")
+    print("\n[1/4] Processing postcodes with H3 indices...")
     postcodes_path = save_postcodes()
     print(f"      Saved: {postcodes_path}")
 
-    print("\n[2/3] Processing property prices...")
+    print("\n[2/4] Processing property prices...")
     postcodes = pl.scan_parquet(postcodes_path)
     property_source = PropertyPricesSource()
     properties = property_source.process(postcodes)
     print("      Joined property prices with postcodes")
 
-    print("\n[3/3] Aggregating at H3 resolutions...")
+    print("\n[3/4] Aggregating at H3 resolutions...")
     saved_paths = save_aggregates(properties)
     for path in saved_paths:
         size_mb = path.stat().st_size / (1024 * 1024)
         print(f"      Saved: {path.name} ({size_mb:.1f} MB)")
 
+    print("\n[4/4] Adding journey times to aggregates...")
+    updated_paths = aggregate_journey_times()
+    if updated_paths:
+        for path in updated_paths:
+            size_mb = path.stat().st_size / (1024 * 1024)
+            print(f"      Updated: {path.name} ({size_mb:.1f} MB)")
+    else:
+        print("      Skipped (no journey time data found)")
+
 
 if __name__ == "__main__":
     run_pipeline()
diff --git a/server/routes/hexagons.py b/server/routes/hexagons.py
index 987c749..afe3777 100644
--- a/server/routes/hexagons.py
+++ b/server/routes/hexagons.py
@@ -77,14 +77,28 @@ def query_hexagons_cached(
     # Filter by year range
     df = df.filter((pl.col("year") >= min_year) & (pl.col("year") <= max_year))
 
+    # Check which journey time columns exist
+    journey_cols = [
+        "median_journey_minutes",
+        "median_pt_easy_minutes",
+        "median_pt_quick_minutes",
+        "median_cycling_minutes",
+    ]
+    available_journey_cols = [c for c in journey_cols if c in df.columns]
+
     # Aggregate across years (weighted by count)
-    df = df.group_by("h3").agg(
+    agg_exprs = [
         pl.col("count").sum().alias("count"),
         (pl.col("avg_price") * pl.col("count")).sum().alias("weighted_price_sum"),
         pl.col("median_price").median().alias("median_price"),
         pl.col("min_price").min().alias("min_price"),
         pl.col("max_price").max().alias("max_price"),
-    )
+    ]
+    for jc in available_journey_cols:
+        # Journey time is same across years, just take first non-null
+        agg_exprs.append(pl.col(jc).first())
+
+    df = df.group_by("h3").agg(agg_exprs)
 
     # Calculate weighted average price
     df = df.with_columns(
@@ -97,16 +111,18 @@ def query_hexagons_cached(
     )
 
     # Build response efficiently using Polars
-    df = df.select(
-        [
-            pl.col("h3"),
-            pl.col("count"),
-            pl.col("avg_price").round(2),
-            pl.col("median_price").round(2),
-            pl.col("min_price"),
-            pl.col("max_price"),
-        ]
-    )
+    select_cols = [
+        pl.col("h3"),
+        pl.col("count"),
+        pl.col("avg_price").round(2),
+        pl.col("median_price").round(2),
+        pl.col("min_price"),
+        pl.col("max_price"),
+    ]
+    for jc in available_journey_cols:
+        select_cols.append(pl.col(jc).round(0))
+
+    df = df.select(select_cols)
 
     return df.to_dicts()
 
diff --git a/server/routes/pois.py b/server/routes/pois.py
index d7e0e58..fcc225b 100644
--- a/server/routes/pois.py
+++ b/server/routes/pois.py
@@ -1,9 +1,5 @@
 """POI (Points of Interest) API endpoint."""
 
-import os
-
-os.environ["POLARS_UNKNOWN_EXTENSION_TYPE_BEHAVIOR"] = "load_as_storage"
-
 from pathlib import Path
 
 from fastapi import APIRouter, Query
@@ -13,36 +9,190 @@ router = APIRouter()
 
 DATA_FILE = Path("data_sources/uk_pois.parquet")
 
-# Categories useful for property buyers
-POI_CATEGORIES = {
-    "schools": [
-        "elementary_school",
-        "school",
-        "high_school",
-        "preschool",
-        "college_university",
-        "private_school",
-    ],
-    "healthcare": [
-        "doctor",
-        "dentist",
-        "pharmacy",
-        "hospital",
-        "public_health_clinic",
-    ],
-    "transport": [
-        "train_station",
-        "bus_station",
-        "metro_station",
-        "light_rail_and_subway_stations",
-    ],
-    "parks": ["park", "national_park", "dog_park"],
-    "emergency": ["police_department", "fire_department"],
-    "supermarkets": ["supermarket", "grocery_store", "convenience_store"],
+# Category groups with emoji and member categories
+POI_CATEGORY_GROUPS: dict[str, dict] = {
+    "schools": {
+        "emoji": "🏫",
+        "label": "Schools",
+        "categories": ["school", "preschool", "college_university", "library"],
+    },
+    "healthcare": {
+        "emoji": "🏥",
+        "label": "Healthcare",
+        "categories": [
+            "doctor",
+            "dentist",
+            "pharmacy",
+            "hospital",
+            "public_health_clinic",
+            "veterinary",
+            "nursing_home",
+            "social_facility",
+        ],
+    },
+    "transport": {
+        "emoji": "🚉",
+        "label": "Transport",
+        "categories": [
+            "train_station",
+            "bus_station",
+            "bus_stop",
+            "metro_station",
+            "light_rail_station",
+            "tram_stop",
+            "ferry_terminal",
+            "airport",
+        ],
+    },
+    "parks": {
+        "emoji": "🌳",
+        "label": "Parks & Leisure",
+        "categories": [
+            "park",
+            "national_park",
+            "nature_reserve",
+            "dog_park",
+            "playground",
+            "garden",
+            "sports_centre",
+            "swimming_pool",
+            "gym",
+            "golf_course",
+            "marina",
+        ],
+    },
+    "emergency": {
+        "emoji": "🚨",
+        "label": "Emergency",
+        "categories": ["police_department", "fire_department"],
+    },
+    "supermarkets": {
+        "emoji": "🛒",
+        "label": "Supermarkets & Grocery",
+        "categories": [
+            "supermarket",
+            "grocery_store",
+            "convenience_store",
+            "bakery",
+            "butcher",
+            "greengrocer",
+            "deli",
+        ],
+    },
+    "shopping": {
+        "emoji": "🛍️",
+        "label": "Shopping",
+        "categories": [
+            "department_store",
+            "clothing_store",
+            "shoe_store",
+            "electronics_store",
+            "hardware_store",
+            "furniture_store",
+            "bookshop",
+            "newsagent",
+            "charity_shop",
+            "shopping_centre",
+            "optician",
+            "off_licence",
+        ],
+    },
+    "food_drink": {
+        "emoji": "🍽️",
+        "label": "Food & Drink",
+        "categories": [
+            "restaurant",
+            "cafe",
+            "pub",
+            "bar",
+            "fast_food",
+            "food_court",
+            "ice_cream",
+            "beer_garden",
+        ],
+    },
+    "personal_care": {
+        "emoji": "💇",
+        "label": "Personal Care",
+        "categories": [
+            "hairdresser",
+            "beauty_salon",
+            "laundry",
+            "dry_cleaning",
+        ],
+    },
+    "finance": {
+        "emoji": "🏦",
+        "label": "Finance",
+        "categories": ["bank", "atm", "bureau_de_change"],
+    },
+    "entertainment": {
+        "emoji": "🎭",
+        "label": "Entertainment & Culture",
+        "categories": [
+            "cinema",
+            "theatre",
+            "nightclub",
+            "community_centre",
+            "arts_centre",
+            "museum",
+            "gallery",
+            "attraction",
+            "zoo",
+            "theme_park",
+            "viewpoint",
+        ],
+    },
+    "accommodation": {
+        "emoji": "🏨",
+        "label": "Accommodation",
+        "categories": [
+            "hotel",
+            "hostel",
+            "guest_house",
+            "campsite",
+            "caravan_site",
+        ],
+    },
+    "religion": {
+        "emoji": "🛐",
+        "label": "Places of Worship",
+        "categories": ["place_of_worship"],
+    },
+    "government": {
+        "emoji": "🏛️",
+        "label": "Government & Public",
+        "categories": [
+            "town_hall",
+            "courthouse",
+            "post_office",
+            "prison",
+            "public_toilets",
+        ],
+    },
+    "automotive": {
+        "emoji": "⛽",
+        "label": "Automotive",
+        "categories": [
+            "petrol_station",
+            "ev_charging",
+            "car_dealer",
+            "car_repair",
+            "parking",
+            "bicycle_parking",
+        ],
+    },
+    "recycling": {
+        "emoji": "♻️",
+        "label": "Recycling & Waste",
+        "categories": ["recycling", "waste_disposal"],
+    },
 }
 
 # Flatten for quick lookup
-ALL_CATEGORIES = {cat for cats in POI_CATEGORIES.values() for cat in cats}
+ALL_CATEGORIES = {
+    cat for group in POI_CATEGORY_GROUPS.values() for cat in group["categories"]
+}
 
 # Cache the dataframe
 _df_cache: pl.DataFrame | None = None
@@ -55,14 +205,9 @@ def get_df() -> pl.DataFrame | None:
         if not DATA_FILE.exists():
             return None
         df = pl.read_parquet(DATA_FILE)
-        # Extract fields we need and filter to relevant categories
-        _df_cache = df.select(
-            pl.col("id"),
-            pl.col("names").struct.field("primary").alias("name"),
-            pl.col("categories").struct.field("primary").alias("category"),
-            pl.col("bbox").struct.field("xmin").alias("lng"),
-            pl.col("bbox").struct.field("ymin").alias("lat"),
-        ).filter(pl.col("category").is_in(ALL_CATEGORIES))
+        _df_cache = df.select("id", "name", "category", "lat", "lng").filter(
+            pl.col("category").is_in(ALL_CATEGORIES)
+        )
     return _df_cache
 
 
@@ -83,23 +228,20 @@ async def get_pois(
     if df is None:
         return {"features": []}
 
-    # Parse bounds
     try:
         south, west, north, east = map(float, bounds.split(","))
     except ValueError:
         return {"features": []}
 
-    # Get categories to include
     requested_groups = [g.strip() for g in categories.split(",")]
     cats_to_include = set()
     for group in requested_groups:
-        if group in POI_CATEGORIES:
-            cats_to_include.update(POI_CATEGORIES[group])
+        if group in POI_CATEGORY_GROUPS:
+            cats_to_include.update(POI_CATEGORY_GROUPS[group]["categories"])
 
     if not cats_to_include:
         return {"features": []}
 
-    # Filter by bounds and categories
     filtered = df.filter(
         (pl.col("lat") >= south)
         & (pl.col("lat") <= north)
@@ -108,7 +250,6 @@ async def get_pois(
         & (pl.col("category").is_in(cats_to_include))
     )
 
-    # Limit results to avoid overwhelming the frontend
     MAX_POIS = 5000
     if len(filtered) > MAX_POIS:
         filtered = filtered.sample(n=MAX_POIS, seed=42)
@@ -118,5 +259,10 @@ async def get_pois(
 
 @router.get("/poi-categories")
 async def get_poi_categories() -> dict:
-    """Get available POI category groups."""
-    return {"categories": list(POI_CATEGORIES.keys())}
+    """Get available POI category groups with emoji and labels."""
+    return {
+        "categories": {
+            key: {"emoji": group["emoji"], "label": group["label"]}
+            for key, group in POI_CATEGORY_GROUPS.items()
+        }
+    }

From c08970c06cb1603326c9f7be3cf208579fb471ec Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Wed, 28 Jan 2026 22:10:51 +0000
Subject: [PATCH 11/62] Format

---
 analysis.ipynb | 708 ++++++++++++++++++++++++++++++++++++++++---------
 1 file changed, 580 insertions(+), 128 deletions(-)

diff --git a/analysis.ipynb b/analysis.ipynb
index 3b4a58a..b209e53 100644
--- a/analysis.ipynb
+++ b/analysis.ipynb
@@ -99,7 +99,7 @@
     "row_count = lf.select(pl.len()).collect().item()\n",
     "london_count = lf.filter(LONDON_FILTER).select(pl.len()).collect().item()\n",
     "print(f\"Total transactions: {row_count:,}\")\n",
-    "print(f\"London transactions: {london_count:,} ({london_count/row_count*100:.1f}%)\")"
+    "print(f\"London transactions: {london_count:,} ({london_count / row_count * 100:.1f}%)\")"
    ]
   },
   {
@@ -208,6 +208,7 @@
     "        pl.col(\"price\").quantile(0.95).alias(\"p95\"),\n",
     "    ).collect()\n",
     "\n",
+    "\n",
     "print(\"=== National Price Statistics ===\")\n",
     "display(get_price_stats(lf))\n",
     "print(\"\\n=== London Price Statistics ===\")\n",
@@ -1115,16 +1116,36 @@
     "        lazy_frame.group_by(\"property_type\")\n",
     "        .agg(pl.len().alias(\"count\"))\n",
     "        .sort(\"count\", descending=True)\n",
-    "        .with_columns(pl.col(\"property_type\").replace(PROPERTY_TYPE_MAP).alias(\"type_name\"))\n",
+    "        .with_columns(\n",
+    "            pl.col(\"property_type\").replace(PROPERTY_TYPE_MAP).alias(\"type_name\")\n",
+    "        )\n",
     "        .collect()\n",
     "    )\n",
     "\n",
+    "\n",
     "property_types_national = get_property_types(lf)\n",
     "property_types_london = get_property_types(lf.filter(LONDON_FILTER))\n",
     "\n",
     "fig = make_subplots(rows=1, cols=2, subplot_titles=(\"National\", \"London\"))\n",
-    "fig.add_trace(go.Bar(x=property_types_national[\"type_name\"], y=property_types_national[\"count\"], name=\"National\"), row=1, col=1)\n",
-    "fig.add_trace(go.Bar(x=property_types_london[\"type_name\"], y=property_types_london[\"count\"], name=\"London\", marker_color=\"crimson\"), row=1, col=2)\n",
+    "fig.add_trace(\n",
+    "    go.Bar(\n",
+    "        x=property_types_national[\"type_name\"],\n",
+    "        y=property_types_national[\"count\"],\n",
+    "        name=\"National\",\n",
+    "    ),\n",
+    "    row=1,\n",
+    "    col=1,\n",
+    ")\n",
+    "fig.add_trace(\n",
+    "    go.Bar(\n",
+    "        x=property_types_london[\"type_name\"],\n",
+    "        y=property_types_london[\"count\"],\n",
+    "        name=\"London\",\n",
+    "        marker_color=\"crimson\",\n",
+    "    ),\n",
+    "    row=1,\n",
+    "    col=2,\n",
+    ")\n",
     "fig.update_layout(title_text=\"Property Type Distribution\", showlegend=False, height=400)\n",
     "fig.show()"
    ]
@@ -2013,12 +2034,34 @@
     "        .collect()\n",
     "    )\n",
     "\n",
+    "\n",
     "tenure_national = get_tenure(lf)\n",
     "tenure_london = get_tenure(lf.filter(LONDON_FILTER))\n",
     "\n",
-    "fig = make_subplots(rows=1, cols=2, specs=[[{\"type\": \"pie\"}, {\"type\": \"pie\"}]], subplot_titles=(\"National\", \"London\"))\n",
-    "fig.add_trace(go.Pie(labels=tenure_national[\"tenure_name\"], values=tenure_national[\"count\"], name=\"National\"), row=1, col=1)\n",
-    "fig.add_trace(go.Pie(labels=tenure_london[\"tenure_name\"], values=tenure_london[\"count\"], name=\"London\"), row=1, col=2)\n",
+    "fig = make_subplots(\n",
+    "    rows=1,\n",
+    "    cols=2,\n",
+    "    specs=[[{\"type\": \"pie\"}, {\"type\": \"pie\"}]],\n",
+    "    subplot_titles=(\"National\", \"London\"),\n",
+    ")\n",
+    "fig.add_trace(\n",
+    "    go.Pie(\n",
+    "        labels=tenure_national[\"tenure_name\"],\n",
+    "        values=tenure_national[\"count\"],\n",
+    "        name=\"National\",\n",
+    "    ),\n",
+    "    row=1,\n",
+    "    col=1,\n",
+    ")\n",
+    "fig.add_trace(\n",
+    "    go.Pie(\n",
+    "        labels=tenure_london[\"tenure_name\"],\n",
+    "        values=tenure_london[\"count\"],\n",
+    "        name=\"London\",\n",
+    "    ),\n",
+    "    row=1,\n",
+    "    col=2,\n",
+    ")\n",
     "fig.update_layout(title_text=\"Freehold vs Leasehold\", height=400)\n",
     "fig.show()"
    ]
@@ -2958,10 +3001,16 @@
     "    .collect()\n",
     ")\n",
     "\n",
-    "fig = px.bar(top_counties.to_pandas(), x=\"count\", y=\"county\", orientation=\"h\",\n",
+    "fig = px.bar(\n",
+    "    top_counties.to_pandas(),\n",
+    "    x=\"count\",\n",
+    "    y=\"county\",\n",
+    "    orientation=\"h\",\n",
     "    title=\"Top 20 Counties by Transaction Volume\",\n",
-    "    color=\"avg_price\", color_continuous_scale=\"Blues\",\n",
-    "    labels={\"count\": \"Transactions\", \"county\": \"County\", \"avg_price\": \"Avg Price\"})\n",
+    "    color=\"avg_price\",\n",
+    "    color_continuous_scale=\"Blues\",\n",
+    "    labels={\"count\": \"Transactions\", \"county\": \"County\", \"avg_price\": \"Avg Price\"},\n",
+    ")\n",
     "fig.update_layout(yaxis={\"categoryorder\": \"total ascending\"}, height=600)\n",
     "fig.show()"
    ]
@@ -4591,15 +4640,29 @@
     "london_boroughs = (\n",
     "    lf.filter(LONDON_FILTER)\n",
     "    .group_by(\"district\")\n",
-    "    .agg(pl.len().alias(\"count\"), pl.col(\"price\").mean().alias(\"avg_price\"), pl.col(\"price\").median().alias(\"median_price\"))\n",
+    "    .agg(\n",
+    "        pl.len().alias(\"count\"),\n",
+    "        pl.col(\"price\").mean().alias(\"avg_price\"),\n",
+    "        pl.col(\"price\").median().alias(\"median_price\"),\n",
+    "    )\n",
     "    .sort(\"avg_price\", descending=True)\n",
     "    .collect()\n",
     ")\n",
     "\n",
-    "fig = px.bar(london_boroughs.to_pandas(), x=\"avg_price\", y=\"district\", orientation=\"h\",\n",
+    "fig = px.bar(\n",
+    "    london_boroughs.to_pandas(),\n",
+    "    x=\"avg_price\",\n",
+    "    y=\"district\",\n",
+    "    orientation=\"h\",\n",
     "    title=\"London Boroughs by Average Price\",\n",
-    "    color=\"count\", color_continuous_scale=\"Reds\",\n",
-    "    labels={\"avg_price\": \"Average Price (£)\", \"district\": \"Borough\", \"count\": \"Transactions\"})\n",
+    "    color=\"count\",\n",
+    "    color_continuous_scale=\"Reds\",\n",
+    "    labels={\n",
+    "        \"avg_price\": \"Average Price (£)\",\n",
+    "        \"district\": \"Borough\",\n",
+    "        \"count\": \"Transactions\",\n",
+    "    },\n",
+    ")\n",
     "fig.update_layout(yaxis={\"categoryorder\": \"total ascending\"}, height=800)\n",
     "fig.show()"
    ]
@@ -4622,11 +4685,16 @@
     "    return (\n",
     "        lazy_frame.with_columns(pl.col(\"date_of_transfer\").dt.year().alias(\"year\"))\n",
     "        .group_by(\"year\")\n",
-    "        .agg(pl.len().alias(\"count\"), pl.col(\"price\").mean().alias(\"avg_price\"), pl.col(\"price\").median().alias(\"median_price\"))\n",
+    "        .agg(\n",
+    "            pl.len().alias(\"count\"),\n",
+    "            pl.col(\"price\").mean().alias(\"avg_price\"),\n",
+    "            pl.col(\"price\").median().alias(\"median_price\"),\n",
+    "        )\n",
     "        .sort(\"year\")\n",
     "        .collect()\n",
     "    )\n",
     "\n",
+    "\n",
     "yearly_national = get_yearly_stats(lf)\n",
     "yearly_london = get_yearly_stats(lf.filter(LONDON_FILTER))"
    ]
@@ -5473,9 +5541,29 @@
    ],
    "source": [
     "fig = go.Figure()\n",
-    "fig.add_trace(go.Scatter(x=yearly_national[\"year\"], y=yearly_national[\"avg_price\"], name=\"National\", mode=\"lines+markers\"))\n",
-    "fig.add_trace(go.Scatter(x=yearly_london[\"year\"], y=yearly_london[\"avg_price\"], name=\"London\", mode=\"lines+markers\", line=dict(color=\"crimson\")))\n",
-    "fig.update_layout(title=\"Average Price by Year\", xaxis_title=\"Year\", yaxis_title=\"Average Price (£)\", height=500)\n",
+    "fig.add_trace(\n",
+    "    go.Scatter(\n",
+    "        x=yearly_national[\"year\"],\n",
+    "        y=yearly_national[\"avg_price\"],\n",
+    "        name=\"National\",\n",
+    "        mode=\"lines+markers\",\n",
+    "    )\n",
+    ")\n",
+    "fig.add_trace(\n",
+    "    go.Scatter(\n",
+    "        x=yearly_london[\"year\"],\n",
+    "        y=yearly_london[\"avg_price\"],\n",
+    "        name=\"London\",\n",
+    "        mode=\"lines+markers\",\n",
+    "        line=dict(color=\"crimson\"),\n",
+    "    )\n",
+    ")\n",
+    "fig.update_layout(\n",
+    "    title=\"Average Price by Year\",\n",
+    "    xaxis_title=\"Year\",\n",
+    "    yaxis_title=\"Average Price (£)\",\n",
+    "    height=500,\n",
+    ")\n",
     "fig.show()"
    ]
   },
@@ -6333,14 +6421,38 @@
     }
    ],
    "source": [
-    "yearly_national_pct = yearly_national.with_columns((pl.col(\"avg_price\").pct_change() * 100).alias(\"yoy_change\"))\n",
-    "yearly_london_pct = yearly_london.with_columns((pl.col(\"avg_price\").pct_change() * 100).alias(\"yoy_change\"))\n",
+    "yearly_national_pct = yearly_national.with_columns(\n",
+    "    (pl.col(\"avg_price\").pct_change() * 100).alias(\"yoy_change\")\n",
+    ")\n",
+    "yearly_london_pct = yearly_london.with_columns(\n",
+    "    (pl.col(\"avg_price\").pct_change() * 100).alias(\"yoy_change\")\n",
+    ")\n",
     "\n",
     "fig = go.Figure()\n",
-    "fig.add_trace(go.Bar(x=yearly_national_pct[\"year\"], y=yearly_national_pct[\"yoy_change\"], name=\"National\", opacity=0.7))\n",
-    "fig.add_trace(go.Bar(x=yearly_london_pct[\"year\"], y=yearly_london_pct[\"yoy_change\"], name=\"London\", opacity=0.7))\n",
+    "fig.add_trace(\n",
+    "    go.Bar(\n",
+    "        x=yearly_national_pct[\"year\"],\n",
+    "        y=yearly_national_pct[\"yoy_change\"],\n",
+    "        name=\"National\",\n",
+    "        opacity=0.7,\n",
+    "    )\n",
+    ")\n",
+    "fig.add_trace(\n",
+    "    go.Bar(\n",
+    "        x=yearly_london_pct[\"year\"],\n",
+    "        y=yearly_london_pct[\"yoy_change\"],\n",
+    "        name=\"London\",\n",
+    "        opacity=0.7,\n",
+    "    )\n",
+    ")\n",
     "fig.add_hline(y=0, line_dash=\"dash\", line_color=\"gray\")\n",
-    "fig.update_layout(title=\"Year-over-Year Price Change (%)\", xaxis_title=\"Year\", yaxis_title=\"Change (%)\", barmode=\"group\", height=500)\n",
+    "fig.update_layout(\n",
+    "    title=\"Year-over-Year Price Change (%)\",\n",
+    "    xaxis_title=\"Year\",\n",
+    "    yaxis_title=\"Change (%)\",\n",
+    "    barmode=\"group\",\n",
+    "    height=500,\n",
+    ")\n",
     "fig.show()"
    ]
   },
@@ -7448,18 +7560,43 @@
     "        lazy_frame.filter(pl.col(\"date_of_transfer\").dt.year() >= start_year)\n",
     "        .with_columns(pl.col(\"date_of_transfer\").dt.truncate(\"1mo\").alias(\"month\"))\n",
     "        .group_by(\"month\")\n",
-    "        .agg(pl.len().alias(\"count\"), pl.col(\"price\").mean().alias(\"avg_price\"), pl.col(\"price\").median().alias(\"median_price\"))\n",
+    "        .agg(\n",
+    "            pl.len().alias(\"count\"),\n",
+    "            pl.col(\"price\").mean().alias(\"avg_price\"),\n",
+    "            pl.col(\"price\").median().alias(\"median_price\"),\n",
+    "        )\n",
     "        .sort(\"month\")\n",
     "        .collect()\n",
     "    )\n",
     "\n",
+    "\n",
     "monthly_national = get_monthly_stats(lf)\n",
     "monthly_london = get_monthly_stats(lf.filter(LONDON_FILTER))\n",
     "\n",
     "fig = go.Figure()\n",
-    "fig.add_trace(go.Scatter(x=monthly_national[\"month\"], y=monthly_national[\"avg_price\"], name=\"National\", mode=\"lines\"))\n",
-    "fig.add_trace(go.Scatter(x=monthly_london[\"month\"], y=monthly_london[\"avg_price\"], name=\"London\", mode=\"lines\", line=dict(color=\"crimson\")))\n",
-    "fig.update_layout(title=\"Monthly Average Price (2015 onwards)\", xaxis_title=\"Month\", yaxis_title=\"Average Price (£)\", height=500)\n",
+    "fig.add_trace(\n",
+    "    go.Scatter(\n",
+    "        x=monthly_national[\"month\"],\n",
+    "        y=monthly_national[\"avg_price\"],\n",
+    "        name=\"National\",\n",
+    "        mode=\"lines\",\n",
+    "    )\n",
+    ")\n",
+    "fig.add_trace(\n",
+    "    go.Scatter(\n",
+    "        x=monthly_london[\"month\"],\n",
+    "        y=monthly_london[\"avg_price\"],\n",
+    "        name=\"London\",\n",
+    "        mode=\"lines\",\n",
+    "        line=dict(color=\"crimson\"),\n",
+    "    )\n",
+    ")\n",
+    "fig.update_layout(\n",
+    "    title=\"Monthly Average Price (2015 onwards)\",\n",
+    "    xaxis_title=\"Month\",\n",
+    "    yaxis_title=\"Average Price (£)\",\n",
+    "    height=500,\n",
+    ")\n",
     "fig.show()"
    ]
   },
@@ -8431,9 +8568,19 @@
     "    .collect()\n",
     ")\n",
     "\n",
-    "fig = px.line(yearly_by_type_london.to_pandas(), x=\"year\", y=\"avg_price\", color=\"type_name\",\n",
+    "fig = px.line(\n",
+    "    yearly_by_type_london.to_pandas(),\n",
+    "    x=\"year\",\n",
+    "    y=\"avg_price\",\n",
+    "    color=\"type_name\",\n",
     "    title=\"London: Average Price by Property Type Over Time\",\n",
-    "    labels={\"avg_price\": \"Average Price (£)\", \"year\": \"Year\", \"type_name\": \"Property Type\"}, markers=True)\n",
+    "    labels={\n",
+    "        \"avg_price\": \"Average Price (£)\",\n",
+    "        \"year\": \"Year\",\n",
+    "        \"type_name\": \"Property Type\",\n",
+    "    },\n",
+    "    markers=True,\n",
+    ")\n",
     "fig.update_layout(height=500)\n",
     "fig.show()"
    ]
@@ -9539,12 +9686,35 @@
     }
    ],
    "source": [
-    "monthly_london_rolling = monthly_london.with_columns(pl.col(\"avg_price\").rolling_mean(window_size=12).alias(\"rolling_12m_avg\"))\n",
+    "monthly_london_rolling = monthly_london.with_columns(\n",
+    "    pl.col(\"avg_price\").rolling_mean(window_size=12).alias(\"rolling_12m_avg\")\n",
+    ")\n",
     "\n",
     "fig = go.Figure()\n",
-    "fig.add_trace(go.Scatter(x=monthly_london_rolling[\"month\"], y=monthly_london_rolling[\"avg_price\"], name=\"Monthly\", mode=\"lines\", opacity=0.5))\n",
-    "fig.add_trace(go.Scatter(x=monthly_london_rolling[\"month\"], y=monthly_london_rolling[\"rolling_12m_avg\"], name=\"12-Month Rolling Avg\", mode=\"lines\", line=dict(width=3, color=\"crimson\")))\n",
-    "fig.update_layout(title=\"London: Monthly Price with 12-Month Rolling Average\", xaxis_title=\"Month\", yaxis_title=\"Average Price (£)\", height=500)\n",
+    "fig.add_trace(\n",
+    "    go.Scatter(\n",
+    "        x=monthly_london_rolling[\"month\"],\n",
+    "        y=monthly_london_rolling[\"avg_price\"],\n",
+    "        name=\"Monthly\",\n",
+    "        mode=\"lines\",\n",
+    "        opacity=0.5,\n",
+    "    )\n",
+    ")\n",
+    "fig.add_trace(\n",
+    "    go.Scatter(\n",
+    "        x=monthly_london_rolling[\"month\"],\n",
+    "        y=monthly_london_rolling[\"rolling_12m_avg\"],\n",
+    "        name=\"12-Month Rolling Avg\",\n",
+    "        mode=\"lines\",\n",
+    "        line=dict(width=3, color=\"crimson\"),\n",
+    "    )\n",
+    ")\n",
+    "fig.update_layout(\n",
+    "    title=\"London: Monthly Price with 12-Month Rolling Average\",\n",
+    "    xaxis_title=\"Month\",\n",
+    "    yaxis_title=\"Average Price (£)\",\n",
+    "    height=500,\n",
+    ")\n",
     "fig.show()"
    ]
   },
@@ -10422,15 +10592,39 @@
     "yearly_indexed = yearly_national.with_columns(\n",
     "    (pl.col(\"avg_price\") / base_price_national * 100).alias(\"national_index\")\n",
     ").join(\n",
-    "    yearly_london.with_columns((pl.col(\"avg_price\") / base_price_london * 100).alias(\"london_index\")).select(\"year\", \"london_index\"),\n",
-    "    on=\"year\"\n",
+    "    yearly_london.with_columns(\n",
+    "        (pl.col(\"avg_price\") / base_price_london * 100).alias(\"london_index\")\n",
+    "    ).select(\"year\", \"london_index\"),\n",
+    "    on=\"year\",\n",
     ")\n",
     "\n",
     "fig = go.Figure()\n",
-    "fig.add_trace(go.Scatter(x=yearly_indexed[\"year\"], y=yearly_indexed[\"national_index\"], name=\"National\", mode=\"lines+markers\"))\n",
-    "fig.add_trace(go.Scatter(x=yearly_indexed[\"year\"], y=yearly_indexed[\"london_index\"], name=\"London\", mode=\"lines+markers\", line=dict(color=\"crimson\")))\n",
-    "fig.add_hline(y=100, line_dash=\"dash\", line_color=\"gray\", annotation_text=\"1995 Baseline\")\n",
-    "fig.update_layout(title=\"Price Index (1995 = 100)\", xaxis_title=\"Year\", yaxis_title=\"Index\", height=500)\n",
+    "fig.add_trace(\n",
+    "    go.Scatter(\n",
+    "        x=yearly_indexed[\"year\"],\n",
+    "        y=yearly_indexed[\"national_index\"],\n",
+    "        name=\"National\",\n",
+    "        mode=\"lines+markers\",\n",
+    "    )\n",
+    ")\n",
+    "fig.add_trace(\n",
+    "    go.Scatter(\n",
+    "        x=yearly_indexed[\"year\"],\n",
+    "        y=yearly_indexed[\"london_index\"],\n",
+    "        name=\"London\",\n",
+    "        mode=\"lines+markers\",\n",
+    "        line=dict(color=\"crimson\"),\n",
+    "    )\n",
+    ")\n",
+    "fig.add_hline(\n",
+    "    y=100, line_dash=\"dash\", line_color=\"gray\", annotation_text=\"1995 Baseline\"\n",
+    ")\n",
+    "fig.update_layout(\n",
+    "    title=\"Price Index (1995 = 100)\",\n",
+    "    xaxis_title=\"Year\",\n",
+    "    yaxis_title=\"Index\",\n",
+    "    height=500,\n",
+    ")\n",
     "fig.show()"
    ]
   },
@@ -12076,12 +12270,24 @@
     "    .collect()\n",
     ")\n",
     "\n",
-    "flats_by_borough = borough_by_type.filter(pl.col(\"property_type\") == \"F\").sort(\"median_price\")\n",
+    "flats_by_borough = borough_by_type.filter(pl.col(\"property_type\") == \"F\").sort(\n",
+    "    \"median_price\"\n",
+    ")\n",
     "\n",
-    "fig = px.bar(flats_by_borough.to_pandas(), x=\"median_price\", y=\"district\", orientation=\"h\",\n",
+    "fig = px.bar(\n",
+    "    flats_by_borough.to_pandas(),\n",
+    "    x=\"median_price\",\n",
+    "    y=\"district\",\n",
+    "    orientation=\"h\",\n",
     "    title=\"Current Median Flat Prices by Borough (2023+)\",\n",
-    "    color=\"count\", color_continuous_scale=\"Viridis\",\n",
-    "    labels={\"median_price\": \"Median Price (£)\", \"district\": \"Borough\", \"count\": \"Sales Volume\"})\n",
+    "    color=\"count\",\n",
+    "    color_continuous_scale=\"Viridis\",\n",
+    "    labels={\n",
+    "        \"median_price\": \"Median Price (£)\",\n",
+    "        \"district\": \"Borough\",\n",
+    "        \"count\": \"Sales Volume\",\n",
+    "    },\n",
+    ")\n",
     "fig.update_layout(yaxis={\"categoryorder\": \"total ascending\"}, height=800)\n",
     "fig.show()"
    ]
@@ -13011,14 +13217,21 @@
    ],
    "source": [
     "# Heatmap: Price by borough and property type\n",
-    "pivot_data = borough_by_type.pivot(on=\"type_name\", index=\"district\", values=\"median_price\").sort(\"Flat/Maisonette\", nulls_last=True)\n",
+    "pivot_data = borough_by_type.pivot(\n",
+    "    on=\"type_name\", index=\"district\", values=\"median_price\"\n",
+    ").sort(\"Flat/Maisonette\", nulls_last=True)\n",
     "\n",
     "fig = px.imshow(\n",
-    "    pivot_data.select([\"Flat/Maisonette\", \"Terraced\", \"Semi-detached\", \"Detached\"]).to_pandas(),\n",
+    "    pivot_data.select(\n",
+    "        [\"Flat/Maisonette\", \"Terraced\", \"Semi-detached\", \"Detached\"]\n",
+    "    ).to_pandas(),\n",
     "    y=pivot_data[\"district\"].to_list(),\n",
     "    x=[\"Flat\", \"Terraced\", \"Semi-detached\", \"Detached\"],\n",
-    "    color_continuous_scale=\"RdYlGn_r\", aspect=\"auto\",\n",
-    "    title=\"Median Price by Borough and Property Type (2023+)\", labels={\"color\": \"Price (£)\"})\n",
+    "    color_continuous_scale=\"RdYlGn_r\",\n",
+    "    aspect=\"auto\",\n",
+    "    title=\"Median Price by Borough and Property Type (2023+)\",\n",
+    "    labels={\"color\": \"Price (£)\"},\n",
+    ")\n",
     "fig.update_layout(height=900)\n",
     "fig.show()"
    ]
@@ -13083,6 +13296,7 @@
     "        return ((end_price / start_price) ** (1 / years) - 1) * 100\n",
     "    return None\n",
     "\n",
+    "\n",
     "borough_yearly_all = (\n",
     "    lf.filter(LONDON_FILTER)\n",
     "    .with_columns(pl.col(\"date_of_transfer\").dt.year().alias(\"year\"))\n",
@@ -13101,8 +13315,16 @@
     "        if len(year_data) > 0:\n",
     "            prices[year] = year_data[\"median_price\"][0]\n",
     "    if all(year in prices for year in years_needed):\n",
-    "        growth_data.append({\"district\": borough, \"price_2014\": prices[2014], \"price_2019\": prices[2019], \"price_2024\": prices[2024],\n",
-    "            \"cagr_10yr\": calculate_cagr(prices[2014], prices[2024], 10), \"cagr_5yr\": calculate_cagr(prices[2019], prices[2024], 5)})\n",
+    "        growth_data.append(\n",
+    "            {\n",
+    "                \"district\": borough,\n",
+    "                \"price_2014\": prices[2014],\n",
+    "                \"price_2019\": prices[2019],\n",
+    "                \"price_2024\": prices[2024],\n",
+    "                \"cagr_10yr\": calculate_cagr(prices[2014], prices[2024], 10),\n",
+    "                \"cagr_5yr\": calculate_cagr(prices[2019], prices[2024], 5),\n",
+    "            }\n",
+    "        )\n",
     "\n",
     "growth_df = pl.DataFrame(growth_data).sort(\"cagr_5yr\", descending=True)\n",
     "growth_df"
@@ -14077,10 +14299,16 @@
    ],
    "source": [
     "# 5-year vs 10-year CAGR scatter\n",
-    "fig = px.scatter(growth_df.to_pandas(), x=\"cagr_10yr\", y=\"cagr_5yr\", text=\"district\",\n",
+    "fig = px.scatter(\n",
+    "    growth_df.to_pandas(),\n",
+    "    x=\"cagr_10yr\",\n",
+    "    y=\"cagr_5yr\",\n",
+    "    text=\"district\",\n",
     "    title=\"Borough Price Growth: 5-Year vs 10-Year CAGR\",\n",
     "    labels={\"cagr_10yr\": \"10-Year CAGR (%)\", \"cagr_5yr\": \"5-Year CAGR (%)\"},\n",
-    "    color=\"price_2024\", color_continuous_scale=\"Viridis\")\n",
+    "    color=\"price_2024\",\n",
+    "    color_continuous_scale=\"Viridis\",\n",
+    ")\n",
     "fig.update_traces(textposition=\"top center\", textfont_size=8)\n",
     "fig.add_hline(y=0, line_dash=\"dash\", line_color=\"gray\")\n",
     "fig.add_vline(x=0, line_dash=\"dash\", line_color=\"gray\")\n",
@@ -15045,11 +15273,20 @@
    ],
    "source": [
     "# 5-Year Price Growth Ranking\n",
-    "fig = px.bar(growth_df.sort(\"cagr_5yr\").to_pandas(), x=\"cagr_5yr\", y=\"district\", orientation=\"h\",\n",
-    "    title=\"5-Year Price Growth by Borough (CAGR %)\", color=\"cagr_5yr\", color_continuous_scale=\"RdYlGn\",\n",
-    "    labels={\"cagr_5yr\": \"5-Year CAGR (%)\", \"district\": \"Borough\"})\n",
+    "fig = px.bar(\n",
+    "    growth_df.sort(\"cagr_5yr\").to_pandas(),\n",
+    "    x=\"cagr_5yr\",\n",
+    "    y=\"district\",\n",
+    "    orientation=\"h\",\n",
+    "    title=\"5-Year Price Growth by Borough (CAGR %)\",\n",
+    "    color=\"cagr_5yr\",\n",
+    "    color_continuous_scale=\"RdYlGn\",\n",
+    "    labels={\"cagr_5yr\": \"5-Year CAGR (%)\", \"district\": \"Borough\"},\n",
+    ")\n",
     "fig.add_vline(x=0, line_dash=\"dash\", line_color=\"black\")\n",
-    "fig.update_layout(yaxis={\"categoryorder\": \"total ascending\"}, height=800, showlegend=False)\n",
+    "fig.update_layout(\n",
+    "    yaxis={\"categoryorder\": \"total ascending\"}, height=800, showlegend=False\n",
+    ")\n",
     "fig.show()"
    ]
   },
@@ -16199,18 +16436,35 @@
    "source": [
     "# Property Type Mix by Borough\n",
     "property_mix = (\n",
-    "    lf.filter(LONDON_FILTER).filter(pl.col(\"date_of_transfer\").dt.year() >= 2020)\n",
-    "    .group_by(\"district\", \"property_type\").agg(pl.len().alias(\"count\"))\n",
-    "    .with_columns(pl.col(\"property_type\").replace(PROPERTY_TYPE_MAP).alias(\"type_name\")).collect()\n",
+    "    lf.filter(LONDON_FILTER)\n",
+    "    .filter(pl.col(\"date_of_transfer\").dt.year() >= 2020)\n",
+    "    .group_by(\"district\", \"property_type\")\n",
+    "    .agg(pl.len().alias(\"count\"))\n",
+    "    .with_columns(pl.col(\"property_type\").replace(PROPERTY_TYPE_MAP).alias(\"type_name\"))\n",
+    "    .collect()\n",
     ")\n",
     "totals = property_mix.group_by(\"district\").agg(pl.col(\"count\").sum().alias(\"total\"))\n",
-    "property_mix_pct = property_mix.join(totals, on=\"district\").with_columns((pl.col(\"count\") / pl.col(\"total\") * 100).alias(\"percentage\"))\n",
-    "flat_pct = property_mix_pct.filter(pl.col(\"property_type\") == \"F\").sort(\"percentage\", descending=True)\n",
+    "property_mix_pct = property_mix.join(totals, on=\"district\").with_columns(\n",
+    "    (pl.col(\"count\") / pl.col(\"total\") * 100).alias(\"percentage\")\n",
+    ")\n",
+    "flat_pct = property_mix_pct.filter(pl.col(\"property_type\") == \"F\").sort(\n",
+    "    \"percentage\", descending=True\n",
+    ")\n",
     "\n",
-    "fig = px.bar(property_mix_pct.to_pandas(), x=\"percentage\", y=\"district\", color=\"type_name\", orientation=\"h\",\n",
+    "fig = px.bar(\n",
+    "    property_mix_pct.to_pandas(),\n",
+    "    x=\"percentage\",\n",
+    "    y=\"district\",\n",
+    "    color=\"type_name\",\n",
+    "    orientation=\"h\",\n",
     "    title=\"Property Type Mix by Borough (2020+)\",\n",
-    "    labels={\"percentage\": \"% of Transactions\", \"district\": \"Borough\", \"type_name\": \"Property Type\"},\n",
-    "    category_orders={\"district\": flat_pct[\"district\"].to_list()})\n",
+    "    labels={\n",
+    "        \"percentage\": \"% of Transactions\",\n",
+    "        \"district\": \"Borough\",\n",
+    "        \"type_name\": \"Property Type\",\n",
+    "    },\n",
+    "    category_orders={\"district\": flat_pct[\"district\"].to_list()},\n",
+    ")\n",
     "fig.update_layout(height=900, barmode=\"stack\", legend_title=\"Property Type\")\n",
     "fig.show()"
    ]
@@ -17194,19 +17448,38 @@
    "source": [
     "# Freehold vs Leasehold by Borough\n",
     "tenure_mix = (\n",
-    "    lf.filter(LONDON_FILTER).filter(pl.col(\"date_of_transfer\").dt.year() >= 2020)\n",
-    "    .group_by(\"district\", \"duration\").agg(pl.len().alias(\"count\"))\n",
-    "    .with_columns(pl.col(\"duration\").replace(TENURE_MAP).alias(\"tenure_name\")).collect()\n",
+    "    lf.filter(LONDON_FILTER)\n",
+    "    .filter(pl.col(\"date_of_transfer\").dt.year() >= 2020)\n",
+    "    .group_by(\"district\", \"duration\")\n",
+    "    .agg(pl.len().alias(\"count\"))\n",
+    "    .with_columns(pl.col(\"duration\").replace(TENURE_MAP).alias(\"tenure_name\"))\n",
+    "    .collect()\n",
+    ")\n",
+    "totals_tenure = tenure_mix.group_by(\"district\").agg(\n",
+    "    pl.col(\"count\").sum().alias(\"total\")\n",
+    ")\n",
+    "tenure_mix_pct = tenure_mix.join(totals_tenure, on=\"district\").with_columns(\n",
+    "    (pl.col(\"count\") / pl.col(\"total\") * 100).alias(\"percentage\")\n",
+    ")\n",
+    "freehold_pct = tenure_mix_pct.filter(pl.col(\"duration\") == \"F\").sort(\n",
+    "    \"percentage\", descending=True\n",
     ")\n",
-    "totals_tenure = tenure_mix.group_by(\"district\").agg(pl.col(\"count\").sum().alias(\"total\"))\n",
-    "tenure_mix_pct = tenure_mix.join(totals_tenure, on=\"district\").with_columns((pl.col(\"count\") / pl.col(\"total\") * 100).alias(\"percentage\"))\n",
-    "freehold_pct = tenure_mix_pct.filter(pl.col(\"duration\") == \"F\").sort(\"percentage\", descending=True)\n",
     "\n",
-    "fig = px.bar(tenure_mix_pct.to_pandas(), x=\"percentage\", y=\"district\", color=\"tenure_name\", orientation=\"h\",\n",
+    "fig = px.bar(\n",
+    "    tenure_mix_pct.to_pandas(),\n",
+    "    x=\"percentage\",\n",
+    "    y=\"district\",\n",
+    "    color=\"tenure_name\",\n",
+    "    orientation=\"h\",\n",
     "    title=\"Freehold vs Leasehold by Borough (2020+)\",\n",
-    "    labels={\"percentage\": \"% of Transactions\", \"district\": \"Borough\", \"tenure_name\": \"Tenure\"},\n",
+    "    labels={\n",
+    "        \"percentage\": \"% of Transactions\",\n",
+    "        \"district\": \"Borough\",\n",
+    "        \"tenure_name\": \"Tenure\",\n",
+    "    },\n",
     "    category_orders={\"district\": freehold_pct[\"district\"].to_list()},\n",
-    "    color_discrete_map={\"Freehold\": \"seagreen\", \"Leasehold\": \"coral\"})\n",
+    "    color_discrete_map={\"Freehold\": \"seagreen\", \"Leasehold\": \"coral\"},\n",
+    ")\n",
     "fig.update_layout(height=900, barmode=\"stack\")\n",
     "fig.show()"
    ]
@@ -18168,19 +18441,39 @@
    "source": [
     "# Market Activity Change (2024 vs 2019)\n",
     "volume_trend = (\n",
-    "    lf.filter(LONDON_FILTER).with_columns(pl.col(\"date_of_transfer\").dt.year().alias(\"year\"))\n",
-    "    .filter(pl.col(\"year\") >= 2019).group_by(\"district\", \"year\").agg(pl.len().alias(\"count\")).collect()\n",
+    "    lf.filter(LONDON_FILTER)\n",
+    "    .with_columns(pl.col(\"date_of_transfer\").dt.year().alias(\"year\"))\n",
+    "    .filter(pl.col(\"year\") >= 2019)\n",
+    "    .group_by(\"district\", \"year\")\n",
+    "    .agg(pl.len().alias(\"count\"))\n",
+    "    .collect()\n",
+    ")\n",
+    "volume_2019 = volume_trend.filter(pl.col(\"year\") == 2019).select(\n",
+    "    \"district\", pl.col(\"count\").alias(\"count_2019\")\n",
+    ")\n",
+    "volume_2024 = volume_trend.filter(pl.col(\"year\") == 2024).select(\n",
+    "    \"district\", pl.col(\"count\").alias(\"count_2024\")\n",
+    ")\n",
+    "volume_change = (\n",
+    "    volume_2019.join(volume_2024, on=\"district\", how=\"inner\")\n",
+    "    .with_columns(\n",
+    "        (\n",
+    "            (pl.col(\"count_2024\") - pl.col(\"count_2019\")) / pl.col(\"count_2019\") * 100\n",
+    "        ).alias(\"pct_change\")\n",
+    "    )\n",
+    "    .sort(\"pct_change\", descending=True)\n",
     ")\n",
-    "volume_2019 = volume_trend.filter(pl.col(\"year\") == 2019).select(\"district\", pl.col(\"count\").alias(\"count_2019\"))\n",
-    "volume_2024 = volume_trend.filter(pl.col(\"year\") == 2024).select(\"district\", pl.col(\"count\").alias(\"count_2024\"))\n",
-    "volume_change = volume_2019.join(volume_2024, on=\"district\", how=\"inner\").with_columns(\n",
-    "    ((pl.col(\"count_2024\") - pl.col(\"count_2019\")) / pl.col(\"count_2019\") * 100).alias(\"pct_change\")\n",
-    ").sort(\"pct_change\", descending=True)\n",
     "\n",
-    "fig = px.bar(volume_change.to_pandas(), x=\"pct_change\", y=\"district\", orientation=\"h\",\n",
+    "fig = px.bar(\n",
+    "    volume_change.to_pandas(),\n",
+    "    x=\"pct_change\",\n",
+    "    y=\"district\",\n",
+    "    orientation=\"h\",\n",
     "    title=\"Market Activity Change: 2024 vs 2019 Transaction Volume (%)\",\n",
-    "    color=\"pct_change\", color_continuous_scale=\"RdYlGn\",\n",
-    "    labels={\"pct_change\": \"Volume Change (%)\", \"district\": \"Borough\"})\n",
+    "    color=\"pct_change\",\n",
+    "    color_continuous_scale=\"RdYlGn\",\n",
+    "    labels={\"pct_change\": \"Volume Change (%)\", \"district\": \"Borough\"},\n",
+    ")\n",
     "fig.add_vline(x=0, line_dash=\"dash\", line_color=\"black\")\n",
     "fig.update_layout(yaxis={\"categoryorder\": \"total ascending\"}, height=800)\n",
     "fig.show()"
@@ -19120,17 +19413,32 @@
    "source": [
     "# New Build Share by Borough\n",
     "new_build_rate = (\n",
-    "    lf.filter(LONDON_FILTER).filter(pl.col(\"date_of_transfer\").dt.year() >= 2020)\n",
-    "    .group_by(\"district\", \"old_new\").agg(pl.len().alias(\"count\")).collect()\n",
+    "    lf.filter(LONDON_FILTER)\n",
+    "    .filter(pl.col(\"date_of_transfer\").dt.year() >= 2020)\n",
+    "    .group_by(\"district\", \"old_new\")\n",
+    "    .agg(pl.len().alias(\"count\"))\n",
+    "    .collect()\n",
+    ")\n",
+    "totals_nb = new_build_rate.group_by(\"district\").agg(\n",
+    "    pl.col(\"count\").sum().alias(\"total\")\n",
+    ")\n",
+    "new_build_pct = (\n",
+    "    new_build_rate.filter(pl.col(\"old_new\") == \"Y\")\n",
+    "    .join(totals_nb, on=\"district\")\n",
+    "    .with_columns((pl.col(\"count\") / pl.col(\"total\") * 100).alias(\"new_build_pct\"))\n",
+    "    .sort(\"new_build_pct\", descending=True)\n",
     ")\n",
-    "totals_nb = new_build_rate.group_by(\"district\").agg(pl.col(\"count\").sum().alias(\"total\"))\n",
-    "new_build_pct = new_build_rate.filter(pl.col(\"old_new\") == \"Y\").join(totals_nb, on=\"district\").with_columns(\n",
-    "    (pl.col(\"count\") / pl.col(\"total\") * 100).alias(\"new_build_pct\")\n",
-    ").sort(\"new_build_pct\", descending=True)\n",
     "\n",
-    "fig = px.bar(new_build_pct.to_pandas(), x=\"new_build_pct\", y=\"district\", orientation=\"h\",\n",
-    "    title=\"New Build Share by Borough (2020+)\", color=\"new_build_pct\", color_continuous_scale=\"Blues\",\n",
-    "    labels={\"new_build_pct\": \"New Build %\", \"district\": \"Borough\"})\n",
+    "fig = px.bar(\n",
+    "    new_build_pct.to_pandas(),\n",
+    "    x=\"new_build_pct\",\n",
+    "    y=\"district\",\n",
+    "    orientation=\"h\",\n",
+    "    title=\"New Build Share by Borough (2020+)\",\n",
+    "    color=\"new_build_pct\",\n",
+    "    color_continuous_scale=\"Blues\",\n",
+    "    labels={\"new_build_pct\": \"New Build %\", \"district\": \"Borough\"},\n",
+    ")\n",
     "fig.update_layout(yaxis={\"categoryorder\": \"total ascending\"}, height=800)\n",
     "fig.show()"
    ]
@@ -20260,21 +20568,66 @@
    "source": [
     "# Budget Finder: Borough Price Ranges\n",
     "recent_prices = (\n",
-    "    lf.filter(LONDON_FILTER).filter(pl.col(\"date_of_transfer\").dt.year() >= 2023)\n",
-    "    .group_by(\"district\").agg(pl.col(\"price\").quantile(0.25).alias(\"p25\"), pl.col(\"price\").median().alias(\"median\"), pl.col(\"price\").quantile(0.75).alias(\"p75\"))\n",
-    "    .sort(\"median\").collect()\n",
+    "    lf.filter(LONDON_FILTER)\n",
+    "    .filter(pl.col(\"date_of_transfer\").dt.year() >= 2023)\n",
+    "    .group_by(\"district\")\n",
+    "    .agg(\n",
+    "        pl.col(\"price\").quantile(0.25).alias(\"p25\"),\n",
+    "        pl.col(\"price\").median().alias(\"median\"),\n",
+    "        pl.col(\"price\").quantile(0.75).alias(\"p75\"),\n",
+    "    )\n",
+    "    .sort(\"median\")\n",
+    "    .collect()\n",
     ")\n",
     "\n",
     "fig = go.Figure()\n",
-    "fig.add_trace(go.Bar(name=\"25th percentile\", y=recent_prices[\"district\"], x=recent_prices[\"p25\"], orientation=\"h\", marker_color=\"lightgreen\"))\n",
-    "fig.add_trace(go.Bar(name=\"Median\", y=recent_prices[\"district\"], x=recent_prices[\"median\"] - recent_prices[\"p25\"], orientation=\"h\", marker_color=\"steelblue\", base=recent_prices[\"p25\"]))\n",
-    "fig.add_trace(go.Bar(name=\"75th percentile\", y=recent_prices[\"district\"], x=recent_prices[\"p75\"] - recent_prices[\"median\"], orientation=\"h\", marker_color=\"coral\", base=recent_prices[\"median\"]))\n",
+    "fig.add_trace(\n",
+    "    go.Bar(\n",
+    "        name=\"25th percentile\",\n",
+    "        y=recent_prices[\"district\"],\n",
+    "        x=recent_prices[\"p25\"],\n",
+    "        orientation=\"h\",\n",
+    "        marker_color=\"lightgreen\",\n",
+    "    )\n",
+    ")\n",
+    "fig.add_trace(\n",
+    "    go.Bar(\n",
+    "        name=\"Median\",\n",
+    "        y=recent_prices[\"district\"],\n",
+    "        x=recent_prices[\"median\"] - recent_prices[\"p25\"],\n",
+    "        orientation=\"h\",\n",
+    "        marker_color=\"steelblue\",\n",
+    "        base=recent_prices[\"p25\"],\n",
+    "    )\n",
+    ")\n",
+    "fig.add_trace(\n",
+    "    go.Bar(\n",
+    "        name=\"75th percentile\",\n",
+    "        y=recent_prices[\"district\"],\n",
+    "        x=recent_prices[\"p75\"] - recent_prices[\"median\"],\n",
+    "        orientation=\"h\",\n",
+    "        marker_color=\"coral\",\n",
+    "        base=recent_prices[\"median\"],\n",
+    "    )\n",
+    ")\n",
     "\n",
     "for budget in [300000, 500000, 750000, 1000000]:\n",
-    "    fig.add_vline(x=budget, line_dash=\"dash\", line_color=\"gray\", annotation_text=f\"£{budget//1000}k\", annotation_position=\"top\")\n",
+    "    fig.add_vline(\n",
+    "        x=budget,\n",
+    "        line_dash=\"dash\",\n",
+    "        line_color=\"gray\",\n",
+    "        annotation_text=f\"£{budget // 1000}k\",\n",
+    "        annotation_position=\"top\",\n",
+    "    )\n",
     "\n",
-    "fig.update_layout(title=\"Borough Price Ranges (2023+) - Find Your Budget\", xaxis_title=\"Price (£)\",\n",
-    "    yaxis={\"categoryorder\": \"total ascending\"}, barmode=\"stack\", height=900, legend_title=\"Price Point\")\n",
+    "fig.update_layout(\n",
+    "    title=\"Borough Price Ranges (2023+) - Find Your Budget\",\n",
+    "    xaxis_title=\"Price (£)\",\n",
+    "    yaxis={\"categoryorder\": \"total ascending\"},\n",
+    "    barmode=\"stack\",\n",
+    "    height=900,\n",
+    "    legend_title=\"Price Point\",\n",
+    ")\n",
     "fig.show()"
    ]
   },
@@ -20288,18 +20641,43 @@
     "comparison_data = (\n",
     "    recent_prices.select(\"district\", \"median\")\n",
     "    .join(growth_df.select(\"district\", \"cagr_5yr\"), on=\"district\", how=\"left\")\n",
-    "    .join(freehold_pct.select(\"district\", \"percentage\").rename({\"percentage\": \"freehold_pct\"}), on=\"district\", how=\"left\")\n",
+    "    .join(\n",
+    "        freehold_pct.select(\"district\", \"percentage\").rename(\n",
+    "            {\"percentage\": \"freehold_pct\"}\n",
+    "        ),\n",
+    "        on=\"district\",\n",
+    "        how=\"left\",\n",
+    "    )\n",
     "    .join(volume_change.select(\"district\", \"count_2024\"), on=\"district\", how=\"left\")\n",
     "    .join(new_build_pct.select(\"district\", \"new_build_pct\"), on=\"district\", how=\"left\")\n",
     ")\n",
     "\n",
-    "comparison_normalized = comparison_data.with_columns([\n",
-    "    (100 - (pl.col(\"median\") - pl.col(\"median\").min()) / (pl.col(\"median\").max() - pl.col(\"median\").min()) * 100).alias(\"affordability_score\"),\n",
-    "    ((pl.col(\"cagr_5yr\") - pl.col(\"cagr_5yr\").min()) / (pl.col(\"cagr_5yr\").max() - pl.col(\"cagr_5yr\").min()) * 100).alias(\"growth_score\"),\n",
-    "    pl.col(\"freehold_pct\").alias(\"freehold_score\"),\n",
-    "    ((pl.col(\"count_2024\") - pl.col(\"count_2024\").min()) / (pl.col(\"count_2024\").max() - pl.col(\"count_2024\").min()) * 100).alias(\"liquidity_score\"),\n",
-    "    ((pl.col(\"new_build_pct\") - pl.col(\"new_build_pct\").min()) / (pl.col(\"new_build_pct\").max() - pl.col(\"new_build_pct\").min()) * 100).alias(\"development_score\"),\n",
-    "])"
+    "comparison_normalized = comparison_data.with_columns(\n",
+    "    [\n",
+    "        (\n",
+    "            100\n",
+    "            - (pl.col(\"median\") - pl.col(\"median\").min())\n",
+    "            / (pl.col(\"median\").max() - pl.col(\"median\").min())\n",
+    "            * 100\n",
+    "        ).alias(\"affordability_score\"),\n",
+    "        (\n",
+    "            (pl.col(\"cagr_5yr\") - pl.col(\"cagr_5yr\").min())\n",
+    "            / (pl.col(\"cagr_5yr\").max() - pl.col(\"cagr_5yr\").min())\n",
+    "            * 100\n",
+    "        ).alias(\"growth_score\"),\n",
+    "        pl.col(\"freehold_pct\").alias(\"freehold_score\"),\n",
+    "        (\n",
+    "            (pl.col(\"count_2024\") - pl.col(\"count_2024\").min())\n",
+    "            / (pl.col(\"count_2024\").max() - pl.col(\"count_2024\").min())\n",
+    "            * 100\n",
+    "        ).alias(\"liquidity_score\"),\n",
+    "        (\n",
+    "            (pl.col(\"new_build_pct\") - pl.col(\"new_build_pct\").min())\n",
+    "            / (pl.col(\"new_build_pct\").max() - pl.col(\"new_build_pct\").min())\n",
+    "            * 100\n",
+    "        ).alias(\"development_score\"),\n",
+    "    ]\n",
+    ")"
    ]
   },
   {
@@ -21228,14 +21606,38 @@
    ],
    "source": [
     "# Borough Scores Heatmap\n",
-    "scores_only = comparison_normalized.select([\"district\", \"affordability_score\", \"growth_score\", \"freehold_score\", \"liquidity_score\", \"development_score\"]).drop_nulls().sort(\"affordability_score\", descending=True)\n",
+    "scores_only = (\n",
+    "    comparison_normalized.select(\n",
+    "        [\n",
+    "            \"district\",\n",
+    "            \"affordability_score\",\n",
+    "            \"growth_score\",\n",
+    "            \"freehold_score\",\n",
+    "            \"liquidity_score\",\n",
+    "            \"development_score\",\n",
+    "        ]\n",
+    "    )\n",
+    "    .drop_nulls()\n",
+    "    .sort(\"affordability_score\", descending=True)\n",
+    ")\n",
     "\n",
     "fig = px.imshow(\n",
-    "    scores_only.select([\"affordability_score\", \"growth_score\", \"freehold_score\", \"liquidity_score\", \"development_score\"]).to_pandas(),\n",
+    "    scores_only.select(\n",
+    "        [\n",
+    "            \"affordability_score\",\n",
+    "            \"growth_score\",\n",
+    "            \"freehold_score\",\n",
+    "            \"liquidity_score\",\n",
+    "            \"development_score\",\n",
+    "        ]\n",
+    "    ).to_pandas(),\n",
     "    y=scores_only[\"district\"].to_list(),\n",
     "    x=[\"Affordability\", \"5yr Growth\", \"Freehold %\", \"Liquidity\", \"New Builds\"],\n",
-    "    color_continuous_scale=\"RdYlGn\", aspect=\"auto\",\n",
-    "    title=\"Borough Scores Heatmap (Higher = Better)\", labels={\"color\": \"Score\"})\n",
+    "    color_continuous_scale=\"RdYlGn\",\n",
+    "    aspect=\"auto\",\n",
+    "    title=\"Borough Scores Heatmap (Higher = Better)\",\n",
+    "    labels={\"color\": \"Score\"},\n",
+    ")\n",
     "fig.update_layout(height=900)\n",
     "fig.show()"
    ]
@@ -22258,17 +22660,49 @@
     "median_afford = value_growth[\"affordability_score\"].median()\n",
     "median_growth = value_growth[\"growth_score\"].median()\n",
     "\n",
-    "fig = px.scatter(value_growth.to_pandas(), x=\"affordability_score\", y=\"growth_score\", text=\"district\",\n",
-    "    size=\"liquidity_score\", color=\"freehold_score\", color_continuous_scale=\"Viridis\",\n",
+    "fig = px.scatter(\n",
+    "    value_growth.to_pandas(),\n",
+    "    x=\"affordability_score\",\n",
+    "    y=\"growth_score\",\n",
+    "    text=\"district\",\n",
+    "    size=\"liquidity_score\",\n",
+    "    color=\"freehold_score\",\n",
+    "    color_continuous_scale=\"Viridis\",\n",
     "    title=\"Value vs Growth Quadrant Analysis\",\n",
-    "    labels={\"affordability_score\": \"Affordability (higher=cheaper)\", \"growth_score\": \"5yr Growth (higher=faster)\", \"liquidity_score\": \"Liquidity\", \"freehold_score\": \"Freehold %\"})\n",
+    "    labels={\n",
+    "        \"affordability_score\": \"Affordability (higher=cheaper)\",\n",
+    "        \"growth_score\": \"5yr Growth (higher=faster)\",\n",
+    "        \"liquidity_score\": \"Liquidity\",\n",
+    "        \"freehold_score\": \"Freehold %\",\n",
+    "    },\n",
+    ")\n",
     "\n",
     "fig.add_hline(y=median_growth, line_dash=\"dash\", line_color=\"gray\")\n",
     "fig.add_vline(x=median_afford, line_dash=\"dash\", line_color=\"gray\")\n",
-    "fig.add_annotation(x=75, y=85, text=\"Best Value<br>(Affordable+Growing)\", showarrow=False, font=dict(size=11))\n",
-    "fig.add_annotation(x=25, y=85, text=\"Premium Growth<br>(Expensive+Growing)\", showarrow=False, font=dict(size=11))\n",
-    "fig.add_annotation(x=75, y=15, text=\"Stable & Affordable<br>(Cheap+Steady)\", showarrow=False, font=dict(size=11))\n",
-    "fig.add_annotation(x=25, y=15, text=\"Caution<br>(Expensive+Slow)\", showarrow=False, font=dict(size=11))\n",
+    "fig.add_annotation(\n",
+    "    x=75,\n",
+    "    y=85,\n",
+    "    text=\"Best Value<br>(Affordable+Growing)\",\n",
+    "    showarrow=False,\n",
+    "    font=dict(size=11),\n",
+    ")\n",
+    "fig.add_annotation(\n",
+    "    x=25,\n",
+    "    y=85,\n",
+    "    text=\"Premium Growth<br>(Expensive+Growing)\",\n",
+    "    showarrow=False,\n",
+    "    font=dict(size=11),\n",
+    ")\n",
+    "fig.add_annotation(\n",
+    "    x=75,\n",
+    "    y=15,\n",
+    "    text=\"Stable & Affordable<br>(Cheap+Steady)\",\n",
+    "    showarrow=False,\n",
+    "    font=dict(size=11),\n",
+    ")\n",
+    "fig.add_annotation(\n",
+    "    x=25, y=15, text=\"Caution<br>(Expensive+Slow)\", showarrow=False, font=dict(size=11)\n",
+    ")\n",
     "fig.update_traces(textposition=\"top center\", textfont_size=8)\n",
     "fig.update_layout(height=700)\n",
     "fig.show()"
@@ -22332,25 +22766,43 @@
    ],
    "source": [
     "# Borough Rankings with Composite Score\n",
-    "WEIGHTS = {\"affordability\": 0.30, \"growth\": 0.25, \"freehold\": 0.15, \"liquidity\": 0.15, \"development\": 0.15}\n",
+    "WEIGHTS = {\n",
+    "    \"affordability\": 0.30,\n",
+    "    \"growth\": 0.25,\n",
+    "    \"freehold\": 0.15,\n",
+    "    \"liquidity\": 0.15,\n",
+    "    \"development\": 0.15,\n",
+    "}\n",
     "\n",
     "ranking = (\n",
     "    comparison_normalized.drop_nulls()\n",
     "    .with_columns(\n",
-    "        (pl.col(\"affordability_score\") * WEIGHTS[\"affordability\"] +\n",
-    "         pl.col(\"growth_score\") * WEIGHTS[\"growth\"] +\n",
-    "         pl.col(\"freehold_score\") * WEIGHTS[\"freehold\"] +\n",
-    "         pl.col(\"liquidity_score\") * WEIGHTS[\"liquidity\"] +\n",
-    "         pl.col(\"development_score\") * WEIGHTS[\"development\"]).alias(\"composite_score\")\n",
+    "        (\n",
+    "            pl.col(\"affordability_score\") * WEIGHTS[\"affordability\"]\n",
+    "            + pl.col(\"growth_score\") * WEIGHTS[\"growth\"]\n",
+    "            + pl.col(\"freehold_score\") * WEIGHTS[\"freehold\"]\n",
+    "            + pl.col(\"liquidity_score\") * WEIGHTS[\"liquidity\"]\n",
+    "            + pl.col(\"development_score\") * WEIGHTS[\"development\"]\n",
+    "        ).alias(\"composite_score\")\n",
+    "    )\n",
+    "    .select(\n",
+    "        [\n",
+    "            \"district\",\n",
+    "            pl.col(\"median\").alias(\"median_price_2023\"),\n",
+    "            \"cagr_5yr\",\n",
+    "            \"freehold_pct\",\n",
+    "            \"composite_score\",\n",
+    "        ]\n",
     "    )\n",
-    "    .select([\"district\", pl.col(\"median\").alias(\"median_price_2023\"), \"cagr_5yr\", \"freehold_pct\", \"composite_score\"])\n",
     "    .sort(\"composite_score\", descending=True)\n",
     "    .with_row_index(\"rank\", offset=1)\n",
     ")\n",
     "\n",
     "print(\"Top 15 Boroughs by Composite Score:\")\n",
-    "print(\"Weights: Affordability 30%, Growth 25%, Freehold 15%, Liquidity 15%, Development 15%\")\n",
-    "print(\"=\"*80)\n",
+    "print(\n",
+    "    \"Weights: Affordability 30%, Growth 25%, Freehold 15%, Liquidity 15%, Development 15%\"\n",
+    ")\n",
+    "print(\"=\" * 80)\n",
     "ranking.head(15)"
    ]
   }

From 30bb9a3d7c8486594266c36a036106154eff9fca Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Wed, 28 Jan 2026 22:12:37 +0000
Subject: [PATCH 12/62] Update packages

---
 Taskfile.yml               |    2 +-
 frontend/package-lock.json |   17 +
 frontend/package.json      |   43 +-
 pyproject.toml             |   10 +-
 uv.lock                    | 1239 +++++++++++++++---------------------
 5 files changed, 567 insertions(+), 744 deletions(-)

diff --git a/Taskfile.yml b/Taskfile.yml
index 7b41e8c..7c96b25 100644
--- a/Taskfile.yml
+++ b/Taskfile.yml
@@ -14,7 +14,7 @@ tasks:
     cmds:
       - uv run python download_land_registry.py
       - uv run python download_arcgis_data.py
-      - uv run python download_pois.py
+      - uv run python -m pipeline.pois
 
   pipeline:
     desc: Run data processing pipeline
diff --git a/frontend/package-lock.json b/frontend/package-lock.json
index 48e04c9..3a0f5f4 100644
--- a/frontend/package-lock.json
+++ b/frontend/package-lock.json
@@ -11,6 +11,7 @@
         "@deck.gl/core": "^9.0.0",
         "@deck.gl/geo-layers": "^9.0.0",
         "@deck.gl/layers": "^9.0.0",
+        "@deck.gl/mapbox": "^9.2.6",
         "@deck.gl/react": "^9.0.0",
         "@radix-ui/react-select": "^2.0.0",
         "@radix-ui/react-slider": "^1.1.0",
@@ -181,6 +182,22 @@
         "@luma.gl/engine": "~9.2.6"
       }
     },
+    "node_modules/@deck.gl/mapbox": {
+      "version": "9.2.6",
+      "resolved": "https://registry.npmjs.org/@deck.gl/mapbox/-/mapbox-9.2.6.tgz",
+      "integrity": "sha512-gyqCHZwiZS8LOYY6LILQQp5YCCf++VFk/wRoGskZvhb/kdEPX2Onv8iV8pXe0h9UyMLO6Mj0wl3HlJWg2ILkrg==",
+      "license": "MIT",
+      "dependencies": {
+        "@luma.gl/constants": "^9.2.6",
+        "@math.gl/web-mercator": "^4.1.0"
+      },
+      "peerDependencies": {
+        "@deck.gl/core": "~9.2.0",
+        "@luma.gl/constants": "~9.2.6",
+        "@luma.gl/core": "~9.2.6",
+        "@math.gl/web-mercator": "^4.1.0"
+      }
+    },
     "node_modules/@deck.gl/mesh-layers": {
       "version": "9.2.6",
       "resolved": "https://registry.npmjs.org/@deck.gl/mesh-layers/-/mesh-layers-9.2.6.tgz",
diff --git a/frontend/package.json b/frontend/package.json
index 019696d..5f527eb 100644
--- a/frontend/package.json
+++ b/frontend/package.json
@@ -11,41 +11,42 @@
     "format:check": "prettier --check \"src/**/*.{ts,tsx,css}\""
   },
   "dependencies": {
-    "react": "^18.2.0",
-    "react-dom": "^18.2.0",
     "@deck.gl/core": "^9.0.0",
-    "@deck.gl/layers": "^9.0.0",
     "@deck.gl/geo-layers": "^9.0.0",
+    "@deck.gl/layers": "^9.0.0",
+    "@deck.gl/mapbox": "^9.2.6",
     "@deck.gl/react": "^9.0.0",
-    "maplibre-gl": "^4.0.0",
-    "react-map-gl": "^7.1.0",
-    "@radix-ui/react-slider": "^1.1.0",
     "@radix-ui/react-select": "^2.0.0",
+    "@radix-ui/react-slider": "^1.1.0",
     "class-variance-authority": "^0.7.0",
     "clsx": "^2.1.0",
+    "maplibre-gl": "^4.0.0",
+    "react": "^18.2.0",
+    "react-dom": "^18.2.0",
+    "react-map-gl": "^7.1.0",
     "tailwind-merge": "^2.2.0",
     "tailwindcss-animate": "^1.0.7"
   },
   "devDependencies": {
-    "webpack": "^5.90.0",
-    "webpack-cli": "^5.1.0",
-    "webpack-dev-server": "^5.0.0",
-    "html-webpack-plugin": "^5.6.0",
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-    "eslint": "^8.57.0",
     "@typescript-eslint/eslint-plugin": "^7.0.0",
     "@typescript-eslint/parser": "^7.0.0",
+    "autoprefixer": "^10.4.0",
+    "css-loader": "^7.0.0",
+    "eslint": "^8.57.0",
     "eslint-plugin-react": "^7.34.0",
     "eslint-plugin-react-hooks": "^4.6.0",
-    "prettier": "^3.2.0"
+    "html-webpack-plugin": "^5.6.0",
+    "postcss": "^8.4.0",
+    "postcss-loader": "^8.0.0",
+    "prettier": "^3.2.0",
+    "style-loader": "^4.0.0",
+    "tailwindcss": "^3.4.0",
+    "ts-loader": "^9.5.0",
+    "typescript": "^5.4.0",
+    "webpack": "^5.90.0",
+    "webpack-cli": "^5.1.0",
+    "webpack-dev-server": "^5.0.0"
   }
-}
\ No newline at end of file
+}
diff --git a/pyproject.toml b/pyproject.toml
index a51fab3..9a1cd5c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -6,7 +6,7 @@ readme = "README.md"
 requires-python = ">=3.12"
 dependencies = [
     "attrs>=22.2.0",
-    "httpx>=0.28.1",
+    "httpx[socks]>=0.28.1",
     "ipywidgets>=8.0.0",
     "jupyter>=1.0.0",
     "nest-asyncio>=1.6.0",
@@ -22,10 +22,12 @@ dependencies = [
     "h3>=3.7.0",
     "overturemaps>=0.18.0",
     "fastexcel>=0.19.0",
+    "osmium>=4.0.0",
+    "matplotlib>=3.10.8",
 ]
 
+[tool.uv]
+environments = ["sys_platform == 'linux' and python_version < '3.14'"]
+
 [dependency-groups]
 dev = ["ruff>=0.8.0"]
-
-[tool.uv.sources]
-journey-client = { path = "./tfl_journey_client" }
diff --git a/uv.lock b/uv.lock
index fedd70a..9a0b00d 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2,12 +2,10 @@ version = 1
 revision = 3
 requires-python = ">=3.12"
 resolution-markers = [
-    "python_full_version >= '3.14' and sys_platform == 'win32'",
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-    "python_full_version < '3.14' and sys_platform == 'emscripten'",
-    "python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.14' and sys_platform == 'linux'",
+]
+supported-markers = [
+    "python_full_version < '3.14' and sys_platform == 'linux'",
 ]
 
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 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "idna" },
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 version = "25.1.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "argon2-cffi-bindings" },
+    { name = "argon2-cffi-bindings", marker = "python_full_version < '3.14' and sys_platform == 'linux'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/0e/89/ce5af8a7d472a67cc819d5d998aa8c82c5d860608c4db9f46f1162d7dab9/argon2_cffi-25.1.0.tar.gz", hash = "sha256:694ae5cc8a42f4c4e2bf2ca0e64e51e23a040c6a517a85074683d3959e1346c1", size = 45706, upload-time = "2025-06-03T06:55:32.073Z" }
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 version = "25.1.0"
 source = { registry = "https://pypi.org/simple" }
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 [[package]]
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 source = { registry = "https://pypi.org/simple" }
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 [[package]]
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From 3d15967420d6484a19b2defc1ecd024f6b54c3bc Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Wed, 28 Jan 2026 22:12:45 +0000
Subject: [PATCH 13/62] Add one more analysis

---
 journey_times_analysis.ipynb | 539 +++++++++++++++++++++++++++++++++++
 1 file changed, 539 insertions(+)
 create mode 100644 journey_times_analysis.ipynb

diff --git a/journey_times_analysis.ipynb b/journey_times_analysis.ipynb
new file mode 100644
index 0000000..8a3cb3a
--- /dev/null
+++ b/journey_times_analysis.ipynb
@@ -0,0 +1,539 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Journey Times Analysis\n",
+    "\n",
+    "Analyzing travel times to Bank station by different transport modes versus distance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-28T19:53:51.298414Z",
+     "iopub.status.busy": "2026-01-28T19:53:51.298328Z",
+     "iopub.status.idle": "2026-01-28T19:53:51.682527Z",
+     "shell.execute_reply": "2026-01-28T19:53:51.682199Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import polars as pl\n",
+    "import matplotlib.pyplot as plt\n",
+    "from math import radians, sin, cos, sqrt, atan2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-28T19:53:51.683859Z",
+     "iopub.status.busy": "2026-01-28T19:53:51.683731Z",
+     "iopub.status.idle": "2026-01-28T19:53:51.797877Z",
+     "shell.execute_reply": "2026-01-28T19:53:51.797505Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total postcodes: 200000\n",
+      "Columns: ['postcode', 'public_transport_easy_minutes', 'public_transport_quick_minutes', 'cycling_minutes', 'error', 'lat', 'long']\n",
+      "With lat/long: 200000\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (5, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>postcode</th><th>public_transport_easy_minutes</th><th>public_transport_quick_minutes</th><th>cycling_minutes</th><th>error</th><th>lat</th><th>long</th></tr><tr><td>str</td><td>i64</td><td>i64</td><td>i64</td><td>null</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;W1J 8HX&quot;</td><td>31</td><td>22</td><td>11</td><td>null</td><td>51.506738</td><td>-0.143261</td></tr><tr><td>&quot;W1J 8HS&quot;</td><td>32</td><td>23</td><td>11</td><td>null</td><td>51.507445</td><td>-0.141877</td></tr><tr><td>&quot;W1J 8HR&quot;</td><td>32</td><td>23</td><td>11</td><td>null</td><td>51.507578</td><td>-0.141757</td></tr><tr><td>&quot;W1J 8HZ&quot;</td><td>31</td><td>22</td><td>11</td><td>null</td><td>51.506578</td><td>-0.14393</td></tr><tr><td>&quot;W1J 8HP&quot;</td><td>32</td><td>23</td><td>11</td><td>null</td><td>51.507541</td><td>-0.141686</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (5, 7)\n",
+       "┌──────────┬──────────────────┬──────────────────┬─────────────────┬───────┬───────────┬───────────┐\n",
+       "│ postcode ┆ public_transport ┆ public_transport ┆ cycling_minutes ┆ error ┆ lat       ┆ long      │\n",
+       "│ ---      ┆ _easy_minutes    ┆ _quick_minutes   ┆ ---             ┆ ---   ┆ ---       ┆ ---       │\n",
+       "│ str      ┆ ---              ┆ ---              ┆ i64             ┆ null  ┆ f64       ┆ f64       │\n",
+       "│          ┆ i64              ┆ i64              ┆                 ┆       ┆           ┆           │\n",
+       "╞══════════╪══════════════════╪══════════════════╪═════════════════╪═══════╪═══════════╪═══════════╡\n",
+       "│ W1J 8HX  ┆ 31               ┆ 22               ┆ 11              ┆ null  ┆ 51.506738 ┆ -0.143261 │\n",
+       "│ W1J 8HS  ┆ 32               ┆ 23               ┆ 11              ┆ null  ┆ 51.507445 ┆ -0.141877 │\n",
+       "│ W1J 8HR  ┆ 32               ┆ 23               ┆ 11              ┆ null  ┆ 51.507578 ┆ -0.141757 │\n",
+       "│ W1J 8HZ  ┆ 31               ┆ 22               ┆ 11              ┆ null  ┆ 51.506578 ┆ -0.14393  │\n",
+       "│ W1J 8HP  ┆ 32               ┆ 23               ┆ 11              ┆ null  ┆ 51.507541 ┆ -0.141686 │\n",
+       "└──────────┴──────────────────┴──────────────────┴─────────────────┴───────┴───────────┴───────────┘"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load the journey times data and join with arcgis for lat/long\n",
+    "jt = pl.read_parquet('data_sources/processed/journey_times_bank_checkpoint.parquet')\n",
+    "arcgis = pl.read_parquet('data_sources/arcgis_data.parquet', columns=['pcds', 'lat', 'long'])\n",
+    "\n",
+    "df = jt.join(arcgis, left_on='postcode', right_on='pcds', how='left')\n",
+    "print(f\"Total postcodes: {df.height}\")\n",
+    "print(f\"Columns: {df.columns}\")\n",
+    "print(f\"With lat/long: {df.filter(pl.col('lat').is_not_null()).height}\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-28T19:53:51.814525Z",
+     "iopub.status.busy": "2026-01-28T19:53:51.814373Z",
+     "iopub.status.idle": "2026-01-28T19:53:51.943769Z",
+     "shell.execute_reply": "2026-01-28T19:53:51.943463Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (5, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>postcode</th><th>lat</th><th>long</th><th>distance_km</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;W1J 8HX&quot;</td><td>51.506738</td><td>-0.143261</td><td>3.852557</td></tr><tr><td>&quot;W1J 8HS&quot;</td><td>51.507445</td><td>-0.141877</td><td>3.744058</td></tr><tr><td>&quot;W1J 8HR&quot;</td><td>51.507578</td><td>-0.141757</td><td>3.733327</td></tr><tr><td>&quot;W1J 8HZ&quot;</td><td>51.506578</td><td>-0.14393</td><td>3.901403</td></tr><tr><td>&quot;W1J 8HP&quot;</td><td>51.507541</td><td>-0.141686</td><td>3.729191</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (5, 4)\n",
+       "┌──────────┬───────────┬───────────┬─────────────┐\n",
+       "│ postcode ┆ lat       ┆ long      ┆ distance_km │\n",
+       "│ ---      ┆ ---       ┆ ---       ┆ ---         │\n",
+       "│ str      ┆ f64       ┆ f64       ┆ f64         │\n",
+       "╞══════════╪═══════════╪═══════════╪═════════════╡\n",
+       "│ W1J 8HX  ┆ 51.506738 ┆ -0.143261 ┆ 3.852557    │\n",
+       "│ W1J 8HS  ┆ 51.507445 ┆ -0.141877 ┆ 3.744058    │\n",
+       "│ W1J 8HR  ┆ 51.507578 ┆ -0.141757 ┆ 3.733327    │\n",
+       "│ W1J 8HZ  ┆ 51.506578 ┆ -0.14393  ┆ 3.901403    │\n",
+       "│ W1J 8HP  ┆ 51.507541 ┆ -0.141686 ┆ 3.729191    │\n",
+       "└──────────┴───────────┴───────────┴─────────────┘"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Bank station coordinates\n",
+    "BANK_LAT = 51.5133\n",
+    "BANK_LON = -0.0886\n",
+    "\n",
+    "def haversine_km(lat1, lon1, lat2, lon2):\n",
+    "    \"\"\"Calculate the great circle distance between two points in km.\"\"\"\n",
+    "    R = 6371  # Earth's radius in km\n",
+    "    \n",
+    "    lat1, lon1, lat2, lon2 = map(radians, [lat1, lon1, lat2, lon2])\n",
+    "    dlat = lat2 - lat1\n",
+    "    dlon = lon2 - lon1\n",
+    "    \n",
+    "    a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2\n",
+    "    c = 2 * atan2(sqrt(a), sqrt(1-a))\n",
+    "    \n",
+    "    return R * c\n",
+    "\n",
+    "# Calculate distance to Bank for each postcode\n",
+    "df = df.with_columns(\n",
+    "    pl.struct(['lat', 'long']).map_elements(\n",
+    "        lambda x: haversine_km(x['lat'], x['long'], BANK_LAT, BANK_LON),\n",
+    "        return_dtype=pl.Float64\n",
+    "    ).alias('distance_km')\n",
+    ")\n",
+    "\n",
+    "df.select(['postcode', 'lat', 'long', 'distance_km']).head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-28T19:53:51.944770Z",
+     "iopub.status.busy": "2026-01-28T19:53:51.944696Z",
+     "iopub.status.idle": "2026-01-28T19:53:51.948131Z",
+     "shell.execute_reply": "2026-01-28T19:53:51.947811Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Postcodes with journey data: 177132/200000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Filter to rows with valid journey times\n",
+    "df_valid = df.filter(\n",
+    "    pl.col('public_transport_easy_minutes').is_not_null() |\n",
+    "    pl.col('public_transport_quick_minutes').is_not_null() |\n",
+    "    pl.col('cycling_minutes').is_not_null()\n",
+    ")\n",
+    "\n",
+    "print(f\"Postcodes with journey data: {df_valid.height}/{df.height}\")\n",
+    "\n",
+    "if df_valid.height == 0:\n",
+    "    print(\"\\nNo journey time data available yet. Run the fetcher first:\")\n",
+    "    print(\"  uv run python -m pipeline.journey_times\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-28T19:53:51.948922Z",
+     "iopub.status.busy": "2026-01-28T19:53:51.948847Z",
+     "iopub.status.idle": "2026-01-28T19:53:51.952849Z",
+     "shell.execute_reply": "2026-01-28T19:53:51.952531Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Journey Time Statistics (minutes):\n",
+      "shape: (1, 6)\n",
+      "┌──────────────┬─────────────┬─────────────┬───────────────┬──────────────┬───────────────────┐\n",
+      "│ PT Easy Mean ┆ PT Easy Min ┆ PT Easy Max ┆ PT Quick Mean ┆ Cycling Mean ┆ Avg Distance (km) │\n",
+      "│ ---          ┆ ---         ┆ ---         ┆ ---           ┆ ---          ┆ ---               │\n",
+      "│ f64          ┆ i64         ┆ i64         ┆ f64           ┆ f64          ┆ f64               │\n",
+      "╞══════════════╪═════════════╪═════════════╪═══════════════╪══════════════╪═══════════════════╡\n",
+      "│ 76.621676    ┆ 0           ┆ 372         ┆ 74.413512     ┆ 51.674563    ┆ 36.287446         │\n",
+      "└──────────────┴─────────────┴─────────────┴───────────────┴──────────────┴───────────────────┘\n",
+      "\n",
+      "Distance Statistics:\n",
+      "shape: (1, 3)\n",
+      "┌──────────┬────────────┬───────────┐\n",
+      "│ Min km   ┆ Max km     ┆ Median km │\n",
+      "│ ---      ┆ ---        ┆ ---       │\n",
+      "│ f64      ┆ f64        ┆ f64       │\n",
+      "╞══════════╪════════════╪═══════════╡\n",
+      "│ 0.013939 ┆ 109.991355 ┆ 19.561729 │\n",
+      "└──────────┴────────────┴───────────┘\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Summary statistics\n",
+    "if df_valid.height > 0:\n",
+    "    print(\"Journey Time Statistics (minutes):\")\n",
+    "    print(df_valid.select([\n",
+    "        pl.col('public_transport_easy_minutes').mean().alias('PT Easy Mean'),\n",
+    "        pl.col('public_transport_easy_minutes').min().alias('PT Easy Min'),\n",
+    "        pl.col('public_transport_easy_minutes').max().alias('PT Easy Max'),\n",
+    "        pl.col('public_transport_quick_minutes').mean().alias('PT Quick Mean'),\n",
+    "        pl.col('cycling_minutes').mean().alias('Cycling Mean'),\n",
+    "        pl.col('distance_km').mean().alias('Avg Distance (km)'),\n",
+    "    ]))\n",
+    "    \n",
+    "    print(\"\\nDistance Statistics:\")\n",
+    "    print(df_valid.select([\n",
+    "        pl.col('distance_km').min().alias('Min km'),\n",
+    "        pl.col('distance_km').max().alias('Max km'),\n",
+    "        pl.col('distance_km').median().alias('Median km'),\n",
+    "    ]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-28T19:53:51.953471Z",
+     "iopub.status.busy": "2026-01-28T19:53:51.953401Z",
+     "iopub.status.idle": "2026-01-28T19:53:53.919178Z",
+     "shell.execute_reply": "2026-01-28T19:53:53.918809Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot: Travel Time vs Distance by Mode\n",
+    "if df_valid.height > 0:\n",
+    "    fig, ax = plt.subplots(figsize=(12, 8))\n",
+    "    \n",
+    "    # Convert to pandas for plotting\n",
+    "    pdf = df_valid.to_pandas()\n",
+    "    \n",
+    "    # Plot each mode    \n",
+    "    if pdf['public_transport_quick_minutes'].notna().any():\n",
+    "        ax.scatter(pdf['distance_km'], pdf['public_transport_quick_minutes'], \n",
+    "                   alpha=0.3, label='Public Transport (Quick)', c='red', s=50)\n",
+    "    \n",
+    "    if pdf['public_transport_easy_minutes'].notna().any():\n",
+    "        ax.scatter(pdf['distance_km'], pdf['public_transport_easy_minutes'], \n",
+    "                   alpha=0.3, label='Public Transport (Easy)', c='green', s=50)\n",
+    "    \n",
+    "    if pdf['cycling_minutes'].notna().any():\n",
+    "        ax.scatter(pdf['distance_km'], pdf['cycling_minutes'], \n",
+    "                   alpha=0.3, label='Cycling', c='orange', s=50)\n",
+    "    \n",
+    "    ax.set_xlabel('Distance to Bank (km)', fontsize=12)\n",
+    "    ax.set_ylabel('Travel Time (minutes)', fontsize=12)\n",
+    "    ax.set_title('Travel Time vs Distance to Bank Station', fontsize=14)\n",
+    "    ax.legend()\n",
+    "    ax.grid(True, alpha=0.3)\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-28T19:53:53.920561Z",
+     "iopub.status.busy": "2026-01-28T19:53:53.920429Z",
+     "iopub.status.idle": "2026-01-28T19:53:55.185911Z",
+     "shell.execute_reply": "2026-01-28T19:53:55.185544Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot: Speed (km/h) vs Distance by Mode\n",
+    "if df_valid.height > 0:\n",
+    "    fig, ax = plt.subplots(figsize=(12, 8))\n",
+    "    \n",
+    "    # Calculate effective speed for each mode\n",
+    "    pdf = df_valid.with_columns([\n",
+    "        (pl.col('distance_km') / (pl.col('public_transport_easy_minutes') / 60)).alias('speed_easy_kmh'),\n",
+    "        (pl.col('distance_km') / (pl.col('public_transport_quick_minutes') / 60)).alias('speed_quick_kmh'),\n",
+    "        (pl.col('distance_km') / (pl.col('cycling_minutes') / 60)).alias('speed_cycling_kmh'),\n",
+    "    ]).to_pandas()\n",
+    "    \n",
+    "    if pdf['speed_easy_kmh'].notna().any():\n",
+    "        ax.scatter(pdf['distance_km'], pdf['speed_easy_kmh'], \n",
+    "                   alpha=0.6, label='Public Transport (Easy)', c='blue', s=50)\n",
+    "    \n",
+    "    if pdf['speed_quick_kmh'].notna().any():\n",
+    "        ax.scatter(pdf['distance_km'], pdf['speed_quick_kmh'], \n",
+    "                   alpha=0.6, label='Public Transport (Quick)', c='green', s=50)\n",
+    "    \n",
+    "    if pdf['speed_cycling_kmh'].notna().any():\n",
+    "        ax.scatter(pdf['distance_km'], pdf['speed_cycling_kmh'], \n",
+    "                   alpha=0.6, label='Cycling', c='orange', s=50)\n",
+    "    \n",
+    "    ax.set_xlabel('Distance to Bank (km)', fontsize=12)\n",
+    "    ax.set_ylabel('Effective Speed (km/h)', fontsize=12)\n",
+    "    ax.set_title('Effective Travel Speed vs Distance to Bank Station', fontsize=14)\n",
+    "    ax.legend()\n",
+    "    ax.grid(True, alpha=0.3)\n",
+    "    ax.set_ylim(0, 100)  # Cap y-axis for readability\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-28T19:53:55.187209Z",
+     "iopub.status.busy": "2026-01-28T19:53:55.187131Z",
+     "iopub.status.idle": "2026-01-28T19:53:56.842052Z",
+     "shell.execute_reply": "2026-01-28T19:53:56.841680Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Time saved by taking quick route:\n",
+      "  Mean: 7.8 minutes\n",
+      "  Max: 97 minutes\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Comparison: Quick vs Easy public transport\n",
+    "if df_valid.height > 0:\n",
+    "    df_both = df_valid.filter(\n",
+    "        pl.col('public_transport_easy_minutes').is_not_null() &\n",
+    "        pl.col('public_transport_quick_minutes').is_not_null()\n",
+    "    )\n",
+    "    \n",
+    "    if df_both.height > 0:\n",
+    "        fig, ax = plt.subplots(figsize=(10, 8))\n",
+    "        \n",
+    "        pdf = df_both.to_pandas()\n",
+    "        \n",
+    "        ax.scatter(pdf['public_transport_easy_minutes'], \n",
+    "                   pdf['public_transport_quick_minutes'],\n",
+    "                   c=pdf['distance_km'], cmap='viridis', s=60, alpha=0.7)\n",
+    "        \n",
+    "        # Add diagonal line (equal times)\n",
+    "        max_val = max(pdf['public_transport_easy_minutes'].max(), \n",
+    "                      pdf['public_transport_quick_minutes'].max())\n",
+    "        ax.plot([0, max_val], [0, max_val], 'r--', alpha=0.5, label='Equal time')\n",
+    "        \n",
+    "        ax.set_xlabel('Easy Route (minutes)', fontsize=12)\n",
+    "        ax.set_ylabel('Quick Route (minutes)', fontsize=12)\n",
+    "        ax.set_title('Quick vs Easy Public Transport Times\\n(color = distance in km)', fontsize=14)\n",
+    "        ax.legend()\n",
+    "        ax.grid(True, alpha=0.3)\n",
+    "        \n",
+    "        plt.colorbar(ax.collections[0], label='Distance (km)')\n",
+    "        plt.tight_layout()\n",
+    "        plt.show()\n",
+    "        \n",
+    "        # Calculate time savings\n",
+    "        savings = df_both.with_columns(\n",
+    "            (pl.col('public_transport_easy_minutes') - pl.col('public_transport_quick_minutes')).alias('time_saved')\n",
+    "        )\n",
+    "        print(\"\\nTime saved by taking quick route:\")\n",
+    "        print(f\"  Mean: {savings['time_saved'].mean():.1f} minutes\")\n",
+    "        print(f\"  Max: {savings['time_saved'].max():.0f} minutes\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-28T19:53:56.842982Z",
+     "iopub.status.busy": "2026-01-28T19:53:56.842908Z",
+     "iopub.status.idle": "2026-01-28T19:53:56.849543Z",
+     "shell.execute_reply": "2026-01-28T19:53:56.849208Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Journey times sorted by distance to Bank:\n",
+      "shape: (177_132, 5)\n",
+      "┌──────────┬─────────────┬───────────────────────────┬───────────────────────────┬─────────────────┐\n",
+      "│ postcode ┆ distance_km ┆ public_transport_easy_min ┆ public_transport_quick_mi ┆ cycling_minutes │\n",
+      "│ ---      ┆ ---         ┆ utes                      ┆ nutes                     ┆ ---             │\n",
+      "│ str      ┆ f64         ┆ ---                       ┆ ---                       ┆ i64             │\n",
+      "│          ┆             ┆ i64                       ┆ i64                       ┆                 │\n",
+      "╞══════════╪═════════════╪═══════════════════════════╪═══════════════════════════╪═════════════════╡\n",
+      "│ EC3V 3LA ┆ 0.0         ┆ 0                         ┆ 0                         ┆ 0               │\n",
+      "│ EC3V 3NR ┆ 0.0         ┆ 1                         ┆ 1                         ┆ 0               │\n",
+      "│ EC3V 3QR ┆ 0.0         ┆ 1                         ┆ 1                         ┆ 0               │\n",
+      "│ EC3V 3QS ┆ 0.0         ┆ 0                         ┆ 0                         ┆ 0               │\n",
+      "│ EC3V 9AQ ┆ 0.0         ┆ 2                         ┆ 2                         ┆ 0               │\n",
+      "│ …        ┆ …           ┆ …                         ┆ …                         ┆ …               │\n",
+      "│ OX15 5JP ┆ 110.0       ┆ null                      ┆ 153                       ┆ null            │\n",
+      "│ OX15 5JT ┆ 110.0       ┆ null                      ┆ 153                       ┆ null            │\n",
+      "│ OX15 5LD ┆ 110.0       ┆ null                      ┆ 153                       ┆ null            │\n",
+      "│ OX15 5ND ┆ 110.0       ┆ null                      ┆ 153                       ┆ null            │\n",
+      "│ OX15 5NE ┆ 110.0       ┆ null                      ┆ 153                       ┆ null            │\n",
+      "└──────────┴─────────────┴───────────────────────────┴───────────────────────────┴─────────────────┘\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Data table: sorted by distance\n",
+    "if df_valid.height > 0:\n",
+    "    print(\"Journey times sorted by distance to Bank:\")\n",
+    "    display_df = df_valid.select([\n",
+    "        'postcode',\n",
+    "        pl.col('distance_km').round(1),\n",
+    "        'public_transport_easy_minutes',\n",
+    "        'public_transport_quick_minutes', \n",
+    "        'cycling_minutes'\n",
+    "    ]).sort('distance_km')\n",
+    "    \n",
+    "    print(display_df)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From c0f88602bfd509dae5a6a57760fff5b8d99e6be8 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Wed, 28 Jan 2026 22:16:34 +0000
Subject: [PATCH 14/62] Move

---
 .../epc_analysis.ipynb                        |  32 +-
 analyses/journey_times_analysis.ipynb         | 539 +++++++++++++
 .../property_analysis.ipynb                   | 752 +++++++++---------
 journey_times_analysis.ipynb                  | 539 -------------
 4 files changed, 931 insertions(+), 931 deletions(-)
 rename epc_analysis.ipynb => analyses/epc_analysis.ipynb (99%)
 create mode 100644 analyses/journey_times_analysis.ipynb
 rename analysis.ipynb => analyses/property_analysis.ipynb (99%)
 delete mode 100644 journey_times_analysis.ipynb

diff --git a/epc_analysis.ipynb b/analyses/epc_analysis.ipynb
similarity index 99%
rename from epc_analysis.ipynb
rename to analyses/epc_analysis.ipynb
index 2a07e87..95f9d14 100644
--- a/epc_analysis.ipynb
+++ b/analyses/epc_analysis.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T22:06:26.543050Z",
@@ -79,7 +79,7 @@
     "plt.rcParams[\"figure.figsize\"] = (12, 5)\n",
     "plt.rcParams[\"figure.dpi\"] = 100\n",
     "\n",
-    "PARQUET = \"data_sources/epc/certificates.parquet\"\n",
+    "PARQUET = \"../data_sources/epc/certificates.parquet\"\n",
     "\n",
     "def scan():\n",
     "    return pl.scan_parquet(PARQUET)\n",
@@ -100,7 +100,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T22:06:27.101365Z",
@@ -183,7 +183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T22:06:29.054902Z",
@@ -240,7 +240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T22:06:29.243014Z",
@@ -288,7 +288,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T22:06:29.389426Z",
@@ -355,7 +355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T22:06:29.941986Z",
@@ -432,7 +432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T22:06:30.516767Z",
@@ -515,7 +515,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T22:06:31.277179Z",
@@ -596,7 +596,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T22:06:31.967996Z",
@@ -648,7 +648,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T22:06:32.077561Z",
@@ -713,7 +713,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T22:06:32.398389Z",
@@ -763,7 +763,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T22:06:32.910558Z",
@@ -822,7 +822,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T22:06:33.048305Z",
@@ -911,7 +911,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T22:06:33.343873Z",
@@ -970,7 +970,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T22:06:33.797899Z",
diff --git a/analyses/journey_times_analysis.ipynb b/analyses/journey_times_analysis.ipynb
new file mode 100644
index 0000000..5cc57ab
--- /dev/null
+++ b/analyses/journey_times_analysis.ipynb
@@ -0,0 +1,539 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Journey Times Analysis\n",
+    "\n",
+    "Analyzing travel times to Bank station by different transport modes versus distance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-28T19:53:51.298414Z",
+     "iopub.status.busy": "2026-01-28T19:53:51.298328Z",
+     "iopub.status.idle": "2026-01-28T19:53:51.682527Z",
+     "shell.execute_reply": "2026-01-28T19:53:51.682199Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import polars as pl\n",
+    "import matplotlib.pyplot as plt\n",
+    "from math import radians, sin, cos, sqrt, atan2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-28T19:53:51.683859Z",
+     "iopub.status.busy": "2026-01-28T19:53:51.683731Z",
+     "iopub.status.idle": "2026-01-28T19:53:51.797877Z",
+     "shell.execute_reply": "2026-01-28T19:53:51.797505Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total postcodes: 220000\n",
+      "Columns: ['postcode', 'public_transport_easy_minutes', 'public_transport_quick_minutes', 'cycling_minutes', 'error', 'lat', 'long']\n",
+      "With lat/long: 220000\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (5, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>postcode</th><th>public_transport_easy_minutes</th><th>public_transport_quick_minutes</th><th>cycling_minutes</th><th>error</th><th>lat</th><th>long</th></tr><tr><td>str</td><td>i64</td><td>i64</td><td>i64</td><td>null</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;W1J 8HX&quot;</td><td>31</td><td>22</td><td>11</td><td>null</td><td>51.506738</td><td>-0.143261</td></tr><tr><td>&quot;W1J 8HS&quot;</td><td>32</td><td>23</td><td>11</td><td>null</td><td>51.507445</td><td>-0.141877</td></tr><tr><td>&quot;W1J 8HR&quot;</td><td>32</td><td>23</td><td>11</td><td>null</td><td>51.507578</td><td>-0.141757</td></tr><tr><td>&quot;W1J 8HZ&quot;</td><td>31</td><td>22</td><td>11</td><td>null</td><td>51.506578</td><td>-0.14393</td></tr><tr><td>&quot;W1J 8HP&quot;</td><td>32</td><td>23</td><td>11</td><td>null</td><td>51.507541</td><td>-0.141686</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (5, 7)\n",
+       "┌──────────┬──────────────────┬──────────────────┬─────────────────┬───────┬───────────┬───────────┐\n",
+       "│ postcode ┆ public_transport ┆ public_transport ┆ cycling_minutes ┆ error ┆ lat       ┆ long      │\n",
+       "│ ---      ┆ _easy_minutes    ┆ _quick_minutes   ┆ ---             ┆ ---   ┆ ---       ┆ ---       │\n",
+       "│ str      ┆ ---              ┆ ---              ┆ i64             ┆ null  ┆ f64       ┆ f64       │\n",
+       "│          ┆ i64              ┆ i64              ┆                 ┆       ┆           ┆           │\n",
+       "╞══════════╪══════════════════╪══════════════════╪═════════════════╪═══════╪═══════════╪═══════════╡\n",
+       "│ W1J 8HX  ┆ 31               ┆ 22               ┆ 11              ┆ null  ┆ 51.506738 ┆ -0.143261 │\n",
+       "│ W1J 8HS  ┆ 32               ┆ 23               ┆ 11              ┆ null  ┆ 51.507445 ┆ -0.141877 │\n",
+       "│ W1J 8HR  ┆ 32               ┆ 23               ┆ 11              ┆ null  ┆ 51.507578 ┆ -0.141757 │\n",
+       "│ W1J 8HZ  ┆ 31               ┆ 22               ┆ 11              ┆ null  ┆ 51.506578 ┆ -0.14393  │\n",
+       "│ W1J 8HP  ┆ 32               ┆ 23               ┆ 11              ┆ null  ┆ 51.507541 ┆ -0.141686 │\n",
+       "└──────────┴──────────────────┴──────────────────┴─────────────────┴───────┴───────────┴───────────┘"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load the journey times data and join with arcgis for lat/long\n",
+    "jt = pl.read_parquet('../data_sources/processed/journey_times_bank_checkpoint.parquet')\n",
+    "arcgis = pl.read_parquet('../data_sources/arcgis_data.parquet', columns=['pcds', 'lat', 'long'])\n",
+    "\n",
+    "df = jt.join(arcgis, left_on='postcode', right_on='pcds', how='left')\n",
+    "print(f\"Total postcodes: {df.height}\")\n",
+    "print(f\"Columns: {df.columns}\")\n",
+    "print(f\"With lat/long: {df.filter(pl.col('lat').is_not_null()).height}\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-28T19:53:51.814525Z",
+     "iopub.status.busy": "2026-01-28T19:53:51.814373Z",
+     "iopub.status.idle": "2026-01-28T19:53:51.943769Z",
+     "shell.execute_reply": "2026-01-28T19:53:51.943463Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (5, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>postcode</th><th>lat</th><th>long</th><th>distance_km</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;W1J 8HX&quot;</td><td>51.506738</td><td>-0.143261</td><td>3.852557</td></tr><tr><td>&quot;W1J 8HS&quot;</td><td>51.507445</td><td>-0.141877</td><td>3.744058</td></tr><tr><td>&quot;W1J 8HR&quot;</td><td>51.507578</td><td>-0.141757</td><td>3.733327</td></tr><tr><td>&quot;W1J 8HZ&quot;</td><td>51.506578</td><td>-0.14393</td><td>3.901403</td></tr><tr><td>&quot;W1J 8HP&quot;</td><td>51.507541</td><td>-0.141686</td><td>3.729191</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (5, 4)\n",
+       "┌──────────┬───────────┬───────────┬─────────────┐\n",
+       "│ postcode ┆ lat       ┆ long      ┆ distance_km │\n",
+       "│ ---      ┆ ---       ┆ ---       ┆ ---         │\n",
+       "│ str      ┆ f64       ┆ f64       ┆ f64         │\n",
+       "╞══════════╪═══════════╪═══════════╪═════════════╡\n",
+       "│ W1J 8HX  ┆ 51.506738 ┆ -0.143261 ┆ 3.852557    │\n",
+       "│ W1J 8HS  ┆ 51.507445 ┆ -0.141877 ┆ 3.744058    │\n",
+       "│ W1J 8HR  ┆ 51.507578 ┆ -0.141757 ┆ 3.733327    │\n",
+       "│ W1J 8HZ  ┆ 51.506578 ┆ -0.14393  ┆ 3.901403    │\n",
+       "│ W1J 8HP  ┆ 51.507541 ┆ -0.141686 ┆ 3.729191    │\n",
+       "└──────────┴───────────┴───────────┴─────────────┘"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Bank station coordinates\n",
+    "BANK_LAT = 51.5133\n",
+    "BANK_LON = -0.0886\n",
+    "\n",
+    "def haversine_km(lat1, lon1, lat2, lon2):\n",
+    "    \"\"\"Calculate the great circle distance between two points in km.\"\"\"\n",
+    "    R = 6371  # Earth's radius in km\n",
+    "    \n",
+    "    lat1, lon1, lat2, lon2 = map(radians, [lat1, lon1, lat2, lon2])\n",
+    "    dlat = lat2 - lat1\n",
+    "    dlon = lon2 - lon1\n",
+    "    \n",
+    "    a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2\n",
+    "    c = 2 * atan2(sqrt(a), sqrt(1-a))\n",
+    "    \n",
+    "    return R * c\n",
+    "\n",
+    "# Calculate distance to Bank for each postcode\n",
+    "df = df.with_columns(\n",
+    "    pl.struct(['lat', 'long']).map_elements(\n",
+    "        lambda x: haversine_km(x['lat'], x['long'], BANK_LAT, BANK_LON),\n",
+    "        return_dtype=pl.Float64\n",
+    "    ).alias('distance_km')\n",
+    ")\n",
+    "\n",
+    "df.select(['postcode', 'lat', 'long', 'distance_km']).head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-28T19:53:51.944770Z",
+     "iopub.status.busy": "2026-01-28T19:53:51.944696Z",
+     "iopub.status.idle": "2026-01-28T19:53:51.948131Z",
+     "shell.execute_reply": "2026-01-28T19:53:51.947811Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Postcodes with journey data: 191297/220000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Filter to rows with valid journey times\n",
+    "df_valid = df.filter(\n",
+    "    pl.col('public_transport_easy_minutes').is_not_null() |\n",
+    "    pl.col('public_transport_quick_minutes').is_not_null() |\n",
+    "    pl.col('cycling_minutes').is_not_null()\n",
+    ")\n",
+    "\n",
+    "print(f\"Postcodes with journey data: {df_valid.height}/{df.height}\")\n",
+    "\n",
+    "if df_valid.height == 0:\n",
+    "    print(\"\\nNo journey time data available yet. Run the fetcher first:\")\n",
+    "    print(\"  uv run python -m pipeline.journey_times\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-28T19:53:51.948922Z",
+     "iopub.status.busy": "2026-01-28T19:53:51.948847Z",
+     "iopub.status.idle": "2026-01-28T19:53:51.952849Z",
+     "shell.execute_reply": "2026-01-28T19:53:51.952531Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Journey Time Statistics (minutes):\n",
+      "shape: (1, 6)\n",
+      "┌──────────────┬─────────────┬─────────────┬───────────────┬──────────────┬───────────────────┐\n",
+      "│ PT Easy Mean ┆ PT Easy Min ┆ PT Easy Max ┆ PT Quick Mean ┆ Cycling Mean ┆ Avg Distance (km) │\n",
+      "│ ---          ┆ ---         ┆ ---         ┆ ---           ┆ ---          ┆ ---               │\n",
+      "│ f64          ┆ i64         ┆ i64         ┆ f64           ┆ f64          ┆ f64               │\n",
+      "╞══════════════╪═════════════╪═════════════╪═══════════════╪══════════════╪═══════════════════╡\n",
+      "│ 76.048709    ┆ 0           ┆ 372         ┆ 73.763485     ┆ 51.144121    ┆ 36.05804          │\n",
+      "└──────────────┴─────────────┴─────────────┴───────────────┴──────────────┴───────────────────┘\n",
+      "\n",
+      "Distance Statistics:\n",
+      "shape: (1, 3)\n",
+      "┌──────────┬────────────┬───────────┐\n",
+      "│ Min km   ┆ Max km     ┆ Median km │\n",
+      "│ ---      ┆ ---        ┆ ---       │\n",
+      "│ f64      ┆ f64        ┆ f64       │\n",
+      "╞══════════╪════════════╪═══════════╡\n",
+      "│ 0.013939 ┆ 109.991355 ┆ 19.140675 │\n",
+      "└──────────┴────────────┴───────────┘\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Summary statistics\n",
+    "if df_valid.height > 0:\n",
+    "    print(\"Journey Time Statistics (minutes):\")\n",
+    "    print(df_valid.select([\n",
+    "        pl.col('public_transport_easy_minutes').mean().alias('PT Easy Mean'),\n",
+    "        pl.col('public_transport_easy_minutes').min().alias('PT Easy Min'),\n",
+    "        pl.col('public_transport_easy_minutes').max().alias('PT Easy Max'),\n",
+    "        pl.col('public_transport_quick_minutes').mean().alias('PT Quick Mean'),\n",
+    "        pl.col('cycling_minutes').mean().alias('Cycling Mean'),\n",
+    "        pl.col('distance_km').mean().alias('Avg Distance (km)'),\n",
+    "    ]))\n",
+    "    \n",
+    "    print(\"\\nDistance Statistics:\")\n",
+    "    print(df_valid.select([\n",
+    "        pl.col('distance_km').min().alias('Min km'),\n",
+    "        pl.col('distance_km').max().alias('Max km'),\n",
+    "        pl.col('distance_km').median().alias('Median km'),\n",
+    "    ]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-28T19:53:51.953471Z",
+     "iopub.status.busy": "2026-01-28T19:53:51.953401Z",
+     "iopub.status.idle": "2026-01-28T19:53:53.919178Z",
+     "shell.execute_reply": "2026-01-28T19:53:53.918809Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot: Travel Time vs Distance by Mode\n",
+    "if df_valid.height > 0:\n",
+    "    fig, ax = plt.subplots(figsize=(12, 8))\n",
+    "    \n",
+    "    # Convert to pandas for plotting\n",
+    "    pdf = df_valid.to_pandas()\n",
+    "    \n",
+    "    # Plot each mode    \n",
+    "    if pdf['public_transport_quick_minutes'].notna().any():\n",
+    "        ax.scatter(pdf['distance_km'], pdf['public_transport_quick_minutes'], \n",
+    "                   alpha=0.3, label='Public Transport (Quick)', c='red', s=50)\n",
+    "    \n",
+    "    if pdf['public_transport_easy_minutes'].notna().any():\n",
+    "        ax.scatter(pdf['distance_km'], pdf['public_transport_easy_minutes'], \n",
+    "                   alpha=0.3, label='Public Transport (Easy)', c='green', s=50)\n",
+    "    \n",
+    "    if pdf['cycling_minutes'].notna().any():\n",
+    "        ax.scatter(pdf['distance_km'], pdf['cycling_minutes'], \n",
+    "                   alpha=0.3, label='Cycling', c='orange', s=50)\n",
+    "    \n",
+    "    ax.set_xlabel('Distance to Bank (km)', fontsize=12)\n",
+    "    ax.set_ylabel('Travel Time (minutes)', fontsize=12)\n",
+    "    ax.set_title('Travel Time vs Distance to Bank Station', fontsize=14)\n",
+    "    ax.legend()\n",
+    "    ax.grid(True, alpha=0.3)\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-28T19:53:53.920561Z",
+     "iopub.status.busy": "2026-01-28T19:53:53.920429Z",
+     "iopub.status.idle": "2026-01-28T19:53:55.185911Z",
+     "shell.execute_reply": "2026-01-28T19:53:55.185544Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot: Speed (km/h) vs Distance by Mode\n",
+    "if df_valid.height > 0:\n",
+    "    fig, ax = plt.subplots(figsize=(12, 8))\n",
+    "    \n",
+    "    # Calculate effective speed for each mode\n",
+    "    pdf = df_valid.with_columns([\n",
+    "        (pl.col('distance_km') / (pl.col('public_transport_easy_minutes') / 60)).alias('speed_easy_kmh'),\n",
+    "        (pl.col('distance_km') / (pl.col('public_transport_quick_minutes') / 60)).alias('speed_quick_kmh'),\n",
+    "        (pl.col('distance_km') / (pl.col('cycling_minutes') / 60)).alias('speed_cycling_kmh'),\n",
+    "    ]).to_pandas()\n",
+    "    \n",
+    "    if pdf['speed_easy_kmh'].notna().any():\n",
+    "        ax.scatter(pdf['distance_km'], pdf['speed_easy_kmh'], \n",
+    "                   alpha=0.6, label='Public Transport (Easy)', c='blue', s=50)\n",
+    "    \n",
+    "    if pdf['speed_quick_kmh'].notna().any():\n",
+    "        ax.scatter(pdf['distance_km'], pdf['speed_quick_kmh'], \n",
+    "                   alpha=0.6, label='Public Transport (Quick)', c='green', s=50)\n",
+    "    \n",
+    "    if pdf['speed_cycling_kmh'].notna().any():\n",
+    "        ax.scatter(pdf['distance_km'], pdf['speed_cycling_kmh'], \n",
+    "                   alpha=0.6, label='Cycling', c='orange', s=50)\n",
+    "    \n",
+    "    ax.set_xlabel('Distance to Bank (km)', fontsize=12)\n",
+    "    ax.set_ylabel('Effective Speed (km/h)', fontsize=12)\n",
+    "    ax.set_title('Effective Travel Speed vs Distance to Bank Station', fontsize=14)\n",
+    "    ax.legend()\n",
+    "    ax.grid(True, alpha=0.3)\n",
+    "    ax.set_ylim(0, 100)  # Cap y-axis for readability\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-28T19:53:55.187209Z",
+     "iopub.status.busy": "2026-01-28T19:53:55.187131Z",
+     "iopub.status.idle": "2026-01-28T19:53:56.842052Z",
+     "shell.execute_reply": "2026-01-28T19:53:56.841680Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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BB5k9ezYff/wx4O8t/Nvf/sb//d//VShWl8tFvXr1ii3mlJ6ejm3bJCYmlnoNy7JIT0+v0PVDRWnt6na7D3uuKCHdv38/xhi2bdsW+GNOaQ7+48S7777LE088wfTp0/n73/8euNeYMWN44okngroit4hIqNNQYZFa6JRTTgHg888/L7fc6tWr2b59O3Xr1iU5OTlw3LKsYsPlDnbwh93yFA2Lq8wQ5CeeeIJzzz2XCRMmlFj0pCISEhI477zz+P7771mzZg0Ab7zxBvHx8QwZMqRYWbfbzV133cXKlSvZtm0b06dP5/TTT2fq1KmMHDmy0vc+nGXLljF79mwGDRrEqlWrmDhxIo8//jjjxo0rsTBTkc6dO/Pee++xb98+lixZwoMPPsjOnTsZPnw433zzTbGyN9xwA3l5eYF9LWfMmEGbNm3KXB36aNm2fUTvkV27dpV53LIsYmNjKx3LkbzXjkZZvZsffPABP/zwA9dccw0//PADL730Eo899hjjxo3jnHPOqZJ7N2vWjMmTJ7N7925+/PFHnn76aRzH4aabbgoMWT1Yae3t8/nYu3dvsSHBcXFxOI5T6gJPKSkpGGNKTfaOdrGuUFZU35NOOgljTJmPg4d/R0VF8dhjj7F+/XrWr1/Pa6+9Rrt27ZgwYQK33357sKoiIlIjKHEVqYVGjx6NbdtMnDix1A+iRYrmaF1xxRXF5mTVrVu31CRg48aNxYYulqdXr16Afw/QioqIiGDWrFkMHjyY5557rsxVWMtT1LP6xhtv8M0337BhwwYuvvhiIiIiynxNo0aNuOyyy5g7dy4nnHAC8+fPJycnp9L3Ls+6desAGDx4cIm5kV9//XW5r/V4PPTp04eHH36Yf/3rXxhjmDNnTrEyF110EYmJibz66qu8++67pKWl8Ze//KVK63CwunXrkpKSUiJ5zcrK4vfffy/zdaXVddOmTWzZsoVOnTpVepgwHNl7rToUfY+HDh1a4tzhvseVZds23bt3569//WsgYf3www8rdN+ikQ0Hzwcu+rq07WqKjpU2vLYsRe/xg3uMa5rY2Fg6dOjAb7/9VuHfewdr2bIlV199NV9++SUxMTGlfn9EROQAJa4itVDbtm2544472Lt3L0OGDCmxx6fjODz66KO88cYb1KlTh7FjxxY737NnTzZu3MiXX34ZOJafn88dd9xR4RhGjx5NTEwMzz33XKlz48rqHQsPD+d///sf559/Ps8//3yleykGDx5M3bp1efPNNwNbWxw6TDgvL4/FixeXeG1WVhaZmZl4PJ4Si6screbNmwOwaNGiYsdXrlzJk08+WaL88uXLSx2aWdSDdmgiHhYWxlVXXcWqVau477778Hg8XHXVVVUUfUk9e/akoKCAN998M3DMFG7tc+i8zoNNnTqVn3/+udhr7rvvPnw+3xHHe6TvtapW1vf4yy+/rJItp1auXFlqD2pZ7wnwD4nfunVr4Hl+fn5gCOvB7T169GgAHn744WLvu7S0tMAw2aIyFVG3bl0sywpMRaipbr31VrKzs7n22mtLfV9v2LCBjRs3ArB79+5i2xcV2b9/P3l5eeX+8UxERDTHVaTWevLJJ0lLS2PixIm0adOGwYMH07p1a9LT0/n000/5/fffiYiI4K233iqx4M8dd9zBp59+ynnnncdll11GVFQUn332GXXq1AksbnI4DRo0YOrUqYwYMYJevXpxwQUX0K5dO/bs2cO3335LixYtSt1zFfxJ2MyZM7nkkksYP348xhjGjx9fofuGh4dz6aWX8sorrzBp0iSaN29Ov379ipXJycnh1FNPpW3btpx00kk0a9aMzMxM5syZw86dO7nrrrtKXaG1NEWr8JZlxIgRtG/fnl69etGrVy/eeecdduzYQZ8+fdi8eTMffvghgwcP5r333iv2umnTpvHKK6/Qr18/WrduTVxcHKtWreLjjz8mISGh1JVcr7/+ep599lm2b9/OsGHDjmiecEXdfPPNTJo0ib/85S989tlnJCYm8vXXX5Oamkq3bt346aefSn3doEGD6Nu3LyNGjCAxMZHPP/+c77//nj59+nDLLbccUSxH816rSkOGDKFFixY888wz/Prrr3Tu3Jk1a9YwZ84c/vznP5f4HlfWZ599xt133x1479arV4/169fz4YcfEhEREVid+mB9+vShW7duDB8+nOjoaGbPns2aNWu46KKLiu2N3K9fP2655RZeeOEFOnfuzLBhwzDGMHPmTLZu3cqtt95a4ueoPDExMfTs2ZOvvvqKUaNG0aZNG2zbZtSoUYEEvya4/vrrWbp0KVOmTOGbb75hwIABNGrUiF27drF69Wq+/fZbpk+fTosWLdi2bRs9evSgW7dudO3alcaNG7N3714++OADCgoKjmj6g4hIrXLMN+ARkZDy+eefm0svvdQ0atTIuN3uwN6Lffr0MX/88UeZr3v33XdNly5dTFhYmElOTja33HKLycjIqPA+rkV+/PFHc+mll5qkpCTj8XhMw4YNzbnnnmvmzJkTKHPoPq5F8vPzzYUXXmgAc+utt1a4zosWLQrU89577y1xPj8/3zz99NNm4MCBpkmTJiYsLMwkJSWZfv36menTp5fYD7UsRXtMlveYNWtWoHxKSoq5+uqrTaNGjUxERITp0qWL+fe//23Wr19fYs/LpUuXmuuvv9507tzZ1KlTx0RGRpo2bdqYm2++ucReoAc77bTTDGDmzp1b4fY6uD6l7eNali+++ML07t3bhIeHm3r16plRo0aZXbt2lbuP64IFC8zEiRNNp06dTHh4uGnYsKG57bbbTHp6eonrU8F9XItU5L1WUeXt41rWHsfG+PdxHTZsmElMTDRRUVGmZ8+e5q233grse/rQQw9V+HqH/qytWrXK3HbbbaZHjx6mXr16Jjw83LRq1cqMHj3arFy5sthri/ZxXbdunXnqqafMCSecYMLCwkzz5s3NuHHjTF5eXqn3fP31103Pnj1NVFRUIP7S9mktqz4HW7NmjTnvvPNMnTp1jGVZZX7fynM0+7ge+vvEmPLfP0Vtduj33Bhj3n77bTNgwABTt25d4/F4TOPGjU3//v3Nc889Z3bv3m2MMWb//v1m3Lhxpl+/fqZhw4YmLCzMNGrUyJxzzjnmk08+qVS9RURqI8uYg/ZaEJFab+3atfTp04fw8HC+/vprTjjhhGCHJFUkNzeXJk2aEBMTw/r166t8uLPUHFdddRVTpkxhw4YNtGjRItjhiIiIHJY+tYhIMW3btmXmzJns3buXP/3pT8ds/p9Uv0mTJrF3716uv/56Ja0iIiJSo2iOq4iUcOaZZzJz5kyWL1/O119/XeZ2LFIzPPXUU+zevZtXXnmFBg0alLqfp4iIiEgoU+IqIqUaMmRIib1NpWa699578Xg8dOvWjRdeeKHY/pwiIiIiNYHmuIqIiIiIiEhI0yQnERERERERCWlKXEVERERERCSkKXEVERERERGRkKbEVURqJWMMJ510EgMHDjxm92zRokWt2zNz4cKFWJbFuHHjih2vjW1xNMaNG4dlWSxcuLBarn/VVVdhWRYbN26sluuX5dVXX8XlcvHLL78c0/uKiEjNo8RVRGqlqVOn8sMPP/DII48EOxSppMmTJ2NZFpMnTw52KHKURo8eTfPmzbn77ruDHYqIiIQ4Ja4iUus4jsO4ceM4/fTT6dOnT7DDqZU+//xzPv/882CHUWPcfPPN/Pbbb/Tq1SvYoVQpj8fD7bffzrx58/jmm2+CHY6IiIQwJa4iUut88sknbNy4kSuvvDLYodRarVu3pnXr1sEOo8aoX78+7du3JyoqKtihVLkRI0bgdrt5+eWXgx2KiIiEMCWuIlLrTJo0CcuyGDZsWKnnMzIyePjhh+natStRUVHEx8fTo0cPHnjgAQoKCoqV/eabbxg8eDAJCQlERETQvn17HnroIbKzsyscT1ZWFg899BDt27cnIiKChIQEBg8eXGoP1MFzHSdPnsyJJ55IVFQU/fv3r1QbVLWcnBz+9re/0bRpUyIiIujcuTMTJ04ss3xpc1xzc3N57rnn6NatG/Hx8URHR9OiRQsuvfRSfvrpJ8A/F3PMmDEAjBkzBsuyAo8iy5cv5+abb6Zz587Ex8cTGRlJly5deOqpp0p8/w6OJTMzk9tuu41GjRoRHh5O165dee+990qNPz8/n3/+85/07NmT2NhYYmJi6NixI3fccQf79+8vVjYlJYXbb7+dE044gfDwcOrXr8+wYcP49ddfK9S2UPoc140bN2JZFldddRV//PEHf/7zn6lbty7R0dEMGDAg0GZHY+HChdSpU4dmzZqxevVqoPhQ7dmzZ9O7d2+ioqJo3LgxDzzwAI7jADBlyhS6detGZGQkzZo14x//+Eep90hMTKR///689957ZGZmHnXMIiJyfHIHOwARkWPJGMOCBQto164ddevWLXE+JSWFM844g9WrV9O9e3duvPFGHMdh9erVPP3009x5553UqVMHgHfffZfLLruM8PBwhg8fToMGDfj000955JFHmDdvHgsXLiQiIqLceHJzcznrrLP47rvvOPHEExk7diy7du3i7bffZt68ecyYMYNLLrmkxOv+8Y9/sGDBAoYOHcrAgQNxuVxV0j5HwnEcLrjgAubPn0+XLl24/PLL2bt3L7fffjtnnnlmha8zevRo3nnnHbp27cqYMWMIDw9ny5YtLFiwgGXLltGtWzcuvPBCUlNT+eCDDxg6dCjdu3cvcZ2JEycye/Zs+vXrx3nnnUd2djYLFy7k3nvvZdmyZcycObPEawoKChg4cCD79+9n2LBhZGdn89Zbb3HppZcyd+7cYot45eTk8Kc//YlvvvmGNm3aBGL9/fffeeWVV7jyyisD761169bRv39/tm7dysCBA7nwwgtJSUlh5syZzJs3j88//5zevXtXvtEPsnHjRvr06UOnTp24+uqrWbduHR988AFnnnkmv/32G0lJSUd03ZkzZzJy5Ehat27NvHnzaNKkSbHzs2bN4tNPP+XCCy/k1FNP5aOPPuKxxx7DGEN8fDyPPfYYQ4cOpX///sycOZO//vWvJCUllTrSoW/fvsyfP5/Fixcf0wXTRESkBjEiIrXIypUrDWBGjhxZ6vlhw4YZwNx3330lzu3cudMUFBQYY4xJS0sz8fHxJjw83Pz000+BMj6fzwwfPtwA5pFHHin2+ubNm5vmzZsXO/bwww8H4nEcJ3D8hx9+MGFhYaZOnTomPT09cPyhhx4ygImOjjY///xzpeo+adIk89BDD1X4sWDBggpfFzDnnHOO8Xq9geM///yzCQsLM4B56KGHym2L1NRUY1mWOemkk4pdwxhjvF6v2b9/f4n7TZo0qdR4Nm3aVOIajuOYq6++2gBm0aJFJWIBzNChQ01eXl7g+Pz58w1gBg0aVKz8nXfeaQAzatSoEvdJTU01GRkZgeennHKKcblcZu7cucXKrVmzxsTGxpouXbqUWodDFX3fD/6ebNiwwQAGME899VSx8vfff78BzJNPPlmh648ePdoAZsOGDcYYY1566SVj27Y55ZRTzL59+4qVLWp/j8djvvvuu8Dx9PR006BBAxMVFWWSk5PNunXrAuc2b95swsLCyqzvBx98YADz4IMPViheERGpfZS4ikitMm/ePAOYO+64o8S5HTt2GMuyTOvWrU1+fn6515k6daoBzI033lji3KZNm4zb7TatWrUqdry0xLVVq1bG4/GYLVu2lLjOtddeawAzderUwLGiBOb2228vN77SnHHGGYFEpyKPQ5PNspx55pkGMMuXLy9x7pprrqlQ4pqWlmYAc+qppxZL4EtzuMS1LMuXLzeAGTduXIlYALN+/foSr2nevLlJSEgIPC8oKDCxsbEmPj6+REJ3qB9++MEA5uqrry71/B133GEA88svvxw29vIS15YtWxqfz1esfNG5iy666LDXNqZ44jpu3DgDmPPPP99kZ2eXKFvU/mPGjClxruiPAw8//HCJc2eddZZxuVyBP/4cbOnSpeW2lYiIiIYKi0itsnfvXoDAcN+Dff/99xhjOPPMM/F4POVe58cffwQodW5ps2bNaNWqFWvXriUjI4PY2NhSr5Gens769evp0KFDiWGYAGeeeSYTJ05kxYoVjBo1qti5I1ldtrr2AP3pp5+Ijo7mxBNPLHHu9NNP57XXXjvsNeLi4jjvvPP4+OOPOfHEE7nkkkvo378/PXv2POz34lD5+fm8+OKLvPXWW6xevZrMzEyMMYHz27dvL/GaOnXq0LJlyxLHmzRpwpIlSwLPV69eTUZGBgMGDCh1qPnBli5dCsCuXbtK7GNbdK2ifzt37lyhupWme/fu2HbxJSuK3k+pqamVutbYsWP54IMPuOqqq5g4cSJud9kfE0obpt2wYcNyz/l8Pnbt2kXjxo2LnUtISABgz549lYpXRERqDyWuIlKrREZGAv65pYdKS0sDKPGhujTp6ekAZc4fbNiwIWvXriU9Pb3cxPVw1zi43MGOdN5idUhLS6Np06alnqtMnO+++y5PPPEE06dP5+9//zvgT2jHjBnDE088UeEVdS+++GJmz55N27ZtA3OPPR4PqampTJgwgby8vBKviY+PL/Vabrc7sNgQVO49sm/fPgA++ugjPvroozLLZWVlHfZa5YmLiytxrCjh9Pl8lbrWV199BcCQIUPKTVoPd9/yzpW2QFZOTg7AcblqsoiIVA0lriJSqyQmJgIHkoqDFfXCbtu27bDXKfpgvmvXrlLP79y5s1i5qr7GwavoVtTkyZPZuHFjhcv379+/QqsVx8fHs3v37lLPlVW30kRFRfHYY4/x2GOPsWHDBhYsWMDLL7/MhAkTyMnJ4ZVXXjnsNZYtW8bs2bMZNGgQH330UbFFq5YuXcqECRMqHE9pjuQ98sILL3DzzTcf1X2PlVmzZjFmzBhGjBjBW2+9xUUXXXRM7lv081j08ykiInIoJa4iUqt06tQJ27ZZs2ZNiXMnn3wytm2zYMECCgoKyh2i2qNHD8A//PbSSy8tdm7Lli2sW7eOVq1aldnbCv7EplWrVvzxxx9s27atRC9e0dDe0oZdHonJkyfz5ZdfVuo1FUlcu3XrxoIFC/jhhx9KDBf++uuvK3W/Ii1btqRly5ZcdtllNGjQgA8//DCQuBYlo6X1Jq5btw6AwYMHl1hp+UhjOVi7du2Ii4tj2bJl7N+/v9zhwkWrBS9ZsqTGJK7Nmzdn4cKFnHnmmQwfPpy33nqrzG2jqlLRz2OXLl2q/V4iIlIzaR9XEalV6tSpQ9euXfn++++LDQEF/7DWYcOGsW7dOh5++OESr01JScHr9QIwdOhQ4uPjmTRpEitXrgyUMcZwzz334PV6ueqqqw4bz+jRoykoKODee+8tNg/z559/ZvLkycTHx3PhhRceWWUPsXDhQox/Ub4KPUqbl1maovm3f//734slk7/88gvTpk2r0DV2795d6r6m+/fvJy8vr9i2QkXzIbds2VKifPPmzQFYtGhRseMrV67kySefrFAs5XG73Vx//fWkpaVx2223lUie09LSAnuR9urVi969ezNjxgzefvvtEtdyHKfSf0g4Fpo1a8bChQtp3rw5I0aMKHMv26r07bffAnDGGWdU+71ERKRmUo+riNQ6f/7zn3nooYdYunQpp5xySrFz//nPf/j11195/PHH+fjjjznrrLMwxrB27Vo+/fRTdu3aRZ06dYiLi2PixIlcdtll9O7dm+HDh5OYmMj8+fNZvnw5vXr14u677z5sLH/961/56KOPmDZtGr/99htnn302KSkpvP3223i9XiZOnFhur20oGD16NNOnT2fu3Ln06NGDc889l3379jFjxgwGDhzInDlzDnuNbdu20aNHD7p160bXrl1p3Lgxe/fu5YMPPqCgoIC77rorULZv375ERkYyfvx49u/fHxheev/999OrVy969erFO++8w44dO+jTpw+bN2/mww8/ZPDgwVWShD3yyCMsXbqUadOmsXTpUs4991zCw8NZv349c+fOZdGiRYFe8hkzZnDmmWcyYsQIxo8fz4knnkhkZCSbN29myZIl7N69u9T51sHWtGnTQM/rZZddhjGm1P2Eq4Ixhs8//5wOHTrQtm3barmHiIjUfOpxFZFa5y9/+Qtut5s33nijxLn69euzdOlSHnjgAXJycnjxxRd57bXX2Lp1K3/729+Ijo4OlL3kkktYsGAB/fr143//+x///Oc/ycjI4IEHHuCLL74o1ktYloiICL744gseeOAB0tPT+ec//8msWbM444wzWLhwYbUlC1XJtm0++OAD/vrXv7Jv3z4mTJjA4sWL+ec//8mdd95ZoWu0aNGCcePGER8fz/z583n++ef56KOPOPHEE/nkk0+46aabAmUTEhJ47733aNu2LRMnTuSBBx7ggQceAPzDiOfMmcPVV1/NunXreOGFF1i1ahXPPvsszzzzTJXUNyIigs8++4xnn32W6OhoJk6cyEsvvcRvv/3GDTfcQIsWLQJlW7ZsyY8//sj9999PZmYmkyZN4pVXXmHFihX069ePGTNmVElM1aFJkyYsXLiQli1bcvnll/POO+9Uy32++uorNm/ezPXXX18t1xcRkeODZQ4emyYiUkuMGjWKjz76iE2bNoV8j6bI8eyKK67gk08+Yd26daVuUyUiIgLqcRWRWuqxxx4jJyeHF154IdihiNRaa9eu5a233uL+++9X0ioiIuVS4ioitVLz5s2ZMmWKeltFgmjr1q089NBDxYaCi4iIlEZDhUVERERERCSkqcdVREREREREQpoSVxEREREREQlp2se1FI7jsH37dmJjY7EsK9jhiIiIiIjUaMYYMjIyaNSoEbZdc/rOcnNzyc/PD3YYhIWFVWibveOZEtdSbN++naZNmwY7DBERERGR48qWLVto0qRJsMOokNzcXFq2SGDnrpxgh0JycjIbNmyo1cmrEtdSFK0yumXLFuLi4oISg+M47N69m8TExBr1V6maRG1c/dTG1U9tXP3UxtVPbVz91MbVT21cvvT0dJo2bVqjVvPPz89n564cNv1yOXGxYUGLIz0jn+ZdppOfn6/EVYorGh4cFxcX1MQ1NzeXuLg4/fKrJmrj6qc2rn5q4+qnNq5+auPqpzaufmrjiqmJ0/DiYsOIiwte4ip+SlxFRERERETKYHAwOEG9v2hVYREREREREQlxSlxFREREREQkpGmo8BFyHKdal8Z2HIeCggJyc3M1T6KaHK6NPR4PLpcrCJGJiIiISKhwjMExJqj3FyWuRyQ/P58NGzbgONU33twYg+M4ZGRk1MhJ7DVBRdq4Tp06JCcn63sgIiIiIhJESlwryRjDjh07cLlcNG3atNp6Q40xeL1e3G63kqZqUl4bG2PIzs4mJSUFgIYNGwYjRBEREREJMqdweaZg3l+UuFaa1+slOzubRo0aERUVVW33UeJa/Q7XxpGRkQCkpKTQoEEDDRsWEREREQkSTZ6sJJ/PB0BYmPZyqg2K/jhRUFAQ5EhERERERGov9bgeIfWC1g76PouIiIjUbqbwf8G8v6jHVUREREREREKcElc55vr378/YsWMr/TrLsnj//ferPB4REREREQltSlxriauuugrLsko8zjnnnGCHVsK4cePo3r17ieM7duzg3HPPPfYBiYiIiEit5WACe7kG5aGhwoDmuNYq55xzDpMmTSp2LDw8PEjRVF5ycnKwQxARERERkSBQj2stEh4eTnJycrFH3bp1A+d///13+vXrR0REBB07duSzzz4rNjx34cKFWJZFampq4DUrVqzAsiw2btwIwN69e7nsssto3LgxUVFRdOnShRkzZlQ4xsmTJ/Pwww/z008/BXqFJ0+eDBQfKrxx40Ysy+Kdd97h9NNPJzIykp49e7J27VqWLVvGySefTExMDOeeey67d+8udo9XX32VDh06EBkZSefOnfnPf/5T6bYUERERkdrBCYGHqMe16uTnl33OtsHtrlhZywKPp3jZ0la2reLteBzH4aKLLiIpKYlvv/2WtLS0I5qHmpuby0knncQ999xDXFwcH330EaNGjaJ169b06tXrsK8fPnw4v/76K3PnzmX+/PkAxMfHl1n+oYceYvz48TRr1oyrr76ayy+/nNjYWCZMmEBUVBSXXnopDz74IC+99BIAb775Jg8++CAvvvgi3bt35/vvv+fGG28kJiaG0aNHV7q+IiIiIiJS/ZS4VpUnnij7XJs2MHLkgef/+AeUtS9oixZw1VUHno8fDzk5JcuNG1fpEOfMmUNMTEyxY/fddx/33Xcf8+fPZ/Xq1cybN49GjRoB8MQTT1R6Tmnjxo256667As9vueUW5s2bxzvvvFOhxDUyMpKYmBjcbneFhgbfddddDBo0CIDbbruNyy67jM8//5xTTz0VgGuuuSbQYwv+RPe5557joosuwhhD06ZNWbNmDa+88ooSVxERERGREKXEtRY588wzAz2PRRISEgD47bffaNq0aSBpBejbt2+l7+Hz+XjiiSd455132LZtG/n5+eTl5REVFXV0wZeha9euga+TkpIA6NKlS7FjKSkpAGRlZbFu3TquueYarr322kAZr9dbbq+uiIiIiNReDsFdIEmLM/kpca0q991X9jn7kKnEd99ddtlDhwWPHVv6UOEjEB0dzQknnHDEr7cL62HMgR+egkN6jv/xj38wYcIExo8fT5cuXYiOjmbs2LHklzc8+ih4DhpWbRW206HHHMc/MyAzMxOAiRMn0rt3b4wxeL1e3G43brd+FEREREREQpU+rVeVysw5rWzZKkpcy9OhQwe2bNnCjh07aNiwIQBLly4tViYxMRHwb0tTtKjTihUripX55ptvGDp0KFdccQXgnzu7du1aOnbsWOFYwsLC8Pl8R1qVMiUlJdGoUSPWr1/PyJEjiyWu1jFoYxEREREROTJKXGuRvLw8du7cWeyY2+2mfv36DBgwgLZt2zJ69Gj+8Y9/kJ6ezt///vdiZU844QSaNm3KuHHjePzxx1m7di3PPfdcsTJt2rThvffeY/HixdStW5fnn3+eXbt2VSpxbdGiBRs2bGDFihU0adKE2NjYKtu25+GHH+bWW28lPj6eQYMGkZWVxYoVK0hNTeWOO+6oknuIiIiIyPHDFP4vmPcXbYdTq8ydO5eGDRsWe5x22mmAfxjwrFmzyMnJoVevXvzlL3/h8ccfL/Z6j8fDjBkzWL16NV27duXpp5/mscceK1bm/vvv58QTT2TQoEH079+f5ORkLrzwwkrFOWzYMM455xzOPPNMEhMTK7WdzuH85S9/4dVXX2XSpEl07dqVAQMGMGXKFFq2bFll9xARERERkaplmYMnLAoA6enpxMfHk5aWRlxcXLFzubm5bNiwgZYtWxIREVFtMYTKMFbLspg1a1alk8+aoCJtfKy+38crx3FISUmhQYMGgTnSUrXUxtVPbVz91MbVT21c/dTG5Svv83WoKop544bhxMVW7VaUlYojI58WLd+uUW1XHfRTJSIiIiIiIiFNiauIiIiIiIiENC3OJOXSSHIRERERqc0M4AT5/qIeVxEREREREQlx6nEVEREREREpg0Nwe1yDee9Qoh7XI6QhtLWD4+hXhYiIiIhIsKnHtZI8Hg+WZbF7924SExOrbauaUNkO53hWXhsbY8jPz2f37t3Ytk1YWPCWQBcRERE5Ivn5oM8wcpxQ4lpJLpeLJk2asHXrVjZu3Fht9zHG4DgOtm0rca0mFWnjqKgomjVrpv3YREREpGbZuBHefReGDYNWrYIdTY3mGP8jmPcXJa5HJCYmhjZt2lBQUFBt93Ach71791KvXj0lTdXkcG3scrnU4y0iIiI1z5o1/qTV64WlS5W4ynFBiesRcrlcuFyuaru+4zh4PB4iIiKUuFYTtbGIiIgcd37+Gd5/HxwH2rWDiy8OdkQiVUKJq4iIiIjI8eC77+Djj/1fd+0KQ4dCNXa01BaG4O6lqpHCfkpcRURERERqMmPgq69gwQL/89694ZxzQNOd5DiixFVEREREpKbbu9f/b//+cMYZSlqrkPZxDQ1KXEVEREREajLL8g8L7tgR2rcPdjQi1UIr0oiIiIiI1DRFKwY7hf1xLpeSVjmuqcdVRERERKQmycuDt96CDRtg/34499xgR3Rc0z6uoUGJq4iIiIhITZGdDW++Cdu2QViYelml1lDiKiIiIiJSE6Snw7RpsHs3REXBFVdAo0bBjkrkmFDiKiIiIiIS6vbtg6lTITUV4uJg1ChITAx2VLWCg4VD8FZpDua9Q4kSVxERERGRUOb1Hkha69XzJ6116gQ7KpFjSomriIiIiEgoc7v9CzB9+SVcfjnExAQ7olpFPa6hQYmriIiIiEgo8nr9SStAu3bQpg3Y2s1Saie980VEREREQs2vv8KLL/q3uymipFVqMb37RURERERCyfLlMHOmf07rsmXBjqbWM8YK+kM0VFhEREREJHQsWgTz5/u/PvlkGDAguPGIhAglriIiIiIiwWaMP2H95hv/89NPh7POAku9bSKgxFVEREREJLgcB+bMgR9+8D8fOBBOOSW4MUmAr/ARzPuLElcRERERkeDyemHHDn/v6gUXQI8ewY5IJOQocRURERERCaawMLjiCti2Ddq2DXY0cgiDjRPENW2N1tMFtKqwiIiIiMixl5MDv/xy4Hl0tJJWkXKox1VERERE5FjKyIA33oBdu8Dng+7dgx2RSMhT4ioiIiIicqzs3w/TpsG+fRAbC40aBTsiOQxT+Ajm/UVDhUVEREREjo2UFHj9dX/SWrcuXH01NGgQ7KjkOPTVV18xZMgQGjVqhGVZvP/++8XOG2N48MEHadiwIZGRkQwYMIDff/+9WJl9+/YxcuRI4uLiqFOnDtdccw2ZmZnHsBbFKXEVEREREaluW7fCpEn+YcINGviT1rp1gx2VHKeysrLo1q0b//73v0s9/8wzz/Cvf/2Ll19+mW+//Zbo6GgGDRpEbm5uoMzIkSNZuXIln332GXPmzOGrr77iuuuuO1ZVKEFDhUVEREREqlN6OkydCvn50KQJjBwJkZHBjkoqyMHCwQrq/Svr3HPP5dxzzy31nDGG8ePHc//99zN06FAApk6dSlJSEu+//z4jRozgt99+Y+7cuSxbtoyTTz4ZgBdeeIHzzjuPZ599lkZBGOKuHlcRERERkeoUFwenngqtW8OVVypplaDasGEDO3fuZMCAAYFj8fHx9O7dmyVLlgCwZMkS6tSpE0haAQYMGIBt23z77bfHPGZQj6uIiIiISPVwHLAL+4n69fM/d7mCG5NUmmMsHBPEHtfCe6enpxc7Hh4eTnh4eKWvt3PnTgCSkpKKHU9KSgqc27lzJw0OmX/tdrtJSEgIlDnW1OMqIiIiIlLVlizxz2nNz/c/tywlrXJUmjZtSnx8fODx5JNPBjukYyqkEteXXnqJrl27EhcXR1xcHH379uWTTz4JnO/fvz+WZRV73HDDDcWusXnzZgYPHkxUVBQNGjTg7rvvxuv1HuuqiIiIiEhtZAx88QXMmwdbtsCvvwY7IjlObNmyhbS0tMDj3nvvPaLrJCcnA7Br165ix3ft2hU4l5ycTEpKSrHzXq+Xffv2BcocayE1VLhJkyY89dRTtGnTBmMMU6ZMYejQofz444906tQJgGuvvZZHHnkk8JqoqKjA1z6fj8GDB5OcnMzixYvZsWMHV155JR6PhyeeeOKY10dEREREahFj4OOPYfly//Ozz4YePYIbkxw1BxsniP19Rfcu6tw7Wi1btiQ5OZnPP/+c7t27A/5hyN9++y033ngjAH379iU1NZXly5dz0kknAfDFF1/gOA69e/c+6hiOREglrkOGDCn2/PHHH+ell15i6dKlgcQ1KiqqzCz/008/ZdWqVcyfP5+kpCS6d+/Oo48+yj333MO4ceMICwur9jqIiIiISC3k8xH+0UdYW7b457Wedx707BnsqKSWyszM5I8//gg837BhAytWrCAhIYFmzZoxduxYHnvsMdq0aUPLli154IEHaNSoERdeeCEAHTp04JxzzuHaa6/l5ZdfpqCggJtvvpkRI0YEZUVhCLGhwgfz+Xy89dZbZGVl0bdv38DxN998k/r169O5c2fuvfdesrOzA+eWLFlCly5dik00HjRoEOnp6axcufKYxi8iIiIitURBAbz1Fp7VqzG2DRddpKT1OFK0HU4wH5X1/fff06NHD3oU9vjfcccd9OjRgwcffBCAv/71r9xyyy1cd9119OzZk8zMTObOnUtERETgGm+++Sbt27fn7LPP5rzzzuO0007jv//9b9U06hEIqR5XgF9++YW+ffuSm5tLTEwMs2bNomPHjgBcfvnlNG/enEaNGvHzzz9zzz33sGbNGv73v/8B/tWvSlsdq+hcWfLy8sjLyws8L1qxy3EcHMep0vpVlOM4GGOCdv/aQG1c/dTG1U9tXP3UxtVPbVz91MbVLC0Ns307xuXCufRSaNfOv4KwBOi9d2z1798fY0yZ5y3L4pFHHik2BfNQCQkJTJ8+vTrCOyIhl7i2a9eOFStWkJaWxnvvvcfo0aP58ssv6dixI9ddd12gXJcuXWjYsCFnn30269ato3Xr1kd8zyeffJKHH364xPHdu3eTm5t7xNc9Go7jkJaWhjEG2w7ZjvEaTW1c/dTG1U9tXP3UxtVPbVz91MbHwMCBZOzdS3R8PPYhi9oIZGRkBDsEqeFCLnENCwvjhBNOAOCkk05i2bJlTJgwgVdeeaVE2aKJwX/88QetW7cmOTmZ7777rliZotWyylv96t577+WOO+4IPE9PT6dp06YkJiZWyQToI+E4DpZlkZiYqP/AVBO1cfVTG1c/tXH1UxtXP7Vx9VMbV4O0NNi3D1q2BMCpXx+ze7fauAwHD0GtaUJlH9faLuQS10M5jlNsGO/BVqxYAUDDhg0B/+pXjz/+OCkpKYENcz/77DPi4uICw41LU9bmvbZtB/UXj2VZQY/heKc2rn5q4+qnNq5+auPqpzaufmrjKrRnD0ydCjk5MHo0NGkCqI3LozaRoxVSieu9997LueeeS7NmzcjIyGD69OksXLiQefPmsW7dOqZPn855551HvXr1+Pnnn7n99tvp168fXbt2BWDgwIF07NiRUaNG8cwzz7Bz507uv/9+brrpplITUxERERGRStm+Hd54A7KzoX59CNLoPJHaJqQS15SUFK688kp27NhBfHw8Xbt2Zd68efzpT39iy5YtzJ8/n/Hjx5OVlUXTpk0ZNmwY999/f+D1LpeLOXPmcOONN9K3b1+io6MZPXp0uZOORUREREQqZONGmDED8vKgUSO44gqIigp2VFLNDBbmCFb2rcr7S4glrq+99lqZ55o2bcqXX3552Gs0b96cjz/+uCrDEhEREZHabs0aePdd8HqhRQu47DLQiD6RYyakElcRERERkZCzeTO8/bZ/i5t27eCSS8Ctj9G1xZHupVqV9xclriIiIiIi5WvSBNq29fewDh0KWmhI5JhT4ioiIiIicihj/P9alj9RvfhicLn8z2uIXF8qu7K/J9e3F8tyEedpQWJkN1yWJ9ihiVSaElcRERERkYMZA/PmQX4+DBniT1Zr0NBgn5PHmrR32Zr1Jfm+NMACDGAT62lM2zqX0DCqT5CjrDk0VDg01JyfQBERERGR6uY48MEH8NNP/ufdu0OzZkENqTIc4+Xnfa+wNWsRHjuaaHcjLMsF+BPazILt/LT3JXwmnybR/YIcrUjFaYC+iIiIiAj4Vwx++21/0mrb8Oc/16ikFWBb1tdsz1pMhKseEa6EQNIK4LLDiXI3xGe8/Lb/DXK9+4IYqUjlqMdVRERERCQvD956CzZs8A8LvuQS/wrCNYgxhs2ZCzCAxy59f1nLsohyNSDTu40d2UtpGXfesQ2yBjLYOEHs7zPqawTU4yoiIiIitV12NkyZ4k9aw8PhiitqVNJqjGFf3hp+3f86KTk/4JgCCpxM/PNaS7IsGwsXO3OWH9tARY6CelxFREREpHbbudP/iIryJ62NGgU7ogpLz9/Myv2TSM37gzwngzxfKmCT59uPx44mNqwZLiuixOtsy43XyTrm8dZEjgHHBHFxptL//lDrKHEVERERkdqtVSv/djeJif5HDZGRv4Xvdz9LlncHEa76eKw4CnwZgIVlWeQ56fjy/qBO+AklklfHFBDmigtO4CJHQEOFRURERKT22bkT9h20OFHHjjUqaTXGsGz3v0jJ/QOfCcNn/IsveexoDF4sXLitSLwml8yCbcVe6xgvYGgY1Ts4wYscAfW4ioiIiEjtsnkzTJ8OERFwzTUQGxvsiCplTeon/LT/DdILtgCQ6csELMKsKOI8sUAGjvFiW25sK4x8JwOfk4PLjsQYQ7ZvF5HuRJIjlbhWhPZxDQ1KXEVERESk9vj9d3jnHSgogAYNwOMJdkSV8v2e1/hl/1v4TD5gsPBvd2Mw5Jss9uZnE+eOwZgsfMaHjQcHH3m+dDw45Pr2Eu6Kp0vCXwhzxQS3MiKVoMRVRERERGqHX3+F//0PHAfatIFLL61RieumzCX8uv9tjPHhIgyHfKzCmX8WYHAwOKR7M0kMS6bAScNn8nCMl1xnH5ZlkxjRlbbxF5MQ0SG4lRGpJCWuIiIiInL8+/57+OgjMAa6dIELLwSXK9hRVYgxhv15q/lh97N4SMO2XBigwIADmMJe16Ik1uCQ63hJDO9Ani+NHN8umsf+iVaxQ4gPa4VlaehpZRhjYYK4qnAw7x1KlLiKiIiIyPFtxQqYM8f/dc+ecN55UEOSN8f4WJs6g43pH5Hr3eqf7WiBhUMYDg4+vCYcX+HHegsbg0OWN5UGES3Agmh3QzrUGUWku14wqyJyVJS4ioiIiMjxrW1b/4rBHTrAmWfWmKQVYH36B2xI/xDLcmOw8G8K4n/4Vw928Fh5GGPhUNSDbOHgw2fyKXAyaB4zUEnrUfBh4wviZizBvHcoUeIqIiIiIscfYw4kqFFRcO21EBYW3JgqKc+XyqaMj3FZ4bjtGA7dydKy3DimABuDy8rHMeEHlTFke3dSL6IT7esMP9ahi1Q5pe8iIiIicnzxeuHdd/3zWovUsKTVcRy2ZCwk17uPcFdd3HYELsuDwSlWzrY8GGxcOIAPBy8YB48VQavYIZxc/y7CXHFBqYNIVVKPq4iIiIgcP/Lz4e23Yd06WLsW2rWrUfu0Znn3sGr/DDZlLKDAScEin0xvCpHuekS46pLp3YkxDpZ1oP/JslyAD48VRoEDWIae9f+PDnWHBq0exxOn8BHM+4sSVxERERE5XuTkwJtvwtat/h7WESNqVNK6J2c1C3f8jVzfXsDCZRkAHFNAVsFObMuN2wrHa/LAANgHpusa/0JOBpvkyO60ix8SpFqIVA8lriIiIiJS82VkwLRpkJICkZEwciQ0aRLsqCosx7uPhTvuJce3B7cVjW25wOQCPizLxgCO8WJb4LGi8JocDF6MAQqXbTKWiyaRvTir0UPYtmYEyvFFiauIiIiI1Gz798PUqf5/Y2Nh1Cho0CDYUVVYvi+TBTseJNu7E7Dxko1NGG484E9J/Xu0Wi4c4yXKXZ867uZkFuykwMkFCohwN+DsBo+RFNUxuJU5DhlsTBCXBgrmvUOJElcRERERqdnWrPEnrXXrwpVX+v+tIdamfcK3Kf/BZ/ZgYfyLLxkHn/HixSLMdmHjBZzCPVoh17eXuuGtiLDjyfHtxrJcdK8/lgaRSlrl+KXEVURERERqtt69/dvfdO5co+a0frf7FX7d/yYGH27/pFUOTFn1p7H5jo8w212YvHqxLHAoIMu7E2Py8dhxdKh7JQ0iTwxaPY53jrFwTPD2/g3mvUOJElcRERERqXk2b4akJAgP9+/X2rdvsCOqlOW7X+PX/W9gChPWQ1mYwv83FBhDuBUD5AN5WPjnuTaOPZfG0f2IC2tx7AIXCRIlriIiIiJSs/z2G7z3HjRr5l+EyV2zPtKuSZ3DLwclrf4hwE5hsnqAKUxeHWNwLAvbisHrGMLsePo3egm3q2btTStyNGrWT7mIiIiI1G4//ggffugfGhwZGexoKi2rYDfL976Kj4LAMYN/BqsLU/jMPzT0wLBhg88UQOEc1+axZylpPYYcLByCOFQ4iPcOJUpcRURERKRmWLIE5s3zf33iiXD++VBDtn1xjJdV+z9gVep75Pj2HHLWFM5ptQp7XQ8kr4UbtmJw8JosIlx16VT38mMYuUhoUOIqIiIiIqHNGFiwAL76yv/81FNhwAD/3NYawOcUsDjln2zIXIDXyS2jlIUPG1dgyLA56AwYvES66nN68qPEeJKPRdgiIUWJq4iIiIiEtoOT1gED4LTTghtPJXidPBbufJzNmV9CGQsxHeBPXi3APmjOq8HmhNghdK93DdGemrM/7fHCBHlVYaNVhQElriIiIiIS6jp1gmXL4Oyz4eSTgx1NhWV79/Dx5ltJ926uxKv8Q4adwhWFwaJx5KmcmnxvdYUpUiMocRURERGR0GPMgaHASUlw220QERHcmCohs2AXszdfR45vb4lz/qG/B/4t6cB6wzHuxvRv+PfqC1QOy8HGIXhzqYN571CiVhARERGR0JKbC2+8AZs2HThWg5LWbO8eZm++PpC0mkMeRQ5egqk0Ua4GDG32KhHuuGqLVaSmUOIqIiIiIqEjMxMmT4Z162DWLPD5gh1RpeT50vl651PFelqtQ/4tSljL6nG1cZEc0YNLWk4nwh1brfGK1BQaKiwiIiIioSE1FaZNg717ISYGRowAlyvYUVXKxsyF7M5dVfisZGpadKS05NXCItKVyIn1rqFN3LnYNWSrn+OdMf5HMO8vSlxFREREJBTs3u1PWtPToU4dGDUK6tULdlSV4nMK2JT5FVZh36qFjcEHhfuzltXTauPC4CbO04QzG95PvYg2xz54kRCnxFVEREREgmv7dv+c1uxsSEz0J61xNW9eZ45vL7ne/bjtCPKc9MACS0X9q1Zg0aXiHBzC7ShOqj9GSWsI0uJMoUGtICIiIiLB9d13/qS1cWMYM6ZGJq0AjnEwGCJdCYW9ribQ+3ogeT2gKIn1WNH0SryRlrH9j2W4IjWKelxFREREJLjOPx9iY+G00yA8PNjRHLEwOxrbCcO2bNxWFAUmE3AVDhl2oEQfLFh4OLvh4zSOOSl4gYvUAEpcRUREROTY27wZmjb179XqdsPZZwc7okrxOnnsyllBjm8PFjYxrsaEuerTwNOJ7dnfkhDeit25q3Dw4h/kaBcmrA7gT1ptPJya9DclrSHOwcIpd+Oi6r+/KHEVERERkWPt22/hk0+gTx8YNMifvNYQxjisz5jHuvS5ZPt2gwGDwcZDVHYXGiW3IMWOwGtyqR/RgX15f+A1efhLFfWzGlxWBGckP0CL2DOCXCORmkGJq4iIiIgcG8bAl1/CwoXBjuSIOI7D93teZH3GPBxTgG158NhRRLoTsEwYWQW7WJ+xmoZRPdiR/SN5vjTqeFrhI59c3168Ti4GQ7ynKf0bPkp8eONgV0mkxlDiKiIiIiLVzxiYO9ff2wpw1llw+uk1prfV6+Txza5n2Jz5BWCwLQ8+k4/Xl0uubz8eK5YYuwcFZjd7cn+lW73R7Mj+gd05KzHGS4SrLhFh8TSLOZ1WsWcT6U4IdpWkgjRUODQocRURERGR6uXzwYcfwk8/+Z+fdx706hXcmCrBGMNP+6ayNWsRAB47uvh5HPKdDLJ9KcS6Esj27SDfl0bfBreTWbCTXF8qFi7iwhrjsaOCUQWRGk+Jq4iIiIhUH2Ng5kxYtQpsGy68ELp2DXZUlbI3bw2bMr8GfLgsT4nzFjYuK5wCk0uuLwuX5WF79ne0jb+AGE8yMZ7kYx+0VBljLIwJXq9nMO8dSrSPq4iIiIhUH8uCTp0gLAxGjKhxSSvAlszF+JxcitYCLo1VuEdrrm8vFm7yfRnHMkSR4556XEVERESkenXqBC1aQHT0YYuGoj15q3HbkRSYVH8PchksXPicPHwmjzC7ZtZVJFQpcRURERGRqpWWBrNnw5AhEB/vP1bDktb0/C3sz1sL2OT7MrEtNx4rhjyThl3GR2gLcIzBZ/JpFF1z5vBK+bQ4U2hQ4ioiIiIiVWfvXpg61Z+8fvghjBoV7IgqZXvWt6zc9wZ78lbiGC8ADg4WHqLdDcknHcd4sa2SH6MdDFg+Ilx1aBzV91iHLnJcU+IqIiIiIlVjxw544w3IyoJ69fw9riHG6+SzJfsX9udvBwwx7no0j+5OuCua39M+ZPmeCficPCzLjdsK97/G5OIzOWR6NxFm18VrcvAZBxs3luWf82qMgzEFhNmxdE64gmhPUhBrKXL8UeIqIiIiIkdv0yaYPh3y8iA52d/TGkLDg40x/JGxhBX7PyG9IAWDwQIMEO3+gGaR7diS8RY+k4fbisG2DwzP9JhoHCcTY3zkO6lEuhpTYNJxTL7/AoBjfIRbYXRLuJrmMf2DUUWpJo4BJ4gr+zplT6uuVZS4ioiIiMjR+f13ePtt8HqheXO47DKIiAh2VAGO4/BVyn9ZmfoRDrmFKwC7iXAlEO1OJt/JYVXaLMLIJsIunrQCWJZFmB1NvpNVmLymER/WigInkwInC6/JJ8pTl7Z1r6BN/KlBqqXI8U2Jq4iIiIgcOceBL77wJ61t2sCll4Kn5F6nweJ18pm95QG25iwHY7Asfz+rQx7Zvh3k+FKIdbfERQaOAR+m1A1vbMsm3I4h38nAZ7LI92Vj2S4i3Q1oEt2b5tH9yUsNnWRdqo6DjRPEXUSDee9QosRVRERERI6cbcPll8OSJXD22eByBTuiYubveJZtOT8UJq0uivZh9fepOhh8ZHjXE2k5gEWB8eKh9DpYloVthWEBpyffg8cVTYSrLuGuWBzHIYWUY1MpkVpIiauIiIiIVI4x/oWYGjXyP4+NhYEDgxtTKTZnLmd9xlcYHLCKhv8aCGwvYhfOc/VRYCzCbYPP+HCMwbbKmtNoAJs64S1x2+phFTlWlLiKiIiISMUZA599BosXw0UXQdeuwY6ohHxfNt/ve5tfU+fgI7/wqMEUJq0WNgSGX9pYOPhw4Tg+rMOMynSMl/iw5kpaaxGDVbiUV/DuL0pcRURERKSiHAdmz4Yff/Q/z84ObjylKHBy+DrlZbbm/IzPl4cVSDqKlmY1GHyFqcCB5NXgYCwLC6fMa/ucfCwsWsWeW611EJGSNNNXRERERA7P64V33/UnrZYFQ4dCnz7BjqqEtelfsi3nF2JdiYE9Vq2Deqyswv4z8HIgmfX3anmNBxcOjinAOWgPEscxeJ18fCaP+LCWtKtz8bGqjogUUo+riIiIiJQvPx/eegvWr/cvvnTxxdChQ7CjKsFnCliXsQgXHtx2OB47Aq8vCwAbBwun2KBL/+Bhd2H6amHbjYj1OOR4t+OQB76iPh4HCxd1w9vQv9GzGiZcyxhjBXUfVxPEe4cSJa4iIiIiUjavF6ZOha1bISwMRoyAVq2CHVUxjvGxK+c31mUuYlfuGsLsKPKdLGLcSeT49mJTgH3QEOCiPlf/sOACDBYWYfSoewknJgxha/ZCfk/7gIyCbQDEeprQJv5CmkafgW1rwKJIMChxFREREZGyud3QogXs3QsjR0KTJsGOqJjduX/w/Z432JO9gTyTTa7JIN+XRbZvPxGWmwgrn3xjAwbbKlpT2ICx8BUmsDaGRpEdObn+UCzLonns2TSPPTvINZNQ4QS5xzWY9w4lSlxFREREpHxnnw29ekFcXLAjKWZpymSW75uBF+9BRy0s3NiOjzBXCi7bS6YTQYFx4TWUWKrJwiLWFUFCWBSmcEiwiIQejXUQERERkeJ27YL33oOCAv9zywq5pHXmxjv5dt+0Q5JWKNyVlTB7Py7Lh7Eg2i4gyvbithyKUlcLiLB91Pd4aBDRgvT8zaTmrwtCTUSkItTjKiIiIiIHbNkCb74JubkQGwuDBgU7ohLe2nADu/J+L/O8Cx9hlhfHGCwLsBzCLS/hxsYADgbbsomwwzHkAT58Jp88X9qxqoLUICWX9Tr29xclriIiIiJSZN06/+rBBQXQtCn06xfsiEqYt+WJcpNWAI/ln73qn8/qZ3DAcrCwcVs2HisS/+zWHLxOGhYWLstT3eGLyBFS4ioiIiIisGoVzJwJPh+0bg3Dh/tXEQ4RjvExf/uzrM76opxS/vmrVuEMVoONv3+Vg/qsDB4rAtvyz2W1jEWBk02kuxHxYS2rLX4ROTpKXEVERERqux9+gNmzwRjo1Akuusi/X2uIMMbw0/4P+D3zSw4sqxQ4ixuHcKuAMMuHZRksY7Atg2OAwvmsFja2ZWGMg8EHeDDGv5OrYwpoFN2XcFf8sa6a1AAmsKRX8O4vSlxFREREarecHPjsM3/SetJJMHgwhNhepXvzN/Jr6kf4TD4UWxPYEGXlE2kXYGFwjIUx/vMWBrdlcIyNbRX1uPpTWMcU4LIiMOTjGIe64SfQOu784FRORCpEiauIiIhIbRYZ6d+f9fffoX9//wrCIWZDxhLynawSfa2RVgGRdgHGgA+bovTU4MJrDG7LwbYcbMLAMhjjAAbH+ChwsrHwEu1uRp+k+4hy1z/W1ZIaQoszhQYlriIiIiK1jePA3r2QmOh/3qSJ/xGituX84p+XWjh71RT2qEZY/qTVKWWHR69xYWFwWQbLcnBZMRjLh2NyAYPHDqNOWFf6JD1GhDvhmNdJRCpHiauIiIhIbeLzwaxZ/h7W0aOhUaNgR3RYXicPjx2FbbnxmQLAIswqwLYMPlNWb5SF17jx2C6gAGNyA73JEa76tIm/lFZxfybMFVr704pI6UJqAsNLL71E165diYuLIy4ujr59+/LJJ58Ezufm5nLTTTdRr149YmJiGDZsGLt27Sp2jc2bNzN48GCioqJo0KABd999N17voRtTi4iIiNRCBQUwYwb8+it4vZBWM/YtjXTXwWcKiHLXA8DGxlXY90oZwygtIMYVh4toIJwwVzJhrkaE2Q05KfFB2tcdraRVKsQxVtAfEmKJa5MmTXjqqadYvnw533//PWeddRZDhw5l5cqVANx+++3Mnj2bd999ly+//JLt27dz0UUXBV7v8/kYPHgw+fn5LF68mClTpjB58mQefPDBYFVJREREJDTk5sK0afDHH+DxwGWXQYcOwY6qQlrG9MHBS7y7EW4rAoODVfi/0thAtCsWyypaksnCARxj0yJuMA2jeh274EWkSoTUUOEhQ4YUe/7444/z0ksvsXTpUpo0acJrr73G9OnTOeusswCYNGkSHTp0YOnSpfTp04dPP/2UVatWMX/+fJKSkujevTuPPvoo99xzD+PGjSMshPYiExERETlmMjOJfPttrOzsA4sxNW0a7KgqrEV0T35Lm0eWdy9JER3YnbcWxxQA/j1bD94uJNyOwG2BQ27hKsMO4MNFGC3jzqd93UsDCa2I1BwhlbgezOfz8e6775KVlUXfvn1Zvnw5BQUFDBgwIFCmffv2NGvWjCVLltCnTx+WLFlCly5dSEpKCpQZNGgQN954IytXrqRHjx6l3isvL4+8vLzA8/T0dAAcx8FxnGqqYfkcx8EYE7T71wZq4+qnNq5+auPqpzaufmrjapaRgXn9deyUFJykJKxRoyApyb9AU4jId3LYmvUjO3NXU+DkEOGKo0lUN5IjO+Ky3ETY8fROuJLFeyaR6d1DXU8LHCcPx7cSFz4cbCxs4sMaE+9piNfkkevbj8/Jo8BJJ9rThFOSniDakwQGHFP1ddf7uHw1uV1MYJul4N1fQjBx/eWXX+jbty+5ubnExMQwa9YsOnbsyIoVKwgLC6NOnTrFyiclJbFz504Adu7cWSxpLTpfdK4sTz75JA8//HCJ47t37yY3N/coa3RkHMchLS0NYwx2iO2ldrxQG1c/tXH1UxtXP7Vx9VMbVzOfj3C3m7ywMDIHD/b3NqakBDuqgN2561iT9gXZvlRsLFx2GA7b2chaYtyJtI8/i1hPA9wk0c0ezebc5ezP24Jx8rHphosMXFY4Ua76hHmjKCi8bphJJt9Jw41N87jzydpvkUX11Vvv4/JlZGQEOwSp4UIucW3Xrh0rVqwgLS2N9957j9GjR/Pll19W6z3vvfde7rjjjsDz9PR0mjZtSmJiInFxwZm07zgOlmWRmJioX37VRG1c/dTG1U9tXP3UxtVPbVz9nGuuIW/bNhJbtgypNv5i5wR+y/kUJ+zAQpoWLqJd9YhzJbGPn/nV2UH/OrcSH9aQBjSgDd1Iy99BWsEOjONlX94ydud8i8/8js+KwrLcOKYAr8nBY8fQvs7lNIk5rdrrovdx+SIiIoIdwhFTj2toCLnENSwsjBNOOAGAk046iWXLljFhwgSGDx9Ofn4+qampxXpdd+3aRXJyMgDJycl89913xa5XtOpwUZnShIeHEx4eXuK4bdtB/cVjWVbQYzjeqY2rn9q4+qmNq5/auPqpjavYhg2wbh2cfbZ/C5iICIiNDak2nrn5LrZmryixKLDBS6azi2xnH43CO5Hu3cmvaXM4Pen6QJm6EY2pG9EYgBamN/vyzmJ71tek5CzHGB9hrmiaRw2kUfRpxIW1OGZ10vu4bGoTOVohl7geynEc8vLyOOmkk/B4PHz++ecMGzYMgDVr1rB582b69u0LQN++fXn88cdJSUmhQYMGAHz22WfExcXRsWPHoNVBRERE5JhZvRree8+/3U39+tC9e7AjKuGzHc/6k9aAQ3uUDA4F7MhbTWJEK7bl/EJGQQqxngYlrmVZFvUiOlEvohOO8eKYAlxWOJalREnkeBJSieu9997LueeeS7NmzcjIyGD69OksXLiQefPmER8fzzXXXMMdd9xBQkICcXFx3HLLLfTt25c+ffoAMHDgQDp27MioUaN45pln2LlzJ/fffz833XRTqT2qIiIiIseVn36CDz7wL7zUvj107hzsiErYkL6UVWmfHnL00P1YLcDgIxdjDPlOFnvy1peauB7MttzYVkh9vJXjgA8LXxlbLx2r+0uIJa4pKSlceeWV7Nixg/j4eLp27cq8efP405/+BMA///lPbNtm2LBh5OXlMWjQIP7zn/8EXu9yuZgzZw433ngjffv2JTo6mtGjR/PII48Eq0oiIiIix8bSpTB3rv/r7t3hggsghIZn5nmz+Gj7w2zN/gkobYXZQ5NXv7SCbUS64/EZb8mXiEitEVKJ62uvvVbu+YiICP7973/z73//u8wyzZs35+OPP67q0ERERERCkzHw5ZewcKH/eZ8+MGiQf25riHAcLx9u+zs7c1bjT1DLcnDy6u919Zo8LGwiXcFZMFPEUP679ljcX0IscRURERGRStq1y5+4Apx1Fpx+ekglrQC/pX/Grty1uKwwfCYfU2qPa5HiPa+O8RHrSSIpon21xykioUuJq4iIiEhNlpzsHxZcUAC9egU7mlKtTJ2LMQ5uVxj4DA4VGfbr72eyLTdtY8/AbYdVb5AiEtKUuIqIiIjUNF4v5ORAbKz/eY8ewY3nMPblb8K2XADYlgdMzmFecWBwZLu4s2kfP6AaoxMpn8HGmODNFzeEzlz1YFIriIiIiNQkeXnwxhsweTJkZQU7msNyHKfY0GDbtnFRsd0e6oY146zkWwNJr4jUXkpcRURERGqKrCyYMgU2boTMTNi/P9gRHZZt24TZ0TjmQPIa5orEhafc19ULa8XI5hOVtIoIoKHCIiIiIjVDWhpMmwZ79kB0NFxxBTRsGOyoKqRlTB9Wpn6CcRyswi16wlzR+Hz5FJCHwRco6yKcUxtcTdf4C3G5lLRK8BlK38DpWN5flLiKiIiIhL69e2HqVH/yGh8Po0ZB/frBjqrCeiQM4/f0heQ72YQ50Vi2f9VglysMF2EYx5BrMvFY4Zzf5GGaRYf2nF0ROfaUuIqIiIiEsl27/ElrVhbUqwdXXulPXkNIlncvmzK/Y3v2zxQ4OUS669A0+iSaRp1EmCuKumFN6Jd0E1/uepF8JwvbceHCv0qwQwE+48VjhdGr/kglrRJyHGPhmOBtMRXMe4cSJa4iIiIioSw6GsLDIS7OPzw4OjrYEQUYY/gj40t+2j+TXG86tuXCtlzsy9/ItuwVrHTPpnfiNSRFtqND/ACi3Qks2zOdXblr8JpcACzLRYOINvRIuIS2cf2CXCMRCVVKXEVERERCWUwMjB7tT14jIoIdTTHrMxexfO90AOI9DbGsA+t+OsZLhjeFb1Je5ozkW6kX3pJm0SfSLPpE9udtYWfuGjAO9SNakRhxQrCqICI1hBJXERERkVDzyy/g80H37v7nITY0GKDAyeGX/R9ijI9YT1KJ87blJs7dkNSCbaxK/ZjTk24KnKsb3pS64U2PZbgiR8xgB3UvVe3j6qfEVURERCSULFsGH3/s/zoxERo3Dm48Zdia9SNZ3j3EuBPLLGNZFpGueHbk/Epa/nbiwxodwwhF5Hii9F1EREQkFBgDX30FH33k/7pnT2gUuone/vzNGBxcVvn9IOF2DPlONvvzNx+jyETkeKQeVxEREZFgMwY++wwWL/Y/P+MM6N8frNBdTdTB8cd9GFZhHYzxHaakSGhyjP8RzPuLElcRERGR4HIcmD0bfvzR//ycc6BPn+DGVAFRrrpgWRhjAslpaQqcXFyWh0h3wjGMTkSONxoqLCIiIhJMK1f6k1bbhgsvrBFJK0DT6JMIt6PJddLLLZfjSyXe05gGEW2PUWQiVcsABiuIDwH1uIqIiIgEV+fOsH07NG8O7dsHO5oKi/U0oFl0T35PX4DbCsdjl9yqJ8eXBkC7+LOxLdexDlFEjiNKXEVERESOtZwccLvB4/HPYx00KNgRHZET640g15fO1uwVWD6LCFccNi58Jp9cJwPb8tCxzmBaxZwe7FBFpIZT4ioiIiJyLGVkwLRp/r1ZR4wAV83tifTYkZza4EY2ZH7DuoyvSMvfjsHBttw0jupO69h+NInqUe4cWJFQZ4yFMcF7Dwfz3qFEiauIiIjIsbJvH0ydCqmp/l7XjAyoUyfYUR0Vtx1Gm7gzaR3bj4yCXXhNHuF2DNHu+kpYRaTKKHEVERERORZ27fL3tGZmQkICXHlljU9aD2ZbLuLDQnffWRGp2ZS4ioiIiFS3LVvgzTchNxeSkmDUKIiJCXZUIlIBDhYOwRs9EMx7hxIlriIiIiLVad06eOstKCiApk3h8sshMjLYUYmI1Cjax1VERESkOkVE+FcOPuEEf09riCWtjvGxN289O7J/YXfuWrxOfrBDEgkpRYszBfNRGT6fjwceeICWLVsSGRlJ69atefTRRzHmwI6wxhgefPBBGjZsSGRkJAMGDOD333+v6qarUupxFREREalOjRvD1VdDYmJIrSDsGB/rM77ij4yFpBVswzFebFxEu+vTKvZ02sSdjccOrSRbRA7v6aef5qWXXmLKlCl06tSJ77//njFjxhAfH8+tt94KwDPPPMO//vUvpkyZQsuWLXnggQcYNGgQq1atIiKi5J7MoUCJq4iIiEhVW7IEmjXzJ60AycnBjecQPuNl2Z5JbMhcjIVFhCselxWGY7xkefeyYt877MxZxakN/o9wl+biitQkixcvZujQoQwePBiAFi1aMGPGDL777jvA39s6fvx47r//foYOHQrA1KlTSUpK4v3332fEiBFBi708GiosIiIiUlWMgfnzYd48eOMNyMoKdkSlWpv+KeszFxHhiiPWk4zHjsS2XLjtcGI8DYh2J7Ij5xd+3Pd2sEMVCTonBB6Vccopp/D555+zdu1aAH766ScWLVrEueeeC8CGDRvYuXMnAwYMCLwmPj6e3r17s2TJkkre7dhRj6uIiIhIVXAc+OgjWL7c//y00yA6OrgxlcLr5PFH+gJcVhhhdunxue1wIlxxbM3+noyCIcR6GhzjKEXkUOnp6cWeh4eHEx4eXqLc3/72N9LT02nfvj0ulwufz8fjjz/OyJEjAdi5cycASUlJxV6XlJQUOBeK1OMqIiIicrR8Ppg505+0WhYMGQKnnlruSxzHYfGen/jf1i+Ys+0rtmWnHJNQd+asJNO7m0hXnXLLhdtx5DtZbMv+4ZjEJSLla9q0KfHx8YHHk08+WWq5d955hzfffJPp06fzww8/MGXKFJ599lmmTJlyjCOuWupxFRERETka+fnwzjvwxx/+xZcuugg6dSqzuOM4zNgyj/k7vyW1IAPH+AcCemwPbWObcVWL82kb16Laws110jEYXJan3HKWZQEWub70csuJHO+OZGXfqr4/wJYtW4iLiwscL623FeDuu+/mb3/7W2CuapcuXdi0aRNPPvkko0ePJrlwzv2uXbto2LBh4HW7du2ie/fu1VSLo6ceVxEREZGj8fXX/qTV44HLLjts0vrUb5N5d/Nn7MtPw225iHJFEGmH4xgfv6b9wbiV/2XF/jXVFq4LNxhTbGuM8tiW+jlEQkFcXFyxR1mJa3Z2NrZdPM1zuVw4jv+PZC1btiQ5OZnPP/88cD49PZ1vv/2Wvn37Vl8FjpJ+E4mIiIgcjX79YPdu/9Dgpk3LLTpz6+d8t+9XbMsiwo7AKurEsSwiCccxhixvDs+veZP/nnw/Ee6wKg83IbwVHlcU+U4m4a7YMst5nTxsbOqFt6ryGERqklDpca2oIUOG8Pjjj9OsWTM6derEjz/+yPPPP8/VV18N+EdTjB07lscee4w2bdoEtsNp1KgRF154YTXUoGoocRURERGprKwsiIryz2f1eOAw20fkevOZve1L3tj0MV58YCDfKcBtuYlwheGy/L0j/oQ2jPSCDObu/IYLm5xZ5aHHhzWiYURnNmV9S5gdjWWVHIBnjCHbt4f4sKYkR3au8hhEpPq88MILPPDAA/zf//0fKSkpNGrUiOuvv54HH3wwUOavf/0rWVlZXHfddaSmpnLaaacxd+7ckN3DFZS4ioiIiFROSgpMmwbdu8PZZx+2+L78dO776UU2ZKVgAAsbl+WABQXGS4HXS6QrnHDbP+fUbbvI9cHXu3+slsQVoHPdoezN30B6wXai3Q1w2wd6dn3GS5Z3Nx47mu51L8GlocIiNUpsbCzjx49n/PjxZZaxLItHHnmERx555NgFdpT0m0hERESkorZt8+/PmpMDq1fD6adDWNnDebdl7eXG758jtSAfYwoXQ7LAxuBx+QizfVgW5Pj8w3I9tstfxLJIL6i+PWDrhDXltAY3892e10nN34rx+rAsFwYfFjbR7vqcVG8kjaK6VVsMIjWFg4VD8IYKB/PeoUSJq4iIiEhFbNgAM2b4VxFu3BhGjiw3aV2Xvp0bv3+BHJ8XLLCsA4shOVjked34bItItxfLgjwnH48dCYDBBJLY6lIvvCUDGz3Ijuxf2Ja9glxfOmF2JMlRnWkSdWIgFhGRUKDEVURERORwVq+Gd9/179faqhUMHw5lrOgJsGj3Ch78+Q3yHQvbMgcWYSpkYTCA13GR5zNEuH14jQ+fMYG+lQ5x1b8oksvy0CT6RJpEn1jt9xKpqYzxP4J5f1HiKiIiIlK+FSvggw/8nx47dIBhw8Bd9keodzfN59WNcygw7lKT1iKW5e9ZLXBchBv/kGGf8eF1fHhsNxc0PqN66iMiUgMpcRUREREpj2X5k9bu3eGCC8AuuQpvkTfXf8IbWz/B67gACyi/q8TCv9WF19h4LIc8pwAbiwFJvWke3bAqayEiUqMpcRUREREpT7duUKcONGtGmd2nwPXLnmRzzg6gaN9HsA+zpoq/1/XAPo1hlpuBDfvwl5Z/rqLgReRomSAvzmS0OBOgxFVERESkOGPgq6/gpJMgJsZ/rHnzcl9y1dJH2ZW3p3C7G/8c1sBHzaJO11I+expTdN6QGFaHp7vfRlJEvSqohIjI8aXssS4iIiIitY3PB7NmwYIF/m1vHOewLxm1ZBw7C5PWInbhCsLFFlUpY9SwZRliXB5ubXe5klYRkTKox1VEREQEoKDAv3Lw2rX+eaynnlrufFav18vIbx8kzVtyv1WX5WBbDj5jY5vSF2jyr1RqEeYy3NDmIk6s274qayMiVcQ/9D+IQ4WDeO9QosRVREREJDfXv0frpk3+FYMvvRTati2zuNfrZcTS+8n05ZR63rIg3OUl1+fxf+gsSl4LxxIbA46x8Ng2d7W7iEENe1dPvUREjhNKXEVERKR2y8ryDwvescO/N+vll5c7p3Vf3j6u+PYRnFI3VzywkrDLNkRQQJ7Pg2MsHFO0irA/sXXbhqe6jqF3onpaRUJZYCp6EO8vSlxFRESktvvwQ3/SGh0NV1wBDcvehiYlYy9XLH+UfJ+NY2z/QkyWg8d2cNmF81o5sA6TyzZEWvn4jI3P2IGk1WMbnuhyHSfVU9IqIlIRSlxFRESkdjvvPMjJgaFDoV7ZiyN9l7Kau3+eiNeEBRJQAOPYFDgGj+0Q7vJiWRbmoFWFLQvcloMbpzCjtXiyy41015xWEZEKU+IqIiIitU9uLkRE+L+Oj4cxY8rdo3Xh9h95cNUb+IzLv9WNdVDiWlimwOfCGIhwFyWvB58tKm/x7xP/SquYxlVfJxGpFv6h/sFbICmY9w4l2g5HREREapdNm2DCBFi16sCxcpLWL3b+yKNrpuJzSiatRS/1Pwxe48JnDv54ZRV7vNRDSauIyJFQj6uIiIjUHmvXwjvvgNcLy5dDhw7lJq0P/TSZpak/UuDzgFVuUSwLjAMFPhu37ZQ492KPu2kRq6RVRORIKHEVERGR2uGXX2DWLHAcaNcOLr643Ez01u8msCZnPcaAzxxYLbhclim2CBOA27J5/5R/4HbrY5dITaR9XEODfoOKiIjI8W/ZMvj4Y/9eNF27+hdicrnKLH7F14+S4tvjn1tm/C+zj+CzY4QrjPf6PKmkVUTkKOm3qIiIiBy/jIGvv4YvvvA/790bzjmn3J7WkYueYHNuGl4ngqKtWg0WjmVw2065w4XBwrL882DrhcUxpedDSlpFajjt4xoa9JtUREREjm9ZWf5/+/eHM84oN2l9dtUs1mem4eAqXE7J/5HRv6qojTEWHpev1EsUJbke20eMK5I3+z5atfUQEanFlLiKiIjI8cuy/D2sbdrACSeUW/SDLd/y3ualGMAuXD24iMsy+Bx/Alvgc5VIXo3x98paGBI8Ebx32hPVUx8RkVpK2+GIiIjI8cXr9Q8P9nr9zy3rsEnrVztX8Y9VH+Iztr93FRuHA5mphcFlGywMjrHwOZY/WTXgGH9Ca2FoGVWX905X0ipyPPH/5Af3IepxFRERkeNJXh689RZs2AC7d8NFF5Vb3Ofz8Zdv/8vKtC2YwEwyC8cAxsI5KGH1J68Eel7tQGmD2+WjdVQir/f9W3XWTkSk1lLiKiIiIseH7Gx4803Ytg3CwqBHj3KL5zteLl30LNtz0ood9/dtmML/t/A54LIJJK+2BZZliHTn+8tbhiHJ/bi1/bBqqJSIiIASVxERETkepKfDtGn+XtaoKBg5Eho3LrN4njefyxePP5C0ltim1X/AwvhXFDb+ea6Bsxa4bP/zPvHdlLSKHMf8i7MFb7huMO8dSpS4ioiISM22bx9MnQqpqRAXB6NGQWJimcU3Z+7hskXjKcB34KApJXcNJK9gjIUpLGAAl+UAcG7i6YzteHEVV0hERA6lxFVERERqLsfxDw9OTYWEBLjySqhTp8zi27P2M3zReHwHJ61AYO2TwsTUKnbCHLRAij+19bh8XNfiIoY1P6MKKyMiIclY/kcw7y9aVVhERERqMNuGCy6AJk3g6qvLTVoBblk+CZ9xSj9piuWvBymc9WrAYOOxHe5td7mSVhGRY0g9riIiIlLz5Of7F2ACaN4crrmGYhurlmJPbgZbsvdS5s4Shcetwl5Xc+h5Y1EvLIppp46lXkTcUQQvIiKVpcRVREREapaVK+GTT/xzWZOS/MfKSVoLCgp4+NcP+HzXLxjjH/R7uG0RLcvfw1o0ONjG4oY2Z3NlyzNxuVxVVhURCX1O4X7Nwby/KHEVERGRmmT5cpgzx59VLl8O551XbvHHf/6A/21ZhlW4mJJl+Zdbskr2px5Q1PNa9C8WFzTpyZgTBlRFDURE5AgocRUREZGaYdEimD/f//XJJ8M555Rb/JZvJ7N031pcrgNJqjFw2O7WQ3SNb8bfOlxQyWBFRKQqKXEVERGR0GaMP2H95hv/89NPh7POKnd48J3LpvDt/jUlivifl1g6uEwnJbTkpV7XHWnkInIcOLCqePDuL0pcRUREJJQ5Dnz0kX9YMMCf/gSnnlruS0Z98y/WZmyn3My0aPJqOUVGtjiV29qfX9mIRUSkGihxFRERkdDlOLBvn7+rdMgQOPHEcotft+Q//JG5lQrt+GeBMf4MtnjPrMVZSZ2UtIoI4P89YYK4l2ow7x1KlLiKiIhI6HK7YcQI2LIFTjihzGJer5dh3zzG/oIM/B9v/N2pRYswlT/UzgqsIGxbNhc3681dHYdUYSVERORoKXEVERGR0JKT49/y5uST/c/Dw8tNWgsKChiy6EHyTS5WYU9r0UjgotHAFqaM5NVgjIXHdjOsWU/Gtj0Xt1sfj0REQo1+M4uIiEjoyMyEadNg1y4oKIC+fcstvjs7jeFLHsW2fdgW+ApzU9syJfY+LCt5NcC/TrqKnomtqqgSInI8MaZoRfLg3V8qNAHk2HnyySfp2bMnsbGxNGjQgAsvvJA1a9YUK9O/f38syyr2uOGGG4qV2bx5M4MHDyYqKooGDRpw99134/V6j2VVREREpLL274fXX/cnrTEx0Kr8RDKzIJdLlzyOZfsO6l0tHBpsir4uvmNrybTVIsIOV9IqIhLiQqrH9csvv+Smm26iZ8+eeL1e7rvvPgYOHMiqVauIjo4OlLv22mt55JFHAs+joqICX/t8PgYPHkxycjKLFy9mx44dXHnllXg8Hp544oljWh8RERGpGHvPHpg719/jWrcuXHml/98ypOVnM+yrZ7DsguLXsYy/txULFw4+YxduZeFPX61DlhJ2HIt/9RldLXUSEZGqE1KJ69y5c4s9nzx5Mg0aNGD58uX069cvcDwqKork5ORSr/Hpp5+yatUq5s+fT1JSEt27d+fRRx/lnnvuYdy4cYSFhVVrHURERKSStm0j8q23sFwuSEqCUaMgNrbM4luz9jB6yQsUkIXnkHOWBW6Xj3yv/yOOyzqQvALFel8x8N8+13JSvRZVWh0ROb4UjegI5v0lxBLXQ6WlpQGQkJBQ7Pibb77JG2+8QXJyMkOGDOGBBx4I9LouWbKELl26kJSUFCg/aNAgbrzxRlauXEmPHj1K3CcvL4+8vLzA8/T0dAAcx8FxnCqvV0U4joMxJmj3rw3UxtVPbVz91MbVT21czbKzMVOnQk4OTtu2WFdcAZGR/m1wSvHljlU8tPINDD7ctoNVyjYRbgzYPrw+FwZw4/jnqGEFElgbm3dPv5OGUXVrxfdW7+PqpzYun9pFjlbIJq6O4zB27FhOPfVUOnfuHDh++eWX07x5cxo1asTPP//MPffcw5o1a/jf//4HwM6dO4slrUDg+c6dO0u915NPPsnDDz9c4vju3bvJzc2tqipViuM4pKWlYYzBtkNqKvJxQ21c/dTG1U9tXP3UxtXP1aMH+b/8QsagQdgZGZCRUWq5T7ev4PNdP9LSigfAdhxsy7+PzaHpqwGMZeFzbBysg7osDG7Lw32dhuHKLCAlM6W6qhVS9D6ufmrj8mWU8XNdExz8R69g3V9COHG96aab+PXXX1m0aFGx49ddd13g6y5dutCwYUPOPvts1q1bR+vWrY/oXvfeey933HFH4Hl6ejpNmzYlMTGRuLi4I6vAUXIcB8uySExM1C+/aqI2rn5q4+qnNq5+auNq4vX692gFnIED2d2tG4lJSWW28bR1C3h17+cHb9EKgI0/eXWV863x97b6/w030bzb729EeyKrtDqhTu/j6qc2Ll9ERESwQ5AaLiQT15tvvpk5c+bw1Vdf0aRJk3LL9u7dG4A//viD1q1bk5yczHfffVeszK5duwDKnBcbHh5OeHh4ieO2bQf1F49lWUGP4XinNq5+auPqpzaufmrjKrZkCfz4I4wZ4x8WDFguV5lt/P7mJUzcNPdAt2rRJq2Az7LwYeEYB7dd+kwwq/B18a66zOr3YBVXpubQ+7j6qY3LpjaRoxVS7yBjDDfffDOzZs3iiy++oGXLlod9zYoVKwBo2LAhAH379uWXX34hJeXA0J/PPvuMuLg4OnbsWC1xi4iISAUYA198AfPmQUoK/PLLYV9y6/cv8ezamcWOWRQmo5Z/AJ0FOMbG55Q+nM4AkVYM755y39HWQERqI2NhgviglLn8tVFI9bjedNNNTJ8+nQ8++IDY2NjAnNT4+HgiIyNZt24d06dP57zzzqNevXr8/PPP3H777fTr14+uXbsCMHDgQDp27MioUaN45pln2LlzJ/fffz833XRTqb2qIiIicgwYA598AkWjos4+G3r2LPcll379ODvz9nHo39mL+lWtg7a5AQvHWNjmQK+rZflvm+RJYsbpf6uaeoiISFCEVOL60ksvAdC/f/9ixydNmsRVV11FWFgY8+fPZ/z48WRlZdG0aVOGDRvG/fffHyjrcrmYM2cON954I3379iU6OprRo0cX2/dVREREjiGfD95/39/Dallw3nmHTVov/OIR9ps0/8JKZfBPdT2QvDrYGOMEhgYX+Gy6x7fjXz2vK/MaIiKHY4z/Ecz7S4glruYw35WmTZvy5ZdfHvY6zZs35+OPP66qsERERORIFRTAu+/C2rVg2/DnP0OXLmUW9/l8nP/Vo2T4MnFXYEKT/5ODhfEvLkwBLowDxtjc0e5iLm7eq4oqIiIiwRRSiauIiIgcZ3Jz/fNZ3W4YPhzatCmzqM/n49JvniXdm42rElO6rEOeWVg83PUKBiR3PdKoRUQkxChxFRERkeoTGwujRkFWFjRrVmaxrIJchi94hALyA0N9odgCwodXWPju9hcraRWRKmOoxO+harq/hNiqwiIiInIcSEuDNWsOPK9Xr9yk1ev18uSv7+M1+SXOWRX4yHbQckzc2OoChjbV8GARkeONelxFRESk6uzZA1OnQmYmjBwJrVsf9iUP//I/ck1usWOOAdvyr+VkGYMpZ5Em8O+f+UjnUZyZXPb8WRERqbmUuIqIiEjV2L4d3ngDsrOhfn1ITCy3+K6cVMavmcXXe3+jlVUXyzKBCasGcPAPDSvqdS0reXVh8dapf6NhZELV1UVE5GDaSzXolLiKiIjI0du4EWbMgLw8aNQIrrgCoqLKLL41ew9jf/g3+/PTsQ6eueTf4waw8TkOlm2wLTAYTGHP64GdW8EFTO9zj5JWEZHjnBJXEREROTpr1vi3vPF6oUULuOwyCA8vs3hK7n5uXjaedF+2f3/Eg08W69Sw8ToOLstgWf6hwwR6XwFjMeOU+2gYpaRVRKqPYyycIPa4BvPeoUSJq4iIiBy57dvh7bfBcaBdO7j4YvB4yiy+cOcvPP3bW2T68jDGVXi06EOZ4dDNbcDGZwDjFDvjwuLD/g8T7ym7V1dERI4fR5S4pqamsnjxYlatWsWePXuwLIv69evToUMH+vbtS926das6ThEREQlFDRtCt27+xHXoULDL3rBg+voveXn9Rzjm0AT14MG/ZbEDpWLdbh7vMoR9eduIcbfCZbnKeZ2IiBwPKpy45ufnM336dCZPnsyiRYtwHKfUcrZtc+qppzJmzBguu+wywssZKiQiIiI1kDH+h237l/0dMqRw+d+yE89X/viYNzYuwDEWFsWLWofZ8cYUDpOzLEO9MC8nJTh8smsmtuUmMTyJngmn0q1OT6xy7i8icsS0kWtIqNA+ri+//DKtWrXihhtuIC4ujn/+858sWrSI7du3k5OTQ3Z2Ntu2bWPRokU8//zzxMfHc8MNN9C6dWteeeWV6q6DiIiIHCvGwLx5/jmtRX/ELkpgyzBtw6fM2PQFxvhXCC5R1Dq0r9Vw8CdFC4PL8tEsKouzkuuQFJFEYngyse44duft5OMdM/ly96cYo093IiLHqwr1uD7xxBPcddddjBkzhvj4+FLLNGzYkIYNG3LKKadw6623kp6ezuuvv86TTz7J9ddfX6VBi4iISBA4Dnz4IaxY4X++YUO5+7R6nTwmrH2POdu+x+u4MBQtsFQKy5+g+re8KV4oxp1HsxgfJ9VtUaxXNcwOo15YIpneDJbuXUjjyKa0ie14VFUUEZHQVKHEdf369bjdlZsOGxcXx9ixY7n55puPKDAREREJIV4vvPcerF7t72EdOrTMpDUtfyvr0r7iH2u/Z1uOjWPcgT1YHXNgX9ZDe14t68A5t+3vzY1zezi5nof64Y3KHAoc445ld94ufkpdpsRVRKqcfxuu4E1FCOa9Q0mFstHKJq1V9VoREREJAXl58NZb/h5Wt9u/cnD79qUW3Zb1A9/uep3Jm1zs9Ybh7z0tPoTXFB0zZY8wdtmGSDuSM5LqkevsPOwCTNGuaDZmrSO9II04T+mjw0REpOY6oqwyIyOD1NRUmjZtGji2fft2Xn75ZfLy8hg2bBi9evWqsiBFREQkSLKz4Y03/NvehIX592ht2bLUovvyNvDt7teYtiWMvd6Dt8QpLTs9MDD4YAYLl+UQYYdzf6dR/Jz2Md78srfXKeK2PeT5ssjxZSlxFZEqVbQeXTDvL0eYuF533XVs2LCBpUuXApCenk6fPn3YunUrtm0zYcIE5s6dS//+/asyVhERETnW9u2D3bshKgquuAIaNSq1WLY3hSU7XmLGJh8pFUg0/SyMOZC8GuNfObhuWBSv9BxLcmQCazI+xzG+w17JZ3zYlk2Yrd0MRESORxVaVfhQixYt4vzzzw88f+ONN9i+fTuLFy9m//79dO3alccee6zKghQREZEgadIERoyAMWNKTVqNMfye9gEzN9zDM7972ZYXF+idOPhx2O0cCs+H227+feItJEcmAHBCbAe8xnvYFYOzfJkkhTeijifhCCopIiKh7ogS1z179tC4cePA8w8//JDTTjuNPn36EBsby5VXXslPP/1UZUGKiIjIMbRrF+zceeB569aQmFhq0T/SZzN/63u8vKE+BebAQK5Dt3U1gf8rzgBO4T6tHtvFiyfdRJOYA/fqFNedKHc0ad79ZYab58sFoHtd7eUqInK8OqLEtU6dOuws/A9aTk4OX3/9NQMHDgycd7vdZGdnV02EIiIicuxs3gyTJsG0af5hwuXI9u5m7raPmL6tAc5BHykOTh0PTmDL6jN1WQ4xHg9v9rmHDnWaFDtXJyyB/onnYIB9+XvwOt7AOcc4ZHrTSfOm0j6uC53ielSioiIiUpMc0RzXU045hf/85z+0b9+euXPnkpuby9ChQwPn165dW6xHVkRERGqAP/6At9+GggJo1sw/r7UcU9a+wzvbYvEGklZ/hurvXS2cu1qYtFpWaQuMGNy2j8YRdbmr+SUkRZW+qNKJdfvgtt0s2v05qQX7cIwTSI4jXdH0qnc6ZzY4F7etnQxERI5XR/Qb/umnn2bgwIEMGzYMgDvvvJNOnToB4PP5ePfddznnnHOqLkoRERGpXr/+CrNmgc8HbdrApZeCp+xFlqas+5zp2zbiMzZlrxoMljHFTpuDtsCxgHaxjXnxxFvYt6fs3l3LsuhWpycd4rrye8Zv7MrdjmN8xHnq0C6uM/GeukdSYxGRCjHGwpgg7uMaxHuHkiNKXE844QTWrFnDqlWriI+Pp0WLFoFz2dnZvPjii3Tr1q2qYhQREZHq9P338NFH/qyyc2f485/BVfa+qQt3rmLiuk8KhwcbSk9c/co+a2galcB/Tr4Nu4Izl8LscDrFd6dTfPcKlRcRkePHEY+p8Xg8pSansbGxxYYNi4iISAj79VeYM8f/9cknw3nngV12IvmfNR8xfdNCfBQltgfS0tKT1MIxwgcNGQboGNuMl3v+Hy7bheM4VVARERE5nh3R4kzg37v1qaeeYtCgQfTo0YPvvvsOgH379vH888/zxx9/VFmQIiIiUk3atvVveXP66TB4cLlJ6/jV7zNt01f4KvnxwXBgfquFxfCmpzOxzy24yunVFREROdgR9bhu3bqVM844gy1bttCmTRtWr15NZmYmAAkJCbzyyits2rSJCRMmVGmwIiIiUgUCWaQFYWFw1VXgLv8jwcu/f8Lbm5ce/tKF/5Y+69XiL60HcVWrsyoVroiIyBElrnfffTcZGRmsWLGCBg0a0KBBg2LnL7zwQuYUDTsSERGR0OH1+hdhSkqCfv38xw6TtD618n/M3r4Ul+VPS/09qFZhklrWDNaDWVgYLml6upJWEalxtDhTaDiixPXTTz/l9ttvp2PHjuzdu7fE+VatWrFly5ajDk5ERESqUH6+f7ubdetg9Wro0gXqlr0ib2ZuCiOXvMJeX3pgbir4U1XbMhgDPlN0pGwufPwpqSdjO5xfJdUQEZHa54gS15ycHBITE8s8n5GRccQBiYiISDXIyYHp02HLFv/w4OHDy0xa9+auYm3qPP72y3acMnLSonzVZRl8TuGTg9g4GCxsHEa1+BPXtx1UlbUREZFa5ogWZ+rYsSNfffVVmefff/99evToccRBiYiISBXKyIDJk/1Ja2QkXHkltG5datHNmZ/z9Y5nuOeXrfisQ4f8llTU+1qcf7NWl2W4ssUAJa0iUqMZE/yHHGGP69ixYxk9ejRdu3blkksuAcBxHP744w8efvhhlixZwsyZM6s0UBERETkC+/fDtGmwbx/ExsKoUXDI2hRF9uT+yg+7p/H/7L13nF9Vnf//POeWT5/ekkmHhAAJEAjSUYqogNhW146u+7Xrgr/vWlZBlq99/bq6+3Xtq7KubVUUEBSQ3iGUkIRAepve51NvOef3x/3MZ2YyJTOTRAic5+MxSebec88999xPPve+zrv969YGAi2xhD6gcNWUS9zo8cVwLBT/sOINvGnxWYfqSgwGg8HwEmZOwvWd73wnu3bt4nOf+xyf/exnAXj1q1+N1hopJV/60pd4/etffyjHaTAYDAaDYS7s2hWJ1trayNI6iXuw1poBbyv3tH2Tf99Wi6clAgjLfsJCqPLfk/sNi/LPSOqmuJT87IxP05qqOxxXZDAYDIaXIHMSrgCf/exnede73sVvf/tbtm7dilKKo446ije+8Y0sW7bsUI7RYDAYDAbDXDnppMgaevTRkcV1P0Lt80z/z3iw605+srsZVY4i0mMK22htEeUQVlOK1xGS0uG/z/xHWpI1h/QyDAaDwfDSZk7Cdffu3TQ2NrJo0SKuvPLKCfsLhQLd3d0sWrTooAdoMBgMBoNhluzaBY2NkExGv0+Td2Jz/y/48Zb7eGCwhRG76XhpGklYjQAtmUy86vJPjZPgx6f/Ay2JmkN3LQaDwWAwMMfkTEuXLuX666+fcv8NN9zA0qVL5zwog8FgMBgMc+SZZ+C66+BnP4NSadqmw94e/t/mR3lgsJ5R0TrRohpt0WVJO4nFVcOyZD0fOOpMNgxtYm++A22yiRgMhhcLWjz/P4a5WVwP9DDyfR8p56SJDQaDwWAwzJUnnoAbbohcg2tqwLKmbf6rnb/nieFUOYZ1ctE6QiRrNVoLxH5ZhJO2pDaR547eB9Ea/mzdw/L0Et644FU0xEycq8FgMBgOnhkL16GhIQYGBiq/9/b2snv37gntBgYG+OUvf8m8efMOyQANBoPBYDDMgAcfhD//Ofr3ySfDpZfCNIvIv9hxNz/asY+JBW+mX5zWRAvYsuwuHBeKU+uaSToJhBBorcmHRdYPPkuvN8D7l72N+ljNnC/LYDAYDAaYhXD913/9V6699logyip4xRVXcMUVV0zaVmvNF77whUMyQIPBYDAYDNOgNdx5J4zUVz/rLLjwwnKNmsn51jM38Ms9Dxyw1M10KA0JS3FO42Jse/R1QghByk4Qt1z2FTq5uf1O3rXkDQdxJoPBYHh+eb5rqZrIi4gZC9eLLrqIdDqN1ppPfvKTvO1tb+Pkk08e10YIQSqV4pRTTmHt2rWHfLAGg8FgMBj24557RkXrhRfC2WdP2/zd932b53J7xm0bW8rmQAhAaUG1A2sb5o8TrWOxhEXGTrFxaAs9pT7jMmwwGAyGg2LGwvWMM87gjDPOACCXy/HGN76R1atXH7aBGQwGw4uJfODxaM9OdmR7CLWmMZbmjKZlNMYnlicxGGbF6tWwbh2cey4cYNH4zXf9P3YV9yLHGWNFOW+wqKRfmgxdbguaGsciE9MkrPi050vbSTqLPWwZ3mmEq8FgOHIZSZ3+fJ7fMLfkTJ///OcP9TgMBoPhRYnWmtvbn+FXOx6jozA0TiD8YsejnNdyDJcffToxy3m+h3rY0FofsPanYWo8zwPAdd3RjVqPugLX1cFHPwpj9+9HEARceMdX6S8VSLh6QhomrQVSRNsnE68jolWgOTpTS0geT5UOeF+FECAEJeXP9HINBoPBYJiUOQnXkVjX6RBCcNVVV82le4PBYHjRcPO+DfzouftQGuYlqnFklOVVacWAV+APe55kwM9zxXEX4Mo5fSW/ICkE3bTl7qctfy9eOIgULk2Jk5mfOocad4URsgfgj/se4PvbbiIbFimvdQBwfNVivr78vbjXXw9nnAErVkQ7phGtYRjy6ru+xHBQIBkLESJaTIjqslLuXJS18Kh4HU8kWo+pqmNhqp6t2d1IceBFCaWjbMVJe3rLrMFgMBgMB2JOb0nXXHPNlPtGMgoa4WowGF7qtOcH+dm2hxFCMD9RNW6fFJK6WIqYZXNf51ZOqV/MBfNWPk8jPbR05h9jY9/3KYS9WMLFEjFCVWLX8C3sy97FosyrWVHzNqSYvlTLS5UrHv93nh7YPl46lmv4be3Ywn/87I28J3EiNd3d8A//AM7U1vq8V+BN93+FXFjEtfU4ESzKf0Ql7kTZzhrJVsr/orwHNK2JNAtT9QQqwJUx4pZFLiyQtpNTnn84yJG2U6zMHDXH2TAYDIbnH135jnz+zm+AORVbVUpN+AmCgG3btnHllVeydu1aurq6DvVYDQaD4Yjins4tDHoFGmNTx7Gm7BgauK1t0wFrZB8J9BU383Tvf1AKB8nYC0jZLcStWhJ2A2l7IVI47Bi6ge2Dv3++h/qC5HX3fJoNg9sRAuS4H006W+A1f9pIbV+OX/c+Cu985/SiNSjx+nu/yoBfQOkooVJFjI55B4r+XRarWo6TrpRtrwuSVRxbMw+tNV2lAVoTjZxcu5IhP0ugwknP76uAfFBgTc1xVDnpQzA7BoPBYHgpMyfhOmlHUrJ06VK+/vWvs3z5cj72sY8dqq4NBoPhiOSRnh240qrUu5yKGifB9uEeuorDf6WRHT52DN+ApwZJ2fMQYvwjRghBzKrBEjF2ZW+mFA48P4N8gfKGez9NLixNuq9qsMBr/rSJqqEC2XSMm199PN8avG/Kvoa8Aq+98yvkVA4pNLalyjGsekySj9GFknEf0THuwyB4We18jqlqIhcU2FfspsZN8+4lr+HNC1/DkmQrXaUehvxs2S04cg8e9IfpKfWxPLOEi+e9Yu6TYjAYDC8E9Avgx3DohOtYzj33XG6++ebD0bXBYDAcMRQCH1se2B3WFpJQKwrhoU9go7XGV/6crLmBCghUMOPjh7099BY3EJO108Y9xq1aSuEg7fkHZz2mFyNhGPK2+/+J4WBy0Vrfk+XVt2wklSsxWJPkT68+jqHqBH9qf2TS9uv6tnHJ3dfgiew4q60tNa41+gY01tIaMeoirDVIBMdUV5HTedqKPZSUx0k1y/nI0W/imKpF1LrV/K9lb+XshrUIIegq9tJR7KGr1IstbF7RdAbvW/q3pKZxJTYYDAaDYaYclkwgjz32GFIeFk1sMBgMRwy1sSR78/0HbFdSIY60yDixQ3bunlI3Tw08wdODT1EKi0hhcUxmJSfUrGFxcsmUwjIXZNkw+BSP9D1AZ7GdXJDDlg4ZO8Pq6jWsqV3L4uTSSY/PBW0EKk/cnr7siRAWoMn5+w7FpR7RKKX42BNfo6tUYCqtv2x7D/GiT29DmtsvXEkxFrkHezqY0PaXO+7l/229YdK+RsJbLQmhirISV8SrHh9DFRM2Pz/rSrq9Pgb8LJawWJJqYUGiady9r3YzvHXRa3mVdy5bs7soKY+EjLEis5SMcQ82GAwGwyFkTsL1uuuum3T7wMAA99xzD7/73e/4+7//+4MamMFgMBzpnNV0FE/27SHUCktMvZg35Oc5q2k59bFD86K/fuBJbmm/kWyQxZUujnTwlMejfQ/z1MCTnFZ/Bhc2vwq535jaCnv53d5fsrewm2wwjNJR0RRf+xTDPPd0/4UNg0/ysvozubD5NVj7JVfSZVfRmSFeFDG9B0OgAq5Y9202DXVhT7PW+9jaxRTiDs+ubMZ37SiF0iRT9y8b/8Af2u6bUgBHqZdGxasfRlbXyo+GUIOlHf7w8k9Sk8zQmmqY0bXUutWcWnfCjNoaDAbDEcfz7a770n5cVpiTcH3Pe94z5b6GhgY+/elPc/XVV891TAaDwfCi4Mymo/jNrsdpKwyyIFEzqZWyr5TDlTavnH9oMgpvGX6Om9p+j68DmmLN485ZZVeRC3Pc33MPcRnn3KbzKvv6vT5+s+fn7CnsIhdkK/VmQZfdRi0CfIaDIR7ouYeYjPOKpgvHnTvpNGOLOIHO44jUlGPUWqHRJJ3mQ3LNRyKBCrli3XfZMLRn0veReW2DdDZnUJZES8GGE1r3a6GxGF04+MYBROsIY8WrFBBqCVohEeQ9G1dY3HLhp6mJG/deg8FgMLywmJNw3bFjx4RtQghqa2vJZKbOnmkwGAwvJWrcJB9deR7/d+Nt7Mr1UR9LkbZjCCEohj69pRwIeMviUzilfvFBn09rzf09d1NQRZrcpglCWQhB2k4T6ICH+x7glLpTSdmRlffJgXXsLYtWhUJiVRxHNZqQAKEFRVUgYSV4tO8B1tadRtoe/c6vcpZSE1tBT/FpbJGc0h25pAZxZRUtydMP+pqPRAIV8k9PXseGoZ2TWp1XPtPOyx7eybajGrn/7KOYSo2e27gagO9uvpX/2Xs/9gyrC430JoQGpZESglBS4yb4ywWfxrZfPPWEDQaDwfDiYU5Pp8WLD/4Fy2AwGF4KnFy/iM+dcDG/3vkYT/e30eflAXCkZHG6nssWnsAF81ZOm8xoprQV97Env4cqu2ra/qrsDD1eD5uGNnJq3Wl4yuOh3vvIhwU0OhKtY44XCISOMtL6ysdXAdkgy6ahp3lZ3Zmj7YRgSdWlDHhbKYTdJKzGCePwVQ5fZVmauZSk3XTQ13yk0VMc5EOPfpuOYh+WHBGtElCgNSes38dJT+wBwHOnVqJCCP5x+Vt59/3/xnPZPThzKIkrKGtiLWiJZ7j+nE8a0WowGAyGFywH/YTKZrP09/dPumq8aNGig+3eYDAYjniOrZnH1Sdeys5sLzuyPSitaYynOb5m/oyyDs+UnlI3nipR49RM204KC601vaUeAAb9Afq8nvL3uJhc9AqB0JH1tRDmcGRN5fixNCVO5tja97C5/zqywR5skcKSMbQO8FQWgUVr+jxW1L79EFzxkcWubBcfWffvDPkFpBgfsqRCzWnrdnHspnYAnlyzkPUntE5qbdXAlUet4g33XUV/OKVB9sCISLye23QMX13zd3PsxGAwGAyGvw5zEq7FYpF//ud/5kc/+hG9vb1TtgvDyYuSGwwGw0sNIQRLMw0szcws2c3hZ1Tt5IMcJVVECoGaLgGEiNRWoAMiCTt544Xp86lyl7Avezcd+QcJtYdA0pw8ldbUy2lKnIIULy3L3pbhfXx83X+Qn6TkjVCaMx/Yxood3QA8ctoSNh87b9J+NPCB+SV+tPkJ+nSiIlpH4lZnQxBILp5/Clef8JZZHmkwGAwvNUZqWz+f5zfM6c3hwx/+MD/96U95/etfzznnnENtbe2hHpfBYDAYZkmdW48rXUqqRNyKT9lOaQVo6tyobI0UFqL8UIwy1uopXY115Q9BvTu1CK92l1Fdt4xjat9BoPJI4eDIqRM2vZgZ9vJ87qmfUAjHi9ayly5n37+Vo3b0EAjBg2cfxY6jGyftRwDvbSrwjR3VSEtj7XeL9Jh2B0Jp+PDyS3n3UefM9nIMBoPBYDggpVKJhx9+mF27dpHP52lsbGTNmjUsXbp0zn3OSbiOlLv53ve+N+cTGwwGg2EiWms6int5dng9g34fAAKNKOfiT9oZjkofz8Lk8gnlaBYkFjI/sYBt2S1YwiIbZPFUJJZs4ZBxMtS7jeTDHBknw7FVqwBI2SkSVpJckEUiUSgmkz9aR5U+pbRI2enK8dNhCRfLcmd07cWgh478gwz5O9A6JGm30JI8g7Sz+JDEAD9f/Hbv/fSUBsdtG2ur3nZ0E4v29nPP2UezZ2EdIlRYctQFWGuotW0+t2w5H9y4Fcva39ItyosNo30faLbeuvA8I1oNBoPBcMi5//77+da3vsWNN96I7/tUV1eTSCTo6+ujVCqxbNky3v/+9/PBD35w1kl95yRchRCcfPLJcznUYDAYDFOQC7L8ueM3bM9txgtLhNqjEA6idIgQkqSVIibjPDXwAM3xBbyq5a3UjSkpkw/z+LrEgN9fducdxdcehVKO7lIXaTvDJfMuI+NED4wap5ZFySVsHtqEFiqKddUKMabOq9YaTVSn1REOp9S+jCqn+pBct9Yh24Z+x+7hm/HUICMuWZqQXcN/pClxKsfW/f0RabEthXlu7Xhs1BoqyjVYK/HEmrb51fzPG9fgxRwANJKgvHYgAFdY/O+j38MH1/8Ea4Yh0dOJ1wsbT+FjKy8+mMsyGAyGlxamjuuMuOyyy3j88cd5+9vfzq233sratWtJJBKV/du3b+fee+/lF7/4Bd/4xje47rrreOUrXznj/uckXF/3utdx++2384EPfGAuhxsMBoNhP0phkT/s+y925p8jbVdjY9Hr9SMRODJBSEhRFUnZVaTsDG2FHfxh33/yhvn/q3L8b/f9nJ25bYR6JL/AxCedRjEcDPDM4NOc3xw9LKSQvKzuLPYV9lII83hKE+owEsz7HW9hcWrdmRNquM4VrTVbBn/FjqHrsUSclL2gIpi11vhqmH35u/B1npPqP4ElY4fkvIebHdmneKT3RnZmN9BZnF++E9FspnIe5967hQfOWMZQdRKExos7429XeeID5TDkOXx8/U8PkDl4vNV1MrSG1847k0+tesNBXZvBYDAYDJNxySWX8Nvf/hbHcSbdv2zZMpYtW8bll1/Opk2baG9vn1X/8sBNJnLVVVexfft23v/+97Nu3Tq6u7vp6+ub8GMwGAyGmbF+8BF25bdQ49QTl3EG/E6UDgGJIizHoAr6vR4Egjq3hd5SJw/13Q7AU4OPsz27lUJQLCdNiuJYJxJtWz/0BF/f/MXK1hNr1nB81WqSVpIap46ElcQRdlQKB4mFRUImOa/5It688O1Yhyi50rC/k93DN2PLJHG7fpyVVwiBa1WRsJrpLqyjI3//QZ9Pa01bYTdPDz7G0wOPsTcf1VId8gfYOPAE93Tfyq0d17Ou7wF25raOWQSYOXd3/IJf7vo8W7MP46k8oCsLAFWDBS7509O0dA1x9gPbUFqjtZgQoKqUIFdyGC7Z2JaHM8E9eDIEWpeNuROuG17fehKvXnAMWT8362syGAwGg+FAfOADH5hStO7PcccdxwUXXDCr/uf05rF8+XIAnnjiCX70ox9N2c5kFTYYDIYDE+qA9QOPYAkbR7pk/QFKYb4ca6rH6M9IvA76fTTEWkjaabZlN7A8cQpPZdcR6ICCKjK29djMv/sb47ZkN3PNhk9z9XFfwpEub1zwVhLtSTYPb0Qi0GMsfBmninMaz+P0urMPabxpW+4efJ0nbS2cso0t4xAK9ubuYH7qvDmff1t2Mw/13sm+wm58VQIEUkgEAi8skQuzBNpHa40su2YvSh3F2tpzaNZTj28sP93+afYWNjBy06SEmAwohA71fVkuvO0ZEkWfwaoEd5+zHMRI9HK0NKEVhBrynovSFq4dYMvZ+IhFc6O1JtRgSYgJn5W1afaVnuMnO7aQcVKcXLOaC5vPIe0cee7XBoPB8FfHuAofFNlsFqXUuG1VVVWz7mdOwvXqq68+ohNlGAwGwwuJPq+Hfr+HpJVGaUWf105IgEAikIxKUI0mpM/rptqpI2ml6S11sTu3lT3eTvq9fqjIoIgDfVO3F/fx/7b8Kx9dfiVxK8EbFvwtncUOnhnawIDfh8RiXmI+x1WtJmWnxx3rK4/nhp/lueHN5MI8CRlneeYYjskcS8yamUtvb/EpbBE/4DPFlVUM+7sohb3E7dmXFNow+Dh/7vgtxbBIxq6m2q7FVx5txZ0UwgIajURGNW7RKB0yHAyyZXgj3cUOTpXn0dT0qmnP8R9bPssj3R30lxailSDleMxPDdIcHyS/3eXCOzbjeCF9dSn+fOFxFOPOpPen5NsobSGExpEKxFzK3QhsqYhbAcdX19CUqMMSFqFWZP0sd3U/wK78Xt679K1UOekDd2cwGAwGwyzYsWMHH/3oR7nrrrsoFkcX1UcqF8zFwDkn4XrNNdfM5TCDwWAwTEKog4qVb8DrxitbTUfcg6n8JsqiKqCn1EFLbAHFsMDG/GMMyF58FHOJANkw/BS3tN3Iy1suIFABNU7tAWNYd+V2cmPb9XSXulBaRaIIxfrBJ6l167hk3utYnlkxg2v3yuJ8eoSwQGmU9ifdH8XDDhFqD0emseVoMojeUhe3d/6BQAU0uM1lkazp9jrwlE+UCEoREhLqcMx4NCVVpN/rZbP/FItzS1heddyEcyut+dL6H3DLPg8vbEGIyH6q8oIdw/Ws7t7LxfduQATQ2Zzmz+etwnetcvKl0WV0rSHn2QQqcrOypQIxt2V2IRRp22N1TQt1sarKwoAlJNVuFSmVZEduNzfuu5V3LHnjnM5hMBgMLx1MHdfZ8s53vhOtNf/5n/9Jc3PzITF6vrQqwBsMBsMLkKSVRgpBf6mTwaAXTViWUvtbXSOksCiEObpLHQx7WQq5kGzcRogQ2ypbW2f4fIjss5rrO37Hrd1/ps6tJ24lWFW1mpNqT6Y10TrhmL35Pfx6z88ZDoaoc+qw5Wg8S6AC+r0+frv3V7xl4dtZlj5q2vPHrXoKQecBxxnqIlK4ONb41PlKB3QVHqYteweD3hY0IZZwaUqczrzUK6iJHcPGoSfIBsM0ui2VB2cxLFAM8wghyrHEY+dElec9kpW+KpENhnhy4KEJwrUUelzz1H9zV9c2pC2IOUH0eqM1UoWkbI81z+2lwcrRvSTDzlc20qKH6CslyfoxENHyhNKQ9VxCNeqfLUU5tXA56dJMra4CTY1ToDpm0Vnqoc8foNatosbJYMvosW9Lm4yTZuPQc3SXemmM1c+gZ4PBYDAYZsZTTz3FunXrOOaYYw5ZnzMSrtdeey1CCD772c8ipeTaa6894DFCCK666qqDHqDBYHhhMuwXebJ3L8NBiZi0WVU7j+bE7OMVDJEwKoV5BoIeNLosVMOKqBSESMYIGix85dEVdLOpex41xSoKOkF9Kjur80YpnEZXkXNhjnwhz/x4Kw/03s+Tg09wcculrKkdLX+mtebOrtsY9Adpjk1cQbWlTYPbSLfXxe2df+LvUx+ip5jj6f597MkN0FfKIYXEEoIlmXri8kRCvQGlA+QUCZ8ia2qWRemLcOSoW2uoSmzo/RZtuTvRhGVLaxqlA/Zkb6Ejfy9HVb2DjYNP4srYuLHmwixKK3ztTTE3Cg3I8qJBqAKeGVrPsD9IplwGqBAU+MTj/5f1g0NIqRFl62iKLO9Y/BgxGWVlDt4i2fJAho0nLUVaghpZoNop0l1M0VaoIlAWRV8SKibNCqwrEvrACKFxRUCVK3CIkmuVlEdboZOeUg/z4tWk7Vos6ZCyknT63TwztIXGRiNcDQaDwXDoOPXUU9mzZ89fX7hec801CCH41Kc+heu6M3IVNsLVYHhxkg88fr3jcf7S/ix9pVyUxRRNxolxeuNS3nHUqUbAzoJiWOCm9p8REmBhVWJbI7kSJWeK/jViFRQE2kNpxTO9LXQVkjRYAYXAQQEWM7PM7S9aR7dr2ottrEgdS1YN88f2G6lyqjgqfTQA7cU2duV3Uu1UTen2I4Sg2q5hb34fX93wGx7s7KMtP0gh9Am1Kl+FIGbZVDsxFiRX8LKGNk6uqx6XVRgi0VoMu3FkmtbU+ZXtvsrxcMcn6SmsK59T4qssUti4soakPR9PDfHcwHVk/fnY+9WAVTqMXLSnFISRO7FCVcRrLsiSC7NknGp6Sl185NGvsK9ojVq4RY6PLXoYR4Yk2nwKrS4AViZk0avyNAebuWd4JYXAwRKKxniOfODSWajCscCxNH4YospZsZQuL2BojRYHvqdCaGwRcnxVDYNhH5ZlEaoiaB+BphAK9uQLNLgdJO0a0vY8BIJ8UDhAzwaDwfDSZqqM7X/N8x9p/PCHP+SDH/wg+/btY9WqVROyDZ9wwgmz7nNGwnX/LFD7/24wGF68dBez3Nu5laf72sj6RZ4eaKOnmMUWFq60saRAac2AV2Bndh0Pde/ka2tfz5KMseDMhC3D6+ks7qU53ko3Fv1+N7oilkYSLY3KK1H+M+vH6M5ncGSIFJqC7zBUjFOXLExbyxOmFq0jKBS7CttZklxGv9/HQ70PVoTr3sIePOVRbddM3rfWdBaH2ZcfxNdD7Bl6hI5sdSS/x5xOoymEPsXQZ8jP0J5fSD7Yx8saFI7MIJAEOkcx7McRSVbWvJuaWBQz66scj3ddS0/hMUBiy0Ql/lcTUAy7CVSeqtjRFPwufNWPEOOTRamyKJ2e0aRYQghCHVAMc3QVO/ngI1+hy7PROipBIwS8Z8ETbMi30nr/IEvW99J2Vg3BKYIWpx/Xgpgdcl71Ju4ZXkkpsFFa0JIcpruYKotUgWNpgjAk1BZBKHGsEBAopbGmCQUWQpOQHgtTabIqT0iIDrNlt2eBROIICLSgECqgGy/MEVKDK90DzIPBYDAYDLOju7ubbdu28d73vreyTQhxUMmZ5lTH1WAwvPjRWvOH3ev5+EO/5vvP3s9D3Tv4875n2JHtYzjwGPQLdJeGaS8M0VkcJusVKQUBzwx08N77/ovNAx3P9yU8r+QDj7b8AF2FoYqVcTKeHnw0qpMqbBrcZlwZK4tW0MiybI3QGoqBQ8F3aR+uIlASRwaVvnrzafzQqoipqdAzSDJRUEW6S12k7QzbslvZkdtOT6mbfBDVAN3f2qq1pruY5YGuHTwz0MGgV4gEXVn4jYx//3FpIB8GdBVj3N15FJ0Fl0LQQS5oI1QlmuKncFLD/8eC9GiyqB2D/0NPcR1CSGyRKCexoizQHCyRINA5cv4eYnYtNVaefDiEHntyPbWtdbK50Whs6VD0Je++99/oKlqV62mgk1c0PsuTw8uYd8cQC57qJ9CSXBjj2eJ8HsquYMCPhLMtNatjO4lZIb6SxGRAXayIFCOzAbal0TpEIwhCWXFBVmMrI40drVCk7BJVMciFRfJhCV+FeEqX3Ywjc+3ILSsoC0vEKYQFlMpyVHrxlDNhMBgMhiOTffv28c53vpP6+noSiQSrV6/mscceq+zXWnP11Vczb948EokEF154IVu2bDlk5/+7v/s71qxZw4MPPsj27dvZsWPHuL/ngknOZDAYJuXGPU/zo+cewBaSxniaTQMdlMYksdlfinkopFbYQtJdyHLV4zfylVNez0utSuSO4R5ub3+G+7q2kg88pBDMS1Rz4fxjOa/5GFLOqOUvKn3ThWvFAbAthyq7hkG/H3tMvGfeD9iXS9CezVAIXDSaUmATakGoZUVnhVoyUIxTl8xjiUjl7G99HV8sZ3p6/R587eGpEj/e8QNc6VIKi2SDYeIyTsapIlSKXbl+2vODeGrs6mkksfwxyYYqeyYZl49mb17Q7/8NFy1oQqOJWw2knYXjRLIXDtGevwfKFsrJ3JUFAilcvHCIhDWPRrfEQFGSC7Ok7Si5kxCybE8dL6Yjt98RZ+uxQhcsHK545HvkhGbEo1mgWd04yEApyZo7dzNv+wBCCJ4+t5W9K2uRaIra5an8Ek5JbyNtBdTHisiSQopoblwrQIhoJVnp6NyuBb4CLxRIKbFkZCPXetQV3JIKR4Y0xrIE5ce5wq9Y66PEUoAIy6mmJFJAqDVaC0raptHOUescedkqDQaD4a/KEVbHtb+/n7POOovzzjuPW265hcbGRrZs2UJtbW2lzde+9jX+7d/+jZ/+9KcsXbqUq666ile96lVs2rSJeDx+0EPetWsXN9xwA0cfffRB9zXCnIXrz372M/7zP/+T7du309/fP34lm2g1fnBw8KAHaDAYDj9aa54Z7GD7cC+hVsQth59vewxLSFK2y/q+fRRVcMB+FBDoyMK2dbiHX+9Yx3tbTjrs43+h8EDXNr69+S4GvDwpJ0bSdtFasyPby3efvYf7Orfyj6suoj4WJRgSI8JrzPdnjdtIURXwVBFL2BR9h8e7ahksxaO6nlaU8KegBYGyGPbjBLZkJHfTQCFJwvFJuT6gylbPyZP+TE8kfIaCQUAQKJ8quwqJoN/rY19hL3VhI7uG8+QCb8Iz1ZGKQEVCeqb4WvH7Pc/x4WMvmjJ+tq/4FF44gCXjBOHUyagENooCnhqkytKsqlrJxuEdhH5A2s4w7BUJlahYM0evmrLg33+7Zle2RF4kkWJ04eZv5z3Ec9lWXvbnnTTuG0ZZgicuWEjn0iiBkyw7ZnvaZnuhhRPSe5Hlvq1yjdaR8woRaWYNSKlBKWxLECiJ0gIpNJaM3p5iVkBtLE/a8XAEDPh2uR9FWB7diEtVoKPZEEKBttDAcAhpy2JFapju4nqq3InZow0Gg8FwZPLVr36VhQsX8uMf/7iybenSpZV/a6355je/yec+9zle97rXAXDdddfR3NzM73//e9761rce9BjOP/98nnrqqedfuH7qU5/i61//Oq2traxdu5bq6upDNiCDwfDX5cm+vfz3tkfZMtRFKQwQQpD1Swz5RWqcOM8MdsxqoW/EAdNTIXd1bOW1NUfTRNPhGfwLiC1DnXx7851kA49FqbpxwivtxPHCgPX9+/jmpr9wzUmvxRISIQQt8YVsy24kbUffo450aIkvpKfUQTbI8nhXLQOlBEnHqwgegLgdEHiRoCkGDoGQWJYi1JKO4SoWVA8Qs8faDUdzFM+GkTP2ev0k7RQ1Tg2e8ugqddFR7MDTDprxVlUpFI6l6Mhm8NXsHjNdhWGKYUDCdibd76scoHFkilLYU4mV2Z+RLaHK49o1nNl0KY3xbTzady978m1kg1xZBEom+g+MWoVHaumWlKbXT1UWABqdQS5u3sjOXCMv++NO6jqzhI5k3UWL6V2QHteXLEea9gQZiqEkbikghySNLt8/GBWvumx1tSvTKlBaoDQEStMYzzI/PYwQYAkbrcESmkCLcXdXVSzbAoUEHeVKdoSgwbU4ucbF1hpP5Q58YwwGg+GljBbRz/N5fmBoaGjc5lgsRiwWm9D8hhtu4FWvehVvfvObufvuu2ltbeXDH/4w/+t//S8AduzYQUdHBxdeOBqGU11dzWmnncaDDz54SITra1/7Wq688kqefvppVq9ePSE502WXXTbrPuckXH/wgx9w6aWXcv311yOlCZM1GI5UHuzawTc33sGgX6QhniZhhfSUcvQWswTl5DmzZcSNUQA9pWHa8gMcd4BjXgzcsm8DA15hgmgdwbVsmuIZNgy0saF/HyfWLeTx3vt4vO8+SrpAV6kNAAub5vgC6p0m9gwrBkpxEvZ40Rr1FyClCyp6npVCm4TlReVXlMXugRpaq4ZIuv4YMTP3h25IyL5CG5awqHPrGfJzlMIBErZHqGIoHVkvHamQUtNfSLB3ePaLmp4Ky+6yk2PJOBpwZTVSOCjtYzGaXEhrGAwdunyXXJhCCpuFiXkcG0pOr38Fq6rX8ul1P2LQ76A22Q1otIohZBYhAkbmaCR+VUpNqAT9pSRFNAhNgzPEpS0bAMGQiNOxtIqqgSKPvWYxA83JScct0YRIhsIEcSuHH1bhWCFZ32XIj1cWGMR+Hsrj0dgyoDmZK9d1FSgduRC7UhEE7mgflC9FR4LWtRSW0PhasCqjWVsTiethX2OLg3cJMxgMBsPhZ+HCheN+//znPz9ptZft27fzne98h0984hP80z/9E48++igf//jHcV2Xyy+/nI6OKA9Jc3PzuOOam5sr+w6WD37wgwCTllGda3KmObsKX3zxxUa0GgxHMH2lPN/ZfA+5wGN+opod2V76SrkZuQQfiEhYWPha0VkYPvjBvsDpL+V4uHsHGSc+pYsrQNJ26SkNc0/nFv7U+Q26vbbKPlVOvFQKJHtzPbhC056tAS3LlsER619k07akJm75lHS0glkKbWLaQ5bLpgTK4tmeRpbXd1MVG61VOrcQnWg5wtc+7YUOlqWW0lOAAIeYFRCzgyghFIJiYNMznKIrny5nyp0dlhDTJrOqjR2PIzMEOk/CaiYX7ENpH4GNryXPFTIMlEsDSRRCOGwreLTv/A7HV6+hyT2FnYMJ6uOrCPw2bHsrCNCqGkQRITwQCiE0SitKoaArn6EmjIEVWSbPrd3CrkJtOT5WsP3ERjpWVOMlDvxIVUhCJXh091KOauhiSMUmtYKrCTdKU+UWqIvnkVKVvct12VassYXGlgpfWeXrHjkuWkYSRNb4JjfP0vgQQtTihTlsGachfuwBx20wGAyG5589e/ZQVTVacnAyaytEFWDWrl3Ll770JQDWrFnDhg0b+O53v8vll1/+Vxnr4ahCMyfheumll3LffffxgQ984FCPx2Aw/JW4r3MbXcUs85PVbB/uoa+UxzsEonUELQAVJYJ5sdNdylIIfWrdya1tY3GkzW3td7CiPhKtgZJ05DLsHqph0IsTjE1mpCIXUNuycK3ROq4j8jPlRBZWgigxUyFwsUUkULQWJByPjONVkg3pyvFzp6iKbB7eQzbwAQeBpj1bxZCXIFSCvO/O2h15LAnbJW1PXZ4lYTfSlDiNvbnbSFrz0YQUgi58XWJzvoHB0MEVAZYIkcKmxj0KV1ZTCHM83v8QVVY3gdbELYcwWASEWPZuhCyCtlA6hqCIJsrqq5RFbaxIOigxz+6nNTNI2Gtz8sP72PzKZoZFSKCtA4rWEU+EhCixdaCBUmCzvn0BiVQJ1w3HtYOxbr4gCWjNDJGwo/jmUhgtVkihsESIJaLV64TlAS6BkqMLFDoqfRRoSXMsx0mZPkChVEAx7KMpcQI17rJZ3yeDwWB4KXHgXPyH//wAVVVV44TrVMybN4/jjhvv73bsscfy29/+FoCWlhYAOjs7mTdvXqVNZ2cnJ5100iEZ8969e1mwYMGk+x566CFOP/30Wfc5J5Ppv//7v7Nr1y4++tGP8vjjj9Pd3U1fX9+En9ny5S9/mVNPPZVMJkNTUxOvf/3refbZZ8e1KRaLfOQjH6G+vp50Os2b3vQmOjs7x7XZvXs3l1xyCclkkqamJv7xH/+RIDh0L+QGw4uBB7u2YwvJoFdg0CsQanVIE+YVAh9HSpoTmUPY6wsTOeJeOoO2SoUUVJRUyAstnu5u4ZneZnqLyTGidSQqNRIcA8VEOQ5yv7SGAlK2R9z2ca2QmAywpaY6VmRlfSenzttNbbxAjIDR6ONxh8+CckkWFHk/O7pNRDGaQ6U4OX9y6+FMkQjOa1lxQG+eZdVvocpZSiFowxFVVLsrGArnMRTGiIkgsj6KBLWx44hZNQghSNppMnYVXd4WYna2EkcaBkvxS6cQ+kvQ2kLKIooQL7QZKsUoKQtLKKqcbZxQtZfqjjwrfttJanuRY+7dQ7MTJa9S01y3IrK0pmSRpAz5/d5TUUT3P5+PVXJzjWQNDpVk5PFsi5Al1YMkbFUubFPJhUyoJb5yCMuxT1JAyi6RcjxsEUbFlATEpc+aqj2syewhJj2UDskGbaTsZlbXvmtaLwGDwWAwHHmcddZZEzTUc889x+LFUfmzpUuX0tLSwl/+8pfK/qGhIR5++GHOOOOMQzKGiy66aFI9eP/99/PqV796Tn3OSbimUinOPPNMvvOd73DqqafS0tJCY2PjhJ/Zcvfdd/ORj3yEhx56iNtuuw3f97nooovI5UYTR1x55ZXceOON/M///A933303bW1tvPGNb6zsD8OQSy65BM/zeOCBB/jpT3/KT37yE66++uq5XKrB8KJl0C9gS4vuYracDfjQu3TUuClaki/+5G3zkzXUuAmG/OK07bTWdJe6yLgltIZn+xrpLSYphhaRO6cu/8BYkaoRDJViBMpmgtwsxzpm3AJnLtjBOQu3ceq83SysGiBWttK6dli2tx68QIniPqPPiiU0SgkKweSJlGZLynZ5z9GnHbBdwm7kpMbP0JQ8HUWJYthHu2chhcS1YsTtemrix+HK8Z+9uEwghaY6PkAuGHGfFmidIQxb0VqS8wX9xQRZP0aoLJSWLHM78MJF7N7cyJLf9ZLNxXimZh6/Xn0muwt1WASEyElSPI2KVomm1e3lGxtfTs53CbSFlIogtPCDaMFCA0EARd+l6DnkizbzU0PYMvpM2DK6RlGuyRrFPWt8ZaPGJA1xZUja8ah2i6StEsenO2iJDSNESKB9hHBojK/m9Kb/TZW7cJJRGwwGg+FI5sorr+Shhx7iS1/6Elu3buXnP/853//+9/nIRz4CRF46V1xxBV/4whe44YYbePrpp3n3u9/N/Pnzef3rX39IxnD66adz0UUXMTw8GjJ2zz33cPHFF/P5z39+Tn3OyVX4ox/9KD/4wQ84/fTTOe200w5ZVuE//elP437/yU9+QlNTE+vWrePcc89lcHCQH/3oR/z85z/n/PPPB+DHP/4xxx57bMXkfOutt7Jp0yZuv/12mpubOemkk/g//+f/8KlPfYprrrkG153aBc1geCmRtuN4YR/50BtfyPIQIRCc2bSUlviL3+KatF3OaTya7z77AM/29+KX65naQjI/VcXSdA2WZTHoF0D4NCWzDHlx+orJchyoqDjyjiAALXQlk6BGkPVtamLjkxmosivo/NQwtpzsPmosoYnLgNwYN+Q5VMdhxOE1MtBpXDssW1pn8706eRZgV1p8cvUrOa523iTHTCRhN3Fiw6fI+rvYnX2AR7P3krFtMk49tkxPen1CCNJ2kvp4kU3DOVK2WxlLEO4mdIbKIlxUsgofm9zH48PLOHH7IGseeA5LhXQtqOKB85dRsh32ebXU2jmSjk+IhYry91ambFS09vGjTa9A69HatlJAoCAILFwnRIVQCuKV8kXVsRJJJ5pzWZkztd81RZ+BQEscoqzgurLgAfOdLCuSRTQOoQ6IySQn1P89y6vfZCytBoPBMFOOsDqup556Ktdffz2f+cxnuPbaa1m6dCnf/OY3ecc73lFp88lPfpJcLsf73/9+BgYGOPvss/nTn/50SGq4Avzwhz/kb/7mb3jta1/Ln//8Zx544AEuu+wyvvCFL/AP//APc+pzTsL1V7/6Fe9617v4yU9+MqeTzpSROrB1dXUArFu3Dt/3x6VuXrlyJYsWLeLBBx/k9NNP58EHH2T16tXjsmS96lWv4kMf+hAbN25kzZo1h3XMBsMLEU+FtOUH8FVIrZukIZ7mtMYlPNG3B6317K2tM/A5zThx3rL0ZIT34n853jbYzXc2P8SAXxq33dOKndkB9mQHOaa6nkCEtCSzpGyPrQP1hFrihdYUvY5Es45aSouBjXKLldjHkVI4CdunJr1/3ezxN0kKhZhDOZzJHpZai6gcj5K0DVczGxk8mVbK2DGuOvHVvG7xibMamhCCjLuEBelqXGsTMRHDkYlpj5FIGhJxGpPD9Pn91DjN9BaHSad2ESuXldEaQgTHxTeyPreKhZt7OeW+TnJSsXdpLY+8fAnalriEhFrQ76epsbtI2gUGwmTFbVgAKVFkvtvPDza+nEqc8bjMwQI0+L6gGMQqc5SwBCc0WgyHo221VpV0TOPmAY3SEi1GF0AEUG3lODHTFiWzUiVsabEofS5HVV9mRKvBYDC8yLn00ku59NJLp9wvhODaa6+dNOvvoUBKyS9/+UsuueQSzj//fNavX8+Xv/xlPvrRj865zzkJV8dx5hRQOxuUUlxxxRWcddZZrFq1CoCOjg5c16WmpmZc27Gpmzs6OiZN7TyybzJKpRKl0ugL50iNJKXUYcmINROUUmitn7fzvxR4KczxsF/k9rbN3N72LJ2FYZTWxCyLUxoW8bKGxTS4KfqKWbRWM4obmNIwq8vSZcy78N8sOpFjq5rp7u5+Uc/xoFfgbXf9hKGgOOUcajTPDfbwliXHUbAeJURQ9F2k1pWoxUllhADFqNUVBEXfQYqyUAGq7SJLMiXylpq2xpwELB31p8sjPaB0mfR+R0IrCG12DdaS8+LIGS4FT3a+efEqvn/WO1iWqZ/z5yQuE8REnFJYJD6NcM2FOTqLbQQqoDG9j1ArQrWJetsiYUdxwAkrRGtBk9tPSTWgipITn9yL0DY7VzTw+FmL0FIwUp3IBgINnaVajrY3sExbWDEHjcDzBNdteTkwJg56zFRpHd2Xo+NdbPMasYXCloqauM+y6jjFYLBcpxaU0uXFBzFe9xL9ohEILbGEQqJosIdYldyHQBKEeUBRF1/D6roPI7SNOgyhAQfDS+H7+PnGzPHhx8zx9Bzx8/LizzV50Kxfv37CtmuuuYa3ve1tvPOd7+Tcc8+ttDnhhBNm3f+chOtb3/pWbrzxxkp9nsPBRz7yETZs2MB999132M4xwpe//GX++Z//ecL27u5uisXpY9YOF0opBgejlxZTdujw8GKf4yGvyG92Ps7uXD+2kCy3Uwgp8MKQ7fv2sK+jnbOq5hHP+wyEhQN+H1csRdMwstsWgpqSoKur60U9xwA/2/oo80KHeeLAcZ6d3btYXr2AIgWWqBrqdJwCI8dNvAORuzBQtqRpoEo6CAExy6c2XqDKVVT5dcQKwfT3R2syyiHQ8qASKCkt6C0kGc6nadGSlhl2Ndqs7G4MLEjV8P4VZ5MuhHQVuqYYtiYf9DDo7SbUHrZ0qXGXYss4faVteGEWISyW+IvYkd+Dh13Jjm0LSdqJYQlJLsiS9XJkqEViYQubkiohRNk+6kXXprRmsdNOMqxm4/B8mnWKbecnEdsKtJ9cT8skef6UFoSBpFnC7mI97V3VDGUTaC1YUY5P1ZXrGX9cwvZ4RTpA9lXjWCE1cY+4TJIoxgi1INAho86/IMTEz4kuC1dHhMSlz8J4H1UkIHc0QkgEcWpix7Ai9RYGe0vA5HP9fPJi/z5+IWDm+PBj5nh6xsY6Gl6cnHTSSVHIypiH3cjv3/ve9/j+979fXpD9K9Zx/du//Vs+9rGPcckll/B3f/d3LFq0CMua6O528sknz6V7PvrRj3LTTTdxzz33jEuj3NLSgud5DAwMjLO6dnZ2VtI6t7S08Mgjj4zrbyTr8Eib/fnMZz7DJz7xicrvQ0NDLFy4kMbGxhmlnD4cKKUQQtDY2Gi+/A4TL+Y51lrzH0/ezKOFNuanqhGWzVBlrw3aYm8xy77cXs5efDT/te3RKNZ1yv5mdt4Rq2tSOvy+fwutLfM4uab+RTnHI/zq4WcY1pHHxlQabiTicFteUdvcgSdy9Pt5tpXqKWBXLKCTHzkqWhOWz/mtzyHl6LmGtMAqwECiLYqJnQSBRqLxlUUujFcSBk2w3I0f8KSb+wpJnskm0OSmaDH5+UcnR+AIyYqqZq464400xNOTnwwY9tvZ0P8LesLNBKKAkBKlQoJiEdBYMo60LNCazX6G3SoAIfBVjJETutqmPu6Sk+2ouEJiYQkbX5UQIhznulwMtnNOjUZoiHX7tLkuAyJPX1NItqaBDrdn0jlWGjxt0xjfx1P5JHknIOfGGc4lkFJhyRHhKaIyN4BSAgvF21of4VmdYrfTTWOySF5KHGEhBNiiACKyBIvKBEYWWLHf+UGQECXOqNlOyi4Rli31rtXMiprXsLL6jVhy8np/LwRezN/HLxTMHB9+zBxPz6GKnTS8cNmxY8dh7X9OwvWcc84B4Mknn5yQUAmYs5LWWvOxj32M66+/nrvuuoulS5eO23/KKafgOA5/+ctfeNOb3gTAs88+y+7duyupm8844wy++MUv0tXVRVNTEwC33XYbVVVVE+oZjRCLxSYt4CulfF6/eIQQz/sYXuy8WOf46f421g+00ZDI4Nj2RDkhBA2JNHty/Qz6Jf799Lfwd/f/bEqr60y9YyKXV4GvFJ2lYf6wez0nLnvFi3KOAQqhx1BQqkShHmj+PC3ZnrUZKDUx5MXJBzbTO2mLMX/C/PQAljXJWYQGoRBibFbi0TGN/O5aAaEokQ9dNKoih2Z6f0Mt2D5QRzjlmCfvTQiwiARcwopzZtMK/s/Jl5JxopeYIW8P+3IP0FV8klAVidv11MVWsnP4Pob8NgQSX+XROiTQJRQ+AoFLhrr40Tzdl+Av7Q6OlWVhTT9xpwjE0NoiUB59fh+2FSKxEAhKugBCj0tmNRxs54JajQhhwe39VG8t0vzyfvoakpFYFVGyrEmFa/m6HRmQD+1o8SZVxFeSfDFGEIxmYlZaoHWU4Gphpp8zm7dwzY6Lac7kKq7AIQKJhadCHHskI0jF5hpFKpdvqtaghcAWASdm2sg4fqWlJWKct+D/UhdbPsM7/PzyYv0+fiFh5vjwY+Z4asycvPgZKbdzuJiTcP3xj398qMcBRO7BP//5z/nDH/5AJpOpxKRWV1eTSCSorq7mfe97H5/4xCeoq6ujqqqKj33sY5xxxhmVmNuLLrqI4447jne961187Wtfo6Ojg8997nN85CMfmVScGgwvRu7r2MqgVwAd1VOtdRNY+z0whBDUxJI83b+P9604g/evOIvvPXf/hL5mk3B4xLIYolBhwKaBdnZle5nP5N4ORzpeGM5Q9I22eqanHikFhdAqZxSGEcG3f5zw2KOTts/RNd1T9i/LpXT2Z//uEtLHRpELHTycA4ruyhk0dOdS5IPpvkf3z4yssaQibinitiDjKI6pKfDpVWeTceJordky+Hu2Dt2Ip4axRAwpLLJ+B7uG74xEqrbRZSGnUCj8Su+eGmb38F5u27cSpQVJK01/FlKxIukYSOHjWiBkiFJRORlflyKBKEZHGwZwXrVC+LD4ln4yO4toCfPVEJtpJTzA5PjKIm75PL3ZpcOvoalxEMuC6kyemBOQL8bw/Oh+C6GJOT6JuE99ZoifdJ5OfbI05h5EIlUJBUiUVsgxmYIr91iPtBRYQrE6tYfm2IgbnkZgsaz6siNGtBoMBoPhyGekystMyOfz7Nixg+OPP37G/c9JuF5++eVzOeyAfOc73wHgFa94xbjtP/7xj3nPe94DwL/+678ipeRNb3oTpVKJV73qVfzHf/xHpa1lWdx000186EMf4owzziCVSnH55ZcftoxZBsMLCaUUP9n6ED/e+iDDfomOwhACgSUEdbEUyzINxKzR//YJy2HIK9JbyvGJVRcQas0PtzxwUGPQGgI0gpAhv0hPaTKX0hcHSdudUvRNpXWKygZlTRJnWq60OrJSMGa3JTSntewgZk9eKVRM0tt0ODKkWoQMhKIc8yoZdUSd5Fo0FAKHjtxMSp9FPUngXccExCxBoGI4UlLtKgb8TnbnN9CSaGH78B95dvB/kCJGxlmIEAI/zFNQA3gqXx5NgMKqZOodHWU00o0DgkE/oN7NEqoYWiTpz9vocB5JOwWEOPH70QhKqogj9bi5LQTgE8f2YMmNvaTaPJQj2HVxHemFBWI9Pp5yplzACXU0+63xfn6bOwulJV3d1TQ3DSIlJBMesZhPruRGscnSJ2F7lHDoDqvI2F5UNqfs7ht5LYwsRICvLVzCShbgsXZXAbjS56T0bubHhqhYY5EsTF3ImvpPTDJig8FgMBgOD+9617tYtmwZf//3f8/FF19MKpWa0GbTpk387Gc/48c//jFf/epXD79wPVzoGZh24vE43/72t/n2t789ZZvFixdz8803H8qhGQwveJRSvOvun7Kuf8+47RqN1pqu4jCDXoET6lpJ2lHdTa01UggsEVn+/nH1hRxb3cynHvsDwSRlN2aDBnwd0l148SZjsIQkZcfIjnEXhumtl/oAX7ua8SVjknYpKtEyJnfv2ERHkolW1enOP2LVFQLSlMiGcQKtxxwTubGOfB2HWlAMbDqyVQyVpotP2t/aCq2p/TP8WtjCYWv2aU6sOZWtgzcihE3CjkqeFYMBBr3d+Co/rh+JQmGVfx9vWd6ZbcASIYiQQBcQugQIhr1u0A4xK4ODRCsfD4ehILLg2kIREyV84XB6/mmW3dJLotsnjEl2vraO/PwYMRQnZvbw+NASfG0T6CjeWFZqp1qEWlLr5Aj7+8v3TlHyHApFl2TSQ6lo/mxbkbYKWFLgK4lCYMuQUAlCFVlipdBoEdlLtY4sqQJBSdlYhNhSYwkFQpCSRebHBlga7yVhjVihJY5Mc2zNuzmu7t3T3CuDwWAwzIqxERvP1/mPADZt2sR3vvMdPve5z/H2t7+dFStWMH/+fOLxOP39/WzevJlsNssb3vAGbr31VlavXj2r/mckXD/wgQ/w6U9/ekLM6YHYtm0bX/va1/je9743q+MMBsPs6MnnOe/P38CfosTFyPddUQVsHGjn1PrFIGDQL1Ltxlmcrqu0vXTRapZk6rnq8ZvYNDh5CakDMkbFHSHftXNCCsE5Tcu4pe0Z4FBda1k1ludPlm1ogR519Y6md8QqNz5fb2S4Gw2AFIwXwmNxpCJDkbxy8dSoVXPEBTXUNsMlm85cFV25NLOp1+pOUZ5WCpuiytOWf4RiOEDKmQdAoAoMensIdIHRWqUjFsTRN4b93aFLoY1ViTsdqXMq8FWOQW83UtgkpU1eJfHUmHhvDYg0NV6eE+/oJpEN8VOSnZc1UGwczRC9ONGHRNPbV0tYFqoj6Z4dGTLP7SOd7+AXOy4uDzkEBEPDCZxYiB9KhICMU0QiCDUUlQsabFfjBVZlXoWIXKtlOZlTqCU2qlwOR1IKBQnLozXWz4mZPVhlEQ0WUjg0Jk5lZc3baUmunfF9MhgMBoPhUOE4Dh//+Mf5+Mc/zmOPPcZ9993Hrl27KBQKnHjiiVx55ZWcd9551NXVHbizSZiRcN2zZw/HHHMMF1xwAX/7t3/LBRdcwMKFCydtu3PnTm6//XZ+/etfc+edd3LRRRfNaWAGg2Fm5H2f8//8r1OK1gntA4/uUpY6N0nOL3HxguMrSXJGWFU7n+sveD9feOpP/GzbI2U3xpkTubxSfh9/MUtXeOtRp/BI7256D6FLtC5nEgbwlE3cDrCEqqwHjE2+NIKasKUsakY03xSa05aKjCgSSImvrHLiJUm1XcOuwaPY2D08JhZ35jRMEQob6oCklWLI21UeY6Rw80EvgS6gdFgefdnyOGbgoYKd+Xq2DDbRW4qyEQ96CUIlSNulcQJdCAtLxthT9Km300ipcERYtpZqgrL7cZ+dorfKxZEeO17XgFc78bG4MD7AovRu6t0eevw0IZK48GhQvXzrycvGtRVIEBrft1FaYFuapCyBBg+Jr2xsGVIbyxOzQwIlCZRFEAq0lgShhY1CSjXu1o0mXFK0xvqB6J5bWNgyxoLUBZza9BmkeEE5UhkMBsOLA2NxnTVr165l7dpDu5A6oyfczTffzP3338/Xv/513v/+9xOGIfX19SxZsoTa2lq01vT397Njxw76+/uxLIuLL76YO++8k7PPPvuQDthgMIznvfddh6dnnsFbA/tyA+QDj8WZei5duGrSdoFS1DhxbCTeHNyGx7qevphZ27CISxYcxy17n2HAy894AWGmhFoSswLS7vhyRWMtjyO2yDHVQsdWnzkgQoAjFE45862FzfJMK5u6q0CPxJoesJdx7aomEa5KK0IdcHT6RLR6rjI4rRX5oAc9RrSOZluO+swHDne1r6AtX4PSAkdGBVVLoUUxdFEFSUM8iy0VoNFasS2rqEpGWXYrohUIscqZjjVKav7fy17De+sfIEhNYSYGpIwE7MJEP6D5zN1vnrSd1pHFXJddrhPSGxOfK8i4JWLSxy7Xd7WlxpE+oZQUAhulJUEoccoJpJQWWEKjiP5ucYeod7JjZl1S4y5nVd37jGg1GAwGw4uaGT/lzjrrLM466yy6u7u56aabePDBB9m8eTN79+4FoL6+nje+8Y2cccYZXHLJJZVSNAaD4fDydP++WR+TC0q8rHEJn1h1Ps2JibWKPRXyT4/dwO1tzxKUXRVnk134pYQjLa44/jwcafGnfc/Qnh9EIgjGuLbOaerKpjYBVMcKlbzDYj+ZOvpbGcH+W+bEwuRy+krt2FLiqdmUNtPYCKz9jLRaawb9XtJONcdkTqI911+Jvw61T6i98tETRWugLO5sP4Z9uRrSTrEisAHiVkB7vppc6KKLaZoTw4ShpsPP4NoBltTYIoxcfFGESJrah1iyvZdHzlyCFILAEnhJMW1horF43oFmV4DQtLiDlfNiCSwRZSAOlIUQKnID1+UcwFKRsAPyflTXVyuJsMYsgmhoiQ2yKr2vYlkWQMJu5NSmfyLlzJ/h6A0Gg8FgODKZ9fJsY2Mj733ve3nve997OMZjMBhmwT3tW+aUQiltx/iXU98wLsPwCKUw4J+fuJk/79uM0lFyGAmEcxRg6iWgeNNOjE+uvpCmRJrvPXsfpTBEhwGOlHhhMDfhWqYunqUunsdXFq4VlmM+p+AQiNYRV+TWxHJsGcU4x6RNSQUzOloKTcIOscSo9dJTRYb9AWJWnPMa30DGqYXUaZUyOBK7Etc6ap0cTUW1K1tHW76ajFMsW1RHsaWiLp6jt5imELr0F+PkiaF1lPxIV7JdaUIkrbv7OfuubVihYrA2wbPHtSCF4mftTbx7fjcHmj2F5vMPTm5tHUt9cog3NT3Ow8OtdIf1aCHJe05kkQUCJbBHwmURKA2WVDiWwgsloY7+38nyotHieA9rq3dhydGZcWU1583/Nmm39YDjMRgMBsPBMDZQ5/k6v8FUAjYYjmB25/qYS+BDXTw5qWgF+O/tj3Fnx1YEYI0pwTHbswiiF+5c4B2w7f5orWeUZfxQHXcoEEJw3rwVtCZrqHYSxCwLKcTUmZEO2CFknAJLavoqWWzHMpqWSRyy5+moZNT0ldo5vmYeSmtiMywaL4AlqTSOtKiOZen1Ouj1OsiHWeYllnDxvMs5pupkADJOK/OSp+GFg4Q6QCAYjeIdL86fHWwGmCBaR0jZHo3xYSSKgSCFFwoCpaOpF6IsXmHp1h7OvWMrVqjYu6iWLSuaRq9bH81Nvclpl2eU1nx2Chfh8ROh+T8n/YYtpXoGqCWaPlGp4wqRS3Go5Jg48OgmOpZifCIqTZM7xKk140WrBF41/zojWg0Gg8HwksEExBgMRyBt+QHu6dzCLW1PRxlI9ejL8EwUTDEI2JsbYEGqZtz2Aa/ADbueJusXZ2hhOzCFGQrXYujzYNdO/tL2LDuGexEIjq5q4IL5x3Ba42LcKYS20ord+S1sHFzHnsJWQh1S7dRxfNVajsmcSNJOH5LrmAlL0vWcWNfKLXs3lV1gdUVTzlZOV9l5Tpu3G4UkF7hjMudGRC61hx5Zfixszj7Jqc3L6Qj2RpZLJN35OL35FFk/xv6fMwGsqpmHBjJONe87ehW2VUAiaYjNpzWxDCnGC+BVde/GV1na849McTWCUAt6S2lcOf3nsRB4HNXYRU28XKsV6CsmKIYOcdtn5cZO1jy8G4Btyxt56KxlqPJ4yiVUkeIobh9QLGMTS2tGxbPWmns2tfLn4okHnkChuHTpOu4YOo6usL7cf+TiPSJaRz4PfliOta3E5UYJtSyhsKzo3432IGdVb43c9cunkAjObfkyVYl5Bx6PwWAwGA4ek5zpoCgWi8Tj05XUmxlGuBoMRxh3dTzLD7fcR38pT8yyK3U5ISqnofVIPOr4MiljaS8M8jd/+QH1iTTntyznla3HsqK6iW9uvHPuJXCmoNrdv5bnRDoKQ/zL+r+waSA6d8p20cBD3Tt5pHsXJ9S18skTLqAuNr6Qta88buv4Dc8MP0GgfWIygRCC9uJu9hV2sq7/Hi6e93bmJxYf0muajrcuXcsTfXvZOtSN0lFpltk+cardAmtb9oKI6o3GLb8c1znWEjm1X/A0SYSnJBLYAlfEyIYlnh58jrjVTn1cMeyHgKI1M0Rzapj2bBV7hmorZ5EIjq1uRgMlFXD50adxWsOaA57TkUlOafg4u7J3cH/nF4FRcar3+8d01zPo2xzTMlARrNH7haIxPkx7vopVj+9j9ZNtADx3fDNPvmwBjggRWuNrC60ltgyxRSSet3Mc2wdG++8dihHqxcCBMkdr3r3iLnTKZqfXPP46hMYSmqBcs1XryJ5aCixsKbClQopyHmUBpcCmxs1zdu1WRgzeAk3KauGclq/QkDzuAGN5aeN7Gwj8J0CHWM7xuLFTn+8hGQwGw0sKpRRf/OIX+e53v0tnZyfPPfccy5Yt46qrrmLJkiW8733vm3WfxlXYYDiCeKxnJ9959m5yvseiVB0tiWpcOX79SQg9WsaTySVTCPQHBbYOd/P9LQ/w5rt+xLl//Fd+sX3dmCMOzvdUAxLJ/GTNtO2yfomvrr+dp/vbaE5kWJSupT6eoiGeYnG6jsZEmsd79/C19X+hFI4RNlrzl87reXroUeJWkobYPDJODWm7mjq3iTq3kV6vi5vafkaf131Q1zIbVlQ3ce1Jl1LjJlFQFq8zpyU5yNqW3Ug5KlPjVnTd+9cwnU4Oz1Qqj9ynKKJSMBT6ZJVDUfnkgiwxOyDjKiypKAUWWgtaM4PMTw8CYAtJa7KafOhjCcnlR5/G3yw5acbXa0mXxvhJKG2NsbmOeg5Iock4RTw1cZ1VoBny4Zim/opoHdleZ+dpcHPUZ3MsX9+N1oKnT27liZctqLhv20Ihy2dNOd6kXt0dQ0me6VmIcAOwprmXQvGvZ/ycTqeW7V4LIZIQiUJWivq4ll9pHonUiEBJioGNryzynsOe/mpq7DyXNa8nBEqhpKQcVqTfzRuW3WBE6zSUCrfT130ZAz1vIDt4NdmhaxjsfRt9Xa+kkPuf53t4BoPB8JLhC1/4Aj/5yU/42te+huu6le2rVq3ihz/84Zz6NBZXg+EIQWvNb3Y9Ti7wWJCoobs0zJbBTnytJ8n4OxKpNzPxqTUM+MVxxx88AteyOLVx8prPI9zbuY1nBjppTdXgypGanh79pQK+CpFCkLAcnuzdy4NdO3jFvOUAdJb28czwEyStNHErOaFfKSzq3CZ6S5082f8A5ze/7hBc08xYXTefH575Nt5973/R7xVmdIxAsbKuk4VVgxXBKqL8skg5UtV0v2OEAD3ZPRYz+gSMLGzI8hpmIYScju6BhcSVUU0baYXY0iewQwp+HETAspoCy5KrqIvVErdsVtfN5+UtR9MySZbqA7Gx7zeEeJOOVghYUd1JVzGD0lHyp5ErHApgeeNwVK92zFUlrCJxK8qEXDuvyMOvWIKbDdl+XCMj8lhrTaAtlJYkbI/EGFE5ws7uanYPN0aSXoKMBaAFyrNG511opBPwb6f8lFuGTmaYSVzTy0Z329LE8Cn5Tvnaonuq9Ghcb86LccG8zaypi7KFK2xKuCxJrGZt8wdnPbcvJQq5n5Ed+gLoAuBC5V6UCIMtZAf/CRXuI1V1xfM3SIPBcGRiXIVnzXXXXcf3v/99LrjgAj74wdHn14knnsjmzZvn1KcRrgbDEcLmoQ62DHUSExaP9u6iqHxU2QCkJxEvM3VQPTy5jEbqc0JXIctU6WO01ty6dzOWAFda+CpkV7aPAa9AoMba3yIh9l9bH60I12eGHscLi2RiNVOOQgpJ3EqwefgJzmh4JYlJBO7h4Ine3XzuievJhzmmdmzRSMrCUSiW1fTSmhmquIuOIIQmVIL9wkPLdUgtQkbckfe/49OL15FnsFt+DARaUNBW5VhHupEwBmxhY2kbT5TIONCaWEa/18sl845lbd3pM5+YSfjdjnfS7+1AaRldezmLrmZUpC5L97Cpfx59pRTVbh5LaITts7yuNOHaYjKgSpeID/kUq1xStkf+OIeOYjU6EJTKllshwBUBLYkhXBmSU6OFZ7WGrd21tGfrx/UdratopD1q+bdEyFdW/JrfDZ1BUY/2MXZcY+9KzAoRaLzAKQvW6E7F7JC48Lm45SliiaByfxSSefFlvHrB15HSPLKnIvCfJTf0JdBFoAoxLqFYAq3iwDD57H9gu2uIxV/+PI3UYDAYXhrs27ePo48+esJ2pRS+P3GxeCYc1FPwoYce4s4776Srq4sPf/jDLF++nHw+z+bNm1mxYgXp9F8vKYrB8GJnT66fQa9Id2mYQKuK4JxMtI4yl2jHg2H8uUoqoC0/yFTRjrnAY29+gLQTJ1CKrUPdDHpFHGmRsJyKcFJaUwg81vXs5o625zh//go6i3ux5GibqYhbSXLBMANeD4nEokNxkdPydP9ernj0lwz5BSwJcaHwghGH1FEJY4uoPq4tQxZm+snEPPpLCRypsKVCoMg4JcJyvHLkyBsJroyoYXn1SewrbGPQ6x9z9omlZCIBND4pUITEIZrbAEEutMaUoomOUVpVEioJAS4uniqVBTn0eF0HNVf/sfGtrB+QbB9eixdaBNpCCoXWAksoqtwix1R3sjTTw3nznuOO9mPo95IsrWkjEbcm+WRrUkGRNX/eQ2rIY93rF1GscknaPktTvRSVQz5w0AhsEVJVrgmbC11yYQyNIAjhibaFFAN3khGPxxElrjnmt/wqdxaesrBltMiAiMZvCYUsJ32mHNcK4FgKxyoRKln5H1plFXlz/WN4wmZQxcsCXnBU6uWcN/9a5AwzO79UKeR+gNZ5ILOfaI0QUqBVBhiikP2xEa4Gg8FwmDnuuOO49957Wbx4fJ6R3/zmN6xZc+A8GJMxJ+HqeR5vfetb+cMf/oDWGiEEr33ta1m+fDlSSi666CKuvPJKPvvZz85pUAaDYSJaa3pLWXw16uqIFlGmUs2ktrUDWV0PnbV1avG4eaCLS2ZwdFdxmEGvSNyyJ2SflULglC2y//ncg6xtWDSLqrLRLMytCu3s+cqGWxjyC6TtWLkUkCbhQKDAV6Iy50LAgswAaaeIa49YlwW+shA4NCVKBNoGHSXtcYRFndvIyXWv4OzG1+IIhx25Z3h64AHaSm3008bU8cmjZY0ALCIraqhCCkpS0mNFa/S3p3wCAlwrjl2uySrKqW2H/SEc6R5U6aEvPn4Ft7W3UgxtLKHIBTFKykZrsIQmYZUohTZdxQwb++dx3rznuHjhBp4YWoCwrEn7TBY9Tr91B9XdBcKYJJYLKFa5lflOWH7FJVhr8JRFSTkUlU2oBQXf4en2Znx1YNEatzwWVHXws4GXE2qLlOMzUndHawi0JBQSW4TYUlfEa3TysiW7XN4nKQq8pf5x4nZAt59GCoEtYiyrfgMva/qHOc/xS4lS8U5ATCpaR4jEq0XgP4xSWaQ0i+sGg8FwuLj66qu5/PLL2bdvH0opfve73/Hss89y3XXXcdNNN82pzzkJ16uuuoqbbrqJ73znO5x33nkcc8wxlX3xeJw3v/nN/OEPfzDC1WA4hAz7JTwVMhILJwSIMQlpomzCI1adsc6mU1td51KmZWIP01NQU5fDSdoujfE0u7J99JZySCEmiNYRQq3IOHF6Sjnu79pOg9vC7tyWyuLZVJRUAVfGqXZqD3w5B8nG/n1sGerEkRZSSCwhCLWKhLcFtgSFJtQKgaY2USBpexQCm5G5rHdTrKptRaPJBcPkgixn1L+cNbWnUe82j7O8LUsfx5LkSvaIXVTVp+ny9vDbPd9jKOyddHwuMRYljqLXb6OoCuS1xNNivGsyAoEsu2crSmERYcWxRsQrEl95uNKl1p39nGqt+dbG/82f2mw0UOPm6Sll8JWFI6NMvwpJQcWI2zlStkdfKcUd7cfQkhlAOpN/PhJZj7Nu2UrVUJFSwuLpSxcy3Dgx9b7WkAtjDPoJispGI1Ba4IcW3bkU0pJEPvhTC6BqJ88xdXvxnASWCBEirPxPsFAoDaGO6rb62kKIMHJvpvy/UWjkmKWCdzc9ggbagjps6VIfO5pVde+lNXX2rOf3pYhSCq2yzOyVxkbrABV2GuFqMBgMh5HXve513HjjjVx77bWkUimuvvpqTj75ZG688UZe+cpXzqnPOQnXX/ziF3zoQx/i/e9/P729E1+Qjj32WP7nf0z2PoPhUKG15sadT6HLpTIEI0K1kiA12i6itqGKUu7AoRCnUzEzF+TJHDpHkELwytZj+NaGuykFAc4UljRVtuw1JzIUQo8nevfy7uVreHrwYUqqSNyavOSO1ppikOOk2jNJ2bNPGjRb7ujYjK9CMnYkmFzLxlNhdN8Q5bhUgSUkvlbsGqznqJpuknaAIMbyTCs1bpJA+QwHQwQ64JS6M7mg5bUV4TgZMStBtVtPbbyRTx/7HZ4cuJcn+u9mwO9FCov5iaU0uM08M/QwWTVAXWwexbDEgN+BFAqhBQqwcdAichFGCISWKBS+8rEq9yban7RSHFu1elbzo7Xm8e4fcEd7gUCnqXULFEKHYuhgSYVEg4jEn1aSYT9Oyi5R7ebZmatjft0gk33uUgNFzrx5G+lciWKVw6MXLyJsmPh40xr6/ST9fhKtI3dhgSYbxigGNtXxEgknoGMoQ8EbOX5sbWTN/FQfCzKDFGWS4WICP5QIIO74ZNwSlozqrwqhCZRCaUmgJNIKx41clv9jnpjcTntYRUE72CLOUZnXcGrDh3AtI6pmgxAWWs+k9nTZN0WkDtDOYDAYRhF61Gnm+Tr/kcg555zDbbfddsj6m5Nw7erqYvXqqV9YLMsin8/PeVAGg2E8D/fs5LH+XRXRCqOCdX8iASvGuQGLsnSatD1zEbYzj5ttTdVMu/8VLcv51fYn6C4O40hrQtdaa4qhT9qJURtL4hUC8oFHa2IpS1MreS67HktYONKdcFy/103aruakmjNnPN65EKiQkgoY8oaxZVipp2shsaQkVCOxjqPuuAJwZQ0Fr5Z0sp9FafD0EN2lYYQQ1LkNnFx7OmvrzppWtO6PlJKT617OyXUTY/gWpZazru8Oukv76PWyKMAVDjE7SUkFBDpEIinpIkqrsvVVEOqg/Lsk0CFxy+XEmrWzsmKH2ufhrm9wV/td9JVWkbZLAOR8t5zZePyn0BKKQFkMFBzyIsWxNbuY7HOX7i9y1k1biRUChqrjPPO6+RTTNpZWWPs96fOhS7+fREBk3UUTakHedwm1BK1x7ZDmTJY9/dVoRGTRRyOFJhUrUhUboq1Uy7AXR6lRF2xRTNAjQ+qTeWrihXL8ssZXUdZgrUXlzWPEwn1ifBtxV9IbZgBBa+JE1tS/z4jWWSKlRNorCP0nZ9DaQ1qLEbLpcA/LYDAYXtI8+uijKKU47bTTxm1/+OGHsSyLtWvXzrrPOQnXhQsXTpvG+P777580i5TBYJg9Wmv+tHcTodbYM83PMrvwz8NGTFgsTddP26Y2luRDK8/iyoevpxD62Fpil92FA61QWpOyXY7KNGDLyFJZF0shhOBVLW8haA/YmdsMCOJWEiEkvirihSXSTg2vbnkLzfEFh/zatNY8M9jOX9o2sn7oHmJ2G1IWOH2eRmlJfzHJ3uFaIIGQuiJypJCRSzdQCgNq3VY+uvwdzEu6tBX2EOqAlJ1hSeqoCWL8YFmeWcNR6RNpK2zn5vY/4OudNMSaiMkEA/4AXcV2pIzK4HiqVFnu0GgCHRDqyGq4puZUzm969YzPO+Tt45Y9H6Co+hjw5hFqiVOO7/SUPUG0AlEd20AT2HGaEllqkiFRdO54ikkn+kk5PPiaZTipkITw8ZVESFXJTAwwFMTRWuDIqFSOQhBoScLxyHlxNAI/lLh2QNL1yJaiLMFCgGUHNFjdDIZ1ZL0YlgxxrTE1kzX4yqIrl0YDdYlC2Var0EiUBqvcVqJ4a/2D7Avq8YjuccKq5tyWj5G062Y8r4ZREsm3kx18Gq0KCDmFB4aKSi7FE28wya4MBoPhMPORj3yET37ykxOE6759+/jqV7/Kww8/POs+5yRc3/72t/ONb3yDN73pTaxYsQKgEmP2gx/8gF//+td85StfmUvXBoNhPzoKQ2zob5/SwnogRiNcD0WG4cmOD7HKyWe0FuUsuJHAOKqqiSWZA7+In9tyNGc1L+OJ3j2EWuOpyOUvYTk0xtPUx1M40qIUBthCckbTEgCSdprXt76HZ4ef4unBR+gutaFUSMJKs7b25RxfvZY699BbVrTW/HLHo/x+98PUpp4kGcsDUXyx1iBFSGNimLp4jm2D9ezL1cBIzc4x9u9TG5byd0efzaraqGBQfazxkI91f6SQLEgeTX1sHu3FTmIyKhFUZVeRs4fIBTlsaROTcUIdWWFBE+oQV7qcWnsmf7PwnTO2Anfkn+DP+z4y8wGWP2Kh0tSmCjSkclhyrLAd/zkOYhYPXrwMJSVBzCIILRyhsIXCV1GJHVsoAi0phC6WiASzKidQCrSFp1xsS6G0qCwwZOIeOS+GbYW4VkhrqkReJMjmYjhWOE4QQznrshXih5LefIq0W8K1Iquv0qP+DkJr6uijpFw6vJrITRqHNy2+kvrY0pnPk2EcscTfUMz/hsB/BK00EK8kaop+LwIeln0MifTfP59DNRgMRyRjw0aer/MfWWzatImTTz55wvY1a9awadOmOfU5J+H62c9+loceeohzzz2XY489FiEEV155JX19fezdu5eLL76YK6+8ck4DMhgM4xn0ChUhN1v0fn9PypytriHOGIvTyJksQOkAS7v8zZITiVnOAXsSQvC6xavZPtxDxo6RdCIrlC1kZVEs1Ir2/BBHVzVwSsNoWRtHuqyqPpXjq9ZSVHmUDonJJPZhrHn5x31P8/MdD9GSeYqYlSdQI1deRkNIJJ6Oqu6hGDr0FlPlaQ7RCBwsdmZ7+OqGWzixbgFvWHQyK6vnHbYx709jrAmNqiS3ksKiJd5KV7GDXJhFoaLtWqCxaI7N49zGCzir4bwpE2jtT19+1wTRWu0WkELjK4EjNY4M8UMrmr3yZ8kLBdUJn8Z0DqUFXigoBpBIRTPYurWfeN5n2wnRooSXGP2MKSRDQZy0XcQRUQKkUEs8ZaO0QKLJBy62HeIpi2I4mnXYEpFLsBQa1/ZJxD2khIXJbhwaGcwnosRK0wQb2VLhhTbDpTj1yTyCyGU4cvNX6AI87S1jY3Yxrq0ItaTKaebfn1vHSbV9vLb1VJakjRvrbJFSUl3/nwz1fQTfewAYRquRLycFOFjOidTU/sgkZTIYDIa/ArFYjM7OTpYtWzZue3t7O7Y9t3e0OfnKuK7Ln/70J3784x+zbNkyVq5cSalU4oQTTuAnP/kJN95445hEHgaD4WBwLRtLiDHxkdOjy6Vxxr5aH5p1ukjhulZAxskRcxRS6qgcz35IAY4d8oqWmYcMnD9vBZcuXMWQX6S7mI0y7wqB0pr+Up7d2X7mJ6u5ctV5uHLi94sQgoSVImVXHVbRWgg8frNzHb5uJ2HnCTXoSb9KJYEWWFKzODMxiV1rsoZFqTpcy+L+rm18cf0feax352Eb9/4cV3UCCStJPsxWtlnCpiXeyoLEYmqdelJWBle6LM+s5KPLP8k5jRfMWLQOeru4ed97J2yfnxykzs2RC2KAJuVEsa4jnyIvFBxd38OKui4aYlma4sPUuAUGRAbQLNnUyyl37uL4h9po2Dc86bkVkoEgQXchQdZ3KQQ2npIEWtJfSKEQFINR0ToWUV5UFyJyV16Y7iLtagIl8UKrYrGdClFOzJTzor5DLSgFNhYBsZIiZYHSkgEvhdBwTHo5i5LzsITk3u5n+Noz17N5aO+M5tgwHinT1DT8lOqGXxFLvBbLXollr8CNv4qquh9R1/gHpN3wfA/TYDAYXhJcdNFFfOYzn2FwcLCybWBggH/6p3/662YVhugl8Z3vfCfvfOc759qFwWCYAQtStTQnqtiR6yKYQdZMpUUlEdOhdCyRIqQxnQMdMlAab7EYEa+6XJ9SCPB1yD+u+x/+7zGXzbB/wftXnsnCdA1/3L2RPbl+fKUQAjJ2jItaV/KWpSezKH34y9pMx2O9u9gy1MXSmm6E0Cg93SxLlFJkXI+kXSIfxMrbNTvzfSzONFDlJMjYcfblB/iPzXfytVPeTEP88FuEGmJNrK4+iUf7HkSKKK5VCokQkriVICbj9Pk9VDk1vL71b6l2ambV/70d1xJSnLBdCs0JdXu5u2M52SBG0vKIWz7F0KFO9nDMwmEydgkpVNkpWFDtQIObY/Fj3Sx7LFoE2HF8Az3zp56nXF6gdYpsSZNwfUqhxXAxjusEhFrj64miVQPoyDrqhTYrqtqRtkSOKRg0U5f9kc9FIbBJyIC4H5USCrWk2c1RLDokZAsxO7qGKidJxk6wr9jHd7fcyhdPfAcpOzbdKQxT4Lon47oT3dMMBoNhzuxvEXg+zn+E8fWvf51zzz2XxYsXs2bNGgCefPJJmpub+a//+q859Tkn4bps2TK++c1vctllk7+Q3nTTTXz84x9n+/btcxqUwWAYxZUWF7Uey3ODHXR5A9O21Xr0hXns+7WesGV2CKFpyQwDmt7i1GUkIgEryi/6UfKivlKWJmbm+mgJyaULV/Gq1mPZ0N9OfymPLSUrqptoSRz+cjYz4ZmBdnKBR8L2ypmbp7dAKsBCk3FGhWtUtgi2DXVxVFUTQgjmJ6vZk+/n/q6tvG7RSYf7MtBas6rqWJ4bfpLd+e1orbGFJGalcGQcDaTtDBfPewOLkrOLvezMP8WAt2OqM7O8qoti6PBYz2IGvCQJy8MLB1nV2k/G9qJYUyJLdqgFSmmOe7iDJU/2ooVmw5pWnj1lXvkjvX/stkYFGiniWFaAJRUFz2FbdyNVqSKJuEdJOSNNxxwV9SLLixFN8UEsR1ZOYYlI0CotpnUVjuZWYMuonqsjfRp0gNZQVJGnQEKGSCEoBUOgq0FE4lUIQUusho5iP4/2buEVzatmNe8Gg8FgMLxQaG1tZf369fz3f/83Tz31FIlEgve+97287W1vw3EOHEY2GXMSrjt37iSbzU65P5vNsmvXrjkNyGAwTOSi+cfyQOd2/tw+iJ5m2U1Pa/2bO1WxIlIouvOZGbVXZZtvqBW3tW9i5cJlBzxmLI60WFN/6DMBHwp2ZXvLCYsOno7iEIszdRTCAooQjcdfOjYeduGqteaR3pt5uPePFP1hUjKgqCS+FgThILbKcWzmeF47/100xptn3f+24ZunrakpBJxQt4/mxBBbBpt4rG8hpy3oImN7hOM8BjS2Vhx3bzvznhkkVJJnzmjhmeMay+JxJFnGyNKMYmDIwXEthNAUfZvOoWq6hzPMq+tDUqIQuLhWQKijXMZKS4SISt5YIqojGxMl3P2eqbalSDolhrw4tpzaXViXh5WJldBK06xCtACNINBRXK+ujDuEcA/Yx46eR1oIBA/1PGeEq8FgMLxQMBbXOZFKpXj/+99/yPo7KFfhqXj00UepqamZa9cGg2E/0k6Mz530GrpLgzzet3PK76+KO+OEbQcjaPWsROvY8wpg0JvoLnokkw89NJD3HZKOB1oxndVVEomWYX/U7VPrsiizS+wq7CDQQVlEKTZnh/nVrrt5betpJO34IR9/qENuaf8R63pvJ8CPNgqIWxArx0fb2Ax5W2gvPjMn4VoMBw/cCGhODHNb1wJWNHaRsUvRHOw3lw17hlmwuR8tBY+esZjeVWn6B6rZ3t9IoTTIypYsw0WXXb0JoBEYSbCkIquttoglPHq9FHE3jlVSZGJFXGuk3m4kVgUQKp+ErYlNIUxr40Wyfgw/lDjWxDZaRy7GMTsg4+aoDUb/p478P5QoCqGD1oKkJUBlQWcrVlcAV9r0e1MvDhsMBoPBcCSwZcsW7rzzTrq6ulBq/HPz6quvnnV/Mxau3/rWt/jWt74FRKL1iiuu4LOf/eyEdoODgwwMDPD2t7991oMxGAxTU+Mm+N4Z7+Cap27kjvbNFMoCaiwj8nRENM5UtI64rk6GY4VjYjNnwnjXzdgkiZSOZBxpI4A92RrqEnkkkTvw5Cg8ZbF1oIGcPyJCI3UYczxcS5ELNEJYOMJCKUFea7773N38eucTXDL/ZM6fdxyLU/XTLhbOlCD0+e3ef2PD0H2T7hdlAacI6PW7eKjnjxxbdQaOnF2spSWi+FGJi8Ibs2f8h+y/dx9HdcIi4xSxZYjWouxkDiOfoe7FVTx3ahO52hgdC2oQSlOXzLO9XxB3a9nU1ozSkEh4xN0sQmrCUFIoOqjAJh73wA4JtYUXauJSM1SKY0mFpRWOHVnPfWXRlCwRs6Ze1k65HvXJHD35FKXAxpajZXFCLQmUxLVCWpL940TrCLJ8C3uCJLbUNMQUoCYI11Ar4tahreFrMBgMBsNfkx/84Ad86EMfoqGhgZaWlnHvMUKIwytcm5qaOP7444HIVbi1tZXW1tZxbYQQpFIpTjnlFD784Q/PejAGg2F64rbLP590Gac2LOFXOx7l2aEOFLoiOoXQoEerth58eiY15/qxIyxLv7iyeLYkqpFC0ltMMeTFqHaLoNUESyEo9gzVsSdbS6jH7ovUYSlwUAS4Fmgdkg01oY7ig2MW7Pb6+en2+7m1fRNnNR7N+5afS9KOxEygQoaDPAJJypqZqMwGA/xw22fp9dpmfK17C9vYmdvI8szsEt3MT76M3dl7mO7z9+t9JzIvXcKW4bhWAnCKkZj04tEjavspTQg0rgrw/NHMvkJokqkiqWQJy1LjdHEqWUJryHouSltR3dbQQtshQgi8wEJrGxECdLGkSuAeYI1FCGhI5nCtkP5CgkLgEJRLrlhSUxMvUOcO01moJbQKzHOzkVjVYAsLVwb0+gmyoUtLLCBpjaQAH1360Frjq4ATa5fMZKoNBoPB8FfAVHGdPV/4whf44he/yKc+9alD1ueMhevb3vY23va2twFw3nnn8bnPfY4LLrjgkA3EYDDMjJjl8KbFp3DpghN4ZrCddT27uafrWR7v2U2gIHoTjpLK2EJWwjJCfeAyHvtbXS053nY7WwRQ7U6dzOlI5JiqJhKWTS5QPNm9gJMb95BxS1giRI3UjRSa3UO17B6uQ+33uNG6PNcIvMAG7UdJiFTkVIzQ2FLgWjZaB0ghuK19I4XQ5/KjTueRvme5r3sDg34OgHqnirPd5ZxZm6Y6NnmWXU8V+emOa2clWgE0mueG1s1auB6VeTWP936HYtg/6f7fta+mOVUc4yEQvRIING4u4JQ/7iJwLR67ZDGBM6ompdBYUjFciuEFFrYTUJUuINAEgRxXMspxfBCCtPTIFmNoLdFalH/KyzoCoIejawRyhsXhhIDqeJGqWLEsXCVaQ9wOsLRPLJTkLA9baNq8DK4ISVgKpQWDYZx+P0Z9LGRJyh/T6WhAba83TMZJcGbDypkNyGAwGAyGFyD9/f28+c1vPqR9zinG9c477zykgzAYDLMnZjmcVLeIk+oW8b4VZ7NzuJfL77uO7kK2LDMjl0tRVqQzkZ9CKKpiJWJ2FHM5VIqjtWRO4lUz49qzf01CHbI7t41Bvx8hJCkrRT4s4muPuEywNHU0SXtqsf2yxqXMT9ayfbibQNk81rmYBZl+5qeGSNgegqgEyq7h+nGiddQqPrY3gRc65WzMGimixFb5wCNpu/gqYNjPkXFS3N25mWeGtxBQwJU2KTuORtNR6OXOgTx3FDdzZsMJJO0kNW6SE2oWIETA3vyzrOv/Cx3FqbL8Ts/23PrZHyQgbbVQDAfY/3Pzu/bV1Ce8/eKwBSVlUTec46Sb9pAY8imlbGL5gKB65PMXfT4tIdgzUIcfSmpqipFoDaM2I59y24pcg9GR0I07PrlSVDc21OWVcyGI0cOiWj0nrwIhIOlE4jPUMDwMXUEzdW4OhGZ5qo8TUt3cN7iEbBDHsRzOrVb0BCV2FyXZUFAlPYR0QNYQqJA+L4tC8+YFZ9GSqJn9oAwGg8FgeIHw5je/mVtvvZUPfvCDh6zPOQnXe+65Z0btzj333Ll0bzAY5sCSTD0/OPPtXPnIb9md7cfXIYEedZ+UCBpiSWKWw978wH5HKxqSeaoTxcgNU0Tei8OlOIjpkw9NTmRWVBpKyjtw878CWmvWDz7Ko3330lPqJNA+uSCHrzyEsIjLNLaMkbEzrKo+iXMaLyBhJSf00xjPcOG8lfxXfpBcUEIh2T1cz+7heoRWSBESKAspp1JDEyOTR6ywSkeJhTSafBAlteouDTDgZykEAcOB5LSm+dhj4oZtbVPKeWzs28YT/btIinqkkMQtj9bUAAtTvfT6HZOce2ZqTc0hg3JPcQOKIjXOIob8NlQ5CdRv2k+iPl6Y9Mxud8DaP+/CzQfkql3WXbKEQpU7ZslEY6EZDh26CmniMR/Liiyto59PHcW4Ispbos+gY4fg6WghRYOQgrQeoLZaIsTBZYj2Q8gVBO1+IwC5IEbc8ugLq3j7vE7e1prHQ2DLEraAfAj/1R7n0UGLvZ4Vxbb6gwgEdbE0b1hwOhe2nHBQYzIYDAbDIcZkFZ41Rx99NFdddRUPPfQQq1evnlAC5+Mf//is+5yTcH3FK14xo0QhYXhoSkYYDIaZsaK6mRsv+CB3dDzH73etZ0+un2Lo0xTPcEbTUs5qXsaq2vkEns/b77+ODQPtALRkhqmKlYByaiUtkHLUSisqBW4OjGY0a64U0F0cOizXOhu01tzXcxv399wOQNxK0lPqpBR6SCHROsRTOTJ2FaXQ496eW3ly4D4WJBYStxK0JpawsmoN1U4dAO856ky2DnVzR/szhHqsi7Uk1HKKOp8zeeoIlBYIVEVXhlpjaYUiJB9o+ktDWLKEr4qUlKanoFgQ1mLbDkqEVLsWA14Xbfkiu3OCnUnNytrRxEDjx3Pge5qyq2cw7vH0l7aidEDGXUDKbmLA28k3HllJdm8t/uIe5i/qIQgkUoJlK2o7c5z+px0IT5NtiPHEJYsoJh1GnYgVloBiYLOpr4VE0sexQ6RQSGmVMwNrpNTlOsZj5K6OXN5tqUAIpAUp1U3oVBGzhmY4C5Pjh9DeA22Di7EcRSzuk3dBaRvHXYXnNBILHiEuhoA60A5J6fOBee1cVguPFE6gl7OwrRiLU42cWrectHPoM0kbDAaDwfDX5vvf/z7pdJq7776bu+++e9w+IcRfT7hO5iochiE7d+7k+9//PkopvvKVr8yla4PBcJBIKblw/kounD91jJwVi/Hb8/8XgQq56qn/YmP2MdQYV1ZRful3rIBSGAmImSIYjZeVIiSYJuduLiiQDws4wqHaSR+SzLmTsTO3hQd778SVMdJ2FW2FvRTDIjErFkVWak2gPXq8fcSkg6+LdJf6yAUDpO00W7JP83DfHbys9jxeVn8+KSfGGxat4aHuHQx4hQOcXU+btXkylBbI8nGBUiitiOSsYme+nfpYZFPsKVoEIQgCIECj2Vfaha0DYjZIJWkvVFHlFliQnqxEzYFl2/FVZ8184JXxB5V+79vaw68fPh2hLSxLYdkj+wRKQd3OPGfesQ0rUHQ1V/H4qxdiJzW2CCt2VAXkQpenulsohC62pZAyShxmleNHowWT8a7ZY6/MkgqkZkG8h45SHfWx/EElHgs17G5L0JmbH42xZOGXHGwnQFZ77Ml38fmdS/jAgqNYKu8F1QE6AGGDbGF+3St5feIyhEjMfRAGg8Fg+OtgLK6zZseOuYUoTcechOvLX/7yKfe95z3v4ZxzzuGuu+7i/PPPn/PADAbD4cUPA7666Q88NfgktjWZRQ6qY0W6807FVXM2tikhonjCuDXxa2bL8C4e6n2SjUNbCFSAFJKFyfmcXn8ia2qOG+cKeyh4avARfFWiJlaHp0rkwyyWsMsxuJqQgED7CHzCEISwkFh4yqPaqUcKQTYY4r6eW6K40nwD39x4B7nAY9r4XxFljZ08vnVMM8aXMhqxdeux28puxKHS5AOJI2wCDbYEQgi0R6gjN2NV7s+RCl9p2vI1zE8NTnqPpyMmk7ys7jWzOwiIWdWA5rbnOrjz2aNYsrybdFUBITSOG9VPFUKhtaCYdPCExXBrikcvWkLoWBBoLKGQ5YzZ+cBm0E8Rj2mqksOAxlcWvpKgBEIIAmWNTlYg0GH0uxAK7SiShZCGul6yOkmgLJxyNuO5vAsEIWzdEqeP+RP3+Ta5Ycmipjr25Pv41u4q/vfKa1no9oDKg0iAsxIhZldiyGAwGAyGlzpzEq7TIaXkrW99K1/+8pe59tprD3X3BoNhjoRasXGgjb25fnwdcGfnozzQuYuWqmDKYxKOjy08fH2gmpIjr/9j8sQKQAvacoP8cd96krbL6poFbBjayM3td5MN8vgqINAhoVa0F3p4qn8zp9at5r3L3ogrnSnONTvyQZbt2WdJWlHG3VyQRWmFKx00Gk8VCHWI2C//r9IBoQ7o9bpois8j49Qw7A9wW8fN3Lanjr6SjsQk+9dxHSM3x6iiaa17YuQ4URFTSgvskXjjsiXRtQIQUAw1eR0QKjtK6KSjHy0mOnS7MiDnuwz7CardA1mHx/PqeZdjWbNfRGhJnsIXHvtv2nqbWbq8EwQEvhVZRjXEYj62owkDyNbEuOfSFeQyDnbllgtCbRECed+mqB2kjK5faYHQAosQJQTS0vihFc2aEuBZlXm3pMJxA8KSRWNTL0XiZAsJyqGuFSutEHrGyzKhgke2JGAS0TpCybPIFwWt6Xr25Lu5Yd8TfHTFJbOeR4PBYDAYjmT27t3LDTfcwO7du/G88TlPvvGNb8y6v0MuXAH6+voYGBg4HF0bDIY58EDXNn67ax07cz2UQp/u4hCeLtIyqfvoKIEGX89EQO6XI3bMr+v79/KQ144WkLA9bLuHEI9S6BHulyxIkOfWzgdoK3bxqZV/T9I+eDfKYlmYxmXUV8ho7L2nimXRypi4VBGl9hEarUMG/T6qnBriVoJQuXSX2ki70FusKSdUmjgTQowvLzQTl9QRySkqv0VWR60hwEIIjWtFYw20wA8tFFESrVCDp6KYUbnf/FtCU0IQqNllh35V03tYW3fRjNruz+X3XU1vWystCwbwPIswGBW/YWix+LF95Oc5dLdWoRFkq6O4Tq3Dytg1kPdsSowsmmhCZZWLPemorE35N8eKShGFI6JVaOJWQNwOUFKQqikwrFLk/BihjlSrF1okHR/NqGg9kHj1Q8EjW22mE63RdcCW7hzNmRg1bpon+3fQUeinJVE7h9k0GAwGg+HI4y9/+QuXXXYZy5YtY/PmzaxatYqdO3eitebkk2dXZm+EOQnX3bt3T7p9YGCAe+65h3/5l3/hnHPOmdOADAbDoeX/briVX+x8mJIKKkZA+f+z999xklzlvT/+PudUVcfJaWc2a4O0CishCUmAAkgIJIIQwgQDF2S+mHsxXNtg7Guwv/4ZY5tgG1/jawP2FxtsEHDBImMJCYEkUEAorNKupM1pZifPdKxwzvn9Ud0905N2drQyEtT7xaDtrqpTp073dM+nnuf5PEKzKl/EUbGIqYQO09U0oVFYBEoY8imfcuBx4tY1cc1oXYCENmJtrofIGoajnVR1AZix3ZkhFhCR1TwytZv/89QNvHfLm5+2eHWFhxQSXXPHlbXKSW0jjI0Qtf6hMDfqVqvDtIapYJx0ejXHqtNoAy1eGWhv9AOdfQ0raq3SSMaeLZ8s2sq43lVY0m6IkrFJlq3VdNJUx2lisRe5ZJXFlab2bFwvq8TSfXxnc0X3W7i47zUnfiHAdbdcz56Ht7D1rCNEkWgSrVjL9ocOc+rjx5AtmtvfeCrlnEdkBdi45rUe4PUDhz1DPazum2rcCHCkJtAKRHxdwtZswwQ40qABq0xs0uRYqiikiCgHaQxqRrILmKhmaEtVkdQi2gtEq2cTavj5vo3LXoexSvx+a3UyHK6Msbd4LBGuCQkJCQm/Mnzwgx/kAx/4AB/+8IdpaWnhP/7jP+jt7eUtb3kLV1111YrGXJFw3bBhw6ImKtZaLrroIj772c+uaEIJCQknh0BH/Pod/8QT08fmbIndfqf8NB2izHgli69dZnd61dZhvOI0md2cGGKeqNOijBGFWRm0s+Ncs82LLJHV7Jh8gpuHfspr17x0hXOIyTutrM6sZ29xF1knR1ZlEUIS2XBOcm595vWYp0EIgSNcSrpIISxTDH2sELXorGWubIWZ6OryzZgsFoEjNdaKOCI4i6wbkHPjtjvaSiyQUhFpZSlFHkbHQlwJCI0gjCQlk6LNqyAEBNohrSJaXH9Zs3ll/7u4qPvE61oBXnfLO3jyodNoay/jeppKeSZaL4zlvJ/vZ9NTw1jgwU0bmHCypIhQqlbZW0vhLfsuTw32kM5EaC1QKt7mKoM2shY9jt899Q6uSItK6/qSEoYOJd8jqCryaZ81nWMMBRopOtFGElqHQpCmNVUFazG1lkQLNX7SGg6NdwHLbe1kiXR8o0CI+B2lV9BWKCEhISEh4bnKzp07+fKXvwyA4zhUKhXy+Tx/9md/xmte8xre/e53n/CYKxKu//Iv/zJPuAoh6OjoYNOmTZx++ukrGTYhIeEkUdEBV978SSbD+TWNUtha5MphsNgW1wyKODV15tfaEv/dfXJcfgNTZtLsY36aqm0IvLnxrkJU4Zahe7ly1QvJO/P7qS4XIQTb257PvtKTVHWZtMqQVhkK4US8fc7/CyGpOyo50sURDqENqegqhriXaDVymoR+41xzH9jjpQnPumpLLSoqkULTlqqQcjSuNLWaV00liiPgcRQWMk6Ib93YnMkIAq2QwhJZSWAUjowdd/tzUyi5dA9XlxTv3vI39KRXL2td53LdLe/g8N51hFUPL1VvgRSfR2rDhXfvZd2BMawQ/Pz5G9i7pRcKYLTPxEgL1XKKzt4JfFcwUW2htd3HUZoAB2UMShhcobELfG3ZSOA6hpHpDEGk6p5YeI7BczVFP8ORyW429Y2g5DhHJ6Agehkq5ZBCk/PCWLBaGsZW1FO9reGJiX42ymXfiYjnVHtv+DpECUm7l1vRuiYkJCQkPAtIXIVPmFwu16hr7e/vZ8+ePZxxxhkAjI6OrmjMFQnX66+/fkUnS0hIeOaZDiq86oefWlC0QtzTUkrQkcXYmqkNs1NP7cnSqw1G9eMYwnmf+9bOF6yNbViOVIb53tE7eOO6laWU1Dm19SzOKp3PjsmfEZqAbq+HUjQdpwvXrJVkXNlaWweDEApXeI24cBw5i6tyRyp5lvoWOSH/5dqOkZU4wiCEpTVVJeuGmEgxNZkj0hKLIJX16WktEBqHUuSihCWjQqq+Q8W6yFpkEgPVyEVJS1eqxOrc0rXMedXBb23+G1q8E09lHSoXeNNtf0RxZC2F8RxoS3XKJQxqkeBIc/Edu1k1OImRknteeAqH1nc1rVRhMsfYsVbc/jKBUaTTYe29Ee8TmzopfOPU+rTOHG21qCV1W6JIEfgujqPJZ6qkHI2jNNr4TJayjBdz9LYVWN0JExPDHKOXo8VW8m5Ie7pMWumaG7aI0+RNxJFSb9yTWJ3YXw2OE7+vJsIi/ZlOTmtdc8Jrm5CQkJCQ8Fzloosu4ic/+Qnbtm3jFa94Bb/3e7/HI488wo033shFF120ojGftjnT448/zoEDBwBYv359Em1NSPgF8867vsBYUF5UOAkskZZEVs08Y2fSNG3NclXJetT16WIwNsKIZjFnbN0td6GZxnLWYLh58G4u6DqLjbmVRQIBlFBctep15JwWdkz+jGI0TUqmKOuolqgLWAcrIgQGJRw8mULWUoqlUGRVCk+FTPtZJqsL193auQ9O6AaAIKd8WlI+kVYcO9JFaTpTSwWO5+g5EaLd4ZQ1Q3TnILQSZaFSHsBYhTFxb1NT80hem51kS/tIo951oUm1qC7eu/VvyTotJzJZxqplPvf4z/iXHXcgRlZDyUGa+AxTw60ExRT5zhIvnNjFqsFJtFL85NLNDA20N8ZQymC1oFJKkekpUTUuShr8QAGCFBGuisV8ZOLa1nqjIKsFtpYyrJQm0oIgVHS3FultnyblRo0rFcLSmS8zXc7E/V0FdHTA0Pgwgl5KYYpSmGosjcDSnZpiqNyFRdDrlunQGRwVR7WXQyYVUggraGt46aqzceUz4oWYkJCQkPBfQRJxPWE++clPUiwWAfjwhz9MsVjkq1/9Klu2bFmRozA8DeH6rW99i/e///3s37+/6fmNGzfyyU9+kmuuuWalQyckJByHYljl3rEnuWP4cfaXhilGVXLSoxwqdk2NLqGX4miSWUbtarzPCauvBYgjv8sXrfHe9c/oYX+K/9+Of2dDbisXdJ3CC3u20Jk68bRLRzq8pPcVPL/zYp4sPMbRykHuG78TgcCTcU/NwFTwTZFYGhmMhciEuNJh2N9PVlnKImIgP8VQqWWW+J+hLoxszRRpKeqRwvoqr2sZJ60inti9lvJ0BkdpnHQQR8kxaK0YHW+jXE5z2pZDGAUppck6ATK0pJwIIeL2MAO5KU7vnFvf3Lze/alTeNfmj+JID2stY5KKvAoAAQAASURBVP5uDpbuZszfi7WWNm+A9bkX0pvZhhQzXxfj1TJ/cu8PuOWpR3GG0ohQooVtfKNoA9ViCr/gcbNzPiIvOXxOO9P96VkzsHgpzfRklrKvyK0r4yhDGEmEiNvmhFrhWT3zfq0lA9hQQijjfwuLci2TUxn62qdZ1V7AWvBDp9bqBpTQZLyQlBsRaYlbi4ZuboH9JY02cbWsFIaUCulypzlY7KndPIHxMM0ZXkBvPmSokJ/pGbsISmmy2YBiVOHKVedwxaqzl9w/ISEhISHhl41TTjml8e9cLsdnPvOZpz3mioTr97//fV73utexfv16/vIv/5Jt27YBcRHuP/3TP3Hdddfx3e9+d8WOUQkJCYuzY2I/n37qJvYXhylGFbSJxWgldJdlpmTsQvYzzxSWjozkmK33zKzfNFxKtDYTGst4OEJxMs2OiUPceOjn/MamS7mkd+uKZpR3Wjm34wWc2/ECUjLNfeN3kFU5MiqHEIKKLjAdjlM1RXxTIU5VNeSdDGk3BXaKFm+aja2jPDI6wGQwu/5WYKxFzTJoWqzGVWCQtWWwNq49dqXhyGAPU4U8uUwFNeelkjLCcSJKlTR7DvSzZfORWeNBWoWNtj59meKiayBRbG05lzeu+wMc6RCaCg+M/huHyj8jMj5KeAgE48FeDhbvoTezjQu630Vk0jw0MshXnnqYmw89TGooBVUJrkbY2LVXBJJ8pUrF87BC4Ice3+29AGdUk7MlOtdNIaUhlQ6JIsnQUBvRqljomdrNjNnGVn6k8ByNqKUMQxxBrTsBpzxNUHbR0y6r148SGUE0KypqLWgUfhjXvCo1k0bgOPXUeYsSmnavjGN8DhZ760cjAY2koD1a0wXa8dk53YW29fZCs17TWqp3NhuwtqWVd22+ihf1nIYU/5W/cwkJCQkJCb94TjnlFO677z66urqanp+cnOTcc89l7969JzzmioTrRz7yEbZv386dd95JLjcT+bjmmmt473vfy8UXX8yHP/zhRLgmJJxkfjD4EH//xPcZC+K2LNpI/EhBI7a3VIT0xFu1xL1IVxZ1rR8RpyVLZuSCZXnJljHGWCKpSUtNb6qTsaDEZ578ISnpcEH3KccfYAle0vtKDJodk/dRCgp4MoVA4og0kgCFptvL0Zvuw5GxQ25WdfD45GHyboVzeo5w//BaCmG66cqNsSylVaQwDTOsWKTFLYiMFoyOt+GoaJaZUn0la5JfCDw3pFDMUC6laMn5hEbV2rlYKpFD1gnoTs8XrgJBh9vHxT2v5fzOKxFCYKzm/tHPs7/4UzKqnazb3WS+F5oKj47u5LMPfYZd41lKYUg0bnDHctiw5sEcyoZgbi8Vec1jP2d/Vw8/3nI6UsQGXNLRFEdzOI5m1ZZRwtBl35EuRt0MDnGk2GjREPtCWAyCIHQQxI7Csp4E4FgcEYKAoOoweKCTVX2TSGvx9QLmTRYMilBD2pt5vp4eL7F0pMq4kc/+ch+zc7JELZk8MArP07xz0w7++qmLOFxpw5p6CLj2uipLOh3Q3ab5+DlvZ2PLqsXfBAkJCQkJzxmEjX9+ked/rrF//360nu+o7/s+R44cWeCI47Mi4frwww/zl3/5l02itU4ul+P666/nQx/60IomlJCQsDC3H3uMT+76NlNBGWMhiFStLYiYlZ4KJ7sQYka8wnIFbN2lGCuIjIrNdWrzUssfBgBPWSJrGSxPMR1oNuZ7GKkW+Mr+ezm3cz2OPBEZ3IwjHV7W91rObD2PR6fv50BpN5GNaHEGGPWfwpNpOrzepmNavQxndKzl4YlDZJ0yG9vGeGRsdVOU0CKwxiJF7NjcTN2OSsQp07Wa4mqkGJlqIwgdMqm5rWtmxrAWlLIEoaRQzOKkDEHkIqWmEKaRwtCRKhMaiZL1LwyBQrGl5Tyu6n87Xan+xnjD1Z0cKt1HxunAk/M/048Wcvzzw30UAlDCh0OQKsyKaNbPYAW9hSle/fj9pKKQ/ukJvCgicFyEsVgrkY5h8lgr1cil5Amm3RRS6pn3Q62GVUlDVM8MEOD7LtpalBuhnNj3t1r0mJ7MUpzKIISlpa1MFCoEAruAA7AljmrPfesJIOsETE77lOmd9Wzze94g2JKdYH2uwB9uu4s/2n0pVT+FLitEWaIwuKmIbFbz6+svSURrQkJCQsKvJN/+9rcb/7755ptpa2trPNZa88Mf/pANGzasaOwVCdd0Os34+Pii28fHx0mn04tuT0hIODF2TR/mn3b/gKmwjKVZtMKs/6yoHPX4kdjZbXJgJkq48L52VoVq/di4WlAJGpG55hkAi6U5CzBGIoVHIaxyoDTGulwnB8tjPDJ5mOd1rl968sdBCMHq7HpWZ2fGeWzqp/zo2OO0ub0LHtPipjm7Yy2PTOylN1MkowIq2pvTuzVOGxbz0oWbe7XWD9FGcniqPV6Hpv1nBFTdhVnUBFikFUPlVtoRRMYFDK60HC52cKzSyqrsNKe3lbHCx5EeF/dc2yRaAQ4U78LYcEHRGkTwz4+0UAgkThQh97mIaOHXafXkGK/Y+SCu1gy3tPGdM87Fd9y4NY0VVIqpOB/AQpgxVNoNIgIh4yvURuI6EZF2ZkRr/cp1vGbauLhWU5zIMH60vXEL2vNCpCCuVY07Cs0jrodtfs5aSMkQyiWK9BF7N9ftuuKddW24jNSckx8BFN2eT8qJsEdSRBMONhKAIsLDHctxxEj2t0+woefEXZoTEhISEhKey1x77bVA/PfV29/+9qZtruuyYcMG/uZv/mZFY6+o8Obyyy/n7/7u77j77rvnbbv33nv51Kc+xUtf+tIVTSghIWE+twzu4GhlPE55tPEf+bPVzcrtk048fRgWEsh21k9j5LiNiNAoDE5NtNo5e8+0m1k8UlwJXcpRgLWW6bBCaAyR0ewrjpz45JfBqH8Yi0GKxaO5eTfN1tZ1uFLT4sUR0jjFFWaLTW1Eo7a38V9oErnGxEIpsLFo00YS1iLV8Wj16GyculqPBxZCj5FyDrDk3QqdqTKtnk/WCbBWcKjQwQNjHUgyuCLLYGXfvOsYqT6BI+ffaCyFgi/vyjJekeiqRe5OIyLRkHSz3wIbx47xyscfwNWaI+2dfOuM86l6brwW9TpezyDSBpmO0JETC9bGGgiKlRSImch8g1lvK1mzLS5NZpoSC7SWtawDu+ANECFiw6S59bPlMoxVCgyzColtuDHXu7DW67EdYbikY4R12QpKaEYrefwn24lGMkghcTOWbF5yZl8/61q6+Pm+I3zkW7exb2TxG7wJCQkJCQm/jBhjMMawbt06hoeHG4+NMfi+zxNPPMGrXvWqFY29oojrJz7xCV7wghdw8cUXc8EFF3DqqacC8MQTT/Czn/2M3t5ePv7xj69oQgkJCc2M+wXuGH4c34QAjZ6ex2e2AFik5cyyROvSdbMwX8jWpZWShrRjagJmniQBIDTNR4LAnTVeEEnKkYvBom0EGg6Xx8m7aSJ7Uvr1zMPYOB31eHSmclRM67wocuyK25DkaFNLU509pIhvQtRFK4BIaYxwCUIHz4sIjUIKi5odxRYQRhKNZIIUHoaU1KQd09BsQkBKRThCMFrNM+m3s6m1zM7pn3F+50sb9brxtUaIOfcw900pbnwqw74JFxNBdp+LXGA9BHDq8FEuf+oRhIW9Xb3ccup2tFTxxdWHlRbhGawQ6FDGfXJl3Cu3nuZeqqTIpgNSXuz822w0FvdRTXkRpck0fqlWqFozaNJaUShkaG8vEUXMe8uqWn/W2a+StbC3DLCqYfTkCUNoVeP3yyBQWN7Yu59TU2kc5ZJVlv/7yHbCaScWrK5Hu5enO9VKRsXzyqY8Do1N8Zkf3svH3nhVU81wQkJCQkLCrwL79s2/WT45OUl7e/uKx1xRxHXjxo08/PDD/PZv/zYTExN89atf5atf/SoTExP8zu/8Djt27Fhx7nJCQkIzo/40k0Gp8Uf3clrZzBaUMxHAmR8xV0QtglhE3MZRKztr/PnnlwKyblATXPMla2ggNJK46nX2jyS0ENYOCYyDmXP4uF+kHAV0p/LHv4gVkHfbATurtndhQuvT4ub56PPevMiH6Uzk1ViJNgKtZ37M3Mi5ApM2VAKPSM8YHmkr0FZirCDUkmIlQ6gEwouFu5Jzb1LEP0paHCHYV1AokaaqSzWn5Bla3D4iW208/vmQy2cfyrN/3CUqC7K7HeQS77mq4wKCXX0D3Hza2bFobcwjFtzCM/G/LKDBuPHSCGmxtYi0sZKRyTy+7+BIg+fouB2Q0rjpCNfRlKbTTI62zqyZpRFhHR9vwVqB40ZNotVRGilrN0SkaRhiPTYKsGrGRAyFQSIwjdctKwJ+e+3PeWv/A7SocdakjlAOPPYNrsFxBX3Zdra1rWVttrshWgGkEPS0ZHnq2CiPHxledO0SEhISEp4b1M2ZfpE/zzU+/vGP89WvfrXx+PWvfz2dnZ2sXr2aHTt2rGjMFXv09/b28rd/+7fs2rWLSqVCpVJh165dfPKTn6S3t3dBlZ2QkHDijPkFAhtR82+dFYlaKOm2LkybBWX98eJCcz6iFjFdcNtxxpHC4qmIbCrCENcJhgYqoUPBTzFZTVENU0uI8FjQlaM4PTTtRE1bDZaqDjinY93yLmaZBKbKE9P3UQwniUzASPUQ4/4IY/4wE8EoVV1uErOlaJKe1Bqe330ut1z5vqYPVCHEnJsGMFtULhbRVS0B1jMUK2mK1RShlhgrY8Hqp5guZdEKnA6/Mb4r9ALjx48zCqZCGK3Gydtz05/X519E3LM24sEhly89lmeqotChJXtExaJ1idf6QGcPX99+IT/cfCa2ZqXciDVbwLFop7YCJp6TadUYK+P2MbPEaxQphsZbOTbeQrHiEYWKSCsGxAQj+zoYG2xDa4l0ZrkUWsAIpiezjBxrRbkGz4kQNnZvlsoirMWVGkcaoggePLKWIFiDNY3fKqwVZJQipzSrvDKv6X6Kf9p2My/vOoBF4QiLQrPjWCsmqJBNQW+6DWeRdPKM5xJEmocOHF188RISEhISEn5J+cxnPsPatWsBuOWWW7j11lu56aabuPrqq/n93//9FY25olThpXj44Yf52Mc+xte//nWCIDjZwyck/MrR4mSRSATxH+uWpetBTwYCi+tEsaWSrldVLv/YlBPRmvZrNa1xj9lS6DVMieriW4YeaSck64bz042tQAlJaOK012roNqllRyj2FkfoPAlRV2stD0/dzo6J25iOxtBGMxUWiGwECJRQSBRSSNIqS3eqj8j6SBTb21+MFJL+XBuff9HbedtPv9C8HgKOE7ht3l+B01VFT3lEVYeo4sxkvgqLTGuc9gBZi7ZKLHbeXYQZoexIhR9ZSlGV01LrSMts055rsufzhHMT39lT4dZ9eXwdj5U66iL0wuH2Cw7uYVffANPpeKyRlrZG3W1DNgsLnkG71IOviFBi0wab1whRS8VVBisFWscuTdYKKn6KapBCqYiPPe9rfPyJq6gGHjaU4NZSkJUBLWcu1wqOHu3CdyW9HQXSqWCmjlYIKpGDrxV7h/pmvTYWx9Vk0hojLL+2/mJe1XErueh7uMIC8ZyMEATGYW/QxeFqB4qI3vQ0WZVa/HWs2XyXw3CplzshISEhIeGXkqGhoYZw/e53v8sb3vAGXvayl7FhwwYuvPDCFY15QsL1scce49Of/jR79uyho6OD17/+9bz2ta8F4IEHHuCP//iPufnmm3Fdl7e+9a0rmlBCQkIz7akcrW6GMT8iipaqNz05CGFRwoCNkyZj8Wnn1BwuRCxvW9NVUk4Uj4EgiBQFncYKO9OOpNbb0xhBOfAwRpCpide6qdOMM3EcQZa1vp4ACkmLk+JAaYzzuzY+reu11vKzse/z84mbUEKRU+0cC48ihERahUHHdaBCIJAUo2nKUYHOVBcv6Ho1W/LnNcY6r3sDr19/Lt848ABxjFiQcQI60mXaUlWksIRaMVHNMF7NEpmFo3VCWbI9ZVpVFScUiJpTrnYNBZPCN7NqVLFIDB3pEvl0FVfqhlgOtaIStlGN2nCl5fS2i+bVW7oyy2PDl3LL3ocJaq68zpRAVee/3tIYLt/9KFuHB9kyOshXnvfCWanBc96ZKY1x4vGEEWAENmWIBoKZXB8RO0arVAhaYkOJiRSY+D1wQc9+/uzRa6gKF9sdwGgKQgHSxum/jsaEEmEEVoHOGYaLWcYjh84WH1dprBUEWpFxfQbHO1EqFvzGCqwVhIGLNZJ0NqAz1ULKDRgKOyn7BldGSCRVk2IibCUKRxAqQghJSlQxpoiUC984sTauqc2lvAW3JyQkJCQ8h1i46um/9vzPMTo6Ojh06BBr167lpptu4s///M+B+Ptxof6uy2HZwvWee+7h8ssvp1qdqYX66le/yic/+UmiKOJ//a//RUtLC7//+7/P7/zO79Df37/EaAkJCctldaaTba1ruXN4F4FZcXb/spBC056p4kcOgVY10xpwhCYyEmsXOr+tublC2o1wlY5biljwtaQaOVAzGJo5ghkxaqEauTjK4NZEhWUmyquwcay5dg4AVyqEEOiTYM40VN3HQ5M/xJNpck4bxypH8HUVV6bwHEFkQkLro22IEAJHOAghaXVXcW7Hy5qEoBCCN53yfPYURnh08jCdmSn6cgWUMA2h5LqavBfQlytyYKqD6WC2o6/FkZrubImB3DSONGgrMDZOO1bCEFnFULGFY+UWwLI6X6DHiXCz5aYiGCEg5WhSzjit6QnW5dZxSv6MpmuPjOGjP7udLzzyGJGWiNDiTShUZZ7bFo7RvPyJHawfH8EKwX1rNzWJ1tm4OZ9QKtC1VFxl0V0Rpi0Ct3lgoyUiktjAwYQSrEBJTX96knuGN2NQCGVwciH0VaHgQMmB2u+CdC1kQ9IdVaRnCSKFtZLJagZBnGrelStw6FhnPDd3Zn2kiL9Aw0iRCj2u7mtlqrCTVm8NjlKMBQWmwzLaANaSVh4XrhXc96igUHFIu2N4iwjXchCSdh2et35gwe0JCQkJCQm/zFx33XW8+c1vZsuWLYyNjXH11VcD8OCDD7J58+YVjbls4fpnf/ZnpNNpvvGNb3DJJZewb98+fuM3foM/+ZM/oVKp8P73v58/+qM/amoym5CQ8PQRQvCC7lP54dEneWZvuRlavSqe0qQcTaBjM6B6DaCxkpLvxQK2ERWNRaYU4DkRGTeMSw5rCaPVyMHWRNfcdGNbSywVIo5++ZGDp4LatqYFiKNXRjZCeg4ShKAn1fK0r/qJws8ITJVOr5/QhJR0ASlUQ5A60sWxLoGtkFOtdKYG0EZTCCcZ9g+yKrOhabxtbf184MyX8aldXyQQBbSBqnZojkfG6dQb2sfZM9FJKUyRdkJ6s0W6M2UybhCnaVuwxiEystYCKa7VHMhPYazEVSGrcgXcKNVI1W1euvgZJSyPFw7wg8N/wrb2K9jYcjlFX/D6b36Z3ZPjtUaqgtSYRIZi3kCujnjlzgcYmJpAS8lNp53Dgc6eBdfTzfqEawNSLSGFUhoQWM82Xf5M3kB8Ll11IZIgLTnXZ403zB6/v64ssVoSlVycfIDoDLHtIdT6yUpHg5AYKUi7IZ4bYazA1Iyhs6rM4WMz/XjDMH6HunXrahGvU5o8SkxhrI8re2hxFS1uhtBoAqMpmy66WmPTsYs2T/P9Ha34oY+3wLeoMYbRQomz1w1wWv/C65SQkJCQkPDLzN/+7d+yYcMGDh06xCc+8Qny+fhG7+DgIL/1W7+1ojGXLVzvvfde3vOe9/Dyl78cgDPOOINPfvKTXHrppbz//e/nE5/4xIomkJCQcHwmfb/hsvvMYHGIiHCphLEgkrWIKLZWiygsLWmfyCiCaKbHqJKGlBPNafcSi81QxxE5MV8LNQlZISyRUWgba5W5GCua6jgrJqTVST3tNGFjNfuKD5OSGYQQVKIS2mpcMSe9UwDW41DZcKismQoMvrFMBDexteV0ALQ1ZJ0U29s3sqWlg60dmnG/ncnAMBmUMU2FrrFQTzsRq/JFhkuwoW0cT0Vxqi+gLQhE47lK5BIaRWgcUiqiPzeFqwyeiiCaP91mC0KBb+H28RF2FG5gtXsX/+enA0z41doLY3HKsWg1DsgZY10ygc+rHn+AntI0gePwvW3PY7Ctc8H1dLM+Z7/sKe66fytuPoJ0/QYFTW+A+itpImJXYCPAsXgyot8dY0/Qj7GyEZm3xOLVBAqV1ggJeLMNr2Yi00rG0X1rIaOqHB2ZEa0z+yvC0OK48RiudOhwW9hf8MnXxosdruPoviMcIunUDNIsV501xt7hiCePrSXUZdqzaaSUWGspVH3GSxXWdLbxP664MGmFk5CQkJDwK4nrunzgAx+Y9/z73ve+FY+5bOE6OTnJ1q1bm56rP7788stXPIGEhISlKUU+Nx66D7vM3qILs1RtrCGjQlw3rovUVmF1nGZaF611hABXaVy1vNqEerR1odnMRhCbOMV9OZuvc2bmM/+KMBRCnxY3zdMhshHaRkjh1OYbn3u22LAWhqop9hfTFCIHY/1aqYvicGUvtw7tw5EOeSdNVqX49pF76XA9hCywNtvHupyiqkPuG9mHpllMhlrR6lXJuQGuNIRakVK6dlOg1kio5kWUdkJ0GLfVCbQi7/m40rBgsq6YLepmqFo4Fki+dl+agl+h4ZpkQZXBChtHuGXtZQBetP8JekrTVFyP75x+HqMtrQuupZf1OfOKJxuO05ViGpNZxNZLgI6AWuQcBUIaDLA7XDWrvrm2e61m1/gKmdJN76l69D++WRJfj7HgiCpHR5aKdgqiENrSHqe3rWa0WiaiHyXb0GYKR3UvemTWC3nnZQ/zo11nc99eyeGJ6dosLDnP4+KtG3jbxeeypnPlGUjGGKQ8uaUBURThOCfdkzEhISEhIQGAb3/721x99dW4rsu3v/3tJfe95pprTnj8ZX+DWWtRqvlPpPrjdPrp/fGYkJCwOEfLkwxVpxqptctlrvPwwj1JDa3pmXCdEGCNxaAwVq9QJsfioe5+PLsMdfGgsUBi456bom5sIzA27u8piNNd9SyDqKOVSfYXR9iQX3kqpiNcHOnh6zJArVVM3L+1Ll6PVlLsLmTxjURbgap1h6lHwA0WbQ3lMMAWcwxOCsYKBaCd9R2CUwcsa7sdtrT1sWtqqOn82grSUuNiKIUuntIIYee0CRKxAZOweFJT1XGqthKmVjc8/+aArad4i+b3jDaCQyPtFKqZRqQVQEQgdCxYHRWRy/mUxrJgBT855VQyUcCdG05jMptf8EVsH5hgy4WH49Y2VqDQBIGDyIbM0erxmmmwWjV6sDpCI21EQBzptohY086+PyJsHJk1AtT8Wx/1WmsLVCuCUmlp0QqQc1zObncoBI+DgaD0E5x8lqreg5IdiEVa3URmhNZMG++98jVMVdI8uP8ohapPynU4c00f67ralzj3whgTEFT+L37pK2i9DzBI2Y6Xfjmp3G+inNUnPCaAX/wCYfHTWDM0c4NIdOHm/h/SrStL1UpISEj4VeMX3Uv1udLH9dprr2VoaIje3l6uvfbaRfcTQqzIoOmEbr1+//vfZ2ho5g+vcrmMEIKvfe1rPPTQQ/Mm9HRCwQkJCTH7iscITNyWZXnYBaOc85/T5D2NtTPb4ohrrV1N5OKpaNl9Xxci5WjQca3mYtMXxEIt5ehGmrAQcZsXAbUo5fyDfRPy9QM/5wNnXL3i+Ukh2Zw/lwcnbsFaS0blUMJB2whHuJQjyf5SJq6ZRKBE/BOYWCAp4rrbMITxoxnCssaVAiEkkbUcGDEcG1f0tsPFp7eixPA8QyklLZERc65x7vXGfU5dpanqOGVVybhu1JqF2+3EulQ0bmBUA5fHDq9mZKxlbhFx41+ZlE82G5DyA6Kcg19MUXVTfOfM8xoic64Q7Vo3xinnHm08Lpc8LAoMmEggVK2vcM2Z2hpi52ArQFjSIiKwEo3XmEnDT1oSuxIv9SISX78jY0uvwgT40VItkmZGK0UBUXSEySDHxlyF1dkjmGgaY6bww0fw3G1IMdPyxlqDNsOApjP/33BUJ115eOmZKzOZqGP0OIWxt6GjncTe2U7t+WNUSl/Ar3yDXNtf4WVedkLjlkZegw0foh6Xjq/egh0hKn6cUvXrpDp/kERhExISEhJOCsaYBf99sjihb6sbbriBG264Yd7zn/3sZ+c9lwjXhISTQ1lXWDrVt5nlCE2BJuuZhr2StXE0ThuJrjkHR1rhRw6O1HF7G3nit/scGUcQrY1zPZvnZpHQcMxNu2GTcKnXN7rSEmiaopB1I6J9pZETntNcTm29gJ3Td1GMJmhxO2lx2pgMxzDWMOJnCLSkfk9Q1ESrqb0eGoPVMHWklbDkoDyN50qkkFR13N4noyRDE4KfPA5nbu1lT2nm5p8UBoGtGS/VrrERCZ1rZhU/I4Ul70Z136JliDpBserx4P71lKopjJHMXUmrBKl0nK7cNz7JlQ8+wn1bN7GjfSN+2Z0RrfVDAOVq1p11lO51k/HT0oIVDB7rJIwUpDUYgQ7jELWSBmEFOlI10yVwpME3CouMj2+a1ZwlqLVVZZH3oetEyIqPHy2e4jtnZQBBWecBh8sHJkk5q7C2D6GPUI2OEERPIUUerEekJaE+gKNa6cy/g9bsa5d5nqUxxlAYfwc6egxII+Wc3rBGY8w0pckPINUXcbztyxq3PPb2mmgFmHtjJMZGe/AnrsPpWTqdKyEhISEh4dnAsoXrvn37nsl5JCQkLEpsNrO8xF07599zjzE4aJpbSwq0iestbdNx8aPQOOhQkXEDHHlid8+UBE9pMDTqD8UsdaptHLHM1mo8Z2ZUm60FKS1C22Yzp1rSYzGM23NNBkW+dvBudk4fRhtDVyrPa9ZcwNkdxzdv6kmt4aKua/jp6I0MVYYY8/MMVyG0AcNVj9j4J44QhnaWG3Lt+GrRIyy7SC9CSkloNTmZQooAbS1GGFqziuFJKE+3sqbVZ7g8RWANnjJoKxuiPKrdOFDSzEkXnhGt3ekqnnQJrMRgFoy2zqZQSXH/ng1Uqqk4UVSDqghkBAiBUYJcV5G+1hKZBwxXPbEDxxhOGRrmiTX9uJmQoOwR+QpjYvHesXqStWccxUubhisvCIZHWzg60hlPNmWQ0uIqTRQ5CGuIAo/Z78nIEotYA9YIkPNrYoW0WB3XPwt3TgZAvT2S0nz+lK/w2zuvO86rPTemCxOBw2V9BV7cNx3vIQSeswaBwCBJpV5IqCcI3H668lfSkrscV/Ud5zzLJ/L/Ex0+CqQQc0UrIKQC04Kx01QKf0dL1+eOP2ZURPt3NGp/F4rgxxhM+DBRdAjHWfs0ryQhISEhISHGGMPnP/95brzxRvbv348Qgo0bN/Jrv/Zr/Lf/9t9WbFy4bOG6fv36FZ0gISHh6bEq046jDIE+vlHLzOdALACEsChhahFTQ6jn/8obS5NorY8hG+mdcSpvJXTJef6Crr/NNBsDucqQcwIqkYM2siG0hBAoaci6ISln4ZRkQRxVVNIiMMQJxDNOtauznXz2qZu58dA9VHQw42AL/OjYo2xp6ecvzn4rPekZQyFjDcZanFk9SLe1voi7Rga5a+RhSlGAJY2xKapG1MaUsyRAs3WQP5mOt8ma+621CCFxhYO2Idoa0kqBgD2D8PL+bjLKZdQfI7IQRB6eKhOa+Ip97ZAVwUw9InGg0ZEGKSyOiBsRWZNG28qSH+IThSwP7N5AELhgBG5R4JRFU/qtEJaUlqyPRjj/8QMQCQ4OdHHbOWeAECjHkGmtYowgKHvkOsqsP+9I040GoyWjYy3s2r8mjs56GhyLqs1ZhwL0LC+E+gKauMdvHEWNxamlOYxs63VFyqJSel6qsqsivrH9y6xK+7xhzQ7+5qkXL7EizTdzJIZf3zjOq9ZM4Mz59XJUL5E+Qkv6hWRSVzIcDdOe7z3phknV0g3Et6Vyi+4jpATjEAb3YKJhpDPXKbmZYPpPar8vC0daZ42MwBBMfgin+99XNP+EhISEhITZWGu55ppr+P73v8/ZZ5/NWWedhbWWnTt3cv3113PjjTfyzW9+c0VjJ4UtCQnPekTtj+rjpwsrDJ4TYaxECotSGkfMFqM1g6NZda1xX9Z4/Pi5erS1ZtwjQNbEa6hVXLe65Gzn4ymDo3wiEzu/ShGbLblKL5nabIHIxCJaSdNUC6oQlHWRLx/YgbGGjPJQIhYV1lp8G7Fz+jD/8+f/zN+f/072lY5xx/Bj7CkOYa2lL93Opb1ncEHXVr51+F7uGDlMq9tHXxq0DQE4VJ6iGPkNN2AlYuGtMbXzQOg7SFVfwRlV5UqPwGoshsD4OMphsiSY8ss40mddrot1mbPBZnmk8EPWZjP4JmSwPI5vHNIqQgiLI2qCmLpoFrSokL3T4HgO0pnTC6fGVDHDo/vWEYYOWHAL4JQEVsSpwVaA60ZkUz6n7TvKebsOksqGPL5qDbdtOhMRgqzVp0ShwhpBtr3C6jOHkBLGRlqR0hD4LsdGWpms1Hrqugby8Y2SKAIdeKDnmBw1JQbU35zM5IfPDiPb2KjJzfkIxzTlEHtOlR+d/UU8L16bN697jL956jKO93tS5w/POMI164oLbhPCBSxRdADmB0JPGkbvQSARx70j5IH1icJH8JwrltzTRo8wk1u9FPHvuol2L3/CCQkJCb+KzPxZ9Is7/3OEz3/+89xxxx388Ic/5CUveUnTtttuu41rr72Wf/u3f+Ntb3vbCY+dCNeEhGc53V4L7nH/AI1R0uIqi7YRrpxv0qSkxmgnjuTVxIGxsWKYEa31aOsMoqZlQ+OQYnHhumRsR1jSjqkZ9SwPKZiTTmIbMaR2N8PDU3ux1pJ3mp3NhRCkhYsjJEcqY/z2z/8ZJePU2pxKIxDsKQ7xVOEo/3HoLiaDMq1uhlY32zROb1rhl0fRJqidfWYtjvcdEllDWqZYnWmnpAtMRdW4l6sQnNdxAed0nMuG3Eaquoo5eJhd04+Tc7Kc3bGesp5iKhon0GHDnCoj02xrPQNljmDMXeyxpzJazrGudWreuY2FvUf7qAYuCIssC1RB1trdxFeSy/ikvJBz9h7g/D17MULyYNdGDlzbSndpksmjregoft+l8wHtA9O0908j3VjMSm0ZGu6k5KcohylwDKQNIhXhOgajLbqS4vjiidgpWJqaeJ0VJRQWNxWg0rq28LGhk6M0joy45awv4Xkzae1gefOaB7nh8LnHPWXO0VyzfmHROhvLyTeXaBrfLrd+fX6a8xKjnugsTnD/hISEhISEhfnyl7/Mhz70oXmiFeIWqn/4h3/Il770pUS4JiT8MtKRapkl15bG2DiNVS2yuxRxPWBkJNaKeP85LjhSWOb20azvYevmQWL+tqUQGJwVmDtBnKRbNy1yxMwtzyolrDZklbfosY5QGGs5WB7jnI715GYJ3HZyREazc/oQodH0ptvmHd/u5hkUE43HxhocoZrMg5SrCSsOctanqbUGbTU9qXY6vQ46aMcGk6xuy/OBba8k62Txtc/tIz/iwYmfM+FPEJiAYlRgFEHGyZJ3utEydjfe0rKVq/pfya7xz/Nk4S6kgDa3Qsl6C6ZuTxVzFCtpTGhxJlzckqi1x61Fqx2DrN1/qL/WD21dx939W+memmRg2wi9p4wR+AqBwElF1ILZtfei4dT1hzkaZSlOZxA5HyHjmxOe0hht8UsZjvvOmP1eMgKUqaUNxxukMmRbfZSMDcRi3W8Q1vJ/196A59FIqLa1ub3/1LsZDbL8YPi0RU+bkRHfvWQHkF10H2s1YHHUwNLX8DSRaoDIDC/jtzsAHJS75bh7CrUBGz3J8bM0alkVz/A1JiQkJCT86vDwww/ziU98YtHtV199NZ/61KdWNHYiXBMSnuUUoyqRaa4bBRtHVxuuvTP7C2xNfC6MFPFxxsYuwoK4d6msReKW/gN64VY7SxGL1pXHdISIHY+ZJadj6RQrr6oOAYEn5/fcjKxupO/6JiRHc2RWSom1Fo1hPCiwKt3RtN2VDgOZLp4qHm5kCcVjzpBurxJWWhpthbQ1VE1Iq5OlPxOPp43FGHj1qWeQdbJUdYWvHfoKTxWewJUu7V473aluyrrEqD9KoAOyKssLuy/leR3n0p9eza2H/5BDlZ82zr22ZZxquVm0F6se+0e7GZ1ooVLwSI85yJDY/KhxzXH/nKDoYULJgxvXM9TRxlBnB6pqmR5tod+OIJUllY2abmzUx0jJkIdHV1OSaaRrYqdia1DKoJcrWus0gqVx7S0SEAIpDY4XYYxEyTilXIi41vm25315zsGi5vQcP/PnZ93Gu6bv4XceuZYj1fbGqTJK8P7tl3HNwL8QhCPA4t4N2oyhZDeZ9CXLu44Vksq+Hj31MNYECLnwTRhrLJYI1zsf5Rzfb8Jr+wsqw7fOqnNdCkGq9c9OfOIJCQkJCQkLMD4+Tl/f4iaGfX19TExMLLp9KRLhmpDwLGewPE40K3IihSHjRihhGj06Z0fdZvdlXQxRqzGVQhPWnGwXirLOHtNCkyHPUmPXIz0Kw8nwsokjas1uvvXrtEBVByiZivuqziI0M2nN0QL9xGytn4y0gjG/QG+qHTln8bq8FoadHFNRacESl1RLQGUiIqo6KC9CCktKwvpcL0pItDEcmppiY2cHLz5lIwC3HvsBTxZ20eF14s0SKzknT87J4xufyWACV7p0yR6+8NTlGMKm80opSKu4v6+18ODBNQwXWjBWYisSb0IhQzAyzsIFcByDS8i5B/bxwJoNBFUP6XgMdcYCWwiL0aKxts1JuFA38zk4keJIpRMhLalsQOg7YAR+RUKUYvnJ4HOJU4GlikWrmHfDI+K2c74y54j4XNrGHVDjOKlloLXE1170JcDGt2ZEDi//23S0XEilWmJ8+mNEehgle+a5G2ozhbUlctk3oGTHM9KLro6X+TWqxf8PrfeCkQjZ/LVsjQUKCJEmnX/XssZ0nD6kewY2rNe6LvR61GzO5Hqc1JlP8yoSEhISEhJitNZL9gdXShFFC/tzHI8VCdd77rmHiy66aMl9Pv3pT/Pud797RZNKSEiY4WhlHABHxqmqWTdE1ZTIgvWiYvmxTSHidjWVSC4peOt/+npqaWOmmb1joyhRa3XydKhHk2W9H+ycOUkhMNYQ6IjMnLRhbeP5CgQpOf/jTgqBFBKBJjSa0ESklNu0jxCC3nQ7hWIZvYC495Sgb02JscN5gqoDylKxPg+NHkRal7R0Oa2rhw9edikdmQzT4TSPTj1MVmWbROtsUjJFRmV4cOJ+9k99DEfMX3ffOmSckErosWe4h+GCiU2cAouoSFQoME5tkXRsbuXZgKse28GaiXG6SwW+t+1cwqqDm417zhoj8DIzgrG5qjK2hipWFEer/Y33inIMPS0TRL7lyNH+Ba/n+NRuSrgRyqtF9WvFxDM3VCJ+fM5XmqLu9Sh8aMHUfJjlrKrUesa8QVCllWLpP7AiS0fuetryY0wV/4Uo2oeUrQiRwtoQY6cAl1zmWlrzJ15/c6JI6ZHv+hzFsbeh9ZHa70v9fRHFjsMiQ7blf+Gl59cLLUaq41tURi5G2EGYF3mN19uKDjLd/3myLiUhISEhIQFrLddffz2p1MLOhr7vr3jsFcVCrr76ah544IFFt3/0ox/lve9974onlZCQMIt6XeKstjZCzDdQirHLaFfTjCMNjjQYJMbOMXS19bpZgas0SiytQmuNWnCERcqVx91mEy1ySmvqc7QIBKFdSFbGpKRDi5tZYL6CDi+PXqIZamQiJoNCnIpaDz3P3m4NoRORXztJV/80qVRYq8fUGKWRbQGpvhAvFX/cPlnYSVmXyDn5Ja87ozIcKT/GdLS4pa3AMjzdQqGaiSWcAVF1cOo1rTZODU5nAtJBwKsefoA1E+OESrFj9fpaj1SJDlT8WmtJ26rpOa9bLaapLQcGc+yZ7qfh7CwMHekyuuRy9MAqiFh5Trg0SDdOc585M7WbNFV+cs5X5x1iLFQtFK1iwkDJGiI7W7BCxVoKBqxoQ4ocU6WvEerdtOTeSE/HX5HLvgaEi7FxT+BM6jK62z9CR+v7EOK/JinJcTbS2v0tMrn/Byl7azM3INJ4qZfS0vl50vm3n+CYDpmen6DSr8eK5lY7VmSQqavJ9NyPc5z3YUJCQkICM67Cv8if5whvf/vb6e3tpa2tbcGf3t7eFRkzwQojri960Yt42ctexo9+9CPOOuuspm0f/OAH+fjHP84f/MEfrGhCCQkJzXR68R+WsWPwTPuY+QmAJ15/CnF6aNYNqIQukVGYWY7D9ShbSkWkF+m1OnscgUGJ46cqL5c4k1fGQSjbPKixoIhb00gEwtY7n4qmfSzQk25DioXv03V6LQxVJmrGS837WGs5UD7GZFScqfWce22xaxUGQaYtYsvqKbSugu0hI1ejrWF/eYxPPHILf3HeqylFJYBF5xOf13Cs8iAgiGxz7W49fdcTERbB4Yl2eq2AKYlTcJCBQIYgNCgLrtS0UOFljz5CZ6lI1XP5/pnPY7i1rTGW0QJdcXFTmvZVhebvx8gyNpnhyUNrMS6kuypIGaewBgWPoak82neQ9TpYYbGOxabsCdy5EEhPo+YYeCmpySqff9z801kpwTVhKlqYFhkiWvHNbkDjW/CxjaSD+N3gAIZQD5JyzyIyRylUbiXlbiXlbSflbceY92BsCSnSSNmy3EmfVKTqJNv2R6RbPog1h7DWR6oBpFy5sHQcB6fzr4G/JgqexER7kGptkhqckJCQkPCM8a//+q/P2NgrirjeeOONnHvuubz0pS9l165djeff85738PGPf5y/+Iu/4GMf+9hJm2RCwq8yXi3FVQjTFE2dp59WJBZt49iMG5LzfFIqxJUaV2rSTkjeq5Jxw0XHF8LW5habMJ0s0QoQ1dR5qNUCtlESBw+FxGAxmFprkdhAqRhV4+uSLr2pxcVISjrknQytXpbB6gSlqIq1FmstI/4kE2EBayVSOEgBDgIJjR8BcYsWCeVIUYokjmdIuQIpBK5UrM62sXt6mDuGduNIpzH+QmgTcbTyANpGcf3tIqnfbU4FJQxTlRyiJPDGHZRf79MaqzulNPmCzyvvfoju6jTFVIpvnHUBx1raY8FqY6foyHdQyjJw6jFS2bDhHq214KEn1vP4/g1EVmICVYtmGqKRDMFYhsh3sKImWEV8p0CGElk5AUcuRyO95noXKQyd7ghX9e/k1nInY1Es4C2gZTdR+loikSPUh2BOi6aZm9MW0AhcjA3QZgQhspT9e5rPJbM4qucXJlqb5yJRznocd+vTEq1zcbyteNmrE9GakJCQsBJ+0dHW51DE9ZlkRRFXz/P49re/zVVXXcXll1/Orbfeykc/+lFuuOEG/v7v/573vOc9J3ueCQm/srR7eVzhoFmqkH0ln2gzorX+XyUsSs6cp9nHdz51Mb2Yi/FSs6rrtoWErrVxZM1aibGSUM93DAZocbL0plvYWxxEYyjroDFmq5vhJX1nUY6qPDZ9kFWyo3EToI62hqOVcU5pWcVrVl/IT0cfZ3dhkPEgjrBO+yUcHBAKJeIWNxZwZt3zi2xs2SukxUaWiUCQTwmEaSGyBgG4UqGE5Jaju3jfmefjSQ/f+KRVs8txORpn1N8Zzw2JwpCVC9SCWMBAyviEZYVTE4nGjQVryg0wxkVguOqJh2ipVpjI5LnpgrOYVhlsJLBGYE184yHfXaJv8zj5rlLjBBYYGm2nUI7FU8YtkfcCIiKqEy1ERTduUzT39qeopW9rgaxKTOY4Rc6OwcmHDRMvi0AKQ793hNdu3NPY7TvlNaxVRc5MHYvTtv3bCaNBLJVGvev8Rapfi0QIh8iM4Kg1jbTghISEhISEhOcOKy7gSafTfO973+PKK6/kec97HtZavvCFL/DWt771ZM4vIeFXnk4vT1eqhWHfhwXijivDznONnc1M85Ole0AuJVqPhxAzArVxvK3fWJRxrMxKqqGLsfOTQ+L61BxpmWJVpptXDpyDtprQRgxkOrly1dmkHY9xv8D/eer77Jo6hBCCFieDQFDWPoEJ6c908ltbXsHmln4u6z2De4/t5QdPPcUDh44xVFAYDHgBLR2aVN4SEmCFaKzdTOA0blFUjSxBpHhywic0hxECssoj63gcLU/S5fWzOrOWfaU9pGRfw9G2FI4yFjwRj2QhMopWp0JGzrgJl0OPp8Z72Tnaz6FCikqlA68iERpszYgp6/lkvICyLzFacdvWM3jR3ie4eevZ6JQkk6tiIomOJGHVpW1VgfXnHWm6gSCA0Yk8uw/2cPnmRzlj4DA5z2+8ZpPlLD/ffwr3H9i0yItbE6/RrBY3C+FoVE202ob81FzU8yTP6zrWeN01oLAc1HlKVcnZqSk8t33WbBe7HS3i1Hc0knTNfKlESm5YZEIJCQkJCQkJz1aWJVxvvPHGRbe9853v5NFHH+Xaa68lm8027Xvdddc9/RkmJPyK05du47zOU7hpcOykj72QNJ35839pOSrE0v1i54+30BhQL2s0xMWxdSGojaAUeFgrFjyHJxVKCEaDaV7QvZU3b7wYdwHn4M5UC7+/7bX8bOxJfnzsUY6Ux7AY+tLtXNp7Bme2buTQdJGbD9zEA0O7eeJgRBBIujNtOFLia4NfdPBLDtkWh5b+aYyMFqxRtRiMhcOFPIGxKBHHrKfDKhNBmbyTohT5XLnqKr5y8IuMBMN0uJ2EZrohWo0VBMbBkxGrvKmGoBwt5/nBvtMZr+QoBYZqqRUyoEoC22ibasmmArwwQucDqtNpRrNtfPus87AohG9J5YK47U0kybZV6d823CRatREcPtbJoaPtvOsFt9OVK8VGR3ZGpHfmylx5xqNs7j3GV+974SIvbvzii0hgvQXeBVLjtYb1iTdqpC9J389ZXaV6PkBjMGnjVOQxk2XElOgzh2rb40pnW2vVU98/tglTzNg06Tg920bkM1csPOeEhISEhIQFEJxQ04Zn5PwJyxSuv/Zrv4YQYsGarPrzX/ziF/niF7/Y9LzWy2mdkZCQsBRCCF7efw4/Hn6E0JQWSK1diSnTTKz1xD+HLUJY1DJEa3M7lcUxs/auR/UcafGUphq5zI40CwESQdpxAcEVfWdy/aYXLyha62SUx2W9Z3JpzxlUdFBrn2P4yhOP8Hc/+xpHShPo0BJNewgLXirERFMIlcKREqsskbaUpx2EaKV1YBqDBhuvRd1sODISaRWCdlKzsptdqShHIRUd8rmn7uSDZ72CN6x9M985+k2OVY8wFRwG4cZCT1gyKmBNaoKsiqOtpcBriFY/8PH9LvAEiFpUU4CjNJmUz8bBYV702JPcct5ZHG7twi96WK3i1GAE1UIKqQzZjioDpx8jlQsbL5A1sGvfakbHWnnbhXfQnSthLPMi3trGztYbe0Z45fb7+d6O8xYxrVrsxdd4HfVIcryDFIZLeh7lzJ4ydVMwrEEbEKi4vysWjeBwmKfXKRK/E9zas3LW+6hRfdw4h7EhQghcZ4D8CbSVSUhISEhISHh2sCzh+qMf/eiZngcAd9xxB3/1V3/F/fffz+DgIN/4xje49tprG9uvv/56vvCFLzQd8/KXv5ybbrqp8Xh8fJz/+T//J9/5zneQUvK6172Ov/u7vyOfTyz/E567PL9rE6tSbRyolOb8Sb4yJ+GVUT9XLFqX23ZndmuS+uN4lPj/ZsTGLCfjmnjNOBFGpxBC4SBRMhY0vek2Xr32PF7cdzprsp3LvgIhBFknxXi1zJ/c9UPuPbaPsp3CdSNsORXfTZWWoJaenPaqaOvgSQ9jI6xrKBcU7UGWVCYishGhjdBWE0UOoXbIiBxzg7HGWiyWVs/jpqOPMhGUObN9gJf2vp77R/+WPbbQqGltcaq0qGrT+j453sd4JUel4hMEnTgFhYxqRstR3DO3NVPh1MNHeOFjTyKAzUeGGDqzDeVptK8IKrEw7lo3SeuqIrnOctN7xxjBrt1rGJ1spTs3xUDrZC3KulCeb73vr2Fb/yA37dBoq+a3Cp39gtdRIdmuAK1rLXWUxVEhbxq4l1xu9jtB1AymLJioljweUzAe9UJfIdJYW0+jz2Dxma+WLRDiyDX0tv0Bjupa5B2SkJCQkJCQ8GxlWcL1sssue6bnAUCpVOLss8/mHe94x6JpxldddVWTzfLc5rZvectbGBwc5JZbbiEMQ37jN36Dd73rXdxwww3P6NwTEp5JhBD0Z1vZXx7C1iXgCYjH+Zx4nLVe96rECtvuLDAFs+CWOIFZiDj62JtJo0QOJQRdqRYu6z2N16w9j5yzeH/T4/GZh+/l4dFBQjGBqwxGC6KqG6eryjhMGEWKUDgoxxCaECkkWhiMURSnHLqzFonEYjFIKpFCoFCi2UhKG0NJ+0hhKEWGyGruHd3LzqlBvnf4ETJqigu7i6TVwuZbfuTw6MhqSr4Dw3m8kgNGNHKWPGsgkJy2+ygvPPQkCNi1doC7t22JV1eAk9ZEkaK1t8jAGcPzzmGsYM/BXkYnWwF48ZZdSAH6OL5KxsSR3hdseYqfPHXagvtYNeu9piK6VsVZAw3rJAu/tupe8rNajTZH6sWc+ltbq1utF8+qWtQ1iP8tcjUhW1/P+CIc2Ut/x8fIpi5Y9Hq0mY7b0Ig8Us7v+5uQkJCQkJDwi2NF5kzj4+McPnyY7du3L7j9kUceYc2aNXR0dJzQuFdffTVXX331kvukUilWrVq14LadO3dy0003cd9993H++ecD8Pd///e84hWv4K//+q8ZGBg4ofkkJDyb6Em3IWTcvkSs0BBp5dha25tYTKyoj9bs0RYVrTPnq287t2sNV/a9gLTjclrrwNMSrACHClPcM3iIUBRBxrOwWoAVCGkaET8hLIFW5L0IbQWOcLBoImEpVwwVU8FYizZQCV20idvHaOHjSYUjJZExREQIYUgrB086BDZukbM+10kxrLK32I5vNvOSvidx5YxSHCy28sTYKnZP9HJ0og1vxEWV47RlXAtC09VSxgSSs3YfpnfnPiJP8ehpq3lg60ZoGEhZdKiQEtr7C/PWwxjBE3v7GRlvb4Q727N1d+HjvdISMPS2TlFzZJqpbTUC69jaEBa3NWh6/0Icib5+088WfRc0so1FLEMd4riqI3Rti1OLsKYRGCxR7SgPcIEQiHDkAOt6Pk/amy+urY0o+XdRKN9EJXwErEaIFPn0JbRkriLtnX6cNUhISEhISEj4r2BFf3++733v413vetei2//7f//vfOADH1jxpJbixz/+Mb29vZx66qm8+93vZmxsxrDm7rvvpr29vSFaAV760pcipeTee+99RuaTkPBfxds2XoESs6JPTyPaulTEdOG2NjPHrOS0s9uQGVszYlrmSMWoSE9Gsaf0EN848k2+e/RmpoLpFcwi5t6hQxTCgMCWGs8tVJ4phMUagdYSV0R0p/J0eS24UqJE3KbHjxTWpGjz0rhSknbiSKCvQwIdkXEUnhRkHZeUcmMH4UYbIEHeTdHiVDlaaWdPsQdrYaic5+YDp/P1J85nx8g6jhZzyFJNtDo29iIShq7WMtJaLt29kzMOx5HWn6w+jZ9t2NLoMWQtRL5D6Du0rZqitafYdJ3WWHYf6GFkqpVsvsKq/nH6V4/hqOOEWhvMTQAnFq1WYKXFpA1Ii5v3kbMirxYwxnL9pnuJ313HywAQDadpgB4VX4eSrcRytoKQnbjOVqRoRwgZL4FQeO7prOv5wiKiNWBk6m84NvFhSv5dCEQt9ThgqvwNjo5/gKnyt5a5FgkJCQkJv6wI+4v/SVhhxPW2227j3e9+96LbX/3qV/OZz3xmxZNajKuuuorrrruOjRs3smfPHj70oQ9x9dVXc/fdd6OUYmhoiN7e3qZjHMehs7OToaGhRcf1fR/fn+mVOD0d/1FsjMGY5f4Bd3IxxmCt/YWd/1eB59oa96Xa2ZLr58nCkXnWMyfKUsfO3eYBaSdNyQTUTZIaUcnjnciKhpCBekBuYZfgxiFzHj8+uYc/f+xvEeh62SPfOXITZ7efwfXr30zaSS80zKIU/CpxMq9p3LmTjkVJgzWiSWAh4rkLAZ4UrM8M4EQTbOtv5ajaRygiqiYkMD7CgrWalAIpJEpoMtIjNOChGl861lryKq6nFUhSUlKJDI+Mr2bPZA+Hix1MTudBQOhrhE7jlUSclCxjCdfRWmq8DpkgRDmGu87awsNtpyALGh3OGOMpV9O7fpxVm8eQ0mJmXd7wVBonZdm66SjplG5EQqu4cQQau0iNa215akJ8cKIDWXttIU4PtimDVOBkAxwvjuzKmsGS1ZZ3bP5ZrUiX2qvBku8Mi0CjUBgGnEmsdYAsjuwg0oMYm8LYEogckEGKNBnvLPpafw/XWbXg7/nY9OeYKn8fJbtxZEvT6y5tN5EZZnTq01jTjrVbnzOfFc9Fnmufx89FkjV+5knWeGmSdUl4uqxIuI6MjNDd3b3o9q6uLoaH59dRPV3e9KY3Nf591llnsX37djZt2sSPf/xjrrhi5e0NPvrRj/LhD3943vMjIyNUq7+YRvXGGKamprDWIuXTTcxMWIjn4hr/r9Wv4a92fo3QRLGj7NMZTMx/OFc0poQl56ZodfKUooCJoEA9bXgx7506llj09egWYrm1LKnbOHZmXpZsGVpdSdwuxxDaiP3lA3x06O/ocjeTVinW53rY2jJAzl06lThbDVgvPLpEK7a+hhLCVhdddUDZmRRVK/DcCFdp2qVHOgrZmkmTktN0+A7GSqRIIRH4Kmq47dqaE68NFX204lqFsZbQaIx1yQUSo0vknQyn2PWMmwqlwCPyFd3VFN0QNy914p9URmIzIKQln60iaG1cz4GLLiRd8pGtWc7wHax1aektYa3ATWmy7RXclIKod6bsEzg83kKr9jilrYQUaUwI1sRyeDJoZX37kZlXwC70usXXqo1ienA723JxGrNVdsbU19EoN9UQ3a6jUdblyv7dUNqwwIjzxWv9vSAxSCyrnUlElKGIgxRplGonk3o7rlpDEOzHmhClcqSc03DDtUyMC2D+91FkJhmbfhRrT69FbhdiFZEeplK4HRn1PKc+K55rPBc/j59rJGv8zJOs8dIUCvPLVRISToQVCdf+/n4efPDBRbfff//99PT0rHhSy+WUU06hu7ub3bt3c8UVV7Bq1ap5gjmKIsbHxxetiwX44Ac/yPvf//7G4+npadauXUtPTw+trYv9QfPMYoxBCEFPT0/y4fcM8Vxc417gvMo2bjx8F4EJWYnJErBkqjCAK6DNdbly7YuRQnLf+E4KYZFpVWEyLDeE61KiFeqRVstBZwJzAsJ19hiOAEcI+tOgBGhrqegAbSPAstdMoMNVUH2KzkILb9zwIi7p2bbouNuU5QuHnuRYWMbKIJ4nYFxJZTqD1QKh6sJckBdVhLGsFu2U/RJbVrWwQzxJhMZVLpYQTYCVGm1nR6IFkZYYnQMrCUyEsRYlJCmrsKFFRoKschgJykyNObWrjuptR5ERYCA9oUBZOtqKTE2FnHp4kEc2rJ2pY21tZV95mqDqIJXltE17EQKqxD9N62pg73AHo5WI8zoe55TOIxghuXNiLVa2NfbrdkfoTRVrAn6uu7CJjcEE3L93IztLVaxnsWkTC25jESmNNeBog7AFrhrYTU92mrZskQrZRV+fueK1/j5oocSm1DjdTgmJQOCSSZ1PR/4N5NJXxNFfINKGyXIZY6EtkyblLvw1N1W6kwqP4qr1CFFZfD6mRGTuwoteTm/v1ufMZ8Vzjefi5/FzjWSNn3mSNV6adPrEMqSeVSza3u2/8PwJKxOu1157Lf/wD//A1VdfzTXXXNO07Vvf+hb/+q//umQq8cni8OHDjI2N0d/fD8ALXvACJicnuf/++znvvPOAOK3ZGMOFF1646DipVGqeOzGAlPIX+sEjhPiFz+GXnefiGvdmOunNtNPu5HiieAhjDVJIHKHQ1hCY8Pifb0tEW7NSsbGlm7dteCUXdJ0BwCsGXsSB8hCR0ewrHuWLB/6TqgnQduG0n9nnt0JgxXLqGBceQwjQAqKaIiwZH4NByrirp6RIr9eGFZLR6jT/svc2lJBc3LuweD2rexVbO7oZPDqKEUHsXgzgGLyOKtWJNFpLrBU4TkQYSYx2GNUV2tstg7nDBFEJBFRstdHHNXZBnjmPsRblaKwsUfJTcZJ13dzKQEo5SAHHKj5TYzOCUU5DalKhqmImvRhQjiFVCbj6oR10FkoorXlw88bGdgtxPW7OrzkjN6+mtYLdg11M+Hl++4xbyHkhzKo1fmNuJ1jLTwbXcsis5u7xDVzW8xRtbtyax9g4/VjUXhNrYdfgALc+ejZWWqxja5m/Frx43zxFfvd5P8adN58yxljiStVmEbtQRx2XiIuyBxDCReAghUPaPYvVPV9A1dx/C1WfH+3cwy2P7+bYdBFrIZ/2ePGpG3np6ZtZ3dHWdJ7IDiKERcqFcg1mkDIDZhhjJ59znxXPNZ6Ln8fPNZI1fuZJ1nhxkjVJeLqsSLj+6Z/+Kbfeeiuvfe1rOfvssznzzDMBePTRR9mxYwfbtm1bMPX2eBSLRXbv3t14vG/fPh566CE6Ozvp7Ozkwx/+MK973etYtWoVe/bs4Q/+4A/YvHkzL3/5ywHYtm0bV111Fb/5m7/JZz7zGcIw5L3vfS9vetObEkfhhF8azu3czH8O/gwkrMv1MlgZwwKucEgJgWccSrqKtfEf40rEpjZ1kVkXV45QuEJhsHHqMRZXKq4euJDXr3sJfemZ/qgtbo4z2zYBcE7HVvqzXXx2zzcZ8ycJzMJtXE4GoibJrJWE1hAZ3RDqsdyQGCJ8O0ZG9tGXaWewMsFXD9zFeZ2byDje/DGF4N3bL2TP9DF2T/k4boCqCSvpalIdFfxyChEKUkojBLR3WLo6i3S2SYaqPqa2tvV2RM3tWmritL7OUuM5IX6YQtZ29LUmMJqs41AuuI1j3RFJalzOCNb6+BbyBZ+X3vc47ZSpZDz29zVntcRRUUHHwIxxlZj1r/3HOhkptPHHz/8OQkrifN45t5CF5OKBwzw+WWFHcTO3j2zhlNwoG3OjZFX8HjEIRqZaufepLew8sjY+txMLV7AgDUJCnzPEu5/3s0XD+1IKWq1l2paZLV5je6vZcWvLWd7h+Dy4SEIc2Ud3+x82ROux6SIf//7tPDE0gqMkrekUQggKVZ+v3vcwt+3cy+++7EU8b93ArPOcSKL9iWULJCQkJCQkJJx8ViRc29rauOeee/jEJz7BjTfeyNe//nUANm3axP/7//6//P7v/z65XO44o8zn5z//OS95yUsaj+vpu29/+9v59Kc/zcMPP8wXvvAFJicnGRgY4GUvexkf+chHmqKlX/rSl3jve9/LFVdcgZSS173udXzqU59ayWUmJDwrWZft5cy2Dfxs/An6Uu3IjOBYdaKWOhzjCInGkJYeHV6e0GgmwgLaxu7ASihkTRYKBHkngysdtrWu412brsFT7uITAC7qOotOr42vHryFx6b3MhkUF9lztgXyiee5SGFrqaOGoG4OVY/qzRISmplr7061Mlyd4ufje7hkkajrqZ09fOyFr+B3f/pljhYtYWixNSkjpaGtq8i63nHSjqEctuHKPOtynZSjEN/ELVfEEhZ/glhIQqzbUiqiGroYK+K78SLu/1oMQ0I/Fm3esCA13nw3un6Kruo0r3r0fnJBwHimldsuOI1ibqbPqLXgl1J4mZD2VXMdly2FcooDI9388Qu/VROti809XtfT28fYUewFWtlb6uWA38Pg/nYKwy0EuvlmgJUWkzXxeigDDmTdKu8+Z3HROnOBghZrWazqSWA52ztIj1OJZ2yrSNVBb8efk03FWTRBpPnkzXeyc3CYNR2tuGqmj24+5WFslsMT0/zvH/yUP7/uStZ2tgPgOutra6cRc3rvzsbYAlLkcNQzX/6SkJCQkJCQsDgrEq4AuVyOD3/4wyuKrC7Gi1/84kaUaCFuvvnm447R2dnJDTfccNLmlJDwbEMIwds2voypsMST00dIOy4b8wOUogqlqEJFByihuLz3HC7r287O6YNUdcCB0jGemD6EtiYWrU2awrI608NvbnrlcUVrna0t6/jQ6dezc3o/Nxy4mYcmn5qTOvz0IlQCW7N0kqSVQQmBtnFarsaghJy174zwcKXCWMPe4rFFhSvAGd39/NuVb+GvH/syT4xNUI0ihNS05aq0ZCJyTh5tMwRasSbTiyschqujy57/bPMqJWPXYmMU1lq00EgkURTv5UwJvPGFxVNvYYpX77yftA4Yz+T59unnE0WCtImd0KNQEQoXN11i7VlDuGnddHy5IrjvyQ1kchGeWk7kMN7nulW7uHHoAur3CbR254tWATqvQRhIxdeSVgHv3nbr8UVrfX2kANMcdQVQRJyv9tHuzET0qybN6s5/IJ+5tPHcz/cfZufgCANtLU2itY4UgjUdrRwcm+TWx/fwGxfHZST59KWMFz5PZEZxVd/CK2Et2oyR8V6IDBb3SUhISEhISEh45lmxcE1ISPjF0e7l+N1TX8etQw/wk5FHGQ8KGCxZleG0lvVc1redS3rORArJGW0bGsc9OLGb24Ye5MniYXwdIgS0OFku7NrGlavOpTfdcULzUEJxZtsm/nL7b/HDYz/nKwdu5VDl2CL1rCcmZAUWYyWOtGQcPev5mmsvFiEMAkladM07OlxGCnN/to8/Pvt6fjJ6Nw9NPkoxKmKthytd1ufWcrhkKAUjpByXShRQiqooIQnRy74aa+O04SZfZQtGGKrlEUQlhzeqFhzPi0JetfN+UlHIsXw7/3nW2ZRFGlE1CCd2P1aOoaW7zMb+I6Tbmq2YjIF7ntiKl9b8xmm3cSKR75QT34SQymKMZHq8WVhaZdE5Da4FZ2bMV258hHzacCKvdx6ox+wtkCXkXG8/udrrboHAZqjYPNLZ1DiuUPX57o6dBFGE6yz+dSaFIJfy+PETe3njBdvJei5KttOeewNjhc8S6TGU7GwYPAGxe7U+UtvvTRTCJFU4ISEh4VeWxJzpWcGyhOs73vEOhBD80z/9E0op3vGOdxz3GCEEn/vc5572BBMSEhamxc3w2rUv4qqB8zlQGiYwEXknzYZcH1IsbIDwvI7NnNO+iSOVUSaCIkpI1mR7aHUXd3ldLlf0nc8Vfefz54/9Kw+NP0nBNDu11g19lkcs8RxpyLsRSghM7en6EMYalACXdpSciQTGWRuWdm955QodXjuvHriay3svY6h6jMhqWp08q9J9fHDHv5BW8di+idCYmqPt8YkNjeaYDVmQUmEtVErDiPI6nJJE6oXHCByXO07ZxrbhI9y89Ry0IxHWolzNxvMPIwSkMyHdsguT8htrY4lF648fOw0vFZHOhPRmF3fOnU89iTy+gOJkBhMp6qtvpMXkDSgbtw+SschNy5DNHcMnHGuf7Y/kEvLCzF5ULU/aAmXbTsVkcGVESlZ56tgotzy2m5/uPsCTx0aJtKYchHTnc3S3ZHEWMADJpVyKfsBEqUzWi42a2nNvwNgyU6WvEep9CJFF4GCtjyXAkT30tL2PjLedwgItdRISEhISEhL+61iWcL3tttuQUmKMQSnFbbfd1nRneiGOtz0hIeHkkFEpTmtdu+z9hRCsyfawJvvM1Oy9c9M1/L3+Ojun91GO/BWNoYQh62g8qZHS1uSirIlfEwsjAViXVmdj07HFqErWSfH8rs1LnuNQaYTvHnmAsaBAWrq8oPtUXtAdtzu56ciDvG3XZyjqOIKZVWNsSHc04pWL1bfqegvTOaZNdeOkWAwKquVjRNOrQUhSJVHrJzvzmenoiEjFH8+7u/vZ070qvmAT1/hKaWnrLtXqPiXMWmZjIQrgocc3kW3x0Qi0ESu6WSsdS+g7HD3Q1VDgDdHqWJC1n9rGc/oOkVEnbtZVv6FhsVwk9qBmfX0EZKjadgxlMirLQ/vH+Mcf3cdUuUJLOoWrJMZYKkHIgbFJJsplNvV04TlqwXPM/m4SQtKZ/w1yqRdSrN5Kyb8Layoop598+grymctxVR/GLOyenZCQkJCQkPBfx7KE6/79+5d8nJCQkFBnVbqL9255HTcc+AH3jDwKs3TM3PtZsyOwcXTPkpEhedejwzmV0BYpmYMYolrrGzC1MULt4LEex5vpCxeYiImgxAu7T2VdtnvB+Y37Rf7ska/x8OQBAhPVxKjlO0fup1NmGNJTs2cIQFmHPF4aAQwpZRZNuFVzrq/+0FjQJhanpjRKUFgLyFpoFFDE/U+B7UcPsH3oAN844wJKqXRtFjUzKBsnHKdbq43x67XA1P5b8R20cjn1zCNUfJfBiTYqgcfjY72c3XtswTWZT+ztWyl6HHyylyiIvyqMYzCZOMqKrEVbaxfppUKuGNjZWLUTuXVpiF+DS+UTzG7zp1EUTR++1eRFhLCr+PSPnqQSGNZ3tSOEYLrqMxwWSbsuxlqmKz57R8c5ta+n6f02XfXpa83TnW+OxAshSHunkfZOo5v3Yq1NbrwmJCQkJDSTpAo/K0hqXBMSEk46/Zlu3n/qr7N34Ch37L6X0dLPqBgfKSSucMipNKHVlHWcvppVLqsyefLKY0+hQrdai5IKjxYyoo+KHSKwU1hhqEaG8apDKXIYyDhERqOtYSosE5iIM9vW8o5Nly8oPiaDIr913z9xuDyGIxQ5lUJKgTWWoi4zGAUAMxHdJokayzFfg3eCreikhPZswNDgFJG/No6SNvrmABqssFxwYA/PP7wHgC2jgzy0elY0udEbB/JrChR9l6wX1dyhBZGW7Dg0wEQ1T3dLgXW946S8iPW9Y5R9j7unTuPs3iFm2uAszdBUjt2PraldABjXYLM1pe3FxwsBQhpaMgXef+YdmBV+sRYNvCz/RNNzGocx3UdgQ4SJF+rrD65l98gUazvaiIzBVYquXJbRYolIGxwlSTkOhYpPoerTmokd5yNt8CPNladvmReJnUsiWhMSEhISEp6dJMI1ISHhGUEIwcZ8P7mBF/Lylkv48egD3De+i7KugoWUctnevpmX9J7HpvxqAAYrE/zRQzdQMj6tKq67lVKSY4AccQ9OqyyYCawtIIVkpDqNFIKedCuX9Z7Blf3byTnpBef0Nzu/y+HyOBnl4cgZASOkQOvFVNfc+KoiMPqExStYbDgQi9ZZQ+usxZkUXHxwJ2cfPgTAves289DAhjlHx7itAS39RaQUVCMXHSnMdCv3FbKUwrged7TQQm/nBGknFpe5dEguHVIIXFq8aIFrmnu9hn+449WxxgV0SkO6Ngt3JsqKsLRky7z/jDsaR8bIWsfX44tAoy157cRp4ICxkorNMGXaMUYS+ZYWWebA5AA/fGINgdbsH5tkcLrAhs522jJpOrIZxoplEOBIiR/BWKlMayZFEGmOTk6zqbeLy7dtOu58EhISEhISEp6drEi4SimXdVda60UcRxISEn6lWJXp4s3rX86rBi7mWHUcYy3dqTa6Um1N+/VnOjincyN3Dj9OVqWaxGUdi0Vbw+V9Z/LrGy6hGFXxlMO6bDepJVr5TAVl7h19EoVoGtdaKEblprRlW/u/xqfcvI+75bvzCmGZnnAZH+2A2aZZtXxak9Zc+cROtg4PYoE7N27j0VXrFsy1NSnL5AA8dnQ1KTdCSYOwsFl4eE6lIVwBDg33sGVgpOn4H0yew6s778dz6sm8s6+hkdjMJ2+baTej0xpSzDSVncVAfpR3nX7PrGuN13Pm62HppGFrLP9x5Hx+c+1eMqmLCKNDWOviyhRuEFHwpzEIxkobuX33q6iGEVkvdmD2I82e0Qm29HaxsTt2w54oVQgijbGWyXKFQ7W+uFtX9fD7V11Cx6zetwkJCQkJCQnPLVYkXP/kT/5knnDVWrN//36++c1vcuqpp/KqV73qpEwwISHhl4dWN0eru7Tb75vWv4iDpVEOlIbp8PLknTRCCKy1lLTPhF9kINPBfzvlxazOdi773LcNPUJFB2RUswNxSQdz+s/OsLjsquX3LoPx4QzTk20LDuQGmtf85BE2joxiheDWzWfxVE8/MEsn1iKRUasmWBshHUOoFYF2sDauq43Sg/zumXeCI+sXRrEs+GHp3ObrsfCNkfPZ6A9ywbpDNTvfOoaK7/DRm67C2FaEAp0JwWOBVbCsahnnnaffu+Aaxc/JugdxbXuTvzLVCP5z8EL6UoIzWgq0pl9LKvU8StUfUPEf5cDUMQ5PbuRY4TwOT53KtA8wHN9QkIKUo6iEEUcmpzmtr5tNPZ0UWnxGi2VGC2UA2nMZTlvVw6WnbqCnJX+cVyohISEhISHh2cyKhOuf/umfLrptcHCQiy66iK1bt650TgkJCb/C9Kbb+IPTX8MX9v6Yx6YOcaQ8hhACYy0Z5fG8zo28beOJiVaAybCMxSLrAspCWQdos7QAtdgTbu9Sp1RQTE+1NVJu5wxMZhhai1UiV3LTqWdzsLW3SbDWCbs07sYiYZiK60prfWEB/vi8b+FUTq25EDesc8nn4DW5Bzg2IrnHngNAFIFU8Ej1FL777RctfL0iDgzrdAgZmg0papNb1zbI9ac9sODx9airFfVLkE1xXQsUQ4d7Ri9CI3lx9zRplaHs30Jr/i1kUufzs32H+NTtP6I7nyXlxl9TadfiKkVoDCmpEELgOYqiH1AKQvIpj5Z0mqIfoK0lLSXj5Qp37TnIPfsOsbazjTc+fzuXbd04f9IJCQkJCQlLIOyCiUf/pedPeAZqXPv7+/kf/+N/8JGPfIRf//VfP9nDJyQk/ArQm27jA9uu4VB5lB0TByhrn4zyOLN9HRtzvSsy0Glx0wgEgYnQxhLZ2NRpqTrM5ScEz6dQdJku5El3VBDCYq1AB4qo6mCNRPoQRR7fOvtc2oIKg20dYC1CE9vsNiYgUFj8yMXWXIXrl//Hz/8WiKU+xgV9PZbtwzt4yJ6NlHH4NpMNFj3CAiYTwkKtfSWc07OPa055vBGOXijiOrsN0NyxdxdaeXhqK0pYLusqc3l3CUE72owR6YN48jT2joyjjWmIVgAlBV35LEcnp7FWIQQoIQiNphwE5FIeB8cnOTo5jRCC9d3ttGbiWmc/jDg0Psnf3XoXlSDkqjOTG6sJCQkJCQnPNZ4Rc6ZcLse+ffueiaETEhJ+RRBCsC7Xw7rcyek3u719PRZL2QQ1J95YVdklpGnNO5f58nXpvp4Tkxm0EKRb4warRsfXo1xNhy3Qc7DMU2pdHPVNpynXBBbCYhufyiK+xWosoiwxVYX1QFsF1vDqgdvi8OkylPXGXs1DxwRCgjUseuvWAsabI1pnRVvP7t3NNafsmnOUwGAXDCrPvb+wr9DOU9V1bMyMc0nHMC/s7kMJEfe4tRZr495J2iycpN3XmmeyXKESRKRchRRxQyBjYbJUYXBqGoD+9paGaAVIuQ5rOto4Nl3k83c9wFmr+1jd0TZv/ISEhISEhIRnLyfsi3k8Hn30UT71qU8lqcIJCQnPGophlX/b92McoWq9T6FZGJ1oBHdxtThSyGI9gfIsUsU/jmtRjqVtssR1tz3I1Y88zNZjQ7XcI+L/Sht/Ijd+atsUCCtwDKia4NRG8rw1hROYr+BUHm9cahTOv2dpqdW05sVMerCprZawnNp2hJcO7F1kBcRxpDwcq+Qp23a6vCoXdx7l/NZ9CDsJgLFlpEijVHyToiufBSzaNI/qOYpNvV1kPRc/1JT9kMgYSlWffaMTGBOL1jWdC4vSnpYcU+Uqdzy1/zizTUhISEhImIV9FvwkrCziunHjxoV7JE5OMjU1RTab5Zvf/ObTnVtCQkLCSeHOkcd5anqI09rWsGvqML4J4++BOV8Ey8tANou2whktZlE1w2Jb77taS+3tGZ/m1Xc8gleOmGzNMtKZhyqxQF36dLPmZxG2VnUrTuy+4+ndRZ4Yjf89OdZsVGQBrSJku8FqMTN3YRFK01mpMjnUzfeL5/HSMx+mI1cCYsOs2WtmWMCoycLBch5DnrwTEUaKPX4Hz88fwJgppGjHmAmy6StxVB8AF56yln+/O8NEuUp3vjlnOeu5bBvorbkGTyGFYEN3B08dG2NVe55VbS2LppJLIUi5ip8+dYBfv+DsE1q/hISEhISEhF8sKxKul1122bw/DIQQdHR0sGnTJt70pjfR2XlixikJCQkJzwSR0fxo6FEcqcg5KTbmetlZODJPtNaFGMwXsNYKhLDAwv1bgwCm/SwLdO8BBKuOTfKKOx4lFUYM97TwrYvOwi+6iMM18byEYBYGjLTYlInrOqVBmxXYRdUuKgwEY8daZ64Ni84bRFYjpEXMEtLWwkY9iZcGYwTjxTw/evxMrjn3PhwVy9QZ8Spq482gjeBIpR2DAxis1ShhqBoVtwIyEVoMIkQLuexrGsd1ZDNcefpmvvbzRyi5DrmUx2yUFLhK0pXP8s5LzucFm9bzni99m4znHLf+2VWKgu+f+PolJCQkJPzKkpgzPTtYkXD9/Oc/f5KnkZCQ8KtA1Q+5b9ch7nx4D0dHp1FSsm19L5ecvYlt6/ti86CTzHhQZNifpsVNUwqrPFE4usTe8flNXcDO2eLI+dk6kyWwezuQmxYWQ+uPjPHynz6O0oYjve3852VnEEgXvBCGUogI7GLtZy0ILYg6NUbGGcRCgKNW8A1mLVrDwadWYYyMP/0ji84YREYjXTt3d1brSeqSUUhLS7bMaKGFA6M9bOo71lgZu8B6hVZwpNIBzOqZiyYygrQTgdUUTIkD1V6eCrYyPvIlfP3/kXGyrEr1c/bW53NJcYCfPjnEeKlCRzaDoySh1kyWq0gpedX203j12dsoByGOEoTL6B0eak1rOmmNk5CQkJCQ8FzjaZkzlUolCoUC3d3dOM4z4vOUkJDwS8LhkUn+/ut3svfoGABpz8Vayy33PckdO/bygjM28Juvvoh0ajEVtzJiUWURCHZNH8ZgF4i2NiNqzWZmB0MtgsgIpDA40sZGTyWwu7OwcWHR2jFV4uo7H0NYy/7VXdzywm1oV8bpvp5Fd4fIIQ8RWayiWfkZEKFApyxhu45FrJ0VDbbmhNKFHzmWZ89jq6iWMzU3YIPJGETLfNEKFnfSkm+ZvSbgyjhvefexVbOEa33rjKi3lnmiFcBY0Ag2p0bYU+3gJ+XtTGqHyA433J1LusSYP8r+8h76Ngzw1tUv5P6dFfaOjqN9gyMV56zt52VnbOHizRuQUtCSTvG8tQPc/uQ+OrKZRdfAWIsfai7esmHZ65aQkJCQkJDw7OCE1eaBAwf4q7/6K77zne9w+PBhIE4TXrNmDW94wxt4z3vew/r160/6RBMSEp67TBbK/O1Xb2ff4Dj9Xa14TrOgKVZ8fvzQboSA91x38Yra3SxGm5clq1JMBkUqJjyuaJ1LcyavQFuBh2asorG0wpbFj51oy7HjtDVkKz4/uuBUjIr9jOvjRV0aEWjcSYUIaieaZWJsUha/X2PdunuuQNQu4IGDLZy7vrTsq/juIy8jqHqNx7T6OPmayzCzLtJY5DFBe3rh6KWjNNOVxcWhtnC40sZc0WotTOsMeSUYMe3cMbWeCAPURb/AEQ6e8DBYqtpnxB/Cpn/C77zy7eC3Ug5C8ukUq1rz894jLz19M/fsPchkuUL7AuLVWsvwdJGOXJpLk16uCQkJCQkJzzlOyN3jO9/5Dtu3b+cf//EfUUrx6le/mje/+c286lWvQkrJX//1X3POOefwve99r3HMH//xH5/0SSckJDy3+PGDe9g/OM7q7rZ5ohUgn0nRkc9y16P7efLQyEk9d1p5XNyzjeHqibjwNlNLIEYJjQDGKhJrWxeuTbUWpWccle4+eyM/vOg0jJL1zbX/CnSoCLsNlXUhYZfGpC3Gs+icwe+PqK6LsKn6ARJjDFIaEJbvH3sx6GhZ87/7qc5ZotXAQBURG/ci5EzgVghLdsTBFQ651MJRZGsFSs5cXyzE459K4PDg1EamdCvazixOYBQTOocjDO1OgUcrbUSNxRONaGtkQyqmjBQSC4QmYiqY4J7xO1nd0caWvm76FzFfOnfdAK8553SKfsjgVIEgmhHelTDk8EScmv4bLzqf/raWeccnJCQkJCQkPLtZtnDduXMnb3jDG1i9ejW33347e/fu5Zvf/Cb//u//zje/+U327t3L7bffzsDAAG984xvZtWsXb3/72/noRz/6TM4/ISHhWU4YaX780B5SnoOjFv/IyWc8/CDizh17F91npVzWd0atX+jyj5lpmSPq/wMgiCyQXvgga3nRg3t45e2PzIhXIea4PcUyL/IV1kgwcY1r2G2orouoro/wV2t0i13gE1oRRg5aK8JI8bm9Fx9HvFoe2N/CDx6/ovZYw+qZ0K61Mz9YcI54RFaScUM8NX9cayHSiv72idqlWYSwKGFYLSZYX5nm0tY95GTAlM4yHuUYj3KUrUefO82WzDGOhDlmi9UZ+VpP0bZUdRlHKAIbIIXD7uIuxoPRJa4zzvx52wvO5V2XPp+efI5j00UOjk9xcHySiVKVTT1dvO/Ki7ny9M1LjpOQkJCQkJDw7GTZqcJ/+Zd/SVdXFz/5yU8WdQy+5JJLuPPOO9m+fTvnnXcevu8nwjUh4Vec6VKV8ekyLZnUkvsJIUh5Dk8ePrkRV4C+dBtp5UFYWfYxdY0rEdR70kz7AAtfhzCWl/zsCU7dF9d+rh0cZ/+a7gVHjgJBUK3X8tb6pmJPqJ3sltWHyGfgW2PPZ3Xgc34mit2jAKxlqij437f9GjODalgdLjxYZFAjaTSQUz5ducKCrYGqoYfrRGzuG6qlUFsyhGxwx+lyypi0QErYnjvKAb+LgkkhMHQ7BVZ7E3xp4jJsbUXtIk3pBAKDRVvdeKaqK4z4x+j0FlrPGaQUvPrsbbzs9C38/MARhqeLCCFY39XOWatXLXnjJCEhISEhYVF+0b1UE1dh4ASE62233cY73/nO47a56ezs5B3veAd//ud/zr/927/x1re+9WlPMiEh4bmLqZsjLaNuVSCoiCqPTB5AIOjPdNCVmknrHCuXOTQ1hbaW/nyegdbWJUab4b6xPVSj5aXVzsciMZR8BWLhj0ylDVf+9HE2HhnDCsFtF25dRLTGqbaVQpZ5KnWhL8UFdZbl1HWHyDZ1iFnNt8ZSWBHXzwa+4vEH1kPNZAoZIPrNvHE6vBIZGSArKarZiLSKKPse2kiUnEm1tRYqoYcfupy55iDdLXHadaeosNEbJyuCeLq1VjqeDNmSqUdIDaAZDHqZ0mmkAG11I7q6MJbQBDhyxqjL2LnzX5yU6/CizYnXQkJCQkJCwi8TyxauY2NjbNiwYVn7bty4EaVUIloTEhJoyabIpjzKfkBmCcfgUqrEsdVHmeg3/OWjsfFb1klxfucpnJnbxP0HRvjJgQMUg1gkZRyHcwcGeM22bWxftWrRca21/OjYY0i5gmibjWVXuaJgEed0N4y4+s7HWH1sEq0kt7xwG/sWEa1YkNZHycwCvVgXEPbGNm8SltMGDpHx5u9aJwgEjz24ZiY9OR0iOmeLPsspraOc3jFIT7pAWmiMFWTcAEdo7th1OgdGeylWBY4ycS2ukXhOyFlrD3DBpt0gQGE40xtCSLBirua2QD26KxCiFZH7PezUD3GEg7ZmCdEaH2Ooz9niSY82t2OJ/RMSEhISEhJ+2Vm2cO3u7mbfvn3L2nffvn309vaueFIJCQm/PKQ9lxecuYHv3PU4nS3ZBSOvU9kp9nceIhABPdlOetOtWGM5Uh3ni7vuZWJwBzZyyHsu3Zkc7V4ObeD2/ft54OhR3nvRRVyxaRP7Jib4twcf5MDEJAAbOzt4zRlbeKowSJubZSIsLKvOtZ65i4VqYKCQgYITP7fWbwjJtB/yytsfoXesQOgo/vPSMzjSt4DAqum4C9cfoBK4TGSrPDnYR2TmG1U1U7MYtoC0nLv50Mz8FsCvOOx8ZP1MpNatIpqSZCzn9RzknO7DSAxV36OgPfKZChkvQABXnPkox6baeHKwn6lyDiksvW2TbO4bojNfapy/Nyxx0/BZTPlZHKlZ3zrK9p5DuKoeqZVIkSfjnUVr5m1M+W3Ua1ulEGhrj5sZ7QgHazWrM6fQn159nL0TEhISEhISFuJjH/sYH/zgB/md3/kd/vf//t8AVKtVfu/3fo+vfOUr+L7Py1/+cv7xH/+Rvr6+X+xkl2DZwvXFL34xn/vc5/jd3/3dJdOFx8fH+dznPsfll19+UiaYkJDw3Ofy87Zw16P7OTI2xequtibxWnWrHOw8TEBIm8jT39rOdFhmd2GQSqCZGmzFhALpBhRtQLFcIu27dHgtrG3tZLRU4R/uuYcvPfQQ9x85SlXHwkkAdx08yNd37aBzQ4mN7Z2kZYpK5GMAuYhqsrP+G04oONAGFVkrcxWw2m90eslWAtqnK1RTLt+77EyGuxZIXY5ADimed25sOhVEio5cmQ09o+w+tpwvh7jG9oy1h5bcKwyIRWt9/ipAdjXvs7F1lHO6DxNGkrKfJSUj2nNl8ulKrdNtbLa0qn2SVe2TCwrLaqh4Yt8a/mN8gHI4E/qVwtKXm+J1W+7n/L6jODKDI3tAD1Io/RWdpg9X5IlshCtdjA6wtbjr/PNYBApPeqRUmgu7LjmpLZISEhISEhJ+Vbjvvvv47Gc/y/bt25uef9/73sf3vvc9vva1r9HW1sZ73/terrvuOn7605/+gmZ6fJadO/ehD32IsbExLr30Uu66664F97nrrru47LLLGBsb44Mf/OBJm2RCQsJzm7W97fzWa19ERz7DweEJRqdKlKoBxYrPfgapCJ82keWU/i4KYYVd00epmgi/5GJCiXR1k1lQVYcMVsb5+fgeJqIJHhse5o79ByiHIdZapBCkHIec6xJFMFnxeezQJOWhFqYOtDO9v53pwy1Upz20Fo3yUlszSrJGUB32ME+1QFmCayBrsBmNfWBG3I635/jui8/im1ecvbBoBZiE5z9/L0rFx0U1R+CufImUu4hZ0hykaM5UnivhdCR4fMcpjccmpbFZiS6LRrYxWE7vGEJaQ9lPIwBHGLKe3xivKQZa7yk7i1BL7n9yMw8d24gjNQP5CVa3xD/dmQLD5VY+9+gl3DO4DiE6cNyNOO4pKLWajJpgozeFthHKSlzpIGpfQbbpJ5ayKenR6rby0r5XcGrL6ctap4SEhISEhGcE+yz4WQHFYpG3vOUt/PM//zMdHTMZYVNT/3/2/jtKsus8z0efvU+o3DlPjhjkwSCDIAmCIEEQpBiUKFO+skVLsizJsrVk+ad15STr2r5atiVLlET5ylawREmmJIIUo5hAgMgYYBAm59zdMx2rK5y09/3jnErdVT3dM5gBSO1nrUJ3nbDDqcapec/37feb43/9r//Ff//v/50HH3yQ22+/nT/8wz/k6aef5tlnn728zq4BKxauN9xwA5/+9Kc5ceIEb3/729myZQsf/ehH+bEf+zE++tGPsnXrVt7+9rdz/PhxPv3pT3PDDeYfGgaDocFt29fyb//xw3z0HbeQTTtUPJ9KEKBGqgzmC2xbO4Tr2BxdmCDUEaDxi7GDb6dgW6Q0E/M+oVKx3ElKz0RKUQ4CSkGAKxwqE3mmJyxK8wKpJQJB5NlUJnMsnC0QeRbouFyOjgQVz0KcyUIoIK3AirdTtOm/aDEyPlcfw8RAFzPdufYDDBV333wyngOgVKwGg0hiSUV/rrSia6e0ZHoh23ZftSI4dbwRuVUigiQQKpxGZLkvVWbILVLykmuqNH5oM1dutKsBVTN0qn1JJgLWFiHFyRxHZ0YYys5RcKstn4tjRQxn5wgii88cuoOFwK4XrRXCwbLGeKC7Qk6GVHUFC0nGSuEKB1kvjhOTlWneNfRePr7hE9zZd9+KrpHBYDAYDN/rzM/Pt7w8r33N9Ro/8zM/w6OPPspDDz3Usn337t0EQdCyfceOHaxfv55nnnnmqoz9jWDFqcIAH/3oR9m5cye//uu/zhe+8AUee+yx+r6xsTF+8id/kl/8xV9ky5Ytb/Q4DQbD9wBrB3v40Yfv4AceuJXZUhVPefyHw3+JLS0sSzLnl6hEjZuwCiRCtn/MqDWx4Ixqz9/iWJ1Fo25qqBVF30MIG+EGaDui4LpUIj82CNIQ+jal8Ry50QUi4RPpNHLejtODUyquVqOAksXwzCwf2PcSvAaP/ZPrmOrLd5yrECF3rzvb8pC04tdSa+PxOXa05LxOeL4LlFu2TU3kOHtyhOuz8fyVq+qiFStCOrp2aUhVImypKCkLS2vQAqUFpWqarmwZWzYMnHRSksZBIdH0UWaTO8N/Gr8dR4a4VvtxCwEDmSIT5R6eP5fjvZsnsayGqB5K9fND/ef47Mxm5qMQrRRCCCQCrTW2sLmh61Z+dMNPkHM6PAwwGAwGg+HvKevWrWt5/+/+3b/j3//7f9/22L/4i7/gpZde4oUXXliyb3x8HNd16enpadk+PDzM+Pj4GzXcN5xVCVeAzZs386lPfQqIVX+xWKRQKNC1wrIUBoPBkE45jKQcqlEKKSRRUupkyltIhF4iuKSOI51t0EqgwtakkVo2Te0MiSBKzI2EkiitCFRE1nJRaLwoJLJDIt+iOiuxelyk1OhiHIGt56T4knUXp3jfwT3YUcT5rh6KLw/DbSXooTV3RQGTcNuOiZax+aGFF7S6Kq/EKKpGuZzi3HgPtqXIZqucP93DwnwBmcxZ2U2i1Qmx3Lj0jo6AIkRZB60kttbxdhH/DFU8Ljvl1ayg6tdunT3HkF0iLzyOzw1yoVKgyy03LvYiNGDL+FN4fWqAB9fvwZIDIOJFwUJIxlzBz4xqDvOjvDj9DPPhLBY263ObeHDoYdZlN678ohgMBoPBcA1os3rmmvcPcPr06RbNlUq1ry1/+vRpfv7nf56vfe1rpNPpazDCa8OqhWszXV1dRrAaDIbLJm25bO8a5aXpY/S4OaqR37LfyQRU/TRat6YLax1HY5eKJ41CYSVKUjUdoEMLpQXptIOnAoJI4SW1XbX2cHoidK1kTlSTg3FfW85N8tCB15BKc6p3gK9u30loWfBqT3y8pWPxGglkKsB1FLPdXQxtngZi0VqsZOpjkSIuM1OsrvDLREO14hBUYuHrn+9Dh/EYNYCloLkpIVARsWgtSayKzVxVUq6mSDkBFd+NvwSFRmlBENroRLjWxGu3rLDFna436UU2Sos4MttWtAoOTY0wnJ/HEopy4KB0kUjNYlkNlyghbCxR4R0D7+Ydg+9e2fwNBoPBYDCsWHvt3r2byclJdu3aVd8WRRFPPPEEn/zkJ/nqV7+K7/vMzs62RF0nJiYYWabE4JvNZRQ2NBgMhjeOdw7diBSScrh0nUaq4COkRic1T7WGKJCEVRsVWLR9/tkhEqg1CGVzfdcawrJFpQSqahN6JfKbKmA13Q4dFbet4YazZ3jPgVeRWnGkf4QvX3dbLFrbIKRCJmOtFl00MF9OMV/JNEtoUk5I2XeYLbVft7qkXRTZVIDrhPiejY5kfPd2QOcUOPUDqWVKaw8oWyAEIoIgdDkyPoJrh4hFVVS1bjxLrm3f4l5sGUPerWLLCD9qP/d5L4MX2Zye76Pop+lNlUGHKDXfcpzWHlJ2qHNrMBgMBsNbkTfbmGmV5kzvfve7ee2119izZ0/9dccdd/Dxj3+8/rvjOHzjG9+on3Pw4EFOnTrFvffeu8qLc+24ooirwWAwXCm3923m7v5tPHXhACnLhiajXSsdkury8GbTRJFGhTIWWXX3oFbh2vG+Hi/ppMtN8eSxE0QaQGJ1z5MbAD3nQiTQloZCCD0BnE+xcWKSdx7YD1qyd2QdT264vhGVrZFoPilUa4dAxbPxI6fpUE3KDQiV5OTFfi5dyTRuyhUhvufgVZ1EZILWGiyod1trSmi0LyGorfXVqJTG8gR7z6xjbf8Ug11zlLwUQWjH7sJ2SD16C/TKCr1W64OEtfkZ1hemOTo7RNaebdkXKIuLlTwpO6Qa2Cz4aXb0jccCWTfW5Wodh4HT6QcuPW+DwWAwGAyXRaFQ4KabbmrZlsvl6O/vr2//xCc+wS/8wi/Q19dHV1cXP/dzP8e9997LPffc82YMeUUY4WowGN5UbGnxU9veQ9py+Mb4ay37hIDsQBmtoDqVjSOvQtOyGLNZwOqaM25cEqelLWC6VKm/V4VZcoELr2dioaeTtlMKBn3IR5wOBznd188Fp5fn1mwF3SRa64tBNbYVgQAVSqQVxYI0pVBIsq7fkupcDRyOXxhgtrxC8yFP4JGOx1fzWhKxaI2fwja215/KRk1JvxaolEJWBRUvxddfvYV33LCX0d5ZcikPNGQcv657e2WFne7ZJcMQAt657iDH5gYp+ikKrocGgsjm7EIvobJiV2YRF7mp+jXBXkvH1kTRGSx7hHTqgZXN3WAwGAwGw1XhN37jN5BS8v3f//14nsfDDz/M7/7u777Zw1oWI1wNBsObTtpy+alt7+U9I7fySy//CWfKU3UtKgRYtkZYCmHrhni04ggsQjcEHACaSEOkNY2VqokzcO33wgQ9812oeRdhKUiHdfdgK9BwxiHV6+F32Xx5+y4iYYGXpOc2B1YFWIlorRktSUtRiRwOVfrpOZehN1fCtSJCJZkrZ5leyBHpFa7SULV5ifh6yCQu2u50XTu+TUTYBpVWWFWLhWqGL718O2v7ptg0OMFw7yzd+RIuIRvELAcn1/DHCxvRWjCULXLH8AlGc3H5n3tHj3Birp9vnrqBop8mVBaV0I1NnrQk1BYpKyQvfA7NjPDeTfvQWqDUHCq6iLT66S78IpbVt7L5GwwGg8HwVuAKaqm+Yf1fIY8//njL+3Q6ze/8zu/wO7/zO1fe+DXCCFeDwfCWYXNhmJ+57hH+8wufZd4qxUJNgTfvIiyN5ShqyrEmFOviFUCLlnu7qkVjm75weq8fRx/PE845iGyISHSeACwd8a4jewlSFt8RN9C/YRY1IJk514PvO6CaoriWxrIihCSp/ypj0eq7eD0a5QimSzmmS50jq8smCqvaoOpTjiOtixvQyS8t7svJcU13eJXWyEAjkmjs2flefN9mXd80OQLOnh/gz0/ex7yfwRIKS8aGS185eRN3DR/jo1teQkjND2x/kZHMPJ965V14ykYndV9toeh3Swyni8xVM1QDJ5mgh9ZVUqm3k8/9KK7bmrpkMBgMBoPBsBKMcDUYDG8pHhi6ka+q13mqtI+g20eFEhVJ5KJ6rkKAlYrribaWxWlEXrUG4Wuw49I23dvHcZSkMpVCuKpFtNphyMOvvcbaqSkiKdg/tI65yQIb336S4W0XOXN8kOnjvVCVEAmE1qgoEYxaIKQilAI/D/5Ac1h2eVpW6rZ7oitaCgSx9A2tUWCII7I1UdukZ5WrsCoWyDh6vWbdBTb2TPCnz7+NI7Mj9fWzCEhLn9HcDF5o89jhXXzp6K0MZoqk7YjbBk+xPjeFwiKIJEJAzvZJWSFomNJ5etIVJBly2R8hl/s4tr0ZIZaV6gaDwWAwGAwdMcLVYDC8ZdBac+jiRbZ1b+aZfZMEwyUWnIDAibWYTZIZrBsCSDoRwtJEvtW0PV4HK6oCqUH74G6eI52G8FwKHQpENlZ7AkgFAe975RVGZmcJLYuv3nILF7tcLN+iciFDbrTMxhvGCQciFs7nyE4J/FKK0LcQUmOnfLy0ppR3iHJqxcXempemitpgriQdqBaRbRN8RYNyNdKPSA9UsbsC5h2b//GdRyj6mdbONVQjl+Pzw1gobKHwQpuCW0Uj+NLxm1gIUvS5JfJ2gNANxe0rG0to7hiZwLY3UCj8BJY1egWTMhgMBoPhzeWtUsf17ztGuBoMhrcEz546zV+99jrPXTjNRVWiMhiAtsCXYEOEIEJABMIH2XQbb3dDl5WGjivceAEnHcVlYoL4aJEsG814Ho++/DL9Cwt4jsOXb72ViZ4eQGNpsJRACIVAM9Y7S25wEik0UWjV182+fHoN816eS6nOdnqy+X1b8boSIStomFa1uxhNbaS3zJFOKZQSHDm8Fq2T8jaL05CTFOQIC0dESAHl0GU0O894sYtq6HA27MXRERYaSyq63Cpowba+GXYOV8ikP2pEq8FgMBgMhjcEI1wNBsM14ZXz5/mTl/dw5OIUSmvWdnfxD269lbdtWM/fHTnC7z77HKfCGYrCIxKqs8qzQKdBVzQiMS1SShCHVuO6N1a1cXL3LRdwE9EKgNR1fVeoVHj05ZfpLpcpp1J8cedOpguFuMua2ZJUpK0QAQxkyvgKwMZ2Y9X65Ovr8Vh+HeuqdGhzim/NcOmSLCNam9qVlsZx4rq4C2fzsWhdWlWosW422VlVDgXboxy4TJZ7KQa5pE+NFgJXhATKYrJcIOeEfGDbWfLZu8gXfmolgzcYDAaD4a3N94A50/cCRrgaDIarQrFaZXxhgVKxyL97+lmeOn2aMIrq+w9NTfHEiZNs7OkB4LSeYV5U2zfWlAFce6/SYFWJDZNq7rtOhKgK0pm4GGx6+yyOG5+iNEgBdndAIDU6FPQXi3RVKsxnMnxh1y6KmUyjz1BiOQH5vjKOXPqNoTU8uXcLPu6y16FZF17qe2eJhgw7qNHmA60ViNYEOx0gBFRmUkRes3NT00EtpsT14rAESiCFw/lyF2kbQhURakWAoKrAlYqRTAWNxZePv4P33/r/QsqeSw/KYDAYDAaDYQUY4WowGN5Qnjxxgj9+6WVePHuWMAjZnEmzv1RGSknedbFkrIy0UnhRxMGpi6hsiE6v4HFic/hSAkIjE/dd4YbIbp+N/dNUwll8ezhegyoiVG3pp9DI7gCZD4nmHY4PDvG1m25ioqeHcipV70ZrUL6ke12RdMFjsSr0Q3ji0Dau6i00FMtHWyWsKNJaO9yJSOUCtBKEZbcu+EW09GRdE8O1PhBEOkWkLRwBWddBa5tIKSpRyGgmw1jBxrUcApXldLHKS+eK3Leh9zImbjAYDAaDwbAUI1wNBsMbxiefeZb/+fwLeFGELSVCNMrThEox73lkHBuJQApByrYpCx/lrCIHpkm8KgccK8LJ+7hZDy9yOD+n0c4wxaluhofm45RfLXCscRRDjE7MMTsSMuUNo8sWxwaGEVajeR0KlCeRmZB1108g0ahEGWoNQejwxKEtgL1iM6VVZfgoIBBNdWnbNJaI9hW7NUiNnQ5x3JCg5KADCSGIDn2IKPncbOIwNaC0RKIRErwoRAqBJQSWkOQz3aTdOF06ZYHSZZ4+eYr7Nqxf+bwNBoPBYHiropdaQVzr/g1GuBoMhjeIz+3bz+8//wJhFNHluggpKVYqLccorSn5AVKIOKAnBFFm5aVj6iR6y0qHpLNVbDtOQS4tzKAZhWrNgEmjgVt6T5FzFRO7L/LAk4dZyKf43H27mD/ViyrZKBWbNWlASLC6A9ZtO0ym4BFqWf/CKAV2Q7ReDTTgN02w3f7aLysVrUIjhCaVpAmrSIAnkItq3i45DdAR9dRhjSYEoiisrxcWCCwEKWm1nGtLi6lyeYUDNBgMBoPBYLg0RrgaDIbLpuh5fOfESZ48cYLP7z9AJQyxpaQcBKQch1B3lkZCCEKt0La67CeJQoBtRURKMDNXBGuEZkWnEdzee4K0A2sOzHDfE3uZ113M9GbR/YrMwAzRjEs066IDgXAUVp+P1TPPxoFqrREAyp7FE4d3LB1Ebb1pW4OjVc4ngEWLTJe2JzRY7Q/phBQgpUYSkasE+FGHPhaPRxM7J0uwpCRSKg72CoFWmgiFFoIZr0JXKlWv0xopRc5dfu2vwWAwGAwGw2owwtVgMFwW3z5+nN9//gWOXJxirlolTBaShkoRAn6TEVOdWoqv1mihsSxBCJddoCzleJT8FL7vgTW6qKEidw+ewJaw4ZWLXPfMOGTg5JjkW/dsR8u4oI7d72P3+00DLHNH/3T9nULgh4JvH7lu6QCS9bVttWYtjXglAlYDIaBl+/TjuhmTXt1du3aO0kRVGNPzHJzdsGJN3fDCEmRth3IQoLSOBS1xxDxl2YxXShTcFP2ZLKFSaDS3jZkyOAaDwWD4HsG4Cr8lMMLVYDCsmqdOnuS/PfkUJ2ZmKPtB686aOBVNeq7NDVdrCKIk2rrKCCLEZWoCbeH5RaCfVtFa5sM3HcESmq3PT7L5pQsAnLh1gHP3DHOXOMP5i3CKMeJRalyK3NJfbJmG0oKKb3NoYgSt5FKB3Um0Nu+vNdYJDYQCoZoab9cPAVirvGVrAcpCBYLyQg+vRz2xIdNKz08iyVrDnOchhUBrXRetGdsmZdlUwoCJygK9qTSTCwsM5nLcv3HD6sZqMBgMBoPBsAxGuBoMhlXhhSH/8/kXOXzxIn6kOosgvehnu0Nqaa+rjrhqMqkqVRUCfYs6KvOhmw5hSdi4Z6ouWg/fNczx2waoLdAcHYBRztXPFEBUr18ai7WSb3NhoYvxue7281tJxu1y4jV5gtvJ8CFlV/EjF61DbFcQqqbzVtKxBqLkteLzmpCtH4yqpX5rTdpxcK34iYMjLYq+z8m5WfJOih+/43a60+lVdmYwGAwGg8HQGSNcDQbDqnjyxAmeO3WaQMUq6nKzVxQK3R2tTPwtwnY8SiULkUrVhSiAhc+jNx7GTto8t72btftnOHFLP2du7OvQWnvmPJtzs/0cuzBIpBcNcknB1eURTem/zUtVUaJjO7eOHGXXurNMV7Lsnxrj5HwfjgypVNKt5zTVtm1sE7U8Z0QgEDURu3Rki963a6wVGTdLJQiIIhWvd9XxetfeTIafvfce3rlpU8fzDQaDwWAwGC4HI1wNBsOyTCws8O1jx3nl/Dgl3+epkyfrohVWlg27GI1G59TqU4SFZvvwOQ6dG0Sk4pNFEq6UVHjf9YexUOhYquFnHZ7+wS0oe+XqWAPHLvZwZHKMctDGYCgCrJWrVrFI5AqIlZ9ORKtiycXbOXqMzQMzVCKLwfwCA4VDDC8Mcrw0wIVJQbWaam2wnf4MQfiibpi0wtE2NaGXrrcV8X+kECitsS1JfzYbr3sFfu297+G6gYFV9GcwGAwGg8GwMoxwNRgMbdFa8zd79/Hne15lrlrFlpK5apWZSvXK2rU0KheB2yl3tp3QikWUY4UcOj+ESLcK0axd5Z1bjpLyI+782knOXtfL+et6AFYkWus+ShpeOD7AueJaCm6ZjYU5AmUzUSoQYseidRVCsO2RSV3Z+k7VfKTmjrWHWd87B2i0sKgAaRGwvesCc2EWhmBuLsdCMY1SsqUXITQWiqjqIILE/bd5+smheplqOhodh1XbXTYNKlGyAvCiCEtKXCnY0tvH1r7VRbUNBoPBYPiuwJgzvSUwwtVgMLTlsb37+d8v7Ma2JOt7e9Bac6FUiuudtrmBtjPDXYy2NCofgdXpyDZyStSEkiIIbXBa676mpcf9m4+Q8QLu+tJxeqYq5Kc9LmwsEKZWHtKNFOw/30eXI3nfjU+ytjCDTPquhC77p0b5xqkdLISZFbW3ZCb1Nb9NojVM3ie8a8s+erNlEBopIYoklh1RVTYF22NNZpbZIEtvb4nu7hKViosKLbLaYrB7Blsqpk73okNR71OopAuRdJ08G1gsXnXiWLyiT0ZrdCLgJxcWGMhled/WbVjyMvK+DQaDwWAwGFaAEa4Gg2EJU+Uyf/Hqq1iWZCifB2CmUsWPIqSQKN0Qj80x0kuJV5WOQC4T7ltMk2uRZqmrb8bxuW/DMbrKHnd/8QS5OQ8/a/Pi+zesSrQq4KlXh7h1/RxvX3MES0QoLYm0QGtB1va5Y+QE23om+ON993HB61p+2O06aDarUiKJtDZ4x5ZX6M3WyvLE60YBlJJYliJQFsPpIvvnFQqJlJDL+QgNBZUmb3nMjecJyk5rwzUTKdH0ahKvkJhkOazuibLWKKWoas2mnl7eu2XrCk80GAwGg+G7DBNxfUtgHo8bDIYlPHH8BNPlCgPZbH1blKxrdTpE1S51T9dSg5MccZl1WwFE0v3dGw7y4NaDDC6UuO9zx8jNeVTyDs99aBPzgyuLigIoDX+3bxu3rp/lgbWHsEUU6zqhsaXCkopQWXihTXe6wsevfxapVcf22k4tAkKRvGQsXJvSg9+7dTcDNdFaT+ethUmTJrRAorBl+76jUDIzUWg/JpWMQcSlYpufNGipobaUdzkH6HZ9ak3edXn/tutI2eY5qMFgMBgMhquHEa4Gg2EJByYvIIRoSf2USWmUtG119KJdFku3NxJajsUNJ6Lt3o0HGMz69FyocM/nj5EuBSz0pHjmQ5spdafqnkeXQmn47J5tbCjMc+/YMYTQRFqgtEQl0VYpNI4VIaXGDy1602V2Dp5aMraOU6uXo2kOd9bmp/nIDc8z3F1paUA0djemLjQasdThGFBKMDfeBW32NfoivigCtJW8ZBJpre1fhnZzkwL6MhlT+sZgMBgMBsNVxwhXg8GwBD+KsBaZEBVSKRxLEoQKWQuvriZ15TJqtS45R8D1Q0cZyAQIYOhUEbcaMjeY4dnv24yXd4mSNaMdDHvrKBWL1v6M4O41R0lZIVqDbnHWBZW050iFFrGQ3TV8cmXzV8TrWJtvtUJjyZDR/EUQgrMLA7h2uHjmS3BkxAUvv1S4KggqDuXZ5cXjkssviBeLXM5nWR+noDud5taRkdWfbDAYDAbDdwniLfAymDWuBoOhDcP5PKFSaK3r5VQcy6LgpDhXmQcFUjbKhdZpWvAqSAx4W9xzWaFAaiNaLcVNY6dZ11Oubzqya5DAtTh7XQ+hW1vTKogUWFK3dN3cXBDB3766A8iyufcYeceLHXN1+68GpeOIpyU0Wgu63RU4Kysgak0JBk1Pqsz9Gw5x29gpBBo/FJwtD5B1/PbldwBXhigtOVfpablEKpIEJYeFmSwqvPSYhFqUKryk1E2b7U2b2jxH4G3rN7SklBsMBoPBYDBcDUzE1WAwLOH+jRtI2zblIKhv00oTzAeIEBBgaYHQsX9S/Ylg4lorNEgR1/uEZGck4ldH4appabCOAkuxdXicka4So6eLyCBRwUJw8ub+JtFaa0kQKJGYKyUJujpeJ7rgW/ztq7tI2zabeie5cfBc3OUlH2cKrCRVWdcsetuFdDVxarASSYpwEs4UMJyf50dufYZda04lol7gOrCpcAFHRhRSVaRoJDlLEZG1fRwRcbzUxwU/V78klbkUM6e6mT7ZgwpWcSsXK0iibvN4t93l2dbfzz+8defK+zYYDAaDwWC4TEzE1WAwLOHG4SFuGR3hudNnWNMlWVjwOD8xx/xCFUdAmIbQbii2uOxnLG0UGnRcB1YimnyABNKTqGwUC74WrdXJaTgu0XLbhpPknJDt+ya46amzXNhQ4KX3rkdbnQWbIBatUWJwpIG5isszR7azc+wU67qnSdsB+ZQXl3cBpNTJ2ta2IwEUQggmSoV6Lx0X0ybZv1IoEHGq76Pb9jCYKy09VgpG0tMcKY7SlWpEToXQhFicLvdwutpHzgnRCg7vWwOBtVTjL4MGsKBRz+jKEo8sKfjjj3y/ibYaDAaD4Xsf4yr8lsAIV4PBsAQhBP/y/vv4tW8+zgsnTrMw7yECDRKEJZBo3ACcElTyYEtBwXIJUSyIsO4qpAUt4hVfIKREp5vV3nLqSzBSmMWRETe8fI4bXz4HQLnLRcuVCS+tQSE4frGHIxNjPLjlAEP5eYB6RDbSFtXIImtFIOJyM+3EqwQiLXnm3JZFo2ydTk20xrpW4MqId2/ex9qe2Y7jdGzNY3tvY33PNF3pOPLqawuVg0hYdb15/mQ/hBZI0CquLnTJawDx3b7uDbVC0brIS6qxXfCXP/gxBnO5lbVjMBgMBoPBcIUY4WowGNoykMvxUzffzuEXzuG7Et9VRGEctMuWLLrmLbJlwXjkU+rSCA2OLetaxxYS1az+dCyIqUpECCqtIL2c6orIZXxmF3K8e+9Brj92HiQcvH2I43cMrSS3FwBfwdHJQc5N9/PIda8xkC0lYlUiEmGZskKKYZqULGOJ2H5XIZs0m66/Ozbbz7H5oaUd1Z7G1h2E4+OHs3O8a/MBtg9MLD9QIfjQ1mf4q0NvBwS2FTE2OkNWxOtvq1WbydO9VEqZhuzsJCwXDasuWusntev/0m0BuJbFYx/7B+wYanMNDAaDwWD4HkRAc2n5N6V/gxGuBoNhGV597Qxd05LrRke5UC5x6vwMKWGRihprSrtLDqW8T4WAAilcLamKCEdIhCXQ6JZ1pgIgkBSGF6iELmFA7PRUV04CREh3NgCleWD3fjacnUIXBHvfNsaxG/twV3gHjzQ8d3wrOoL373iVgexCkz4TBJGkGtm4MkJIwUyYpdeOxauIk57jI5P+Ts338ucH7mnqIPkiq3+ZNbkeCRjMzvFP7ngCayVhUWBT3ywgEEKTL5QpzaYoTqcpzmaIEuOmTl+ctWWpzbu1JhGtK0wNvoR43dLXx2d++GP0pFdeJ9dgMBgMBoPhjcAIV4PB0JHnXj5BOuVgC8lIpsCcLlGtBJBqCNdMRdA1Iyn2K8qEuKEgsASRBDsRrVJAhMLN+9ipCCyNtiAsS4QWCC8xbRIC5YZ0d/loBA/sPsR1p8bxhc2T922lfGMKWygCpXEu4UekgfOzGVwC3nvdPnrSFQTxGtYwKSnjWBEA1cgmbYUoBOPVAgXHIy1CZKISvchiz+R6vnjsJuKYMxDQnAS9hIzt85EdL61YtEIc/bXsiK6eBSwB02f6YLGf1WJx2cFEqR5plW0OWo42jsNSCN6/bRu/+b73Y1lW29MMBoPBYDAYriZGuBoMhrYopalUAxw7FnlCwGBfgdPnpgnCCMeOBYxA0DttYUWCcFRQJiLnumgLyoGPEBq3u0Kqr4KVSkKUEiZOdyMiieW3iioZ2FQ8C7fHY9+WUTacneIbt12PvTmiV5XJWT4gCJTqKF41cHEhRahcfuCGl0lZDXdkhMBBESqJr2XsfCygFDlkZYhtaSrKZT7KUAldil6a1y+s4cDUCHVVp5YXrY4M+fjNTzHSNb+qa/61Y1voGVjAdUMqM+nGZGh03RI77aCJNaBb0oMvg/iyYFuCf7Tzdv71296OJY0RvcFgMBgMhjcHI1wNhjeBqfkST71+gmdeP8F82SObdrhrx3ruv3kTo/1dHc+reAHP7T/Fk68dY3ymiGNZ3LBxmHfespntawfrNVdraK05em6KJ189xqtHzxNEEYPdOd5+y2buun49+UyqY19SCrIZh+nZhujr783h+SGTU/NUPYVtSaQUqFCRuaAZIE339QXKaxWH5i6g/Cput6aQm0NYGiliV95zr/XhRBZRTte9ggQ6NiBSGhVIvOk0k/3d/J9H7sG3bHbIcXxlYRFHRwWCQMUpuRaJRhPgR/Dy0fVkFDxy4yukE9FaSw/WiaOuIyOk1lSVhSUEgZLMhhksoUDB+EI3+y6Mcma+l1DVooxJK2qxImyWkxE/cce3GMiVWRVaM5EawRERaFiYWuTW2yxga1HX5m6TbRrQlm5ckMtEEEdaf/n+d/KPdu5a8rdlMBgMBoPBcC0xwtVguMY8u+8kf/DF55ieL+PYEse2mJovcfTsFF98Zh+P3ncD168fYrpYJpNy6clnWD/Uw/mpef7HZ7/DqYkZwijCsmLn2+Pj03xrzxEeuHUrP/6+O3FsC601pyZm+Mtv7eH5/acJlSKbcpBSMDFd5NVj5/ncU3v52Y/cz7a1Ax3HevfOTXz2q3vQWsfCRcDYcDfZrMvUdImFcpUo1AShYmy4m1s/uJ4/nd9DpeKBC/muMmkXbDtCJMG6k6f6KJxNEeY0YT6OwFqJJspXqrz/5Vf51o07mCx0482mEN0hObeKY8ditaIcQi1JyRAnqauqiB2CvdBmz74CfWnFB27Zg52kAkNN78WCLtKgtMASGkdoQi1wpCKMLAJlUQ5S7B5fz8RCd+PkWju1Gq0txAVs+9NFfuim5xjIlVitaDxRzCdraTXVuTTKb9yeW7KDdezWnFTZWZo2rPXq04MXIQBLSP7BzTfzj2+7/bLbMRgMBoPBYHijMMLVYLiGvHL0HL/32NNU/YC1g93IppIuxbLHyYkZ/ttfPo5lxY68QgjyaZexgW7Kns9cyUNKKFV9lIojlLZlEamILz23Dyngpo0jfOOlwzz12nFmFipYUpB2HFKOTV9XFtuShJHi9OQsv/FX3+ZXfvQhxga62473vjs28/XvHGBqpsRAXz7eKKCnK0NPIYMfhMzMlom0pveDBX5/6rm6kMqmPTIpH4lCClAazh4eIHveQgQCuwhyQKAdjQqgr1ziw8+/RKFS5aHX9vHp++4mjCSOgr5CCU3chhSCUNsEkY2MFFJotBZEGg4e7+Pd246zsX+67XxqGs8Sul4KxxERoRYIARKNFoLZaobJhabItyBOD1ZAmIQ8F7kkOdLnn939TSyhO5Z27USxavF3U7sA8MsWc2cb17p5CM0ux9oCETYdoDWurahy5em8rrT4Z3fdzT+/+94rbstgMBgMhu96TB3XtwRGuBoM1witNX/17VeYLVXIpBzOTc0jhSCbdomU4tTEDFU/JIoUYaTJpGwQUKx47D85QaQ0QoJjxVFaSwrCSFEKPIplD8sS/MnfvUhPPo2KYHahmugeQTUIOXNhlrlShS1j/Ti2xdrBLk5fmOPLzx/gE++/u+2Y14728kMfvJ0/++zznJ+Yo683R8qNbxt+GDEzX0YDwdvhS1MH6zdW1/bJZ2IJJYmDgOf2DpI5ZyOSNa0iEqTHBdUxTX+5yEeffYms5zOby/K3u25FuwItIO0pevNlhBAEKhaullAIIEIS6rie6fMv2/zje/fRl610/AyaM2yl0Cgdr9210CgEUmrmKileOrMBrUXjhBCEkolNr2xj7av5+Tu+kvxWO20FTr4aXhzv5aXqTQAEVYvZ430t+1vEaxJtRQuwNUpqhAJpK7q6PaarTnzgapVzE7YS3Oj18UPbbrr8RgwGg8FgMBjeYIxwNRiuEa8dO89z+05R8QN0k2eP1pogqkUl4ygqIl5j6tgWjq2ZK1XjUjIKlNB4QUgY6ZY6qSpJ2S15ARagklRSkbjaWkIwV6pycnyGrWsGkFJSyKR4+vUT/MA7b6U7l2477ofu30Eu4/K5r73C2fE5VBQ3LIRgZLCLV2+Y5KgfRzilVPTky7hOnKJroSiWYPLgENlxp+WJpQDsBYtN+2Z5+ORLuDpgsrfAY3fdRjmTQobgzAh6eoN6ORpLQKAtQt1wtrUJ2f1yP//kvlfozlQTdZf0IUAvsuVtKrpTb1ckQrTku3z7yHXMVJKIZ1KXtWHE1K5ejOZn7vpbMpl4f+yirJM9y4hXrfn6+FaOVccACMoWMyf62hzX/vR4XJCnSiQks5VUY3ztluCuAIHgTjlCuezz1L7jfP/9t6zsRIPBYDAYvpcxEde3BEa4GgzXAC8I+dTfPstcqUrKtUk5DeFV9gKiSBE1n6A1pUqAlGG9DmqyGT9Mwmmi8aPlfqaJ2xKx0JNCxPpLa0SkmVmoUKr65DIuhWyKC7Mlzl6YpTs30nH8996+mTtv3cDrB89xfnIOpTRDA138P6e/yInSDACSkIHuEs3Gs9NTPv3jPaTOe6hF4hFg/ewF3nv4Fewo4mx/H3+7+TbUgkV2HuwSaN/CGolq04qFplYkizhZKDkcPrmBVFfAn7x+H46MWN89xa6Rk4zki0nd2FjIaS1a+tctjQqmynm+fPBmil6mfh1bRWtto67/vnPoGB/YsY9m36JGSnMtw7jNt42GPz99O8UoCxqKk2kqU4WmAxb3SV1t14K9QkGmGOGlUpDLAOU25y6ecGcEsFV0M2hnOSMDjp1vn25tMBgMBoPB8GZghKvBcA34+u7DHD13EduSuHZDtGqt8YOwrabQWqPUMmqjSc+0CJOm30MFjozXylpCEGmNH0RMF8vkMm5dcEXL9ZNg2xY7b1zHzhvXEYYhb/vCb3PRi8WSbYX0dy+0CLiFySp673qsdGvdz2ZZdcPkGewo4lTPIH+35VZYsHFLGukoVCiQlqZ7w9wi3SVQCs5M9DI13wW2JogkQmgi5XBwapSjM8PcMXqce9ceSyKuuinPthWlBeML3Xz10I0U/US0KtqWvLGlYjA3x4aeKXYMnGdd90ybKyXq4lUI3bLiVANKCf701G1UdA4VwcJEnupspun0xYVbWfLeLkF6WlAZslCLzIcbJ7Qp8NrhYxbAdfSy3e6rDyFUV5BvbDAYDAaDwfAGY4SrwXCVCSPFN18+jGtbSCFQStdNmcpekEQFrzALRHf4XUCoNLaMU3ulEERKUSx7cf/VgLRrM9iTX3FXfhRy7+d/i9kgXkuadct05/0W0To7o4kOrcctW9CUgbxYNn59yy3ckjvBntFNqCRUG4s70EqQHSrh5kOgFmON04BPnu9ntpSv1xqtyUNLREgiQmXx/LnNpO2A20ZOE0tQ3UYPCs4We/jC/lupRm7SOUm2bevi0rTlM5qf5yM3vEjO9S9xlWLxGkd5myyVNHzm7E4qKkcUCOZOdxN6Tst5S9ACEWm0jJuyqlA4JZge0JATDWFc+0Pq1E5tc5s/tG10s93pS5rRhJFmrK9zWSaDwWAwGP4+IXQbe4tr3L+BN8B+0mAwLMupyRnOTc0z3Fcg5doEYZz6GkaKMLr6Ua1YQMW/1+RMEEZorZldqHLrljFG+gqdTm+hGoZ88O/+gNmgDGi6MkV6Cn6yRjR+zU5pyjN92PNtIq1as272Yn1AoWXx0potddFaE446lNjZgNG7zrfMRGk4fjYRrfWt8ay0hlBZhNrGFhFKC148v5Fa4FAvirZqDcdmBvjs3l1UAzfOr45AaLFItCpsGeFaEbeMnF6BaF1MnIrshYI/O3s380GeoOIyc7Ln0qKV2lpZAUIgQ0HmgmC6T8No+37qan6FpLSgx2pEfEtVn0zK5r4bNq68EYPBYDAYDIarjBGuBsNVpuYU7NgWQ72x4ArCqC5gxSpExuWitEY3GTlJKRifLpJNO7zv7h0raqMSBnzsm3/MkfmLAGTcMvlsSE06CuDCpENJ9ZKasJcYIqE195w+xKMHdnPXmSOdO5KQHSqz4cFTuNmwvrlatXnh0CZmy0tFtkYko9AoLYi0hSUiSn6aQ9Pt1+4emRriiwd3EqpkrIsFKyQ1ZhVo6M8W2do3sYIrtZSF0OEz47sol1MsTGWZO1NABc0JL4sWyTZv14AV14/NjINdAcYuaxhLcLUgI1y6hAvEf6tTxQq7tq5l61j/G9OJwWAwGAwGwxvAW0q4PvHEE3zwgx9kbGwMIQSPPfZYy36tNf/23/5bRkdHyWQyPPTQQxw+fLjlmOnpaT7+8Y/T1dVFT08Pn/jEJ1hYWLiGszAYWsmnXWzbIggjBrpzDPcVGmtb9SVyP3TT6wqIo66aUMVWQcWqz9mpebww4qsvHGT3oTPLRn+LfpWffeqveW0mjoB250r0Fvy6YEXAmfM2voiFuagktxahEUIjiHjn8b3sPHcCAM9evEohzsGxUz5b33GMDe86jZsL6nvnq7DnxAa0tliOOGVYE2lZz4odX+hectzhqUH+9uBOQmW1uBy3IOLrobSgO13hPVv3knH9FX8Utazjw+VuHhu/jZnJXqZO9FKazKPC5nlc4slFBFYFcqfBqcDMhjauwcvR6VgVR6G7lENUVZyZmufCfIld29bwTx+9B3EtnqgYDAaDwWAwrJC3lHAtlUrceuut/M7v/E7b/b/+67/Ob/3Wb/GpT32K5557jlwux8MPP0y1Wq0f8/GPf5y9e/fyta99jS984Qs88cQT/ORP/uS1moLBsIR1Qz1sHu1jdqGCEIKxgS42rxnAdqylEb7luEzxWush0snyTRHXgh3szpFybL7z2nH+v3/+TX77s09S8YIl5097Zf7pU3/Fk+PHABjsniOXDuqCFQETE1m01RQJTSx7pdTIMOKhw69y/eRZtIDHN9/IK6OblgzSdiLW3zzOyPqGm63WMFNMs+/EZlaq1uqpw/WfSRfJApGi5/L5A7vq62Lr1WPqC1g0Qqh6awPZBT56wwsM5oot01vJx3F0YYDnZ67j4qku5se7FgnWtoNvfRNAZgpy58H2QVkacivoGBqXq177J351uSly0sEWkoy26fPSeGHEtjX9/PQH7uVf/eADFLLtSyMZDAaDwWAwvFm8pcyZHnnkER555JG2+7TW/OZv/ia/8iu/woc+9CEA/uRP/oTh4WEee+wxPvaxj7F//36+8pWv8MILL3DHHXcA8Nu//du8//3v57/+1//K2NgblF9nMKwCIQQP7drOwdMXKJY9CtkU3bk0vfkMF+dKoET7kintWKYsaMfjaz+S86SI10tOFcsEkWKoJ4+UgidePY4Ukn/+0fvr0TYvCvkve77Ba1PnQcBw9xyWpRuiFThzqgudslpSnu20D5UUtgp42+uv40xNEEnJ17fcwrH+pam7QmiGt15k7Q3jSTtxPdtTkwXOTg2vYsK1gcUpwwLoy5QAjdaCou/y+y++ozF4tfRyOlaI0nHq8UCmyA/f8jxdqWq93ZXywsxaDpXGmDzeS2U+2xjakrEmLG46FHQdB6khcjSVQQhz7dpootPwatsVVHXsYj2Uz/Fb73uU0UwBx5b0F3J10zCDwWAwGAwNjDnTW4O3lHBdjuPHjzM+Ps5DDz1U39bd3c3dd9/NM888w8c+9jGeeeYZenp66qIV4KGHHkJKyXPPPcdHPvKRtm17nofnefX38/PzACilUG9SSQilVFIOxZSkuFpcy2v8tps2cPD0BF/ffZiK55PLpKj6Qb3czaqyMlepLQSNCiuWEOQybr00zsxCmdmFCoVsCingK88fYMf6QR7atQ0hBM9OnOClC6eRQjPQNYMtdaN7DWdO9uJ6EuuChAi0Ddm1Jxi9Oc3ZJ8d4dO/LjIYlxm2br267lTM9A0itW0u+CM3GnWdZf/M4AoHSAongxHgX4zMDl58WoiVZx+fGgfNoJTk2PcDnDt4et9dcCrcWPpWajO0BEg2MFmb5vh0vk3WCehmdxc8NOj1HOF7q4cDsGOcPDaFCp8n5t/mDEUu31VDgFMECwoymvCa+tuilaTIyGcMlr5MGR0tUpOiy03zy4Q9w69hIywHLll96gzk9OcsLB04xVSzjWBabx/q587p1ZFLOpU++xpj78dXHXOOrj7nGVx9zjZfHXBfDlfJdI1zHx8cBGB5ujb4MDw/X942PjzM0NNSy37Zt+vr66se04z//5//Mf/gP/2HJ9gsXLrSkIV9LlFLMzc2htUbKt1RG9/cM1/oaf+D2jYwWbF46dIZTE7OM5C2Gs9nLX7660shbYjIrBaRSThxxJa7d6vtBouEUlhRoDX/99WfZd/g477tzBy+MH2StEqzJRkAvouk7Z+ZEnsF5G+k3RGjXumm6RvsQAro2uJT8raROH2L/DTvJ5PvZ1lK2J45g9q2dZXSrQFRGEUCgJP5Cjt6FIXqvQMNIFFv6SuTK6zk6PcThybXc4NAInNZeIt6QczwsaeNaAWOFWbb0T+N4a8Fb2rbo8LsGzlYKnCuP0nWql66UALfd6Jb58JJxpTxQmzRBT+vhi0vRCmCdm6qf2hYFRJC1HPKWi1MRPL17L6POtb+3lKs+f7f7EEfOTuEFDfOtVw4c4ZvPvco7btnETZuWWCa/qZj78dXHXOOrj7nGVx9zjZenWCxe+iCDYRm+a4Tr1eSXf/mX+YVf+IX6+/n5edatW8fg4CBdXW9OLUOlFEIIBgcHzc3vKvFmXOMPjIywZcM6/tXvfYHphQphVCt38gbUcu2A41ioqJYTG9de1TouLdOMJQWObZFxFRcq45ye9Xlt7XGq6TPIugtTzPj+fjKnfIh8tAtIGLrxDNamMuXkuPCWKZ6YWc+5gbezvxjC9AKg0Ui0FgihGbt+nNGdZ5luav7QhQEOjA8AV2aqtrVvgnXrX+NP9t7G2bleEKV4PWsEdhlElNRazQXcu+EoA10lHCtkTdcMGSdAA8sXvkmi5clvkYbvTG3jnO/i2Oc5UXWIImvRh3rpcLnQsRmTVdJ4w0CVljY0tIRXa78eqJRp+yz7IgziEgSKsVyK4ZEu5vH4zrELPHqfy/qBnkuO6Y2iXPX51Fcf59Uj4/QU0hQy2XpaehBGHJxY4Og39vHj78/zrp1brtm4LoW5H199zDW++phrfPUx13h50unvYv+EN8Ao84r7N3z3CNeRkTilbWJigtHRxtP4iYkJdu7cWT9mcnKy5bwwDJmenq6f345UKkUqlVqyXUr5pt54hBBv+hi+13kzrvHuw2eZK1cJwta70NVIoBEClNJEWiO0QC3jYhxGmjAKkVJwXf8Qe8+MMx/MkbqxNcp35kwf2XEHFYJOE699vW6Cvs1lumbK7HzmNM+9axM667DxwROkj29FHlaEVTsuOSM13YPzjF0/wcC6uZZ024OTA+w/u+aKawTtHDnB29cf5uj0IKfnBuKNCkQAaEEkYsGXdqv8g3ufYiBXuuy+aqL1xbl1nK8W0LJMdxoGR2c4d6a/NS36Eg0JQCtw5jSVzresuAzuouWxijZ/QyFs9mI3Jz+IKAYe0ZAmn3Y5vTDHC0fOsHGobxWzvTIee3ovz+0/TU8+gxCi5bLYtsVIXxfj00U+/Y2X2bV9Lb35TMe2rjXmfnz1Mdf46mOu8dXHXOPOmGtiuFK+a4Trpk2bGBkZ4Rvf+EZdqM7Pz/Pcc8/x0z/90wDce++9zM7Osnv3bm6//XYAvvnNb6KU4u67736zhm4wtLD3+HhLiuTVRIg4iqqCla9frHohhy+OM63nYTKF3lZCphTT01Dy+rDKEqsqUG6stNbeeYL8cMDAhQXe9tUjuF7Irc+d4fl3bUK60LNpjq3bLhCUJXl8MnmfVDpsLHElFl77zg9xaGL0ikXrPWsPc9faExy92McX998Wb1RxhLVmbqAccByfH7r3GfqvQLSSjP3Zuc2UVYa+jFdXj4WogsyGqLJNw9a3QwNNe1MzoB3iBa4dELrVcKstEWyeaFgQSylQSsU1hZ248bnytVkKcfbiHH/z5Gv81ROvUvUD5ktVpBRkUy6DPXn6Cpl65HWwJ8e5i/M8s/cE77/7+msyPoPBYDAYDG993lLCdWFhgSNHjtTfHz9+nD179tDX18f69ev5F//iX/Brv/ZrbNu2jU2bNvFv/s2/YWxsjA9/+MMAXH/99bzvfe/jJ37iJ/jUpz5FEAT87M/+LB/72MeMo7DhLYEXhLxy9ByXKt/6RuDY8RPfIIxWZbqjhebiXBnZo9EVCzXrMOt4lPw+sMBeELE4s2D9PcfJDoQMn53n/m8cwQ4jpodyvHzvukZ7gJTgFiI8BEgblMISGq0h1Banpns5NH7lonVHzynuXHOCfWcG+dqx2xBKtKT3aBmXZ3Vtn3det4+h/JWtt9Eavvh/b8Z+t8ISSYRbwPTnelnwc6x7aJKcFXDu+CCzC/mGk1Lt49BNU9bgTkNqRrCw9tKfV0fxqoDzMFJ1oSkrSyemWM31WR37EuV53gAOnbnAf/jjv+P0hTkqno8tJbYlEVJQqvqUxqep+gXG+rsQQmBJiRCCV4+dZ9NoHy8ePsPMQoW0Y3P9+iHu2L6OjPvWM3AyGAwGg8FwdXlLCdcXX3yRd73rXfX3tXWnP/ZjP8Yf/dEf8Uu/9EuUSiV+8id/ktnZWe6//36+8pWvtOTM/9mf/Rk/+7M/y7vf/W6klHz/938/v/Vbv3XN52IwtONvnniN2YXKku1vtI7Nppx4jWYQEq1SJSulIBIQxCk9c1WPsu5tLKZUsfDZ9u4jWBnNmhOz3PutY1g6YmJNF888tJnIaRVEjeiqxFcSXzWER6nqcOJ8b+wedQUXYnvPOR687jn2HL2Jpw7djHBV2ztcPlPhketfZX3vRYRYtUlzHa3hC5+8k+p0gcLOKRiIo+hSQrS/i7FHx0nnKgRlB+uiQ2YOoiwEeRoCNhGxIgB3BlLFWs2iZJ9i2QGKJL1YRJArS/ovpihWfLTW2OnWlKwo0nTlU1iWwAtCLCm4bmzgMme/Ml46fJZ//ftfYL5craetBzoiVAopJdmUg1KKiZki2bTbkhr89L4TvHziHFXPRxCnFX9l90GGewv82Ltv557rN1zVsRsMBoPBYHhr8ZYSrg888EAcFeiAEIJf/dVf5Vd/9Vc7HtPX18enP/3pqzE8g+GKWKh4fOvlIziWJAhbVyO+UcZMloDr1g8xMbvAfKmKJSVRFK2iBV0fiAoEs7mLkO5tiYRqW7PtowcRlmDjoSnu+M4JpNac3dTL8w9sRNnt17C0pAYnkcZQQXHcR0sbotpRq5eSbxvZx0e2vcJ///ajjM/GazaFL9Do+C6XXOCcW+Xh61+jP7eAvIKiaFrDZ//gDlSQQ/uSaMbGGmikfysX6Nf4Cykm9g7jzaaRgJwDd0oT5uN0ZaHB8sAqgWyatwhpNYJYwR9IqmRhSYlMSh3ZTZ9DFMV/b329cT3ZC/Ml1vZ3c/vmtZd9DS7FvhPj/Kc//Trz5SqZlIMQgkh5iMTmOooU5apPNu0ShhEXZ0v05NKEkWJ8poi0ADTFsl9fm51LO5zyZ/jtz38HwIhXg8FgMFwbtOaapMst17/hrSVcDYbvZV49ep7p+TL5TJqyd2WOuZ34xR9+gNdPjHPqwixSCrxglaIVqKmkuTUl6O3BOW8hQoG2NcFAxM33HAUhkaFix6vjCA3Htg+w5/51sXJeQS9ag+dJormIC2ot0eIaL6sY8x2Dh/nwttc4cEjURWs8CxHbAkex2MaCD9/yAv25MhoIELERklypVI5FdRTBY791B5DveKQ/LJg910VxvEBQdRvC09JIX5AqsqwQdecgLNCIui5OMW5MEoB0IHGKUIp8HEuScm28IMKxJGhNECp6u7Nkcy7nZopkUy4/9sDtVy1VOAgjPvnZpxifno+j2iJ2rbakJFQKC4ElBZHSeH6I61gsVDy8IGR8uogfhDjCYmqugrSol3CaK1URQlCp+vzR117k1i1jJm3YYDAYDIa/JxjhajBcI+ZKVTTQnUsxNV8iWsW600shBXzy5z9KbyHDZ5/eS8q1WagsX8ylldaxFNdWSM3ksU5Zce3WJBCaPWdxZnwNI7dPYGfhiYe3sf7oNAduHYnFyQq8kYMIjp0ZYmY+h3BB6aYIrRBLLXOX4cG1e3lk016mZzT/e/+PLtkvEBDBTz3wdzgpjUagkpznAJcQTagiHAIuXdJUcPFsgW//353UnZM8gXAVVner2VbYA+WpLH4pLuKqJdSdobReNL2l+cpWGYRPXGqoZhfcwd9J+tBLimlX4/sh2ZSL61jMlKpUvAAhBJmsg0hJJmdLjPYW+PEH7+TOreuWNnaFaK156vUTfPrrL/Hy4bOEkUKjKUU+tiWxLUGoQGmNFAIpBEEUxSWb0FS8kPGZYj3unk5ZS9bkhpGi6oUcOnOBFw+d5u03bX7D52EwGAwGg+GthxGuBsM1IuVYaK3pLeRIXyxS9v2WzI/LThcWMNidZ8/hs3Tl08zOlZkqllfRQGuvs2tnyY13IxZFQYWI0BEUDoacnlvP2gfPUC7AgZ2jSSsapcWyKbhBKHj1wAZ8aWM5EbaICLRI5G5zf8uL15Tl8S92foH+tMd//ubDzBaHOx77zx7+AikrFvcQ60ZFHIzVOATYKATOMlVbtYJXvr2Fo3uaTKc0qAULZ1MVOdiUJpxod2/BrW8TSqNrwc3FteBEc6Q7RiLJnleU1xKfV6t30yxeBQgPCmcEPTuyPLJ5Iw/vuo6j56Y4OTFDpOLavdqKs7CzKYeb149w59Z1ZFNXJ0r5uaf28pfffJmp+UpstGSJuFaxiKOwUok4VT5SRIl41QrCUKG15vSFWQKlEBIiNNUgxLUtZJOplG1JtNaUK74RrgaDwWC4Jgjd9HX9JvVvMMLVYLhmbF83SC7t4gUhQ315zl2YwwtXk8rbHgFMzZX4wy8/j5ACX6lVKODWA6sbF8if72nbhwwF7zr2GltnzvN3wU7Gnx1h3YNnWo6qBZEFeonxkVKCA/vX4lvxelYdSWQmRGqN1rFs17Xoo66Nbal4zbtlPnbj81SdLv7L47uWFa3/8pEvsLhsXBwZhgzg64AIhwiLirLJyKVlipSCPY9v5fietfXhaA16zkKkFanbSy1myGEVhndNMvnKIN7colRcFWf9ak3D7KoDtifJnlJUhyHK0JourMAuQXocsCTFSpXD+05w4PQF/sG7buOf33d/S6TyWvDasfN85vFXsG2L7lyKsudjC4tIaQQCIYn/PiJNxrHxo9jtWmuNH4SxaZQK6zWDg0gRAF4U4UhJxnXqKcO2Jal4IcfOT1/TORoMBoPBYHjzMJWADYZrxNrBHnZuHWOmWGGkN89wX4GM2/rsaNVSIzHijdAEWuNHly9aF9aVsc/n608V6y/AjiLed/hltk2NgxZYKKrTabw5d1GbAqUlkZZEShIqiVKSwLc4+aX1lHQqdtHVoLVAKYklG/FW0VzUVNTCjLVcZU13aoEfvvEF0m7AJ790D3PF4Q7XLGgrWhfjChAEyRl2PRhar1aj4YW/voH9j20jnLVQZYmat1BTNsLVZB+cw1nfiNT6ZQhLaZxcwPCuSdwur+Vyy6Dp0i/5nPSSt7YvyZ+W5I9B6gK4FyE1CfnjkB2XSCRoyKZdxvq6mK9U+Z9ffpZvvXp0+YlfBb758hEqXkB/Vxbbii+8Y8epvkprhBDIxFlYCChkUqSc+O8/0hpPKSp+/OBA6ziduGbW50eKsh80mffFn/pikzODwWAwGAzfuxjhajBcQ374wZ2MDXRxbqrIUG+ebesGGe0r1CNJ0HEpI3W1U3e2azjcrc5sbqlq8jZUyExk26aiuEHAowd2s372IqG0+NL22znSP4yOBHPHujr0IdDJS4WCY99Yw8U+B23TbFxMGFhIobBkEm2tjy9uQyTOPlIo1ndP8cM3vUDGDfi9L98D9Db11nzNAv7Ze796SdFaI1XvVRCoRjuRFjz/ylZePbaZICcJ5lOEJQuRjkjftUD+B6dwd1QBUBFUF0BVU+hI4i842JmQvu0z1CPHKnYL1gKw4p/NQrn+qn3ETWO0Qkl6WpKekqTmJFLFk4v/bDR+EGFbkpHeAkpr/uxbLzNfrq7sArwBzJWqvHzoDF25+Gp25zN186V0TZyq5DoICAJF1Q8oe0H9scQSkuuglEaKOALrJ+7IkYqF8Nr+Tn9/BoPBYDAYvtcwqcIGwzVk7WAPv/QjD/Kpzz/NsXPTBGFEyrUZG+iiWKoSRIpcxiXj2sk/2qGQcxjq7uLFfSco+XFqcT2bVhL/tuK00KUSwcuG2OOZtmo543l84NBu+ktFfNvmS9ftYrzQG+d8Soi85W8hc3NpKuNDzGwooWpC0opfWlFPLXZkhEATKlmv2WmJiJztk7YDdgxMcPPIGRCK3/vS+zqGplP4/NQjX1+JuXEdIQAdAA4KSaQ1Gnhhdh1H9TCs8QnyKaqejOvcCA3HUmSDFEqCm6nQe3MFN60h7aOVIKzaRJ5Fuq+KW/DxiylkNfGeupSgbhp7LWO6/nkvnleiBZsffAx25Tg7Pc8zB07y8K7rVn4hroCFilf/2wVIuzY9+QwX50qxcE05eH5Yj6IGUYQfNf011tKg21ATrwiBH8Zpw14Q4DoWN28evRbTMxgMBoPB8BbACFeD4RqzYbiXX/vEI+w9Ps6eI+eYL1fJpl1u3jTKUE+O3YfOMD5dxJKS9cO97D16nv/7zT1x5Immf+yLple79aBKY1c1lqcQKjHpSQnClKg7FfmpCDtqfxtIhQEfPvA83ZUyZcfli9fdzlS+FuGK3X+l3TlV8+LFDCcnRrg+LcAjdghqXvIp4yin1gIhNLaMsKQiUpJASfKux11jJ9nQM0XG8YnQ/P4XO4tWmOGnHnkaITubGCgFh6aHeP3CWua9DEIoBrMlbh0+yUAhLpNTDFN8++JWKjqNPTzLutE5yuMZyudyhHNuvL40F+J0R+TWlbDzIVoJdG25sgAnGxDZEh1J0t0e4XQKIQV6te4KTWtqOx4iBK5j1a2lLCuuCLv/1OQ1E66ubSGlqNeLBVg32IMfRBTLHlIKcmmXMFJ4QUCoNGqR0dRyKA2W0IRKUfEDXMtmzUA399248WpNyWAwGAyGOkLFrzezf4MRrgbDm4IlJbdsGeOWLWNL9m0YiWuRhmHI9//KH3N2cq5lfz2hdklOcUO8Wp4iNacQYaviscsaxxZ43RKvoLHoXMfTs2xOdw8g9QW+sP125jK51nFITW6s1PZcpTQnzo8gHCBKbrjtxImAQElcqxZJ1kihkEJw89BZdgyej9vTEb//pfcvnmad7aPHed9t++uRZ9FGIM5W0zx28DZmqnlUUxvT1TxHZoZY2zXNtpGjnPHW4mkbQYUeN+4ws6ZCZk01iQQrpEgixqFER0mfjkIHMjaaUmC5Ch0mY1E6eViwOB16MW0mt5zdtIhFo2tb+E1falIIvGCp0dTVYqA7x6aRPvafnKCQjdOFbVuyZU0/EzNFpubKBGHNjKk2yNX1ESWXsCubJuM6PHr39Qx2d66lazAYDAaD4XsLI1wNhrcgSmm+7//530xOL3Q+qMM//KWnSM1ECAXKojWNWGtkqPEKOjb2WQ4h+M6GHexes5mKk2oswkyac7IB2ZF2wlWz79tr0CMgIhBJN6K2JHeRLou0RGuFELGoCZRFygq5afBs3JoK+dRXHm3VfE3t7Bg8zHtvO9yaYrsoe7rs2/zV/juY87PYIsKRqr5fawi0xcm5fsarXYwMzmDJKr1uHBFWTTm6S8qv2hoCEJZGWhBFgIrPEyqJSIciSX9unny7nN9225s2txOvOl7veWZqnvFSRH9Xjr58hlBp+gvZ9m1dBYQQPLhrGwdOTVKu+mTTccqwbUnWDHQz0ttFqeoxNV9mcnaBQK3eTVsCtpR0Z1O85/br+KF33foGz8JgMBgMBsNbGSNcDYa3GGGk+Df/vy8tL1o7oRWpomovWoE5eQG5bgRZKzuziLH5aW6cOM03ttyMkhKEiEXr0o4YvH1ykQGSRkeaY386gn99KhZ2i8VWROtdJ6l9EmmBJBatltDcu/YojhNRXBD8ybcfXSIYaxpw54YjvP3GQ/HmJkOjxd08d24Lc34GR4ZYiwYlBDgiohraVKou5YpgbS8oXfM4bq0v21KCVWiEq5csMa7JXF1zohKL22m9bpekNuc2wl/KeGex4jFf8bg451LIprl7x/pLt7sKtNacGJ/m6LkpokjT15Xlli2jdWfgt920kd0HT/PU6yfoCiO6cun62luNpuIHVIMQLHF5BYuF4MYNw/zLj76DmzaOXPNyPwaDwWD4e0zbagDXuH+DEa4Gw1uNz3/ndb710pHLOlf6IAMdGyEt+of9/PAE0h5FdBBQG2Ymee+RV7CU4mK2wMtrNnfsZ/jes+RHyk33UYFakBz/682AhbaDtjdZoUGHxGtdRePcIHTicjEqYig9yeMv3sLj3NJ5ogLee9Oz7Fg/1RI3btZ3NZSCg9MjAEtEa41QyfrJ5VIXusdrc1QsULUWKJaK1bi+anJ1hQapUUFcv/RSAdUV7Gz0sci8yQtCPD+MyxZZkumFCvlsihvWda5vu1oOnprkM4+/yv6TE1T9ABBIKRjpK/DwndfxyD07cGyLf/bht9GVS/OdV49zZnKOmtCXQtCTzxBqTSgUXjFc+XdwMt+x/i7+n489yPY1g2/YvAwGg8FgMHz3YISrwfAmMjtb4oXnjrF/31kqlQAfxbMnzxIG0SqcghtYQRLha7bVFXBGTdBtj3aMUm2/cI53HX8doTUneod4dXRD+w4E9N4yTveGUks0MlqQnPjrrY3DIjrqMKGBgNicScY/rSKocwI16HB+bmmkcHGg8YO7nmLz6OyiWGgDiSDUGlvAhUqBSuhgd3A2qCoQyLjGqIaK79JOdQsRR2GVitf31rbV+1bx5ISMbYCjioWKBCq61Oe4ws+5OVV6UcZxPO4AKSXZlIOQglMXZ9k03LeytpfhtWPn+c3PPMFMsUxfV5aB7iwicfi9OLvAH33lBcZnivz4I3eRSTn8xAfu4fvediPP7j3JxbkSUgrWD/Uy0JvjP/35N+jLZ5lbqKK1ZkVeEzqOKv/zD91vRKvBYDAY3hSW2Iq8Cf0bjHA1GN4UtNY88fgBPvOXzzI7W0YIwXy5SqnqEylN1hFU+2yUu8w6VAVLvJXahLHO7Jimp7jUBKomgG4eP8nbTh4A4NDAGN/afCNatOlXwMDOs/TtKNGcDFu96HDmi63RWWdeEhXUcp5C8VpQASKA/H7BwlZrRVG4B296kS2js+3bpBFxrYnXMJJNe1qJRWtL7jJaCyItsGRTZVkNoRJEWkLicKu1RiCQTZFYIWPDprBi16Ot/sX0CmbVhsXfUu2+tRZNKYoUa/u7mK16fOnFA3ziPXeRdi//Nl+q+nzq888wV6qybqin5cGHa1sM9xWYL1f52gsH2bF+iPtv3gTAcG+BD91/U0tb+05NoLSmkE3hOhZeEK2sALGAj953E+/Ztf2y52EwGAwGg+G7HyNcDYZrjNaaP/s/T/HZv3qRatVHSkE1iIhQaEugpMbyNZmLAZVBB+W0F69CNdUErUX+akI2cSc6s26KnuJQp4Fwx9mj3HH2KACvjmzg6fXXtY/0aui7cZL+bQuIOLSICuDcE2sonyk0DSo+Nj0uqI4Q12tdPO5kg0qO12Uobu3sbtw0BB64/lVu2TDOcs8eW5eDCnKpKgKNQgJRfTy+stu0IpAyItSSKGr0ouotxxNQWsRLNTVECFRVEhXd2Gk4iNOOnUJA+UyOsOS0Rkiv0joVQTyew+enwBJ85ulXefnkOR64cTMP3bKNkd7CJdtYzHP7TjE+Nc9of1fHaH1XNs18yeMbuw/ztps2djyuJ5fBteP6xP1dOSZmiqB0vZZvp0n90Ntv4Zc/9u5Vj91gMBgMBsP3FqssSGAwGK4ErTV/+efP8BeffoZSKS6vUvECVKQQEUhfI8PYWEkGGqfY2X1V0FTXK/nHf5gWaBlvn9lcokd3EK1A3q9yy/mTALywdmtn0Zp0UDzVw8RLw6jAAgXjT421itamgclIkjmb3F6sugcTEItAJYGkVE785tJs6j3PbZtPs5KEmWadmHd9RvLzKCXqIslXS5/ZaR27AXflKsSxZIFKXq19CkIlW5yLwwWHyLdQgUQ4GqcQEBQd5vb3Jo2zwnWuqziGRuqSqPUBhIFCCIFjSYrlKn/51Kv8mz//KvtOT6ys0Sae338KIQS2tfxn1JNPc/jMBc5PzXc8ZrSvwE0bR5gtVRjr76KQSeHaNmnbqps4NZNxbf7tj7zbiFaDwWAwvPlo/ea/DCbiajBcSx7/1n4e++vd+F5cYzMMl5rUyGR9qLYETlnhd2m03UHJKBCBInIlwgItBUFWMNvnk/a7lh3LQirDV7bfRl9lgddHOjjQ1sWWIPIlpYksk68MkiqUKJ1s037T+svMuIXUIDbRGnmtibgK4EnkCoTrT7/ny6Tdy6u+LYBbR04zvtBNoCwUIi6p2jxsDUpLLKno6760m7NG4CsrLqujwUpFWMn4VCipTGaYebWfqOTEY0jmrAXx48J2kdeVLmBJ2mrO5l5idqgUXbk0g915lNacnZrjN7/wHf7jjzzMcM/Ka5/Ol6o4lxCtEKcNL1R8SlW/4zFCCN6zazuvHDtHqeKzdc0ApyZnWah4CClBx0ZOkVIMdOX4Hz/9IbatNWtaDQaDwWAwxBjhajBcI4Ig4stf3MPsbAmVhP7aPj/TIEJQKY2MBJavCO32qbQiVFyc9HEelmRPOIgQZnt80iLT9nhLRXRXy0xn40jpua4+znX1LZO+mqzfRKBDiXAj5o70IKq9y1d4SUhPWmS1JCNkLMAFOBVNVBFEPVYs5C6RPvtz7/si9elfRqqtBrb1TTI+fJLd45sSV2DVuiZWS6RUjA3OkLI7r81tbVfg+RbemSzRVBphK1QoqU5k8GddqPk3J40JDYRJOnc7LdguIrt4IBqIQFwis9p1bPqSOq5SCNb0dXN6ao4n9h3jB++7BT8MefHYWZ46cILJuQVcx+aW9SO8fccmxvoaDyRyGZcwuvQDgyBSWFLWS+N04vZta/jYAzv5i8f3cGFugZG+AlrlmStXKVd9gkixeaSPf/8P38vawZ5L9mswGAwGg+HvD0a4GgzXiKeePMhrr5wmDGMhsOzSPt2UBtwWjfAV824APwKB1CykAzJ7BWm7vWh1opD3HXqZgXKRz11/Z128LouIa5HW9KW/4IISIBc5By+2/W0ME6kF6UmL1DiIUCMDTWVYULlkhFHxj975DTpo9lXh+7CQSjPaP8P0XAEvtOtZN0JANu0x1DtPLtM5YrgEDVHZYe5AH+GMu7RmLe0ze0QUn6trrspNC2mbhW5LhrJO9tfW3Tavb17cvoDBnnyLiJRSkHYsvvnaEXZtWcMnv/I0xyenUVrjWhZKa145cY7PPPMq775pKz903630F7Lctm0Nuw+dIVKxMO3E3EKVzWP9lxSbQgg+dO+NrOnv5isvHmT/6Qn8IMK2JOuHe3nnzZt53x07GOjOLduOwWAwGAzXlCWpTW9C/wYjXA2Ga8HMTIlPf/ppgjBWHisyU42IU2wtiJVOsj3U2HMR5zdFsAtqea/pV8GynbZtpQOfRw/uZrA0j2/ZpMJgZQPXAAItNFqJWLTStGa1JliXtQ9uas6K82WtxfpwSRsh/+TBxymsRki2aVIB016a3fObcSxFX1eZvq4KpaqD5zsIocmmPFKL0pC1jge0XEUigSacTBHOu/Hn1Dx+Fb9EszCFugBVEqI0aCe+lkKQrHEG6SWXo8llSjaL2UVfnosv/UBPjrH+pWnc2ZTLxWKJ//LY44zPFhntyePaNpFSXCyWWahWmJhb4A++9QKf272P996yjbu3rKevkGVyJo6OtjNeqngBSmse3LUVuTgHu911E4I7r1vHHdvXcnZqjpliBcuSbBjqJZd2L3m+wWAwGAyGv58Y4WowXAO+9c19TEwkxjUrfGomVGy2FNmxUZMMNFZVY1Uizt6uYUejiGj+eRvLah+azPlVPnDgRXorJaqOwxe3386FfPfqJqDFysa9rG7RcX3ZsHFcO81rE/IT7/la+zWtq3jiqIGLFYc9pc1LBGguHZBLtxfvTct62yLQpKwQC0WpJJt3tDSyRLQmBBlQWRqTV/FPbUGYAemAUwJXCGzLohqEK0qRFkBPIcO29YNtBabWmtlylVIQsGGgB0tKwiji6MQ0cxUPIeK1qmGkmFmo8PXXjvDc4dPcuX0te14/y7mpeQa6c/VIrlKK2VKVhbLPfTdt5F23bV3S57LjFYK1Az2sHehZ1XkGg8FgMBj+fmKEq8FwlalUfJ5++jBuyqY0vwrtJSDMStyiRoYaoUCGmpPfJyBfE5KawosOEtlWJHVXSnzw4IvkvSolN83f7rid2cxy5jwekOo4npb1mjVWtCBUt0QLw3bZzMn+j7/zO8sbMTVHe5fhbCXHvtKGljRmsYJod7x2t/1BjghJ2w2nZ7erTUS4FmldupmwEEdZWzqrCdwwjrpqG6IM5EKLlGPjhWE85loIGVpcmmsZxV35NDs2DXUsRzNbruJFEX1dWSwp0Vpz4sIsc+UqKceuR0ttKakEIRk3Hujzx0/z/nt3sGffWc5enEMpRe3D6sqm+eB9N/AjD+3CeSNyug0Gg8FgMBg6YISrwXCVOHJkgr/56xd4cfcJZmZKqLBzaZt2BC6IINZQtfWQp9+noUl3FnY7WFq21XDdlRIf3v88mcBnLp3lb3fcwUKq/fpX7JD+HXPMHO1CVS4xMJ2kMSe/1+kUbVW6bkYkA1A2NBse1/SslCF3bjxGX750yf7raq2Du9UzU+so6i6iSCBlHCVd2kDn5hdPTKBJyQDH0i1N2Klo6cltmvcACnS842oNWI0SR8oBKSRCCPLpFAtVryFedWzOJKRAaY2QgnTaZvNAF7LDOlQ/jPDCiJRt0ZNJA1D2A+YqVRzbak3xFXE3Jc9n63A/p6bmOD49w6//00d55eh5jp+fIowUfYUsd12/nr6ubMdraTAYDAaDwfBGYYSrwXAV+NTvfZ3HPvdyvKYV0JGqR8tWUvVEAcKSsWi1iE2SBA3RGmgKe1ysZVoruWlmMjlKbpovXreLitMpkhpw3Q8exy86FM9n8Cvt18nWBi/CRXPQLOOSq6AWiAvjt94AqEVLGbszJT505wv05S4hWlvaZkl6bqThyekN+DpPFAmUFug5iQgF2BrZHa6ocdn0uy0jHKmQi6Ow7fRvm20BQE9rlHTx0OunW6AjkBb4tsKNLBxbUkinKFd9Iq1RVhzRdR2LTSP9/OD9N3F6fJyvHzjHQFcW27JYqHhoqK8ZnZgtMtKTZ3y+yFylim1JJucWCMKoHlldMpYkNN2XS7P/zCRnZ+a5c8c67tyxrvOlMxgMBoPhexChOyZjXbP+DUa4GgxvKGEY8okf/1+cPjMdb9CxqdJqbjga4rWgiBbReu7diaVspOna4yK1WFYFh5bFl7fvQmiN38G0SaZ9tn3kBABuIcC3ZoFlImgKaA4yNgu1xJAIKx6jQIMjk/WxsRNxkIIgq1vO68os8LH7niGT8ldcyrSl/4RIw9cmdyAtiVKC6EQG71AWNe2AEgipkb0BztYyzuZK25IyGkhLvx6BlO0MmhSopCRNWHYaJ9acf5tEvAexaG0j7Ouuxou2Cyduy48i/CCubxopTdp16CqkqRJyw9phfvoD93L9umGUUpw83cupeY+nDpyk7Act6dCOJcmlXeY9j5lShXMzxZb+wkjhOjapWuRVg0aTsuOvh6zrMFOqcrFYZtNQ39KJGAwGg8FgMFwDjHA1GK4ArTWHD41z5PA4lbLH//6jJ6hUE2Wn9KoFqwawBcqO5YzQoESi8jJApCnsdpG0F61bp87TV1ng+bXbAAisTrmpYPfOsuWRyaa5gJgYa2jBxW64zUKr6XedrL0UOvk9gigjkJGII5ciXrcZWvEx6WlB4IPXp8lnKnz4zhfJpnyU7myIdCm0hq9NbkRaEq3Ae74L/1AuHmdKgaviiOxFl+iCS3Q+Rfq+2bobsEiWDMtA4GTp+KRBR6BCibA0OoKZQ911cyUWjT8AdC+dHy4kab+Lg7RagGtZpITE0RZSCPI5l96eLLYtmZqv8A/ffTvXrxuun7PvzASP7z2Gl5Raam7PjxR+pYolRdtM5lBrQj+gEgQ4loUjY7Hcm4/Tymufi7UCx2CDwWAwGAyGq4URrgbDZXLk8Dif+cvnOHHsAuVylXOTxSYTndWJViVA2wJti4b6ESDD+K2yNJxVdJ9PIdooIQFcP3Gat5/ch9AwkevmZO9Qx/4y62ZY//YLLds8r9EWsHT9aH0xaizgRBJIba43KkON1x3Xbg0dkBLCFESpJu2rwFkALM39t+2nJ1uKA9M6LruzWnmkNTw5tQnLyqABf38e/2AOkVaIlErqzWi0BumE6EDgH8uiM4rU7Un0UYGlFVHJIpA2TiaMReWiMjQqkCA1QmpK57IEs5m2n3NkQdRFZ9G6+JouPl9r1vR3M5Zv1NoNI8XZqTmuXzfMzs1j9e2//ti3eHH/EYJI1ftraVI02hRN/S3uVteivFGEJQWRio+Yr1QppFNsGOi9xGQMBoPBYPgepekB/pvWv8EIV4Phcjh08Dy/98mvMTtdotCV4siJYuOmoi8j0uoItC3jd0ontVlFzTiYiTUX6Dk/2qEBzW3nj3PXmcMA7B1ex8mewY799d86zsAN8y3boghO/c32Sw9WEa9nbQ7babA8jazEzse2ByIRPdoCx072JYK8Jnaz1Yje9EI92gmglMCSK794Fz2XF+Y3Y4vYoEqHAv9ALk4LTqmkDG1DfUZa4CsLS4I+nMNbF1G1LPxzGTZsvEgqFxB4FlJGWK6O034TAasjkE4sDr2ZFOefGGsvOq3YPXjFClw0TJdrqFChvAgvFaK0Zr7sUfVDto4O8PPfd3/dwff//emv8IUX97Otf3mDJN30ixSCqJO1cvK5Rkqz9+wEN4wNMVfxePS2HfQXjAmTwWAwGAyGNw8jXA2GVRIEEX/6R08yN1NmeLSbPa+duWzRCqAtgbZkLFgBBLEBTyIiph6okD/WRrQmqvbe04e4dfwEALvHNvP8mq3tc2419N95ioFt1cYmDWEIxz+zAtHaPK+aeNUgQo0o6frNRIcNXSs12D6k5jVeJhboNYMDXbY4dXiUobta11zWUncvxULYEK01wrMp1IKFzMcmTPVSqVqglMT3LVQkiSTYJUFxfw+V9Rpnweb0ayN0DxfpHlgg0A5aRVhu1HDyFYKwajF/osDFlwbjD6kNYW6pEdNqkAKy2JQrATOygkDQV8jy4K1beGjn9noK73/6q2/y+Rf3t/XFIpl3XQ03RWLVpeoBAbYQBJFi37lJ7ti0lg/ecf3lT8hgMBgMhu9ympLL3rT+DUa4Ggyr5vVXT3Pu7Az9g3lOnp4iCFZX5mYx2hINhSVAOYLIFVg+TL1rFvfoQNvzhFa84+Q+rr9wFoCn11/HqyMb62tQl2T6arCyVYIgfl+ZgfGvr0CwJu2hmm6ctZqiGqyqpmb9pEXiglxb75q8LB9cpeMyOLXIq5IcfHkTQ4OzrN80GU8/SRkWUi97l572M+ye34S9KDVWlWMxKazGtjCSREoSBRZKNdKwIV7TKpSGSOB7LtN7+ik6PdiZWPiGZUmqO8DOhESeRXkyg/AlksTleVH0ObJouChfJt3SZSiX445t6/jhd9yKbUlG+7pIOfHtWmvNr37m6/zVs6+3Pb8uWNteP90qaOsLbVsPjnScsh0pzXtv2ca6/p4rm5TBYDAYDAbDFWKEq8GwSvbtPUMYKXw/YnY2KXqqmhc/LP9cTNkQZCy0HddDlVGSWishTMlYyAJTDxdJvd4fu/g2mdfWuhgtznL95Fm0gMc33cjBwbUt/SxZqyrgwre307qydQUkAnXJrDRYgcby2nXachjKitfrSh9UU1WewLd55lu3YDt7GB27WG9A6Th/dnFZ0kALXpkdYzrqars8VEjdWJtaE7VaEHjWksFpiGunegLhCajEmlMFFn7QUJ+VqSZHZhH/p90aUoAovXT+q8EJBSkV/20Usim2jrU+tNBa80ff3M3fPLe37fka2pcmqu1dziiq3kBMxrXxQ8W+s5NtTzEYDAaDwWC4lhjhajCsknI5LpcyPVsmDMNYWCbpl0kssXGwaNoqoNJvE2UFdkUjgyT4KBpRVxGBFprpXT75l7qRoSBIhN5it99zXX08ueF6yk6K4/0Nh9klXG5+iW682orWqsYq6xadJDQtEb3m6J8AbF/jpxpXybIUXtXlpaeu4x3vK1MolJtOFomjbRz985XFC3MbqSoXS8SxQ90aPkT2BWBrCAS48f7AXypaieJNYV5jzVmISOAEK7kmoqXczRKW23cpAshFNqGMnYG3jPa37A4jxX/9/Lf5i6deaZvue3midVE79c9KIIRACCh5/urmYTAYDAbD9xrGnOktgRGuBsMqyedTKKUplaroJtHaliT/VQtBedghSgvceYUMQctkd1NZFKng4n0ehf1ZRCDi+p9NYjAVBlgqouzGob29w+sb/XRipcJVL/p9uWzdssKttJZ0rZ+XGDjV51bblbgk1xexiiQtGE2pmOHUsRFuvu0YWuu6CEsOI9KC/QsjVJVb70YQrwdVTeFVayDAHvQJz6eQTkgUWijkEvdey4cop4mURJYklgdCLXNpkokomaw/jhLH52aTKpK5XwZCgVuGyFFIIejJZ3jb9Rvr+yuVgH/4yT/n4PmpZRpp8/uKv+gWTwSCMEIDhUyq7RkGg8FgMBgM15IriQ8YDH8vuenmdUhLMD9fQasOyqApWokCPy8JcxJnoSFaSdZIqtrvAiY+XiZ/LIX0SQSxhni5JVm/yocOPM8HDu0mFa4kPHiJcbUbem0t63KiVWvy0w3zo8VItcgYaBmUlmgE0tIcPzBGad6l6LuMl7oZL3VTDlw8ZfHK/FouBl1Lhrp4KacAUjuLiLRCzduEC06cBqwaB1rVWIhWuyTWvI0MRXy9O023SX1rJ051jjJxmR+1+A7qN8axYtGoQCYZ51GkyaQcfuD+m+lJTJiOT0zxtn/3u0tE65Lmm9YUs/jI1UTda87CWmNLyft3XreKkw0Gg8FgMBiuDibiajCsku07RpmeLqGjNsqkZgy8aJdTVajpEBmJhmkRcYqwEBoUzL9vgZ7vuIg5iYgUIilBiiXIWFU+cPBFurwKJTdFJvDwbIcVoRf9XLyvOTp3CY2jtUZ0TdH1kQoLr/YSnGlTIqXmGry4IZ0Y8daEYDIehcCyQyq2xROnr2NS5Ym0rB+zEKQIlGbn1vF6U7UAai2qq2tRVwH2sE/27dNUn+2B8TituHlW2oZqnyBK6vDIoPOctRD1viKXuhmT9Bs1dmsB98gCkWpdX9smkNlK8pAgyQ7GtiUff9cuHr0zdvGdWyjz/f/tTwlClYxnySVt9Ndk8NW4UG1cujrSOFkpjRSCzUN93L557fKnGQwGg8HwvY5JFX5LYISrwbAKgiDit3/jqxQXvKU3kWWEnww0qWKESlkoAdJv5JQqS1B6Z4WuJ1xUZBO5rRHLPn+eh0++TDbwmUtn+cL22ymmV1FT81I3uw5iezFKa0gJnHQee8s8vdsmKL2Sp/T4AGLxrNtcGwFEqXbuTYLpKEO1XzFd6cdNFptWAhdPNcT5niNj7Nx6rv6+nU6Lt2vy60q4XT7TX92Es6DjtcMSgpwgyIGW8VkyoG20Nb7+om6erBwaojUAK2w6uKaxU8l5AeA2add2nl3NfythkipsSf71D7yLj9x3EwCzzaJVLPMxNovWpbO47DXO3dk0//GH3nt5JxsMBoPBYDC8wRjhajCsgi9/4WWefOJAe53AMqatEQgUWBI7UnWhUWYW7yNpCt9yEREol2RNaxyZHS7P8p5zL+HIiKlsgS9u31Vf37oiVvKEbgXHKIC0AKnw59JULqbIDlfJ7Vyg8p0cOszQqp5EQ1UmEcXISqKWi4hcTXmNRlkCP3IpqzYHAWCx91g/N25eus6z0bMmJQOkgGoljd8l8LtaI7y1CUmfeG1rm56EEHU3ZGVTP0hEi0RrbQ4iSf8m1rHKJ3aCFm3cnZt/14207J9+9N66aN17eoKf+v2/Ya5cvbRoXcKVPJaNP7T+Qpb/+U8+wnVjg1fQlsFgMBgM3yNovbynybXo32DWuBoMK6HqBXzj6QP8n08/zULFbxvcahux1BqUQng+IlC1Q9FAUc5S/jmH1EmJqEKQgcCxqHZLvH5JvzPLe8dfwlEh4/kePr/jztWJ1pWgOyyLbEIBJIIaBFiKiy8N4hcdEDDwcxMIQloEUy2iGMXXRVng50T8qKxpTS9CE/QqlAuyCs6cQJZFG9enmEBlOk0D0KRlgJRxOZ2zB5pKySjiNlX8shfA7iBadSIUIzuJtDal/cqw9btDC4gc0G7jOF2bYpD0uWi9c93AKXmvLbjtprV8/MHbAXjp2Bk+8bufYa5cXT4zSbT7eeVfbF2Ow+d/8ce4bmzoitsyGAwGg8FgeKMwEVeD4RJcmFngk3/2bV575RSlucRFZyUCQWtEGMU1WhVIP0JoDVKwcM8M5VvTyDM21hkLr0viZd3YJjdhPpWhmnKY6e7hq1t3Elor/N/1EusqV5U5Gmh0rnGGUAJhaXQkubh7gJF3nEdaMPAvz3Dxf4yCctGIeM4idkWOXPC7RD09F0BY8dpLFUHQo+sCFw12BXRVEOVApRZfZ7HknSa+bBZRHCnVMD+RJ6q6rWKxZs7kgbxUWrRkyd1RqIbzsCZeK1uLxtabq4nX2oaacF38dCAZk9CwdrCLqWqFJw+dYH1vNz/zB49R8oJ20+10GWiM6soQSnC/HKQ7+wY/IDEYDAaDwWC4QoxwNRiWoVz1+e0/+zYHjk2Qdx2KSqN0LHxa9OGSNZ0aEapEtMY7hQbpRSw8MIef7sP5jouMoJqhXse1uZ2FVIbP7biLsuMSSWt1A2+jYdqZzS6LF5FeEJQy1AW11kAoEFKQHvRoHtbAz59Ha7j4WA59qp8gZ8XrPlOAlXQoiM2otEBHAiyNtnUjTJkMS0RglWJzJO22DnRmWtPb12K3hBACiUZrQXEyy6mXRlqNFKza9W+/prXekkjStDtc7lpUvZ5C3JhW3FXzelbdCIIuzvCptXPDukEGe/Kcnp7jDx5/nqOnL1Lxw9VJ0OaOO7pMLbOvNiZfc0s1T0UFRJHCskxCjsFgMBgMdUy27puOEa4GwzI89+oJDp2YZHSgwJFXzhJFivqdS4JWbfRAFIHSiDauwzM/PAfn+rEmnVi9hKCshmK7dfw4C26Go30jIKCYap8a25HFAml1ZzfwInJToC2NVdVIJXBKCrukkCoWbva8jd9t466LF33qAMp7u5DlArI/wkoFeCqFlrFhkohAWHGbOohHJkTYVlTppFaqVYHQWbpfNP0iiGvB+iWHM6+MUpnJYE9pdJ562SERJe7BHb50dOI8rEQitNsgkvTlWrQV0SRkazual9MujrIuMu7aNNzLYE8eiE2/Xp0YR/is/ovxUq5a9QHRiDzXCtBqEIEmNRfhlASiR2BZEikv+y/HYDAYDAaD4apghKvB0AGtNY8/fxgp4MLkPPNVH1vE6aK1IJdY7NIURchAtW1v+l9VESe60Z4FuRBm7EaNUK256+xhdp0/TiQkU5k8s5n8FSjPS01umX3ViPx0bViC3ESE7el6mizEAVR9LEX52Cil4YCuj00SjGeovlZAuAqZiQ+Wqools41oaiggkEihcdMebiqgVEkRFqjXq60PUSZrZEPQTZV/+vqajokEOgKvmOLsK6NE8zb5sxo7BN/XRJnW8kOt11O0vI8sYkOldiTjqB8nG9dQyUQg165pbQ0vtIryWuQ1EbDrh3rQGhaqHmem5+pR2Za/qWUjpXrlfx81k6xIY5c1qTmFXLyOWIDvhdxw/RhCGOFqMBgMBoPhrYURrgZDB6peyLnJOXLZFMcPTcTpoY7AinRrndLaz2rQ0e3s/L+oYisXveDGaagzdr2EjNCK+08e4MYLpwF4Yc2WWLTCilI8O7HsaZ1skedD8gui3jVCY/mtonVxM2Lcofxng0QjgBIIW6N9AbbGEpqUHZCVHv60i47iNqWvsaSF6oaUDVESHW3pR9TWlQp0fbARWsdiVYUClCCoOEzsG0BctChM6rogc0pxG1GKRupvcyqvgLRj4zgSUoLZqtfetC9ZJxu5iWhdbFLctK2+Qy96X0MCEXTnUpy8OMv0QoVi1atlk6/i8159vlLqQoRbUUjVvgMRaWxb8rZ7t626bYPBYDAYvpcRnUw4r2H/BiNcDYaO1MRSueTheSFaCoK8hQwUImy6iQjA6yxap/9dFeGnCGZtLE8iSrWTQCrFg8dfY+v0OBp4YuMN7B9ct3ggjX5WyGVp3cmAfFDLfxWQCEkR1kLLbdBxGnB00SEIbEQmAlfEYs4XaCWIpI0+62CH8QnKTiKdoUBP2aSqCr8bgq44qhmXDmpMvflePZaaICxb6EgQBRbF83nKp/LICxapokY0iTIBOAsaK4AwJVBuEhlFYAvBxqFefvAdt3LPjvU8fuA4v/f1Zyl7fquIrLEoEqthqWBt7rj2YGDJ2mdAwpznMXfBizfVxGrU5jK/gYFPqXRsfGXrJIW6Ee0XEdhasOuOjVy/Y+yN69RgMBgMBoPhDcIIV4OhA5mUw0Bvjr0X5tFotCVBCPyCRXqmKc+yFNDJOmnu35fxtUskBdITiGojb9WOIt57dA/r5y4SCck3N93E0f7R9g1dSsBcwpun0/F1Jn3yvmyJJmohsL2oyYBKLwrUNvUmRVzapWgRLch6arBQwFoZFzptagch0CKOXKdLkuiMojIKQXdietQ0H20BSpPdI1AX1nGhEIEliKoWIhI4GqSv2z6NFCQuwl6cVqsl3Lx9mI9/4C7etnMTrhPfAj+Uy/C1Vw5xePwiJT9sbaDdpeskWhd3Xj+hdXtLZLc21+Zoc3O6cfNxzaJ6Nd5JAipDFkJFuAsaGdJ48qJB2IL0WI4f/4f3m/WtBoPBYDAsod3T6Gvdv8EIV4OhA0IIHrhzO6/sO0NU+8e85+PMNWV0ljuL1uKvllHCIipbCK2RR1ItAm7HxbOsn7tIKC2+unUnp7sHOrS0wvFezklaw5mQHLHY1BCLUAEyUEhfNY5b1I/udBPVolGHtd2gahevSQW787GaS02B3w0qTVwHVUHUD2v+zibMidh9uRwrNotGG6LuhtSemh/RQHeO3/zXP0A+2+rA1JvLcNeWtew5Nd553KxCtDZ33Gl7cyS9XdmcNoI3Xii7iv6bkYLyqIVXBbeosPy4IZmzsAZSfP977qCvK3cZDRsMBoPBYDBcfYxwNRiW4Z6dG/nLL3YxU6xAJSQzR2LKJBAlv2PQq/hrZYQEv2zDgsB6sYCIWo9+fWgd3dUSR/tGGC/0Xv4gL+XRo+I1qtoiTg+tR+00zsEKbsGNBSGJJtJg+QrpqZXro0g3HGuXeSgY7xIIX6Fd0XDmVSAtgQzAOSpwy8n6XxWnsQYuS/Nok7civLSSE0A6ZfOuu7cvEa0A56Zn+Z/ffrGl3bZjX41obddAmyiqqNV0Xdz34mup9dI05NWORQiiDFRSFgLIp10yaZd1/d2899btq2zMYDAYDAaD4dphhKvBsAzZlMvNa4bY/9JpMl5j+7Ki9T+WkVasM8J5C/vFfF205vwqVduJ67IKwVMbrr+yAdZEqxeCZYHVCNfZpYjUVIA7FySppQKvx8bvdQhTFr3PzSCyNgqJcuLxCWKTnmZ9e8khCIEQIENNlIqjrR31lAShNXY1qverZKzERAVEWeIkdVbjGrgQOYC76GrXOghb17W2oyZah/sLPHjvUnE2s1DhPb/+h8lcOgSJBXQMra+URRdUhLQ+dOi0XhZo7xp1+X2DJiNt+gpZhnsK/PNH3saGwSt4eGIwGAwGw/cwxpzprYERrgZDB0oljz/8vW/yla/sIRMkKZqVEEuruiPwYor/nzIy0ViVky72gVxdtPZWFnj00G4mc118bcutaLGaRYqLqK2L1BprxqPvtRJRVjJ3XR6hITPp4c5HcWapLZLSMprMpE960kdUInTKQqfdeoS1HvKTlw7m1TNzm6KgVqDQtoWyBCjdIso0NWMksKoRUoPwFDLQRK4kKAhkUcQGTjqOEAsgtGgvWpWGiI4Ouc2HZtIOvV1Z3nXPdm7evqZlf8nz+eBv/HFnlS6aIq1Ximj8FFGbL6GOU2kTUl9F1FWEcQkciNcPW4lgXj/SxUfvv40Hb97CSE9h5fMwGAwGg8FgeBMwwtVgaIPWmv/zB9/m83/7EkqB1IAXItBLRasQlCihf426aC2PW+iX++pHDi3M8f7Du0mHAYFlkQoDqs7SlNV2CJYG3ESkIdLIUJM/5yME2BVF32sLaAukamgxrQSRK1EC7EBhRaClIHIW/++fpAsvYyLccuiig4QGuxIRpiXaEmgZl7HRIu4PpWPRGsTnxqVuIMgpIt8i4+l609AqWoXSEDT67vTgoBlLCjJph7GhHh68dzs/8oHbW4yHtNZ86pvPMbNQaZlXLSP3DRWtTe239XdYTrReig6HiDD+m8iNh0ifepkgLeK08W4Uh799gg1WluF786Z2q8FgMBgMnTDeTG8JjHA1GBK01pw+foEXvnOYl547yvN7TqFr/5j3QkTiqttO1YW/ZBO8lkPP2+AD805do4zNT/HI4ZdxVMRkrpsvbdtF1XHbjmE5L586XoRUGrQgM+7hTgd1ISm0jlNQRSPnVYYaGUR19avtOKQqvZDIkW0F6LLXqcM1qJ1rVxTaAmXHhk8iilODha8Qi3JxIweCALJNjrqapF5qLdIa6XqJHL1kIeiisSW7ensy3HfLRm7aNsY9t21mdLBrybFHJqb4i6debjOJpnleQVC8bbualnI/zf0tZRXfUs3RVw3OvCIzHcVuyoBsKt8kAK3iSPTJExf533/0JJMXinz4+3YZ8WowGAwGg+EtixGuBgNQKlb5s9//Fi88dZiJ8zOUqiEin4531kRr8z/qExFYHK1QvbcL/VQeEm3YvOZy48wEDx17FVspzhb6+Mq22wisy/zfTmnw44ipVVGkL3ikLsTR1iVCUrcvDwPJsULHxkehQjurDCleKjWVWJzJSCEEWIHGCnXjIUCCl4cggpSUoBv7I1uALWLDomSda0O0Lo+WMDyQ5/O//VOXPPZX/u9XKQdRy7aWgGi7kjSrpfkzaDZhqnX2RrS7aHt6KiJ7IULZcUTb9pceJjQcOHierVuGkJbkS19+hY0bBrht54YrGJTBYDAYDAbD1cMIV8Pfe2anF/j1X/4r9r5yisBLhEw+iYh6YRx0q4kurSFSCK2JHEVmIY3zuKI6GOAPWIiwIQK3Tp3nweOvIbXmeM8QX99yS2zK1IG6jkn6qKXTOkWf9LgXpysnjrx2MWq40CZj66SDliyDDCMQIt6uOha1uSpoAf//9u47TqrqYB/4c+6dtn2XraywS2/SFAVXsVElFqJoFI0txhbQKCa2JKImb9AUSwzqm+hPjNGomFdNVFREQEUwihIEFQEpCixL277T7j2/P+7cmbnTdmbZYWfh+X4+Izt3bjn37DDyzGm6ANx2wOkTsOnGGExjDKyEZlcgIIHIiYsA6/IxceTb7Rg3tP3wdcuzr2Pdjrr4O3RGS6sM+9OcPbi9/dsLs+3to0s463Vjkil/7NAKGMFV1yW+3rgbOTlOuFx2LHn3CwZXIiKimNhXOBMwuNIRrbG+FY/f9y98+d9vIcOChRCKEVrDB3xqOoRm7KRn6UCeAtkmYHf7YW/2w7vfhube2ZA2Y/9mhwu6ULCxuBzL+hydcDKmYBbRJdCsweHX4NrnhXOvF4ovlOCktOxthNt27jH8dWn+x+z27NchnBEfh6mEthSWZAnmTiHgMrv+hm1XNEDoOnRNQHcESh5+qxHniSSkhM/tw6BeidfD/f1ry/Hm2q8TlhGRITPVca6RY5IjXgp+6RD3/0MxXtABxRcYM2yPrnTFJ+Hcr8HmltBUwOaJ2CHylIHnLS0eeDx+vPf+Bpz5vVEYOqQyXqGIiIiIukxnjuAi6la8Hh/efvVTbFq/wxJaAUS3tGpaMLRCkVA0AWU/YPPokDYB3SHgPOBHzrdtwW7EtXlF+OfQcVjaZ3jc0CoQGHsoAfh15K/Yj5K1jSj4vBmunR4Ir4SUxgRLUgpEJrkO9TYNG/+qevwQPi0s0aLDX+olOizY+BgI2kKGz0wMYyZjc9Zhn4TijX+2WF2ghTROaPMD/j0tcY99fuV/8Uysca3tlB9aohfbOVGiE0f+AsN/DxFUj0T2bh3ZOzVLsFa8RmDN3+pH9l4digbYPXFaq+PQ/BrcbV4seOp9tLXFaaYlIiIi6kIMrnTE+nz1NmzdtBs+vzW16rpu/MXQdcCvQXh8UPxG92BAAqoxK6tEYKmRFg3CD2iqQM22r1G+ryF4rgPZeZbxpyL8Ed7c6NNQ/sF+uDQJRZMQwXa5sLDaqb1ERHC6YsXtM+5ND7tAeyk0oseMOR+wFIFZayN2kYoxy3Bwpt5AEWIVSwpjQilzSZ2oHSIKI6Q5AZFEpbBh0/qdMYut6zqe+eBTaHr0zSVVtcmE11RCqykyYUZWngYIr0TODg2ueh2uJglbi/GeVd0Sud/5YW+RkEJCt0XPQJ2o9Td4ycDbbcfOA/j0061JFJqIiOgIomfAgxhc6ci19I21aGl2W7bpkIDLAWg6FLcPilcLdVE1M6QfEB6ja6vZSuho9WH8/i8xomEbzlr/MWyaP+p6cVvAfBrKPzwQ3EeYEytFJRDrcSLyPEkQUc8EhE8HvBoUt88I63q80GiUSfiN/c1BqDJ4rtA5jVZdYQmsSRYqcB1A0RLcnDlDrjl+1Kcjd08b8mw2+LzRdQ8Ai9dtxLf7GyzbUh6xkii8xqyv6N9TzKowv8mI+K5CeI2WVpsbsLcavxMhgaz9OoTPWOpG9UrodoTW622vTPFuza9DUQTe/yB2N2oiIiKirsQxrnTE0XUdb7z6GT54dz16VuWEtksdcNgBnx+KNzAIUzVigAwPFBKBb74kFD+gSIlTGr5ApbYfuiKwOr8f/IlmDg4LE0qLH6Wf1MfczWgQDRtjm+h8qfQZlmFNbNKYXVj1+ABVgWjzQXPZAUVYU52mQ2n1Qmn1GMEVgFQF9DwXtBwHYFPDbi1UZ7GKHjW0M7L8IqI1OuL14FhZr5EiVa+OrL1uuIQCd5sPRSW5Udd0+/x46r3V0MxFWuPk8g6LE1oTvt7eCaTRoqo7AEejbuki7WiUyJE6bB4JzYHgRF5qW/RpwiV6m0gdsNkU7NxZD12XljVviYiIjmRCxh6qdCivTwyudATQdR3/WbkJaz/dhj21Dfhi/Q7s29sEePyBeYokdCkhFBXw+owWSCAijJkns57brvtxWv16lHsboAkFy0qGYZutFPZ6L3z5gQCI8BmDw47d1Yqize7guqrQ9dgfTGFhy7odwfMnXxmWaGlt1ZUSwieh6l4jjJql9mmw7W2C8PjCbkFA+ADV3Qyl2Q5/SS6k0xYInDp0NbLpz1rslPt6SEBXhBGEdQnFL6G6NdibfbC1+OGwqVBsAlJKHD9+UNThy7/6BjsONFrL0FEaQpM1JQissX7nMcVqWQcgfEZotbdKOA9YF7oVAnA1GOvlwmFcSfUkWAIpSW1tPuTlZbX7XQkRERHRocbgSoe1ZYvX429PLkftznpofg0ybFIgVUoITYfwa1B0ANCNF9VQU2FUaA37B71T92LCgXUo9jXDL1QsKzwataIA9gYvCpq80LJVtPXMgrvMaZzPPJlfonBdE+weCekI/BU0WwE1HcLnDwYQy2zDpvBCCRlqHQ07IKoRVjNCqlQAaVMDS/qEvR5o4ZSqgJ5tB1QjWQqPD7a6xkAra5xld9p8sO2qh68i32ixljLuLLyRMwnHFHEv4fcq/DoUH2Bv8MBZb0wiZHeoUFUBXZeo7N0Dx5zQ33KYrku8s24j7GrYFxEJZ/RNQnhrcER5k+4VLWXoi5CIebegA0IDHC0S2bvN92f0uYQGwCMDYf4gbilwoNvtQ3VViXXNYiIiIqIMwOBKh63XX/kUf31kMVrcvkCrZdhYS7c3MJuutIYPW6ApMLDOaVBEt1UhgdFNW1Hia4ZbsePdwuHYb8+D0CXg1aDnqVBbNeRubobi9aOtZzYgBGyNPhR81QJFCVzHbG0zk6ZNgVTsgMcXCpZxWuQAGGNShYLIxGoJpboMttrKQEuoVBVAN7oJ6zYVepYd0qYYrb/murBeP2x7miyhNRYhAfh1qM1eaAWq0eVYl8bYVrNMkY3G7XRvlgLBZYVMqlc3ZhsWgK3FDymE0Z1VCHi9GnpVF+PKG6cgJ9dlOW5fcwu272tAfpYTtY3NoesebLcbPewW4ozTjSsQWs0u0dau6BI5O/xw1msQmoBQEjdPq17A5m7nenGKGMlmUzE+Ros1ERERUVdjcKXD0p66Rvz1z4vR4gmbqEcPtKh6vFDdWnR3VTW5/qtmKPwstx9cuh9rcvug0ZZtbNR0CF3C0eCHP1sFhETO1lZAA1SPjqy9fqMLsjnxUHhXX6PfshGa7TYIrz9xaDWP8evGeZSw7rmBLrWhJuawcGQeKAGpqtDyXUZgDdaTBDQdam1DYAmg9iOPkALqgVboWXbj/mwqoIhQQ6IwuvrqqrHcTbDs4ac2W5kloNmtA2QFAHh1SJsCW6PPmHU48OWC16fDbldxTM0AVPcviyqbT9eh6xJf796X2ljguDcLa2jtAMUbWI9VDfsyRQcEJLJr/cjdZbxvJSR0h7C+T8zvAyK+oOhwHg874JhjqjB6dFWqZyAiIiJKO84qTIelV178D1rcfmPcqMcPpdkDtckDtbHNCK2RbNFjMmMFk2zNEwyTPsWG9wqHhUKrXzMe5ilbNdhadKhuHfkbm5H9bavxQni3z3hjWlUFMpXumro0rq3pxsOvR80MLAPdlYWmQ2gSUAX8+c5QaPVrUBvbYK9rhKO2weieKpTgDMGWFusYBATse5shfBqUVi80FdAcCjSbgOZUoDsUQFWg2wPdkM37N1tkA2XV7QLSHhFadQlFVWBr8sFR74HTZYPDocJuV1FQmIXefUvx6arNeOmZD40JrcIUZLmwfmdd54TWQJktp2rvvJHdgDUJ1WM8bG268WjVoXp1qB4J137Ncqji19r/AiPB5RIKO21JSS6uv24iHA5+n0lERNSdzZs3D8cffzzy8vJQVlaG73//+9iwYYNlH7fbjVmzZqG4uBi5ubmYMWMGdu/e3UUlTg6DKx2WlixeB+g6lGY3lBYP4NMgNT12ULTF+GsQERQEgHJvA87Z9wmObv02en+fBrTTQiokoLR5AS08OEtLeLN0W4438ZK0NrNJTTPOqWmA22OUI9Cap7ls8Oc5oWXbIBTF6ImqqtBynfAVuiDtKuDXoDS0wr67EWp9mzFbb9ylaBIHWOHVYd/dBEXTITTdaFFUrK2nUhXQnQp0m7CkLN0MuGGtrWa4VRu8cNa1wXHAA5uqwGa3IbcgC8U9C1FUXgDFrsDutOH9d9Zj2zd7LGUaf++jce6lY5IKrXHfBkb3bBkI6oofED4ElgwSyKrzw+aOeO/pCM7kHE6xKYBidJeOVQwBJJ5kKewyWVkO/OnhH6KoKCf+/kREREcqc0WGrnykYPny5Zg1axZWrVqFxYsXw+fzYcqUKWhpaQnuc/PNN+Pf//43Fi5ciOXLl2Pnzp0477zzOrvmOhW/WqfD0v7dDVCbPBGz6MbQzvhB87hKz36c2vAFVKmj0nsAX2b3gi4Cx/o0wBfWJTnREjYSEG1eyOywCZuSaR6L6lYrja7Pls0i2OIqfT74sm1AaY/gq5r5oWeWza9BrW+B0uY1WmDN8yYl0E811u66DmVvM1DoDK5zG324gLQJ6DYAirW+grcqAXg1uHa0wCbNOxXQdIkWjw9uXYfN6w8eqwYmqXrntTX48U+nAAAufuQ5eLTIJtKOE8H/JBCnCs17EjogHdYx1IpXImuPH659sReKNcKu0Uo+vmYA7vz52Xjwz29h6fKv4HSo7bSExyhS2FuhrKwAD/xxJioqCtu5MSIiIuoO3nzzTcvzBQsWoKysDKtXr8Ypp5yChoYGPPnkk3juuecwYcIEAMBTTz2FoUOHYtWqVTjhhBO6otjtYnClw866z7ZBNHutM/HGEqulNVwggPZx1+HEhg1QIPGdswfeLxhqhFYpjdDqjx024hEA4PZCuhyJm8Qiv2GTCO2vx5hmNiyeCACOVj+8tfuBikB4Db+Wxw/b/hYovhhlP9hJiyAgpETWpn3QilywOwDbgVb4cpyQDjV8N0CTEH5jvKfRMhvqGuysbYHdbYyxlTB6LUtVCbZE+/0a/JoOCGOZHFVRIAG8vWgtzjh3DK59/lVs3d8Qv5gKopY3SnhXyX7BEH5/5h9+QGnSkNUk4dzvhz9HGC3L0ljGxtEQe+bgiFNhYP8yXHvV6cjOduDcc8bg/Q83QpOAM8sOYVMgfdEnkWEN5EIAPYpykOVyokePHMy8aBzGjRuQxI0REREdubr7Oq4NDca/h3r0MP5NuHr1avh8PkyaNCm4z5AhQ1BVVYWVK1cyuBKlW3OjGxs37MQf7ngpMKkQYrd+CiQ9EdOglh04vnkzBIAtrjKszB9khFZNNwKrHnb+VMYh6hLw+SEddkQnxUAbmdm1OU5Lq/WQQGtrBHurHz6/Ztyv2f3W44Ntd1N0I505kVPMJrqYd5FwRyElbE1eKF4Njj2tsO1rg68oC77irFCdiUBXWCkhzVZfATgOeGBvM0KpK9uGNp8emqU4/J4D5ZYA/IEu2E3Nbkx99G/J3EDS4TXp4caB/WyKglyXA5eMHYUv3tqML7/YEfr1SCBrn5b0FwRm8KzsWYBf3XY2evcy/qczbEgljh1djY9XfwNdl8jOccDr1eH1+ABNWlbpcThUjBhRhSsvH49hQ49K8maIiIgokzQ2NlqeO51OOJ3OhMfouo6bbroJJ510EoYPHw4AqK2thcPhQGFhoWXf8vJy1NbWdmqZOxODK3V733y9G88veB+ffvQN2prd0BrbjBcOMrQe3bIdxzRvBQBsyK7Ex7n9jRPoujGONJIQqYVXTTe6f0aWURGhUByuvTEOWnTrqQBg374PMi8LerYDaPXC1uYLnS88eybbbTkFZuspIAEdcOw1JqjylWQHx7dKsyyKgNAlbA0+2Jp8wSK1+WX88b6RV5MS209wpHYz5tshzvcBqVAE0CMnG6X5Obh+wglwf9uC1zfsBto0qJG/z/Bm0HbY7CruvPUsVFWVWLbfdfvZ+OW9L6OxYT/a3D4oQsDutEHTdOiaBrtqw+SJwzDzBzU46qii1G6GiIiIMkrv3r0tz+fOnYu777474TGzZs3CunXr8MEHH6SxZIcGgyt1a6ve/xL/c/s/4fX6AQjA4w2MJYwVCNHumNZwXsUOAFibU4W1OdWh88XqXmtKJelIGK2kSlj3WcUIxqLVA6HrgfNJQFVjn8O8nhZ/5lkJQPg0qPtawlbDkaEXg+dKvuiWYxJldWl0BVa8OnS7CkjAUdcCaBK6S4XutENAQkhAbfLB1uKHYnZ3FQJQ45dLRj4RwM5TsqN+xwKB223v/sIP68ByN3ZFYEBFKX4wbgROHtwXhU4nfvzrv8LT6I7K0UIa1zC6SMcfEy0Vo/X2lFOG4Oije0W97nI5cN+9M7Bk6ad4a8k32PxNHfyajsKCbJw4bgDOP/c4HFXJwEpERHRwAl/Cd+n1gW+//Rb5+fnBre21ts6ePRuvvfYa3nvvPfTqFfp3REVFBbxeL+rr6y2trrt370ZFRUXnFr0TMbhSt/Xg//wbb768OmyLjD8briKSbLUL2ZjVE/ttudhnzwtt9PrbHzubAqEISDNoSQl4/RCt7lBXZ5MMhNfIgCMDXYpjtLZadtN0KOYMxp0p1vnMllwA4WlN9YTK6NzbAqkoRoBTFGPd11jhLUZgj7pkYD3XXeOzUvpiIq6wHBmY7ynQKhx7d7uiYFSfSlxSMxqThw+ACBz89F+XY9/uxpjT+0oBQDcCe7xQLRXAYVdxVGURpp05KnjeSIqiYNSIKkyeeByUzrh/IiIiykj5+fmW4BqPlBI33HADXn75ZSxbtgx9+/a1vD5mzBjY7XYsWbIEM2bMAABs2LAB27dvR01NTVrK3hkYXKlbcbt9+O8nW/DEnxZje8SyJ8b4TxlqbTX/TDK0KlLH6OatWJ/TG55Aa6sltPp8KU3mk5DZ4unTjJCqBSZ58mkIzjcbHlTMNVqVsPVmzftthxDGZEmH7ovC8CQW46ICkEJAqgKKWwMQ6DJtt4WNfRWQMVpbY96ClGittCfsAp50q2tY5u6Zn4uxA6vQv6wHshx2NLW5sWT9ZuxpaoEuJfJdTpw8uA9OGFCNsf17w2UPfZzu3lWPj1dtNsbwKgIy1vtGEZDmWrthPQSkMH7NOVlO9KwoxPTzxuCYY/u0U3AiIiIiw6xZs/Dcc8/h1VdfRV5eXnDcakFBAbKyslBQUICrrroKc+bMQY8ePZCfn48bbrgBNTU1GTsxE8DgSt3I9i178OSf38H2LXuwc/t+64u6hPD4woJc4M8kQ6tN13Bqwxfo6T2AEn8T3i4caQ2OXn+gmawT0l9Yl17h8wP+iPVfY7WshXcJjvdaHEJRUi92R2816j5iNSUak1MJLTB2VUoIv2bMGBxoYZWKaH/W5wBvroKGvo5290sYXkPzQgEAcp0OvHnbVbBFhOFrJyT3Yb764y1oaXHD7rBBc/sghIzdk9ucJNqmQMhAl2lFoKK8AMeM6YMJk4Zj1OiquK2tRERElH5CNx5def1UPPbYYwCA0047zbL9qaeewhVXXAEAePDBB6EoCmbMmAGPx4OpU6fi0Ucf7YTSpk+3Cq5333037rnnHsu2wYMH46uvvgIAuN1u3HLLLXj++ectv4Dy8vKuKC51ot276vH4A29h+5Y9qN/fbH1RBkKrXw8FLgnArrQb6gDAoftwev16lPga4RMq1uZUWY9z+zrvRizL20hraE0mnMSaACreurFCIOFasbGOMc8dq8tpVAtvzH7C1h/j3JLQJCD1UBl1M7yqMUNr8Kzh670KwF2sorGPA7AnF+xE4GSx5nE25Trt+OCX10SF1lQc2NcMQCA3xwm32wchhNGqHKtMEoAioKsKHDYFP7l2Ik4Y2x/lFQUMrERERJSyeP/mCOdyuTB//nzMnz//EJSoc3Sr4AoARx99NN55553gc5stdAs333wzXn/9dSxcuBAFBQWYPXs2zjvvPKxYsaIrikqdaMkba7Fpwy60NLvh90a0Ovo0I7QCoW6XzuTe2k6fG5P3r0WhrwUexY53C4eHugdLCXhizB7cUZEfIn6/dW3WZCUze7GiIGG/2HjXNINkrPOpZvOgDMyqHNEsG6tMicqpy9A5BQBNh4wxEZWMUV6pAI19HXD3UKE5lZRnRI61a7bdhvOPH47bzjk9+RPFoagKAIni4lzUN7ZB82sJf20icMy0M0bh+9PHHPT1iYiIiA433S642my2mLNdNTQ04Mknn8Rzzz2HCRMmADCaw4cOHYpVq1ZldH9tSuzAvha89OxKtDS5o190uyF8CLU6+nxAduIZ1ky5/jaM3/o5vP4WtKpOLCkcjgZbjvFiZ4fWSOZarJ3VoBbe6hrsohtIc6lMShU1E7MS3XKtArCrxpjbNm94IRKfq71yGwcZ5RVhZ4xxnuaj7HD3UCG8EsIBY6yomnplvnLTpRhYUdL+jinqXV0MIQRysp3Iy3WhqckNoejQ/Hp0eBWAsCk4Y8oI/HT25E4vCxEREdHhoNsF140bN6KyshIulws1NTWYN28eqqqqsHr1avh8PkyaNCm475AhQ1BVVYWVK1cmDK4ejwcejyf43FzcV9d16HrXdGjXdWPCmq66fqbQdR2/vu15tDa7o/OL1wNogBCBsZJeL5DlSC7ASImTmjYgO9eL/fYsvFM4Ai2qK5Qj3X7r8ijhOjL+0zLTLsK68HY0uQYKYVnOJmwqXHOfgwmtqmIE1HgUBVCdQKsnOoAKI3waf0iIeHVpNgwbCRVCDYTW8B7OEcXS7ALeUhtsmjHmQwbqIdVfyVUnH4v+ZT3S8nds1LHV6FGSi8aGVlRVFWPbtr1oa/PCblMBSPj9gc8WXcCZ68Cc28/CqeMHA0DK5eFnRfqxjtOPdZx+rOP0Yx0nxnqhg9Wtguu4ceOwYMECDB48GLt27cI999yDk08+GevWrUNtbS0cDodlLSIAKC8vD86kFc+8efOixs4CwJ49e+B2x2jlOwR0XUdDQwOklEf0Ehcff7gRre566UmAAABC8ElEQVQmVA0Im/pbSqNl1WM31jrVAqHQUWi0DibZjLndPxa93FvwZfYAFCtOFBsnT7xOa0fFSlWREy0l0wU45knDW1pj/JkMEfGDSH5yJOg64IkYBxx4zwoBlPbONWYJjjc5kXnfqgLptAOKYsyDZSlXiKdARXGZDcIjjR7GTgHdHONqKXL8+z//+GE4fdhA1NXVJXePHTDle4Px3tIvoKo6jh1TjuZmD5qa3dA0CSl1SE0iJ9eFM84ejaGDijpcFn5WpB/rOP1Yx+nHOk4/1nFiTU1NXV2EjsuMZVyPeN0quE6bNi3488iRIzFu3DhUV1fjxRdfRFZWVofPe8cdd2DOnDnB542NjejduzdKS0uTWispHXRdhxACpaWlR/SH39uvvIqtXzdYN/q8wP5mY1IbwMgndjvgsge6mcYPLE7dC49izEArFKB1wNHYvqkeUm8zur6mJbTG+bTxa6HyhpdZSiMMJvqQijyn+R6JanVNghDGOqqqEvu49sK0lEBz2Bc8AjCbWM2W1u1fH4i9JExwfwHpdEDaVcCmhgXXGN2Ee9nRptqgtBnlkirgzzFaMqUiQuHVbNWO8MSPv4/j+1UnvqdOMHFKKaTuwOuvformpmbY7TYIoaDN7YPUJcoqCvDjayZg0NCeB3UdflakH+s4/VjH6cc6Tj/WcWIul6uri0DdXLcKrpEKCwsxaNAgbNq0CZMnT4bX60V9fb2l1XX37t0xx8SGczqdcDqjx0UqitKlHzxCiC4vQ1eq39+MrZv3WnOTzwex1/jGTgJG2FJtAASgJ+59W+Xeg5rGr/F+wVDsdPYwzhGY3FZ6wyZ46iwJJybSA6kuEK7CZ+sVAhCqUbB43Wosy86ELXcjpRFik70VIYz1U5VA/9yYx5kDTuPdjzB+D2boF8LS2Bms4wRlkg7VCK8aIG2Bc8a5njQb2QMvSz8Atw49SwGkhPQhNMtwWDkcqoKlP7s8qldGOk09czSOGdMXH63chM/XbEdbmxdFPXJw3Nj+GDO2L3JyO+d/4kf6Z8WhwDpOP9Zx+rGO0491HB/rhA5Wtw6uzc3N2Lx5My699FKMGTMGdrsdS5YswYwZMwAAGzZswPbt21FTU9PFJaWOaGxsg98f1gLa5gbqW0LPFTO0wuh2q9jjdg4d0FaLcU0bIaREH8+eYHAFYLR8dnZobU9wGZgY1zXHiypKIPUlCMBCiT8TcDIctnZbqZMiFAAdbK0WArAFvnwQgICIOykTANhbdGNYrDDCqwCgeiSE1KG5AuN6NaP6bA4VI3tV4IcnHYNJRw/okuVlyioKcPa5Y3D2uZwtmIiIqFtq799jh+L61L2C689+9jOcffbZqK6uxs6dOzF37lyoqoqZM2eioKAAV111FebMmYMePXogPz8fN9xwA2pqajijcDdlt6uhnp71zRBtoQm0jFQSmDjI4wHyc42fYyyLMqzlWxzbvAUAsDGrJ/6TNyD0oq4futAaOXkRELX0S7BM4Wu7er2h+1LCgqqiBIJrB8ujpnhsojG4ZnOqEIiaiSleWFSM1mYhJYTHmJ1YQhiTPVnqCpCKgO6wA3YFjgZAbZPwuwRUtzTncoLqlVB8EtIuoCuAcKoY178KD19yJpx2ewo3SkRERESZplsF1++++w4zZ87Evn37UFpaivHjx2PVqlUoLS0FADz44INQFAUzZsyAx+PB1KlT8eijj3ZxqamjcnJccDptcNc1QLSGjaG02YzQpumArllDKxBqsZQSx7RsxdEt3wIA1uf0xmc5fUKhyO83zpEuZtBL1MoX6zVVDY1zNUOqDPSD9mvGc4c9NK61owLBMaXxsLFIGQr/UaEV0c+FYuwmYe0/LERodylDra4SEJqE2uaF9AB6rgs5O3xo6uuA7hRQApM0BS/nlxBOAYcX+OmUExlaiYiI6OCwxTUjdKvg+vzzzyd83eVyYf78+Zg/f/4hKhGlU06uC6KpFaKpNbTRZgvNKiwA5OaEXpMyFMakjrHNmzGobRcA4LPcvlif0zu0r6YlPw70YKQaWsNfU5SwsbBmC2zgT68PcCB2i21KUvwgjNXqqgc+zGOFVnNNVjMcCwERCKgyfAKqYHfniHVtIoK/0AGlyQ2XMCZhau5th5ZlttwCUjG6GiseHf39TgytLEvt/oiIiIgoI3Wr4EpHll9f/zd46hpCy3za7UBW2CRakQHKnKpcANABJdBq91H+QGzKCszcqkuj1VIi/jqtmSJ8nGtwW9hzvxbqOpyq4DeHB9naqgeWD1LCJohSBKRNhYhc7gcCqk2BgIDm81u7Q4ftExWmI8OrBJQWD1wA7I0aPD1s8BSqkKqA4pNw1mtwHtBwwtlHH9y9EREREVHGYHCljDRtwM+AwJhWCRjdgSMDmvk8PNjpMri8ykd5A7DZVYY9joJA11u0v8zMoZJs2DRbOMPDm7lN1437VSPO1V73ZCkBn9/42eyW3JHwG7l8UGBZHelQQ9cJa9YWKqAIAU3TrbMoR4bvJNazFX4dwuODKhzIrtOQXWcNyQ6nDWNP6J/6PRERERFF4jquGSHT25zoCDStfxKhNVzgNbvux8iWbRC6BHQJKYE9tnwjJGnSGpa6k1j3HgztSSyXE7nd6zO6Svt8MLrmplAv5r4ef9Sat9Jph8xyBIOotKuhcbjSqH+/pkOa44oF4q8dGzE5UyzCF/tLCLtdxaDBFTjmuL7J3xcRERERZTS2uFJG+fV1TwDusNmDc7OTag10SR8mNKxDD38znNKPj3P7R4eaQBfi6I0ZTlWMoJlqy2is/f3+0BI8EoDHBzgdHSuTP6K11W60tIrIbYFwKnSjJVWxKWaOTXg/QghIaY55jb4XISWEX4N0GB9jNruC3FwXehTn4PyZNcjK7sB9EREREVFGYnCljHHB0beiub4ttMFhT2ryoRzNjUkNnyPP34Y2xYGNrorYO0pkxqxsKXfLDds/bMbkpM5l7qdLI/ya405V1VhOyK8BamCm4kSzC5vnMa8f2Ts50Moqwvc1y6cogN2GH14/Cd98vQtfrtmOhrqmsEmd4lxXhmYLljCCqozY1+lQUVCRDyEUKKpAUY8cnPeDcTjxlEGJ64WIiIgoSUKG5sfsqusTgytliLOqb4Tml6FGUofDaB1sR4G/FRMbPke27kWzzYV38oejWc2KvbN+kNMIm2NNdd2YKMrcFv66KThLboztHblu+M/hY12TWRLHPD58siSbGhpb6g+05jrt0WUMD6yJmKE1Xh0LgfeXrMOfX5iNz1ZuwsO/+Cf2722K/yuJuJ4Iv0ZAdo4Dg0ZVobBnIVxZdgwb3gvHn9AfhUU5ICIiIqLDC4MrdamGhlZcNOw2ADC6hQoRCq3tZLJiXxMmNKyDU/rRoGZjScFwtCpxuocmWq/VXIYlEV03xob6fEaocgXCa6Lgak58dLAiZ+c1z5nMUjhmV9vwc6hKKPAGJ39CqEtuMmXWI4OlTPzFgBBoafJAVRUcN34QzrtyPP72yGK4W32xyxzj+EitTR5cft3pGDKqqv3yEhEREXUYZ2fKBJycibrMnj17cNGw2yADy9YYodUeGoeZnR33WFVqOK3xCzilH3tteXi7cCRaVWfsnZMJYmYLaawGUb8faGkNTGYUYE5wFN6yav5shsK2toMPrn4tOhCaLb9+H+DxAl5vaAysKTBBVfBzLjy4Rq23Gii/prffIiwC6+RGfhGQ6LDAa737lQY3nXVxDXr1KYUSOSNyvGvG8cozK9o/noiIiIi6PQZX6hLrVm3EZaPuCbWyKooRWrVAUMvPNXaME/w0oeLDvIHY4SjCkoLh8Cj26H1lkq2HFiIsxAZaI9vc0buZs/N6fUZ5gwFWGEvNtLYZrzU1W4NnqjP4ejwJdgiUT9OsrcGRsydHBt/I7sVmMPRrgeWE4k3jGwit/sgW4EBZYu0fdq47/3BB8Ge7w4YLrz0dxWX5sNnCyhNZP+0E6eVvrE34OhEREREdHthVmA652m178LNzH7IGG1ug26vXBxQVxA0sdt0Pn2K8bXc5emCXvSi5caNJrA0aU+Q428hJkcInPIp3jeYWY+Zeu93YR9ej1y6NpEujxTZW99uIQBgKsDogfYDN1k6dJKgHnx+w22JP1GSG1qhu19K4Znj5IthsCnJyrGNPjz1pIKr6l0HXJfbvaTK+xDDvJdmxwOw5Q0REROnWocaQTr4+scWVDq3vNtXiRzV3WYOJOdFRaxvQozBuaBnStgPTD3yCPH9raGOsCZAAa6CREftGipeRNM26NI/lmFjBLsEkTB6vcX8erxGGvd7obr66bvzs8QAtLaFAbH5Yhk8OZQZmXbcGPfO5pVwRQTPRerYSgNdvPPy6EVI13Viz1eOzhNbgDMLmeqxRgTrkuaU/j9qWnePEpTdORmVVMbJzHNZldIiIiIiIwrDFlQ6pO3/wEHSpwLIupwDgdQPFRbEPkhIjW7djZOt2AECVdx/W27Itr8c4KPa5wkORQKj7a8T14PNFh9bw1tZkxoIqirXFVNeNoBqvPOHM7rztfcMWa9ZfTQsdr4daSINLy+h6YH2ZBOfVpXFsHGZoFZpuNJIm2PGO318Q1dpqGjyiN37yy3Pw+gsf4cN31mN/XVOCQkXLKXCltD8RERFRyjg3U0ZgiysdEi/9+W2cWTkLdTsaQqHODICtbUBBYewDpcTxLZuDoXVNTjXWZ/WytkDGOCb0czsFC65zqhvdYN1uoLHJGNca2dIJJL8EzcGK1XIaT6yxvUAotEYsIyN0HaKj427N/c0uw1LCaVNw633nokdprvErVQBXlh0P/v3HeH31vXC5EofLPoMqMOtX0/G7p6/Bz393fkpFefRfN6VWdiIiIiLqltjiSml3x/kPYc37GyAVxVjCxdJNWAXyYrfGKVJHTdNG9PXUQQL4OLc/vs6qNF5MFLYsS7xEjEkNMmfcNVtXfUYXXjOYRobi8AmbOqMra1q7w0pA6kZraLx68vshhDEWVoYF20QRVoSH/OClJDweH1r3tuK5JbdHHaOnsHbuUX1KcFSfEjz90GLU7Wxod/+8oiyUlRUkfX4iIiIi6r7Y4kpp9fc/vGaE1liBz2kHXK6YIU6VGk5p/BJ9PXXQIbAib3AotLbHnFnXbLU0x4Waz3XdWN6moQloaQNa3dZJmMzwqqrWR3sTKiWjs4JvpIilcITPnzjc67oxEZOURiAN1E1gXuToR3hojepaDezcurfTbuXpJbejqDg34T7ZeU68+OFdnXZNIiIionjMfwt12aOrKyBDsMWV0sbd4sFzf1wUWqM12BIqjRDo9RuT/mRnITjmNfAQAFy6D5pQ8F7eEOxwFrd/wfCQ6tNC1wlfn9XvDy1vY86cq6qAjFgH1Tz2YIS3NnY0rHZgNuSkr6TrxgRRalhItyztg4hAHBFaw1qls7LjrKHbQc998Av89z+b8asfPwWfLzTWVrUruOOBi3DSpBGdej0iIiIiymwMrpQWa97/CvN/8Uyoq6gZwCJbHI1ZfgLrhwZmDFIE/IqKdwuORr7Whr32/PgXigybWqglEUBo9l2/FgqSsZKdGXC1sAmJUlmWJVG5DkaKwTXl0goRaJ32G3WnKqGu0uFizUQc2EdRBSZfOC7VK7dr1Nj++Nfa33T6eYmIiIio+2FwpU733w82YP6dz+HbL/eExrQGZxCOiFZhS9Vka2708u7H11k9AQBexY69ij32RcJn05XSCJw+LdS3VdNDYdVsRQy/ZryEp6rWwNqR8Gpe8xDrUMSOXEonvMU1smu0ua/Zqh2oyKoB5ajonUSLOBEREVF3xFmFMwKDK3Uqr8eHJ+95Bd9+WRv9Yry1TzUd+fBgYuM65Oge6FCwKavCaIGNdUz4WFWfZm0lNZe3URRAaPEDZKIPoMhWzngTPEWG2rCxop0myQAcXFMVOLgxtDFDa1jSjwjlql1gzh8v7vj1iIiIiIiSwOBKneqco2YFfooITzlZxiRIMfRw12OCZyNcug8NajZ2OgphzIwbI7i6PUZX4HBKjKAmADgCrbX++OuRWoRfLzKQRgbZVFpUDyJICrQz02+HzxzrZGHr6soYkzBFdhcWAr/+23UYOLKqM0tBRERElFniLcF4KK9PDK7Uec4ouQaWKGWOlyzMN7ruxlDmb8TpbV/Drkjsd+RjSf4weEQgcEb+JfX7Aa/POtlSe6HQYTcCmJZkeI0lvHvsIfzgEBF/Jn9gB+KsORFVjEbW4POIazz075sweFSf1K9FRERERJQiBlfqFAsfeSt6oxDGGq1SxkxfR/kO4JS2TVChYzfysMw1CD7YA8FLWI/x+YA2T+C8SC2c2W1GC2KsCYaA6JlyzbJH3ktHxq12sLW1w+NVAaOcqprCxWKMO45VgLD9Hn/nNlQP6plyEYmIiIiIOoLBlQ7a/1z1ON5/9VNY0o5NNVo7zSVWIpJQru7GqW0boUDiO1sh3s8aAE0TxlI15vhKRQAup7HWqhkazXOlFMwQKIc/9usyunwxW1YV5ZBMupRSaI3XApzseNfwsaxmv+TIQyIu8fg7t6N6UEUqpSQiIiLqxjg7UyZgcKWDcs+lj2LlojXRL9jtMFpNYwenZsWFNc5eKNJbsdLVD7oIWzPVDIcaosez+jXAkeLbVkqj27Ivzuu6DihJBOFUW0+T3T9s1mXRGcE4mYmlwq8d3BcJuwdn5Trx1/d+geKSBMsTERERERGlAYMrddjb//gwOrRKaUzEZLaYAqGZfrNdsLW0wC+MkPiFszLxcjORMxNJaXQZtqcaXJG4GdNcvzVy+Zeo86TwbVfkecxldmIF08CA/5S7Bycqj6ZZW6VjBdhYrdZxTllYkosFH/4KTpcz1VISERERER00pf1diGKbf9s/rBv8fiA3OxSOtEAKCnSxHdO2FZNbv4RdRixfE4uU0SHKExjjmmqrZLKJUNeNwGfOHGc+zCVgkr1uvGV/EhSvQ2Na22vR1bTo+9E1KA41pa7W5b2KUFJRiPo9zR0pJREREVH3pmfAgxhcqeO8bWF9bxUB5OdadzAbXKVETesmDG3dgWLFi0p/feITSxlq/TTDWZvbOjNxqq2fsUJnvMQYHlTNmYTDr2eWK94jnohzdTiwpirsPo7qV4YX1sxDYWlu+8cBqOjdAxVVJZBSwuc7iJmZiYiIiIgOAoMrdUjt1trgz1IAcLpihjZF8+Pkpi/R37sHuhD4MHcAthX1BnKyY59YiNDSLIAR8twea/DUZXSYTEQC8EcE11RnJg4vX6KXkSCM6jogBIQQnRdY2wvLwUIJCAHMuG4Scguy8MxHd6O0sjDqVGbBFFXgqL4lqBrUE542L+wOG/KLcjqr1EREREREKWFwpQ5RXS4AgLTbIbKyYu5jl36cvve/qPLsgw6B9wqOxjdZPQPL0gTGwuZkAdmBR6wA1tIavQarFpiwSUvQfTc80/r90fulIbSGX1ZEPqSEmpeD0urS1K/b0bIFQ63x2qDR1Zh26XgAgM1mw/X3noce5QXILciCalMgFAU2m4oe5fkYckwfHNWvHFJKNDe0YWTNQAZXIiIiIuoynJyJOqS0ohC6IiBsNkTPogQ4dR9Ob92AEn8zfELBsuJR2O0sMl40Z/ANbzFtbo2+SHNL7ItremjyIV0ay9koES2P5o8+vzETcfi2VHVwLdYgVQXsNthdDjjysuAoyIG3Ic69dcCP7joXky88AT8/54/Y8U1dVEO0ogqMqBmI/3nhBsv2Y8YPxqCRvfDd5j0YPLoKQihQ1NB3WVJK7N/diKxcJ06aNrLTyktERERElCoGV+qQqeXXQ9jtcV+3QUe27oVHseHd7CHY53cCWisQ3jorJdDaFlgay5q2ZGOT8UPEJELBCOn2Gmu8qopxvKYHWhgRCpo+H+DTQrMbWy7Q3lTDnUjTUFpZiPHTj0NhSR40Tcdzf3wd/jbPQS3LZXOoGH/WMZhx3UQoioK/rrgbn6/chOceeB17dhyAUAR6D6zAFXecg6pBPaOOd2U7ceXtZ+Mv976C2u37kJ2fhbyCLAgh0NbqRdOBZjizHJhxzekYNKrqICqAiIiIqBtLZYhauq5PDK6UuqkVPzFaWhO0RLYoTizJGQIAaFAD41kljKBq0qK7+Uopgaaw2WsjlqkJxk1z7KvdBths1nCqaUYrq6bHDq2hiyW+USDl1tZYe1dWF+OOv1yNAWHhb/uX3+G9f30G3Ww9TlF57x743mUn4/xZk6GEjQkeUTMA8xb+NOnzVA2swA3zfoClr6zGx+9+gX21DZBSwpnlwMiagTht+hgMH9sv5fIREREREXUmBldKydSKn0CoanSgEwKF/hZkSy922o0uwcHAGkus0KppxpjWSLoeP7x6fcZDVYyQqoctwZMotJrH+zXA4wVEIPwJhFp5Uwmt5nq0qmIEabexdE9haR5+u/CnKK8qsex+8wM/RMPeJqz54GtI83qRAVaIqHA9+Pi+uOSmaRhz+jBLYD0Y5b164KLZk3HmD0/Eji17oGsSRaV5qKgqhjjYbtJERERERJ2AwZWSNrX4aojApEzBoBZQ4m/ChJavoAqJxTlDsdeWF/9EsUKrxxtapzUWXTdCISLCa/AE0lg3VpeBF0Ti4KpLY5Inc4kac8InwBgX63QYP4cHt4h7DvJrRtlys42f29wAgILiXPzu/+ZEhVYAcDhs+M1zs/CXu/8Pi5770FhaKN7aqkJAURXUTBmBX/zlR2kLk3mFORhyDCdgIiIiIrJgV+GMwOBK7ZrqugRwOSFyYoeanr4DOLVtI2zQUafkokGNPcswgOjQKgHpcRutpu3RAuE12PIqghkVUgZCaGBfXRpBMFZ41XUjYCYKtr5AkFUUyzUtHxxmN19hTE4lmlqC0wiXVBTidy/PQc++8WcRVhQF194zAyNqBuJf/+89bPjvNnjbvJZLKKpAdl4WJl8wDj/+1XS2gBIRERHREYnBlRKa4rjYaPEzW1rDSYlq/36c1LYZCnTstBVhefZAaFIJtE6G7wtj9l+zK2+gG6x0e4zlapJlBl/FSIjSPHnkF1FSQvj9gXVhAzMOm2WI3Nec1Mk8j9kF1+yq69es66WaIdncpijIzncBEBCKwDEnD8HP5l8JV7aj3dsRQuCkaaNw3GlD8d8PN+LL1d9g3arNcLu9KCrJx6DR1Tj7ipNR0CM3+ToiIiIios4TWMmxS69PDK4Unxla4bBBb2iMCJgCA+V+jLPvgVBVbHWW4sOs/tBFrO6uEtC10F86CcDvg/T5YnYbToqe4BNEiFBmNrsQxxiTq9gixuoKJRS2I/cP7yKiCEu3ZVe2Az2rS1FeVYLxZx+D8WcdC4cr/ozLsTizHBg78WiMnXh0SscRERERER0JGFwppimOi42ApmmAO3rW256yCSfoOwAP8LXogf/4e0A6dCgiesIg6W4DPL7ABEsIrL0aHTojG2gTvR53n/BW0QTdalW7almzNLhvSl1xJfoMOwo3P3w5XFkO9BpQDtUWZ5wqERERERF1GIMrWSx9YQXm/fARI2Sa3WEjSYla5GCrKEATHFgjyoyAu+8A9IJ8KC5ncD/Z0Gj8LIRxviQlEx+D+8SaQEkY3XaDE/MqAk6nHXanHVJKKDYVFVXFmHjZWDx+y0tJlyvc6ecfh9sev6ZDxxIRERFRN8HJmTICgysBALZ9+S2uO+52aF7NmNQoRoulkBICErqiQAL4QO8d1uoZGB/a0Ahdz4FwOoHGJuu40FjdbwHLMjdAcqE1VCgByxzDgXNV9ivD79+4DcU9i+Iequs66urq8PjP/pnyB8LVv74AM66fnNIxRERERETUMZ2zECR1a28+vRhXj/y5EVqFYrSe6rrloWh+jNe2o0b7LjguVYatrWrR1AJ5oD56u/ltVTvfWqX+nVKoDNl5Lky+5ET870e/SRhaw71e93jSV8ouyMLf1tzH0EpEREREdAixxfUId8uke/D58i8DAVQYs+5GsEkNp8pvUSmboQuBL1GM/Xp22Gy94bMuBUKkpgO2JN5eMYJvUi2uDtVYDEeTEIpATn4Wjq4ZiEkXnYia741Oeazpm/v/ijN6XJ1wn+9deSouu/0cFJbmp3RuIiIiIurG2FU4IzC4HsGm2C8KexYjtAoBBzRMkNtRilb4FRXLlCrsF4H1XHUZI7yGSL8fIlF4TXIipXDOHAde2v5nbFm/E7Xb9qCloRVVgypQVFqAir6lUJSOdyJ4c/9fASAqwP5m4U8xvGZQUsvbEBERERFR52NwPUIFQ6u5BEzkxEmKApf0YZK+FUXSDa9Q8a5SjT1maAUCa6kibnBN+O1QeGgN/CnaCa+uXCde2Wl06x00uhqDRlcn3L+jzABLRERERMQW18zAMa5HoCn2iwDVZoRWqccMrbnSizP0b1Ak3WgTNryl9LWG1iixZh+Ot2tEaAXaDa2lvXsEQysRERERER1Z2OJ6hAmGViCia7A1OObCixz40CQcWKL0QZNwxj9pMH8mOR9wxLdGiUKrI8uOXzwzG+OmjEzu3EREREREdNhhcD2CWEKr5g9sjQiNgae1IhdLlWocgAttwp74xOaKNFHa79YQK7QqqkBxZRHOvnoiTp5+HHr2LWv3PEREREREacGuwhmBwfUIMcV+kTEHEhA3tFbIZrTBhgaRBQDYKfJSu0hkCE3QkhoeWB1ZdkycOR5HDShDSUUB+o/qgx7lhcjJz0rt+kREREREdFhicD2Mbf9qO248aS5a6luNjKrawkIrEN4i2ls24hT5LdzShkViAFrba2VNhh7726Hw0FpQmoen1/8OLpfr4K9HRERERESHJQbXw9T5Pa9GQ12j8cTMiboGwLLaKgCgnzyAE+UOCAnsEdlwI7U1UBOKWOrG/Em1KRhyfH/85pWfweXiMjNERERElKkkkhkCl97rE4PrYWhG+VVo3NscCIzmcjMwlmpF6CkADJN7cZxeCwDYJIqwSlRCihQnm07Q715qmnGxwHqvxZVFOHbiCFz2i3NR1rs4tesQEREREdERicH1MPL16m/wryffNEIrAEtoVSLGm0qJ0bIOI+UeAMAXogSrRbkRdnUdUFIIr4m+BNICMxfrgM2h4rmNDyd/XiIiIiKirqYHHl15fWJwPRy899IqPHHX37Frw57QRhH4jxCAiE6WQ7AfI+QeSABrlHKsQ4l1MqWILr5x6TqSWQZHsSn4+yaGViIiIiIiSh2Dazc3s8912PvtgdAGM0O20913MwrRXxzARlGEr0Woy24wgkogOA1xZICNNyV4nLBbNbQST/z3D+3dChERERERUUwMrt3YZPXC6I0SMbr5GmFSSAkZCJY+oWIR+kGPCLjScgQOet2qAaP74M+rftPh44mIiIiIuhYnZ8oEKc7CQ5nCElqlHgqXccam2qWGKXILhsq9gS0iKrRGO7i/JEefNAiP/ue3UFIZL0tERERERBSBLa7d0Fn5l4aeyLAxpnECokv6MVFuRQ/ZhkK4sQWFcIv2fvUy6qckRrwGTbvqdNz82NUpHEFERERElIEOsgdip1yfGFy7I0+L1/ghPLSqsddezZZeTJZbkS89cAsblog+YaE1bLmcOJILrYG9hIKhJwzA9X+8DEOOH9DufRARERERESWDwbWbaWlpCQRWkwSU2KE1X3owSW5FjvSiRTiwWPRBk3C2f5GINV+TaWntOaAnZj1wGUaeOgyu7CSuQURERERElCQG127kjmm/xidvrY3YKmLO5NtDtmGS3Aqn9KNROLFY9EGrcLR/EWn8R0IkFVj7ndQbP73/GgwdNzCJvYmIiIiIuhnOzZQRGFy7id/MfCB2aI2jFK1wSj/2iSy8a+keHI/ZbVi2c2ZDYUU+7n/jl+gzvDdEMuu9EhERERERdRCDazex/IWVEVsSh8UNohh+RcF25MMnYnclDp5HSgCB7scSEEr8c1f0LcX0n5yBGTedmVS5iYiIiIiIDhaDazdwyzm/itgSO1j2ko3YjZxgUN0sito/ua5bnsYLrf1GVuGel3+O8qrS9s9JRERERHS4kHrEHDNdcH1icO0O1r72VdizGMFSSgzGfozVd6JO5OAd9IHW7hqtiAqtp114ItZ98BUa9jUBAJzZTgwYXY0rf30hhp0w+CDugIiIiIiIqOMYXDPcGY6LEu8gJUbK3Rgl9wAA9sMFrb0RqjHWolqsvXAwxSQiIiIiOjxxHdeMwODanUmJ47AbQ/V9gAD+K8qxVpTFnGU4/BiGViIiIiIi6k4YXDPYr865L+5rQkrUYCf6ox4A8InoiS9FceIT6hqCXY0FIITA277nO6ewREREREREaZLEQEjqCu11ET4eteiPekgIrMBR+FL2CLwiQwPI9fCHFloDSgBDxw1kaCUiIiIiSobswgcBYItrRpqsXJBwSRoA2IAe6I0m/AcV+FbkGxv1wDtbwOguLMK2wdim2lU88tm9GDh0QFrKTkRERERE1NkYXDPMZOWC+C9KGejpK9AgnHhZDoAea/ZgCes4ViEAqeO6By7HGVecjpyC7E4uNRERERERUfowuGaQRKE1W/pwOr7FalmGWpELQMQOrZECofXVhr8hOy+r8wpLRERERHQk4KzCGeGwHeM6f/589OnTBy6XC+PGjcN//vOfri5SQolCa570YCq2ogfaMA61ELK9Tu/W1xbrCxlaiYiIiIio2zosg+sLL7yAOXPmYO7cufj0008xatQoTJ06FXV1dV1dtJgShdYi6cZUbEUuvGiCA++gCtKy3E28EdwC+eX5XOqGiIiIiOhgmC2uXfmgwzO4PvDAA7j66qtx5ZVXYtiwYXj88ceRnZ2N//f//l9XFy0lPTyNmCK3Igt+HIALb6IvWoSj/QOFgj999D/4564n0l9IIiIiIiKiNDvsgqvX68Xq1asxadKk4DZFUTBp0iSsXLmyC0sWW7zW1krZjBP2fQUHNNQhG2+hD9wiwZBkIQChQHXY8din92Po8QPTVGIiIiIiIqJD67CbnGnv3r3QNA3l5eWW7eXl5fjqq69iHuPxeODxeILPGxsbAQC6rkPX9fQVFoi77E0fNMImdexQ8rBc9oImFMRbICe/tAB5PbJx57M3of/IagBIe7kPB7quQ0rJukoj1nH6sY7Tj3Wcfqzj9GMdpx/rOLFuXS9d3V2XXYUBHIbBtSPmzZuHe+65J2r7nj174Ha703rt6mMrY27fJXpiR0ErtvTKRi8Zf03XX//rdsvzTB3Hm4l0XUdDQwOklFCUw67zQUZgHacf6zj9WMfpxzpOP9Zx+rGOE2tqaurqIlA3d9gF15KSEqiqit27d1u27969GxUVFTGPueOOOzBnzpzg88bGRvTu3RulpaXIz89Pa3m3fboz5nahCMhjKrHts52QeuxvWd70Pp/Ooh32dF2HEAKlpaX8H0yasI7Tj3Wcfqzj9GMdpx/rOP1Yx4m5XK6uLgJ1c4ddcHU4HBgzZgyWLFmC73//+wCMD5IlS5Zg9uzZMY9xOp1wOp1R2xVFSfsHz9v+F+OOc5VSQuoyZnBdrC9Ma7mOFEKIQ/J7PpKxjtOPdZx+rOP0Yx2nH+s4/VjH8XXrOpHo4q7CXXfpTHLYBVcAmDNnDi6//HIcd9xxGDt2LB566CG0tLTgyiuv7OqidQqGViIiIiIiOpIclsH1wgsvxJ49e3DXXXehtrYWo0ePxptvvhk1YVOmWKwvTLiWq2nAuL54bOXvDkGJiIiIiIgIACdnyhCHZXAFgNmzZ8ftGpyJ2guvv1n5M4wbN+4QloiIiIiIiCgzHLbBtTsK7wK8atUq9OnTB2VlZd17TAAREREREdFBYiLKUGPHju3qIhARERERkdlVuCsfxOBKREREREREmY3BlYiIiIiIiDIax7gSERERERHFIaWE7MLuul157UzCFlciIiIiIiLKaGxxJSIiIiIiiqerJ0hiiysAtrgSERERERFRhmNwJSIiIiIioozGrsJERERERETxsKtwRmCLKxEREREREWU0trgSERERERHFwxbXjMAWVyIiIiIiIspoDK5ERERERESU0dhVmIiIiIiIKA6pS0i967rrduW1MwlbXImIiIiIiCijMbgSERERERFRRmNXYSIiIiIiorhk4NGV1ye2uBIREREREVFGY4srERERERFRPLo0Hl15fWKLKxEREREREWU2BlciIiIiIiLKaOwqTEREREREFBcnZ8oEbHElIiIiIiKijMYW1xikNL7VaGxs7LIy6LqOpqYmuFwuKAq/X0gH1nH6sY7Tj3Wcfqzj9GMdpx/rOP1Yx4mZ/642/53dnXj8niP6+pmCwTWGpqYmAEDv3r27uCRERERERIePpqYmFBQUdHUxkuJwOFBRUYE/vXNfVxcFFRUVcDgcXV2MLiVkd/zaI810XcfOnTuRl5cHIUSXlKGxsRG9e/fGt99+i/z8/C4pw+GOdZx+rOP0Yx2nH+s4/VjH6cc6Tj/WcWJSSjQ1NaGysrJbtUi73W54vd6uLgYcDgdcLldXF6NLscU1BkVR0KtXr64uBgAgPz+fH35pxjpOP9Zx+rGO0491nH6s4/RjHacf6zi+7tLSGs7lch3xgTFTdJ+vO4iIiIiIiOiIxOBKREREREREGY3BNUM5nU7MnTsXTqezq4ty2GIdpx/rOP1Yx+nHOk4/1nH6sY7Tj3VMlF6cnImIiIiIiIgyGltciYiIiIiIKKMxuBIREREREVFGY3AlIiIiIiKijMbgmoHmz5+PPn36wOVyYdy4cfjPf/7T1UXqtu6++24IISyPIUOGBF93u92YNWsWiouLkZubixkzZmD37t1dWOLM99577+Hss89GZWUlhBB45ZVXLK9LKXHXXXehZ8+eyMrKwqRJk7Bx40bLPvv378cll1yC/Px8FBYW4qqrrkJzc/MhvIvM1l4dX3HFFVHv6zPOOMOyD+s4sXnz5uH4449HXl4eysrK8P3vfx8bNmyw7JPM58P27dtx5plnIjs7G2VlZfj5z38Ov99/KG8lYyVTx6eddlrUe/m6666z7MM6ju+xxx7DyJEjg+uG1tTUYNGiRcHX+R4+eO3VMd/DRIcOg2uGeeGFFzBnzhzMnTsXn376KUaNGoWpU6eirq6uq4vWbR199NHYtWtX8PHBBx8EX7v55pvx73//GwsXLsTy5cuxc+dOnHfeeV1Y2szX0tKCUaNGYf78+TFf/93vfoc//elPePzxx/HRRx8hJycHU6dOhdvtDu5zySWXYP369Vi8eDFee+01vPfee7jmmmsO1S1kvPbqGADOOOMMy/v6H//4h+V11nFiy5cvx6xZs7Bq1SosXrwYPp8PU6ZMQUtLS3Cf9j4fNE3DmWeeCa/Xiw8//BBPP/00FixYgLvuuqsrbinjJFPHAHD11Vdb3su/+93vgq+xjhPr1asX7rvvPqxevRqffPIJJkyYgOnTp2P9+vUA+B7uDO3VMcD3MNEhIymjjB07Vs6aNSv4XNM0WVlZKefNm9eFpeq+5s6dK0eNGhXztfr6emm32+XChQuD27788ksJQK5cufIQlbB7AyBffvnl4HNd12VFRYX8/e9/H9xWX18vnU6n/Mc//iGllPKLL76QAOTHH38c3GfRokVSCCF37NhxyMreXUTWsZRSXn755XL69Olxj2Edp66urk4CkMuXL5dSJvf58MYbb0hFUWRtbW1wn8cee0zm5+dLj8dzaG+gG4isYymlPPXUU+VPf/rTuMewjlNXVFQkn3jiCb6H08isYyn5HiY6lNjimkG8Xi9Wr16NSZMmBbcpioJJkyZh5cqVXViy7m3jxo2orKxEv379cMkll2D79u0AgNWrV8Pn81nqe8iQIaiqqmJ9d9CWLVtQW1trqdOCggKMGzcuWKcrV65EYWEhjjvuuOA+kyZNgqIo+Oijjw55mburZcuWoaysDIMHD8b111+Pffv2BV9jHaeuoaEBANCjRw8AyX0+rFy5EiNGjEB5eXlwn6lTp6KxsdHSGkOGyDo2PfvssygpKcHw4cNxxx13oLW1Nfga6zh5mqbh+eefR0tLC2pqavgeToPIOjbxPUx0aNi6ugAUsnfvXmiaZvlwA4Dy8nJ89dVXXVSq7m3cuHFYsGABBg8ejF27duGee+7BySefjHXr1qG2thYOhwOFhYWWY8rLy1FbW9s1Be7mzHqL9R42X6utrUVZWZnldZvNhh49erDek3TGGWfgvPPOQ9++fbF582bceeedmDZtGlauXAlVVVnHKdJ1HTfddBNOOukkDB8+HACS+nyora2N+V43X6OQWHUMABdffDGqq6tRWVmJtWvX4rbbbsOGDRvwf//3fwBYx8n4/PPPUVNTA7fbjdzcXLz88ssYNmwY1qxZw/dwJ4lXxwDfw0SHEoMrHdamTZsW/HnkyJEYN24cqqur8eKLLyIrK6sLS0bUcRdddFHw5xEjRmDkyJHo378/li1bhokTJ3ZhybqnWbNmYd26dZbx79S54tVx+LjrESNGoGfPnpg4cSI2b96M/v37H+pidkuDBw/GmjVr0NDQgJdeegmXX345li9f3tXFOqzEq+Nhw4bxPUx0CLGrcAYpKSmBqqpRM/7t3r0bFRUVXVSqw0thYSEGDRqETZs2oaKiAl6vF/X19ZZ9WN8dZ9ZbovdwRUVF1GRjfr8f+/fvZ713UL9+/VBSUoJNmzYBYB2nYvbs2XjttdewdOlS9OrVK7g9mc+HioqKmO918zUyxKvjWMaNGwcAlvcy6zgxh8OBAQMGYMyYMZg3bx5GjRqFhx9+mO/hThSvjmPhe5gofRhcM4jD4cCYMWOwZMmS4DZd17FkyRLLWArquObmZmzevBk9e/bEmDFjYLfbLfW9YcMGbN++nfXdQX379kVFRYWlThsbG/HRRx8F67Smpgb19fVYvXp1cJ93330Xuq4H/4dPqfnuu++wb98+9OzZEwDrOBlSSsyePRsvv/wy3n33XfTt29fyejKfDzU1Nfj8888tXxIsXrwY+fn5wW6ER7L26jiWNWvWAIDlvcw6To2u6/B4PHwPp5FZx7HwPUyURl09OxRZPf/889LpdMoFCxbIL774Ql5zzTWysLDQMhsdJe+WW26Ry5Ytk1u2bJErVqyQkyZNkiUlJbKurk5KKeV1110nq6qq5Lvvvis/+eQTWVNTI2tqarq41JmtqalJfvbZZ/Kzzz6TAOQDDzwgP/vsM7lt2zYppZT33XefLCwslK+++qpcu3atnD59uuzbt69sa2sLnuOMM86QxxxzjPzoo4/kBx98IAcOHChnzpzZVbeUcRLVcVNTk/zZz34mV65cKbds2SLfeecdeeyxx8qBAwdKt9sdPAfrOLHrr79eFhQUyGXLlsldu3YFH62trcF92vt88Pv9cvjw4XLKlClyzZo18s0335SlpaXyjjvu6Ipbyjjt1fGmTZvkvffeKz/55BO5ZcsW+eqrr8p+/frJU045JXgO1nFit99+u1y+fLncsmWLXLt2rbz99tulEEK+/fbbUkq+hztDojrme5jo0GJwzUCPPPKIrKqqkg6HQ44dO1auWrWqq4vUbV144YWyZ8+e0uFwyKOOOkpeeOGFctOmTcHX29ra5E9+8hNZVFQks7Oz5bnnnit37drVhSXOfEuXLpUAoh6XX365lNJYEudXv/qVLC8vl06nU06cOFFu2LDBco59+/bJmTNnytzcXJmfny+vvPJK2dTU1AV3k5kS1XFra6ucMmWKLC0tlXa7XVZXV8urr7466sst1nFiseoXgHzqqaeC+yTz+bB161Y5bdo0mZWVJUtKSuQtt9wifT7fIb6bzNReHW/fvl2ecsopskePHtLpdMoBAwbIn//857KhocFyHtZxfD/60Y9kdXW1dDgcsrS0VE6cODEYWqXke7gzJKpjvoeJDi0hpZSHrn2XiIiIiIiIKDUc40pEREREREQZjcGViIiIiIiIMhqDKxEREREREWU0BlciIiIiIiLKaAyuRERERERElNEYXImIiIiIiCijMbgSERERERFRRmNwJSIiIiIioozG4EpEREeUn/zkJ5g8eXKnnrNPnz644oorOvWcXeWEE07Arbfe2tXFICIismBwJSJKswULFkAIEfexatWqri5ilMgy5ufn49RTT8Xrr7+e9mu/8cYbuPvuu9Ny7i1btuCJJ57AnXfemZbzp9MXX3yBu+++G1u3bk3rdW677TbMnz8ftbW1ab0OERFRKmxdXQAioiPFvffei759+0ZtHzBgQBeUpn2TJ0/GZZddBikltm3bhsceewxnn302Fi1ahKlTp6btum+88Qbmz5+flvD68MMPo2/fvjj99NM79bwbNmyAoqT3u+AvvvgC99xzD0477TT06dMnbdeZPn068vPz8eijj+Lee+9N23WIiIhSweBKRHSITJs2Dccdd1xXFyNpgwYNwg9/+MPg8xkzZmDYsGF4+OGH0xpc08Xn8+HZZ5/Fdddd1+nndjqdnX7OrqIoCs4//3z87W9/wz333AMhRFcXiYiIiF2FiYgyyR/+8AeceOKJKC4uRlZWFsaMGYOXXnopar/Fixdj/PjxKCwsRG5uLgYPHhzs/trc3IycnBz89Kc/jTruu+++g6qqmDdvXsplGzp0KEpKSrB582bL9rq6Olx11VUoLy+Hy+XCqFGj8PTTT1v2WbZsGYQQWLZsmWX71q1bIYTAggULAABXXHEF5s+fD8DaXdmk6zoeeughHH300XC5XCgvL8e1116LAwcOtFv+Dz74AHv37sWkSZNilu3FF1/EPffcg6OOOgp5eXk4//zz0dDQAI/Hg5tuugllZWXIzc3FlVdeCY/HYzlH5BhXs3v4ihUrMGfOHJSWliInJwfnnnsu9uzZYzlWCBGzdTn8nAsWLMAFF1wAADj99NOD9RJen4sWLcLJJ5+MnJwc5OXl4cwzz8T69est56ytrcWVV16JXr16wel0omfPnpg+fXpU9+PJkydj27ZtWLNmTbv1SkREdCiwxZWI6BBpaGjA3r17LduEECguLg4+f/jhh3HOOefgkksugdfrxfPPP48LLrgAr732Gs4880wAwPr163HWWWdh5MiRuPfee+F0OrFp0yasWLECAJCbm4tzzz0XL7zwAh544AGoqho8/z/+8Q9IKXHJJZd0qPwHDhxA//79g9va2tpw2mmnYdOmTZg9ezb69u2LhQsX4oorrkB9fX3M8JzItddei507d2Lx4sV45plnYr6+YMECXHnllbjxxhuxZcsW/PnPf8Znn32GFStWwG63xz33hx9+CCEEjjnmmJivz5s3D1lZWbj99tuxadMmPPLII7Db7VAUBQcOHMDdd9+NVatWYcGCBejbty/uuuuudu/nhhtuQFFREebOnYutW7fioYcewuzZs/HCCy8kXykATjnlFNx4443405/+hDvvvBNDhw4FgOCfzzzzDC6//HJMnToV999/P1pbW/HYY49h/Pjx+Oyzz4Jdi2fMmIH169fjhhtuQJ8+fVBXV4fFixdj+/btlu7HY8aMAQCsWLEibn0REREdUpKIiNLqqaeekgBiPpxOp2Xf1tZWy3Ov1yuHDx8uJ0yYENz24IMPSgByz549ca/51ltvSQBy0aJFlu0jR46Up556artlBiCvuuoquWfPHllXVyc/+eQTecYZZ0gA8ve//31wv4ceekgCkH//+98tZa6pqZG5ubmysbFRSinl0qVLJQC5dOlSy3W2bNkiAcinnnoquG3WrFky1v+e3n//fQlAPvvss5btb775ZsztkX74wx/K4uLiqO1m2YYPHy69Xm9w+8yZM6UQQk6bNs2yf01NjayurrZsq66ulpdffnnwufk7nzRpktR1Pbj95ptvlqqqyvr6+uA2AHLu3LlR5Yo858KFC2PWYVNTkywsLJRXX321ZXttba0sKCgIbj9w4EDU7y8Rh8Mhr7/++qT2JSIiSjd2FSYiOkTmz5+PxYsXWx6LFi2y7JOVlRX8+cCBA2hoaMDJJ5+MTz/9NLi9sLAQAPDqq69C1/WY15o0aRIqKyvx7LPPBretW7cOa9eutYxbTeTJJ59EaWkpysrKcNxxx2HJkiW49dZbMWfOnOA+b7zxBioqKjBz5szgNrvdjhtvvBHNzc1Yvnx5UtdKxsKFC1FQUIDJkydj7969wceYMWOQm5uLpUuXJjx+3759KCoqivv6ZZddZmmxHTduHKSU+NGPfmTZb9y4cfj222/h9/vbLfM111xj6ep88sknQ9M0bNu2rd1jk7V48WLU19dj5syZlnpRVRXjxo0L1ktWVhYcDgeWLVuWVNfqoqKiqB4CREREXYVdhYmIDpGxY8e2OznTa6+9ht/85jdYs2aNZRxlePi58MIL8cQTT+DHP/4xbr/9dkycOBHnnXcezj///ODMtoqi4JJLLsFjjz2G1tZWZGdn49lnn4XL5QqOlWzP9OnTMXv2bHi9Xnz88cf47W9/i9bWVsvsudu2bcPAgQOjZtQ1u7B2ZkDbuHEjGhoaUFZWFvP1urq6ds8hpYz7WlVVleV5QUEBAKB3795R23VdR0NDg6WbdzLnNINzMsExWRs3bgQATJgwIebr+fn5AIwJpO6//37ccsstKC8vxwknnICzzjoLl112GSoqKqKOk1JyYiYiIsoYDK5ERBni/fffxznnnINTTjkFjz76KHr27Am73Y6nnnoKzz33XHC/rKwsvPfee1i6dClef/11vPnmm3jhhRcwYcIEvP3228ExrZdddhl+//vf45VXXsHMmTPx3HPP4ayzzgoGsvb06tUrOJHR9773PZSUlGD27Nk4/fTTcd5556V0b/ECkKZpSZ9D13WUlZVZWpHDlZaWJjy+uLg4YWAMHwuczPZEIbgzjk22bsxW92eeeSZmALXZQv+rv+mmm3D22WfjlVdewVtvvYVf/epXmDdvHt59992osaz19fUoKSlJqgxERETpxuBKRJQh/vnPf8LlcuGtt96yLK/y1FNPRe2rKAomTpyIiRMn4oEHHsBvf/tb/OIXv8DSpUuDYXP48OE45phj8Oyzz6JXr17Yvn07HnnkkQ6X79prr8WDDz6IX/7ylzj33HMhhEB1dTXWrl0LXdctra5fffUVAKC6uhpAqKWxvr7ecs5YLbLxQm7//v3xzjvv4KSTTrJ0qU7WkCFD8Oyzz6KhoSHp8H4oFBUVRdWL1+vFrl27LNsS1QsAlJWVRc2YHG//W265Bbfccgs2btyI0aNH449//CP+/ve/B/fZsWMHvF5vsOWciIioq3GMKxFRhlBVFUIIS0vb1q1b8corr1j2279/f9Sxo0ePBoCoZVouvfRSvP3223jooYdQXFyMadOmdbh8NpsNt9xyC7788ku8+uqrAIyW2NraWsssuX6/H4888ghyc3Nx6qmnAjACrKqqeO+99yznfPTRR6Ouk5OTAyA65P7gBz+Apmn49a9/HXWM3++P2j9STU0NpJRYvXp1u/d6KPXv3z+qXv7yl79EtbjGq5epU6ciPz8fv/3tb+Hz+aLOby6/09raCrfbHXXtvLy8qPeNWUcnnnhi6jdERESUBmxxJSI6RBYtWhRsiQx34oknol+/fjjzzDPxwAMP4IwzzsDFF1+Muro6zJ8/HwMGDMDatWuD+99777147733cOaZZ6K6uhp1dXV49NFH0atXL4wfP95y7osvvhi33norXn75ZVx//fUJl4tJxhVXXIG77roL999/P77//e/jmmuuwf/+7//iiiuuwOrVq9GnTx+89NJLWLFiBR566CHk5eUBMMaFXnDBBXjkkUcghED//v3x2muvxRyXai7FcuONN2Lq1KlQVRUXXXQRTj31VFx77bWYN28e1qxZgylTpsBut2Pjxo1YuHAhHn74YZx//vlxyz5+/HgUFxfjnXfeiTsetCv8+Mc/xnXXXYcZM2Zg8uTJ+O9//4u33norqpvu6NGjoaoq7r//fjQ0NMDpdGLChAkoKyvDY489hksvvRTHHnssLrroIpSWlmL79u14/fXXcdJJJ+HPf/4zvv76a0ycOBE/+MEPMGzYMNhsNrz88svYvXs3LrroIsu1Fi9ejKqqKi6FQ0REmaMrpzQmIjoSJFoOBxFLwTz55JNy4MCB0ul0yiFDhsinnnpKzp0717I8zJIlS+T06dNlZWWldDgcsrKyUs6cOVN+/fXXMa//ve99TwKQH374YdJlBiBnzZoV87W7777bsizL7t275ZVXXilLSkqkw+GQI0aMsNyTac+ePXLGjBkyOztbFhUVyWuvvVauW7cuqg78fr+84YYbZGlpqRRCRC2N85e//EWOGTNGZmVlyby8PDlixAh56623yp07d7Z7XzfeeKMcMGCAZZu5HM7ChQst283f28cff2zZbv4+wpcjirccTuSxsZYF0jRN3nbbbbKkpERmZ2fLqVOnyk2bNkWdU0op//rXv8p+/fpJVVWjzrN06VI5depUWVBQIF0ul+zfv7+84oor5CeffCKllHLv3r1y1qxZcsiQITInJ0cWFBTIcePGyRdffNFyDU3TZM+ePeUvf/nLhHVJRER0KAkpk5ghgoiIuq1zzz0Xn3/+OTZt2tTVRely33zzDYYMGYJFixZh4sSJXV2cjPTKK6/g4osvxubNm9GzZ8+uLg4REREAjnElIjqs7dq1C6+//jouvfTSri5KRujXrx+uuuoq3HfffV1dlIx1//33Y/bs2QytRESUUdjiSkR0GNqyZQtWrFiBJ554Ah9//DE2b94cc6kUIiIiou6ALa5ERIeh5cuX49JLL8WWLVvw9NNPM7QSERFRt8YWVyIiIiIiIspobHElIiIiIiKijMbgSkRERERERBmNwZWIiIiIiIgyGoMrERERERERZTQGVyIiIiIiIspoDK5ERERERESU0RhciYiIiIiIKKMxuBIREREREVFGY3AlIiIiIiKijPb/AYZNgWfYSWduAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Time saved by taking quick route:\n",
+      "  Mean: 7.6 minutes\n",
+      "  Max: 97 minutes\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Comparison: Quick vs Easy public transport\n",
+    "if df_valid.height > 0:\n",
+    "    df_both = df_valid.filter(\n",
+    "        pl.col('public_transport_easy_minutes').is_not_null() &\n",
+    "        pl.col('public_transport_quick_minutes').is_not_null()\n",
+    "    )\n",
+    "    \n",
+    "    if df_both.height > 0:\n",
+    "        fig, ax = plt.subplots(figsize=(10, 8))\n",
+    "        \n",
+    "        pdf = df_both.to_pandas()\n",
+    "        \n",
+    "        ax.scatter(pdf['public_transport_easy_minutes'], \n",
+    "                   pdf['public_transport_quick_minutes'],\n",
+    "                   c=pdf['distance_km'], cmap='viridis', s=60, alpha=0.7)\n",
+    "        \n",
+    "        # Add diagonal line (equal times)\n",
+    "        max_val = max(pdf['public_transport_easy_minutes'].max(), \n",
+    "                      pdf['public_transport_quick_minutes'].max())\n",
+    "        ax.plot([0, max_val], [0, max_val], 'r--', alpha=0.5, label='Equal time')\n",
+    "        \n",
+    "        ax.set_xlabel('Easy Route (minutes)', fontsize=12)\n",
+    "        ax.set_ylabel('Quick Route (minutes)', fontsize=12)\n",
+    "        ax.set_title('Quick vs Easy Public Transport Times\\n(color = distance in km)', fontsize=14)\n",
+    "        ax.legend()\n",
+    "        ax.grid(True, alpha=0.3)\n",
+    "        \n",
+    "        plt.colorbar(ax.collections[0], label='Distance (km)')\n",
+    "        plt.tight_layout()\n",
+    "        plt.show()\n",
+    "        \n",
+    "        # Calculate time savings\n",
+    "        savings = df_both.with_columns(\n",
+    "            (pl.col('public_transport_easy_minutes') - pl.col('public_transport_quick_minutes')).alias('time_saved')\n",
+    "        )\n",
+    "        print(\"\\nTime saved by taking quick route:\")\n",
+    "        print(f\"  Mean: {savings['time_saved'].mean():.1f} minutes\")\n",
+    "        print(f\"  Max: {savings['time_saved'].max():.0f} minutes\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-28T19:53:56.842982Z",
+     "iopub.status.busy": "2026-01-28T19:53:56.842908Z",
+     "iopub.status.idle": "2026-01-28T19:53:56.849543Z",
+     "shell.execute_reply": "2026-01-28T19:53:56.849208Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Journey times sorted by distance to Bank:\n",
+      "shape: (191_297, 5)\n",
+      "┌──────────┬─────────────┬───────────────────────────┬───────────────────────────┬─────────────────┐\n",
+      "│ postcode ┆ distance_km ┆ public_transport_easy_min ┆ public_transport_quick_mi ┆ cycling_minutes │\n",
+      "│ ---      ┆ ---         ┆ utes                      ┆ nutes                     ┆ ---             │\n",
+      "│ str      ┆ f64         ┆ ---                       ┆ ---                       ┆ i64             │\n",
+      "│          ┆             ┆ i64                       ┆ i64                       ┆                 │\n",
+      "╞══════════╪═════════════╪═══════════════════════════╪═══════════════════════════╪═════════════════╡\n",
+      "│ EC3V 3LA ┆ 0.0         ┆ 0                         ┆ 0                         ┆ 0               │\n",
+      "│ EC3V 3NR ┆ 0.0         ┆ 1                         ┆ 1                         ┆ 0               │\n",
+      "│ EC3V 3QR ┆ 0.0         ┆ 1                         ┆ 1                         ┆ 0               │\n",
+      "│ EC3V 3QS ┆ 0.0         ┆ 0                         ┆ 0                         ┆ 0               │\n",
+      "│ EC3V 9AQ ┆ 0.0         ┆ 2                         ┆ 2                         ┆ 0               │\n",
+      "│ …        ┆ …           ┆ …                         ┆ …                         ┆ …               │\n",
+      "│ OX15 5JP ┆ 110.0       ┆ null                      ┆ 153                       ┆ null            │\n",
+      "│ OX15 5JT ┆ 110.0       ┆ null                      ┆ 153                       ┆ null            │\n",
+      "│ OX15 5LD ┆ 110.0       ┆ null                      ┆ 153                       ┆ null            │\n",
+      "│ OX15 5ND ┆ 110.0       ┆ null                      ┆ 153                       ┆ null            │\n",
+      "│ OX15 5NE ┆ 110.0       ┆ null                      ┆ 153                       ┆ null            │\n",
+      "└──────────┴─────────────┴───────────────────────────┴───────────────────────────┴─────────────────┘\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Data table: sorted by distance\n",
+    "if df_valid.height > 0:\n",
+    "    print(\"Journey times sorted by distance to Bank:\")\n",
+    "    display_df = df_valid.select([\n",
+    "        'postcode',\n",
+    "        pl.col('distance_km').round(1),\n",
+    "        'public_transport_easy_minutes',\n",
+    "        'public_transport_quick_minutes', \n",
+    "        'cycling_minutes'\n",
+    "    ]).sort('distance_km')\n",
+    "    \n",
+    "    print(display_df)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/analysis.ipynb b/analyses/property_analysis.ipynb
similarity index 99%
rename from analysis.ipynb
rename to analyses/property_analysis.ipynb
index b209e53..2727a2d 100644
--- a/analysis.ipynb
+++ b/analyses/property_analysis.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -20,7 +20,7 @@
        "polars.config.Config"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -50,14 +50,14 @@
     }
    ],
    "source": [
-    "DATA_PATH = Path(\"./data_sources/pp-complete.parquet\")\n",
+    "DATA_PATH = Path(\"../data_sources/pp-complete.parquet\")\n",
     "lf = pl.scan_parquet(DATA_PATH)\n",
     "print(f\"Schema: {lf.collect_schema()}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -83,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -125,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -217,7 +217,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -1152,7 +1152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -1175,13 +1175,13 @@
          },
          "labels": [
           "U",
-          "Leasehold",
-          "Freehold"
+          "Freehold",
+          "Leasehold"
          ],
          "name": "National",
          "type": "pie",
          "values": {
-          "bdata": "FAIAAPG5bgBzcGcB",
+          "bdata": "FAIAAHNwZwHxuW4A",
           "dtype": "u4"
          }
         },
@@ -2076,7 +2076,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -3017,7 +3017,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -4677,7 +4677,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4701,7 +4701,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -5569,7 +5569,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -6458,7 +6458,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -7602,7 +7602,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -7638,37 +7638,11 @@
          },
          "yaxis": "y"
         },
-        {
-         "hovertemplate": "Property Type=Terraced<br>Year=%{x}<br>Average Price (£)=%{y}<extra></extra>",
-         "legendgroup": "Terraced",
-         "line": {
-          "color": "#EF553B",
-          "dash": "solid"
-         },
-         "marker": {
-          "symbol": "circle"
-         },
-         "mode": "lines+markers",
-         "name": "Terraced",
-         "orientation": "v",
-         "showlegend": true,
-         "type": "scatter",
-         "x": {
-          "bdata": "ywcAAMwHAADNBwAAzgcAAM8HAADQBwAA0QcAANIHAADTBwAA1AcAANUHAADWBwAA1wcAANgHAADZBwAA2gcAANsHAADcBwAA3QcAAN4HAADfBwAA4AcAAOEHAADiBwAA4wcAAOQHAADlBwAA5gcAAOcHAADoBwAA6QcAAA==",
-          "dtype": "i4"
-         },
-         "xaxis": "x",
-         "y": {
-          "bdata": "cph/WyoB+EDmW75F0275QAjetIpYnf1AF+zrVa+ZAEFE1CEGEbUDQe5Jd9N0AAhBrs1quM5OCUE0CeRsExQNQcNllA9QfA9B6KlaheJpEUEce6AslI4SQRM0w213XBRB1WxKwtkDF0EdGHF0S14XQefbnEvtqhdBq+jYexh4G0EOo1mWC0QcQToiNyy85x1BDZav3HRpIEEzefthii0iQTSHLIP+eyJBk4HmHejMI0GFBBzUEj8kQRZNkIJ6ciRBIleyz6VGJEFAAsiGV80lQW2snlz6dSZBT2ES0DK2KUE6oC7G64MoQZhoE/39ZChB+R6N+15RJkE=",
-          "dtype": "f8"
-         },
-         "yaxis": "y"
-        },
         {
          "hovertemplate": "Property Type=Other<br>Year=%{x}<br>Average Price (£)=%{y}<extra></extra>",
          "legendgroup": "Other",
          "line": {
-          "color": "#00cc96",
+          "color": "#EF553B",
           "dash": "solid"
          },
          "marker": {
@@ -7690,6 +7664,32 @@
          },
          "yaxis": "y"
         },
+        {
+         "hovertemplate": "Property Type=Semi-detached<br>Year=%{x}<br>Average Price (£)=%{y}<extra></extra>",
+         "legendgroup": "Semi-detached",
+         "line": {
+          "color": "#00cc96",
+          "dash": "solid"
+         },
+         "marker": {
+          "symbol": "circle"
+         },
+         "mode": "lines+markers",
+         "name": "Semi-detached",
+         "orientation": "v",
+         "showlegend": true,
+         "type": "scatter",
+         "x": {
+          "bdata": "ywcAAMwHAADNBwAAzgcAAM8HAADQBwAA0QcAANIHAADTBwAA1AcAANUHAADWBwAA1wcAANgHAADZBwAA2gcAANsHAADcBwAA3QcAAN4HAADfBwAA4AcAAOEHAADiBwAA4wcAAOQHAADlBwAA5gcAAOcHAADoBwAA6QcAAA==",
+          "dtype": "i4"
+         },
+         "xaxis": "x",
+         "y": {
+          "bdata": "/hlp6rqA+kBlfGGuPsr7QA3U2BzOPgBBFEkc5hF5AkEOH7hbPVkFQdQ3zroWSglBRetdezepC0HMh86Iur0PQSIquL97URFBK/ijBo0AE0FRJPfJ+SsUQfxVLXPAuRVB2S3vDChDGEGx1QCPrekYQTqhOT1RhRdBTia8aVJ2GkHuXCtHJx8bQQshg4NcLhxBNqxBkbkiHkGNK6rjU9ggQfYdpg056SFBYO87hnc/I0Hwpamcz/QjQaHX3l6M0yNBQSK1/+HPI0Heua3ksNAkQQ/lJFlsMyZBXFAo+AdbKEEly3AWY9koQReAqH3otidBgRctZr/SJkE=",
+          "dtype": "f8"
+         },
+         "yaxis": "y"
+        },
         {
          "hovertemplate": "Property Type=Flat/Maisonette<br>Year=%{x}<br>Average Price (£)=%{y}<extra></extra>",
          "legendgroup": "Flat/Maisonette",
@@ -7717,8 +7717,8 @@
          "yaxis": "y"
         },
         {
-         "hovertemplate": "Property Type=Semi-detached<br>Year=%{x}<br>Average Price (£)=%{y}<extra></extra>",
-         "legendgroup": "Semi-detached",
+         "hovertemplate": "Property Type=Terraced<br>Year=%{x}<br>Average Price (£)=%{y}<extra></extra>",
+         "legendgroup": "Terraced",
          "line": {
           "color": "#FFA15A",
           "dash": "solid"
@@ -7727,7 +7727,7 @@
           "symbol": "circle"
          },
          "mode": "lines+markers",
-         "name": "Semi-detached",
+         "name": "Terraced",
          "orientation": "v",
          "showlegend": true,
          "type": "scatter",
@@ -7737,7 +7737,7 @@
          },
          "xaxis": "x",
          "y": {
-          "bdata": "/hlp6rqA+kBlfGGuPsr7QA3U2BzOPgBBFEkc5hF5AkEOH7hbPVkFQdQ3zroWSglBRetdezepC0HMh86Iur0PQSIquL97URFBK/ijBo0AE0FRJPfJ+SsUQfxVLXPAuRVB2S3vDChDGEGx1QCPrekYQTqhOT1RhRdBTia8aVJ2GkHuXCtHJx8bQQshg4NcLhxBNqxBkbkiHkGNK6rjU9ggQfYdpg056SFBYO87hnc/I0Hwpamcz/QjQaHX3l6M0yNBQSK1/+HPI0Heua3ksNAkQQ/lJFlsMyZBXFAo+AdbKEEly3AWY9koQReAqH3otidBgRctZr/SJkE=",
+          "bdata": "cph/WyoB+EDmW75F0275QAjetIpYnf1AF+zrVa+ZAEFE1CEGEbUDQe5Jd9N0AAhBrs1quM5OCUE0CeRsExQNQcNllA9QfA9B6KlaheJpEUEce6AslI4SQRM0w213XBRB1WxKwtkDF0EdGHF0S14XQefbnEvtqhdBq+jYexh4G0EOo1mWC0QcQToiNyy85x1BDZav3HRpIEEzefthii0iQTSHLIP+eyJBk4HmHejMI0GFBBzUEj8kQRZNkIJ6ciRBIleyz6VGJEFAAsiGV80lQW2snlz6dSZBT2ES0DK2KUE6oC7G64MoQZhoE/39ZChB+R6N+15RJkE=",
           "dtype": "f8"
          },
          "yaxis": "y"
@@ -8587,7 +8587,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -9720,7 +9720,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -10640,7 +10640,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -12294,7 +12294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -12338,9 +12338,9 @@
           "NEWHAM",
           "EALING",
           "HARINGEY",
-          "RICHMOND UPON THAMES",
           "LAMBETH",
           "TOWER HAMLETS",
+          "RICHMOND UPON THAMES",
           "SOUTHWARK",
           "HACKNEY",
           "WANDSWORTH",
@@ -12353,7 +12353,7 @@
          ],
          "yaxis": "y",
          "z": {
-          "bdata": "AAAAAICEDkEAAAAAoH8XQQAAAACAohlBAAAAAOB0HkEAAAAAAL0PQQAAAAAguBhBAAAAAABMHUEAAAAAaE0jQQAAAACgLBBBAAAAAKDwGUEAAAAAIJodQQAAAAAAiCNBAAAAAMD3EEEAAAAAoPAZQQAAAACwUyBBAAAAAKSuJkEAAAAAIGURQQAAAADg/RxBAAAAAByzIkEAAAAAsBcqQQAAAACATxJBAAAAAJAFIEEAAAAAwOsiQQAAAAAQAitBAAAAAKCdEkEAAAAAAEwdQQAAAADQoSBBAAAAAOAbKEEAAAAAELUSQQAAAACgYRxBAAAAADx+I0EAAAAAgIQuQQAAAAAg1hNBAAAAAICEHkEAAAAAkHYiQQAAAADwNipBAAAAAMBiFEEAAAAAsFMgQQAAAAB29yJBAAAAAFAPLkEAAAAAKnUVQQAAAAAAvR9BAAAAAPDvIEEAAAAAIEcmQQAAAACghRZBAAAAAJB2IkEAAAAAECAmQQAAAABYeSpBAAAAAKB/F0EAAAAAABchQQAAAAAQICZBAAAAAODIMEEAAAAAyKIXQQAAAADA6yJBAAAAALCmJ0EAAAAArFM+QQAAAADAzRdBAAAAANASI0EAAAAAINYjQQAAAABYCChBAAAAAOAbGEEAAAAA4P0cQQAAAAAAFyFBAAAAAJgzJkEAAAAA0D8YQQAAAAAgWSNBAAAAAPBCKEEAAAAAINYzQQAAAADMYRhBAAAAACDWI0EAAAAAINYjQQAAAABw7CtBAAAAAABqGEEAAAAAQHcbQQAAAAAgmh1BAAAAABA+IUEAAAAAwMcYQQAAAADQEiNBAAAAAKAOJUEAAAAAINYzQQAAAABAdxtBAAAAAOCqJUEAAAAANoUyQQAAAAA4nDxBAAAAAKBhHEEAAAAAwD4qQQAAAADgbi9BAAAAALIUOEEAAAAAoGEcQQAAAACAoilBAAAAAGA2LkEAAAAAQCQ0QQAAAACgYRxBAAAAACC4KEEAAAAAfHkpQQAAAABwKDJBAAAAAADPHEEAAAAA8LMqQQAAAAA2hTJBAAAAALA1NUEAAAAAkAUgQQAAAADgyDBBAAAAAMBcNUEAAAAAQAY5QQAAAADQoSBBAAAAAMAgL0EAAAAAYOM2QQAAAACIKkFBAAAAAAARIkEAAAAAKKU0QQAAAAAUqDtBAAAAAODIQEEAAAAAAIgjQQAAAADAXDVBAAAAAGqCOEEAAAAAyIo7QQAAAABwmSRBAAAAAOAVOUEAAAAAGtdDQQAAAAC0Lk1BAAAAANJHKEEAAAAAeCw/QQAAAAAAAPh/AAAAAAAA+H8AAAAAjE8qQQAAAAASkUFBAAAAANqxVEEAAAAAqMtYQQAAAADg/SxBAAAAAGDjRkEAAAAA1fxZQQAAAAAheV9B",
+          "bdata": "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",
           "dtype": "f8",
           "shape": "33, 4"
          }
@@ -13238,7 +13238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -13284,7 +13284,7 @@
        "└────────────────────────┴────────────┴────────────┴────────────┴───────────┴───────────┘"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -13332,7 +13332,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -14318,7 +14318,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -15292,7 +15292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -15302,67 +15302,11 @@
         "plotlyServerURL": "https://plot.ly"
        },
        "data": [
-        {
-         "hovertemplate": "Property Type=Flat/Maisonette<br>% of Transactions=%{x}<br>Borough=%{y}<extra></extra>",
-         "legendgroup": "Flat/Maisonette",
-         "marker": {
-          "color": "#636efa",
-          "pattern": {
-           "shape": ""
-          }
-         },
-         "name": "Flat/Maisonette",
-         "orientation": "h",
-         "showlegend": true,
-         "textposition": "auto",
-         "type": "bar",
-         "x": {
-          "bdata": "TNHWu3IsU0AVGL70LeNCQOnhY+/6E0hA7COwqp0KUUAAV/jZr+NKQDIQVmIHRUBApl6CT7R5UkBXiYvZKtFSQD5lr7Am1D9ArDAL0tz6S0BEkxDOpYhFQFkvpYsYukpAkhaOAoSkSkDkl1gK05NQQI32deNeCD5A+lWiwSrUQ0CEcgySoPVIQB35YLuRHVZAdiOcaIoaREDFjLZfCZhGQCkJ0Lcb6EJAB1I/dOlMU0Bm2pHswZtRQCKRNZ4mpktAVy2Y9imsUUDqiHfQLHk2QJYQkPNTCTxAl9ImA55DQ0A1jmAfE/pCQG0KJsJOeVFAODUJuXhEOkDr1gAQQVhBQFIRfb90CFNA",
-          "dtype": "f8"
-         },
-         "xaxis": "x",
-         "y": [
-          "CITY OF WESTMINSTER",
-          "HARROW",
-          "BARNET",
-          "HAMMERSMITH AND FULHAM",
-          "GREENWICH",
-          "HILLINGDON",
-          "ISLINGTON",
-          "CAMDEN",
-          "BROMLEY",
-          "HARINGEY",
-          "WALTHAM FOREST",
-          "BRENT",
-          "LEWISHAM",
-          "WANDSWORTH",
-          "REDBRIDGE",
-          "MERTON",
-          "EALING",
-          "TOWER HAMLETS",
-          "KINGSTON UPON THAMES",
-          "HOUNSLOW",
-          "SUTTON",
-          "CITY OF LONDON",
-          "SOUTHWARK",
-          "NEWHAM",
-          "LAMBETH",
-          "HAVERING",
-          "BARKING AND DAGENHAM",
-          "RICHMOND UPON THAMES",
-          "CROYDON",
-          "KENSINGTON AND CHELSEA",
-          "BEXLEY",
-          "ENFIELD",
-          "HACKNEY"
-         ],
-         "yaxis": "y"
-        },
         {
          "hovertemplate": "Property Type=Detached<br>% of Transactions=%{x}<br>Borough=%{y}<extra></extra>",
          "legendgroup": "Detached",
          "marker": {
-          "color": "#EF553B",
+          "color": "#636efa",
           "pattern": {
            "shape": ""
           }
@@ -15373,155 +15317,44 @@
          "textposition": "auto",
          "type": "bar",
          "x": {
-          "bdata": "WfXZSN/bIkBnloyDAf0GQJsmRqRSFfM/rUe2si4aE0A66QFXC5IRQO15T9J1tvA/nHzPklwnsj90Z65JGr3tP0KjQxni+r8/+HMSZDLW+j9jw1UnjyouQAsarqogJ+g/4y6jcHx02D9c5qgf+Y8ZQK8MXhjacyJA32TLlJFc4j+JQEK563AgQGimDN6ZgCNAEGEavplHJkDwD+plKXwKQEIkhz2I5Pg/8FaxxzOM1j+oDOpOzpQkQPDVIjv/CQ5ACzdxUOh1IECDq1TdV2nyP0uCWWO9ORdAd4kk09sD8D9MBY4wa5IGQHQMhETWeNo/Z9eCPl8v0j+Ea4LWMqAAQEfORwDxV/c/",
+          "bdata": "8FaxxzOM1j+JQEK563AgQK1HtrIuGhNAQiSHPYjk+D9HzkcA8Vf3P2PDVSePKi5A8A/qZSl8CkD4cxJkMtb6P+15T9J1tvA/EGEavplHJkBZ9dlI39siQGimDN6ZgCNAXOaoH/mPGUDw1SI7/wkOQJx8z5JcJ7I/S4JZY705F0CDq1TdV2nyP4RrgtYyoABACxquqiAn6D+vDF4Y2nMiQOMuo3B8dNg/CzdxUOh1IECbJkakUhXzP99ky5SRXOI/dAyERNZ42j866QFXC5IRQHeJJNPbA/A/QqNDGeL6vz90Z65JGr3tP2eWjIMB/QZAqAzqTs6UJEBMBY4wa5IGQGfXgj5fL9I/",
           "dtype": "f8"
          },
          "xaxis": "x",
          "y": [
-          "KINGSTON UPON THAMES",
-          "EALING",
-          "SOUTHWARK",
-          "ENFIELD",
-          "REDBRIDGE",
-          "WANDSWORTH",
-          "TOWER HAMLETS",
-          "BARKING AND DAGENHAM",
-          "CITY OF LONDON",
-          "LEWISHAM",
-          "BROMLEY",
-          "KENSINGTON AND CHELSEA",
-          "HAMMERSMITH AND FULHAM",
-          "RICHMOND UPON THAMES",
-          "HARROW",
-          "CITY OF WESTMINSTER",
-          "SUTTON",
-          "HAVERING",
-          "HILLINGDON",
-          "BRENT",
-          "CAMDEN",
           "ISLINGTON",
-          "CROYDON",
-          "MERTON",
-          "BARNET",
-          "WALTHAM FOREST",
-          "BEXLEY",
-          "LAMBETH",
-          "HOUNSLOW",
-          "NEWHAM",
-          "HACKNEY",
-          "GREENWICH",
-          "HARINGEY"
-         ],
-         "yaxis": "y"
-        },
-        {
-         "hovertemplate": "Property Type=Semi-detached<br>% of Transactions=%{x}<br>Borough=%{y}<extra></extra>",
-         "legendgroup": "Semi-detached",
-         "marker": {
-          "color": "#00cc96",
-          "pattern": {
-           "shape": ""
-          }
-         },
-         "name": "Semi-detached",
-         "orientation": "h",
-         "showlegend": true,
-         "textposition": "auto",
-         "type": "bar",
-         "x": {
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          "legendgroup": "Terraced",
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+         "legendgroup": "Other",
+         "marker": {
+          "color": "#00cc96",
+          "pattern": {
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+         "textposition": "auto",
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+         "legendgroup": "Flat/Maisonette",
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+         "textposition": "auto",
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+         "legendgroup": "Semi-detached",
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           "HILLINGDON",
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-          "BRENT",
-          "TOWER HAMLETS",
-          "ISLINGTON",
-          "CAMDEN",
+          "CROYDON",
           "HARINGEY",
           "SUTTON",
-          "MERTON",
-          "EALING",
-          "CITY OF WESTMINSTER",
-          "KINGSTON UPON THAMES",
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-          "BARKING AND DAGENHAM",
-          "HACKNEY",
-          "RICHMOND UPON THAMES",
-          "CITY OF LONDON",
-          "NEWHAM",
-          "HAMMERSMITH AND FULHAM",
+          "GREENWICH",
           "LAMBETH",
-          "BEXLEY"
+          "CAMDEN",
+          "BROMLEY",
+          "CITY OF WESTMINSTER",
+          "BARNET",
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@@ -16471,7 +16471,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
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-          "BROMLEY",
-          "SUTTON",
-          "LAMBETH",
-          "EALING",
-          "GREENWICH",
-          "BEXLEY",
+          "TOWER HAMLETS",
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-          "ISLINGTON",
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+          "BARKING AND DAGENHAM",
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+          "LEWISHAM",
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+          "CAMDEN",
+          "WANDSWORTH",
+          "KINGSTON UPON THAMES",
+          "GREENWICH",
+          "NEWHAM",
+          "KENSINGTON AND CHELSEA",
+          "LAMBETH",
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+          "BEXLEY",
+          "HAVERING",
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@@ -16552,44 +16552,44 @@
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-          "KINGSTON UPON THAMES",
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-          "TOWER HAMLETS",
-          "EALING",
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+          "GREENWICH",
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+          "KENSINGTON AND CHELSEA",
+          "EALING",
+          "KINGSTON UPON THAMES",
+          "BARNET",
+          "CROYDON",
+          "REDBRIDGE",
+          "HILLINGDON",
+          "HAMMERSMITH AND FULHAM",
+          "BROMLEY",
+          "HAVERING",
+          "LAMBETH",
+          "HARINGEY",
+          "TOWER HAMLETS",
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+          "MERTON",
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          "yaxis": "y"
         }
@@ -17486,7 +17486,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -18481,7 +18481,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -19445,7 +19445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -19463,7 +19463,7 @@
          "orientation": "h",
          "type": "bar",
          "x": {
-          "bdata": "AAAAAOTTEUEAAAAAIOIRQQAAAADA6xJBAAAAAIDAFEEAAAAAgMAUQQAAAADgvBJBAAAAAGByFEEAAAAAwFwVQQAAAACAwBRBAAAAAIDAFEEAAAAAYHIUQQAAAACAwBRBAAAAAGDjFkEAAAAA4KoVQQAAAACgDhVBAAAAAIAxF0EAAAAA4CEXQQAAAAAAcBdBAAAAAOCqFUEAAAAA8EIYQQAAAADgGxhBAAAAAIAxF0EAAAAAAGoYQQAAAAAAahhBAAAAAMA+GkEAAAAAQHcbQQAAAADg/RxBAAAAAKBhHEEAAAAAgIQeQQAAAADgbh9BAAAAANASI0EAAAAAMIwhQQAAAABAJCRB",
+          "bdata": "AAAAAOTTEUEAAAAAIOIRQQAAAADA6xJBAAAAAIDAFEEAAAAAgMAUQQAAAADgvBJBAAAAAGByFEEAAAAAwFwVQQAAAACAwBRBAAAAAIDAFEEAAAAAYHIUQQAAAACAwBRBAAAAAGDjFkEAAAAA4KoVQQAAAACgDhVBAAAAAOAhF0EAAAAAgDEXQQAAAAAAcBdBAAAAAOCqFUEAAAAA8EIYQQAAAADgGxhBAAAAAABqGEEAAAAAgDEXQQAAAAAAahhBAAAAAMA+GkEAAAAAQHcbQQAAAADg/RxBAAAAAKBhHEEAAAAA4G4fQQAAAACAhB5BAAAAANASI0EAAAAAMIwhQQAAAABAJCRB",
           "dtype": "f8"
          },
          "y": [
@@ -19482,21 +19482,21 @@
           "TOWER HAMLETS",
           "BROMLEY",
           "HARROW",
-          "WALTHAM FOREST",
           "EALING",
+          "WALTHAM FOREST",
           "MERTON",
           "BRENT",
           "LAMBETH",
           "KINGSTON UPON THAMES",
-          "BARNET",
           "HARINGEY",
+          "BARNET",
           "SOUTHWARK",
           "HACKNEY",
           "WANDSWORTH",
           "ISLINGTON",
           "RICHMOND UPON THAMES",
-          "HAMMERSMITH AND FULHAM",
           "CAMDEN",
+          "HAMMERSMITH AND FULHAM",
           "CITY OF LONDON",
           "CITY OF WESTMINSTER",
           "KENSINGTON AND CHELSEA"
@@ -19519,21 +19519,21 @@
           375000,
           355000,
           345000,
-          380000,
           379000,
+          380000,
           384000,
           355000,
           397500,
           395000,
-          380000,
           400000,
+          380000,
           400000,
           430000,
           450000,
           475000,
           465000,
-          500000,
           515000,
+          500000,
           625000,
           575000,
           660000
@@ -19545,7 +19545,7 @@
          "orientation": "h",
          "type": "bar",
          "x": {
-          "bdata": "AAAAAHAF80AAAAAAgB/6QAAAAABABvlAAAAAAAD59UAAAAAAgDH3QAAAAABAPABBAAAAAADb+kAAAAAA2G/3QAAAAAAA2/pAAAAAAICE/kAAAAAAvJsBQQAAAAAAFwFBAAAAAADb+kAAAAAAABcBQQAAAAAAiANBAAAAAAC9/0AAAAAAgPv/QAAAAACA2ABBAAAAAIDABEEAAAAAoCwAQQAAAABQXQFBAAAAAACIA0EAAAAAABcBQQAAAABM8AFBAAAAAIBPAkEAAAAAgDEHQQAAAABAjwdBAAAAAEB3C0EAAAAAoCwQQQAAAACAhA5BAAAAACQjEEEAAAAAAGoYQQAAAAAAvR9B",
+          "bdata": "AAAAAHAF80AAAAAAgB/6QAAAAABABvlAAAAAAAD59UAAAAAAgDH3QAAAAABAPABBAAAAAADb+kAAAAAA2G/3QAAAAAAA2/pAAAAAAICE/kAAAAAAvJsBQQAAAAAAFwFBAAAAAADb+kAAAAAAABcBQQAAAAAAiANBAAAAAID7/0AAAAAAAL3/QAAAAACA2ABBAAAAAIDABEEAAAAAoCwAQQAAAABQXQFBAAAAAAAXAUEAAAAAAIgDQQAAAABM8AFBAAAAAIBPAkEAAAAAgDEHQQAAAABAjwdBAAAAAEB3C0EAAAAAgIQOQQAAAACgLBBBAAAAACQjEEEAAAAAAGoYQQAAAAAAvR9B",
           "dtype": "f8"
          },
          "y": [
@@ -19564,21 +19564,21 @@
           "TOWER HAMLETS",
           "BROMLEY",
           "HARROW",
-          "WALTHAM FOREST",
           "EALING",
+          "WALTHAM FOREST",
           "MERTON",
           "BRENT",
           "LAMBETH",
           "KINGSTON UPON THAMES",
-          "BARNET",
           "HARINGEY",
+          "BARNET",
           "SOUTHWARK",
           "HACKNEY",
           "WANDSWORTH",
           "ISLINGTON",
           "RICHMOND UPON THAMES",
-          "HAMMERSMITH AND FULHAM",
           "CAMDEN",
+          "HAMMERSMITH AND FULHAM",
           "CITY OF LONDON",
           "CITY OF WESTMINSTER",
           "KENSINGTON AND CHELSEA"
@@ -19627,7 +19627,7 @@
          "orientation": "h",
          "type": "bar",
          "x": {
-          "bdata": "AAAAAADb6kAAAAAAAB0AQQAAAADAXPVAAAAAAIAx90AAAAAAAPn1QAAAAAAAiANBAAAAAEAkBEEAAAAAVDYBQQAAAADAXAVBAAAAAEAkBEEAAAAAxMoBQQAAAAAATP1AAAAAAIAxB0EAAAAAgDEHQQAAAADA6wJBAAAAAIBPAkEAAAAAQAYJQQAAAAAARg5BAAAAAMDNB0EAAAAAwD4KQQAAAACwBQ1BAAAAAAC9D0EAAAAAANsKQQAAAAAaDhBBAAAAAMAgD0EAAAAAgMAUQQAAAABgZhZBAAAAAKB/F0EAAAAAIJodQQAAAACwUyBBAAAAAB53JEEAAAAA2O0xQQAAAABQ2jFB",
+          "bdata": "AAAAAADb6kAAAAAAAB0AQQAAAADAXPVAAAAAAIAx90AAAAAAAPn1QAAAAAAAiANBAAAAAEAkBEEAAAAAVDYBQQAAAADAXAVBAAAAAEAkBEEAAAAAxMoBQQAAAAAATP1AAAAAAIAxB0EAAAAAgDEHQQAAAADA6wJBAAAAAEAGCUEAAAAAgE8CQQAAAAAARg5BAAAAAMDNB0EAAAAAwD4KQQAAAACwBQ1BAAAAAADbCkEAAAAAAL0PQQAAAAAaDhBBAAAAAMAgD0EAAAAAgMAUQQAAAABgZhZBAAAAAKB/F0EAAAAAsFMgQQAAAAAgmh1BAAAAAB53JEEAAAAA2O0xQQAAAABQ2jFB",
           "dtype": "f8"
          },
          "y": [
@@ -19646,21 +19646,21 @@
           "TOWER HAMLETS",
           "BROMLEY",
           "HARROW",
-          "WALTHAM FOREST",
           "EALING",
+          "WALTHAM FOREST",
           "MERTON",
           "BRENT",
           "LAMBETH",
           "KINGSTON UPON THAMES",
-          "BARNET",
           "HARINGEY",
+          "BARNET",
           "SOUTHWARK",
           "HACKNEY",
           "WANDSWORTH",
           "ISLINGTON",
           "RICHMOND UPON THAMES",
-          "HAMMERSMITH AND FULHAM",
           "CAMDEN",
+          "HAMMERSMITH AND FULHAM",
           "CITY OF LONDON",
           "CITY OF WESTMINSTER",
           "KENSINGTON AND CHELSEA"
@@ -20633,7 +20633,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20682,7 +20682,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -20721,28 +20721,28 @@
           "TOWER HAMLETS",
           "BROMLEY",
           "HARROW",
-          "WALTHAM FOREST",
           "EALING",
+          "WALTHAM FOREST",
           "MERTON",
           "BRENT",
           "LAMBETH",
           "KINGSTON UPON THAMES",
-          "BARNET",
           "HARINGEY",
+          "BARNET",
           "SOUTHWARK",
           "HACKNEY",
           "WANDSWORTH",
           "ISLINGTON",
           "RICHMOND UPON THAMES",
-          "HAMMERSMITH AND FULHAM",
           "CAMDEN",
+          "HAMMERSMITH AND FULHAM",
           "CITY OF LONDON",
           "CITY OF WESTMINSTER",
           "KENSINGTON AND CHELSEA"
          ],
          "yaxis": "y",
          "z": {
-          "bdata": "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",
+          "bdata": "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",
           "dtype": "f8",
           "shape": "33, 5"
          }
@@ -21644,7 +21644,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -21659,12 +21659,12 @@
          "legendgroup": "",
          "marker": {
           "color": {
-           "bdata": "KhSythFUUUCpeICeowVOQDL7ywPGFVJAD+AITkoOU0DdlMOyM1dEQBd53ZmdYU5A6dnJd5tDT0BXNZfOo8lFQLbi3TnK7EVAnI/B+RicSUApCfyKoNtQQLl3+9L7RVBAOh3jfD3aH0CL9vMHra1QQNhZO4dmmU1AMpdkHRuJSkAQrfNWTk5HQMIvk4Yq10xAQ8Ijqvs9RUBnQgyvnU06QFyhvfPsFE1AP0Fk9uQ2SEAnTU7oKDFEQMZ7d+F54jlAbfovygqYM0DltKKst20+QFhDGMN+ADZA8+x+fhdUTUCM+vLsPo08QJLDHkRy+DNAH3t+qdycI0DLT8R4+EEuQOr4n0wvzjhA",
+           "bdata": "KhSythFUUUCpeICeowVOQDL7ywPGFVJAD+AITkoOU0DdlMOyM1dEQBd53ZmdYU5A6dnJd5tDT0BXNZfOo8lFQLbi3TnK7EVAnI/B+RicSUApCfyKoNtQQLl3+9L7RVBAOh3jfD3aH0CL9vMHra1QQNhZO4dmmU1AEK3zVk5OR0Ayl2QdG4lKQMIvk4Yq10xAQ8Ijqvs9RUBnQgyvnU06QFyhvfPsFE1AJ01O6CgxREA/QWT25DZIQMZ7d+F54jlAbfovygqYM0DltKKst20+QFhDGMN+ADZA8+x+fhdUTUCSwx5EcvgzQIz68uw+jTxAH3t+qdycI0DLT8R4+EEuQOr4n0wvzjhA",
            "dtype": "f8"
           },
           "coloraxis": "coloraxis",
           "size": {
-           "bdata": "73seye3IOUDUqUuvFgVVQHwdzOTjeEpA1w/1aVQnT0AbZT4gVmtFQKKHROmxxUVAt9pvsO4zSkA64n3PI7lNQIEP9mZdDE5A48OPB1fWRUBmPCZEofdFQDc3ETotL0pA64vuWD7XTED0ALRR5gNVQOn6fGSJ0UJAS/vEHn85S0AheE6CVLlQQHGs+g/Qj0ZABPcar/InREC3Jh7K6tFRQBlITGRl9EFAUMYaixUtU0C8g+IzGxxHQFOXXi0KBk9ARXsgw/+SR0AAAAAAAABZQPlce7Ix/UZAlPx3c1xYSEDvM2TThW5GQCs4xYsTM0ZAAAAAAAAAAAAbIHtFnQNMQKH4zAbHYUJA",
+           "bdata": "73seye3IOUDUqUuvFgVVQHwdzOTjeEpA1w/1aVQnT0AbZT4gVmtFQKKHROmxxUVAt9pvsO4zSkA64n3PI7lNQIEP9mZdDE5A48OPB1fWRUBmPCZEofdFQDc3ETotL0pA64vuWD7XTED0ALRR5gNVQOn6fGSJ0UJAIXhOglS5UEBL+8QefzlLQHGs+g/Qj0ZABPcar/InREC3Jh7K6tFRQBlITGRl9EFAvIPiMxscR0BQxhqLFS1TQFOXXi0KBk9ARXsgw/+SR0AAAAAAAABZQPlce7Ix/UZAlPx3c1xYSEArOMWLEzNGQO8zZNOFbkZAAAAAAAAAAAAbIHtFnQNMQKH4zAbHYUJA",
            "dtype": "f8"
           },
           "sizemode": "area",
@@ -21691,21 +21691,21 @@
           "TOWER HAMLETS",
           "BROMLEY",
           "HARROW",
-          "WALTHAM FOREST",
           "EALING",
+          "WALTHAM FOREST",
           "MERTON",
           "BRENT",
           "LAMBETH",
           "KINGSTON UPON THAMES",
-          "BARNET",
           "HARINGEY",
+          "BARNET",
           "SOUTHWARK",
           "HACKNEY",
           "WANDSWORTH",
           "ISLINGTON",
           "RICHMOND UPON THAMES",
-          "HAMMERSMITH AND FULHAM",
           "CAMDEN",
+          "HAMMERSMITH AND FULHAM",
           "CITY OF LONDON",
           "CITY OF WESTMINSTER",
           "KENSINGTON AND CHELSEA"
@@ -21721,7 +21721,7 @@
          },
          "xaxis": "x",
          "y": {
-          "bdata": "XmMq/gfiVUD5M0jxhCdJQOE8TEudL1hAAAAAAAAAWUDi4DlnVP5HQJhpn+iO/lZAxpALfp6cVUD285ct1oVCQM08s9HOx01AmSSyVzc8VkDeEBQPDPpWQA+p0hLyIlhA6aeTP0kNPUCAdgwQY9FSQOE3efdzrFVAINmxb242VEB6k8W6vyVRQK0E6zIlhFNAQjfSMif4U0BIEbRKrXY2QIoXenKgm1FAxSb8YuTcTUBp9SmUU3ZKQAEeYlHz5z9AfpJps7iKLkBUPqTgH+BDQAmgB5+4klFAuT1rUff7UEAZUBz7EG9OQPkwP1ZS0EBAAAAAAAAAAABgOTq+qaw0QEgRtEqtdjZA",
+          "bdata": "XmMq/gfiVUD5M0jxhCdJQOE8TEudL1hAAAAAAAAAWUDi4DlnVP5HQJhpn+iO/lZAxpALfp6cVUD285ct1oVCQM08s9HOx01AmSSyVzc8VkDeEBQPDPpWQA+p0hLyIlhA6aeTP0kNPUCAdgwQY9FSQOE3efdzrFVAepPFur8lUUAg2bFvbjZUQK0E6zIlhFNAQjfSMif4U0BIEbRKrXY2QIoXenKgm1FAafUplFN2SkDFJvxi5NxNQAEeYlHz5z9AfpJps7iKLkBUPqTgH+BDQAmgB5+4klFAuT1rUff7UED5MD9WUtBAQBlQHPsQb05AAAAAAAAAAABgOTq+qaw0QEgRtEqtdjZA",
           "dtype": "f8"
          },
          "yaxis": "y"
@@ -22710,7 +22710,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -22759,7 +22759,7 @@
        "└──────┴──────────────────────┴───────────────────┴──────────┴──────────────┴─────────────────┘"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/journey_times_analysis.ipynb b/journey_times_analysis.ipynb
deleted file mode 100644
index 8a3cb3a..0000000
--- a/journey_times_analysis.ipynb
+++ /dev/null
@@ -1,539 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Journey Times Analysis\n",
-    "\n",
-    "Analyzing travel times to Bank station by different transport modes versus distance."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-01-28T19:53:51.298414Z",
-     "iopub.status.busy": "2026-01-28T19:53:51.298328Z",
-     "iopub.status.idle": "2026-01-28T19:53:51.682527Z",
-     "shell.execute_reply": "2026-01-28T19:53:51.682199Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import polars as pl\n",
-    "import matplotlib.pyplot as plt\n",
-    "from math import radians, sin, cos, sqrt, atan2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-01-28T19:53:51.683859Z",
-     "iopub.status.busy": "2026-01-28T19:53:51.683731Z",
-     "iopub.status.idle": "2026-01-28T19:53:51.797877Z",
-     "shell.execute_reply": "2026-01-28T19:53:51.797505Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total postcodes: 200000\n",
-      "Columns: ['postcode', 'public_transport_easy_minutes', 'public_transport_quick_minutes', 'cycling_minutes', 'error', 'lat', 'long']\n",
-      "With lat/long: 200000\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div><style>\n",
-       ".dataframe > thead > tr,\n",
-       ".dataframe > tbody > tr {\n",
-       "  text-align: right;\n",
-       "  white-space: pre-wrap;\n",
-       "}\n",
-       "</style>\n",
-       "<small>shape: (5, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>postcode</th><th>public_transport_easy_minutes</th><th>public_transport_quick_minutes</th><th>cycling_minutes</th><th>error</th><th>lat</th><th>long</th></tr><tr><td>str</td><td>i64</td><td>i64</td><td>i64</td><td>null</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;W1J 8HX&quot;</td><td>31</td><td>22</td><td>11</td><td>null</td><td>51.506738</td><td>-0.143261</td></tr><tr><td>&quot;W1J 8HS&quot;</td><td>32</td><td>23</td><td>11</td><td>null</td><td>51.507445</td><td>-0.141877</td></tr><tr><td>&quot;W1J 8HR&quot;</td><td>32</td><td>23</td><td>11</td><td>null</td><td>51.507578</td><td>-0.141757</td></tr><tr><td>&quot;W1J 8HZ&quot;</td><td>31</td><td>22</td><td>11</td><td>null</td><td>51.506578</td><td>-0.14393</td></tr><tr><td>&quot;W1J 8HP&quot;</td><td>32</td><td>23</td><td>11</td><td>null</td><td>51.507541</td><td>-0.141686</td></tr></tbody></table></div>"
-      ],
-      "text/plain": [
-       "shape: (5, 7)\n",
-       "┌──────────┬──────────────────┬──────────────────┬─────────────────┬───────┬───────────┬───────────┐\n",
-       "│ postcode ┆ public_transport ┆ public_transport ┆ cycling_minutes ┆ error ┆ lat       ┆ long      │\n",
-       "│ ---      ┆ _easy_minutes    ┆ _quick_minutes   ┆ ---             ┆ ---   ┆ ---       ┆ ---       │\n",
-       "│ str      ┆ ---              ┆ ---              ┆ i64             ┆ null  ┆ f64       ┆ f64       │\n",
-       "│          ┆ i64              ┆ i64              ┆                 ┆       ┆           ┆           │\n",
-       "╞══════════╪══════════════════╪══════════════════╪═════════════════╪═══════╪═══════════╪═══════════╡\n",
-       "│ W1J 8HX  ┆ 31               ┆ 22               ┆ 11              ┆ null  ┆ 51.506738 ┆ -0.143261 │\n",
-       "│ W1J 8HS  ┆ 32               ┆ 23               ┆ 11              ┆ null  ┆ 51.507445 ┆ -0.141877 │\n",
-       "│ W1J 8HR  ┆ 32               ┆ 23               ┆ 11              ┆ null  ┆ 51.507578 ┆ -0.141757 │\n",
-       "│ W1J 8HZ  ┆ 31               ┆ 22               ┆ 11              ┆ null  ┆ 51.506578 ┆ -0.14393  │\n",
-       "│ W1J 8HP  ┆ 32               ┆ 23               ┆ 11              ┆ null  ┆ 51.507541 ┆ -0.141686 │\n",
-       "└──────────┴──────────────────┴──────────────────┴─────────────────┴───────┴───────────┴───────────┘"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Load the journey times data and join with arcgis for lat/long\n",
-    "jt = pl.read_parquet('data_sources/processed/journey_times_bank_checkpoint.parquet')\n",
-    "arcgis = pl.read_parquet('data_sources/arcgis_data.parquet', columns=['pcds', 'lat', 'long'])\n",
-    "\n",
-    "df = jt.join(arcgis, left_on='postcode', right_on='pcds', how='left')\n",
-    "print(f\"Total postcodes: {df.height}\")\n",
-    "print(f\"Columns: {df.columns}\")\n",
-    "print(f\"With lat/long: {df.filter(pl.col('lat').is_not_null()).height}\")\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-01-28T19:53:51.814525Z",
-     "iopub.status.busy": "2026-01-28T19:53:51.814373Z",
-     "iopub.status.idle": "2026-01-28T19:53:51.943769Z",
-     "shell.execute_reply": "2026-01-28T19:53:51.943463Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div><style>\n",
-       ".dataframe > thead > tr,\n",
-       ".dataframe > tbody > tr {\n",
-       "  text-align: right;\n",
-       "  white-space: pre-wrap;\n",
-       "}\n",
-       "</style>\n",
-       "<small>shape: (5, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>postcode</th><th>lat</th><th>long</th><th>distance_km</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;W1J 8HX&quot;</td><td>51.506738</td><td>-0.143261</td><td>3.852557</td></tr><tr><td>&quot;W1J 8HS&quot;</td><td>51.507445</td><td>-0.141877</td><td>3.744058</td></tr><tr><td>&quot;W1J 8HR&quot;</td><td>51.507578</td><td>-0.141757</td><td>3.733327</td></tr><tr><td>&quot;W1J 8HZ&quot;</td><td>51.506578</td><td>-0.14393</td><td>3.901403</td></tr><tr><td>&quot;W1J 8HP&quot;</td><td>51.507541</td><td>-0.141686</td><td>3.729191</td></tr></tbody></table></div>"
-      ],
-      "text/plain": [
-       "shape: (5, 4)\n",
-       "┌──────────┬───────────┬───────────┬─────────────┐\n",
-       "│ postcode ┆ lat       ┆ long      ┆ distance_km │\n",
-       "│ ---      ┆ ---       ┆ ---       ┆ ---         │\n",
-       "│ str      ┆ f64       ┆ f64       ┆ f64         │\n",
-       "╞══════════╪═══════════╪═══════════╪═════════════╡\n",
-       "│ W1J 8HX  ┆ 51.506738 ┆ -0.143261 ┆ 3.852557    │\n",
-       "│ W1J 8HS  ┆ 51.507445 ┆ -0.141877 ┆ 3.744058    │\n",
-       "│ W1J 8HR  ┆ 51.507578 ┆ -0.141757 ┆ 3.733327    │\n",
-       "│ W1J 8HZ  ┆ 51.506578 ┆ -0.14393  ┆ 3.901403    │\n",
-       "│ W1J 8HP  ┆ 51.507541 ┆ -0.141686 ┆ 3.729191    │\n",
-       "└──────────┴───────────┴───────────┴─────────────┘"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Bank station coordinates\n",
-    "BANK_LAT = 51.5133\n",
-    "BANK_LON = -0.0886\n",
-    "\n",
-    "def haversine_km(lat1, lon1, lat2, lon2):\n",
-    "    \"\"\"Calculate the great circle distance between two points in km.\"\"\"\n",
-    "    R = 6371  # Earth's radius in km\n",
-    "    \n",
-    "    lat1, lon1, lat2, lon2 = map(radians, [lat1, lon1, lat2, lon2])\n",
-    "    dlat = lat2 - lat1\n",
-    "    dlon = lon2 - lon1\n",
-    "    \n",
-    "    a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2\n",
-    "    c = 2 * atan2(sqrt(a), sqrt(1-a))\n",
-    "    \n",
-    "    return R * c\n",
-    "\n",
-    "# Calculate distance to Bank for each postcode\n",
-    "df = df.with_columns(\n",
-    "    pl.struct(['lat', 'long']).map_elements(\n",
-    "        lambda x: haversine_km(x['lat'], x['long'], BANK_LAT, BANK_LON),\n",
-    "        return_dtype=pl.Float64\n",
-    "    ).alias('distance_km')\n",
-    ")\n",
-    "\n",
-    "df.select(['postcode', 'lat', 'long', 'distance_km']).head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-01-28T19:53:51.944770Z",
-     "iopub.status.busy": "2026-01-28T19:53:51.944696Z",
-     "iopub.status.idle": "2026-01-28T19:53:51.948131Z",
-     "shell.execute_reply": "2026-01-28T19:53:51.947811Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Postcodes with journey data: 177132/200000\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Filter to rows with valid journey times\n",
-    "df_valid = df.filter(\n",
-    "    pl.col('public_transport_easy_minutes').is_not_null() |\n",
-    "    pl.col('public_transport_quick_minutes').is_not_null() |\n",
-    "    pl.col('cycling_minutes').is_not_null()\n",
-    ")\n",
-    "\n",
-    "print(f\"Postcodes with journey data: {df_valid.height}/{df.height}\")\n",
-    "\n",
-    "if df_valid.height == 0:\n",
-    "    print(\"\\nNo journey time data available yet. Run the fetcher first:\")\n",
-    "    print(\"  uv run python -m pipeline.journey_times\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-01-28T19:53:51.948922Z",
-     "iopub.status.busy": "2026-01-28T19:53:51.948847Z",
-     "iopub.status.idle": "2026-01-28T19:53:51.952849Z",
-     "shell.execute_reply": "2026-01-28T19:53:51.952531Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Journey Time Statistics (minutes):\n",
-      "shape: (1, 6)\n",
-      "┌──────────────┬─────────────┬─────────────┬───────────────┬──────────────┬───────────────────┐\n",
-      "│ PT Easy Mean ┆ PT Easy Min ┆ PT Easy Max ┆ PT Quick Mean ┆ Cycling Mean ┆ Avg Distance (km) │\n",
-      "│ ---          ┆ ---         ┆ ---         ┆ ---           ┆ ---          ┆ ---               │\n",
-      "│ f64          ┆ i64         ┆ i64         ┆ f64           ┆ f64          ┆ f64               │\n",
-      "╞══════════════╪═════════════╪═════════════╪═══════════════╪══════════════╪═══════════════════╡\n",
-      "│ 76.621676    ┆ 0           ┆ 372         ┆ 74.413512     ┆ 51.674563    ┆ 36.287446         │\n",
-      "└──────────────┴─────────────┴─────────────┴───────────────┴──────────────┴───────────────────┘\n",
-      "\n",
-      "Distance Statistics:\n",
-      "shape: (1, 3)\n",
-      "┌──────────┬────────────┬───────────┐\n",
-      "│ Min km   ┆ Max km     ┆ Median km │\n",
-      "│ ---      ┆ ---        ┆ ---       │\n",
-      "│ f64      ┆ f64        ┆ f64       │\n",
-      "╞══════════╪════════════╪═══════════╡\n",
-      "│ 0.013939 ┆ 109.991355 ┆ 19.561729 │\n",
-      "└──────────┴────────────┴───────────┘\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Summary statistics\n",
-    "if df_valid.height > 0:\n",
-    "    print(\"Journey Time Statistics (minutes):\")\n",
-    "    print(df_valid.select([\n",
-    "        pl.col('public_transport_easy_minutes').mean().alias('PT Easy Mean'),\n",
-    "        pl.col('public_transport_easy_minutes').min().alias('PT Easy Min'),\n",
-    "        pl.col('public_transport_easy_minutes').max().alias('PT Easy Max'),\n",
-    "        pl.col('public_transport_quick_minutes').mean().alias('PT Quick Mean'),\n",
-    "        pl.col('cycling_minutes').mean().alias('Cycling Mean'),\n",
-    "        pl.col('distance_km').mean().alias('Avg Distance (km)'),\n",
-    "    ]))\n",
-    "    \n",
-    "    print(\"\\nDistance Statistics:\")\n",
-    "    print(df_valid.select([\n",
-    "        pl.col('distance_km').min().alias('Min km'),\n",
-    "        pl.col('distance_km').max().alias('Max km'),\n",
-    "        pl.col('distance_km').median().alias('Median km'),\n",
-    "    ]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-01-28T19:53:51.953471Z",
-     "iopub.status.busy": "2026-01-28T19:53:51.953401Z",
-     "iopub.status.idle": "2026-01-28T19:53:53.919178Z",
-     "shell.execute_reply": "2026-01-28T19:53:53.918809Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot: Travel Time vs Distance by Mode\n",
-    "if df_valid.height > 0:\n",
-    "    fig, ax = plt.subplots(figsize=(12, 8))\n",
-    "    \n",
-    "    # Convert to pandas for plotting\n",
-    "    pdf = df_valid.to_pandas()\n",
-    "    \n",
-    "    # Plot each mode    \n",
-    "    if pdf['public_transport_quick_minutes'].notna().any():\n",
-    "        ax.scatter(pdf['distance_km'], pdf['public_transport_quick_minutes'], \n",
-    "                   alpha=0.3, label='Public Transport (Quick)', c='red', s=50)\n",
-    "    \n",
-    "    if pdf['public_transport_easy_minutes'].notna().any():\n",
-    "        ax.scatter(pdf['distance_km'], pdf['public_transport_easy_minutes'], \n",
-    "                   alpha=0.3, label='Public Transport (Easy)', c='green', s=50)\n",
-    "    \n",
-    "    if pdf['cycling_minutes'].notna().any():\n",
-    "        ax.scatter(pdf['distance_km'], pdf['cycling_minutes'], \n",
-    "                   alpha=0.3, label='Cycling', c='orange', s=50)\n",
-    "    \n",
-    "    ax.set_xlabel('Distance to Bank (km)', fontsize=12)\n",
-    "    ax.set_ylabel('Travel Time (minutes)', fontsize=12)\n",
-    "    ax.set_title('Travel Time vs Distance to Bank Station', fontsize=14)\n",
-    "    ax.legend()\n",
-    "    ax.grid(True, alpha=0.3)\n",
-    "    \n",
-    "    plt.tight_layout()\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-01-28T19:53:53.920561Z",
-     "iopub.status.busy": "2026-01-28T19:53:53.920429Z",
-     "iopub.status.idle": "2026-01-28T19:53:55.185911Z",
-     "shell.execute_reply": "2026-01-28T19:53:55.185544Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot: Speed (km/h) vs Distance by Mode\n",
-    "if df_valid.height > 0:\n",
-    "    fig, ax = plt.subplots(figsize=(12, 8))\n",
-    "    \n",
-    "    # Calculate effective speed for each mode\n",
-    "    pdf = df_valid.with_columns([\n",
-    "        (pl.col('distance_km') / (pl.col('public_transport_easy_minutes') / 60)).alias('speed_easy_kmh'),\n",
-    "        (pl.col('distance_km') / (pl.col('public_transport_quick_minutes') / 60)).alias('speed_quick_kmh'),\n",
-    "        (pl.col('distance_km') / (pl.col('cycling_minutes') / 60)).alias('speed_cycling_kmh'),\n",
-    "    ]).to_pandas()\n",
-    "    \n",
-    "    if pdf['speed_easy_kmh'].notna().any():\n",
-    "        ax.scatter(pdf['distance_km'], pdf['speed_easy_kmh'], \n",
-    "                   alpha=0.6, label='Public Transport (Easy)', c='blue', s=50)\n",
-    "    \n",
-    "    if pdf['speed_quick_kmh'].notna().any():\n",
-    "        ax.scatter(pdf['distance_km'], pdf['speed_quick_kmh'], \n",
-    "                   alpha=0.6, label='Public Transport (Quick)', c='green', s=50)\n",
-    "    \n",
-    "    if pdf['speed_cycling_kmh'].notna().any():\n",
-    "        ax.scatter(pdf['distance_km'], pdf['speed_cycling_kmh'], \n",
-    "                   alpha=0.6, label='Cycling', c='orange', s=50)\n",
-    "    \n",
-    "    ax.set_xlabel('Distance to Bank (km)', fontsize=12)\n",
-    "    ax.set_ylabel('Effective Speed (km/h)', fontsize=12)\n",
-    "    ax.set_title('Effective Travel Speed vs Distance to Bank Station', fontsize=14)\n",
-    "    ax.legend()\n",
-    "    ax.grid(True, alpha=0.3)\n",
-    "    ax.set_ylim(0, 100)  # Cap y-axis for readability\n",
-    "    \n",
-    "    plt.tight_layout()\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-01-28T19:53:55.187209Z",
-     "iopub.status.busy": "2026-01-28T19:53:55.187131Z",
-     "iopub.status.idle": "2026-01-28T19:53:56.842052Z",
-     "shell.execute_reply": "2026-01-28T19:53:56.841680Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x800 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Time saved by taking quick route:\n",
-      "  Mean: 7.8 minutes\n",
-      "  Max: 97 minutes\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Comparison: Quick vs Easy public transport\n",
-    "if df_valid.height > 0:\n",
-    "    df_both = df_valid.filter(\n",
-    "        pl.col('public_transport_easy_minutes').is_not_null() &\n",
-    "        pl.col('public_transport_quick_minutes').is_not_null()\n",
-    "    )\n",
-    "    \n",
-    "    if df_both.height > 0:\n",
-    "        fig, ax = plt.subplots(figsize=(10, 8))\n",
-    "        \n",
-    "        pdf = df_both.to_pandas()\n",
-    "        \n",
-    "        ax.scatter(pdf['public_transport_easy_minutes'], \n",
-    "                   pdf['public_transport_quick_minutes'],\n",
-    "                   c=pdf['distance_km'], cmap='viridis', s=60, alpha=0.7)\n",
-    "        \n",
-    "        # Add diagonal line (equal times)\n",
-    "        max_val = max(pdf['public_transport_easy_minutes'].max(), \n",
-    "                      pdf['public_transport_quick_minutes'].max())\n",
-    "        ax.plot([0, max_val], [0, max_val], 'r--', alpha=0.5, label='Equal time')\n",
-    "        \n",
-    "        ax.set_xlabel('Easy Route (minutes)', fontsize=12)\n",
-    "        ax.set_ylabel('Quick Route (minutes)', fontsize=12)\n",
-    "        ax.set_title('Quick vs Easy Public Transport Times\\n(color = distance in km)', fontsize=14)\n",
-    "        ax.legend()\n",
-    "        ax.grid(True, alpha=0.3)\n",
-    "        \n",
-    "        plt.colorbar(ax.collections[0], label='Distance (km)')\n",
-    "        plt.tight_layout()\n",
-    "        plt.show()\n",
-    "        \n",
-    "        # Calculate time savings\n",
-    "        savings = df_both.with_columns(\n",
-    "            (pl.col('public_transport_easy_minutes') - pl.col('public_transport_quick_minutes')).alias('time_saved')\n",
-    "        )\n",
-    "        print(\"\\nTime saved by taking quick route:\")\n",
-    "        print(f\"  Mean: {savings['time_saved'].mean():.1f} minutes\")\n",
-    "        print(f\"  Max: {savings['time_saved'].max():.0f} minutes\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-01-28T19:53:56.842982Z",
-     "iopub.status.busy": "2026-01-28T19:53:56.842908Z",
-     "iopub.status.idle": "2026-01-28T19:53:56.849543Z",
-     "shell.execute_reply": "2026-01-28T19:53:56.849208Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Journey times sorted by distance to Bank:\n",
-      "shape: (177_132, 5)\n",
-      "┌──────────┬─────────────┬───────────────────────────┬───────────────────────────┬─────────────────┐\n",
-      "│ postcode ┆ distance_km ┆ public_transport_easy_min ┆ public_transport_quick_mi ┆ cycling_minutes │\n",
-      "│ ---      ┆ ---         ┆ utes                      ┆ nutes                     ┆ ---             │\n",
-      "│ str      ┆ f64         ┆ ---                       ┆ ---                       ┆ i64             │\n",
-      "│          ┆             ┆ i64                       ┆ i64                       ┆                 │\n",
-      "╞══════════╪═════════════╪═══════════════════════════╪═══════════════════════════╪═════════════════╡\n",
-      "│ EC3V 3LA ┆ 0.0         ┆ 0                         ┆ 0                         ┆ 0               │\n",
-      "│ EC3V 3NR ┆ 0.0         ┆ 1                         ┆ 1                         ┆ 0               │\n",
-      "│ EC3V 3QR ┆ 0.0         ┆ 1                         ┆ 1                         ┆ 0               │\n",
-      "│ EC3V 3QS ┆ 0.0         ┆ 0                         ┆ 0                         ┆ 0               │\n",
-      "│ EC3V 9AQ ┆ 0.0         ┆ 2                         ┆ 2                         ┆ 0               │\n",
-      "│ …        ┆ …           ┆ …                         ┆ …                         ┆ …               │\n",
-      "│ OX15 5JP ┆ 110.0       ┆ null                      ┆ 153                       ┆ null            │\n",
-      "│ OX15 5JT ┆ 110.0       ┆ null                      ┆ 153                       ┆ null            │\n",
-      "│ OX15 5LD ┆ 110.0       ┆ null                      ┆ 153                       ┆ null            │\n",
-      "│ OX15 5ND ┆ 110.0       ┆ null                      ┆ 153                       ┆ null            │\n",
-      "│ OX15 5NE ┆ 110.0       ┆ null                      ┆ 153                       ┆ null            │\n",
-      "└──────────┴─────────────┴───────────────────────────┴───────────────────────────┴─────────────────┘\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Data table: sorted by distance\n",
-    "if df_valid.height > 0:\n",
-    "    print(\"Journey times sorted by distance to Bank:\")\n",
-    "    display_df = df_valid.select([\n",
-    "        'postcode',\n",
-    "        pl.col('distance_km').round(1),\n",
-    "        'public_transport_easy_minutes',\n",
-    "        'public_transport_quick_minutes', \n",
-    "        'cycling_minutes'\n",
-    "    ]).sort('distance_km')\n",
-    "    \n",
-    "    print(display_df)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

From 2131da96aa6958f3de63e329ac3f5b91e4f1ab7a Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Fri, 30 Jan 2026 14:23:03 +0000
Subject: [PATCH 15/62] Move pipelines

---
 Taskfile.yml                                  |   7 +-
 download_arcgis_data.py                       | 129 ------------------
 download_land_registry.py                     | 114 ----------------
 pipeline/download/arcgis.py                   |  71 ++++++++++
 .../download/deprivation_data.py              |   6 -
 pipeline/download/price_paid.py               |  91 ++++++++++++
 6 files changed, 166 insertions(+), 252 deletions(-)
 delete mode 100644 download_arcgis_data.py
 delete mode 100644 download_land_registry.py
 create mode 100644 pipeline/download/arcgis.py
 rename download_deprivation_data.py => pipeline/download/deprivation_data.py (90%)
 create mode 100644 pipeline/download/price_paid.py

diff --git a/Taskfile.yml b/Taskfile.yml
index 7c96b25..52a0dde 100644
--- a/Taskfile.yml
+++ b/Taskfile.yml
@@ -12,9 +12,10 @@ tasks:
     deps:
       - install
     cmds:
-      - uv run python download_land_registry.py
-      - uv run python download_arcgis_data.py
-      - uv run python -m pipeline.pois
+      - uv run -m pipeline.download.arcgis
+      - uv run -m pipeline.download.pois
+      - uv run -m pipeline.download.deprivation_data
+      - uv run -m pipeline.download.price_paid
 
   pipeline:
     desc: Run data processing pipeline
diff --git a/download_arcgis_data.py b/download_arcgis_data.py
deleted file mode 100644
index 34b8eff..0000000
--- a/download_arcgis_data.py
+++ /dev/null
@@ -1,129 +0,0 @@
-#!/usr/bin/env python3
-"""Download ArcGIS data and convert to Parquet."""
-
-# Run it with:
-#   uv run download_arcgis_data.py
-
-import time
-import zipfile
-import httpx
-import polars as pl
-from pathlib import Path
-from tqdm import tqdm
-
-URL = "https://www.arcgis.com/sharing/rest/content/items/077631e063eb4e1ab43575d01381ec33/data"
-
-BASE_DATA_PATH = Path("./data_sources")
-BASE_DATA_PATH.mkdir(exist_ok=True)
-DOWNLOAD_PATH = BASE_DATA_PATH / "arcgis_data.zip"
-EXTRACT_PATH = BASE_DATA_PATH / "arcgis_extracted"
-PARQUET_PATH = BASE_DATA_PATH / "arcgis_data.parquet"
-
-MAX_RETRIES = 3
-
-
-def download_with_progress(url: str, output_path: Path) -> None:
-    """Download a file with progress bar and retry logic."""
-    for attempt in range(1, MAX_RETRIES + 1):
-        try:
-            with httpx.stream(
-                "GET",
-                url,
-                follow_redirects=True,
-                timeout=httpx.Timeout(30.0, read=None),
-            ) as response:
-                response.raise_for_status()  # pyright: ignore[reportUnusedCallResult]
-                total = int(response.headers.get("content-length", 0))
-
-                with (
-                    open(output_path, "wb") as f,
-                    tqdm(
-                        total=total,
-                        unit="B",
-                        unit_scale=True,
-                        unit_divisor=1024,
-                        desc="Downloading",
-                    ) as pbar,
-                ):
-                    for chunk in response.iter_bytes(chunk_size=8192):
-                        f.write(chunk)
-                        pbar.update(len(chunk))
-                return  # Success
-        except (httpx.ConnectError, httpx.ReadTimeout) as e:
-            if attempt < MAX_RETRIES:
-                wait = 2**attempt
-                print(f"Attempt {attempt} failed: {e}. Retrying in {wait}s...")
-                time.sleep(wait)
-            else:
-                raise
-
-
-def extract_zip(zip_path: Path, extract_path: Path) -> list[Path]:
-    """Extract ZIP file and return list of extracted files."""
-    print("Extracting ZIP file...")
-    extract_path.mkdir(exist_ok=True)
-
-    with zipfile.ZipFile(zip_path, "r") as zf:
-        zf.extractall(extract_path)
-        return [extract_path / name for name in zf.namelist()]
-
-
-def find_data_file(extract_path: Path) -> Path:
-    """Find the main data file (CSV, XLSX, or similar) in extracted files."""
-    # Look for common data file extensions
-    for ext in ["*.csv", "*.xlsx", "*.xls", "*.json", "*.geojson"]:
-        files = list(extract_path.rglob(ext))
-        if files:
-            # Return the largest file if multiple found
-            return max(files, key=lambda f: f.stat().st_size)
-
-    raise FileNotFoundError(f"No data file found in {extract_path}")
-
-
-def convert_to_parquet(data_path: Path, parquet_path: Path) -> None:
-    """Convert data file to Parquet using Polars."""
-    print(f"Converting {data_path.name} to Parquet...")
-
-    suffix = data_path.suffix.lower()
-
-    if suffix == ".csv":
-        df = pl.read_csv(data_path, try_parse_dates=True)
-    elif suffix in [".xlsx", ".xls"]:
-        df = pl.read_excel(data_path)
-    elif suffix in [".json", ".geojson"]:
-        df = pl.read_json(data_path)
-    else:
-        raise ValueError(f"Unsupported file format: {suffix}")
-
-    df.write_parquet(parquet_path, compression="zstd")
-    print(f"Saved to {parquet_path}")
-    print(f"Rows: {df.height:,}")
-    print(f"Columns: {df.columns}")
-    print(f"Original size: {data_path.stat().st_size / 1024**2:.1f} MB")
-    print(f"Parquet size: {parquet_path.stat().st_size / 1024**2:.1f} MB")
-
-
-def main() -> None:
-    if PARQUET_PATH.exists():
-        print(f"Parquet already exists at {PARQUET_PATH}, skipping")
-        return
-
-    if not DOWNLOAD_PATH.exists():
-        download_with_progress(URL, DOWNLOAD_PATH)
-    else:
-        print(f"File already exists at {DOWNLOAD_PATH}, skipping download")
-
-    # Check if it's a ZIP file
-    if zipfile.is_zipfile(DOWNLOAD_PATH):
-        extracted_files = extract_zip(DOWNLOAD_PATH, EXTRACT_PATH)
-        print(f"Extracted {len(extracted_files)} files")
-        data_file = find_data_file(EXTRACT_PATH)
-    else:
-        # Not a ZIP, treat as direct data file
-        data_file = DOWNLOAD_PATH
-
-    convert_to_parquet(data_file, PARQUET_PATH)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/download_land_registry.py b/download_land_registry.py
deleted file mode 100644
index c07a640..0000000
--- a/download_land_registry.py
+++ /dev/null
@@ -1,114 +0,0 @@
-#!/usr/bin/env python3
-"""Download Land Registry price paid data and convert to Parquet."""
-
-# Run it with:
-#   uv run download_land_registry.py
-
-# The download failed in this environment due to network restrictions, but the script will work on your local machine. The ~5GB CSV should compress to roughly ~1GB in Parquet format with ZSTD compression.
-
-import time
-import httpx
-import polars as pl
-from pathlib import Path
-from tqdm import tqdm
-
-URL = "http://prod.publicdata.landregistry.gov.uk.s3-website-eu-west-1.amazonaws.com/pp-complete.csv"
-
-BASE_DATA_PATH = Path("./data_sources")
-BASE_DATA_PATH.mkdir(exist_ok=True)
-CSV_PATH = BASE_DATA_PATH / "pp-complete.csv"
-PARQUET_PATH = BASE_DATA_PATH / "pp-complete.parquet"
-
-MAX_RETRIES = 3
-
-
-def download_with_progress(url: str, output_path: Path) -> None:
-    """Download a file with progress bar and retry logic."""
-    for attempt in range(1, MAX_RETRIES + 1):
-        try:
-            with httpx.stream(
-                "GET",
-                url,
-                follow_redirects=True,
-                timeout=httpx.Timeout(30.0, read=None),
-            ) as response:
-                response.raise_for_status()  # pyright: ignore[reportUnusedCallResult]
-                total = int(response.headers.get("content-length", 0))
-
-                with (
-                    open(output_path, "wb") as f,
-                    tqdm(
-                        total=total,
-                        unit="B",
-                        unit_scale=True,
-                        unit_divisor=1024,
-                        desc="Downloading",
-                    ) as pbar,
-                ):
-                    for chunk in response.iter_bytes(chunk_size=8192):
-                        f.write(chunk)
-                        pbar.update(len(chunk))
-                return  # Success
-        except (httpx.ConnectError, httpx.ReadTimeout) as e:
-            if attempt < MAX_RETRIES:
-                wait = 2**attempt
-                print(f"Attempt {attempt} failed: {e}. Retrying in {wait}s...")
-                time.sleep(wait)
-            else:
-                raise
-
-
-def convert_to_parquet(csv_path: Path, parquet_path: Path) -> None:
-    """Convert CSV to Parquet using Polars."""
-    print("Converting to Parquet...")
-
-    # https://www.gov.uk/guidance/about-the-price-paid-data
-    # Land Registry CSV columns
-    columns = [
-        "transaction_id",
-        "price",
-        "date_of_transfer",
-        "postcode",
-        "property_type",
-        "old_new",
-        "duration",
-        "paon",
-        "saon",
-        "street",
-        "locality",
-        "town_city",
-        "district",
-        "county",
-        "ppd_category",
-        "record_status",
-    ]
-
-    df = pl.read_csv(
-        csv_path,
-        has_header=False,
-        new_columns=columns,
-        try_parse_dates=True,
-    )
-
-    df.write_parquet(parquet_path, compression="zstd")
-    print(f"Saved to {parquet_path}")
-    print(f"Rows: {df.height:,}")
-    print(f"CSV size: {csv_path.stat().st_size / 1024**2:.1f} MB")
-    print(f"Parquet size: {parquet_path.stat().st_size / 1024**2:.1f} MB")
-
-
-def main() -> None:
-    if PARQUET_PATH.exists():
-        print(f"Parquet already exists at {PARQUET_PATH}, skipping")
-        return
-
-    if not CSV_PATH.exists():
-        download_with_progress(URL, CSV_PATH)
-    else:
-        print(f"CSV already exists at {CSV_PATH}, skipping download")
-
-    convert_to_parquet(CSV_PATH, PARQUET_PATH)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/pipeline/download/arcgis.py b/pipeline/download/arcgis.py
new file mode 100644
index 0000000..cdd284c
--- /dev/null
+++ b/pipeline/download/arcgis.py
@@ -0,0 +1,71 @@
+import zipfile
+import httpx
+import polars as pl
+from pathlib import Path
+from tqdm import tqdm
+
+URL = "https://www.arcgis.com/sharing/rest/content/items/077631e063eb4e1ab43575d01381ec33/data"
+
+BASE_DATA_PATH = Path("./data_sources")
+BASE_DATA_PATH.mkdir(exist_ok=True)
+DOWNLOAD_PATH = BASE_DATA_PATH / "arcgis_data.zip"
+EXTRACT_PATH = BASE_DATA_PATH / "arcgis_extracted"
+PARQUET_PATH = BASE_DATA_PATH / "arcgis_data.parquet"
+
+
+def download_with_progress(url: str, output_path: Path) -> None:
+    with httpx.stream(
+        "GET",
+        url,
+        follow_redirects=True,
+        timeout=httpx.Timeout(30.0, read=None),
+    ) as response:
+        response.raise_for_status()  # pyright: ignore[reportUnusedCallResult]
+        total = int(response.headers.get("content-length", 0))
+
+        with (
+            open(output_path, "wb") as f,
+            tqdm(
+                total=total,
+                unit="B",
+                unit_scale=True,
+                unit_divisor=1024,
+                desc="Downloading",
+            ) as pbar,
+        ):
+            for chunk in response.iter_bytes(chunk_size=8192):
+                f.write(chunk)
+                pbar.update(len(chunk))
+        return 
+   
+
+
+def extract_zip(zip_path: Path, extract_path: Path) -> None:
+    extract_path.mkdir(exist_ok=True)
+
+    with zipfile.ZipFile(zip_path, "r") as zf:
+        zf.extractall(extract_path)
+
+
+def convert_to_parquet(data_path: Path, parquet_path: Path) -> None:
+    df = pl.scan_csv(data_path / 'Data/NSPL_MAY_2025_UK.csv', try_parse_dates=True)
+    print(f"Columns: {df.columns}")
+    df.sink_parquet(parquet_path, compression="zstd")
+    print(f"Saved to {parquet_path}")
+
+
+def main() -> None:
+    if PARQUET_PATH.exists():
+        print(f"Parquet already exists at {PARQUET_PATH}, skipping")
+        return
+
+    if not DOWNLOAD_PATH.exists():
+        download_with_progress(URL, DOWNLOAD_PATH)
+    else:
+        print(f"File already exists at {DOWNLOAD_PATH}, skipping download")
+
+    extract_zip(DOWNLOAD_PATH, EXTRACT_PATH)
+    convert_to_parquet(EXTRACT_PATH, PARQUET_PATH)
+
+if __name__ == "__main__":
+    main()
diff --git a/download_deprivation_data.py b/pipeline/download/deprivation_data.py
similarity index 90%
rename from download_deprivation_data.py
rename to pipeline/download/deprivation_data.py
index 5094002..6d42180 100644
--- a/download_deprivation_data.py
+++ b/pipeline/download/deprivation_data.py
@@ -1,6 +1,3 @@
-#!/usr/bin/env python3
-"""Download IoD2025 Deprivation Scores and convert to Parquet."""
-
 import httpx
 import polars as pl
 from pathlib import Path
@@ -14,8 +11,6 @@ PARQUET_PATH = BASE_DATA_PATH / "IoD2025_Scores.parquet"
 
 
 def download_file(url: str, output_path: Path) -> None:
-    """Download file from URL."""
-    print(f"Downloading from {url}...")
     with httpx.stream("GET", url, follow_redirects=True, timeout=60) as response:
         response.raise_for_status()
         total = int(response.headers.get("content-length", 0))
@@ -33,7 +28,6 @@ def download_file(url: str, output_path: Path) -> None:
 
 
 def convert_to_parquet(xlsx_path: Path, parquet_path: Path) -> None:
-    """Convert Excel sheet 2 to Parquet."""
     print("Reading Excel file (sheet 2)...")
 
     # Read the 2nd sheet (index 1) - IoD2025 Scores
diff --git a/pipeline/download/price_paid.py b/pipeline/download/price_paid.py
new file mode 100644
index 0000000..38e0f69
--- /dev/null
+++ b/pipeline/download/price_paid.py
@@ -0,0 +1,91 @@
+import httpx
+import polars as pl
+from pathlib import Path
+from tqdm import tqdm
+
+URL = "http://prod.publicdata.landregistry.gov.uk.s3-website-eu-west-1.amazonaws.com/pp-complete.csv"
+
+BASE_DATA_PATH = Path("./data_sources")
+BASE_DATA_PATH.mkdir(exist_ok=True)
+CSV_PATH = BASE_DATA_PATH / "price-paid-complete.csv"
+PARQUET_PATH = BASE_DATA_PATH / "price-paid-complete.parquet"
+
+
+def download_with_progress(url: str, output_path: Path) -> None:
+    with httpx.stream(
+        "GET",
+        url,
+        follow_redirects=True,
+        timeout=httpx.Timeout(30.0, read=None),
+    ) as response:
+        response.raise_for_status()  # pyright: ignore[reportUnusedCallResult]
+        total = int(response.headers.get("content-length", 0))
+
+        with (
+            open(output_path, "wb") as f,
+            tqdm(
+                total=total,
+                unit="B",
+                unit_scale=True,
+                unit_divisor=1024,
+                desc="Downloading",
+            ) as pbar,
+        ):
+            for chunk in response.iter_bytes(chunk_size=8192):
+                f.write(chunk)
+                pbar.update(len(chunk))
+        return
+
+      
+def convert_to_parquet(csv_path: Path, parquet_path: Path) -> None:
+    """Convert CSV to Parquet using Polars."""
+    print("Converting to Parquet...")
+
+    # https://www.gov.uk/guidance/about-the-price-paid-data
+    # Land Registry CSV columns
+    columns = [
+        "transaction_id",
+        "price",
+        "date_of_transfer",
+        "postcode",
+        "property_type",
+        "old_new",
+        "duration",
+        "paon",
+        "saon",
+        "street",
+        "locality",
+        "town_city",
+        "district",
+        "county",
+        "ppd_category",
+        "record_status",
+    ]
+
+    df = pl.read_csv(
+        csv_path,
+        has_header=False,
+        new_columns=columns,
+        try_parse_dates=True,
+    )
+
+    df.write_parquet(parquet_path, compression="zstd")
+    print(f"Saved to {parquet_path}")
+    print(f"Rows: {df.height:,}")
+
+
+def main() -> None:
+    if PARQUET_PATH.exists():
+        print(f"Parquet already exists at {PARQUET_PATH}, skipping")
+        return
+
+    if not CSV_PATH.exists():
+        download_with_progress(URL, CSV_PATH)
+    else:
+        print(f"CSV already exists at {CSV_PATH}, skipping download")
+
+    convert_to_parquet(CSV_PATH, PARQUET_PATH)
+
+
+if __name__ == "__main__":
+    main()

From 68b6dcf65e11c3d9c95ba22eba41638d7f012708 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Fri, 30 Jan 2026 14:44:48 +0000
Subject: [PATCH 16/62] Join epc & pp

---
 pipeline/epc_pp.py          |  87 ++++++++++++++++++++++
 pipeline/fuzzy_join.py      | 139 ++++++++++++++++++++++++++++++++++++
 pipeline/test_fuzzy_join.py |  52 ++++++++++++++
 3 files changed, 278 insertions(+)
 create mode 100644 pipeline/epc_pp.py
 create mode 100644 pipeline/fuzzy_join.py
 create mode 100644 pipeline/test_fuzzy_join.py

diff --git a/pipeline/epc_pp.py b/pipeline/epc_pp.py
new file mode 100644
index 0000000..1c50ec2
--- /dev/null
+++ b/pipeline/epc_pp.py
@@ -0,0 +1,87 @@
+import polars as pl
+from .fuzzy_join import fuzzy_join_on_postcode
+
+
+pl.Config.set_tbl_cols(-1)
+
+
+
+epc = pl.scan_csv('data_sources/epc/certificates.csv').select(
+    pl.col('ADDRESS').alias('epc_address'),
+    'POSTCODE',
+    'CURRENT_ENERGY_RATING',
+    'POTENTIAL_ENERGY_RATING',
+    pl.col('PROPERTY_TYPE').alias('epc_property_type'),
+    'BUILT_FORM',
+    'INSPECTION_DATE',
+    'TOTAL_FLOOR_AREA',
+    'NUMBER_HABITABLE_ROOMS',
+    'FLOOR_HEIGHT',
+    'CONSTRUCTION_AGE_BAND'
+).sort('INSPECTION_DATE', descending=True).group_by('epc_address').first()
+
+
+print("EPC dataset")
+print(epc.head().collect())
+
+# https://www.gov.uk/guidance/about-the-price-paid-data
+property_type_map = {"D": "Detached", "S": "Semi-Detached", "T": "Terraced", "F": "Flats/Maisonettes", "O": "Other"}
+duration_map = {"F": "Freehold", "L": "Leasehold"}
+
+price_paid = (pl.scan_parquet('data_sources/pp-complete.parquet').select(
+    "price",
+    "date_of_transfer",
+    pl.col('property_type').alias("pp_property_type").replace(property_type_map),
+    "postcode",
+    'paon',
+    'saon',
+    'street',
+    'locality',
+    'town_city',
+    pl.col('duration').replace(duration_map)
+).filter(pl.col('pp_property_type') != 'Other').with_columns(
+        pl.concat_str(
+            [pl.col('saon'), pl.col('paon'), pl.col('street')],
+            separator=' ',
+            ignore_nulls=True,
+        ).alias('pp_address'),
+    )
+    .sort('date_of_transfer')
+    .group_by('pp_address', 'postcode', maintain_order=True)
+    .agg(
+        pl.struct(
+            pl.col('date_of_transfer').dt.year().alias('year'),
+            'price',
+        ).alias('historical_prices'),
+        pl.col('pp_property_type').last(),
+        pl.col('duration').last(),
+        pl.col('price').last().alias('latest_price'),
+        pl.col('date_of_transfer').last(),
+    )   
+)
+
+print("Price paid dataset")
+print(price_paid.head().collect())
+
+price_paid_df = price_paid.collect()
+epc_df = epc.collect()
+
+joined = fuzzy_join_on_postcode(
+    left=price_paid_df,
+    right=epc_df,
+    left_address_col='pp_address',
+    right_address_col='epc_address',
+    left_postcode_col='postcode',
+    right_postcode_col='POSTCODE',
+    score_threshold=80,
+).drop('POSTCODE')
+
+matched_count = joined.filter(pl.col('epc_address').is_not_null() & pl.col('pp_address').is_not_null()).height
+print(f"Unique properties: {price_paid_df.height}")
+print(f"Matched: {matched_count} ({100 * matched_count / price_paid_df.height:.1f}%)")
+print(f"Unmatched: {price_paid_df.height - matched_count}")
+
+joined = joined.rename({col: col.lower() for col in joined.columns})
+
+print(joined.head())
+joined.write_parquet('data_sources/processed/epc_pp.parquet')
diff --git a/pipeline/fuzzy_join.py b/pipeline/fuzzy_join.py
new file mode 100644
index 0000000..ed85286
--- /dev/null
+++ b/pipeline/fuzzy_join.py
@@ -0,0 +1,139 @@
+import re
+from concurrent.futures import ProcessPoolExecutor
+from os import cpu_count
+
+import polars as pl
+from thefuzz import fuzz
+from tqdm import tqdm
+
+_NUMBER_RE = re.compile(r'\d+')
+
+
+def fuzzy_join_on_postcode(
+    left: pl.DataFrame,
+    right: pl.DataFrame,
+    left_address_col: str,
+    right_address_col: str,
+    left_postcode_col: str,
+    right_postcode_col: str,
+    score_threshold: int = 80,
+) -> pl.DataFrame:
+    """Fuzzy join two DataFrames by matching addresses within postcode buckets.
+
+    Returns the left DataFrame with all right columns appended.
+    Unmatched rows have null right columns.
+    """
+
+    def _normalize(s: pl.Expr) -> pl.Expr:
+        return (
+            s.str.to_uppercase()
+            .str.replace_all(r'[,.\-]', ' ')
+            .str.replace_all(r'\s+', ' ')
+            .str.strip_chars()
+        )
+
+    left = left.with_columns(
+        _normalize(pl.col(left_address_col)).alias('_left_address'),
+        pl.col(left_postcode_col).str.strip_chars().str.to_uppercase().alias('_left_postcode'),
+    )
+    right = right.with_columns(
+        _normalize(pl.col(right_address_col)).alias('_right_address'),
+        pl.col(right_postcode_col).str.strip_chars().str.to_uppercase().alias('_right_postcode'),
+    )
+
+    # Deduplicate right side on normalized address + postcode so that
+    # variant spellings of the same address don't consume multiple slots.
+    right = right.unique(subset=['_right_address', '_right_postcode'], keep='first')
+
+    # Group right side by postcode for fast lookup
+    right_by_postcode: dict[str, list[tuple[int, str]]] = {}
+    for i, (postcode, address) in enumerate(
+        zip(right['_right_postcode'], right['_right_address'])
+    ):
+        if postcode is not None:
+            right_by_postcode.setdefault(postcode, []).append((i, address))
+
+    # Group left side by postcode
+    left_by_postcode: dict[str, list[tuple[int, str]]] = {}
+    for left_row, (postcode, address) in enumerate(
+        zip(left['_left_postcode'], left['_left_address'])
+    ):
+        if address is not None and postcode is not None:
+            left_by_postcode.setdefault(postcode, []).append((left_row, address))
+
+    # Build tasks for each postcode bucket
+    tasks = [
+        (left_entries, right_by_postcode[postcode], score_threshold)
+        for postcode, left_entries in left_by_postcode.items()
+        if postcode in right_by_postcode
+    ]
+
+    # Score all pairwise matches in parallel, then greedily assign from
+    # highest score downward so best pairs lock in first.
+    all_pairs: list[tuple[int, int, int]] = []  # (score, left_row, right_row)
+    with ProcessPoolExecutor(max_workers=cpu_count()) as executor:
+        for pairs in tqdm(
+            executor.map(_score_bucket, tasks, chunksize=64),
+            total=len(tasks),
+            desc='Fuzzy matching',
+        ):
+            all_pairs.extend(pairs)
+
+    # Sort descending by score so best matches are assigned first
+    all_pairs.sort(key=lambda t: (t[0], -t[1]), reverse=True)
+
+    match_indices: list[int | None] = [None] * len(left)
+    matched_left: set[int] = set()
+    matched_right: set[int] = set()
+
+    for score, left_row, right_row in all_pairs:
+        if left_row in matched_left or right_row in matched_right:
+            continue
+        match_indices[left_row] = right_row
+        matched_left.add(left_row)
+        matched_right.add(right_row)
+
+    # Select right columns (excluding internal helpers)
+    right_cols = right.select(pl.exclude('_right_address', '_right_postcode'))
+    right_matched = right_cols[
+        [i if i is not None else 0 for i in match_indices]
+    ]
+
+    # Null out unmatched rows
+    mask = pl.Series('_matched', [i is not None for i in match_indices])
+    right_matched = right_matched.with_columns(
+        pl.when(mask).then(pl.col(c)).otherwise(pl.lit(None)).alias(c)
+        for c in right_matched.columns
+    )
+
+    left_clean = left.select(pl.exclude('_left_address', '_left_postcode'))
+    return pl.concat([left_clean, right_matched], how='horizontal')
+
+
+def _numbers_compatible(a: str, b: str) -> bool:
+    """Check that numeric tokens (flat/house numbers) in the shorter set are a subset of the longer.
+
+    Returns False if one address has numbers and the other doesn't.
+    """
+    nums_a = set(_NUMBER_RE.findall(a))
+    nums_b = set(_NUMBER_RE.findall(b))
+    smaller, larger = (nums_a, nums_b) if len(nums_a) <= len(nums_b) else (nums_b, nums_a)
+    if not smaller and larger:
+        return False
+    return smaller.issubset(larger)
+
+
+def _score_bucket(
+    args: tuple[list[tuple[int, str]], list[tuple[int, str]], int],
+) -> list[tuple[int, int, int]]:
+    """Score all address pairs within a single postcode bucket."""
+    left_entries, right_entries, score_threshold = args
+    pairs = []
+    for left_row, left_address in left_entries:
+        for right_row, right_address in right_entries:
+            if not _numbers_compatible(left_address, right_address):
+                continue
+            score = fuzz.token_sort_ratio(left_address, right_address)
+            if score >= score_threshold:
+                pairs.append((score, left_row, right_row))
+    return pairs
diff --git a/pipeline/test_fuzzy_join.py b/pipeline/test_fuzzy_join.py
new file mode 100644
index 0000000..7197009
--- /dev/null
+++ b/pipeline/test_fuzzy_join.py
@@ -0,0 +1,52 @@
+import polars as pl
+
+from fuzzy_join import fuzzy_join_on_postcode
+
+POSTCODE = "E14 2DG"
+
+# Price paid: unique addresses for this postcode
+pp = (
+    pl.scan_parquet("data_sources/pp-complete.parquet")
+    .filter(pl.col("postcode") == POSTCODE)
+    .select("paon", "saon", "street", "postcode")
+    .collect()
+    .unique()
+    .sort("saon")
+    .with_columns(
+        pl.concat_str(
+            [pl.col("saon"), pl.col("paon"), pl.col("street")],
+            separator=" ",
+            ignore_nulls=True,
+        ).alias("pp_address"),
+    )
+)
+
+# EPC: latest inspection per address for this postcode
+epc = (
+    pl.scan_csv("data_sources/epc/certificates.csv")
+    .select("ADDRESS", "POSTCODE", "INSPECTION_DATE")
+    .filter(pl.col("POSTCODE").str.strip_chars() == POSTCODE)
+    .sort("INSPECTION_DATE", descending=True)
+    .collect()
+    .unique("ADDRESS")
+    .sort("ADDRESS")
+)
+
+print(f"Price paid: {len(pp)} unique addresses")
+print(f"EPC: {len(epc)} unique addresses")
+
+result = fuzzy_join_on_postcode(
+    left=pp,
+    right=epc,
+    left_address_col="pp_address",
+    right_address_col="ADDRESS",
+    left_postcode_col="postcode",
+    right_postcode_col="POSTCODE",
+    score_threshold=80,
+
+)
+
+snapshot = result.select("pp_address", "ADDRESS").sort("pp_address")
+
+with pl.Config(tbl_rows=-1, tbl_cols=-1, fmt_str_lengths=80):
+    print(snapshot)

From 6122ee44dace020aa062cdfb1b8cb9373def3a0c Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Fri, 30 Jan 2026 18:33:48 +0000
Subject: [PATCH 17/62] Update H3 pipeline

---
 pipeline/config.py                            |  10 -
 pipeline/epc_pp.py                            |  32 ++-
 pipeline/fuzzy_join.py                        | 208 +++++++++++-------
 pipeline/processors/__init__.py               |   0
 pipeline/processors/h3_aggregator.py          |  42 ----
 .../processors/journey_times_aggregator.py    | 127 -----------
 pipeline/run.py                               |  43 +---
 pipeline/sources/__init__.py                  |   0
 pipeline/sources/postcodes.py                 |  49 -----
 pipeline/sources/property_prices.py           |  41 ----
 pipeline/test_fuzzy_join.py                   |   9 +-
 pipeline/wide.py                              | 143 ++++++++++++
 pyproject.toml                                |   7 +-
 13 files changed, 291 insertions(+), 420 deletions(-)
 delete mode 100644 pipeline/processors/__init__.py
 delete mode 100644 pipeline/processors/h3_aggregator.py
 delete mode 100644 pipeline/processors/journey_times_aggregator.py
 delete mode 100644 pipeline/sources/__init__.py
 delete mode 100644 pipeline/sources/postcodes.py
 delete mode 100644 pipeline/sources/property_prices.py
 create mode 100644 pipeline/wide.py

diff --git a/pipeline/config.py b/pipeline/config.py
index 116a078..ca7ca24 100644
--- a/pipeline/config.py
+++ b/pipeline/config.py
@@ -10,13 +10,3 @@ AGGREGATES_DIR = PROCESSED_DIR / "aggregates"
 # https://h3geo.org/docs/core-library/restable/#average-area-in-m2
 H3_RESOLUTIONS = [7, 8, 9, 10, 11]
 DEFAULT_H3_RESOLUTION = 8
-
-# Year filters
-MIN_YEAR = 1995
-MAX_YEAR = 2024
-DEFAULT_MIN_YEAR = 2020
-DEFAULT_MAX_YEAR = 2024
-
-# Price filters
-DEFAULT_MIN_PRICE = 0
-DEFAULT_MAX_PRICE = 100_000_000
diff --git a/pipeline/epc_pp.py b/pipeline/epc_pp.py
index 1c50ec2..9d7d480 100644
--- a/pipeline/epc_pp.py
+++ b/pipeline/epc_pp.py
@@ -18,7 +18,7 @@ epc = pl.scan_csv('data_sources/epc/certificates.csv').select(
     'NUMBER_HABITABLE_ROOMS',
     'FLOOR_HEIGHT',
     'CONSTRUCTION_AGE_BAND'
-).sort('INSPECTION_DATE', descending=True).group_by('epc_address').first()
+).filter(pl.col('epc_address').is_not_null()).sort('INSPECTION_DATE', descending=True).group_by('epc_address', 'POSTCODE').first()
 
 
 print("EPC dataset")
@@ -39,7 +39,8 @@ price_paid = (pl.scan_parquet('data_sources/pp-complete.parquet').select(
     'locality',
     'town_city',
     pl.col('duration').replace(duration_map)
-).filter(pl.col('pp_property_type') != 'Other').with_columns(
+)
+.filter(pl.col('pp_property_type') != 'Other').with_columns(
         pl.concat_str(
             [pl.col('saon'), pl.col('paon'), pl.col('street')],
             separator=' ',
@@ -58,30 +59,27 @@ price_paid = (pl.scan_parquet('data_sources/pp-complete.parquet').select(
         pl.col('price').last().alias('latest_price'),
         pl.col('date_of_transfer').last(),
     )   
-)
+).filter(pl.col('pp_address').is_not_null())
 
 print("Price paid dataset")
 print(price_paid.head().collect())
 
-price_paid_df = price_paid.collect()
-epc_df = epc.collect()
-
 joined = fuzzy_join_on_postcode(
-    left=price_paid_df,
-    right=epc_df,
+    left=price_paid,
+    right=epc,
     left_address_col='pp_address',
     right_address_col='epc_address',
     left_postcode_col='postcode',
     right_postcode_col='POSTCODE',
-    score_threshold=80,
-).drop('POSTCODE')
+).drop('POSTCODE').collect()
 
-matched_count = joined.filter(pl.col('epc_address').is_not_null() & pl.col('pp_address').is_not_null()).height
-print(f"Unique properties: {price_paid_df.height}")
-print(f"Matched: {matched_count} ({100 * matched_count / price_paid_df.height:.1f}%)")
-print(f"Unmatched: {price_paid_df.height - matched_count}")
+matched = joined.filter(pl.col('epc_address').is_not_null() & pl.col('pp_address').is_not_null())
+total = joined.height
+print(f"Unique properties: {total}")
+print(f"Matched: {matched.height} ({100 * matched.height / total:.1f}%)")
+print(f"Unmatched: {total - matched.height}")
 
-joined = joined.rename({col: col.lower() for col in joined.columns})
+matched = matched.rename({col: col.lower() for col in joined.columns})
 
-print(joined.head())
-joined.write_parquet('data_sources/processed/epc_pp.parquet')
+print(matched.head())
+matched.write_parquet('data_sources/processed/epc_pp.parquet')
diff --git a/pipeline/fuzzy_join.py b/pipeline/fuzzy_join.py
index ed85286..f516f22 100644
--- a/pipeline/fuzzy_join.py
+++ b/pipeline/fuzzy_join.py
@@ -1,6 +1,9 @@
 import re
+import shutil
+import tempfile
 from concurrent.futures import ProcessPoolExecutor
 from os import cpu_count
+from pathlib import Path
 
 import polars as pl
 from thefuzz import fuzz
@@ -9,105 +12,143 @@ from tqdm import tqdm
 _NUMBER_RE = re.compile(r'\d+')
 
 
+def _normalize(s: pl.Expr) -> pl.Expr:
+    return (
+        s.str.to_uppercase()
+        .str.replace_all(r'[,.\-]', ' ')
+        .str.replace_all(r'\s+', ' ')
+        .str.strip_chars()
+    )
+
+
 def fuzzy_join_on_postcode(
-    left: pl.DataFrame,
-    right: pl.DataFrame,
+    left: pl.LazyFrame,
+    right: pl.LazyFrame,
     left_address_col: str,
     right_address_col: str,
     left_postcode_col: str,
     right_postcode_col: str,
-    score_threshold: int = 80,
-) -> pl.DataFrame:
-    """Fuzzy join two DataFrames by matching addresses within postcode buckets.
+) -> pl.LazyFrame:
+    """Fuzzy join two LazyFrames by matching addresses within postcode buckets.
 
-    Returns the left DataFrame with all right columns appended.
-    Unmatched rows have null right columns.
+    Sinks each side to a temporary parquet file so the upstream pipeline
+    executes only once.  The matching phase collects just three narrow
+    columns (index, address, postcode) via projection pushdown, and the
+    final join reads the remaining columns lazily.
+
+    Returns a LazyFrame with all left and right columns.  Unmatched rows
+    have null right columns.
     """
 
-    def _normalize(s: pl.Expr) -> pl.Expr:
-        return (
-            s.str.to_uppercase()
-            .str.replace_all(r'[,.\-]', ' ')
-            .str.replace_all(r'\s+', ' ')
-            .str.strip_chars()
+    tmpdir = tempfile.mkdtemp(prefix='fuzzy_join_')
+    left_path = Path(tmpdir) / 'left.parquet'
+    right_path = Path(tmpdir) / 'right.parquet'
+
+    try:
+        # Materialise each side exactly once, with a row index, to temp parquet.
+        left.with_row_index('_left_idx').sink_parquet(left_path)
+        right.with_row_index('_right_idx').sink_parquet(right_path)
+
+        # Collect only the narrow columns needed for matching (projection pushdown).
+        left_match = (
+            pl.scan_parquet(left_path)
+            .select(
+                '_left_idx',
+                _normalize(pl.col(left_address_col)).alias('_left_address'),
+                pl.col(left_postcode_col).str.strip_chars().str.to_uppercase().alias('_left_postcode'),
+            )
+            .collect()
         )
 
-    left = left.with_columns(
-        _normalize(pl.col(left_address_col)).alias('_left_address'),
-        pl.col(left_postcode_col).str.strip_chars().str.to_uppercase().alias('_left_postcode'),
-    )
-    right = right.with_columns(
-        _normalize(pl.col(right_address_col)).alias('_right_address'),
-        pl.col(right_postcode_col).str.strip_chars().str.to_uppercase().alias('_right_postcode'),
-    )
+        right_match = (
+            pl.scan_parquet(right_path)
+            .select(
+                '_right_idx',
+                _normalize(pl.col(right_address_col)).alias('_right_address'),
+                pl.col(right_postcode_col).str.strip_chars().str.to_uppercase().alias('_right_postcode'),
+            )
+            .unique(subset=['_right_address', '_right_postcode'], keep='first')
+            .collect()
+        )
 
-    # Deduplicate right side on normalized address + postcode so that
-    # variant spellings of the same address don't consume multiple slots.
-    right = right.unique(subset=['_right_address', '_right_postcode'], keep='first')
-
-    # Group right side by postcode for fast lookup
-    right_by_postcode: dict[str, list[tuple[int, str]]] = {}
-    for i, (postcode, address) in enumerate(
-        zip(right['_right_postcode'], right['_right_address'])
-    ):
-        if postcode is not None:
-            right_by_postcode.setdefault(postcode, []).append((i, address))
-
-    # Group left side by postcode
-    left_by_postcode: dict[str, list[tuple[int, str]]] = {}
-    for left_row, (postcode, address) in enumerate(
-        zip(left['_left_postcode'], left['_left_address'])
-    ):
-        if address is not None and postcode is not None:
-            left_by_postcode.setdefault(postcode, []).append((left_row, address))
-
-    # Build tasks for each postcode bucket
-    tasks = [
-        (left_entries, right_by_postcode[postcode], score_threshold)
-        for postcode, left_entries in left_by_postcode.items()
-        if postcode in right_by_postcode
-    ]
-
-    # Score all pairwise matches in parallel, then greedily assign from
-    # highest score downward so best pairs lock in first.
-    all_pairs: list[tuple[int, int, int]] = []  # (score, left_row, right_row)
-    with ProcessPoolExecutor(max_workers=cpu_count()) as executor:
-        for pairs in tqdm(
-            executor.map(_score_bucket, tasks, chunksize=64),
-            total=len(tasks),
-            desc='Fuzzy matching',
+        # Group right side by postcode for fast lookup
+        right_by_postcode: dict[str, list[tuple[int, str]]] = {}
+        for idx, postcode, address in zip(
+            right_match['_right_idx'], right_match['_right_postcode'], right_match['_right_address']
         ):
-            all_pairs.extend(pairs)
+            if postcode is not None:
+                right_by_postcode.setdefault(postcode, []).append((idx, address))
 
-    # Sort descending by score so best matches are assigned first
-    all_pairs.sort(key=lambda t: (t[0], -t[1]), reverse=True)
+        # Group left side by postcode
+        left_by_postcode: dict[str, list[tuple[int, str]]] = {}
+        for idx, postcode, address in zip(
+            left_match['_left_idx'], left_match['_left_postcode'], left_match['_left_address']
+        ):
+            if address is not None and postcode is not None:
+                left_by_postcode.setdefault(postcode, []).append((idx, address))
 
-    match_indices: list[int | None] = [None] * len(left)
-    matched_left: set[int] = set()
-    matched_right: set[int] = set()
+        del left_match, right_match
 
-    for score, left_row, right_row in all_pairs:
-        if left_row in matched_left or right_row in matched_right:
-            continue
-        match_indices[left_row] = right_row
-        matched_left.add(left_row)
-        matched_right.add(right_row)
+        # Build tasks for each postcode bucket
+        tasks = [
+            (left_entries, right_by_postcode[postcode])
+            for postcode, left_entries in left_by_postcode.items()
+            if postcode in right_by_postcode
+        ]
 
-    # Select right columns (excluding internal helpers)
-    right_cols = right.select(pl.exclude('_right_address', '_right_postcode'))
-    right_matched = right_cols[
-        [i if i is not None else 0 for i in match_indices]
-    ]
+        # Score all pairwise matches in parallel, then greedily assign from
+        # highest score downward so best pairs lock in first.
+        all_pairs: list[tuple[int, int, int]] = []  # (score, left_idx, right_idx)
+        with ProcessPoolExecutor(max_workers=cpu_count()) as executor:
+            for pairs in tqdm(
+                executor.map(_score_bucket, tasks, chunksize=64),
+                total=len(tasks),
+                desc='Fuzzy matching',
+            ):
+                all_pairs.extend(pairs)
 
-    # Null out unmatched rows
-    mask = pl.Series('_matched', [i is not None for i in match_indices])
-    right_matched = right_matched.with_columns(
-        pl.when(mask).then(pl.col(c)).otherwise(pl.lit(None)).alias(c)
-        for c in right_matched.columns
-    )
+        del tasks, left_by_postcode, right_by_postcode
 
-    left_clean = left.select(pl.exclude('_left_address', '_left_postcode'))
-    return pl.concat([left_clean, right_matched], how='horizontal')
+        # Sort descending by score so best matches are assigned first
+        all_pairs.sort(key=lambda t: (t[0], -t[1]), reverse=True)
+
+        matches: list[tuple[int, int]] = []
+        matched_left: set[int] = set()
+        matched_right: set[int] = set()
+
+        for _score, left_idx, right_idx in all_pairs:
+            if left_idx in matched_left or right_idx in matched_right:
+                continue
+            matches.append((left_idx, right_idx))
+            matched_left.add(left_idx)
+            matched_right.add(right_idx)
+
+        del all_pairs, matched_left, matched_right
+
+        # Build a small mapping LazyFrame and join back to the cached parquets.
+        if matches:
+            mapping = pl.LazyFrame({
+                '_left_idx': pl.Series([m[0] for m in matches], dtype=pl.UInt32),
+                '_right_idx': pl.Series([m[1] for m in matches], dtype=pl.UInt32),
+            })
+        else:
+            mapping = pl.LazyFrame({
+                '_left_idx': pl.Series([], dtype=pl.UInt32),
+                '_right_idx': pl.Series([], dtype=pl.UInt32),
+            })
+
+        left_cached = pl.scan_parquet(left_path)
+        right_cached = pl.scan_parquet(right_path)
+
+        return (
+            left_cached
+            .join(mapping, on='_left_idx', how='left')
+            .join(right_cached, on='_right_idx', how='left')
+            .drop('_left_idx', '_right_idx')
+        )
+    except BaseException:
+        shutil.rmtree(tmpdir, ignore_errors=True)
+        raise
 
 
 def _numbers_compatible(a: str, b: str) -> bool:
@@ -127,13 +168,12 @@ def _score_bucket(
     args: tuple[list[tuple[int, str]], list[tuple[int, str]], int],
 ) -> list[tuple[int, int, int]]:
     """Score all address pairs within a single postcode bucket."""
-    left_entries, right_entries, score_threshold = args
+    left_entries, right_entries = args
     pairs = []
     for left_row, left_address in left_entries:
         for right_row, right_address in right_entries:
             if not _numbers_compatible(left_address, right_address):
                 continue
             score = fuzz.token_sort_ratio(left_address, right_address)
-            if score >= score_threshold:
-                pairs.append((score, left_row, right_row))
+            pairs.append((score, left_row, right_row))
     return pairs
diff --git a/pipeline/processors/__init__.py b/pipeline/processors/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/pipeline/processors/h3_aggregator.py b/pipeline/processors/h3_aggregator.py
deleted file mode 100644
index 94653e9..0000000
--- a/pipeline/processors/h3_aggregator.py
+++ /dev/null
@@ -1,42 +0,0 @@
-from pathlib import Path
-import polars as pl
-
-from pipeline.config import AGGREGATES_DIR, H3_RESOLUTIONS
-
-
-def aggregate(df: pl.LazyFrame, resolution: int) -> pl.LazyFrame:
-    """Aggregate property data by H3 cell and year."""
-    h3_col = f"h3_res{resolution}"
-
-    return (
-        df.group_by(h3_col, "year")
-        .agg(
-            pl.len().alias("count"),
-            pl.col("price").mean().alias("avg_price"),
-            pl.col("price").median().alias("median_price"),
-            pl.col("price").min().alias("min_price"),
-            pl.col("price").max().alias("max_price"),
-        )
-        .rename({h3_col: "h3"})
-    )
-
-
-def aggregate_all(df: pl.LazyFrame) -> dict[int, pl.LazyFrame]:
-    """Aggregate at all H3 resolutions."""
-    return {res: aggregate(df, res) for res in H3_RESOLUTIONS}
-
-
-def save_aggregates(df: pl.LazyFrame, output_dir: Path | None = None) -> list[Path]:
-    """Aggregate and save at all H3 resolutions."""
-    output_dir = output_dir or AGGREGATES_DIR
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    saved_paths = []
-    aggregates = aggregate_all(df)
-
-    for res, agg_df in aggregates.items():
-        output_path = output_dir / f"res{res}.parquet"
-        agg_df.collect().write_parquet(output_path)
-        saved_paths.append(output_path)
-
-    return saved_paths
diff --git a/pipeline/processors/journey_times_aggregator.py b/pipeline/processors/journey_times_aggregator.py
deleted file mode 100644
index 5f6dfb9..0000000
--- a/pipeline/processors/journey_times_aggregator.py
+++ /dev/null
@@ -1,127 +0,0 @@
-"""Aggregate journey times data by H3 hexagonal cells."""
-
-from pathlib import Path
-
-import polars as pl
-
-from pipeline.config import AGGREGATES_DIR, H3_RESOLUTIONS, PROCESSED_DIR
-
-JOURNEY_COLS = [
-    "public_transport_easy_minutes",
-    "public_transport_quick_minutes",
-    "cycling_minutes",
-]
-
-AGGREGATE_COLS = [
-    "median_pt_easy_minutes",
-    "median_pt_quick_minutes",
-    "median_cycling_minutes",
-    "median_journey_minutes",
-]
-
-
-def aggregate_journey_times(
-    journey_times_path: Path | None = None,
-    postcodes_h3_path: Path | None = None,
-    aggregates_dir: Path | None = None,
-) -> list[Path]:
-    """
-    Add journey times to existing H3 aggregate parquet files.
-
-    Joins journey_times_bank_checkpoint.parquet with postcodes_h3.parquet on postcode,
-    aggregates by H3 cell, then merges into existing res{N}.parquet files.
-    """
-    journey_times_path = (
-        journey_times_path
-        or PROCESSED_DIR / "journey_times_bank_checkpoint.parquet"
-    )
-    postcodes_h3_path = postcodes_h3_path or PROCESSED_DIR / "postcodes_h3.parquet"
-    aggregates_dir = aggregates_dir or AGGREGATES_DIR
-
-    # Load journey times data
-    journey_df = pl.read_parquet(journey_times_path).select(
-        ["postcode"] + JOURNEY_COLS
-    )
-
-    # Filter out rows where all journey time columns are null
-    journey_df = journey_df.filter(
-        pl.any_horizontal(pl.col(c).is_not_null() for c in JOURNEY_COLS)
-    )
-
-    if journey_df.height == 0:
-        print("No valid journey times found")
-        return []
-
-    # Load postcodes with H3 indices
-    postcodes_df = pl.read_parquet(postcodes_h3_path)
-
-    # Join on postcode to get H3 indices
-    joined_df = journey_df.join(postcodes_df, on="postcode", how="inner")
-
-    if joined_df.height == 0:
-        print("No matching postcodes found")
-        return []
-
-    print(f"Joined {joined_df.height} postcodes with journey times")
-
-    updated_paths = []
-
-    for resolution in H3_RESOLUTIONS:
-        h3_col = f"h3_res{resolution}"
-        parquet_path = aggregates_dir / f"res{resolution}.parquet"
-
-        if not parquet_path.exists():
-            print(f"Skipping resolution {resolution} - {parquet_path} not found")
-            continue
-
-        if h3_col not in joined_df.columns:
-            print(f"Skipping resolution {resolution} - column {h3_col} not found")
-            continue
-
-        # Aggregate journey times by H3 cell
-        journey_agg = (
-            joined_df.group_by(h3_col)
-            .agg(
-                pl.col("public_transport_easy_minutes")
-                .median()
-                .alias("median_pt_easy_minutes"),
-                pl.col("public_transport_quick_minutes")
-                .median()
-                .alias("median_pt_quick_minutes"),
-                pl.col("cycling_minutes")
-                .median()
-                .alias("median_cycling_minutes"),
-                pl.col("public_transport_quick_minutes")
-                .median()
-                .alias("median_journey_minutes"),
-            )
-            .rename({h3_col: "h3"})
-        )
-
-        # Load existing parquet
-        existing_df = pl.read_parquet(parquet_path)
-
-        # Drop existing journey time columns if present
-        existing_df = existing_df.drop(
-            [c for c in AGGREGATE_COLS if c in existing_df.columns]
-        )
-
-        # Left join journey times onto existing data
-        updated_df = existing_df.join(journey_agg, on="h3", how="left")
-
-        # Save back to parquet
-        updated_df.write_parquet(parquet_path)
-        updated_paths.append(parquet_path)
-        matched = updated_df.filter(
-            pl.col("median_journey_minutes").is_not_null()
-        ).height
-        print(
-            f"Updated {parquet_path.name}: {matched} rows with journey times "
-            f"(out of {updated_df.height} total)"
-        )
-
-    return updated_paths
-
-
-if __name__ == "__main__":
-    aggregate_journey_times()
diff --git a/pipeline/run.py b/pipeline/run.py
index 630a892..6a03ed6 100644
--- a/pipeline/run.py
+++ b/pipeline/run.py
@@ -1,45 +1,6 @@
 """Pipeline CLI to process property data with H3 spatial indexing."""
 
-import polars as pl
-
-from pipeline.sources.postcodes import save_postcodes
-from pipeline.sources.property_prices import PropertyPricesSource
-from pipeline.processors.h3_aggregator import save_aggregates
-from pipeline.processors.journey_times_aggregator import aggregate_journey_times
-
-
-def run_pipeline():
-    """Run the full data processing pipeline."""
-    print("=" * 60)
-    print("Property Map Data Pipeline")
-    print("=" * 60)
-
-    # Step 1: Process postcodes with H3 indices
-    print("\n[1/4] Processing postcodes with H3 indices...")
-    postcodes_path = save_postcodes()
-    print(f"      Saved: {postcodes_path}")
-
-    print("\n[2/4] Processing property prices...")
-    postcodes = pl.scan_parquet(postcodes_path)
-    property_source = PropertyPricesSource()
-    properties = property_source.process(postcodes)
-    print("      Joined property prices with postcodes")
-
-    print("\n[3/4] Aggregating at H3 resolutions...")
-    saved_paths = save_aggregates(properties)
-    for path in saved_paths:
-        size_mb = path.stat().st_size / (1024 * 1024)
-        print(f"      Saved: {path.name} ({size_mb:.1f} MB)")
-
-    print("\n[4/4] Adding journey times to aggregates...")
-    updated_paths = aggregate_journey_times()
-    if updated_paths:
-        for path in updated_paths:
-            size_mb = path.stat().st_size / (1024 * 1024)
-            print(f"      Updated: {path.name} ({size_mb:.1f} MB)")
-    else:
-        print("      Skipped (no journey time data found)")
-
+from pipeline.wide import run
 
 if __name__ == "__main__":
-    run_pipeline()
+    run()
diff --git a/pipeline/sources/__init__.py b/pipeline/sources/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/pipeline/sources/postcodes.py b/pipeline/sources/postcodes.py
deleted file mode 100644
index d0105b5..0000000
--- a/pipeline/sources/postcodes.py
+++ /dev/null
@@ -1,49 +0,0 @@
-from pathlib import Path
-import polars as pl
-import h3
-
-from pipeline.config import DATA_DIR, H3_RESOLUTIONS, PROCESSED_DIR
-
-
-def lat_long_to_h3(lat: float, long: float, resolution: int) -> str:
-    """Convert lat/long to H3 index at given resolution."""
-    return h3.latlng_to_cell(lat, long, resolution)
-
-
-def load_postcodes() -> pl.LazyFrame:
-    """Load postcode data from arcgis parquet file."""
-    return pl.scan_parquet(DATA_DIR / "arcgis_data.parquet").select(
-        pl.col("pcds").alias("postcode"),
-        pl.col("lat"),
-        pl.col("long"),
-    )
-
-
-def process_postcodes() -> pl.LazyFrame:
-    """Process postcodes and add H3 indices at multiple resolutions."""
-    df = load_postcodes().collect()
-
-    for res in H3_RESOLUTIONS:
-        col_name = f"h3_res{res}"
-        df = df.with_columns(
-            pl.struct(["lat", "long"])
-            .map_elements(
-                # Capture res by value using default argument to avoid closure bug
-                lambda x, res=res: lat_long_to_h3(x["lat"], x["long"], res),
-                return_dtype=pl.Utf8,
-            )
-            .alias(col_name)
-        )
-
-    return df.lazy()
-
-
-def save_postcodes(output_path: Path | None = None) -> Path:
-    """Process and save postcodes with H3 indices."""
-    output_path = output_path or PROCESSED_DIR / "postcodes_h3.parquet"
-    output_path.parent.mkdir(parents=True, exist_ok=True)
-
-    df = process_postcodes().collect()
-    df.write_parquet(output_path)
-
-    return output_path
diff --git a/pipeline/sources/property_prices.py b/pipeline/sources/property_prices.py
deleted file mode 100644
index 4d61b0c..0000000
--- a/pipeline/sources/property_prices.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import polars as pl
-
-from pipeline.base import DataSource
-from pipeline.config import DATA_DIR, H3_RESOLUTIONS
-
-
-class PropertyPricesSource(DataSource):
-    """Land Registry property prices data source."""
-
-    @property
-    def name(self) -> str:
-        return "property_prices"
-
-    def load(self) -> pl.LazyFrame:
-        """Load raw property prices data."""
-        return pl.scan_parquet(DATA_DIR / "pp-complete.parquet")
-
-    def process(self, postcodes: pl.LazyFrame) -> pl.LazyFrame:
-        """Process and join with postcode coordinates and H3 indices."""
-        prices = self.load().select(
-            pl.col("price"),
-            pl.col("date_of_transfer").dt.year().alias("year"),
-            pl.col("property_type"),
-            pl.col("postcode"),
-        )
-
-        joined = prices.join(
-            postcodes,
-            on="postcode",
-            how="inner",
-        )
-
-        h3_cols = [pl.col(f"h3_res{res}") for res in H3_RESOLUTIONS]
-        return joined.select(
-            pl.col("price"),
-            pl.col("year"),
-            pl.col("property_type"),
-            pl.col("lat"),
-            pl.col("long"),
-            *h3_cols,
-        )
diff --git a/pipeline/test_fuzzy_join.py b/pipeline/test_fuzzy_join.py
index 7197009..7a73a73 100644
--- a/pipeline/test_fuzzy_join.py
+++ b/pipeline/test_fuzzy_join.py
@@ -9,7 +9,6 @@ pp = (
     pl.scan_parquet("data_sources/pp-complete.parquet")
     .filter(pl.col("postcode") == POSTCODE)
     .select("paon", "saon", "street", "postcode")
-    .collect()
     .unique()
     .sort("saon")
     .with_columns(
@@ -27,14 +26,10 @@ epc = (
     .select("ADDRESS", "POSTCODE", "INSPECTION_DATE")
     .filter(pl.col("POSTCODE").str.strip_chars() == POSTCODE)
     .sort("INSPECTION_DATE", descending=True)
-    .collect()
     .unique("ADDRESS")
     .sort("ADDRESS")
 )
 
-print(f"Price paid: {len(pp)} unique addresses")
-print(f"EPC: {len(epc)} unique addresses")
-
 result = fuzzy_join_on_postcode(
     left=pp,
     right=epc,
@@ -42,9 +37,7 @@ result = fuzzy_join_on_postcode(
     right_address_col="ADDRESS",
     left_postcode_col="postcode",
     right_postcode_col="POSTCODE",
-    score_threshold=80,
-
-)
+).collect()
 
 snapshot = result.select("pp_address", "ADDRESS").sort("pp_address")
 
diff --git a/pipeline/wide.py b/pipeline/wide.py
new file mode 100644
index 0000000..3e170d9
--- /dev/null
+++ b/pipeline/wide.py
@@ -0,0 +1,143 @@
+"""Build a wide property dataframe and H3 aggregates from epc_pp output."""
+
+import polars as pl
+import h3
+
+from pipeline.config import AGGREGATES_DIR, H3_RESOLUTIONS, DATA_DIR, PROCESSED_DIR
+
+
+def _build_wide() -> pl.DataFrame:
+    """Build the wide dataframe by joining epc_pp with all auxiliary data."""
+    print("Loading epc_pp...")
+    wide = pl.read_parquet(PROCESSED_DIR / "epc_pp.parquet")
+    print(f"  {wide.shape[0]:,} rows")
+
+    # GPS coordinates + LSOA from ArcGIS
+    print("Joining GPS coordinates...")
+    arcgis = pl.read_parquet(DATA_DIR / "arcgis_data.parquet").select(
+        pl.col("pcds").alias("postcode"),
+        "lat",
+        pl.col("long").alias("lon"),
+        "lsoa21",
+    )
+    wide = wide.join(arcgis, on="postcode", how="inner")
+    print(f"  {wide.shape[0]:,} rows after GPS join")
+
+    # Journey times (optional)
+    journey_path = PROCESSED_DIR / "journey_times_bank_checkpoint.parquet"
+    if journey_path.exists():
+        print("Joining journey times...")
+        journey_times = pl.read_parquet(journey_path).select(
+            "postcode",
+            "public_transport_easy_minutes",
+            "public_transport_quick_minutes",
+            "cycling_minutes",
+        )
+        wide = wide.join(journey_times, on="postcode", how="left")
+
+    # Index of Deprivation
+    iod_path = DATA_DIR / "IoD2025_Scores.parquet"
+    if iod_path.exists():
+        print("Joining IoD scores...")
+        iod = pl.read_parquet(iod_path).drop(
+            "LSOA name (2021)",
+            "Local Authority District code (2024)",
+            "Local Authority District name (2024)",
+        )
+        # Rename IoD columns to clean snake_case
+        iod = iod.rename(_IOD_RENAMES)
+        wide = wide.join(
+            iod, left_on="lsoa21", right_on="lsoa_code", how="left"
+        )
+
+    return wide
+
+
+_IOD_RENAMES = {
+    "LSOA code (2021)": "lsoa_code",
+    "Index of Multiple Deprivation (IMD) Score": "imd_score",
+    "Income Score (rate)": "income_score",
+    "Employment Score (rate)": "employment_score",
+    "Education, Skills and Training Score": "education_score",
+    "Health Deprivation and Disability Score": "health_score",
+    "Crime Score": "crime_score",
+    "Barriers to Housing and Services Score": "housing_barriers_score",
+    "Living Environment Score": "living_environment_score",
+    "Income Deprivation Affecting Children Index (IDACI) Score (rate)": "idaci_score",
+    "Income Deprivation Affecting Older People (IDAOPI) Score (rate)": "idaopi_score",
+    "Children and Young People Sub-domain Score": "children_young_people_score",
+    "Adult Skills Sub-domain Score": "adult_skills_score",
+    "Geographical Barriers Sub-domain Score": "geographical_barriers_score",
+    "Wider Barriers Sub-domain Score": "wider_barriers_score",
+    "Indoors Sub-domain Score": "indoors_score",
+    "Outdoors Sub-domain Score": "outdoors_score",
+}
+
+
+def _add_h3_indices(df: pl.DataFrame) -> pl.DataFrame:
+    """Compute H3 indices from lat/lon for all configured resolutions."""
+    print("Computing H3 indices...")
+    # Compute per unique postcode for efficiency, then join back
+    postcodes = df.select("postcode", "lat", "lon").unique(subset=["postcode"])
+
+    for res in H3_RESOLUTIONS:
+        col_name = f"h3_res{res}"
+        postcodes = postcodes.with_columns(
+            pl.struct(["lat", "lon"])
+            .map_elements(
+                lambda x, r=res: h3.latlng_to_cell(x["lat"], x["lon"], r),
+                return_dtype=pl.Utf8,
+            )
+            .alias(col_name)
+        )
+        print(f"  res{res}: {postcodes[col_name].n_unique():,} unique cells")
+
+    h3_cols = [f"h3_res{res}" for res in H3_RESOLUTIONS]
+    return df.join(
+        postcodes.select("postcode", *h3_cols), on="postcode", how="left"
+    )
+
+
+def _aggregate_to_h3(df: pl.DataFrame) -> None:
+    """Aggregate min/max of every numeric feature per H3 cell at each resolution."""
+    AGGREGATES_DIR.mkdir(parents=True, exist_ok=True)
+
+    exclude = {"lat", "lon"}
+    numeric_cols = [
+        col
+        for col, dtype in zip(df.columns, df.dtypes)
+        if dtype.is_numeric() and not col.startswith("h3_res") and col not in exclude
+    ]
+
+    agg_exprs = [pl.len().alias("count")]
+    for col in numeric_cols:
+        agg_exprs.append(pl.col(col).min().alias(f"min_{col}"))
+        agg_exprs.append(pl.col(col).max().alias(f"max_{col}"))
+
+    print("Aggregating to H3 cells...")
+    for res in H3_RESOLUTIONS:
+        h3_col = f"h3_res{res}"
+        result = df.group_by(h3_col).agg(agg_exprs).rename({h3_col: "h3"})
+        path = AGGREGATES_DIR / f"res{res}.parquet"
+        result.write_parquet(path)
+        size_mb = path.stat().st_size / (1024 * 1024)
+        print(f"  {path.name}: {result.shape[0]:,} cells ({size_mb:.1f} MB)")
+
+
+def run():
+    """Run the full wide pipeline: build wide df, compute H3, aggregate."""
+    wide = _build_wide()
+
+    wide_path = PROCESSED_DIR / "wide.parquet"
+    wide.write_parquet(wide_path)
+    size_mb = wide_path.stat().st_size / (1024 * 1024)
+    print(f"Wrote {wide_path} ({size_mb:.1f} MB)")
+
+    wide = _add_h3_indices(wide)
+    _aggregate_to_h3(wide)
+
+    print("Done.")
+
+
+if __name__ == "__main__":
+    run()
diff --git a/pyproject.toml b/pyproject.toml
index 9a1cd5c..a0e6a9f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -24,10 +24,15 @@ dependencies = [
     "fastexcel>=0.19.0",
     "osmium>=4.0.0",
     "matplotlib>=3.10.8",
+    "thefuzz>=0.22.1",
+    "python-levenshtein>=0.27.3",
 ]
 
 [tool.uv]
 environments = ["sys_platform == 'linux' and python_version < '3.14'"]
 
 [dependency-groups]
-dev = ["ruff>=0.8.0"]
+dev = [
+    "pytest>=9.0.2",
+    "ruff>=0.8.0",
+]

From d4fe881ef49793ff9093237db3fc5121da070375 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Fri, 30 Jan 2026 18:34:12 +0000
Subject: [PATCH 18/62] Update map to do filtering

---
 frontend/src/App.tsx                |  84 ++++++++++----
 frontend/src/components/Filters.tsx | 157 ++++++++++----------------
 frontend/src/components/Map.tsx     | 166 ++++++++++++----------------
 frontend/src/lib/constants.ts       |  20 +---
 frontend/src/types.ts               |  35 +++---
 server/config.py                    |  12 --
 server/routes/hexagons.py           | 149 +++++++++++--------------
 server/routes/pois.py               |  98 ++++++++++++----
 8 files changed, 349 insertions(+), 372 deletions(-)

diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx
index f4838fb..1ba8e38 100644
--- a/frontend/src/App.tsx
+++ b/frontend/src/App.tsx
@@ -1,9 +1,9 @@
-import { useState, useEffect, useCallback, useRef } from 'react';
+import { useState, useEffect, useCallback, useRef, useMemo } from 'react';
 import Map from './components/Map';
 import Filters from './components/Filters';
-import { DEFAULT_FILTERS } from './lib/constants';
 import type {
-  Filters as FiltersType,
+  FeatureMeta,
+  FeatureFilters,
   Bounds,
   HexagonData,
   ViewChangeParams,
@@ -11,7 +11,6 @@ import type {
   POI,
   POIResponse,
   POICategoriesMap,
-  ColorMode,
 } from './types';
 
 const DEBOUNCE_MS = 150;
@@ -42,8 +41,10 @@ function getApiBaseUrl(): string {
 }
 
 export default function App() {
-  const [filters, setFilters] = useState<FiltersType>(DEFAULT_FILTERS);
-  const [data, setData] = useState<HexagonData[]>([]);
+  const [features, setFeatures] = useState<FeatureMeta[]>([]);
+  const [filters, setFilters] = useState<FeatureFilters>({});
+  const [activeFeature, setActiveFeature] = useState<string | null>(null);
+  const [rawData, setRawData] = useState<HexagonData[]>([]);
   const [resolution, setResolution] = useState<number>(8);
   const [bounds, setBounds] = useState<Bounds | null>(null);
   const [loading, setLoading] = useState<boolean>(false);
@@ -51,8 +52,6 @@ export default function App() {
   const debounceRef = useRef<ReturnType<typeof setTimeout> | null>(null);
   const abortControllerRef = useRef<AbortController | null>(null);
 
-  const [colorMode, setColorMode] = useState<ColorMode>('price');
-
   // POI state
   const [pois, setPois] = useState<POI[]>([]);
   const [poiCategories, setPOICategories] = useState<POICategoriesMap>({});
@@ -60,8 +59,21 @@ export default function App() {
   const poiDebounceRef = useRef<ReturnType<typeof setTimeout> | null>(null);
   const poiAbortControllerRef = useRef<AbortController | null>(null);
 
-  // Fetch POI category definitions from server on mount
+  // Fetch feature metadata + POI categories on mount
   useEffect(() => {
+    fetch(`${getApiBaseUrl()}/api/features`)
+      .then((res) => res.json())
+      .then((json: { features: FeatureMeta[] }) => {
+        setFeatures(json.features);
+        // Initialize filters with full range for each feature
+        const initial: FeatureFilters = {};
+        for (const f of json.features) {
+          initial[f.name] = [f.min, f.max];
+        }
+        setFilters(initial);
+      })
+      .catch((err) => console.error('Failed to fetch features:', err));
+
     fetch(`${getApiBaseUrl()}/api/poi-categories`)
       .then((res) => res.json())
       .then((json: { categories: POICategoriesMap }) => {
@@ -70,7 +82,7 @@ export default function App() {
       .catch((err) => console.error('Failed to fetch POI categories:', err));
   }, []);
 
-  // Debounced fetch when dependencies change
+  // Debounced fetch when resolution/bounds change (no filter params sent)
   useEffect(() => {
     if (!bounds) return;
 
@@ -89,17 +101,13 @@ export default function App() {
         const boundsStr = `${bounds.south},${bounds.west},${bounds.north},${bounds.east}`;
         const params = new URLSearchParams({
           resolution: resolution.toString(),
-          min_year: filters.minYear.toString(),
-          max_year: filters.maxYear.toString(),
-          min_price: filters.minPrice.toString(),
-          max_price: filters.maxPrice.toString(),
           bounds: boundsStr,
         });
         const res = await fetch(`${getApiBaseUrl()}/api/hexagons?${params}`, {
           signal: abortControllerRef.current.signal,
         });
         const json: ApiResponse = await res.json();
-        setData(json.features || []);
+        setRawData(json.features || []);
       } catch (err) {
         if (err instanceof Error && err.name !== 'AbortError') {
           console.error('Failed to fetch data:', err);
@@ -114,7 +122,36 @@ export default function App() {
         clearTimeout(debounceRef.current);
       }
     };
-  }, [filters, resolution, bounds]);
+  }, [resolution, bounds]);
+
+  // Client-side filtering
+  const data = useMemo(() => {
+    if (features.length === 0) return rawData;
+
+    return rawData.filter((hex) => {
+      if (activeFeature) {
+        // Only apply the active feature's filter
+        const range = filters[activeFeature];
+        if (!range) return true;
+        const minVal = hex[`min_${activeFeature}`];
+        const maxVal = hex[`max_${activeFeature}`];
+        if (minVal == null || maxVal == null) return true;
+        return (minVal as number) <= range[1] && (maxVal as number) >= range[0];
+      }
+      // Apply ALL filters as intersection
+      for (const f of features) {
+        const range = filters[f.name];
+        if (!range) continue;
+        // Skip features where filter is at full range
+        if (range[0] === f.min && range[1] === f.max) continue;
+        const minVal = hex[`min_${f.name}`];
+        const maxVal = hex[`max_${f.name}`];
+        if (minVal == null || maxVal == null) continue;
+        if ((minVal as number) > range[1] || (maxVal as number) < range[0]) return false;
+      }
+      return true;
+    });
+  }, [rawData, filters, activeFeature, features]);
 
   // Fetch POIs when bounds or selected categories change
   useEffect(() => {
@@ -171,17 +208,24 @@ export default function App() {
   return (
     <div className="h-screen flex">
       <Filters
+        features={features}
         filters={filters}
-        onChange={setFilters}
+        activeFeature={activeFeature}
+        onFiltersChange={setFilters}
+        onActiveFeatureChange={setActiveFeature}
         zoom={zoom}
         poiCategories={poiCategories}
         selectedPOICategories={selectedPOICategories}
         onPOICategoriesChange={setSelectedPOICategories}
-        colorMode={colorMode}
-        onColorModeChange={setColorMode}
       />
       <div className="flex-1 relative">
-        <Map data={data} pois={pois} onViewChange={handleViewChange} colorMode={colorMode} />
+        <Map
+          data={data}
+          pois={pois}
+          onViewChange={handleViewChange}
+          activeFeature={activeFeature}
+          features={features}
+        />
         {loading && (
           <div className="absolute top-4 right-4 bg-white px-3 py-1 rounded shadow">Loading...</div>
         )}
diff --git a/frontend/src/components/Filters.tsx b/frontend/src/components/Filters.tsx
index b956df8..181d685 100644
--- a/frontend/src/components/Filters.tsx
+++ b/frontend/src/components/Filters.tsx
@@ -1,32 +1,38 @@
 import { useState, useRef, useEffect } from 'react';
 import { Slider } from './ui/slider';
 import { Label } from './ui/label';
-import { YEAR_MIN, YEAR_MAX, YEAR_STEP, PRICE_MIN, PRICE_MAX, PRICE_STEP } from '../lib/constants';
-import type { Filters as FiltersType, POICategoriesMap, ColorMode } from '../types';
+import type { FeatureMeta, FeatureFilters, POICategoriesMap } from '../types';
 
 interface FiltersProps {
-  filters: FiltersType;
-  onChange: (filters: FiltersType) => void;
+  features: FeatureMeta[];
+  filters: FeatureFilters;
+  activeFeature: string | null;
+  onFiltersChange: (filters: FeatureFilters) => void;
+  onActiveFeatureChange: (feature: string | null) => void;
   zoom: number;
   poiCategories: POICategoriesMap;
   selectedPOICategories: Set<string>;
   onPOICategoriesChange: (categories: Set<string>) => void;
-  colorMode: ColorMode;
-  onColorModeChange: (mode: ColorMode) => void;
+}
+
+function formatValue(value: number): string {
+  if (Math.abs(value) >= 1_000_000) return `${(value / 1_000_000).toFixed(1)}M`;
+  if (Math.abs(value) >= 1_000) return `${(value / 1_000).toFixed(1)}k`;
+  if (Number.isInteger(value)) return value.toString();
+  return value.toFixed(2);
 }
 
 export default function Filters({
+  features,
   filters,
-  onChange,
+  activeFeature,
+  onFiltersChange,
+  onActiveFeatureChange,
   zoom,
   poiCategories,
   selectedPOICategories,
   onPOICategoriesChange,
-  colorMode,
-  onColorModeChange,
 }: FiltersProps) {
-  const update = (key: keyof FiltersType, value: number) => onChange({ ...filters, [key]: value });
-
   const [dropdownOpen, setDropdownOpen] = useState(false);
   const dropdownRef = useRef<HTMLDivElement>(null);
 
@@ -63,99 +69,53 @@ export default function Filters({
   const selectedCount = selectedPOICategories.size;
 
   return (
-    <div className="w-72 p-4 bg-white shadow-lg space-y-6 overflow-y-auto max-h-screen">
+    <div className="w-72 p-4 bg-white shadow-lg space-y-4 overflow-y-auto max-h-screen">
       <h1 className="text-xl font-bold">UK Property Prices</h1>
 
       <div className="text-sm text-slate-500">Zoom: {zoom.toFixed(1)}</div>
 
-      <div className="space-y-2">
-        <Label>
-          Year Range: {filters.minYear} - {filters.maxYear}
-        </Label>
-        <Slider
-          min={YEAR_MIN}
-          max={YEAR_MAX}
-          step={YEAR_STEP}
-          value={[filters.minYear, filters.maxYear]}
-          onValueChange={([min, max]) => onChange({ ...filters, minYear: min, maxYear: max })}
-        />
-      </div>
+      {features.map((feature) => {
+        const range = filters[feature.name] || [feature.min, feature.max];
+        const isActive = activeFeature === feature.name;
+        const step = (feature.max - feature.min) / 100;
 
-      <div className="space-y-2">
-        <Label>Min Price: £{filters.minPrice.toLocaleString()}</Label>
-        <Slider
-          min={PRICE_MIN}
-          max={PRICE_MAX}
-          step={PRICE_STEP}
-          value={[filters.minPrice]}
-          onValueChange={([v]) => update('minPrice', v)}
-        />
-      </div>
-
-      <div className="space-y-2">
-        <Label>Max Price: £{filters.maxPrice.toLocaleString()}</Label>
-        <Slider
-          min={PRICE_MIN}
-          max={PRICE_MAX}
-          step={PRICE_STEP}
-          value={[filters.maxPrice]}
-          onValueChange={([v]) => update('maxPrice', v)}
-        />
-      </div>
-
-      <div className="space-y-2">
-        <Label>Color By</Label>
-        <div className="flex gap-2">
-          <button
-            className={`flex-1 px-3 py-1.5 text-sm rounded ${colorMode === 'price' ? 'bg-slate-800 text-white' : 'bg-slate-100 text-slate-700'}`}
-            onClick={() => onColorModeChange('price')}
-          >
-            Price
-          </button>
-          <button
-            className={`flex-1 px-3 py-1.5 text-sm rounded ${colorMode === 'journey_time' ? 'bg-slate-800 text-white' : 'bg-slate-100 text-slate-700'}`}
-            onClick={() => onColorModeChange('journey_time')}
-          >
-            Journey Time
-          </button>
-        </div>
-      </div>
-
-      {colorMode === 'price' ? (
-        <div className="p-3 bg-slate-100 rounded text-xs">
-          <div className="mb-2 font-medium">Average Price</div>
+        return (
           <div
-            className="h-4 rounded"
-            style={{
-              background:
-                'linear-gradient(to right, rgb(46, 204, 113), rgb(241, 196, 15), rgb(231, 76, 60), rgb(142, 68, 173))',
-            }}
-          ></div>
-          <div className="flex justify-between mt-1">
-            <span>£0</span>
-            <span>£200k</span>
-            <span>£400k</span>
-            <span>£800k+</span>
+            key={feature.name}
+            className={`space-y-1 p-2 rounded ${isActive ? 'ring-2 ring-blue-400 bg-blue-50' : ''}`}
+          >
+            <Label className="text-xs">
+              {feature.label}: {formatValue(range[0])} - {formatValue(range[1])}
+            </Label>
+            <Slider
+              min={feature.min}
+              max={feature.max}
+              step={step}
+              value={[range[0], range[1]]}
+              onValueChange={([min, max]) => {
+                onFiltersChange({ ...filters, [feature.name]: [min, max] });
+              }}
+              onPointerDown={() => onActiveFeatureChange(feature.name)}
+              onPointerUp={() => onActiveFeatureChange(null)}
+            />
           </div>
+        );
+      })}
+
+      <div className="p-3 bg-slate-100 rounded text-xs">
+        <div className="mb-2 font-medium">Color Scale</div>
+        <div
+          className="h-4 rounded"
+          style={{
+            background:
+              'linear-gradient(to right, rgb(46, 204, 113), rgb(241, 196, 15), rgb(231, 76, 60), rgb(142, 68, 173))',
+          }}
+        ></div>
+        <div className="flex justify-between mt-1">
+          <span>Low</span>
+          <span>High</span>
         </div>
-      ) : (
-        <div className="p-3 bg-slate-100 rounded text-xs">
-          <div className="mb-2 font-medium">Journey Time to Bank</div>
-          <div
-            className="h-4 rounded"
-            style={{
-              background:
-                'linear-gradient(to right, rgb(46, 204, 113), rgb(241, 196, 15), rgb(231, 76, 60), rgb(142, 68, 173))',
-            }}
-          ></div>
-          <div className="flex justify-between mt-1">
-            <span>0 min</span>
-            <span>30 min</span>
-            <span>60 min</span>
-            <span>120+ min</span>
-          </div>
-        </div>
-      )}
+      </div>
 
       <div className="space-y-2" ref={dropdownRef}>
         <Label>Points of Interest</Label>
@@ -199,7 +159,7 @@ export default function Filters({
             </div>
             <div className="max-h-64 overflow-y-auto py-1">
               {categoryKeys.map((key) => {
-                const { emoji, label } = poiCategories[key];
+                const { emoji, label, count } = poiCategories[key];
                 return (
                   <label
                     key={key}
@@ -211,9 +171,10 @@ export default function Filters({
                       onChange={() => toggleCategory(key)}
                       className="rounded"
                     />
-                    <span className="text-sm">
+                    <span className="text-sm flex-1">
                       {emoji} {label}
                     </span>
+                    <span className="text-xs text-slate-400">{count.toLocaleString()}</span>
                   </label>
                 );
               })}
diff --git a/frontend/src/components/Map.tsx b/frontend/src/components/Map.tsx
index a3e8ec2..6353cac 100644
--- a/frontend/src/components/Map.tsx
+++ b/frontend/src/components/Map.tsx
@@ -6,13 +6,14 @@ import { H3HexagonLayer } from '@deck.gl/geo-layers';
 import { IconLayer } from '@deck.gl/layers';
 import type { PickingInfo } from '@deck.gl/core';
 import 'maplibre-gl/dist/maplibre-gl.css';
-import type { HexagonData, ViewState, ViewChangeParams, Bounds, POI, ColorMode } from '../types';
+import type { HexagonData, ViewState, ViewChangeParams, Bounds, POI, FeatureMeta } from '../types';
 
 interface MapProps {
   data: HexagonData[];
   pois: POI[];
   onViewChange: (params: ViewChangeParams) => void;
-  colorMode: ColorMode;
+  activeFeature: string | null;
+  features: FeatureMeta[];
 }
 
 // Twemoji CDN base URL
@@ -185,66 +186,31 @@ const INITIAL_VIEW: ViewState = {
 
 const MAP_STYLE = 'https://basemaps.cartocdn.com/gl/positron-gl-style/style.json';
 
-interface ColorStop {
-  price: number;
-  color: [number, number, number];
-}
-
-// Continuous color scale from green (low) -> yellow -> red -> purple (high)
-const COLOR_SCALE: ColorStop[] = [
-  { price: 0, color: [46, 204, 113] }, // Green
-  { price: 200000, color: [241, 196, 15] }, // Yellow
-  { price: 400000, color: [231, 76, 60] }, // Red
-  { price: 800000, color: [142, 68, 173] }, // Purple
+// Gradient stops for normalized [0,1] values
+const GRADIENT: { t: number; color: [number, number, number] }[] = [
+  { t: 0, color: [46, 204, 113] },    // Green
+  { t: 0.33, color: [241, 196, 15] },  // Yellow
+  { t: 0.66, color: [231, 76, 60] },   // Red
+  { t: 1, color: [142, 68, 173] },     // Purple
 ];
 
-function interpolateColor(
-  c1: [number, number, number],
-  c2: [number, number, number],
-  t: number
-): [number, number, number] {
-  return [
-    Math.round(c1[0] + (c2[0] - c1[0]) * t),
-    Math.round(c1[1] + (c2[1] - c1[1]) * t),
-    Math.round(c1[2] + (c2[2] - c1[2]) * t),
-  ];
-}
+function normalizedToColor(t: number): [number, number, number] {
+  if (t <= 0) return GRADIENT[0].color;
+  if (t >= 1) return GRADIENT[GRADIENT.length - 1].color;
 
-function scaleToColor(
-  value: number | null | undefined,
-  scale: ColorStop[]
-): [number, number, number] {
-  if (value == null || isNaN(value)) return [128, 128, 128];
-
-  if (value <= scale[0].price) return scale[0].color;
-  if (value >= scale[scale.length - 1].price) return scale[scale.length - 1].color;
-
-  for (let i = 0; i < scale.length - 1; i++) {
-    const lower = scale[i];
-    const upper = scale[i + 1];
-    if (value >= lower.price && value <= upper.price) {
-      const t = (value - lower.price) / (upper.price - lower.price);
-      return interpolateColor(lower.color, upper.color, t);
+  for (let i = 0; i < GRADIENT.length - 1; i++) {
+    const lo = GRADIENT[i];
+    const hi = GRADIENT[i + 1];
+    if (t >= lo.t && t <= hi.t) {
+      const frac = (t - lo.t) / (hi.t - lo.t);
+      return [
+        Math.round(lo.color[0] + (hi.color[0] - lo.color[0]) * frac),
+        Math.round(lo.color[1] + (hi.color[1] - lo.color[1]) * frac),
+        Math.round(lo.color[2] + (hi.color[2] - lo.color[2]) * frac),
+      ];
     }
   }
-
-  return scale[scale.length - 1].color;
-}
-
-function priceToColor(price: number | null | undefined): [number, number, number] {
-  return scaleToColor(price, COLOR_SCALE);
-}
-
-// Journey time color scale: green (short) -> yellow -> orange -> red (long)
-const JOURNEY_COLOR_SCALE: ColorStop[] = [
-  { price: 0, color: [46, 204, 113] }, // Green
-  { price: 30, color: [241, 196, 15] }, // Yellow
-  { price: 60, color: [231, 76, 60] }, // Red
-  { price: 120, color: [142, 68, 173] }, // Purple
-];
-
-function journeyTimeToColor(minutes: number | null | undefined): [number, number, number] {
-  return scaleToColor(minutes, JOURNEY_COLOR_SCALE);
+  return GRADIENT[GRADIENT.length - 1].color;
 }
 
 function zoomToResolution(zoom: number): number {
@@ -271,7 +237,6 @@ function getBoundsFromViewState(viewState: ViewState, width: number, height: num
   const halfWidthDeg = (width / 2) * degreesPerPixelLng;
 
   // Latitude uses Mercator projection (non-linear)
-  // Convert center lat to pixel y, offset by half height, convert back to lat
   const latRad = (clampedLat * Math.PI) / 180;
   const mercatorY = (1 - Math.log(Math.tan(latRad) + 1 / Math.cos(latRad)) / Math.PI) / 2;
   const centerPixelY = mercatorY * worldSize;
@@ -281,7 +246,7 @@ function getBoundsFromViewState(viewState: ViewState, width: number, height: num
 
   // Convert pixel Y back to latitude
   const pixelYToLat = (pixelY: number): number => {
-    const mercY = Math.max(0.001, Math.min(0.999, pixelY / worldSize)); // Clamp to avoid edge cases
+    const mercY = Math.max(0.001, Math.min(0.999, pixelY / worldSize));
     const latRadians = Math.atan(Math.sinh(Math.PI * (1 - 2 * mercY)));
     return (latRadians * 180) / Math.PI;
   };
@@ -315,7 +280,7 @@ function DeckOverlay({
   return null;
 }
 
-export default function Map({ data, pois, onViewChange, colorMode }: MapProps) {
+export default function Map({ data, pois, onViewChange, activeFeature, features }: MapProps) {
   const containerRef = useRef<HTMLDivElement>(null);
   const [viewState, setViewState] = useState<ViewState>(INITIAL_VIEW);
   const [dimensions, setDimensions] = useState<Dimensions>({ width: 0, height: 0 });
@@ -355,7 +320,6 @@ export default function Map({ data, pois, onViewChange, colorMode }: MapProps) {
     const map = evt.target;
     for (const layer of map.getStyle().layers || []) {
       if (layer.type !== 'symbol') continue;
-      // Stronger white halo so text pops over hex fills
       map.setPaintProperty(layer.id, 'text-halo-color', 'rgba(255,255,255,1)');
       map.setPaintProperty(layer.id, 'text-halo-width', 2);
       map.setPaintProperty(layer.id, 'text-color', '#222');
@@ -383,24 +347,32 @@ export default function Map({ data, pois, onViewChange, colorMode }: MapProps) {
     }
   }, []);
 
+  // Determine which feature to use for coloring
+  const colorFeatureName = activeFeature || (features.length > 0 ? features[0].name : null);
+  const colorFeatureMeta = features.find((f) => f.name === colorFeatureName) || null;
+
   const layers = useMemo(
     () => [
       new H3HexagonLayer<HexagonData>({
         id: 'h3-hexagons',
         data,
         getHexagon: (d) => d.h3,
-        getFillColor: (d) =>
-          colorMode === 'journey_time'
-            ? journeyTimeToColor(d.median_journey_minutes)
-            : priceToColor(d.avg_price),
+        getFillColor: (d) => {
+          if (!colorFeatureName || !colorFeatureMeta) return [128, 128, 128] as [number, number, number];
+          const val = d[`min_${colorFeatureName}`];
+          if (val == null) return [128, 128, 128] as [number, number, number];
+          const range = colorFeatureMeta.max - colorFeatureMeta.min;
+          if (range === 0) return GRADIENT[0].color;
+          const t = ((val as number) - colorFeatureMeta.min) / range;
+          return normalizedToColor(t);
+        },
         updateTriggers: {
-          getFillColor: colorMode,
+          getFillColor: [colorFeatureName, colorFeatureMeta],
         },
         extruded: false,
         pickable: true,
         opacity: 0.5,
         highPrecision: true,
-        // Render below labels so road names, place names etc. stay visible
         // @ts-expect-error beforeId is a MapboxOverlay interleave prop, not typed in LayerProps
         beforeId: LABEL_LAYER_ID,
       }),
@@ -420,41 +392,39 @@ export default function Map({ data, pois, onViewChange, colorMode }: MapProps) {
         onHover: handlePoiHover,
       }),
     ],
-    [data, pois, handlePoiHover, colorMode]
+    [data, pois, handlePoiHover, colorFeatureName, colorFeatureMeta]
   );
 
-  const getTooltip = useCallback(({ object }: { object?: HexagonData }) => {
-    if (!object || !('h3' in object)) return null;
+  const getTooltip = useCallback(
+    ({ object }: { object?: HexagonData }) => {
+      if (!object || !('h3' in object)) return null;
 
-    const hex = object as HexagonData;
-    const journeyLines: string[] = [];
-    if (hex.median_pt_quick_minutes != null)
-      journeyLines.push(`🚇 Quick PT: ${hex.median_pt_quick_minutes} min`);
-    if (hex.median_pt_easy_minutes != null)
-      journeyLines.push(`🚌 Easy PT: ${hex.median_pt_easy_minutes} min`);
-    if (hex.median_cycling_minutes != null)
-      journeyLines.push(`🚲 Cycling: ${hex.median_cycling_minutes} min`);
-    const journeyTimeHtml =
-      journeyLines.length > 0
-        ? `<div style="color: #0066cc; margin-top: 4px; font-size: 12px;">${journeyLines.join('<br/>')}</div>`
-        : '';
+      const hex = object;
+      const lines: string[] = [];
+      lines.push(`<strong>${(hex.count as number).toLocaleString()} properties</strong>`);
 
-    return {
-      html: `<div style="padding: 8px; font-size: 14px;">
-        <strong>Avg: £${hex.avg_price?.toLocaleString() || 'N/A'}</strong>
-        <div style="color: #666; font-size: 12px;">
-          ${hex.count} sales<br/>
-          Range: £${hex.min_price?.toLocaleString()} - £${hex.max_price?.toLocaleString()}
-        </div>
-        ${journeyTimeHtml}
-      </div>`,
-      style: {
-        backgroundColor: 'white',
-        borderRadius: '4px',
-        boxShadow: '0 2px 4px rgba(0,0,0,0.2)',
-      },
-    };
-  }, []);
+      for (const f of features) {
+        const minVal = hex[`min_${f.name}`];
+        const maxVal = hex[`max_${f.name}`];
+        if (minVal != null && maxVal != null) {
+          const minStr = typeof minVal === 'number' ? minVal.toLocaleString(undefined, { maximumFractionDigits: 1 }) : String(minVal);
+          const maxStr = typeof maxVal === 'number' ? maxVal.toLocaleString(undefined, { maximumFractionDigits: 1 }) : String(maxVal);
+          const highlight = f.name === colorFeatureName ? 'font-weight: bold;' : '';
+          lines.push(`<div style="${highlight}">${f.label}: ${minStr} - ${maxStr}</div>`);
+        }
+      }
+
+      return {
+        html: `<div style="padding: 8px; font-size: 12px;">${lines.join('')}</div>`,
+        style: {
+          backgroundColor: 'white',
+          borderRadius: '4px',
+          boxShadow: '0 2px 4px rgba(0,0,0,0.2)',
+        },
+      };
+    },
+    [features, colorFeatureName]
+  );
 
   return (
     <div className="flex-1 h-full relative" ref={containerRef}>
diff --git a/frontend/src/lib/constants.ts b/frontend/src/lib/constants.ts
index 0b7504d..890093b 100644
--- a/frontend/src/lib/constants.ts
+++ b/frontend/src/lib/constants.ts
@@ -1,19 +1 @@
-import type { Filters } from '../types';
-
-// Filter configuration constants
-// Should match backend pipeline/config.py
-
-export const YEAR_MIN = 1995;
-export const YEAR_MAX = 2024;
-export const YEAR_STEP = 1;
-
-export const PRICE_MIN = 0;
-export const PRICE_MAX = 5000000; // £5M max for slider, but no server-side cap
-export const PRICE_STEP = 50000;
-
-export const DEFAULT_FILTERS: Filters = {
-  minYear: 2020,
-  maxYear: YEAR_MAX,
-  minPrice: PRICE_MIN,
-  maxPrice: PRICE_MAX,
-};
+// No hardcoded filter constants - features are discovered dynamically from the API.
diff --git a/frontend/src/types.ts b/frontend/src/types.ts
index d372bc2..7416021 100644
--- a/frontend/src/types.ts
+++ b/frontend/src/types.ts
@@ -1,8 +1,17 @@
-export interface Filters {
-  minYear: number;
-  maxYear: number;
-  minPrice: number;
-  maxPrice: number;
+export interface FeatureMeta {
+  name: string;
+  min: number;
+  max: number;
+  label: string;
+}
+
+// Filters: feature name -> [selectedMin, selectedMax]
+export type FeatureFilters = Record<string, [number, number]>;
+
+export interface HexagonData {
+  h3: string;
+  count: number;
+  [key: string]: string | number | null;
 }
 
 export interface Bounds {
@@ -12,21 +21,6 @@ export interface Bounds {
   east: number;
 }
 
-export interface HexagonData {
-  h3: string;
-  count: number;
-  avg_price: number;
-  median_price: number;
-  min_price: number;
-  max_price: number;
-  median_journey_minutes: number | null;
-  median_pt_easy_minutes: number | null;
-  median_pt_quick_minutes: number | null;
-  median_cycling_minutes: number | null;
-}
-
-export type ColorMode = 'price' | 'journey_time';
-
 export interface ViewState {
   longitude: number;
   latitude: number;
@@ -60,6 +54,7 @@ export interface POIResponse {
 export interface POICategoryInfo {
   emoji: string;
   label: string;
+  count: number;
 }
 
 export type POICategoriesMap = Record<string, POICategoryInfo>;
diff --git a/server/config.py b/server/config.py
index 798a6bd..fcb27d9 100644
--- a/server/config.py
+++ b/server/config.py
@@ -4,12 +4,6 @@ from pipeline.config import (
     AGGREGATES_DIR,
     H3_RESOLUTIONS as VALID_RESOLUTIONS,
     DEFAULT_H3_RESOLUTION as DEFAULT_RESOLUTION,
-    MIN_YEAR,
-    MAX_YEAR,
-    DEFAULT_MIN_YEAR,
-    DEFAULT_MAX_YEAR,
-    DEFAULT_MIN_PRICE,
-    DEFAULT_MAX_PRICE,
 )
 
 # Extra area to return beyond requested bounds (0.2 = 20%)
@@ -20,11 +14,5 @@ __all__ = [
     "AGGREGATES_DIR",
     "VALID_RESOLUTIONS",
     "DEFAULT_RESOLUTION",
-    "MIN_YEAR",
-    "MAX_YEAR",
-    "DEFAULT_MIN_YEAR",
-    "DEFAULT_MAX_YEAR",
-    "DEFAULT_MIN_PRICE",
-    "DEFAULT_MAX_PRICE",
     "BOUNDS_BUFFER_PERCENT",
 ]
diff --git a/server/routes/hexagons.py b/server/routes/hexagons.py
index afe3777..c12177e 100644
--- a/server/routes/hexagons.py
+++ b/server/routes/hexagons.py
@@ -10,10 +10,6 @@ from server.config import (
     AGGREGATES_DIR,
     VALID_RESOLUTIONS,
     DEFAULT_RESOLUTION,
-    DEFAULT_MIN_YEAR,
-    DEFAULT_MAX_YEAR,
-    DEFAULT_MIN_PRICE,
-    DEFAULT_MAX_PRICE,
     BOUNDS_BUFFER_PERCENT,
 )
 
@@ -22,6 +18,38 @@ router = APIRouter()
 # Cache loaded dataframes in memory (one per resolution)
 _df_cache: dict[int, pl.DataFrame] = {}
 
+# Discovered features (computed once on first load)
+_features_cache: list[dict] | None = None
+
+
+def _snake_to_label(name: str) -> str:
+    """Convert snake_case feature name to a human-readable label."""
+    return name.replace("_", " ").title()
+
+
+def _discover_features(df: pl.DataFrame) -> list[dict]:
+    """Discover features from column pairs min_X / max_X."""
+    features = []
+    seen = set()
+    for col in df.columns:
+        if col.startswith("min_"):
+            name = col[4:]
+            max_col = f"max_{name}"
+            if max_col in df.columns and name not in seen:
+                seen.add(name)
+                global_min = df[col].min()
+                global_max = df[max_col].max()
+                if global_min is not None and global_max is not None:
+                    features.append(
+                        {
+                            "name": name,
+                            "min": float(global_min),
+                            "max": float(global_max),
+                            "label": _snake_to_label(name),
+                        }
+                    )
+    return features
+
 
 def preload_dataframes() -> None:
     """Load all resolution dataframes into cache on startup."""
@@ -38,25 +66,41 @@ def get_cached_df(resolution: int) -> pl.DataFrame | None:
         # Load and add H3 cell centroids for fast bbox filtering
         df = pl.read_parquet(parquet_path)
 
-        # Pre-compute cell centroids for bbox filtering (much faster than is_in)
+        # Pre-compute cell centroids for bbox filtering
         centroids = [h3.cell_to_latlng(cell) for cell in df["h3"].to_list()]
         df = df.with_columns(
             [
-                pl.Series("lat", [c[0] for c in centroids]),
-                pl.Series("lng", [c[1] for c in centroids]),
+                pl.Series("_lat", [c[0] for c in centroids]),
+                pl.Series("_lng", [c[1] for c in centroids]),
             ]
         )
         _df_cache[resolution] = df
     return _df_cache[resolution]
 
 
+def get_features() -> list[dict]:
+    """Get discovered features, computing from the first available resolution."""
+    global _features_cache
+    if _features_cache is None:
+        for resolution in VALID_RESOLUTIONS:
+            df = get_cached_df(resolution)
+            if df is not None:
+                _features_cache = _discover_features(df)
+                break
+        if _features_cache is None:
+            _features_cache = []
+    return _features_cache
+
+
+@router.get("/features")
+async def get_features_endpoint() -> dict:
+    """Return discovered feature metadata with global min/max ranges."""
+    return {"features": get_features()}
+
+
 @lru_cache(maxsize=128)
 def query_hexagons_cached(
     resolution: int,
-    min_year: int,
-    max_year: int,
-    min_price: int,
-    max_price: int,
     bounds_tuple: tuple[float, float, float, float],
 ) -> list[dict]:
     """Cached query - returns features list."""
@@ -64,65 +108,18 @@ def query_hexagons_cached(
 
     df = get_cached_df(resolution)
     if df is None:
-        return [], False
+        return []
 
-    # Fast bbox filter using pre-computed centroids (O(1) per row)
+    # Fast bbox filter using pre-computed centroids
     df = df.filter(
-        (pl.col("lat") >= south)
-        & (pl.col("lat") <= north)
-        & (pl.col("lng") >= west)
-        & (pl.col("lng") <= east)
+        (pl.col("_lat") >= south)
+        & (pl.col("_lat") <= north)
+        & (pl.col("_lng") >= west)
+        & (pl.col("_lng") <= east)
     )
 
-    # Filter by year range
-    df = df.filter((pl.col("year") >= min_year) & (pl.col("year") <= max_year))
-
-    # Check which journey time columns exist
-    journey_cols = [
-        "median_journey_minutes",
-        "median_pt_easy_minutes",
-        "median_pt_quick_minutes",
-        "median_cycling_minutes",
-    ]
-    available_journey_cols = [c for c in journey_cols if c in df.columns]
-
-    # Aggregate across years (weighted by count)
-    agg_exprs = [
-        pl.col("count").sum().alias("count"),
-        (pl.col("avg_price") * pl.col("count")).sum().alias("weighted_price_sum"),
-        pl.col("median_price").median().alias("median_price"),
-        pl.col("min_price").min().alias("min_price"),
-        pl.col("max_price").max().alias("max_price"),
-    ]
-    for jc in available_journey_cols:
-        # Journey time is same across years, just take first non-null
-        agg_exprs.append(pl.col(jc).first())
-
-    df = df.group_by("h3").agg(agg_exprs)
-
-    # Calculate weighted average price
-    df = df.with_columns(
-        (pl.col("weighted_price_sum") / pl.col("count")).alias("avg_price")
-    ).drop("weighted_price_sum")
-
-    # Filter by price range
-    df = df.filter(
-        (pl.col("avg_price") >= min_price) & (pl.col("avg_price") <= max_price)
-    )
-
-    # Build response efficiently using Polars
-    select_cols = [
-        pl.col("h3"),
-        pl.col("count"),
-        pl.col("avg_price").round(2),
-        pl.col("median_price").round(2),
-        pl.col("min_price"),
-        pl.col("max_price"),
-    ]
-    for jc in available_journey_cols:
-        select_cols.append(pl.col(jc).round(0))
-
-    df = df.select(select_cols)
+    # Drop internal centroid columns before returning
+    df = df.drop("_lat", "_lng")
 
     return df.to_dicts()
 
@@ -135,13 +132,9 @@ async def get_hexagons(
         le=max(VALID_RESOLUTIONS),
         description=f"H3 resolution ({min(VALID_RESOLUTIONS)}-{max(VALID_RESOLUTIONS)})",
     ),
-    min_year: int = Query(DEFAULT_MIN_YEAR, description="Minimum year filter"),
-    max_year: int = Query(DEFAULT_MAX_YEAR, description="Maximum year filter"),
-    min_price: float = Query(DEFAULT_MIN_PRICE, description="Minimum average price"),
-    max_price: float = Query(DEFAULT_MAX_PRICE, description="Maximum average price"),
     bounds: str | None = Query(None, description="Bounding box: south,west,north,east"),
 ) -> dict:
-    """Get aggregated property data as GeoJSON hexagons within bounds."""
+    """Get aggregated property data as hexagons within bounds."""
     if resolution not in VALID_RESOLUTIONS:
         resolution = DEFAULT_RESOLUTION
 
@@ -165,9 +158,7 @@ async def get_hexagons(
     west -= lng_buffer
     east += lng_buffer
 
-    # Round bounds to reduce cache misses (0.01 degree ≈ 1km precision)
-    # Always expand bounds (floor for min, ceil for max) to prevent hexagons
-    # popping in when crossing rounding boundaries
+    # Round bounds to reduce cache misses (0.01 degree ~ 1km precision)
     precision = 0.01
     bounds_tuple = (
         math.floor(south / precision) * precision,
@@ -176,14 +167,6 @@ async def get_hexagons(
         math.ceil(east / precision) * precision,
     )
 
-    # Convert prices to int for cache key hashability
-    features = query_hexagons_cached(
-        resolution,
-        min_year,
-        max_year,
-        int(min_price),
-        int(max_price),
-        bounds_tuple,
-    )
+    features = query_hexagons_cached(resolution, bounds_tuple)
 
     return {"features": features}
diff --git a/server/routes/pois.py b/server/routes/pois.py
index fcc225b..edf48db 100644
--- a/server/routes/pois.py
+++ b/server/routes/pois.py
@@ -9,8 +9,11 @@ router = APIRouter()
 
 DATA_FILE = Path("data_sources/uk_pois.parquet")
 
-# Category groups with emoji and member categories
-POI_CATEGORY_GROUPS: dict[str, dict] = {
+# Group definitions: maps a group key to its display metadata and the
+# individual POI categories it contains.  Categories are matched against
+# the values that actually exist in the loaded parquet so that the
+# selector only shows groups with real data.
+_GROUP_DEFS: dict[str, dict] = {
     "schools": {
         "emoji": "🏫",
         "label": "Schools",
@@ -189,33 +192,80 @@ POI_CATEGORY_GROUPS: dict[str, dict] = {
     },
 }
 
-# Flatten for quick lookup
-ALL_CATEGORIES = {
-    cat for group in POI_CATEGORY_GROUPS.values() for cat in group["categories"]
-}
+# Built at startup from the data — only groups whose member categories
+# actually appear in the parquet file are included.
+_active_groups: dict[str, dict] = {}
+
+# Reverse lookup: category value -> group key (built at startup)
+_cat_to_group: dict[str, str] = {}
 
 # Cache the dataframe
 _df_cache: pl.DataFrame | None = None
 
 
+def _load_and_build() -> pl.DataFrame | None:
+    """Load the parquet, build category groups from actual data."""
+    global _df_cache, _active_groups, _cat_to_group
+
+    if not DATA_FILE.exists():
+        return None
+
+    df = pl.read_parquet(DATA_FILE).select("id", "name", "category", "lat", "lng")
+
+    # Distinct categories present in the data
+    data_categories: set[str] = set(
+        df.select("category").unique().to_series().to_list()
+    )
+
+    # Per-category counts for the response
+    counts: dict[str, int] = dict(
+        df.group_by("category")
+        .agg(pl.len().alias("n"))
+        .iter_rows()
+    )
+
+    # Build reverse map from every known category to its group
+    cat_to_group: dict[str, str] = {}
+    for key, gdef in _GROUP_DEFS.items():
+        for cat in gdef["categories"]:
+            cat_to_group[cat] = key
+
+    # Only keep categories that belong to a known group
+    known_categories = data_categories & cat_to_group.keys()
+
+    # Build active groups — only those with at least one matching category
+    active: dict[str, dict] = {}
+    for key, gdef in _GROUP_DEFS.items():
+        present = [c for c in gdef["categories"] if c in known_categories]
+        if present:
+            active[key] = {
+                "emoji": gdef["emoji"],
+                "label": gdef["label"],
+                "categories": present,
+                "count": sum(counts.get(c, 0) for c in present),
+            }
+
+    _active_groups = active
+    _cat_to_group = cat_to_group
+
+    # Filter dataframe to only known categories
+    _df_cache = df.filter(pl.col("category").is_in(known_categories))
+    return _df_cache
+
+
 def get_df() -> pl.DataFrame | None:
-    """Load and cache the POI dataframe."""
-    global _df_cache
+    """Return cached POI dataframe, loading if necessary."""
     if _df_cache is None:
-        if not DATA_FILE.exists():
-            return None
-        df = pl.read_parquet(DATA_FILE)
-        _df_cache = df.select("id", "name", "category", "lat", "lng").filter(
-            pl.col("category").is_in(ALL_CATEGORIES)
-        )
+        return _load_and_build()
     return _df_cache
 
 
 def preload_pois() -> None:
     """Preload POI data on startup."""
-    df = get_df()
+    df = _load_and_build()
     if df is not None:
-        print(f"Loaded {len(df):,} POIs")
+        n_groups = len(_active_groups)
+        print(f"Loaded {len(df):,} POIs across {n_groups} category groups")
 
 
 @router.get("/pois")
@@ -234,10 +284,10 @@ async def get_pois(
         return {"features": []}
 
     requested_groups = [g.strip() for g in categories.split(",")]
-    cats_to_include = set()
+    cats_to_include: set[str] = set()
     for group in requested_groups:
-        if group in POI_CATEGORY_GROUPS:
-            cats_to_include.update(POI_CATEGORY_GROUPS[group]["categories"])
+        if group in _active_groups:
+            cats_to_include.update(_active_groups[group]["categories"])
 
     if not cats_to_include:
         return {"features": []}
@@ -259,10 +309,14 @@ async def get_pois(
 
 @router.get("/poi-categories")
 async def get_poi_categories() -> dict:
-    """Get available POI category groups with emoji and labels."""
+    """Get available POI category groups derived from loaded data."""
     return {
         "categories": {
-            key: {"emoji": group["emoji"], "label": group["label"]}
-            for key, group in POI_CATEGORY_GROUPS.items()
+            key: {
+                "emoji": group["emoji"],
+                "label": group["label"],
+                "count": group["count"],
+            }
+            for key, group in _active_groups.items()
         }
     }

From 5c39f3128353d676b02c2a6fe22b19e5eed971bd Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Fri, 30 Jan 2026 18:35:15 +0000
Subject: [PATCH 19/62] POI vibe coding

---
 analyses/poi_analysis.ipynb              | 4891 ++++++++++++++++++++++
 pipeline/{ => download}/pois/__init__.py |    0
 pipeline/{ => download}/pois/__main__.py |  117 +-
 pipeline/download/pois/config.py         |   34 +
 pipeline/pois/config.py                  |  147 -
 uv.lock                                  |  146 +-
 6 files changed, 5137 insertions(+), 198 deletions(-)
 create mode 100644 analyses/poi_analysis.ipynb
 rename pipeline/{ => download}/pois/__init__.py (100%)
 rename pipeline/{ => download}/pois/__main__.py (58%)
 create mode 100644 pipeline/download/pois/config.py
 delete mode 100644 pipeline/pois/config.py

diff --git a/analyses/poi_analysis.ipynb b/analyses/poi_analysis.ipynb
new file mode 100644
index 0000000..16e0330
--- /dev/null
+++ b/analyses/poi_analysis.ipynb
@@ -0,0 +1,4891 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# POI Category Cardinality Analysis\n",
+    "\n",
+    "Analysis of UK Points of Interest extracted from OpenStreetMap, showing the cardinality (count) of each POI type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "polars.config.Config"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import polars as pl\n",
+    "import plotly.express as px\n",
+    "from pathlib import Path\n",
+    "\n",
+    "pl.Config.set_tbl_rows(50)\n",
+    "pl.Config.set_fmt_str_lengths(60)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total POIs: 26,920,704\n",
+      "Schema: Schema({'id': String, 'name': String, 'category': String, 'lat': Float64, 'lng': Float64, 'osm_tags': String})\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (5, 6)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>id</th><th>name</th><th>category</th><th>lat</th><th>lng</th><th>osm_tags</th></tr><tr><td>str</td><td>str</td><td>str</td><td>f64</td><td>f64</td><td>str</td></tr></thead><tbody><tr><td>&quot;n129&quot;</td><td>&quot;Vodafone&quot;</td><td>&quot;mobile_phone&quot;</td><td>53.959357</td><td>-1.081517</td><td>&quot;{&quot;addr:city&quot;: &quot;York&quot;, &quot;addr:housenumber&quot;: &quot;29-30&quot;, &quot;addr:pos…</td></tr><tr><td>&quot;n99880&quot;</td><td>&quot;&quot;</td><td>&quot;crossing&quot;</td><td>51.525085</td><td>-0.15358</td><td>&quot;{&quot;crossing&quot;: &quot;unmarked&quot;, &quot;crossing:island&quot;: &quot;no&quot;, &quot;highway&quot;:…</td></tr><tr><td>&quot;n99884&quot;</td><td>&quot;&quot;</td><td>&quot;waste_basket&quot;</td><td>51.524364</td><td>-0.152816</td><td>&quot;{&quot;amenity&quot;: &quot;waste_basket&quot;, &quot;waste&quot;: &quot;dog_excrement;trash&quot;}&quot;</td></tr><tr><td>&quot;n99918&quot;</td><td>&quot;&quot;</td><td>&quot;life_ring&quot;</td><td>51.525734</td><td>-0.157812</td><td>&quot;{&quot;emergency&quot;: &quot;life_ring&quot;}&quot;</td></tr><tr><td>&quot;n99939&quot;</td><td>&quot;&quot;</td><td>&quot;traffic_signals&quot;</td><td>51.522443</td><td>-0.155462</td><td>&quot;{&quot;highway&quot;: &quot;traffic_signals&quot;, &quot;traffic_signals:direction&quot;: …</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (5, 6)\n",
+       "┌────────┬──────────┬─────────────────┬───────────┬───────────┬─────────────────────────────────┐\n",
+       "│ id     ┆ name     ┆ category        ┆ lat       ┆ lng       ┆ osm_tags                        │\n",
+       "│ ---    ┆ ---      ┆ ---             ┆ ---       ┆ ---       ┆ ---                             │\n",
+       "│ str    ┆ str      ┆ str             ┆ f64       ┆ f64       ┆ str                             │\n",
+       "╞════════╪══════════╪═════════════════╪═══════════╪═══════════╪═════════════════════════════════╡\n",
+       "│ n129   ┆ Vodafone ┆ mobile_phone    ┆ 53.959357 ┆ -1.081517 ┆ {\"addr:city\": \"York\",           │\n",
+       "│        ┆          ┆                 ┆           ┆           ┆ \"addr:housenumber\": \"29-30\",    │\n",
+       "│        ┆          ┆                 ┆           ┆           ┆ \"addr:pos…                      │\n",
+       "│ n99880 ┆          ┆ crossing        ┆ 51.525085 ┆ -0.15358  ┆ {\"crossing\": \"unmarked\",        │\n",
+       "│        ┆          ┆                 ┆           ┆           ┆ \"crossing:island\": \"no\",        │\n",
+       "│        ┆          ┆                 ┆           ┆           ┆ \"highway\":…                     │\n",
+       "│ n99884 ┆          ┆ waste_basket    ┆ 51.524364 ┆ -0.152816 ┆ {\"amenity\": \"waste_basket\",     │\n",
+       "│        ┆          ┆                 ┆           ┆           ┆ \"waste\": \"dog_excrement;trash\"} │\n",
+       "│ n99918 ┆          ┆ life_ring       ┆ 51.525734 ┆ -0.157812 ┆ {\"emergency\": \"life_ring\"}      │\n",
+       "│ n99939 ┆          ┆ traffic_signals ┆ 51.522443 ┆ -0.155462 ┆ {\"highway\": \"traffic_signals\",  │\n",
+       "│        ┆          ┆                 ┆           ┆           ┆ \"traffic_signals:direction\": …  │\n",
+       "└────────┴──────────┴─────────────────┴───────────┴───────────┴─────────────────────────────────┘"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "DATA_PATH = Path(\"../data_sources/uk_pois.parquet\")\n",
+    "\n",
+    "df = pl.read_parquet(DATA_PATH)\n",
+    "print(f\"Total POIs: {len(df):,}\")\n",
+    "print(f\"Schema: {df.schema}\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 1. Overall Category Cardinality"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unique categories: 10,926\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (10_926, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>category</th><th>count</th></tr><tr><td>str</td><td>u32</td></tr></thead><tbody><tr><td>&quot;yes&quot;</td><td>6149240</td></tr><tr><td>&quot;house&quot;</td><td>4867889</td></tr><tr><td>&quot;residential&quot;</td><td>2975285</td></tr><tr><td>&quot;service&quot;</td><td>2397521</td></tr><tr><td>&quot;footway&quot;</td><td>1690239</td></tr><tr><td>&quot;semidetached_house&quot;</td><td>710064</td></tr><tr><td>&quot;garage&quot;</td><td>536802</td></tr><tr><td>&quot;track&quot;</td><td>506554</td></tr><tr><td>&quot;detached&quot;</td><td>451014</td></tr><tr><td>&quot;path&quot;</td><td>419601</td></tr><tr><td>&quot;unclassified&quot;</td><td>320382</td></tr><tr><td>&quot;crossing&quot;</td><td>318014</td></tr><tr><td>&quot;garden&quot;</td><td>308411</td></tr><tr><td>&quot;parking&quot;</td><td>272518</td></tr><tr><td>&quot;bus_stop / platform&quot;</td><td>242090</td></tr><tr><td>&quot;terrace&quot;</td><td>219344</td></tr><tr><td>&quot;tertiary&quot;</td><td>216299</td></tr><tr><td>&quot;parking_space&quot;</td><td>206980</td></tr><tr><td>&quot;apartments&quot;</td><td>175306</td></tr><tr><td>&quot;cycleway&quot;</td><td>173504</td></tr><tr><td>&quot;primary&quot;</td><td>152330</td></tr><tr><td>&quot;bench&quot;</td><td>149125</td></tr><tr><td>&quot;street_lamp&quot;</td><td>120327</td></tr><tr><td>&quot;give_way&quot;</td><td>118935</td></tr><tr><td>&quot;pitch&quot;</td><td>117031</td></tr><tr><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;cage&quot;</td><td>1</td></tr><tr><td>&quot;post_office / appliance&quot;</td><td>1</td></tr><tr><td>&quot;club / commercial&quot;</td><td>1</td></tr><tr><td>&quot;home_help_service_agency&quot;</td><td>1</td></tr><tr><td>&quot;decoration&quot;</td><td>1</td></tr><tr><td>&quot;picnic_table / viewpoint&quot;</td><td>1</td></tr><tr><td>&quot;bird_hide / tower&quot;</td><td>1</td></tr><tr><td>&quot;entertainment / yes&quot;</td><td>1</td></tr><tr><td>&quot;terminal / aerohangar&quot;</td><td>1</td></tr><tr><td>&quot;valeting / yes&quot;</td><td>1</td></tr><tr><td>&quot;Waste Disposal Services&quot;</td><td>1</td></tr><tr><td>&quot;museum / tourism&quot;</td><td>1</td></tr><tr><td>&quot;attraction / stadium&quot;</td><td>1</td></tr><tr><td>&quot;radome&quot;</td><td>1</td></tr><tr><td>&quot;social_facility / static_caravan&quot;</td><td>1</td></tr><tr><td>&quot;cake_supplies / yes&quot;</td><td>1</td></tr><tr><td>&quot;rope_ladder&quot;</td><td>1</td></tr><tr><td>&quot;packaging_supplies&quot;</td><td>1</td></tr><tr><td>&quot;locksmith / office&quot;</td><td>1</td></tr><tr><td>&quot;fitness_centre / container&quot;</td><td>1</td></tr><tr><td>&quot;trampoline_park / leisure&quot;</td><td>1</td></tr><tr><td>&quot;vacant / garden / retail&quot;</td><td>1</td></tr><tr><td>&quot;miniature_golf / retail&quot;</td><td>1</td></tr><tr><td>&quot;acupuncture;herbalist / shop&quot;</td><td>1</td></tr><tr><td>&quot;Elec Sub-station&quot;</td><td>1</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (10_926, 2)\n",
+       "┌──────────────────────────────────┬─────────┐\n",
+       "│ category                         ┆ count   │\n",
+       "│ ---                              ┆ ---     │\n",
+       "│ str                              ┆ u32     │\n",
+       "╞══════════════════════════════════╪═════════╡\n",
+       "│ yes                              ┆ 6149240 │\n",
+       "│ house                            ┆ 4867889 │\n",
+       "│ residential                      ┆ 2975285 │\n",
+       "│ service                          ┆ 2397521 │\n",
+       "│ footway                          ┆ 1690239 │\n",
+       "│ semidetached_house               ┆ 710064  │\n",
+       "│ garage                           ┆ 536802  │\n",
+       "│ track                            ┆ 506554  │\n",
+       "│ detached                         ┆ 451014  │\n",
+       "│ path                             ┆ 419601  │\n",
+       "│ unclassified                     ┆ 320382  │\n",
+       "│ crossing                         ┆ 318014  │\n",
+       "│ garden                           ┆ 308411  │\n",
+       "│ parking                          ┆ 272518  │\n",
+       "│ bus_stop / platform              ┆ 242090  │\n",
+       "│ terrace                          ┆ 219344  │\n",
+       "│ tertiary                         ┆ 216299  │\n",
+       "│ parking_space                    ┆ 206980  │\n",
+       "│ apartments                       ┆ 175306  │\n",
+       "│ cycleway                         ┆ 173504  │\n",
+       "│ primary                          ┆ 152330  │\n",
+       "│ bench                            ┆ 149125  │\n",
+       "│ street_lamp                      ┆ 120327  │\n",
+       "│ give_way                         ┆ 118935  │\n",
+       "│ pitch                            ┆ 117031  │\n",
+       "│ …                                ┆ …       │\n",
+       "│ cage                             ┆ 1       │\n",
+       "│ post_office / appliance          ┆ 1       │\n",
+       "│ club / commercial                ┆ 1       │\n",
+       "│ home_help_service_agency         ┆ 1       │\n",
+       "│ decoration                       ┆ 1       │\n",
+       "│ picnic_table / viewpoint         ┆ 1       │\n",
+       "│ bird_hide / tower                ┆ 1       │\n",
+       "│ entertainment / yes              ┆ 1       │\n",
+       "│ terminal / aerohangar            ┆ 1       │\n",
+       "│ valeting / yes                   ┆ 1       │\n",
+       "│ Waste Disposal Services          ┆ 1       │\n",
+       "│ museum / tourism                 ┆ 1       │\n",
+       "│ attraction / stadium             ┆ 1       │\n",
+       "│ radome                           ┆ 1       │\n",
+       "│ social_facility / static_caravan ┆ 1       │\n",
+       "│ cake_supplies / yes              ┆ 1       │\n",
+       "│ rope_ladder                      ┆ 1       │\n",
+       "│ packaging_supplies               ┆ 1       │\n",
+       "│ locksmith / office               ┆ 1       │\n",
+       "│ fitness_centre / container       ┆ 1       │\n",
+       "│ trampoline_park / leisure        ┆ 1       │\n",
+       "│ vacant / garden / retail         ┆ 1       │\n",
+       "│ miniature_golf / retail          ┆ 1       │\n",
+       "│ acupuncture;herbalist / shop     ┆ 1       │\n",
+       "│ Elec Sub-station                 ┆ 1       │\n",
+       "└──────────────────────────────────┴─────────┘"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "category_counts = (\n",
+    "    df.group_by(\"category\")\n",
+    "    .agg(pl.len().alias(\"count\"))\n",
+    "    .sort(\"count\", descending=True)\n",
+    ")\n",
+    "print(f\"Unique categories: {len(category_counts):,}\")\n",
+    "category_counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
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+         "legendgroup": "6149240",
+         "marker": {
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+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "6149240",
+         "orientation": "h",
+         "showlegend": true,
+         "textposition": "auto",
+         "type": "bar",
+         "x": {
+          "bdata": "eNRdAA==",
+          "dtype": "u4"
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+         "xaxis": "x",
+         "y": [
+          "yes"
+         ],
+         "yaxis": "y"
+        },
+        {
+         "hovertemplate": "Number of POIs=%{x}<br>Category=%{y}<extra></extra>",
+         "legendgroup": "4867889",
+         "marker": {
+          "color": "#EF553B",
+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "4867889",
+         "orientation": "h",
+         "showlegend": true,
+         "textposition": "auto",
+         "type": "bar",
+         "x": {
+          "bdata": "MUdKAA==",
+          "dtype": "u4"
+         },
+         "xaxis": "x",
+         "y": [
+          "house"
+         ],
+         "yaxis": "y"
+        },
+        {
+         "hovertemplate": "Number of POIs=%{x}<br>Category=%{y}<extra></extra>",
+         "legendgroup": "2975285",
+         "marker": {
+          "color": "#00cc96",
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+         },
+         "name": "2975285",
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+         "textposition": "auto",
+         "type": "bar",
+         "x": {
+          "bdata": "NWYtAA==",
+          "dtype": "u4"
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+         "xaxis": "x",
+         "y": [
+          "residential"
+         ],
+         "yaxis": "y"
+        },
+        {
+         "hovertemplate": "Number of POIs=%{x}<br>Category=%{y}<extra></extra>",
+         "legendgroup": "2397521",
+         "marker": {
+          "color": "#ab63fa",
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+         "name": "2397521",
+         "orientation": "h",
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+         "textposition": "auto",
+         "type": "bar",
+         "x": {
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+         "xaxis": "x",
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+          "service"
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+         "yaxis": "y"
+        },
+        {
+         "hovertemplate": "Number of POIs=%{x}<br>Category=%{y}<extra></extra>",
+         "legendgroup": "1690239",
+         "marker": {
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+          "pattern": {
+           "shape": ""
+          }
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+         "name": "1690239",
+         "orientation": "h",
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+         "textposition": "auto",
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+         "xaxis": "x",
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+          "footway"
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+         "yaxis": "y"
+        },
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+         "legendgroup": "710064",
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+         "orientation": "h",
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+         "textposition": "auto",
+         "type": "bar",
+         "x": {
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+          "dtype": "u4"
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+         "xaxis": "x",
+         "y": [
+          "semidetached_house"
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+         "yaxis": "y"
+        },
+        {
+         "hovertemplate": "Number of POIs=%{x}<br>Category=%{y}<extra></extra>",
+         "legendgroup": "536802",
+         "marker": {
+          "color": "#FF6692",
+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "536802",
+         "orientation": "h",
+         "showlegend": true,
+         "textposition": "auto",
+         "type": "bar",
+         "x": {
+          "bdata": "4jAIAA==",
+          "dtype": "u4"
+         },
+         "xaxis": "x",
+         "y": [
+          "garage"
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+         "yaxis": "y"
+        },
+        {
+         "hovertemplate": "Number of POIs=%{x}<br>Category=%{y}<extra></extra>",
+         "legendgroup": "506554",
+         "marker": {
+          "color": "#B6E880",
+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "506554",
+         "orientation": "h",
+         "showlegend": true,
+         "textposition": "auto",
+         "type": "bar",
+         "x": {
+          "bdata": "uroHAA==",
+          "dtype": "u4"
+         },
+         "xaxis": "x",
+         "y": [
+          "track"
+         ],
+         "yaxis": "y"
+        },
+        {
+         "hovertemplate": "Number of POIs=%{x}<br>Category=%{y}<extra></extra>",
+         "legendgroup": "451014",
+         "marker": {
+          "color": "#FF97FF",
+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "451014",
+         "orientation": "h",
+         "showlegend": true,
+         "textposition": "auto",
+         "type": "bar",
+         "x": {
+          "bdata": "xuEGAA==",
+          "dtype": "u4"
+         },
+         "xaxis": "x",
+         "y": [
+          "detached"
+         ],
+         "yaxis": "y"
+        },
+        {
+         "hovertemplate": "Number of POIs=%{x}<br>Category=%{y}<extra></extra>",
+         "legendgroup": "419601",
+         "marker": {
+          "color": "#FECB52",
+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "419601",
+         "orientation": "h",
+         "showlegend": true,
+         "textposition": "auto",
+         "type": "bar",
+         "x": {
+          "bdata": "EWcGAA==",
+          "dtype": "u4"
+         },
+         "xaxis": "x",
+         "y": [
+          "path"
+         ],
+         "yaxis": "y"
+        },
+        {
+         "hovertemplate": "Number of POIs=%{x}<br>Category=%{y}<extra></extra>",
+         "legendgroup": "320382",
+         "marker": {
+          "color": "#636efa",
+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "320382",
+         "orientation": "h",
+         "showlegend": true,
+         "textposition": "auto",
+         "type": "bar",
+         "x": {
+          "bdata": "fuMEAA==",
+          "dtype": "u4"
+         },
+         "xaxis": "x",
+         "y": [
+          "unclassified"
+         ],
+         "yaxis": "y"
+        },
+        {
+         "hovertemplate": "Number of POIs=%{x}<br>Category=%{y}<extra></extra>",
+         "legendgroup": "318014",
+         "marker": {
+          "color": "#EF553B",
+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "318014",
+         "orientation": "h",
+         "showlegend": true,
+         "textposition": "auto",
+         "type": "bar",
+         "x": {
+          "bdata": "PtoEAA==",
+          "dtype": "u4"
+         },
+         "xaxis": "x",
+         "y": [
+          "crossing"
+         ],
+         "yaxis": "y"
+        },
+        {
+         "hovertemplate": "Number of POIs=%{x}<br>Category=%{y}<extra></extra>",
+         "legendgroup": "308411",
+         "marker": {
+          "color": "#00cc96",
+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "308411",
+         "orientation": "h",
+         "showlegend": true,
+         "textposition": "auto",
+         "type": "bar",
+         "x": {
+          "bdata": "u7QEAA==",
+          "dtype": "u4"
+         },
+         "xaxis": "x",
+         "y": [
+          "garden"
+         ],
+         "yaxis": "y"
+        },
+        {
+         "hovertemplate": "Number of POIs=%{x}<br>Category=%{y}<extra></extra>",
+         "legendgroup": "272518",
+         "marker": {
+          "color": "#ab63fa",
+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "272518",
+         "orientation": "h",
+         "showlegend": true,
+         "textposition": "auto",
+         "type": "bar",
+         "x": {
+          "bdata": "higEAA==",
+          "dtype": "u4"
+         },
+         "xaxis": "x",
+         "y": [
+          "parking"
+         ],
+         "yaxis": "y"
+        },
+        {
+         "hovertemplate": "Number of POIs=%{x}<br>Category=%{y}<extra></extra>",
+         "legendgroup": "242090",
+         "marker": {
+          "color": "#FFA15A",
+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "242090",
+         "orientation": "h",
+         "showlegend": true,
+         "textposition": "auto",
+         "type": "bar",
+         "x": {
+          "bdata": "qrEDAA==",
+          "dtype": "u4"
+         },
+         "xaxis": "x",
+         "y": [
+          "bus_stop / platform"
+         ],
+         "yaxis": "y"
+        },
+        {
+         "hovertemplate": "Number of POIs=%{x}<br>Category=%{y}<extra></extra>",
+         "legendgroup": "219344",
+         "marker": {
+          "color": "#19d3f3",
+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "219344",
+         "orientation": "h",
+         "showlegend": true,
+         "textposition": "auto",
+         "type": "bar",
+         "x": {
+          "bdata": "0FgDAA==",
+          "dtype": "u4"
+         },
+         "xaxis": "x",
+         "y": [
+          "terrace"
+         ],
+         "yaxis": "y"
+        },
+        {
+         "hovertemplate": "Number of POIs=%{x}<br>Category=%{y}<extra></extra>",
+         "legendgroup": "216299",
+         "marker": {
+          "color": "#FF6692",
+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "216299",
+         "orientation": "h",
+         "showlegend": true,
+         "textposition": "auto",
+         "type": "bar",
+         "x": {
+          "bdata": "60wDAA==",
+          "dtype": "u4"
+         },
+         "xaxis": "x",
+         "y": [
+          "tertiary"
+         ],
+         "yaxis": "y"
+        },
+        {
+         "hovertemplate": "Number of POIs=%{x}<br>Category=%{y}<extra></extra>",
+         "legendgroup": "206980",
+         "marker": {
+          "color": "#B6E880",
+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "206980",
+         "orientation": "h",
+         "showlegend": true,
+         "textposition": "auto",
+         "type": "bar",
+         "x": {
+          "bdata": "hCgDAA==",
+          "dtype": "u4"
+         },
+         "xaxis": "x",
+         "y": [
+          "parking_space"
+         ],
+         "yaxis": "y"
+        },
+        {
+         "hovertemplate": "Number of POIs=%{x}<br>Category=%{y}<extra></extra>",
+         "legendgroup": "175306",
+         "marker": {
+          "color": "#FF97FF",
+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "175306",
+         "orientation": "h",
+         "showlegend": true,
+         "textposition": "auto",
+         "type": "bar",
+         "x": {
+          "bdata": "yqwCAA==",
+          "dtype": "u4"
+         },
+         "xaxis": "x",
+         "y": [
+          "apartments"
+         ],
+         "yaxis": "y"
+        },
+        {
+         "hovertemplate": "Number of POIs=%{x}<br>Category=%{y}<extra></extra>",
+         "legendgroup": "173504",
+         "marker": {
+          "color": "#FECB52",
+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "173504",
+         "orientation": "h",
+         "showlegend": true,
+         "textposition": "auto",
+         "type": "bar",
+         "x": {
+          "bdata": "wKUCAA==",
+          "dtype": "u4"
+         },
+         "xaxis": "x",
+         "y": [
+          "cycleway"
+         ],
+         "yaxis": "y"
+        },
+        {
+         "hovertemplate": "Number of POIs=%{x}<br>Category=%{y}<extra></extra>",
+         "legendgroup": "152330",
+         "marker": {
+          "color": "#636efa",
+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "152330",
+         "orientation": "h",
+         "showlegend": true,
+         "textposition": "auto",
+         "type": "bar",
+         "x": {
+          "bdata": "ClMCAA==",
+          "dtype": "u4"
+         },
+         "xaxis": "x",
+         "y": [
+          "primary"
+         ],
+         "yaxis": "y"
+        },
+        {
+         "hovertemplate": "Number of POIs=%{x}<br>Category=%{y}<extra></extra>",
+         "legendgroup": "149125",
+         "marker": {
+          "color": "#EF553B",
+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "149125",
+         "orientation": "h",
+         "showlegend": true,
+         "textposition": "auto",
+         "type": "bar",
+         "x": {
+          "bdata": "hUYCAA==",
+          "dtype": "u4"
+         },
+         "xaxis": "x",
+         "y": [
+          "bench"
+         ],
+         "yaxis": "y"
+        },
+        {
+         "hovertemplate": "Number of POIs=%{x}<br>Category=%{y}<extra></extra>",
+         "legendgroup": "120327",
+         "marker": {
+          "color": "#00cc96",
+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "120327",
+         "orientation": "h",
+         "showlegend": true,
+         "textposition": "auto",
+         "type": "bar",
+         "x": {
+          "bdata": "B9YBAA==",
+          "dtype": "u4"
+         },
+         "xaxis": "x",
+         "y": [
+          "street_lamp"
+         ],
+         "yaxis": "y"
+        },
+        {
+         "hovertemplate": "Number of POIs=%{x}<br>Category=%{y}<extra></extra>",
+         "legendgroup": "118935",
+         "marker": {
+          "color": "#ab63fa",
+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "118935",
+         "orientation": "h",
+         "showlegend": true,
+         "textposition": "auto",
+         "type": "bar",
+         "x": {
+          "bdata": "l9ABAA==",
+          "dtype": "u4"
+         },
+         "xaxis": "x",
+         "y": [
+          "give_way"
+         ],
+         "yaxis": "y"
+        },
+        {
+         "hovertemplate": "Number of POIs=%{x}<br>Category=%{y}<extra></extra>",
+         "legendgroup": "117031",
+         "marker": {
+          "color": "#FFA15A",
+          "pattern": {
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+          }
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+         "orientation": "h",
+         "showlegend": true,
+         "textposition": "auto",
+         "type": "bar",
+         "x": {
+          "bdata": "J8kBAA==",
+          "dtype": "u4"
+         },
+         "xaxis": "x",
+         "y": [
+          "pitch"
+         ],
+         "yaxis": "y"
+        },
+        {
+         "hovertemplate": "Number of POIs=%{x}<br>Category=%{y}<extra></extra>",
+         "legendgroup": "108357",
+         "marker": {
+          "color": "#19d3f3",
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+         "orientation": "h",
+         "showlegend": true,
+         "textposition": "auto",
+         "type": "bar",
+         "x": {
+          "bdata": "RacBAA==",
+          "dtype": "u4"
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+         "xaxis": "x",
+         "y": [
+          "trunk"
+         ],
+         "yaxis": "y"
+        },
+        {
+         "hovertemplate": "Number of POIs=%{x}<br>Category=%{y}<extra></extra>",
+         "legendgroup": "103815",
+         "marker": {
+          "color": "#FF6692",
+          "pattern": {
+           "shape": ""
+          }
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+         "name": "103815",
+         "orientation": "h",
+         "showlegend": true,
+         "textposition": "auto",
+         "type": "bar",
+         "x": {
+          "bdata": "h5UBAA==",
+          "dtype": "u4"
+         },
+         "xaxis": "x",
+         "y": [
+          "secondary"
+         ],
+         "yaxis": "y"
+        },
+        {
+         "hovertemplate": "Number of POIs=%{x}<br>Category=%{y}<extra></extra>",
+         "legendgroup": "103072",
+         "marker": {
+          "color": "#B6E880",
+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "103072",
+         "orientation": "h",
+         "showlegend": true,
+         "textposition": "auto",
+         "type": "bar",
+         "x": {
+          "bdata": "oJIBAA==",
+          "dtype": "u4"
+         },
+         "xaxis": "x",
+         "y": [
+          "steps"
+         ],
+         "yaxis": "y"
+        },
+        {
+         "hovertemplate": "Number of POIs=%{x}<br>Category=%{y}<extra></extra>",
+         "legendgroup": "88906",
+         "marker": {
+          "color": "#FF97FF",
+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "88906",
+         "orientation": "h",
+         "showlegend": true,
+         "textposition": "auto",
+         "type": "bar",
+         "x": {
+          "bdata": "SlsBAA==",
+          "dtype": "u4"
+         },
+         "xaxis": "x",
+         "y": [
+          "static_caravan"
+         ],
+         "yaxis": "y"
+        },
+        {
+         "hovertemplate": "Number of POIs=%{x}<br>Category=%{y}<extra></extra>",
+         "legendgroup": "88103",
+         "marker": {
+          "color": "#FECB52",
+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "88103",
+         "orientation": "h",
+         "showlegend": true,
+         "textposition": "auto",
+         "type": "bar",
+         "x": {
+          "bdata": "J1gBAA==",
+          "dtype": "u4"
+         },
+         "xaxis": "x",
+         "y": [
+          "rail"
+         ],
+         "yaxis": "y"
+        },
+        {
+         "hovertemplate": "Number of POIs=%{x}<br>Category=%{y}<extra></extra>",
+         "legendgroup": "87937",
+         "marker": {
+          "color": "#636efa",
+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "87937",
+         "orientation": "h",
+         "showlegend": true,
+         "textposition": "auto",
+         "type": "bar",
+         "x": {
+          "bdata": "gVcBAA==",
+          "dtype": "u4"
+         },
+         "xaxis": "x",
+         "y": [
+          "post_box"
+         ],
+         "yaxis": "y"
+        },
+        {
+         "hovertemplate": "Number of POIs=%{x}<br>Category=%{y}<extra></extra>",
+         "legendgroup": "81487",
+         "marker": {
+          "color": "#EF553B",
+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "81487",
+         "orientation": "h",
+         "showlegend": true,
+         "textposition": "auto",
+         "type": "bar",
+         "x": {
+          "bdata": "Tz4BAA==",
+          "dtype": "u4"
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+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "Top 40 POI Categories by Count"
+        },
+        "xaxis": {
+         "anchor": "y",
+         "categoryarray": [
+          6149240,
+          4867889,
+          2975285,
+          2397521,
+          1690239,
+          710064,
+          536802,
+          506554,
+          451014,
+          419601,
+          320382,
+          318014,
+          308411,
+          272518,
+          242090,
+          219344,
+          216299,
+          206980,
+          175306,
+          173504,
+          152330,
+          149125,
+          120327,
+          118935,
+          117031,
+          108357,
+          103815,
+          103072,
+          88906,
+          88103,
+          87937,
+          81487,
+          77276,
+          76916,
+          74586,
+          73465,
+          71613,
+          70511,
+          69933,
+          61426
+         ],
+         "categoryorder": "array",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "Number of POIs"
+         }
+        },
+        "yaxis": {
+         "anchor": "x",
+         "categoryorder": "total ascending",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "Category"
+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "top_n = 40\n",
+    "fig = px.bar(\n",
+    "    category_counts.head(top_n).to_pandas(),\n",
+    "    x=\"count\",\n",
+    "    y=\"category\",\n",
+    "    orientation=\"h\",\n",
+    "    title=f\"Top {top_n} POI Categories by Count\",\n",
+    "    labels={\"count\": \"Number of POIs\", \"category\": \"Category\"},\n",
+    "    color=\"count\",\n",
+    "    color_continuous_scale=\"Blues\",\n",
+    ")\n",
+    "fig.update_layout(yaxis={\"categoryorder\": \"total ascending\"}, height=900)\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 2. Long Tail Distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "hovertemplate": "Category Rank=%{x}<br>Number of POIs=%{y}<extra></extra>",
+         "legendgroup": "",
+         "line": {
+          "color": "#636efa",
+          "dash": "solid"
+         },
+         "marker": {
+          "symbol": "circle"
+         },
+         "mode": "lines",
+         "name": "",
+         "showlegend": false,
+         "type": "scattergl",
+         "x": {
+          "bdata": 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",
+          "dtype": "u4"
+         },
+         "xaxis": "x",
+         "y": {
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+            "ticks": "",
+            "zerolinecolor": "white"
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+            "linecolor": "white",
+            "ticks": ""
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+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
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+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
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+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "POI Category Frequency Distribution (Log Scale)"
+        },
+        "xaxis": {
+         "anchor": "y",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "Category Rank"
+         }
+        },
+        "yaxis": {
+         "anchor": "x",
+         "domain": [
+          0,
+          1
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+         "title": {
+          "text": "Number of POIs"
+         },
+         "type": "log"
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "category_counts_sorted = category_counts.with_row_index(\"rank\", offset=1)\n",
+    "\n",
+    "fig = px.line(\n",
+    "    category_counts_sorted.to_pandas(),\n",
+    "    x=\"rank\",\n",
+    "    y=\"count\",\n",
+    "    title=\"POI Category Frequency Distribution (Log Scale)\",\n",
+    "    labels={\"rank\": \"Category Rank\", \"count\": \"Number of POIs\"},\n",
+    "    log_y=True,\n",
+    ")\n",
+    "fig.update_layout(height=500)\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Categories covering 80% of all POIs: 12\n",
+      "Total unique categories: 10926\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
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+         "hovertemplate": "Category Rank=%{x}<br>Cumulative % of POIs=%{y}<extra></extra>",
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+          "dash": "solid"
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+         "mode": "lines",
+         "name": "",
+         "showlegend": false,
+         "type": "scattergl",
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",
+          "dtype": "u4"
+         },
+         "xaxis": "x",
+         "y": {
+          "bdata": 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+          "scene": {
+           "xaxis": {
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+            "linecolor": "white",
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+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
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+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
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+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
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+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "Cumulative POI Coverage by Category Rank"
+        },
+        "xaxis": {
+         "anchor": "y",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "Category Rank"
+         }
+        },
+        "yaxis": {
+         "anchor": "x",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "Cumulative % of POIs"
+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cumulative = category_counts_sorted.with_columns(\n",
+    "    (pl.col(\"count\").cum_sum() / pl.col(\"count\").sum() * 100).alias(\"cumulative_pct\")\n",
+    ")\n",
+    "\n",
+    "top_80 = cumulative.filter(pl.col(\"cumulative_pct\") <= 80)\n",
+    "print(f\"Categories covering 80% of all POIs: {len(top_80)}\")\n",
+    "print(f\"Total unique categories: {len(cumulative)}\")\n",
+    "\n",
+    "fig = px.line(\n",
+    "    cumulative.to_pandas(),\n",
+    "    x=\"rank\",\n",
+    "    y=\"cumulative_pct\",\n",
+    "    title=\"Cumulative POI Coverage by Category Rank\",\n",
+    "    labels={\"rank\": \"Category Rank\", \"cumulative_pct\": \"Cumulative % of POIs\"},\n",
+    ")\n",
+    "fig.add_hline(y=80, line_dash=\"dash\", line_color=\"red\", annotation_text=\"80%\")\n",
+    "fig.add_hline(y=95, line_dash=\"dash\", line_color=\"orange\", annotation_text=\"95%\")\n",
+    "fig.update_layout(height=500)\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 3. Composite Categories\n",
+    "\n",
+    "Some POIs match multiple OSM tag keys, producing composite categories joined with ` / `."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "POIs with composite categories: 591,594 (2.2%)\n",
+      "Unique composite categories: 7,594\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (30, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>category</th><th>count</th></tr><tr><td>str</td><td>u32</td></tr></thead><tbody><tr><td>&quot;bus_stop / platform&quot;</td><td>242090</td></tr><tr><td>&quot;place_of_worship / church&quot;</td><td>18708</td></tr><tr><td>&quot;pub / yes&quot;</td><td>16104</td></tr><tr><td>&quot;place_of_worship / yes&quot;</td><td>14087</td></tr><tr><td>&quot;chalet / yes&quot;</td><td>9994</td></tr><tr><td>&quot;fast_food / yes&quot;</td><td>8420</td></tr><tr><td>&quot;platform / platform&quot;</td><td>8124</td></tr><tr><td>&quot;restaurant / yes&quot;</td><td>7993</td></tr><tr><td>&quot;convenience / yes&quot;</td><td>7775</td></tr><tr><td>&quot;cafe / yes&quot;</td><td>7130</td></tr><tr><td>&quot;community_centre / yes&quot;</td><td>6704</td></tr><tr><td>&quot;pharmacy / pharmacy&quot;</td><td>5726</td></tr><tr><td>&quot;stop / stop_position&quot;</td><td>5701</td></tr><tr><td>&quot;hairdresser / yes&quot;</td><td>5377</td></tr><tr><td>&quot;shelter / yes&quot;</td><td>5055</td></tr><tr><td>&quot;community_centre / public&quot;</td><td>4851</td></tr><tr><td>&quot;hotel / yes&quot;</td><td>4847</td></tr><tr><td>&quot;car_repair / yes&quot;</td><td>4466</td></tr><tr><td>&quot;social_facility / yes&quot;</td><td>4357</td></tr><tr><td>&quot;supermarket / yes&quot;</td><td>3926</td></tr><tr><td>&quot;toilets / yes&quot;</td><td>3752</td></tr><tr><td>&quot;dentist / dentist&quot;</td><td>3420</td></tr><tr><td>&quot;convenience / retail&quot;</td><td>3254</td></tr><tr><td>&quot;fast_food / retail&quot;</td><td>3159</td></tr><tr><td>&quot;doctors / yes / doctor&quot;</td><td>2946</td></tr><tr><td>&quot;station / station&quot;</td><td>2861</td></tr><tr><td>&quot;car / yes&quot;</td><td>2706</td></tr><tr><td>&quot;yes / yes&quot;</td><td>2661</td></tr><tr><td>&quot;sports_centre / yes&quot;</td><td>2552</td></tr><tr><td>&quot;clothes / yes&quot;</td><td>2352</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (30, 2)\n",
+       "┌───────────────────────────┬────────┐\n",
+       "│ category                  ┆ count  │\n",
+       "│ ---                       ┆ ---    │\n",
+       "│ str                       ┆ u32    │\n",
+       "╞═══════════════════════════╪════════╡\n",
+       "│ bus_stop / platform       ┆ 242090 │\n",
+       "│ place_of_worship / church ┆ 18708  │\n",
+       "│ pub / yes                 ┆ 16104  │\n",
+       "│ place_of_worship / yes    ┆ 14087  │\n",
+       "│ chalet / yes              ┆ 9994   │\n",
+       "│ fast_food / yes           ┆ 8420   │\n",
+       "│ platform / platform       ┆ 8124   │\n",
+       "│ restaurant / yes          ┆ 7993   │\n",
+       "│ convenience / yes         ┆ 7775   │\n",
+       "│ cafe / yes                ┆ 7130   │\n",
+       "│ community_centre / yes    ┆ 6704   │\n",
+       "│ pharmacy / pharmacy       ┆ 5726   │\n",
+       "│ stop / stop_position      ┆ 5701   │\n",
+       "│ hairdresser / yes         ┆ 5377   │\n",
+       "│ shelter / yes             ┆ 5055   │\n",
+       "│ community_centre / public ┆ 4851   │\n",
+       "│ hotel / yes               ┆ 4847   │\n",
+       "│ car_repair / yes          ┆ 4466   │\n",
+       "│ social_facility / yes     ┆ 4357   │\n",
+       "│ supermarket / yes         ┆ 3926   │\n",
+       "│ toilets / yes             ┆ 3752   │\n",
+       "│ dentist / dentist         ┆ 3420   │\n",
+       "│ convenience / retail      ┆ 3254   │\n",
+       "│ fast_food / retail        ┆ 3159   │\n",
+       "│ doctors / yes / doctor    ┆ 2946   │\n",
+       "│ station / station         ┆ 2861   │\n",
+       "│ car / yes                 ┆ 2706   │\n",
+       "│ yes / yes                 ┆ 2661   │\n",
+       "│ sports_centre / yes       ┆ 2552   │\n",
+       "│ clothes / yes             ┆ 2352   │\n",
+       "└───────────────────────────┴────────┘"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "composite = df.filter(pl.col(\"category\").str.contains(\" / \"))\n",
+    "print(f\"POIs with composite categories: {len(composite):,} ({len(composite) / len(df) * 100:.1f}%)\")\n",
+    "\n",
+    "composite_counts = (\n",
+    "    composite.group_by(\"category\")\n",
+    "    .agg(pl.len().alias(\"count\"))\n",
+    "    .sort(\"count\", descending=True)\n",
+    ")\n",
+    "print(f\"Unique composite categories: {len(composite_counts):,}\")\n",
+    "composite_counts.head(30)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 4. Named vs Unnamed POIs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
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+        "barmode": "stack",
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+            "baxis": {
+             "endlinecolor": "#2a3f5f",
+             "gridcolor": "white",
+             "linecolor": "white",
+             "minorgridcolor": "white",
+             "startlinecolor": "#2a3f5f"
+            },
+            "type": "carpet"
+           }
+          ],
+          "choropleth": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
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+            "type": "choropleth"
+           }
+          ],
+          "contour": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
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+             [
+              0.2222222222222222,
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+              0.3333333333333333,
+              "#9c179e"
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+              "#bd3786"
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+             [
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+             [
+              0.7777777777777778,
+              "#fb9f3a"
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+             [
+              0.8888888888888888,
+              "#fdca26"
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+              1,
+              "#f0f921"
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+            "type": "contour"
+           }
+          ],
+          "contourcarpet": [
+           {
+            "colorbar": {
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+             "ticks": ""
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+            "type": "contourcarpet"
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+          ],
+          "heatmap": [
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+            "colorscale": [
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+            "type": "heatmap"
+           }
+          ],
+          "histogram": [
+           {
+            "marker": {
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+            "type": "histogram"
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+          "histogram2d": [
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+            "type": "histogram2d"
+           }
+          ],
+          "histogram2dcontour": [
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+              "#fdca26"
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+             [
+              1,
+              "#f0f921"
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+            ],
+            "type": "histogram2dcontour"
+           }
+          ],
+          "mesh3d": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "type": "mesh3d"
+           }
+          ],
+          "parcoords": [
+           {
+            "line": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "parcoords"
+           }
+          ],
+          "pie": [
+           {
+            "automargin": true,
+            "type": "pie"
+           }
+          ],
+          "scatter": [
+           {
+            "fillpattern": {
+             "fillmode": "overlay",
+             "size": 10,
+             "solidity": 0.2
+            },
+            "type": "scatter"
+           }
+          ],
+          "scatter3d": [
+           {
+            "line": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatter3d"
+           }
+          ],
+          "scattercarpet": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattercarpet"
+           }
+          ],
+          "scattergeo": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattergeo"
+           }
+          ],
+          "scattergl": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattergl"
+           }
+          ],
+          "scattermap": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattermap"
+           }
+          ],
+          "scattermapbox": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattermapbox"
+           }
+          ],
+          "scatterpolar": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatterpolar"
+           }
+          ],
+          "scatterpolargl": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatterpolargl"
+           }
+          ],
+          "scatterternary": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatterternary"
+           }
+          ],
+          "surface": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "surface"
+           }
+          ],
+          "table": [
+           {
+            "cells": {
+             "fill": {
+              "color": "#EBF0F8"
+             },
+             "line": {
+              "color": "white"
+             }
+            },
+            "header": {
+             "fill": {
+              "color": "#C8D4E3"
+             },
+             "line": {
+              "color": "white"
+             }
+            },
+            "type": "table"
+           }
+          ]
+         },
+         "layout": {
+          "annotationdefaults": {
+           "arrowcolor": "#2a3f5f",
+           "arrowhead": 0,
+           "arrowwidth": 1
+          },
+          "autotypenumbers": "strict",
+          "coloraxis": {
+           "colorbar": {
+            "outlinewidth": 0,
+            "ticks": ""
+           }
+          },
+          "colorscale": {
+           "diverging": [
+            [
+             0,
+             "#8e0152"
+            ],
+            [
+             0.1,
+             "#c51b7d"
+            ],
+            [
+             0.2,
+             "#de77ae"
+            ],
+            [
+             0.3,
+             "#f1b6da"
+            ],
+            [
+             0.4,
+             "#fde0ef"
+            ],
+            [
+             0.5,
+             "#f7f7f7"
+            ],
+            [
+             0.6,
+             "#e6f5d0"
+            ],
+            [
+             0.7,
+             "#b8e186"
+            ],
+            [
+             0.8,
+             "#7fbc41"
+            ],
+            [
+             0.9,
+             "#4d9221"
+            ],
+            [
+             1,
+             "#276419"
+            ]
+           ],
+           "sequential": [
+            [
+             0,
+             "#0d0887"
+            ],
+            [
+             0.1111111111111111,
+             "#46039f"
+            ],
+            [
+             0.2222222222222222,
+             "#7201a8"
+            ],
+            [
+             0.3333333333333333,
+             "#9c179e"
+            ],
+            [
+             0.4444444444444444,
+             "#bd3786"
+            ],
+            [
+             0.5555555555555556,
+             "#d8576b"
+            ],
+            [
+             0.6666666666666666,
+             "#ed7953"
+            ],
+            [
+             0.7777777777777778,
+             "#fb9f3a"
+            ],
+            [
+             0.8888888888888888,
+             "#fdca26"
+            ],
+            [
+             1,
+             "#f0f921"
+            ]
+           ],
+           "sequentialminus": [
+            [
+             0,
+             "#0d0887"
+            ],
+            [
+             0.1111111111111111,
+             "#46039f"
+            ],
+            [
+             0.2222222222222222,
+             "#7201a8"
+            ],
+            [
+             0.3333333333333333,
+             "#9c179e"
+            ],
+            [
+             0.4444444444444444,
+             "#bd3786"
+            ],
+            [
+             0.5555555555555556,
+             "#d8576b"
+            ],
+            [
+             0.6666666666666666,
+             "#ed7953"
+            ],
+            [
+             0.7777777777777778,
+             "#fb9f3a"
+            ],
+            [
+             0.8888888888888888,
+             "#fdca26"
+            ],
+            [
+             1,
+             "#f0f921"
+            ]
+           ]
+          },
+          "colorway": [
+           "#636efa",
+           "#EF553B",
+           "#00cc96",
+           "#ab63fa",
+           "#FFA15A",
+           "#19d3f3",
+           "#FF6692",
+           "#B6E880",
+           "#FF97FF",
+           "#FECB52"
+          ],
+          "font": {
+           "color": "#2a3f5f"
+          },
+          "geo": {
+           "bgcolor": "white",
+           "lakecolor": "white",
+           "landcolor": "#E5ECF6",
+           "showlakes": true,
+           "showland": true,
+           "subunitcolor": "white"
+          },
+          "hoverlabel": {
+           "align": "left"
+          },
+          "hovermode": "closest",
+          "mapbox": {
+           "style": "light"
+          },
+          "paper_bgcolor": "white",
+          "plot_bgcolor": "#E5ECF6",
+          "polar": {
+           "angularaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "radialaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "scene": {
+           "xaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "yaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "zaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           }
+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#2a3f5f"
+           }
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "Named vs Unnamed POIs (Top 30 Categories)"
+        },
+        "xaxis": {
+         "anchor": "y",
+         "domain": [
+          0,
+          1
+         ],
+         "tickangle": -45,
+         "title": {
+          "text": "Count"
+         }
+        },
+        "yaxis": {
+         "anchor": "x",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "count"
+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "named_stats = (\n",
+    "    df.with_columns((pl.col(\"name\") != \"\").alias(\"has_name\"))\n",
+    "    .group_by(\"category\", \"has_name\")\n",
+    "    .agg(pl.len().alias(\"count\"))\n",
+    ")\n",
+    "\n",
+    "named_rate = (\n",
+    "    named_stats.pivot(on=\"has_name\", index=\"category\", values=\"count\")\n",
+    "    .rename({\"true\": \"named\", \"false\": \"unnamed\"})\n",
+    "    .with_columns(\n",
+    "        pl.col(\"named\").fill_null(0),\n",
+    "        pl.col(\"unnamed\").fill_null(0),\n",
+    "    )\n",
+    "    .with_columns(\n",
+    "        (pl.col(\"named\") + pl.col(\"unnamed\")).alias(\"total\"),\n",
+    "        (pl.col(\"named\") / (pl.col(\"named\") + pl.col(\"unnamed\")) * 100).alias(\"named_pct\"),\n",
+    "    )\n",
+    "    .sort(\"total\", descending=True)\n",
+    ")\n",
+    "\n",
+    "top_cats = named_rate.head(30)\n",
+    "fig = px.bar(\n",
+    "    top_cats.to_pandas(),\n",
+    "    x=\"category\",\n",
+    "    y=[\"named\", \"unnamed\"],\n",
+    "    title=\"Named vs Unnamed POIs (Top 30 Categories)\",\n",
+    "    labels={\"value\": \"Count\", \"category\": \"Category\"},\n",
+    "    barmode=\"stack\",\n",
+    ")\n",
+    "fig.update_layout(height=600, xaxis_tickangle=-45)\n",
+    "fig.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/pipeline/pois/__init__.py b/pipeline/download/pois/__init__.py
similarity index 100%
rename from pipeline/pois/__init__.py
rename to pipeline/download/pois/__init__.py
diff --git a/pipeline/pois/__main__.py b/pipeline/download/pois/__main__.py
similarity index 58%
rename from pipeline/pois/__main__.py
rename to pipeline/download/pois/__main__.py
index ac6c0b1..091eae3 100644
--- a/pipeline/pois/__main__.py
+++ b/pipeline/download/pois/__main__.py
@@ -1,7 +1,8 @@
-"""Single-pass POI extraction from OSM PBF file using pyosmium."""
-
 import json
+import shutil
 import urllib.request
+from pathlib import Path
+from tempfile import mkdtemp
 
 import osmium
 import polars as pl
@@ -10,21 +11,18 @@ from tqdm import tqdm
 from .config import (
     GB_PBF_FILE,
     GEOFABRIK_GB_URL,
-    OSM_TAG_MAPPING,
     OUTPUT_FILE,
-    TAG_KEYS_TO_CHECK,
+    POI_TAG_KEYS,
     UK_BBOX_EAST,
     UK_BBOX_NORTH,
     UK_BBOX_SOUTH,
     UK_BBOX_WEST,
 )
 
-# Approximate element count for the GB PBF extract (for progress estimation).
-ESTIMATED_ELEMENTS = 500_000_000
+BATCH_SIZE = 50_000
 
 
 def download_pbf() -> None:
-    """Download Great Britain PBF extract from Geofabrik."""
     GB_PBF_FILE.parent.mkdir(parents=True, exist_ok=True)
     tmp = GB_PBF_FILE.with_suffix(".pbf.tmp")
     print(f"Downloading {GEOFABRIK_GB_URL}")
@@ -46,12 +44,14 @@ def download_pbf() -> None:
 
 
 class POIHandler(osmium.SimpleHandler):
-    """Streams OSM data, filters to UK bbox, extracts matching POIs."""
+    """Streams OSM data, filters to UK bbox, extracts matching POIs in batches."""
 
-    def __init__(self, progress: tqdm) -> None:
+    def __init__(self, progress: tqdm, tmp_dir: Path) -> None:
         super().__init__()
-        self.pois: list[dict] = []
-        self._poi_count = 0
+        self._batch: list[dict] = []
+        self._tmp_dir = tmp_dir
+        self._batch_num = 0
+        self.poi_count = 0
         self._progress = progress
 
     def _in_uk(self, lat: float, lon: float) -> bool:
@@ -61,12 +61,8 @@ class POIHandler(osmium.SimpleHandler):
         )
 
     def _match_tags(self, tags: osmium.osm.TagList) -> str | None:
-        for key in TAG_KEYS_TO_CHECK:
-            if key in tags:
-                value = tags[key]
-                if value in TAG_KEYS_TO_CHECK[key]:
-                    return OSM_TAG_MAPPING[(key, value)]
-        return None
+        parts = [tags[key] for key in POI_TAG_KEYS if key in tags]
+        return " / ".join(parts) if parts else None
 
     def _get_name(self, tags: osmium.osm.TagList) -> str:
         return tags.get("name:en", tags.get("name", ""))
@@ -74,10 +70,19 @@ class POIHandler(osmium.SimpleHandler):
     def _tags_to_json(self, tags: osmium.osm.TagList) -> str:
         return json.dumps({tag.k: tag.v for tag in tags})
 
+    def _flush_batch(self) -> None:
+        if not self._batch:
+            return
+        df = pl.DataFrame(self._batch)
+        out = self._tmp_dir / f"batch_{self._batch_num:05d}.parquet"
+        df.write_parquet(out)
+        self._batch_num += 1
+        self._batch.clear()
+
     def _add_poi(
         self, osm_id: str, tags: osmium.osm.TagList, category: str, lat: float, lng: float
     ) -> None:
-        self.pois.append(
+        self._batch.append(
             {
                 "id": osm_id,
                 "name": self._get_name(tags),
@@ -87,8 +92,10 @@ class POIHandler(osmium.SimpleHandler):
                 "osm_tags": self._tags_to_json(tags),
             }
         )
-        self._poi_count += 1
-        self._progress.set_postfix(pois=f"{self._poi_count:,}", refresh=False)
+        self.poi_count += 1
+        self._progress.set_postfix(pois=f"{self.poi_count:,}", refresh=False)
+        if len(self._batch) >= BATCH_SIZE:
+            self._flush_batch()
 
     def _tick(self) -> None:
         self._progress.update(1)
@@ -139,42 +146,52 @@ def main() -> None:
     print(
         f"UK bbox: ({UK_BBOX_WEST}, {UK_BBOX_SOUTH}, {UK_BBOX_EAST}, {UK_BBOX_NORTH})"
     )
-    print(f"Categories: {len(OSM_TAG_MAPPING)}")
+    print(f"Tag keys: {POI_TAG_KEYS}")
     print()
 
-    with tqdm(
-        total=ESTIMATED_ELEMENTS,
-        unit=" elements",
-        unit_scale=True,
-        desc="Streaming",
-        smoothing=0.05,
-        mininterval=1.0,
-    ) as progress:
-        handler = POIHandler(progress)
-        handler.apply_file(str(GB_PBF_FILE), locations=True)
-
-    print(f"Extracted {len(handler.pois):,} POIs")
-
-    if not handler.pois:
-        print("No POIs found.")
+    if OUTPUT_FILE.exists():
+        print("POIs are up-to-date")
         return
 
-    df = pl.DataFrame(handler.pois)
+    tmp_dir = Path(mkdtemp(prefix="pois_"))
+    try:
+        with tqdm(
+            unit=" elements",
+            unit_scale=True,
+            desc="Streaming",
+            smoothing=0.05,
+            mininterval=1.0,
+        ) as progress:
+            handler = POIHandler(progress, tmp_dir)
+            handler.apply_file(str(GB_PBF_FILE), locations=True)
+            handler._flush_batch()  # write any remaining POIs
 
-    OUTPUT_FILE.parent.mkdir(parents=True, exist_ok=True)
-    df.write_parquet(OUTPUT_FILE)
-    print(f"Saved to {OUTPUT_FILE}")
+        print(f"Extracted {handler.poi_count:,} POIs")
 
-    print("\n=== Summary ===")
-    print(f"Total POIs: {len(df):,}")
-    print("\nPOIs by category:")
-    category_counts = (
-        df.group_by("category")
-        .agg(pl.len().alias("count"))
-        .sort("count", descending=True)
-    )
-    for row in category_counts.iter_rows(named=True):
-        print(f"  {row['category']}: {row['count']:,}")
+        batch_files = sorted(tmp_dir.glob("batch_*.parquet"))
+        if not batch_files:
+            print("No POIs found.")
+            return
+
+        df = pl.concat([pl.scan_parquet(f) for f in batch_files])
+
+        OUTPUT_FILE.parent.mkdir(parents=True, exist_ok=True)
+        df.sink_parquet(OUTPUT_FILE)
+        print(f"Saved to {OUTPUT_FILE}")
+
+        print("\n=== Summary ===")
+        print(f"Total POIs: {handler.poi_count:,}")
+        print("\nPOIs by category:")
+        category_counts = (
+            df.group_by("category")
+            .agg(pl.len().alias("count"))
+            .sort("count", descending=True)
+            .collect()
+        )
+        for row in category_counts.iter_rows(named=True):
+            print(f"  {row['category']}: {row['count']:,}")
+    finally:
+        shutil.rmtree(tmp_dir, ignore_errors=True)
 
 
 if __name__ == "__main__":
diff --git a/pipeline/download/pois/config.py b/pipeline/download/pois/config.py
new file mode 100644
index 0000000..2a063c6
--- /dev/null
+++ b/pipeline/download/pois/config.py
@@ -0,0 +1,34 @@
+from pathlib import Path
+
+DATA_DIR = Path("./data_sources")
+GB_PBF_FILE = DATA_DIR / "great-britain-latest.osm.pbf"
+OUTPUT_FILE = DATA_DIR / "uk_pois.parquet"
+
+GEOFABRIK_GB_URL = (
+    "https://download.geofabrik.de/europe/great-britain-latest.osm.pbf"
+)
+
+# UK bounding box (west, south, east, north) — used for way centroid filtering
+UK_BBOX_WEST = -7.57
+UK_BBOX_SOUTH = 49.96
+UK_BBOX_EAST = 1.68
+UK_BBOX_NORTH = 58.64
+
+# OSM tag keys that indicate a POI. Any element with one of these keys is kept,
+# regardless of the specific value.  When multiple keys match, their values are
+# concatenated with " / ".
+POI_TAG_KEYS: list[str] = [
+    "amenity",
+    "shop",
+    "leisure",
+    "tourism",
+    "railway",
+    "aeroway",
+    "highway",
+    "public_transport",
+    "station",
+    "building",
+    "military",
+    "emergency",
+    "healthcare",
+]
diff --git a/pipeline/pois/config.py b/pipeline/pois/config.py
deleted file mode 100644
index 14fb439..0000000
--- a/pipeline/pois/config.py
+++ /dev/null
@@ -1,147 +0,0 @@
-"""Configuration for POI extraction from OpenStreetMap."""
-
-from pathlib import Path
-
-# File paths
-DATA_DIR = Path(__file__).parent.parent.parent / "data_sources"
-GB_PBF_FILE = DATA_DIR / "great-britain-latest.osm.pbf"
-OUTPUT_FILE = DATA_DIR / "uk_pois.parquet"
-
-# Geofabrik download URL for Great Britain (~1.1GB PBF, much faster than planet XML)
-GEOFABRIK_GB_URL = (
-    "https://download.geofabrik.de/europe/great-britain-latest.osm.pbf"
-)
-
-# UK bounding box (west, south, east, north) — used for way centroid filtering
-UK_BBOX_WEST = -7.57
-UK_BBOX_SOUTH = 49.96
-UK_BBOX_EAST = 1.68
-UK_BBOX_NORTH = 58.64
-
-# OSM tag mapping to categories
-# Maps (tag_key, tag_value) -> category name
-OSM_TAG_MAPPING: dict[tuple[str, str], str] = {
-    # Education
-    ("amenity", "school"): "school",
-    ("amenity", "kindergarten"): "preschool",
-    ("amenity", "college"): "college_university",
-    ("amenity", "university"): "college_university",
-    ("amenity", "library"): "library",
-    ("amenity", "language_school"): "school",
-    ("amenity", "music_school"): "school",
-    ("amenity", "driving_school"): "school",
-    # Healthcare
-    ("amenity", "hospital"): "hospital",
-    ("amenity", "clinic"): "public_health_clinic",
-    ("amenity", "doctors"): "doctor",
-    ("amenity", "dentist"): "dentist",
-    ("amenity", "pharmacy"): "pharmacy",
-    ("amenity", "veterinary"): "veterinary",
-    ("amenity", "nursing_home"): "nursing_home",
-    ("amenity", "social_facility"): "social_facility",
-    # Transport
-    ("railway", "station"): "train_station",
-    ("railway", "halt"): "train_station",
-    ("railway", "tram_stop"): "tram_stop",
-    ("amenity", "bus_station"): "bus_station",
-    ("amenity", "ferry_terminal"): "ferry_terminal",
-    ("public_transport", "station"): "train_station",
-    ("public_transport", "stop_position"): "bus_stop",
-    ("station", "subway"): "metro_station",
-    ("station", "light_rail"): "light_rail_station",
-    ("aeroway", "aerodrome"): "airport",
-    ("highway", "bus_stop"): "bus_stop",
-    # Parks & Leisure
-    ("leisure", "park"): "park",
-    ("leisure", "nature_reserve"): "nature_reserve",
-    ("leisure", "dog_park"): "dog_park",
-    ("leisure", "playground"): "playground",
-    ("leisure", "sports_centre"): "sports_centre",
-    ("leisure", "swimming_pool"): "swimming_pool",
-    ("leisure", "fitness_centre"): "gym",
-    ("leisure", "golf_course"): "golf_course",
-    ("leisure", "garden"): "garden",
-    ("leisure", "marina"): "marina",
-    ("boundary", "national_park"): "national_park",
-    # Emergency
-    ("amenity", "police"): "police_department",
-    ("amenity", "fire_station"): "fire_department",
-    # Shopping
-    ("shop", "supermarket"): "supermarket",
-    ("shop", "convenience"): "convenience_store",
-    ("shop", "grocery"): "grocery_store",
-    ("shop", "bakery"): "bakery",
-    ("shop", "butcher"): "butcher",
-    ("shop", "greengrocer"): "greengrocer",
-    ("shop", "deli"): "deli",
-    ("shop", "department_store"): "department_store",
-    ("shop", "clothes"): "clothing_store",
-    ("shop", "shoes"): "shoe_store",
-    ("shop", "electronics"): "electronics_store",
-    ("shop", "hardware"): "hardware_store",
-    ("shop", "furniture"): "furniture_store",
-    ("shop", "car"): "car_dealer",
-    ("shop", "car_repair"): "car_repair",
-    ("shop", "hairdresser"): "hairdresser",
-    ("shop", "beauty"): "beauty_salon",
-    ("shop", "optician"): "optician",
-    ("shop", "newsagent"): "newsagent",
-    ("shop", "books"): "bookshop",
-    ("shop", "charity"): "charity_shop",
-    ("shop", "alcohol"): "off_licence",
-    ("shop", "laundry"): "laundry",
-    ("shop", "dry_cleaning"): "dry_cleaning",
-    ("shop", "mall"): "shopping_centre",
-    # Food & Drink
-    ("amenity", "restaurant"): "restaurant",
-    ("amenity", "cafe"): "cafe",
-    ("amenity", "pub"): "pub",
-    ("amenity", "bar"): "bar",
-    ("amenity", "fast_food"): "fast_food",
-    ("amenity", "food_court"): "food_court",
-    ("amenity", "ice_cream"): "ice_cream",
-    ("amenity", "biergarten"): "beer_garden",
-    # Finance
-    ("amenity", "bank"): "bank",
-    ("amenity", "atm"): "atm",
-    ("amenity", "bureau_de_change"): "bureau_de_change",
-    # Entertainment & Culture
-    ("amenity", "cinema"): "cinema",
-    ("amenity", "theatre"): "theatre",
-    ("amenity", "nightclub"): "nightclub",
-    ("amenity", "community_centre"): "community_centre",
-    ("amenity", "arts_centre"): "arts_centre",
-    ("tourism", "museum"): "museum",
-    ("tourism", "gallery"): "gallery",
-    ("tourism", "attraction"): "attraction",
-    ("tourism", "zoo"): "zoo",
-    ("tourism", "theme_park"): "theme_park",
-    ("tourism", "viewpoint"): "viewpoint",
-    # Accommodation
-    ("tourism", "hotel"): "hotel",
-    ("tourism", "hostel"): "hostel",
-    ("tourism", "guest_house"): "guest_house",
-    ("tourism", "camp_site"): "campsite",
-    ("tourism", "caravan_site"): "caravan_site",
-    # Religion
-    ("amenity", "place_of_worship"): "place_of_worship",
-    # Government & Public
-    ("amenity", "townhall"): "town_hall",
-    ("amenity", "courthouse"): "courthouse",
-    ("amenity", "post_office"): "post_office",
-    ("amenity", "prison"): "prison",
-    ("amenity", "recycling"): "recycling",
-    ("amenity", "waste_disposal"): "waste_disposal",
-    ("amenity", "toilets"): "public_toilets",
-    # Fuel
-    ("amenity", "fuel"): "petrol_station",
-    ("amenity", "charging_station"): "ev_charging",
-    # Parking
-    ("amenity", "parking"): "parking",
-    ("amenity", "bicycle_parking"): "bicycle_parking",
-}
-
-# Build reverse lookup: tag_key -> set of tag_values we care about
-TAG_KEYS_TO_CHECK: dict[str, set[str]] = {}
-for (key, value), _ in OSM_TAG_MAPPING.items():
-    TAG_KEYS_TO_CHECK.setdefault(key, set()).add(value)
diff --git a/uv.lock b/uv.lock
index 9a0b00d..c661144 100644
--- a/uv.lock
+++ b/uv.lock
@@ -627,6 +627,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/0e/61/66938bbb5fc52dbdf84594873d5b51fb1f7c7794e9c0f5bd885f30bc507b/idna-3.11-py3-none-any.whl", hash = "sha256:771a87f49d9defaf64091e6e6fe9c18d4833f140bd19464795bc32d966ca37ea", size = 71008, upload-time = "2025-10-12T14:55:18.883Z" },
 ]
 
+[[package]]
+name = "iniconfig"
+version = "2.3.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/72/34/14ca021ce8e5dfedc35312d08ba8bf51fdd999c576889fc2c24cb97f4f10/iniconfig-2.3.0.tar.gz", hash = "sha256:c76315c77db068650d49c5b56314774a7804df16fee4402c1f19d6d15d8c4730", size = 20503, upload-time = "2025-10-18T21:55:43.219Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/cb/b1/3846dd7f199d53cb17f49cba7e651e9ce294d8497c8c150530ed11865bb8/iniconfig-2.3.0-py3-none-any.whl", hash = "sha256:f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12", size = 7484, upload-time = "2025-10-18T21:55:41.639Z" },
+]
+
 [[package]]
 name = "ipykernel"
 version = "7.1.0"
@@ -1045,6 +1054,47 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/82/3d/14ce75ef66813643812f3093ab17e46d3a206942ce7376d31ec2d36229e7/lark-1.3.1-py3-none-any.whl", hash = "sha256:c629b661023a014c37da873b4ff58a817398d12635d3bbb2c5a03be7fe5d1e12", size = 113151, upload-time = "2025-10-27T18:25:54.882Z" },
 ]
 
+[[package]]
+name = "levenshtein"
+version = "0.27.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "rapidfuzz", marker = "python_full_version < '3.14' and sys_platform == 'linux'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/82/56/dcf68853b062e3b94bdc3d011cc4198779abc5b9dc134146a062920ce2e2/levenshtein-0.27.3.tar.gz", hash = "sha256:1ac326b2c84215795163d8a5af471188918b8797b4953ec87aaba22c9c1f9fc0", size = 393269, upload-time = "2025-11-01T12:14:31.04Z" }
+wheels = [
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+    { url = "https://files.pythonhosted.org/packages/c7/ac/85820f70fed5ecb5f1d9a55f1e1e2090ef62985ef41db289b5ac5ec56e28/rapidfuzz-3.14.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:32eeafa3abce138bb725550c0e228fc7eaeec7059aa8093d9cbbec2b58c2371a", size = 4265011, upload-time = "2025-11-01T11:54:15.087Z" },
+    { url = "https://files.pythonhosted.org/packages/a4/72/3fbf12819fc6afc8ec75a45204013b40979d068971e535a7f3512b05e765/rapidfuzz-3.14.3-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1b86daa7419b5e8b180690efd1fdbac43ff19230803282521c5b5a9c83977655", size = 1382810, upload-time = "2025-11-01T11:54:25.571Z" },
+    { url = "https://files.pythonhosted.org/packages/0f/18/0f1991d59bb7eee28922a00f79d83eafa8c7bfb4e8edebf4af2a160e7196/rapidfuzz-3.14.3-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c7bd1816db05d6c5ffb3a4df0a2b7b56fb8c81ef584d08e37058afa217da91b1", size = 3166349, upload-time = "2025-11-01T11:54:27.195Z" },
+    { url = "https://files.pythonhosted.org/packages/0d/f0/baa958b1989c8f88c78bbb329e969440cf330b5a01a982669986495bb980/rapidfuzz-3.14.3-cp314-cp314t-manylinux_2_31_armv7l.whl", hash = "sha256:33da4bbaf44e9755b0ce192597f3bde7372fe2e381ab305f41b707a95ac57aa7", size = 1214994, upload-time = "2025-11-01T11:54:28.821Z" },
+    { url = "https://files.pythonhosted.org/packages/e4/a0/cd12ec71f9b2519a3954febc5740291cceabc64c87bc6433afcb36259f3b/rapidfuzz-3.14.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:3fecce764cf5a991ee2195a844196da840aba72029b2612f95ac68a8b74946bf", size = 2403919, upload-time = "2025-11-01T11:54:30.393Z" },
+    { url = "https://files.pythonhosted.org/packages/0b/ce/019bd2176c1644098eced4f0595cb4b3ef52e4941ac9a5854f209d0a6e16/rapidfuzz-3.14.3-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:ecd7453e02cf072258c3a6b8e930230d789d5d46cc849503729f9ce475d0e785", size = 2508346, upload-time = "2025-11-01T11:54:32.048Z" },
+    { url = "https://files.pythonhosted.org/packages/23/f8/be16c68e2c9e6c4f23e8f4adbb7bccc9483200087ed28ff76c5312da9b14/rapidfuzz-3.14.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:ea188aa00e9bcae8c8411f006a5f2f06c4607a02f24eab0d8dc58566aa911f35", size = 4274105, upload-time = "2025-11-01T11:54:33.701Z" },
+]
+
 [[package]]
 name = "referencing"
 version = "0.37.0"
@@ -2198,6 +2330,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/6a/9e/2064975477fdc887e47ad42157e214526dcad8f317a948dee17e1659a62f/terminado-0.18.1-py3-none-any.whl", hash = "sha256:a4468e1b37bb318f8a86514f65814e1afc977cf29b3992a4500d9dd305dcceb0", size = 14154, upload-time = "2024-03-12T14:34:36.569Z" },
 ]
 
+[[package]]
+name = "thefuzz"
+version = "0.22.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "rapidfuzz", marker = "python_full_version < '3.14' and sys_platform == 'linux'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/81/4b/d3eb25831590d6d7d38c2f2e3561d3ba41d490dc89cd91d9e65e7c812508/thefuzz-0.22.1.tar.gz", hash = "sha256:7138039a7ecf540da323792d8592ef9902b1d79eb78c147d4f20664de79f3680", size = 19993, upload-time = "2024-01-19T19:18:23.135Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/82/4f/1695e70ceb3604f19eda9908e289c687ea81c4fecef4d90a9d1d0f2f7ae9/thefuzz-0.22.1-py3-none-any.whl", hash = "sha256:59729b33556850b90e1093c4cf9e618af6f2e4c985df193fdf3c5b5cf02ca481", size = 8245, upload-time = "2024-01-19T19:18:20.362Z" },
+]
+
 [[package]]
 name = "tinycss2"
 version = "1.4.0"

From 0cea9b873c932c9f1fa6d55464ea02d2f5c5a22c Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sat, 31 Jan 2026 10:18:31 +0000
Subject: [PATCH 20/62] Stop doing manual caching

---
 download_pois.py                      |  54 ------------
 pipeline/download/arcgis.py           |  33 ++++----
 pipeline/download/deprivation_data.py |  21 ++---
 pipeline/download/pois/__main__.py    | 114 ++++++++------------------
 pipeline/download/pois/config.py      |  24 +++---
 pipeline/download/price_paid.py       |  27 +++---
 6 files changed, 82 insertions(+), 191 deletions(-)
 delete mode 100644 download_pois.py

diff --git a/download_pois.py b/download_pois.py
deleted file mode 100644
index fd7a06d..0000000
--- a/download_pois.py
+++ /dev/null
@@ -1,54 +0,0 @@
-"""Download POI data for the UK from Overture Maps."""
-
-from pathlib import Path
-
-import overturemaps
-import pyarrow as pa
-import pyarrow.parquet as pq
-from tqdm import tqdm
-
-# UK bounding box (west, south, east, north)
-UK_BBOX = (-8.65, 49.86, 1.77, 60.86)
-
-OUTPUT_DIR = Path("data_sources")
-OUTPUT_FILE = OUTPUT_DIR / "uk_pois.parquet"
-
-
-def main():
-    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
-
-    if OUTPUT_FILE.exists():
-        print(f"POI file already exists: {OUTPUT_FILE}")
-        print("Delete it manually to re-download.")
-        return
-
-    print("Downloading UK POI data from Overture Maps...")
-    print(f"Bounding box: {UK_BBOX}")
-    print("This may take several minutes...")
-
-    reader = overturemaps.record_batch_reader("place", bbox=UK_BBOX)
-
-    # Read all batches
-    batches = []
-    with tqdm(desc="Downloading batches", unit=" batches") as pbar:
-        for batch in reader:
-            batches.append(batch)
-            pbar.update(1)
-            pbar.set_postfix(rows=sum(b.num_rows for b in batches))
-
-    if not batches:
-        print("No data found in bounding box!")
-        return
-
-    # Combine batches into a table and write
-    table = pa.Table.from_batches(batches, schema=reader.schema)
-
-    print(f"\nWriting {table.num_rows:,} POIs to {OUTPUT_FILE}...")
-    pq.write_table(table, OUTPUT_FILE)
-
-    print(f"Download complete: {OUTPUT_FILE}")
-    print(f"File size: {OUTPUT_FILE.stat().st_size / 1024 / 1024:.1f} MB")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/pipeline/download/arcgis.py b/pipeline/download/arcgis.py
index cdd284c..1277fc6 100644
--- a/pipeline/download/arcgis.py
+++ b/pipeline/download/arcgis.py
@@ -1,3 +1,5 @@
+import argparse
+import tempfile
 import zipfile
 import httpx
 import polars as pl
@@ -6,12 +8,6 @@ from tqdm import tqdm
 
 URL = "https://www.arcgis.com/sharing/rest/content/items/077631e063eb4e1ab43575d01381ec33/data"
 
-BASE_DATA_PATH = Path("./data_sources")
-BASE_DATA_PATH.mkdir(exist_ok=True)
-DOWNLOAD_PATH = BASE_DATA_PATH / "arcgis_data.zip"
-EXTRACT_PATH = BASE_DATA_PATH / "arcgis_extracted"
-PARQUET_PATH = BASE_DATA_PATH / "arcgis_data.parquet"
-
 
 def download_with_progress(url: str, output_path: Path) -> None:
     with httpx.stream(
@@ -36,8 +32,8 @@ def download_with_progress(url: str, output_path: Path) -> None:
             for chunk in response.iter_bytes(chunk_size=8192):
                 f.write(chunk)
                 pbar.update(len(chunk))
-        return 
-   
+        return
+
 
 
 def extract_zip(zip_path: Path, extract_path: Path) -> None:
@@ -49,23 +45,24 @@ def extract_zip(zip_path: Path, extract_path: Path) -> None:
 
 def convert_to_parquet(data_path: Path, parquet_path: Path) -> None:
     df = pl.scan_csv(data_path / 'Data/NSPL_MAY_2025_UK.csv', try_parse_dates=True)
-    print(f"Columns: {df.columns}")
+    print(f"Columns: {df.collect_schema().names()}")
+    parquet_path.parent.mkdir(parents=True, exist_ok=True)
     df.sink_parquet(parquet_path, compression="zstd")
     print(f"Saved to {parquet_path}")
 
 
 def main() -> None:
-    if PARQUET_PATH.exists():
-        print(f"Parquet already exists at {PARQUET_PATH}, skipping")
-        return
+    parser = argparse.ArgumentParser(description="Download and convert ArcGIS postcode data")
+    parser.add_argument("--output", type=Path, required=True, help="Output parquet file path")
+    args = parser.parse_args()
 
-    if not DOWNLOAD_PATH.exists():
-        download_with_progress(URL, DOWNLOAD_PATH)
-    else:
-        print(f"File already exists at {DOWNLOAD_PATH}, skipping download")
+    with tempfile.TemporaryDirectory() as cache_dir:
+        download_path = Path(cache_dir) / "arcgis_data.zip"
+        extract_path = Path(cache_dir) / "arcgis_extracted"
 
-    extract_zip(DOWNLOAD_PATH, EXTRACT_PATH)
-    convert_to_parquet(EXTRACT_PATH, PARQUET_PATH)
+        download_with_progress(URL, download_path)
+        extract_zip(download_path, extract_path)
+        convert_to_parquet(extract_path, args.output)
 
 if __name__ == "__main__":
     main()
diff --git a/pipeline/download/deprivation_data.py b/pipeline/download/deprivation_data.py
index 6d42180..e73cb96 100644
--- a/pipeline/download/deprivation_data.py
+++ b/pipeline/download/deprivation_data.py
@@ -1,14 +1,11 @@
+import argparse
 import httpx
 import polars as pl
 from pathlib import Path
+import tempfile
 
 URL = "https://assets.publishing.service.gov.uk/media/691ded34513046b952c500bd/File_5_IoD2025_Scores_for_the_Indices_of_Deprivation.xlsx"
 
-BASE_DATA_PATH = Path("./data_sources")
-BASE_DATA_PATH.mkdir(exist_ok=True)
-XLSX_PATH = BASE_DATA_PATH / "IoD2025_Scores.xlsx"
-PARQUET_PATH = BASE_DATA_PATH / "IoD2025_Scores.parquet"
-
 
 def download_file(url: str, output_path: Path) -> None:
     with httpx.stream("GET", url, follow_redirects=True, timeout=60) as response:
@@ -41,17 +38,17 @@ def convert_to_parquet(xlsx_path: Path, parquet_path: Path) -> None:
 
     df.write_parquet(parquet_path, compression="zstd")
     print(f"Saved to {parquet_path}")
-    print(f"Excel size: {xlsx_path.stat().st_size / 1024 / 1024:.1f} MB")
-    print(f"Parquet size: {parquet_path.stat().st_size / 1024 / 1024:.1f} MB")
 
 
 def main() -> None:
-    if not XLSX_PATH.exists():
-        download_file(URL, XLSX_PATH)
-    else:
-        print(f"Excel file already exists at {XLSX_PATH}, skipping download")
+    parser = argparse.ArgumentParser(description="Download and convert Index of Deprivation data")
+    parser.add_argument("--output", type=Path, required=True, help="Output parquet file path")
+    args = parser.parse_args()
 
-    convert_to_parquet(XLSX_PATH, PARQUET_PATH)
+    with tempfile.TemporaryDirectory() as cache_dir:
+        xlsx_path = Path(cache_dir) / "IoD2025_Scores.xlsx"
+        download_file(URL, xlsx_path)
+        convert_to_parquet(xlsx_path, args.output)
 
 
 if __name__ == "__main__":
diff --git a/pipeline/download/pois/__main__.py b/pipeline/download/pois/__main__.py
index 091eae3..7b06c06 100644
--- a/pipeline/download/pois/__main__.py
+++ b/pipeline/download/pois/__main__.py
@@ -1,5 +1,5 @@
-import json
-import shutil
+import argparse
+import tempfile
 import urllib.request
 from pathlib import Path
 from tempfile import mkdtemp
@@ -9,9 +9,9 @@ import polars as pl
 from tqdm import tqdm
 
 from .config import (
-    GB_PBF_FILE,
+    BATCH_SIZE,
     GEOFABRIK_GB_URL,
-    OUTPUT_FILE,
+    MIN_OCCURENCE_COUNT,
     POI_TAG_KEYS,
     UK_BBOX_EAST,
     UK_BBOX_NORTH,
@@ -19,12 +19,11 @@ from .config import (
     UK_BBOX_WEST,
 )
 
-BATCH_SIZE = 50_000
 
 
-def download_pbf() -> None:
-    GB_PBF_FILE.parent.mkdir(parents=True, exist_ok=True)
-    tmp = GB_PBF_FILE.with_suffix(".pbf.tmp")
+def download_pbf(pbf_file: Path) -> None:
+    pbf_file.parent.mkdir(parents=True, exist_ok=True)
+    tmp = pbf_file.with_suffix(".pbf.tmp")
     print(f"Downloading {GEOFABRIK_GB_URL}")
 
     with (
@@ -39,13 +38,11 @@ def download_pbf() -> None:
             f.write(chunk)
             bar.update(len(chunk))
 
-    tmp.rename(GB_PBF_FILE)
-    print(f"Saved to {GB_PBF_FILE}")
+    tmp.rename(pbf_file)
+    print(f"Saved to {pbf_file}")
 
 
 class POIHandler(osmium.SimpleHandler):
-    """Streams OSM data, filters to UK bbox, extracts matching POIs in batches."""
-
     def __init__(self, progress: tqdm, tmp_dir: Path) -> None:
         super().__init__()
         self._batch: list[dict] = []
@@ -60,16 +57,12 @@ class POIHandler(osmium.SimpleHandler):
             and UK_BBOX_WEST <= lon <= UK_BBOX_EAST
         )
 
-    def _match_tags(self, tags: osmium.osm.TagList) -> str | None:
-        parts = [tags[key] for key in POI_TAG_KEYS if key in tags]
-        return " / ".join(parts) if parts else None
+    def _match_tags(self, tags: osmium.osm.TagList) -> list[str]:
+        return [f"{key}/{tags[key]}" for key in POI_TAG_KEYS if key in tags]
 
     def _get_name(self, tags: osmium.osm.TagList) -> str:
         return tags.get("name:en", tags.get("name", ""))
 
-    def _tags_to_json(self, tags: osmium.osm.TagList) -> str:
-        return json.dumps({tag.k: tag.v for tag in tags})
-
     def _flush_batch(self) -> None:
         if not self._batch:
             return
@@ -89,7 +82,6 @@ class POIHandler(osmium.SimpleHandler):
                 "category": category,
                 "lat": lat,
                 "lng": lng,
-                "osm_tags": self._tags_to_json(tags),
             }
         )
         self.poi_count += 1
@@ -107,54 +99,27 @@ class POIHandler(osmium.SimpleHandler):
         lat, lon = n.location.lat, n.location.lon
         if not self._in_uk(lat, lon):
             return
-        category = self._match_tags(n.tags)
-        if category:
+        categories = self._match_tags(n.tags)
+        for category in categories:
             self._add_poi(f"n{n.id}", n.tags, category, lat, lon)
 
-    def way(self, w: osmium.osm.Way) -> None:
-        self._tick()
-        category = self._match_tags(w.tags)
-        if not category:
-            return
-
-        lats = []
-        lons = []
-        for node in w.nodes:
-            try:
-                lats.append(node.location.lat)
-                lons.append(node.location.lon)
-            except osmium.InvalidLocationError:
-                continue
-
-        if not lats:
-            return
-
-        centroid_lat = sum(lats) / len(lats)
-        centroid_lng = sum(lons) / len(lons)
-
-        if not self._in_uk(centroid_lat, centroid_lng):
-            return
-
-        self._add_poi(f"w{w.id}", w.tags, category, centroid_lat, centroid_lng)
-
 
 def main() -> None:
-    if not GB_PBF_FILE.exists():
-        download_pbf()
+    parser = argparse.ArgumentParser(description="Download and extract POIs from OpenStreetMap")
+    parser.add_argument("--output", type=Path, required=True, help="Output parquet file path")
+    args = parser.parse_args()
 
-    print(f"=== POI Extraction from {GB_PBF_FILE} ===")
-    print(
-        f"UK bbox: ({UK_BBOX_WEST}, {UK_BBOX_SOUTH}, {UK_BBOX_EAST}, {UK_BBOX_NORTH})"
-    )
-    print(f"Tag keys: {POI_TAG_KEYS}")
-    print()
+    with tempfile.TemporaryDirectory() as cache_dir:
+        pbf_file = Path(cache_dir) / "great-britain-latest.osm.pbf"
 
-    if OUTPUT_FILE.exists():
-        print("POIs are up-to-date")
-        return
+        if not pbf_file.exists():
+            download_pbf(pbf_file)
+        else:
+            print(f"Using cached PBF file at {pbf_file}")
 
-    tmp_dir = Path(mkdtemp(prefix="pois_"))
-    try:
+        print(f"Tag keys: {POI_TAG_KEYS}")
+
+        tmp_dir = Path(mkdtemp(prefix="pois_"))
         with tqdm(
             unit=" elements",
             unit_scale=True,
@@ -163,35 +128,26 @@ def main() -> None:
             mininterval=1.0,
         ) as progress:
             handler = POIHandler(progress, tmp_dir)
-            handler.apply_file(str(GB_PBF_FILE), locations=True)
+            handler.apply_file(str(pbf_file), locations=True)
             handler._flush_batch()  # write any remaining POIs
 
         print(f"Extracted {handler.poi_count:,} POIs")
 
         batch_files = sorted(tmp_dir.glob("batch_*.parquet"))
-        if not batch_files:
-            print("No POIs found.")
-            return
-
         df = pl.concat([pl.scan_parquet(f) for f in batch_files])
 
-        OUTPUT_FILE.parent.mkdir(parents=True, exist_ok=True)
-        df.sink_parquet(OUTPUT_FILE)
-        print(f"Saved to {OUTPUT_FILE}")
-
-        print("\n=== Summary ===")
-        print(f"Total POIs: {handler.poi_count:,}")
-        print("\nPOIs by category:")
-        category_counts = (
+        # Only keep categories with enough occurrences
+        valid_categories = (
             df.group_by("category")
             .agg(pl.len().alias("count"))
-            .sort("count", descending=True)
-            .collect()
+            .filter(pl.col("count") >= MIN_OCCURENCE_COUNT)
         )
-        for row in category_counts.iter_rows(named=True):
-            print(f"  {row['category']}: {row['count']:,}")
-    finally:
-        shutil.rmtree(tmp_dir, ignore_errors=True)
+        df = df.join(valid_categories.select("category"), on="category", how="semi")
+
+        args.output.parent.mkdir(parents=True, exist_ok=True)
+        df.sink_parquet(args.output)
+        print(f"Saved to {args.output}")
+        print(f"Total POIs: {handler.poi_count:,}")
 
 
 if __name__ == "__main__":
diff --git a/pipeline/download/pois/config.py b/pipeline/download/pois/config.py
index 2a063c6..eb677a6 100644
--- a/pipeline/download/pois/config.py
+++ b/pipeline/download/pois/config.py
@@ -4,31 +4,29 @@ DATA_DIR = Path("./data_sources")
 GB_PBF_FILE = DATA_DIR / "great-britain-latest.osm.pbf"
 OUTPUT_FILE = DATA_DIR / "uk_pois.parquet"
 
+
+BATCH_SIZE = 50_000
+
+MIN_OCCURENCE_COUNT = 20
+
 GEOFABRIK_GB_URL = (
     "https://download.geofabrik.de/europe/great-britain-latest.osm.pbf"
 )
 
-# UK bounding box (west, south, east, north) — used for way centroid filtering
 UK_BBOX_WEST = -7.57
 UK_BBOX_SOUTH = 49.96
 UK_BBOX_EAST = 1.68
 UK_BBOX_NORTH = 58.64
 
-# OSM tag keys that indicate a POI. Any element with one of these keys is kept,
-# regardless of the specific value.  When multiple keys match, their values are
-# concatenated with " / ".
 POI_TAG_KEYS: list[str] = [
     "amenity",
-    "shop",
-    "leisure",
-    "tourism",
-    "railway",
-    "aeroway",
-    "highway",
-    "public_transport",
-    "station",
     "building",
-    "military",
+    "craft",
     "emergency",
     "healthcare",
+    "leisure",
+    "office",
+    "shop",
+    "tourism",
+    "public_transport",
 ]
diff --git a/pipeline/download/price_paid.py b/pipeline/download/price_paid.py
index 38e0f69..fac4ec1 100644
--- a/pipeline/download/price_paid.py
+++ b/pipeline/download/price_paid.py
@@ -1,3 +1,5 @@
+import argparse
+import tempfile
 import httpx
 import polars as pl
 from pathlib import Path
@@ -5,11 +7,6 @@ from tqdm import tqdm
 
 URL = "http://prod.publicdata.landregistry.gov.uk.s3-website-eu-west-1.amazonaws.com/pp-complete.csv"
 
-BASE_DATA_PATH = Path("./data_sources")
-BASE_DATA_PATH.mkdir(exist_ok=True)
-CSV_PATH = BASE_DATA_PATH / "price-paid-complete.csv"
-PARQUET_PATH = BASE_DATA_PATH / "price-paid-complete.parquet"
-
 
 def download_with_progress(url: str, output_path: Path) -> None:
     with httpx.stream(
@@ -36,7 +33,7 @@ def download_with_progress(url: str, output_path: Path) -> None:
                 pbar.update(len(chunk))
         return
 
-      
+
 def convert_to_parquet(csv_path: Path, parquet_path: Path) -> None:
     """Convert CSV to Parquet using Polars."""
     print("Converting to Parquet...")
@@ -69,22 +66,22 @@ def convert_to_parquet(csv_path: Path, parquet_path: Path) -> None:
         try_parse_dates=True,
     )
 
+    parquet_path.parent.mkdir(parents=True, exist_ok=True)
+    print(f"Columns: {df.collect_schema().names()}")
     df.write_parquet(parquet_path, compression="zstd")
     print(f"Saved to {parquet_path}")
-    print(f"Rows: {df.height:,}")
 
 
 def main() -> None:
-    if PARQUET_PATH.exists():
-        print(f"Parquet already exists at {PARQUET_PATH}, skipping")
-        return
+    parser = argparse.ArgumentParser(description="Download and convert Land Registry price-paid data")
+    parser.add_argument("--output", type=Path, required=True, help="Output parquet file path")
+    args = parser.parse_args()
 
-    if not CSV_PATH.exists():
-        download_with_progress(URL, CSV_PATH)
-    else:
-        print(f"CSV already exists at {CSV_PATH}, skipping download")
+    with tempfile.TemporaryDirectory() as cache_dir:
+        csv_path = Path(cache_dir) / "price-paid-complete.csv"
 
-    convert_to_parquet(CSV_PATH, PARQUET_PATH)
+        download_with_progress(URL, csv_path)
+        convert_to_parquet(csv_path, args.output)
 
 
 if __name__ == "__main__":

From bf2d5de156b990450621943295099eb3e83239fc Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sat, 31 Jan 2026 10:18:54 +0000
Subject: [PATCH 21/62] Rewrite server in rust

---
 server-rs/Cargo.lock      | 2740 +++++++++++++++++++++++++++++++++++++
 server-rs/Cargo.toml      |   19 +
 server-rs/src/consts.rs   |   11 +
 server-rs/src/data.rs     |  405 ++++++
 server-rs/src/index.rs    |  130 ++
 server-rs/src/main.rs     |  109 ++
 server-rs/src/routes.rs   |  461 +++++++
 server/__init__.py        |    0
 server/config.py          |   18 -
 server/main.py            |   35 -
 server/routes/__init__.py |    0
 server/routes/hexagons.py |  172 ---
 server/routes/pois.py     |  322 -----
 13 files changed, 3875 insertions(+), 547 deletions(-)
 create mode 100644 server-rs/Cargo.lock
 create mode 100644 server-rs/Cargo.toml
 create mode 100644 server-rs/src/consts.rs
 create mode 100644 server-rs/src/data.rs
 create mode 100644 server-rs/src/index.rs
 create mode 100644 server-rs/src/main.rs
 create mode 100644 server-rs/src/routes.rs
 delete mode 100644 server/__init__.py
 delete mode 100644 server/config.py
 delete mode 100644 server/main.py
 delete mode 100644 server/routes/__init__.py
 delete mode 100644 server/routes/hexagons.py
 delete mode 100644 server/routes/pois.py

diff --git a/server-rs/Cargo.lock b/server-rs/Cargo.lock
new file mode 100644
index 0000000..ddb90e7
--- /dev/null
+++ b/server-rs/Cargo.lock
@@ -0,0 +1,2740 @@
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diff --git a/server-rs/Cargo.toml b/server-rs/Cargo.toml
new file mode 100644
index 0000000..5ddaa7d
--- /dev/null
+++ b/server-rs/Cargo.toml
@@ -0,0 +1,19 @@
+[package]
+name = "property-map-server"
+version = "0.1.0"
+edition = "2021"
+
+[dependencies]
+axum = "0.8"
+tower-http = { version = "0.6", features = ["cors", "fs", "compression-gzip"] }
+tokio = { version = "1", features = ["full"] }
+polars = { version = "0.46", features = ["parquet", "lazy", "dtype-struct", "dtype-u8", "dtype-u16", "dtype-i8", "dtype-i16"] }
+h3o = "0.7"
+serde = { version = "1", features = ["derive"] }
+serde_json = "1"
+rayon = "1"
+rustc-hash = "2"
+
+[profile.release]
+opt-level = 3
+lto = true
diff --git a/server-rs/src/consts.rs b/server-rs/src/consts.rs
new file mode 100644
index 0000000..7ccf49e
--- /dev/null
+++ b/server-rs/src/consts.rs
@@ -0,0 +1,11 @@
+/// Lower percentile for feature range reporting
+pub const FEATURE_PERCENTILE_LOW: f64 = 2.0;
+
+/// Upper percentile for feature range reporting
+pub const FEATURE_PERCENTILE_HIGH: f64 = 98.0;
+
+pub const HISTOGRAM_BINS: usize = 100;
+
+/// H3 resolutions to precompute at startup (covers typical zoom levels)
+pub const H3_PRECOMPUTE_MIN: u8 = 4;
+pub const H3_PRECOMPUTE_MAX: u8 = 12;
diff --git a/server-rs/src/data.rs b/server-rs/src/data.rs
new file mode 100644
index 0000000..16928c3
--- /dev/null
+++ b/server-rs/src/data.rs
@@ -0,0 +1,405 @@
+use polars::prelude::*;
+use polars::lazy::frame::LazyFrame;
+use rayon::prelude::*;
+use serde::Serialize;
+use std::path::Path;
+
+use crate::consts::{FEATURE_PERCENTILE_LOW, FEATURE_PERCENTILE_HIGH, HISTOGRAM_BINS, H3_PRECOMPUTE_MIN, H3_PRECOMPUTE_MAX};
+
+/// Columns to exclude from feature discovery (not numeric features)
+const EXCLUDED_COLUMNS: &[&str] = &["lat", "lon"];
+
+/// H3 valid resolution range (0-15)
+pub const MIN_RESOLUTION: u8 = 0;
+pub const MAX_RESOLUTION: u8 = 15;
+pub const DEFAULT_RESOLUTION: u8 = 8;
+
+/// Returns true if the polars DataType is numeric (integer or float)
+fn is_numeric_dtype(dtype: &DataType) -> bool {
+    matches!(
+        dtype,
+        DataType::Int8
+            | DataType::Int16
+            | DataType::Int32
+            | DataType::Int64
+            | DataType::UInt8
+            | DataType::UInt16
+            | DataType::UInt32
+            | DataType::UInt64
+            | DataType::Float32
+            | DataType::Float64
+    )
+}
+
+/// Histogram for a single feature column
+#[derive(Serialize, Clone)]
+pub struct Histogram {
+    /// Left edge of first bin
+    pub min: f64,
+    /// Right edge of last bin
+    pub max: f64,
+    /// Width of each bin
+    pub bin_width: f64,
+    /// Count of values in each bin
+    pub counts: Vec<u64>,
+}
+
+/// Precomputed statistics for a single feature
+pub struct FeatureStats {
+    pub p_low: f64,
+    pub p_high: f64,
+    pub histogram: Histogram,
+}
+
+/// Columnar storage for all property data.
+/// Feature values use NaN as the null sentinel.
+pub struct PropertyData {
+    pub lat: Vec<f64>,
+    pub lon: Vec<f64>,
+    /// Dynamically discovered numeric feature column names
+    pub feature_names: Vec<String>,
+    /// Number of feature columns
+    pub num_features: usize,
+    /// Row-major flat array: feature_data[row * num_features + feat_idx].
+    /// NaN = null. Contiguous layout for cache-friendly per-row access.
+    pub feature_data: Vec<f64>,
+    /// Precomputed stats (percentiles + histogram) for each feature
+    pub feature_stats: Vec<FeatureStats>,
+}
+
+/// Approximate a percentile from a histogram using linear interpolation.
+/// `p` is in [0, 100]. `total` is the sum of all bin counts.
+fn percentile_from_histogram(counts: &[u64], min: f64, bin_width: f64, total: usize, p: f64) -> f64 {
+    let target = (p / 100.0) * (total as f64 - 1.0);
+    let mut cumulative = 0u64;
+    for (i, &c) in counts.iter().enumerate() {
+        let prev = cumulative;
+        cumulative += c;
+        if cumulative as f64 > target {
+            // Interpolate within this bin
+            let frac = if c > 0 {
+                (target - prev as f64) / c as f64
+            } else {
+                0.0
+            };
+            return min + (i as f64 + frac) * bin_width;
+        }
+    }
+    // Fallback: right edge of last bin
+    min + counts.len() as f64 * bin_width
+}
+
+/// Build a histogram and compute approximate percentiles in O(n) — no sort needed.
+fn compute_feature_stats(vals: &[f64]) -> FeatureStats {
+    // Single pass: min, max, count (skipping NaN)
+    let mut min = f64::INFINITY;
+    let mut max = f64::NEG_INFINITY;
+    let mut count = 0usize;
+    for &v in vals {
+        if !v.is_nan() {
+            if v < min { min = v; }
+            if v > max { max = v; }
+            count += 1;
+        }
+    }
+
+    if count == 0 {
+        return FeatureStats {
+            p_low: 0.0,
+            p_high: 0.0,
+            histogram: Histogram {
+                min: 0.0,
+                max: 0.0,
+                bin_width: 1.0,
+                counts: vec![0; HISTOGRAM_BINS],
+            },
+        };
+    }
+
+    // Build histogram over full range (second pass, no sort)
+    let range = if max == min { 1.0 } else { max - min };
+    let bin_max = min + range * (1.0 + 1e-9);
+    let bin_width = (bin_max - min) / HISTOGRAM_BINS as f64;
+
+    let mut counts = vec![0u64; HISTOGRAM_BINS];
+    for &v in vals {
+        if !v.is_nan() {
+            let bin = ((v - min) / bin_width) as usize;
+            counts[bin.min(HISTOGRAM_BINS - 1)] += 1;
+        }
+    }
+
+    // Approximate percentiles from the histogram
+    let p_low = percentile_from_histogram(&counts, min, bin_width, count, FEATURE_PERCENTILE_LOW);
+    let p_high = percentile_from_histogram(&counts, min, bin_width, count, FEATURE_PERCENTILE_HIGH);
+
+    FeatureStats {
+        p_low,
+        p_high,
+        histogram: Histogram {
+            min,
+            max,
+            bin_width,
+            counts,
+        },
+    }
+}
+
+/// Convert a polars Column to Vec<f64> using NaN for null values
+fn column_to_f64_vec(c: &Column) -> Vec<f64> {
+    let s = c.cast(&DataType::Float64).unwrap();
+    let ca = s.f64().unwrap();
+    ca.into_iter().map(|v| v.unwrap_or(f64::NAN)).collect()
+}
+
+/// Precompute H3 cell IDs for all rows at commonly used resolutions.
+/// Returns a Vec indexed by resolution (0..16), where non-precomputed
+/// resolutions have an empty Vec.
+pub fn precompute_h3(lat: &[f64], lon: &[f64]) -> Vec<Vec<u64>> {
+    eprintln!(
+        "Precomputing H3 cells for resolutions {}..{}...",
+        H3_PRECOMPUTE_MIN, H3_PRECOMPUTE_MAX
+    );
+
+    let resolutions: Vec<u8> = (H3_PRECOMPUTE_MIN..=H3_PRECOMPUTE_MAX).collect();
+    let computed: Vec<(u8, Vec<u64>)> = resolutions
+        .into_par_iter()
+        .map(|res| {
+            let h3_res = h3o::Resolution::try_from(res).unwrap();
+            let cells: Vec<u64> = lat
+                .iter()
+                .zip(lon.iter())
+                .map(|(&la, &lo)| {
+                    h3o::LatLng::new(la, lo)
+                        .map(|c| u64::from(c.to_cell(h3_res)))
+                        .unwrap_or(0)
+                })
+                .collect();
+            eprintln!("  Resolution {} done ({} cells)", res, cells.len());
+            (res, cells)
+        })
+        .collect();
+
+    let mut result: Vec<Vec<u64>> = (0..16).map(|_| Vec::new()).collect();
+    for (res, cells) in computed {
+        result[res as usize] = cells;
+    }
+
+    eprintln!("H3 precomputation complete.");
+    result
+}
+
+impl PropertyData {
+    pub fn load(parquet_path: &Path) -> Self {
+        eprintln!("Loading parquet from {:?}...", parquet_path);
+
+        // Scan schema to discover numeric feature columns
+        let mut lf = LazyFrame::scan_parquet(parquet_path, Default::default())
+            .expect("Failed to scan parquet");
+        let schema = lf.collect_schema().expect("Failed to read schema");
+
+        let feature_names: Vec<String> = schema
+            .iter()
+            .filter(|(name, dtype)| {
+                is_numeric_dtype(dtype) && !EXCLUDED_COLUMNS.contains(&name.as_str())
+            })
+            .map(|(name, _)| name.to_string())
+            .collect();
+
+        let num_features = feature_names.len();
+        eprintln!("Discovered {} numeric feature columns", num_features);
+
+        // Read only the columns we need
+        let mut cols_needed: Vec<String> = vec!["lat".into(), "lon".into()];
+        cols_needed.extend(feature_names.iter().cloned());
+
+        let df = LazyFrame::scan_parquet(parquet_path, Default::default())
+            .expect("Failed to scan parquet")
+            .select(
+                cols_needed
+                    .iter()
+                    .map(|c| col(c.as_str()).cast(DataType::Float64))
+                    .collect::<Vec<_>>(),
+            )
+            .collect()
+            .expect("Failed to read parquet");
+
+        let row_count = df.height();
+        eprintln!("Loaded {} rows", row_count);
+
+        // Extract lat/lon using bulk iterator
+        let lat_series = df.column("lat").unwrap().cast(&DataType::Float64).unwrap();
+        let lat: Vec<f64> = lat_series.f64().unwrap().into_iter().map(|v| v.unwrap_or(0.0)).collect();
+
+        let lon_series = df.column("lon").unwrap().cast(&DataType::Float64).unwrap();
+        let lon: Vec<f64> = lon_series.f64().unwrap().into_iter().map(|v| v.unwrap_or(0.0)).collect();
+
+        // Extract feature columns (column-major, for cache-friendly histogram computation)
+        eprintln!("Extracting feature columns...");
+        let col_major: Vec<Vec<f64>> = feature_names
+            .iter()
+            .map(|name| {
+                let s = df.column(name.as_str()).unwrap();
+                column_to_f64_vec(s)
+            })
+            .collect();
+
+        // Compute histograms in parallel (column-major is ideal for per-column iteration)
+        eprintln!("Computing histograms...");
+        let feature_stats: Vec<FeatureStats> = col_major
+            .par_iter()
+            .enumerate()
+            .map(|(i, vals)| {
+                let stats = compute_feature_stats(vals);
+                eprintln!(
+                    "  {}: p{}={:.2}, p{}={:.2}, {} bins",
+                    feature_names[i],
+                    FEATURE_PERCENTILE_LOW, stats.p_low,
+                    FEATURE_PERCENTILE_HIGH, stats.p_high,
+                    stats.histogram.counts.len()
+                );
+                stats
+            })
+            .collect();
+
+        // Sort all rows by spatial locality so that grid queries access
+        // contiguous memory (sequential reads instead of random DRAM accesses).
+        // Uses the same 0.01° grid cell as the spatial index for the sort key.
+        eprintln!("Sorting rows by spatial locality...");
+        let grid_cell_size = 0.01_f64;
+        let min_lat_val = lat.iter().cloned().fold(f64::INFINITY, f64::min) - grid_cell_size;
+        let min_lon_val = lon.iter().cloned().fold(f64::INFINITY, f64::min) - grid_cell_size;
+        let max_lon_val = lon.iter().cloned().fold(f64::NEG_INFINITY, f64::max) + grid_cell_size;
+        let grid_cols = ((max_lon_val - min_lon_val) / grid_cell_size).ceil() as u64 + 1;
+
+        let mut perm: Vec<u32> = (0..row_count as u32).collect();
+        perm.sort_unstable_by_key(|&i| {
+            let r = ((lat[i as usize] - min_lat_val) / grid_cell_size) as u64;
+            let c = ((lon[i as usize] - min_lon_val) / grid_cell_size) as u64;
+            r * grid_cols + c
+        });
+
+        // Apply permutation to lat/lon
+        let lat: Vec<f64> = perm.iter().map(|&i| lat[i as usize]).collect();
+        let lon: Vec<f64> = perm.iter().map(|&i| lon[i as usize]).collect();
+
+        // Transpose to row-major AND apply spatial permutation in one pass.
+        // Result: all features for one row are contiguous, and spatially
+        // nearby rows are adjacent in memory.
+        eprintln!("Transposing to row-major layout (spatially sorted)...");
+        let mut feature_data = vec![f64::NAN; row_count * num_features];
+        for (new_row, &old_row) in perm.iter().enumerate() {
+            let old = old_row as usize;
+            let dst_base = new_row * num_features;
+            for (feat_idx, col_vec) in col_major.iter().enumerate() {
+                feature_data[dst_base + feat_idx] = col_vec[old];
+            }
+        }
+
+        eprintln!("Data loading complete.");
+
+        PropertyData {
+            lat,
+            lon,
+            feature_names,
+            num_features,
+            feature_data,
+            feature_stats,
+        }
+    }
+}
+
+/// Point of Interest data
+#[derive(Serialize)]
+pub struct POI {
+    pub id: String,
+    pub name: String,
+    pub category: String,
+    pub lat: f64,
+    pub lng: f64,
+    pub emoji: String,
+}
+
+/// Columnar storage for POI data
+pub struct POIData {
+    pub id: Vec<String>,
+    pub name: Vec<String>,
+    pub category: Vec<String>,
+    pub lat: Vec<f64>,
+    pub lng: Vec<f64>,
+    pub emoji: Vec<String>,
+}
+
+impl POIData {
+    pub fn load(parquet_path: &Path) -> Self {
+        eprintln!("Loading POI data from {:?}...", parquet_path);
+
+        let df = LazyFrame::scan_parquet(parquet_path, Default::default())
+            .expect("Failed to scan POI parquet")
+            .collect()
+            .expect("Failed to read POI parquet");
+
+        let row_count = df.height();
+        eprintln!("Loaded {} POIs", row_count);
+
+        // Extract columns
+        let id: Vec<String> = df.column("id")
+            .unwrap()
+            .str()
+            .unwrap()
+            .into_iter()
+            .map(|v| v.unwrap_or("").to_string())
+            .collect();
+
+        let name: Vec<String> = df.column("name")
+            .unwrap()
+            .str()
+            .unwrap()
+            .into_iter()
+            .map(|v| v.unwrap_or("").to_string())
+            .collect();
+
+        let category: Vec<String> = df.column("category")
+            .unwrap()
+            .str()
+            .unwrap()
+            .into_iter()
+            .map(|v| v.unwrap_or("").to_string())
+            .collect();
+
+        let lat: Vec<f64> = df.column("lat")
+            .unwrap()
+            .f64()
+            .unwrap()
+            .into_iter()
+            .map(|v| v.unwrap_or(0.0))
+            .collect();
+
+        let lng: Vec<f64> = df.column("lng")
+            .unwrap()
+            .f64()
+            .unwrap()
+            .into_iter()
+            .map(|v| v.unwrap_or(0.0))
+            .collect();
+
+        let emoji: Vec<String> = df.column("emoji")
+            .unwrap()
+            .str()
+            .unwrap()
+            .into_iter()
+            .map(|v| v.unwrap_or("").to_string())
+            .collect();
+
+        eprintln!("POI data loading complete.");
+
+        POIData {
+            id,
+            name,
+            category,
+            lat,
+            lng,
+            emoji,
+        }
+    }
+}
diff --git a/server-rs/src/index.rs b/server-rs/src/index.rs
new file mode 100644
index 0000000..5b705ed
--- /dev/null
+++ b/server-rs/src/index.rs
@@ -0,0 +1,130 @@
+/// Grid-based spatial index for fast rectangle queries over property rows.
+///
+/// Divides the UK bounding box into cells of ~0.01 degrees (~1km),
+/// each storing indices of rows whose lat/lon falls within that cell.
+
+pub struct GridIndex {
+    min_lat: f64,
+    min_lon: f64,
+    cell_size: f64,
+    cols: usize,
+    rows: usize,
+    /// cells[row * cols + col] = vec of row indices
+    cells: Vec<Vec<u32>>,
+}
+
+impl GridIndex {
+    /// Build the grid index from lat/lon arrays.
+    pub fn build(lat: &[f64], lon: &[f64], cell_size: f64) -> Self {
+        // Compute bounding box with a small margin
+        let mut min_lat = f64::INFINITY;
+        let mut max_lat = f64::NEG_INFINITY;
+        let mut min_lon = f64::INFINITY;
+        let mut max_lon = f64::NEG_INFINITY;
+
+        for i in 0..lat.len() {
+            let la = lat[i];
+            let lo = lon[i];
+            if la < min_lat {
+                min_lat = la;
+            }
+            if la > max_lat {
+                max_lat = la;
+            }
+            if lo < min_lon {
+                min_lon = lo;
+            }
+            if lo > max_lon {
+                max_lon = lo;
+            }
+        }
+
+        // Add margin
+        min_lat -= cell_size;
+        min_lon -= cell_size;
+        max_lat += cell_size;
+        max_lon += cell_size;
+
+        let rows = ((max_lat - min_lat) / cell_size).ceil() as usize + 1;
+        let cols = ((max_lon - min_lon) / cell_size).ceil() as usize + 1;
+
+        eprintln!(
+            "Building grid index: {}x{} cells ({} total), cell_size={}",
+            rows,
+            cols,
+            rows * cols,
+            cell_size
+        );
+
+        let mut cells: Vec<Vec<u32>> = vec![Vec::new(); rows * cols];
+
+        for i in 0..lat.len() {
+            let r = ((lat[i] - min_lat) / cell_size) as usize;
+            let c = ((lon[i] - min_lon) / cell_size) as usize;
+            let idx = r * cols + c;
+            cells[idx].push(i as u32);
+        }
+
+        eprintln!("Grid index built.");
+
+        GridIndex {
+            min_lat,
+            min_lon,
+            cell_size,
+            cols,
+            rows,
+            cells,
+        }
+    }
+
+    /// Query all row indices within the given bounding box.
+    pub fn query(&self, south: f64, west: f64, north: f64, east: f64) -> Vec<u32> {
+        let (r_min, r_max, c_min, c_max) = self.clamp_bounds(south, west, north, east);
+
+        let mut result = Vec::new();
+        for r in r_min..=r_max {
+            let row_start = r * self.cols;
+            for c in c_min..=c_max {
+                result.extend_from_slice(&self.cells[row_start + c]);
+            }
+        }
+
+        result
+    }
+
+    /// Iterate all row indices in bounds without allocating a Vec.
+    #[inline]
+    pub fn for_each_in_bounds(
+        &self,
+        south: f64,
+        west: f64,
+        north: f64,
+        east: f64,
+        mut f: impl FnMut(u32),
+    ) {
+        let (r_min, r_max, c_min, c_max) = self.clamp_bounds(south, west, north, east);
+
+        for r in r_min..=r_max {
+            let row_start = r * self.cols;
+            for c in c_min..=c_max {
+                for &row_idx in &self.cells[row_start + c] {
+                    f(row_idx);
+                }
+            }
+        }
+    }
+
+    fn clamp_bounds(&self, south: f64, west: f64, north: f64, east: f64) -> (usize, usize, usize, usize) {
+        let r_min = ((south - self.min_lat) / self.cell_size) as isize;
+        let r_max = ((north - self.min_lat) / self.cell_size) as isize;
+        let c_min = ((west - self.min_lon) / self.cell_size) as isize;
+        let c_max = ((east - self.min_lon) / self.cell_size) as isize;
+
+        let r_min = r_min.max(0) as usize;
+        let r_max = (r_max.min(self.rows as isize - 1)).max(0) as usize;
+        let c_min = c_min.max(0) as usize;
+        let c_max = (c_max.min(self.cols as isize - 1)).max(0) as usize;
+
+        (r_min, r_max, c_min, c_max)
+    }
+}
diff --git a/server-rs/src/main.rs b/server-rs/src/main.rs
new file mode 100644
index 0000000..a3bc3ea
--- /dev/null
+++ b/server-rs/src/main.rs
@@ -0,0 +1,109 @@
+mod consts;
+mod data;
+mod index;
+mod routes;
+
+use std::path::PathBuf;
+use std::sync::Arc;
+
+use axum::routing::get;
+use axum::Router;
+use tower_http::compression::CompressionLayer;
+use tower_http::cors::{Any, CorsLayer};
+use tower_http::services::ServeDir;
+
+use routes::AppState;
+
+#[tokio::main]
+async fn main() {
+    let parquet_path = PathBuf::from(
+        std::env::args()
+            .nth(1)
+            .unwrap_or_else(|| "data_sources/processed/wide.parquet".to_string()),
+    );
+    if !parquet_path.exists() {
+        eprintln!("Error: {} not found.", parquet_path.display());
+        std::process::exit(1);
+    }
+
+    // Load property data and build indices
+    let property_data = data::PropertyData::load(&parquet_path);
+    let grid = index::GridIndex::build(&property_data.lat, &property_data.lon, 0.01);
+    let h3_cells = data::precompute_h3(&property_data.lat, &property_data.lon);
+
+    // Load POI data and build spatial index
+    // Derive POI path from the data parquet path (same directory)
+    let poi_path = parquet_path
+        .parent()
+        .and_then(|p| p.parent())
+        .map(|p| p.join("filtered_uk_pois.parquet"))
+        .unwrap_or_else(|| PathBuf::from("data_sources/filtered_uk_pois.parquet"));
+
+    let poi_data = if poi_path.exists() {
+        data::POIData::load(&poi_path)
+    } else {
+        eprintln!("Warning: {} not found. POI endpoints will be unavailable.", poi_path.display());
+        data::POIData {
+            id: Vec::new(),
+            name: Vec::new(),
+            category: Vec::new(),
+            lat: Vec::new(),
+            lng: Vec::new(),
+            emoji: Vec::new(),
+        }
+    };
+    let poi_grid = index::GridIndex::build(&poi_data.lat, &poi_data.lng, 0.01);
+
+    let state = Arc::new(AppState {
+        data: property_data,
+        grid,
+        h3_cells,
+        poi_data,
+        poi_grid,
+    });
+
+    let cors = CorsLayer::new()
+        .allow_origin(Any)
+        .allow_methods(Any)
+        .allow_headers(Any);
+
+    // API routes
+    let state_features = state.clone();
+    let state_hexagons = state.clone();
+    let state_pois = state.clone();
+    let state_poi_categories = state.clone();
+
+    let api = Router::new()
+        .route(
+            "/api/features",
+            get(move || routes::get_features(state_features.clone())),
+        )
+        .route(
+            "/api/hexagons",
+            get(move |query| routes::get_hexagons(state_hexagons.clone(), query)),
+        )
+        .route(
+            "/api/pois",
+            get(move |query| routes::get_pois(state_pois.clone(), query)),
+        )
+        .route(
+            "/api/poi-categories",
+            get(move || routes::get_poi_categories(state_poi_categories.clone())),
+        );
+
+    // Static file serving for frontend
+    let frontend_dist = PathBuf::from("frontend/dist");
+    let app = if frontend_dist.exists() {
+        api.fallback_service(ServeDir::new(frontend_dist))
+    } else {
+        api
+    };
+
+    let app = app.layer(cors).layer(CompressionLayer::new().gzip(true));
+
+    let addr = "0.0.0.0:8001";
+    eprintln!("Server listening on {}", addr);
+
+    let listener = tokio::net::TcpListener::bind(addr).await.unwrap();
+    axum::serve(listener, app).await.unwrap();
+}
diff --git a/server-rs/src/routes.rs b/server-rs/src/routes.rs
new file mode 100644
index 0000000..baa3099
--- /dev/null
+++ b/server-rs/src/routes.rs
@@ -0,0 +1,461 @@
+use std::fmt::Write;
+use std::sync::Arc;
+
+use axum::extract::Query;
+use axum::http::StatusCode;
+use axum::response::{IntoResponse, Json};
+use rustc_hash::FxHashMap;
+use serde::{Deserialize, Serialize};
+
+use crate::data::{Histogram, PropertyData, POIData, POI, DEFAULT_RESOLUTION, MAX_RESOLUTION, MIN_RESOLUTION};
+use crate::index::GridIndex;
+
+/// Shared application state
+pub struct AppState {
+    pub data: PropertyData,
+    pub grid: GridIndex,
+    /// h3_cells[resolution][row_idx] = precomputed H3 cell ID.
+    /// Empty Vec for resolutions not precomputed.
+    pub h3_cells: Vec<Vec<u64>>,
+    pub poi_data: POIData,
+    pub poi_grid: GridIndex,
+}
+
+const BOUNDS_BUFFER_PERCENT: f64 = 0.2;
+
+// ── /api/features ──
+
+#[derive(Serialize)]
+pub struct FeatureInfo {
+    name: String,
+    min: f64,
+    max: f64,
+    label: String,
+    histogram: Histogram,
+}
+
+#[derive(Serialize)]
+pub struct FeaturesResponse {
+    features: Vec<FeatureInfo>,
+}
+
+fn snake_to_label(name: &str) -> String {
+    name.split('_')
+        .map(|word| {
+            let mut chars = word.chars();
+            match chars.next() {
+                None => String::new(),
+                Some(c) => {
+                    let mut s = c.to_uppercase().to_string();
+                    s.extend(chars);
+                    s
+                }
+            }
+        })
+        .collect::<Vec<_>>()
+        .join(" ")
+}
+
+pub async fn get_features(state: Arc<AppState>) -> Json<FeaturesResponse> {
+    let features = state
+        .data
+        .feature_names
+        .iter()
+        .enumerate()
+        .map(|(i, name): (usize, &String)| {
+            let stats = &state.data.feature_stats[i];
+            FeatureInfo {
+                name: name.clone(),
+                min: stats.p_low,
+                max: stats.p_high,
+                label: snake_to_label(name),
+                histogram: stats.histogram.clone(),
+            }
+        })
+        .collect();
+
+    Json(FeaturesResponse { features })
+}
+
+// ── /api/hexagons ──
+
+#[derive(Deserialize)]
+pub struct HexagonParams {
+    resolution: Option<u8>,
+    bounds: Option<String>,
+    /// Comma-separated filters: `name:min:max,...`
+    /// Rows must have non-NaN values within [min,max] for each filter.
+    filters: Option<String>,
+}
+
+struct ParsedFilter {
+    feat_idx: usize,
+    min: f64,
+    max: f64,
+}
+
+/// Per-cell accumulator for aggregating features
+struct CellAgg {
+    count: u32,
+    mins: Vec<f64>,
+    maxs: Vec<f64>,
+}
+
+impl CellAgg {
+    fn new(num_features: usize) -> Self {
+        CellAgg {
+            count: 0,
+            mins: vec![f64::INFINITY; num_features],
+            maxs: vec![f64::NEG_INFINITY; num_features],
+        }
+    }
+
+    /// Add a row using row-major feature_data layout.
+    /// feature_data[row * num_features + feat_idx] — all features for one row
+    /// are contiguous, so this reads a single cache line per ~8 features.
+    #[inline]
+    fn add_row(&mut self, feature_data: &[f64], row: usize, num_features: usize) {
+        self.count += 1;
+        let base = row * num_features;
+        let row_slice = &feature_data[base..base + num_features];
+        for (i, &v) in row_slice.iter().enumerate() {
+            if v.is_finite() {
+                if v < self.mins[i] {
+                    self.mins[i] = v;
+                }
+                if v > self.maxs[i] {
+                    self.maxs[i] = v;
+                }
+            }
+        }
+    }
+
+}
+
+/// Write the hexagons JSON response directly to a String buffer,
+/// avoiding serde_json::Value allocations entirely.
+fn write_hexagons_json(
+    buf: &mut String,
+    groups: &FxHashMap<u64, CellAgg>,
+    min_keys: &[String],
+    max_keys: &[String],
+    num_features: usize,
+) {
+    buf.push_str("{\"features\":[");
+    let mut first = true;
+    for (&cell_id, agg) in groups {
+        if !first {
+            buf.push(',');
+        }
+        first = false;
+
+        let cell = h3o::CellIndex::try_from(cell_id).unwrap();
+        write!(buf, "{{\"h3\":\"{}\",\"count\":{}", cell, agg.count).unwrap();
+
+        for i in 0..num_features {
+            if agg.mins[i] != f64::INFINITY {
+                write!(
+                    buf,
+                    ",\"{}\":{},\"{}\":{}",
+                    min_keys[i], agg.mins[i], max_keys[i], agg.maxs[i]
+                )
+                .unwrap();
+            }
+        }
+        buf.push('}');
+    }
+    buf.push_str("]}");
+}
+
+pub async fn get_hexagons(
+    state: Arc<AppState>,
+    Query(params): Query<HexagonParams>,
+) -> Result<impl IntoResponse, (StatusCode, String)> {
+    let resolution = params.resolution.unwrap_or(DEFAULT_RESOLUTION);
+    if resolution > MAX_RESOLUTION {
+        return Err((
+            StatusCode::BAD_REQUEST,
+            format!(
+                "resolution must be between {} and {}",
+                MIN_RESOLUTION, MAX_RESOLUTION
+            ),
+        ));
+    }
+
+    let bounds_str = params
+        .bounds
+        .ok_or((StatusCode::BAD_REQUEST, "bounds parameter is required".into()))?;
+
+    let parts: Vec<f64> = bounds_str
+        .split(',')
+        .map(|s| s.trim().parse::<f64>())
+        .collect::<Result<Vec<_>, _>>()
+        .map_err(|_| {
+            (
+                StatusCode::BAD_REQUEST,
+                "Invalid bounds format. Use: south,west,north,east".into(),
+            )
+        })?;
+
+    if parts.len() != 4 {
+        return Err((
+            StatusCode::BAD_REQUEST,
+            "Invalid bounds format. Use: south,west,north,east".into(),
+        ));
+    }
+
+    let (mut south, mut west, mut north, mut east) = (parts[0], parts[1], parts[2], parts[3]);
+
+    // Apply bounds buffer (20%)
+    let lat_range = north - south;
+    let lng_range = east - west;
+    south -= lat_range * BOUNDS_BUFFER_PERCENT;
+    north += lat_range * BOUNDS_BUFFER_PERCENT;
+    west -= lng_range * BOUNDS_BUFFER_PERCENT;
+    east += lng_range * BOUNDS_BUFFER_PERCENT;
+
+    // Quantize to 0.01 degree precision
+    let precision = 0.01;
+    south = (south / precision).floor() * precision;
+    west = (west / precision).floor() * precision;
+    north = (north / precision).ceil() * precision;
+    east = (east / precision).ceil() * precision;
+
+    // Parse filters: `name:min:max,...`
+    let parsed_filters: Vec<ParsedFilter> = params
+        .filters
+        .as_deref()
+        .filter(|s| !s.is_empty())
+        .map(|s| {
+            s.split(',')
+                .filter_map(|entry| {
+                    let parts: Vec<&str> = entry.splitn(3, ':').collect();
+                    if parts.len() != 3 {
+                        return None;
+                    }
+                    let name = parts[0].trim();
+                    let min = parts[1].trim().parse::<f64>().ok()?;
+                    let max = parts[2].trim().parse::<f64>().ok()?;
+                    let feat_idx = state.data.feature_names.iter().position(|n| n == name)?;
+                    Some(ParsedFilter { feat_idx, min, max })
+                })
+                .collect()
+        })
+        .unwrap_or_default();
+
+    // Move CPU-heavy work off the async executor
+    let json_body = tokio::task::spawn_blocking(move || {
+        let t0 = std::time::Instant::now();
+
+        let num_features = state.data.num_features;
+        let feature_data = &state.data.feature_data;
+
+        // Pre-compute JSON key strings once
+        let min_keys: Vec<String> = state
+            .data
+            .feature_names
+            .iter()
+            .map(|n| format!("min_{}", n))
+            .collect();
+        let max_keys: Vec<String> = state
+            .data
+            .feature_names
+            .iter()
+            .map(|n| format!("max_{}", n))
+            .collect();
+
+        // Use precomputed H3 cells if available
+        let h3_cells_for_res: Option<&[u64]> = state
+            .h3_cells
+            .get(resolution as usize)
+            .filter(|v| !v.is_empty())
+            .map(|v| v.as_slice());
+
+        // Aggregate using FxHashMap (fast non-crypto hash for integer keys)
+        // and grid visitor (no intermediate Vec<u32> allocation)
+        let mut groups: FxHashMap<u64, CellAgg> = FxHashMap::default();
+
+        // Row-level filter check: value must be non-NaN and within [min, max]
+        let row_passes = |row: usize| -> bool {
+            parsed_filters.iter().all(|f| {
+                let v = feature_data[row * num_features + f.feat_idx];
+                v.is_finite() && v >= f.min && v <= f.max
+            })
+        };
+
+        if let Some(precomputed) = h3_cells_for_res {
+            // Fast path: precomputed H3 + visitor pattern
+            state.grid.for_each_in_bounds(south, west, north, east, |row_idx| {
+                let row = row_idx as usize;
+                if !row_passes(row) {
+                    return;
+                }
+                let cell_id = precomputed[row];
+                groups
+                    .entry(cell_id)
+                    .or_insert_with(|| CellAgg::new(num_features))
+                    .add_row(feature_data, row, num_features);
+            });
+        } else {
+            // Fallback: compute H3 on-the-fly
+            let h3_res = h3o::Resolution::try_from(resolution).unwrap();
+            state.grid.for_each_in_bounds(south, west, north, east, |row_idx| {
+                let row = row_idx as usize;
+                if !row_passes(row) {
+                    return;
+                }
+                let cell_id = h3o::LatLng::new(state.data.lat[row], state.data.lon[row])
+                    .map(|c| u64::from(c.to_cell(h3_res)))
+                    .unwrap_or(0);
+                groups
+                    .entry(cell_id)
+                    .or_insert_with(|| CellAgg::new(num_features))
+                    .add_row(feature_data, row, num_features);
+            });
+        }
+
+        let t_agg = t0.elapsed();
+
+        // Write JSON directly (no serde_json::Value allocation overhead)
+        let mut json_buf = String::with_capacity(groups.len() * 128);
+        write_hexagons_json(
+            &mut json_buf,
+            &groups,
+            &min_keys,
+            &max_keys,
+            num_features,
+        );
+
+        let t_total = t0.elapsed();
+        eprintln!(
+            "hexagons: res={} cells={} agg={:?} json={:?} total={:?} bytes={}",
+            resolution,
+            groups.len(),
+            t_agg,
+            t_total - t_agg,
+            t_total,
+            json_buf.len()
+        );
+
+        json_buf
+    })
+    .await
+    .unwrap();
+
+    Ok(([("content-type", "application/json")], json_body))
+}
+
+// ── /api/pois ──
+
+#[derive(Deserialize)]
+pub struct POIParams {
+    bounds: Option<String>,
+    /// Comma-separated list of categories to filter by
+    categories: Option<String>,
+}
+
+#[derive(Serialize)]
+pub struct POIsResponse {
+    pois: Vec<POI>,
+}
+
+pub async fn get_pois(
+    state: Arc<AppState>,
+    Query(params): Query<POIParams>,
+) -> Result<Json<POIsResponse>, (StatusCode, String)> {
+    let bounds_str = params
+        .bounds
+        .ok_or((StatusCode::BAD_REQUEST, "bounds parameter is required".into()))?;
+
+    let parts: Vec<f64> = bounds_str
+        .split(',')
+        .map(|s| s.trim().parse::<f64>())
+        .collect::<Result<Vec<_>, _>>()
+        .map_err(|_| {
+            (
+                StatusCode::BAD_REQUEST,
+                "Invalid bounds format. Use: south,west,north,east".into(),
+            )
+        })?;
+
+    if parts.len() != 4 {
+        return Err((
+            StatusCode::BAD_REQUEST,
+            "Invalid bounds format. Use: south,west,north,east".into(),
+        ));
+    }
+
+    let (south, west, north, east) = (parts[0], parts[1], parts[2], parts[3]);
+
+    // Parse category filter if provided
+    let category_filter: Option<Vec<String>> = params
+        .categories
+        .as_deref()
+        .filter(|s| !s.is_empty())
+        .map(|s| s.split(',').map(|c| c.trim().to_string()).collect());
+
+    // Move CPU-heavy work off the async executor
+    let result = tokio::task::spawn_blocking(move || {
+        // Spatial query using grid index
+        let row_indices = state.poi_grid.query(south, west, north, east);
+
+        let pois: Vec<POI> = row_indices
+            .iter()
+            .filter_map(|&row_idx| {
+                let row = row_idx as usize;
+
+                // Apply category filter if specified
+                if let Some(ref categories) = category_filter {
+                    if !categories.contains(&state.poi_data.category[row]) {
+                        return None;
+                    }
+                }
+
+                Some(POI {
+                    id: state.poi_data.id[row].clone(),
+                    name: state.poi_data.name[row].clone(),
+                    category: state.poi_data.category[row].clone(),
+                    lat: state.poi_data.lat[row],
+                    lng: state.poi_data.lng[row],
+                    emoji: state.poi_data.emoji[row].clone(),
+                })
+            })
+            .take(5000)
+            .collect();
+
+        POIsResponse { pois }
+    })
+    .await
+    .unwrap();
+
+    Ok(Json(result))
+}
+
+// ── /api/poi-categories ──
+
+#[derive(Serialize)]
+pub struct POICategoriesResponse {
+    categories: Vec<String>,
+}
+
+pub async fn get_poi_categories(state: Arc<AppState>) -> Json<POICategoriesResponse> {
+    // Compute unique categories
+    let result = tokio::task::spawn_blocking(move || {
+        let mut categories: Vec<String> = state
+            .poi_data
+            .category
+            .iter()
+            .cloned()
+            .collect::<std::collections::HashSet<_>>()
+            .into_iter()
+            .collect();
+
+        categories.sort();
+
+        POICategoriesResponse { categories }
+    })
+    .await
+    .unwrap();
+
+    Json(result)
+}
diff --git a/server/__init__.py b/server/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/server/config.py b/server/config.py
deleted file mode 100644
index fcb27d9..0000000
--- a/server/config.py
+++ /dev/null
@@ -1,18 +0,0 @@
-"""Server configuration - imports shared values from pipeline config."""
-
-from pipeline.config import (
-    AGGREGATES_DIR,
-    H3_RESOLUTIONS as VALID_RESOLUTIONS,
-    DEFAULT_H3_RESOLUTION as DEFAULT_RESOLUTION,
-)
-
-# Extra area to return beyond requested bounds (0.2 = 20%)
-# Makes panning smoother by preloading nearby hexagons
-BOUNDS_BUFFER_PERCENT = 0.2
-
-__all__ = [
-    "AGGREGATES_DIR",
-    "VALID_RESOLUTIONS",
-    "DEFAULT_RESOLUTION",
-    "BOUNDS_BUFFER_PERCENT",
-]
diff --git a/server/main.py b/server/main.py
deleted file mode 100644
index d404a51..0000000
--- a/server/main.py
+++ /dev/null
@@ -1,35 +0,0 @@
-from contextlib import asynccontextmanager
-from pathlib import Path
-from fastapi import FastAPI
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.staticfiles import StaticFiles
-
-from server.routes import hexagons, pois
-
-
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    # Startup: preload all parquet files
-    hexagons.preload_dataframes()
-    pois.preload_pois()
-    yield
-    # Shutdown: nothing to clean up
-
-
-app = FastAPI(title="Property Map API", lifespan=lifespan)
-
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=False,  # Cannot use True with wildcard origins
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-app.include_router(hexagons.router, prefix="/api")
-app.include_router(pois.router, prefix="/api")
-
-# Mount static files for production (frontend build)
-frontend_dist = Path(__file__).parent.parent / "frontend" / "dist"
-if frontend_dist.exists():
-    app.mount("/", StaticFiles(directory=frontend_dist, html=True), name="static")
diff --git a/server/routes/__init__.py b/server/routes/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/server/routes/hexagons.py b/server/routes/hexagons.py
deleted file mode 100644
index c12177e..0000000
--- a/server/routes/hexagons.py
+++ /dev/null
@@ -1,172 +0,0 @@
-import math
-from functools import lru_cache
-from fastapi import APIRouter, Query, HTTPException
-import polars as pl
-import h3
-
-from tqdm import tqdm
-
-from server.config import (
-    AGGREGATES_DIR,
-    VALID_RESOLUTIONS,
-    DEFAULT_RESOLUTION,
-    BOUNDS_BUFFER_PERCENT,
-)
-
-router = APIRouter()
-
-# Cache loaded dataframes in memory (one per resolution)
-_df_cache: dict[int, pl.DataFrame] = {}
-
-# Discovered features (computed once on first load)
-_features_cache: list[dict] | None = None
-
-
-def _snake_to_label(name: str) -> str:
-    """Convert snake_case feature name to a human-readable label."""
-    return name.replace("_", " ").title()
-
-
-def _discover_features(df: pl.DataFrame) -> list[dict]:
-    """Discover features from column pairs min_X / max_X."""
-    features = []
-    seen = set()
-    for col in df.columns:
-        if col.startswith("min_"):
-            name = col[4:]
-            max_col = f"max_{name}"
-            if max_col in df.columns and name not in seen:
-                seen.add(name)
-                global_min = df[col].min()
-                global_max = df[max_col].max()
-                if global_min is not None and global_max is not None:
-                    features.append(
-                        {
-                            "name": name,
-                            "min": float(global_min),
-                            "max": float(global_max),
-                            "label": _snake_to_label(name),
-                        }
-                    )
-    return features
-
-
-def preload_dataframes() -> None:
-    """Load all resolution dataframes into cache on startup."""
-    for resolution in tqdm(VALID_RESOLUTIONS, desc="Loading parquet files"):
-        get_cached_df(resolution)
-
-
-def get_cached_df(resolution: int) -> pl.DataFrame | None:
-    """Get cached dataframe for resolution, loading from disk if needed."""
-    if resolution not in _df_cache:
-        parquet_path = AGGREGATES_DIR / f"res{resolution}.parquet"
-        if not parquet_path.exists():
-            return None
-        # Load and add H3 cell centroids for fast bbox filtering
-        df = pl.read_parquet(parquet_path)
-
-        # Pre-compute cell centroids for bbox filtering
-        centroids = [h3.cell_to_latlng(cell) for cell in df["h3"].to_list()]
-        df = df.with_columns(
-            [
-                pl.Series("_lat", [c[0] for c in centroids]),
-                pl.Series("_lng", [c[1] for c in centroids]),
-            ]
-        )
-        _df_cache[resolution] = df
-    return _df_cache[resolution]
-
-
-def get_features() -> list[dict]:
-    """Get discovered features, computing from the first available resolution."""
-    global _features_cache
-    if _features_cache is None:
-        for resolution in VALID_RESOLUTIONS:
-            df = get_cached_df(resolution)
-            if df is not None:
-                _features_cache = _discover_features(df)
-                break
-        if _features_cache is None:
-            _features_cache = []
-    return _features_cache
-
-
-@router.get("/features")
-async def get_features_endpoint() -> dict:
-    """Return discovered feature metadata with global min/max ranges."""
-    return {"features": get_features()}
-
-
-@lru_cache(maxsize=128)
-def query_hexagons_cached(
-    resolution: int,
-    bounds_tuple: tuple[float, float, float, float],
-) -> list[dict]:
-    """Cached query - returns features list."""
-    south, west, north, east = bounds_tuple
-
-    df = get_cached_df(resolution)
-    if df is None:
-        return []
-
-    # Fast bbox filter using pre-computed centroids
-    df = df.filter(
-        (pl.col("_lat") >= south)
-        & (pl.col("_lat") <= north)
-        & (pl.col("_lng") >= west)
-        & (pl.col("_lng") <= east)
-    )
-
-    # Drop internal centroid columns before returning
-    df = df.drop("_lat", "_lng")
-
-    return df.to_dicts()
-
-
-@router.get("/hexagons")
-async def get_hexagons(
-    resolution: int = Query(
-        DEFAULT_RESOLUTION,
-        ge=min(VALID_RESOLUTIONS),
-        le=max(VALID_RESOLUTIONS),
-        description=f"H3 resolution ({min(VALID_RESOLUTIONS)}-{max(VALID_RESOLUTIONS)})",
-    ),
-    bounds: str | None = Query(None, description="Bounding box: south,west,north,east"),
-) -> dict:
-    """Get aggregated property data as hexagons within bounds."""
-    if resolution not in VALID_RESOLUTIONS:
-        resolution = DEFAULT_RESOLUTION
-
-    if not bounds:
-        raise HTTPException(status_code=400, detail="bounds parameter is required")
-
-    try:
-        south, west, north, east = map(float, bounds.split(","))
-    except ValueError:
-        raise HTTPException(
-            status_code=400, detail="Invalid bounds format. Use: south,west,north,east"
-        )
-
-    # Expand bounds by buffer percentage for smoother panning
-    lat_range = north - south
-    lng_range = east - west
-    lat_buffer = lat_range * BOUNDS_BUFFER_PERCENT
-    lng_buffer = lng_range * BOUNDS_BUFFER_PERCENT
-    south -= lat_buffer
-    north += lat_buffer
-    west -= lng_buffer
-    east += lng_buffer
-
-    # Round bounds to reduce cache misses (0.01 degree ~ 1km precision)
-    precision = 0.01
-    bounds_tuple = (
-        math.floor(south / precision) * precision,
-        math.floor(west / precision) * precision,
-        math.ceil(north / precision) * precision,
-        math.ceil(east / precision) * precision,
-    )
-
-    features = query_hexagons_cached(resolution, bounds_tuple)
-
-    return {"features": features}
diff --git a/server/routes/pois.py b/server/routes/pois.py
deleted file mode 100644
index edf48db..0000000
--- a/server/routes/pois.py
+++ /dev/null
@@ -1,322 +0,0 @@
-"""POI (Points of Interest) API endpoint."""
-
-from pathlib import Path
-
-from fastapi import APIRouter, Query
-import polars as pl
-
-router = APIRouter()
-
-DATA_FILE = Path("data_sources/uk_pois.parquet")
-
-# Group definitions: maps a group key to its display metadata and the
-# individual POI categories it contains.  Categories are matched against
-# the values that actually exist in the loaded parquet so that the
-# selector only shows groups with real data.
-_GROUP_DEFS: dict[str, dict] = {
-    "schools": {
-        "emoji": "🏫",
-        "label": "Schools",
-        "categories": ["school", "preschool", "college_university", "library"],
-    },
-    "healthcare": {
-        "emoji": "🏥",
-        "label": "Healthcare",
-        "categories": [
-            "doctor",
-            "dentist",
-            "pharmacy",
-            "hospital",
-            "public_health_clinic",
-            "veterinary",
-            "nursing_home",
-            "social_facility",
-        ],
-    },
-    "transport": {
-        "emoji": "🚉",
-        "label": "Transport",
-        "categories": [
-            "train_station",
-            "bus_station",
-            "bus_stop",
-            "metro_station",
-            "light_rail_station",
-            "tram_stop",
-            "ferry_terminal",
-            "airport",
-        ],
-    },
-    "parks": {
-        "emoji": "🌳",
-        "label": "Parks & Leisure",
-        "categories": [
-            "park",
-            "national_park",
-            "nature_reserve",
-            "dog_park",
-            "playground",
-            "garden",
-            "sports_centre",
-            "swimming_pool",
-            "gym",
-            "golf_course",
-            "marina",
-        ],
-    },
-    "emergency": {
-        "emoji": "🚨",
-        "label": "Emergency",
-        "categories": ["police_department", "fire_department"],
-    },
-    "supermarkets": {
-        "emoji": "🛒",
-        "label": "Supermarkets & Grocery",
-        "categories": [
-            "supermarket",
-            "grocery_store",
-            "convenience_store",
-            "bakery",
-            "butcher",
-            "greengrocer",
-            "deli",
-        ],
-    },
-    "shopping": {
-        "emoji": "🛍️",
-        "label": "Shopping",
-        "categories": [
-            "department_store",
-            "clothing_store",
-            "shoe_store",
-            "electronics_store",
-            "hardware_store",
-            "furniture_store",
-            "bookshop",
-            "newsagent",
-            "charity_shop",
-            "shopping_centre",
-            "optician",
-            "off_licence",
-        ],
-    },
-    "food_drink": {
-        "emoji": "🍽️",
-        "label": "Food & Drink",
-        "categories": [
-            "restaurant",
-            "cafe",
-            "pub",
-            "bar",
-            "fast_food",
-            "food_court",
-            "ice_cream",
-            "beer_garden",
-        ],
-    },
-    "personal_care": {
-        "emoji": "💇",
-        "label": "Personal Care",
-        "categories": [
-            "hairdresser",
-            "beauty_salon",
-            "laundry",
-            "dry_cleaning",
-        ],
-    },
-    "finance": {
-        "emoji": "🏦",
-        "label": "Finance",
-        "categories": ["bank", "atm", "bureau_de_change"],
-    },
-    "entertainment": {
-        "emoji": "🎭",
-        "label": "Entertainment & Culture",
-        "categories": [
-            "cinema",
-            "theatre",
-            "nightclub",
-            "community_centre",
-            "arts_centre",
-            "museum",
-            "gallery",
-            "attraction",
-            "zoo",
-            "theme_park",
-            "viewpoint",
-        ],
-    },
-    "accommodation": {
-        "emoji": "🏨",
-        "label": "Accommodation",
-        "categories": [
-            "hotel",
-            "hostel",
-            "guest_house",
-            "campsite",
-            "caravan_site",
-        ],
-    },
-    "religion": {
-        "emoji": "🛐",
-        "label": "Places of Worship",
-        "categories": ["place_of_worship"],
-    },
-    "government": {
-        "emoji": "🏛️",
-        "label": "Government & Public",
-        "categories": [
-            "town_hall",
-            "courthouse",
-            "post_office",
-            "prison",
-            "public_toilets",
-        ],
-    },
-    "automotive": {
-        "emoji": "⛽",
-        "label": "Automotive",
-        "categories": [
-            "petrol_station",
-            "ev_charging",
-            "car_dealer",
-            "car_repair",
-            "parking",
-            "bicycle_parking",
-        ],
-    },
-    "recycling": {
-        "emoji": "♻️",
-        "label": "Recycling & Waste",
-        "categories": ["recycling", "waste_disposal"],
-    },
-}
-
-# Built at startup from the data — only groups whose member categories
-# actually appear in the parquet file are included.
-_active_groups: dict[str, dict] = {}
-
-# Reverse lookup: category value -> group key (built at startup)
-_cat_to_group: dict[str, str] = {}
-
-# Cache the dataframe
-_df_cache: pl.DataFrame | None = None
-
-
-def _load_and_build() -> pl.DataFrame | None:
-    """Load the parquet, build category groups from actual data."""
-    global _df_cache, _active_groups, _cat_to_group
-
-    if not DATA_FILE.exists():
-        return None
-
-    df = pl.read_parquet(DATA_FILE).select("id", "name", "category", "lat", "lng")
-
-    # Distinct categories present in the data
-    data_categories: set[str] = set(
-        df.select("category").unique().to_series().to_list()
-    )
-
-    # Per-category counts for the response
-    counts: dict[str, int] = dict(
-        df.group_by("category")
-        .agg(pl.len().alias("n"))
-        .iter_rows()
-    )
-
-    # Build reverse map from every known category to its group
-    cat_to_group: dict[str, str] = {}
-    for key, gdef in _GROUP_DEFS.items():
-        for cat in gdef["categories"]:
-            cat_to_group[cat] = key
-
-    # Only keep categories that belong to a known group
-    known_categories = data_categories & cat_to_group.keys()
-
-    # Build active groups — only those with at least one matching category
-    active: dict[str, dict] = {}
-    for key, gdef in _GROUP_DEFS.items():
-        present = [c for c in gdef["categories"] if c in known_categories]
-        if present:
-            active[key] = {
-                "emoji": gdef["emoji"],
-                "label": gdef["label"],
-                "categories": present,
-                "count": sum(counts.get(c, 0) for c in present),
-            }
-
-    _active_groups = active
-    _cat_to_group = cat_to_group
-
-    # Filter dataframe to only known categories
-    _df_cache = df.filter(pl.col("category").is_in(known_categories))
-    return _df_cache
-
-
-def get_df() -> pl.DataFrame | None:
-    """Return cached POI dataframe, loading if necessary."""
-    if _df_cache is None:
-        return _load_and_build()
-    return _df_cache
-
-
-def preload_pois() -> None:
-    """Preload POI data on startup."""
-    df = _load_and_build()
-    if df is not None:
-        n_groups = len(_active_groups)
-        print(f"Loaded {len(df):,} POIs across {n_groups} category groups")
-
-
-@router.get("/pois")
-async def get_pois(
-    categories: str = Query(..., description="Comma-separated category groups"),
-    bounds: str = Query(..., description="Bounding box: south,west,north,east"),
-) -> dict:
-    """Get POIs within bounds for specified category groups."""
-    df = get_df()
-    if df is None:
-        return {"features": []}
-
-    try:
-        south, west, north, east = map(float, bounds.split(","))
-    except ValueError:
-        return {"features": []}
-
-    requested_groups = [g.strip() for g in categories.split(",")]
-    cats_to_include: set[str] = set()
-    for group in requested_groups:
-        if group in _active_groups:
-            cats_to_include.update(_active_groups[group]["categories"])
-
-    if not cats_to_include:
-        return {"features": []}
-
-    filtered = df.filter(
-        (pl.col("lat") >= south)
-        & (pl.col("lat") <= north)
-        & (pl.col("lng") >= west)
-        & (pl.col("lng") <= east)
-        & (pl.col("category").is_in(cats_to_include))
-    )
-
-    MAX_POIS = 5000
-    if len(filtered) > MAX_POIS:
-        filtered = filtered.sample(n=MAX_POIS, seed=42)
-
-    return {"features": filtered.to_dicts()}
-
-
-@router.get("/poi-categories")
-async def get_poi_categories() -> dict:
-    """Get available POI category groups derived from loaded data."""
-    return {
-        "categories": {
-            key: {
-                "emoji": group["emoji"],
-                "label": group["label"],
-                "count": group["count"],
-            }
-            for key, group in _active_groups.items()
-        }
-    }

From 0153e46478b95d2e25a11919044217dc37440bbe Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sat, 31 Jan 2026 10:19:48 +0000
Subject: [PATCH 22/62] Clean up

---
 .gitignore     |  2 ++
 CLAUDE.md      | 94 +++++++++++++++++++++++++++-----------------------
 main.py        |  6 ----
 pyproject.toml |  1 +
 uv.lock        | 33 ++++++++++++++++++
 5 files changed, 87 insertions(+), 49 deletions(-)
 delete mode 100644 main.py

diff --git a/.gitignore b/.gitignore
index accfc66..84ca392 100644
--- a/.gitignore
+++ b/.gitignore
@@ -5,3 +5,5 @@ tfl_journey_client
 **/node_modules
 **/__pycache__
 **/dist
+server-rs/target
+.task
diff --git a/CLAUDE.md b/CLAUDE.md
index 9a7302a..f78b5c9 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -4,66 +4,74 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
 
 ## Project Overview
 
-Property Map is a full-stack geospatial web application that visualizes UK property price data aggregated by H3 hexagonal spatial indices. It combines Land Registry price data with postcode geolocation to create an interactive map for exploring property markets.
+Property Map is a full-stack geospatial application for visualizing UK property data on an interactive map. It combines Land Registry price-paid data, EPC energy certificates, postcode geolocation, TFL journey times, Index of Deprivation scores, and OpenStreetMap POIs into a single wide parquet file, then serves aggregated H3 hexagon statistics and POI data via a Rust backend.
 
 ## Commands
 
-All commands use [Task](https://taskfile.dev) runner. Install with: `curl -1sLf 'https://dl.cloudsmith.io/public/task/task/setup.deb.sh' | sudo -E bash`
+All commands use [Task](https://taskfile.dev) runner.
 
 ```bash
-# Initial setup (downloads ~GB of data, runs pipeline)
-task prepare
+task prepare        # Full setup: install deps, download data (~GB), run pipeline
+task server         # Rust backend on :8001 (cargo run --release)
+task frontend       # Webpack dev server on :3030 (proxies /api to :8001)
 
-# Development (run in separate terminals)
-task server      # FastAPI backend on :8001
-task frontend    # Webpack dev server on :3030 (proxies /api to :8001)
-
-# Code quality
-task lint        # Lint Python (ruff) + TypeScript (ESLint + Prettier)
-task format      # Auto-fix formatting
-task typecheck   # TypeScript type checking
-task check       # All checks (lint + typecheck + build)
-
-# Production
-task build       # Build frontend
-task prod        # Serve built frontend via FastAPI
+task lint           # Lint Python (ruff) + TypeScript (ESLint + Prettier)
+task format         # Auto-fix formatting (ruff + ESLint + Prettier)
+task typecheck      # TypeScript type checking
+task check          # All checks (lint + typecheck + build)
+task test           # Run Python tests (fuzzy join)
+task build          # Build frontend for production
 ```
 
+Python commands use `uv run`. Frontend commands use `npm run` from `frontend/`.
+
 ## Architecture
 
-```
-frontend/          React + TypeScript SPA (deck.gl/MapLibre for visualization)
-  src/App.tsx      Main component with filters and map state
-  src/components/  Map.tsx (deck.gl H3HexagonLayer), Filters UI
+### Data Pipeline (`pipeline/`)
 
-server/            FastAPI backend
-  main.py          App setup, CORS, static file mounting
-  routes/hexagons.py  GET /api/hexagons - returns aggregated price data
+Python + Polars. Orchestrated by `pipeline/run.py` which builds `data_sources/processed/wide.parquet`:
 
-pipeline/          Data processing (Polars + H3)
-  config.py        Central config (H3 resolutions 6-11, year/price ranges)
-  sources/         Postcode loading, property price joins
-  processors/      H3 aggregation (count, avg/median/min/max by cell+year)
+1. **Download** (`pipeline/download/`) — Fetches raw data into `data_sources/`:
+   - `arcgis.py` — Postcode → lat/lon/LSOA mappings
+   - `price_paid.py` — Land Registry price-paid records
+   - `pois/` — OpenStreetMap POIs via osmium (PBF parsing)
+   - `deprivation_data.py` — English Indices of Deprivation 2025
+2. **Join** (`pipeline/epc_pp.py`) — Fuzzy-joins EPC certificates with price-paid by address within postcode buckets → `epc_pp.parquet`
+3. **Widen** (`pipeline/run.py`) — Joins epc_pp with GPS coords, journey times, IoD scores, POI proximity counts, derives `price_per_sqm` and numeric `construction_age_band`
+4. **Transform POIs** (`pipeline/download/pois/transform.py`) — Drops unwanted categories, remaps to friendly names + emoji → `filtered_uk_pois.parquet`
 
-tfl_journey_client/  Generated TFL API client (local package)
-```
+Shared utilities live in `pipeline/utils/` (haversine distance for both numpy and Polars expressions, fuzzy address matching).
 
-## Data Flow
+### Backend (`server-rs/`)
 
-1. **Download**: Land Registry prices + ArcGIS postcode→lat/lon mappings → `data_sources/`
-2. **Pipeline**: Join data, compute H3 indices, aggregate stats → `data_sources/processed/aggregates/*.parquet`
-3. **Serve**: Load parquet files into memory, filter by bounds/year/price, return as GeoJSON-like response
-4. **Visualize**: Frontend fetches on viewport change, renders hexagons colored by average price
+Rust + Axum. Loads `wide.parquet` and `filtered_uk_pois.parquet` into memory at startup with precomputed H3 indices (resolutions 7–11) and grid-based spatial indices (0.01° cells).
 
-## Tech Stack
+**API endpoints:**
+- `GET /api/features` — Numeric column metadata with histograms and percentiles
+- `GET /api/hexagons` — H3 aggregates filtered by bounds, resolution, and feature min/max
+- `GET /api/pois` — POIs by bounds with optional category filter (max 5000)
+- `GET /api/poi-categories` — Available POI categories
 
-- **Frontend**: React 18, TypeScript, Webpack, TailwindCSS, deck.gl, MapLibre GL
-- **Backend**: Python 3.12, FastAPI, Polars, H3
-- **Package managers**: `uv` (Python), `npm` (frontend)
+Also serves `frontend/dist/` as static fallback.
+
+### Frontend (`frontend/`)
+
+React 18 + TypeScript SPA. deck.gl `H3HexagonLayer` over MapLibre GL basemap. Debounces API calls (150ms) on viewport changes. TailwindCSS for styling.
 
 ## Key Implementation Details
 
-- Backend caches dataframes in memory and uses LRU cache on queries
-- Bounds rounded to 0.01° precision to improve cache hits
-- Results capped at 50,000 hexagons per request (truncated flag in response)
-- Frontend debounces API calls on map movement
+- Bounds quantized to 0.01° to improve cache hits on both backend and frontend
+- H3 hexagon results capped at 50,000 per request (truncated flag in response)
+- POI proximity counting uses a spatial grid (0.05° cells, ~5km) to avoid O(n×m) distance checks
+- Fuzzy address matching uses `thefuzz.token_sort_ratio` with numeric token compatibility checks, parallelized across postcode buckets
+- The Rust server writes JSON via direct string buffer (avoids serde_json::Value allocations)
+- POI transform validates exhaustive category coverage — pipeline fails if any OSM category is unmapped
+
+## Data Sources
+
+- **Land Registry** — Price Paid bulk download
+- **EPC** — Energy Performance Certificates (domestic)
+- **ArcGIS** — Postcode → GPS/LSOA lookup
+- **OpenStreetMap** — POIs from Geofabrik Great Britain PBF
+- **IoD 2025** — English Indices of Deprivation (LSOA-level scores)
+- **TFL API** — Journey time calculations to configurable destinations
diff --git a/main.py b/main.py
deleted file mode 100644
index b4bdfc2..0000000
--- a/main.py
+++ /dev/null
@@ -1,6 +0,0 @@
-def main():
-    print("Hello from property-map!")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/pyproject.toml b/pyproject.toml
index a0e6a9f..b214f71 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -26,6 +26,7 @@ dependencies = [
     "matplotlib>=3.10.8",
     "thefuzz>=0.22.1",
     "python-levenshtein>=0.27.3",
+    "scipy>=1.17.0",
 ]
 
 [tool.uv]
diff --git a/uv.lock b/uv.lock
index c661144..7f06e32 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1588,6 +1588,7 @@ dependencies = [
     { name = "pyarrow", marker = "python_full_version < '3.14' and sys_platform == 'linux'" },
     { name = "python-dateutil", marker = "python_full_version < '3.14' and sys_platform == 'linux'" },
     { name = "python-levenshtein", marker = "python_full_version < '3.14' and sys_platform == 'linux'" },
+    { name = "scipy", marker = "python_full_version < '3.14' and sys_platform == 'linux'" },
     { name = "thefuzz", marker = "python_full_version < '3.14' and sys_platform == 'linux'" },
     { name = "tqdm", marker = "python_full_version < '3.14' and sys_platform == 'linux'" },
     { name = "uvicorn", marker = "python_full_version < '3.14' and sys_platform == 'linux'" },
@@ -1619,6 +1620,7 @@ requires-dist = [
     { name = "pyarrow", specifier = ">=15.0.0" },
     { name = "python-dateutil", specifier = ">=2.8.0" },
     { name = "python-levenshtein", specifier = ">=0.27.3" },
+    { name = "scipy", specifier = ">=1.17.0" },
     { name = "thefuzz", specifier = ">=0.22.1" },
     { name = "tqdm", specifier = ">=4.67.1" },
     { name = "uvicorn", specifier = ">=0.34.0" },
@@ -2192,6 +2194,37 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/6a/5b/aaf1dfbcc53a2811f6cc0a1759de24e4b03e02ba8762daabd9b6bd8c59e3/ruff-0.14.14-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:16bc890fb4cc9781bb05beb5ab4cd51be9e7cb376bf1dd3580512b24eb3fda2b", size = 11315626, upload-time = "2026-01-22T22:30:36.848Z" },
 ]
 
+[[package]]
+name = "scipy"
+version = "1.17.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy", marker = "python_full_version < '3.14' and sys_platform == 'linux'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/56/3e/9cca699f3486ce6bc12ff46dc2031f1ec8eb9ccc9a320fdaf925f1417426/scipy-1.17.0.tar.gz", hash = "sha256:2591060c8e648d8b96439e111ac41fd8342fdeff1876be2e19dea3fe8930454e", size = 30396830, upload-time = "2026-01-10T21:34:23.009Z" }
+wheels = [
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+    { url = "https://files.pythonhosted.org/packages/e2/82/a2d684dfddb87ba1b3ea325df7c3293496ee9accb3a19abe9429bce94755/scipy-1.17.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:33af70d040e8af9d5e7a38b5ed3b772adddd281e3062ff23fec49e49681c38cf", size = 34909905, upload-time = "2026-01-10T21:30:28.704Z" },
+    { url = "https://files.pythonhosted.org/packages/ef/5e/e565bd73991d42023eb82bb99e51c5b3d9e2c588ca9d4b3e2cc1d3ca62a6/scipy-1.17.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:f9eb55bb97d00f8b7ab95cb64f873eb0bf54d9446264d9f3609130381233483f", size = 37457743, upload-time = "2026-01-10T21:30:34.819Z" },
+]
+
 [[package]]
 name = "send2trash"
 version = "2.1.0"

From e1b38a1b954f847c4ff558c869192bdd8c1936ee Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sat, 31 Jan 2026 10:49:43 +0000
Subject: [PATCH 23/62] Extarct utils

---
 pipeline/journey_times/__main__.py      |  27 +---
 pipeline/utils/__init__.py              |   5 +
 pipeline/{ => utils}/fuzzy_join.py      |   0
 pipeline/utils/haversine.py             |  36 +++++
 pipeline/utils/poi_counts.py            | 192 ++++++++++++++++++++++++
 pipeline/{ => utils}/test_fuzzy_join.py |   3 +-
 pipeline/utils/test_haversine.py        | 135 +++++++++++++++++
 pipeline/utils/test_poi_counts.py       |  85 +++++++++++
 8 files changed, 458 insertions(+), 25 deletions(-)
 create mode 100644 pipeline/utils/__init__.py
 rename pipeline/{ => utils}/fuzzy_join.py (100%)
 create mode 100644 pipeline/utils/haversine.py
 create mode 100644 pipeline/utils/poi_counts.py
 rename pipeline/{ => utils}/test_fuzzy_join.py (91%)
 create mode 100644 pipeline/utils/test_haversine.py
 create mode 100644 pipeline/utils/test_poi_counts.py

diff --git a/pipeline/journey_times/__main__.py b/pipeline/journey_times/__main__.py
index 0951ae4..40e8452 100644
--- a/pipeline/journey_times/__main__.py
+++ b/pipeline/journey_times/__main__.py
@@ -8,6 +8,7 @@ from tqdm import tqdm
 from .config import DESTINATIONS, MAX_CONCURRENT, MAX_POSTCODES, OUTPUT_DIR, MAX_DISTANCE_KM
 from .results import CheckpointSaver, results_to_dataframe, save_results
 from .tfl_client import fetch_journey_times
+from pipeline.utils import haversine_km_expr
 
 
 def main():
@@ -28,31 +29,9 @@ def main():
     postcodes_df = pl.read_parquet(OUTPUT_DIR / "postcodes_h3.parquet")
     print(f"Loaded {postcodes_df.height:,} postcodes")
 
-    # Filter to postcodes within 150km of destination using Haversine formula
-    earth_radius_km = 6371
-
-    dest_lat_rad = destination.lat * 3.14159265359 / 180
-    dest_lon_rad = destination.lon * 3.14159265359 / 180
-
+    # Filter to postcodes within range of destination
     postcodes_df = postcodes_df.with_columns(
-        (
-            2
-            * earth_radius_km
-            * (
-                (
-                    ((pl.lit(dest_lat_rad) - pl.col("lat") * 3.14159265359 / 180) / 2).sin()
-                    ** 2
-                    + pl.lit(dest_lat_rad).cos()
-                    * (pl.col("lat") * 3.14159265359 / 180).cos()
-                    * (
-                        (pl.lit(dest_lon_rad) - pl.col("long") * 3.14159265359 / 180) / 2
-                    ).sin()
-                    ** 2
-                )
-                .sqrt()
-                .arcsin()
-            )
-        ).alias("distance_km")
+        haversine_km_expr("lat", "long", destination.lat, destination.lon).alias("distance_km")
     ).filter(pl.col("distance_km") <= MAX_DISTANCE_KM)
 
     print(f"Filtered to {postcodes_df.height:,} postcodes within {MAX_DISTANCE_KM}km")
diff --git a/pipeline/utils/__init__.py b/pipeline/utils/__init__.py
new file mode 100644
index 0000000..8f435ac
--- /dev/null
+++ b/pipeline/utils/__init__.py
@@ -0,0 +1,5 @@
+from .fuzzy_join import fuzzy_join_on_postcode
+from .haversine import haversine_km, haversine_km_expr
+from .poi_counts import POI_GROUPS, count_pois_within_radius
+
+__all__ = ["fuzzy_join_on_postcode", "haversine_km", "haversine_km_expr", "POI_GROUPS", "count_pois_within_radius"]
diff --git a/pipeline/fuzzy_join.py b/pipeline/utils/fuzzy_join.py
similarity index 100%
rename from pipeline/fuzzy_join.py
rename to pipeline/utils/fuzzy_join.py
diff --git a/pipeline/utils/haversine.py b/pipeline/utils/haversine.py
new file mode 100644
index 0000000..ff4a4bf
--- /dev/null
+++ b/pipeline/utils/haversine.py
@@ -0,0 +1,36 @@
+import math
+
+import numpy as np
+import polars as pl
+
+_EARTH_RADIUS_KM = 6371.0
+
+
+def haversine_km(lat1: np.ndarray, lon1: np.ndarray, lat2: float, lon2: float) -> np.ndarray:
+    """Compute haversine distance in km between arrays (lat1, lon1) and a single point (lat2, lon2)."""
+    lat1_rad = np.radians(lat1)
+    lon1_rad = np.radians(lon1)
+    lat2_rad = np.radians(lat2)
+    lon2_rad = np.radians(lon2)
+    dlat = lat2_rad - lat1_rad
+    dlon = lon2_rad - lon1_rad
+    a = np.sin(dlat / 2) ** 2 + np.cos(lat1_rad) * np.cos(lat2_rad) * np.sin(dlon / 2) ** 2
+    c = 2 * np.arcsin(np.sqrt(a))
+    return _EARTH_RADIUS_KM * c
+
+
+def haversine_km_expr(
+    lat_col: str, lon_col: str, dest_lat: float, dest_lon: float
+) -> pl.Expr:
+    """Polars expression computing haversine distance in km to a fixed point."""
+    dest_lat_rad = math.radians(dest_lat)
+    dest_lon_rad = math.radians(dest_lon)
+
+    lat_rad = pl.col(lat_col).radians()
+    lon_rad = pl.col(lon_col).radians()
+
+    dlat = pl.lit(dest_lat_rad) - lat_rad
+    dlon = pl.lit(dest_lon_rad) - lon_rad
+
+    a = (dlat / 2).sin() ** 2 + pl.lit(dest_lat_rad).cos() * lat_rad.cos() * (dlon / 2).sin() ** 2
+    return 2 * _EARTH_RADIUS_KM * a.sqrt().arcsin()
diff --git a/pipeline/utils/poi_counts.py b/pipeline/utils/poi_counts.py
new file mode 100644
index 0000000..6ab700c
--- /dev/null
+++ b/pipeline/utils/poi_counts.py
@@ -0,0 +1,192 @@
+"""Count POIs within a radius of properties, optimized via postcode deduplication."""
+
+import os
+import tempfile
+
+import numpy as np
+import polars as pl
+
+from .haversine import haversine_km
+
+# POI category groups for proximity counting
+POI_GROUPS = {
+    "restaurants": ["Restaurant", "Fast Food"],
+    "groceries": ["Greengrocer", "Grocery Shop", "Supermarket", "Convenience Store"],
+    "parks": ["Park", "Garden", "Nature Reserve"],
+    "public_transport": ["Station", "Stop", "Bus Station"],
+}
+
+
+def _count_pois_per_postcode(
+    postcodes_df: pl.DataFrame, pois: pl.DataFrame, radius_km: float = 2.0
+) -> pl.DataFrame:
+    """
+    For each unique postcode, count POIs within radius_km by category group.
+    Uses spatial grid with vectorized distance calculations.
+    """
+    print(f"Counting POIs within {radius_km}km per postcode...")
+
+    n_postcodes = len(postcodes_df)
+    n_pois = len(pois)
+    print(f"  {n_postcodes:,} postcodes, {n_pois:,} POIs")
+
+    # Build spatial grid for POIs (0.05 degree cells ~5.5km)
+    grid_size = 0.05
+    print("  Building POI spatial grid...")
+
+    # Convert to numpy arrays
+    poi_lats = pois["lat"].to_numpy()
+    poi_lngs = pois["lng"].to_numpy()
+    poi_cats = pois["category"].to_numpy()
+
+    # Compute grid coordinates for all POIs
+    poi_grid_lats = np.floor(poi_lats / grid_size).astype(np.int32)
+    poi_grid_lngs = np.floor(poi_lngs / grid_size).astype(np.int32)
+
+    # Build grid cell lookup using numpy indexing
+    poi_grid = {}
+    for i in range(n_pois):
+        key = (poi_grid_lats[i], poi_grid_lngs[i])
+        if key not in poi_grid:
+            poi_grid[key] = []
+        poi_grid[key].append(i)
+
+    # Convert grid values to numpy arrays for faster indexing
+    for key in poi_grid:
+        poi_grid[key] = np.array(poi_grid[key], dtype=np.int32)
+
+    print(f"  POI grid has {len(poi_grid):,} occupied cells")
+
+    # Pre-compute category masks
+    category_masks = {}
+    for group, categories in POI_GROUPS.items():
+        mask = np.isin(poi_cats, categories)
+        category_masks[group] = mask
+        print(f"  {group}: {mask.sum():,} POIs")
+
+    # Extract postcode coordinates as numpy arrays
+    pc_lats = postcodes_df["lat"].to_numpy()
+    pc_lons = postcodes_df["lon"].to_numpy()
+    pc_codes = postcodes_df["postcode"].to_list()
+
+    # Initialize result arrays
+    result_counts = {group: np.zeros(n_postcodes, dtype=np.int32) for group in POI_GROUPS}
+
+    # Process in batches with progress
+    batch_size = 50000
+    n_batches = (n_postcodes + batch_size - 1) // batch_size
+
+    print(f"  Processing {n_postcodes:,} postcodes in {n_batches} batches...")
+
+    for batch_idx in range(n_batches):
+        start_idx = batch_idx * batch_size
+        end_idx = min(start_idx + batch_size, n_postcodes)
+
+        if batch_idx % 5 == 0:
+            print(f"  Batch {batch_idx + 1}/{n_batches}: postcodes {start_idx:,} - {end_idx:,}")
+
+        # Process batch
+        for i in range(start_idx, end_idx):
+            pc_lat = pc_lats[i]
+            pc_lon = pc_lons[i]
+
+            # Find grid cells to check (3x3 grid)
+            grid_lat = int(np.floor(pc_lat / grid_size))
+            grid_lng = int(np.floor(pc_lon / grid_size))
+
+            # Collect nearby POI indices
+            nearby_indices = []
+            for dlat in [-1, 0, 1]:
+                for dlng in [-1, 0, 1]:
+                    cell_key = (grid_lat + dlat, grid_lng + dlng)
+                    if cell_key in poi_grid:
+                        nearby_indices.append(poi_grid[cell_key])
+
+            if not nearby_indices:
+                continue
+
+            # Concatenate all nearby POI indices
+            nearby = np.concatenate(nearby_indices)
+
+            # Vectorized distance calculation for all nearby POIs
+            distances = haversine_km(
+                poi_lats[nearby],
+                poi_lngs[nearby],
+                pc_lat,
+                pc_lon
+            )
+
+            # Filter by radius
+            within_mask = distances <= radius_km
+            within_indices = nearby[within_mask]
+
+            if len(within_indices) == 0:
+                continue
+
+            # Count by category group using pre-computed masks
+            for group, cat_mask in category_masks.items():
+                result_counts[group][i] = cat_mask[within_indices].sum()
+
+    # Build result dataframe
+    result_data = {"postcode": pc_codes}
+    for group in POI_GROUPS:
+        result_data[f"{group}_{int(radius_km)}km"] = result_counts[group]
+
+    result = pl.DataFrame(result_data)
+    print("  Completed POI counting")
+    return result
+
+
+def count_pois_within_radius(
+    properties: pl.DataFrame, pois: pl.DataFrame, radius_km: float = 2.0
+) -> dict[str, pl.Series]:
+    """
+    Count POIs within radius for properties, optimized by deduplicating postcodes.
+
+    Returns dict of {column_name: count_series} aligned to properties dataframe.
+    """
+    # Get unique postcodes with coordinates
+    print("Deduplicating postcodes...")
+    unique_postcodes = (
+        properties
+        .select(["postcode", "lat", "lon"])
+        .unique(subset=["postcode"])
+    )
+
+    print(f"  {len(properties):,} properties → {len(unique_postcodes):,} unique postcodes")
+
+    # Count POIs per postcode
+    postcode_counts = _count_pois_per_postcode(unique_postcodes, pois, radius_km)
+
+    # Write to temp file to avoid memory duplication during join
+    print("  Writing postcode counts to temp file...")
+    with tempfile.NamedTemporaryFile(suffix=".parquet", delete=False) as tmp:
+        tmp_path = tmp.name
+        postcode_counts.write_parquet(tmp_path)
+
+    del postcode_counts  # Free memory
+
+    # Join using lazy evaluation
+    print("  Joining counts back to properties (lazy)...")
+    count_cols = [f"{group}_{int(radius_km)}km" for group in POI_GROUPS]
+
+    # Convert properties to lazy frame, join, then collect
+    result_lazy = (
+        properties.lazy()
+        .select("postcode")
+        .join(
+            pl.scan_parquet(tmp_path),
+            on="postcode",
+            how="left"
+        )
+        .select(count_cols)
+        .fill_null(0)
+    )
+
+    result_df = result_lazy.collect()
+
+    # Clean up temp file
+    os.unlink(tmp_path)
+
+    # Extract as dict of Series
+    return {col: result_df[col] for col in count_cols}
diff --git a/pipeline/test_fuzzy_join.py b/pipeline/utils/test_fuzzy_join.py
similarity index 91%
rename from pipeline/test_fuzzy_join.py
rename to pipeline/utils/test_fuzzy_join.py
index 7a73a73..e9a1cb5 100644
--- a/pipeline/test_fuzzy_join.py
+++ b/pipeline/utils/test_fuzzy_join.py
@@ -1,6 +1,6 @@
 import polars as pl
 
-from fuzzy_join import fuzzy_join_on_postcode
+from pipeline.utils import fuzzy_join_on_postcode
 
 POSTCODE = "E14 2DG"
 
@@ -41,5 +41,6 @@ result = fuzzy_join_on_postcode(
 
 snapshot = result.select("pp_address", "ADDRESS").sort("pp_address")
 
+print('Testing the matching between EPC and PP addresses')
 with pl.Config(tbl_rows=-1, tbl_cols=-1, fmt_str_lengths=80):
     print(snapshot)
diff --git a/pipeline/utils/test_haversine.py b/pipeline/utils/test_haversine.py
new file mode 100644
index 0000000..639eba7
--- /dev/null
+++ b/pipeline/utils/test_haversine.py
@@ -0,0 +1,135 @@
+import numpy as np
+import polars as pl
+import pytest
+
+from pipeline.utils.haversine import haversine_km, haversine_km_expr
+
+
+class TestHaversineKm:
+    """Test numpy-based haversine distance calculation."""
+
+    def test_same_point(self):
+        """Distance from a point to itself should be zero."""
+        lat = np.array([51.5074])
+        lon = np.array([-0.1278])
+        dist = haversine_km(lat, lon, 51.5074, -0.1278)
+        assert np.allclose(dist, 0.0, atol=1e-10)
+
+    def test_known_distance_london_to_paris(self):
+        """Test distance from London to Paris (~344 km)."""
+        # London coordinates
+        london_lat = np.array([51.5074])
+        london_lon = np.array([-0.1278])
+        # Paris coordinates
+        paris_lat = 48.8566
+        paris_lon = 2.3522
+
+        dist = haversine_km(london_lat, london_lon, paris_lat, paris_lon)
+        # Expected distance is approximately 344 km
+        assert np.allclose(dist[0], 344, rtol=0.01)
+
+    def test_known_distance_new_york_to_london(self):
+        """Test distance from New York to London (~5570 km)."""
+        ny_lat = np.array([40.7128])
+        ny_lon = np.array([-74.0060])
+        london_lat = 51.5074
+        london_lon = -0.1278
+
+        dist = haversine_km(ny_lat, ny_lon, london_lat, london_lon)
+        # Expected distance is approximately 5570 km
+        assert np.allclose(dist[0], 5570, rtol=0.01)
+
+    def test_multiple_points(self):
+        """Test calculating distances from multiple points to a single destination."""
+        lats = np.array([51.5074, 48.8566, 40.7128])  # London, Paris, NYC
+        lons = np.array([-0.1278, 2.3522, -74.0060])
+        # Distance to Edinburgh
+        edinburgh_lat = 55.9533
+        edinburgh_lon = -3.1883
+
+        dists = haversine_km(lats, lons, edinburgh_lat, edinburgh_lon)
+
+        # All distances should be positive
+        assert np.all(dists > 0)
+        # London to Edinburgh should be shortest (~530 km)
+        assert dists[0] < dists[1] < dists[2]
+        assert np.allclose(dists[0], 530, rtol=0.02)
+
+    def test_equator_points(self):
+        """Test distance along the equator."""
+        # Two points on the equator, 1 degree apart
+        lat = np.array([0.0])
+        lon1 = np.array([0.0])
+        lon2 = 1.0
+
+        dist = haversine_km(lat, lon1, 0.0, lon2)
+        # 1 degree at equator ≈ 111 km
+        assert np.allclose(dist[0], 111.2, rtol=0.01)
+
+
+class TestHaversineKmExpr:
+    """Test Polars expression-based haversine distance calculation."""
+
+    def test_same_point(self):
+        """Distance from a point to itself should be zero."""
+        df = pl.DataFrame({"lat": [51.5074], "lon": [-0.1278]})
+        result = df.select(haversine_km_expr("lat", "lon", 51.5074, -0.1278).alias("dist"))
+        assert result["dist"][0] == pytest.approx(0.0, abs=1e-10)
+
+    def test_known_distance_london_to_paris(self):
+        """Test distance from London to Paris (~344 km)."""
+        df = pl.DataFrame({"lat": [51.5074], "lon": [-0.1278]})
+        result = df.select(haversine_km_expr("lat", "lon", 48.8566, 2.3522).alias("dist"))
+        assert result["dist"][0] == pytest.approx(344, rel=0.01)
+
+    def test_known_distance_new_york_to_london(self):
+        """Test distance from New York to London (~5570 km)."""
+        df = pl.DataFrame({"lat": [40.7128], "lon": [-74.0060]})
+        result = df.select(haversine_km_expr("lat", "lon", 51.5074, -0.1278).alias("dist"))
+        assert result["dist"][0] == pytest.approx(5570, rel=0.01)
+
+    def test_multiple_points(self):
+        """Test calculating distances from multiple points to a single destination."""
+        df = pl.DataFrame({
+            "lat": [51.5074, 48.8566, 40.7128],  # London, Paris, NYC
+            "lon": [-0.1278, 2.3522, -74.0060],
+        })
+        # Distance to Edinburgh
+        result = df.select(haversine_km_expr("lat", "lon", 55.9533, -3.1883).alias("dist"))
+
+        dists = result["dist"].to_numpy()
+        # All distances should be positive
+        assert np.all(dists > 0)
+        # London to Edinburgh should be shortest (~530 km)
+        assert dists[0] < dists[1] < dists[2]
+        assert dists[0] == pytest.approx(530, rel=0.02)
+
+    def test_equator_points(self):
+        """Test distance along the equator."""
+        df = pl.DataFrame({"lat": [0.0], "lon": [0.0]})
+        result = df.select(haversine_km_expr("lat", "lon", 0.0, 1.0).alias("dist"))
+        # 1 degree at equator ≈ 111 km
+        assert result["dist"][0] == pytest.approx(111.2, rel=0.01)
+
+
+class TestHaversineConsistency:
+    """Test that both implementations give consistent results."""
+
+    def test_numpy_and_polars_match(self):
+        """Both implementations should give identical results."""
+        # Test data
+        lats = np.array([51.5074, 48.8566, 40.7128, 55.9533, 52.5200])
+        lons = np.array([-0.1278, 2.3522, -74.0060, -3.1883, 13.4050])
+        dest_lat = 41.9028  # Rome
+        dest_lon = 12.4964
+
+        # Numpy version
+        numpy_dists = haversine_km(lats, lons, dest_lat, dest_lon)
+
+        # Polars version
+        df = pl.DataFrame({"lat": lats, "lon": lons})
+        polars_result = df.select(haversine_km_expr("lat", "lon", dest_lat, dest_lon).alias("dist"))
+        polars_dists = polars_result["dist"].to_numpy()
+
+        # Should be identical (or at least very close due to floating point)
+        assert np.allclose(numpy_dists, polars_dists, rtol=1e-10)
diff --git a/pipeline/utils/test_poi_counts.py b/pipeline/utils/test_poi_counts.py
new file mode 100644
index 0000000..a7366f5
--- /dev/null
+++ b/pipeline/utils/test_poi_counts.py
@@ -0,0 +1,85 @@
+import polars as pl
+import pytest
+
+from pipeline.utils.poi_counts import POI_GROUPS, count_pois_within_radius
+
+
+@pytest.fixture
+def pois():
+    """POIs clustered around two locations: central London and 10km away."""
+    return pl.DataFrame({
+        "lat": [51.5074, 51.5075, 51.5080, 51.5076, 51.5073, 51.60],
+        "lng": [-0.1278, -0.1280, -0.1275, -0.1279, -0.1277, -0.20],
+        "category": [
+            "Restaurant",
+            "Fast Food",
+            "Supermarket",
+            "Park",
+            "Station",
+            "Restaurant",   # too far from any property
+        ],
+    })
+
+
+@pytest.fixture
+def properties():
+    """Two properties at the same postcode near central London, one at a distant postcode."""
+    return pl.DataFrame({
+        "postcode": ["EC1A 1BB", "EC1A 1BB", "ZZ99 9ZZ"],
+        "lat": [51.5074, 51.5074, 55.0],
+        "lon": [-0.1278, -0.1278, -3.0],
+    })
+
+
+def test_counts_pois_within_radius(properties, pois):
+    result = count_pois_within_radius(properties, pois, radius_km=2.0)
+
+    assert set(result.keys()) == {f"{g}_2km" for g in POI_GROUPS}
+
+    # Result Series must be aligned to properties (3 rows)
+    for col, series in result.items():
+        assert len(series) == 3, f"{col} has {len(series)} rows, expected 3"
+
+    # First two rows share a postcode near the central London cluster
+    assert result["restaurants_2km"][0] == 2   # Restaurant + Fast Food
+    assert result["groceries_2km"][0] == 1     # Supermarket
+    assert result["parks_2km"][0] == 1         # Park
+    assert result["public_transport_2km"][0] == 1  # Station
+
+    # Second row is the same postcode, so same counts
+    assert result["restaurants_2km"][1] == result["restaurants_2km"][0]
+
+    # Third row (ZZ99 9ZZ) is far from all POIs → zero counts
+    for group in POI_GROUPS:
+        assert result[f"{group}_2km"][2] == 0
+
+
+def test_no_pois_returns_zeros(properties):
+    empty_pois = pl.DataFrame({
+        "lat": pl.Series([], dtype=pl.Float64),
+        "lng": pl.Series([], dtype=pl.Float64),
+        "category": pl.Series([], dtype=pl.String),
+    })
+    result = count_pois_within_radius(properties, empty_pois, radius_km=2.0)
+
+    for group in POI_GROUPS:
+        col = f"{group}_2km"
+        assert col in result
+        assert result[col].to_list() == [0, 0, 0]
+
+
+def test_custom_radius(pois):
+    """A tiny radius should exclude POIs that are even slightly away."""
+    properties = pl.DataFrame({
+        "postcode": ["EC1A 1BB"],
+        "lat": [51.5074],
+        "lon": [-0.1278],
+    })
+
+    # 0.01 km = 10m — only the POI at the exact same location should match
+    result = count_pois_within_radius(properties, pois, radius_km=0.01)
+    # The Restaurant at (51.5074, -0.1278) is at distance 0
+    assert result["restaurants_0km"][0] >= 1
+    # POIs >100m away should not be counted
+    total = sum(result[f"{g}_0km"][0] for g in POI_GROUPS)
+    assert total <= 2  # at most the co-located POIs

From 38b0cf1ea110f6953ef5c8f7424059b586f3be8a Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sat, 31 Jan 2026 12:48:29 +0000
Subject: [PATCH 24/62] Move transform logic around

---
 Taskfile.yml                                  | 155 ++++-
 pipeline/base.py                              |  22 -
 pipeline/config.py                            |  12 -
 .../download/{pois/__main__.py => pois.py}    |  39 +-
 pipeline/download/pois/__init__.py            |   0
 pipeline/download/pois/config.py              |  32 -
 pipeline/epc_pp.py                            |  85 ---
 pipeline/run.py                               |   6 -
 pipeline/transform/join_epc_pp.py             |  98 +++
 pipeline/transform/merge.py                   | 127 ++++
 pipeline/transform/poi_proximity.py           |  42 ++
 pipeline/transform/transform_poi.py           | 644 ++++++++++++++++++
 pipeline/utils/test_fuzzy_join.py             |   4 +-
 pipeline/wide.py                              | 143 ----
 14 files changed, 1073 insertions(+), 336 deletions(-)
 delete mode 100644 pipeline/base.py
 delete mode 100644 pipeline/config.py
 rename pipeline/download/{pois/__main__.py => pois.py} (90%)
 delete mode 100644 pipeline/download/pois/__init__.py
 delete mode 100644 pipeline/download/pois/config.py
 delete mode 100644 pipeline/epc_pp.py
 delete mode 100644 pipeline/run.py
 create mode 100644 pipeline/transform/join_epc_pp.py
 create mode 100644 pipeline/transform/merge.py
 create mode 100644 pipeline/transform/poi_proximity.py
 create mode 100644 pipeline/transform/transform_poi.py
 delete mode 100644 pipeline/wide.py

diff --git a/Taskfile.yml b/Taskfile.yml
index 52a0dde..ceb4a47 100644
--- a/Taskfile.yml
+++ b/Taskfile.yml
@@ -1,38 +1,136 @@
 version: '3'
 
+vars:
+  DATA_DIR: data
+  ARCGIS_OUTPUT: "{{.DATA_DIR}}/arcgis_data.parquet"
+  PRICE_PAID_OUTPUT: "{{.DATA_DIR}}/price-paid-complete.parquet"
+  IOD_OUTPUT: "{{.DATA_DIR}}/IoD2025_Scores.parquet"
+  POIS_RAW_OUTPUT: "{{.DATA_DIR}}/uk_pois.parquet"
+  POIS_FILTERED_OUTPUT: "{{.DATA_DIR}}/filtered_uk_pois.parquet"
+  POI_PROXIMITY_OUTPUT: "{{.DATA_DIR}}/poi_proximity.parquet"
+  EPC_PP_OUTPUT: "{{.DATA_DIR}}/epc_pp.parquet"
+  WIDE_OUTPUT: "{{.DATA_DIR}}/wide.parquet"
+  EPC_CSV: "{{.DATA_DIR}}/epc/certificates.csv"
+  JOURNEY_TIMES: "{{.DATA_DIR}}/journey_times_bank_checkpoint.parquet"
+
 tasks:
   install:
-    desc: Install dependencies, generate client, and download data
+    desc: Install dependencies
     cmds:
       - uv sync
       - cd frontend && npm install
 
-  download:
-    desc: Download data
-    deps:
-      - install
+  download:arcgis:
+    desc: Download and convert ArcGIS postcode data
+    sources:
+      - pipeline/download/arcgis.py
+    generates:
+      - "{{.ARCGIS_OUTPUT}}"
     cmds:
-      - uv run -m pipeline.download.arcgis
-      - uv run -m pipeline.download.pois
-      - uv run -m pipeline.download.deprivation_data
-      - uv run -m pipeline.download.price_paid
+      - uv run python -m pipeline.download.arcgis --output {{.ARCGIS_OUTPUT}}
 
-  pipeline:
-    desc: Run data processing pipeline
-    deps:
-      - download
+  download:price-paid:
+    desc: Download and convert Land Registry price-paid data
+    sources:
+      - pipeline/download/price_paid.py
+    generates:
+      - "{{.PRICE_PAID_OUTPUT}}"
     cmds:
-      - uv run python -m pipeline.run
+      - uv run python -m pipeline.download.price_paid --output {{.PRICE_PAID_OUTPUT}}
+
+  download:deprivation:
+    desc: Download and convert Index of Deprivation data
+    sources:
+      - pipeline/download/deprivation_data.py
+    generates:
+      - "{{.IOD_OUTPUT}}"
+    cmds:
+      - uv run python -m pipeline.download.deprivation_data --output {{.IOD_OUTPUT}}
+
+  download:pois:
+    desc: Download and extract POIs from OpenStreetMap
+    sources:
+      - pipeline/download/pois.py
+    generates:
+      - "{{.POIS_RAW_OUTPUT}}"
+    cmds:
+      - uv run python -m pipeline.download.pois --output {{.POIS_RAW_OUTPUT}}
+
+  transform:pois:
+    desc: Transform raw POIs to filtered version with friendly names
+    deps:
+      - download:pois
+    sources:
+      - pipeline/transform/transform_poi.py
+      - "{{.POIS_RAW_OUTPUT}}"
+    generates:
+      - "{{.POIS_FILTERED_OUTPUT}}"
+    cmds:
+      - uv run python -m pipeline.transform.transform_poi --input {{.POIS_RAW_OUTPUT}} --output {{.POIS_FILTERED_OUTPUT}}
+
+  transform:epc-pp:
+    desc: Fuzzy join EPC and Price Paid data
+    deps:
+      - download:price-paid
+    sources:
+      - pipeline/transform/join_epc_pp.py
+      - pipeline/utils/fuzzy_join.py
+      - "{{.PRICE_PAID_OUTPUT}}"
+      - "{{.EPC_CSV}}"
+    generates:
+      - "{{.EPC_PP_OUTPUT}}"
+    cmds:
+      - uv run python -m pipeline.transform.join_epc_pp --epc {{.EPC_CSV}} --price-paid {{.PRICE_PAID_OUTPUT}} --output {{.EPC_PP_OUTPUT}}
+
+  transform:poi-proximity:
+    desc: Compute POI proximity counts per postcode
+    deps:
+      - download:arcgis
+      - transform:pois
+    sources:
+      - pipeline/transform/poi_proximity.py
+      - pipeline/utils/poi_counts.py
+      - "{{.ARCGIS_OUTPUT}}"
+      - "{{.POIS_FILTERED_OUTPUT}}"
+    generates:
+      - "{{.POI_PROXIMITY_OUTPUT}}"
+    cmds:
+      - uv run python -m pipeline.transform.poi_proximity --arcgis {{.ARCGIS_OUTPUT}} --pois {{.POIS_FILTERED_OUTPUT}} --output {{.POI_PROXIMITY_OUTPUT}}
+
+  transform:wide:
+    desc: Build wide property dataframe with all joins
+    deps:
+      - join:epc-pp
+      - download:arcgis
+      - download:deprivation
+      - transform:poi-proximity
+    sources:
+      - pipeline/transform/merge.py
+      - "{{.EPC_PP_OUTPUT}}"
+      - "{{.ARCGIS_OUTPUT}}"
+      - "{{.IOD_OUTPUT}}"
+      - "{{.POI_PROXIMITY_OUTPUT}}"
+    generates:
+      - "{{.WIDE_OUTPUT}}"
+    cmds:
+      - uv run python -m pipeline.transform.merge --epc-pp {{.EPC_PP_OUTPUT}} --arcgis {{.ARCGIS_OUTPUT}} --iod {{.IOD_OUTPUT}} --poi-proximity {{.POI_PROXIMITY_OUTPUT}} --journey-times {{.JOURNEY_TIMES}} --output {{.WIDE_OUTPUT}}
 
   prepare:
     desc: Prepare the application (install, download data, run pipeline)
     deps:
-      - pipeline
+      - transform:wide
+
+  test:
+    cmds:
+      - uv run -m pipeline.utils.test_fuzzy_join
+      - uv run pytest pipeline/utils/test_haversine.py
+      - uv run pytest pipeline/utils/test_poi_counts.py
 
   server:
-    desc: Run FastAPI backend on port 8001
+    desc: Run Rust backend on port 8001
+    dir: server-rs
     cmds:
-      - uv run fastapi dev server/main.py --port 8001
+      - cargo run --release -- {{.WIDE_OUTPUT}}
 
   frontend:
     desc: Run frontend dev server on port 3030 (proxies /api to :8001)
@@ -46,16 +144,13 @@ tasks:
     cmds:
       - npm run build
 
-  prod:
-    desc: Run production server (serves built frontend)
-    cmds:
-      - uv run fastapi run server/main.py --port 8001
 
   lint:
-    desc: Lint all code (Python and TypeScript)
+    desc: Lint all code (Python, TypeScript, and Rust)
     cmds:
       - task: lint:python
       - task: lint:frontend
+      - task: lint:rust
 
   lint:python:
     desc: Lint Python code with ruff
@@ -69,11 +164,19 @@ tasks:
       - npm run lint
       - npm run format:check
 
+  lint:rust:
+    desc: Lint Rust code with clippy and check formatting
+    dir: server-rs
+    cmds:
+      - cargo clippy -- -D warnings
+      - cargo fmt --check
+
   format:
-    desc: Format all code (Python and TypeScript)
+    desc: Format all code (Python, TypeScript, and Rust)
     cmds:
       - task: format:python
       - task: format:frontend
+      - task: format:rust
 
   format:python:
     desc: Format Python code with ruff
@@ -88,6 +191,12 @@ tasks:
       - npm run lint:fix
       - npm run format
 
+  format:rust:
+    desc: Format Rust code with cargo fmt
+    dir: server-rs
+    cmds:
+      - cargo fmt
+
   check:
     desc: Run all checks (lint, typecheck, build)
     cmds:
diff --git a/pipeline/base.py b/pipeline/base.py
deleted file mode 100644
index 8394792..0000000
--- a/pipeline/base.py
+++ /dev/null
@@ -1,22 +0,0 @@
-from abc import ABC, abstractmethod
-import polars as pl
-
-
-class DataSource(ABC):
-    """Base class for all data sources."""
-
-    @property
-    @abstractmethod
-    def name(self) -> str:
-        """Unique identifier for this data source."""
-        pass
-
-    @abstractmethod
-    def load(self) -> pl.LazyFrame:
-        """Load raw data as LazyFrame."""
-        pass
-
-    @abstractmethod
-    def process(self, postcodes: pl.LazyFrame) -> pl.LazyFrame:
-        """Process and join with postcode coordinates."""
-        pass
diff --git a/pipeline/config.py b/pipeline/config.py
deleted file mode 100644
index ca7ca24..0000000
--- a/pipeline/config.py
+++ /dev/null
@@ -1,12 +0,0 @@
-"""Shared configuration for the pipeline and server."""
-
-from pathlib import Path
-
-DATA_DIR = Path(__file__).parent.parent / "data_sources"
-PROCESSED_DIR = DATA_DIR / "processed"
-AGGREGATES_DIR = PROCESSED_DIR / "aggregates"
-
-# H3 resolutions to generate and serve
-# https://h3geo.org/docs/core-library/restable/#average-area-in-m2
-H3_RESOLUTIONS = [7, 8, 9, 10, 11]
-DEFAULT_H3_RESOLUTION = 8
diff --git a/pipeline/download/pois/__main__.py b/pipeline/download/pois.py
similarity index 90%
rename from pipeline/download/pois/__main__.py
rename to pipeline/download/pois.py
index 7b06c06..2694fc2 100644
--- a/pipeline/download/pois/__main__.py
+++ b/pipeline/download/pois.py
@@ -8,17 +8,35 @@ import osmium
 import polars as pl
 from tqdm import tqdm
 
-from .config import (
-    BATCH_SIZE,
-    GEOFABRIK_GB_URL,
-    MIN_OCCURENCE_COUNT,
-    POI_TAG_KEYS,
-    UK_BBOX_EAST,
-    UK_BBOX_NORTH,
-    UK_BBOX_SOUTH,
-    UK_BBOX_WEST,
+from pathlib import Path
+
+
+BATCH_SIZE = 50_000
+
+MIN_OCCURENCE_COUNT = 20
+
+GEOFABRIK_GB_URL = (
+    "https://download.geofabrik.de/europe/great-britain-latest.osm.pbf"
 )
 
+UK_BBOX_WEST = -7.57
+UK_BBOX_SOUTH = 49.96
+UK_BBOX_EAST = 1.68
+UK_BBOX_NORTH = 58.64
+
+POI_TAG_KEYS: list[str] = [
+    "amenity",
+    "building",
+    "craft",
+    "emergency",
+    "healthcare",
+    "leisure",
+    "office",
+    "shop",
+    "tourism",
+    "public_transport",
+]
+
 
 
 def download_pbf(pbf_file: Path) -> None:
@@ -144,10 +162,9 @@ def main() -> None:
         )
         df = df.join(valid_categories.select("category"), on="category", how="semi")
 
-        args.output.parent.mkdir(parents=True, exist_ok=True)
+        print(f"Total POIs: {handler.poi_count:,}")
         df.sink_parquet(args.output)
         print(f"Saved to {args.output}")
-        print(f"Total POIs: {handler.poi_count:,}")
 
 
 if __name__ == "__main__":
diff --git a/pipeline/download/pois/__init__.py b/pipeline/download/pois/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/pipeline/download/pois/config.py b/pipeline/download/pois/config.py
deleted file mode 100644
index eb677a6..0000000
--- a/pipeline/download/pois/config.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from pathlib import Path
-
-DATA_DIR = Path("./data_sources")
-GB_PBF_FILE = DATA_DIR / "great-britain-latest.osm.pbf"
-OUTPUT_FILE = DATA_DIR / "uk_pois.parquet"
-
-
-BATCH_SIZE = 50_000
-
-MIN_OCCURENCE_COUNT = 20
-
-GEOFABRIK_GB_URL = (
-    "https://download.geofabrik.de/europe/great-britain-latest.osm.pbf"
-)
-
-UK_BBOX_WEST = -7.57
-UK_BBOX_SOUTH = 49.96
-UK_BBOX_EAST = 1.68
-UK_BBOX_NORTH = 58.64
-
-POI_TAG_KEYS: list[str] = [
-    "amenity",
-    "building",
-    "craft",
-    "emergency",
-    "healthcare",
-    "leisure",
-    "office",
-    "shop",
-    "tourism",
-    "public_transport",
-]
diff --git a/pipeline/epc_pp.py b/pipeline/epc_pp.py
deleted file mode 100644
index 9d7d480..0000000
--- a/pipeline/epc_pp.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import polars as pl
-from .fuzzy_join import fuzzy_join_on_postcode
-
-
-pl.Config.set_tbl_cols(-1)
-
-
-
-epc = pl.scan_csv('data_sources/epc/certificates.csv').select(
-    pl.col('ADDRESS').alias('epc_address'),
-    'POSTCODE',
-    'CURRENT_ENERGY_RATING',
-    'POTENTIAL_ENERGY_RATING',
-    pl.col('PROPERTY_TYPE').alias('epc_property_type'),
-    'BUILT_FORM',
-    'INSPECTION_DATE',
-    'TOTAL_FLOOR_AREA',
-    'NUMBER_HABITABLE_ROOMS',
-    'FLOOR_HEIGHT',
-    'CONSTRUCTION_AGE_BAND'
-).filter(pl.col('epc_address').is_not_null()).sort('INSPECTION_DATE', descending=True).group_by('epc_address', 'POSTCODE').first()
-
-
-print("EPC dataset")
-print(epc.head().collect())
-
-# https://www.gov.uk/guidance/about-the-price-paid-data
-property_type_map = {"D": "Detached", "S": "Semi-Detached", "T": "Terraced", "F": "Flats/Maisonettes", "O": "Other"}
-duration_map = {"F": "Freehold", "L": "Leasehold"}
-
-price_paid = (pl.scan_parquet('data_sources/pp-complete.parquet').select(
-    "price",
-    "date_of_transfer",
-    pl.col('property_type').alias("pp_property_type").replace(property_type_map),
-    "postcode",
-    'paon',
-    'saon',
-    'street',
-    'locality',
-    'town_city',
-    pl.col('duration').replace(duration_map)
-)
-.filter(pl.col('pp_property_type') != 'Other').with_columns(
-        pl.concat_str(
-            [pl.col('saon'), pl.col('paon'), pl.col('street')],
-            separator=' ',
-            ignore_nulls=True,
-        ).alias('pp_address'),
-    )
-    .sort('date_of_transfer')
-    .group_by('pp_address', 'postcode', maintain_order=True)
-    .agg(
-        pl.struct(
-            pl.col('date_of_transfer').dt.year().alias('year'),
-            'price',
-        ).alias('historical_prices'),
-        pl.col('pp_property_type').last(),
-        pl.col('duration').last(),
-        pl.col('price').last().alias('latest_price'),
-        pl.col('date_of_transfer').last(),
-    )   
-).filter(pl.col('pp_address').is_not_null())
-
-print("Price paid dataset")
-print(price_paid.head().collect())
-
-joined = fuzzy_join_on_postcode(
-    left=price_paid,
-    right=epc,
-    left_address_col='pp_address',
-    right_address_col='epc_address',
-    left_postcode_col='postcode',
-    right_postcode_col='POSTCODE',
-).drop('POSTCODE').collect()
-
-matched = joined.filter(pl.col('epc_address').is_not_null() & pl.col('pp_address').is_not_null())
-total = joined.height
-print(f"Unique properties: {total}")
-print(f"Matched: {matched.height} ({100 * matched.height / total:.1f}%)")
-print(f"Unmatched: {total - matched.height}")
-
-matched = matched.rename({col: col.lower() for col in joined.columns})
-
-print(matched.head())
-matched.write_parquet('data_sources/processed/epc_pp.parquet')
diff --git a/pipeline/run.py b/pipeline/run.py
deleted file mode 100644
index 6a03ed6..0000000
--- a/pipeline/run.py
+++ /dev/null
@@ -1,6 +0,0 @@
-"""Pipeline CLI to process property data with H3 spatial indexing."""
-
-from pipeline.wide import run
-
-if __name__ == "__main__":
-    run()
diff --git a/pipeline/transform/join_epc_pp.py b/pipeline/transform/join_epc_pp.py
new file mode 100644
index 0000000..8717dc4
--- /dev/null
+++ b/pipeline/transform/join_epc_pp.py
@@ -0,0 +1,98 @@
+import argparse
+import polars as pl
+from pathlib import Path
+from ..utils import fuzzy_join_on_postcode
+
+
+pl.Config.set_tbl_cols(-1)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Fuzzy join EPC and Price Paid data")
+    parser.add_argument("--epc", type=Path, required=True, help="EPC certificates CSV file")
+    parser.add_argument("--price-paid", type=Path, required=True, help="Price paid parquet file")
+    parser.add_argument("--output", type=Path, required=True, help="Output parquet file path")
+    args = parser.parse_args()
+
+    epc = pl.scan_csv(args.epc).select(
+        pl.col('ADDRESS').alias('epc_address'),
+        'POSTCODE',
+        'CURRENT_ENERGY_RATING',
+        'POTENTIAL_ENERGY_RATING',
+        pl.col('PROPERTY_TYPE').alias('epc_property_type'),
+        'BUILT_FORM',
+        'INSPECTION_DATE',
+        'TOTAL_FLOOR_AREA',
+        'NUMBER_HABITABLE_ROOMS',
+        'FLOOR_HEIGHT',
+        'CONSTRUCTION_AGE_BAND'
+    ).filter(pl.col('epc_address').is_not_null()).sort('INSPECTION_DATE', descending=True).group_by('epc_address', 'POSTCODE').first()
+
+
+    print("EPC dataset")
+    print(epc.head().collect())
+
+    # https://www.gov.uk/guidance/about-the-price-paid-data
+    property_type_map = {"D": "Detached", "S": "Semi-Detached", "T": "Terraced", "F": "Flats/Maisonettes", "O": "Other"}
+    duration_map = {"F": "Freehold", "L": "Leasehold"}
+
+    price_paid = (pl.scan_parquet(args.price_paid).select(
+        "price",
+        "date_of_transfer",
+        pl.col('property_type').alias("pp_property_type").replace(property_type_map),
+        "postcode",
+        'paon',
+        'saon',
+        'street',
+        'locality',
+        'town_city',
+        pl.col('duration').replace(duration_map)
+    )
+    .filter(pl.col('pp_property_type') != 'Other').with_columns(
+            pl.concat_str(
+                [pl.col('saon'), pl.col('paon'), pl.col('street')],
+                separator=' ',
+                ignore_nulls=True,
+            ).alias('pp_address'),
+        )
+        .sort('date_of_transfer')
+        .group_by('pp_address', 'postcode', maintain_order=True)
+        .agg(
+            pl.struct(
+                pl.col('date_of_transfer').dt.year().alias('year'),
+                'price',
+            ).alias('historical_prices'),
+            pl.col('pp_property_type').last(),
+            pl.col('duration').last(),
+            pl.col('price').last().alias('latest_price'),
+            pl.col('date_of_transfer').last(),
+        )
+    ).filter(pl.col('pp_address').is_not_null())
+
+    print("Price paid dataset")
+    print(price_paid.head().collect())
+
+    joined = fuzzy_join_on_postcode(
+        left=price_paid,
+        right=epc,
+        left_address_col='pp_address',
+        right_address_col='epc_address',
+        left_postcode_col='postcode',
+        right_postcode_col='POSTCODE',
+    ).drop('POSTCODE').collect()
+
+    matched = joined.filter(pl.col('epc_address').is_not_null() & pl.col('pp_address').is_not_null())
+    total = joined.height
+    print(f"Unique properties: {total}")
+    print(f"Matched: {matched.height} ({100 * matched.height / total:.1f}%)")
+    print(f"Unmatched: {total - matched.height}")
+
+    matched = matched.rename({col: col.lower() for col in joined.columns})
+
+    print(matched.head())
+    matched.write_parquet(args.output)
+    print(f"Wrote {args.output}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/pipeline/transform/merge.py b/pipeline/transform/merge.py
new file mode 100644
index 0000000..1ea4459
--- /dev/null
+++ b/pipeline/transform/merge.py
@@ -0,0 +1,127 @@
+import argparse
+import polars as pl
+from pathlib import Path
+
+
+def _build_wide(
+    epc_pp_path: Path,
+    arcgis_path: Path,
+    iod_path: Path | None,
+    poi_proximity_path: Path | None,
+    journey_times_path: Path | None,
+) -> pl.DataFrame:
+    """Build the wide dataframe by joining epc_pp with all auxiliary data."""
+    print("Loading epc_pp...")
+    wide = pl.read_parquet(epc_pp_path)
+    print(f"  {wide.shape[0]:,} rows, {wide.estimated_size('mb'):.1f} MB")
+
+    # GPS coordinates + LSOA from ArcGIS
+    print("Joining GPS coordinates...")
+    arcgis = pl.read_parquet(arcgis_path).select(
+        pl.col("pcds").alias("postcode"),
+        "lat",
+        pl.col("long").alias("lon"),
+        "lsoa21",
+    )
+    wide = wide.join(arcgis, on="postcode", how="inner")
+    print(f"  {wide.shape[0]:,} rows after GPS join, {wide.estimated_size('mb'):.1f} MB")
+
+    # Journey times (optional)
+    if journey_times_path and journey_times_path.exists():
+        print("Joining journey times...")
+        journey_times = pl.read_parquet(journey_times_path).select(
+            "postcode",
+            "public_transport_easy_minutes",
+            "public_transport_quick_minutes",
+            "cycling_minutes",
+        )
+        wide = wide.join(journey_times, on="postcode", how="left")
+        print(f"  {wide.estimated_size('mb'):.1f} MB after journey times")
+
+    # Index of Deprivation
+    if iod_path and iod_path.exists():
+        print("Joining IoD scores...")
+        iod = pl.read_parquet(iod_path)
+        wide = wide.join(
+            iod, left_on="lsoa21", right_on="LSOA code (2021)", how="left"
+        )
+        print(f"  {wide.estimated_size('mb'):.1f} MB after IoD")
+
+    # POI proximity counts (pre-computed per postcode)
+    if poi_proximity_path and poi_proximity_path.exists():
+        print("Joining POI proximity counts...")
+        poi_counts = pl.read_parquet(poi_proximity_path)
+        wide = wide.join(poi_counts, on="postcode", how="left")
+        print(f"  {wide.estimated_size('mb'):.1f} MB after POI counts")
+
+    # Convert construction_age_band to numeric year
+    if "construction_age_band" in wide.columns:
+        wide = wide.with_columns(
+            pl.col("construction_age_band")
+            .str.replace("England and Wales: ", "")
+            .str.replace(" onwards", "")
+            .str.extract(r"(\d{4})", 1)
+            .cast(pl.UInt16, strict=False)
+            .alias("construction_age_band"),
+        )
+
+    # Derived columns
+    wide = wide.with_columns(
+        (pl.col("latest_price") / pl.col("total_floor_area")).alias("Price per sqm"),
+    ).drop(
+        'date_of_transfer',
+        'inspection_date',
+        'floor_height',
+        'lsoa21',
+        'LSOA code (2021)',
+        'Local Authority District code (2024)',
+        'Local Authority District name (2024)',
+        'imd_score',
+        'housing_barriers_score',
+        'idaci_score',
+        'idaopi_score',
+        'children_young_people_score',
+        'adult_skills_score',
+        'geographical_barriers_score',
+        'wider_barriers_score',
+    ).rename({
+        'construction_age_band': "Approximate construction age",
+        "income_score": "Income Score (rate)",
+        "employment_score": "Employment Score (rate)",
+        "education_score": "Education, Skills and Training Score",
+        "health_score": "Health Deprivation and Disability Score",
+        "crime_score": "Crime Score",
+    })
+
+    return wide
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Build wide property dataframe with all joins")
+    parser.add_argument("--epc-pp", type=Path, required=True, help="EPC-Price Paid joined parquet file")
+    parser.add_argument("--arcgis", type=Path, required=True, help="ArcGIS postcode data parquet file")
+    parser.add_argument("--iod", type=Path, help="Index of Deprivation parquet file (optional)")
+    parser.add_argument("--poi-proximity", type=Path, help="POI proximity counts parquet file (optional)")
+    parser.add_argument("--journey-times", type=Path, help="Journey times parquet file (optional)")
+    parser.add_argument("--output", type=Path, required=True, help="Output parquet file path")
+    args = parser.parse_args()
+
+    wide = _build_wide(
+        epc_pp_path=args.epc_pp,
+        arcgis_path=args.arcgis,
+        iod_path=args.iod,
+        poi_proximity_path=args.poi_proximity,
+        journey_times_path=args.journey_times,
+    )
+
+    print(f"Columns: {wide.columns}")
+    print(f"Rows: {wide.height}")
+
+    wide.write_parquet(args.output)
+    size_mb = args.output.stat().st_size / (1024 * 1024)
+    
+    print(f"Wrote {args.output} ({size_mb:.1f} MB)")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/pipeline/transform/poi_proximity.py b/pipeline/transform/poi_proximity.py
new file mode 100644
index 0000000..dce09cc
--- /dev/null
+++ b/pipeline/transform/poi_proximity.py
@@ -0,0 +1,42 @@
+"""Compute POI proximity counts per postcode from ArcGIS + filtered POIs."""
+
+import argparse
+from pathlib import Path
+
+import polars as pl
+
+from pipeline.utils.poi_counts import _count_pois_per_postcode
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Count POIs within radius per postcode"
+    )
+    parser.add_argument(
+        "--arcgis", type=Path, required=True, help="ArcGIS postcode parquet"
+    )
+    parser.add_argument(
+        "--pois", type=Path, required=True, help="Filtered POIs parquet"
+    )
+    parser.add_argument(
+        "--output", type=Path, required=True, help="Output parquet path"
+    )
+    args = parser.parse_args()
+
+    postcodes = pl.read_parquet(args.arcgis).select(
+        pl.col("pcds").alias("postcode"),
+        "lat",
+        pl.col("long").alias("lon"),
+    )
+
+    pois = pl.read_parquet(args.pois)
+
+    result = _count_pois_per_postcode(postcodes, pois, radius_km=2)
+
+    result.write_parquet(args.output)
+    size_mb = args.output.stat().st_size / (1024 * 1024)
+    print(f"Wrote {args.output} ({size_mb:.1f} MB)")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/pipeline/transform/transform_poi.py b/pipeline/transform/transform_poi.py
new file mode 100644
index 0000000..d9caeb5
--- /dev/null
+++ b/pipeline/transform/transform_poi.py
@@ -0,0 +1,644 @@
+import argparse
+from pathlib import Path
+
+import polars as pl
+
+
+DROP_CATEGORIES = {
+    "amenity/advice",
+    "amenity/atm",
+    "amenity/bbq",
+    "amenity/bench",
+    "amenity/bicycle_parking",
+    "amenity/clock",
+    "amenity/fixme",
+    "amenity/grit_bin",
+    "amenity/hunting_stand",
+    "amenity/motorcycle_parking",
+    "amenity/notice_board",
+    "amenity/parking",
+    "amenity/parking_entrance",
+    "amenity/parking_space",
+    "amenity/post_box",
+    "amenity/telephone",
+    "amenity/toilets",
+    "amenity/vacuum_cleaner",
+    "amenity/waste_basket",
+    "building/air_shaft",
+    "building/apartments",
+    "building/detached",
+    "building/entrance",
+    "building/entry",
+    "building/garage",
+    "building/garages",
+    "building/house",
+    "building/hut",
+    "building/no",
+    "building/office",
+    "building/public",
+    "building/residential",
+    "building/roof",
+    "building/shed",
+    "building/terrace",
+    "building/yes",
+    "emergency/access_point",
+    "emergency/ambulance_station",
+    "emergency/assembly_point",
+    "emergency/bleed_control_kit",
+    "emergency/defibrillator",
+    "emergency/designated",
+    "emergency/dry_riser_inlet",
+    "emergency/emergency_ward_entrance",
+    "emergency/fire_alarm_box",
+    "emergency/fire_extinguisher",
+    "emergency/fire_hydrant",
+    "emergency/fire_service_inlet",
+    "emergency/first_aid_kit",
+    "emergency/life_ring",
+    "emergency/lifeguard",
+    "emergency/no",
+    "emergency/phone",
+    "emergency/rescue_equipment",
+    "emergency/siren",
+    "emergency/throw_bag",
+    "emergency/water_rescue",
+    "emergency/yes",
+    "leisure/firepit",
+    "leisure/fishing",
+    "leisure/picnic_table",
+    "office/company",
+    "office/yes",
+    "tourism/apartment",
+    "tourism/apartments",
+    "tourism/camp_pitch",
+    "tourism/information",
+    "tourism/village_sign",
+    "tourism/yes",
+}
+
+# (friendly_name, emoji) for every category we keep
+CATEGORY_MAP: dict[str, tuple[str, str]] = {
+    # amenity
+    "amenity/animal_boarding": ("Animal Boarding", "🐾"),
+    "amenity/animal_breeding": ("Animal Breeding", "🐣"),
+    "amenity/animal_shelter": ("Animal Shelter", "🏠"),
+    "amenity/arts_centre": ("Arts Centre", "🎨"),
+    "amenity/bank": ("Bank", "🏦"),
+    "amenity/bar": ("Bar", "🍸"),
+    "amenity/bicycle_rental": ("Bike Rental", "🚲"),
+    "amenity/bicycle_repair_station": ("Bike Repair", "🔧"),
+    "amenity/binoculars": ("Public Binoculars", "🔭"),
+    "amenity/boat_rental": ("Boat Rental", "⛵"),
+    "amenity/boat_storage": ("Boat Storage", "🚢"),
+    "amenity/boot_scraper": ("Boot Scraper", "🥾"),
+    "amenity/bureau_de_change": ("Currency Exchange", "💱"),
+    "amenity/bus_station": ("Bus Station", "🚌"),
+    "amenity/cafe": ("Café", "☕"),
+    "amenity/car_rental": ("Car Rental", "🚗"),
+    "amenity/car_sharing": ("Car Sharing", "🚙"),
+    "amenity/car_wash": ("Car Wash", "🧽"),
+    "amenity/care_home": ("Care Home", "🏥"),
+    "amenity/casino": ("Casino", "🎰"),
+    "amenity/charging_station": ("EV Charging", "🔌"),
+    "amenity/check_in": ("Check-In Point", "✅"),
+    "amenity/childcare": ("Childcare", "👶"),
+    "amenity/cinema": ("Cinema", "🎬"),
+    "amenity/clinic": ("Clinic", "🩺"),
+    "amenity/club": ("Club", "🏛️"),
+    "amenity/college": ("College", "🎓"),
+    "amenity/community_centre": ("Community Centre", "🤝"),
+    "amenity/compressed_air": ("Compressed Air", "💨"),
+    "amenity/conference_centre": ("Conference Centre", "📋"),
+    "amenity/courthouse": ("Courthouse", "⚖️"),
+    "amenity/coworking_space": ("Co-working Space", "💻"),
+    "amenity/crematorium": ("Crematorium", "🕯️"),
+    "amenity/dancing_school": ("Dance School", "💃"),
+    "amenity/dentist": ("Dentist", "🦷"),
+    "amenity/doctors": ("Doctor", "👨‍⚕️"),
+    "amenity/dojo": ("Dojo", "🥋"),
+    "amenity/donation_box": ("Donation Box", "📦"),
+    "amenity/dressing_room": ("Dressing Room", "👗"),
+    "amenity/drinking_water": ("Drinking Water", "🚰"),
+    "amenity/driving_school": ("Driving School", "🚦"),
+    "amenity/escooter_rental": ("E-Scooter Rental", "🛴"),
+    "amenity/events_venue": ("Events Venue", "🎪"),
+    "amenity/fast_food": ("Fast Food", "🍔"),
+    "amenity/feeding_place": ("Feeding Place", "🍽️"),
+    "amenity/ferry_terminal": ("Ferry Terminal", "⛴️"),
+    "amenity/fire_station": ("Fire Station", "🚒"),
+    "amenity/food_court": ("Food Court", "🍴"),
+    "amenity/fountain": ("Fountain", "⛲"),
+    "amenity/fuel": ("Fuel Station", "⛽"),
+    "amenity/gambling": ("Gambling", "🎲"),
+    "amenity/grave_yard": ("Graveyard", "🪦"),
+    "amenity/hall": ("Hall", "🏛️"),
+    "amenity/hookah_lounge": ("Hookah Lounge", "💨"),
+    "amenity/hospital": ("Hospital", "🏥"),
+    "amenity/ice_cream": ("Ice Cream", "🍦"),
+    "amenity/internet_cafe": ("Internet Café", "🌐"),
+    "amenity/kick-scooter_rental": ("Kick Scooter Rental", "🛴"),
+    "amenity/kindergarten": ("Kindergarten", "💒"),
+    "amenity/language_school": ("Language School", "🗣️"),
+    "amenity/letter_box": ("Letter Box", "📮"),
+    "amenity/library": ("Library", "📚"),
+    "amenity/loading_dock": ("Loading Dock", "📥"),
+    "amenity/lounge": ("Lounge", "🛋️"),
+    "amenity/lounger": ("Public Lounger", "🪑"),
+    "amenity/marketplace": ("Market", "🛒"),
+    "amenity/money_transfer": ("Money Transfer", "💸"),
+    "amenity/mounting_block": ("Mounting Block", "🐴"),
+    "amenity/music_school": ("Music School", "🎵"),
+    "amenity/music_venue": ("Music Venue", "🎶"),
+    "amenity/nightclub": ("Nightclub", "🪩"),
+    "amenity/nursing_home": ("Nursing Home", "🏠"),
+    "amenity/parcel_locker": ("Parcel Locker", "📦"),
+    "amenity/payment_terminal": ("Payment Terminal", "💳"),
+    "amenity/pharmacy": ("Pharmacy", "💊"),
+    "amenity/photo_booth": ("Photo Booth", "📸"),
+    "amenity/piano": ("Public Piano", "🎹"),
+    "amenity/place_of_worship": ("Place of Worship", "⛪"),
+    "amenity/police": ("Police Station", "🚔"),
+    "amenity/post_depot": ("Post Depot", "📬"),
+    "amenity/post_office": ("Post Office", "🏤"),
+    "amenity/prep_school": ("Prep School", "📖"),
+    "amenity/pub": ("Pub", "🍺"),
+    "amenity/public_bookcase": ("Public Bookcase", "📕"),
+    "amenity/public_building": ("Public Building", "🏢"),
+    "amenity/reception_desk": ("Reception Desk", "🛎️"),
+    "amenity/recycling": ("Recycling", "♻️"),
+    "amenity/restaurant": ("Restaurant", "🍽️"),
+    "amenity/sanitary_dump_station": ("Sanitary Dump Station", "🚿"),
+    "amenity/school": ("School", "🏫"),
+    "amenity/scout_hut": ("Scout Hut", "⚜️"),
+    "amenity/shelter": ("Shelter", "🛖"),
+    "amenity/shower": ("Public Shower", "🚿"),
+    "amenity/smoking_area": ("Smoking Area", "🚬"),
+    "amenity/social_centre": ("Social Centre", "🏘️"),
+    "amenity/social_club": ("Social Club", "🤝"),
+    "amenity/social_facility": ("Social Facility", "🫂"),
+    "amenity/stripclub": ("Strip Club", "🔞"),
+    "amenity/studio": ("Studio", "🎙️"),
+    "amenity/table": ("Public Table", "🪑"),
+    "amenity/taxi": ("Taxi Stand", "🚕"),
+    "amenity/telescope": ("Public Telescope", "🔭"),
+    "amenity/theatre": ("Theatre", "🎭"),
+    "amenity/ticket_validator": ("Ticket Validator", "🎫"),
+    "amenity/townhall": ("Town Hall", "🏛️"),
+    "amenity/training": ("Training Centre", "📝"),
+    "amenity/trolley_bay": ("Trolley Bay", "🛒"),
+    "amenity/university": ("University", "🏫"),
+    "amenity/vehicle_inspection": ("Vehicle Inspection", "🔍"),
+    "amenity/vending_machine": ("Vending Machine", "🏧"),
+    "amenity/veterinary": ("Vet", "🐕"),
+    "amenity/washing_machine": ("Washing Machine", "🧺"),
+    "amenity/washingline": ("Washing Line", "👕"),
+    "amenity/waste_disposal": ("Waste Disposal", "🗑️"),
+    "amenity/waste_transfer_station": ("Waste Transfer Station", "🚛"),
+    "amenity/water_point": ("Water Point", "💧"),
+    "amenity/watering_place": ("Watering Place", "🚰"),
+    "amenity/weighbridge": ("Weighbridge", "⚖️"),
+    # building
+    "building/barn": ("Barn", "🏚️"),
+    "building/bunker": ("Bunker", "🏗️"),
+    "building/chapel": ("Chapel", "⛪"),
+    "building/church": ("Church", "⛪"),
+    "building/commercial": ("Commercial Building", "🏬"),
+    "building/construction": ("Construction Site", "🚧"),
+    "building/farm": ("Farmhouse", "🌾"),
+    "building/greenhouse": ("Greenhouse", "🌿"),
+    "building/industrial": ("Industrial Building", "🏭"),
+    "building/kiosk": ("Kiosk", "🏪"),
+    "building/retail": ("Retail Building", "🏬"),
+    "building/ruins": ("Ruins", "🏚️"),
+    "building/school": ("School Building", "🏫"),
+    "building/semidetached_house": ("Semi-Detached House", "🏠"),
+    "building/service": ("Service Building", "🔧"),
+    "building/university": ("University Building", "🎓"),
+    "building/warehouse": ("Warehouse", "🏭"),
+    # craft
+    "craft/agricultural_engines": ("Agricultural Engines", "🚜"),
+    "craft/atelier": ("Atelier", "🎨"),
+    "craft/blacksmith": ("Blacksmith", "🔨"),
+    "craft/bookbinder": ("Bookbinder", "📖"),
+    "craft/brewery": ("Brewery", "🍺"),
+    "craft/builder": ("Builder", "🧱"),
+    "craft/carpenter": ("Carpenter", "🪚"),
+    "craft/caterer": ("Caterer", "🍱"),
+    "craft/cleaning": ("Cleaning Service", "🧹"),
+    "craft/confectionery": ("Confectioner", "🍬"),
+    "craft/distillery": ("Distillery", "🥃"),
+    "craft/dressmaker": ("Dressmaker", "👗"),
+    "craft/electrician": ("Electrician", "⚡"),
+    "craft/electronics_repair": ("Electronics Repair", "🔌"),
+    "craft/floorer": ("Flooring Specialist", "🪵"),
+    "craft/gardener": ("Gardener", "🌱"),
+    "craft/glaziery": ("Glazier", "🪟"),
+    "craft/handicraft": ("Handicraft", "✂️"),
+    "craft/hvac": ("HVAC", "❄️"),
+    "craft/jeweller": ("Jeweller", "💎"),
+    "craft/joiner": ("Joiner", "🪚"),
+    "craft/key_cutter": ("Key Cutter", "🔑"),
+    "craft/locksmith": ("Locksmith", "🔐"),
+    "craft/metal_construction": ("Metal Fabrication", "🔩"),
+    "craft/painter": ("Painter & Decorator", "🖌️"),
+    "craft/photographer": ("Photographer", "📷"),
+    "craft/photographic_laboratory": ("Photo Lab", "🖼️"),
+    "craft/plumber": ("Plumber", "🔧"),
+    "craft/pottery": ("Pottery", "🏺"),
+    "craft/printer": ("Printer", "🖨️"),
+    "craft/roofer": ("Roofer", "🏠"),
+    "craft/sawmill": ("Sawmill", "🪵"),
+    "craft/scaffolder": ("Scaffolder", "🏗️"),
+    "craft/sculptor": ("Sculptor", "🗿"),
+    "craft/shoemaker": ("Shoemaker", "👞"),
+    "craft/signmaker": ("Sign Maker", "🪧"),
+    "craft/stonemason": ("Stonemason", "🪨"),
+    "craft/tailor": ("Tailor", "🧵"),
+    "craft/upholsterer": ("Upholsterer", "🛋️"),
+    "craft/watchmaker": ("Watchmaker", "⌚"),
+    "craft/window_construction": ("Window Fitter", "🪟"),
+    "craft/winery": ("Winery", "🍷"),
+    "craft/yes": ("Craft Workshop", "🛠️"),
+    # healthcare
+    "healthcare/alternative": ("Alternative Medicine", "🌿"),
+    "healthcare/audiologist": ("Audiologist", "👂"),
+    "healthcare/centre": ("Health Centre", "🏥"),
+    "healthcare/clinic": ("Health Clinic", "🩺"),
+    "healthcare/counselling": ("Counselling", "🧠"),
+    "healthcare/dentist": ("Dental Practice", "🦷"),
+    "healthcare/doctor": ("GP Surgery", "👨‍⚕️"),
+    "healthcare/hospital": ("Hospital", "🏥"),
+    "healthcare/laboratory": ("Medical Lab", "🔬"),
+    "healthcare/optometrist": ("Optometrist", "👁️"),
+    "healthcare/pharmacy": ("Pharmacy", "💊"),
+    "healthcare/physiotherapist": ("Physiotherapist", "🏃"),
+    "healthcare/podiatrist": ("Podiatrist", "🦶"),
+    "healthcare/psychotherapist": ("Psychotherapist", "🧠"),
+    "healthcare/rehabilitation": ("Rehabilitation Centre", "♿"),
+    "healthcare/vaccination_centre": ("Vaccination Centre", "💉"),
+    "healthcare/yes": ("Healthcare Facility", "🏥"),
+    # leisure
+    "leisure/adult_gaming_centre": ("Adult Gaming Centre", "🎮"),
+    "leisure/amusement_arcade": ("Amusement Arcade", "🕹️"),
+    "leisure/bandstand": ("Bandstand", "🎺"),
+    "leisure/bathing_place": ("Bathing Spot", "🏖️"),
+    "leisure/bird_hide": ("Bird Hide", "🐦"),
+    "leisure/bowling_alley": ("Bowling Alley", "🎳"),
+    "leisure/common": ("Common Land", "🌳"),
+    "leisure/dance": ("Dance Venue", "💃"),
+    "leisure/dog_park": ("Dog Park", "🐕"),
+    "leisure/escape_game": ("Escape Room", "🔓"),
+    "leisure/fitness_centre": ("Gym", "🏋️"),
+    "leisure/fitness_station": ("Outdoor Gym", "💪"),
+    "leisure/garden": ("Garden", "🌷"),
+    "leisure/golf_course": ("Golf Course", "⛳"),
+    "leisure/hackerspace": ("Hackerspace", "💻"),
+    "leisure/horse_riding": ("Horse Riding", "🐎"),
+    "leisure/indoor_play": ("Indoor Play Area", "🧒"),
+    "leisure/marina": ("Marina", "⚓"),
+    "leisure/miniature_golf": ("Mini Golf", "⛳"),
+    "leisure/nature_reserve": ("Nature Reserve", "🦔"),
+    "leisure/outdoor_seating": ("Outdoor Seating", "🪑"),
+    "leisure/park": ("Park", "🌳"),
+    "leisure/pitch": ("Sports Pitch", "⚽"),
+    "leisure/playground": ("Playground", "🛝"),
+    "leisure/sauna": ("Sauna", "🧖"),
+    "leisure/slipway": ("Slipway", "🚤"),
+    "leisure/social_club": ("Social Club", "🍻"),
+    "leisure/sports_centre": ("Sports Centre", "🏟️"),
+    "leisure/sports_hall": ("Sports Hall", "🏀"),
+    "leisure/swimming_pool": ("Swimming Pool", "🏊"),
+    "leisure/tanning_salon": ("Tanning Salon", "☀️"),
+    "leisure/track": ("Running Track", "🏃"),
+    "leisure/trampoline_park": ("Trampoline Park", "🤸"),
+    "leisure/water_park": ("Water Park", "🌊"),
+    "leisure/wildlife_hide": ("Wildlife Hide", "🦌"),
+    "leisure/yes": ("Leisure Facility", "🎉"),
+    # office
+    "office/accountant": ("Accountant", "🧮"),
+    "office/advertising_agency": ("Advertising Agency", "📢"),
+    "office/architect": ("Architect", "📐"),
+    "office/association": ("Association", "🏛️"),
+    "office/charity": ("Charity", "❤️"),
+    "office/construction_company": ("Construction Company", "🏗️"),
+    "office/consulting": ("Consulting Firm", "📊"),
+    "office/courier": ("Courier Service", "📦"),
+    "office/coworking": ("Co-working Space", "💻"),
+    "office/design": ("Design Studio", "🎨"),
+    "office/diplomatic": ("Diplomatic Office", "🏛️"),
+    "office/educational_institution": ("Education Office", "🎓"),
+    "office/employment_agency": ("Employment Agency", "💼"),
+    "office/energy_supplier": ("Energy Supplier", "⚡"),
+    "office/engineer": ("Engineering Firm", "⚙️"),
+    "office/estate_agent": ("Estate Agent", "🏠"),
+    "office/financial": ("Financial Services", "💰"),
+    "office/financial_advisor": ("Financial Advisor", "📈"),
+    "office/foundation": ("Foundation", "🏛️"),
+    "office/government": ("Government Office", "🏛️"),
+    "office/graphic_design": ("Graphic Design", "🖌️"),
+    "office/healthcare": ("Healthcare Office", "🏥"),
+    "office/home_care": ("Home Care Service", "🏠"),
+    "office/insurance": ("Insurance", "🛡️"),
+    "office/interior_design": ("Interior Design", "🛋️"),
+    "office/it": ("IT Company", "💻"),
+    "office/lawyer": ("Lawyer", "⚖️"),
+    "office/logistics": ("Logistics", "🚚"),
+    "office/marketing": ("Marketing Agency", "📣"),
+    "office/mortgage": ("Mortgage Broker", "🏦"),
+    "office/moving_company": ("Moving Company", "📦"),
+    "office/newspaper": ("Newspaper Office", "📰"),
+    "office/ngo": ("NGO", "🌍"),
+    "office/notary": ("Notary", "📜"),
+    "office/political_party": ("Political Party", "🗳️"),
+    "office/politician": ("Politician Office", "🏛️"),
+    "office/property_management": ("Property Management", "🏘️"),
+    "office/recruitment": ("Recruitment Agency", "👥"),
+    "office/religion": ("Religious Office", "✝️"),
+    "office/research": ("Research Office", "🔬"),
+    "office/security": ("Security Company", "🔒"),
+    "office/solicitor": ("Solicitor", "⚖️"),
+    "office/surveyor": ("Surveyor", "📏"),
+    "office/tax_advisor": ("Tax Advisor", "🧾"),
+    "office/taxi": ("Taxi Office", "🚕"),
+    "office/telecommunication": ("Telecoms Office", "📡"),
+    "office/therapist": ("Therapist", "🧠"),
+    "office/travel_agent": ("Travel Agent", "✈️"),
+    "office/union": ("Trade Union", "✊"),
+    "office/university": ("University Office", "🎓"),
+    "office/vacant": ("Vacant Office", "🏚️"),
+    "office/web_design": ("Web Design", "🌐"),
+    # public_transport
+    "public_transport/entrance": ("Transport Entrance", "🚪"),
+    "public_transport/platform": ("Platform", "🚉"),
+    "public_transport/station": ("Station", "🚉"),
+    "public_transport/stop_position": ("Stop", "🚏"),
+    # shop
+    "shop/accessories": ("Accessories Shop", "👜"),
+    "shop/agrarian": ("Farm Supply Shop", "🌾"),
+    "shop/alcohol": ("Off-Licence", "🍷"),
+    "shop/antiques": ("Antiques Shop", "🏺"),
+    "shop/appliance": ("Appliance Shop", "🔌"),
+    "shop/art": ("Art Shop", "🎨"),
+    "shop/baby_goods": ("Baby Shop", "🍼"),
+    "shop/bag": ("Bag Shop", "👜"),
+    "shop/bakery": ("Bakery", "🥐"),
+    "shop/bathroom": ("Bathroom Shop", "🛁"),
+    "shop/bathroom_furnishing": ("Bathroom Furnishings", "🚿"),
+    "shop/beauty": ("Beauty Shop", "💄"),
+    "shop/bed": ("Bed Shop", "🛏️"),
+    "shop/beverages": ("Drinks Shop", "🥤"),
+    "shop/bicycle": ("Bike Shop", "🚲"),
+    "shop/boat": ("Boat Shop", "⛵"),
+    "shop/bookmaker": ("Bookmaker", "🏇"),
+    "shop/books": ("Bookshop", "📚"),
+    "shop/boutique": ("Boutique", "👗"),
+    "shop/building_materials": ("Building Materials", "🧱"),
+    "shop/butcher": ("Butcher", "🥩"),
+    "shop/camera": ("Camera Shop", "📷"),
+    "shop/candles": ("Candle Shop", "🕯️"),
+    "shop/car": ("Car Dealership", "🚗"),
+    "shop/car;car_repair": ("Car Sales & Repair", "🚗"),
+    "shop/car_parts": ("Car Parts", "🔩"),
+    "shop/car_repair": ("Car Repair", "🔧"),
+    "shop/caravan": ("Caravan Dealer", "🚐"),
+    "shop/carpet": ("Carpet Shop", "🧶"),
+    "shop/catalogue": ("Catalogue Shop", "📋"),
+    "shop/charity": ("Charity Shop", "❤️"),
+    "shop/cheese": ("Cheese Shop", "🧀"),
+    "shop/chemist": ("Chemist", "🧪"),
+    "shop/chocolate": ("Chocolate Shop", "🍫"),
+    "shop/clothes": ("Clothes Shop", "👕"),
+    "shop/coffee": ("Coffee Shop", "☕"),
+    "shop/collector": ("Collector Shop", "🏆"),
+    "shop/computer": ("Computer Shop", "🖥️"),
+    "shop/confectionery": ("Sweet Shop", "🍬"),
+    "shop/convenience": ("Convenience Store", "🏪"),
+    "shop/copyshop": ("Copy Shop", "🖨️"),
+    "shop/cosmetics": ("Cosmetics Shop", "💅"),
+    "shop/country_store": ("Country Store", "🏡"),
+    "shop/craft": ("Craft Shop", "✂️"),
+    "shop/curtain": ("Curtain Shop", "🪟"),
+    "shop/dairy": ("Dairy Shop", "🥛"),
+    "shop/deli": ("Delicatessen", "🧆"),
+    "shop/department_store": ("Department Store", "🏬"),
+    "shop/discount": ("Discount Store", "💲"),
+    "shop/doityourself": ("DIY Store", "🔨"),
+    "shop/doors": ("Door Shop", "🚪"),
+    "shop/dry_cleaning": ("Dry Cleaner", "👔"),
+    "shop/e-cigarette": ("Vape Shop", "💨"),
+    "shop/electrical": ("Electrical Shop", "⚡"),
+    "shop/electronics": ("Electronics Shop", "📱"),
+    "shop/erotic": ("Adult Shop", "🔞"),
+    "shop/esoteric": ("Esoteric Shop", "🔮"),
+    "shop/estate_agent": ("Estate Agent", "🏠"),
+    "shop/fabric": ("Fabric Shop", "🧵"),
+    "shop/fan": ("Fan Shop", "🏅"),
+    "shop/farm": ("Farm Shop", "🥕"),
+    "shop/fashion_accessories": ("Fashion Accessories", "👒"),
+    "shop/fireplace": ("Fireplace Shop", "🔥"),
+    "shop/fishing": ("Fishing Shop", "🎣"),
+    "shop/flooring": ("Flooring Shop", "🪵"),
+    "shop/florist": ("Florist", "💐"),
+    "shop/food": ("Food Shop", "🍞"),
+    "shop/frame": ("Framing Shop", "🖼️"),
+    "shop/frozen_food": ("Frozen Food Shop", "🧊"),
+    "shop/fuel": ("Fuel Shop", "⛽"),
+    "shop/funeral_directors": ("Funeral Director", "⚰️"),
+    "shop/furniture": ("Furniture Shop", "🪑"),
+    "shop/games": ("Games Shop", "🎮"),
+    "shop/garden_centre": ("Garden Centre", "🌻"),
+    "shop/gas": ("Gas Shop", "🔥"),
+    "shop/general": ("General Store", "🏪"),
+    "shop/gift": ("Gift Shop", "🎁"),
+    "shop/glaziery": ("Glazier", "🪟"),
+    "shop/greengrocer": ("Greengrocer", "🥬"),
+    "shop/grocery": ("Grocery Shop", "🛒"),
+    "shop/haberdashery": ("Haberdashery", "🧵"),
+    "shop/hairdresser": ("Hairdresser", "💇"),
+    "shop/hairdresser_supply": ("Hairdresser Supply", "💇"),
+    "shop/hardware": ("Hardware Shop", "🔩"),
+    "shop/health": ("Health Shop", "🌿"),
+    "shop/health_food": ("Health Food Shop", "🥗"),
+    "shop/hearing_aids": ("Hearing Aid Shop", "👂"),
+    "shop/herbalist": ("Herbalist", "🌿"),
+    "shop/hifi": ("Hi-Fi Shop", "🔊"),
+    "shop/household": ("Household Shop", "🏠"),
+    "shop/household_linen": ("Linen Shop", "🛏️"),
+    "shop/houseware": ("Houseware Shop", "🍳"),
+    "shop/ice_cream": ("Ice Cream Shop", "🍦"),
+    "shop/interior_decoration": ("Interior Decoration", "🖼️"),
+    "shop/jewelry": ("Jewellery Shop", "💍"),
+    "shop/kiosk": ("Kiosk", "🏪"),
+    "shop/kitchen": ("Kitchen Shop", "🍳"),
+    "shop/laundry": ("Laundry", "🧺"),
+    "shop/leather": ("Leather Shop", "🧳"),
+    "shop/lighting": ("Lighting Shop", "💡"),
+    "shop/locksmith": ("Locksmith", "🔐"),
+    "shop/mall": ("Shopping Centre", "🏬"),
+    "shop/massage": ("Massage Parlour", "💆"),
+    "shop/medical_supply": ("Medical Supply", "🩺"),
+    "shop/military_surplus": ("Military Surplus", "🎖️"),
+    "shop/mobile_phone": ("Mobile Phone Shop", "📱"),
+    "shop/mobile_phone_accessories": ("Phone Accessories", "📱"),
+    "shop/mobility": ("Mobility Shop", "♿"),
+    "shop/mobility_scooter": ("Mobility Scooter Shop", "🦽"),
+    "shop/model": ("Model Shop", "✈️"),
+    "shop/money_lender": ("Money Lender", "💰"),
+    "shop/motorcycle": ("Motorcycle Shop", "🏍️"),
+    "shop/motorcycle_repair": ("Motorcycle Repair", "🔧"),
+    "shop/music": ("Music Shop", "🎵"),
+    "shop/musical_instrument": ("Musical Instrument Shop", "🎸"),
+    "shop/newsagent": ("Newsagent", "📰"),
+    "shop/nutrition_supplements": ("Nutrition Shop", "💪"),
+    "shop/optician": ("Optician", "👓"),
+    "shop/outdoor": ("Outdoor Shop", "🏕️"),
+    "shop/outpost": ("Outpost", "📦"),
+    "shop/paint": ("Paint Shop", "🎨"),
+    "shop/party": ("Party Shop", "🎈"),
+    "shop/pastry": ("Pastry Shop", "🥐"),
+    "shop/pawnbroker": ("Pawnbroker", "💰"),
+    "shop/perfumery": ("Perfumery", "🌸"),
+    "shop/pet": ("Pet Shop", "🐾"),
+    "shop/pet_grooming": ("Pet Grooming", "🐩"),
+    "shop/photo": ("Photo Shop", "📸"),
+    "shop/piercing": ("Piercing Studio", "💎"),
+    "shop/plant_hire": ("Plant Hire", "🚜"),
+    "shop/pottery": ("Pottery Shop", "🏺"),
+    "shop/printer_ink": ("Ink & Toner Shop", "🖨️"),
+    "shop/printing": ("Print Shop", "🖨️"),
+    "shop/psychic": ("Psychic", "🔮"),
+    "shop/pyrotechnics": ("Fireworks Shop", "🎆"),
+    "shop/religion": ("Religious Shop", "✝️"),
+    "shop/rental": ("Rental Shop", "🔑"),
+    "shop/repair": ("Repair Shop", "🔧"),
+    "shop/scuba_diving": ("Scuba Diving Shop", "🤿"),
+    "shop/seafood": ("Fishmonger", "🐟"),
+    "shop/second_hand": ("Second-Hand Shop", "♻️"),
+    "shop/security": ("Security Shop", "🔒"),
+    "shop/sewing": ("Sewing Shop", "🪡"),
+    "shop/shoe_repair": ("Shoe Repair", "👞"),
+    "shop/shoes": ("Shoe Shop", "👟"),
+    "shop/sports": ("Sports Shop", "⚽"),
+    "shop/stationery": ("Stationery Shop", "✏️"),
+    "shop/storage_rental": ("Self Storage", "📦"),
+    "shop/supermarket": ("Supermarket", "🛒"),
+    "shop/swimming_pool": ("Pool Supplies", "🏊"),
+    "shop/tailor": ("Tailor", "🧵"),
+    "shop/tattoo": ("Tattoo Studio", "🖋️"),
+    "shop/taxi": ("Taxi Booking", "🚕"),
+    "shop/tea": ("Tea Shop", "🫖"),
+    "shop/telecommunication": ("Telecoms Shop", "📡"),
+    "shop/ticket": ("Ticket Office", "🎫"),
+    "shop/tiles": ("Tile Shop", "🔲"),
+    "shop/tobacco": ("Tobacconist", "🚬"),
+    "shop/tool_hire": ("Tool Hire", "🧰"),
+    "shop/toys": ("Toy Shop", "🧸"),
+    "shop/trade": ("Trade Supplier", "🏭"),
+    "shop/travel_agency": ("Travel Agency", "✈️"),
+    "shop/trophy": ("Trophy Shop", "🏆"),
+    "shop/tyres": ("Tyre Shop", "🛞"),
+    "shop/vacant": ("Vacant Shop", "🏚️"),
+    "shop/variety_store": ("Variety Store", "🏪"),
+    "shop/video": ("Video Shop", "📀"),
+    "shop/video_games": ("Video Game Shop", "🎮"),
+    "shop/watches": ("Watch Shop", "⌚"),
+    "shop/water_sports": ("Water Sports Shop", "🏄"),
+    "shop/weapons": ("Weapons Shop", "🗡️"),
+    "shop/wedding": ("Wedding Shop", "💒"),
+    "shop/wholesale": ("Wholesaler", "📦"),
+    "shop/wigs": ("Wig Shop", "💇"),
+    "shop/window_blind": ("Blinds Shop", "🪟"),
+    "shop/windows": ("Window Shop", "🪟"),
+    "shop/wine": ("Wine Shop", "🍷"),
+    "shop/wool": ("Wool Shop", "🧶"),
+    "shop/yes": ("Shop", "🛍️"),
+    # tourism
+    "tourism/artwork": ("Public Artwork", "🎨"),
+    "tourism/attraction": ("Tourist Attraction", "📸"),
+    "tourism/camp_site": ("Campsite", "⛺"),
+    "tourism/caravan_site": ("Caravan Site", "🚐"),
+    "tourism/chalet": ("Chalet", "🏔️"),
+    "tourism/gallery": ("Gallery", "🖼️"),
+    "tourism/guest_house": ("Guest House", "🏡"),
+    "tourism/hostel": ("Hostel", "🛏️"),
+    "tourism/hotel": ("Hotel", "🏨"),
+    "tourism/motel": ("Motel", "🏨"),
+    "tourism/museum": ("Museum", "🏛️"),
+    "tourism/picnic_site": ("Picnic Site", "🧺"),
+    "tourism/preserved_railway": ("Heritage Railway", "🚂"),
+    "tourism/theme_park": ("Theme Park", "🎢"),
+    "tourism/viewpoint": ("Viewpoint", "🔭"),
+    "tourism/zoo": ("Zoo", "🦁"),
+}
+
+
+def transform(input_path: Path) -> pl.LazyFrame:
+    lf = pl.scan_parquet(input_path)
+
+    # Get all unique categories present in the data
+    all_categories = lf.select("category").unique().collect().to_series().to_list()
+
+    # Verify every non-dropped category has a mapping
+    unmapped = []
+    for cat in all_categories:
+        if cat not in DROP_CATEGORIES and cat not in CATEGORY_MAP:
+            unmapped.append(cat)
+    if unmapped:
+        raise ValueError(
+            f"Categories missing from CATEGORY_MAP: {sorted(unmapped)}"
+        )
+
+    # Verify every CATEGORY_MAP key actually exists in the data (catch typos)
+    mapped_but_absent = []
+    all_set = set(all_categories)
+    for cat in CATEGORY_MAP:
+        if cat not in all_set:
+            mapped_but_absent.append(cat)
+    if mapped_but_absent:
+        raise ValueError(
+            f"CATEGORY_MAP contains categories not in data: {sorted(mapped_but_absent)}"
+        )
+
+    # Drop unwanted categories
+    lf = lf.filter(~pl.col("category").is_in(list(DROP_CATEGORIES)))
+
+    # Build name and emoji lookup expressions
+    name_mapping = {k: v[0] for k, v in CATEGORY_MAP.items()}
+    emoji_mapping = {k: v[1] for k, v in CATEGORY_MAP.items()}
+
+    # Check no friendly names are missing (defensive)
+    missing_names = [k for k, v in CATEGORY_MAP.items() if not v[0]]
+    if missing_names:
+        raise ValueError(f"Empty friendly names for: {missing_names}")
+    missing_emojis = [k for k, v in CATEGORY_MAP.items() if not v[1]]
+    if missing_emojis:
+        raise ValueError(f"Empty emojis for: {missing_emojis}")
+
+    lf = lf.with_columns(
+        pl.col("category").replace_strict(name_mapping).alias("category"),
+        pl.col("category").replace_strict(emoji_mapping).alias("emoji"),
+    )
+
+    return lf
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Transform raw POIs to filtered version with friendly names")
+    parser.add_argument("--input", type=Path, required=True, help="Raw POIs parquet file")
+    parser.add_argument("--output", type=Path, required=True, help="Output filtered POIs parquet file")
+    args = parser.parse_args()
+
+    df = transform(args.input).collect()
+
+    df.write_parquet(args.output)
+
+    size_mb = args.output.stat().st_size / (1024 * 1024)
+    print(f"Wrote {args.output} ({size_mb:.1f} MB, {len(df):,} POIs)")
+    print(f"\nCategories ({df['category'].n_unique()}):")
+    counts = df.group_by("category", "emoji").len().sort("len", descending=True)
+    for row in counts.iter_rows(named=True):
+        print(f"  {row['emoji']} {row['category']}: {row['len']:,}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/pipeline/utils/test_fuzzy_join.py b/pipeline/utils/test_fuzzy_join.py
index e9a1cb5..93c573c 100644
--- a/pipeline/utils/test_fuzzy_join.py
+++ b/pipeline/utils/test_fuzzy_join.py
@@ -6,7 +6,7 @@ POSTCODE = "E14 2DG"
 
 # Price paid: unique addresses for this postcode
 pp = (
-    pl.scan_parquet("data_sources/pp-complete.parquet")
+    pl.scan_parquet("data/price-paid-complete.parquet")
     .filter(pl.col("postcode") == POSTCODE)
     .select("paon", "saon", "street", "postcode")
     .unique()
@@ -22,7 +22,7 @@ pp = (
 
 # EPC: latest inspection per address for this postcode
 epc = (
-    pl.scan_csv("data_sources/epc/certificates.csv")
+    pl.scan_csv("data/epc/certificates.csv")
     .select("ADDRESS", "POSTCODE", "INSPECTION_DATE")
     .filter(pl.col("POSTCODE").str.strip_chars() == POSTCODE)
     .sort("INSPECTION_DATE", descending=True)
diff --git a/pipeline/wide.py b/pipeline/wide.py
deleted file mode 100644
index 3e170d9..0000000
--- a/pipeline/wide.py
+++ /dev/null
@@ -1,143 +0,0 @@
-"""Build a wide property dataframe and H3 aggregates from epc_pp output."""
-
-import polars as pl
-import h3
-
-from pipeline.config import AGGREGATES_DIR, H3_RESOLUTIONS, DATA_DIR, PROCESSED_DIR
-
-
-def _build_wide() -> pl.DataFrame:
-    """Build the wide dataframe by joining epc_pp with all auxiliary data."""
-    print("Loading epc_pp...")
-    wide = pl.read_parquet(PROCESSED_DIR / "epc_pp.parquet")
-    print(f"  {wide.shape[0]:,} rows")
-
-    # GPS coordinates + LSOA from ArcGIS
-    print("Joining GPS coordinates...")
-    arcgis = pl.read_parquet(DATA_DIR / "arcgis_data.parquet").select(
-        pl.col("pcds").alias("postcode"),
-        "lat",
-        pl.col("long").alias("lon"),
-        "lsoa21",
-    )
-    wide = wide.join(arcgis, on="postcode", how="inner")
-    print(f"  {wide.shape[0]:,} rows after GPS join")
-
-    # Journey times (optional)
-    journey_path = PROCESSED_DIR / "journey_times_bank_checkpoint.parquet"
-    if journey_path.exists():
-        print("Joining journey times...")
-        journey_times = pl.read_parquet(journey_path).select(
-            "postcode",
-            "public_transport_easy_minutes",
-            "public_transport_quick_minutes",
-            "cycling_minutes",
-        )
-        wide = wide.join(journey_times, on="postcode", how="left")
-
-    # Index of Deprivation
-    iod_path = DATA_DIR / "IoD2025_Scores.parquet"
-    if iod_path.exists():
-        print("Joining IoD scores...")
-        iod = pl.read_parquet(iod_path).drop(
-            "LSOA name (2021)",
-            "Local Authority District code (2024)",
-            "Local Authority District name (2024)",
-        )
-        # Rename IoD columns to clean snake_case
-        iod = iod.rename(_IOD_RENAMES)
-        wide = wide.join(
-            iod, left_on="lsoa21", right_on="lsoa_code", how="left"
-        )
-
-    return wide
-
-
-_IOD_RENAMES = {
-    "LSOA code (2021)": "lsoa_code",
-    "Index of Multiple Deprivation (IMD) Score": "imd_score",
-    "Income Score (rate)": "income_score",
-    "Employment Score (rate)": "employment_score",
-    "Education, Skills and Training Score": "education_score",
-    "Health Deprivation and Disability Score": "health_score",
-    "Crime Score": "crime_score",
-    "Barriers to Housing and Services Score": "housing_barriers_score",
-    "Living Environment Score": "living_environment_score",
-    "Income Deprivation Affecting Children Index (IDACI) Score (rate)": "idaci_score",
-    "Income Deprivation Affecting Older People (IDAOPI) Score (rate)": "idaopi_score",
-    "Children and Young People Sub-domain Score": "children_young_people_score",
-    "Adult Skills Sub-domain Score": "adult_skills_score",
-    "Geographical Barriers Sub-domain Score": "geographical_barriers_score",
-    "Wider Barriers Sub-domain Score": "wider_barriers_score",
-    "Indoors Sub-domain Score": "indoors_score",
-    "Outdoors Sub-domain Score": "outdoors_score",
-}
-
-
-def _add_h3_indices(df: pl.DataFrame) -> pl.DataFrame:
-    """Compute H3 indices from lat/lon for all configured resolutions."""
-    print("Computing H3 indices...")
-    # Compute per unique postcode for efficiency, then join back
-    postcodes = df.select("postcode", "lat", "lon").unique(subset=["postcode"])
-
-    for res in H3_RESOLUTIONS:
-        col_name = f"h3_res{res}"
-        postcodes = postcodes.with_columns(
-            pl.struct(["lat", "lon"])
-            .map_elements(
-                lambda x, r=res: h3.latlng_to_cell(x["lat"], x["lon"], r),
-                return_dtype=pl.Utf8,
-            )
-            .alias(col_name)
-        )
-        print(f"  res{res}: {postcodes[col_name].n_unique():,} unique cells")
-
-    h3_cols = [f"h3_res{res}" for res in H3_RESOLUTIONS]
-    return df.join(
-        postcodes.select("postcode", *h3_cols), on="postcode", how="left"
-    )
-
-
-def _aggregate_to_h3(df: pl.DataFrame) -> None:
-    """Aggregate min/max of every numeric feature per H3 cell at each resolution."""
-    AGGREGATES_DIR.mkdir(parents=True, exist_ok=True)
-
-    exclude = {"lat", "lon"}
-    numeric_cols = [
-        col
-        for col, dtype in zip(df.columns, df.dtypes)
-        if dtype.is_numeric() and not col.startswith("h3_res") and col not in exclude
-    ]
-
-    agg_exprs = [pl.len().alias("count")]
-    for col in numeric_cols:
-        agg_exprs.append(pl.col(col).min().alias(f"min_{col}"))
-        agg_exprs.append(pl.col(col).max().alias(f"max_{col}"))
-
-    print("Aggregating to H3 cells...")
-    for res in H3_RESOLUTIONS:
-        h3_col = f"h3_res{res}"
-        result = df.group_by(h3_col).agg(agg_exprs).rename({h3_col: "h3"})
-        path = AGGREGATES_DIR / f"res{res}.parquet"
-        result.write_parquet(path)
-        size_mb = path.stat().st_size / (1024 * 1024)
-        print(f"  {path.name}: {result.shape[0]:,} cells ({size_mb:.1f} MB)")
-
-
-def run():
-    """Run the full wide pipeline: build wide df, compute H3, aggregate."""
-    wide = _build_wide()
-
-    wide_path = PROCESSED_DIR / "wide.parquet"
-    wide.write_parquet(wide_path)
-    size_mb = wide_path.stat().st_size / (1024 * 1024)
-    print(f"Wrote {wide_path} ({size_mb:.1f} MB)")
-
-    wide = _add_h3_indices(wide)
-    _aggregate_to_h3(wide)
-
-    print("Done.")
-
-
-if __name__ == "__main__":
-    run()

From a316556a03172c0f4e1786d340fe6d10ebbb38e3 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sat, 31 Jan 2026 12:48:36 +0000
Subject: [PATCH 25/62] Update notebooks

---
 analyses/epc_analysis.ipynb           |  668 +---
 analyses/journey_times_analysis.ipynb |   57 +-
 analyses/poi_analysis.ipynb           | 4891 -------------------------
 analyses/property_analysis.ipynb      |  720 ++--
 analyses/wide.ipynb                   |   93 +
 5 files changed, 645 insertions(+), 5784 deletions(-)
 delete mode 100644 analyses/poi_analysis.ipynb
 create mode 100644 analyses/wide.ipynb

diff --git a/analyses/epc_analysis.ipynb b/analyses/epc_analysis.ipynb
index 95f9d14..4954b8f 100644
--- a/analyses/epc_analysis.ipynb
+++ b/analyses/epc_analysis.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T22:06:26.543050Z",
@@ -26,7 +26,99 @@
      "output_type": "stream",
      "text": [
       "Rows: 28,847,961\n",
-      "Columns: 93\n"
+      "Columns: LMK_KEY\n",
+      "ADDRESS1\n",
+      "ADDRESS2\n",
+      "ADDRESS3\n",
+      "POSTCODE\n",
+      "BUILDING_REFERENCE_NUMBER\n",
+      "CURRENT_ENERGY_RATING\n",
+      "POTENTIAL_ENERGY_RATING\n",
+      "CURRENT_ENERGY_EFFICIENCY\n",
+      "POTENTIAL_ENERGY_EFFICIENCY\n",
+      "PROPERTY_TYPE\n",
+      "BUILT_FORM\n",
+      "INSPECTION_DATE\n",
+      "LOCAL_AUTHORITY\n",
+      "CONSTITUENCY\n",
+      "COUNTY\n",
+      "LODGEMENT_DATE\n",
+      "TRANSACTION_TYPE\n",
+      "ENVIRONMENT_IMPACT_CURRENT\n",
+      "ENVIRONMENT_IMPACT_POTENTIAL\n",
+      "ENERGY_CONSUMPTION_CURRENT\n",
+      "ENERGY_CONSUMPTION_POTENTIAL\n",
+      "CO2_EMISSIONS_CURRENT\n",
+      "CO2_EMISS_CURR_PER_FLOOR_AREA\n",
+      "CO2_EMISSIONS_POTENTIAL\n",
+      "LIGHTING_COST_CURRENT\n",
+      "LIGHTING_COST_POTENTIAL\n",
+      "HEATING_COST_CURRENT\n",
+      "HEATING_COST_POTENTIAL\n",
+      "HOT_WATER_COST_CURRENT\n",
+      "HOT_WATER_COST_POTENTIAL\n",
+      "TOTAL_FLOOR_AREA\n",
+      "ENERGY_TARIFF\n",
+      "MAINS_GAS_FLAG\n",
+      "FLOOR_LEVEL\n",
+      "FLAT_TOP_STOREY\n",
+      "FLAT_STOREY_COUNT\n",
+      "MAIN_HEATING_CONTROLS\n",
+      "MULTI_GLAZE_PROPORTION\n",
+      "GLAZED_TYPE\n",
+      "GLAZED_AREA\n",
+      "EXTENSION_COUNT\n",
+      "NUMBER_HABITABLE_ROOMS\n",
+      "NUMBER_HEATED_ROOMS\n",
+      "LOW_ENERGY_LIGHTING\n",
+      "NUMBER_OPEN_FIREPLACES\n",
+      "HOTWATER_DESCRIPTION\n",
+      "HOT_WATER_ENERGY_EFF\n",
+      "HOT_WATER_ENV_EFF\n",
+      "FLOOR_DESCRIPTION\n",
+      "FLOOR_ENERGY_EFF\n",
+      "FLOOR_ENV_EFF\n",
+      "WINDOWS_DESCRIPTION\n",
+      "WINDOWS_ENERGY_EFF\n",
+      "WINDOWS_ENV_EFF\n",
+      "WALLS_DESCRIPTION\n",
+      "WALLS_ENERGY_EFF\n",
+      "WALLS_ENV_EFF\n",
+      "SECONDHEAT_DESCRIPTION\n",
+      "SHEATING_ENERGY_EFF\n",
+      "SHEATING_ENV_EFF\n",
+      "ROOF_DESCRIPTION\n",
+      "ROOF_ENERGY_EFF\n",
+      "ROOF_ENV_EFF\n",
+      "MAINHEAT_DESCRIPTION\n",
+      "MAINHEAT_ENERGY_EFF\n",
+      "MAINHEAT_ENV_EFF\n",
+      "MAINHEATCONT_DESCRIPTION\n",
+      "MAINHEATC_ENERGY_EFF\n",
+      "MAINHEATC_ENV_EFF\n",
+      "LIGHTING_DESCRIPTION\n",
+      "LIGHTING_ENERGY_EFF\n",
+      "LIGHTING_ENV_EFF\n",
+      "MAIN_FUEL\n",
+      "WIND_TURBINE_COUNT\n",
+      "HEAT_LOSS_CORRIDOR\n",
+      "UNHEATED_CORRIDOR_LENGTH\n",
+      "FLOOR_HEIGHT\n",
+      "PHOTO_SUPPLY\n",
+      "SOLAR_WATER_HEATING_FLAG\n",
+      "MECHANICAL_VENTILATION\n",
+      "ADDRESS\n",
+      "LOCAL_AUTHORITY_LABEL\n",
+      "CONSTITUENCY_LABEL\n",
+      "POSTTOWN\n",
+      "CONSTRUCTION_AGE_BAND\n",
+      "LODGEMENT_DATETIME\n",
+      "TENURE\n",
+      "FIXED_LIGHTING_OUTLETS_COUNT\n",
+      "LOW_ENERGY_FIXED_LIGHT_COUNT\n",
+      "UPRN\n",
+      "UPRN_SOURCE\n",
+      "REPORT_TYPE\n"
      ]
     },
     {
@@ -66,7 +158,7 @@
        "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -79,7 +171,7 @@
     "plt.rcParams[\"figure.figsize\"] = (12, 5)\n",
     "plt.rcParams[\"figure.dpi\"] = 100\n",
     "\n",
-    "PARQUET = \"../data_sources/epc/certificates.parquet\"\n",
+    "PARQUET = \"../data/epc/certificates.parquet\"\n",
     "\n",
     "def scan():\n",
     "    return pl.scan_parquet(PARQUET)\n",
@@ -87,7 +179,8 @@
     "# Quick row count\n",
     "row_count = scan().select(pl.len()).collect().item()\n",
     "print(f\"Rows: {row_count:,}\")\n",
-    "print(f\"Columns: {len(scan().collect_schema().names())}\")\n",
+    "print(f\"Columns: {\"\\n\".join(scan().collect_schema().names())}\")\n",
+    "\n",
     "scan().head(3).collect()"
    ]
   },
@@ -100,7 +193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T22:06:27.101365Z",
@@ -143,7 +236,7 @@
        "└──────────────────────────────┴────────┴────────────┴──────────┘"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -183,7 +276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T22:06:29.054902Z",
@@ -193,6 +286,27 @@
     }
    },
    "outputs": [
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mKeyboardInterrupt\u001b[39m                         Traceback (most recent call last)",
+      "\u001b[36mFile \u001b[39m\u001b[32m/volumes/syncthing/Projects/property-map/.venv/lib/python3.12/site-packages/zmq/backend/cython/_zmq.py:179\u001b[39m, in \u001b[36mzmq.backend.cython._zmq._check_rc\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m    174\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"internal utility for checking zmq return condition\u001b[39;00m\n\u001b[32m    175\u001b[39m \n\u001b[32m    176\u001b[39m \u001b[33;03mand raising the appropriate Exception class\u001b[39;00m\n\u001b[32m    177\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m    178\u001b[39m errno: C.int = _zmq_errno()\n\u001b[32m--> \u001b[39m\u001b[32m179\u001b[39m PyErr_CheckSignals()\n\u001b[32m    180\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m errno == \u001b[32m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m error_without_errno:\n\u001b[32m    181\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[32m0\u001b[39m\n",
+      "\u001b[31mKeyboardInterrupt\u001b[39m: "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Exception ignored in: 'zmq.backend.cython._zmq.Frame.__dealloc__'\n",
+      "Traceback (most recent call last):\n",
+      "  File \"zmq/backend/cython/_zmq.py\", line 179, in zmq.backend.cython._zmq._check_rc\n",
+      "KeyboardInterrupt: \n"
+     ]
+    },
     {
      "data": {
       "image/png": 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@@ -283,67 +397,52 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Energy Efficiency by Property Type"
+    "## PROPERTY_TYPE and BUILT_FORM Cardinality"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-01-28T22:06:29.389426Z",
-     "iopub.status.busy": "2026-01-28T22:06:29.389348Z",
-     "iopub.status.idle": "2026-01-28T22:06:29.941153Z",
-     "shell.execute_reply": "2026-01-28T22:06:29.940746Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div><style>\n",
-       ".dataframe > thead > tr,\n",
-       ".dataframe > tbody > tr {\n",
-       "  text-align: right;\n",
-       "  white-space: pre-wrap;\n",
-       "}\n",
-       "</style>\n",
-       "<small>shape: (5, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>PROPERTY_TYPE</th><th>mean_efficiency</th><th>median_efficiency</th><th>count</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>u32</td></tr></thead><tbody><tr><td>&quot;Flat&quot;</td><td>69.745031</td><td>72.0</td><td>8236696</td></tr><tr><td>&quot;Maisonette&quot;</td><td>64.894323</td><td>68.0</td><td>710695</td></tr><tr><td>&quot;House&quot;</td><td>63.306193</td><td>65.0</td><td>17437884</td></tr><tr><td>&quot;Bungalow&quot;</td><td>60.703275</td><td>63.0</td><td>2448109</td></tr><tr><td>&quot;Park home&quot;</td><td>45.450847</td><td>48.0</td><td>14577</td></tr></tbody></table></div>"
-      ],
-      "text/plain": [
-       "shape: (5, 4)\n",
-       "┌───────────────┬─────────────────┬───────────────────┬──────────┐\n",
-       "│ PROPERTY_TYPE ┆ mean_efficiency ┆ median_efficiency ┆ count    │\n",
-       "│ ---           ┆ ---             ┆ ---               ┆ ---      │\n",
-       "│ str           ┆ f64             ┆ f64               ┆ u32      │\n",
-       "╞═══════════════╪═════════════════╪═══════════════════╪══════════╡\n",
-       "│ Flat          ┆ 69.745031       ┆ 72.0              ┆ 8236696  │\n",
-       "│ Maisonette    ┆ 64.894323       ┆ 68.0              ┆ 710695   │\n",
-       "│ House         ┆ 63.306193       ┆ 65.0              ┆ 17437884 │\n",
-       "│ Bungalow      ┆ 60.703275       ┆ 63.0              ┆ 2448109  │\n",
-       "│ Park home     ┆ 45.450847       ┆ 48.0              ┆ 14577    │\n",
-       "└───────────────┴─────────────────┴───────────────────┴──────────┘"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PROPERTY_TYPE — 5 distinct values:\n",
+      "  House                            17,437,884\n",
+      "  Flat                              8,236,696\n",
+      "  Bungalow                          2,448,109\n",
+      "  Maisonette                          710,695\n",
+      "  Park home                            14,577\n",
+      "\n",
+      "BUILT_FORM — 9 distinct values:\n",
+      "  Semi-Detached                     8,777,318\n",
+      "  Mid-Terrace                       7,972,697\n",
+      "  Detached                          6,428,144\n",
+      "  End-Terrace                       4,132,264\n",
+      "  NO DATA!                            548,769\n",
+      "  Enclosed End-Terrace                450,732\n",
+      "  Enclosed Mid-Terrace                377,198\n",
+      "  Not Recorded                        155,103\n",
+      "  None                                  5,736\n",
+      "\n"
+     ]
     }
    ],
    "source": [
-    "eff_by_type = (\n",
-    "    scan()\n",
-    "    .group_by(\"PROPERTY_TYPE\")\n",
-    "    .agg(\n",
-    "        pl.col(\"CURRENT_ENERGY_EFFICIENCY\").mean().alias(\"mean_efficiency\"),\n",
-    "        pl.col(\"CURRENT_ENERGY_EFFICIENCY\").median().alias(\"median_efficiency\"),\n",
-    "        pl.len().alias(\"count\"),\n",
+    "for col_name in [\"PROPERTY_TYPE\", \"BUILT_FORM\"]:\n",
+    "    counts = (\n",
+    "        scan()\n",
+    "        .group_by(col_name)\n",
+    "        .len()\n",
+    "        .sort(\"len\", descending=True)\n",
+    "        .collect()\n",
     "    )\n",
-    "    .filter(pl.col(\"count\") > 1000)\n",
-    "    .sort(\"mean_efficiency\", descending=True)\n",
-    "    .collect()\n",
-    ")\n",
-    "eff_by_type"
+    "    print(f\"{col_name} — {len(counts)} distinct values:\")\n",
+    "    for row in counts.iter_rows(named=True):\n",
+    "        print(f\"  {row[col_name]!s:30s} {row['len']:>12,}\")\n",
+    "    print()"
    ]
   },
   {
@@ -423,89 +522,6 @@
     "plt.show()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## CO2 Emissions Distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-01-28T22:06:30.516767Z",
-     "iopub.status.busy": "2026-01-28T22:06:30.516692Z",
-     "iopub.status.idle": "2026-01-28T22:06:31.276152Z",
-     "shell.execute_reply": "2026-01-28T22:06:31.275784Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean: 3.8, Median: 3.2, P99: 15.0\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "co2_stats = (\n",
-    "    scan()\n",
-    "    .select(pl.col(\"CO2_EMISSIONS_CURRENT\").cast(pl.Float64, strict=False))\n",
-    "    .filter(pl.col(\"CO2_EMISSIONS_CURRENT\").is_not_null() & (pl.col(\"CO2_EMISSIONS_CURRENT\") > 0))\n",
-    "    .select(\n",
-    "        pl.col(\"CO2_EMISSIONS_CURRENT\").mean().alias(\"mean\"),\n",
-    "        pl.col(\"CO2_EMISSIONS_CURRENT\").median().alias(\"median\"),\n",
-    "        pl.col(\"CO2_EMISSIONS_CURRENT\").quantile(0.99).alias(\"p99\"),\n",
-    "    )\n",
-    "    .collect()\n",
-    ")\n",
-    "p99 = co2_stats[\"p99\"].item()\n",
-    "mean_val = co2_stats[\"mean\"].item()\n",
-    "median_val = co2_stats[\"median\"].item()\n",
-    "print(f\"Mean: {mean_val:.1f}, Median: {median_val:.1f}, P99: {p99:.1f}\")\n",
-    "\n",
-    "# Use hash-based sampling to avoid collecting full column\n",
-    "co2_sample = (\n",
-    "    scan()\n",
-    "    .select(pl.col(\"CO2_EMISSIONS_CURRENT\").cast(pl.Float64, strict=False))\n",
-    "    .filter(\n",
-    "        pl.col(\"CO2_EMISSIONS_CURRENT\").is_not_null()\n",
-    "        & (pl.col(\"CO2_EMISSIONS_CURRENT\") > 0)\n",
-    "        & (pl.col(\"CO2_EMISSIONS_CURRENT\") < p99)\n",
-    "    )\n",
-    "    .collect()\n",
-    ")\n",
-    "co2_vals = co2_sample.to_series()\n",
-    "if len(co2_vals) > 500_000:\n",
-    "    co2_vals = co2_vals.sample(n=500_000, seed=42)\n",
-    "\n",
-    "fig, ax = plt.subplots()\n",
-    "ax.hist(co2_vals.to_list(), bins=100, edgecolor=\"none\", alpha=0.7)\n",
-    "ax.set_xlabel(\"CO2 Emissions (tonnes/year)\")\n",
-    "ax.set_ylabel(\"Count\")\n",
-    "ax.set_title(\"Distribution of Current CO2 Emissions (99th percentile, 500k sample)\")\n",
-    "ax.axvline(mean_val, color=\"red\", linestyle=\"--\", label=f\"Mean: {mean_val:.1f}\")\n",
-    "ax.axvline(median_val, color=\"orange\", linestyle=\"--\", label=f\"Median: {median_val:.1f}\")\n",
-    "ax.legend()\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "del co2_sample, co2_vals"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -529,7 +545,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_793155/4276860801.py:36: MatplotlibDeprecationWarning: The 'labels' parameter of boxplot() has been renamed 'tick_labels' since Matplotlib 3.9; support for the old name will be dropped in 3.11.\n",
+      "/tmp/ipykernel_1434749/4276860801.py:36: MatplotlibDeprecationWarning: The 'labels' parameter of boxplot() has been renamed 'tick_labels' since Matplotlib 3.9; support for the old name will be dropped in 3.11.\n",
       "  ax.boxplot(data, labels=labels, vert=True)\n"
      ]
     },
@@ -639,71 +655,6 @@
     "plt.show()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Tenure Distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-01-28T22:06:32.077561Z",
-     "iopub.status.busy": "2026-01-28T22:06:32.077482Z",
-     "iopub.status.idle": "2026-01-28T22:06:32.397533Z",
-     "shell.execute_reply": "2026-01-28T22:06:32.397108Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_793155/3225898348.py:23: UserWarning: Tight layout not applied. tight_layout cannot make Axes width small enough to accommodate all Axes decorations\n",
-      "  plt.tight_layout()\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1400x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "tenure = (\n",
-    "    scan()\n",
-    "    .filter(pl.col(\"TENURE\").is_not_null() & (pl.col(\"TENURE\") != \"\"))\n",
-    "    .group_by(\"TENURE\")\n",
-    "    .agg(\n",
-    "        pl.len().alias(\"count\"),\n",
-    "        pl.col(\"CURRENT_ENERGY_EFFICIENCY\").mean().alias(\"mean_eff\"),\n",
-    "    )\n",
-    "    .sort(\"count\", descending=True)\n",
-    "    .collect()\n",
-    ")\n",
-    "\n",
-    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
-    "\n",
-    "axes[0].pie(tenure[\"count\"].to_list(), labels=tenure[\"TENURE\"].to_list(), autopct=\"%1.1f%%\")\n",
-    "axes[0].set_title(\"Tenure Distribution\")\n",
-    "\n",
-    "tenure_sorted = tenure.sort(\"mean_eff\", descending=True)\n",
-    "axes[1].barh(tenure_sorted[\"TENURE\"].to_list(), tenure_sorted[\"mean_eff\"].to_list())\n",
-    "axes[1].set_xlabel(\"Mean Energy Efficiency\")\n",
-    "axes[1].set_title(\"Mean Energy Efficiency by Tenure\")\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -753,309 +704,6 @@
     "plt.tight_layout()\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Energy Efficiency vs Floor Area"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-01-28T22:06:32.910558Z",
-     "iopub.status.busy": "2026-01-28T22:06:32.910472Z",
-     "iopub.status.idle": "2026-01-28T22:06:33.047282Z",
-     "shell.execute_reply": "2026-01-28T22:06:33.046951Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sample = (\n",
-    "    scan()\n",
-    "    .filter(\n",
-    "        pl.col(\"TOTAL_FLOOR_AREA\").is_not_null()\n",
-    "        & pl.col(\"CURRENT_ENERGY_EFFICIENCY\").is_not_null()\n",
-    "        & (pl.col(\"TOTAL_FLOOR_AREA\").cast(pl.Float64, strict=False) > 0)\n",
-    "        & (pl.col(\"TOTAL_FLOOR_AREA\").cast(pl.Float64, strict=False) < 500)\n",
-    "    )\n",
-    "    .select(\n",
-    "        pl.col(\"TOTAL_FLOOR_AREA\").cast(pl.Float64, strict=False),\n",
-    "        pl.col(\"CURRENT_ENERGY_EFFICIENCY\"),\n",
-    "    )\n",
-    "    .collect()\n",
-    "    .sample(n=50_000, seed=42)\n",
-    ")\n",
-    "\n",
-    "fig, ax = plt.subplots()\n",
-    "ax.scatter(sample[\"TOTAL_FLOOR_AREA\"], sample[\"CURRENT_ENERGY_EFFICIENCY\"],\n",
-    "           alpha=0.05, s=1)\n",
-    "ax.set_xlabel(\"Total Floor Area (m²)\")\n",
-    "ax.set_ylabel(\"Current Energy Efficiency\")\n",
-    "ax.set_title(\"Energy Efficiency vs Floor Area (50k sample)\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "del sample"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Current vs Potential Energy Efficiency"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-01-28T22:06:33.048305Z",
-     "iopub.status.busy": "2026-01-28T22:06:33.048217Z",
-     "iopub.status.idle": "2026-01-28T22:06:33.342900Z",
-     "shell.execute_reply": "2026-01-28T22:06:33.342606Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean current efficiency: 65.0\n",
-      "Mean potential efficiency: 79.2\n",
-      "Mean improvement potential: 14.2 points\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "improvement = (\n",
-    "    scan()\n",
-    "    .filter(\n",
-    "        pl.col(\"CURRENT_ENERGY_EFFICIENCY\").is_not_null()\n",
-    "        & pl.col(\"POTENTIAL_ENERGY_EFFICIENCY\").is_not_null()\n",
-    "    )\n",
-    "    .group_by(\"PROPERTY_TYPE\")\n",
-    "    .agg(\n",
-    "        pl.col(\"CURRENT_ENERGY_EFFICIENCY\").mean().alias(\"current\"),\n",
-    "        pl.col(\"POTENTIAL_ENERGY_EFFICIENCY\").mean().alias(\"potential\"),\n",
-    "        (pl.col(\"POTENTIAL_ENERGY_EFFICIENCY\") - pl.col(\"CURRENT_ENERGY_EFFICIENCY\")).mean().alias(\"mean_improvement\"),\n",
-    "        pl.len().alias(\"count\"),\n",
-    "    )\n",
-    "    .filter(pl.col(\"count\") > 1000)\n",
-    "    .sort(\"mean_improvement\", descending=True)\n",
-    "    .collect()\n",
-    ")\n",
-    "\n",
-    "# Overall stats\n",
-    "overall = (\n",
-    "    scan()\n",
-    "    .filter(\n",
-    "        pl.col(\"CURRENT_ENERGY_EFFICIENCY\").is_not_null()\n",
-    "        & pl.col(\"POTENTIAL_ENERGY_EFFICIENCY\").is_not_null()\n",
-    "    )\n",
-    "    .select(\n",
-    "        pl.col(\"CURRENT_ENERGY_EFFICIENCY\").mean().alias(\"current\"),\n",
-    "        pl.col(\"POTENTIAL_ENERGY_EFFICIENCY\").mean().alias(\"potential\"),\n",
-    "    )\n",
-    "    .collect()\n",
-    ")\n",
-    "print(f\"Mean current efficiency: {overall['current'].item():.1f}\")\n",
-    "print(f\"Mean potential efficiency: {overall['potential'].item():.1f}\")\n",
-    "print(f\"Mean improvement potential: {overall['potential'].item() - overall['current'].item():.1f} points\")\n",
-    "\n",
-    "fig, ax = plt.subplots()\n",
-    "x = range(len(improvement))\n",
-    "width = 0.35\n",
-    "ax.bar([i - width/2 for i in x], improvement[\"current\"].to_list(), width, label=\"Current\")\n",
-    "ax.bar([i + width/2 for i in x], improvement[\"potential\"].to_list(), width, label=\"Potential\")\n",
-    "ax.set_xticks(list(x))\n",
-    "ax.set_xticklabels(improvement[\"PROPERTY_TYPE\"].to_list(), rotation=45, ha=\"right\")\n",
-    "ax.set_ylabel(\"Energy Efficiency Score\")\n",
-    "ax.set_title(\"Current vs Potential Energy Efficiency by Property Type\")\n",
-    "ax.legend()\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Top 20 Local Authorities by Number of Certificates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-01-28T22:06:33.343873Z",
-     "iopub.status.busy": "2026-01-28T22:06:33.343778Z",
-     "iopub.status.idle": "2026-01-28T22:06:33.797103Z",
-     "shell.execute_reply": "2026-01-28T22:06:33.796854Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1200x700 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "la_counts = (\n",
-    "    scan()\n",
-    "    .filter(pl.col(\"LOCAL_AUTHORITY_LABEL\").is_not_null() & (pl.col(\"LOCAL_AUTHORITY_LABEL\") != \"\"))\n",
-    "    .group_by(\"LOCAL_AUTHORITY_LABEL\")\n",
-    "    .agg(\n",
-    "        pl.len().alias(\"count\"),\n",
-    "        pl.col(\"CURRENT_ENERGY_EFFICIENCY\").mean().alias(\"mean_eff\"),\n",
-    "    )\n",
-    "    .sort(\"count\", descending=True)\n",
-    "    .head(20)\n",
-    "    .collect()\n",
-    ")\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(12, 7))\n",
-    "eff_values = la_counts[\"mean_eff\"].to_list()\n",
-    "colors_la = plt.cm.RdYlGn([(v - 50) / 30 for v in eff_values])\n",
-    "ax.barh(\n",
-    "    la_counts[\"LOCAL_AUTHORITY_LABEL\"].to_list()[::-1],\n",
-    "    la_counts[\"count\"].to_list()[::-1],\n",
-    "    color=colors_la[::-1],\n",
-    ")\n",
-    "ax.set_xlabel(\"Number of Certificates\")\n",
-    "ax.set_title(\"Top 20 Local Authorities (color = mean energy efficiency)\")\n",
-    "ax.xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f\"{x/1e3:.0f}k\"))\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Summary Statistics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-01-28T22:06:33.797899Z",
-     "iopub.status.busy": "2026-01-28T22:06:33.797826Z",
-     "iopub.status.idle": "2026-01-28T22:06:36.111277Z",
-     "shell.execute_reply": "2026-01-28T22:06:36.111013Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div><style>\n",
-       ".dataframe > thead > tr,\n",
-       ".dataframe > tbody > tr {\n",
-       "  text-align: right;\n",
-       "  white-space: pre-wrap;\n",
-       "}\n",
-       "</style>\n",
-       "<small>shape: (9, 10)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>column</th><th>count</th><th>null_count</th><th>mean</th><th>std</th><th>min</th><th>q25</th><th>median</th><th>q75</th><th>max</th></tr><tr><td>str</td><td>u32</td><td>u32</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;CURRENT_ENERGY_EFFICIENCY&quot;</td><td>28847961</td><td>0</td><td>64.953829</td><td>14.289267</td><td>0.0</td><td>58.0</td><td>67.0</td><td>74.0</td><td>13060.0</td></tr><tr><td>&quot;POTENTIAL_ENERGY_EFFICIENCY&quot;</td><td>28847961</td><td>0</td><td>79.197588</td><td>11.097917</td><td>0.0</td><td>75.0</td><td>81.0</td><td>85.0</td><td>13071.0</td></tr><tr><td>&quot;ENERGY_CONSUMPTION_CURRENT&quot;</td><td>28847961</td><td>0</td><td>253.700647</td><td>173.148972</td><td>-320866.0</td><td>174.0</td><td>233.0</td><td>308.0</td><td>312273.0</td></tr><tr><td>&quot;CO2_EMISSIONS_CURRENT&quot;</td><td>28847961</td><td>0</td><td>3.819772</td><td>3.585926</td><td>-2854.8</td><td>2.1</td><td>3.2</td><td>4.7</td><td>4863.0</td></tr><tr><td>&quot;TOTAL_FLOOR_AREA&quot;</td><td>28847959</td><td>2</td><td>86.904086</td><td>118.035704</td><td>0.0</td><td>59.48</td><td>77.0</td><td>99.0</td><td>530331.552</td></tr><tr><td>&quot;HEATING_COST_CURRENT&quot;</td><td>28840897</td><td>7064</td><td>708.726959</td><td>644.316629</td><td>-944269.0</td><td>364.0</td><td>571.0</td><td>860.0</td><td>201800.0</td></tr><tr><td>&quot;LIGHTING_COST_CURRENT&quot;</td><td>28840912</td><td>7049</td><td>82.130655</td><td>589.723862</td><td>-768.0</td><td>54.0</td><td>73.0</td><td>100.0</td><td>3.15769e6</td></tr><tr><td>&quot;HOT_WATER_COST_CURRENT&quot;</td><td>28841075</td><td>6886</td><td>154.051112</td><td>102.004094</td><td>-38254.0</td><td>94.0</td><td>120.0</td><td>183.0</td><td>66675.0</td></tr><tr><td>&quot;NUMBER_HABITABLE_ROOMS&quot;</td><td>25398115</td><td>3449846</td><td>4.215609</td><td>1.915856</td><td>0.0</td><td>3.0</td><td>4.0</td><td>5.0</td><td>3424.0</td></tr></tbody></table></div>"
-      ],
-      "text/plain": [
-       "shape: (9, 10)\n",
-       "┌───────────────────┬──────────┬────────────┬────────────┬───┬───────┬────────┬───────┬────────────┐\n",
-       "│ column            ┆ count    ┆ null_count ┆ mean       ┆ … ┆ q25   ┆ median ┆ q75   ┆ max        │\n",
-       "│ ---               ┆ ---      ┆ ---        ┆ ---        ┆   ┆ ---   ┆ ---    ┆ ---   ┆ ---        │\n",
-       "│ str               ┆ u32      ┆ u32        ┆ f64        ┆   ┆ f64   ┆ f64    ┆ f64   ┆ f64        │\n",
-       "╞═══════════════════╪══════════╪════════════╪════════════╪═══╪═══════╪════════╪═══════╪════════════╡\n",
-       "│ CURRENT_ENERGY_EF ┆ 28847961 ┆ 0          ┆ 64.953829  ┆ … ┆ 58.0  ┆ 67.0   ┆ 74.0  ┆ 13060.0    │\n",
-       "│ FICIENCY          ┆          ┆            ┆            ┆   ┆       ┆        ┆       ┆            │\n",
-       "│ POTENTIAL_ENERGY_ ┆ 28847961 ┆ 0          ┆ 79.197588  ┆ … ┆ 75.0  ┆ 81.0   ┆ 85.0  ┆ 13071.0    │\n",
-       "│ EFFICIENCY        ┆          ┆            ┆            ┆   ┆       ┆        ┆       ┆            │\n",
-       "│ ENERGY_CONSUMPTIO ┆ 28847961 ┆ 0          ┆ 253.700647 ┆ … ┆ 174.0 ┆ 233.0  ┆ 308.0 ┆ 312273.0   │\n",
-       "│ N_CURRENT         ┆          ┆            ┆            ┆   ┆       ┆        ┆       ┆            │\n",
-       "│ CO2_EMISSIONS_CUR ┆ 28847961 ┆ 0          ┆ 3.819772   ┆ … ┆ 2.1   ┆ 3.2    ┆ 4.7   ┆ 4863.0     │\n",
-       "│ RENT              ┆          ┆            ┆            ┆   ┆       ┆        ┆       ┆            │\n",
-       "│ TOTAL_FLOOR_AREA  ┆ 28847959 ┆ 2          ┆ 86.904086  ┆ … ┆ 59.48 ┆ 77.0   ┆ 99.0  ┆ 530331.552 │\n",
-       "│ HEATING_COST_CURR ┆ 28840897 ┆ 7064       ┆ 708.726959 ┆ … ┆ 364.0 ┆ 571.0  ┆ 860.0 ┆ 201800.0   │\n",
-       "│ ENT               ┆          ┆            ┆            ┆   ┆       ┆        ┆       ┆            │\n",
-       "│ LIGHTING_COST_CUR ┆ 28840912 ┆ 7049       ┆ 82.130655  ┆ … ┆ 54.0  ┆ 73.0   ┆ 100.0 ┆ 3.15769e6  │\n",
-       "│ RENT              ┆          ┆            ┆            ┆   ┆       ┆        ┆       ┆            │\n",
-       "│ HOT_WATER_COST_CU ┆ 28841075 ┆ 6886       ┆ 154.051112 ┆ … ┆ 94.0  ┆ 120.0  ┆ 183.0 ┆ 66675.0    │\n",
-       "│ RRENT             ┆          ┆            ┆            ┆   ┆       ┆        ┆       ┆            │\n",
-       "│ NUMBER_HABITABLE_ ┆ 25398115 ┆ 3449846    ┆ 4.215609   ┆ … ┆ 3.0   ┆ 4.0    ┆ 5.0   ┆ 3424.0     │\n",
-       "│ ROOMS             ┆          ┆            ┆            ┆   ┆       ┆        ┆       ┆            │\n",
-       "└───────────────────┴──────────┴────────────┴────────────┴───┴───────┴────────┴───────┴────────────┘"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numeric_cols = [\n",
-    "    \"CURRENT_ENERGY_EFFICIENCY\", \"POTENTIAL_ENERGY_EFFICIENCY\",\n",
-    "    \"ENERGY_CONSUMPTION_CURRENT\", \"CO2_EMISSIONS_CURRENT\",\n",
-    "    \"TOTAL_FLOOR_AREA\", \"HEATING_COST_CURRENT\", \"LIGHTING_COST_CURRENT\",\n",
-    "    \"HOT_WATER_COST_CURRENT\", \"NUMBER_HABITABLE_ROOMS\",\n",
-    "]\n",
-    "\n",
-    "# Compute stats lazily per column to avoid loading all at once\n",
-    "rows = []\n",
-    "for c in numeric_cols:\n",
-    "    s = (\n",
-    "        scan()\n",
-    "        .select(pl.col(c).cast(pl.Float64, strict=False))\n",
-    "        .select(\n",
-    "            pl.lit(c).alias(\"column\"),\n",
-    "            pl.col(c).count().alias(\"count\"),\n",
-    "            pl.col(c).null_count().alias(\"null_count\"),\n",
-    "            pl.col(c).mean().alias(\"mean\"),\n",
-    "            pl.col(c).std().alias(\"std\"),\n",
-    "            pl.col(c).min().alias(\"min\"),\n",
-    "            pl.col(c).quantile(0.25).alias(\"q25\"),\n",
-    "            pl.col(c).median().alias(\"median\"),\n",
-    "            pl.col(c).quantile(0.75).alias(\"q75\"),\n",
-    "            pl.col(c).max().alias(\"max\"),\n",
-    "        )\n",
-    "        .collect()\n",
-    "    )\n",
-    "    rows.append(s)\n",
-    "\n",
-    "pl.concat(rows)"
-   ]
   }
  ],
  "metadata": {
diff --git a/analyses/journey_times_analysis.ipynb b/analyses/journey_times_analysis.ipynb
index 5cc57ab..0cd6b88 100644
--- a/analyses/journey_times_analysis.ipynb
+++ b/analyses/journey_times_analysis.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T19:53:51.298414Z",
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T19:53:51.683859Z",
@@ -43,9 +43,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Total postcodes: 220000\n",
+      "Total postcodes: 810000\n",
       "Columns: ['postcode', 'public_transport_easy_minutes', 'public_transport_quick_minutes', 'cycling_minutes', 'error', 'lat', 'long']\n",
-      "With lat/long: 220000\n"
+      "With lat/long: 810000\n"
      ]
     },
     {
@@ -76,7 +76,7 @@
        "└──────────┴──────────────────┴──────────────────┴─────────────────┴───────┴───────────┴───────────┘"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -95,7 +95,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 12,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T19:53:51.814525Z",
@@ -132,7 +132,7 @@
        "└──────────┴───────────┴───────────┴─────────────┘"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -168,7 +168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 13,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T19:53:51.944770Z",
@@ -182,7 +182,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Postcodes with journey data: 191297/220000\n"
+      "Postcodes with journey data: 638898/810000\n"
      ]
     }
    ],
@@ -203,7 +203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 14,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T19:53:51.948922Z",
@@ -224,7 +224,7 @@
       "│ ---          ┆ ---         ┆ ---         ┆ ---           ┆ ---          ┆ ---               │\n",
       "│ f64          ┆ i64         ┆ i64         ┆ f64           ┆ f64          ┆ f64               │\n",
       "╞══════════════╪═════════════╪═════════════╪═══════════════╪══════════════╪═══════════════════╡\n",
-      "│ 76.048709    ┆ 0           ┆ 372         ┆ 73.763485     ┆ 51.144121    ┆ 36.05804          │\n",
+      "│ 77.660431    ┆ 0           ┆ 388         ┆ 72.591016     ┆ 74.812994    ┆ 34.564262         │\n",
       "└──────────────┴─────────────┴─────────────┴───────────────┴──────────────┴───────────────────┘\n",
       "\n",
       "Distance Statistics:\n",
@@ -234,7 +234,7 @@
       "│ ---      ┆ ---        ┆ ---       │\n",
       "│ f64      ┆ f64        ┆ f64       │\n",
       "╞══════════╪════════════╪═══════════╡\n",
-      "│ 0.013939 ┆ 109.991355 ┆ 19.140675 │\n",
+      "│ 0.013939 ┆ 109.991355 ┆ 28.407258 │\n",
       "└──────────┴────────────┴───────────┘\n"
      ]
     }
@@ -262,7 +262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 15,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T19:53:51.953471Z",
@@ -274,7 +274,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x800 with 1 Axes>"
       ]
@@ -330,7 +330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 16,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T19:53:53.920561Z",
@@ -342,7 +342,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x800 with 1 Axes>"
       ]
@@ -388,7 +388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 17,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T19:53:55.187209Z",
@@ -400,7 +400,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x800 with 2 Axes>"
       ]
@@ -414,8 +414,8 @@
      "text": [
       "\n",
       "Time saved by taking quick route:\n",
-      "  Mean: 7.6 minutes\n",
-      "  Max: 97 minutes\n"
+      "  Mean: 9.2 minutes\n",
+      "  Max: 147 minutes\n"
      ]
     }
    ],
@@ -462,7 +462,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T19:53:56.842982Z",
@@ -477,7 +477,7 @@
      "output_type": "stream",
      "text": [
       "Journey times sorted by distance to Bank:\n",
-      "shape: (191_297, 5)\n",
+      "shape: (638_898, 5)\n",
       "┌──────────┬─────────────┬───────────────────────────┬───────────────────────────┬─────────────────┐\n",
       "│ postcode ┆ distance_km ┆ public_transport_easy_min ┆ public_transport_quick_mi ┆ cycling_minutes │\n",
       "│ ---      ┆ ---         ┆ utes                      ┆ nutes                     ┆ ---             │\n",
@@ -490,13 +490,24 @@
       "│ EC3V 3QS ┆ 0.0         ┆ 0                         ┆ 0                         ┆ 0               │\n",
       "│ EC3V 9AQ ┆ 0.0         ┆ 2                         ┆ 2                         ┆ 0               │\n",
       "│ …        ┆ …           ┆ …                         ┆ …                         ┆ …               │\n",
-      "│ OX15 5JP ┆ 110.0       ┆ null                      ┆ 153                       ┆ null            │\n",
       "│ OX15 5JT ┆ 110.0       ┆ null                      ┆ 153                       ┆ null            │\n",
       "│ OX15 5LD ┆ 110.0       ┆ null                      ┆ 153                       ┆ null            │\n",
       "│ OX15 5ND ┆ 110.0       ┆ null                      ┆ 153                       ┆ null            │\n",
       "│ OX15 5NE ┆ 110.0       ┆ null                      ┆ 153                       ┆ null            │\n",
+      "│ OX7 5TG  ┆ 110.0       ┆ null                      ┆ 241                       ┆ null            │\n",
       "└──────────┴─────────────┴───────────────────────────┴───────────────────────────┴─────────────────┘\n"
      ]
+    },
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
+      "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
+      "\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
+      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
     }
    ],
    "source": [
diff --git a/analyses/poi_analysis.ipynb b/analyses/poi_analysis.ipynb
deleted file mode 100644
index 16e0330..0000000
--- a/analyses/poi_analysis.ipynb
+++ /dev/null
@@ -1,4891 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# POI Category Cardinality Analysis\n",
-    "\n",
-    "Analysis of UK Points of Interest extracted from OpenStreetMap, showing the cardinality (count) of each POI type."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "polars.config.Config"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import polars as pl\n",
-    "import plotly.express as px\n",
-    "from pathlib import Path\n",
-    "\n",
-    "pl.Config.set_tbl_rows(50)\n",
-    "pl.Config.set_fmt_str_lengths(60)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total POIs: 26,920,704\n",
-      "Schema: Schema({'id': String, 'name': String, 'category': String, 'lat': Float64, 'lng': Float64, 'osm_tags': String})\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div><style>\n",
-       ".dataframe > thead > tr,\n",
-       ".dataframe > tbody > tr {\n",
-       "  text-align: right;\n",
-       "  white-space: pre-wrap;\n",
-       "}\n",
-       "</style>\n",
-       "<small>shape: (5, 6)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>id</th><th>name</th><th>category</th><th>lat</th><th>lng</th><th>osm_tags</th></tr><tr><td>str</td><td>str</td><td>str</td><td>f64</td><td>f64</td><td>str</td></tr></thead><tbody><tr><td>&quot;n129&quot;</td><td>&quot;Vodafone&quot;</td><td>&quot;mobile_phone&quot;</td><td>53.959357</td><td>-1.081517</td><td>&quot;{&quot;addr:city&quot;: &quot;York&quot;, &quot;addr:housenumber&quot;: &quot;29-30&quot;, &quot;addr:pos…</td></tr><tr><td>&quot;n99880&quot;</td><td>&quot;&quot;</td><td>&quot;crossing&quot;</td><td>51.525085</td><td>-0.15358</td><td>&quot;{&quot;crossing&quot;: &quot;unmarked&quot;, &quot;crossing:island&quot;: &quot;no&quot;, &quot;highway&quot;:…</td></tr><tr><td>&quot;n99884&quot;</td><td>&quot;&quot;</td><td>&quot;waste_basket&quot;</td><td>51.524364</td><td>-0.152816</td><td>&quot;{&quot;amenity&quot;: &quot;waste_basket&quot;, &quot;waste&quot;: &quot;dog_excrement;trash&quot;}&quot;</td></tr><tr><td>&quot;n99918&quot;</td><td>&quot;&quot;</td><td>&quot;life_ring&quot;</td><td>51.525734</td><td>-0.157812</td><td>&quot;{&quot;emergency&quot;: &quot;life_ring&quot;}&quot;</td></tr><tr><td>&quot;n99939&quot;</td><td>&quot;&quot;</td><td>&quot;traffic_signals&quot;</td><td>51.522443</td><td>-0.155462</td><td>&quot;{&quot;highway&quot;: &quot;traffic_signals&quot;, &quot;traffic_signals:direction&quot;: …</td></tr></tbody></table></div>"
-      ],
-      "text/plain": [
-       "shape: (5, 6)\n",
-       "┌────────┬──────────┬─────────────────┬───────────┬───────────┬─────────────────────────────────┐\n",
-       "│ id     ┆ name     ┆ category        ┆ lat       ┆ lng       ┆ osm_tags                        │\n",
-       "│ ---    ┆ ---      ┆ ---             ┆ ---       ┆ ---       ┆ ---                             │\n",
-       "│ str    ┆ str      ┆ str             ┆ f64       ┆ f64       ┆ str                             │\n",
-       "╞════════╪══════════╪═════════════════╪═══════════╪═══════════╪═════════════════════════════════╡\n",
-       "│ n129   ┆ Vodafone ┆ mobile_phone    ┆ 53.959357 ┆ -1.081517 ┆ {\"addr:city\": \"York\",           │\n",
-       "│        ┆          ┆                 ┆           ┆           ┆ \"addr:housenumber\": \"29-30\",    │\n",
-       "│        ┆          ┆                 ┆           ┆           ┆ \"addr:pos…                      │\n",
-       "│ n99880 ┆          ┆ crossing        ┆ 51.525085 ┆ -0.15358  ┆ {\"crossing\": \"unmarked\",        │\n",
-       "│        ┆          ┆                 ┆           ┆           ┆ \"crossing:island\": \"no\",        │\n",
-       "│        ┆          ┆                 ┆           ┆           ┆ \"highway\":…                     │\n",
-       "│ n99884 ┆          ┆ waste_basket    ┆ 51.524364 ┆ -0.152816 ┆ {\"amenity\": \"waste_basket\",     │\n",
-       "│        ┆          ┆                 ┆           ┆           ┆ \"waste\": \"dog_excrement;trash\"} │\n",
-       "│ n99918 ┆          ┆ life_ring       ┆ 51.525734 ┆ -0.157812 ┆ {\"emergency\": \"life_ring\"}      │\n",
-       "│ n99939 ┆          ┆ traffic_signals ┆ 51.522443 ┆ -0.155462 ┆ {\"highway\": \"traffic_signals\",  │\n",
-       "│        ┆          ┆                 ┆           ┆           ┆ \"traffic_signals:direction\": …  │\n",
-       "└────────┴──────────┴─────────────────┴───────────┴───────────┴─────────────────────────────────┘"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "DATA_PATH = Path(\"../data_sources/uk_pois.parquet\")\n",
-    "\n",
-    "df = pl.read_parquet(DATA_PATH)\n",
-    "print(f\"Total POIs: {len(df):,}\")\n",
-    "print(f\"Schema: {df.schema}\")\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "## 1. Overall Category Cardinality"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Unique categories: 10,926\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div><style>\n",
-       ".dataframe > thead > tr,\n",
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-       "  text-align: right;\n",
-       "  white-space: pre-wrap;\n",
-       "}\n",
-       "</style>\n",
-       "<small>shape: (10_926, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>category</th><th>count</th></tr><tr><td>str</td><td>u32</td></tr></thead><tbody><tr><td>&quot;yes&quot;</td><td>6149240</td></tr><tr><td>&quot;house&quot;</td><td>4867889</td></tr><tr><td>&quot;residential&quot;</td><td>2975285</td></tr><tr><td>&quot;service&quot;</td><td>2397521</td></tr><tr><td>&quot;footway&quot;</td><td>1690239</td></tr><tr><td>&quot;semidetached_house&quot;</td><td>710064</td></tr><tr><td>&quot;garage&quot;</td><td>536802</td></tr><tr><td>&quot;track&quot;</td><td>506554</td></tr><tr><td>&quot;detached&quot;</td><td>451014</td></tr><tr><td>&quot;path&quot;</td><td>419601</td></tr><tr><td>&quot;unclassified&quot;</td><td>320382</td></tr><tr><td>&quot;crossing&quot;</td><td>318014</td></tr><tr><td>&quot;garden&quot;</td><td>308411</td></tr><tr><td>&quot;parking&quot;</td><td>272518</td></tr><tr><td>&quot;bus_stop / platform&quot;</td><td>242090</td></tr><tr><td>&quot;terrace&quot;</td><td>219344</td></tr><tr><td>&quot;tertiary&quot;</td><td>216299</td></tr><tr><td>&quot;parking_space&quot;</td><td>206980</td></tr><tr><td>&quot;apartments&quot;</td><td>175306</td></tr><tr><td>&quot;cycleway&quot;</td><td>173504</td></tr><tr><td>&quot;primary&quot;</td><td>152330</td></tr><tr><td>&quot;bench&quot;</td><td>149125</td></tr><tr><td>&quot;street_lamp&quot;</td><td>120327</td></tr><tr><td>&quot;give_way&quot;</td><td>118935</td></tr><tr><td>&quot;pitch&quot;</td><td>117031</td></tr><tr><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;cage&quot;</td><td>1</td></tr><tr><td>&quot;post_office / appliance&quot;</td><td>1</td></tr><tr><td>&quot;club / commercial&quot;</td><td>1</td></tr><tr><td>&quot;home_help_service_agency&quot;</td><td>1</td></tr><tr><td>&quot;decoration&quot;</td><td>1</td></tr><tr><td>&quot;picnic_table / viewpoint&quot;</td><td>1</td></tr><tr><td>&quot;bird_hide / tower&quot;</td><td>1</td></tr><tr><td>&quot;entertainment / yes&quot;</td><td>1</td></tr><tr><td>&quot;terminal / aerohangar&quot;</td><td>1</td></tr><tr><td>&quot;valeting / yes&quot;</td><td>1</td></tr><tr><td>&quot;Waste Disposal Services&quot;</td><td>1</td></tr><tr><td>&quot;museum / tourism&quot;</td><td>1</td></tr><tr><td>&quot;attraction / stadium&quot;</td><td>1</td></tr><tr><td>&quot;radome&quot;</td><td>1</td></tr><tr><td>&quot;social_facility / static_caravan&quot;</td><td>1</td></tr><tr><td>&quot;cake_supplies / yes&quot;</td><td>1</td></tr><tr><td>&quot;rope_ladder&quot;</td><td>1</td></tr><tr><td>&quot;packaging_supplies&quot;</td><td>1</td></tr><tr><td>&quot;locksmith / office&quot;</td><td>1</td></tr><tr><td>&quot;fitness_centre / container&quot;</td><td>1</td></tr><tr><td>&quot;trampoline_park / leisure&quot;</td><td>1</td></tr><tr><td>&quot;vacant / garden / retail&quot;</td><td>1</td></tr><tr><td>&quot;miniature_golf / retail&quot;</td><td>1</td></tr><tr><td>&quot;acupuncture;herbalist / shop&quot;</td><td>1</td></tr><tr><td>&quot;Elec Sub-station&quot;</td><td>1</td></tr></tbody></table></div>"
-      ],
-      "text/plain": [
-       "shape: (10_926, 2)\n",
-       "┌──────────────────────────────────┬─────────┐\n",
-       "│ category                         ┆ count   │\n",
-       "│ ---                              ┆ ---     │\n",
-       "│ str                              ┆ u32     │\n",
-       "╞══════════════════════════════════╪═════════╡\n",
-       "│ yes                              ┆ 6149240 │\n",
-       "│ house                            ┆ 4867889 │\n",
-       "│ residential                      ┆ 2975285 │\n",
-       "│ service                          ┆ 2397521 │\n",
-       "│ footway                          ┆ 1690239 │\n",
-       "│ semidetached_house               ┆ 710064  │\n",
-       "│ garage                           ┆ 536802  │\n",
-       "│ track                            ┆ 506554  │\n",
-       "│ detached                         ┆ 451014  │\n",
-       "│ path                             ┆ 419601  │\n",
-       "│ unclassified                     ┆ 320382  │\n",
-       "│ crossing                         ┆ 318014  │\n",
-       "│ garden                           ┆ 308411  │\n",
-       "│ parking                          ┆ 272518  │\n",
-       "│ bus_stop / platform              ┆ 242090  │\n",
-       "│ terrace                          ┆ 219344  │\n",
-       "│ tertiary                         ┆ 216299  │\n",
-       "│ parking_space                    ┆ 206980  │\n",
-       "│ apartments                       ┆ 175306  │\n",
-       "│ cycleway                         ┆ 173504  │\n",
-       "│ primary                          ┆ 152330  │\n",
-       "│ bench                            ┆ 149125  │\n",
-       "│ street_lamp                      ┆ 120327  │\n",
-       "│ give_way                         ┆ 118935  │\n",
-       "│ pitch                            ┆ 117031  │\n",
-       "│ …                                ┆ …       │\n",
-       "│ cage                             ┆ 1       │\n",
-       "│ post_office / appliance          ┆ 1       │\n",
-       "│ club / commercial                ┆ 1       │\n",
-       "│ home_help_service_agency         ┆ 1       │\n",
-       "│ decoration                       ┆ 1       │\n",
-       "│ picnic_table / viewpoint         ┆ 1       │\n",
-       "│ bird_hide / tower                ┆ 1       │\n",
-       "│ entertainment / yes              ┆ 1       │\n",
-       "│ terminal / aerohangar            ┆ 1       │\n",
-       "│ valeting / yes                   ┆ 1       │\n",
-       "│ Waste Disposal Services          ┆ 1       │\n",
-       "│ museum / tourism                 ┆ 1       │\n",
-       "│ attraction / stadium             ┆ 1       │\n",
-       "│ radome                           ┆ 1       │\n",
-       "│ social_facility / static_caravan ┆ 1       │\n",
-       "│ cake_supplies / yes              ┆ 1       │\n",
-       "│ rope_ladder                      ┆ 1       │\n",
-       "│ packaging_supplies               ┆ 1       │\n",
-       "│ locksmith / office               ┆ 1       │\n",
-       "│ fitness_centre / container       ┆ 1       │\n",
-       "│ trampoline_park / leisure        ┆ 1       │\n",
-       "│ vacant / garden / retail         ┆ 1       │\n",
-       "│ miniature_golf / retail          ┆ 1       │\n",
-       "│ acupuncture;herbalist / shop     ┆ 1       │\n",
-       "│ Elec Sub-station                 ┆ 1       │\n",
-       "└──────────────────────────────────┴─────────┘"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "category_counts = (\n",
-    "    df.group_by(\"category\")\n",
-    "    .agg(pl.len().alias(\"count\"))\n",
-    "    .sort(\"count\", descending=True)\n",
-    ")\n",
-    "print(f\"Unique categories: {len(category_counts):,}\")\n",
-    "category_counts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.plotly.v1+json": {
-       "config": {
-        "plotlyServerURL": "https://plot.ly"
-       },
-       "data": [
-        {
-         "hovertemplate": "Number of POIs=%{x}<br>Category=%{y}<extra></extra>",
-         "legendgroup": "6149240",
-         "marker": {
-          "color": "#636efa",
-          "pattern": {
-           "shape": ""
-          }
-         },
-         "name": "6149240",
-         "orientation": "h",
-         "showlegend": true,
-         "textposition": "auto",
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-         "xaxis": "x",
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-          "yes"
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-         "yaxis": "y"
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-         },
-         "xaxis": "x",
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-          "house"
-         ],
-         "yaxis": "y"
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-         "xaxis": "x",
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-          "residential"
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-         "yaxis": "y"
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-          "footway"
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-          "semidetached_house"
-         ],
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-         "hovertemplate": "Number of POIs=%{x}<br>Category=%{y}<extra></extra>",
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-         "x": {
-          "bdata": "4jAIAA==",
-          "dtype": "u4"
-         },
-         "xaxis": "x",
-         "y": [
-          "garage"
-         ],
-         "yaxis": "y"
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-         "x": {
-          "bdata": "uroHAA==",
-          "dtype": "u4"
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-         "xaxis": "x",
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-          "track"
-         ],
-         "yaxis": "y"
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-         "hovertemplate": "Number of POIs=%{x}<br>Category=%{y}<extra></extra>",
-         "legendgroup": "451014",
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-         "x": {
-          "bdata": "xuEGAA==",
-          "dtype": "u4"
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-         "xaxis": "x",
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-          "detached"
-         ],
-         "yaxis": "y"
-        },
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-         "hovertemplate": "Number of POIs=%{x}<br>Category=%{y}<extra></extra>",
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-          "dtype": "u4"
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-         "xaxis": "x",
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-          "path"
-         ],
-         "yaxis": "y"
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-          "bdata": "fuMEAA==",
-          "dtype": "u4"
-         },
-         "xaxis": "x",
-         "y": [
-          "unclassified"
-         ],
-         "yaxis": "y"
-        },
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-         "x": {
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-         "xaxis": "x",
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-          "crossing"
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-         "yaxis": "y"
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-   "source": [
-    "top_n = 40\n",
-    "fig = px.bar(\n",
-    "    category_counts.head(top_n).to_pandas(),\n",
-    "    x=\"count\",\n",
-    "    y=\"category\",\n",
-    "    orientation=\"h\",\n",
-    "    title=f\"Top {top_n} POI Categories by Count\",\n",
-    "    labels={\"count\": \"Number of POIs\", \"category\": \"Category\"},\n",
-    "    color=\"count\",\n",
-    "    color_continuous_scale=\"Blues\",\n",
-    ")\n",
-    "fig.update_layout(yaxis={\"categoryorder\": \"total ascending\"}, height=900)\n",
-    "fig.show()"
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-   "source": [
-    "---\n",
-    "## 2. Long Tail Distribution"
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-   "execution_count": 11,
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DAACrAwAArAMAAK0DAACuAwAArwMAALADAACxAwAAsgMAALMDAAC0AwAAtQMAALYDAAC3AwAAuAMAALkDAAC6AwAAuwMAALwDAAC9AwAAvgMAAL8DAADAAwAAwQMAAMIDAADDAwAAxAMAAMUDAADGAwAAxwMAAMgDAADJAwAAygMAAMsDAADMAwAAzQMAAM4DAADPAwAA0AMAANEDAADSAwAA0wMAANQDAADVAwAA1gMAANcDAADYAwAA2QMAANoDAADbAwAA3AMAAN0DAADeAwAA3wMAAOADAADhAwAA4gMAAOMDAADkAwAA5QMAAOYDAADnAwAA6AMAAOkDAADqAwAA6wMAAOwDAADtAwAA7gMAAO8DAADwAwAA8QMAAPIDAADzAwAA9AMAAPUDAAD2AwAA9wMAAPgDAAD5AwAA+gMAAPsDAAD8AwAA/QMAAP4DAAD/AwAAAAQAAAEEAAACBAAAAwQAAAQEAAAFBAAABgQAAAcEAAAIBAAACQQAAAoEAAALBAAADAQAAA0EAAAOBAAADwQAABAEAAARBAAAEgQAABMEAAAUBAAAFQQAABYEAAAXBAAAGAQAABkEAAAaBAAAGwQAABwEAAAdBAAAHgQAAB8EAAAgBAAAIQQAACIEAAAjBAAAJAQAACUEAAAmBAAAJwQAACgEAAApBAAAKgQAACsEAAAsBAAALQQAAC4EAAAvBAAAMAQAADEEAAAyBAAAMwQAADQEAAA1BAAANgQAADcEAAA4BAAAOQQAADoEAAA7BAAAPAQAAD0EAAA+BAAAPwQAAEAEAABBBAAAQgQAAEMEAABEBAAARQQAAEYEAABHBAAASAQAAEkEAABKBAAASwQAAEwEAABNBAAATgQAAE8EAABQBAAAUQQAAFIEAABTBAAAVAQAAFUEAABWBAAAVwQAAFgEAABZBAAAWgQAAFsEAABcBAAAXQQAAF4EAABfBAAAYAQAAGEEAABiBAAAYwQAAGQEAABlBAAAZgQAAGcEAABoBAAAaQQAAGoEAABrBAAAbAQAAG0EAABuBAAAbwQAAHAEAABxBAAAcgQAAHMEAAB0BAAAdQQAAHYEAAB3BAAAeAQAAHkEAAB6BAAAewQAAHwEAAB9BAAAfgQAAH8EAACABAAAgQQAAIIEAACDBAAAhAQAAIUEAACGBAAAhwQAAIgEAACJBAAAigQAAIsEAACMBAAAjQQAAI4EAACPBAAAkAQAAJEEAACSBAAAkwQAAJQEAACVBAAAlgQAAJcEAACYBAAAmQQAAJoEAACbBAAAnAQAAJ0EAACeBAAAnwQAAKAEAAChBAAAogQAAKMEAACkBAAApQQAAKYEAACnBAAAqAQAAKkEAACqBAAAqwQAAKwEAACtBAAArgQAAK8EAACwBAAAsQQAALIEAACzBAAAtAQAALUEAAC2BAAAtwQAALgEAAC5BAAAugQAALsEAAC8BAAAvQQAAL4EAAC/BAAAwAQAAMEEAADCBAAAwwQAAMQEAADFBAAAxgQAAMcEAADIBAAAyQQAAMoEAADLBAAAzAQAAM0EAADOBAAAzwQAANAEAADRBAAA0gQAANMEAADUBAAA1QQAANYEAADXBAAA2AQAANkEAADaBAAA2wQAANwEAADdBAAA3gQAAN8EAADgBAAA4QQAAOIEAADjBAAA5AQAAOUEAADmBAAA5wQAAOgEAADpBAAA6gQAAOsEAADsBAAA7QQAAO4EAADvBAAA8AQAAPEEAADyBAAA8wQAAPQEAAD1BAAA9gQAAPcEAAD4BAAA+QQAAPoEAAD7BAAA/AQAAP0EAAD+BAAA/wQAAAAFAAABBQAAAgUAAAMFAAAEBQAABQUAAAYFAAAHBQAACAUAAAkFAAAKBQAACwUAAAwFAAANBQAADgUAAA8FAAAQBQAAEQUAABIFAAATBQAAFAUAABUFAAAWBQAAFwUAABgFAAAZBQAAGgUAABsFAAAcBQAAHQUAAB4FAAAfBQAAIAUAACEFAAAiBQAAIwUAACQFAAAlBQAAJgUAACcFAAAoBQAAKQUAACoFAAArBQAALAUAAC0FAAAuBQAALwUAADAFAAAxBQAAMgUAADMFAAA0BQAANQUAADYFAAA3BQAAOAUAADkFAAA6BQAAOwUAADwFAAA9BQAAPgUAAD8FAABABQAAQQUAAEIFAABDBQAARAUAAEUFAABGBQAARwUAAEgFAABJBQAASgUAAEsFAABMBQAATQUAAE4FAABPBQAAUAUAAFEFAABSBQAAUwUAAFQFAABVBQAAVgUAAFcFAABYBQAAWQUAAFoFAABbBQAAXAUAAF0FAABeBQAAXwUAAGAFAABhBQAAYgUAAGMFAABkBQAAZQUAAGYFAABnBQAAaAUAAGkFAABqBQAAawUAAGwFAABtBQAAbgUAAG8FAABwBQAAcQUAAHIFAABzBQAAdAUAAHUFAAB2BQAAdwUAAHgFAAB5BQAAegUAAHsFAAB8BQAAfQUAAH4FAAB/BQAAgAUAAIEFAACCBQAAgwUAAIQFAACFBQAAhgUAAIcFAACIBQAAiQUAAIoFAACLBQAAjAUAAI0FAACOBQAAjwUAAJAFAACRBQAAkgUAAJMFAACUBQAAlQUAAJYFAACXBQAAmAUAAJkFAACaBQAAmwUAAJwFAACdBQAAngUAAJ8FAACgBQAAoQUAAKIFAACjBQAApAUAAKUFAACmBQAApwUAAKgFAACpBQAAqgUAAKsFAACsBQAArQUAAK4FAACvBQAAsAUAALEFAACyBQAAswUAALQFAAC1BQAAtgUAALcFAAC4BQAAuQUAALoFAAC7BQAAvAUAAL0FAAC+BQAAvwUAAMAFAADBBQAAwgUAAMMFAADEBQAAxQUAAMYFAADHBQAAyAUAAMkFAADKBQAAywUAAMwFAADNBQAAzgUAAM8FAADQBQAA0QUAANIFAADTBQAA1AUAANUFAADWBQAA1wUAANgFAADZBQAA2gUAANsFAADcBQAA3QUAAN4FAADfBQAA4AUAAOEFAADiBQAA4wUAAOQFAADlBQAA5gUAAOcFAADoBQAA6QUAAOoFAADrBQAA7AUAAO0FAADuBQAA7wUAAPAFAADxBQAA8gUAAPMFAAD0BQAA9QUAAPYFAAD3BQAA+AUAAPkFAAD6BQAA+wUAAPwFAAD9BQAA/gUAAP8FAAAABgAAAQYAAAIGAAADBgAABAYAAAUGAAAGBgAABwYAAAgGAAAJBgAACgYAAAsGAAAMBgAADQYAAA4GAAAPBgAAEAYAABEGAAASBgAAEwYAABQGAAAVBgAAFgYAABcGAAAYBgAAGQYAABoGAAAbBgAAHAYAAB0GAAAeBgAAHwYAACAGAAAhBgAAIgYAACMGAAAkBgAAJQYAACYGAAAnBgAAKAYAACkGAAAqBgAAKwYAACwGAAAtBgAALgYAAC8GAAAwBgAAMQYAADIGAAAzBgAANAYAADUGAAA2BgAANwYAADgGAAA5BgAAOgYAADsGAAA8BgAAPQYAAD4GAAA/BgAAQAYAAEEGAABCBgAAQwYAAEQGAABFBgAARgYAAEcGAABIBgAASQYAAEoGAABLBgAATAYAAE0GAABOBgAATwYAAFAGAABRBgAAUgYAAFMGAABUBgAAVQYAAFYGAABXBgAAWAYAAFkGAABaBgAAWwYAAFwGAABdBgAAXgYAAF8GAABgBgAAYQYAAGIGAABjBgAAZAYAAGUGAABmBgAAZwYAAGgGAABpBgAAagYAAGsGAABsBgAAbQYAAG4GAABvBgAAcAYAAHEGAAByBgAAcwYAAHQGAAB1BgAAdgYAAHcGAAB4BgAAeQYAAHoGAAB7BgAAfAYAAH0GAAB+BgAAfwYAAIAGAACBBgAAggYAAIMGAACEBgAAhQYAAIYGAACHBgAAiAYAAIkGAACKBgAAiwYAAIwGAACNBgAAjgYAAI8GAACQBgAAkQYAAJIGAACTBgAAlAYAAJUGAACWBgAAlwYAAJgGAACZBgAAmgYAAJsGAACcBgAAnQYAAJ4GAACfBgAAoAYAAKEGAACiBgAAowYAAKQGAAClBgAApgYAAKcGAACoBgAAqQYAAKoGAACrBgAArAYAAK0GAACuBgAArwYAALAGAACxBgAAsgYAALMGAAC0BgAAtQYAALYGAAC3BgAAuAYAALkGAAC6BgAAuwYAALwGAAC9BgAAvgYAAL8GAADABgAAwQYAAMIGAADDBgAAxAYAAMUGAADGBgAAxwYAAMgGAADJBgAAygYAAMsGAADMBgAAzQYAAM4GAADPBgAA0AYAANEGAADSBgAA0wYAANQGAADVBgAA1gYAANcGAADYBgAA2QYAANoGAADbBgAA3AYAAN0GAADeBgAA3wYAAOAGAADhBgAA4gYAAOMGAADkBgAA5QYAAOYGAADnBgAA6AYAAOkGAADqBgAA6wYAAOwGAADtBgAA7gYAAO8GAADwBgAA8QYAAPIGAADzBgAA9AYAAPUGAAD2BgAA9wYAAPgGAAD5BgAA+gYAAPsGAAD8BgAA/QYAAP4GAAD/BgAAAAcAAAEHAAACBwAAAwcAAAQHAAAFBwAABgcAAAcHAAAIBwAACQcAAAoHAAALBwAADAcAAA0HAAAOBwAADwcAABAHAAARBwAAEgcAABMHAAAUBwAAFQcAABYHAAAXBwAAGAcAABkHAAAaBwAAGwcAABwHAAAdBwAAHgcAAB8HAAAgBwAAIQcAACIHAAAjBwAAJAcAACUHAAAmBwAAJwcAACgHAAApBwAAKgcAACsHAAAsBwAALQcAAC4HAAAvBwAAMAcAADEHAAAyBwAAMwcAADQHAAA1BwAANgcAADcHAAA4BwAAOQcAADoHAAA7BwAAPAcAAD0HAAA+BwAAPwcAAEAHAABBBwAAQgcAAEMHAABEBwAARQcAAEYHAABHBwAASAcAAEkHAABKBwAASwcAAEwHAABNBwAATgcAAE8HAABQBwAAUQcAAFIHAABTBwAAVAcAAFUHAABWBwAAVwcAAFgHAABZBwAAWgcAAFsHAABcBwAAXQcAAF4HAABfBwAAYAcAAGEHAABiBwAAYwcAAGQHAABlBwAAZgcAAGcHAABoBwAAaQcAAGoHAABrBwAAbAcAAG0HAABuBwAAbwcAAHAHAABxBwAAcgcAAHMHAAB0BwAAdQcAAHYHAAB3BwAAeAcAAHkHAAB6BwAAewcAAHwHAAB9BwAAfgcAAH8HAACABwAAgQcAAIIHAACDBwAAhAcAAIUHAACGBwAAhwcAAIgHAACJBwAAigcAAIsHAACMBwAAjQcAAI4HAACPBwAAkAcAAJEHAACSBwAAkwcAAJQHAACVBwAAlgcAAJcHAACYBwAAmQcAAJoHAACbBwAAnAcAAJ0HAACeBwAAnwcAAKAHAAChBwAAogcAAKMHAACkBwAApQcAAKYHAACnBwAAqAcAAKkHAACqBwAAqwcAAKwHAACtBwAArgcAAK8HAACwBwAAsQcAALIHAACzBwAAtAcAALUHAAC2BwAAtwcAALgHAAC5BwAAugcAALsHAAC8BwAAvQcAAL4HAAC/BwAAwAcAAMEHAADCBwAAwwcAAMQHAADFBwAAxgcAAMcHAADIBwAAyQcAAMoHAADLBwAAzAcAAM0HAADOBwAAzwcAANAHAADRBwAA0gcAANMHAADUBwAA1QcAANYHAADXBwAA2AcAANkHAADaBwAA2wcAANwHAADdBwAA3gcAAN8HAADgBwAA4QcAAOIHAADjBwAA5AcAAOUHAADmBwAA5wcAAOgHAADpBwAA6gcAAOsHAADsBwAA7QcAAO4HAADvBwAA8AcAAPEHAADyBwAA8wcAAPQHAAD1BwAA9gcAAPcHAAD4BwAA+QcAAPoHAAD7BwAA/AcAAP0HAAD+BwAA/wcAAAAIAAABCAAAAggAAAMIAAAECAAABQgAAAYIAAAHCAAACAgAAAkIAAAKCAAACwgAAAwIAAANCAAADggAAA8IAAAQCAAAEQgAABIIAAATCAAAFAgAABUIAAAWCAAAFwgAABgIAAAZCAAAGggAABsIAAAcCAAAHQgAAB4IAAAfCAAAIAgAACEIAAAiCAAAIwgAACQIAAAlCAAAJggAACcIAAAoCAAAKQgAACoIAAArCAAALAgAAC0IAAAuCAAALwgAADAIAAAxCAAAMggAADMIAAA0CAAANQgAADYIAAA3CAAAOAgAADkIAAA6CAAAOwgAADwIAAA9CAAAPggAAD8IAABACAAAQQgAAEIIAABDCAAARAgAAEUIAABGCAAARwgAAEgIAABJCAAASggAAEsIAABMCAAATQgAAE4IAABPCAAAUAgAAFEIAABSCAAAUwgAAFQIAABVCAAAVggAAFcIAABYCAAAWQgAAFoIAABbCAAAXAgAAF0IAABeCAAAXwgAAGAIAABhCAAAYggAAGMIAABkCAAAZQgAAGYIAABnCAAAaAgAAGkIAABqCAAAawgAAGwIAABtCAAAbggAAG8IAABwCAAAcQgAAHIIAABzCAAAdAgAAHUIAAB2CAAAdwgAAHgIAAB5CAAAeggAAHsIAAB8CAAAfQgAAH4IAAB/CAAAgAgAAIEIAACCCAAAgwgAAIQIAACFCAAAhggAAIcIAACICAAAiQgAAIoIAACLCAAAjAgAAI0IAACOCAAAjwgAAJAIAACRCAAAkggAAJMIAACUCAAAlQgAAJYIAACXCAAAmAgAAJkIAACaCAAAmwgAAJwIAACdCAAAnggAAJ8IAACgCAAAoQgAAKIIAACjCAAApAgAAKUIAACmCAAApwgAAKgIAACpCAAAqggAAKsIAACsCAAArQgAAK4IAACvCAAAsAgAALEIAACyCAAAswgAALQIAAC1CAAAtggAALcIAAC4CAAAuQgAALoIAAC7CAAAvAgAAL0IAAC+CAAAvwgAAMAIAADBCAAAwggAAMMIAADECAAAxQgAAMYIAADHCAAAyAgAAMkIAADKCAAAywgAAMwIAADNCAAAzggAAM8IAADQCAAA0QgAANIIAADTCAAA1AgAANUIAADWCAAA1wgAANgIAADZCAAA2ggAANsIAADcCAAA3QgAAN4IAADfCAAA4AgAAOEIAADiCAAA4wgAAOQIAADlCAAA5ggAAOcIAADoCAAA6QgAAOoIAADrCAAA7AgAAO0IAADuCAAA7wgAAPAIAADxCAAA8ggAAPMIAAD0CAAA9QgAAPYIAAD3CAAA+AgAAPkIAAD6CAAA+wgAAPwIAAD9CAAA/ggAAP8IAAAACQAAAQkAAAIJAAADCQAABAkAAAUJAAAGCQAABwkAAAgJAAAJCQAACgkAAAsJAAAMCQAADQkAAA4JAAAPCQAAEAkAABEJAAASCQAAEwkAABQJAAAVCQAAFgkAABcJAAAYCQAAGQkAABoJAAAbCQAAHAkAAB0JAAAeCQAAHwkAACAJAAAhCQAAIgkAACMJAAAkCQAAJQkAACYJAAAnCQAAKAkAACkJAAAqCQAAKwkAACwJAAAtCQAALgkAAC8JAAAwCQAAMQkAADIJAAAzCQAANAkAADUJAAA2CQAANwkAADgJAAA5CQAAOgkAADsJAAA8CQAAPQkAAD4JAAA/CQAAQAkAAEEJAABCCQAAQwkAAEQJAABFCQAARgkAAEcJAABICQAASQkAAEoJAABLCQAATAkAAE0JAABOCQAATwkAAFAJAABRCQAAUgkAAFMJAABUCQAAVQkAAFYJAABXCQAAWAkAAFkJAABaCQAAWwkAAFwJAABdCQAAXgkAAF8JAABgCQAAYQkAAGIJAABjCQAAZAkAAGUJAABmCQAAZwkAAGgJAABpCQAAagkAAGsJAABsCQAAbQkAAG4JAABvCQAAcAkAAHEJAAByCQAAcwkAAHQJAAB1CQAAdgkAAHcJAAB4CQAAeQkAAHoJAAB7CQAAfAkAAH0JAAB+CQAAfwkAAIAJAACBCQAAggkAAIMJAACECQAAhQkAAIYJAACHCQAAiAkAAIkJAACKCQAAiwkAAIwJAACNCQAAjgkAAI8JAACQCQAAkQkAAJIJAACTCQAAlAkAAJUJAACWCQAAlwkAAJgJAACZCQAAmgkAAJsJAACcCQAAnQkAAJ4JAACfCQAAoAkAAKEJAACiCQAAowkAAKQJAAClCQAApgkAAKcJAACoCQAAqQkAAKoJAACrCQAArAkAAK0JAACuCQAArwkAALAJAACxCQAAsgkAALMJAAC0CQAAtQkAALYJAAC3CQAAuAkAALkJAAC6CQAAuwkAALwJAAC9CQAAvgkAAL8JAADACQAAwQkAAMIJAADDCQAAxAkAAMUJAADGCQAAxwkAAMgJAADJCQAAygkAAMsJAADMCQAAzQkAAM4JAADPCQAA0AkAANEJAADSCQAA0wkAANQJAADVCQAA1gkAANcJAADYCQAA2QkAANoJAADbCQAA3AkAAN0JAADeCQAA3wkAAOAJAADhCQAA4gkAAOMJAADkCQAA5QkAAOYJAADnCQAA6AkAAOkJAADqCQAA6wkAAOwJAADtCQAA7gkAAO8JAADwCQAA8QkAAPIJAADzCQAA9AkAAPUJAAD2CQAA9wkAAPgJAAD5CQAA+gkAAPsJAAD8CQAA/QkAAP4JAAD/CQAAAAoAAAEKAAACCgAAAwoAAAQKAAAFCgAABgoAAAcKAAAICgAACQoAAAoKAAALCgAADAoAAA0KAAAOCgAADwoAABAKAAARCgAAEgoAABMKAAAUCgAAFQoAABYKAAAXCgAAGAoAABkKAAAaCgAAGwoAABwKAAAdCgAAHgoAAB8KAAAgCgAAIQoAACIKAAAjCgAAJAoAACUKAAAmCgAAJwoAACgKAAApCgAAKgoAACsKAAAsCgAALQoAAC4KAAAvCgAAMAoAADEKAAAyCgAAMwoAADQKAAA1CgAANgoAADcKAAA4CgAAOQoAADoKAAA7CgAAPAoAAD0KAAA+CgAAPwoAAEAKAABBCgAAQgoAAEMKAABECgAARQoAAEYKAABHCgAASAoAAEkKAABKCgAASwoAAEwKAABNCgAATgoAAE8KAABQCgAAUQoAAFIKAABTCgAAVAoAAFUKAABWCgAAVwoAAFgKAABZCgAAWgoAAFsKAABcCgAAXQoAAF4KAABfCgAAYAoAAGEKAABiCgAAYwoAAGQKAABlCgAAZgoAAGcKAABoCgAAaQoAAGoKAABrCgAAbAoAAG0KAABuCgAAbwoAAHAKAABxCgAAcgoAAHMKAAB0CgAAdQoAAHYKAAB3CgAAeAoAAHkKAAB6CgAAewoAAHwKAAB9CgAAfgoAAH8KAACACgAAgQoAAIIKAACDCgAAhAoAAIUKAACGCgAAhwoAAIgKAACJCgAAigoAAIsKAACMCgAAjQoAAI4KAACPCgAAkAoAAJEKAACSCgAAkwoAAJQKAACVCgAAlgoAAJcKAACYCgAAmQoAAJoKAACbCgAAnAoAAJ0KAACeCgAAnwoAAKAKAAChCgAAogoAAKMKAACkCgAApQoAAKYKAACnCgAAqAoAAKkKAACqCgAAqwoAAKwKAACtCgAArgoAAK8KAACwCgAAsQoAALIKAACzCgAAtAoAALUKAAC2CgAAtwoAALgKAAC5CgAAugoAALsKAAC8CgAAvQoAAL4KAAC/CgAAwAoAAMEKAADCCgAAwwoAAMQKAADFCgAAxgoAAMcKAADICgAAyQoAAMoKAADLCgAAzAoAAM0KAADOCgAAzwoAANAKAADRCgAA0goAANMKAADUCgAA1QoAANYKAADXCgAA2AoAANkKAADaCgAA2woAANwKAADdCgAA3goAAN8KAADgCgAA4QoAAOIKAADjCgAA5AoAAOUKAADmCgAA5woAAOgKAADpCgAA6goAAOsKAADsCgAA7QoAAO4KAADvCgAA8AoAAPEKAADyCgAA8woAAPQKAAD1CgAA9goAAPcKAAD4CgAA+QoAAPoKAAD7CgAA/AoAAP0KAAD+CgAA/woAAAALAAABCwAAAgsAAAMLAAAECwAABQsAAAYLAAAHCwAACAsAAAkLAAAKCwAACwsAAAwLAAANCwAADgsAAA8LAAAQCwAAEQsAABILAAATCwAAFAsAABULAAAWCwAAFwsAABgLAAAZCwAAGgsAABsLAAAcCwAAHQsAAB4LAAAfCwAAIAsAACELAAAiCwAAIwsAACQLAAAlCwAAJgsAACcLAAAoCwAAKQsAACoLAAArCwAALAsAAC0LAAAuCwAALwsAADALAAAxCwAAMgsAADMLAAA0CwAANQsAADYLAAA3CwAAOAsAADkLAAA6CwAAOwsAADwLAAA9CwAAPgsAAD8LAABACwAAQQsAAEILAABDCwAARAsAAEULAABGCwAARwsAAEgLAABJCwAASgsAAEsLAABMCwAATQsAAE4LAABPCwAAUAsAAFELAABSCwAAUwsAAFQLAABVCwAAVgsAAFcLAABYCwAAWQsAAFoLAABbCwAAXAsAAF0LAABeCwAAXwsAAGALAABhCwAAYgsAAGMLAABkCwAAZQsAAGYLAABnCwAAaAsAAGkLAABqCwAAawsAAGwLAABtCwAAbgsAAG8LAABwCwAAcQsAAHILAABzCwAAdAsAAHULAAB2CwAAdwsAAHgLAAB5CwAAegsAAHsLAAB8CwAAfQsAAH4LAAB/CwAAgAsAAIELAACCCwAAgwsAAIQLAACFCwAAhgsAAIcLAACICwAAiQsAAIoLAACLCwAAjAsAAI0LAACOCwAAjwsAAJALAACRCwAAkgsAAJMLAACUCwAAlQsAAJYLAACXCwAAmAsAAJkLAACaCwAAmwsAAJwLAACdCwAAngsAAJ8LAACgCwAAoQsAAKILAACjCwAApAsAAKULAACmCwAApwsAAKgLAACpCwAAqgsAAKsLAACsCwAArQsAAK4LAACvCwAAsAsAALELAACyCwAAswsAALQLAAC1CwAAtgsAALcLAAC4CwAAuQsAALoLAAC7CwAAvAsAAL0LAAC+CwAAvwsAAMALAADBCwAAwgsAAMMLAADECwAAxQsAAMYLAADHCwAAyAsAAMkLAADKCwAAywsAAMwLAADNCwAAzgsAAM8LAADQCwAA0QsAANILAADTCwAA1AsAANULAADWCwAA1wsAANgLAADZCwAA2gsAANsLAADcCwAA3QsAAN4LAADfCwAA4AsAAOELAADiCwAA4wsAAOQLAADlCwAA5gsAAOcLAADoCwAA6QsAAOoLAADrCwAA7AsAAO0LAADuCwAA7wsAAPALAADxCwAA8gsAAPMLAAD0CwAA9QsAAPYLAAD3CwAA+AsAAPkLAAD6CwAA+wsAAPwLAAD9CwAA/gsAAP8LAAAADAAAAQwAAAIMAAADDAAABAwAAAUMAAAGDAAABwwAAAgMAAAJDAAACgwAAAsMAAAMDAAADQwAAA4MAAAPDAAAEAwAABEMAAASDAAAEwwAABQMAAAVDAAAFgwAABcMAAAYDAAAGQwAABoMAAAbDAAAHAwAAB0MAAAeDAAAHwwAACAMAAAhDAAAIgwAACMMAAAkDAAAJQwAACYMAAAnDAAAKAwAACkMAAAqDAAAKwwAACwMAAAtDAAALgwAAC8MAAAwDAAAMQwAADIMAAAzDAAANAwAADUMAAA2DAAANwwAADgMAAA5DAAAOgwAADsMAAA8DAAAPQwAAD4MAAA/DAAAQAwAAEEMAABCDAAAQwwAAEQMAABFDAAARgwAAEcMAABIDAAASQwAAEoMAABLDAAATAwAAE0MAABODAAATwwAAFAMAABRDAAAUgwAAFMMAABUDAAAVQwAAFYMAABXDAAAWAwAAFkMAABaDAAAWwwAAFwMAABdDAAAXgwAAF8MAABgDAAAYQwAAGIMAABjDAAAZAwAAGUMAABmDAAAZwwAAGgMAABpDAAAagwAAGsMAABsDAAAbQwAAG4MAABvDAAAcAwAAHEMAAByDAAAcwwAAHQMAAB1DAAAdgwAAHcMAAB4DAAAeQwAAHoMAAB7DAAAfAwAAH0MAAB+DAAAfwwAAIAMAACBDAAAggwAAIMMAACEDAAAhQwAAIYMAACHDAAAiAwAAIkMAACKDAAAiwwAAIwMAACNDAAAjgwAAI8MAACQDAAAkQwAAJIMAACTDAAAlAwAAJUMAACWDAAAlwwAAJgMAACZDAAAmgwAAJsMAACcDAAAnQwAAJ4MAACfDAAAoAwAAKEMAACiDAAAowwAAKQMAAClDAAApgwAAKcMAACoDAAAqQwAAKoMAACrDAAArAwAAK0MAACuDAAArwwAALAMAACxDAAAsgwAALMMAAC0DAAAtQwAALYMAAC3DAAAuAwAALkMAAC6DAAAuwwAALwMAAC9DAAAvgwAAL8MAADADAAAwQwAAMIMAADDDAAAxAwAAMUMAADGDAAAxwwAAMgMAADJDAAAygwAAMsMAADMDAAAzQwAAM4MAADPDAAA0AwAANEMAADSDAAA0wwAANQMAADVDAAA1gwAANcMAADYDAAA2QwAANoMAADbDAAA3AwAAN0MAADeDAAA3wwAAOAMAADhDAAA4gwAAOMMAADkDAAA5QwAAOYMAADnDAAA6AwAAOkMAADqDAAA6wwAAOwMAADtDAAA7gwAAO8MAADwDAAA8QwAAPIMAADzDAAA9AwAAPUMAAD2DAAA9wwAAPgMAAD5DAAA+gwAAPsMAAD8DAAA/QwAAP4MAAD/DAAAAA0AAAENAAACDQAAAw0AAAQNAAAFDQAABg0AAAcNAAAIDQAACQ0AAAoNAAALDQAADA0AAA0NAAAODQAADw0AABANAAARDQAAEg0AABMNAAAUDQAAFQ0AABYNAAAXDQAAGA0AABkNAAAaDQAAGw0AABwNAAAdDQAAHg0AAB8NAAAgDQAAIQ0AACINAAAjDQAAJA0AACUNAAAmDQAAJw0AACgNAAApDQAAKg0AACsNAAAsDQAALQ0AAC4NAAAvDQAAMA0AADENAAAyDQAAMw0AADQNAAA1DQAANg0AADcNAAA4DQAAOQ0AADoNAAA7DQAAPA0AAD0NAAA+DQAAPw0AAEANAABBDQAAQg0AAEMNAABEDQAARQ0AAEYNAABHDQAASA0AAEkNAABKDQAASw0AAEwNAABNDQAATg0AAE8NAABQDQAAUQ0AAFINAABTDQAAVA0AAFUNAABWDQAAVw0AAFgNAABZDQAAWg0AAFsNAABcDQAAXQ0AAF4NAABfDQAAYA0AAGENAABiDQAAYw0AAGQNAABlDQAAZg0AAGcNAABoDQAAaQ0AAGoNAABrDQAAbA0AAG0NAABuDQAAbw0AAHANAABxDQAAcg0AAHMNAAB0DQAAdQ0AAHYNAAB3DQAAeA0AAHkNAAB6DQAAew0AAHwNAAB9DQAAfg0AAH8NAACADQAAgQ0AAIINAACDDQAAhA0AAIUNAACGDQAAhw0AAIgNAACJDQAAig0AAIsNAACMDQAAjQ0AAI4NAACPDQAAkA0AAJENAACSDQAAkw0AAJQNAACVDQAAlg0AAJcNAACYDQAAmQ0AAJoNAACbDQAAnA0AAJ0NAACeDQAAnw0AAKANAAChDQAAog0AAKMNAACkDQAApQ0AAKYNAACnDQAAqA0AAKkNAACqDQAAqw0AAKwNAACtDQAArg0AAK8NAACwDQAAsQ0AALINAACzDQAAtA0AALUNAAC2DQAAtw0AALgNAAC5DQAAug0AALsNAAC8DQAAvQ0AAL4NAAC/DQAAwA0AAMENAADCDQAAww0AAMQNAADFDQAAxg0AAMcNAADIDQAAyQ0AAMoNAADLDQAAzA0AAM0NAADODQAAzw0AANANAADRDQAA0g0AANMNAADUDQAA1Q0AANYNAADXDQAA2A0AANkNAADaDQAA2w0AANwNAADdDQAA3g0AAN8NAADgDQAA4Q0AAOINAADjDQAA5A0AAOUNAADmDQAA5w0AAOgNAADpDQAA6g0AAOsNAADsDQAA7Q0AAO4NAADvDQAA8A0AAPENAADyDQAA8w0AAPQNAAD1DQAA9g0AAPcNAAD4DQAA+Q0AAPoNAAD7DQAA/A0AAP0NAAD+DQAA/w0AAAAOAAABDgAAAg4AAAMOAAAEDgAABQ4AAAYOAAAHDgAACA4AAAkOAAAKDgAACw4AAAwOAAANDgAADg4AAA8OAAAQDgAAEQ4AABIOAAATDgAAFA4AABUOAAAWDgAAFw4AABgOAAAZDgAAGg4AABsOAAAcDgAAHQ4AAB4OAAAfDgAAIA4AACEOAAAiDgAAIw4AACQOAAAlDgAAJg4AACcOAAAoDgAAKQ4AACoOAAArDgAALA4AAC0OAAAuDgAALw4AADAOAAAxDgAAMg4AADMOAAA0DgAANQ4AADYOAAA3DgAAOA4AADkOAAA6DgAAOw4AADwOAAA9DgAAPg4AAD8OAABADgAAQQ4AAEIOAABDDgAARA4AAEUOAABGDgAARw4AAEgOAABJDgAASg4AAEsOAABMDgAATQ4AAE4OAABPDgAAUA4AAFEOAABSDgAAUw4AAFQOAABVDgAAVg4AAFcOAABYDgAAWQ4AAFoOAABbDgAAXA4AAF0OAABeDgAAXw4AAGAOAABhDgAAYg4AAGMOAABkDgAAZQ4AAGYOAABnDgAAaA4AAGkOAABqDgAAaw4AAGwOAABtDgAAbg4AAG8OAABwDgAAcQ4AAHIOAABzDgAAdA4AAHUOAAB2DgAAdw4AAHgOAAB5DgAAeg4AAHsOAAB8DgAAfQ4AAH4OAAB/DgAAgA4AAIEOAACCDgAAgw4AAIQOAACFDgAAhg4AAIcOAACIDgAAiQ4AAIoOAACLDgAAjA4AAI0OAACODgAAjw4AAJAOAACRDgAAkg4AAJMOAACUDgAAlQ4AAJYOAACXDgAAmA4AAJkOAACaDgAAmw4AAJwOAACdDgAAng4AAJ8OAACgDgAAoQ4AAKIOAACjDgAApA4AAKUOAACmDgAApw4AAKgOAACpDgAAqg4AAKsOAACsDgAArQ4AAK4OAACvDgAAsA4AALEOAACyDgAAsw4AALQOAAC1DgAAtg4AALcOAAC4DgAAuQ4AALoOAAC7DgAAvA4AAL0OAAC+DgAAvw4AAMAOAADBDgAAwg4AAMMOAADEDgAAxQ4AAMYOAADHDgAAyA4AAMkOAADKDgAAyw4AAMwOAADNDgAAzg4AAM8OAADQDgAA0Q4AANIOAADTDgAA1A4AANUOAADWDgAA1w4AANgOAADZDgAA2g4AANsOAADcDgAA3Q4AAN4OAADfDgAA4A4AAOEOAADiDgAA4w4AAOQOAADlDgAA5g4AAOcOAADoDgAA6Q4AAOoOAADrDgAA7A4AAO0OAADuDgAA7w4AAPAOAADxDgAA8g4AAPMOAAD0DgAA9Q4AAPYOAAD3DgAA+A4AAPkOAAD6DgAA+w4AAPwOAAD9DgAA/g4AAP8OAAAADwAAAQ8AAAIPAAADDwAABA8AAAUPAAAGDwAABw8AAAgPAAAJDwAACg8AAAsPAAAMDwAADQ8AAA4PAAAPDwAAEA8AABEPAAASDwAAEw8AABQPAAAVDwAAFg8AABcPAAAYDwAAGQ8AABoPAAAbDwAAHA8AAB0PAAAeDwAAHw8AACAPAAAhDwAAIg8AACMPAAAkDwAAJQ8AACYPAAAnDwAAKA8AACkPAAAqDwAAKw8AACwPAAAtDwAALg8AAC8PAAAwDwAAMQ8AADIPAAAzDwAANA8AADUPAAA2DwAANw8AADgPAAA5DwAAOg8AADsPAAA8DwAAPQ8AAD4PAAA/DwAAQA8AAEEPAABCDwAAQw8AAEQPAABFDwAARg8AAEcPAABIDwAASQ8AAEoPAABLDwAATA8AAE0PAABODwAATw8AAFAPAABRDwAAUg8AAFMPAABUDwAAVQ8AAFYPAABXDwAAWA8AAFkPAABaDwAAWw8AAFwPAABdDwAAXg8AAF8PAABgDwAAYQ8AAGIPAABjDwAAZA8AAGUPAABmDwAAZw8AAGgPAABpDwAAag8AAGsPAABsDwAAbQ8AAG4PAABvDwAAcA8AAHEPAAByDwAAcw8AAHQPAAB1DwAAdg8AAHcPAAB4DwAAeQ8AAHoPAAB7DwAAfA8AAH0PAAB+DwAAfw8AAIAPAACBDwAAgg8AAIMPAACEDwAAhQ8AAIYPAACHDwAAiA8AAIkPAACKDwAAiw8AAIwPAACNDwAAjg8AAI8PAACQDwAAkQ8AAJIPAACTDwAAlA8AAJUPAACWDwAAlw8AAJgPAACZDwAAmg8AAJsPAACcDwAAnQ8AAJ4PAACfDwAAoA8AAKEPAACiDwAAow8AAKQPAAClDwAApg8AAKcPAACoDwAAqQ8AAKoPAACrDwAArA8AAK0PAACuDwAArw8AALAPAACxDwAAsg8AALMPAAC0DwAAtQ8AALYPAAC3DwAAuA8AALkPAAC6DwAAuw8AALwPAAC9DwAAvg8AAL8PAADADwAAwQ8AAMIPAADDDwAAxA8AAMUPAADGDwAAxw8AAMgPAADJDwAAyg8AAMsPAADMDwAAzQ8AAM4PAADPDwAA0A8AANEPAADSDwAA0w8AANQPAADVDwAA1g8AANcPAADYDwAA2Q8AANoPAADbDwAA3A8AAN0PAADeDwAA3w8AAOAPAADhDwAA4g8AAOMPAADkDwAA5Q8AAOYPAADnDwAA6A8AAOkPAADqDwAA6w8AAOwPAADtDwAA7g8AAO8PAADwDwAA8Q8AAPIPAADzDwAA9A8AAPUPAAD2DwAA9w8AAPgPAAD5DwAA+g8AAPsPAAD8DwAA/Q8AAP4PAAD/DwAAABAAAAEQAAACEAAAAxAAAAQQAAAFEAAABhAAAAcQAAAIEAAACRAAAAoQAAALEAAADBAAAA0QAAAOEAAADxAAABAQAAAREAAAEhAAABMQAAAUEAAAFRAAABYQAAAXEAAAGBAAABkQAAAaEAAAGxAAABwQAAAdEAAAHhAAAB8QAAAgEAAAIRAAACIQAAAjEAAAJBAAACUQAAAmEAAAJxAAACgQAAApEAAAKhAAACsQAAAsEAAALRAAAC4QAAAvEAAAMBAAADEQAAAyEAAAMxAAADQQAAA1EAAANhAAADcQAAA4EAAAORAAADoQAAA7EAAAPBAAAD0QAAA+EAAAPxAAAEAQAABBEAAAQhAAAEMQAABEEAAARRAAAEYQAABHEAAASBAAAEkQAABKEAAASxAAAEwQAABNEAAAThAAAE8QAABQEAAAURAAAFIQAABTEAAAVBAAAFUQAABWEAAAVxAAAFgQAABZEAAAWhAAAFsQAABcEAAAXRAAAF4QAABfEAAAYBAAAGEQAABiEAAAYxAAAGQQAABlEAAAZhAAAGcQAABoEAAAaRAAAGoQAABrEAAAbBAAAG0QAABuEAAAbxAAAHAQAABxEAAAchAAAHMQAAB0EAAAdRAAAHYQAAB3EAAAeBAAAHkQAAB6EAAAexAAAHwQAAB9EAAAfhAAAH8QAACAEAAAgRAAAIIQAACDEAAAhBAAAIUQAACGEAAAhxAAAIgQAACJEAAAihAAAIsQAACMEAAAjRAAAI4QAACPEAAAkBAAAJEQAACSEAAAkxAAAJQQAACVEAAAlhAAAJcQAACYEAAAmRAAAJoQAACbEAAAnBAAAJ0QAACeEAAAnxAAAKAQAAChEAAAohAAAKMQAACkEAAApRAAAKYQAACnEAAAqBAAAKkQAACqEAAAqxAAAKwQAACtEAAArhAAAK8QAACwEAAAsRAAALIQAACzEAAAtBAAALUQAAC2EAAAtxAAALgQAAC5EAAAuhAAALsQAAC8EAAAvRAAAL4QAAC/EAAAwBAAAMEQAADCEAAAwxAAAMQQAADFEAAAxhAAAMcQAADIEAAAyRAAAMoQAADLEAAAzBAAAM0QAADOEAAAzxAAANAQAADREAAA0hAAANMQAADUEAAA1RAAANYQAADXEAAA2BAAANkQAADaEAAA2xAAANwQAADdEAAA3hAAAN8QAADgEAAA4RAAAOIQAADjEAAA5BAAAOUQAADmEAAA5xAAAOgQAADpEAAA6hAAAOsQAADsEAAA7RAAAO4QAADvEAAA8BAAAPEQAADyEAAA8xAAAPQQAAD1EAAA9hAAAPcQAAD4EAAA+RAAAPoQAAD7EAAA/BAAAP0QAAD+EAAA/xAAAAARAAABEQAAAhEAAAMRAAAEEQAABREAAAYRAAAHEQAACBEAAAkRAAAKEQAACxEAAAwRAAANEQAADhEAAA8RAAAQEQAAEREAABIRAAATEQAAFBEAABURAAAWEQAAFxEAABgRAAAZEQAAGhEAABsRAAAcEQAAHREAAB4RAAAfEQAAIBEAACERAAAiEQAAIxEAACQRAAAlEQAAJhEAACcRAAAoEQAAKREAACoRAAArEQAALBEAAC0RAAAuEQAALxEAADARAAAxEQAAMhEAADMRAAA0EQAANREAADYRAAA3EQAAOBEAADkRAAA6EQAAOxEAADwRAAA9EQAAPhEAAD8RAABAEQAAQREAAEIRAABDEQAARBEAAEURAABGEQAARxEAAEgRAABJEQAAShEAAEsRAABMEQAATREAAE4RAABPEQAAUBEAAFERAABSEQAAUxEAAFQRAABVEQAAVhEAAFcRAABYEQAAWREAAFoRAABbEQAAXBEAAF0RAABeEQAAXxEAAGARAABhEQAAYhEAAGMRAABkEQAAZREAAGYRAABnEQAAaBEAAGkRAABqEQAAaxEAAGwRAABtEQAAbhEAAG8RAABwEQAAcREAAHIRAABzEQAAdBEAAHURAAB2EQAAdxEAAHgRAAB5EQAAehEAAHsRAAB8EQAAfREAAH4RAAB/EQAAgBEAAIERAACCEQAAgxEAAIQRAACFEQAAhhEAAIcRAACIEQAAiREAAIoRAACLEQAAjBEAAI0RAACOEQAAjxEAAJARAACREQAAkhEAAJMRAACUEQAAlREAAJYRAACXEQAAmBEAAJkRAACaEQAAmxEAAJwRAACdEQAAnhEAAJ8RAACgEQAAoREAAKIRAACjEQAApBEAAKURAACmEQAApxEAAKgRAACpEQAAqhEAAKsRAACsEQAArREAAK4RAACvEQAAsBEAALERAACyEQAAsxEAALQRAAC1EQAAthEAALcRAAC4EQAAuREAALoRAAC7EQAAvBEAAL0RAAC+EQAAvxEAAMARAADBEQAAwhEAAMMRAADEEQAAxREAAMYRAADHEQAAyBEAAMkRAADKEQAAyxEAAMwRAADNEQAAzhEAAM8RAADQEQAA0REAANIRAADTEQAA1BEAANURAADWEQAA1xEAANgRAADZEQAA2hEAANsRAADcEQAA3REAAN4RAADfEQAA4BEAAOERAADiEQAA4xEAAOQRAADlEQAA5hEAAOcRAADoEQAA6REAAOoRAADrEQAA7BEAAO0RAADuEQAA7xEAAPARAADxEQAA8hEAAPMRAAD0EQAA9REAAPYRAAD3EQAA+BEAAPkRAAD6EQAA+xEAAPwRAAD9EQAA/hEAAP8RAAAAEgAAARIAAAISAAADEgAABBIAAAUSAAAGEgAABxIAAAgSAAAJEgAAChIAAAsSAAAMEgAADRIAAA4SAAAPEgAAEBIAABESAAASEgAAExIAABQSAAAVEgAAFhIAABcSAAAYEgAAGRIAABoSAAAbEgAAHBIAAB0SAAAeEgAAHxIAACASAAAhEgAAIhIAACMSAAAkEgAAJRIAACYSAAAnEgAAKBIAACkSAAAqEgAAKxIAACwSAAAtEgAALhIAAC8SAAAwEgAAMRIAADISAAAzEgAANBIAADUSAAA2EgAANxIAADgSAAA5EgAAOhIAADsSAAA8EgAAPRIAAD4SAAA/EgAAQBIAAEESAABCEgAAQxIAAEQSAABFEgAARhIAAEcSAABIEgAASRIAAEoSAABLEgAATBIAAE0SAABOEgAATxIAAFASAABREgAAUhIAAFMSAABUEgAAVRIAAFYSAABXEgAAWBIAAFkSAABaEgAAWxIAAFwSAABdEgAAXhIAAF8SAABgEgAAYRIAAGISAABjEgAAZBIAAGUSAABmEgAAZxIAAGgSAABpEgAAahIAAGsSAABsEgAAbRIAAG4SAABvEgAAcBIAAHESAAByEgAAcxIAAHQSAAB1EgAAdhIAAHcSAAB4EgAAeRIAAHoSAAB7EgAAfBIAAH0SAAB+EgAAfxIAAIASAACBEgAAghIAAIMSAACEEgAAhRIAAIYSAACHEgAAiBIAAIkSAACKEgAAixIAAIwSAACNEgAAjhIAAI8SAACQEgAAkRIAAJISAACTEgAAlBIAAJUSAACWEgAAlxIAAJgSAACZEgAAmhIAAJsSAACcEgAAnRIAAJ4SAACfEgAAoBIAAKESAACiEgAAoxIAAKQSAAClEgAAphIAAKcSAACoEgAAqRIAAKoSAACrEgAArBIAAK0SAACuEgAArxIAALASAACxEgAAshIAALMSAAC0EgAAtRIAALYSAAC3EgAAuBIAALkSAAC6EgAAuxIAALwSAAC9EgAAvhIAAL8SAADAEgAAwRIAAMISAADDEgAAxBIAAMUSAADGEgAAxxIAAMgSAADJEgAAyhIAAMsSAADMEgAAzRIAAM4SAADPEgAA0BIAANESAADSEgAA0xIAANQSAADVEgAA1hIAANcSAADYEgAA2RIAANoSAADbEgAA3BIAAN0SAADeEgAA3xIAAOASAADhEgAA4hIAAOMSAADkEgAA5RIAAOYSAADnEgAA6BIAAOkSAADqEgAA6xIAAOwSAADtEgAA7hIAAO8SAADwEgAA8RIAAPISAADzEgAA9BIAAPUSAAD2EgAA9xIAAPgSAAD5EgAA+hIAAPsSAAD8EgAA/RIAAP4SAAD/EgAAABMAAAETAAACEwAAAxMAAAQTAAAFEwAABhMAAAcTAAAIEwAACRMAAAoTAAALEwAADBMAAA0TAAAOEwAADxMAABATAAAREwAAEhMAABMTAAAUEwAAFRMAABYTAAAXEwAAGBMAABkTAAAaEwAAGxMAABwTAAAdEwAAHhMAAB8TAAAgEwAAIRMAACITAAAjEwAAJBMAACUTAAAmEwAAJxMAACgTAAApEwAAKhMAACsTAAAsEwAALRMAAC4TAAAvEwAAMBMAADETAAAyEwAAMxMAADQTAAA1EwAANhMAADcTAAA4EwAAORMAADoTAAA7EwAAPBMAAD0TAAA+EwAAPxMAAEATAABBEwAAQhMAAEMTAABEEwAARRMAAEYTAABHEwAASBMAAEkTAABKEwAASxMAAEwTAABNEwAAThMAAE8TAABQEwAAURMAAFITAABTEwAAVBMAAFUTAABWEwAAVxMAAFgTAABZEwAAWhMAAFsTAABcEwAAXRMAAF4TAABfEwAAYBMAAGETAABiEwAAYxMAAGQTAABlEwAAZhMAAGcTAABoEwAAaRMAAGoTAABrEwAAbBMAAG0TAABuEwAAbxMAAHATAABxEwAAchMAAHMTAAB0EwAAdRMAAHYTAAB3EwAAeBMAAHkTAAB6EwAAexMAAHwTAAB9EwAAfhMAAH8TAACAEwAAgRMAAIITAACDEwAAhBMAAIUTAACGEwAAhxMAAIgTAACJEwAAihMAAIsTAACMEwAAjRMAAI4TAACPEwAAkBMAAJETAACSEwAAkxMAAJQTAACVEwAAlhMAAJcTAACYEwAAmRMAAJoTAACbEwAAnBMAAJ0TAACeEwAAnxMAAKATAAChEwAAohMAAKMTAACkEwAApRMAAKYTAACnEwAAqBMAAKkTAACqEwAAqxMAAKwTAACtEwAArhMAAK8TAACwEwAAsRMAALITAACzEwAAtBMAALUTAAC2EwAAtxMAALgTAAC5EwAAuhMAALsTAAC8EwAAvRMAAL4TAAC/EwAAwBMAAMETAADCEwAAwxMAAMQTAADFEwAAxhMAAMcTAADIEwAAyRMAAMoTAADLEwAAzBMAAM0TAADOEwAAzxMAANATAADREwAA0hMAANMTAADUEwAA1RMAANYTAADXEwAA2BMAANkTAADaEwAA2xMAANwTAADdEwAA3hMAAN8TAADgEwAA4RMAAOITAADjEwAA5BMAAOUTAADmEwAA5xMAAOgTAADpEwAA6hMAAOsTAADsEwAA7RMAAO4TAADvEwAA8BMAAPETAADyEwAA8xMAAPQTAAD1EwAA9hMAAPcTAAD4EwAA+RMAAPoTAAD7EwAA/BMAAP0TAAD+EwAA/xMAAAAUAAABFAAAAhQAAAMUAAAEFAAABRQAAAYUAAAHFAAACBQAAAkUAAAKFAAACxQAAAwUAAANFAAADhQAAA8UAAAQFAAAERQAABIUAAATFAAAFBQAABUUAAAWFAAAFxQAABgUAAAZFAAAGhQAABsUAAAcFAAAHRQAAB4UAAAfFAAAIBQAACEUAAAiFAAAIxQAACQUAAAlFAAAJhQAACcUAAAoFAAAKRQAACoUAAArFAAALBQAAC0UAAAuFAAALxQAADAUAAAxFAAAMhQAADMUAAA0FAAANRQAADYUAAA3FAAAOBQAADkUAAA6FAAAOxQAADwUAAA9FAAAPhQAAD8UAABAFAAAQRQAAEIUAABDFAAARBQAAEUUAABGFAAARxQAAEgUAABJFAAAShQAAEsUAABMFAAATRQAAE4UAABPFAAAUBQAAFEUAABSFAAAUxQAAFQUAABVFAAAVhQAAFcUAABYFAAAWRQAAFoUAABbFAAAXBQAAF0UAABeFAAAXxQAAGAUAABhFAAAYhQAAGMUAABkFAAAZRQAAGYUAABnFAAAaBQAAGkUAABqFAAAaxQAAGwUAABtFAAAbhQAAG8UAABwFAAAcRQAAHIUAABzFAAAdBQAAHUUAAB2FAAAdxQAAHgUAAB5FAAAehQAAHsUAAB8FAAAfRQAAH4UAAB/FAAAgBQAAIEUAACCFAAAgxQAAIQUAACFFAAAhhQAAIcUAACIFAAAiRQAAIoUAACLFAAAjBQAAI0UAACOFAAAjxQAAJAUAACRFAAAkhQAAJMUAACUFAAAlRQAAJYUAACXFAAAmBQAAJkUAACaFAAAmxQAAJwUAACdFAAAnhQAAJ8UAACgFAAAoRQAAKIUAACjFAAApBQAAKUUAACmFAAApxQAAKgUAACpFAAAqhQAAKsUAACsFAAArRQAAK4UAACvFAAAsBQAALEUAACyFAAAsxQAALQUAAC1FAAAthQAALcUAAC4FAAAuRQAALoUAAC7FAAAvBQAAL0UAAC+FAAAvxQAAMAUAADBFAAAwhQAAMMUAADEFAAAxRQAAMYUAADHFAAAyBQAAMkUAADKFAAAyxQAAMwUAADNFAAAzhQAAM8UAADQFAAA0RQAANIUAADTFAAA1BQAANUUAADWFAAA1xQAANgUAADZFAAA2hQAANsUAADcFAAA3RQAAN4UAADfFAAA4BQAAOEUAADiFAAA4xQAAOQUAADlFAAA5hQAAOcUAADoFAAA6RQAAOoUAADrFAAA7BQAAO0UAADuFAAA7xQAAPAUAADxFAAA8hQAAPMUAAD0FAAA9RQAAPYUAAD3FAAA+BQAAPkUAAD6FAAA+xQAAPwUAAD9FAAA/hQAAP8UAAAAFQAAARUAAAIVAAADFQAABBUAAAUVAAAGFQAABxUAAAgVAAAJFQAAChUAAAsVAAAMFQAADRUAAA4VAAAPFQAAEBUAABEVAAASFQAAExUAABQVAAAVFQAAFhUAABcVAAAYFQAAGRUAABoVAAAbFQAAHBUAAB0VAAAeFQAAHxUAACAVAAAhFQAAIhUAACMVAAAkFQAAJRUAACYVAAAnFQAAKBUAACkVAAAqFQAAKxUAACwVAAAtFQAALhUAAC8VAAAwFQAAMRUAADIVAAAzFQAANBUAADUVAAA2FQAANxUAADgVAAA5FQAAOhUAADsVAAA8FQAAPRUAAD4VAAA/FQAAQBUAAEEVAABCFQAAQxUAAEQVAABFFQAARhUAAEcVAABIFQAASRUAAEoVAABLFQAATBUAAE0VAABOFQAATxUAAFAVAABRFQAAUhUAAFMVAABUFQAAVRUAAFYVAABXFQAAWBUAAFkVAABaFQAAWxUAAFwVAABdFQAAXhUAAF8VAABgFQAAYRUAAGIVAABjFQAAZBUAAGUVAABmFQAAZxUAAGgVAABpFQAAahUAAGsVAABsFQAAbRUAAG4VAABvFQAAcBUAAHEVAAByFQAAcxUAAHQVAAB1FQAAdhUAAHcVAAB4FQAAeRUAAHoVAAB7FQAAfBUAAH0VAAB+FQAAfxUAAIAVAACBFQAAghUAAIMVAACEFQAAhRUAAIYVAACHFQAAiBUAAIkVAACKFQAAixUAAIwVAACNFQAAjhUAAI8VAACQFQAAkRUAAJIVAACTFQAAlBUAAJUVAACWFQAAlxUAAJgVAACZFQAAmhUAAJsVAACcFQAAnRUAAJ4VAACfFQAAoBUAAKEVAACiFQAAoxUAAKQVAAClFQAAphUAAKcVAACoFQAAqRUAAKoVAACrFQAArBUAAK0VAACuFQAArxUAALAVAACxFQAAshUAALMVAAC0FQAAtRUAALYVAAC3FQAAuBUAALkVAAC6FQAAuxUAALwVAAC9FQAAvhUAAL8VAADAFQAAwRUAAMIVAADDFQAAxBUAAMUVAADGFQAAxxUAAMgVAADJFQAAyhUAAMsVAADMFQAAzRUAAM4VAADPFQAA0BUAANEVAADSFQAA0xUAANQVAADVFQAA1hUAANcVAADYFQAA2RUAANoVAADbFQAA3BUAAN0VAADeFQAA3xUAAOAVAADhFQAA4hUAAOMVAADkFQAA5RUAAOYVAADnFQAA6BUAAOkVAADqFQAA6xUAAOwVAADtFQAA7hUAAO8VAADwFQAA8RUAAPIVAADzFQAA9BUAAPUVAAD2FQAA9xUAAPgVAAD5FQAA+hUAAPsVAAD8FQAA/RUAAP4VAAD/FQAAABYAAAEWAAACFgAAAxYAAAQWAAAFFgAABhYAAAcWAAAIFgAACRYAAAoWAAALFgAADBYAAA0WAAAOFgAADxYAABAWAAARFgAAEhYAABMWAAAUFgAAFRYAABYWAAAXFgAAGBYAABkWAAAaFgAAGxYAABwWAAAdFgAAHhYAAB8WAAAgFgAAIRYAACIWAAAjFgAAJBYAACUWAAAmFgAAJxYAACgWAAApFgAAKhYAACsWAAAsFgAALRYAAC4WAAAvFgAAMBYAADEWAAAyFgAAMxYAADQWAAA1FgAANhYAADcWAAA4FgAAORYAADoWAAA7FgAAPBYAAD0WAAA+FgAAPxYAAEAWAABBFgAAQhYAAEMWAABEFgAARRYAAEYWAABHFgAASBYAAEkWAABKFgAASxYAAEwWAABNFgAAThYAAE8WAABQFgAAURYAAFIWAABTFgAAVBYAAFUWAABWFgAAVxYAAFgWAABZFgAAWhYAAFsWAABcFgAAXRYAAF4WAABfFgAAYBYAAGEWAABiFgAAYxYAAGQWAABlFgAAZhYAAGcWAABoFgAAaRYAAGoWAABrFgAAbBYAAG0WAABuFgAAbxYAAHAWAABxFgAAchYAAHMWAAB0FgAAdRYAAHYWAAB3FgAAeBYAAHkWAAB6FgAAexYAAHwWAAB9FgAAfhYAAH8WAACAFgAAgRYAAIIWAACDFgAAhBYAAIUWAACGFgAAhxYAAIgWAACJFgAAihYAAIsWAACMFgAAjRYAAI4WAACPFgAAkBYAAJEWAACSFgAAkxYAAJQWAACVFgAAlhYAAJcWAACYFgAAmRYAAJoWAACbFgAAnBYAAJ0WAACeFgAAnxYAAKAWAAChFgAAohYAAKMWAACkFgAApRYAAKYWAACnFgAAqBYAAKkWAACqFgAAqxYAAKwWAACtFgAArhYAAK8WAACwFgAAsRYAALIWAACzFgAAtBYAALUWAAC2FgAAtxYAALgWAAC5FgAAuhYAALsWAAC8FgAAvRYAAL4WAAC/FgAAwBYAAMEWAADCFgAAwxYAAMQWAADFFgAAxhYAAMcWAADIFgAAyRYAAMoWAADLFgAAzBYAAM0WAADOFgAAzxYAANAWAADRFgAA0hYAANMWAADUFgAA1RYAANYWAADXFgAA2BYAANkWAADaFgAA2xYAANwWAADdFgAA3hYAAN8WAADgFgAA4RYAAOIWAADjFgAA5BYAAOUWAADmFgAA5xYAAOgWAADpFgAA6hYAAOsWAADsFgAA7RYAAO4WAADvFgAA8BYAAPEWAADyFgAA8xYAAPQWAAD1FgAA9hYAAPcWAAD4FgAA+RYAAPoWAAD7FgAA/BYAAP0WAAD+FgAA/xYAAAAXAAABFwAAAhcAAAMXAAAEFwAABRcAAAYXAAAHFwAACBcAAAkXAAAKFwAACxcAAAwXAAANFwAADhcAAA8XAAAQFwAAERcAABIXAAATFwAAFBcAABUXAAAWFwAAFxcAABgXAAAZFwAAGhcAABsXAAAcFwAAHRcAAB4XAAAfFwAAIBcAACEXAAAiFwAAIxcAACQXAAAlFwAAJhcAACcXAAAoFwAAKRcAACoXAAArFwAALBcAAC0XAAAuFwAALxcAADAXAAAxFwAAMhcAADMXAAA0FwAANRcAADYXAAA3FwAAOBcAADkXAAA6FwAAOxcAADwXAAA9FwAAPhcAAD8XAABAFwAAQRcAAEIXAABDFwAARBcAAEUXAABGFwAARxcAAEgXAABJFwAAShcAAEsXAABMFwAATRcAAE4XAABPFwAAUBcAAFEXAABSFwAAUxcAAFQXAABVFwAAVhcAAFcXAABYFwAAWRcAAFoXAABbFwAAXBcAAF0XAABeFwAAXxcAAGAXAABhFwAAYhcAAGMXAABkFwAAZRcAAGYXAABnFwAAaBcAAGkXAABqFwAAaxcAAGwXAABtFwAAbhcAAG8XAABwFwAAcRcAAHIXAABzFwAAdBcAAHUXAAB2FwAAdxcAAHgXAAB5FwAAehcAAHsXAAB8FwAAfRcAAH4XAAB/FwAAgBcAAIEXAACCFwAAgxcAAIQXAACFFwAAhhcAAIcXAACIFwAAiRcAAIoXAACLFwAAjBcAAI0XAACOFwAAjxcAAJAXAACRFwAAkhcAAJMXAACUFwAAlRcAAJYXAACXFwAAmBcAAJkXAACaFwAAmxcAAJwXAACdFwAAnhcAAJ8XAACgFwAAoRcAAKIXAACjFwAApBcAAKUXAACmFwAApxcAAKgXAACpFwAAqhcAAKsXAACsFwAArRcAAK4XAACvFwAAsBcAALEXAACyFwAAsxcAALQXAAC1FwAAthcAALcXAAC4FwAAuRcAALoXAAC7FwAAvBcAAL0XAAC+FwAAvxcAAMAXAADBFwAAwhcAAMMXAADEFwAAxRcAAMYXAADHFwAAyBcAAMkXAADKFwAAyxcAAMwXAADNFwAAzhcAAM8XAADQFwAA0RcAANIXAADTFwAA1BcAANUXAADWFwAA1xcAANgXAADZFwAA2hcAANsXAADcFwAA3RcAAN4XAADfFwAA4BcAAOEXAADiFwAA4xcAAOQXAADlFwAA5hcAAOcXAADoFwAA6RcAAOoXAADrFwAA7BcAAO0XAADuFwAA7xcAAPAXAADxFwAA8hcAAPMXAAD0FwAA9RcAAPYXAAD3FwAA+BcAAPkXAAD6FwAA+xcAAPwXAAD9FwAA/hcAAP8XAAAAGAAAARgAAAIYAAADGAAABBgAAAUYAAAGGAAABxgAAAgYAAAJGAAAChgAAAsYAAAMGAAADRgAAA4YAAAPGAAAEBgAABEYAAASGAAAExgAABQYAAAVGAAAFhgAABcYAAAYGAAAGRgAABoYAAAbGAAAHBgAAB0YAAAeGAAAHxgAACAYAAAhGAAAIhgAACMYAAAkGAAAJRgAACYYAAAnGAAAKBgAACkYAAAqGAAAKxgAACwYAAAtGAAALhgAAC8YAAAwGAAAMRgAADIYAAAzGAAANBgAADUYAAA2GAAANxgAADgYAAA5GAAAOhgAADsYAAA8GAAAPRgAAD4YAAA/GAAAQBgAAEEYAABCGAAAQxgAAEQYAABFGAAARhgAAEcYAABIGAAASRgAAEoYAABLGAAATBgAAE0YAABOGAAATxgAAFAYAABRGAAAUhgAAFMYAABUGAAAVRgAAFYYAABXGAAAWBgAAFkYAABaGAAAWxgAAFwYAABdGAAAXhgAAF8YAABgGAAAYRgAAGIYAABjGAAAZBgAAGUYAABmGAAAZxgAAGgYAABpGAAAahgAAGsYAABsGAAAbRgAAG4YAABvGAAAcBgAAHEYAAByGAAAcxgAAHQYAAB1GAAAdhgAAHcYAAB4GAAAeRgAAHoYAAB7GAAAfBgAAH0YAAB+GAAAfxgAAIAYAACBGAAAghgAAIMYAACEGAAAhRgAAIYYAACHGAAAiBgAAIkYAACKGAAAixgAAIwYAACNGAAAjhgAAI8YAACQGAAAkRgAAJIYAACTGAAAlBgAAJUYAACWGAAAlxgAAJgYAACZGAAAmhgAAJsYAACcGAAAnRgAAJ4YAACfGAAAoBgAAKEYAACiGAAAoxgAAKQYAAClGAAAphgAAKcYAACoGAAAqRgAAKoYAACrGAAArBgAAK0YAACuGAAArxgAALAYAACxGAAAshgAALMYAAC0GAAAtRgAALYYAAC3GAAAuBgAALkYAAC6GAAAuxgAALwYAAC9GAAAvhgAAL8YAADAGAAAwRgAAMIYAADDGAAAxBgAAMUYAADGGAAAxxgAAMgYAADJGAAAyhgAAMsYAADMGAAAzRgAAM4YAADPGAAA0BgAANEYAADSGAAA0xgAANQYAADVGAAA1hgAANcYAADYGAAA2RgAANoYAADbGAAA3BgAAN0YAADeGAAA3xgAAOAYAADhGAAA4hgAAOMYAADkGAAA5RgAAOYYAADnGAAA6BgAAOkYAADqGAAA6xgAAOwYAADtGAAA7hgAAO8YAADwGAAA8RgAAPIYAADzGAAA9BgAAPUYAAD2GAAA9xgAAPgYAAD5GAAA+hgAAPsYAAD8GAAA/RgAAP4YAAD/GAAAABkAAAEZAAACGQAAAxkAAAQZAAAFGQAABhkAAAcZAAAIGQAACRkAAAoZAAALGQAADBkAAA0ZAAAOGQAADxkAABAZAAARGQAAEhkAABMZAAAUGQAAFRkAABYZAAAXGQAAGBkAABkZAAAaGQAAGxkAABwZAAAdGQAAHhkAAB8ZAAAgGQAAIRkAACIZAAAjGQAAJBkAACUZAAAmGQAAJxkAACgZAAApGQAAKhkAACsZAAAsGQAALRkAAC4ZAAAvGQAAMBkAADEZAAAyGQAAMxkAADQZAAA1GQAANhkAADcZAAA4GQAAORkAADoZAAA7GQAAPBkAAD0ZAAA+GQAAPxkAAEAZAABBGQAAQhkAAEMZAABEGQAARRkAAEYZAABHGQAASBkAAEkZAABKGQAASxkAAEwZAABNGQAAThkAAE8ZAABQGQAAURkAAFIZAABTGQAAVBkAAFUZAABWGQAAVxkAAFgZAABZGQAAWhkAAFsZAABcGQAAXRkAAF4ZAABfGQAAYBkAAGEZAABiGQAAYxkAAGQZAABlGQAAZhkAAGcZAABoGQAAaRkAAGoZAABrGQAAbBkAAG0ZAABuGQAAbxkAAHAZAABxGQAAchkAAHMZAAB0GQAAdRkAAHYZAAB3GQAAeBkAAHkZAAB6GQAAexkAAHwZAAB9GQAAfhkAAH8ZAACAGQAAgRkAAIIZAACDGQAAhBkAAIUZAACGGQAAhxkAAIgZAACJGQAAihkAAIsZAACMGQAAjRkAAI4ZAACPGQAAkBkAAJEZAACSGQAAkxkAAJQZAACVGQAAlhkAAJcZAACYGQAAmRkAAJoZAACbGQAAnBkAAJ0ZAACeGQAAnxkAAKAZAAChGQAAohkAAKMZAACkGQAApRkAAKYZAACnGQAAqBkAAKkZAACqGQAAqxkAAKwZAACtGQAArhkAAK8ZAACwGQAAsRkAALIZAACzGQAAtBkAALUZAAC2GQAAtxkAALgZAAC5GQAAuhkAALsZAAC8GQAAvRkAAL4ZAAC/GQAAwBkAAMEZAADCGQAAwxkAAMQZAADFGQAAxhkAAMcZAADIGQAAyRkAAMoZAADLGQAAzBkAAM0ZAADOGQAAzxkAANAZAADRGQAA0hkAANMZAADUGQAA1RkAANYZAADXGQAA2BkAANkZAADaGQAA2xkAANwZAADdGQAA3hkAAN8ZAADgGQAA4RkAAOIZAADjGQAA5BkAAOUZAADmGQAA5xkAAOgZAADpGQAA6hkAAOsZAADsGQAA7RkAAO4ZAADvGQAA8BkAAPEZAADyGQAA8xkAAPQZAAD1GQAA9hkAAPcZAAD4GQAA+RkAAPoZAAD7GQAA/BkAAP0ZAAD+GQAA/xkAAAAaAAABGgAAAhoAAAMaAAAEGgAABRoAAAYaAAAHGgAACBoAAAkaAAAKGgAACxoAAAwaAAANGgAADhoAAA8aAAAQGgAAERoAABIaAAATGgAAFBoAABUaAAAWGgAAFxoAABgaAAAZGgAAGhoAABsaAAAcGgAAHRoAAB4aAAAfGgAAIBoAACEaAAAiGgAAIxoAACQaAAAlGgAAJhoAACcaAAAoGgAAKRoAACoaAAArGgAALBoAAC0aAAAuGgAALxoAADAaAAAxGgAAMhoAADMaAAA0GgAANRoAADYaAAA3GgAAOBoAADkaAAA6GgAAOxoAADwaAAA9GgAAPhoAAD8aAABAGgAAQRoAAEIaAABDGgAARBoAAEUaAABGGgAARxoAAEgaAABJGgAAShoAAEsaAABMGgAATRoAAE4aAABPGgAAUBoAAFEaAABSGgAAUxoAAFQaAABVGgAAVhoAAFcaAABYGgAAWRoAAFoaAABbGgAAXBoAAF0aAABeGgAAXxoAAGAaAABhGgAAYhoAAGMaAABkGgAAZRoAAGYaAABnGgAAaBoAAGkaAABqGgAAaxoAAGwaAABtGgAAbhoAAG8aAABwGgAAcRoAAHIaAABzGgAAdBoAAHUaAAB2GgAAdxoAAHgaAAB5GgAAehoAAHsaAAB8GgAAfRoAAH4aAAB/GgAAgBoAAIEaAACCGgAAgxoAAIQaAACFGgAAhhoAAIcaAACIGgAAiRoAAIoaAACLGgAAjBoAAI0aAACOGgAAjxoAAJAaAACRGgAAkhoAAJMaAACUGgAAlRoAAJYaAACXGgAAmBoAAJkaAACaGgAAmxoAAJwaAACdGgAAnhoAAJ8aAACgGgAAoRoAAKIaAACjGgAApBoAAKUaAACmGgAApxoAAKgaAACpGgAAqhoAAKsaAACsGgAArRoAAK4aAACvGgAAsBoAALEaAACyGgAAsxoAALQaAAC1GgAAthoAALcaAAC4GgAAuRoAALoaAAC7GgAAvBoAAL0aAAC+GgAAvxoAAMAaAADBGgAAwhoAAMMaAADEGgAAxRoAAMYaAADHGgAAyBoAAMkaAADKGgAAyxoAAMwaAADNGgAAzhoAAM8aAADQGgAA0RoAANIaAADTGgAA1BoAANUaAADWGgAA1xoAANgaAADZGgAA2hoAANsaAADcGgAA3RoAAN4aAADfGgAA4BoAAOEaAADiGgAA4xoAAOQaAADlGgAA5hoAAOcaAADoGgAA6RoAAOoaAADrGgAA7BoAAO0aAADuGgAA7xoAAPAaAADxGgAA8hoAAPMaAAD0GgAA9RoAAPYaAAD3GgAA+BoAAPkaAAD6GgAA+xoAAPwaAAD9GgAA/hoAAP8aAAAAGwAAARsAAAIbAAADGwAABBsAAAUbAAAGGwAABxsAAAgbAAAJGwAAChsAAAsbAAAMGwAADRsAAA4bAAAPGwAAEBsAABEbAAASGwAAExsAABQbAAAVGwAAFhsAABcbAAAYGwAAGRsAABobAAAbGwAAHBsAAB0bAAAeGwAAHxsAACAbAAAhGwAAIhsAACMbAAAkGwAAJRsAACYbAAAnGwAAKBsAACkbAAAqGwAAKxsAACwbAAAtGwAALhsAAC8bAAAwGwAAMRsAADIbAAAzGwAANBsAADUbAAA2GwAANxsAADgbAAA5GwAAOhsAADsbAAA8GwAAPRsAAD4bAAA/GwAAQBsAAEEbAABCGwAAQxsAAEQbAABFGwAARhsAAEcbAABIGwAASRsAAEobAABLGwAATBsAAE0bAABOGwAATxsAAFAbAABRGwAAUhsAAFMbAABUGwAAVRsAAFYbAABXGwAAWBsAAFkbAABaGwAAWxsAAFwbAABdGwAAXhsAAF8bAABgGwAAYRsAAGIbAABjGwAAZBsAAGUbAABmGwAAZxsAAGgbAABpGwAAahsAAGsbAABsGwAAbRsAAG4bAABvGwAAcBsAAHEbAAByGwAAcxsAAHQbAAB1GwAAdhsAAHcbAAB4GwAAeRsAAHobAAB7GwAAfBsAAH0bAAB+GwAAfxsAAIAbAACBGwAAghsAAIMbAACEGwAAhRsAAIYbAACHGwAAiBsAAIkbAACKGwAAixsAAIwbAACNGwAAjhsAAI8bAACQGwAAkRsAAJIbAACTGwAAlBsAAJUbAACWGwAAlxsAAJgbAACZGwAAmhsAAJsbAACcGwAAnRsAAJ4bAACfGwAAoBsAAKEbAACiGwAAoxsAAKQbAAClGwAAphsAAKcbAACoGwAAqRsAAKobAACrGwAArBsAAK0bAACuGwAArxsAALAbAACxGwAAshsAALMbAAC0GwAAtRsAALYbAAC3GwAAuBsAALkbAAC6GwAAuxsAALwbAAC9GwAAvhsAAL8bAADAGwAAwRsAAMIbAADDGwAAxBsAAMUbAADGGwAAxxsAAMgbAADJGwAAyhsAAMsbAADMGwAAzRsAAM4bAADPGwAA0BsAANEbAADSGwAA0xsAANQbAADVGwAA1hsAANcbAADYGwAA2RsAANobAADbGwAA3BsAAN0bAADeGwAA3xsAAOAbAADhGwAA4hsAAOMbAADkGwAA5RsAAOYbAADnGwAA6BsAAOkbAADqGwAA6xsAAOwbAADtGwAA7hsAAO8bAADwGwAA8RsAAPIbAADzGwAA9BsAAPUbAAD2GwAA9xsAAPgbAAD5GwAA+hsAAPsbAAD8GwAA/RsAAP4bAAD/GwAAABwAAAEcAAACHAAAAxwAAAQcAAAFHAAABhwAAAccAAAIHAAACRwAAAocAAALHAAADBwAAA0cAAAOHAAADxwAABAcAAARHAAAEhwAABMcAAAUHAAAFRwAABYcAAAXHAAAGBwAABkcAAAaHAAAGxwAABwcAAAdHAAAHhwAAB8cAAAgHAAAIRwAACIcAAAjHAAAJBwAACUcAAAmHAAAJxwAACgcAAApHAAAKhwAACscAAAsHAAALRwAAC4cAAAvHAAAMBwAADEcAAAyHAAAMxwAADQcAAA1HAAANhwAADccAAA4HAAAORwAADocAAA7HAAAPBwAAD0cAAA+HAAAPxwAAEAcAABBHAAAQhwAAEMcAABEHAAARRwAAEYcAABHHAAASBwAAEkcAABKHAAASxwAAEwcAABNHAAAThwAAE8cAABQHAAAURwAAFIcAABTHAAAVBwAAFUcAABWHAAAVxwAAFgcAABZHAAAWhwAAFscAABcHAAAXRwAAF4cAABfHAAAYBwAAGEcAABiHAAAYxwAAGQcAABlHAAAZhwAAGccAABoHAAAaRwAAGocAABrHAAAbBwAAG0cAABuHAAAbxwAAHAcAABxHAAAchwAAHMcAAB0HAAAdRwAAHYcAAB3HAAAeBwAAHkcAAB6HAAAexwAAHwcAAB9HAAAfhwAAH8cAACAHAAAgRwAAIIcAACDHAAAhBwAAIUcAACGHAAAhxwAAIgcAACJHAAAihwAAIscAACMHAAAjRwAAI4cAACPHAAAkBwAAJEcAACSHAAAkxwAAJQcAACVHAAAlhwAAJccAACYHAAAmRwAAJocAACbHAAAnBwAAJ0cAACeHAAAnxwAAKAcAAChHAAAohwAAKMcAACkHAAApRwAAKYcAACnHAAAqBwAAKkcAACqHAAAqxwAAKwcAACtHAAArhwAAK8cAACwHAAAsRwAALIcAACzHAAAtBwAALUcAAC2HAAAtxwAALgcAAC5HAAAuhwAALscAAC8HAAAvRwAAL4cAAC/HAAAwBwAAMEcAADCHAAAwxwAAMQcAADFHAAAxhwAAMccAADIHAAAyRwAAMocAADLHAAAzBwAAM0cAADOHAAAzxwAANAcAADRHAAA0hwAANMcAADUHAAA1RwAANYcAADXHAAA2BwAANkcAADaHAAA2xwAANwcAADdHAAA3hwAAN8cAADgHAAA4RwAAOIcAADjHAAA5BwAAOUcAADmHAAA5xwAAOgcAADpHAAA6hwAAOscAADsHAAA7RwAAO4cAADvHAAA8BwAAPEcAADyHAAA8xwAAPQcAAD1HAAA9hwAAPccAAD4HAAA+RwAAPocAAD7HAAA/BwAAP0cAAD+HAAA/xwAAAAdAAABHQAAAh0AAAMdAAAEHQAABR0AAAYdAAAHHQAACB0AAAkdAAAKHQAACx0AAAwdAAANHQAADh0AAA8dAAAQHQAAER0AABIdAAATHQAAFB0AABUdAAAWHQAAFx0AABgdAAAZHQAAGh0AABsdAAAcHQAAHR0AAB4dAAAfHQAAIB0AACEdAAAiHQAAIx0AACQdAAAlHQAAJh0AACcdAAAoHQAAKR0AACodAAArHQAALB0AAC0dAAAuHQAALx0AADAdAAAxHQAAMh0AADMdAAA0HQAANR0AADYdAAA3HQAAOB0AADkdAAA6HQAAOx0AADwdAAA9HQAAPh0AAD8dAABAHQAAQR0AAEIdAABDHQAARB0AAEUdAABGHQAARx0AAEgdAABJHQAASh0AAEsdAABMHQAATR0AAE4dAABPHQAAUB0AAFEdAABSHQAAUx0AAFQdAABVHQAAVh0AAFcdAABYHQAAWR0AAFodAABbHQAAXB0AAF0dAABeHQAAXx0AAGAdAABhHQAAYh0AAGMdAABkHQAAZR0AAGYdAABnHQAAaB0AAGkdAABqHQAAax0AAGwdAABtHQAAbh0AAG8dAABwHQAAcR0AAHIdAABzHQAAdB0AAHUdAAB2HQAAdx0AAHgdAAB5HQAAeh0AAHsdAAB8HQAAfR0AAH4dAAB/HQAAgB0AAIEdAACCHQAAgx0AAIQdAACFHQAAhh0AAIcdAACIHQAAiR0AAIodAACLHQAAjB0AAI0dAACOHQAAjx0AAJAdAACRHQAAkh0AAJMdAACUHQAAlR0AAJYdAACXHQAAmB0AAJkdAACaHQAAmx0AAJwdAACdHQAAnh0AAJ8dAACgHQAAoR0AAKIdAACjHQAApB0AAKUdAACmHQAApx0AAKgdAACpHQAAqh0AAKsdAACsHQAArR0AAK4dAACvHQAAsB0AALEdAACyHQAAsx0AALQdAAC1HQAAth0AALcdAAC4HQAAuR0AALodAAC7HQAAvB0AAL0dAAC+HQAAvx0AAMAdAADBHQAAwh0AAMMdAADEHQAAxR0AAMYdAADHHQAAyB0AAMkdAADKHQAAyx0AAMwdAADNHQAAzh0AAM8dAADQHQAA0R0AANIdAADTHQAA1B0AANUdAADWHQAA1x0AANgdAADZHQAA2h0AANsdAADcHQAA3R0AAN4dAADfHQAA4B0AAOEdAADiHQAA4x0AAOQdAADlHQAA5h0AAOcdAADoHQAA6R0AAOodAADrHQAA7B0AAO0dAADuHQAA7x0AAPAdAADxHQAA8h0AAPMdAAD0HQAA9R0AAPYdAAD3HQAA+B0AAPkdAAD6HQAA+x0AAPwdAAD9HQAA/h0AAP8dAAAAHgAAAR4AAAIeAAADHgAABB4AAAUeAAAGHgAABx4AAAgeAAAJHgAACh4AAAseAAAMHgAADR4AAA4eAAAPHgAAEB4AABEeAAASHgAAEx4AABQeAAAVHgAAFh4AABceAAAYHgAAGR4AABoeAAAbHgAAHB4AAB0eAAAeHgAAHx4AACAeAAAhHgAAIh4AACMeAAAkHgAAJR4AACYeAAAnHgAAKB4AACkeAAAqHgAAKx4AACweAAAtHgAALh4AAC8eAAAwHgAAMR4AADIeAAAzHgAANB4AADUeAAA2HgAANx4AADgeAAA5HgAAOh4AADseAAA8HgAAPR4AAD4eAAA/HgAAQB4AAEEeAABCHgAAQx4AAEQeAABFHgAARh4AAEceAABIHgAASR4AAEoeAABLHgAATB4AAE0eAABOHgAATx4AAFAeAABRHgAAUh4AAFMeAABUHgAAVR4AAFYeAABXHgAAWB4AAFkeAABaHgAAWx4AAFweAABdHgAAXh4AAF8eAABgHgAAYR4AAGIeAABjHgAAZB4AAGUeAABmHgAAZx4AAGgeAABpHgAAah4AAGseAABsHgAAbR4AAG4eAABvHgAAcB4AAHEeAAByHgAAcx4AAHQeAAB1HgAAdh4AAHceAAB4HgAAeR4AAHoeAAB7HgAAfB4AAH0eAAB+HgAAfx4AAIAeAACBHgAAgh4AAIMeAACEHgAAhR4AAIYeAACHHgAAiB4AAIkeAACKHgAAix4AAIweAACNHgAAjh4AAI8eAACQHgAAkR4AAJIeAACTHgAAlB4AAJUeAACWHgAAlx4AAJgeAACZHgAAmh4AAJseAACcHgAAnR4AAJ4eAACfHgAAoB4AAKEeAACiHgAAox4AAKQeAAClHgAAph4AAKceAACoHgAAqR4AAKoeAACrHgAArB4AAK0eAACuHgAArx4AALAeAACxHgAAsh4AALMeAAC0HgAAtR4AALYeAAC3HgAAuB4AALkeAAC6HgAAux4AALweAAC9HgAAvh4AAL8eAADAHgAAwR4AAMIeAADDHgAAxB4AAMUeAADGHgAAxx4AAMgeAADJHgAAyh4AAMseAADMHgAAzR4AAM4eAADPHgAA0B4AANEeAADSHgAA0x4AANQeAADVHgAA1h4AANceAADYHgAA2R4AANoeAADbHgAA3B4AAN0eAADeHgAA3x4AAOAeAADhHgAA4h4AAOMeAADkHgAA5R4AAOYeAADnHgAA6B4AAOkeAADqHgAA6x4AAOweAADtHgAA7h4AAO8eAADwHgAA8R4AAPIeAADzHgAA9B4AAPUeAAD2HgAA9x4AAPgeAAD5HgAA+h4AAPseAAD8HgAA/R4AAP4eAAD/HgAAAB8AAAEfAAACHwAAAx8AAAQfAAAFHwAABh8AAAcfAAAIHwAACR8AAAofAAALHwAADB8AAA0fAAAOHwAADx8AABAfAAARHwAAEh8AABMfAAAUHwAAFR8AABYfAAAXHwAAGB8AABkfAAAaHwAAGx8AABwfAAAdHwAAHh8AAB8fAAAgHwAAIR8AACIfAAAjHwAAJB8AACUfAAAmHwAAJx8AACgfAAApHwAAKh8AACsfAAAsHwAALR8AAC4fAAAvHwAAMB8AADEfAAAyHwAAMx8AADQfAAA1HwAANh8AADcfAAA4HwAAOR8AADofAAA7HwAAPB8AAD0fAAA+HwAAPx8AAEAfAABBHwAAQh8AAEMfAABEHwAARR8AAEYfAABHHwAASB8AAEkfAABKHwAASx8AAEwfAABNHwAATh8AAE8fAABQHwAAUR8AAFIfAABTHwAAVB8AAFUfAABWHwAAVx8AAFgfAABZHwAAWh8AAFsfAABcHwAAXR8AAF4fAABfHwAAYB8AAGEfAABiHwAAYx8AAGQfAABlHwAAZh8AAGcfAABoHwAAaR8AAGofAABrHwAAbB8AAG0fAABuHwAAbx8AAHAfAABxHwAAch8AAHMfAAB0HwAAdR8AAHYfAAB3HwAAeB8AAHkfAAB6HwAAex8AAHwfAAB9HwAAfh8AAH8fAACAHwAAgR8AAIIfAACDHwAAhB8AAIUfAACGHwAAhx8AAIgfAACJHwAAih8AAIsfAACMHwAAjR8AAI4fAACPHwAAkB8AAJEfAACSHwAAkx8AAJQfAACVHwAAlh8AAJcfAACYHwAAmR8AAJofAACbHwAAnB8AAJ0fAACeHwAAnx8AAKAfAAChHwAAoh8AAKMfAACkHwAApR8AAKYfAACnHwAAqB8AAKkfAACqHwAAqx8AAKwfAACtHwAArh8AAK8fAACwHwAAsR8AALIfAACzHwAAtB8AALUfAAC2HwAAtx8AALgfAAC5HwAAuh8AALsfAAC8HwAAvR8AAL4fAAC/HwAAwB8AAMEfAADCHwAAwx8AAMQfAADFHwAAxh8AAMcfAADIHwAAyR8AAMofAADLHwAAzB8AAM0fAADOHwAAzx8AANAfAADRHwAA0h8AANMfAADUHwAA1R8AANYfAADXHwAA2B8AANkfAADaHwAA2x8AANwfAADdHwAA3h8AAN8fAADgHwAA4R8AAOIfAADjHwAA5B8AAOUfAADmHwAA5x8AAOgfAADpHwAA6h8AAOsfAADsHwAA7R8AAO4fAADvHwAA8B8AAPEfAADyHwAA8x8AAPQfAAD1HwAA9h8AAPcfAAD4HwAA+R8AAPofAAD7HwAA/B8AAP0fAAD+HwAA/x8AAAAgAAABIAAAAiAAAAMgAAAEIAAABSAAAAYgAAAHIAAACCAAAAkgAAAKIAAACyAAAAwgAAANIAAADiAAAA8gAAAQIAAAESAAABIgAAATIAAAFCAAABUgAAAWIAAAFyAAABggAAAZIAAAGiAAABsgAAAcIAAAHSAAAB4gAAAfIAAAICAAACEgAAAiIAAAIyAAACQgAAAlIAAAJiAAACcgAAAoIAAAKSAAACogAAArIAAALCAAAC0gAAAuIAAALyAAADAgAAAxIAAAMiAAADMgAAA0IAAANSAAADYgAAA3IAAAOCAAADkgAAA6IAAAOyAAADwgAAA9IAAAPiAAAD8gAABAIAAAQSAAAEIgAABDIAAARCAAAEUgAABGIAAARyAAAEggAABJIAAASiAAAEsgAABMIAAATSAAAE4gAABPIAAAUCAAAFEgAABSIAAAUyAAAFQgAABVIAAAViAAAFcgAABYIAAAWSAAAFogAABbIAAAXCAAAF0gAABeIAAAXyAAAGAgAABhIAAAYiAAAGMgAABkIAAAZSAAAGYgAABnIAAAaCAAAGkgAABqIAAAayAAAGwgAABtIAAAbiAAAG8gAABwIAAAcSAAAHIgAABzIAAAdCAAAHUgAAB2IAAAdyAAAHggAAB5IAAAeiAAAHsgAAB8IAAAfSAAAH4gAAB/IAAAgCAAAIEgAACCIAAAgyAAAIQgAACFIAAAhiAAAIcgAACIIAAAiSAAAIogAACLIAAAjCAAAI0gAACOIAAAjyAAAJAgAACRIAAAkiAAAJMgAACUIAAAlSAAAJYgAACXIAAAmCAAAJkgAACaIAAAmyAAAJwgAACdIAAAniAAAJ8gAACgIAAAoSAAAKIgAACjIAAApCAAAKUgAACmIAAApyAAAKggAACpIAAAqiAAAKsgAACsIAAArSAAAK4gAACvIAAAsCAAALEgAACyIAAAsyAAALQgAAC1IAAAtiAAALcgAAC4IAAAuSAAALogAAC7IAAAvCAAAL0gAAC+IAAAvyAAAMAgAADBIAAAwiAAAMMgAADEIAAAxSAAAMYgAADHIAAAyCAAAMkgAADKIAAAyyAAAMwgAADNIAAAziAAAM8gAADQIAAA0SAAANIgAADTIAAA1CAAANUgAADWIAAA1yAAANggAADZIAAA2iAAANsgAADcIAAA3SAAAN4gAADfIAAA4CAAAOEgAADiIAAA4yAAAOQgAADlIAAA5iAAAOcgAADoIAAA6SAAAOogAADrIAAA7CAAAO0gAADuIAAA7yAAAPAgAADxIAAA8iAAAPMgAAD0IAAA9SAAAPYgAAD3IAAA+CAAAPkgAAD6IAAA+yAAAPwgAAD9IAAA/iAAAP8gAAAAIQAAASEAAAIhAAADIQAABCEAAAUhAAAGIQAAByEAAAghAAAJIQAACiEAAAshAAAMIQAADSEAAA4hAAAPIQAAECEAABEhAAASIQAAEyEAABQhAAAVIQAAFiEAABchAAAYIQAAGSEAABohAAAbIQAAHCEAAB0hAAAeIQAAHyEAACAhAAAhIQAAIiEAACMhAAAkIQAAJSEAACYhAAAnIQAAKCEAACkhAAAqIQAAKyEAACwhAAAtIQAALiEAAC8hAAAwIQAAMSEAADIhAAAzIQAANCEAADUhAAA2IQAANyEAADghAAA5IQAAOiEAADshAAA8IQAAPSEAAD4hAAA/IQAAQCEAAEEhAABCIQAAQyEAAEQhAABFIQAARiEAAEchAABIIQAASSEAAEohAABLIQAATCEAAE0hAABOIQAATyEAAFAhAABRIQAAUiEAAFMhAABUIQAAVSEAAFYhAABXIQAAWCEAAFkhAABaIQAAWyEAAFwhAABdIQAAXiEAAF8hAABgIQAAYSEAAGIhAABjIQAAZCEAAGUhAABmIQAAZyEAAGghAABpIQAAaiEAAGshAABsIQAAbSEAAG4hAABvIQAAcCEAAHEhAAByIQAAcyEAAHQhAAB1IQAAdiEAAHchAAB4IQAAeSEAAHohAAB7IQAAfCEAAH0hAAB+IQAAfyEAAIAhAACBIQAAgiEAAIMhAACEIQAAhSEAAIYhAACHIQAAiCEAAIkhAACKIQAAiyEAAIwhAACNIQAAjiEAAI8hAACQIQAAkSEAAJIhAACTIQAAlCEAAJUhAACWIQAAlyEAAJghAACZIQAAmiEAAJshAACcIQAAnSEAAJ4hAACfIQAAoCEAAKEhAACiIQAAoyEAAKQhAAClIQAApiEAAKchAACoIQAAqSEAAKohAACrIQAArCEAAK0hAACuIQAAryEAALAhAACxIQAAsiEAALMhAAC0IQAAtSEAALYhAAC3IQAAuCEAALkhAAC6IQAAuyEAALwhAAC9IQAAviEAAL8hAADAIQAAwSEAAMIhAADDIQAAxCEAAMUhAADGIQAAxyEAAMghAADJIQAAyiEAAMshAADMIQAAzSEAAM4hAADPIQAA0CEAANEhAADSIQAA0yEAANQhAADVIQAA1iEAANchAADYIQAA2SEAANohAADbIQAA3CEAAN0hAADeIQAA3yEAAOAhAADhIQAA4iEAAOMhAADkIQAA5SEAAOYhAADnIQAA6CEAAOkhAADqIQAA6yEAAOwhAADtIQAA7iEAAO8hAADwIQAA8SEAAPIhAADzIQAA9CEAAPUhAAD2IQAA9yEAAPghAAD5IQAA+iEAAPshAAD8IQAA/SEAAP4hAAD/IQAAACIAAAEiAAACIgAAAyIAAAQiAAAFIgAABiIAAAciAAAIIgAACSIAAAoiAAALIgAADCIAAA0iAAAOIgAADyIAABAiAAARIgAAEiIAABMiAAAUIgAAFSIAABYiAAAXIgAAGCIAABkiAAAaIgAAGyIAABwiAAAdIgAAHiIAAB8iAAAgIgAAISIAACIiAAAjIgAAJCIAACUiAAAmIgAAJyIAACgiAAApIgAAKiIAACsiAAAsIgAALSIAAC4iAAAvIgAAMCIAADEiAAAyIgAAMyIAADQiAAA1IgAANiIAADciAAA4IgAAOSIAADoiAAA7IgAAPCIAAD0iAAA+IgAAPyIAAEAiAABBIgAAQiIAAEMiAABEIgAARSIAAEYiAABHIgAASCIAAEkiAABKIgAASyIAAEwiAABNIgAATiIAAE8iAABQIgAAUSIAAFIiAABTIgAAVCIAAFUiAABWIgAAVyIAAFgiAABZIgAAWiIAAFsiAABcIgAAXSIAAF4iAABfIgAAYCIAAGEiAABiIgAAYyIAAGQiAABlIgAAZiIAAGciAABoIgAAaSIAAGoiAABrIgAAbCIAAG0iAABuIgAAbyIAAHAiAABxIgAAciIAAHMiAAB0IgAAdSIAAHYiAAB3IgAAeCIAAHkiAAB6IgAAeyIAAHwiAAB9IgAAfiIAAH8iAACAIgAAgSIAAIIiAACDIgAAhCIAAIUiAACGIgAAhyIAAIgiAACJIgAAiiIAAIsiAACMIgAAjSIAAI4iAACPIgAAkCIAAJEiAACSIgAAkyIAAJQiAACVIgAAliIAAJciAACYIgAAmSIAAJoiAACbIgAAnCIAAJ0iAACeIgAAnyIAAKAiAAChIgAAoiIAAKMiAACkIgAApSIAAKYiAACnIgAAqCIAAKkiAACqIgAAqyIAAKwiAACtIgAAriIAAK8iAACwIgAAsSIAALIiAACzIgAAtCIAALUiAAC2IgAAtyIAALgiAAC5IgAAuiIAALsiAAC8IgAAvSIAAL4iAAC/IgAAwCIAAMEiAADCIgAAwyIAAMQiAADFIgAAxiIAAMciAADIIgAAySIAAMoiAADLIgAAzCIAAM0iAADOIgAAzyIAANAiAADRIgAA0iIAANMiAADUIgAA1SIAANYiAADXIgAA2CIAANkiAADaIgAA2yIAANwiAADdIgAA3iIAAN8iAADgIgAA4SIAAOIiAADjIgAA5CIAAOUiAADmIgAA5yIAAOgiAADpIgAA6iIAAOsiAADsIgAA7SIAAO4iAADvIgAA8CIAAPEiAADyIgAA8yIAAPQiAAD1IgAA9iIAAPciAAD4IgAA+SIAAPoiAAD7IgAA/CIAAP0iAAD+IgAA/yIAAAAjAAABIwAAAiMAAAMjAAAEIwAABSMAAAYjAAAHIwAACCMAAAkjAAAKIwAACyMAAAwjAAANIwAADiMAAA8jAAAQIwAAESMAABIjAAATIwAAFCMAABUjAAAWIwAAFyMAABgjAAAZIwAAGiMAABsjAAAcIwAAHSMAAB4jAAAfIwAAICMAACEjAAAiIwAAIyMAACQjAAAlIwAAJiMAACcjAAAoIwAAKSMAACojAAArIwAALCMAAC0jAAAuIwAALyMAADAjAAAxIwAAMiMAADMjAAA0IwAANSMAADYjAAA3IwAAOCMAADkjAAA6IwAAOyMAADwjAAA9IwAAPiMAAD8jAABAIwAAQSMAAEIjAABDIwAARCMAAEUjAABGIwAARyMAAEgjAABJIwAASiMAAEsjAABMIwAATSMAAE4jAABPIwAAUCMAAFEjAABSIwAAUyMAAFQjAABVIwAAViMAAFcjAABYIwAAWSMAAFojAABbIwAAXCMAAF0jAABeIwAAXyMAAGAjAABhIwAAYiMAAGMjAABkIwAAZSMAAGYjAABnIwAAaCMAAGkjAABqIwAAayMAAGwjAABtIwAAbiMAAG8jAABwIwAAcSMAAHIjAABzIwAAdCMAAHUjAAB2IwAAdyMAAHgjAAB5IwAAeiMAAHsjAAB8IwAAfSMAAH4jAAB/IwAAgCMAAIEjAACCIwAAgyMAAIQjAACFIwAAhiMAAIcjAACIIwAAiSMAAIojAACLIwAAjCMAAI0jAACOIwAAjyMAAJAjAACRIwAAkiMAAJMjAACUIwAAlSMAAJYjAACXIwAAmCMAAJkjAACaIwAAmyMAAJwjAACdIwAAniMAAJ8jAACgIwAAoSMAAKIjAACjIwAApCMAAKUjAACmIwAApyMAAKgjAACpIwAAqiMAAKsjAACsIwAArSMAAK4jAACvIwAAsCMAALEjAACyIwAAsyMAALQjAAC1IwAAtiMAALcjAAC4IwAAuSMAALojAAC7IwAAvCMAAL0jAAC+IwAAvyMAAMAjAADBIwAAwiMAAMMjAADEIwAAxSMAAMYjAADHIwAAyCMAAMkjAADKIwAAyyMAAMwjAADNIwAAziMAAM8jAADQIwAA0SMAANIjAADTIwAA1CMAANUjAADWIwAA1yMAANgjAADZIwAA2iMAANsjAADcIwAA3SMAAN4jAADfIwAA4CMAAOEjAADiIwAA4yMAAOQjAADlIwAA5iMAAOcjAADoIwAA6SMAAOojAADrIwAA7CMAAO0jAADuIwAA7yMAAPAjAADxIwAA8iMAAPMjAAD0IwAA9SMAAPYjAAD3IwAA+CMAAPkjAAD6IwAA+yMAAPwjAAD9IwAA/iMAAP8jAAAAJAAAASQAAAIkAAADJAAABCQAAAUkAAAGJAAAByQAAAgkAAAJJAAACiQAAAskAAAMJAAADSQAAA4kAAAPJAAAECQAABEkAAASJAAAEyQAABQkAAAVJAAAFiQAABckAAAYJAAAGSQAABokAAAbJAAAHCQAAB0kAAAeJAAAHyQAACAkAAAhJAAAIiQAACMkAAAkJAAAJSQAACYkAAAnJAAAKCQAACkkAAAqJAAAKyQAACwkAAAtJAAALiQAAC8kAAAwJAAAMSQAADIkAAAzJAAANCQAADUkAAA2JAAANyQAADgkAAA5JAAAOiQAADskAAA8JAAAPSQAAD4kAAA/JAAAQCQAAEEkAABCJAAAQyQAAEQkAABFJAAARiQAAEckAABIJAAASSQAAEokAABLJAAATCQAAE0kAABOJAAATyQAAFAkAABRJAAAUiQAAFMkAABUJAAAVSQAAFYkAABXJAAAWCQAAFkkAABaJAAAWyQAAFwkAABdJAAAXiQAAF8kAABgJAAAYSQAAGIkAABjJAAAZCQAAGUkAABmJAAAZyQAAGgkAABpJAAAaiQAAGskAABsJAAAbSQAAG4kAABvJAAAcCQAAHEkAAByJAAAcyQAAHQkAAB1JAAAdiQAAHckAAB4JAAAeSQAAHokAAB7JAAAfCQAAH0kAAB+JAAAfyQAAIAkAACBJAAAgiQAAIMkAACEJAAAhSQAAIYkAACHJAAAiCQAAIkkAACKJAAAiyQAAIwkAACNJAAAjiQAAI8kAACQJAAAkSQAAJIkAACTJAAAlCQAAJUkAACWJAAAlyQAAJgkAACZJAAAmiQAAJskAACcJAAAnSQAAJ4kAACfJAAAoCQAAKEkAACiJAAAoyQAAKQkAAClJAAApiQAAKckAACoJAAAqSQAAKokAACrJAAArCQAAK0kAACuJAAAryQAALAkAACxJAAAsiQAALMkAAC0JAAAtSQAALYkAAC3JAAAuCQAALkkAAC6JAAAuyQAALwkAAC9JAAAviQAAL8kAADAJAAAwSQAAMIkAADDJAAAxCQAAMUkAADGJAAAxyQAAMgkAADJJAAAyiQAAMskAADMJAAAzSQAAM4kAADPJAAA0CQAANEkAADSJAAA0yQAANQkAADVJAAA1iQAANckAADYJAAA2SQAANokAADbJAAA3CQAAN0kAADeJAAA3yQAAOAkAADhJAAA4iQAAOMkAADkJAAA5SQAAOYkAADnJAAA6CQAAOkkAADqJAAA6yQAAOwkAADtJAAA7iQAAO8kAADwJAAA8SQAAPIkAADzJAAA9CQAAPUkAAD2JAAA9yQAAPgkAAD5JAAA+iQAAPskAAD8JAAA/SQAAP4kAAD/JAAAACUAAAElAAACJQAAAyUAAAQlAAAFJQAABiUAAAclAAAIJQAACSUAAAolAAALJQAADCUAAA0lAAAOJQAADyUAABAlAAARJQAAEiUAABMlAAAUJQAAFSUAABYlAAAXJQAAGCUAABklAAAaJQAAGyUAABwlAAAdJQAAHiUAAB8lAAAgJQAAISUAACIlAAAjJQAAJCUAACUlAAAmJQAAJyUAACglAAApJQAAKiUAACslAAAsJQAALSUAAC4lAAAvJQAAMCUAADElAAAyJQAAMyUAADQlAAA1JQAANiUAADclAAA4JQAAOSUAADolAAA7JQAAPCUAAD0lAAA+JQAAPyUAAEAlAABBJQAAQiUAAEMlAABEJQAARSUAAEYlAABHJQAASCUAAEklAABKJQAASyUAAEwlAABNJQAATiUAAE8lAABQJQAAUSUAAFIlAABTJQAAVCUAAFUlAABWJQAAVyUAAFglAABZJQAAWiUAAFslAABcJQAAXSUAAF4lAABfJQAAYCUAAGElAABiJQAAYyUAAGQlAABlJQAAZiUAAGclAABoJQAAaSUAAGolAABrJQAAbCUAAG0lAABuJQAAbyUAAHAlAABxJQAAciUAAHMlAAB0JQAAdSUAAHYlAAB3JQAAeCUAAHklAAB6JQAAeyUAAHwlAAB9JQAAfiUAAH8lAACAJQAAgSUAAIIlAACDJQAAhCUAAIUlAACGJQAAhyUAAIglAACJJQAAiiUAAIslAACMJQAAjSUAAI4lAACPJQAAkCUAAJElAACSJQAAkyUAAJQlAACVJQAAliUAAJclAACYJQAAmSUAAJolAACbJQAAnCUAAJ0lAACeJQAAnyUAAKAlAAChJQAAoiUAAKMlAACkJQAApSUAAKYlAACnJQAAqCUAAKklAACqJQAAqyUAAKwlAACtJQAAriUAAK8lAACwJQAAsSUAALIlAACzJQAAtCUAALUlAAC2JQAAtyUAALglAAC5JQAAuiUAALslAAC8JQAAvSUAAL4lAAC/JQAAwCUAAMElAADCJQAAwyUAAMQlAADFJQAAxiUAAMclAADIJQAAySUAAMolAADLJQAAzCUAAM0lAADOJQAAzyUAANAlAADRJQAA0iUAANMlAADUJQAA1SUAANYlAADXJQAA2CUAANklAADaJQAA2yUAANwlAADdJQAA3iUAAN8lAADgJQAA4SUAAOIlAADjJQAA5CUAAOUlAADmJQAA5yUAAOglAADpJQAA6iUAAOslAADsJQAA7SUAAO4lAADvJQAA8CUAAPElAADyJQAA8yUAAPQlAAD1JQAA9iUAAPclAAD4JQAA+SUAAPolAAD7JQAA/CUAAP0lAAD+JQAA/yUAAAAmAAABJgAAAiYAAAMmAAAEJgAABSYAAAYmAAAHJgAACCYAAAkmAAAKJgAACyYAAAwmAAANJgAADiYAAA8mAAAQJgAAESYAABImAAATJgAAFCYAABUmAAAWJgAAFyYAABgmAAAZJgAAGiYAABsmAAAcJgAAHSYAAB4mAAAfJgAAICYAACEmAAAiJgAAIyYAACQmAAAlJgAAJiYAACcmAAAoJgAAKSYAAComAAArJgAALCYAAC0mAAAuJgAALyYAADAmAAAxJgAAMiYAADMmAAA0JgAANSYAADYmAAA3JgAAOCYAADkmAAA6JgAAOyYAADwmAAA9JgAAPiYAAD8mAABAJgAAQSYAAEImAABDJgAARCYAAEUmAABGJgAARyYAAEgmAABJJgAASiYAAEsmAABMJgAATSYAAE4mAABPJgAAUCYAAFEmAABSJgAAUyYAAFQmAABVJgAAViYAAFcmAABYJgAAWSYAAFomAABbJgAAXCYAAF0mAABeJgAAXyYAAGAmAABhJgAAYiYAAGMmAABkJgAAZSYAAGYmAABnJgAAaCYAAGkmAABqJgAAayYAAGwmAABtJgAAbiYAAG8mAABwJgAAcSYAAHImAABzJgAAdCYAAHUmAAB2JgAAdyYAAHgmAAB5JgAAeiYAAHsmAAB8JgAAfSYAAH4mAAB/JgAAgCYAAIEmAACCJgAAgyYAAIQmAACFJgAAhiYAAIcmAACIJgAAiSYAAIomAACLJgAAjCYAAI0mAACOJgAAjyYAAJAmAACRJgAAkiYAAJMmAACUJgAAlSYAAJYmAACXJgAAmCYAAJkmAACaJgAAmyYAAJwmAACdJgAAniYAAJ8mAACgJgAAoSYAAKImAACjJgAApCYAAKUmAACmJgAApyYAAKgmAACpJgAAqiYAAKsmAACsJgAArSYAAK4mAACvJgAAsCYAALEmAACyJgAAsyYAALQmAAC1JgAAtiYAALcmAAC4JgAAuSYAALomAAC7JgAAvCYAAL0mAAC+JgAAvyYAAMAmAADBJgAAwiYAAMMmAADEJgAAxSYAAMYmAADHJgAAyCYAAMkmAADKJgAAyyYAAMwmAADNJgAAziYAAM8mAADQJgAA0SYAANImAADTJgAA1CYAANUmAADWJgAA1yYAANgmAADZJgAA2iYAANsmAADcJgAA3SYAAN4mAADfJgAA4CYAAOEmAADiJgAA4yYAAOQmAADlJgAA5iYAAOcmAADoJgAA6SYAAOomAADrJgAA7CYAAO0mAADuJgAA7yYAAPAmAADxJgAA8iYAAPMmAAD0JgAA9SYAAPYmAAD3JgAA+CYAAPkmAAD6JgAA+yYAAPwmAAD9JgAA/iYAAP8mAAAAJwAAAScAAAInAAADJwAABCcAAAUnAAAGJwAABycAAAgnAAAJJwAACicAAAsnAAAMJwAADScAAA4nAAAPJwAAECcAABEnAAASJwAAEycAABQnAAAVJwAAFicAABcnAAAYJwAAGScAABonAAAbJwAAHCcAAB0nAAAeJwAAHycAACAnAAAhJwAAIicAACMnAAAkJwAAJScAACYnAAAnJwAAKCcAACknAAAqJwAAKycAACwnAAAtJwAALicAAC8nAAAwJwAAMScAADInAAAzJwAANCcAADUnAAA2JwAANycAADgnAAA5JwAAOicAADsnAAA8JwAAPScAAD4nAAA/JwAAQCcAAEEnAABCJwAAQycAAEQnAABFJwAARicAAEcnAABIJwAASScAAEonAABLJwAATCcAAE0nAABOJwAATycAAFAnAABRJwAAUicAAFMnAABUJwAAVScAAFYnAABXJwAAWCcAAFknAABaJwAAWycAAFwnAABdJwAAXicAAF8nAABgJwAAYScAAGInAABjJwAAZCcAAGUnAABmJwAAZycAAGgnAABpJwAAaicAAGsnAABsJwAAbScAAG4nAABvJwAAcCcAAHEnAAByJwAAcycAAHQnAAB1JwAAdicAAHcnAAB4JwAAeScAAHonAAB7JwAAfCcAAH0nAAB+JwAAfycAAIAnAACBJwAAgicAAIMnAACEJwAAhScAAIYnAACHJwAAiCcAAIknAACKJwAAiycAAIwnAACNJwAAjicAAI8nAACQJwAAkScAAJInAACTJwAAlCcAAJUnAACWJwAAlycAAJgnAACZJwAAmicAAJsnAACcJwAAnScAAJ4nAACfJwAAoCcAAKEnAACiJwAAoycAAKQnAAClJwAApicAAKcnAACoJwAAqScAAKonAACrJwAArCcAAK0nAACuJwAArycAALAnAACxJwAAsicAALMnAAC0JwAAtScAALYnAAC3JwAAuCcAALknAAC6JwAAuycAALwnAAC9JwAAvicAAL8nAADAJwAAwScAAMInAADDJwAAxCcAAMUnAADGJwAAxycAAMgnAADJJwAAyicAAMsnAADMJwAAzScAAM4nAADPJwAA0CcAANEnAADSJwAA0ycAANQnAADVJwAA1icAANcnAADYJwAA2ScAANonAADbJwAA3CcAAN0nAADeJwAA3ycAAOAnAADhJwAA4icAAOMnAADkJwAA5ScAAOYnAADnJwAA6CcAAOknAADqJwAA6ycAAOwnAADtJwAA7icAAO8nAADwJwAA8ScAAPInAADzJwAA9CcAAPUnAAD2JwAA9ycAAPgnAAD5JwAA+icAAPsnAAD8JwAA/ScAAP4nAAD/JwAAACgAAAEoAAACKAAAAygAAAQoAAAFKAAABigAAAcoAAAIKAAACSgAAAooAAALKAAADCgAAA0oAAAOKAAADygAABAoAAARKAAAEigAABMoAAAUKAAAFSgAABYoAAAXKAAAGCgAABkoAAAaKAAAGygAABwoAAAdKAAAHigAAB8oAAAgKAAAISgAACIoAAAjKAAAJCgAACUoAAAmKAAAJygAACgoAAApKAAAKigAACsoAAAsKAAALSgAAC4oAAAvKAAAMCgAADEoAAAyKAAAMygAADQoAAA1KAAANigAADcoAAA4KAAAOSgAADooAAA7KAAAPCgAAD0oAAA+KAAAPygAAEAoAABBKAAAQigAAEMoAABEKAAARSgAAEYoAABHKAAASCgAAEkoAABKKAAASygAAEwoAABNKAAATigAAE8oAABQKAAAUSgAAFIoAABTKAAAVCgAAFUoAABWKAAAVygAAFgoAABZKAAAWigAAFsoAABcKAAAXSgAAF4oAABfKAAAYCgAAGEoAABiKAAAYygAAGQoAABlKAAAZigAAGcoAABoKAAAaSgAAGooAABrKAAAbCgAAG0oAABuKAAAbygAAHAoAABxKAAAcigAAHMoAAB0KAAAdSgAAHYoAAB3KAAAeCgAAHkoAAB6KAAAeygAAHwoAAB9KAAAfigAAH8oAACAKAAAgSgAAIIoAACDKAAAhCgAAIUoAACGKAAAhygAAIgoAACJKAAAiigAAIsoAACMKAAAjSgAAI4oAACPKAAAkCgAAJEoAACSKAAAkygAAJQoAACVKAAAligAAJcoAACYKAAAmSgAAJooAACbKAAAnCgAAJ0oAACeKAAAnygAAKAoAAChKAAAoigAAKMoAACkKAAApSgAAKYoAACnKAAAqCgAAKkoAACqKAAAqygAAKwoAACtKAAArigAAK8oAACwKAAAsSgAALIoAACzKAAAtCgAALUoAAC2KAAAtygAALgoAAC5KAAAuigAALsoAAC8KAAAvSgAAL4oAAC/KAAAwCgAAMEoAADCKAAAwygAAMQoAADFKAAAxigAAMcoAADIKAAAySgAAMooAADLKAAAzCgAAM0oAADOKAAAzygAANAoAADRKAAA0igAANMoAADUKAAA1SgAANYoAADXKAAA2CgAANkoAADaKAAA2ygAANwoAADdKAAA3igAAN8oAADgKAAA4SgAAOIoAADjKAAA5CgAAOUoAADmKAAA5ygAAOgoAADpKAAA6igAAOsoAADsKAAA7SgAAO4oAADvKAAA8CgAAPEoAADyKAAA8ygAAPQoAAD1KAAA9igAAPcoAAD4KAAA+SgAAPooAAD7KAAA/CgAAP0oAAD+KAAA/ygAAAApAAABKQAAAikAAAMpAAAEKQAABSkAAAYpAAAHKQAACCkAAAkpAAAKKQAACykAAAwpAAANKQAADikAAA8pAAAQKQAAESkAABIpAAATKQAAFCkAABUpAAAWKQAAFykAABgpAAAZKQAAGikAABspAAAcKQAAHSkAAB4pAAAfKQAAICkAACEpAAAiKQAAIykAACQpAAAlKQAAJikAACcpAAAoKQAAKSkAACopAAArKQAALCkAAC0pAAAuKQAALykAADApAAAxKQAAMikAADMpAAA0KQAANSkAADYpAAA3KQAAOCkAADkpAAA6KQAAOykAADwpAAA9KQAAPikAAD8pAABAKQAAQSkAAEIpAABDKQAARCkAAEUpAABGKQAARykAAEgpAABJKQAASikAAEspAABMKQAATSkAAE4pAABPKQAAUCkAAFEpAABSKQAAUykAAFQpAABVKQAAVikAAFcpAABYKQAAWSkAAFopAABbKQAAXCkAAF0pAABeKQAAXykAAGApAABhKQAAYikAAGMpAABkKQAAZSkAAGYpAABnKQAAaCkAAGkpAABqKQAAaykAAGwpAABtKQAAbikAAG8pAABwKQAAcSkAAHIpAABzKQAAdCkAAHUpAAB2KQAAdykAAHgpAAB5KQAAeikAAHspAAB8KQAAfSkAAH4pAAB/KQAAgCkAAIEpAACCKQAAgykAAIQpAACFKQAAhikAAIcpAACIKQAAiSkAAIopAACLKQAAjCkAAI0pAACOKQAAjykAAJApAACRKQAAkikAAJMpAACUKQAAlSkAAJYpAACXKQAAmCkAAJkpAACaKQAAmykAAJwpAACdKQAAnikAAJ8pAACgKQAAoSkAAKIpAACjKQAApCkAAKUpAACmKQAApykAAKgpAACpKQAAqikAAKspAACsKQAArSkAAK4pAACvKQAAsCkAALEpAACyKQAAsykAALQpAAC1KQAAtikAALcpAAC4KQAAuSkAALopAAC7KQAAvCkAAL0pAAC+KQAAvykAAMApAADBKQAAwikAAMMpAADEKQAAxSkAAMYpAADHKQAAyCkAAMkpAADKKQAAyykAAMwpAADNKQAAzikAAM8pAADQKQAA0SkAANIpAADTKQAA1CkAANUpAADWKQAA1ykAANgpAADZKQAA2ikAANspAADcKQAA3SkAAN4pAADfKQAA4CkAAOEpAADiKQAA4ykAAOQpAADlKQAA5ikAAOcpAADoKQAA6SkAAOopAADrKQAA7CkAAO0pAADuKQAA7ykAAPApAADxKQAA8ikAAPMpAAD0KQAA9SkAAPYpAAD3KQAA+CkAAPkpAAD6KQAA+ykAAPwpAAD9KQAA/ikAAP8pAAAAKgAAASoAAAIqAAADKgAABCoAAAUqAAAGKgAAByoAAAgqAAAJKgAACioAAAsqAAAMKgAADSoAAA4qAAAPKgAAECoAABEqAAASKgAAEyoAABQqAAAVKgAAFioAABcqAAAYKgAAGSoAABoqAAAbKgAAHCoAAB0qAAAeKgAAHyoAACAqAAAhKgAAIioAACMqAAAkKgAAJSoAACYqAAAnKgAAKCoAACkqAAAqKgAAKyoAACwqAAAtKgAALioAAC8qAAAwKgAAMSoAADIqAAAzKgAANCoAADUqAAA2KgAANyoAADgqAAA5KgAAOioAADsqAAA8KgAAPSoAAD4qAAA/KgAAQCoAAEEqAABCKgAAQyoAAEQqAABFKgAARioAAEcqAABIKgAASSoAAEoqAABLKgAATCoAAE0qAABOKgAATyoAAFAqAABRKgAAUioAAFMqAABUKgAAVSoAAFYqAABXKgAAWCoAAFkqAABaKgAAWyoAAFwqAABdKgAAXioAAF8qAABgKgAAYSoAAGIqAABjKgAAZCoAAGUqAABmKgAAZyoAAGgqAABpKgAAaioAAGsqAABsKgAAbSoAAG4qAABvKgAAcCoAAHEqAAByKgAAcyoAAHQqAAB1KgAAdioAAHcqAAB4KgAAeSoAAHoqAAB7KgAAfCoAAH0qAAB+KgAAfyoAAIAqAACBKgAAgioAAIMqAACEKgAAhSoAAIYqAACHKgAAiCoAAIkqAACKKgAAiyoAAIwqAACNKgAAjioAAI8qAACQKgAAkSoAAJIqAACTKgAAlCoAAJUqAACWKgAAlyoAAJgqAACZKgAAmioAAJsqAACcKgAAnSoAAJ4qAACfKgAAoCoAAKEqAACiKgAAoyoAAKQqAAClKgAApioAAKcqAACoKgAAqSoAAKoqAACrKgAArCoAAK0qAACuKgAA",
-          "dtype": "u4"
-         },
-         "xaxis": "x",
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-        },
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-          0,
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-   "source": [
-    "category_counts_sorted = category_counts.with_row_index(\"rank\", offset=1)\n",
-    "\n",
-    "fig = px.line(\n",
-    "    category_counts_sorted.to_pandas(),\n",
-    "    x=\"rank\",\n",
-    "    y=\"count\",\n",
-    "    title=\"POI Category Frequency Distribution (Log Scale)\",\n",
-    "    labels={\"rank\": \"Category Rank\", \"count\": \"Number of POIs\"},\n",
-    "    log_y=True,\n",
-    ")\n",
-    "fig.update_layout(height=500)\n",
-    "fig.show()"
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-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Categories covering 80% of all POIs: 12\n",
-      "Total unique categories: 10926\n"
-     ]
-    },
-    {
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",
-          "dtype": "u4"
-         },
-         "xaxis": "x",
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jWEAHBgyjoONYQEEcBj6241hAUyDXucvjWEBmJKg14eNYQFEWUJL241hAFfbOzwvkWECywyTuIORYQCh/Ue015FhATxYsrkrkWEBiJHJfX+RYQE8gj/Fz5FhAOhysg4jkWEDY83bXnORYQDowhPyw5FhAnmyRIcXkWEDFDeEX2eRYQNolnP7s5FhA7j1X5QDlWEACVhLMFOVYQLTA5mQo5VhAZiu7/TvlWEAYlo+WT+VYQI9lpgBj5VhA3iKUS3blWEAbV+2GieVYQEMCsrKc5VhARZtNv6/lWEAyq1S8wuVYQL4NdWvV5VhADtXX6+flWEBfnDps+uVYQJzaCN0M5lhAxY9CPh/mWEDbu+ePMeZYQPHnjOFD5lhAuO/f9FXmWEB/9zIIaOZYQDJ28Qt65lhA0msbAIzmWEBe2LDkneZYQLCpiJqv5lhA7vHLQMHmWEAEKObH0uZYQAfVaz/k5lhACoLxtvXmWED5peIeB+dYQOjJ04YY51hAw2Qw3ynnWECf/4w3O+dYQFOIwHBM51hACBH0qV3nWEBa7ECVbudYQIS1ZGF/51hAiGxfDpDnWECMI1q7oOdYQFQ/lzmx51hACtI/qMHnWECr21MH0udYQDlc01bi51hAn8oph/LnWEDyr+unAuhYQEWVrcgS6FhAcWhGyiLoWECcO9/LMuhYQMgOeM1C6FhA4Fh8v1LoWEDkGeyhYuhYQNVRx3Ry6FhAxomiR4LoWECjOOkKkuhYQGxem76h6FhAD3IkU7HoWECd/BjYwOhYQPDrTy7Q6FhAHcldZd/oWEBJpmuc7uhYQGL65MP96FhAe05e6wzpWECUotcSHOlYQJltvCor6VhAnjihQjrpWECPevFKSelYQIG8QVNY6VhA/McW/mbpWEA9OC56delYQH6oRfaD6VhAvxhdcpLpWEDYdkvPoOlYQPLUOSyv6VhA+KmTeb3pWEDWbMSny+lYQGYLo5fZ6VhAuw7EWOfpWED9iFAK9elYQD4D3bsC6lhAgH1pbRDqWECubmEPHupYQMjWxKEr6lhA4j4oNDnqWED9povGRupYQBcP71hU6lhAHe6922HqWEAlzYxeb+pYQBcjx9F86lhACnkBRYrqWED9zju4l+pYQN2b4Rul6lhAvGiHf7LqWECIrJjTv+pYQFTwqSfN6lhADKsmbNrqWEDEZaOw5+pYQHwgIPX06lhAIFIIKgLrWECx+ltPD+tYQEKjr3Qc61hArDnaeinrWEAV0ASBNutYQETLcVhD61hAXz1KIFDrWEBmJo7YXOtYQFqGPYFp61hATebsKXbrWEBBRpzSgutYQDWmS3uP61hAFn1mFJzrWED2U4GtqOtYQMKhBze161hAe2b5sMHrWEA0K+sqzutYQNlmSJXa61hAaxkR8ObrWEDoQkU78+tYQGZseYb/61hA5JWt0QvsWEBPNk0NGOxYQKZNWDkk7FhA/GRjZTDsWEBA89mBPOxYQG/4u45I7FhAnv2dm1TsWECn8FaJYOxYQIfR5lds7FhAaLJ2JnjsWEBJkwb1g+xYQBbrAbSP7FhA0LloY5vsWEB2/zoDp+xYQAm8eJOy7FhAh+8hFL7sWEAGI8uUyexYQEm7tubU7FhAZkF5GeDsWECDxztM6+xYQJ9N/n727FhAqUosogHtWECxR1rFDO1YQLpEiOgX7VhAsLgh/CLtWEClLLsPLu1YQDjzbdU47VhAy7kgm0PtWEBL9z5RTu1YQLaryPdY7VhAImBSnmPtWECOFNxEbu1YQOY/0dt47VhAPmvGcoPtWECXlrsJju1YQNs4HJGY7VhAINt8GKPtWEBlfd2fre1YQJaWqRe47VhAsybhf8LtWEDQthjozO1YQNq9u0DX7VhA48RemeHtWEDaQm3i6+1YQNDAeyv27VhAsrX1ZADuWECBIduOCu5YQDwELKkU7lhA9+Z8wx7uWECeQDnOKO5YQEaa9dgy7lhA7vOx4zzuWECVTW7uRu5YQCkelulQ7lhAqWUp1VruWEAqrbzAZO5YQJZru5xu7lhA76AlaXjuWED6sT33ge5YQPE5wXWL7lhA58FE9JTuWEDLwDNjnu5YQK6/ItKn7lhAfjV9MbHuWEBNq9eQuu5YQB0hMvDD7lhA2A34P83uWECBcSmA1u5YQCnVWsDf7lhAvq/38OjuWEBSipQh8u5YQNTbnEL77lhAVC2lYwTvWEDC9Rh1De9YQC++jIYW71hAnYYAmB/vWEAKT3SpKO9YQGSOU6sx71hAvc0yrTrvWEAEhH2fQ+9YQEo6yJFM71hAfGd+dFXvWECIggs4Xu9YQH8UBOxm71hAYx1okG/vWEAgFKMVeO9YQN0K3pqA71hAmQEZIInvWEBCb7+Vke9YQOvcZQua71hAlEoMgaLvWEAqLx7nqu9YQL8TME2z71hAQW+to7vvWEDDyir6w+9YQDGdE0HM71hAn2/8h9TvWED6uFC/3O9YQFQCpfbk71hAr0v5Le3vWED1C7lV9e9YQD3MeH3971hAg4w4pQXwWEC3w2O9DfBYQNdx+sUV8FhA9h+Rzh3wWEAWzifXJfBYQDV8vt8t8FhAQaHA2DXwWEBNxsLRPfBYQEViMLtF8FhAPv6dpE3wWEA2mguOVfBYQBut5Gdd8FhAAMC9QWXwWEDRSQIMbfBYQKHTRtZ08FhAc12LoHzwWEAwXjtbhPBYQO5e6xWM8FhAq1+b0JPwWEBV17Z7m/BYQP9O0iaj8FhAlT1ZwqrwWEArLOBdsvBYQK6R0um58FhAMPfEdcHwWECzXLcByfBYQCI5FX7Q8FhAkRVz+tfwWEAA8tB23/BYQG/OLvPm8FhAyiH4X+7wWEAldcHM9fBYQG0/9in98FhAoYCWdwTxWEDVwTbFC/FYQPV5QgMT8VhAFjJOQRrxWEA26ll/IfFYQC+QPJ4o8VhAKTYfvS/xWEAh3AHcNvFYQPNvu9s98VhAxQN120TxWECXly7bS/FYQFWiU8tS8VhAE614u1nxWEDSt52rYPFYQJDCwptn8VhAO0RTfG7xWEDlxeNcdfFYQHy+3y188VhAE7fb/oLxWECqr9fPifFYQECo06CQ8VhA16DPcZfxWEBbEDcznvFYQN5/nvSk8VhAYu8FtqvxWEDlXm13svFYQFVFQCm58VhAsaJ+y7/xWEANAL1txvFYQFbUZgDN8VhAnagQk9PxWEDmfLol2vFYQBvIz6jg8VhAUBPlK+fxWEBx1WWf7fFYQJKX5hL08VhAs1lnhvrxWEDUG+j5APJYQM7LP04H8lhAtfICkw3yWECbGcbXE/JYQIFAiRwa8lhAZ2dMYSDyWEA6BXuWJvJYQAyjqcss8lhAy7dD8TLyWEB3Q0kHOfJYQCLPTh0/8lhAzVpUM0XyWEBlXcU5S/JYQP1fNkBR8lhAlGKnRlfyWEAZ3IM9XfJYQJ1VYDRj8lhAIc88K2nyWECRv4QSb/JYQNqdo9p08lhAJHzConryWEBtWuFqgPJYQLY4ADOG8lhA7I2K64vyWEAh4xSkkfJYQFc4n1yX8lhAjI0pFZ3yWECuWR++ovJYQNAlFWeo8lhA8vEKEK7yWEAUvgC5s/JYQDaK9mG58lhARM1X+77yWEBTELmUxPJYQGFTGi7K8lhAWw3nt8/yWEBWx7NB1fJYQFCBgMva8lhATDtNVeDyWEBG9Rnf5fJYQEGv5mjr8lhAO2mz8vDyWEA2I4B89vJYQB1UuPb78lhABIXwcAHzWEDrtSjrBvNYQNLmYGUM81hAuReZ3xHzWECgSNFZF/NYQHPwdMQc81hAR5gYLyLzWEAaQLyZJ/NYQO7nXwQt81hAwY8DbzLzWECUN6fZN/NYQFRWtjQ981hAFHXFj0LzWEDUk9TqR/NYQIApTzZN81hAK7/JgVLzWEDYVETNV/NYQITqvhhd81hAHPekVGLzWEC1A4uQZ/NYQE0Qccxs81hA0pPC+HHzWEBDjn8Vd/NYQKH/pyJ881hA/nDQL4HzWEBc4vg8hvNYQLpTIUqL81hABDy1R5DzWEA6m7Q1lfNYQHD6syOa81hAp1mzEZ/zWEDduLL/o/NYQBMYsu2o81hANu4czK3zWEBZxIeqsvNYQGcRXnm381hAdl40SLzzWECGqwoXwfNYQJX44OXF81hApEW3tMrzWECzko2Dz/NYQMLfY1LU81hAvaOlEdnzWEC5Z+fQ3fNYQKCilIDi81hAid1BMOfzWEBwGO/f6/NYQFhTnI/w81hAQI5JP/XzWEAoyfbu+fNYQPx6D4/+81hA0CwoLwP0WECk3kDPB/RYQHiQWW8M9FhATEJyDxH0WEAh9IqvFfRYQPWlo08a9FhAyVe87x70WECJgECAI/RYQEmpxBAo9FhACtJIoSz0WEDL+swxMfRYQHiavLI19FhAJDqsMzr0WEDS2Zu0PvRYQGvw9iVD9FhABAdSl0f0WECdHa0ITPRYQCOrc2pQ9FhAqTg6zFT0WEAaPWweWfRYQI1BnnBd9FhA/kXQwmH0WEBxSgIVZvRYQOJONGdq9FhAQMrRqW70WECfRW/scvRYQP7ADC939FhAXDyqcXv0WEC6t0e0f/RYQBgz5faD9FhAdq6COYj0WEDVKSB8jPRYQCAcKa+Q9FhAag4y4pT0WEC1ADsVmfRYQOxprzid9FhAI9MjXKH0WEBHswNwpfRYQGqT44Op9FhAeuouiK30WECKQXqMsfRYQIYPMYG19FhAb1RTZrn0WEBXmXVLvfRYQEDelzDB9FhAKCO6FcX0WEAQaNz6yPRYQPms/t/M9FhA4vEgxdD0WEC3ra6a1PRYQItpPHDY9FhAYCXKRdz0WEA14Vcb4PRYQAqd5fDj9FhA31hzxuf0WEC0FAGc6/RYQHVH+mHv9FhAN3rzJ/P0WED4rOzt9vRYQLrf5bP69FhAZ4lKav70WEAVM68gAvVYQMLcE9cF9VhAcIZ4jQn1WEAeMN1DDfVYQMvZQfoQ9VhAeYOmsBT1WEAmLQtnGPVYQNTWbx0c9VhAgYDU0x/1WEAboaR6I/VYQLbBdCEn9VhAUOJEyCr1WEDqAhVvLvVYQIQj5RUy9VhAHkS1vDX1WEC3ZIVjOfVYQD78wPo89VhAxZP8kUD1WEBLKzgpRPVYQNLCc8BH9VhAWFqvV0v1WEDe8eruTvVYQGSJJoZS9VhA6yBiHVb1WEBxuJ20WfVYQPhP2Utd9VhAal6A02D1WEDdbCdbZPVYQFB7zuJn9VhArwDhWmv1WEAOhvPSbvVYQG0LBkty9VhAzJAYw3X1WEAsFis7efVYQIqbPbN89VhA6SBQK4D1WEA1Hc6Tg/VYQIAZTPyG9VhAzBXKZIr1WEAXEkjNjfVYQGMOxjWR9VhArgpEnpT1WEDmfS33l/VYQB7xFlCb9VhAVmQAqZ71WECO1+kBovVYQMVK01ql9VhA/r28s6j1WEA1MaYMrPVYQG2kj2Wv9VhApRd5vrL1WEDcimIXtvVYQBX+S3C59VhATXE1ybz1WECE5B4iwPVYQKnOc2vD9VhAzbjItMb1WEDxoh3+yfVYQBaNckfN9VhAOXfHkND1WEBdYRza0/VYQIJLcSPX9VhAk6wxXdr1WECjDfKW3fVYQLRustDg9VhAxM9yCuT1WEDVMDNE5/VYQOWR833q9VhA9vKzt+31WEAGVHTx8PVYQBe1NCv09VhAKBb1ZPf1WEA4d7We+vVYQEnYddj99VhARrChAgH2WEBDiM0sBPZYQD9g+VYH9lhAPDglgQr2WEA6EFGrDfZYQDfofNUQ9lhAM8Co/xP2WEAdD0AaF/ZYQAZe1zQa9lhA8KxuTx32WEDZ+wVqIPZYQMJKnYQj9lhAq5k0nyb2WECU6Mu5KfZYQH43Y9Qs9lhAVP1l3y/2WEApw2jqMvZYQP+Ia/U19lhA1E5uADn2WECqFHELPPZYQIDacxY/9lhAVqB2IUL2WEArZnksRfZYQAEsfDdI9lhAw2jqMkv2WECFpVguTvZYQEfixilR9lhACR81JVT2WEDLW6MgV/ZYQI2YERxa9lhAT9V/F132WEASEu4SYPZYQNNOXA5j9lhAlYvKCWb2WEBXyDgFafZYQAZ8EvFr9lhAtC/s3G72WEBj48XIcfZYQBGXn7R09lhAwEp5oHf2WEBu/lKMevZYQByyLHh99lhAymUGZID2WEB5GeBPg/ZYQCfNuTuG9lhAwvf+F4n2WEBdIkT0i/ZYQPhMidCO9lhAknfOrJH2WEAtohOJlPZYQMjMWGWX9lhAY/edQZr2WEDqmE4OnfZYQHE6/9qf9lhA+duvp6L2WECAfWB0pfZYQAYfEUGo9lhAjcDBDav2WEAVYnLarfZYQJwDI6ew9lhAI6XTc7P2WECqRoRAtvZYQDHoNA259lhAuYnl2bv2WEAsogGXvvZYQJ+6HVTB9lhAE9M5EcT2WECG61XOxvZYQPoDcovJ9lhAbhyOSMz2WEDOqxX2zvZYQC07naPR9lhAjsokUdT2WEDtWaz+1vZYQE3pM6zZ9lhArXi7Wdz2WEANCEMH3/ZYQGyXyrTh9lhAzCZSYuT2WEAtttkP5/ZYQIxFYb3p9lhA7NToauz2WEA429sI7/ZYQIThzqbx9lhA0efBRPT2WEAd7rTi9vZYQGn0p4D59lhAtvqaHvz2WEACAY68/vZYQE4HgVoB91hAmg10+AP3WEDnE2eWBvdYQDMaWjQJ91hAfyBN0gv3WEDMJkBwDvdYQASknv4Q91hAPCH9jBP3WEB1nlsbFvdYQK0buqkY91hA5pgYOBv3WEAfFnfGHfdYQFiT1VQg91hAkBA04yL3WEC1BP5hJfdYQNr4x+An91hA/+yRXyr3WEAk4VveLPdYQEnVJV0v91hAbsnv2zH3WECTvblaNPdYQLixg9k291hA3aVNWDn3WEABmhfXO/dYQCeO4VU+91hATIKr1ED3WEBxdnVTQ/dYQILhqsJF91hAlEzgMUj3WECltxWhSvdYQLYiSxBN91hAx42Af0/3WEDZ+LXuUfdYQOpj611U91hA6EWMvVb3WEDmJy0dWfdYQOQJznxb91hA4etu3F33WEDezQ88YPdYQNyvsJti91hA2pFR+2T3WEDYc/JaZ/dYQNZVk7pp91hA0zc0Gmz3WEDRGdV5bvdYQM/7ddlw91hAuFSCKXP3WECjrY55dfdYQIwGm8l391hAd1+nGXr3WEBhuLNpfPdYQEsRwLl+91hANWrMCYH3WEAfw9hZg/dYQAkc5amF91hA83Tx+Yf3WEDdzf1JivdYQMgmCpqM91hAsX8W6o73WECc2CI6kfdYQIUxL4qT91hAXAGnypX3WEAz0R4LmPdYQAmhlkua91hA33AOjJz3WEC2QIbMnvdYQI0Q/gyh91hAY+B1TaP3WEA5sO2NpfdYQA+AZc6n91hA5k/dDqr3WEC9H1VPrPdYQJPvzI+u91hAab9E0LD3WEBAj7wQs/dYQBZfNFG191hA7S6skbf3WECvdY/CufdYQHK8cvO791hANQNWJL73WED4STlVwPdYQLuQHIbC91hAftf/tsT3WEBBHuPnxvdYQANlxhjJ91hAxqupScv3WECJ8ox6zfdYQEs5cKvP91hADoBT3NH3WEDRxjYN1PdYQJQNGj7W91hAV1T9btj3WEAGEkyQ2vdYQLXPmrHc91hAZY3p0t73WEATSzj04PdYQMMIhxXj91hAcsbVNuX3WEAhhCRY5/dYQNFBc3np91hAf//Bmuv3WEAvvRC87fdYQN56X93v91hAjTiu/vH3WEA89vwf9PdYQOuzS0H291hAm3GaYvj3WEBJL+mD+vdYQPnsN6X891hAqKqGxv73WEBXaNXnAPhYQAYmJAkD+FhAteNyKgX4WEBlocFLB/hYQADWe10J+FhAnAo2bwv4WEA3P/CADfhYQNNzqpIP+FhAbqhkpBH4WEAK3R62E/hYQKUR2ccV+FhAQUaT2Rf4WEDdek3rGfhYQHivB/0b+FhAE+TBDh74WECvGHwgIPhYQEtNNjIi+FhA0/hbNCT4WEBbpIE2JvhYQONPpzgo+FhAa/vMOir4WEDypvI8LPhYQHpSGD8u+FhAAv49QTD4WECKqWNDMvhYQBJViUU0+FhAmgCvRzb4WEAirNRJOPhYQKpX+ks6+FhAHnqLPjz4WECSnBwxPvhYQAe/rSNA+FhAeuE+FkL4WEDvA9AIRPhYQGMmYftF+FhA10jy7Uf4WEBMa4PgSfhYQMCNFNNL+FhANLClxU34WECo0ja4T/hYQB31x6pR+FhAkRdZnVP4WEAFOuqPVfhYQHpce4JX+FhA7n4MdVn4WEBioZ1nW/hYQNfDLlpd+FhAN10rPV/4WECY9icgYfhYQPiPJANj+FhAWSkh5mT4WEC6wh3JZvhYQBpcGqxo+FhAe/UWj2r4WEDcjhNybPhYQDwoEFVu+FhAncEMOHD4WED9WgkbcvhYQF70Bf5z+FhAv40C4XX4WEAfJ//Dd/hYQIDA+6Z5+FhA4Vn4iXv4WEBB8/RsffhYQKKM8U9/+FhAAibuMoH4WEBjv+oVg/hYQMRY5/iE+FhAEWlPzIb4WEBeebefiPhYQKuJH3OK+FhA+JmHRoz4WEBFqu8ZjvhYQJG6V+2P+FhA38q/wJH4WEAs2yeUk/hYQHnrj2eV+FhAxvv3Opf4WEATDGAOmfhYQGAcyOGa+FhArSwwtZz4WED6PJiInvhYQEdNAFyg+FhAlF1oL6L4WEDhbdACpPhYQC5+ONal+FhAZwUMmqf4WEChjN9dqfhYQNoTsyGr+FhAE5uG5az4WEBNIlqprvhYQIapLW2w+FhAwDABMbL4WED5t9T0s/hYQDI/qLi1+FhAbMZ7fLf4WEClTU9AufhYQN/UIgS7+FhAF1z2x7z4WEBR48mLvvhYQItqnU/A+FhAxPFwE8L4WEDp76/Hw/hYQBDu7nvF+FhANewtMMf4WEBb6mzkyPhYQIDoq5jK+FhApubqTMz4WEDM5CkBzvhYQPLiaLXP+FhAGOGnadH4WEA93+Yd0/hYQGPdJdLU+FhAiNtkhtb4WECv2aM62PhYQMFOTt/Z+FhA0sP4g9v4WEDkOKMo3fhYQPetTc3e+FhACSP4ceD4WEAbmKIW4vhYQC0NTbvj+FhAP4L3X+X4WEBR96EE5/hYQGRsTKno+FhAduH2Ter4WECIVqHy6/hYQJrLS5ft+FhArED2O+/4WEC+taDg8PhYQNAqS4Xy+FhA4p/1KfT4WED0FKDO9fhYQAaKSnP3+FhAGP/0F/n4WEAqdJ+8+vhYQD3pSWH8+FhAT170Bf74WEBOSgqb//hYQEw2IDAB+VhASiI2xQL5WEBIDkxaBPlYQEf6Ye8F+VhARuZ3hAf5WEBE0o0ZCflYQEO+o64K+VhAQaq5Qwz5WEBAls/YDflYQD6C5W0P+VhAPG77AhH5WEA7WhGYEvlYQDpGJy0U+VhAODI9whX5WEA3HlNXF/lYQDUKaewY+VhAM/Z+gRr5WEAy4pQWHPlYQDDOqqsd+VhAL7rAQB/5WEAtptbVIPlYQCyS7Goi+VhAK34CACT5WEApahiVJflYQCdWLion+VhAJUJEvyj5WEAkLlpUKvlYQA+R29kr+VhA+vNcXy35WEDlVt7kLvlYQM+5X2ow+VhAuhzh7zH5WEClf2J1M/lYQJDi4/o0+VhAe0VlgDb5WEBlqOYFOPlYQFELaIs5+VhAO27pEDv5WEAm0WqWPPlYQBE07Bs++VhA/JZtoT/5WEDn+e4mQflYQNFccKxC+VhAvb/xMUT5WECnInO3RflYQJKF9DxH+VhAfOh1wkj5WEBoS/dHSvlYQFOueM1L+VhAKohlQ035WEABYlK5TvlYQNg7Py9Q+VhAsBUspVH5WECH7xgbU/lYQF7JBZFU+VhANaPyBlb5WEAMfd98V/lYQONWzPJY+VhAujC5aFr5WECSCqbeW/lYQGnkklRd+VhAQL5/yl75WEAXmGxAYPlYQO5xWbZh+VhAxktGLGP5WECKnJ6SZPlYQE3t9vhl+VhAET5PX2f5WEDUjqfFaPlYQJjf/ytq+VhAWzBYkmv5WEAfgbD4bPlYQOLRCF9u+VhApiJhxW/5WEBpc7krcflYQC3EEZJy+VhA8BRq+HP5WEC0ZcJedflYQHi2GsV2+VhAOwdzK3j5WED/V8uReflYQMKoI/h6+VhAhvl7Xnz5WEBKStTEfflYQA2bLCt/+VhA0euEkYD5WECBs0jogflYQDB7DD+D+VhA4ULQlYT5WECRCpTshflYQEDSV0OH+VhA8Zkbmoj5WECgYd/wiflYQFApo0eL+VhAAPFmnoz5WECwuCr1jflYQGCA7kuP+VhAEEiyopD5WEDAD3b5kflYQHDXOVCT+VhAIJ/9ppT5WEDQZsH9lflYQIAuhVSX+VhAMPZIq5j5WEDfvQwCmvlYQI+F0Fib+VhAQE2Ur5z5WEDvFFgGnvlYQIxTh02f+VhAKJK2lKD5WEDE0OXboflYQGEPFSOj+VhA/U1EaqT5WECZjHOxpflYQDbLovim+VhA0gnSP6j5WEBuSAGHqflYQAqHMM6q+VhApsVfFaz5WEBDBI9crflYQN9CvqOu+VhAe4Ht6q/5WEAYwBwysflYQLT+S3my+VhAUD17wLP5WEDte6oHtflYQIm62U62+VhAJfkIlrf5WEDCNzjduPlYQF52ZyS6+VhA+rSWa7v5WECX88WyvPlYQDMy9fm9+VhA0HAkQb/5WEBsr1OIwPlYQAjugs/B+VhApCyyFsP5WEBAa+FdxPlYQNypEKXF+VhAeeg/7Mb5WEAVJ28zyPlYQLJlnnrJ+VhAOhs5ssr5WEDD0NPpy/lYQEyGbiHN+VhA1TsJWc75WEBd8aOQz/lYQOamPsjQ+VhAb1zZ/9H5WED3EXQ30/lYQIDHDm/U+VhACH2pptX5WECRMkTe1vlYQBro3hXY+VhAop15Tdn5WEArUxSF2vlYQLQIr7zb+VhAPL5J9Nz5WEDFc+Qr3vlYQE0pf2Pf+VhA1t4Zm+D5WEBflLTS4flYQOhJTwrj+VhAcf/pQeT5WED5tIR55flYQIJqH7Hm+VhACyC66Of5WECT1VQg6flYQByL71fq+VhApUCKj+v5WEAt9iTH7PlYQLarv/7t+VhAPmFaNu/5WEDHFvVt8PlYQFDMj6Xx+VhAxfiVzfL5WEA6JZz18/lYQK9Roh31+VhAJH6oRfb5WECZqq5t9/lYQA7XtJX4+VhAgwO7vfn5WED4L8Hl+vlYQG1cxw38+VhA4ojNNf35WEBXtdNd/vlYQMzh2YX/+VhAQQ7grQD6WEC3OubVAfpYQCtn7P0C+lhAoJPyJQT6WEAVwPhNBfpYQIvs/nUG+lhA/xgFngf6WEB0RQvGCPpYQOpxEe4J+lhAX54XFgv6WEDTyh0+DPpYQEn3I2YN+lhAviMqjg76WEAzUDC2D/pYQKh8Nt4Q+lhAHak8BhL6WECS1UIuE/pYQAcCSVYU+lhAfC5PfhX6WEDxWlWmFvpYQGaHW84X+lhA27Nh9hj6WEBQ4GceGvpYQMUMbkYb+lhAJ7DfXhz6WECIU1F3HfpYQOn2wo8e+lhAS5o0qB/6WECsPabAIPpYQA7hF9kh+lhAb4SJ8SL6WEDRJ/sJJPpYQDLLbCIl+lhAlG7eOib6WED0EVBTJ/pYQFa1wWso+lhAt1gzhCn6WEAY/KScKvpYQHqfFrUr+lhA20KIzSz6WEA95vnlLfpYQJ6Ja/4u+lhAAC3dFjD6WEBh0E4vMfpYQMNzwEcy+lhAJBcyYDP6WECFuqN4NPpYQOddFZE1+lhASAGHqTb6WECqpPjBN/pYQAtIato4+lhAbevb8jn6WEDOjk0LO/pYQDAyvyM8+lhAkdUwPD36WEDyeKJUPvpYQFMcFG0/+lhAtL+FhUD6WEAWY/edQfpYQHcGabZC+lhAxSBGv0P6WEATOyPIRPpYQGFVANFF+lhAr2/d2Ub6WED8ibriR/pYQEqkl+tI+lhAmL509En6WEDm2FH9SvpYQDTzLgZM+lhAgg0MD036WEDPJ+kXTvpYQB1CxiBP+lhAalyjKVD6WEC4doAyUfpYQAaRXTtS+lhAVKs6RFP6WECixRdNVPpYQPDf9FVV+lhAPfrRXlb6WECLFK9nV/pYQNgujHBY+lhAJklpeVn6WEB1Y0aCWvpYQMJ9I4tb+lhAEJgAlFz6WEBest2cXfpYQKvMuqVe+lhA+eaXrl/6WEBHAXW3YPpYQJUbUsBh+lhA4zUvyWL6WEAwUAzSY/pYQH5q6dpk+lhAzITG42X6WEAZn6PsZvpYQGe5gPVn+lhAtdNd/mj6WEAD7joHavpYQFEIGBBr+lhAniL1GGz6WEDsPNIhbfpYQDpXrypu+lhAdOj3I2/6WECueUAdcPpYQOkKiRZx+lhAI5zRD3L6WEBdLRoJc/pYQJa+YgJ0+lhA0U+r+3T6WEAL4fP0dfpYQEVyPO52+lhAgAOF53f6WEC6lM3gePpYQPMlFtp5+lhALbde03r6WEBoSKfMe/pYQKLZ78V8+lhA3Go4v336WEAX/IC4fvpYQFCNybF/+lhAih4Sq4D6WEDFr1qkgfpYQP9Ao52C+lhAOdLrloP6WEBzYzSQhPpYQK30fImF+lhA54XFgob6WEAhFw58h/pYQFyoVnWI+lhAljmfbon6WEDQyudnivpYQApcMGGL+lhARO14Woz6WEB+fsFTjfpYQLgPCk2O+lhA86BSRo/6WEAtMps/kPpYQGfD4ziR+lhAoVQsMpL6WEDb5XQrk/pYQBV3vSSU+lhATwgGHpX6WECKmU4XlvpYQMQqlxCX+lhA/rvfCZj6WEA4TSgDmfpYQF9V3OyZ+lhAhV2Q1pr6WECrZUTAm/pYQNJt+Kmc+lhA+HWsk536WEAffmB9nvpYQEWGFGef+lhAbI7IUKD6WECSlnw6ofpYQLmeMCSi+lhA36bkDaP6WEAGr5j3o/pYQCy3TOGk+lhAU78Ay6X6WEB5x7S0pvpYQKDPaJ6n+lhAx9cciKj6WEDt39BxqfpYQBTohFuq+lhAOvA4Rav6WEBh+OwurPpYQIcAoRit+lhArghVAq76WEDUEAnsrvpYQPsYvdWv+lhAISFxv7D6WEBIKSWpsfpYQG4x2ZKy+lhAlTmNfLP6WEC7QUFmtPpYQOFJ9U+1+lhACFKpObb6WEAuWl0jt/pYQFViEQ24+lhAe2rF9rj6WECicnngufpYQMh6Lcq6+lhA74Lhs7v6WEAVi5WdvPpYQCkKtXe9+lhAO4nUUb76WEBOCPQrv/pYQGGHEwbA+lhAdAYz4MD6WECHhVK6wfpYQJoEcpTC+lhArIORbsP6WEDAArFIxPpYQNKB0CLF+lhA5QDw/MX6WED4fw/XxvpYQAv/LrHH+lhAHn5Oi8j6WEAx/W1lyfpYQEN8jT/K+lhAVvusGcv6WEBqeszzy/pYQHz5683M+lhAj3gLqM36WECh9yqCzvpYQLV2SlzP+lhAyPVpNtD6WEDadIkQ0fpYQO3zqOrR+lhAAHPIxNL6WEAT8uee0/pYQCZxB3nU+lhAOPAmU9X6WEBMb0Yt1vpYQF/uZQfX+lhAcW2F4df6WECE7KS72PpYQJdrxJXZ+lhAqurjb9r6WEC9aQNK2/pYQNDoIiTc+lhA4mdC/tz6WED25mHY3fpYQAhmgbLe+lhAG+WgjN/6WEAuZMBm4PpYQEHj30Dh+lhAVGL/GuL6WEBn4R714vpYQHlgPs/j+lhAjN9dqeT6WECfXn2D5fpYQLLdnF3m+lhAxVy8N+f6WEDY29sR6PpYQOta++vo+lhA/tkaxun6WEAQWTqg6vpYQCPYWXrr+lhAN1d5VOz6WEBJ1pgu7fpYQEjMI/nt+lhASMKuw+76WEBHuDmO7/pYQEauxFjw+lhARqRPI/H6WEBEmtrt8fpYQESQZbjy+lhAQ4bwgvP6WEBCfHtN9PpYQEFyBhj1+lhAQWiR4vX6WEBAXhyt9vpYQD9Up3f3+lhAPkoyQvj6WEA+QL0M+fpYQD02SNf5+lhAPCzTofr6WEA8Il5s+/pYQDsY6Tb8+lhAOg50Af36WEA5BP/L/fpYQDj6iZb++lhAN/AUYf/6WEA25p8rAPtYQDbcKvYA+1hANdK1wAH7WEA0yECLAvtYQDS+y1UD+1hAM7RWIAT7WEAyquHqBPtYQDKgbLUF+1hAMZb3fwb7WEAwjIJKB/tYQC+CDRUI+1hAL3iY3wj7WEAubiOqCftYQC1krnQK+1hALFo5Pwv7WEArUMQJDPtYQCpGT9QM+1hAKjzang37WEApMmVpDvtYQCgo8DMP+1hAJx57/g/7WEAnFAbJEPtYQCYKkZMR+1hAJQAcXhL7WEAl9qYoE/tYQCTsMfMT+1hAI+K8vRT7WEAi2EeIFftYQCHO0lIW+1hAIMRdHRf7WEAfuujnF/tYQB+wc7IY+1hAHqb+fBn7WEAdnIlHGvtYQAkJgAIb+1hA9HV2vRv7WEDg4mx4HPtYQMxPYzMd+1hAt7xZ7h37WECjKVCpHvtYQI+WRmQf+1hAegM9HyD7WEBmcDPaIPtYQFHdKZUh+1hAPUogUCL7WEAptxYLI/tYQBQkDcYj+1hAAJEDgST7WEDs/fk7JftYQNdq8PYl+1hAw9fmsSb7WECuRN1sJ/tYQJqx0yco+1hAhh7K4ij7WEBxi8CdKftYQF34tlgq+1hASWWtEyv7WEA00qPOK/tYQCA/moks+1hAC6yQRC37WED3GIf/LftYQOOFfbou+1hAzvJzdS/7WEC5X2owMPtYQKXMYOsw+1hAkDlXpjH7WEB8pk1hMvtYQGgTRBwz+1hAU4A61zP7WEA/7TCSNPtYQCtaJ001+1hAFscdCDb7WEACNBTDNvtYQO2gCn43+1hA2Q0BOTj7WEDFevfzOPtYQLDn7a45+1hAnFTkaTr7WECIwdokO/tYQHMu0d87+1hAX5vHmjz7WEBKCL5VPftYQDZ1tBA++1hAIuKqyz77WEANT6GGP/tYQPm7l0FA+1hA5SiO/ED7WEDQlYS3QftYQLwCe3JC+1hAp29xLUP7WECT3GfoQ/tYQH9JXqNE+1hAarZUXkX7WEBWI0sZRvtYQEKQQdRG+1hALf03j0f7WEAZai5KSPtYQPBNkPVI+1hAyTHyoEn7WECgFVRMSvtYQHj5tfdK+1hAUN0Xo0v7WEAowXlOTPtYQACl2/lM+1hA2Ig9pU37WECwbJ9QTvtYQIhQAfxO+1hAYDRjp0/7WEA4GMVSUPtYQBD8Jv5Q+1hA6N+IqVH7WEDAw+pUUvtYQJinTABT+1hAcIuuq1P7WEBIbxBXVPtYQCBTcgJV+1hA+DbUrVX7WEDQGjZZVvtYQKj+lwRX+1hAgOL5r1f7WEBYxltbWPtYQDCqvQZZ+1hACI4fsln7WEDgcYFdWvtYQLdV4whb+1hAjzlFtFv7WEBoHadfXPtYQD8BCQtd+1hAF+Vqtl37WEDwyMxhXvtYQMesLg1f+1hAn5CQuF/7WEB4dPJjYPtYQE9YVA9h+1hAJzy2umH7WED/HxhmYvtYQNcDehFj+1hAr+fbvGP7WECHyz1oZPtYQF+vnxNl+1hAN5MBv2X7WEAPd2NqZvtYQOdaxRVn+1hAvz4nwWf7WECXIolsaPtYQG8G6xdp+1hAR+pMw2n7WEAfzq5uavtYQPexEBpr+1hAzpVyxWv7WECnedRwbPtYQH9dNhxt+1hAVkGYx237WEAvJfpybvtYQAcJXB5v+1hA3uy9yW/7WEC30B91cPtYQI+0gSBx+1hAZpjjy3H7WEA/fEV3cvtYQBZgpyJz+1hA7kMJznP7WEDHJ2t5dPtYQJ4LzSR1+1hAdu8u0HX7WEBP05B7dvtYQCa38iZ3+1hA/ppU0nf7WEDWfrZ9ePtYQK5iGCl5+1hAhkZ61Hn7WEBeKtx/evtYQDYOPit7+1hADvKf1nv7WEDm1QGCfPtYQL65Yy19+1hAlp3F2H37WEBugSeEfvtYQEZliS9/+1hACsBWy3/7WEDPGiRngPtYQJN18QKB+1hAV9C+noH7WEAcK4w6gvtYQOCFWdaC+1hApOAmcoP7WEBoO/QNhPtYQC2WwamE+1hA8fCORYX7WEC1S1zhhftYQHqmKX2G+1hAPgH3GIf7WEACXMS0h/tYQMe2kVCI+1hAixFf7Ij7WEBPbCyIiftYQBTH+SOK+1hA2CHHv4r7WECcfJRbi/tYQGHXYfeL+1hAJTIvk4z7WEDqjPwujftYQK7nycqN+1hAckKXZo77WEA3nWQCj/tYQPv3MZ6P+1hAwFL/OZD7WECErczVkPtYQEgImnGR+1hADWNnDZL7WEDRvTSpkvtYQJUYAkWT+1hAWXPP4JP7WEAezpx8lPtYQOIoahiV+1hApoM3tJX7WEBr3gRQlvtYQC850uuW+1hA85Ofh5f7WEC47mwjmPtYQHxJOr+Y+1hAQKQHW5n7WEAF/9T2mftYQMlZopKa+1hAjbRvLpv7WEBSDz3Km/tYQBZqCmac+1hA2sTXAZ37WECeH6WdnftYQGN6cjme+1hAJ9U/1Z77WEDrLw1xn/tYQLCK2gyg+1hAdOWnqKD7WEA4QHVEoftYQP2aQuCh+1hAwfUPfKL7WECFUN0Xo/tYQEqrqrOj+1hADgZ4T6T7WEDTYEXrpPtYQJe7Eoel+1hAXBbgIqb7WEAgca2+pvtYQOTLelqn+1hAqSZI9qf7WEBtgRWSqPtYQDHc4i2p+1hA9jawyan7WEC6kX1lqvtYQH7sSgGr+1hAQ0cYnav7WEAHouU4rPtYQMv8stSs+1hAj1eAcK37WEBUsk0MrvtYQBgNG6iu+1hA3GfoQ6/7WEChwrXfr/tYQGUdg3uw+1hAKXhQF7H7WEDu0h2zsftYQLIt606y+1hAdoi46rL7WEA744WGs/tYQP89UyK0+1hAw5ggvrT7WECH8+1ZtftYQExOu/W1+1hAEKmIkbb7WEDUA1Ytt/tYQJleI8m3+1hAXbnwZLj7WEAhFL4AuftYQOZui5y5+1hAl0DEKLr7WEBIEv20uvtYQPjjNUG7+1hAqbVuzbv7WEBah6dZvPtYQAtZ4OW8+1hAuyoZcr37WEBr/FH+vftYQBzOioq++1hAzZ/DFr/7WEB9cfyiv/tYQC5DNS/A+1hA3xRuu8D7WECQ5qZHwftYQEC439PB+1hA8YkYYML7WECiW1HswvtYQFItinjD+1hAA//CBMT7WEC00PuQxPtYQGWiNB3F+1hAFXRtqcX7WEDGRaY1xvtYQHcX38HG+1hAKOkXTsf7WEDYulDax/tYQImMiWbI+1hAOl7C8sj7WEDqL/t+yftYQJsBNAvK+1hATNNsl8r7WED9pKUjy/tYQK123q/L+1hAXkgXPMz7WEAPGlDIzPtYQL/riFTN+1hAcL3B4M37WEAhj/pszvtYQNJgM/nO+1hAgjJshc/7WEAzBKUR0PtYQOTV3Z3Q+1hAlKcWKtH7WEBFeU+20ftYQPZKiELS+1hApxzBztL7WEBX7vla0/tYQAjAMufT+1hAuZFrc9T7WEBpY6T/1PtYQBo13YvV+1hAygYWGNb7WEB72E6k1vtYQCyqhzDX+1hA3HvAvNf7WECNTflI2PtYQD4fMtXY+1hA7vBqYdn7WECfwqPt2ftYQFCU3Hna+1hAAWYVBtv7WECxN06S2/tYQGIJhx7c+1hAE9u/qtz7WEDDrPg23ftYQHR+McPd+1hAJVBqT977WEDWIaPb3vtYQIbz22ff+1hAN8UU9N/7WEDolk2A4PtYQJhohgzh+1hASTq/mOH7WED6C/gk4vtYQKvdMLHi+1hAW69pPeP7WEAMgaLJ4/tYQL1S21Xk+1hAbiQU4uT7WEAe9kxu5ftYQM/Hhfrl+1hAgJm+hub7WEAwa/cS5/tYQOE8MJ/n+1hAkg5pK+j7WEBD4KG36PtYQPOx2kPp+1hApIMT0On7WEBVVUxc6vtYQAUnhejq+1hAtvi9dOv7WEBnyvYA7PtYQBicL43s+1hAyG1oGe37WEB5P6Gl7ftYQCoR2jHu+1hA2uISvu77WECKtEtK7/tYQDuGhNbv+1hA7Fe9YvD7WECdKfbu8PtYQE37Lnvx+1hA/sxnB/L7WECvnqCT8vtYQEznRBDz+1hA6S/pjPP7WECGeI0J9PtYQCPBMYb0+1hAwQnWAvX7WEBdUnp/9ftYQPuaHvz1+1hAmOPCePb7WEA0LGf19vtYQNJ0C3L3+1hAbr2v7vf7WEAMBlRr+PtYQKlO+Of4+1hARpecZPn7WEDj30Dh+ftYQIAo5V36+1hAHXGJ2vr7WEC6uS1X+/tYQFgC0tP7+1hA9Ep2UPz7WECRkxrN/PtYQC/cvkn9+1hAyyRjxv37WEBpbQdD/vtYQAa2q7/++1hAo/5PPP/7WEBAR/S4//tYQN2PmDUA/FhAetg8sgD8WEAXIeEuAfxYQLRphasB/FhAUbIpKAL8WEDu+s2kAvxYQItDciED/FhAKIwWngP8WEDG1LoaBPxYQGIdX5cE/FhAAGYDFAX8WECdrqeQBfxYQDr3Sw0G/FhA1z/wiQb8WEB0iJQGB/xYQBHROIMH/FhArhnd/wf8WEBLYoF8CPxYQOiqJfkI/FhAhfPJdQn8WEAiPG7yCfxYQL+EEm8K/FhAXc226wr8WED5FVtoC/xYQJde/+QL/FhANKejYQz8WEDQ70feDPxYQG447FoN/FhAC4GQ1w38WECoyTRUDvxYQEUS2dAO/FhA4lp9TQ/8WEB/oyHKD/xYQBzsxUYQ/FhAuTRqwxD8WEBWfQ5AEfxYQPTFsrwR/FhAkA5XORL8WEAtV/u1EvxYQMufnzIT/FhAZ+hDrxP8WEAFMegrFPxYQKJ5jKgU/FhAP8IwJRX8WEDcCtWhFfxYQHlTeR4W/FhAFpwdmxb8WECz5MEXF/xYQFEtZpQX/FhA7XUKERj8WECKvq6NGPxYQCcHUwoZ/FhAxE/3hhn8WEBimJsDGvxYQP7gP4Aa/FhAnCnk/Br8WEA5coh5G/xYQNa6LPYb/FhAcwPRchz8WEAQTHXvHPxYQK2UGWwd/FhASt296B38WEDnJWJlHvxYQIRuBuIe/FhAIbeqXh/8WEC//07bH/xYQFtI81cg/FhA+ZCX1CD8WECV2TtRIfxYQDIi4M0h/FhA0GqESiL8WEBssyjHIvxYQAr8zEMj/FhAp0RxwCP8WEBEjRU9JPxYQOHVubkk/FhAfh5eNiX8WEAbZwKzJfxYQLivpi8m/FhAVvhKrCb8WEDyQO8oJ/xYQJCJk6Un/FhALdI3Iij8WEDJGtyeKPxYQGdjgBsp/FhAA6wkmCn8WECh9MgUKvxYQD49bZEq/FhA24URDiv8WEB4zrWKK/xYQBUXWgcs/FhAsl/+gyz8WEBPqKIALfxYQOzwRn0t/FhAiTnr+S38WEAmgo92LvxYQMTKM/Mu/FhAYBPYby/8WED+W3zsL/xYQJukIGkw/FhAOO3E5TD8WEDVNWliMfxYQHF+Dd8x/FhAD8exWzL8WECsD1bYMvxYQElY+lQz/FhA5qCe0TP8WECD6UJONPxYQCAy58o0/FhAvXqLRzX8WEBbwy/ENfxYQPcL1EA2/FhAlVR4vTb8WEAynRw6N/xYQM7lwLY3/FhAbC5lMzj8WEAJdwmwOPxYQKa/rSw5/FhAQwhSqTn8WEDgUPYlOvxYQGoQBpM6/FhA888VADv8WEB8jyVtO/xYQAZPNdo7/FhAjw5FRzz8WEAYzlS0PPxYQKKNZCE9/FhAK010jj38WEC1DIT7PfxYQD/Mk2g+/FhAyIuj1T78WEBRS7NCP/xYQNsKw68//FhAZMrSHED8WEDtieKJQPxYQHdJ8vZA/FhAAAkCZEH8WECKyBHRQfxYQBOIIT5C/FhAnUcxq0L8WEAmB0EYQ/xYQK/GUIVD/FhAOYZg8kP8WEDCRXBfRPxYQEwFgMxE/FhA1sSPOUX8WEBfhJ+mRfxYQOhDrxNG/FhAcgO/gEb8WED7ws7tRvxYQISC3lpH/FhADkLux0f8WECXAf40SPxYQCHBDaJI/FhAq4AdD0n8WEA0QC18SfxYQL3/POlJ/FhARr9MVkr8WEDQflzDSvxYQFk+bDBL/FhA4v17nUv8WEBsvYsKTPxYQPZ8m3dM/FhAfzyr5Ez8WEAJ/LpRTfxYQJK7yr5N/FhAG3vaK078WEClOuqYTvxYQC76+QVP/FhAuLkJc0/8WEBBeRngT/xYQMs4KU1Q/FhAVPg4ulD8WEDet0gnUfxYQGd3WJRR/FhA8DZoAVL8WEB69nduUvxYQAO2h9tS/FhAjXWXSFP8WEAWNae1U/xYQKD0tiJU/FhAKbTGj1T8WECyc9b8VPxYQDwz5mlV/FhAxfL11lX8WEBOsgVEVvxYQNhxFbFW/FhAYjElHlf8WEDr8DSLV/xYQHWwRPhX/FhA/m9UZVj8WECHL2TSWPxYQBHvcz9Z/FhAmq6DrFn8WEAjbpMZWvxYQK0to4Za/FhAN+2y81r8WEDArMJgW/xYQEls0s1b/FhA0yviOlz8WEBc6/GnXPxYQOWqARVd/FhAb2oRgl38WED4KSHvXfxYQILpMFxe/FhADKlAyV78WECVaFA2X/xYQB4oYKNf/FhAqOdvEGD8WEAxp399YPxYQLpmj+pg/FhARCafV2H8WEDO5a7EYfxYQFelvjFi/FhA4WTOnmL8WEBqJN4LY/xYQPPj7Xhj/FhAfKP95WP8WEAGYw1TZPxYQI8iHcBk/FhAGeIsLWX8WECjoTyaZfxYQCxhTAdm/FhAtSBcdGb8WEA/4GvhZvxYQMife05n/FhAUV+Lu2f8WEDbHpsoaPxYQGTeqpVo/FhA7p26Amn8WEB4XcpvafxYQAEd2txp/FhAitzpSWr8WEAUnPm2avxYQJ1bCSRr/FhAJhsZkWv8WECv2ij+a/xYQDmaOGts/FhAw1lI2Gz8WEBMGVhFbfxYQNbYZ7Jt/FhAX5h3H278WEDoV4eMbvxYQHIXl/lu/FhA+9amZm/8WECElrbTb/xYQA5WxkBw/FhAmBXWrXD8WEAh1eUacfxYQKuU9Ydx/FhANFQF9XH8WEC9ExVicvxYQEfTJM9y/FhA0JI0PHP8WEBaUkSpc/xYQOMRVBZ0/FhAbdFjg3T8WED2kHPwdPxYQH9Qg111/FhACRCTynX8WECSz6I3dvxYQBuPsqR2/FhApU7CEXf8WEAvDtJ+d/xYQLjN4et3/FhAQo3xWHj8WEDLTAHGePxYQFQMETN5/FhA3ssgoHn8WEBnizANevxYQPBKQHp6/FhAegpQ53r8WEAEyl9Ue/xYQI2Jb8F7/FhAFkl/Lnz8WECgCI+bfPxYQCnIngh9/FhAsoeudX38WEA8R77iffxYQMUGzk9+/FhAT8bdvH78WEDZhe0pf/xYQGJF/ZZ//FhA6wQNBID8WEB1xBxxgPxYQP6DLN6A/FhAh0M8S4H8WEARA0y4gfxYQJrCWyWC/FhAEPnWgoL8WECGL1LggvxYQPxlzT2D/FhAcpxIm4P8WEDo0sP4g/xYQF0JP1aE/FhA0z+6s4T8WEBJdjURhfxYQL+ssG6F/FhANeMrzIX8WECqGacphvxYQCBQIoeG/FhAload5Ib8WEAMvRhCh/xYQILzk5+H/FhA+CkP/Yf8WEBtYIpaiPxYQOOWBbiI/FhAWc2AFYn8WEDPA/xyifxYQEU6d9CJ/FhAunDyLYr8WEAwp22LivxYQKbd6OiK/FhAHBRkRov8WECSSt+ji/xYQAeBWgGM/FhAfbfVXoz8WEDy7VC8jPxYQGgkzBmN/FhA3lpHd438WEBUkcLUjfxYQMrHPTKO/FhAP/64j478WEC1NDTtjvxYQCtrr0qP/FhAoaEqqI/8WEAX2KUFkPxYQIwOIWOQ/FhAAkWcwJD8WEB4excekfxYQO6xknuR/FhAZOgN2ZH8WEDZHok2kvxYQE9VBJSS/FhAxYt/8ZL8WEA7wvpOk/xYQLH4dayT/FhAJy/xCZT8WECcZWxnlPxYQBKc58SU/FhAiNJiIpX8WED+CN5/lfxYQHQ/Wd2V/FhA6XXUOpb8WEBfrE+YlvxYQNXiyvWW/FhASxlGU5f8WEDBT8Gwl/xYQDaGPA6Y/FhArLy3a5j8WEAi8zLJmPxYQJgpriaZ/FhADmAphJn8WECElqThmfxYQPnMHz+a/FhAbwObnJr8WEDlORb6mvxYQFtwkVeb/FhA0aYMtZv8WEBG3YcSnPxYQLwTA3Cc/FhAMkp+zZz8WECogPkqnfxYQB63dIid/FhAk+3v5Z38WEAJJGtDnvxYQH9a5qCe/FhA9ZBh/p78WEBrx9xbn/xYQOH9V7mf/FhAVjTTFqD8WEDMak50oPxYQEKhydGg/FhAuNdEL6H8WEAuDsCMofxYQKNEO+qh/FhAGXu2R6L8WECPsTGlovxYQAXorAKj/FhAex4oYKP8WEDwVKO9o/xYQGaLHhuk/FhA3MGZeKT8WEBS+BTWpPxYQMgukDOl/FhAPmULkaX8WECzm4bupfxYQCjSAUym/FhAngh9qab8WEAUP/gGp/xYQIp1c2Sn/FhAAKzuwaf8WEB14mkfqPxYQOsY5Xyo/FhAYU9g2qj8WEDXhds3qfxYQE28VpWp/FhAwvLR8qn8WEA4KU1QqvxYQK5fyK2q/FhAJJZDC6v8WECazL5oq/xYQBADOsar/FhAhTm1I6z8WED7bzCBrPxYQHGmq96s/FhA59wmPK38WEBdE6KZrfxYQNJJHfet/FhASICYVK78WEC+thOyrvxYQDTtjg+v/FhAqiMKba/8WEAgWoXKr/xYQJWQACiw/FhAC8d7hbD8WECB/fbisPxYQPczckCx/FhAbWrtnbH8WEDioGj7sfxYQFjX41iy/FhAzg1ftrL8WEBERNoTs/xYQLp6VXGz/FhAL7HQzrP8WECl50sstPxYQBsex4m0/FhAkVRC57T8WEAHi71EtfxYQHzBOKK1/FhA8vez/7X8WEBoLi9dtvxYQN5kqrq2/FhAVJslGLf8WEDK0aB1t/xYQD8IHNO3/FhAtT6XMLj8WEArdRKOuPxYQKGrjeu4/FhAF+IISbn8WECMGISmufxYQAJP/wO6/FhAeIV6Ybr8WEDuu/W+uvxYQGTycBy7/FhA2ijsebv8WEBPX2fXu/xYQMWV4jS8/FhAO8xdkrz8WECxAtnvvPxYQCc5VE29/FhAnG/Pqr38WEASpkoIvvxYQIjcxWW+/FhA/RJBw778WEBzSbwgv/xYQOl/N36//FhAXray27/8WEDU7C05wPxYQEojqZbA/FhAwFkk9MD8WEA2kJ9RwfxYQKzGGq/B/FhAIf2VDML8WECXMxFqwvxYQA1qjMfC/FhAg6AHJcP8WED51oKCw/xYQG4N/t/D/FhA5EN5PcT8WEBaevSaxPxYQNCwb/jE/FhARufqVcX8WEC7HWazxfxYQDFU4RDG/FhAp4pcbsb8WEAdwdfLxvxYQJP3UinH/FhACS7Ohsf8WEB+ZEnkx/xYQPSaxEHI/FhAatE/n8j8WEDgB7v8yPxYQFY+NlrJ/FhAy3Sxt8n8WEBBqywVyvxYQLfhp3LK/FhALRgj0Mr8WECjTp4ty/xYQBiFGYvL/FhAjruU6Mv8WEAE8g9GzPxYQHooi6PM/FhA8F4GAc38WEBmlYFezfxYQNvL/LvN/FhAUQJ4Gc78WEDHOPN2zvxYQD1vbtTO/FhAs6XpMc/8WEAo3GSPz/xYQJ4S4OzP/FhAFElbStD8WECKf9an0PxYQAC2UQXR/FhAdezMYtH8WEDrIkjA0fxYQGFZwx3S/FhA148+e9L8WEBNxrnY0vxYQMP8NDbT/FhAODOwk9P8WECuaSvx0/xYQCSgpk7U/FhAmtYhrNT8WEAQDZ0J1fxYQIVDGGfV/FhA+3mTxNX8WEBxsA4i1vxYQOfmiX/W/FhAXR0F3db8WEDSU4A61/xYQEiK+5fX/FhAvcB29df8WEAz9/FS2PxYQJak2KDY/FhA+FG/7tj8WEBa/6U82fxYQLysjIrZ/FhAH1pz2Nn8WECAB1om2vxYQOO0QHTa/FhARWInwtr8WECnDw4Q2/xYQAm99F3b/FhAbGrbq9v8WEDNF8L52/xYQDDFqEfc/FhAknKPldz8WED0H3bj3PxYQFbNXDHd/FhAuXpDf938WEAaKCrN3fxYQH3VEBve/FhA34L3aN78WEBBMN623vxYQKPdxATf/FhABourUt/8WEBnOJKg3/xYQMrleO7f/FhAK5NfPOD8WECOQEaK4PxYQPDtLNjg/FhAUpsTJuH8WEC0SPpz4fxYQBf24MHh/FhAeaPHD+L8WEDbUK5d4vxYQD3+lKvi/FhAn6t7+eL8WEACWWJH4/xYQGQGSZXj/FhAxrMv4+P8WEAoYRYx5PxYQIoO/X7k/FhA7LvjzOT8WEBPacoa5fxYQLEWsWjl/FhAE8SXtuX8WEB1cX4E5vxYQNgeZVLm/FhAOcxLoOb8WECceTLu5vxYQP4mGTzn/FhAYNT/ief8WEDCgebX5/xYQCQvzSXo/FhAhtyzc+j8WEDpiZrB6PxYQEo3gQ/p/FhAreRnXen8WEAPkk6r6fxYQHE/Nfnp/FhA0+wbR+r8WEA2mgKV6vxYQJdH6eLq/FhA+vTPMOv8WEBcorZ+6/xYQL5Pnczr/FhAIP2DGuz8WECDqmpo7PxYQORXUbbs/FhARwU4BO38WECpsh5S7fxYQAtgBaDt/FhAbQ3s7e38WEDQutI77vxYQDJouYnu/FhAlBWg1+78WED2woYl7/xYQFhwbXPv/FhAuh1Uwe/8WEAdyzoP8PxYQH94IV3w/FhA4SUIq/D8WEBD0+748PxYQKWA1Ubx/FhACC68lPH8WEBp26Li8fxYQMyIiTDy/FhALjZwfvL8WECQ41bM8vxYQPKQPRrz/FhAVT4kaPP8WEC26wq28/xYQBmZ8QP0/FhAe0bYUfT8WEDd876f9PxYQD+hpe30/FhAok6MO/X8WEAD/HKJ9fxYQGapWdf1/FhAyFZAJfb8WEAqBCdz9vxYQIyxDcH2/FhA7170Dvf8WEBQDNtc9/xYQLO5war3/FhAFWeo+Pf8WEB3FI9G+PxYQNnBdZT4/FhAO29c4vj8WECdHEMw+fxYQADKKX75/FhAYncQzPn8WEDEJPcZ+vxYQCbS3Wf6/FhAiH/Etfr8WEDrLKsD+/xYQE3akVH7/FhAr4d4n/v8WEARNV/t+/xYQHTiRTv8/FhA1Y8sifz8WEA4PRPX/PxYQJrq+ST9/FhA/Jfgcv38WEBeRcfA/fxYQMHyrQ7+/FhAIqCUXP78WECFTXuq/vxYQOf6Yfj+/FhASahIRv/8WECrVS+U//xYQA4DFuL//FhAb7D8LwD9WEDSXeN9AP1YQDQLyssA/VhAlriwGQH9WED4ZZdnAf1YQFoTfrUB/VhAvMBkAwL9WEAfbktRAv1YQIAbMp8C/VhA48gY7QL9WEBFdv86A/1YQKcj5ogD/VhACdHM1gP9WEBsfrMkBP1YQM4rmnIE/VhAMNmAwAT9WECShmcOBf1YQPQzTlwF/VhAVuE0qgX9WEC5jhv4Bf1YQBs8AkYG/VhAfenokwb9WEDfls/hBv1YQEFEti8H/VhApPGcfQf9WEAGn4PLB/1YQGhMahkI/VhAyvlQZwj9WEAspze1CP1YQI5UHgMJ/VhA8QEFUQn9WEBSr+ueCf1YQLVc0uwJ/VhAFwq5Ogr9WEB5t5+ICv1YQNtkhtYK/VhAPhJtJAv9WECfv1NyC/1YQAJtOsAL/VhAZBohDgz9WEDGxwdcDP1YQCh17qkM/VhAiyLV9wz9WEDsz7tFDf1YQE99opMN/VhAsSqJ4Q39WEAT2G8vDv1YQHWFVn0O/VhA2DI9yw79WEA54CMZD/1YQJyNCmcP/VhA/jrxtA/9WEBg6NcCEP1YQMKVvlAQ/VhAJUOlnhD9WECG8IvsEP1YQOmdcjoR/VhAS0tZiBH9WECt+D/WEf1YQA+mJiQS/VhAcVMNchL9WEDUAPS/Ev1YQDau2g0T/VhAmFvBWxP9WED6CKipE/1YQF22jvcT/VhAvmN1RRT9WEAhEVyTFP1YQIO+QuEU/VhA5WspLxX9WEBHGRB9Ff1YQKrG9soV/VhAC3TdGBb9WEBuIcRmFv1YQNDOqrQW/VhAMnyRAhf9WECUKXhQF/1YQPfWXp4X/VhAWIRF7Bf9WEC7MSw6GP1YQB3fEogY/VhAf4z51Rj9WEDhOeAjGf1YQETnxnEZ/VhApZStvxn9WEAIQpQNGv1YQGnvelsa/VhAzJxhqRr9WEAuSkj3Gv1YQJD3LkUb/VhA8qQVkxv9WEBVUvzgG/1YQLf/4i4c/VhAGa3JfBz9WEB7WrDKHP1YQN0Hlxgd/VhAQLV9Zh39WECiYmS0Hf1YQAQQSwIe/VhAZr0xUB79WEDIahieHv1YQCoY/+se/VhAjcXlOR/9WEDvcsyHH/1YQFEgs9Uf/VhAs82ZIyD9WEAWe4BxIP1YQHcoZ78g/VhA2tVNDSH9WEA8gzRbIf1YQJ4wG6kh/VhAAN4B9yH9WEBii+hEIv1YQMQ4z5Ii/VhAJ+a14CL9WECIk5wuI/1YQOtAg3wj/VhATe5pyiP9WECvm1AYJP1YQBFJN2Yk/VhAdPYdtCT9WEDVowQCJf1YQDhR608l/VhAmv7RnSX9WED8q7jrJf1YQF5Znzkm/VhAwQaGhyb9WEAitGzVJv1YQIVhUyMn/VhA5w46cSf9WEBJvCC/J/1YQKtpBw0o/VhADhfuWij9WEBwxNSoKP1YQNJxu/Yo/VhANB+iRCn9WECWzIiSKf1YQPh5b+Ap/VhAWydWLir9WEC91Dx8Kv1YQB+CI8oq/VhAgS8KGCv9WEDj3PBlK/1YQEaK17Mr/VhApze+ASz9WEAK5aRPLP1YQGySi50s/VhAzj9y6yz9WEAw7Vg5Lf1YQJOaP4ct/VhA4b6RxS39WEAv4+MDLv1YQH4HNkIu/VhAzSuIgC79WEAbUNq+Lv1YQGp0LP0u/VhAuJh+Oy/9WEAGvdB5L/1YQFbhIrgv/VhApAV19i/9WEDyKcc0MP1YQEFOGXMw/VhAj3JrsTD9WEDelr3vMP1YQC27Dy4x/VhAe99hbDH9WEDJA7SqMf1YQBgoBukx/VhAZ0xYJzL9WEC1cKplMv1YQASV/KMy/VhAUrlO4jL9WECh3aAgM/1YQO8B814z/VhAPiZFnTP9WECMSpfbM/1YQNpu6Rk0/VhAKpM7WDT9WEB4t42WNP1YQMbb39Q0/VhAFQAyEzX9WEBjJIRRNf1YQLJI1o81/VhAAW0ozjX9WEBPkXoMNv1YQJ21zEo2/VhA7NkeiTb9WEA7/nDHNv1YQIkiwwU3/VhA2EYVRDf9WEAma2eCN/1YQHWPucA3/VhAxLML/zf9WEAS2F09OP1YQGD8r3s4/VhAryACujj9WED+RFT4OP1YQExppjY5/VhAm434dDn9WEDpsUqzOf1YQDfWnPE5/VhAh/ruLzr9WEDVHkFuOv1YQCNDk6w6/VhAcmfl6jr9WEDAizcpO/1YQA+wiWc7/VhAXdTbpTv9WECs+C3kO/1YQPocgCI8/VhASUHSYDz9WECYZSSfPP1YQOaJdt08/VhANK7IGz39WECD0hpaPf1YQNL2bJg9/VhAIBu/1j39WEBvPxEVPv1YQL1jY1M+/VhADIi1kT79WEBbrAfQPv1YQKnQWQ4//VhA9/SrTD/9WEBGGf6KP/1YQJQ9UMk//VhA42GiB0D9WEAyhvRFQP1YQICqRoRA/VhAzs6YwkD9WEAd8+oAQf1YQGwXPT9B/VhAujuPfUH9WEAJYOG7Qf1YQFeEM/pB/VhApqiFOEL9WED1zNd2Qv1YQEPxKbVC/VhAkRV880L9WEDgOc4xQ/1YQC9eIHBD/VhAfYJyrkP9WEDLpsTsQ/1YQBrLFitE/VhAaO9oaUT9WEC3E7unRP1YQAY4DeZE/VhAVFxfJEX9WECigLFiRf1YQPGkA6FF/VhAQMlV30X9WECO7acdRv1YQN0R+ltG/VhAKzZMmkb9WEB6Wp7YRv1YQMl+8BZH/VhAF6NCVUf9WEBlx5STR/1YQLTr5tFH/VhAAxA5EEj9WEBRNItOSP1YQKBY3YxI/VhA7nwvy0j9WEA9oYEJSf1YQIzF00dJ/VhA2uklhkn9WEAoDnjESf1YQHcyygJK/VhAxlYcQUr9WEAUe25/Sv1YQGOfwL1K/VhAscMS/Er9WED/52Q6S/1YQE4Mt3hL/VhAnTAJt0v9WEDrVFv1S/1YQDl5rTNM/VhAiJ3/cUz9WEDXwVGwTP1YQCXmo+5M/VhAdAr2LE39WEDCLkhrTf1YQBFTmqlN/VhAYHfs5039WECumz4mTv1YQPy/kGRO/VhAS+Tiok79WECaCDXhTv1YQOgshx9P/VhAN1HZXU/9WECFdSucT/1YQNOZfdpP/VhAIr7PGFD9WEBx4iFXUP1YQL8GdJVQ/VhADivG01D9WEBcTxgSUf1YQKtzalBR/VhA+pe8jlH9WEBIvA7NUf1YQJbgYAtS/VhA5QSzSVL9WEA0KQWIUv1YQIJNV8ZS/VhA0XGpBFP9WEAflvtCU/1YQG66TYFT/VhAvd6fv1P9WEALA/L9U/1YQFknRDxU/VhAp0uWelT9WED3b+i4VP1YQEWUOvdU/VhAk7iMNVX9WEDi3N5zVf1YQDABMbJV/VhAfyWD8FX9WEDOSdUuVv1YQBxuJ21W/VhAapJ5q1b9WEC5tsvpVv1YQAjbHShX/VhAVv9vZlf9WEClI8KkV/1YQPNHFONX/VhAQmxmIVj9WECRkLhfWP1YQN+0Cp5Y/VhALdlc3Fj9WEB8/a4aWf1YQMshAVlZ/VhAGUZTl1n9WEBoaqXVWf1YQLaO9xNa/VhABLNJUlr9WEBU15uQWv1YQKL77c5a/VhA8B9ADVv9WEA/RJJLW/1YQI1o5Ilb/VhA3Iw2yFv9WEArsYgGXP1YQHnV2kRc/VhAx/ksg1z9WEAWHn/BXP1YQGVC0f9c/VhAs2YjPl39WEABi3V8Xf1YQFCvx7pd/VhAn9MZ+V39WEDt92s3Xv1YQDwcvnVe/VhAikAQtF79WEDYZGLyXv1YQCiJtDBf/VhAdq0Gb1/9WEDE0VitX/1YQBP2qutf/VhAYRr9KWD9WECwPk9oYP1YQP9ioaZg/VhATYfz5GD9WECbq0UjYf1YQOrPl2Fh/VhAOfTpn2H9WECHGDzeYf1YQNY8jhxi/VhAJGHgWmL9WEBzhTKZYv1YQMKphNdi/VhAEM7WFWP9WEBe8ihUY/1YQK0We5Jj/VhA/DrN0GP9WEBKXx8PZP1YQJmDcU1k/VhA56fDi2T9WEA2zBXKZP1YQITwZwhl/VhA0xS6RmX9WEAhOQyFZf1YQG9dXsNl/VhAvoGwAWb9WEANpgJAZv1YQFvKVH5m/VhAqu6mvGb9WED4Evn6Zv1YQEc3Szln/VhAlludd2f9WEDkf++1Z/1YQDKkQfRn/VhAgciTMmj9WEDQ7OVwaP1YQB4ROK9o/VhAbTWK7Wj9WEC7Wdwraf1YQAp+Lmpp/VhAWaKAqGn9WECnxtLmaf1YQPXqJCVq/VhARA93Y2r9WECSM8mhav1YQOFXG+Bq/VhAMHxtHmv9WEB+oL9ca/1YQMzEEZtr/VhAG+lj2Wv9WEBqDbYXbP1YQLgxCFZs/VhAB1ZalGz9WEBVeqzSbP1YQKSe/hBt/VhA8sJQT239WEBB56KNbf1YQI8L9ctt/VhA3i9HCm79WEAtVJlIbv1YQHt464Zu/VhAyZw9xW79WEAYwY8Db/1YQGfl4UFv/VhAtQk0gG/9WEAELoa+b/1YQFJS2Pxv/VhAoHYqO3D9WEDwmnx5cP1YQD6/zrdw/VhAjOMg9nD9WEDbB3M0cf1YQCksxXJx/VhAeFAXsXH9WEDHdGnvcf1YQBWZuy1y/VhAY70NbHL9WECy4V+qcv1YQAEGsuhy/VhATyoEJ3P9WECeTlZlc/1YQOxyqKNz/VhAO5f64XP9WECKu0wgdP1YQNjfnl50/VhAJgTxnHT9WEB1KEPbdP1YQMRMlRl1/VhAEnHnV3X9WEBglTmWdf1YQK+5i9R1/VhA/d3dEnb9WEBMAjBRdv1YQJsmgo92/VhA6UrUzXb9WEA3byYMd/1YQIaTeEp3/VhA1bfKiHf9WEAj3BzHd/1YQHIAbwV4/VhAwCTBQ3j9WEAPSROCeP1YQF5tZcB4/VhArJG3/nj9WED6tQk9ef1YQEnaW3t5/VhAmP6tuXn9WEDmIgD4ef1YQDVHUjZ6/VhAg2ukdHr9WEDRj/ayev1YQCG0SPF6/VhAb9iaL3v9WEC9/Oxte/1YQAwhP6x7/VhAWkWR6nv9WECpaeMofP1YQPiNNWd8/VhARrKHpXz9WECU1tnjfP1YQOP6KyJ9/VhAMh9+YH39WECAQ9Ceff1YQM5nIt19/VhAHYx0G379WEBssMZZfv1YQLrUGJh+/VhACflq1n79WEBXHb0Uf/1YQKZBD1N//VhA9WVhkX/9WEBDirPPf/1YQJGuBQ6A/VhA4NJXTID9WEAu96mKgP1YQH0b/MiA/VhAzD9OB4H9WEAaZKBFgf1YQGiI8oOB/VhAt6xEwoH9WEAG0ZYAgv1YQFT16D6C/VhAoxk7fYL9WEDxPY27gv1YQEBi3/mC/VhAj4YxOIP9WEDdqoN2g/1YQCvP1bSD/VhAevMn84P9WEDJF3oxhP1YQBc8zG+E/VhAZmAeroT9WEC0hHDshP1YQAKpwiqF/VhAUs0UaYX9WECg8Wanhf1YQO4VueWF/VhAPDoLJIb9WECLXl1ihv1YQNqCr6CG/VhAKKcB34b9WEB3y1Mdh/1YQMXvpVuH/VhAFBT4mYf9WEBjOErYh/1YQLFcnBaI/VhA/4DuVIj9WEBOpUCTiP1YQJ3JktGI/VhA6+3kD4n9WEA6EjdOif1YQIg2iYyJ/VhA11rbyon9WEAmfy0Jiv1YQHSjf0eK/VhAwsfRhYr9WEAR7CPEiv1YQGAQdgKL/VhArjTIQIv9WED9WBp/i/1YQEt9bL2L/VhAmaG++4v9WEDoxRA6jP1YQDfqYniM/VhAhQ61toz9WEDUMgf1jP1YQCJXWTON/VhAcXurcY39WEDAn/2vjf1YQA7ET+6N/VhAXOihLI79WECrDPRqjv1YQPowRqmO/VhASFWY5479WECWeeolj/1YQOWdPGSP/VhANMKOoo/9WECC5uDgj/1YQNEKMx+Q/VhAHy+FXZD9WEBtU9ebkP1YQLx3KdqQ/VhAC5x7GJH9WEBZwM1Wkf1YQKjkH5WR/VhA9ghy05H9WEBFLcQRkv1YQJRRFlCS/VhA4nVojpL9WEAwmrrMkv1YQH++DAuT/VhAzuJeSZP9WEAcB7GHk/1YQGsrA8aT/VhAuU9VBJT9WEAIdKdClP1YQFeY+YCU/VhApbxLv5T9WEDz4J39lP1YQEIF8DuV/VhAkSlCepX9WEDfTZS4lf1YQC5y5vaV/VhAfJY4NZb9WEDKuopzlv1YQBnf3LGW/VhAaAMv8Jb9WEC2J4Eul/1YQARM02yX/VhAU3Alq5f9WECilHfpl/1YQPC4ySeY/VhAP90bZpj9WECNAW6kmP1YQNwlwOKY/VhAK0oSIZn9WEB5bmRfmf1YQMeStp2Z/VhAFrcI3Jn9WEBl21oamv1YQLP/rFia/VhAAiT/lpr9WEBQSFHVmv1YQIvjDgSb/VhAxn7MMpv9WEABGophm/1YQDy1R5Cb/VhAd1AFv5v9WECy68Ltm/1YQO2GgByc/VhAJyI+S5z9WEBivft5nP1YQJ1Yuaic/VhA2PN215z9WEATjzQGnf1YQE4q8jSd/VhAicWvY539WEDEYG2Snf1YQP/7KsGd/VhAOpfo7539WEB0MqYenv1YQK/NY02e/VhA6mghfJ79WEAlBN+qnv1YQGCfnNme/VhAmzpaCJ/9WEDW1Rc3n/1YQBFx1WWf/VhATAyTlJ/9WECHp1DDn/1YQMJCDvKf/VhA/N3LIKD9WEA3eYlPoP1YQHIUR36g/VhAra8EraD9WEDoSsLboP1YQCPmfwqh/VhAXoE9OaH9WECZHPtnof1YQNS3uJah/VhAD1N2xaH9WEBK7jP0of1YQISJ8SKi/VhAvySvUaL9WED6v2yAov1YQDVbKq+i/VhAcPbn3aL9WECrkaUMo/1YQOYsYzuj/VhAIcggaqP9WEBcY96Yo/1YQJf+m8ej/VhA0plZ9qP9WEAMNRclpP1YQEfQ1FOk/VhAgmuSgqT9WEC9BlCxpP1YQPihDeCk/VhAMz3LDqX9WEBu2Ig9pf1YQKlzRmyl/VhA5A4Em6X9WEAfqsHJpf1YQFlFf/il/VhAlOA8J6b9WEDPe/pVpv1YQAoXuISm/VhARbJ1s6b9WECATTPipv1YQLro8BCn/VhA9YOuP6f9WEAwH2xup/1YQGu6KZ2n/VhAplXny6f9WEDh8KT6p/1YQByMYimo/VhAVicgWKj9WECRwt2GqP1YQMxdm7Wo/VhAB/lY5Kj9WEBClBYTqf1YQH0v1EGp/VhAuMqRcKn9WEDzZU+fqf1YQC4BDc6p/VhAaZzK/Kn9WECkN4grqv1YQN7SRVqq/VhAGW4Diar9WEBUCcG3qv1YQI+kfuaq/VhAyj88Fav9WEAF2/lDq/1YQEB2t3Kr/VhAexF1oav9WEC2rDLQq/1YQPFH8P6r/VhALOOtLaz9WEBmfmtcrP1YQKEZKYus/VhA3LTmuaz9WEAXUKTorP1YQFLrYRet/VhAjYYfRq39WEDIId10rf1YQAO9mqOt/VhAPlhY0q39WEB58xUBrv1YQLOO0y+u/VhA7imRXq79WEApxU6Nrv1YQGRgDLyu/VhAn/vJ6q79WEDalocZr/1YQBUyRUiv/VhAUM0Cd6/9WECLaMClr/1YQMYDftSv/VhAAZ87A7D9WEA7OvkxsP1YQHbVtmCw/VhAsXB0j7D9WEDsCzK+sP1YQCen7+yw/VhAYkKtG7H9WECd3WpKsf1YQNh4KHmx/VhAExTmp7H9WEBOr6PWsf1YQIhKYQWy/VhAw+UeNLL9WED+gNxisv1YQDkcmpGy/VhAdLdXwLL9WECvUhXvsv1YQOrt0h2z/VhAJYmQTLP9WEBgJE57s/1YQJu/C6qz/VhA1lrJ2LP9WEAQ9oYHtP1YQEuRRDa0/VhAhiwCZbT9WEDBx7+TtP1YQPxifcK0/VhAN/468bT9WEBymfgftf1YQK00tk61/VhA6M9zfbX9WEAjazGstf1YQF4G79q1/VhAmKGsCbb9WEDTPGo4tv1YQA7YJ2e2/VhASXPllbb9WECEDqPEtv1YQL+pYPO2/VhA+kQeIrf9WEA14NtQt/1YQHB7mX+3/VhAqxZXrrf9WEDmsRTdt/1YQCBN0gu4/VhAW+iPOrj9WECWg01puP1YQNEeC5i4/VhADLrIxrj9WEBHVYb1uP1YQILwQyS5/VhAvYsBU7n9WED4Jr+Buf1YQDPCfLC5/VhAbV0637n9WECo+PcNuv1YQOOTtTy6/VhAHi9za7r9WEBZyjCauv1YQJRl7si6/VhAzwCs97r9WEAKnGkmu/1YQEU3J1W7/VhAgNLkg7v9WEC7baKyu/1YQPUIYOG7/VhAMKQdELz9WEBrP9s+vP1YQKbamG28/VhA4XVWnLz9WEAcERTLvP1YQFes0fm8/VhAkkePKL39WEDN4kxXvf1YQAh+Coa9/VhAQhnItL39WEB9tIXjvf1YQLhPQxK+/VhA8+oAQb79WEAuhr5vvv1YQGkhfJ6+/VhApLw5zb79WEDfV/f7vv1YQBrztCq//VhAVY5yWb/9WECQKTCIv/1YQMrE7ba//VhABWCr5b/9WEBA+2gUwP1YQHqWJkPA/VhAtTHkccD9WEDwzKGgwP1YQCtoX8/A/VhAZgMd/sD9WEChntoswf1YQNw5mFvB/VhAF9VVisH9WEBScBO5wf1YQI0L0efB/VhAx6aOFsL9WEACQkxFwv1YQD3dCXTC/VhAeHjHosL9WECzE4XRwv1YQO6uQgDD/VhAKUoAL8P9WEBk5b1dw/1YQJ+Ae4zD/VhA2hs5u8P9WEAVt/bpw/1YQE9StBjE/VhAiu1xR8T9WEDFiC92xP1YQAAk7aTE/VhAO7+q08T9WEB2WmgCxf1YQLH1JTHF/VhA7JDjX8X9WEAnLKGOxf1YQGLHXr3F/VhAnGIc7MX9WEDX/dkaxv1YQBKZl0nG/VhATTRVeMb9WECIzxKnxv1YQMNq0NXG/VhA/gWOBMf9WEA5oUszx/1YQHQ8CWLH/VhAr9fGkMf9WEDqcoS/x/1YQCQOQu7H/VhAX6n/HMj9WECaRL1LyP1YQNXfenrI/VhAEHs4qcj9WEBLFvbXyP1YQIaxswbJ/VhAwUxxNcn9WED85y5kyf1YQDeD7JLJ/VhAch6qwcn9WECsuWfwyf1YQOdUJR/K/VhAIvDiTcr9WEBdi6B8yv1YQJgmXqvK/VhA08Eb2sr9WEAOXdkIy/1YQEn4ljfL/VhAhJNUZsv9WEC/LhKVy/1YQPrJz8PL/VhANGWN8sv9WEBvAEshzP1YQKqbCFDM/VhA5TbGfsz9WEAg0oOtzP1YQFttQdzM/VhAlgj/Cs39WEDRo7w5zf1YQAw/emjN/VhAR9o3l839WECBdfXFzf1YQLwQs/TN/VhA96twI879WEAyRy5Szv1YQG3i64DO/VhAqH2pr879WEDjGGfezv1YQB60JA3P/VhAWU/iO8/9WECU6p9qz/1YQM+FXZnP/VhACSEbyM/9WEBEvNj2z/1YQH9XliXQ/VhAuvJTVND9WED1jRGD0P1YQDApz7HQ/VhAa8SM4ND9WECmX0oP0f1YQOH6Bz7R/VhAHJbFbNH9WEBWMYOb0f1YQJHMQMrR/VhAzGf++NH9WEAHA7wn0v1YQEKeeVbS/VhAfTk3hdL9WEC41PSz0v1YQPNvsuLS/VhALgtwEdP9WEBppi1A0/1YQKRB627T/VhA3tyondP9WEAZeGbM0/1YQFQTJPvT/VhAj67hKdT9WEDKSZ9Y1P1YQAXlXIfU/VhAQIAattT9WEB7G9jk1P1YQLa2lRPV/VhA8VFTQtX9WEAs7RBx1f1YQGaIzp/V/VhAoSOMztX9WEDcvkn91f1YQBdaByzW/VhAUvXEWtb9WECNkIKJ1v1YQMgrQLjW/VhAA8f95tb9WEA+YrsV1/1YQHn9eETX/VhAtJg2c9f9WEDuM/Sh1/1YQCnPsdDX/VhAZGpv/9f9WECfBS0u2P1YQNqg6lzY/VhAFTyoi9j9WEBQ12W62P1YQIpyI+nY/VhAxQ3hF9n9WEAAqZ5G2f1YQDtEXHXZ/VhAdt8ZpNn9WECwetfS2f1YQOsVlQHa/VhAJrFSMNr9WEBhTBBf2v1YQJznzY3a/VhA14KLvNr9WEASHknr2v1YQE25Bhrb/VhAiFTESNv9WEDD74F32/1YQP6KP6bb/VhAOCb91Nv9WEBzwboD3P1YQK5ceDLc/VhA6fc1Ydz9WEAkk/OP3P1YQF8usb7c/VhAmslu7dz9WEDVZCwc3f1YQBAA6krd/VhAS5uned39WECGNmWo3f1YQMDRItfd/VhA+2zgBd79WEA2CJ403v1YQHGjW2Pe/VhArD4Zkt79WEDn2dbA3v1YQCJ1lO/e/VhAXRBSHt/9WECYqw9N3/1YQNNGzXvf/VhADuKKqt/9WEBIfUjZ3/1YQIMYBgjg/VhAvrPDNuD9WED5ToFl4P1YQDTqPpTg/VhAb4X8wuD9WECqILrx4P1YQOW7dyDh/VhAIFc1T+H9WEBb8vJ94f1YQJWNsKzh/VhA0Chu2+H9WEALxCsK4v1YQEZf6Tji/VhAgfqmZ+L9WEC8lWSW4v1YQPcwIsXi/VhAMszf8+L9WEBtZ50i4/1YQKgCW1Hj/VhA450YgOP9WEAdOdau4/1YQFjUk93j/VhAk29RDOT9WEDOCg875P1YQAmmzGnk/VhAREGKmOT9WEB/3EfH5P1YQLp3Bfbk/VhA9RLDJOX9WEAwroBT5f1YQGpJPoLl/VhApeT7sOX9WEDgf7nf5f1YQBsbdw7m/VhAVrY0Peb9WECRUfJr5v1YQMzsr5rm/VhAB4htyeb9WEBCIyv45v1YQH2+6Cbn/VhAuFmmVef9WEDy9GOE5/1YQC2QIbPn/VhAaCvf4ef9WECjxpwQ6P1YQN5hWj/o/VhAGf0Xbuj9WEBUmNWc6P1YQI8zk8vo/VhAys5Q+uj9WEAFag4p6f1YQEAFzFfp/VhAeqCJhun9WEC1O0e16f1YQPDWBOTp/VhAK3LCEur9WEBmDYBB6v1YQKGoPXDq/VhA3EP7nur9WEAX37jN6v1YQFJ6dvzq/VhAjRU0K+v9WEDIsPFZ6/1YQAJMr4jr/VhAPedst+v9WEB4girm6/1YQLMd6BTs/VhA7rilQ+z9WEApVGNy7P1YQGTvIKHs/VhAn4rez+z9WEDaJZz+7P1YQBXBWS3t/VhAT1wXXO39WECK99SK7f1YQMWSkrnt/VhAAC5Q6O39WEA7yQ0X7v1YQHZky0Xu/VhAsf+IdO79WEDsmkaj7v1YQCc2BNLu/VhAYtHBAO/9WECdbH8v7/1YQNcHPV7v/VhAEqP6jO/9WEBNPri77/1YQIjZderv/VhAw3QzGfD9WED+D/FH8P1YQDmrrnbw/VhAdEZspfD9WECv4SnU8P1YQOp85wLx/VhAJBilMfH9WEBfs2Jg8f1YQJpOII/x/VhA1OndvfH9WEAPhZvs8f1YQEogWRvy/VhAhbsWSvL9WEDAVtR48v1YQPvxkafy/VhANo1P1vL9WEBxKA0F8/1YQKzDyjPz/VhA516IYvP9WEAi+kWR8/1YQFyVA8Dz/VhAlzDB7vP9WEDSy34d9P1YQA1nPEz0/VhASAL6evT9WECDnbep9P1YQL44ddj0/VhA+dMyB/X9WEA0b/A19f1YQG8KrmT1/VhAqaVrk/X9WEDkQCnC9f1YQB/c5vD1/VhAWnekH/b9WECVEmJO9v1YQNCtH332/VhAC0ndq/b9WEBG5Jra9v1YQIF/WAn3/VhAvBoWOPf9WED3tdNm9/1YQDFRkZX3/VhAbOxOxPf9WECnhwzz9/1YQOIiyiH4/VhAHb6HUPj9WEBYWUV/+P1YQJP0Aq74/VhAzo/A3Pj9WEAJK34L+f1YQETGOzr5/VhAfmH5aPn9WEC5/LaX+f1YQPSXdMb5/VhALzMy9fn9WEBqzu8j+v1YQKVprVL6/VhA4ARrgfr9WEAboCiw+v1YQFY75t76/VhAkdajDfv9WEDMcWE8+/1YQAYNH2v7/VhAQajcmfv9WEB8Q5rI+/1YQLfeV/f7/VhA8nkVJvz9WEAtFdNU/P1YQGiwkIP8/VhAo0tOsvz9WEDe5gvh/P1YQBmCyQ/9/VhAVB2HPv39WECOuERt/f1YQMlTApz9/VhABO+/yv39WEA/in35/f1YQHolOyj+/VhAtcD4Vv79WEDwW7aF/v1YQCv3c7T+/VhAZpIx4/79WEChLe8R//1YQNzIrED//VhAFmRqb//9WEBR/yee//1YQIya5cz//VhAxzWj+//9WEAC0WAqAP5YQD1sHlkA/lhAeAfchwD+WECzopm2AP5YQO49V+UA/lhAKdkUFAH+WEBjdNJCAf5YQJ4PkHEB/lhA2apNoAH+WEAURgvPAf5YQE/hyP0B/lhAinyGLAL+WEDFF0RbAv5YQACzAYoC/lhAO06/uAL+WEB26XznAv5YQLGEOhYD/lhA6x/4RAP+WEAmu7VzA/5YQGFWc6ID/lhAnPEw0QP+WEDXjO7/A/5YQBIorC4E/lhATcNpXQT+WECIXieMBP5YQMP55LoE/lhA/pSi6QT+WEA4MGAYBf5YQHPLHUcF/lhArmbbdQX+WEDpAZmkBf5YQCSdVtMF/lhAXzgUAgb+WECa09EwBv5YQNVuj18G/lhAEApNjgb+WEBLpQq9Bv5YQIZAyOsG/lhAwNuFGgf+WED7dkNJB/5YQDYSAXgH/lhAca2+pgf+WECsSHzVB/5YQOfjOQQI/lhAIn/3Mgj+WEBdGrVhCP5YQJi1cpAI/lhA01Awvwj+WEAO7O3tCP5YQEiHqxwJ/lhAgyJpSwn+WEC+vSZ6Cf5YQPlY5KgJ/lhANPSh1wn+WEBvj18GCv5YQKoqHTUK/lhA5cXaYwr+WEAfYZiSCv5YQFr8VcEK/lhAlZcT8Ar+WEDQMtEeC/5YQAvOjk0L/lhARWlMfAv+WECABAqrC/5YQLufx9kL/lhA9jqFCAz+WEAx1kI3DP5YQGxxAGYM/lhApwy+lAz+WEDip3vDDP5YQB1DOfIM/lhAWN72IA3+WECSebRPDf5YQM0Ucn4N/lhACLAvrQ3+WEBDS+3bDf5YQH7mqgoO/lhAuYFoOQ7+WED0HCZoDv5YQC+445YO/lhAVsoMtg7+WEB+3DXVDv5YQKXuXvQO/lhAzACIEw/+WED0ErEyD/5YQBol2lEP/lhAQjcDcQ/+WEBqSSyQD/5YQJBbVa8P/lhAuG1+zg/+WEDff6ftD/5YQAaS0AwQ/lhALqT5KxD+WEBVtiJLEP5YQHzIS2oQ/lhAo9p0iRD+WEDL7J2oEP5YQPL+xscQ/lhAGRHw5hD+WEBBIxkGEf5YQGg1QiUR/lhAj0drRBH+WEC3WZRjEf5YQN1rvYIR/lhABX7moRH+WEAskA/BEf5YQFOiOOAR/lhAe7Rh/xH+WECixooeEv5YQMnYsz0S/lhA8OrcXBL+WEAY/QV8Ev5YQD8PL5sS/lhAZiFYuhL+WECOM4HZEv5YQLVFqvgS/lhA3FfTFxP+WEADavw2E/5YQCp8JVYT/lhAUo5OdRP+WEB5oHeUE/5YQKCyoLMT/lhAyMTJ0hP+WEDu1vLxE/5YQBbpGxEU/lhAPvtEMBT+WEBkDW5PFP5YQIwfl24U/lhAszHAjRT+WEDaQ+msFP5YQAJWEswU/lhAKWg76xT+WEBQemQKFf5YQHeMjSkV/lhAn562SBX+WEDGsN9nFf5YQO3CCIcV/lhAFdUxphX+WEA851rFFf5YQGP5g+QV/lhAiwutAxb+WECxHdYiFv5YQNkv/0EW/lhAAEIoYRb+WEAnVFGAFv5YQE9mep8W/lhAdnijvhb+WECdiszdFv5YQMWc9fwW/lhA7K4eHBf+WEATwUc7F/5YQDrTcFoX/lhAYuWZeRf+WECJ98KYF/5YQLAJ7LcX/lhA2BsV1xf+WED+LT72F/5YQCZAZxUY/lhATlKQNBj+WEB0ZLlTGP5YQJx24nIY/lhAw4gLkhj+WEDqmjSxGP5YQBKtXdAY/lhAOb+G7xj+WEBg0a8OGf5YQIfj2C0Z/lhAr/UBTRn+WEDWBytsGf5YQP0ZVIsZ/lhAJSx9qhn+WEBMPqbJGf5YQHNQz+gZ/lhAm2L4Bxr+WEDBdCEnGv5YQOmGSkYa/lhAEJlzZRr+WEA3q5yEGv5YQF+9xaMa/lhAhs/uwhr+WECt4RfiGv5YQNTzQAEb/lhA/AVqIBv+WEAjGJM/G/5YQEoqvF4b/lhAcTzlfRv+WECZTg6dG/5YQMBgN7wb/lhA53Jg2xv+WEAOhYn6G/5YQDaXshkc/lhAXanbOBz+WECEuwRYHP5YQKzNLXcc/lhA0t9Wlhz+WED68X+1HP5YQCIEqdQc/lhASBbS8xz+WEBwKPsSHf5YQJc6JDId/lhAvkxNUR3+WEDmXnZwHf5YQA1xn48d/lhANIPIrh3+WEBblfHNHf5YQIOnGu0d/lhAqrlDDB7+WEDRy2wrHv5YQPndlUoe/lhAIPC+aR7+WEBHAuiIHv5YQG8UEage/lhAlSY6xx7+WEC9OGPmHv5YQORKjAUf/lhAC121JB/+WEAzb95DH/5YQFqBB2Mf/lhAgZMwgh/+WECopVmhH/5YQNC3gsAf/lhA98mr3x/+WEAe3NT+H/5YQEbu/R0g/lhAbQAnPSD+WECUElBcIP5YQLwkeXsg/lhA4jaimiD+WEAKScu5IP5YQDFb9Ngg/lhAWG0d+CD+WECAf0YXIf5YQKeRbzYh/lhAzqOYVSH+WED2tcF0If5YQB3I6pMh/lhARNoTsyH+WEBr7DzSIf5YQJP+ZfEh/lhAuhCPECL+WEDhIrgvIv5YQAk14U4i/lhAL0cKbiL+WEBXWTONIv5YQH9rXKwi/lhApX2FyyL+WEDNj67qIv5YQPSh1wkj/lhAG7QAKSP+WEBDxilII/5YQGrYUmcj/lhAkep7hiP+WEC4/KSlI/5YQN8OzsQj/lhAByH34yP+WEAuMyADJP5YQFVFSSIk/lhAfVdyQST+WECkaZtgJP5YQMt7xH8k/lhA8o3tniT+WEAaoBa+JP5YQEGyP90k/lhAaMRo/CT+WECQ1pEbJf5YQLboujol/lhA3vrjWSX+WEAGDQ15Jf5YQCwfNpgl/lhAVDFftyX+WEB7Q4jWJf5YQKJVsfUl/lhAymfaFCb+WEDxeQM0Jv5YQBiMLFMm/lhAP55Vcib+WEBnsH6RJv5YQI7Cp7Am/lhAtdTQzyb+WEDd5vnuJv5YQAT5Ig4n/lhAKwtMLSf+WEBTHXVMJ/5YQHkvnmsn/lhAoUHHiif+WEDIU/CpJ/5YQO9lGckn/lhAF3hC6Cf+WEA+imsHKP5YQGWclCYo/lhAjK69RSj+WEC0wOZkKP5YQNvSD4Qo/lhAAuU4oyj+WEAq92HCKP5YQFEJi+Eo/lhAeBu0ACn+WECgLd0fKf5YQMY/Bj8p/lhA7lEvXin+WEAVZFh9Kf5YQDx2gZwp/lhAZIiquyn+WECLmtPaKf5YQLKs/Pkp/lhA2r4lGSr+WEAB0U44Kv5YQCjjd1cq/lhAT/Wgdir+WEB3B8qVKv5YQJ4Z87Qq/lhAxSsc1Cr+WEDtPUXzKv5YQBNQbhIr/lhAO2KXMSv+WEBidMBQK/5YQImG6W8r/lhAsZgSjyv+WEDYqjuuK/5YQP+8ZM0r/lhAJ8+N7Cv+WEBN4bYLLP5YQHXz3yos/lhAnAUJSiz+WEDDFzJpLP5YQOspW4gs/lhAEjyEpyz+WEA5Tq3GLP5YQGBg1uUs/lhAiHL/BC3+WECvhCgkLf5YQNaWUUMt/lhA/qh6Yi3+WEAlu6OBLf5YQEzNzKAt/lhAdN/1vy3+WECa8R7fLf5YQMIDSP4t/lhA6RVxHS7+WEAQKJo8Lv5YQDg6w1su/lhAX0zsei7+WECGXhWaLv5YQK5wPrku/lhA1YJn2C7+WED8lJD3Lv5YQCOnuRYv/lhAS7niNS/+WEByywtVL/5YQJndNHQv/lhAwe9dky/+WEDnAYeyL/5YQA8UsNEv/lhANybZ8C/+WEBdOAIQMP5YQIVKKy8w/lhArFxUTjD+WEDTbn1tMP5YQPuApoww/lhAIpPPqzD+WEBJpfjKMP5YQHC3Ieow/lhAmMlKCTH+WEC/23MoMf5YQObtnEcx/lhADgDGZjH+WEA1Eu+FMf5YQFwkGKUx/lhAhDZBxDH+WECqSGrjMf5YQNJakwIy/lhA+Wy8ITL+WEAgf+VAMv5YQEiRDmAy/lhAb6M3fzL+WECWtWCeMv5YQL7Hib0y/lhA5dmy3DL+WEAM7Nv7Mv5YQDP+BBsz/lhAWxAuOjP+WECCIldZM/5YQKk0gHgz/lhA0UaplzP+WED3WNK2M/5YQB9r+9Uz/lhARn0k9TP+WEBtj00UNP5YQJWhdjM0/lhAvLOfUjT+WEDjxchxNP5YQAvY8ZA0/lhAMeoasDT+WEBZ/EPPNP5YQIAObe40/lhApyCWDTX+WEDPMr8sNf5YQPZE6Es1/lhAHVcRazX+WEBEaTqKNf5YQGx7Y6k1/lhAk42MyDX+WEC6n7XnNf5YQOKx3gY2/lhACcQHJjb+WEAw1jBFNv5YQFjoWWQ2/lhAfvqCgzb+WECmDKyiNv5YQM0e1cE2/lhA9DD+4Db+WEAcQycAN/5YQENVUB83/lhAamd5Pjf+WECSeaJdN/5YQLmLy3w3/lhA4J30mzf+WEAHsB27N/5YQC/CRto3/lhAVtRv+Tf+WEB95pgYOP5YQKX4wTc4/lhAywrrVjj+WEDzHBR2OP5YQBovPZU4/lhAQUFmtDj+WEBpU4/TOP5YQJBluPI4/lhAt3fhETn+WEDfiQoxOf5YQAacM1A5/lhALa5cbzn+WEBUwIWOOf5YQHzSrq05/lhAo+TXzDn+WEDK9gDsOf5YQPIIKgs6/lhAGBtTKjr+WEBALXxJOv5YQGg/pWg6/lhAjlHOhzr+WEC2Y/emOv5YQN11IMY6/lhABIhJ5Tr+WEAsmnIEO/5YQFOsmyM7/lhAer7EQjv+WECh0O1hO/5YQMniFoE7/lhA8PQ/oDv+WEAXB2m/O/5YQD8Zkt47/lhAZiu7/Tv+WECNPeQcPP5YQLVPDTw8/lhA22E2Wzz+WEADdF96PP5YQCqGiJk8/lhAUZixuDz+WEB5qtrXPP5YQJ+8A/c8/lhAx84sFj3+WEDv4FU1Pf5YQBXzflQ9/lhAPQWocz3+WEBkF9GSPf5YQIsp+rE9/lhAszsj0T3+WEDaTUzwPf5YQAFgdQ8+/lhAKHKeLj7+WEBQhMdNPv5YQHeW8Gw+/lhAnqgZjD7+WEDGukKrPv5YQO3Ma8o+/lhAFN+U6T7+WEA88b0IP/5YQGID5yc//lhAihUQRz/+WECxJzlmP/5YQNg5YoU//lhAAEyLpD/+WEAnXrTDP/5YQE5w3eI//lhAdoIGAkD+WECdlC8hQP5YQMSmWEBA/lhA67iBX0D+WEATy6p+QP5YQDrd051A/lhAYe/8vED+WECJASbcQP5YQK8TT/tA/lhA1yV4GkH+WED+N6E5Qf5YQCVKylhB/lhATVzzd0H+WEB0bhyXQf5YQJuARbZB/lhAw5Ju1UH+WEDqpJf0Qf5YQBG3wBNC/lhAOMnpMkL+WEBg2xJSQv5YQIftO3FC/lhArv9kkEL+WEDWEY6vQv5YQPwjt85C/lhAJDbg7UL+WEBMSAkNQ/5YQHJaMixD/lhAmmxbS0P+WEDBfoRqQ/5YQOiQrYlD/lhAEKPWqEP+WEA3tf/HQ/5YQF7HKOdD/lhAhdlRBkT+WECt63olRP5YQNT9o0RE/lhA+w/NY0T+WEAjIvaCRP5YQEo0H6JE/lhAcUZIwUT+WECYWHHgRP5YQL9qmv9E/lhA53zDHkX+WEAOj+w9Rf5YQDWhFV1F/lhAXbM+fEX+WECDxWebRf5YQKvXkLpF/lhA0um52UX+WED5++L4Rf5YQCEODBhG/lhASCA1N0b+WEBvMl5WRv5YQJdEh3VG/lhAvlawlEb+WEDlaNmzRv5YQAx7AtNG/lhANI0r8kb+WEBbn1QRR/5YQIKxfTBH/lhAqsOmT0f+WEDQ1c9uR/5YQPjn+I1H/lhAIPohrUf+WEBGDEvMR/5YQG4edOtH/lhAlTCdCkj+WEC8QsYpSP5YQORU70hI/lhAC2cYaEj+WEAyeUGHSP5YQFmLaqZI/lhAgZ2TxUj+WECor7zkSP5YQM/B5QNJ/lhA99MOI0n+WEAe5jdCSf5YQEX4YGFJ/lhAbQqKgEn+WECTHLOfSf5YQLsu3L5J/lhA4kAF3kn+WEAJUy79Sf5YQDFlVxxK/lhAWHeAO0r+WEB/ialaSv5YQKeb0nlK/lhAzq37mEr+WED1vyS4Sv5YQBzSTddK/lhAROR29kr+WEBr9p8VS/5YQJIIyTRL/lhAuhryU0v+WEDgLBtzS/5YQAg/RJJL/lhAMFFtsUv+WEBWY5bQS/5YQH51v+9L/lhApYfoDkz+WEDMmREuTP5YQPSrOk1M/lhAG75jbEz+WEBC0IyLTP5YQGnitapM/lhAkfTeyUz+WEC4BgjpTP5YQN8YMQhN/lhABitaJ03+WEAuPYNGTf5YQFVPrGVN/lhAfGHVhE3+WECjc/6jTf5YQMuFJ8NN/lhA8pdQ4k3+WEAZqnkBTv5YQEG8oiBO/lhAZ87LP07+WECP4PReTv5YQLbyHX5O/lhA3QRHnU7+WEAFF3C8Tv5YQCwpmdtO/lhAUzvC+k7+WEB7TesZT/5YQKJfFDlP/lhAyXE9WE/+WEDwg2Z3T/5YQBiWj5ZP/lhAP6i4tU/+WEBmuuHUT/5YQI7MCvRP/lhAtN4zE1D+WEDc8FwyUP5YQAQDhlFQ/lhAKhWvcFD+WEBSJ9iPUP5YQHk5Aa9Q/lhAoEsqzlD+WEDIXVPtUP5YQO9vfAxR/lhAFoKlK1H+WEA9lM5KUf5YQGWm92lR/lhAjLggiVH+WECzykmoUf5YQNvccsdR/lhAAu+b5lH+WEApAcUFUv5YQFET7iRS/lhAdyUXRFL+WECfN0BjUv5YQMZJaYJS/lhA7VuSoVL+WEAVbrvAUv5YQDyA5N9S/lhAY5IN/1L+WECKpDYeU/5YQLK2Xz1T/lhA2ciIXFP+WEAA27F7U/5YQCjt2ppT/lhAT/8DulP+WEB2ES3ZU/5YQJ4jVvhT/lhAxDV/F1T+WEDsR6g2VP5YQBNa0VVU/lhAOmz6dFT+WEBifiOUVP5YQImQTLNU/lhAsKJ10lT+WEDYtJ7xVP5YQP/GxxBV/lhAJtnwL1X+WEBN6xlPVf5YQHT9Qm5V/lhAnA9sjVX+WEDDIZWsVf5YQOozvstV/lhAEUbn6lX+WEA5WBAKVv5YQGBqOSlW/lhAh3xiSFb+WECvjotnVv5YQNagtIZW/lhA/bLdpVb+WEAlxQbFVv5YQEvXL+RW/lhAc+lYA1f+WECa+4EiV/5YQMENq0FX/lhA6R/UYFf+WEAQMv1/V/5YQDdEJp9X/lhAX1ZPvlf+WECGaHjdV/5YQK16ofxX/lhA1IzKG1j+WED8nvM6WP5YQCOxHFpY/lhASsNFeVj+WEBy1W6YWP5YQJjnl7dY/lhAwPnA1lj+WEDoC+r1WP5YQA4eExVZ/lhANjA8NFn+WEBdQmVTWf5YQIRUjnJZ/lhArGa3kVn+WEDTeOCwWf5YQPqKCdBZ/lhAIZ0y71n+WEBJr1sOWv5YQHDBhC1a/lhAl9OtTFr+WEC/5dZrWv5YQOb3/4pa/lhADQopqlr+WEA1HFLJWv5YQFsue+ha/lhAg0CkB1v+WECqUs0mW/5YQNFk9kVb/lhA+XYfZVv+WEAgiUiEW/5YQEebcaNb/lhAbq2awlv+WECWv8PhW/5YQL3R7ABc/lhA5OMVIFz+WEAM9j4/XP5YQDMIaF5c/lhAWhqRfVz+WECCLLqcXP5YQKg+47tc/lhA0FAM21z+WED3YjX6XP5YQB51Xhld/lhARoeHOF3+WEBtmbBXXf5YQJSr2XZd/lhAvL0Cll3+WEDizyu1Xf5YQAriVNRd/lhAMfR9813+WEBYBqcSXv5YQIAY0DFe/lhApyr5UF7+WEDOPCJwXv5YQPVOS49e/lhAHWF0rl7+WEBEc53NXv5YQGuFxuxe/lhAk5fvC1/+WEC6qRgrX/5YQOG7QUpf/lhACc5qaV/+WEAv4JOIX/5YQFfyvKdf/lhAfgTmxl/+WEClFg/mX/5YQM0oOAVg/lhA9DphJGD+WEAbTYpDYP5YQEJfs2Jg/lhAanHcgWD+WECRgwWhYP5YQLiVLsBg/lhA4KdX32D+WEAHuoD+YP5YQC7MqR1h/lhAVt7SPGH+WEB88PtbYf5YQKQCJXth/lhAyxROmmH+WEDyJne5Yf5YQBo5oNhh/lhAQUvJ92H+WEBoXfIWYv5YQJBvGzZi/lhAt4FEVWL+WEDek210Yv5YQAWmlpNi/lhALbi/smL+WEBUyujRYv5YQHvcEfFi/lhAo+46EGP+WEDJAGQvY/5YQPESjU5j/lhAGSW2bWP+WEA/N9+MY/5YQGdJCKxj/lhAjlsxy2P+WEC1bVrqY/5YQN1/gwlk/lhABJKsKGT+WEArpNVHZP5YQFK2/mZk/lhAesgnhmT+WECh2lClZP5YQMjsecRk/lhA8P6i42T+WEAXEcwCZf5YQD4j9SFl/lhAZjUeQWX+WECMR0dgZf5YQLRZcH9l/lhA22uZnmX+WEACfsK9Zf5YQCqQ69xl/lhAUKIU/GX+WEB4tD0bZv5YQKDGZjpm/lhAxtiPWWb+WEDu6rh4Zv5YQBX94Zdm/lhAPA8Lt2b+WEBkITTWZv5YQIszXfVm/lhAskWGFGf+WEDZV68zZ/5YQAFq2FJn/lhAKHwBcmf+WEBPjiqRZ/5YQHegU7Bn/lhAnrJ8z2f+WEDFxKXuZ/5YQO3Wzg1o/lhAE+n3LGj+WEA7+yBMaP5YQGINSmto/lhAiR9zimj+WECxMZypaP5YQNhDxcho/lhA/1Xu52j+WEAmaBcHaf5YQE56QCZp/lhAdYxpRWn+WECcnpJkaf5YQMSwu4Np/lhA68Lkomn+WEAS1Q3Caf5YQDrnNuFp/lhAYPlfAGr+WECIC4kfav5YQK8dsj5q/lhA1i/bXWr+WED+QQR9av5YQCVULZxq/lhATGZWu2r+WEB0eH/aav5YQJuKqPlq/lhAwpzRGGv+WEDprvo3a/5YQBHBI1dr/lhAONNMdmv+WEBf5XWVa/5YQIf3nrRr/lhArQnI02v+WEDVG/Hya/5YQPwtGhJs/lhAI0BDMWz+WEBLUmxQbP5YQHJklW9s/lhAmXa+jmz+WEDBiOetbP5YQOiaEM1s/lhAD6057Gz+WEA2v2ILbf5YQF7Riypt/lhAheO0SW3+WECs9d1obf5YQNQHB4ht/lhA+hkwp23+WEAiLFnGbf5YQEo+guVt/lhAcFCrBG7+WECYYtQjbv5YQL90/UJu/lhA5oYmYm7+WEAOmU+Bbv5YQDSreKBu/lhAXL2hv27+WECDz8rebv5YQKrh8/1u/lhA0vMcHW/+WED5BUY8b/5YQCAYb1tv/lhASCqYem/+WEBvPMGZb/5YQJZO6rhv/lhAvWAT2G/+WEDlcjz3b/5YQAyFZRZw/lhAM5eONXD+WEBbqbdUcP5YQIG74HNw/lhAqc0Jk3D+WEDR3zKycP5YQPfxW9Fw/lhAHwSF8HD+WEBGFq4Pcf5YQG0o1y5x/lhAlToATnH+WEC8TCltcf5YQONeUoxx/lhACnF7q3H+WEAyg6TKcf5YQFmVzelx/lhAgKf2CHL+WECouR8ocv5YQM/LSEdy/lhA9t1xZnL+WEAe8JqFcv5YQEQCxKRy/lhAbBTtw3L+WECTJhbjcv5YQLo4PwJz/lhA4kpoIXP+WEAJXZFAc/5YQDBvul9z/lhAWIHjfnP+WEB/kwyec/5YQKalNb1z/lhAzbde3HP+WED1yYf7c/5YQBzcsBp0/lhAQ+7ZOXT+WEBrAANZdP5YQJESLHh0/lhAuSRVl3T+WEDgNn62dP5YQAdJp9V0/lhAL1vQ9HT+WEBWbfkTdf5YQH1/IjN1/lhApZFLUnX+WEDMo3Rxdf5YQPO1nZB1/lhAGsjGr3X+WEBC2u/Odf5YQGnsGO51/lhAkP5BDXb+WEC4EGssdv5YQN4ilEt2/lhABjW9anb+WEAtR+aJdv5YQFRZD6l2/lhAfGs4yHb+WECjfWHndv5YQMqPigZ3/lhA8qGzJXf+WEAYtNxEd/5YQEDGBWR3/lhAZ9gug3f+WECO6leid/5YQLb8gMF3/lhA3Q6q4Hf+WEAEIdP/d/5YQCwz/B54/lhAU0UlPnj+WEB6V05deP5YQKFpd3x4/lhAyXugm3j+WEDwjcm6eP5YQBeg8tl4/lhAP7Ib+Xj+WEBlxEQYef5YQI3WbTd5/lhAtOiWVnn+WEDb+r91ef5YQAMN6ZR5/lhAKh8StHn+WEBRMTvTef5YQHlDZPJ5/lhAoFWNEXr+WEDHZ7Ywev5YQO553096/lhAFowIb3r+WEA9njGOev5YQGSwWq16/lhAjMKDzHr+WECy1Kzrev5YQNrm1Qp7/lhAAvn+KXv+WEAoCyhJe/5YQFAdUWh7/lhAdy96h3v+WECeQaOme/5YQMZTzMV7/lhA7WX15Hv+WEAUeB4EfP5YQDuKRyN8/lhAY5xwQnz+WECKrplhfP5YQLHAwoB8/lhA2dLrn3z+WEAA5RS/fP5YQCf3Pd58/lhATwln/Xz+WEB1G5Acff5YQJ0tuTt9/lhAxD/iWn3+WEDrUQt6ff5YQBNkNJl9/lhAOnZduH3+WEBhiIbXff5YQImar/Z9/lhAsKzYFX7+WEDXvgE1fv5YQP7QKlR+/lhAJuNTc37+WEBN9XySfv5YQHQHprF+/lhAmxnP0H7+WEDCK/jvfv5YQOo9IQ9//lhAEVBKLn/+WEA4YnNNf/5YQGB0nGx//lhAh4bFi3/+WECumO6qf/5YQNaqF8p//lhA/LxA6X/+WEAkz2kIgP5YQEvhkieA/lhAcvO7RoD+WECaBeVlgP5YQMEXDoWA/lhA6Ck3pID+WEAQPGDDgP5YQDdOieKA/lhAXmCyAYH+WECFctsggf5YQK2EBECB/lhA1JYtX4H+WED7qFZ+gf5YQCO7f52B/lhASc2ovIH+WEBx39Hbgf5YQJjx+vqB/lhAvwMkGoL+WEDnFU05gv5YQA4odliC/lhANTqfd4L+WEBdTMiWgv5YQIRe8bWC/lhAq3Aa1YL+WEDSgkP0gv5YQPqUbBOD/lhAIaeVMoP+WEBIub5Rg/5YQHDL53CD/lhAlt0QkIP+WEC+7zmvg/5YQOYBY86D/lhADBSM7YP+WEA0JrUMhP5YQFs43iuE/lhAgkoHS4T+WECqXDBqhP5YQNFuWYmE/lhA+ICCqIT+WEAfk6vHhP5YQEel1OaE/lhAbrf9BYX+WECVySYlhf5YQL3bT0SF/lhA5O14Y4X+WEALAKKChf5YQDMSy6GF/lhAWST0wIX+WECBNh3ghf5YQKhIRv+F/lhAz1pvHob+WED3bJg9hv5YQB5/wVyG/lhARZHqe4b+WEBsoxObhv5YQJS1PLqG/lhAu8dl2Yb+WEDi2Y74hv5YQAnstxeH/lhAMf7gNof+WEBYEApWh/5YQH8iM3WH/lhApjRclIf+WEDORoWzh/5YQPVYrtKH/lhAHGvX8Yf+WEBEfQARiP5YQGqPKTCI/lhAkqFST4j+WEC6s3tuiP5YQODFpI2I/lhACNjNrIj+WEAv6vbLiP5YQFb8H+uI/lhAfg5JCon+WEClIHIpif5YQMwym0iJ/lhA80TEZ4n+WEAbV+2Gif5YQEJpFqaJ/lhAaXs/xYn+WECRjWjkif5YQLifkQOK/lhA37G6Ior+WEAHxONBiv5YQC3WDGGK/lhAVeg1gIr+WEB8+l6fiv5YQKMMiL6K/lhAyx6x3Yr+WEDyMNr8iv5YQBlDAxyL/lhAQVUsO4v+WEBoZ1Vai/5YQI95fnmL/lhAtounmIv+WEDendC3i/5YQAWw+daL/lhALMIi9ov+WEBU1EsVjP5YQHrmdDSM/lhAovidU4z+WEDKCsdyjP5YQPAc8JGM/lhAGC8ZsYz+WEA/QULQjP5YQGZTa++M/lhAjmWUDo3+WEC1d70tjf5YQNyJ5kyN/lhAA5wPbI3+WEArrjiLjf5YQFLAYaqN/lhAedKKyY3+WECh5LPojf5YQMj23AeO/lhA7wgGJ47+WEAXGy9Gjv5YQD0tWGWO/lhAZT+BhI7+WECMUaqjjv5YQLNj08KO/lhA23X84Y7+WEACiCUBj/5YQCmaTiCP/lhAUKx3P4/+WEB3vqBej/5YQJ/QyX2P/lhAxuLynI/+WEDt9Bu8j/5YQBUHRduP/lhAPBlu+o/+WEBjK5cZkP5YQIo9wDiQ/lhAsk/pV5D+WEDZYRJ3kP5YQAB0O5aQ/lhAKIZktZD+WEBOmI3UkP5YQHaqtvOQ/lhAnrzfEpH+WEDEzggykf5YQOzgMVGR/lhAE/NacJH+WEA6BYSPkf5YQGIXra6R/lhAiSnWzZH+WECwO//skf5YQNdNKAyS/lhA/19RK5L+WEAmcnpKkv5YQE2Eo2mS/lhAdZbMiJL+WECcqPWnkv5YQMO6HseS/lhA68xH5pL+WEAR33AFk/5YQDnxmSST/lhAYAPDQ5P+WECHFexik/5YQK8nFYKT/lhA1jk+oZP+WED9S2fAk/5YQCRekN+T/lhATHC5/pP+WEBzguIdlP5YQJqUCz2U/lhAwqY0XJT+WEDpuF17lP5YQBDLhpqU/lhAON2vuZT+WEBe79jYlP5YQIYBAviU/lhArRMrF5X+WEDUJVQ2lf5YQPw3fVWV/lhAI0qmdJX+WEBKXM+Tlf5YQHJu+LKV/lhAmYAh0pX+WEDAkkrxlf5YQOekcxCW/lhAD7ecL5b+WEA2ycVOlv5YQF3b7m2W/lhAhe0XjZb+WECr/0Cslv5YQNMRasuW/lhA+yOT6pb+WEAhNrwJl/5YQElI5SiX/lhAcFoOSJf+WECXbDdnl/5YQL9+YIaX/lhA5pCJpZf+WEANo7LEl/5YQDS12+OX/lhAW8cEA5j+WECD2S0imP5YQKrrVkGY/lhA0f1/YJj+WED5D6l/mP5YQCAi0p6Y/lhARzT7vZj+WEBuRiTdmP5YQJZYTfyY/lhAvWp2G5n+WEDkfJ86mf5YQAyPyFmZ/lhAMqHxeJn+WEBasxqYmf5YQILFQ7eZ/lhAqNds1pn+WEDQ6ZX1mf5YQPf7vhSa/lhAHg7oM5r+WEBGIBFTmv5YQG0yOnKa/lhAlERjkZr+WEC7Voywmv5YQONotc+a/lhACnve7pr+WEAxjQcOm/5YQFmfMC2b/lhAgLFZTJv+WECnw4Jrm/5YQM/Vq4qb/lhA9efUqZv+WEAd+v3Im/5YQEQMJ+ib/lhAax5QB5z+WECTMHkmnP5YQLpCokWc/lhA4VTLZJz+WEAIZ/SDnP5YQDB5HaOc/lhAV4tGwpz+WEB+nW/hnP5YQKavmACd/lhAzcHBH53+WED00+o+nf5YQBzmE16d/lhAQvg8fZ3+WEBqCmacnf5YQJEcj7ud/lhAuC642p3+WEDgQOH5nf5YQAdTChme/lhALmUzOJ7+WEBWd1xXnv5YQH2JhXae/lhApJuulZ7+WEDLrde0nv5YQPO/ANSe/lhAGtIp857+WEBB5FISn/5YQGn2ezGf/lhAjwilUJ/+WEC3Gs5vn/5YQN4s946f/lhABT8grp/+WEAtUUnNn/5YQFRjcuyf/lhAe3WbC6D+WECjh8QqoP5YQMmZ7Umg/lhA8asWaaD+WEAYvj+IoP5YQD/QaKeg/lhAZ+KRxqD+WECO9LrloP5YQLUG5ASh/lhA3BgNJKH+WEAEKzZDof5YQCs9X2Kh/lhAUk+IgaH+WEB6YbGgof5YQKFz2r+h/lhAyIUD36H+WEDwlyz+of5YQBaqVR2i/lhAPrx+PKL+WEBlzqdbov5YQIzg0Hqi/lhAtPL5maL+WEDbBCO5ov5YQAIXTNii/lhAKil196L+WEBRO54Wo/5YQHhNxzWj/lhAn1/wVKP+WEDHcRl0o/5YQO6DQpOj/lhAFZZrsqP+WEA9qJTRo/5YQGO6vfCj/lhAi8zmD6T+WECz3g8vpP5YQNnwOE6k/lhAAQNibaT+WEAoFYuMpP5YQE8ntKuk/lhAdzndyqT+WECeSwbqpP5YQMVdLwml/lhA7G9YKKX+WEAUgoFHpf5YQDuUqmal/lhAYqbThaX+WECKuPykpf5YQLHKJcSl/lhA2NxO46X+WEAA73cCpv5YQCYBoSGm/lhAThPKQKb+WEB1JfNfpv5YQJw3HH+m/lhAxElFnqb+WEDrW269pv5YQBJul9ym/lhAOoDA+6b+WEBhkukap/5YQIikEjqn/lhAr7Y7Waf+WEDXyGR4p/5YQP7ajZen/lhAJe22tqf+WEBN/9/Vp/5YQHMRCfWn/lhAmyMyFKj+WEDCNVszqP5YQOlHhFKo/lhAEVqtcaj+WEA4bNaQqP5YQF9+/6+o/lhAh5Aoz6j+WECtolHuqP5YQNW0eg2p/lhA/MajLKn+WEAj2cxLqf5YQEvr9Wqp/lhAcv0eiqn+WECZD0ipqf5YQMAhccip/lhA6DOa56n+WEAPRsMGqv5YQDZY7CWq/lhAXmoVRar+WECFfD5kqv5YQKyOZ4Oq/lhA1KCQoqr+WED6srnBqv5YQCLF4uCq/lhASdcLAKv+WEBw6TQfq/5YQJj7XT6r/lhAvw2HXav+WEDmH7B8q/5YQA4y2Zur/lhANUQCu6v+WEBcVivaq/5YQINoVPmr/lhAq3p9GKz+WEDSjKY3rP5YQPmez1as/lhAIbH4daz+WEBHwyGVrP5YQG/VSrSs/lhAludz06z+WEC9+ZzyrP5YQOULxhGt/lhADB7vMK3+WEAzMBhQrf5YQFtCQW+t/lhAglRqjq3+WECpZpOtrf5YQNB4vMyt/lhA+Irl663+WEAfnQ4Lrv5YQEavNyqu/lhAbsFgSa7+WECU04lorv5YQLzlsoeu/lhA5Pfbpq7+WEAKCgXGrv5YQDIcLuWu/lhAWS5XBK/+WECAQIAjr/5YQKhSqUKv/lhAz2TSYa/+WED2dvuAr/5YQB2JJKCv/lhARZtNv6/+WEBsrXber/5YQJO/n/2v/lhAu9HIHLD+WEDi4/E7sP5YQAn2Gluw/lhAMQhEerD+WEBXGm2ZsP5YQH8slriw/lhApj6/17D+WEDNUOj2sP5YQPViERax/lhAG3U6NbH+WEAw/s5Esf5YQEOHY1Sx/lhAVhD4Y7H+WEBrmYxzsf5YQH4iIYOx/lhAkau1krH+WEClNEqisf5YQLm93rGx/lhAzEZzwbH+WEDgzwfRsf5YQPRYnOCx/lhAB+Iw8LH+WEAba8X/sf5YQC/0WQ+y/lhAQn3uHrL+WEBWBoMusv5YQGqPFz6y/lhAfRisTbL+WECRoUBdsv5YQKQq1Wyy/lhAuLNpfLL+WEDMPP6Lsv5YQN/Fkpuy/lhA804nq7L+WEAH2Lu6sv5YQBphUMqy/lhALurk2bL+WEBCc3npsv5YQFX8Dfmy/lhAaYWiCLP+WEB9DjcYs/5YQJCXyyez/lhAoyBgN7P+WEC4qfRGs/5YQMsyiVaz/lhA3rsdZrP+WEDyRLJ1s/5YQAbORoWz/lhAGVfblLP+WEAt4G+ks/5YQEFpBLSz/lhAVPKYw7P+WEBoey3Ts/5YQHwEwuKz/lhAj41W8rP+WECjFusBtP5YQLeffxG0/lhAyigUIbT+WEDesagwtP5YQPI6PUC0/lhABcTRT7T+WEAZTWZftP5YQCzW+m60/lhAQF+PfrT+WEBU6COOtP5YQGdxuJ20/lhAe/pMrbT+WECPg+G8tP5YQKIMdsy0/lhAtpUK3LT+WEDKHp/rtP5YQN2nM/u0/lhA8DDICrX+WEAFulwatf5YQBhD8Sm1/lhAK8yFObX+WEBAVRpJtf5YQFPerli1/lhAZmdDaLX+WEB68Nd3tf5YQI55bIe1/lhAoQIBl7X+WEC1i5Wmtf5YQMkUKra1/lhA3J2+xbX+WEDwJlPVtf5YQASw5+S1/lhAFzl89LX+WEArwhAEtv5YQD9LpRO2/lhAUtQ5I7b+WEBmXc4ytv5YQHnmYkK2/lhAjW/3Ubb+WECh+Ithtv5YQLSBIHG2/lhAyAq1gLb+WEDck0mQtv5YQO8c3p+2/lhAA6Zyr7b+WEAXLwe/tv5YQCq4m862/lhAPkEw3rb+WEBSysTttv5YQGVTWf22/lhAeNztDLf+WECNZYIct/5YQKDuFiy3/lhAs3erO7f+WEDIAEBLt/5YQNuJ1Fq3/lhA7hJparf+WEACnP15t/5YQBYlkom3/lhAKa4mmbf+WEA9N7uot/5YQFHAT7i3/lhAZEnkx7f+WEB40njXt/5YQIxbDee3/lhAn+Sh9rf+WECzbTYGuP5YQMf2yhW4/lhA2n9fJbj+WEDuCPQ0uP5YQAGSiES4/lhAFRsdVLj+WEAppLFjuP5YQDwtRnO4/lhAULbagrj+WEBkP2+SuP5YQHfIA6K4/lhAi1GYsbj+WECf2izBuP5YQLJjwdC4/lhAxuxV4Lj+WEDZdervuP5YQO3+fv+4/lhAAIgTD7n+WEAUEageuf5YQCiaPC65/lhAOyPRPbn+WEBPrGVNuf5YQGM1+ly5/lhAdr6ObLn+WECKRyN8uf5YQJ7Qt4u5/lhAsVlMm7n+WEDF4uCquf5YQNlrdbq5/lhA7PQJyrn+WED/fZ7Zuf5YQBQHM+m5/lhAJ5DH+Ln+WEA6GVwIuv5YQE6i8Be6/lhAYiuFJ7r+WEB1tBk3uv5YQIk9rka6/lhAncZCVrr+WECwT9dluv5YQMTYa3W6/lhA2GEAhbr+WEDr6pSUuv5YQP9zKaS6/lhAE/29s7r+WEAmhlLDuv5YQDoP59K6/lhATph74rr+WEBhIRDyuv5YQHWqpAG7/lhAiDM5Ebv+WECcvM0gu/5YQLBFYjC7/lhAw872P7v+WEDXV4tPu/5YQOvgH1+7/lhA/mm0brv+WEAS80h+u/5YQCZ83Y27/lhAOQVynbv+WEBMjgatu/5YQGEXm7y7/lhAdKAvzLv+WECHKcTbu/5YQJyyWOu7/lhArzvt+rv+WEDCxIEKvP5YQNZNFhq8/lhA6taqKbz+WED9Xz85vP5YQBHp00i8/lhAJXJoWLz+WEA4+/xnvP5YQEyEkXe8/lhAYA0mh7z+WEBzlrqWvP5YQIcfT6a8/lhAm6jjtbz+WECuMXjFvP5YQMK6DNW8/lhA1UOh5Lz+WEDpzDX0vP5YQP1VygO9/lhAEN9eE73+WEAkaPMivf5YQDjxhzK9/lhAS3ocQr3+WEBfA7FRvf5YQHOMRWG9/lhAhhXacL3+WECanm6Avf5YQK4nA5C9/lhAwbCXn73+WEDUOSyvvf5YQOnCwL69/lhA/EtVzr3+WEAP1endvf5YQCRefu29/lhAN+cS/b3+WEBKcKcMvv5YQF75Oxy+/lhAcoLQK77+WECFC2U7vv5YQJmU+Uq+/lhArR2OWr7+WEDApiJqvv5YQNQvt3m+/lhA6LhLib7+WED7QeCYvv5YQA/LdKi+/lhAI1QJuL7+WEA23Z3Hvv5YQEpmMte+/lhAXe/G5r7+WEBxeFv2vv5YQIUB8AW//lhAmIqEFb/+WECsExklv/5YQMCcrTS//lhA0yVCRL/+WEDnrtZTv/5YQPs3a2O//lhADsH/cr/+WEAiSpSCv/5YQDbTKJK//lhASVy9ob/+WEBc5VGxv/5YQHFu5sC//lhAhPd60L/+WECXgA/gv/5YQKwJpO+//lhAv5I4/7/+WEDSG80OwP5YQOakYR7A/lhA+i32LcD+WEANt4o9wP5YQCFAH03A/lhANcmzXMD+WEBIUkhswP5YQFzb3HvA/lhAcGRxi8D+WECD7QWbwP5YQJd2mqrA/lhAqv8uusD+WEC+iMPJwP5YQNIRWNnA/lhA5Zrs6MD+WED5I4H4wP5YQA2tFQjB/lhAIDaqF8H+WEA0vz4nwf5YQEdI0zbB/lhAW9FnRsH+WEBvWvxVwf5YQILjkGXB/lhAlmwldcH+WECq9bmEwf5YQL1+TpTB/lhA0Qfjo8H+WEDkkHezwf5YQPgZDMPB/lhADKOg0sH+WEAfLDXiwf5YQDO1yfHB/lhARz5eAcL+WEBax/IQwv5YQG5QhyDC/lhAgtkbMML+WECVYrA/wv5YQKjrRE/C/lhAvXTZXsL+WEDQ/W1uwv5YQOOGAn7C/lhA+A+XjcL+WEALmSudwv5YQB4iwKzC/lhAMqtUvML+WEBGNOnLwv5YQFm9fdvC/lhAbUYS68L+WECBz6b6wv5YQJRYOwrD/lhAqOHPGcP+WEC8amQpw/5YQM/z+DjD/lhA43yNSMP+WED3BSJYw/5YQAqPtmfD/lhAHhhLd8P+WEAxod+Gw/5YQEUqdJbD/lhAWbMIpsP+WEBsPJ21w/5YQIDFMcXD/lhAlE7G1MP+WECn11rkw/5YQLtg7/PD/lhAz+mDA8T+WEDichgTxP5YQPb7rCLE/lhACoVBMsT+WEAdDtZBxP5YQDCXalHE/lhARSD/YMT+WEBYqZNwxP5YQGsyKIDE/lhAgLu8j8T+WECTRFGfxP5YQKbN5a7E/lhAulZ6vsT+WEDO3w7OxP5YQOFoo93E/lhA9fE37cT+WEAJe8z8xP5YQBwEYQzF/lhAMI31G8X+WEBEFoorxf5YQFefHjvF/lhAayizSsX+WEB/sUdaxf5YQJI63GnF/lhApsNwecX+WEC5TAWJxf5YQM3VmZjF/lhA4V4uqMX+WED058K3xf5YQAhxV8fF/lhAHPrr1sX+WEAvg4Dmxf5YQEMMFfbF/lhAV5WpBcb+WEBqHj4Vxv5YQH6n0iTG/lhAkjBnNMb+WECluftDxv5YQLhCkFPG/lhAzcskY8b+WEDgVLlyxv5YQPPdTYLG/lhACGfikcb+WEAb8Hahxv5YQC55C7HG/lhAQgKgwMb+WEBWizTQxv5YQGkUyd/G/lhAfZ1d78b+WECRJvL+xv5YQKSvhg7H/lhAuDgbHsf+WEDMwa8tx/5YQN9KRD3H/lhA89PYTMf+WEAGXW1cx/5YQBrmAWzH/lhALm+We8f+WEBB+CqLx/5YQFWBv5rH/lhAaQpUqsf+WEB8k+i5x/5YQJAcfcnH/lhApKUR2cf+WEC3Lqbox/5YQMu3OvjH/lhA30DPB8j+WEDyyWMXyP5YQAZT+CbI/lhAGtyMNsj+WEAtZSFGyP5YQEDutVXI/lhAVXdKZcj+WEBoAN90yP5YQHuJc4TI/lhAjxIIlMj+WECjm5yjyP5YQLYkMbPI/lhAyq3Fwsj+WEDeNlrSyP5YQPG/7uHI/lhABUmD8cj+WEAZ0hcByf5YQCxbrBDJ/lhAQORAIMn+WEBUbdUvyf5YQGf2aT/J/lhAe3/+Tsn+WECOCJNeyf5YQKKRJ27J/lhAtRq8fcn+WEDJo1CNyf5YQN0s5ZzJ/lhA8LV5rMn+WEAEPw68yf5YQBjIosvJ/lhAK1E328n+WEA/2svqyf5YQFNjYPrJ/lhAZuz0Ccr+WEB6dYkZyv5YQI3+HSnK/lhAoYeyOMr+WEC1EEdIyv5YQMiZ21fK/lhA3CJwZ8r+WEDwqwR3yv5YQAM1mYbK/lhAF74tlsr+WEArR8Klyv5YQD7QVrXK/lhAUlnrxMr+WEBm4n/Uyv5YQHlrFOTK/lhAjPSo88r+WEChfT0Dy/5YQLQG0hLL/lhAx49mIsv+WEDcGPsxy/5YQO+hj0HL/lhAAiskUcv+WEAWtLhgy/5YQCo9TXDL/lhAPcbhf8v+WEBRT3aPy/5YQGXYCp/L/lhAeGGfrsv+WECM6jO+y/5YQKBzyM3L/lhAs/xc3cv+WEDHhfHsy/5YQNsOhvzL/lhA7pcaDMz+WEACIa8bzP5YQBWqQyvM/lhAKTPYOsz+WEA9vGxKzP5YQFBFAVrM/lhAZM6Vacz+WEB4Vyp5zP5YQIvgvojM/lhAn2lTmMz+WECz8uenzP5YQMZ7fLfM/lhA2gQRx8z+WEDujaXWzP5YQAEXOubM/lhAFKDO9cz+WEApKWMFzf5YQDyy9xTN/lhATzuMJM3+WEBkxCA0zf5YQHdNtUPN/lhAitZJU83+WECeX95izf5YQLLocnLN/lhAxXEHgs3+WEDZ+puRzf5YQO2DMKHN/lhAAA3FsM3+WEAUllnAzf5YQCgf7s/N/lhAO6iC383+WEBPMRfvzf5YQGK6q/7N/lhAdkNADs7+WECKzNQdzv5YQJ1VaS3O/lhAsd79PM7+WEDFZ5JMzv5YQNjwJlzO/lhA7Hm7a87+WEAAA1B7zv5YQBOM5IrO/lhAJxV5ms7+WEA7ng2qzv5YQE4nornO/lhAYrA2yc7+WEB2OcvYzv5YQInCX+jO/lhAnEv0987+WECx1IgHz/5YQMRdHRfP/lhA1+axJs/+WEDrb0Y2z/5YQP/42kXP/lhAEoJvVc/+WEAmCwRlz/5YQDqUmHTP/lhATR0thM/+WEBhpsGTz/5YQHUvVqPP/lhAiLjqss/+WECcQX/Cz/5YQLDKE9LP/lhAw1Oo4c/+WEDX3Dzxz/5YQOpl0QDQ/lhA/u5lEND+WEASePof0P5YQCUBjy/Q/lhAOYojP9D+WEBNE7hO0P5YQGCcTF7Q/lhAdCXhbdD+WECIrnV90P5YQJs3Co3Q/lhAr8CenND+WEDDSTOs0P5YQNbSx7vQ/lhA6Vtcy9D+WED+5PDa0P5YQBFuherQ/lhAJPcZ+tD+WEA5gK4J0f5YQEwJQxnR/lhAX5LXKNH+WEBzG2w40f5YQIekAEjR/lhAmi2VV9H+WECutiln0f5YQMI/vnbR/lhA1chShtH+WEDpUeeV0f5YQP3ae6XR/lhAEGQQtdH+WEAk7aTE0f5YQDh2OdTR/lhAS//N49H+WEBeiGLz0f5YQHIR9wLS/lhAhpqLEtL+WECZIyAi0v5YQK2stDHS/lhAwTVJQdL+WEDUvt1Q0v5YQOhHcmDS/lhA/NAGcNL+WEAPWpt/0v5YQCPjL4/S/lhAN2zEntL+WEBK9Viu0v5YQF5+7b3S/lhAcQeCzdL+WECFkBbd0v5YQJkZq+zS/lhArKI//NL+WEDAK9QL0/5YQNS0aBvT/lhA5z39KtP+WED7xpE60/5YQA9QJkrT/lhAItm6WdP+WEA2Yk9p0/5YQErr43jT/lhAXXR4iNP+WEBw/QyY0/5YQIWGoafT/lhAmA82t9P+WECrmMrG0/5YQMAhX9bT/lhA06rz5dP+WEDmM4j10/5YQPq8HAXU/lhADkaxFNT+WEAhz0Uk1P5YQDVY2jPU/lhASeFuQ9T+WEBcagNT1P5YQHDzl2LU/lhAhHwsctT+WECXBcGB1P5YQKuOVZHU/lhAvhfqoNT+WEDSoH6w1P5YQOYpE8DU/lhA+bKnz9T+WEANPDzf1P5YQCHF0O7U/lhANE5l/tT+WEBI1/kN1f5YQFxgjh3V/lhAb+kiLdX+WECDcrc81f5YQJf7S0zV/lhAqoTgW9X+WEC+DXVr1f5YQNKWCXvV/lhA5R+eitX+WED4qDKa1f5YQA0yx6nV/lhAILtbudX+WEAzRPDI1f5YQEfNhNjV/lhAW1YZ6NX+WEBu36331f5YQIJoQgfW/lhAlvHWFtb+WECpemsm1v5YQL0DADbW/lhA0YyURdb+WEDkFSlV1v5YQPievWTW/lhADChSdNb+WEAfseaD1v5YQDM6e5PW/lhARsMPo9b+WEBaTKSy1v5YQG7VOMLW/lhAgV7N0db+WECV52Hh1v5YQKlw9vDW/lhAvPmKANf+WEDQgh8Q1/5YQOQLtB/X/lhA95RIL9f+WEALHt0+1/5YQB+ncU7X/lhAMjAGXtf+WEBFuZpt1/5YQFpCL33X/lhAbcvDjNf+WECAVFic1/5YQJXd7KvX/lhAqGaBu9f+WEC77xXL1/5YQM94qtrX/lhA4wE/6tf+WED2itP51/5YQAoUaAnY/lhAHp38GNj+WEAxJpEo2P5YQEWvJTjY/lhAWTi6R9j+WEBswU5X2P5YQIBK42bY/lhAlNN3dtj+WECnXAyG2P5YQLvloJXY/lhAzm41pdj+WEDi98m02P5YQPaAXsTY/lhACQrz09j+WEAdk4fj2P5YQDEcHPPY/lhARKWwAtn+WEBYLkUS2f5YQGy32SHZ/lhAf0BuMdn+WECTyQJB2f5YQKdSl1DZ/lhAutsrYNn+WEDNZMBv2f5YQOLtVH/Z/lhA9Xbpjtn+WEAIAH6e2f5YQByJEq7Z/lhAMBKnvdn+WEBDmzvN2f5YQFck0NzZ/lhAa61k7Nn+WEB+Nvn72f5YQJK/jQva/lhApkgiG9r+WEC50bYq2v5YQMxaSzra/lhA4ePfSdr+WED0bHRZ2v5YQAf2CGna/lhAHH+deNr+WEAvCDKI2v5YQEKRxpfa/lhAVhpbp9r+WEBqo++22v5YQH0shMba/lhAkbUY1tr+WEClPq3l2v5YQLjHQfXa/lhAzFDWBNv+WEDg2WoU2/5YQPNi/yPb/lhAB+yTM9v+WEAadShD2/5YQC7+vFLb/lhAQodRYtv+WEBVEOZx2/5YQGmZeoHb/lhAfSIPkdv+WECQq6Og2/5YQKQ0OLDb/lhAuL3Mv9v+WEDLRmHP2/5YQN/P9d7b/lhA81iK7tv+WEAG4h7+2/5YQBprsw3c/lhALvRHHdz+WEBBfdws3P5YQFQGcTzc/lhAaY8FTNz+WEB8GJpb3P5YQI+hLmvc/lhAoyrDetz+WEC3s1eK3P5YQMo87Jnc/lhA3sWAqdz+WEDyThW53P5YQAXYqcjc/lhAGWE+2Nz+WEAt6tLn3P5YQEBzZ/fc/lhAVPz7Bt3+WEBohZAW3f5YQHsOJSbd/lhAj5e5Nd3+WECiIE5F3f5YQLap4lTd/lhAyjJ3ZN3+WEDduwt03f5YQPFEoIPd/lhABc40k93+WEAYV8mi3f5YQCzgXbLd/lhAQGnywd3+WEBT8obR3f5YQGd7G+Hd/lhAewSw8N3+WECOjUQA3v5YQKEW2Q/e/lhAtp9tH97+WEDJKAIv3v5YQNyxlj7e/lhA8TorTt7+WEAExL9d3v5YQBdNVG3e/lhAK9bofN7+WEA/X32M3v5YQFLoEZze/lhAZnGmq97+WEB6+jq73v5YQI2Dz8re/lhAoQxk2t7+WEC1lfjp3v5YQMgejfne/lhA3KchCd/+WEDwMLYY3/5YQAO6Sijf/lhAF0PfN9/+WEAqzHNH3/5YQD5VCFff/lhAUt6cZt/+WEBlZzF23/5YQHnwxYXf/lhAjXlald/+WECgAu+k3/5YQLSLg7Tf/lhAyBQYxN/+WEDbnazT3/5YQO8mQePf/lhAA7DV8t/+WEAWOWoC4P5YQCnC/hHg/lhAPkuTIeD+WEBR1Ccx4P5YQGRdvEDg/lhAeOZQUOD+WECMb+Vf4P5YQJ/4eW/g/lhAs4EOf+D+WEDHCqOO4P5YQNqTN57g/lhA7hzMreD+WEACpmC94P5YQBUv9czg/lhAKbiJ3OD+WEA9QR7s4P5YQFDKsvvg/lhAZFNHC+H+WEB43Nsa4f5YQItlcCrh/lhAn+4EOuH+WECyd5lJ4f5YQMYALlnh/lhA2onCaOH+WEDtEld44f5YQAGc64fh/lhAFSWAl+H+WEAorhSn4f5YQDw3qbbh/lhAUMA9xuH+WEBjSdLV4f5YQHbSZuXh/lhAi1v79OH+WECe5I8E4v5YQLFtJBTi/lhAxva4I+L+WEDZf00z4v5YQOwI4kLi/lhAAJJ2UuL+WEAUGwti4v5YQCekn3Hi/lhAOy00geL+WEBPtsiQ4v5YQGI/XaDi/lhAdsjxr+L+WECKUYa/4v5YQJ3aGs/i/lhAsGOv3uL+WEDF7EPu4v5YQNh12P3i/lhA6/5sDeP+WED/hwEd4/5YQBMRlizj/lhAJpoqPOP+WEA6I79L4/5YQE6sU1vj/lhAYTXoauP+WEB1vnx64/5YQIlHEYrj/lhAnNClmeP+WECwWTqp4/5YQMTizrjj/lhA12tjyOP+WEDr9PfX4/5YQP59jOfj/lhAEgch9+P+WEAmkLUG5P5YQDkZShbk/lhATaLeJeT+WEBhK3M15P5YQHS0B0Xk/lhAiD2cVOT+WECcxjBk5P5YQK9PxXPk/lhAw9hZg+T+WEDXYe6S5P5YQOrqgqLk/lhA/XMXsuT+WEAS/avB5P5YQCWGQNHk/lhAOA/V4OT+WEBNmGnw5P5YQGAh/v/k/lhAc6qSD+X+WECHMycf5f5YQJu8uy7l/lhArkVQPuX+WEDCzuRN5f5YQNZXeV3l/lhA6eANbeX+WED9aaJ85f5YQBHzNozl/lhAJHzLm+X+WEA4BWCr5f5YQEyO9Lrl/lhAXxeJyuX+WEBzoB3a5f5YQIYpsunl/lhAmrJG+eX+WECuO9sI5v5YQMHEbxjm/lhA1U0EKOb+WEDp1pg35v5YQPxfLUfm/lhAEOnBVub+WEAkclZm5v5YQDf76nXm/lhAS4R/heb+WEBfDRSV5v5YQHKWqKTm/lhAhR89tOb+WECaqNHD5v5YQK0xZtPm/lhAwLr64ub+WEDUQ4/y5v5YQOjMIwLn/lhA+1W4Eef+WEAP30wh5/5YQCNo4TDn/lhANvF1QOf+WEBKegpQ5/5YQF4Dn1/n/lhAcYwzb+f+WECFFch+5/5YQJmeXI7n/lhArCfxnef+WEDAsIWt5/5YQNQ5Gr3n/lhA58KuzOf+WED7S0Pc5/5YQA7V1+vn/lhAIl5s++f+WEA25wAL6P5YQElwlRro/lhAXfkpKuj+WEBxgr456P5YQIQLU0no/lhAmJTnWOj+WECsHXxo6P5YQL+mEHjo/lhA0i+lh+j+WEDnuDmX6P5YQPpBzqbo/lhADctituj+WEAiVPfF6P5YQDXdi9Xo/lhASGYg5ej+WEBc77T06P5YQHB4SQTp/lhAgwHeE+n+WECXinIj6f5YQKsTBzPp/lhAvpybQun+WEDSJTBS6f5YQOauxGHp/lhA+TdZcen+WEANwe2A6f5YQCFKgpDp/lhANNMWoOn+WEBIXKuv6f5YQFvlP7/p/lhAb27Uzun+WECD92je6f5YQJaA/e3p/lhAqgmS/en+WEC+kiYN6v5YQNEbuxzq/lhA5aRPLOr+WED5LeQ76v5YQAy3eEvq/lhAIEANW+r+WEA0yaFq6v5YQEdSNnrq/lhAWtvKier+WEBvZF+Z6v5YQILt86jq/lhAlXaIuOr+WECp/xzI6v5YQL2Isdfq/lhA0BFG5+r+WEDkmtr26v5YQPgjbwbr/lhAC60DFuv+WEAfNpgl6/5YQDO/LDXr/lhARkjBROv+WEBZ0VVU6/5YQG5a6mPr/lhAgeN+c+v+WECUbBOD6/5YQKn1p5Lr/lhAvH48ouv+WEDPB9Gx6/5YQOOQZcHr/lhA9xn60Ov+WEAKo47g6/5YQB4sI/Dr/lhAMrW3/+v+WEBFPkwP7P5YQFnH4B7s/lhAbVB1Luz+WECA2Qk+7P5YQJRink3s/lhAqOsyXez+WEC7dMds7P5YQM/9W3zs/lhA4obwi+z+WED2D4Wb7P5YQAqZGavs/lhAHSKuuuz+WEAxq0LK7P5YQEU019ns/lhAWL1r6ez+WEBsRgD57P5YQIDPlAjt/lhAk1gpGO3+WECn4b0n7f5YQLtqUjft/lhAzvPmRu3+WEDhfHtW7f5YQPYFEGbt/lhACY+kde3+WEAcGDmF7f5YQDChzZTt/lhARCpipO3+WEBXs/az7f5YQGs8i8Pt/lhAf8Uf0+3+WECSTrTi7f5YQKbXSPLt/lhAumDdAe7+WEDN6XER7v5YQOFyBiHu/lhA9fuaMO7+WEAIhS9A7v5YQBwOxE/u/lhAMJdYX+7+WEBDIO1u7v5YQFepgX7u/lhAajIWju7+WEB+u6qd7v5YQJJEP63u/lhApc3TvO7+WEC5VmjM7v5YQM3f/Nvu/lhA4GiR6+7+WED08SX77v5YQAh7ugrv/lhAGwRPGu/+WEAujeMp7/5YQEMWeDnv/lhAVp8MSe/+WEBpKKFY7/5YQH6xNWjv/lhAkTrKd+/+WECkw16H7/5YQLhM85bv/lhAzNWHpu/+WEDfXhy27/5YQPPnsMXv/lhAB3FF1e/+WEAa+tnk7/5YQC6DbvTv/lhAQgwDBPD+WEBVlZcT8P5YQGkeLCPw/lhAfafAMvD+WECQMFVC8P5YQKS56VHw/lhAt0J+YfD+WEDLyxJx8P5YQN9Up4Dw/lhA8t07kPD+WEAGZ9Cf8P5YQBrwZK/w/lhALXn5vvD+WEBBAo7O8P5YQFWLIt7w/lhAaBS37fD+WEB8nUv98P5YQJAm4Azx/lhAo690HPH+WEC2OAks8f5YQMvBnTvx/lhA3koyS/H+WEDx08Za8f5YQAZdW2rx/lhAGebvefH+WEAsb4SJ8f5YQED4GJnx/lhAVIGtqPH+WEBnCkK48f5YQHuT1sfx/lhAjxxr1/H+WECipf/m8f5YQLYulPbx/lhAyrcoBvL+WEDdQL0V8v5YQPHJUSXy/lhABVPmNPL+WEAY3HpE8v5YQCxlD1Ty/lhAP+6jY/L+WEBTdzhz8v5YQGcAzYLy/lhAeolhkvL+WECOEvah8v5YQKKbirHy/lhAtSQfwfL+WEDJrbPQ8v5YQN02SODy/lhA8L/c7/L+WEAESXH/8v5YQBfSBQ/z/lhAK1uaHvP+WEA+5C4u8/5YQFJtwz3z/lhAZvZXTfP+WEB5f+xc8/5YQI0IgWzz/lhAoZEVfPP+WEC0GqqL8/5YQMijPpvz/lhA3CzTqvP+WEDvtWe68/5YQAM//Mnz/lhAF8iQ2fP+WEAqUSXp8/5YQD3aufjz/lhAUmNOCPT+WEBl7OIX9P5YQHh1dyf0/lhAjP4LN/T+WECgh6BG9P5YQLMQNVb0/lhAx5nJZfT+WEDbIl519P5YQO6r8oT0/lhAAjWHlPT+WEAWvhuk9P5YQClHsLP0/lhAPdBEw/T+WEBRWdnS9P5YQGTibeL0/lhAeGsC8vT+WECM9JYB9f5YQJ99KxH1/lhAswbAIPX+WEDGj1Qw9f5YQNoY6T/1/lhA7qF9T/X+WEABKxJf9f5YQBW0pm71/lhAKT07fvX+WEA8xs+N9f5YQFBPZJ31/lhAZNj4rPX+WEB3YY289f5YQIrqIcz1/lhAn3O22/X+WECy/Err9f5YQMWF3/r1/lhA2g50Cvb+WEDtlwga9v5YQAAhnSn2/lhAFKoxOfb+WEAoM8ZI9v5YQDu8Wlj2/lhAT0XvZ/b+WEBjzoN39v5YQHZXGIf2/lhAiuCslvb+WECeaUGm9v5YQLHy1bX2/lhAxXtqxfb+WEDZBP/U9v5YQOyNk+T2/lhAABco9Pb+WEAToLwD9/5YQCcpURP3/lhAO7LlIvf+WEBOO3oy9/5YQGLEDkL3/lhAdk2jUff+WECJ1jdh9/5YQJ1fzHD3/lhAsehggPf+WEDEcfWP9/5YQNj6iZ/3/lhA7IMer/f+WED/DLO+9/5YQBKWR873/lhAJx/c3ff+WEA6qHDt9/5YQE0xBf33/lhAYrqZDPj+WEB1Qy4c+P5YQIjMwiv4/lhAnFVXO/j+WECw3utK+P5YQMNngFr4/lhA1/AUavj+WEDreal5+P5YQP4CPon4/lhAEozSmPj+WEAmFWeo+P5YQDme+7f4/lhATSeQx/j+WEBhsCTX+P5YQHQ5ueb4/lhAiMJN9vj+WECbS+IF+f5YQK/UdhX5/lhAw10LJfn+WEDW5p80+f5YQOpvNET5/lhA/vjIU/n+WEARgl1j+f5YQCUL8nL5/lhAOZSGgvn+WEBMHRuS+f5YQGCmr6H5/lhAdC9Esfn+WECHuNjA+f5YQJpBbdD5/lhAr8oB4Pn+WEDCU5bv+f5YQNXcKv/5/lhA6mW/Dvr+WED97lMe+v5YQBB46C36/lhAJAF9Pfr+WEA4ihFN+v5YQEsTplz6/lhAX5w6bPr+WEBzJc97+v5YQIauY4v6/lhAmjf4mvr+WECuwIyq+v5YQMFJIbr6/lhA1dK1yfr+WEDoW0rZ+v5YQPzk3uj6/lhAEG5z+Pr+WEAj9wcI+/5YQDeAnBf7/lhASwkxJ/v+WEBeksU2+/5YQHIbWkb7/lhAhaTuVfv+WECZLYNl+/5YQK22F3X7/lhAwD+shPv+WEDUyECU+/5YQOhR1aP7/lhA+9pps/v+WEAPZP7C+/5YQCLtktL7/lhANnYn4vv+WEBK/7vx+/5YQF2IUAH8/lhAcRHlEPz+WECFmnkg/P5YQJgjDjD8/lhArKyiP/z+WEDANTdP/P5YQNO+y178/lhA5kdgbvz+WED70PR9/P5YQA5aiY38/lhAIeMdnfz+WEA2bLKs/P5YQEn1Rrz8/lhAXH7by/z+WEBwB3Db/P5YQISQBOv8/lhAlxmZ+vz+WECroi0K/f5YQL8rwhn9/lhA0rRWKf3+WEDmPes4/f5YQPrGf0j9/lhADVAUWP3+WEAh2ahn/f5YQDViPXf9/lhASOvRhv3+WEBcdGaW/f5YQG/9+qX9/lhAg4aPtf3+WECXDyTF/f5YQKqYuNT9/lhAviFN5P3+WEDSquHz/f5YQOUzdgP+/lhA+bwKE/7+WEANRp8i/v5YQCDPMzL+/lhANFjIQf7+WEBI4VxR/v5YQFtq8WD+/lhAbvOFcP7+WECDfBqA/v5YQJYFr4/+/lhAqY5Dn/7+WEC+F9iu/v5YQNGgbL7+/lhA5CkBzv7+WED4spXd/v5YQAw8Ku3+/lhAH8W+/P7+WEAzTlMM//5YQEfX5xv//lhAWmB8K//+WEBu6RA7//5YQIJypUr//lhAlfs5Wv/+WECphM5p//5YQL0NY3n//lhA0Jb3iP/+WEDkH4yY//5YQPeoIKj//lhACzK1t//+WEAfu0nH//5YQDJE3tb//lhARs1y5v/+WEBaVgf2//5YQG3fmwUA/1hAgWgwFQD/WECV8cQkAP9YQKh6WTQA/1hAvAPuQwD/WEDQjIJTAP9YQOMVF2MA/1hA9p6rcgD/WEALKECCAP9YQB6x1JEA/1hAMTppoQD/WEBGw/2wAP9YQFlMksAA/1hAbNUm0AD/WECAXrvfAP9YQJTnT+8A/1hAp3Dk/gD/WEC7+XgOAf9YQM+CDR4B/1hA4guiLQH/WED2lDY9Af9YQAoey0wB/1hAHadfXAH/WEAxMPRrAf9YQES5iHsB/1hAWEIdiwH/WEBsy7GaAf9YQH9URqoB/1hAk93auQH/WECnZm/JAf9YQLrvA9kB/1hAzniY6AH/WEDiAS34Af9YQPWKwQcC/1hACRRWFwL/WEAdneomAv9YQDAmfzYC/1hARK8TRgL/WEBYOKhVAv9YQGvBPGUC/1hAfkrRdAL/WECT02WEAv9YQKZc+pMC/1hAueWOowL/WEDNbiOzAv9YQOH3t8IC/1hA9IBM0gL/WEAICuHhAv9YQByTdfEC/1hALxwKAQP/WEBDpZ4QA/9YQFcuMyAD/1hAarfHLwP/WEB+QFw/A/9YQJLJ8E4D/1hApVKFXgP/WEC52xluA/9YQMxkrn0D/1hA4O1CjQP/WEDzdtecA/9YQAcAbKwD/1hAG4kAvAP/WEAuEpXLA/9YQEKbKdsD/1hAViS+6gP/WEBprVL6A/9YQH025wkE/1hAkb97GQT/WECkSBApBP9YQLjRpDgE/1hAy1o5SAT/WEDf481XBP9YQPNsYmcE/1hABvb2dgT/WEAaf4uGBP9YQC4IIJYE/1hAQZG0pQT/WEBVGkm1BP9YQGmj3cQE/1hAfCxy1AT/WECQtQbkBP9YQKQ+m/ME/1hAt8cvAwX/WEDKUMQSBf9YQN/ZWCIF/1hA8mLtMQX/WEAF7IFBBf9YQBp1FlEF/1hALf6qYAX/WEBAhz9wBf9YQFQQ1H8F/1hAaJlojwX/WEB7Iv2eBf9YQI+rka4F/1hAozQmvgX/WEC2vbrNBf9YQMpGT90F/1hA3s/j7AX/WEDxWHj8Bf9YQAXiDAwG/1hAGWuhGwb/WEAs9DUrBv9YQEB9yjoG/1hAUwZfSgb/WEBnj/NZBv9YQHsYiGkG/1hAjqEceQb/WECiKrGIBv9YQLazRZgG/1hAyTzapwb/WEDdxW63Bv9YQPFOA8cG/1hABNiX1gb/WEAYYSzmBv9YQCzqwPUG/1hAP3NVBQf/WEBS/OkUB/9YQGeFfiQH/1hAeg4TNAf/WECNl6dDB/9YQKIgPFMH/1hAtanQYgf/WEDIMmVyB/9YQNy7+YEH/1hA8ESOkQf/WEADziKhB/9YQBdXt7AH/1hAK+BLwAf/WEA+aeDPB/9YQFLydN8H/1hAZnsJ7wf/WEB5BJ7+B/9YQI2NMg4I/1hAoBbHHQj/WEC0n1stCP9YQMgo8DwI/1hA27GETAj/WEDvOhlcCP9YQAPErWsI/1hAFk1Cewj/WEAq1taKCP9YQD5fa5oI/1hAUej/qQj/WEBlcZS5CP9YQHn6KMkI/1hAjIO92Aj/WECgDFLoCP9YQLSV5vcI/1hAxx57Bwn/WEDapw8XCf9YQO8wpCYJ/1hAAro4Ngn/WEAVQ81FCf9YQCnMYVUJ/1hAPVX2ZAn/WEBQ3op0Cf9YQGRnH4QJ/1hAePCzkwn/WECLeUijCf9YQJ8C3bIJ/1hAs4txwgn/WEDGFAbSCf9YQNqdmuEJ/1hA7iYv8Qn/WEABsMMACv9YQBU5WBAK/1hAKMLsHwr/WEA8S4EvCv9YQFDUFT8K/1hAY12qTgr/WEB35j5eCv9YQItv020K/1hAnvhnfQr/WECygfyMCv9YQMYKkZwK/1hA2ZMlrAr/WEDtHLq7Cv9YQAGmTssK/1hAFC/j2gr/WEAnuHfqCv9YQDxBDPoK/1hAT8qgCQv/WEBiUzUZC/9YQHfcySgL/1hAimVeOAv/WECd7vJHC/9YQLF3h1cL/1hAxQAcZwv/WEDYibB2C/9YQOwSRYYL/1hAAJzZlQv/WEATJW6lC/9YQCeuArUL/1hAOzeXxAv/WEBOwCvUC/9YQGFJwOML/1hAdtJU8wv/WECJW+kCDP9YQJzkfRIM/1hAsG0SIgz/WEDE9qYxDP9YQNd/O0EM/1hA6wjQUAz/WED/kWRgDP9YQBIb+W8M/1hAJqSNfwz/WEA6LSKPDP9YQE22tp4M/1hAYT9Lrgz/WEB1yN+9DP9YQIhRdM0M/1hAnNoI3Qz/WECvY53sDP9YQMPsMfwM/1hA13XGCw3/WEDq/lobDf9YQP6H7yoN/1hAEhGEOg3/WEAlmhhKDf9YQDkjrVkN/1hATaxBaQ3/WEBgNdZ4Df9YQHS+aogN/1hAiEf/lw3/WECb0JOnDf9YQK5ZKLcN/1hAw+K8xg3/WEDWa1HWDf9YQOn05eUN/1hA/n169Q3/WEARBw8FDv9YQCSQoxQO/1hAOBk4JA7/WEBMoswzDv9YQF8rYUMO/1hAc7T1Ug7/WECHPYpiDv9YQJrGHnIO/1hArk+zgQ7/WEDC2EeRDv9YQNVh3KAO/1hA6epwsA7/WED8cwXADv9YQBD9mc8O/1hAJIYu3w7/WEA3D8PuDv9YQEuYV/4O/1hAXyHsDQ//WEByqoAdD/9YQIYzFS0P/1hAmrypPA//WECtRT5MD/9YQMHO0lsP/1hA1Vdnaw//WEDo4Pt6D/9YQPxpkIoP/1hAEPMkmg//WEAjfLmpD/9YQDYFTrkP/1hAS47iyA//WEBeF3fYD/9YQHGgC+gP/1hAhSmg9w//WECZsjQHEP9YQKw7yRYQ/1hAwMRdJhD/WEDUTfI1EP9YQOfWhkUQ/1hA+18bVRD/WEAP6a9kEP9YQCJyRHQQ/1hANvvYgxD/WEBKhG2TEP9YQF0NAqMQ/1hAcZaWshD/WECEHyvCEP9YQJiov9EQ/1hArDFU4RD/WEC/uujwEP9YQNNDfQAR/1hA58wREBH/WED6VaYfEf9YQA7fOi8R/1hAImjPPhH/WEA18WNOEf9YQEl6+F0R/1hAXQONbRH/WEBwjCF9Ef9YQIMVtowR/1hAmJ5KnBH/WECrJ9+rEf9YQL6wc7sR/1hA0zkIyxH/WEDmwpzaEf9YQPlLMeoR/1hADdXF+RH/WEAhXloJEv9YQDTn7hgS/1hASHCDKBL/WEBc+Rc4Ev9YQG+CrEcS/1hAgwtBVxL/WECXlNVmEv9YQKodanYS/1hAvqb+hRL/WEDSL5OVEv9YQOW4J6US/1hA+UG8tBL/WEAMy1DEEv9YQCBU5dMS/1hANN154xL/WEBHZg7zEv9YQFvvogIT/1hAb3g3EhP/WECCAcwhE/9YQJaKYDET/1hAqhP1QBP/WEC9nIlQE/9YQNElHmAT/1hA5a6ybxP/WED4N0d/E/9YQAvB244T/1hAIEpwnhP/WEAz0wSuE/9YQEZcmb0T/1hAWuUtzRP/WEBubsLcE/9YQIH3VuwT/1hAlYDr+xP/WECpCYALFP9YQLySFBsU/1hA0BupKhT/WEDkpD06FP9YQPct0kkU/1hACrdmWRT/WEAfQPtoFP9YQDLJj3gU/1hARVIkiBT/WEBa27iXFP9YQG1kTacU/1hAgO3hthT/WECUdnbGFP9YQKj/CtYU/1hAu4if5RT/WEDPETT1FP9YQOOayAQV/1hA9iNdFBX/WEAKrfEjFf9YQB42hjMV/1hAMb8aQxX/WEBFSK9SFf9YQFjRQ2IV/1hAbFrYcRX/WECA42yBFf9YQJNsAZEV/1hAp/WVoBX/WEC7fiqwFf9YQM4Hv78V/1hA4pBTzxX/WED2GejeFf9YQAmjfO4V/1hAHSwR/hX/WEAxtaUNFv9YQEQ+Oh0W/1hAWMfOLBb/WEBsUGM8Fv9YQH/Z90sW/1hAkmKMWxb/WECn6yBrFv9YQLp0tXoW/1hAzf1Jihb/WEDhht6ZFv9YQPUPc6kW/1hACJkHuRb/WEAcIpzIFv9YQDCrMNgW/1hAQzTF5xb/WEBXvVn3Fv9YQGtG7gYX/1hAfs+CFhf/WECSWBcmF/9YQKbhqzUX/1hAuWpARRf/WEDN89RUF/9YQOB8aWQX/1hA9AX+cxf/WEAIj5KDF/9YQBsYJ5MX/1hAL6G7ohf/WEBDKlCyF/9YQFaz5MEX/1hAajx50Rf/WEB+xQ3hF/9YQJFOovAX/1hApdc2ABj/WEC5YMsPGP9YQMzpXx8Y/1hA33L0Lhj/WED0+4g+GP9YQAeFHU4Y/1hAGg6yXRj/WEAvl0ZtGP9YQEIg23wY/1hAValvjBj/WEBpMgScGP9YQH27mKsY/1hAkEQtuxj/WECkzcHKGP9YQLhWVtoY/1hAy9/q6Rj/WEDfaH/5GP9YQPPxEwkZ/1hABnuoGBn/WEAaBD0oGf9YQC6N0TcZ/1hAQRZmRxn/WEBVn/pWGf9YQGgoj2YZ/1hAfLEjdhn/WECQOriFGf9YQKPDTJUZ/1hAt0zhpBn/WEDL1XW0Gf9YQN5eCsQZ/1hA8uee0xn/WEAGcTPjGf9YQBn6x/IZ/1hALYNcAhr/WEBBDPERGv9YQFSVhSEa/1hAZx4aMRr/WEB8p65AGv9YQI8wQ1Aa/1hAornXXxr/WEC2QmxvGv9YQMrLAH8a/1hA3VSVjhr/WEDx3SmeGv9YQAVnvq0a/1hAGPBSvRr/WEAseefMGv9YQEACfNwa/1hAU4sQ7Br/WEBnFKX7Gv9YQHudOQsb/1hAjibOGhv/WECir2IqG/9YQLY49zkb/1hAycGLSRv/WEDdSiBZG/9YQPDTtGgb/1hABF1JeBv/WEAY5t2HG/9YQCtvcpcb/1hAP/gGpxv/WEBTgZu2G/9YQGYKMMYb/1hAepPE1Rv/WECOHFnlG/9YQKGl7fQb/1hAtC6CBBz/WEDJtxYUHP9YQNxAqyMc/1hA78k/Mxz/WEAEU9RCHP9YQBfcaFIc/1hAKmX9YRz/WEA+7pFxHP9YQFJ3JoEc/1hAZQC7kBz/WEB5iU+gHP9YQI0S5K8c/1hAoJt4vxz/WEC0JA3PHP9YQMitod4c/1hA2zY27hz/WEDuv8r9HP9YQANJXw0d/1hAFtLzHB3/WEApW4gsHf9YQD3kHDwd/1hAUW2xSx3/WEBk9kVbHf9YQHh/2mod/1hAjAhveh3/WECfkQOKHf9YQLMamJkd/1hAx6MsqR3/WEDaLMG4Hf9YQO61Vcgd/1hAAj/q1x3/WEAVyH7nHf9YQClRE/cd/1hAPNqnBh7/WEBQYzwWHv9YQGTs0CUe/1hAd3VlNR7/WECL/vlEHv9YQJ+HjlQe/1hAshAjZB7/WEDGmbdzHv9YQNoiTIMe/1hA7avgkh7/WEABNXWiHv9YQBW+CbIe/1hAKEeewR7/WEA70DLRHv9YQFBZx+Ae/1hAY+Jb8B7/WEB2a/D/Hv9YQIv0hA8f/1hAnn0ZHx//WECxBq4uH/9YQMWPQj4f/1hA2RjXTR//WEDsoWtdH/9YQAArAG0f/1hAFLSUfB//WEAnPSmMH/9YQDvGvZsf/1hAT09Sqx//WEBi2Oa6H/9YQHZhe8of/1hAiuoP2h//WECdc6TpH/9YQLH8OPkf/1hAxIXNCCD/WEDYDmIYIP9YQOyX9icg/1hA/yCLNyD/WEATqh9HIP9YQCcztFYg/1hAOrxIZiD/WEBORd11IP9YQGLOcYUg/1hAdVcGlSD/WECJ4JqkIP9YQJ1pL7Qg/1hAsPLDwyD/WEDDe1jTIP9YQNgE7eIg/1hA642B8iD/WED+FhYCIf9YQBKgqhEh/1hAJik/ISH/WEA5stMwIf9YQE07aEAh/1hAYcT8TyH/WEB0TZFfIf9YQIjWJW8h/1hAnF+6fiH/WECv6E6OIf9YQMNx450h/1hA1/p3rSH/WEDqgwy9If9YQP4Mocwh/1hAEpY13CH/WEAlH8rrIf9YQDmoXvsh/1hATDHzCiL/WEBguocaIv9YQHRDHCoi/1hAh8ywOSL/WECbVUVJIv9YQK/e2Vgi/1hAwmduaCL/WEDW8AJ4Iv9YQOp5l4ci/1hA/QIslyL/WEAQjMCmIv9YQCUVVbYi/1hAOJ7pxSL/WEBLJ37VIv9YQGCwEuUi/1hAczmn9CL/WECGwjsEI/9YQJpL0BMj/1hArtRkIyP/WEDBXfkyI/9YQNXmjUIj/1hA6W8iUiP/WED8+LZhI/9YQBCCS3Ej/1hAJAvggCP/WEA3lHSQI/9YQEsdCaAj/1hAX6adryP/WEByLzK/I/9YQIa4xs4j/1hAmUFb3iP/WECtyu/tI/9YQMFThP0j/1hA1NwYDST/WEDoZa0cJP9YQPzuQSwk/1hAD3jWOyT/WEAjAWtLJP9YQDeK/1ok/1hAShOUaiT/WEBenCh6JP9YQHIlvYkk/1hAha5RmST/WECYN+aoJP9YQK3Aergk/1hAwEkPyCT/WEDT0qPXJP9YQOdbOOck/1hA++TM9iT/WEAObmEGJf9YQCL39RUl/1hANoCKJSX/WEBJCR81Jf9YQF2Ss0Ql/1hAcRtIVCX/WECEpNxjJf9YQJctcXMl/1hArLYFgyX/WEC/P5qSJf9YQNLILqIl/1hA51HDsSX/WED62lfBJf9YQA1k7NAl/1hAIe2A4CX/WEA1dhXwJf9YQEj/qf8l/1hAXIg+Dyb/WEBwEdMeJv9YQIOaZy4m/1hAlyP8PSb/WECrrJBNJv9YQL41JV0m/1hA0r65bCb/WEDmR058Jv9YQPnQ4osm/1hADVp3myb/WEAg4wurJv9YQDRsoLom/1hASPU0yib/WEBbfsnZJv9YQG8HXukm/1hAg5Dy+Cb/WECWGYcIJ/9YQKqiGxgn/1hAviuwJyf/WEDRtEQ3J/9YQOU92UYn/1hA+cZtVif/WEAMUAJmJ/9YQB/ZlnUn/1hANGIrhSf/WEBH67+UJ/9YQFp0VKQn/1hAbv3osyf/WECChn3DJ/9YQJUPEtMn/1hAqZim4if/WEC9ITvyJ/9YQNCqzwEo/1hA5DNkESj/WED4vPggKP9YQAtGjTAo/1hAH88hQCj/WEAzWLZPKP9YQEbhSl8o/1hAWmrfbij/WEBu83N+KP9YQIF8CI4o/1hAlQWdnSj/WECojjGtKP9YQLwXxrwo/1hA0KBazCj/WEDjKe/bKP9YQPeyg+so/1hACzwY+yj/WEAexawKKf9YQDJOQRop/1hARtfVKSn/WEBZYGo5Kf9YQGzp/kgp/1hAgXKTWCn/WECU+ydoKf9YQKeEvHcp/1hAvA1Rhyn/WEDPluWWKf9YQOIfeqYp/1hA9qgOtin/WEAKMqPFKf9YQB27N9Up/1hAMUTM5Cn/WEBFzWD0Kf9YQFhW9QMq/1hAbN+JEyr/WECAaB4jKv9YQJPxsjIq/1hAp3pHQir/WEC7A9xRKv9YQM6McGEq/1hA4hUFcSr/WED1npmAKv9YQAkoLpAq/1hAHbHCnyr/WEAwOlevKv9YQETD674q/1hAWEyAzir/WEBr1RTeKv9YQH9eqe0q/1hAk+c9/Sr/WECmcNIMK/9YQLr5Zhwr/1hAzoL7Kyv/WEDhC5A7K/9YQPSUJEsr/1hACR65Wiv/WEAcp01qK/9YQC8w4nkr/1hARLl2iSv/WEBXQguZK/9YQGrLn6gr/1hAflQ0uCv/WECS3cjHK/9YQKVmXdcr/1hAue/x5iv/WEDNeIb2K/9YQOABGwYs/1hA9IqvFSz/WEAIFEQlLP9YQBud2DQs/1hALyZtRCz/WEBDrwFULP9YQFY4lmMs/1hAasEqcyz/WEB9Sr+CLP9YQJHTU5Is/1hApVzooSz/WEC45XyxLP9YQMxuEcEs/1hA4Pel0Cz/WEDzgDrgLP9YQAcKz+8s/1hAG5Nj/yz/WEAuHPgOLf9YQEKljB4t/1hAVS4hLi3/WEBpt7U9Lf9YQHxASk0t/1hAkMneXC3/WECkUnNsLf9YQLfbB3wt/1hAy2Sciy3/WEDf7TCbLf9YQPJ2xaot/1hABgBaui3/WEAaie7JLf9YQC0Sg9kt/1hAQZsX6S3/WEBVJKz4Lf9YQGitQAgu/1hAezbVFy7/WECQv2knLv9YQKNI/jYu/1hAttGSRi7/WEDKWidWLv9YQN7ju2Uu/1hA8WxQdS7/WEAF9uSELv9YQBl/eZQu/1hALAgOpC7/WEBAkaKzLv9YQFQaN8Mu/1hAZ6PL0i7/WEB7LGDiLv9YQI+19PEu/1hAoj6JAS//WEC2xx0RL/9YQMpQsiAv/1hA3dlGMC//WEDxYts/L/9YQATsb08v/1hAGHUEXy//WEAs/phuL/9YQD+HLX4v/1hAUxDCjS//WEBnmVadL/9YQHoi66wv/1hAjqt/vC//WECiNBTML/9YQLW9qNsv/1hAyEY96y//WEDdz9H6L/9YQPBYZgow/1hAA+L6GTD/WEAYa48pMP9YQCv0Izkw/1hAPn24SDD/WEBSBk1YMP9YQGaP4Wcw/1hAeRh2dzD/WECNoQqHMP9YQKEqn5Yw/1hAtLMzpjD/WEDIPMi1MP9YQNzFXMUw/1hA707x1DD/WEAD2IXkMP9YQBdhGvQw/1hAKuquAzH/WEA+c0MTMf9YQFH81yIx/1hAZYVsMjH/WEB5DgFCMf9YQIyXlVEx/1hAoCAqYTH/WEC0qb5wMf9YQMcyU4Ax/1hA27vnjzH/WEDvRHyfMf9YQALOEK8x/1hAFlelvjH/WEAq4DnOMf9YQD1pzt0x/1hAUPJi7TH/WEBle/f8Mf9YQHgEjAwy/1hAi40gHDL/WECgFrUrMv9YQLOfSTsy/1hAxijeSjL/WEDasXJaMv9YQO46B2oy/1hAAcSbeTL/WEAVTTCJMv9YQCnWxJgy/1hAPF9ZqDL/WEBQ6O23Mv9YQGRxgscy/1hAd/oW1zL/WECLg6vmMv9YQJ8MQPYy/1hAspXUBTP/WEDGHmkVM/9YQNmn/SQz/1hA7TCSNDP/WEABuiZEM/9YQBRDu1Mz/1hAKMxPYzP/WEA8VeRyM/9YQE/eeIIz/1hAY2cNkjP/WEB38KGhM/9YQIp5NrEz/1hAngLLwDP/WECyi1/QM/9YQMUU9N8z/1hA2J2I7zP/WEDtJh3/M/9YQACwsQ40/1hAEzlGHjT/WEAowtotNP9YQDtLbz00/1hATtQDTTT/WEBiXZhcNP9YQHbmLGw0/1hAiW/BezT/WECd+FWLNP9YQLGB6po0/1hAxAp/qjT/WEDYkxO6NP9YQOwcqMk0/1hA/6U82TT/WEATL9HoNP9YQCa4Zfg0/1hAOkH6BzX/WEBOyo4XNf9YQGFTIyc1/1hAddy3NjX/WECJZUxGNf9YQJzu4FU1/1hAsHd1ZTX/WEDDAAp1Nf9YQNeJnoQ1/1hA6xIzlDX/WED+m8ejNf9YQBIlXLM1/1hAJq7wwjX/WEA5N4XSNf9YQE3AGeI1/1hAYEmu8TX/WEB00kIBNv9YQIhb1xA2/1hAm+RrIDb/WECvbQAwNv9YQMP2lD82/1hA1n8pTzb/WEDqCL5eNv9YQP6RUm42/1hAERvnfTb/WEAkpHuNNv9YQDktEJ02/1hATLakrDb/WEBfPzm8Nv9YQHTIzcs2/1hAh1Fi2zb/WECa2vbqNv9YQK5ji/o2/1hAwuwfCjf/WEDVdbQZN/9YQOn+SCk3/1hA/YfdODf/WEAQEXJIN/9YQCSaBlg3/1hAOCObZzf/WEBLrC93N/9YQF81xIY3/1hAc75Yljf/WECGR+2lN/9YQJrQgbU3/1hArVkWxTf/WEDB4qrUN/9YQNVrP+Q3/1hA6PTT8zf/WED8fWgDOP9YQBAH/RI4/1hAI5CRIjj/WEA3GSYyOP9YQEuiukE4/1hAXitPUTj/WEBytONgOP9YQIY9eHA4/1hAmcYMgDj/WECsT6GPOP9YQMHYNZ84/1hA1GHKrjj/WEDn6l6+OP9YQPxz8804/1hAD/2H3Tj/WEAihhztOP9YQDYPsfw4/1hASphFDDn/WEBdIdobOf9YQHGqbis5/1hAhTMDOzn/WECYvJdKOf9YQKxFLFo5/1hAwM7AaTn/WEDTV1V5Of9YQOfg6Yg5/1hA+2l+mDn/WEAO8xKoOf9YQCJ8p7c5/1hANQU8xzn/WEBJjtDWOf9YQF0XZeY5/1hAcKD59Tn/WECEKY4FOv9YQJiyIhU6/1hAqzu3JDr/WEC/xEs0Ov9YQNNN4EM6/1hA5tZ0Uzr/WED6XwljOv9YQA7pnXI6/1hAIXIygjr/WEA0+8aROv9YQEmEW6E6/1hAXA3wsDr/WEBvloTAOv9YQIQfGdA6/1hAl6it3zr/WECqMULvOv9YQL661v46/1hA0kNrDjv/WEDlzP8dO/9YQPlVlC07/1hADd8oPTv/WEAgaL1MO/9YQDTxUVw7/1hASHrmazv/WEBbA3t7O/9YQG+MD4s7/1hAghWkmjv/WECWnjiqO/9YQKonzbk7/1hAvbBhyTv/WEDROfbYO/9YQOXCiug7/1hA+Esf+Dv/WEAM1bMHPP9YQCBeSBc8/1hAM+fcJjz/WEBHcHE2PP9YQFv5BUY8/1hAboKaVTz/WECCCy9lPP9YQJaUw3Q8/1hAqR1YhDz/WEC8puyTPP9YQNEvgaM8/1hA5LgVszz/WED3QarCPP9YQAvLPtI8/1hAH1TT4Tz/WEAy3WfxPP9YQEZm/AA9/1hAWu+QED3/WEBteCUgPf9YQIEBui89/1hAlYpOPz3/WECoE+NOPf9YQLycd149/1hA0CUMbj3/WEDjrqB9Pf9YQPc3NY09/1hACsHJnD3/WEAeSl6sPf9YQDHT8rs9/1hARVyHyz3/WEBZ5RvbPf9YQGxusOo9/1hAgPdE+j3/WECUgNkJPv9YQKcJbhk+/1hAu5ICKT7/WEDPG5c4Pv9YQOKkK0g+/1hA9i3AVz7/WEAJt1RnPv9YQB1A6XY+/1hAMcl9hj7/WEBEUhKWPv9YQFjbpqU+/1hAbGQ7tT7/WEB/7c/EPv9YQJN2ZNQ+/1hAp//44z7/WEC6iI3zPv9YQM4RIgM//1hA4pq2Ej//WED1I0siP/9YQAit3zE//1hAHTZ0QT//WEAwvwhRP/9YQENInWA//1hAWNExcD//WEBrWsZ/P/9YQH7jWo8//1hAkmzvnj//WECm9YOuP/9YQLl+GL4//1hAzQetzT//WEDhkEHdP/9YQPQZ1uw//1hACKNq/D//WEAcLP8LQP9YQC+1kxtA/1hAQz4oK0D/WEBXx7w6QP9YQGpQUUpA/1hAftnlWUD/WECRYnppQP9YQKXrDnlA/1hAuXSjiED/WEDM/TeYQP9YQOCGzKdA/1hA9A9ht0D/WEAHmfXGQP9YQBsiitZA/1hAL6se5kD/WEBCNLP1QP9YQFa9RwVB/1hAakbcFEH/WEB9z3AkQf9YQJBYBTRB/1hApeGZQ0H/WEC4ai5TQf9YQMvzwmJB/1hA4HxXckH/WEDzBeyBQf9YQAaPgJFB/1hAGhgVoUH/WEAuoamwQf9YQEEqPsBB/1hAVbPSz0H/WEBpPGffQf9YQHzF++5B/1hAkE6Q/kH/WECk1yQOQv9YQLdguR1C/1hAy+lNLUL/WEDecuI8Qv9YQPL7dkxC/1hABoULXEL/WEAZDqBrQv9YQC2XNHtC/1hAQSDJikL/WEBUqV2aQv9YQGgy8qlC/1hAfLuGuUL/WECPRBvJQv9YQKPNr9hC/1hAt1ZE6EL/WEDK39j3Qv9YQN5obQdD/1hA8vEBF0P/WEAFe5YmQ/9YQBgEKzZD/1hALY2/RUP/WEBAFlRVQ/9YQFOf6GRD/1hAZyh9dEP/WEB7sRGEQ/9YQI46ppND/1hAosM6o0P/WEC2TM+yQ/9YQMnVY8JD/1hA3V740UP/WEDx54zhQ/9YQARxIfFD/1hAGPq1AET/WEAsg0oQRP9YQD8M3x9E/1hAU5VzL0T/WEBmHgg/RP9YQHqnnE5E/1hAjjAxXkT/WEChucVtRP9YQLVCWn1E/1hAycvujET/WEDcVIOcRP9YQPDdF6xE/1hABGesu0T/WEAX8EDLRP9YQCt51dpE/1hAPwJq6kT/WEBSi/75RP9YQGUUkwlF/1hAep0nGUX/WECNJrwoRf9YQKCvUDhF/1hAtTjlR0X/WEDIwXlXRf9YQNtKDmdF/1hA79OidkX/WEADXTeGRf9YQBbmy5VF/1hAKm9gpUX/WEA++PS0Rf9YQFGBicRF/1hAZQoe1EX/WEB5k7LjRf9YQIwcR/NF/1hAn6XbAkb/WEC0LnASRv9YQMe3BCJG/1hA2kCZMUb/WEDuyS1BRv9YQAJTwlBG/1hAFdxWYEb/WEApZetvRv9YQD3uf39G/1hAUHcUj0b/WEBkAKmeRv9YQHiJPa5G/1hAixLSvUb/WECfm2bNRv9YQLMk+9xG/1hAxq2P7Eb/WEDaNiT8Rv9YQO2/uAtH/1hAAUlNG0f/WEAV0uEqR/9YQChbdjpH/1hAPOQKSkf/WEBQbZ9ZR/9YQGP2M2lH/1hAd3/IeEf/WECLCF2IR/9YQJ6R8ZdH/1hAshqGp0f/WEDGoxq3R/9YQNksr8ZH/1hA7LVD1kf/WEABP9jlR/9YQBTIbPVH/1hAJ1EBBUj/WEA82pUUSP9YQE9jKiRI/1hAYuy+M0j/WEB2dVNDSP9YQIr+51JI/1hAnYd8Ykj/WECxEBFySP9YQMWZpYFI/1hA2CI6kUj/WEDsq86gSP9YQAA1Y7BI/1hAE773v0j/WEAnR4zPSP9YQDrQIN9I/1hATlm17kj/WEBi4kn+SP9YQHVr3g1J/1hAifRyHUn/WECdfQctSf9YQLAGnDxJ/1hAxI8wTEn/WEDYGMVbSf9YQOuhWWtJ/1hA/yruekn/WEATtIKKSf9YQCY9F5pJ/1hAOsarqUn/WEBOT0C5Sf9YQGHY1MhJ/1hAdGFp2En/WECJ6v3nSf9YQJxzkvdJ/1hAr/wmB0r/WEDDhbsWSv9YQNcOUCZK/1hA6pfkNUr/WED+IHlFSv9YQBKqDVVK/1hAJTOiZEr/WEA5vDZ0Sv9YQE1Fy4NK/1hAYM5fk0r/WEB0V/SiSv9YQIjgiLJK/1hAm2kdwkr/WECv8rHRSv9YQMJ7RuFK/1hA1gTb8Er/WEDqjW8AS/9YQP0WBBBL/1hAEaCYH0v/WEAlKS0vS/9YQDiywT5L/1hATDtWTkv/WEBgxOpdS/9YQHNNf21L/1hAh9YTfUv/WECbX6iMS/9YQK7oPJxL/1hAwXHRq0v/WEDW+mW7S/9YQOmD+spL/1hA/AyP2kv/WEARliPqS/9YQCQfuPlL/1hAN6hMCUz/WEBLMeEYTP9YQF+6dShM/1hAckMKOEz/WECGzJ5HTP9YQJpVM1dM/1hArd7HZkz/WEDBZ1x2TP9YQNXw8IVM/1hA6HmFlUz/WED8AhqlTP9YQBCMrrRM/1hAIxVDxEz/WEA3ntfTTP9YQEonbONM/1hAXrAA80z/WEByOZUCTf9YQIXCKRJN/1hAmUu+IU3/WECt1FIxTf9YQMBd50BN/1hA1OZ7UE3/WEDobxBgTf9YQPv4pG9N/1hAD4I5f03/WEAjC86OTf9YQDaUYp5N/1hASR33rU3/WEBepou9Tf9YQHEvIM1N/1hAhLi03E3/WECYQUnsTf9YQKzK3ftN/1hAv1NyC07/WEDT3AYbTv9YQOdlmypO/1hA+u4vOk7/WEAOeMRJTv9YQCIBWVlO/1hANYrtaE7/WEBIE4J4Tv9YQF2cFohO/1hAcCWrl07/WECDrj+nTv9YQJg31LZO/1hAq8Boxk7/WEC+Sf3VTv9YQNLSkeVO/1hA5lsm9U7/WED55LoET/9YQA1uTxRP/1hAIffjI0//WEA0gHgzT/9YQEgJDUNP/1hAXJKhUk//WEBvGzZiT/9YQIOkynFP/1hAli1fgU//WECqtvOQT/9YQL4/iKBP/1hA0cgcsE//WEDlUbG/T/9YQPnaRc9P/1hADGTa3k//WEAg7W7uT/9YQDR2A/5P/1hAR/+XDVD/WEBbiCwdUP9YQG8RwSxQ/1hAgppVPFD/WECWI+pLUP9YQKqsfltQ/1hAvTUTa1D/WEDQvqd6UP9YQOVHPIpQ/1hA+NDQmVD/WEALWmWpUP9YQB/j+bhQ/1hAM2yOyFD/WEBG9SLYUP9YQFp+t+dQ/1hAbgdM91D/WECBkOAGUf9YQJUZdRZR/1hAqaIJJlH/WEC8K541Uf9YQNC0MkVR/1hA5D3HVFH/WED3xltkUf9YQAtQ8HNR/1hAHtmEg1H/WEAyYhmTUf9YQEbrraJR/1hAWXRCslH/WEBt/dbBUf9YQIGGa9FR/1hAlA8A4VH/WEComJTwUf9YQLwhKQBS/1hAz6q9D1L/WEDjM1IfUv9YQPe85i5S/1hACkZ7PlL/WEAdzw9OUv9YQDJYpF1S/1hAReE4bVL/WEBYas18Uv9YQG3zYYxS/1hAgHz2m1L/WECTBYurUv9YQKeOH7tS/1hAuxe0ylL/WEDOoEjaUv9YQOIp3elS/1hA9rJx+VL/WEAJPAYJU/9YQB3FmhhT/1hAMU4vKFP/WEBE18M3U/9YQFhgWEdT/1hAbOnsVlP/WEB/coFmU/9YQJP7FXZT/1hApoSqhVP/WEC6DT+VU/9YQM6W06RT/1hA4R9otFP/WED1qPzDU/9YQAkykdNT/1hAHLsl41P/WEAwRLryU/9YQETNTgJU/1hAV1bjEVT/WEBr33chVP9YQH9oDDFU/1hAkvGgQFT/WEClejVQVP9YQLoDyl9U/1hAzYxeb1T/WEDgFfN+VP9YQPSeh45U/1hACCgcnlT/WEAbsbCtVP9YQC86Rb1U/1hAQ8PZzFT/WEBWTG7cVP9YQGrVAuxU/1hAfl6X+1T/WECR5ysLVf9YQKVwwBpV/1hAuflUKlX/WEDMguk5Vf9YQOALfklV/1hA9JQSWVX/WEAHHqdoVf9YQBunO3hV/1hALjDQh1X/WEBCuWSXVf9YQFZC+aZV/1hAacuNtlX/WEB9VCLGVf9YQJHdttVV/1hApGZL5VX/WEC479/0Vf9YQMx4dARW/1hA3wEJFFb/WEDyip0jVv9YQAcUMjNW/1hAGp3GQlb/WEAtJltSVv9YQEKv72FW/1hAVTiEcVb/WEBowRiBVv9YQHxKrZBW/1hAkNNBoFb/WECjXNavVv9YQLflar9W/1hAy27/zlb/WEDe95PeVv9YQPKAKO5W/1hABgq9/Vb/WEAZk1ENV/9YQCwc5hxX/1hAQaV6LFf/WEBULg88V/9YQGe3o0tX/1hAe0A4W1f/WECPycxqV/9YQKJSYXpX/1hAttv1iVf/WEDKZIqZV/9YQN3tHqlX/1hA8XazuFf/WEAFAEjIV/9YQBiJ3NdX/1hALBJx51f/WEBAmwX3V/9YQFMkmgZY/1hAZ60uFlj/WEB6NsMlWP9YQI6/VzVY/1hAokjsRFj/WEC10YBUWP9YQMlaFWRY/1hA3eOpc1j/WEDwbD6DWP9YQAT20pJY/1hAGH9nolj/WEArCPyxWP9YQD+RkMFY/1hAUxol0Vj/WEBmo7ngWP9YQHksTvBY/1hAjrXi/1j/WEChPncPWf9YQLTHCx9Z/1hAyVCgLln/WEDc2TQ+Wf9YQO9iyU1Z/1hAA+xdXVn/WEAXdfJsWf9YQCr+hnxZ/1hAPocbjFn/WEBSELCbWf9YQGWZRKtZ/1hAeSLZuln/WECNq23KWf9YQKA0AtpZ/1hAtL2W6Vn/WEDIRiv5Wf9YQNvPvwha/1hA71hUGFr/WEAC4ugnWv9YQBZrfTda/1hAKvQRR1r/WEA9faZWWv9YQFEGO2Za/1hAZY/PdVr/WEB4GGSFWv9YQIyh+JRa/1hAoCqNpFr/WECzsyG0Wv9YQMc8tsNa/1hA28VK01r/WEDuTt/iWv9YQAHYc/Ja/1hAFmEIAlv/WEAp6pwRW/9YQDxzMSFb/1hAUPzFMFv/WEBkhVpAW/9YQHcO709b/1hAi5eDX1v/WECfIBhvW/9YQLKprH5b/1hAxjJBjlv/WEDau9WdW/9YQO1Eaq1b/1hAAc7+vFv/WEAVV5PMW/9YQCjgJ9xb/1hAPGm861v/WEBQ8lD7W/9YQGN75Qpc/1hAdwR6Glz/WECKjQ4qXP9YQJ4Wozlc/1hAsp83SVz/WEDFKMxYXP9YQNmxYGhc/1hA7Tr1d1z/WEAAxImHXP9YQBRNHpdc/1hAKNayplz/WEA7X0e2XP9YQE7o28Vc/1hAY3Fw1Vz/WEB2+gTlXP9YQImDmfRc/1hAngwuBF3/WECxlcITXf9YQMQeVyNd/1hA2KfrMl3/WEDsMIBCXf9YQP+5FFJd/1hAE0OpYV3/WEAnzD1xXf9YQDpV0oBd/1hATt5mkF3/WEBiZ/ufXf9YQHXwj69d/1hAiXkkv13/WECdArnOXf9YQLCLTd5d/1hAxBTi7V3/WEDXnXb9Xf9YQOsmCw1e/1hA/6+fHF7/WEASOTQsXv9YQCbCyDte/1hAOktdS17/WEBN1PFaXv9YQGFdhmpe/1hAdeYael7/WECIb6+JXv9YQJz4Q5le/1hAsIHYqF7/WEDDCm24Xv9YQNaTAche/1hA6xyW117/WED+pSrnXv9YQBEvv/Ze/1hAJbhTBl//WEA5QegVX/9YQEzKfCVf/1hAYFMRNV//WEB03KVEX/9YQIdlOlRf/1hAm+7OY1//WECvd2NzX/9YQMIA+IJf/1hA1YmMkl//WEDqEiGiX/9YQP2btbFf/1hAECVKwV//WEAlrt7QX/9YQDg3c+Bf/1hAS8AH8F//WEBfSZz/X/9YQHPSMA9g/1hAhlvFHmD/WECa5FkuYP9YQK5t7j1g/1hAwfaCTWD/WEDVfxddYP9YQOkIrGxg/1hA/JFAfGD/WEAQG9WLYP9YQCSkaZtg/1hANy3+qmD/WEBLtpK6YP9YQF4/J8pg/1hAcsi72WD/WECGUVDpYP9YQJna5Phg/1hArWN5CGH/WEDB7A0YYf9YQNR1oidh/1hA6P42N2H/WED8h8tGYf9YQA8RYFZh/1hAI5r0ZWH/WEA3I4l1Yf9YQEqsHYVh/1hAXTWylGH/WEByvkakYf9YQIVH27Nh/1hAmNBvw2H/WECsWQTTYf9YQMDimOJh/1hA02st8mH/WEDn9MEBYv9YQPt9VhFi/1hADgfrIGL/WEAikH8wYv9YQDYZFEBi/1hASaKoT2L/WEBdKz1fYv9YQHG00W5i/1hAhD1mfmL/WECYxvqNYv9YQKxPj51i/1hAv9gjrWL/WEDTYbi8Yv9YQObqTMxi/1hA+nPh22L/WEAO/XXrYv9YQCGGCvti/1hANQ+fCmP/WEBJmDMaY/9YQFwhyClj/1hAcKpcOWP/WECEM/FIY/9YQJe8hVhj/1hAqkUaaGP/WEC/zq53Y/9YQNJXQ4dj/1hA5eDXlmP/WED6aWymY/9YQA3zALZj/1hAIHyVxWP/WEA0BSrVY/9YQEiOvuRj/1hAWxdT9GP/WEBvoOcDZP9YQIMpfBNk/1hAlrIQI2T/WECqO6UyZP9YQL7EOUJk/1hA0U3OUWT/WEDl1mJhZP9YQPlf93Bk/1hADOmLgGT/WEAgciCQZP9YQDP7tJ9k/1hAR4RJr2T/WEBbDd6+ZP9YQG6Wcs5k/1hAgh8H3mT/WECWqJvtZP9YQKkxMP1k/1hAvbrEDGX/WEDRQ1kcZf9YQOTM7Stl/1hA+FWCO2X/WEAM3xZLZf9YQB9oq1pl/1hAMvE/amX/WEBHetR5Zf9YQFoDaYll/1hAbYz9mGX/WECCFZKoZf9YQJWeJrhl/1hAqCe7x2X/WEC8sE/XZf9YQNA55OZl/1hA48J49mX/WED3Sw0GZv9YQAvVoRVm/1hAHl42JWb/WEAy58o0Zv9YQEZwX0Rm/1hAWfnzU2b/WEBtgohjZv9YQIELHXNm/1hAlJSxgmb/WECoHUaSZv9YQLum2qFm/1hAzy9vsWb/WEDjuAPBZv9YQPZBmNBm/1hACsss4Gb/WEAeVMHvZv9YQDHdVf9m/1hARWbqDmf/WEBZ734eZ/9YQGx4Ey5n/1hAgAGoPWf/WECTijxNZ/9YQKcT0Vxn/1hAupxlbGf/WEDOJfp7Z/9YQOKujotn/1hA9Tcjm2f/WEAJwbeqZ/9YQB1KTLpn/1hAMNPgyWf/WEBEXHXZZ/9YQFjlCeln/1hAa26e+Gf/WEB/9zIIaP9YQJOAxxdo/1hApglcJ2j/WEC5kvA2aP9YQM4bhUZo/1hA4aQZVmj/WED0La5laP9YQAi3QnVo/1hAHEDXhGj/WEAvyWuUaP9YQENSAKRo/1hAV9uUs2j/WEBqZCnDaP9YQH7tvdJo/1hAknZS4mj/WECl/+bxaP9YQLmIewFp/1hAzREQEWn/WEDgmqQgaf9YQPQjOTBp/1hACK3NP2n/WEAbNmJPaf9YQC+/9l5p/1hAQkiLbmn/WEBW0R9+af9YQGpatI1p/1hAfeNInWn/WECRbN2saf9YQKX1cbxp/1hAuH4GzGn/WEDMB5vbaf9YQOCQL+tp/1hA8xnE+mn/WEAGo1gKav9YQBss7Rlq/1hALrWBKWr/WEBBPhY5av9YQFbHqkhq/1hAaVA/WGr/WEB82dNnav9YQJBiaHdq/1hApOv8hmr/WEC3dJGWav9YQMv9JaZq/1hA34a6tWr/WEDyD0/Fav9YQAaZ49Rq/1hAGiJ45Gr/WEAtqwz0av9YQEE0oQNr/1hAVb01E2v/WEBoRsoia/9YQHzPXjJr/1hAj1jzQWv/WECj4YdRa/9YQLdqHGFr/1hAyvOwcGv/WEDefEWAa/9YQPIF2o9r/1hABY9un2v/WEAZGAOva/9YQC2hl75r/1hAQCoszmv/WEBUs8Dda/9YQGg8Ve1r/1hAe8Xp/Gv/WECOTn4MbP9YQKPXEhxs/1hAtmCnK2z/WEDJ6Ts7bP9YQN5y0Eps/1hA8ftkWmz/WEAEhflpbP9YQBgOjnls/1hALJciiWz/WEA/ILeYbP9YQFOpS6hs/1hAZzLgt2z/WEB6u3THbP9YQI5ECdds/1hAos2d5mz/WEC1VjL2bP9YQMnfxgVt/1hA3WhbFW3/WEDw8e8kbf9YQAR7hDRt/1hAFwQZRG3/WEArja1Tbf9YQD8WQmNt/1hAUp/Wcm3/WEBmKGuCbf9YQHqx/5Ft/1hAjTqUoW3/WEChwyixbf9YQLVMvcBt/1hAyNVR0G3/WEDcXubfbf9YQPDneu9t/1hAA3EP/23/WEAW+qMObv9YQCuDOB5u/1hAPgzNLW7/WEBRlWE9bv9YQGYe9kxu/1hAeaeKXG7/WECMMB9sbv9YQKC5s3tu/1hAtEJIi27/WEDHy9yabv9YQNtUcapu/1hA790Fum7/WEACZ5rJbv9YQBbwLtlu/1hAKnnD6G7/WEA9Alj4bv9YQFGL7Adv/1hAZBSBF2//WEB4nRUnb/9YQIwmqjZv/1hAn68+Rm//WECzONNVb/9YQMfBZ2Vv/1hA2kr8dG//WEDu05CEb/9YQAFdJZRv/1hAFea5o2//WEApb06zb/9YQDz44sJv/1hAUIF30m//WEBkCgzib/9YQHeToPFv/1hAixw1AXD/WECepckQcP9YQLIuXiBw/1hAxrfyL3D/WEDZQIc/cP9YQO3JG09w/1hAAVOwXnD/WEAU3ERucP9YQChl2X1w/1hAPO5tjXD/WEBPdwKdcP9YQGIAl6xw/1hAd4krvHD/WECKEsDLcP9YQJ2bVNtw/1hAsiTp6nD/WEDFrX36cP9YQNg2Egpx/1hA7L+mGXH/WEAASTspcf9YQBPSzzhx/1hAJ1tkSHH/WEA75PhXcf9YQE5tjWdx/1hAYvYhd3H/WEB2f7aGcf9YQIkIS5Zx/1hAnZHfpXH/WECxGnS1cf9YQMSjCMVx/1hA2Cyd1HH/WEDrtTHkcf9YQP8+xvNx/1hAE8haA3L/WEAmUe8Scv9YQDragyJy/1hATmMYMnL/WEBh7KxBcv9YQHV1QVFy/1hAif7VYHL/WECch2pwcv9YQLAQ/39y/1hAxJmTj3L/WEDXIiifcv9YQOqrvK5y/1hA/zRRvnL/WEASvuXNcv9YQCVHet1y/1hAOtAO7XL/WEBNWaP8cv9YQGDiNwxz/1hAdGvMG3P/WECI9GArc/9YQJt99Tpz/1hArwaKSnP/WEDDjx5ac/9YQNYYs2lz/1hA6qFHeXP/WED+KtyIc/9YQBG0cJhz/1hAJT0FqHP/WEA5xpm3c/9YQExPLsdz/1hAYNjC1nP/WEBzYVfmc/9YQIfq6/Vz/1hAm3OABXT/WECu/BQVdP9YQMKFqSR0/1hA1g4+NHT/WEDpl9JDdP9YQP0gZ1N0/1hAEar7YnT/WEAkM5BydP9YQDi8JIJ0/1hATEW5kXT/WEBfzk2hdP9YQHJX4rB0/1hAh+B2wHT/WECaaQvQdP9YQK3yn990/1hAwns073T/WEDVBMn+dP9YQOiNXQ51/1hA/BbyHXX/WEAQoIYtdf9YQCMpGz11/1hAN7KvTHX/WEBLO0Rcdf9YQF7E2Gt1/1hAck1te3X/WECG1gGLdf9YQJlflpp1/1hAregqqnX/WEDAcb+5df9YQNT6U8l1/1hA6IPo2HX/WED7DH3odf9YQA+WEfh1/1hAIx+mB3b/WEA2qDoXdv9YQEoxzyZ2/1hAXrpjNnb/WEBxQ/hFdv9YQIXMjFV2/1hAmVUhZXb/WECs3rV0dv9YQMBnSoR2/1hA1PDek3b/WEDneXOjdv9YQPoCCLN2/1hAD4ycwnb/WEAiFTHSdv9YQDWexeF2/1hASSda8Xb/WEBdsO4Ad/9YQHA5gxB3/1hAhMIXIHf/WECYS6wvd/9YQKvUQD93/1hAv13VTnf/WEDT5mled/9YQOZv/m13/1hA+viSfXf/WEAOgieNd/9YQCELvJx3/1hANZRQrHf/WEBIHeW7d/9YQFymect3/1hAby8O23f/WECDuKLqd/9YQJdBN/p3/1hAqsrLCXj/WEC+U2AZeP9YQNLc9Ch4/1hA5WWJOHj/WED57h1IeP9YQA14sld4/1hAIAFHZ3j/WEA0itt2eP9YQEcTcIZ4/1hAW5wElnj/WEBvJZmleP9YQIKuLbV4/1hAljfCxHj/WECqwFbUeP9YQL1J6+N4/1hA0dJ/83j/WEDlWxQDef9YQPjkqBJ5/1hADG49Inn/WEAg99Exef9YQDOAZkF5/1hARgn7UHn/WEBbko9gef9YQG4bJHB5/1hAgaS4f3n/WECWLU2Pef9YQKm24Z55/1hAvD92rnn/WEDQyAq+ef9YQORRn815/1hA99oz3Xn/WEALZMjsef9YQB/tXPx5/1hAMnbxC3r/WEBG/4Ubev9YQFqIGit6/1hAbRGvOnr/WECBmkNKev9YQJUj2Fl6/1hAqKxsaXr/WEC8NQF5ev9YQM++lYh6/1hA40cqmHr/WED30L6nev9YQApaU7d6/1hAHuPnxnr/WEAybHzWev9YQEX1EOZ6/1hAWX6l9Xr/WEBtBzoFe/9YQICQzhR7/1hAlBljJHv/WECoovcze/9YQLsrjEN7/1hAzrQgU3v/WEDjPbVie/9YQPbGSXJ7/1hACVDegXv/WEAe2XKRe/9YQDFiB6F7/1hAROubsHv/WEBYdDDAe/9YQGz9xM97/1hAf4ZZ33v/WECTD+7ue/9YQKeYgv57/1hAuiEXDnz/WEDOqqsdfP9YQOIzQC18/1hA9bzUPHz/WEAJRmlMfP9YQBzP/Vt8/1hAMFiSa3z/WEBE4SZ7fP9YQFdqu4p8/1hAa/NPmnz/WEB/fOSpfP9YQJIFebl8/1hApo4NyXz/WEC6F6LYfP9YQM2gNuh8/1hA4SnL93z/WED1sl8Hff9YQAg89BZ9/1hAHMWIJn3/WEAwTh02ff9YQEPXsUV9/1hAVmBGVX3/WEBr6dpkff9YQH5yb3R9/1hAkfsDhH3/WEClhJiTff9YQLkNLaN9/1hAzJbBsn3/WEDgH1bCff9YQPSo6tF9/1hABzJ/4X3/WEAbuxPxff9YQC9EqAB+/1hAQs08EH7/WEBWVtEffv9YQGrfZS9+/1hAfWj6Pn7/WECR8Y5Ofv9YQKR6I15+/1hAuAO4bX7/WEDMjEx9fv9YQN8V4Yx+/1hA8551nH7/WEAHKAqsfv9YQBqxnrt+/1hALjozy37/WEBCw8fafv9YQFVMXOp+/1hAadXw+X7/WEB9XoUJf/9YQJDnGRl//1hAo3CuKH//WEC4+UI4f/9YQMuC10d//1hA3gtsV3//WEDzlABnf/9YQAYelXZ//1hAGacphn//WEAtML6Vf/9YQEG5UqV//1hAVELntH//WEBoy3vEf/9YQHxUENR//1hAj92k43//WECjZjnzf/9YQLfvzQKA/1hAynhiEoD/WEDdAfchgP9YQPKKizGA/1hABRQgQYD/WEAYnbRQgP9YQCwmSWCA/1hAQK/db4D/WEBTOHJ/gP9YQGfBBo+A/1hAe0qbnoD/WECO0y+ugP9YQKJcxL2A/1hAtuVYzYD/WEDJbu3cgP9YQN33geyA/1hA8YAW/ID/WEAECqsLgf9YQBiTPxuB/1hAKxzUKoH/WEA/pWg6gf9YQFMu/UmB/1hAZreRWYH/WEB6QCZpgf9YQI7JuniB/1hAoVJPiIH/WEC12+OXgf9YQMlkeKeB/1hA3O0Mt4H/WEDwdqHGgf9YQAQANtaB/1hAF4nK5YH/WEAqEl/1gf9YQD+b8wSC/1hAUiSIFIL/WEBlrRwkgv9YQHo2sTOC/1hAjb9FQ4L/WECgSNpSgv9YQLTRbmKC/1hAyFoDcoL/WEDb45eBgv9YQO9sLJGC/1hAA/bAoIL/WEAWf1Wwgv9YQCoI6r+C/1hAPpF+z4L/WEBRGhPfgv9YQGWjp+6C/1hAeCw8/oL/WECMtdANg/9YQKA+ZR2D/1hAs8f5LIP/WEDHUI48g/9YQNvZIkyD/1hA7mK3W4P/WEAC7Etrg/9YQBZ14HqD/1hAKf50ioP/WEA9hwmag/9YQFEQnqmD/1hAZJkyuYP/WEB4IsfIg/9YQIyrW9iD/1hAnzTw54P/WECyvYT3g/9YQMdGGQeE/1hA2s+tFoT/WEDtWEImhP9YQAHi1jWE/1hAFWtrRYT/WEAo9P9UhP9YQDx9lGSE/1hAUAYpdIT/WEBjj72DhP9YQHcYUpOE/1hAi6HmooT/WECeKnuyhP9YQLKzD8KE/1hAxjyk0YT/WEDZxTjhhP9YQO1OzfCE/1hAANhhAIX/WEAUYfYPhf9YQCjqih+F/1hAO3MfL4X/WEBP/LM+hf9YQGOFSE6F/1hAdg7dXYX/WECKl3Fthf9YQJ4gBn2F/1hAsamajIX/WEDFMi+chf9YQNm7w6uF/1hA7ERYu4X/WED/zezKhf9YQBRXgdqF/1hAJ+AV6oX/WEA6aar5hf9YQE/yPgmG/1hAYnvTGIb/WEB1BGgohv9YQImN/DeG/1hAnRaRR4b/WECwnyVXhv9YQMQoumaG/1hA2LFOdob/WEDrOuOFhv9YQP/Dd5WG/1hAE00MpYb/WEAm1qC0hv9YQDpfNcSG/1hATujJ04b/WEBhcV7jhv9YQHX68vKG/1hAiIOHAof/WECcDBwSh/9YQLCVsCGH/1hAwx5FMYf/WEDXp9lAh/9YQOswblCH/1hA/rkCYIf/WEASQ5dvh/9YQCbMK3+H/1hAOVXAjof/WEBN3lSeh/9YQGFn6a2H/1hAdPB9vYf/WECHeRLNh/9YQJwCp9yH/1hAr4s77If/WEDCFND7h/9YQNadZAuI/1hA6ib5Goj/WED9r40qiP9YQBE5IjqI/1hAJcK2SYj/WEA4S0tZiP9YQEzU32iI/1hAYF10eIj/WEBz5giIiP9YQIZvnZeI/1hAm/gxp4j/WECugca2iP9YQMEKW8aI/1hA1pPv1Yj/WEDpHITliP9YQPylGPWI/1hAEC+tBIn/WEAkuEEUif9YQDdB1iOJ/1hAS8pqM4n/WEBfU/9Cif9YQHLck1KJ/1hAhmUoYon/WECa7rxxif9YQK13UYGJ/1hAwQDmkIn/WEDUiXqgif9YQOgSD7CJ/1hA/Jujv4n/WEAPJTjPif9YQCOuzN6J/1hANzdh7on/WEBKwPX9if9YQF5Jig2K/1hActIeHYr/WECFW7Msiv9YQJnkRzyK/1hArW3cS4r/WEDA9nBbiv9YQNR/BWuK/1hA6Aiaeor/WED7kS6Kiv9YQA4bw5mK/1hAI6RXqYr/WEA2Ley4iv9YQEm2gMiK/1hAXT8V2Ir/WEBxyKnniv9YQIRRPveK/1hAmNrSBov/WECsY2cWi/9YQL/s+yWL/1hA03WQNYv/WEDn/iRFi/9YQPqHuVSL/1hADhFOZIv/WEAimuJzi/9YQDUjd4OL/1hASawLk4v/WEBcNaCii/9YQHC+NLKL/1hAhEfJwYv/WECX0F3Ri/9YQKtZ8uCL/1hAv+KG8Iv/WEDSaxsAjP9YQOb0rw+M/1hA+n1EH4z/WEANB9kujP9YQCGQbT6M/1hANRkCToz/WEBIopZdjP9YQFsrK22M/1hAcLS/fIz/WECDPVSMjP9YQJbG6JuM/1hAq099q4z/WEC+2BG7jP9YQNFhpsqM/1hA5eo62oz/WED5c8/pjP9YQAz9Y/mM/1hAIIb4CI3/WEA0D40Yjf9YQEeYISiN/1hAWyG2N43/WEBvqkpHjf9YQIIz31aN/1hAlrxzZo3/WECqRQh2jf9YQL3OnIWN/1hA0VcxlY3/WEDk4MWkjf9YQPhpWrSN/1hADPPuw43/WEAffIPTjf9YQDMFGOON/1hAR46s8o3/WEBaF0ECjv9YQG6g1RGO/1hAgilqIY7/WECVsv4wjv9YQKk7k0CO/1hAvcQnUI7/WEDQTbxfjv9YQOPWUG+O/1hA+F/lfo7/WEAL6XmOjv9YQB5yDp6O/1hAMvuirY7/WEBGhDe9jv9YQFkNzMyO/1hAbZZg3I7/WECBH/Xrjv9YQJSoifuO/1hAqDEeC4//WEC8urIaj/9YQM9DRyqP/1hA48zbOY//WED3VXBJj/9YQArfBFmP/1hAHmiZaI//WEAy8S14j/9YQEV6woeP/1hAWQNXl4//WEBsjOumj/9YQIAVgLaP/1hAlJ4Uxo//WECnJ6nVj/9YQLuwPeWP/1hAzznS9I//WEDiwmYEkP9YQPZL+xOQ/1hACtWPI5D/WEAdXiQzkP9YQDDnuEKQ/1hARXBNUpD/WEBY+eFhkP9YQGuCdnGQ/1hAgAsLgZD/WECTlJ+QkP9YQKYdNKCQ/1hAuqbIr5D/WEDOL12/kP9YQOG48c6Q/1hA9UGG3pD/WEAJyxrukP9YQBxUr/2Q/1hAMN1DDZH/WEBEZtgckf9YQFfvbCyR/1hAangBPJH/WEB/AZZLkf9YQJKKKluR/1hApRO/apH/WEC5nFN6kf9YQM0l6ImR/1hA4K58mZH/WED0NxGpkf9YQAjBpbiR/1hAG0o6yJH/WEAv087Xkf9YQENcY+eR/1hAVuX39pH/WEBqbowGkv9YQH73IBaS/1hAkYC1JZL/WEClCUo1kv9YQLiS3kSS/1hAzBtzVJL/WEDgpAdkkv9YQPMtnHOS/1hAB7cwg5L/WEAbQMWSkv9YQC7JWaKS/1hAQlLusZL/WEBW24LBkv9YQGlkF9GS/1hAfe2r4JL/WECRdkDwkv9YQKT/1P+S/1hAt4hpD5P/WEDMEf4ek/9YQN+aki6T/1hA8iMnPpP/WEAHrbtNk/9YQBo2UF2T/1hALb/kbJP/WEBBSHl8k/9YQFXRDYyT/1hAaFqim5P/WEB84zark/9YQJBsy7qT/1hAo/VfypP/WEC3fvTZk/9YQMsHiemT/1hA3pAd+ZP/WEDyGbIIlP9YQAajRhiU/1hAGSzbJ5T/WEAttW83lP9YQEA+BEeU/1hAVMeYVpT/WEBoUC1mlP9YQHvZwXWU/1hAj2JWhZT/WECj6+qUlP9YQLZ0f6SU/1hAyv0TtJT/WEDehqjDlP9YQPEPPdOU/1hABZnR4pT/WEAZImbylP9YQCyr+gGV/1hAPzSPEZX/WEBUvSMhlf9YQGdGuDCV/1hAes9MQJX/WECOWOFPlf9YQKLhdV+V/1hAtWoKb5X/WEDJ855+lf9YQN18M46V/1hA8AXInZX/WEAEj1ytlf9YQBgY8byV/1hAK6GFzJX/WEA/Khrclf9YQFOzruuV/1hAZjxD+5X/WEB6xdcKlv9YQI5ObBqW/1hAodcAKpb/WEC1YJU5lv9YQMjpKUmW/1hA3HK+WJb/WEDw+1Jolv9YQAOF53eW/1hAFw58h5b/WEArlxCXlv9YQD4gpaaW/1hAUqk5tpb/WEBmMs7Flv9YQHm7YtWW/1hAjET35Jb/WEChzYv0lv9YQLRWIASX/1hAx9+0E5f/WEDcaEkjl/9YQO/x3TKX/1hAAntyQpf/WEAWBAdSl/9YQCqNm2GX/1hAPRYwcZf/WEBRn8SAl/9YQGUoWZCX/1hAeLHtn5f/WECMOoKvl/9YQKDDFr+X/1hAs0yrzpf/WEDH1T/el/9YQNte1O2X/1hA7udo/Zf/WEACcf0MmP9YQBX6kRyY/1hAKYMmLJj/WEA9DLs7mP9YQFCVT0uY/1hAZB7kWpj/WEB4p3hqmP9YQIswDXqY/1hAn7mhiZj/WECzQjaZmP9YQMbLyqiY/1hA2lRfuJj/WEDu3fPHmP9YQAFniNeY/1hAFPAc55j/WEApebH2mP9YQDwCRgaZ/1hAT4vaFZn/WEBjFG8lmf9YQHedAzWZ/1hAiiaYRJn/WECeryxUmf9YQLI4wWOZ/1hAxcFVc5n/WEDZSuqCmf9YQO3TfpKZ/1hAAF0Topn/WEAT5qexmf9YQChvPMGZ/1hAO/jQ0Jn/WEBOgWXgmf9YQGMK+u+Z/1hAdpOO/5n/WECJHCMPmv9YQJ2ltx6a/1hAsS5MLpr/WEDEt+A9mv9YQNhAdU2a/1hA7MkJXZr/WED/Up5smv9YQBPcMnya/1hAJ2XHi5r/WEA67lubmv9YQE538Kqa/1hAYgCFupr/WEB1iRnKmv9YQIkSrtma/1hAnJtC6Zr/WECwJNf4mv9YQMStawib/1hA1zYAGJv/WEDrv5Qnm/9YQP9IKTeb/1hAEtK9Rpv/WEAmW1JWm/9YQDrk5mWb/1hATW17dZv/WEBh9g+Fm/9YQHV/pJSb/1hAiAg5pJv/WECbkc2zm/9YQLAaYsOb/1hAw6P20pv/WEDWLIvim/9YQOq1H/Kb/1hA/j60AZz/WEARyEgRnP9YQCVR3SCc/1hAOdpxMJz/WEBMYwZAnP9YQGDsmk+c/1hAdHUvX5z/WECH/sNunP9YQJuHWH6c/1hArxDtjZz/WEDCmYGdnP9YQNYiFq2c/1hA6quqvJz/WED9ND/MnP9YQBG+09uc/1hAJEdo65z/WEA40Pz6nP9YQExZkQqd/1hAX+IlGp3/WEBza7opnf9YQIf0Tjmd/1hAmn3jSJ3/WECuBnhYnf9YQMKPDGid/1hA1Rihd53/WEDooTWHnf9YQP0qypad/1hAELRepp3/WEAjPfO1nf9YQDjGh8Wd/1hAS08c1Z3/WEBe2LDknf9YQHJhRfSd/1hAhurZA57/WECZc24Tnv9YQK38AiOe/1hAwYWXMp7/WEDUDixCnv9YQOiXwFGe/1hA/CBVYZ7/WEAPqulwnv9YQCMzfoCe/1hAN7wSkJ7/WEBKRaefnv9YQF7OO6+e/1hAcVfQvp7/WECF4GTOnv9YQJlp+d2e/1hArPKN7Z7/WEDAeyL9nv9YQNQEtwyf/1hA541LHJ//WED7FuArn/9YQA+gdDuf/1hAIikJS5//WEA2sp1an/9YQEo7Mmqf/1hAXcTGeZ//WEBwTVuJn/9YQIXW75if/1hAmF+EqJ//WECr6Bi4n/9YQMBxrcef/1hA0/pB15//WEDmg9bmn/9YQPoMa/af/1hADpb/BaD/WEAhH5QVoP9YQDWoKCWg/1hASTG9NKD/WEBculFEoP9YQHBD5lOg/1hAhMx6Y6D/WECXVQ9zoP9YQKveo4Kg/1hAv2c4kqD/WEDS8MyhoP9YQOZ5YbGg/1hA+QL2wKD/WEANjIrQoP9YQCEVH+Cg/1hANJ6z76D/WEBIJ0j/oP9YQFyw3A6h/1hAbzlxHqH/WECDwgUuof9YQJdLmj2h/1hAqtQuTaH/WEC+XcNcof9YQNHmV2yh/1hA5W/se6H/WED4+ICLof9YQAyCFZuh/1hAIAuqqqH/WEAzlD66of9YQEcd08mh/1hAW6Zn2aH/WEBuL/zoof9YQIK4kPih/1hAlkElCKL/WECpyrkXov9YQL1TTiei/1hA0dziNqL/WEDkZXdGov9YQPfuC1ai/1hADHigZaL/WEAfATV1ov9YQDKKyYSi/1hARhNelKL/WEBanPKjov9YQG0lh7Oi/1hAga4bw6L/WECVN7DSov9YQKjAROKi/1hAvEnZ8aL/WEDQ0m0Bo/9YQONbAhGj/1hA9+SWIKP/WEALbiswo/9YQB73vz+j/1hAMoBUT6P/WEBGCeleo/9YQFmSfW6j/1hAbRsSfqP/WECApKaNo/9YQJQtO52j/1hAqLbPrKP/WEC7P2S8o/9YQM/I+Muj/1hA41GN26P/WED22iHro/9YQApktvqj/1hAHu1KCqT/WEAxdt8ZpP9YQET/cymk/1hAWYgIOaT/WEBsEZ1IpP9YQH+aMVik/1hAlCPGZ6T/WECnrFp3pP9YQLo174ak/1hAzr6DlqT/WEDiRximpP9YQPXQrLWk/1hACVpBxaT/WEAd49XUpP9YQDBsauSk/1hARPX+86T/WEBYfpMDpf9YQGsHKBOl/1hAf5C8IqX/WECTGVEypf9YQKai5UGl/1hAuit6UaX/WEDNtA5hpf9YQOE9o3Cl/1hA9cY3gKX/WEAIUMyPpf9YQBzZYJ+l/1hAMGL1rqX/WEBD64m+pf9YQFd0Hs6l/1hAa/2y3aX/WEB+hkftpf9YQJIP3Pyl/1hApphwDKb/WEC5IQUcpv9YQMyqmSum/1hA4TMuO6b/WED0vMJKpv9YQAdGV1qm/1hAHM/raab/WEAvWIB5pv9YQELhFImm/1hAVmqpmKb/WEBq8z2opv9YQH180rem/1hAkQVnx6b/WECljvvWpv9YQLgXkOam/1hAzKAk9qb/WEDgKbkFp/9YQPOyTRWn/1hABzziJKf/WEAbxXY0p/9YQC5OC0Sn/1hAQtefU6f/WEBVYDRjp/9YQGnpyHKn/1hAfXJdgqf/WECQ+/GRp/9YQKSEhqGn/1hAuA0bsaf/WEDLlq/Ap/9YQN8fRNCn/1hA86jY36f/WEAGMm3vp/9YQBq7Af+n/1hALkSWDqj/WEBBzSoeqP9YQFRWvy2o/1hAad9TPaj/WEB8aOhMqP9YQI/xfFyo/1hApHoRbKj/WEC3A6Z7qP9YQMqMOouo/1hA3hXPmqj/WEDynmOqqP9YQAUo+Lmo/1hAGbGMyaj/WEAtOiHZqP9YQEDDteio/1hAVExK+Kj/WEBo1d4Hqf9YQHtecxep/1hAj+cHJ6n/WECicJw2qf9YQLb5MEap/1hAyoLFVan/WEDdC1plqf9YQPGU7nSp/1hABR6DhKn/WEAYpxeUqf9YQCwwrKOp/1hAP7lAs6n/WEBTQtXCqf9YQGfLadKp/1hAelT+4an/WECO3ZLxqf9YQKJmJwGq/1hAte+7EKr/WEDJeFAgqv9YQNwB5S+q/1hA8Ip5P6r/WEAEFA5Pqv9YQBedol6q/1hAKyY3bqr/WEA/r8t9qv9YQFI4YI2q/1hAZsH0nKr/WEB6Somsqv9YQI3THbyq/1hAoFyyy6r/WEC15Ubbqv9YQMhu2+qq/1hA2/dv+qr/WEDwgAQKq/9YQAMKmRmr/1hAFpMtKav/WEAqHMI4q/9YQD6lVkir/1hAUS7rV6v/WEBlt39nq/9YQHlAFHer/1hAjMmohqv/WECgUj2Wq/9YQLTb0aWr/1hAx2Rmtav/WEDb7frEq/9YQO92j9Sr/1hAAgAk5Kv/WEAWibjzq/9YQCkSTQOs/1hAPZvhEqz/WEBRJHYirP9YQGStCjKs/1hAeDafQaz/WECMvzNRrP9YQJ9IyGCs/1hAs9FccKz/WEDHWvF/rP9YQNrjhY+s/1hA7mwan6z/WEAC9q6urP9YQBV/Q76s/1hAKAjYzaz/WEA9kWzdrP9YQFAaAe2s/1hAY6OV/Kz/WEB4LCoMrf9YQIu1vhut/1hAnj5TK63/WECyx+c6rf9YQMZQfEqt/1hA2dkQWq3/WEDtYqVprf9YQAHsOXmt/1hAFHXOiK3/WEAo/mKYrf9YQDyH96et/1hATxCMt63/WEBjmSDHrf9YQHcitdat/1hAiqtJ5q3/WECeNN71rf9YQLG9cgWu/1hAxUYHFa7/WEDZz5skrv9YQOxYMDSu/1hAAOLEQ67/WEAUa1lTrv9YQCf07WKu/1hAO32Ccq7/WEBPBheCrv9YQGKPq5Gu/1hAdhhAoa7/WECKodSwrv9YQJ0qacCu/1hAsLP9z67/WEDFPJLfrv9YQNjFJu+u/1hA6067/q7/WEAA2E8Or/9YQBNh5B2v/1hAJup4La//WEA6cw09r/9YQE78oUyv/1hAYYU2XK//WEB1Dstrr/9YQImXX3uv/1hAnCD0iq//WECwqYiar/9YQMQyHaqv/1hA17uxua//WEDrREbJr/9YQP7N2tiv/1hAEldv6K//WEAm4AP4r/9YQDlpmAew/1hATfIsF7D/WEBhe8EmsP9YQHQEVjaw/1hAiI3qRbD/WECcFn9VsP9YQK+fE2Ww/1hAwyiodLD/WEDXsTyEsP9YQOo60ZOw/1hA/sNlo7D/WEASTfqysP9YQCXWjsKw/1hAOF8j0rD/WEBN6LfhsP9YQGBxTPGw/1hAc/rgALH/WECHg3UQsf9YQJsMCiCx/1hArpWeL7H/WEDCHjM/sf9YQNanx06x/1hA6TBcXrH/WED9ufBtsf9YQBFDhX2x/1hAJMwZjbH/WEA4Va6csf9YQEzeQqyx/1hAX2fXu7H/WEBz8GvLsf9YQIZ5ANux/1hAmgKV6rH/WECtiyn6sf9YQMEUvgmy/1hA1Z1SGbL/WEDoJucosv9YQPyveziy/1hAEDkQSLL/WEAjwqRXsv9YQDdLOWey/1hAS9TNdrL/WEBeXWKGsv9YQHLm9pWy/1hAhW+LpbL/WECZ+B+1sv9YQK2BtMSy/1hAwApJ1LL/WEDUk93jsv9YQOgccvOy/1hA+6UGA7P/WEAPL5sSs/9YQCO4LyKz/1hANkHEMbP/WEBKylhBs/9YQF5T7VCz/1hAcdyBYLP/WECEZRZws/9YQJnuqn+z/1hArHc/j7P/WEC/ANSes/9YQNSJaK6z/1hA5xL9vbP/WED6m5HNs/9YQA4lJt2z/1hAIq667LP/WEA1N0/8s/9YQEnA4wu0/1hAXUl4G7T/WEBw0gwrtP9YQIRboTq0/1hAmOQ1SrT/WECrbcpZtP9YQL/2Xmm0/1hA03/zeLT/WEDmCIiItP9YQPqRHJi0/1hADRuxp7T/WEAhpEW3tP9YQDUt2sa0/1hASLZu1rT/WEBcPwPmtP9YQHDIl/W0/1hAg1EsBbX/WECX2sAUtf9YQKtjVSS1/1hAvuzpM7X/WEDSdX5Dtf9YQOb+ElO1/1hA+YenYrX/WEAMETxytf9YQCGa0IG1/1hANCNlkbX/WEBHrPmgtf9YQFw1jrC1/1hAb74iwLX/WECCR7fPtf9YQJbQS9+1/1hAqlng7rX/WEC94nT+tf9YQNFrCQ62/1hA5fSdHbb/WED4fTIttv9YQAwHxzy2/1hAIJBbTLb/WEAzGfBbtv9YQEeihGu2/1hAWisZe7b/WEButK2Ktv9YQII9Qpq2/1hAlcbWqbb/WECpT2u5tv9YQL3Y/8i2/1hA0GGU2Lb/WEDk6ijotv9YQPhzvfe2/1hAC/1RB7f/WEAfhuYWt/9YQDMPeya3/1hARpgPNrf/WEBaIaRFt/9YQG6qOFW3/1hAgTPNZLf/WECUvGF0t/9YQKlF9oO3/1hAvM6Kk7f/WEDPVx+jt/9YQOPgs7K3/1hA92lIwrf/WEAK89zRt/9YQB58ceG3/1hAMgUG8bf/WEBFjpoAuP9YQFkXLxC4/1hAbaDDH7j/WECAKVgvuP9YQJSy7D64/1hAqDuBTrj/WEC7xBVeuP9YQM9Nqm24/1hA4tY+fbj/WED2X9OMuP9YQArpZ5y4/1hAHXL8q7j/WEAx+5C7uP9YQEWEJcu4/1hAWA262rj/WEBslk7quP9YQIAf4/m4/1hAk6h3Cbn/WECnMQwZuf9YQLu6oCi5/1hAzkM1OLn/WEDhzMlHuf9YQPZVXle5/1hACd/yZrn/WEAcaId2uf9YQDHxG4a5/1hARHqwlbn/WEBXA0Wluf9YQGuM2bS5/1hAfxVuxLn/WECSngLUuf9YQKYnl+O5/1hAurAr87n/WEDNOcACuv9YQOHCVBK6/1hA9UvpIbr/WEAI1X0xuv9YQBteEkG6/1hAMOemULr/WEBDcDtguv9YQFb5z2+6/1hAaoJkf7r/WEB+C/mOuv9YQJGUjZ66/1hApR0irrr/WEC5pra9uv9YQMwvS826/1hA4Ljf3Lr/WED0QXTsuv9YQAfLCPy6/1hAG1SdC7v/WEAv3TEbu/9YQEJmxiq7/1hAVu9aOrv/WEBpeO9Ju/9YQH0BhFm7/1hAkYoYabv/WECkE614u/9YQLicQYi7/1hAzCXWl7v/WEDfrmqnu/9YQPM3/7a7/1hAB8GTxrv/WEAaSijWu/9YQC7TvOW7/1hAQlxR9bv/WEBV5eUEvP9YQGhuehS8/1hAffcOJLz/WECQgKMzvP9YQKMJOEO8/1hAuJLMUrz/WEDLG2FivP9YQN6k9XG8/1hA8i2Kgbz/WEAGtx6RvP9YQBlAs6C8/1hALclHsLz/WEBBUty/vP9YQFTbcM+8/1hAaGQF37z/WEB87ZnuvP9YQI92Lv68/1hAo//CDb3/WEC2iFcdvf9YQMoR7Cy9/1hA3pqAPL3/WEDxIxVMvf9YQAWtqVu9/1hAGTY+a73/WEAsv9J6vf9YQEBIZ4q9/1hAVNH7mb3/WEBnWpCpvf9YQHvjJLm9/1hAj2y5yL3/WECi9U3Yvf9YQLZ+4ue9/1hAygd3973/WEDdkAsHvv9YQPAZoBa+/1hABaM0Jr7/WEAYLMk1vv9YQCu1XUW+/1hAPz7yVL7/WEBTx4Zkvv9YQGZQG3S+/1hAetmvg77/WECOYkSTvv9YQKHr2KK+/1hAtXRtsr7/WEDJ/QHCvv9YQNyGltG+/1hA8A8r4b7/WEAEmb/wvv9YQBciVAC//1hAK6voD7//WEA+NH0fv/9YQFK9ES+//1hAZkamPr//WEB5zzpOv/9YQI1Yz12//1hAoeFjbb//WEC0avh8v/9YQMjzjIy//1hA3HwhnL//WEDvBbarv/9YQAOPSru//1hAFxjfyr//WEAqoXPav/9YQD0qCOq//1hAUrOc+b//WEBlPDEJwP9YQHjFxRjA/1hAjU5aKMD/WECg1+43wP9YQLNgg0fA/1hAx+kXV8D/WEDbcqxmwP9YQO77QHbA/1hAAoXVhcD/WEAWDmqVwP9YQCmX/qTA/1hAPSCTtMD/WEBRqSfEwP9YQGQyvNPA/1hAeLtQ48D/WECMROXywP9YQJ/NeQLB/1hAs1YOEsH/WEDG36Ihwf9YQNpoNzHB/1hA7vHLQMH/WEABe2BQwf9YQBUE9V/B/1hAKY2Jb8H/WEA8Fh5/wf9YQFCfso7B/1hAZChHnsH/WEB3sdutwf9YQIs6cL3B/1hAn8MEzcH/WECyTJncwf9YQMXVLezB/1hA2l7C+8H/WEDt51YLwv9YQABx6xrC/1hAFPp/KsL/WEAogxQ6wv9YQDsMqUnC/1hAT5U9WcL/WEBjHtJowv9YQHanZnjC/1hAijD7h8L/WECeuY+Xwv9YQLFCJKfC/1hAxMu4tsL/WEDZVE3Gwv9YQOzd4dXC/1hA/2Z25cL/WEAU8Ar1wv9YQCd5nwTD/1hAOgI0FMP/WEBOi8gjw/9YQGIUXTPD/1hAdZ3xQsP/WECJJoZSw/9YQJ2vGmLD/1hAsDivccP/WEDEwUOBw/9YQNhK2JDD/1hA69NsoMP/WED/XAGww/9YQBLmlb/D/1hAJm8qz8P/WEA6+L7ew/9YQE2BU+7D/1hAYQro/cP/WEB1k3wNxP9YQIgcER3E/1hAnKWlLMT/WECwLjo8xP9YQMO3zkvE/1hA10BjW8T/WEDryfdqxP9YQP5SjHrE/1hAEtwgisT/WEAmZbWZxP9YQDnuSanE/1hATHfeuMT/WEBhAHPIxP9YQHSJB9jE/1hAhxKc58T/WECbmzD3xP9YQK8kxQbF/1hAwq1ZFsX/WEDWNu4lxf9YQOq/gjXF/1hA/UgXRcX/WEAR0qtUxf9YQCVbQGTF/1hAOOTUc8X/WEBMbWmDxf9YQGD2/ZLF/1hAc3+SosX/WECHCCeyxf9YQJqRu8HF/1hArhpQ0cX/WEDCo+Tgxf9YQNUsefDF/1hA6bUNAMb/WED9PqIPxv9YQBDINh/G/1hAJFHLLsb/WEA42l8+xv9YQEtj9E3G/1hAX+yIXcb/WEBzdR1txv9YQIb+sXzG/1hAmYdGjMb/WECuENubxv9YQMGZb6vG/1hA1CIEu8b/WEDpq5jKxv9YQPw0LdrG/1hAD77B6cb/WEAjR1b5xv9YQDfQ6gjH/1hASll/GMf/WEBe4hMox/9YQHJrqDfH/1hAhfQ8R8f/WECZfdFWx/9YQK0GZmbH/1hAwI/6dcf/WEDUGI+Fx/9YQOihI5XH/1hA+yq4pMf/WEAPtEy0x/9YQCI94cPH/1hANsZ108f/WEBKTwrjx/9YQF3YnvLH/1hAcWEzAsj/WECF6scRyP9YQJhzXCHI/1hArPzwMMj/WEDAhYVAyP9YQNMOGlDI/1hA55euX8j/WED7IENvyP9YQA6q137I/1hAITNsjsj/WEA2vACeyP9YQElFla3I/1hAXM4pvcj/WEBwV77MyP9YQITgUtzI/1hAl2nn68j/WECr8nv7yP9YQL97EAvJ/1hA0gSlGsn/WEDmjTkqyf9YQPoWzjnJ/1hADaBiScn/WEAhKfdYyf9YQDWyi2jJ/1hASDsgeMn/WEBcxLSHyf9YQHBNSZfJ/1hAg9bdpsn/WECXX3K2yf9YQKroBsbJ/1hAvnGb1cn/WEDS+i/lyf9YQOWDxPTJ/1hA+QxZBMr/WEANlu0Tyv9YQCAfgiPK/1hANKgWM8r/WEBIMatCyv9YQFu6P1LK/1hAbkPUYcr/WECDzGhxyv9YQJZV/YDK/1hAqd6RkMr/WEC+Zyagyv9YQNHwuq/K/1hA5HlPv8r/WED4AuTOyv9YQAyMeN7K/1hAHxUN7sr/WEAznqH9yv9YQEcnNg3L/1hAWrDKHMv/WEBuOV8sy/9YQILC8zvL/1hAlUuIS8v/WECo1Bxby/9YQL1dsWrL/1hA0OZFesv/WEDjb9qJy/9YQPf4bpnL/1hAC4IDqcv/WEAeC5i4y/9YQDKULMjL/1hARh3B18v/WEBZplXny/9YQG0v6vbL/1hAgbh+Bsz/WECUQRMWzP9YQKjKpyXM/1hAvFM8Ncz/WEDP3NBEzP9YQONlZVTM/1hA9u75Y8z/WEAKeI5zzP9YQB4BI4PM/1hAMYq3ksz/WEBFE0yizP9YQFmc4LHM/1hAbCV1wcz/WECArgnRzP9YQJQ3nuDM/1hAp8Ay8Mz/WEC7Scf/zP9YQM/SWw/N/1hA4lvwHs3/WED15IQuzf9YQApuGT7N/1hAHfetTc3/WEAwgEJdzf9YQEUJ12zN/1hAWJJrfM3/WEBrGwCMzf9YQH+klJvN/1hAky0pq83/WECmtr26zf9YQLo/UsrN/1hAzsjm2c3/WEDhUXvpzf9YQPXaD/nN/1hACWSkCM7/WEAc7TgYzv9YQDB2zSfO/1hARP9hN87/WEBXiPZGzv9YQGsRi1bO/1hAfpofZs7/WECSI7R1zv9YQKasSIXO/1hAuTXdlM7/WEDNvnGkzv9YQOFHBrTO/1hA9NCaw87/WEAIWi/Tzv9YQBzjw+LO/1hAL2xY8s7/WEBD9ewBz/9YQFd+gRHP/1hAagcWIc//WEB9kKowz/9YQJIZP0DP/1hApaLTT8//WEC4K2hfz/9YQMy0/G7P/1hA4D2Rfs//WEDzxiWOz/9YQAdQup3P/1hAG9lOrc//WEAuYuO8z/9YQELrd8zP/1hAVnQM3M//WEBp/aDrz/9YQH2GNfvP/1hAkQ/KCtD/WECkmF4a0P9YQLgh8ynQ/1hAzKqHOdD/WEDfMxxJ0P9YQPO8sFjQ/1hABkZFaND/WEAaz9l30P9YQC5YbofQ/1hAQeECl9D/WEBVapem0P9YQGnzK7bQ/1hAfHzAxdD/WECQBVXV0P9YQKSO6eTQ/1hAtxd+9ND/WEDKoBIE0f9YQN8ppxPR/1hA8rI7I9H/WEAFPNAy0f9YQBrFZELR/1hALU75UdH/WEBA141h0f9YQFRgInHR/1hAaOm2gNH/WEB7ckuQ0f9YQI/735/R/1hAo4R0r9H/WEC2DQm/0f9YQMqWnc7R/1hA3h8y3tH/WEDxqMbt0f9YQAUyW/3R/1hAGbvvDNL/WEAsRIQc0v9YQEDNGCzS/1hAU1atO9L/WEBn30FL0v9YQHto1lrS/1hAjvFqatL/WECiev950v9YQLYDlInS/1hAyYwomdL/WEDdFb2o0v9YQPGeUbjS/1hABCjmx9L/WEAYsXrX0v9YQCw6D+fS/1hAP8Oj9tL/WEBSTDgG0/9YQGfVzBXT/1hAel5hJdP/WECN5/U00/9YQKFwikTT/1hAtfkeVNP/WEDIgrNj0/9YQNwLSHPT/1hA8JTcgtP/WEADHnGS0/9YQBenBaLT/1hAKzCasdP/WEA+uS7B0/9YQFFCw9DT/1hAZstX4NP/WEB5VOzv0/9YQIzdgP/T/1hAoWYVD9T/WEC076ke1P9YQMd4Pi7U/1hA2wHTPdT/WEDvimdN1P9YQAIU/FzU/1hAFp2QbNT/WEAqJiV81P9YQD2vuYvU/1hAUThOm9T/WEBlweKq1P9YQHhKd7rU/1hAjNMLytT/WECgXKDZ1P9YQLPlNOnU/1hAx27J+NT/WEDa910I1f9YQO6A8hfV/1hAAgqHJ9X/WEAVkxs31f9YQCkcsEbV/1hAPaVEVtX/WEBQLtll1f9YQGS3bXXV/1hAeEAChdX/WECLyZaU1f9YQJ9SK6TV/1hAs9u/s9X/WEDGZFTD1f9YQNnt6NLV/1hA7nZ94tX/WEABABLy1f9YQBSJpgHW/1hAKBI7Edb/WEA8m88g1v9YQE8kZDDW/1hAY634P9b/WEB3No1P1v9YQIq/IV/W/1hAnki2btb/WECy0Up+1v9YQMVa343W/1hA2eNzndb/WEDtbAit1v9YQAD2nLzW/1hAFH8xzNb/WEAoCMbb1v9YQDuRWuvW/1hATxrv+tb/WEBio4MK1/9YQHYsGBrX/1hAirWsKdf/WECdPkE51/9YQLHH1UjX/1hAxVBqWNf/WEDY2f5n1/9YQOxik3fX/1hAAOwnh9f/WEATdbyW1/9YQCb+UKbX/1hAO4fltdf/WEBOEHrF1/9YQGGZDtXX/1hAdiKj5Nf/WECJqzf01/9YQJw0zAPY/1hAsL1gE9j/WEDERvUi2P9YQNfPiTLY/1hA61geQtj/WED/4bJR2P9YQBJrR2HY/1hAJvTbcNj/WEA6fXCA2P9YQE0GBZDY/1hAYY+Zn9j/WEB1GC6v2P9YQIihwr7Y/1hAnCpXztj/WECvs+vd2P9YQMM8gO3Y/1hA18UU/dj/WEDqTqkM2f9YQP7XPRzZ/1hAEmHSK9n/WEAl6mY72f9YQDlz+0rZ/1hATfyPWtn/WEBghSRq2f9YQHQOuXnZ/1hAiJdNidn/WECbIOKY2f9YQK6pdqjZ/1hAwzILuNn/WEDWu5/H2f9YQOlENNfZ/1hA/s3I5tn/WEARV1322f9YQCTg8QXa/1hAOGmGFdr/WEBM8hol2v9YQF97rzTa/1hAcwRERNr/WECHjdhT2v9YQJoWbWPa/1hArp8Bc9r/WEDCKJaC2v9YQNWxKpLa/1hA6Tq/odr/WED9w1O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-        },
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-    "cumulative = category_counts_sorted.with_columns(\n",
-    "    (pl.col(\"count\").cum_sum() / pl.col(\"count\").sum() * 100).alias(\"cumulative_pct\")\n",
-    ")\n",
-    "\n",
-    "top_80 = cumulative.filter(pl.col(\"cumulative_pct\") <= 80)\n",
-    "print(f\"Categories covering 80% of all POIs: {len(top_80)}\")\n",
-    "print(f\"Total unique categories: {len(cumulative)}\")\n",
-    "\n",
-    "fig = px.line(\n",
-    "    cumulative.to_pandas(),\n",
-    "    x=\"rank\",\n",
-    "    y=\"cumulative_pct\",\n",
-    "    title=\"Cumulative POI Coverage by Category Rank\",\n",
-    "    labels={\"rank\": \"Category Rank\", \"cumulative_pct\": \"Cumulative % of POIs\"},\n",
-    ")\n",
-    "fig.add_hline(y=80, line_dash=\"dash\", line_color=\"red\", annotation_text=\"80%\")\n",
-    "fig.add_hline(y=95, line_dash=\"dash\", line_color=\"orange\", annotation_text=\"95%\")\n",
-    "fig.update_layout(height=500)\n",
-    "fig.show()"
-   ]
-  },
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-   "metadata": {},
-   "source": [
-    "---\n",
-    "## 3. Composite Categories\n",
-    "\n",
-    "Some POIs match multiple OSM tag keys, producing composite categories joined with ` / `."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "POIs with composite categories: 591,594 (2.2%)\n",
-      "Unique composite categories: 7,594\n"
-     ]
-    },
-    {
-     "data": {
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-       "<small>shape: (30, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>category</th><th>count</th></tr><tr><td>str</td><td>u32</td></tr></thead><tbody><tr><td>&quot;bus_stop / platform&quot;</td><td>242090</td></tr><tr><td>&quot;place_of_worship / church&quot;</td><td>18708</td></tr><tr><td>&quot;pub / yes&quot;</td><td>16104</td></tr><tr><td>&quot;place_of_worship / yes&quot;</td><td>14087</td></tr><tr><td>&quot;chalet / yes&quot;</td><td>9994</td></tr><tr><td>&quot;fast_food / yes&quot;</td><td>8420</td></tr><tr><td>&quot;platform / platform&quot;</td><td>8124</td></tr><tr><td>&quot;restaurant / yes&quot;</td><td>7993</td></tr><tr><td>&quot;convenience / yes&quot;</td><td>7775</td></tr><tr><td>&quot;cafe / yes&quot;</td><td>7130</td></tr><tr><td>&quot;community_centre / yes&quot;</td><td>6704</td></tr><tr><td>&quot;pharmacy / pharmacy&quot;</td><td>5726</td></tr><tr><td>&quot;stop / stop_position&quot;</td><td>5701</td></tr><tr><td>&quot;hairdresser / yes&quot;</td><td>5377</td></tr><tr><td>&quot;shelter / yes&quot;</td><td>5055</td></tr><tr><td>&quot;community_centre / public&quot;</td><td>4851</td></tr><tr><td>&quot;hotel / yes&quot;</td><td>4847</td></tr><tr><td>&quot;car_repair / yes&quot;</td><td>4466</td></tr><tr><td>&quot;social_facility / yes&quot;</td><td>4357</td></tr><tr><td>&quot;supermarket / yes&quot;</td><td>3926</td></tr><tr><td>&quot;toilets / yes&quot;</td><td>3752</td></tr><tr><td>&quot;dentist / dentist&quot;</td><td>3420</td></tr><tr><td>&quot;convenience / retail&quot;</td><td>3254</td></tr><tr><td>&quot;fast_food / retail&quot;</td><td>3159</td></tr><tr><td>&quot;doctors / yes / doctor&quot;</td><td>2946</td></tr><tr><td>&quot;station / station&quot;</td><td>2861</td></tr><tr><td>&quot;car / yes&quot;</td><td>2706</td></tr><tr><td>&quot;yes / yes&quot;</td><td>2661</td></tr><tr><td>&quot;sports_centre / yes&quot;</td><td>2552</td></tr><tr><td>&quot;clothes / yes&quot;</td><td>2352</td></tr></tbody></table></div>"
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-       "shape: (30, 2)\n",
-       "┌───────────────────────────┬────────┐\n",
-       "│ category                  ┆ count  │\n",
-       "│ ---                       ┆ ---    │\n",
-       "│ str                       ┆ u32    │\n",
-       "╞═══════════════════════════╪════════╡\n",
-       "│ bus_stop / platform       ┆ 242090 │\n",
-       "│ place_of_worship / church ┆ 18708  │\n",
-       "│ pub / yes                 ┆ 16104  │\n",
-       "│ place_of_worship / yes    ┆ 14087  │\n",
-       "│ chalet / yes              ┆ 9994   │\n",
-       "│ fast_food / yes           ┆ 8420   │\n",
-       "│ platform / platform       ┆ 8124   │\n",
-       "│ restaurant / yes          ┆ 7993   │\n",
-       "│ convenience / yes         ┆ 7775   │\n",
-       "│ cafe / yes                ┆ 7130   │\n",
-       "│ community_centre / yes    ┆ 6704   │\n",
-       "│ pharmacy / pharmacy       ┆ 5726   │\n",
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-       "│ hairdresser / yes         ┆ 5377   │\n",
-       "│ shelter / yes             ┆ 5055   │\n",
-       "│ community_centre / public ┆ 4851   │\n",
-       "│ hotel / yes               ┆ 4847   │\n",
-       "│ car_repair / yes          ┆ 4466   │\n",
-       "│ social_facility / yes     ┆ 4357   │\n",
-       "│ supermarket / yes         ┆ 3926   │\n",
-       "│ toilets / yes             ┆ 3752   │\n",
-       "│ dentist / dentist         ┆ 3420   │\n",
-       "│ convenience / retail      ┆ 3254   │\n",
-       "│ fast_food / retail        ┆ 3159   │\n",
-       "│ doctors / yes / doctor    ┆ 2946   │\n",
-       "│ station / station         ┆ 2861   │\n",
-       "│ car / yes                 ┆ 2706   │\n",
-       "│ yes / yes                 ┆ 2661   │\n",
-       "│ sports_centre / yes       ┆ 2552   │\n",
-       "│ clothes / yes             ┆ 2352   │\n",
-       "└───────────────────────────┴────────┘"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "composite = df.filter(pl.col(\"category\").str.contains(\" / \"))\n",
-    "print(f\"POIs with composite categories: {len(composite):,} ({len(composite) / len(df) * 100:.1f}%)\")\n",
-    "\n",
-    "composite_counts = (\n",
-    "    composite.group_by(\"category\")\n",
-    "    .agg(pl.len().alias(\"count\"))\n",
-    "    .sort(\"count\", descending=True)\n",
-    ")\n",
-    "print(f\"Unique composite categories: {len(composite_counts):,}\")\n",
-    "composite_counts.head(30)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "## 4. Named vs Unnamed POIs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
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-          "ternary": {
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-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
-           },
-           "baxis": {
-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
-           },
-           "bgcolor": "#E5ECF6",
-           "caxis": {
-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
-           }
-          },
-          "title": {
-           "x": 0.05
-          },
-          "xaxis": {
-           "automargin": true,
-           "gridcolor": "white",
-           "linecolor": "white",
-           "ticks": "",
-           "title": {
-            "standoff": 15
-           },
-           "zerolinecolor": "white",
-           "zerolinewidth": 2
-          },
-          "yaxis": {
-           "automargin": true,
-           "gridcolor": "white",
-           "linecolor": "white",
-           "ticks": "",
-           "title": {
-            "standoff": 15
-           },
-           "zerolinecolor": "white",
-           "zerolinewidth": 2
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-        },
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-        },
-        "xaxis": {
-         "anchor": "y",
-         "domain": [
-          0,
-          1
-         ],
-         "tickangle": -45,
-         "title": {
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-         }
-        },
-        "yaxis": {
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-       }
-      }
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "named_stats = (\n",
-    "    df.with_columns((pl.col(\"name\") != \"\").alias(\"has_name\"))\n",
-    "    .group_by(\"category\", \"has_name\")\n",
-    "    .agg(pl.len().alias(\"count\"))\n",
-    ")\n",
-    "\n",
-    "named_rate = (\n",
-    "    named_stats.pivot(on=\"has_name\", index=\"category\", values=\"count\")\n",
-    "    .rename({\"true\": \"named\", \"false\": \"unnamed\"})\n",
-    "    .with_columns(\n",
-    "        pl.col(\"named\").fill_null(0),\n",
-    "        pl.col(\"unnamed\").fill_null(0),\n",
-    "    )\n",
-    "    .with_columns(\n",
-    "        (pl.col(\"named\") + pl.col(\"unnamed\")).alias(\"total\"),\n",
-    "        (pl.col(\"named\") / (pl.col(\"named\") + pl.col(\"unnamed\")) * 100).alias(\"named_pct\"),\n",
-    "    )\n",
-    "    .sort(\"total\", descending=True)\n",
-    ")\n",
-    "\n",
-    "top_cats = named_rate.head(30)\n",
-    "fig = px.bar(\n",
-    "    top_cats.to_pandas(),\n",
-    "    x=\"category\",\n",
-    "    y=[\"named\", \"unnamed\"],\n",
-    "    title=\"Named vs Unnamed POIs (Top 30 Categories)\",\n",
-    "    labels={\"value\": \"Count\", \"category\": \"Category\"},\n",
-    "    barmode=\"stack\",\n",
-    ")\n",
-    "fig.update_layout(height=600, xaxis_tickangle=-45)\n",
-    "fig.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/analyses/property_analysis.ipynb b/analyses/property_analysis.ipynb
index 2727a2d..3889fa3 100644
--- a/analyses/property_analysis.ipynb
+++ b/analyses/property_analysis.ipynb
@@ -1174,14 +1174,14 @@
           ]
          },
          "labels": [
+          "Leasehold",
           "U",
-          "Freehold",
-          "Leasehold"
+          "Freehold"
          ],
          "name": "National",
          "type": "pie",
          "values": {
-          "bdata": "FAIAAHNwZwHxuW4A",
+          "bdata": "8bluABQCAABzcGcB",
           "dtype": "u4"
          }
         },
@@ -1197,14 +1197,14 @@
           ]
          },
          "labels": [
-          "Freehold",
           "U",
+          "Freehold",
           "Leasehold"
          ],
          "name": "London",
          "type": "pie",
          "values": {
-          "bdata": "6VQcAFwAAAD1TR8A",
+          "bdata": "XAAAAOlUHAD1TR8A",
           "dtype": "u4"
          }
         }
@@ -7612,89 +7612,11 @@
         "plotlyServerURL": "https://plot.ly"
        },
        "data": [
-        {
-         "hovertemplate": "Property Type=Detached<br>Year=%{x}<br>Average Price (£)=%{y}<extra></extra>",
-         "legendgroup": "Detached",
-         "line": {
-          "color": "#636efa",
-          "dash": "solid"
-         },
-         "marker": {
-          "symbol": "circle"
-         },
-         "mode": "lines+markers",
-         "name": "Detached",
-         "orientation": "v",
-         "showlegend": true,
-         "type": "scatter",
-         "x": {
-          "bdata": "ywcAAMwHAADNBwAAzgcAAM8HAADQBwAA0QcAANIHAADTBwAA1AcAANUHAADWBwAA1wcAANgHAADZBwAA2gcAANsHAADcBwAA3QcAAN4HAADfBwAA4AcAAOEHAADiBwAA4wcAAOQHAADlBwAA5gcAAOcHAADoBwAA6QcAAA==",
-          "dtype": "i4"
-         },
-         "xaxis": "x",
-         "y": {
-          "bdata": "he7aSLi/CEFoN1HZAJAKQUY4cXYqNA5BmCYOQ6h8EkElIzG0u48UQeBpcT5PfxhBonCi/DDEGUGMRRiD7RUcQcQ/kuu+EB9BJEZk1BEQIUGZxLYWBcshQb27SvI8YyNB5+vvv8SyJUHh5E4zurMnQYAXJGVJ1yRBXOxwvdgiKEFRsmsTwYMnQUyGYB0SMydBNIUCsvpAKUHS8WO8cv8rQepZmB6R/yxBCNnlsjI8L0E1vqw6OicwQVbzCW9KyzBBA2/mqxRzMEEny9kScZkxQTPTgYu31jJB362yvuYNNUGL5FNq7ig1QVr2KFX/FDRBOL3pTarhMkE=",
-          "dtype": "f8"
-         },
-         "yaxis": "y"
-        },
-        {
-         "hovertemplate": "Property Type=Other<br>Year=%{x}<br>Average Price (£)=%{y}<extra></extra>",
-         "legendgroup": "Other",
-         "line": {
-          "color": "#EF553B",
-          "dash": "solid"
-         },
-         "marker": {
-          "symbol": "circle"
-         },
-         "mode": "lines+markers",
-         "name": "Other",
-         "orientation": "v",
-         "showlegend": true,
-         "type": "scatter",
-         "x": {
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          "legendgroup": "Terraced",
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@@ -7741,6 +7663,84 @@
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+         "legendgroup": "Other",
+         "line": {
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+        {
+         "hovertemplate": "Property Type=Detached<br>Year=%{x}<br>Average Price (£)=%{y}<extra></extra>",
+         "legendgroup": "Detached",
+         "line": {
+          "color": "#ab63fa",
+          "dash": "solid"
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+         "marker": {
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+         "legendgroup": "Semi-detached",
+         "line": {
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+         "marker": {
+          "symbol": "circle"
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+         "mode": "lines+markers",
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-         "legendgroup": "3069",
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+          "TOWER HAMLETS"
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-         "legendgroup": "7611",
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-         "name": "7611",
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@@ -11222,7 +11222,7 @@
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+          "RICHMOND UPON THAMES"
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-         "legendgroup": "Detached",
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-          "LAMBETH",
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-          "HARROW",
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-          "TOWER HAMLETS",
-          "HAVERING",
-          "KINGSTON UPON THAMES",
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-          "BARKING AND DAGENHAM",
-          "HARINGEY",
-          "WANDSWORTH",
-          "RICHMOND UPON THAMES",
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-          "BROMLEY",
-          "CITY OF WESTMINSTER",
-          "GREENWICH",
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-          "BROMLEY",
-          "GREENWICH",
-          "CAMDEN",
-          "BEXLEY",
-          "HOUNSLOW",
-          "REDBRIDGE",
+          "CROYDON",
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+          "MERTON",
+          "BROMLEY",
+          "BARNET",
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-          "LAMBETH"
+          "REDBRIDGE",
+          "NEWHAM",
+          "RICHMOND UPON THAMES",
+          "SUTTON",
+          "LEWISHAM",
+          "GREENWICH",
+          "HARINGEY",
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+          "KINGSTON UPON THAMES",
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          "legendgroup": "Semi-detached",
          "marker": {
-          "color": "#FFA15A",
+          "color": "#00cc96",
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@@ -15541,43 +15429,155 @@
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           "shape": "33, 5"
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@@ -21659,12 +21659,12 @@
          "legendgroup": "",
          "marker": {
           "color": {
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           "coloraxis": "coloraxis",
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           "sizemode": "area",
@@ -21697,8 +21697,8 @@
           "BRENT",
           "LAMBETH",
           "KINGSTON UPON THAMES",
-          "HARINGEY",
           "BARNET",
+          "HARINGEY",
           "SOUTHWARK",
           "HACKNEY",
           "WANDSWORTH",
@@ -21721,7 +21721,7 @@
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          "xaxis": "x",
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diff --git a/analyses/wide.ipynb b/analyses/wide.ipynb
new file mode 100644
index 0000000..44a6378
--- /dev/null
+++ b/analyses/wide.ipynb
@@ -0,0 +1,93 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3053488d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (5, 47)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>pp_address</th><th>postcode</th><th>historical_prices</th><th>pp_property_type</th><th>duration</th><th>latest_price</th><th>date_of_transfer</th><th>epc_address</th><th>current_energy_rating</th><th>potential_energy_rating</th><th>epc_property_type</th><th>built_form</th><th>inspection_date</th><th>total_floor_area</th><th>number_habitable_rooms</th><th>floor_height</th><th>construction_age_band</th><th>lat</th><th>lon</th><th>lsoa21</th><th>public_transport_easy_minutes</th><th>public_transport_quick_minutes</th><th>cycling_minutes</th><th>LSOA name (2021)</th><th>Local Authority District code (2024)</th><th>Local Authority District name (2024)</th><th>imd_score</th><th>income_score</th><th>employment_score</th><th>education_score</th><th>health_score</th><th>crime_score</th><th>housing_barriers_score</th><th>living_environment_score</th><th>idaci_score</th><th>idaopi_score</th><th>children_young_people_score</th><th>adult_skills_score</th><th>geographical_barriers_score</th><th>wider_barriers_score</th><th>indoors_score</th><th>outdoors_score</th><th>restaurants_2km</th><th>groceries_2km</th><th>parks_2km</th><th>public_transport_2km</th><th>price_per_sqm</th></tr><tr><td>str</td><td>str</td><td>list[struct[2]]</td><td>str</td><td>str</td><td>i64</td><td>datetime[μs]</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>f64</td><td>i64</td><td>f64</td><td>u16</td><td>f64</td><td>f64</td><td>str</td><td>i64</td><td>i64</td><td>i64</td><td>str</td><td>str</td><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i32</td><td>i32</td><td>i32</td><td>i32</td><td>f64</td></tr></thead><tbody><tr><td>&quot; 92 JOYES ROAD&quot;</td><td>&quot;CT19 6HL&quot;</td><td>[{1995,50000}, {2001,98500}]</td><td>&quot;Semi-Detached&quot;</td><td>&quot;Freehold&quot;</td><td>98500</td><td>2001-03-30 00:00:00</td><td>&quot;92, Joyes Road&quot;</td><td>&quot;D&quot;</td><td>&quot;C&quot;</td><td>&quot;House&quot;</td><td>&quot;Semi-Detached&quot;</td><td>&quot;2014-04-25&quot;</td><td>164.0</td><td>6</td><td>null</td><td>1930</td><td>51.091719</td><td>1.183558</td><td>&quot;E01024498&quot;</td><td>null</td><td>null</td><td>null</td><td>&quot;Folkestone and Hythe 003C&quot;</td><td>&quot;E07000112&quot;</td><td>&quot;Folkestone and Hythe&quot;</td><td>57.36</td><td>0.527</td><td>0.358</td><td>65.878</td><td>1.561</td><td>1.1</td><td>20.542</td><td>8.876</td><td>0.583</td><td>0.51</td><td>1.764</td><td>0.392</td><td>32.265</td><td>1.321</td><td>-0.46</td><td>-0.106</td><td>58</td><td>20</td><td>0</td><td>3</td><td>600.609756</td></tr><tr><td>&quot; 7 LUTON STREET&quot;</td><td>&quot;HX1 4NG&quot;</td><td>[{1995,16000}]</td><td>&quot;Terraced&quot;</td><td>&quot;Freehold&quot;</td><td>16000</td><td>1995-01-01 00:00:00</td><td>&quot;7, Luton Street&quot;</td><td>&quot;G&quot;</td><td>&quot;C&quot;</td><td>&quot;House&quot;</td><td>&quot;Enclosed Mid-Terrace&quot;</td><td>&quot;2015-03-18&quot;</td><td>99.0</td><td>4</td><td>null</td><td>1900</td><td>53.723918</td><td>-1.882346</td><td>&quot;E01010964&quot;</td><td>null</td><td>null</td><td>null</td><td>&quot;Calderdale 012B&quot;</td><td>&quot;E08000033&quot;</td><td>&quot;Calderdale&quot;</td><td>78.601</td><td>0.78</td><td>0.418</td><td>69.597</td><td>2.013</td><td>1.266</td><td>16.115</td><td>62.178</td><td>0.776</td><td>0.976</td><td>1.031</td><td>0.548</td><td>24.718</td><td>1.212</td><td>1.256</td><td>0.536</td><td>48</td><td>6</td><td>0</td><td>3</td><td>161.616162</td></tr><tr><td>&quot; OLD OAK COTTAGE ROPE WALK&quot;</td><td>&quot;TR4 8DW&quot;</td><td>[{1995,50000}, {2000,125000}, {2010,245000}]</td><td>&quot;Detached&quot;</td><td>&quot;Freehold&quot;</td><td>245000</td><td>2010-04-09 00:00:00</td><td>&quot;Old Oak Cottage, Rope Walk, Mo…</td><td>&quot;G&quot;</td><td>&quot;D&quot;</td><td>&quot;House&quot;</td><td>&quot;Detached&quot;</td><td>&quot;2024-08-22&quot;</td><td>134.0</td><td>8</td><td>2.27</td><td>1900</td><td>50.283886</td><td>-5.209508</td><td>&quot;E01018826&quot;</td><td>null</td><td>null</td><td>null</td><td>&quot;Cornwall 040B&quot;</td><td>&quot;E06000052&quot;</td><td>&quot;Cornwall&quot;</td><td>19.044</td><td>0.195</td><td>0.106</td><td>11.685</td><td>-0.24</td><td>-0.725</td><td>29.989</td><td>47.506</td><td>0.321</td><td>0.174</td><td>0.08</td><td>0.157</td><td>61.226</td><td>-1.92</td><td>1.144</td><td>-0.941</td><td>1</td><td>2</td><td>0</td><td>0</td><td>1828.358209</td></tr><tr><td>&quot; 147 WESTLANDS ROAD&quot;</td><td>&quot;HU5 5NY&quot;</td><td>[{1995,17000}, {2011,58500}, … {2021,145000}]</td><td>&quot;Terraced&quot;</td><td>&quot;Freehold&quot;</td><td>145000</td><td>2021-09-24 00:00:00</td><td>&quot;147, Westlands Road&quot;</td><td>&quot;C&quot;</td><td>&quot;B&quot;</td><td>&quot;House&quot;</td><td>&quot;Mid-Terrace&quot;</td><td>&quot;2020-07-22&quot;</td><td>67.0</td><td>4</td><td>null</td><td>1930</td><td>53.753695</td><td>-0.41399</td><td>&quot;E01012803&quot;</td><td>null</td><td>null</td><td>null</td><td>&quot;Kingston upon Hull 023E&quot;</td><td>&quot;E06000010&quot;</td><td>&quot;Kingston upon Hull, City of&quot;</td><td>19.998</td><td>0.22</td><td>0.122</td><td>26.551</td><td>0.178</td><td>0.357</td><td>18.601</td><td>21.688</td><td>0.325</td><td>0.185</td><td>0.688</td><td>0.244</td><td>31.781</td><td>0.857</td><td>0.316</td><td>-0.34</td><td>7</td><td>3</td><td>0</td><td>0</td><td>2164.179104</td></tr><tr><td>&quot; 70 SCOTLAND ROAD&quot;</td><td>&quot;CA11 9JD&quot;</td><td>[{1995,23000}, {1999,23500}, … {2024,134000}]</td><td>&quot;Semi-Detached&quot;</td><td>&quot;Freehold&quot;</td><td>134000</td><td>2024-10-01 00:00:00</td><td>&quot;70, Scotland Road&quot;</td><td>&quot;E&quot;</td><td>&quot;D&quot;</td><td>&quot;House&quot;</td><td>&quot;Semi-Detached&quot;</td><td>&quot;2011-03-29&quot;</td><td>65.68</td><td>4</td><td>2.44</td><td>1900</td><td>54.671753</td><td>-2.759846</td><td>&quot;E01019331&quot;</td><td>null</td><td>null</td><td>null</td><td>&quot;Eden 004F&quot;</td><td>&quot;E06000064&quot;</td><td>&quot;Westmorland and Furness&quot;</td><td>11.688</td><td>0.159</td><td>0.082</td><td>29.708</td><td>-0.836</td><td>-0.476</td><td>17.131</td><td>7.314</td><td>0.317</td><td>0.101</td><td>0.805</td><td>0.258</td><td>37.771</td><td>-1.003</td><td>-0.331</td><td>-0.758</td><td>29</td><td>11</td><td>0</td><td>12</td><td>2040.194884</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (5, 47)\n",
+       "┌───────────┬──────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
+       "│ pp_addres ┆ postcode ┆ historica ┆ pp_proper ┆ … ┆ groceries ┆ parks_2km ┆ public_tr ┆ price_per │\n",
+       "│ s         ┆ ---      ┆ l_prices  ┆ ty_type   ┆   ┆ _2km      ┆ ---       ┆ ansport_2 ┆ _sqm      │\n",
+       "│ ---       ┆ str      ┆ ---       ┆ ---       ┆   ┆ ---       ┆ i32       ┆ km        ┆ ---       │\n",
+       "│ str       ┆          ┆ list[stru ┆ str       ┆   ┆ i32       ┆           ┆ ---       ┆ f64       │\n",
+       "│           ┆          ┆ ct[2]]    ┆           ┆   ┆           ┆           ┆ i32       ┆           │\n",
+       "╞═══════════╪══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
+       "│ 92 JOYES  ┆ CT19 6HL ┆ [{1995,50 ┆ Semi-Deta ┆ … ┆ 20        ┆ 0         ┆ 3         ┆ 600.60975 │\n",
+       "│ ROAD      ┆          ┆ 000}, {20 ┆ ched      ┆   ┆           ┆           ┆           ┆ 6         │\n",
+       "│           ┆          ┆ 01,98500} ┆           ┆   ┆           ┆           ┆           ┆           │\n",
+       "│           ┆          ┆ ]         ┆           ┆   ┆           ┆           ┆           ┆           │\n",
+       "│ 7 LUTON   ┆ HX1 4NG  ┆ [{1995,16 ┆ Terraced  ┆ … ┆ 6         ┆ 0         ┆ 3         ┆ 161.61616 │\n",
+       "│ STREET    ┆          ┆ 000}]     ┆           ┆   ┆           ┆           ┆           ┆ 2         │\n",
+       "│ OLD OAK   ┆ TR4 8DW  ┆ [{1995,50 ┆ Detached  ┆ … ┆ 2         ┆ 0         ┆ 0         ┆ 1828.3582 │\n",
+       "│ COTTAGE   ┆          ┆ 000}, {20 ┆           ┆   ┆           ┆           ┆           ┆ 09        │\n",
+       "│ ROPE WALK ┆          ┆ 00,125000 ┆           ┆   ┆           ┆           ┆           ┆           │\n",
+       "│           ┆          ┆ }, …      ┆           ┆   ┆           ┆           ┆           ┆           │\n",
+       "│ 147       ┆ HU5 5NY  ┆ [{1995,17 ┆ Terraced  ┆ … ┆ 3         ┆ 0         ┆ 0         ┆ 2164.1791 │\n",
+       "│ WESTLANDS ┆          ┆ 000}, {20 ┆           ┆   ┆           ┆           ┆           ┆ 04        │\n",
+       "│ ROAD      ┆          ┆ 11,58500} ┆           ┆   ┆           ┆           ┆           ┆           │\n",
+       "│           ┆          ┆ , ……      ┆           ┆   ┆           ┆           ┆           ┆           │\n",
+       "│ 70        ┆ CA11 9JD ┆ [{1995,23 ┆ Semi-Deta ┆ … ┆ 11        ┆ 0         ┆ 12        ┆ 2040.1948 │\n",
+       "│ SCOTLAND  ┆          ┆ 000}, {19 ┆ ched      ┆   ┆           ┆           ┆           ┆ 84        │\n",
+       "│ ROAD      ┆          ┆ 99,23500} ┆           ┆   ┆           ┆           ┆           ┆           │\n",
+       "│           ┆          ┆ , ……      ┆           ┆   ┆           ┆           ┆           ┆           │\n",
+       "└───────────┴──────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴───────────┘"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import polars as pl\n",
+    "\n",
+    "\n",
+    "pl.scan_parquet('../data_sources/processed/wide.parquet').head().collect()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d492243",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 400f733956dd6cb48b9c7cd459e67cf8635315b1 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sat, 31 Jan 2026 12:48:47 +0000
Subject: [PATCH 26/62] Small changes

---
 .gitignore            | 1 +
 .vscode/settings.json | 4 +++-
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/.gitignore b/.gitignore
index 84ca392..172268f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -7,3 +7,4 @@ tfl_journey_client
 **/dist
 server-rs/target
 .task
+data
diff --git a/.vscode/settings.json b/.vscode/settings.json
index 1a89293..f99b994 100644
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@@ -2,6 +2,8 @@
     "files.exclude": {
         "*.venv": true,
         "**/__pycache__": true,
-        "**/node_modules": true
+        "**/node_modules": true,
+        "**/.ruff_cache":true,
+        "**/.pytest_cache":true
     }
 }
\ No newline at end of file

From 51967fa880972c9bc776917a69b93b608779c040 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sat, 31 Jan 2026 12:49:56 +0000
Subject: [PATCH 27/62] Improve map

---
 frontend/src/App.tsx                       | 240 ++++++++++++++++----
 frontend/src/components/DataSources.tsx    |  49 ++++
 frontend/src/components/Filters.tsx        | 213 +++++++-----------
 frontend/src/components/Map.tsx            | 247 ++++++---------------
 frontend/src/components/POIPane.tsx        | 140 ++++++++++++
 frontend/src/components/PropertiesPane.tsx | 224 +++++++++++++++++++
 frontend/src/types.ts                      |  34 ++-
 7 files changed, 794 insertions(+), 353 deletions(-)
 create mode 100644 frontend/src/components/DataSources.tsx
 create mode 100644 frontend/src/components/POIPane.tsx
 create mode 100644 frontend/src/components/PropertiesPane.tsx

diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx
index 1ba8e38..4f30c11 100644
--- a/frontend/src/App.tsx
+++ b/frontend/src/App.tsx
@@ -1,6 +1,9 @@
 import { useState, useEffect, useCallback, useRef, useMemo } from 'react';
 import Map from './components/Map';
 import Filters from './components/Filters';
+import POIPane from './components/POIPane';
+import { PropertiesPane } from './components/PropertiesPane';
+import DataSources from './components/DataSources';
 import type {
   FeatureMeta,
   FeatureFilters,
@@ -10,7 +13,9 @@ import type {
   ApiResponse,
   POI,
   POIResponse,
-  POICategoriesMap,
+  POICategoriesResponse,
+  Property,
+  HexagonPropertiesResponse,
 } from './types';
 
 const DEBOUNCE_MS = 150;
@@ -44,6 +49,7 @@ export default function App() {
   const [features, setFeatures] = useState<FeatureMeta[]>([]);
   const [filters, setFilters] = useState<FeatureFilters>({});
   const [activeFeature, setActiveFeature] = useState<string | null>(null);
+  const [dragValue, setDragValue] = useState<[number, number] | null>(null);
   const [rawData, setRawData] = useState<HexagonData[]>([]);
   const [resolution, setResolution] = useState<number>(8);
   const [bounds, setBounds] = useState<Bounds | null>(null);
@@ -54,35 +60,41 @@ export default function App() {
 
   // POI state
   const [pois, setPois] = useState<POI[]>([]);
-  const [poiCategories, setPOICategories] = useState<POICategoriesMap>({});
+  const [poiCategories, setPOICategories] = useState<string[]>([]);
   const [selectedPOICategories, setSelectedPOICategories] = useState<Set<string>>(new Set());
   const poiDebounceRef = useRef<ReturnType<typeof setTimeout> | null>(null);
   const poiAbortControllerRef = useRef<AbortController | null>(null);
 
+  // Hexagon properties state
+  const [selectedHexagon, setSelectedHexagon] = useState<{ h3: string; resolution: number } | null>(null);
+  const [properties, setProperties] = useState<Property[]>([]);
+  const [propertiesTotal, setPropertiesTotal] = useState(0);
+  const [propertiesOffset, setPropertiesOffset] = useState(0);
+  const [loadingProperties, setLoadingProperties] = useState(false);
+  const [rightPaneTab, setRightPaneTab] = useState<'pois' | 'properties'>('pois');
+
+  // Derive enabled features from filter keys
+  const enabledFeatures = useMemo(() => new Set(Object.keys(filters)), [filters]);
+
   // Fetch feature metadata + POI categories on mount
   useEffect(() => {
     fetch(`${getApiBaseUrl()}/api/features`)
       .then((res) => res.json())
       .then((json: { features: FeatureMeta[] }) => {
         setFeatures(json.features);
-        // Initialize filters with full range for each feature
-        const initial: FeatureFilters = {};
-        for (const f of json.features) {
-          initial[f.name] = [f.min, f.max];
-        }
-        setFilters(initial);
+        // Start with no filters (empty object)
       })
       .catch((err) => console.error('Failed to fetch features:', err));
 
     fetch(`${getApiBaseUrl()}/api/poi-categories`)
       .then((res) => res.json())
-      .then((json: { categories: POICategoriesMap }) => {
+      .then((json: POICategoriesResponse) => {
         setPOICategories(json.categories);
       })
       .catch((err) => console.error('Failed to fetch POI categories:', err));
   }, []);
 
-  // Debounced fetch when resolution/bounds change (no filter params sent)
+  // Debounced fetch when resolution/bounds/filters change
   useEffect(() => {
     if (!bounds) return;
 
@@ -103,6 +115,14 @@ export default function App() {
           resolution: resolution.toString(),
           bounds: boundsStr,
         });
+        // Build filters param: name:min:max,...
+        const filterEntries = Object.entries(filters);
+        if (filterEntries.length > 0) {
+          const filtersStr = filterEntries
+            .map(([name, [min, max]]) => `${name}:${min}:${max}`)
+            .join(',');
+          params.set('filters', filtersStr);
+        }
         const res = await fetch(`${getApiBaseUrl()}/api/hexagons?${params}`, {
           signal: abortControllerRef.current.signal,
         });
@@ -122,36 +142,19 @@ export default function App() {
         clearTimeout(debounceRef.current);
       }
     };
-  }, [resolution, bounds]);
+  }, [resolution, bounds, filters]);
 
-  // Client-side filtering
+  // Client-side filtering: only during drag preview
   const data = useMemo(() => {
-    if (features.length === 0) return rawData;
+    if (!activeFeature || !dragValue) return rawData;
 
     return rawData.filter((hex) => {
-      if (activeFeature) {
-        // Only apply the active feature's filter
-        const range = filters[activeFeature];
-        if (!range) return true;
-        const minVal = hex[`min_${activeFeature}`];
-        const maxVal = hex[`max_${activeFeature}`];
-        if (minVal == null || maxVal == null) return true;
-        return (minVal as number) <= range[1] && (maxVal as number) >= range[0];
-      }
-      // Apply ALL filters as intersection
-      for (const f of features) {
-        const range = filters[f.name];
-        if (!range) continue;
-        // Skip features where filter is at full range
-        if (range[0] === f.min && range[1] === f.max) continue;
-        const minVal = hex[`min_${f.name}`];
-        const maxVal = hex[`max_${f.name}`];
-        if (minVal == null || maxVal == null) continue;
-        if ((minVal as number) > range[1] || (maxVal as number) < range[0]) return false;
-      }
-      return true;
+      const minVal = hex[`min_${activeFeature}`];
+      const maxVal = hex[`max_${activeFeature}`];
+      if (minVal == null || maxVal == null) return false;
+      return (minVal as number) <= dragValue[1] && (maxVal as number) >= dragValue[0];
     });
-  }, [rawData, filters, activeFeature, features]);
+  }, [rawData, activeFeature, dragValue]);
 
   // Fetch POIs when bounds or selected categories change
   useEffect(() => {
@@ -181,7 +184,7 @@ export default function App() {
           signal: poiAbortControllerRef.current.signal,
         });
         const json: POIResponse = await res.json();
-        setPois(json.features || []);
+        setPois(json.pois || []);
       } catch (err) {
         if (err instanceof Error && err.name !== 'AbortError') {
           console.error('Failed to fetch POIs:', err);
@@ -205,18 +208,123 @@ export default function App() {
     []
   );
 
+  const handleAddFilter = useCallback(
+    (name: string) => {
+      const meta = features.find((f) => f.name === name);
+      if (!meta) return;
+      setFilters((prev) => ({ ...prev, [name]: [meta.min, meta.max] }));
+    },
+    [features]
+  );
+
+  const handleRemoveFilter = useCallback((name: string) => {
+    setFilters((prev) => {
+      const next = { ...prev };
+      delete next[name];
+      return next;
+    });
+  }, []);
+
+  const handleDragStart = useCallback(
+    (name: string) => {
+      setActiveFeature(name);
+      setDragValue(filters[name] || null);
+    },
+    [filters]
+  );
+
+  const handleDragChange = useCallback((value: [number, number]) => {
+    setDragValue(value);
+  }, []);
+
+  const handleDragEnd = useCallback(() => {
+    if (activeFeature && dragValue) {
+      setFilters((prev) => ({ ...prev, [activeFeature]: dragValue }));
+    }
+    setActiveFeature(null);
+    setDragValue(null);
+  }, [activeFeature, dragValue]);
+
+  const fetchHexagonProperties = useCallback(
+    async (h3: string, res: number, offset = 0) => {
+      setLoadingProperties(true);
+      try {
+        const params = new URLSearchParams({
+          h3,
+          resolution: res.toString(),
+          limit: '100',
+          offset: offset.toString(),
+        });
+
+        // Add current filters
+        const filterEntries = Object.entries(filters);
+        if (filterEntries.length > 0) {
+          const filterStr = filterEntries
+            .map(([name, [min, max]]) => `${name}:${min}:${max}`)
+            .join(',');
+          params.append('filters', filterStr);
+        }
+
+        const response = await fetch(`${getApiBaseUrl()}/api/hexagon-properties?${params}`);
+        const data: HexagonPropertiesResponse = await response.json();
+
+        if (offset === 0) {
+          setProperties(data.properties);
+        } else {
+          setProperties((prev) => [...prev, ...data.properties]);
+        }
+        setPropertiesTotal(data.total);
+        setPropertiesOffset(offset + data.properties.length);
+      } catch (err) {
+        console.error('Failed to fetch properties:', err);
+      } finally {
+        setLoadingProperties(false);
+      }
+    },
+    [filters]
+  );
+
+  const handleHexagonClick = useCallback(
+    (h3: string) => {
+      if (selectedHexagon?.h3 === h3) {
+        // Deselect if clicking same hexagon
+        setSelectedHexagon(null);
+        setProperties([]);
+      } else {
+        setSelectedHexagon({ h3, resolution });
+        setPropertiesOffset(0);
+        setRightPaneTab('properties'); // Auto-switch to properties tab
+        fetchHexagonProperties(h3, resolution, 0);
+      }
+    },
+    [selectedHexagon, resolution, fetchHexagonProperties]
+  );
+
+  const handleLoadMoreProperties = useCallback(() => {
+    if (selectedHexagon) {
+      fetchHexagonProperties(selectedHexagon.h3, selectedHexagon.resolution, propertiesOffset);
+    }
+  }, [selectedHexagon, propertiesOffset, fetchHexagonProperties]);
+
+  const handleCloseProperties = useCallback(() => {
+    setSelectedHexagon(null);
+    setProperties([]);
+  }, []);
+
   return (
     <div className="h-screen flex">
       <Filters
         features={features}
         filters={filters}
         activeFeature={activeFeature}
-        onFiltersChange={setFilters}
-        onActiveFeatureChange={setActiveFeature}
+        dragValue={dragValue}
+        enabledFeatures={enabledFeatures}
+        onAddFilter={handleAddFilter}
+        onRemoveFilter={handleRemoveFilter}
+        onDragStart={handleDragStart}
+        onDragChange={handleDragChange}
+        onDragEnd={handleDragEnd}
         zoom={zoom}
-        poiCategories={poiCategories}
-        selectedPOICategories={selectedPOICategories}
-        onPOICategoriesChange={setSelectedPOICategories}
       />
       <div className="flex-1 relative">
         <Map
@@ -224,11 +332,61 @@ export default function App() {
           pois={pois}
           onViewChange={handleViewChange}
           activeFeature={activeFeature}
+          dragValue={dragValue}
           features={features}
+          selectedHexagonId={selectedHexagon?.h3 || null}
+          onHexagonClick={handleHexagonClick}
         />
         {loading && (
           <div className="absolute top-4 right-4 bg-white px-3 py-1 rounded shadow">Loading...</div>
         )}
+        <DataSources />
+      </div>
+      <div className="w-72 bg-white shadow-lg z-10 flex flex-col">
+        {/* Tab headers */}
+        <div className="flex border-b border-gray-200">
+          <button
+            className={`flex-1 p-3 ${
+              rightPaneTab === 'pois'
+                ? 'border-b-2 border-blue-500 font-semibold'
+                : 'text-gray-600'
+            }`}
+            onClick={() => setRightPaneTab('pois')}
+          >
+            POIs {pois.length > 0 && `(${pois.length})`}
+          </button>
+          <button
+            className={`flex-1 p-3 ${
+              rightPaneTab === 'properties'
+                ? 'border-b-2 border-blue-500 font-semibold'
+                : 'text-gray-600'
+            }`}
+            onClick={() => setRightPaneTab('properties')}
+          >
+            Properties {propertiesTotal > 0 && `(${propertiesTotal})`}
+          </button>
+        </div>
+
+        {/* Tab content */}
+        <div className="flex-1 overflow-hidden">
+          {rightPaneTab === 'pois' ? (
+            <POIPane
+              categories={poiCategories}
+              selectedCategories={selectedPOICategories}
+              onCategoriesChange={setSelectedPOICategories}
+              poiCount={pois.length}
+            />
+          ) : (
+            <PropertiesPane
+              properties={properties}
+              total={propertiesTotal}
+              loading={loadingProperties}
+              hexagonId={selectedHexagon?.h3 || null}
+              onLoadMore={handleLoadMoreProperties}
+              onClose={handleCloseProperties}
+            />
+          )}
+        </div>
       </div>
     </div>
   );
diff --git a/frontend/src/components/DataSources.tsx b/frontend/src/components/DataSources.tsx
new file mode 100644
index 0000000..1d558bf
--- /dev/null
+++ b/frontend/src/components/DataSources.tsx
@@ -0,0 +1,49 @@
+export default function DataSources() {
+  const sources = [
+    {
+      name: 'Land Registry',
+      url: 'https://www.gov.uk/government/statistical-data-sets/price-paid-data-downloads',
+    },
+    {
+      name: 'EPC',
+      url: 'https://epc.opendatacommunities.org/downloads/domestic',
+    },
+    {
+      name: 'ArcGIS Postcodes',
+      url: 'https://www.arcgis.com/sharing/rest/content/items/077631e063eb4e1ab43575d01381ec33/data',
+    },
+    {
+      name: 'OpenStreetMap',
+      url: 'https://www.openstreetmap.org/copyright',
+    },
+    {
+      name: 'IoD 2025',
+      url: 'https://www.gov.uk/government/statistics/english-indices-of-deprivation-2025',
+    },
+    {
+      name: 'TfL API',
+      url: 'https://api-portal.tfl.gov.uk/',
+    },
+  ];
+
+  return (
+    <div className="absolute bottom-2 right-2 bg-white/90 backdrop-blur-sm px-3 py-2 rounded shadow-lg text-xs max-w-xs">
+      <div className="font-semibold mb-1 text-gray-700">Data Sources</div>
+      <div className="flex flex-wrap gap-x-2 gap-y-0.5">
+        {sources.map((source, idx) => (
+          <span key={source.name}>
+            <a
+              href={source.url}
+              target="_blank"
+              rel="noopener noreferrer"
+              className="text-blue-600 hover:text-blue-800 hover:underline"
+            >
+              {source.name}
+            </a>
+            {idx < sources.length - 1 && <span className="text-gray-400">•</span>}
+          </span>
+        ))}
+      </div>
+    </div>
+  );
+}
diff --git a/frontend/src/components/Filters.tsx b/frontend/src/components/Filters.tsx
index 181d685..3bcb29e 100644
--- a/frontend/src/components/Filters.tsx
+++ b/frontend/src/components/Filters.tsx
@@ -1,18 +1,19 @@
-import { useState, useRef, useEffect } from 'react';
 import { Slider } from './ui/slider';
 import { Label } from './ui/label';
-import type { FeatureMeta, FeatureFilters, POICategoriesMap } from '../types';
+import type { FeatureMeta, FeatureFilters } from '../types';
 
 interface FiltersProps {
   features: FeatureMeta[];
   filters: FeatureFilters;
   activeFeature: string | null;
-  onFiltersChange: (filters: FeatureFilters) => void;
-  onActiveFeatureChange: (feature: string | null) => void;
+  dragValue: [number, number] | null;
+  enabledFeatures: Set<string>;
+  onAddFilter: (name: string) => void;
+  onRemoveFilter: (name: string) => void;
+  onDragStart: (name: string) => void;
+  onDragChange: (value: [number, number]) => void;
+  onDragEnd: () => void;
   zoom: number;
-  poiCategories: POICategoriesMap;
-  selectedPOICategories: Set<string>;
-  onPOICategoriesChange: (categories: Set<string>) => void;
 }
 
 function formatValue(value: number): string {
@@ -26,47 +27,17 @@ export default function Filters({
   features,
   filters,
   activeFeature,
-  onFiltersChange,
-  onActiveFeatureChange,
+  dragValue,
+  enabledFeatures,
+  onAddFilter,
+  onRemoveFilter,
+  onDragStart,
+  onDragChange,
+  onDragEnd,
   zoom,
-  poiCategories,
-  selectedPOICategories,
-  onPOICategoriesChange,
 }: FiltersProps) {
-  const [dropdownOpen, setDropdownOpen] = useState(false);
-  const dropdownRef = useRef<HTMLDivElement>(null);
-
-  // Close dropdown when clicking outside
-  useEffect(() => {
-    function handleClickOutside(event: MouseEvent) {
-      if (dropdownRef.current && !dropdownRef.current.contains(event.target as Node)) {
-        setDropdownOpen(false);
-      }
-    }
-    document.addEventListener('mousedown', handleClickOutside);
-    return () => document.removeEventListener('mousedown', handleClickOutside);
-  }, []);
-
-  const toggleCategory = (key: string) => {
-    const newSet = new Set(selectedPOICategories);
-    if (newSet.has(key)) {
-      newSet.delete(key);
-    } else {
-      newSet.add(key);
-    }
-    onPOICategoriesChange(newSet);
-  };
-
-  const selectAll = () => {
-    onPOICategoriesChange(new Set(Object.keys(poiCategories)));
-  };
-
-  const selectNone = () => {
-    onPOICategoriesChange(new Set());
-  };
-
-  const categoryKeys = Object.keys(poiCategories);
-  const selectedCount = selectedPOICategories.size;
+  const availableFeatures = features.filter((f) => !enabledFeatures.has(f.name));
+  const enabledFeatureList = features.filter((f) => enabledFeatures.has(f.name));
 
   return (
     <div className="w-72 p-4 bg-white shadow-lg space-y-4 overflow-y-auto max-h-screen">
@@ -74,9 +45,31 @@ export default function Filters({
 
       <div className="text-sm text-slate-500">Zoom: {zoom.toFixed(1)}</div>
 
-      {features.map((feature) => {
-        const range = filters[feature.name] || [feature.min, feature.max];
+      {/* Add filter dropdown */}
+      {availableFeatures.length > 0 && (
+        <select
+          className="w-full p-2 border rounded text-sm bg-white"
+          value=""
+          onChange={(e) => {
+            if (e.target.value) onAddFilter(e.target.value);
+          }}
+        >
+          <option value="" disabled>
+            + Add filter...
+          </option>
+          {availableFeatures.map((f) => (
+            <option key={f.name} value={f.name}>
+              {f.label}
+            </option>
+          ))}
+        </select>
+      )}
+
+      {/* Active filters */}
+      {enabledFeatureList.map((feature) => {
         const isActive = activeFeature === feature.name;
+        const displayValue =
+          isActive && dragValue ? dragValue : filters[feature.name] || [feature.min, feature.max];
         const step = (feature.max - feature.min) / 100;
 
         return (
@@ -84,19 +77,26 @@ export default function Filters({
             key={feature.name}
             className={`space-y-1 p-2 rounded ${isActive ? 'ring-2 ring-blue-400 bg-blue-50' : ''}`}
           >
-            <Label className="text-xs">
-              {feature.label}: {formatValue(range[0])} - {formatValue(range[1])}
-            </Label>
+            <div className="flex items-center justify-between">
+              <Label className="text-xs">
+                {feature.label}: {formatValue(displayValue[0])} - {formatValue(displayValue[1])}
+              </Label>
+              <button
+                onClick={() => onRemoveFilter(feature.name)}
+                className="text-slate-400 hover:text-slate-700 text-sm px-1"
+                title="Remove filter"
+              >
+                x
+              </button>
+            </div>
             <Slider
               min={feature.min}
               max={feature.max}
               step={step}
-              value={[range[0], range[1]]}
-              onValueChange={([min, max]) => {
-                onFiltersChange({ ...filters, [feature.name]: [min, max] });
-              }}
-              onPointerDown={() => onActiveFeatureChange(feature.name)}
-              onPointerUp={() => onActiveFeatureChange(null)}
+              value={[displayValue[0], displayValue[1]]}
+              onValueChange={([min, max]) => onDragChange([min, max])}
+              onPointerDown={() => onDragStart(feature.name)}
+              onPointerUp={() => onDragEnd()}
             />
           </div>
         );
@@ -104,82 +104,33 @@ export default function Filters({
 
       <div className="p-3 bg-slate-100 rounded text-xs">
         <div className="mb-2 font-medium">Color Scale</div>
-        <div
-          className="h-4 rounded"
-          style={{
-            background:
-              'linear-gradient(to right, rgb(46, 204, 113), rgb(241, 196, 15), rgb(231, 76, 60), rgb(142, 68, 173))',
-          }}
-        ></div>
-        <div className="flex justify-between mt-1">
-          <span>Low</span>
-          <span>High</span>
-        </div>
-      </div>
-
-      <div className="space-y-2" ref={dropdownRef}>
-        <Label>Points of Interest</Label>
-        <button
-          onClick={() => setDropdownOpen(!dropdownOpen)}
-          className="w-full flex items-center justify-between px-3 py-2 text-sm border border-slate-300 rounded hover:border-slate-400 bg-white"
-        >
-          <span className="truncate text-left">
-            {selectedCount === 0
-              ? 'Select categories...'
-              : selectedCount === categoryKeys.length
-                ? 'All categories'
-                : `${selectedCount} selected`}
-          </span>
-          <svg
-            className={`w-4 h-4 ml-2 flex-shrink-0 transition-transform ${dropdownOpen ? 'rotate-180' : ''}`}
-            fill="none"
-            stroke="currentColor"
-            viewBox="0 0 24 24"
-          >
-            <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M19 9l-7 7-7-7" />
-          </svg>
-        </button>
-
-        {dropdownOpen && (
-          <div className="border border-slate-300 rounded shadow-lg bg-white">
-            <div className="flex gap-2 px-3 py-2 border-b border-slate-200">
-              <button
-                onClick={selectAll}
-                className="text-xs text-blue-600 hover:text-blue-800"
-              >
-                All
-              </button>
-              <span className="text-xs text-slate-300">|</span>
-              <button
-                onClick={selectNone}
-                className="text-xs text-blue-600 hover:text-blue-800"
-              >
-                None
-              </button>
+        {activeFeature ? (
+          <>
+            <div
+              className="h-4 rounded"
+              style={{
+                background:
+                  'linear-gradient(to right, rgb(46, 204, 113), rgb(241, 196, 15), rgb(231, 76, 60), rgb(142, 68, 173))',
+              }}
+            ></div>
+            <div className="flex justify-between mt-1">
+              <span>Low</span>
+              <span>High</span>
             </div>
-            <div className="max-h-64 overflow-y-auto py-1">
-              {categoryKeys.map((key) => {
-                const { emoji, label, count } = poiCategories[key];
-                return (
-                  <label
-                    key={key}
-                    className="flex items-center gap-2 px-3 py-1.5 hover:bg-slate-50 cursor-pointer"
-                  >
-                    <input
-                      type="checkbox"
-                      checked={selectedPOICategories.has(key)}
-                      onChange={() => toggleCategory(key)}
-                      className="rounded"
-                    />
-                    <span className="text-sm flex-1">
-                      {emoji} {label}
-                    </span>
-                    <span className="text-xs text-slate-400">{count.toLocaleString()}</span>
-                  </label>
-                );
-              })}
+          </>
+        ) : (
+          <>
+            <div
+              className="h-4 rounded"
+              style={{
+                background: 'linear-gradient(to right, rgb(209, 226, 243), rgb(33, 102, 172))',
+              }}
+            ></div>
+            <div className="flex justify-between mt-1">
+              <span>Few</span>
+              <span>Many</span>
             </div>
-          </div>
+          </>
         )}
       </div>
     </div>
diff --git a/frontend/src/components/Map.tsx b/frontend/src/components/Map.tsx
index 6353cac..3aa94c8 100644
--- a/frontend/src/components/Map.tsx
+++ b/frontend/src/components/Map.tsx
@@ -13,169 +13,24 @@ interface MapProps {
   pois: POI[];
   onViewChange: (params: ViewChangeParams) => void;
   activeFeature: string | null;
+  dragValue: [number, number] | null;
   features: FeatureMeta[];
+  selectedHexagonId: string | null;
+  onHexagonClick: (h3: string) => void;
 }
 
 // Twemoji CDN base URL
 const TWEMOJI_BASE = 'https://cdn.jsdelivr.net/gh/twitter/twemoji@14.0.2/assets/72x72/';
 
-// Map category to Twemoji codepoint (emoji unicode -> hex)
-const POI_EMOJI_CODES: Record<string, string> = {
-  // Education
-  school: '1f3eb', // 🏫
-  preschool: '1f476', // 👶
-  college_university: '1f393', // 🎓
-  library: '1f4da', // 📚
-  // Healthcare
-  doctor: '1f3e5', // 🏥
-  dentist: '1f9b7', // 🦷
-  pharmacy: '1f48a', // 💊
-  hospital: '1f3e5',
-  public_health_clinic: '1f3e5',
-  veterinary: '1f43e', // 🐾
-  nursing_home: '1f3e0', // 🏠
-  social_facility: '1f91d', // 🤝
-  // Transport
-  train_station: '1f689', // 🚉
-  bus_station: '1f68c', // 🚌
-  bus_stop: '1f68f', // 🚏
-  metro_station: '1f687', // 🚇
-  light_rail_station: '1f687',
-  tram_stop: '1f68a', // 🚊
-  ferry_terminal: '26f4', // ⛴
-  airport: '2708', // ✈
-  // Parks & Leisure
-  park: '1f333', // 🌳
-  national_park: '1f3de', // 🏞
-  nature_reserve: '1f33f', // 🌿
-  dog_park: '1f415', // 🐕
-  playground: '1f3a0', // 🎠
-  garden: '1f33a', // 🌺
-  sports_centre: '1f3c3', // 🏃
-  swimming_pool: '1f3ca', // 🏊
-  gym: '1f4aa', // 💪
-  golf_course: '26f3', // ⛳
-  marina: '26f5', // ⛵
-  // Emergency
-  police_department: '1f694', // 🚔
-  fire_department: '1f692', // 🚒
-  // Supermarkets & Grocery
-  supermarket: '1f6d2', // 🛒
-  grocery_store: '1f6d2',
-  convenience_store: '1f3ea', // 🏪
-  bakery: '1f35e', // 🍞
-  butcher: '1f969', // 🥩
-  greengrocer: '1f966', // 🥦
-  deli: '1f9c0', // 🧀
-  // Shopping
-  department_store: '1f3ec', // 🏬
-  clothing_store: '1f455', // 👕
-  shoe_store: '1f45f', // 👟
-  electronics_store: '1f4f1', // 📱
-  hardware_store: '1f527', // 🔧
-  furniture_store: '1fa91', // 🪑
-  bookshop: '1f4d6', // 📖
-  newsagent: '1f4f0', // 📰
-  charity_shop: '1f49c', // 💜
-  shopping_centre: '1f6cd', // 🛍
-  optician: '1f453', // 👓
-  off_licence: '1f37a', // 🍺
-  // Food & Drink
-  restaurant: '1f37d', // 🍽
-  cafe: '2615', // ☕
-  pub: '1f37b', // 🍻
-  bar: '1f378', // 🍸
-  fast_food: '1f354', // 🍔
-  food_court: '1f372', // 🍲
-  ice_cream: '1f366', // 🍦
-  beer_garden: '1f37a', // 🍺
-  // Personal Care
-  hairdresser: '1f487', // 💇
-  beauty_salon: '1f484', // 💄
-  laundry: '1f9fa', // 🧺
-  dry_cleaning: '1f455', // 👕
-  // Finance
-  bank: '1f3e6', // 🏦
-  atm: '1f4b3', // 💳
-  bureau_de_change: '1f4b1', // 💱
-  // Entertainment & Culture
-  cinema: '1f3ac', // 🎬
-  theatre: '1f3ad', // 🎭
-  nightclub: '1f483', // 💃
-  community_centre: '1f3db', // 🏛
-  arts_centre: '1f3a8', // 🎨
-  museum: '1f3db', // 🏛
-  gallery: '1f5bc', // 🖼
-  attraction: '2b50', // ⭐
-  zoo: '1f418', // 🐘
-  theme_park: '1f3a2', // 🎢
-  viewpoint: '1f301', // 🌁
-  // Accommodation
-  hotel: '1f3e8', // 🏨
-  hostel: '1f6cf', // 🛏
-  guest_house: '1f3e1', // 🏡
-  campsite: '26fa', // ⛺
-  caravan_site: '1f699', // 🚙
-  // Religion
-  place_of_worship: '1f6d0', // 🛐
-  // Government & Public
-  town_hall: '1f3db', // 🏛
-  courthouse: '2696', // ⚖
-  post_office: '1f4ee', // 📮
-  prison: '1f513', // 🔓
-  public_toilets: '1f6bb', // 🚻
-  // Automotive
-  petrol_station: '26fd', // ⛽
-  ev_charging: '1f50c', // 🔌
-  car_dealer: '1f697', // 🚗
-  car_repair: '1f527', // 🔧
-  parking: '1f17f', // 🅿
-  bicycle_parking: '1f6b2', // 🚲
-  // Recycling & Waste
-  recycling: '267b', // ♻
-  waste_disposal: '1f5d1', // 🗑
-};
-
-function getPOIIconUrl(category: string): string {
-  const code = POI_EMOJI_CODES[category] || '1f4cd'; // 📍 default
-  return `${TWEMOJI_BASE}${code}.png`;
+// Convert emoji to Twemoji URL
+function emojiToTwemojiUrl(emoji: string): string {
+  // Convert emoji to Unicode codepoint hex
+  const codePoint = emoji.codePointAt(0);
+  if (!codePoint) return `${TWEMOJI_BASE}1f4cd.png`; // Default pin
+  const hex = codePoint.toString(16);
+  return `${TWEMOJI_BASE}${hex}.png`;
 }
 
-// Tooltip emojis (these render fine in HTML)
-const TOOLTIP_EMOJIS: Record<string, string> = {
-  school: '🏫', preschool: '👶', college_university: '🎓', library: '📚',
-  doctor: '🏥', dentist: '🦷', pharmacy: '💊', hospital: '🏥',
-  public_health_clinic: '🏥', veterinary: '🐾', nursing_home: '🏠', social_facility: '🤝',
-  train_station: '🚉', bus_station: '🚌', bus_stop: '🚏', metro_station: '🚇',
-  light_rail_station: '🚇', tram_stop: '🚊', ferry_terminal: '⛴️', airport: '✈️',
-  park: '🌳', national_park: '🏞️', nature_reserve: '🌿', dog_park: '🐕',
-  playground: '🎠', garden: '🌺', sports_centre: '🏃', swimming_pool: '🏊',
-  gym: '💪', golf_course: '⛳', marina: '⛵',
-  police_department: '🚔', fire_department: '🚒',
-  supermarket: '🛒', grocery_store: '🛒', convenience_store: '🏪',
-  bakery: '🍞', butcher: '🥩', greengrocer: '🥦', deli: '🧀',
-  department_store: '🏬', clothing_store: '👕', shoe_store: '👟',
-  electronics_store: '📱', hardware_store: '🔧', furniture_store: '🪑',
-  bookshop: '📖', newsagent: '📰', charity_shop: '💜', shopping_centre: '🛍️',
-  optician: '👓', off_licence: '🍺',
-  restaurant: '🍽️', cafe: '☕', pub: '🍻', bar: '🍸',
-  fast_food: '🍔', food_court: '🍲', ice_cream: '🍦', beer_garden: '🍺',
-  hairdresser: '💇', beauty_salon: '💄', laundry: '🧺', dry_cleaning: '👕',
-  bank: '🏦', atm: '💳', bureau_de_change: '💱',
-  cinema: '🎬', theatre: '🎭', nightclub: '💃', community_centre: '🏛️',
-  arts_centre: '🎨', museum: '🏛️', gallery: '🖼️', attraction: '⭐',
-  zoo: '🐘', theme_park: '🎢', viewpoint: '🌁',
-  hotel: '🏨', hostel: '🛏️', guest_house: '🏡', campsite: '⛺', caravan_site: '🚙',
-  place_of_worship: '🛐',
-  town_hall: '🏛️', courthouse: '⚖️', post_office: '📮', prison: '🔓', public_toilets: '🚻',
-  petrol_station: '⛽', ev_charging: '🔌', car_dealer: '🚗', car_repair: '🔧',
-  parking: '🅿️', bicycle_parking: '🚲',
-  recycling: '♻️', waste_disposal: '🗑️',
-};
-
-function getTooltipEmoji(category: string): string {
-  return TOOLTIP_EMOJIS[category] || '📍';
-}
 
 const INITIAL_VIEW: ViewState = {
   longitude: -1.5,
@@ -280,7 +135,16 @@ function DeckOverlay({
   return null;
 }
 
-export default function Map({ data, pois, onViewChange, activeFeature, features }: MapProps) {
+// Sequential blue scale for count-based coloring
+function countToColor(t: number): [number, number, number] {
+  // light blue (209, 226, 243) -> dark blue (33, 102, 172)
+  const r = Math.round(209 + (33 - 209) * t);
+  const g = Math.round(226 + (102 - 226) * t);
+  const b = Math.round(243 + (172 - 243) * t);
+  return [r, g, b];
+}
+
+export default function Map({ data, pois, onViewChange, activeFeature, dragValue, features, selectedHexagonId, onHexagonClick }: MapProps) {
   const containerRef = useRef<HTMLDivElement>(null);
   const [viewState, setViewState] = useState<ViewState>(INITIAL_VIEW);
   const [dimensions, setDimensions] = useState<Dimensions>({ width: 0, height: 0 });
@@ -347,9 +211,31 @@ export default function Map({ data, pois, onViewChange, activeFeature, features
     }
   }, []);
 
-  // Determine which feature to use for coloring
-  const colorFeatureName = activeFeature || (features.length > 0 ? features[0].name : null);
-  const colorFeatureMeta = features.find((f) => f.name === colorFeatureName) || null;
+  // Compute count range for count-based coloring
+  const countRange = useMemo(() => {
+    if (data.length === 0) return { min: 0, max: 1 };
+    let min = Infinity;
+    let max = -Infinity;
+    for (const d of data) {
+      const c = d.count as number;
+      if (c < min) min = c;
+      if (c > max) max = c;
+    }
+    if (min === max) return { min, max: min + 1 };
+    return { min, max };
+  }, [data]);
+
+  // Determine color mode
+  const colorFeatureMeta = activeFeature ? features.find((f) => f.name === activeFeature) || null : null;
+
+  const handleHexagonClick = useCallback(
+    (info: PickingInfo<HexagonData>) => {
+      if (info.object && 'h3' in info.object) {
+        onHexagonClick(info.object.h3);
+      }
+    },
+    [onHexagonClick]
+  );
 
   const layers = useMemo(
     () => [
@@ -358,21 +244,33 @@ export default function Map({ data, pois, onViewChange, activeFeature, features
         data,
         getHexagon: (d) => d.h3,
         getFillColor: (d) => {
-          if (!colorFeatureName || !colorFeatureMeta) return [128, 128, 128] as [number, number, number];
-          const val = d[`min_${colorFeatureName}`];
-          if (val == null) return [128, 128, 128] as [number, number, number];
-          const range = colorFeatureMeta.max - colorFeatureMeta.min;
-          if (range === 0) return GRADIENT[0].color;
-          const t = ((val as number) - colorFeatureMeta.min) / range;
-          return normalizedToColor(t);
+          if (activeFeature && dragValue && colorFeatureMeta) {
+            // Drag mode: color by feature value using gradient
+            const val = d[`min_${activeFeature}`];
+            if (val == null) return [128, 128, 128] as [number, number, number];
+            const range = dragValue[1] - dragValue[0];
+            if (range === 0) return GRADIENT[0].color;
+            const t = ((val as number) - dragValue[0]) / range;
+            return normalizedToColor(Math.max(0, Math.min(1, t)));
+          }
+          // Normal mode: color by count using blue scale
+          const c = d.count as number;
+          const t = (c - countRange.min) / (countRange.max - countRange.min);
+          return countToColor(Math.max(0, Math.min(1, t)));
         },
+        getLineColor: (d) => (d.h3 === selectedHexagonId ? [255, 255, 255, 255] : [0, 0, 0, 0]) as [number, number, number, number],
+        getLineWidth: (d) => (d.h3 === selectedHexagonId ? 2 : 0),
+        lineWidthUnits: 'pixels',
         updateTriggers: {
-          getFillColor: [colorFeatureName, colorFeatureMeta],
+          getFillColor: [activeFeature, dragValue, countRange, colorFeatureMeta],
+          getLineColor: [selectedHexagonId],
+          getLineWidth: [selectedHexagonId],
         },
         extruded: false,
         pickable: true,
         opacity: 0.5,
         highPrecision: true,
+        onClick: handleHexagonClick,
         // @ts-expect-error beforeId is a MapboxOverlay interleave prop, not typed in LayerProps
         beforeId: LABEL_LAYER_ID,
       }),
@@ -381,7 +279,7 @@ export default function Map({ data, pois, onViewChange, activeFeature, features
         data: pois,
         getPosition: (d) => [d.lng, d.lat],
         getIcon: (d) => ({
-          url: getPOIIconUrl(d.category),
+          url: emojiToTwemojiUrl(d.emoji),
           width: 72,
           height: 72,
         }),
@@ -392,7 +290,7 @@ export default function Map({ data, pois, onViewChange, activeFeature, features
         onHover: handlePoiHover,
       }),
     ],
-    [data, pois, handlePoiHover, colorFeatureName, colorFeatureMeta]
+    [data, pois, handlePoiHover, handleHexagonClick, activeFeature, dragValue, countRange, colorFeatureMeta, selectedHexagonId]
   );
 
   const getTooltip = useCallback(
@@ -409,7 +307,7 @@ export default function Map({ data, pois, onViewChange, activeFeature, features
         if (minVal != null && maxVal != null) {
           const minStr = typeof minVal === 'number' ? minVal.toLocaleString(undefined, { maximumFractionDigits: 1 }) : String(minVal);
           const maxStr = typeof maxVal === 'number' ? maxVal.toLocaleString(undefined, { maximumFractionDigits: 1 }) : String(maxVal);
-          const highlight = f.name === colorFeatureName ? 'font-weight: bold;' : '';
+          const highlight = f.name === activeFeature ? 'font-weight: bold;' : '';
           lines.push(`<div style="${highlight}">${f.label}: ${minStr} - ${maxStr}</div>`);
         }
       }
@@ -423,7 +321,7 @@ export default function Map({ data, pois, onViewChange, activeFeature, features
         },
       };
     },
-    [features, colorFeatureName]
+    [features, activeFeature]
   );
 
   return (
@@ -434,6 +332,7 @@ export default function Map({ data, pois, onViewChange, activeFeature, features
         onLoad={handleMapLoad as never}
         mapStyle={MAP_STYLE}
         style={{ width: '100%', height: '100%' }}
+        attributionControl={false}
       >
         <DeckOverlay layers={layers} getTooltip={getTooltip as never} />
       </MapGL>
@@ -447,10 +346,8 @@ export default function Map({ data, pois, onViewChange, activeFeature, features
             zIndex: 9999,
           }}
         >
-          <strong>
-            {getTooltipEmoji(popupInfo.category)} {popupInfo.name}
-          </strong>
-          <div className="text-gray-500 text-xs">{popupInfo.category.replace(/_/g, ' ')}</div>
+          <strong>{popupInfo.name}</strong>
+          <div className="text-gray-500 text-xs">{popupInfo.category}</div>
         </div>
       )}
     </div>
diff --git a/frontend/src/components/POIPane.tsx b/frontend/src/components/POIPane.tsx
new file mode 100644
index 0000000..961d038
--- /dev/null
+++ b/frontend/src/components/POIPane.tsx
@@ -0,0 +1,140 @@
+import { useState, useRef, useEffect } from 'react';
+import { Label } from './ui/label';
+
+interface POIPaneProps {
+  categories: string[];
+  selectedCategories: Set<string>;
+  onCategoriesChange: (categories: Set<string>) => void;
+  poiCount: number;
+}
+
+export default function POIPane({
+  categories,
+  selectedCategories,
+  onCategoriesChange,
+  poiCount,
+}: POIPaneProps) {
+  const [dropdownOpen, setDropdownOpen] = useState(false);
+  const [searchTerm, setSearchTerm] = useState('');
+  const dropdownRef = useRef<HTMLDivElement>(null);
+
+  // Close dropdown when clicking outside
+  useEffect(() => {
+    function handleClickOutside(event: MouseEvent) {
+      if (dropdownRef.current && !dropdownRef.current.contains(event.target as Node)) {
+        setDropdownOpen(false);
+      }
+    }
+    document.addEventListener('mousedown', handleClickOutside);
+    return () => document.removeEventListener('mousedown', handleClickOutside);
+  }, []);
+
+  const toggleCategory = (category: string) => {
+    const newSet = new Set(selectedCategories);
+    if (newSet.has(category)) {
+      newSet.delete(category);
+    } else {
+      newSet.add(category);
+    }
+    onCategoriesChange(newSet);
+  };
+
+  const selectAll = () => {
+    onCategoriesChange(new Set(categories));
+  };
+
+  const selectNone = () => {
+    onCategoriesChange(new Set());
+  };
+
+  const filteredCategories = categories.filter((cat) =>
+    cat.toLowerCase().includes(searchTerm.toLowerCase())
+  );
+
+  const selectedCount = selectedCategories.size;
+
+  return (
+    <div className="w-72 p-4 bg-white shadow-lg space-y-4 overflow-y-auto max-h-screen">
+      <h2 className="text-xl font-bold">Points of Interest</h2>
+
+      <div className="space-y-2" ref={dropdownRef}>
+        <Label>Categories</Label>
+        <button
+          onClick={() => setDropdownOpen(!dropdownOpen)}
+          className="w-full flex items-center justify-between px-3 py-2 text-sm border border-slate-300 rounded hover:border-slate-400 bg-white"
+        >
+          <span className="truncate text-left">
+            {selectedCount === 0
+              ? 'Select categories...'
+              : selectedCount === categories.length
+                ? 'All categories'
+                : `${selectedCount} selected`}
+          </span>
+          <svg
+            className={`w-4 h-4 ml-2 flex-shrink-0 transition-transform ${dropdownOpen ? 'rotate-180' : ''}`}
+            fill="none"
+            stroke="currentColor"
+            viewBox="0 0 24 24"
+          >
+            <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M19 9l-7 7-7-7" />
+          </svg>
+        </button>
+
+        {dropdownOpen && (
+          <div className="border border-slate-300 rounded shadow-lg bg-white">
+            <div className="flex gap-2 px-3 py-2 border-b border-slate-200">
+              <button onClick={selectAll} className="text-xs text-blue-600 hover:text-blue-800">
+                All
+              </button>
+              <span className="text-xs text-slate-300">|</span>
+              <button onClick={selectNone} className="text-xs text-blue-600 hover:text-blue-800">
+                None
+              </button>
+            </div>
+            <div className="px-3 py-2 border-b border-slate-200">
+              <input
+                type="text"
+                placeholder="Search categories..."
+                value={searchTerm}
+                onChange={(e) => setSearchTerm(e.target.value)}
+                className="w-full px-2 py-1 text-sm border border-slate-300 rounded"
+              />
+            </div>
+            <div className="max-h-96 overflow-y-auto py-1">
+              {filteredCategories.map((category) => (
+                <label
+                  key={category}
+                  className="flex items-center gap-2 px-3 py-1.5 hover:bg-slate-50 cursor-pointer"
+                >
+                  <input
+                    type="checkbox"
+                    checked={selectedCategories.has(category)}
+                    onChange={() => toggleCategory(category)}
+                    className="rounded"
+                  />
+                  <span className="text-sm flex-1">{category}</span>
+                </label>
+              ))}
+            </div>
+          </div>
+        )}
+      </div>
+
+      {selectedCount > 0 && (
+        <div className="p-3 bg-blue-50 rounded text-sm">
+          <div className="font-medium text-blue-900">
+            {poiCount.toLocaleString()} POI{poiCount !== 1 ? 's' : ''} visible
+          </div>
+          <div className="text-xs text-blue-700 mt-1">
+            {selectedCount} categor{selectedCount !== 1 ? 'ies' : 'y'} selected
+          </div>
+        </div>
+      )}
+
+      <div className="p-3 bg-slate-100 rounded text-xs text-slate-600">
+        <p>Select categories to display POIs on the map.</p>
+        <p className="mt-2">Zoom in for better visibility of individual locations.</p>
+      </div>
+    </div>
+  );
+}
diff --git a/frontend/src/components/PropertiesPane.tsx b/frontend/src/components/PropertiesPane.tsx
new file mode 100644
index 0000000..07b03bd
--- /dev/null
+++ b/frontend/src/components/PropertiesPane.tsx
@@ -0,0 +1,224 @@
+import React, { useMemo, useState } from 'react';
+import { Property } from '../types';
+
+interface PropertiesPaneProps {
+  properties: Property[];
+  total: number;
+  loading: boolean;
+  hexagonId: string | null;
+  onLoadMore: () => void;
+  onClose: () => void;
+}
+
+type SortBy = 'price' | 'size' | 'energy';
+
+export function PropertiesPane({
+  properties,
+  total,
+  loading,
+  hexagonId,
+  onLoadMore,
+  onClose,
+}: PropertiesPaneProps) {
+  const [sortBy, setSortBy] = useState<SortBy>('price');
+
+  // Sort properties
+  const sortedProperties = useMemo(() => {
+    return [...properties].sort((a, b) => {
+      switch (sortBy) {
+        case 'price':
+          return ((b.latest_price as number) || 0) - ((a.latest_price as number) || 0);
+        case 'size':
+          return ((b.total_floor_area as number) || 0) - ((a.total_floor_area as number) || 0);
+        case 'energy':
+          return (a.current_energy_rating || 'Z').localeCompare(
+            b.current_energy_rating || 'Z'
+          );
+      }
+    });
+  }, [properties, sortBy]);
+
+  if (!hexagonId) {
+    return (
+      <div className="flex items-center justify-center h-full text-gray-500">
+        Click a hexagon to view properties
+      </div>
+    );
+  }
+
+  return (
+    <div className="flex flex-col h-full">
+      {/* Header */}
+      <div className="p-4 border-b border-gray-200">
+        <div className="flex justify-between items-center">
+          <h2 className="text-lg font-semibold">Properties in Hexagon</h2>
+          <button
+            onClick={onClose}
+            className="text-gray-500 hover:text-gray-700 text-2xl leading-none"
+          >
+            ×
+          </button>
+        </div>
+        <p className="text-sm text-gray-600">
+          Showing {properties.length} of {total} properties
+        </p>
+      </div>
+
+      {/* Sort controls */}
+      <div className="p-2 border-b border-gray-200">
+        <select
+          value={sortBy}
+          onChange={(e) => setSortBy(e.target.value as SortBy)}
+          className="w-full p-2 border border-gray-300 rounded text-sm"
+        >
+          <option value="price">Price (High to Low)</option>
+          <option value="size">Size (Large to Small)</option>
+          <option value="energy">Energy Rating (Best to Worst)</option>
+        </select>
+      </div>
+
+      {/* Properties list */}
+      <div className="flex-1 overflow-y-auto">
+        {loading && properties.length === 0 ? (
+          <div className="p-4">Loading...</div>
+        ) : (
+          <>
+            {sortedProperties.map((property, idx) => (
+              <PropertyCard key={idx} property={property} />
+            ))}
+            {properties.length < total && (
+              <button
+                onClick={onLoadMore}
+                disabled={loading}
+                className="w-full p-4 text-blue-600 hover:bg-blue-50 disabled:opacity-50"
+              >
+                {loading ? 'Loading...' : `Load More (${total - properties.length} remaining)`}
+              </button>
+            )}
+          </>
+        )}
+      </div>
+    </div>
+  );
+}
+
+// Property card component showing all fields
+function PropertyCard({ property }: { property: Property }) {
+  const formatNumber = (value: number | undefined, decimals = 0): string => {
+    if (value === undefined) return '';
+    return decimals > 0 ? value.toFixed(decimals) : Math.round(value).toLocaleString();
+  };
+
+  return (
+    <div className="p-4 border-b border-gray-100 hover:bg-gray-50">
+      {/* Address */}
+      <div className="font-semibold">{property.address || 'Unknown Address'}</div>
+      <div className="text-sm text-gray-600">{property.postcode}</div>
+
+      {/* Price */}
+      {property.latest_price && (
+        <div className="mt-2 text-lg font-bold text-green-700">
+          £{formatNumber(property.latest_price as number)}
+          {property.price_per_sqm && (
+            <span className="text-sm font-normal text-gray-600">
+              {' '}
+              (£{formatNumber(property.price_per_sqm as number)}/m²)
+            </span>
+          )}
+        </div>
+      )}
+
+      {/* Property details grid */}
+      <div className="mt-2 grid grid-cols-2 gap-2 text-sm">
+        {property.property_type && (
+          <div>
+            <span className="text-gray-600">Type:</span> {property.property_type}
+          </div>
+        )}
+        {property.built_form && (
+          <div>
+            <span className="text-gray-600">Form:</span> {property.built_form}
+          </div>
+        )}
+        {property.total_floor_area && (
+          <div>
+            <span className="text-gray-600">Area:</span> {formatNumber(property.total_floor_area as number)}m²
+          </div>
+        )}
+        {property.number_habitable_rooms && (
+          <div>
+            <span className="text-gray-600">Rooms:</span>{' '}
+            {formatNumber(property.number_habitable_rooms as number)}
+          </div>
+        )}
+        {property.current_energy_rating && (
+          <div>
+            <span className="text-gray-600">Energy:</span> {property.current_energy_rating}
+          </div>
+        )}
+        {property.potential_energy_rating && (
+          <div>
+            <span className="text-gray-600">Potential:</span> {property.potential_energy_rating}
+          </div>
+        )}
+        {property.construction_age_band !== undefined && (
+          <div>
+            <span className="text-gray-600">Built (age):</span> {formatNumber(property.construction_age_band as number)}
+          </div>
+        )}
+
+        {/* Journey times */}
+        {property.public_transport_easy_minutes && (
+          <div>
+            <span className="text-gray-600">PT (easy):</span>{' '}
+            {formatNumber(property.public_transport_easy_minutes as number)}min
+          </div>
+        )}
+        {property.public_transport_quick_minutes && (
+          <div>
+            <span className="text-gray-600">PT (quick):</span>{' '}
+            {formatNumber(property.public_transport_quick_minutes as number)}min
+          </div>
+        )}
+        {property.cycling_minutes && (
+          <div>
+            <span className="text-gray-600">Cycling:</span>{' '}
+            {formatNumber(property.cycling_minutes as number)}min
+          </div>
+        )}
+
+        {/* Deprivation scores */}
+        {property.income_score !== undefined && (
+          <div>
+            <span className="text-gray-600">Income:</span>{' '}
+            {formatNumber(property.income_score as number, 1)}
+          </div>
+        )}
+        {property.employment_score !== undefined && (
+          <div>
+            <span className="text-gray-600">Employment:</span>{' '}
+            {formatNumber(property.employment_score as number, 1)}
+          </div>
+        )}
+        {property.education_score !== undefined && (
+          <div>
+            <span className="text-gray-600">Education:</span>{' '}
+            {formatNumber(property.education_score as number, 1)}
+          </div>
+        )}
+        {property.health_score !== undefined && (
+          <div>
+            <span className="text-gray-600">Health:</span>{' '}
+            {formatNumber(property.health_score as number, 1)}
+          </div>
+        )}
+        {property.crime_score !== undefined && (
+          <div>
+            <span className="text-gray-600">Crime:</span>{' '}
+            {formatNumber(property.crime_score as number, 1)}
+          </div>
+        )}
+      </div>
+    </div>
+  );
+}
diff --git a/frontend/src/types.ts b/frontend/src/types.ts
index 7416021..8412774 100644
--- a/frontend/src/types.ts
+++ b/frontend/src/types.ts
@@ -45,16 +45,38 @@ export interface POI {
   category: string;
   lat: number;
   lng: number;
+  emoji: string;
 }
 
 export interface POIResponse {
-  features: POI[];
+  pois: POI[];
 }
 
-export interface POICategoryInfo {
-  emoji: string;
-  label: string;
-  count: number;
+export interface POICategoriesResponse {
+  categories: string[];
 }
 
-export type POICategoriesMap = Record<string, POICategoryInfo>;
+export interface Property {
+  // String fields
+  address?: string;
+  postcode?: string;
+  property_type?: string;
+  built_form?: string;
+  current_energy_rating?: string;
+  potential_energy_rating?: string;
+
+  // Numeric fields
+  lat: number;
+  lon: number;
+
+  // All other numeric features (dynamic, including construction_age_band)
+  [key: string]: string | number | undefined;
+}
+
+export interface HexagonPropertiesResponse {
+  properties: Property[];
+  total: number;
+  limit: number;
+  offset: number;
+  truncated: boolean;
+}

From 85f5770e09a0f8c62975257bcce991a5aaed629c Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sat, 31 Jan 2026 12:50:01 +0000
Subject: [PATCH 28/62] Add property listing

---
 server-rs/src/data.rs   |  93 +++++++++++++++++++++--
 server-rs/src/main.rs   |   5 ++
 server-rs/src/routes.rs | 163 ++++++++++++++++++++++++++++++++++++++++
 3 files changed, 254 insertions(+), 7 deletions(-)

diff --git a/server-rs/src/data.rs b/server-rs/src/data.rs
index 16928c3..6a3cb48 100644
--- a/server-rs/src/data.rs
+++ b/server-rs/src/data.rs
@@ -6,7 +6,7 @@ use std::path::Path;
 
 use crate::consts::{FEATURE_PERCENTILE_LOW, FEATURE_PERCENTILE_HIGH, HISTOGRAM_BINS, H3_PRECOMPUTE_MIN, H3_PRECOMPUTE_MAX};
 
-/// Columns to exclude from feature discovery (not numeric features)
+/// Columns to exclude from feature discovery
 const EXCLUDED_COLUMNS: &[&str] = &["lat", "lon"];
 
 /// H3 valid resolution range (0-15)
@@ -65,6 +65,13 @@ pub struct PropertyData {
     pub feature_data: Vec<f64>,
     /// Precomputed stats (percentiles + histogram) for each feature
     pub feature_stats: Vec<FeatureStats>,
+    /// String fields for property details
+    pub address: Vec<String>,
+    pub postcode: Vec<String>,
+    pub property_type: Vec<String>,
+    pub built_form: Vec<String>,
+    pub current_energy_rating: Vec<String>,
+    pub potential_energy_rating: Vec<String>,
 }
 
 /// Approximate a percentile from a histogram using linear interpolation.
@@ -213,14 +220,34 @@ impl PropertyData {
         let mut cols_needed: Vec<String> = vec!["lat".into(), "lon".into()];
         cols_needed.extend(feature_names.iter().cloned());
 
+        // Add string columns (using actual column names from parquet)
+        let string_cols = vec![
+            "pp_address", "postcode", "pp_property_type", "built_form",
+            "current_energy_rating", "potential_energy_rating"
+        ];
+
+        // Build selection with proper casting
+        let mut select_exprs: Vec<polars::prelude::Expr> = vec![];
+
+        // lat/lon as f64
+        select_exprs.push(col("lat").cast(DataType::Float64));
+        select_exprs.push(col("lon").cast(DataType::Float64));
+
+        // numeric features as f64
+        for name in &feature_names {
+            select_exprs.push(col(name.as_str()).cast(DataType::Float64));
+        }
+
+        // string columns as string (check if they exist in schema)
+        for &s_col in &string_cols {
+            if schema.get(s_col).is_some() {
+                select_exprs.push(col(s_col).cast(DataType::String));
+            }
+        }
+
         let df = LazyFrame::scan_parquet(parquet_path, Default::default())
             .expect("Failed to scan parquet")
-            .select(
-                cols_needed
-                    .iter()
-                    .map(|c| col(c.as_str()).cast(DataType::Float64))
-                    .collect::<Vec<_>>(),
-            )
+            .select(select_exprs)
             .collect()
             .expect("Failed to read parquet");
 
@@ -262,6 +289,44 @@ impl PropertyData {
             })
             .collect();
 
+        // Extract string columns (before permutation)
+        eprintln!("Extracting string columns...");
+        let address_raw: Vec<String> = if let Ok(col) = df.column("pp_address") {
+            col.str().unwrap().into_iter().map(|v| v.unwrap_or("").to_string()).collect()
+        } else {
+            vec![String::new(); row_count]
+        };
+
+        let postcode_raw: Vec<String> = if let Ok(col) = df.column("postcode") {
+            col.str().unwrap().into_iter().map(|v| v.unwrap_or("").to_string()).collect()
+        } else {
+            vec![String::new(); row_count]
+        };
+
+        let property_type_raw: Vec<String> = if let Ok(col) = df.column("pp_property_type") {
+            col.str().unwrap().into_iter().map(|v| v.unwrap_or("").to_string()).collect()
+        } else {
+            vec![String::new(); row_count]
+        };
+
+        let built_form_raw: Vec<String> = if let Ok(col) = df.column("built_form") {
+            col.str().unwrap().into_iter().map(|v| v.unwrap_or("").to_string()).collect()
+        } else {
+            vec![String::new(); row_count]
+        };
+
+        let current_energy_rating_raw: Vec<String> = if let Ok(col) = df.column("current_energy_rating") {
+            col.str().unwrap().into_iter().map(|v| v.unwrap_or("").to_string()).collect()
+        } else {
+            vec![String::new(); row_count]
+        };
+
+        let potential_energy_rating_raw: Vec<String> = if let Ok(col) = df.column("potential_energy_rating") {
+            col.str().unwrap().into_iter().map(|v| v.unwrap_or("").to_string()).collect()
+        } else {
+            vec![String::new(); row_count]
+        };
+
         // Sort all rows by spatial locality so that grid queries access
         // contiguous memory (sequential reads instead of random DRAM accesses).
         // Uses the same 0.01° grid cell as the spatial index for the sort key.
@@ -283,6 +348,14 @@ impl PropertyData {
         let lat: Vec<f64> = perm.iter().map(|&i| lat[i as usize]).collect();
         let lon: Vec<f64> = perm.iter().map(|&i| lon[i as usize]).collect();
 
+        // Apply permutation to string columns
+        let address: Vec<String> = perm.iter().map(|&i| address_raw[i as usize].clone()).collect();
+        let postcode: Vec<String> = perm.iter().map(|&i| postcode_raw[i as usize].clone()).collect();
+        let property_type: Vec<String> = perm.iter().map(|&i| property_type_raw[i as usize].clone()).collect();
+        let built_form: Vec<String> = perm.iter().map(|&i| built_form_raw[i as usize].clone()).collect();
+        let current_energy_rating: Vec<String> = perm.iter().map(|&i| current_energy_rating_raw[i as usize].clone()).collect();
+        let potential_energy_rating: Vec<String> = perm.iter().map(|&i| potential_energy_rating_raw[i as usize].clone()).collect();
+
         // Transpose to row-major AND apply spatial permutation in one pass.
         // Result: all features for one row are contiguous, and spatially
         // nearby rows are adjacent in memory.
@@ -305,6 +378,12 @@ impl PropertyData {
             num_features,
             feature_data,
             feature_stats,
+            address,
+            postcode,
+            property_type,
+            built_form,
+            current_energy_rating,
+            potential_energy_rating,
         }
     }
 }
diff --git a/server-rs/src/main.rs b/server-rs/src/main.rs
index a3bc3ea..4756e66 100644
--- a/server-rs/src/main.rs
+++ b/server-rs/src/main.rs
@@ -72,6 +72,7 @@ async fn main() {
     let state_hexagons = state.clone();
     let state_pois = state.clone();
     let state_poi_categories = state.clone();
+    let state_hexagon_properties = state.clone();
 
     let api = Router::new()
         .route(
@@ -89,6 +90,10 @@ async fn main() {
         .route(
             "/api/poi-categories",
             get(move || routes::get_poi_categories(state_poi_categories.clone())),
+        )
+        .route(
+            "/api/hexagon-properties",
+            get(move |query| routes::get_hexagon_properties(state_hexagon_properties.clone(), query)),
         );
 
     // Static file serving for frontend
diff --git a/server-rs/src/routes.rs b/server-rs/src/routes.rs
index baa3099..9f3d251 100644
--- a/server-rs/src/routes.rs
+++ b/server-rs/src/routes.rs
@@ -1,4 +1,5 @@
 use std::fmt::Write;
+use std::str::FromStr;
 use std::sync::Arc;
 
 use axum::extract::Query;
@@ -459,3 +460,165 @@ pub async fn get_poi_categories(state: Arc<AppState>) -> Json<POICategoriesRespo
 
     Json(result)
 }
+
+// ── /api/hexagon-properties ──
+
+#[derive(Deserialize)]
+pub struct HexagonPropertiesParams {
+    pub h3: String,
+    pub resolution: u8,
+    pub filters: Option<String>,
+    pub limit: Option<usize>,
+    pub offset: Option<usize>,
+}
+
+#[derive(Serialize)]
+pub struct Property {
+    // String fields
+    pub address: Option<String>,
+    pub postcode: Option<String>,
+    pub property_type: Option<String>,
+    pub built_form: Option<String>,
+    pub current_energy_rating: Option<String>,
+    pub potential_energy_rating: Option<String>,
+
+    // Numeric fields
+    pub lat: f64,
+    pub lon: f64,
+
+    // All other numeric features stored as dynamic map
+    #[serde(flatten)]
+    pub features: FxHashMap<String, f64>,
+}
+
+#[derive(Serialize)]
+pub struct HexagonPropertiesResponse {
+    pub properties: Vec<Property>,
+    pub total: usize,
+    pub limit: usize,
+    pub offset: usize,
+    pub truncated: bool,
+}
+
+/// Helper function to check if a row passes all filters
+fn row_passes_filters(row: usize, filters: &[ParsedFilter], feature_data: &[f64], num_features: usize) -> bool {
+    filters.iter().all(|f| {
+        let v = feature_data[row * num_features + f.feat_idx];
+        v.is_finite() && v >= f.min && v <= f.max
+    })
+}
+
+pub async fn get_hexagon_properties(
+    state: Arc<AppState>,
+    Query(params): Query<HexagonPropertiesParams>,
+) -> Result<Json<HexagonPropertiesResponse>, (StatusCode, String)> {
+    // 1. Parse H3 cell ID
+    let cell = h3o::CellIndex::from_str(&params.h3)
+        .map_err(|e| (StatusCode::BAD_REQUEST, format!("Invalid H3 cell: {}", e)))?;
+    let cell_u64: u64 = cell.into();
+
+    // 2. Validate resolution
+    let resolution = params.resolution as usize;
+    if resolution >= state.h3_cells.len() || state.h3_cells[resolution].is_empty() {
+        return Err((StatusCode::BAD_REQUEST, "Invalid or non-precomputed resolution".to_string()));
+    }
+
+    // 3. Parse filters (reuse existing filter parsing logic from get_hexagons)
+    let parsed_filters: Vec<ParsedFilter> = params
+        .filters
+        .as_deref()
+        .filter(|s| !s.is_empty())
+        .map(|s| {
+            s.split(',')
+                .filter_map(|entry| {
+                    let parts: Vec<&str> = entry.splitn(3, ':').collect();
+                    if parts.len() != 3 {
+                        return None;
+                    }
+                    let name = parts[0].trim();
+                    let min = parts[1].trim().parse::<f64>().ok()?;
+                    let max = parts[2].trim().parse::<f64>().ok()?;
+                    let feat_idx = state.data.feature_names.iter().position(|n| n == name)?;
+                    Some(ParsedFilter { feat_idx, min, max })
+                })
+                .collect()
+        })
+        .unwrap_or_default();
+
+    // Move CPU-heavy work off the async executor
+    let result = tokio::task::spawn_blocking(move || {
+        let h3_data = &state.h3_cells[resolution];
+        let num_features = state.data.num_features;
+        let feature_data = &state.data.feature_data;
+
+        // 4. Find all rows with matching H3 cell
+        let matching_rows: Vec<usize> = h3_data
+            .iter()
+            .enumerate()
+            .filter_map(|(idx, &h3_cell)| {
+                if h3_cell == cell_u64 {
+                    // Apply feature filters
+                    if row_passes_filters(idx, &parsed_filters, feature_data, num_features) {
+                        Some(idx)
+                    } else {
+                        None
+                    }
+                } else {
+                    None
+                }
+            })
+            .collect();
+
+        let total = matching_rows.len();
+        let limit = params.limit.unwrap_or(100).min(500);
+        let offset = params.offset.unwrap_or(0);
+        let truncated = total > offset + limit;
+
+        // 5. Extract properties for paginated subset
+        let properties: Vec<Property> = matching_rows
+            .iter()
+            .skip(offset)
+            .take(limit)
+            .map(|&row| {
+                // Build dynamic features map
+                let mut features = FxHashMap::default();
+                let base = row * num_features;
+                for (feat_idx, feat_name) in state.data.feature_names.iter().enumerate() {
+                    let v = feature_data[base + feat_idx];
+                    if v.is_finite() {
+                        features.insert(feat_name.clone(), v);
+                    }
+                }
+
+                // Helper to get non-empty string
+                let get_string = |s: &str| -> Option<String> {
+                    if s.is_empty() { None } else { Some(s.to_string()) }
+                };
+
+                Property {
+                    address: get_string(&state.data.address[row]),
+                    postcode: get_string(&state.data.postcode[row]),
+                    property_type: get_string(&state.data.property_type[row]),
+                    built_form: get_string(&state.data.built_form[row]),
+                    current_energy_rating: get_string(&state.data.current_energy_rating[row]),
+                    potential_energy_rating: get_string(&state.data.potential_energy_rating[row]),
+                    lat: state.data.lat[row],
+                    lon: state.data.lon[row],
+                    features,
+                }
+            })
+            .collect();
+
+        HexagonPropertiesResponse {
+            properties,
+            total,
+            limit,
+            offset,
+            truncated,
+        }
+    })
+    .await
+    .unwrap();
+
+    Ok(Json(result))
+}

From 4c258018c319cbcc6055791dbb37569a4ea802c1 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sat, 31 Jan 2026 13:07:09 +0000
Subject: [PATCH 29/62] Format python

---
 Taskfile.yml                          |  54 ++++------
 pipeline/download/arcgis.py           |  12 ++-
 pipeline/download/deprivation_data.py |   8 +-
 pipeline/download/pois.py             |  22 ++--
 pipeline/download/price_paid.py       |   8 +-
 pipeline/journey_times/config.py      |   4 -
 pipeline/journey_times/tfl_client.py  |   4 +-
 pipeline/transform/join_epc_pp.py     | 139 ++++++++++++++++----------
 pipeline/transform/merge.py           | 100 +++++++++++-------
 pipeline/transform/transform_poi.py   |  16 +--
 pipeline/utils/__init__.py            |   8 +-
 pipeline/utils/fuzzy_join.py          |  77 ++++++++------
 pipeline/utils/haversine.py           |  13 ++-
 pipeline/utils/poi_counts.py          |  31 +++---
 pipeline/utils/test_fuzzy_join.py     |   2 +-
 pipeline/utils/test_haversine.py      |  30 ++++--
 pipeline/utils/test_poi_counts.py     |  68 +++++++------
 17 files changed, 348 insertions(+), 248 deletions(-)

diff --git a/Taskfile.yml b/Taskfile.yml
index ceb4a47..b6334c6 100644
--- a/Taskfile.yml
+++ b/Taskfile.yml
@@ -21,47 +21,43 @@ tasks:
       - cd frontend && npm install
 
   download:arcgis:
+    internal: true
     desc: Download and convert ArcGIS postcode data
-    sources:
-      - pipeline/download/arcgis.py
     generates:
       - "{{.ARCGIS_OUTPUT}}"
     cmds:
       - uv run python -m pipeline.download.arcgis --output {{.ARCGIS_OUTPUT}}
 
   download:price-paid:
+    internal: true
     desc: Download and convert Land Registry price-paid data
-    sources:
-      - pipeline/download/price_paid.py
     generates:
       - "{{.PRICE_PAID_OUTPUT}}"
     cmds:
       - uv run python -m pipeline.download.price_paid --output {{.PRICE_PAID_OUTPUT}}
 
   download:deprivation:
+    internal: true
     desc: Download and convert Index of Deprivation data
-    sources:
-      - pipeline/download/deprivation_data.py
     generates:
       - "{{.IOD_OUTPUT}}"
     cmds:
       - uv run python -m pipeline.download.deprivation_data --output {{.IOD_OUTPUT}}
 
   download:pois:
+    internal: true
     desc: Download and extract POIs from OpenStreetMap
-    sources:
-      - pipeline/download/pois.py
     generates:
       - "{{.POIS_RAW_OUTPUT}}"
     cmds:
       - uv run python -m pipeline.download.pois --output {{.POIS_RAW_OUTPUT}}
 
   transform:pois:
+    internal: true
     desc: Transform raw POIs to filtered version with friendly names
     deps:
       - download:pois
     sources:
-      - pipeline/transform/transform_poi.py
       - "{{.POIS_RAW_OUTPUT}}"
     generates:
       - "{{.POIS_FILTERED_OUTPUT}}"
@@ -69,12 +65,11 @@ tasks:
       - uv run python -m pipeline.transform.transform_poi --input {{.POIS_RAW_OUTPUT}} --output {{.POIS_FILTERED_OUTPUT}}
 
   transform:epc-pp:
+    internal: true
     desc: Fuzzy join EPC and Price Paid data
     deps:
       - download:price-paid
     sources:
-      - pipeline/transform/join_epc_pp.py
-      - pipeline/utils/fuzzy_join.py
       - "{{.PRICE_PAID_OUTPUT}}"
       - "{{.EPC_CSV}}"
     generates:
@@ -83,13 +78,12 @@ tasks:
       - uv run python -m pipeline.transform.join_epc_pp --epc {{.EPC_CSV}} --price-paid {{.PRICE_PAID_OUTPUT}} --output {{.EPC_PP_OUTPUT}}
 
   transform:poi-proximity:
+    internal: true
     desc: Compute POI proximity counts per postcode
     deps:
       - download:arcgis
       - transform:pois
     sources:
-      - pipeline/transform/poi_proximity.py
-      - pipeline/utils/poi_counts.py
       - "{{.ARCGIS_OUTPUT}}"
       - "{{.POIS_FILTERED_OUTPUT}}"
     generates:
@@ -97,7 +91,7 @@ tasks:
     cmds:
       - uv run python -m pipeline.transform.poi_proximity --arcgis {{.ARCGIS_OUTPUT}} --pois {{.POIS_FILTERED_OUTPUT}} --output {{.POI_PROXIMITY_OUTPUT}}
 
-  transform:wide:
+  prepare:
     desc: Build wide property dataframe with all joins
     deps:
       - join:epc-pp
@@ -105,7 +99,6 @@ tasks:
       - download:deprivation
       - transform:poi-proximity
     sources:
-      - pipeline/transform/merge.py
       - "{{.EPC_PP_OUTPUT}}"
       - "{{.ARCGIS_OUTPUT}}"
       - "{{.IOD_OUTPUT}}"
@@ -115,36 +108,37 @@ tasks:
     cmds:
       - uv run python -m pipeline.transform.merge --epc-pp {{.EPC_PP_OUTPUT}} --arcgis {{.ARCGIS_OUTPUT}} --iod {{.IOD_OUTPUT}} --poi-proximity {{.POI_PROXIMITY_OUTPUT}} --journey-times {{.JOURNEY_TIMES}} --output {{.WIDE_OUTPUT}}
 
-  prepare:
-    desc: Prepare the application (install, download data, run pipeline)
-    deps:
-      - transform:wide
-
   test:
     cmds:
       - uv run -m pipeline.utils.test_fuzzy_join
       - uv run pytest pipeline/utils/test_haversine.py
       - uv run pytest pipeline/utils/test_poi_counts.py
 
-  server:
+  dev:server:
     desc: Run Rust backend on port 8001
     dir: server-rs
     cmds:
       - cargo run --release -- {{.WIDE_OUTPUT}}
 
-  frontend:
+  dev:frontend:
     desc: Run frontend dev server on port 3030 (proxies /api to :8001)
     dir: frontend
     cmds:
       - npm run dev
 
-  build:
+  build:server:
+    desc: Build server for production
+    dir: frontend
+    cmds:
+      - cargo build --release
+
+  build:frontend:
     desc: Build frontend for production
     dir: frontend
     cmds:
+      - npm run typecheck
       - npm run build
 
-
   lint:
     desc: Lint all code (Python, TypeScript, and Rust)
     cmds:
@@ -195,17 +189,13 @@ tasks:
     desc: Format Rust code with cargo fmt
     dir: server-rs
     cmds:
-      - cargo fmt
+      - cargo fmt --all
 
   check:
     desc: Run all checks (lint, typecheck, build)
     cmds:
       - task: lint
-      - task: typecheck
-      - task: build
+      - task: build:server
+      - task: build:frontend
+      - task: test
 
-  typecheck:
-    desc: Type check frontend TypeScript code
-    dir: frontend
-    cmds:
-      - npm run typecheck
diff --git a/pipeline/download/arcgis.py b/pipeline/download/arcgis.py
index 1277fc6..de0fa0c 100644
--- a/pipeline/download/arcgis.py
+++ b/pipeline/download/arcgis.py
@@ -35,7 +35,6 @@ def download_with_progress(url: str, output_path: Path) -> None:
         return
 
 
-
 def extract_zip(zip_path: Path, extract_path: Path) -> None:
     extract_path.mkdir(exist_ok=True)
 
@@ -44,7 +43,7 @@ def extract_zip(zip_path: Path, extract_path: Path) -> None:
 
 
 def convert_to_parquet(data_path: Path, parquet_path: Path) -> None:
-    df = pl.scan_csv(data_path / 'Data/NSPL_MAY_2025_UK.csv', try_parse_dates=True)
+    df = pl.scan_csv(data_path / "Data/NSPL_MAY_2025_UK.csv", try_parse_dates=True)
     print(f"Columns: {df.collect_schema().names()}")
     parquet_path.parent.mkdir(parents=True, exist_ok=True)
     df.sink_parquet(parquet_path, compression="zstd")
@@ -52,8 +51,12 @@ def convert_to_parquet(data_path: Path, parquet_path: Path) -> None:
 
 
 def main() -> None:
-    parser = argparse.ArgumentParser(description="Download and convert ArcGIS postcode data")
-    parser.add_argument("--output", type=Path, required=True, help="Output parquet file path")
+    parser = argparse.ArgumentParser(
+        description="Download and convert ArcGIS postcode data"
+    )
+    parser.add_argument(
+        "--output", type=Path, required=True, help="Output parquet file path"
+    )
     args = parser.parse_args()
 
     with tempfile.TemporaryDirectory() as cache_dir:
@@ -64,5 +67,6 @@ def main() -> None:
         extract_zip(download_path, extract_path)
         convert_to_parquet(extract_path, args.output)
 
+
 if __name__ == "__main__":
     main()
diff --git a/pipeline/download/deprivation_data.py b/pipeline/download/deprivation_data.py
index e73cb96..db75969 100644
--- a/pipeline/download/deprivation_data.py
+++ b/pipeline/download/deprivation_data.py
@@ -41,8 +41,12 @@ def convert_to_parquet(xlsx_path: Path, parquet_path: Path) -> None:
 
 
 def main() -> None:
-    parser = argparse.ArgumentParser(description="Download and convert Index of Deprivation data")
-    parser.add_argument("--output", type=Path, required=True, help="Output parquet file path")
+    parser = argparse.ArgumentParser(
+        description="Download and convert Index of Deprivation data"
+    )
+    parser.add_argument(
+        "--output", type=Path, required=True, help="Output parquet file path"
+    )
     args = parser.parse_args()
 
     with tempfile.TemporaryDirectory() as cache_dir:
diff --git a/pipeline/download/pois.py b/pipeline/download/pois.py
index 2694fc2..cd7345d 100644
--- a/pipeline/download/pois.py
+++ b/pipeline/download/pois.py
@@ -8,16 +8,12 @@ import osmium
 import polars as pl
 from tqdm import tqdm
 
-from pathlib import Path
-
 
 BATCH_SIZE = 50_000
 
 MIN_OCCURENCE_COUNT = 20
 
-GEOFABRIK_GB_URL = (
-    "https://download.geofabrik.de/europe/great-britain-latest.osm.pbf"
-)
+GEOFABRIK_GB_URL = "https://download.geofabrik.de/europe/great-britain-latest.osm.pbf"
 
 UK_BBOX_WEST = -7.57
 UK_BBOX_SOUTH = 49.96
@@ -38,7 +34,6 @@ POI_TAG_KEYS: list[str] = [
 ]
 
 
-
 def download_pbf(pbf_file: Path) -> None:
     pbf_file.parent.mkdir(parents=True, exist_ok=True)
     tmp = pbf_file.with_suffix(".pbf.tmp")
@@ -91,7 +86,12 @@ class POIHandler(osmium.SimpleHandler):
         self._batch.clear()
 
     def _add_poi(
-        self, osm_id: str, tags: osmium.osm.TagList, category: str, lat: float, lng: float
+        self,
+        osm_id: str,
+        tags: osmium.osm.TagList,
+        category: str,
+        lat: float,
+        lng: float,
     ) -> None:
         self._batch.append(
             {
@@ -123,8 +123,12 @@ class POIHandler(osmium.SimpleHandler):
 
 
 def main() -> None:
-    parser = argparse.ArgumentParser(description="Download and extract POIs from OpenStreetMap")
-    parser.add_argument("--output", type=Path, required=True, help="Output parquet file path")
+    parser = argparse.ArgumentParser(
+        description="Download and extract POIs from OpenStreetMap"
+    )
+    parser.add_argument(
+        "--output", type=Path, required=True, help="Output parquet file path"
+    )
     args = parser.parse_args()
 
     with tempfile.TemporaryDirectory() as cache_dir:
diff --git a/pipeline/download/price_paid.py b/pipeline/download/price_paid.py
index fac4ec1..56ebc4e 100644
--- a/pipeline/download/price_paid.py
+++ b/pipeline/download/price_paid.py
@@ -73,8 +73,12 @@ def convert_to_parquet(csv_path: Path, parquet_path: Path) -> None:
 
 
 def main() -> None:
-    parser = argparse.ArgumentParser(description="Download and convert Land Registry price-paid data")
-    parser.add_argument("--output", type=Path, required=True, help="Output parquet file path")
+    parser = argparse.ArgumentParser(
+        description="Download and convert Land Registry price-paid data"
+    )
+    parser.add_argument(
+        "--output", type=Path, required=True, help="Output parquet file path"
+    )
     args = parser.parse_args()
 
     with tempfile.TemporaryDirectory() as cache_dir:
diff --git a/pipeline/journey_times/config.py b/pipeline/journey_times/config.py
index f2401cc..f86a422 100644
--- a/pipeline/journey_times/config.py
+++ b/pipeline/journey_times/config.py
@@ -1,11 +1,7 @@
 """Configuration constants for journey times processing."""
 
-from pathlib import Path
-
 from .models import Destination
 
-DATA_DIR = Path("./data_sources")
-OUTPUT_DIR = DATA_DIR / "processed"
 
 MAX_DELAY = 10
 REQUESTS_PER_MIN = 500
diff --git a/pipeline/journey_times/tfl_client.py b/pipeline/journey_times/tfl_client.py
index f214f7c..33fc794 100644
--- a/pipeline/journey_times/tfl_client.py
+++ b/pipeline/journey_times/tfl_client.py
@@ -99,9 +99,7 @@ async def fetch_journey_for_mode(
                 journeys = data.get("journeys", [])
                 if journeys:
                     durations = [
-                        j["duration"]
-                        for j in journeys
-                        if j.get("duration") is not None
+                        j["duration"] for j in journeys if j.get("duration") is not None
                     ]
                     if durations:
                         return min(durations)
diff --git a/pipeline/transform/join_epc_pp.py b/pipeline/transform/join_epc_pp.py
index 8717dc4..741848a 100644
--- a/pipeline/transform/join_epc_pp.py
+++ b/pipeline/transform/join_epc_pp.py
@@ -9,79 +9,108 @@ pl.Config.set_tbl_cols(-1)
 
 def main():
     parser = argparse.ArgumentParser(description="Fuzzy join EPC and Price Paid data")
-    parser.add_argument("--epc", type=Path, required=True, help="EPC certificates CSV file")
-    parser.add_argument("--price-paid", type=Path, required=True, help="Price paid parquet file")
-    parser.add_argument("--output", type=Path, required=True, help="Output parquet file path")
+    parser.add_argument(
+        "--epc", type=Path, required=True, help="EPC certificates CSV file"
+    )
+    parser.add_argument(
+        "--price-paid", type=Path, required=True, help="Price paid parquet file"
+    )
+    parser.add_argument(
+        "--output", type=Path, required=True, help="Output parquet file path"
+    )
     args = parser.parse_args()
 
-    epc = pl.scan_csv(args.epc).select(
-        pl.col('ADDRESS').alias('epc_address'),
-        'POSTCODE',
-        'CURRENT_ENERGY_RATING',
-        'POTENTIAL_ENERGY_RATING',
-        pl.col('PROPERTY_TYPE').alias('epc_property_type'),
-        'BUILT_FORM',
-        'INSPECTION_DATE',
-        'TOTAL_FLOOR_AREA',
-        'NUMBER_HABITABLE_ROOMS',
-        'FLOOR_HEIGHT',
-        'CONSTRUCTION_AGE_BAND'
-    ).filter(pl.col('epc_address').is_not_null()).sort('INSPECTION_DATE', descending=True).group_by('epc_address', 'POSTCODE').first()
-
+    epc = (
+        pl.scan_csv(args.epc)
+        .select(
+            pl.col("ADDRESS").alias("epc_address"),
+            "POSTCODE",
+            "CURRENT_ENERGY_RATING",
+            "POTENTIAL_ENERGY_RATING",
+            pl.col("PROPERTY_TYPE").alias("epc_property_type"),
+            "BUILT_FORM",
+            "INSPECTION_DATE",
+            "TOTAL_FLOOR_AREA",
+            "NUMBER_HABITABLE_ROOMS",
+            "FLOOR_HEIGHT",
+            "CONSTRUCTION_AGE_BAND",
+        )
+        .filter(pl.col("epc_address").is_not_null())
+        .sort("INSPECTION_DATE", descending=True)
+        .group_by("epc_address", "POSTCODE")
+        .first()
+    )
 
     print("EPC dataset")
     print(epc.head().collect())
 
     # https://www.gov.uk/guidance/about-the-price-paid-data
-    property_type_map = {"D": "Detached", "S": "Semi-Detached", "T": "Terraced", "F": "Flats/Maisonettes", "O": "Other"}
+    property_type_map = {
+        "D": "Detached",
+        "S": "Semi-Detached",
+        "T": "Terraced",
+        "F": "Flats/Maisonettes",
+        "O": "Other",
+    }
     duration_map = {"F": "Freehold", "L": "Leasehold"}
 
-    price_paid = (pl.scan_parquet(args.price_paid).select(
-        "price",
-        "date_of_transfer",
-        pl.col('property_type').alias("pp_property_type").replace(property_type_map),
-        "postcode",
-        'paon',
-        'saon',
-        'street',
-        'locality',
-        'town_city',
-        pl.col('duration').replace(duration_map)
-    )
-    .filter(pl.col('pp_property_type') != 'Other').with_columns(
-            pl.concat_str(
-                [pl.col('saon'), pl.col('paon'), pl.col('street')],
-                separator=' ',
-                ignore_nulls=True,
-            ).alias('pp_address'),
+    price_paid = (
+        pl.scan_parquet(args.price_paid)
+        .select(
+            "price",
+            "date_of_transfer",
+            pl.col("property_type")
+            .alias("pp_property_type")
+            .replace(property_type_map),
+            "postcode",
+            "paon",
+            "saon",
+            "street",
+            "locality",
+            "town_city",
+            pl.col("duration").replace(duration_map),
         )
-        .sort('date_of_transfer')
-        .group_by('pp_address', 'postcode', maintain_order=True)
+        .filter(pl.col("pp_property_type") != "Other")
+        .with_columns(
+            pl.concat_str(
+                [pl.col("saon"), pl.col("paon"), pl.col("street")],
+                separator=" ",
+                ignore_nulls=True,
+            ).alias("pp_address"),
+        )
+        .sort("date_of_transfer")
+        .group_by("pp_address", "postcode", maintain_order=True)
         .agg(
             pl.struct(
-                pl.col('date_of_transfer').dt.year().alias('year'),
-                'price',
-            ).alias('historical_prices'),
-            pl.col('pp_property_type').last(),
-            pl.col('duration').last(),
-            pl.col('price').last().alias('latest_price'),
-            pl.col('date_of_transfer').last(),
+                pl.col("date_of_transfer").dt.year().alias("year"),
+                "price",
+            ).alias("historical_prices"),
+            pl.col("pp_property_type").last(),
+            pl.col("duration").last(),
+            pl.col("price").last().alias("latest_price"),
+            pl.col("date_of_transfer").last(),
         )
-    ).filter(pl.col('pp_address').is_not_null())
+    ).filter(pl.col("pp_address").is_not_null())
 
     print("Price paid dataset")
     print(price_paid.head().collect())
 
-    joined = fuzzy_join_on_postcode(
-        left=price_paid,
-        right=epc,
-        left_address_col='pp_address',
-        right_address_col='epc_address',
-        left_postcode_col='postcode',
-        right_postcode_col='POSTCODE',
-    ).drop('POSTCODE').collect()
+    joined = (
+        fuzzy_join_on_postcode(
+            left=price_paid,
+            right=epc,
+            left_address_col="pp_address",
+            right_address_col="epc_address",
+            left_postcode_col="postcode",
+            right_postcode_col="POSTCODE",
+        )
+        .drop("POSTCODE")
+        .collect()
+    )
 
-    matched = joined.filter(pl.col('epc_address').is_not_null() & pl.col('pp_address').is_not_null())
+    matched = joined.filter(
+        pl.col("epc_address").is_not_null() & pl.col("pp_address").is_not_null()
+    )
     total = joined.height
     print(f"Unique properties: {total}")
     print(f"Matched: {matched.height} ({100 * matched.height / total:.1f}%)")
diff --git a/pipeline/transform/merge.py b/pipeline/transform/merge.py
index 1ea4459..26b4076 100644
--- a/pipeline/transform/merge.py
+++ b/pipeline/transform/merge.py
@@ -24,7 +24,9 @@ def _build_wide(
         "lsoa21",
     )
     wide = wide.join(arcgis, on="postcode", how="inner")
-    print(f"  {wide.shape[0]:,} rows after GPS join, {wide.estimated_size('mb'):.1f} MB")
+    print(
+        f"  {wide.shape[0]:,} rows after GPS join, {wide.estimated_size('mb'):.1f} MB"
+    )
 
     # Journey times (optional)
     if journey_times_path and journey_times_path.exists():
@@ -42,9 +44,7 @@ def _build_wide(
     if iod_path and iod_path.exists():
         print("Joining IoD scores...")
         iod = pl.read_parquet(iod_path)
-        wide = wide.join(
-            iod, left_on="lsoa21", right_on="LSOA code (2021)", how="left"
-        )
+        wide = wide.join(iod, left_on="lsoa21", right_on="LSOA code (2021)", how="left")
         print(f"  {wide.estimated_size('mb'):.1f} MB after IoD")
 
     # POI proximity counts (pre-computed per postcode)
@@ -66,44 +66,68 @@ def _build_wide(
         )
 
     # Derived columns
-    wide = wide.with_columns(
-        (pl.col("latest_price") / pl.col("total_floor_area")).alias("Price per sqm"),
-    ).drop(
-        'date_of_transfer',
-        'inspection_date',
-        'floor_height',
-        'lsoa21',
-        'LSOA code (2021)',
-        'Local Authority District code (2024)',
-        'Local Authority District name (2024)',
-        'imd_score',
-        'housing_barriers_score',
-        'idaci_score',
-        'idaopi_score',
-        'children_young_people_score',
-        'adult_skills_score',
-        'geographical_barriers_score',
-        'wider_barriers_score',
-    ).rename({
-        'construction_age_band': "Approximate construction age",
-        "income_score": "Income Score (rate)",
-        "employment_score": "Employment Score (rate)",
-        "education_score": "Education, Skills and Training Score",
-        "health_score": "Health Deprivation and Disability Score",
-        "crime_score": "Crime Score",
-    })
+    wide = (
+        wide.with_columns(
+            (pl.col("latest_price") / pl.col("total_floor_area")).alias(
+                "Price per sqm"
+            ),
+        )
+        .drop(
+            "date_of_transfer",
+            "inspection_date",
+            "floor_height",
+            "lsoa21",
+            "LSOA code (2021)",
+            "Local Authority District code (2024)",
+            "Local Authority District name (2024)",
+            "imd_score",
+            "housing_barriers_score",
+            "idaci_score",
+            "idaopi_score",
+            "children_young_people_score",
+            "adult_skills_score",
+            "geographical_barriers_score",
+            "wider_barriers_score",
+        )
+        .rename(
+            {
+                "construction_age_band": "Approximate construction age",
+                "income_score": "Income Score (rate)",
+                "employment_score": "Employment Score (rate)",
+                "education_score": "Education, Skills and Training Score",
+                "health_score": "Health Deprivation and Disability Score",
+                "crime_score": "Crime Score",
+            }
+        )
+    )
 
     return wide
 
 
 def main():
-    parser = argparse.ArgumentParser(description="Build wide property dataframe with all joins")
-    parser.add_argument("--epc-pp", type=Path, required=True, help="EPC-Price Paid joined parquet file")
-    parser.add_argument("--arcgis", type=Path, required=True, help="ArcGIS postcode data parquet file")
-    parser.add_argument("--iod", type=Path, help="Index of Deprivation parquet file (optional)")
-    parser.add_argument("--poi-proximity", type=Path, help="POI proximity counts parquet file (optional)")
-    parser.add_argument("--journey-times", type=Path, help="Journey times parquet file (optional)")
-    parser.add_argument("--output", type=Path, required=True, help="Output parquet file path")
+    parser = argparse.ArgumentParser(
+        description="Build wide property dataframe with all joins"
+    )
+    parser.add_argument(
+        "--epc-pp", type=Path, required=True, help="EPC-Price Paid joined parquet file"
+    )
+    parser.add_argument(
+        "--arcgis", type=Path, required=True, help="ArcGIS postcode data parquet file"
+    )
+    parser.add_argument(
+        "--iod", type=Path, help="Index of Deprivation parquet file (optional)"
+    )
+    parser.add_argument(
+        "--poi-proximity",
+        type=Path,
+        help="POI proximity counts parquet file (optional)",
+    )
+    parser.add_argument(
+        "--journey-times", type=Path, help="Journey times parquet file (optional)"
+    )
+    parser.add_argument(
+        "--output", type=Path, required=True, help="Output parquet file path"
+    )
     args = parser.parse_args()
 
     wide = _build_wide(
@@ -119,7 +143,7 @@ def main():
 
     wide.write_parquet(args.output)
     size_mb = args.output.stat().st_size / (1024 * 1024)
-    
+
     print(f"Wrote {args.output} ({size_mb:.1f} MB)")
 
 
diff --git a/pipeline/transform/transform_poi.py b/pipeline/transform/transform_poi.py
index d9caeb5..5a4608b 100644
--- a/pipeline/transform/transform_poi.py
+++ b/pipeline/transform/transform_poi.py
@@ -584,9 +584,7 @@ def transform(input_path: Path) -> pl.LazyFrame:
         if cat not in DROP_CATEGORIES and cat not in CATEGORY_MAP:
             unmapped.append(cat)
     if unmapped:
-        raise ValueError(
-            f"Categories missing from CATEGORY_MAP: {sorted(unmapped)}"
-        )
+        raise ValueError(f"Categories missing from CATEGORY_MAP: {sorted(unmapped)}")
 
     # Verify every CATEGORY_MAP key actually exists in the data (catch typos)
     mapped_but_absent = []
@@ -623,9 +621,15 @@ def transform(input_path: Path) -> pl.LazyFrame:
 
 
 def main():
-    parser = argparse.ArgumentParser(description="Transform raw POIs to filtered version with friendly names")
-    parser.add_argument("--input", type=Path, required=True, help="Raw POIs parquet file")
-    parser.add_argument("--output", type=Path, required=True, help="Output filtered POIs parquet file")
+    parser = argparse.ArgumentParser(
+        description="Transform raw POIs to filtered version with friendly names"
+    )
+    parser.add_argument(
+        "--input", type=Path, required=True, help="Raw POIs parquet file"
+    )
+    parser.add_argument(
+        "--output", type=Path, required=True, help="Output filtered POIs parquet file"
+    )
     args = parser.parse_args()
 
     df = transform(args.input).collect()
diff --git a/pipeline/utils/__init__.py b/pipeline/utils/__init__.py
index 8f435ac..cb7663a 100644
--- a/pipeline/utils/__init__.py
+++ b/pipeline/utils/__init__.py
@@ -2,4 +2,10 @@ from .fuzzy_join import fuzzy_join_on_postcode
 from .haversine import haversine_km, haversine_km_expr
 from .poi_counts import POI_GROUPS, count_pois_within_radius
 
-__all__ = ["fuzzy_join_on_postcode", "haversine_km", "haversine_km_expr", "POI_GROUPS", "count_pois_within_radius"]
+__all__ = [
+    "fuzzy_join_on_postcode",
+    "haversine_km",
+    "haversine_km_expr",
+    "POI_GROUPS",
+    "count_pois_within_radius",
+]
diff --git a/pipeline/utils/fuzzy_join.py b/pipeline/utils/fuzzy_join.py
index f516f22..4ddd602 100644
--- a/pipeline/utils/fuzzy_join.py
+++ b/pipeline/utils/fuzzy_join.py
@@ -9,14 +9,14 @@ import polars as pl
 from thefuzz import fuzz
 from tqdm import tqdm
 
-_NUMBER_RE = re.compile(r'\d+')
+_NUMBER_RE = re.compile(r"\d+")
 
 
 def _normalize(s: pl.Expr) -> pl.Expr:
     return (
         s.str.to_uppercase()
-        .str.replace_all(r'[,.\-]', ' ')
-        .str.replace_all(r'\s+', ' ')
+        .str.replace_all(r"[,.\-]", " ")
+        .str.replace_all(r"\s+", " ")
         .str.strip_chars()
     )
 
@@ -40,22 +40,25 @@ def fuzzy_join_on_postcode(
     have null right columns.
     """
 
-    tmpdir = tempfile.mkdtemp(prefix='fuzzy_join_')
-    left_path = Path(tmpdir) / 'left.parquet'
-    right_path = Path(tmpdir) / 'right.parquet'
+    tmpdir = tempfile.mkdtemp(prefix="fuzzy_join_")
+    left_path = Path(tmpdir) / "left.parquet"
+    right_path = Path(tmpdir) / "right.parquet"
 
     try:
         # Materialise each side exactly once, with a row index, to temp parquet.
-        left.with_row_index('_left_idx').sink_parquet(left_path)
-        right.with_row_index('_right_idx').sink_parquet(right_path)
+        left.with_row_index("_left_idx").sink_parquet(left_path)
+        right.with_row_index("_right_idx").sink_parquet(right_path)
 
         # Collect only the narrow columns needed for matching (projection pushdown).
         left_match = (
             pl.scan_parquet(left_path)
             .select(
-                '_left_idx',
-                _normalize(pl.col(left_address_col)).alias('_left_address'),
-                pl.col(left_postcode_col).str.strip_chars().str.to_uppercase().alias('_left_postcode'),
+                "_left_idx",
+                _normalize(pl.col(left_address_col)).alias("_left_address"),
+                pl.col(left_postcode_col)
+                .str.strip_chars()
+                .str.to_uppercase()
+                .alias("_left_postcode"),
             )
             .collect()
         )
@@ -63,18 +66,23 @@ def fuzzy_join_on_postcode(
         right_match = (
             pl.scan_parquet(right_path)
             .select(
-                '_right_idx',
-                _normalize(pl.col(right_address_col)).alias('_right_address'),
-                pl.col(right_postcode_col).str.strip_chars().str.to_uppercase().alias('_right_postcode'),
+                "_right_idx",
+                _normalize(pl.col(right_address_col)).alias("_right_address"),
+                pl.col(right_postcode_col)
+                .str.strip_chars()
+                .str.to_uppercase()
+                .alias("_right_postcode"),
             )
-            .unique(subset=['_right_address', '_right_postcode'], keep='first')
+            .unique(subset=["_right_address", "_right_postcode"], keep="first")
             .collect()
         )
 
         # Group right side by postcode for fast lookup
         right_by_postcode: dict[str, list[tuple[int, str]]] = {}
         for idx, postcode, address in zip(
-            right_match['_right_idx'], right_match['_right_postcode'], right_match['_right_address']
+            right_match["_right_idx"],
+            right_match["_right_postcode"],
+            right_match["_right_address"],
         ):
             if postcode is not None:
                 right_by_postcode.setdefault(postcode, []).append((idx, address))
@@ -82,7 +90,9 @@ def fuzzy_join_on_postcode(
         # Group left side by postcode
         left_by_postcode: dict[str, list[tuple[int, str]]] = {}
         for idx, postcode, address in zip(
-            left_match['_left_idx'], left_match['_left_postcode'], left_match['_left_address']
+            left_match["_left_idx"],
+            left_match["_left_postcode"],
+            left_match["_left_address"],
         ):
             if address is not None and postcode is not None:
                 left_by_postcode.setdefault(postcode, []).append((idx, address))
@@ -103,7 +113,7 @@ def fuzzy_join_on_postcode(
             for pairs in tqdm(
                 executor.map(_score_bucket, tasks, chunksize=64),
                 total=len(tasks),
-                desc='Fuzzy matching',
+                desc="Fuzzy matching",
             ):
                 all_pairs.extend(pairs)
 
@@ -127,24 +137,27 @@ def fuzzy_join_on_postcode(
 
         # Build a small mapping LazyFrame and join back to the cached parquets.
         if matches:
-            mapping = pl.LazyFrame({
-                '_left_idx': pl.Series([m[0] for m in matches], dtype=pl.UInt32),
-                '_right_idx': pl.Series([m[1] for m in matches], dtype=pl.UInt32),
-            })
+            mapping = pl.LazyFrame(
+                {
+                    "_left_idx": pl.Series([m[0] for m in matches], dtype=pl.UInt32),
+                    "_right_idx": pl.Series([m[1] for m in matches], dtype=pl.UInt32),
+                }
+            )
         else:
-            mapping = pl.LazyFrame({
-                '_left_idx': pl.Series([], dtype=pl.UInt32),
-                '_right_idx': pl.Series([], dtype=pl.UInt32),
-            })
+            mapping = pl.LazyFrame(
+                {
+                    "_left_idx": pl.Series([], dtype=pl.UInt32),
+                    "_right_idx": pl.Series([], dtype=pl.UInt32),
+                }
+            )
 
         left_cached = pl.scan_parquet(left_path)
         right_cached = pl.scan_parquet(right_path)
 
         return (
-            left_cached
-            .join(mapping, on='_left_idx', how='left')
-            .join(right_cached, on='_right_idx', how='left')
-            .drop('_left_idx', '_right_idx')
+            left_cached.join(mapping, on="_left_idx", how="left")
+            .join(right_cached, on="_right_idx", how="left")
+            .drop("_left_idx", "_right_idx")
         )
     except BaseException:
         shutil.rmtree(tmpdir, ignore_errors=True)
@@ -158,7 +171,9 @@ def _numbers_compatible(a: str, b: str) -> bool:
     """
     nums_a = set(_NUMBER_RE.findall(a))
     nums_b = set(_NUMBER_RE.findall(b))
-    smaller, larger = (nums_a, nums_b) if len(nums_a) <= len(nums_b) else (nums_b, nums_a)
+    smaller, larger = (
+        (nums_a, nums_b) if len(nums_a) <= len(nums_b) else (nums_b, nums_a)
+    )
     if not smaller and larger:
         return False
     return smaller.issubset(larger)
diff --git a/pipeline/utils/haversine.py b/pipeline/utils/haversine.py
index ff4a4bf..96fc02a 100644
--- a/pipeline/utils/haversine.py
+++ b/pipeline/utils/haversine.py
@@ -6,7 +6,9 @@ import polars as pl
 _EARTH_RADIUS_KM = 6371.0
 
 
-def haversine_km(lat1: np.ndarray, lon1: np.ndarray, lat2: float, lon2: float) -> np.ndarray:
+def haversine_km(
+    lat1: np.ndarray, lon1: np.ndarray, lat2: float, lon2: float
+) -> np.ndarray:
     """Compute haversine distance in km between arrays (lat1, lon1) and a single point (lat2, lon2)."""
     lat1_rad = np.radians(lat1)
     lon1_rad = np.radians(lon1)
@@ -14,7 +16,10 @@ def haversine_km(lat1: np.ndarray, lon1: np.ndarray, lat2: float, lon2: float) -
     lon2_rad = np.radians(lon2)
     dlat = lat2_rad - lat1_rad
     dlon = lon2_rad - lon1_rad
-    a = np.sin(dlat / 2) ** 2 + np.cos(lat1_rad) * np.cos(lat2_rad) * np.sin(dlon / 2) ** 2
+    a = (
+        np.sin(dlat / 2) ** 2
+        + np.cos(lat1_rad) * np.cos(lat2_rad) * np.sin(dlon / 2) ** 2
+    )
     c = 2 * np.arcsin(np.sqrt(a))
     return _EARTH_RADIUS_KM * c
 
@@ -32,5 +37,7 @@ def haversine_km_expr(
     dlat = pl.lit(dest_lat_rad) - lat_rad
     dlon = pl.lit(dest_lon_rad) - lon_rad
 
-    a = (dlat / 2).sin() ** 2 + pl.lit(dest_lat_rad).cos() * lat_rad.cos() * (dlon / 2).sin() ** 2
+    a = (dlat / 2).sin() ** 2 + pl.lit(dest_lat_rad).cos() * lat_rad.cos() * (
+        dlon / 2
+    ).sin() ** 2
     return 2 * _EARTH_RADIUS_KM * a.sqrt().arcsin()
diff --git a/pipeline/utils/poi_counts.py b/pipeline/utils/poi_counts.py
index 6ab700c..b97df5e 100644
--- a/pipeline/utils/poi_counts.py
+++ b/pipeline/utils/poi_counts.py
@@ -70,7 +70,9 @@ def _count_pois_per_postcode(
     pc_codes = postcodes_df["postcode"].to_list()
 
     # Initialize result arrays
-    result_counts = {group: np.zeros(n_postcodes, dtype=np.int32) for group in POI_GROUPS}
+    result_counts = {
+        group: np.zeros(n_postcodes, dtype=np.int32) for group in POI_GROUPS
+    }
 
     # Process in batches with progress
     batch_size = 50000
@@ -83,7 +85,9 @@ def _count_pois_per_postcode(
         end_idx = min(start_idx + batch_size, n_postcodes)
 
         if batch_idx % 5 == 0:
-            print(f"  Batch {batch_idx + 1}/{n_batches}: postcodes {start_idx:,} - {end_idx:,}")
+            print(
+                f"  Batch {batch_idx + 1}/{n_batches}: postcodes {start_idx:,} - {end_idx:,}"
+            )
 
         # Process batch
         for i in range(start_idx, end_idx):
@@ -109,12 +113,7 @@ def _count_pois_per_postcode(
             nearby = np.concatenate(nearby_indices)
 
             # Vectorized distance calculation for all nearby POIs
-            distances = haversine_km(
-                poi_lats[nearby],
-                poi_lngs[nearby],
-                pc_lat,
-                pc_lon
-            )
+            distances = haversine_km(poi_lats[nearby], poi_lngs[nearby], pc_lat, pc_lon)
 
             # Filter by radius
             within_mask = distances <= radius_km
@@ -147,13 +146,13 @@ def count_pois_within_radius(
     """
     # Get unique postcodes with coordinates
     print("Deduplicating postcodes...")
-    unique_postcodes = (
-        properties
-        .select(["postcode", "lat", "lon"])
-        .unique(subset=["postcode"])
+    unique_postcodes = properties.select(["postcode", "lat", "lon"]).unique(
+        subset=["postcode"]
     )
 
-    print(f"  {len(properties):,} properties → {len(unique_postcodes):,} unique postcodes")
+    print(
+        f"  {len(properties):,} properties → {len(unique_postcodes):,} unique postcodes"
+    )
 
     # Count POIs per postcode
     postcode_counts = _count_pois_per_postcode(unique_postcodes, pois, radius_km)
@@ -174,11 +173,7 @@ def count_pois_within_radius(
     result_lazy = (
         properties.lazy()
         .select("postcode")
-        .join(
-            pl.scan_parquet(tmp_path),
-            on="postcode",
-            how="left"
-        )
+        .join(pl.scan_parquet(tmp_path), on="postcode", how="left")
         .select(count_cols)
         .fill_null(0)
     )
diff --git a/pipeline/utils/test_fuzzy_join.py b/pipeline/utils/test_fuzzy_join.py
index 93c573c..bd3f03b 100644
--- a/pipeline/utils/test_fuzzy_join.py
+++ b/pipeline/utils/test_fuzzy_join.py
@@ -41,6 +41,6 @@ result = fuzzy_join_on_postcode(
 
 snapshot = result.select("pp_address", "ADDRESS").sort("pp_address")
 
-print('Testing the matching between EPC and PP addresses')
+print("Testing the matching between EPC and PP addresses")
 with pl.Config(tbl_rows=-1, tbl_cols=-1, fmt_str_lengths=80):
     print(snapshot)
diff --git a/pipeline/utils/test_haversine.py b/pipeline/utils/test_haversine.py
index 639eba7..d1c46af 100644
--- a/pipeline/utils/test_haversine.py
+++ b/pipeline/utils/test_haversine.py
@@ -73,29 +73,39 @@ class TestHaversineKmExpr:
     def test_same_point(self):
         """Distance from a point to itself should be zero."""
         df = pl.DataFrame({"lat": [51.5074], "lon": [-0.1278]})
-        result = df.select(haversine_km_expr("lat", "lon", 51.5074, -0.1278).alias("dist"))
+        result = df.select(
+            haversine_km_expr("lat", "lon", 51.5074, -0.1278).alias("dist")
+        )
         assert result["dist"][0] == pytest.approx(0.0, abs=1e-10)
 
     def test_known_distance_london_to_paris(self):
         """Test distance from London to Paris (~344 km)."""
         df = pl.DataFrame({"lat": [51.5074], "lon": [-0.1278]})
-        result = df.select(haversine_km_expr("lat", "lon", 48.8566, 2.3522).alias("dist"))
+        result = df.select(
+            haversine_km_expr("lat", "lon", 48.8566, 2.3522).alias("dist")
+        )
         assert result["dist"][0] == pytest.approx(344, rel=0.01)
 
     def test_known_distance_new_york_to_london(self):
         """Test distance from New York to London (~5570 km)."""
         df = pl.DataFrame({"lat": [40.7128], "lon": [-74.0060]})
-        result = df.select(haversine_km_expr("lat", "lon", 51.5074, -0.1278).alias("dist"))
+        result = df.select(
+            haversine_km_expr("lat", "lon", 51.5074, -0.1278).alias("dist")
+        )
         assert result["dist"][0] == pytest.approx(5570, rel=0.01)
 
     def test_multiple_points(self):
         """Test calculating distances from multiple points to a single destination."""
-        df = pl.DataFrame({
-            "lat": [51.5074, 48.8566, 40.7128],  # London, Paris, NYC
-            "lon": [-0.1278, 2.3522, -74.0060],
-        })
+        df = pl.DataFrame(
+            {
+                "lat": [51.5074, 48.8566, 40.7128],  # London, Paris, NYC
+                "lon": [-0.1278, 2.3522, -74.0060],
+            }
+        )
         # Distance to Edinburgh
-        result = df.select(haversine_km_expr("lat", "lon", 55.9533, -3.1883).alias("dist"))
+        result = df.select(
+            haversine_km_expr("lat", "lon", 55.9533, -3.1883).alias("dist")
+        )
 
         dists = result["dist"].to_numpy()
         # All distances should be positive
@@ -128,7 +138,9 @@ class TestHaversineConsistency:
 
         # Polars version
         df = pl.DataFrame({"lat": lats, "lon": lons})
-        polars_result = df.select(haversine_km_expr("lat", "lon", dest_lat, dest_lon).alias("dist"))
+        polars_result = df.select(
+            haversine_km_expr("lat", "lon", dest_lat, dest_lon).alias("dist")
+        )
         polars_dists = polars_result["dist"].to_numpy()
 
         # Should be identical (or at least very close due to floating point)
diff --git a/pipeline/utils/test_poi_counts.py b/pipeline/utils/test_poi_counts.py
index a7366f5..a5786b6 100644
--- a/pipeline/utils/test_poi_counts.py
+++ b/pipeline/utils/test_poi_counts.py
@@ -7,28 +7,32 @@ from pipeline.utils.poi_counts import POI_GROUPS, count_pois_within_radius
 @pytest.fixture
 def pois():
     """POIs clustered around two locations: central London and 10km away."""
-    return pl.DataFrame({
-        "lat": [51.5074, 51.5075, 51.5080, 51.5076, 51.5073, 51.60],
-        "lng": [-0.1278, -0.1280, -0.1275, -0.1279, -0.1277, -0.20],
-        "category": [
-            "Restaurant",
-            "Fast Food",
-            "Supermarket",
-            "Park",
-            "Station",
-            "Restaurant",   # too far from any property
-        ],
-    })
+    return pl.DataFrame(
+        {
+            "lat": [51.5074, 51.5075, 51.5080, 51.5076, 51.5073, 51.60],
+            "lng": [-0.1278, -0.1280, -0.1275, -0.1279, -0.1277, -0.20],
+            "category": [
+                "Restaurant",
+                "Fast Food",
+                "Supermarket",
+                "Park",
+                "Station",
+                "Restaurant",  # too far from any property
+            ],
+        }
+    )
 
 
 @pytest.fixture
 def properties():
     """Two properties at the same postcode near central London, one at a distant postcode."""
-    return pl.DataFrame({
-        "postcode": ["EC1A 1BB", "EC1A 1BB", "ZZ99 9ZZ"],
-        "lat": [51.5074, 51.5074, 55.0],
-        "lon": [-0.1278, -0.1278, -3.0],
-    })
+    return pl.DataFrame(
+        {
+            "postcode": ["EC1A 1BB", "EC1A 1BB", "ZZ99 9ZZ"],
+            "lat": [51.5074, 51.5074, 55.0],
+            "lon": [-0.1278, -0.1278, -3.0],
+        }
+    )
 
 
 def test_counts_pois_within_radius(properties, pois):
@@ -41,9 +45,9 @@ def test_counts_pois_within_radius(properties, pois):
         assert len(series) == 3, f"{col} has {len(series)} rows, expected 3"
 
     # First two rows share a postcode near the central London cluster
-    assert result["restaurants_2km"][0] == 2   # Restaurant + Fast Food
-    assert result["groceries_2km"][0] == 1     # Supermarket
-    assert result["parks_2km"][0] == 1         # Park
+    assert result["restaurants_2km"][0] == 2  # Restaurant + Fast Food
+    assert result["groceries_2km"][0] == 1  # Supermarket
+    assert result["parks_2km"][0] == 1  # Park
     assert result["public_transport_2km"][0] == 1  # Station
 
     # Second row is the same postcode, so same counts
@@ -55,11 +59,13 @@ def test_counts_pois_within_radius(properties, pois):
 
 
 def test_no_pois_returns_zeros(properties):
-    empty_pois = pl.DataFrame({
-        "lat": pl.Series([], dtype=pl.Float64),
-        "lng": pl.Series([], dtype=pl.Float64),
-        "category": pl.Series([], dtype=pl.String),
-    })
+    empty_pois = pl.DataFrame(
+        {
+            "lat": pl.Series([], dtype=pl.Float64),
+            "lng": pl.Series([], dtype=pl.Float64),
+            "category": pl.Series([], dtype=pl.String),
+        }
+    )
     result = count_pois_within_radius(properties, empty_pois, radius_km=2.0)
 
     for group in POI_GROUPS:
@@ -70,11 +76,13 @@ def test_no_pois_returns_zeros(properties):
 
 def test_custom_radius(pois):
     """A tiny radius should exclude POIs that are even slightly away."""
-    properties = pl.DataFrame({
-        "postcode": ["EC1A 1BB"],
-        "lat": [51.5074],
-        "lon": [-0.1278],
-    })
+    properties = pl.DataFrame(
+        {
+            "postcode": ["EC1A 1BB"],
+            "lat": [51.5074],
+            "lon": [-0.1278],
+        }
+    )
 
     # 0.01 km = 10m — only the POI at the exact same location should match
     result = count_pois_within_radius(properties, pois, radius_km=0.01)

From 0fde087c3dbb25ace3fe2d6d8e09518d87c91649 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sat, 31 Jan 2026 13:07:18 +0000
Subject: [PATCH 30/62] Format the map

---
 frontend/src/App.tsx                       |  8 +--
 frontend/src/components/Map.tsx            | 75 ++++++++++++++++------
 frontend/src/components/PropertiesPane.tsx | 10 +--
 3 files changed, 64 insertions(+), 29 deletions(-)

diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx
index 4f30c11..ed9ec6f 100644
--- a/frontend/src/App.tsx
+++ b/frontend/src/App.tsx
@@ -66,7 +66,9 @@ export default function App() {
   const poiAbortControllerRef = useRef<AbortController | null>(null);
 
   // Hexagon properties state
-  const [selectedHexagon, setSelectedHexagon] = useState<{ h3: string; resolution: number } | null>(null);
+  const [selectedHexagon, setSelectedHexagon] = useState<{ h3: string; resolution: number } | null>(
+    null
+  );
   const [properties, setProperties] = useState<Property[]>([]);
   const [propertiesTotal, setPropertiesTotal] = useState(0);
   const [propertiesOffset, setPropertiesOffset] = useState(0);
@@ -347,9 +349,7 @@ export default function App() {
         <div className="flex border-b border-gray-200">
           <button
             className={`flex-1 p-3 ${
-              rightPaneTab === 'pois'
-                ? 'border-b-2 border-blue-500 font-semibold'
-                : 'text-gray-600'
+              rightPaneTab === 'pois' ? 'border-b-2 border-blue-500 font-semibold' : 'text-gray-600'
             }`}
             onClick={() => setRightPaneTab('pois')}
           >
diff --git a/frontend/src/components/Map.tsx b/frontend/src/components/Map.tsx
index 3aa94c8..bc81e29 100644
--- a/frontend/src/components/Map.tsx
+++ b/frontend/src/components/Map.tsx
@@ -31,7 +31,6 @@ function emojiToTwemojiUrl(emoji: string): string {
   return `${TWEMOJI_BASE}${hex}.png`;
 }
 
-
 const INITIAL_VIEW: ViewState = {
   longitude: -1.5,
   latitude: 53.5,
@@ -43,10 +42,10 @@ const MAP_STYLE = 'https://basemaps.cartocdn.com/gl/positron-gl-style/style.json
 
 // Gradient stops for normalized [0,1] values
 const GRADIENT: { t: number; color: [number, number, number] }[] = [
-  { t: 0, color: [46, 204, 113] },    // Green
-  { t: 0.33, color: [241, 196, 15] },  // Yellow
-  { t: 0.66, color: [231, 76, 60] },   // Red
-  { t: 1, color: [142, 68, 173] },     // Purple
+  { t: 0, color: [46, 204, 113] }, // Green
+  { t: 0.33, color: [241, 196, 15] }, // Yellow
+  { t: 0.66, color: [231, 76, 60] }, // Red
+  { t: 1, color: [142, 68, 173] }, // Purple
 ];
 
 function normalizedToColor(t: number): [number, number, number] {
@@ -144,7 +143,16 @@ function countToColor(t: number): [number, number, number] {
   return [r, g, b];
 }
 
-export default function Map({ data, pois, onViewChange, activeFeature, dragValue, features, selectedHexagonId, onHexagonClick }: MapProps) {
+export default function Map({
+  data,
+  pois,
+  onViewChange,
+  activeFeature,
+  dragValue,
+  features,
+  selectedHexagonId,
+  onHexagonClick,
+}: MapProps) {
   const containerRef = useRef<HTMLDivElement>(null);
   const [viewState, setViewState] = useState<ViewState>(INITIAL_VIEW);
   const [dimensions, setDimensions] = useState<Dimensions>({ width: 0, height: 0 });
@@ -180,15 +188,18 @@ export default function Map({ data, pois, onViewChange, activeFeature, dragValue
   }, []);
 
   // Make place labels more legible over the colored hexagons
-  const handleMapLoad = useCallback((evt: { target: MapRef['getMap'] extends () => infer M ? M : never }) => {
-    const map = evt.target;
-    for (const layer of map.getStyle().layers || []) {
-      if (layer.type !== 'symbol') continue;
-      map.setPaintProperty(layer.id, 'text-halo-color', 'rgba(255,255,255,1)');
-      map.setPaintProperty(layer.id, 'text-halo-width', 2);
-      map.setPaintProperty(layer.id, 'text-color', '#222');
-    }
-  }, []);
+  const handleMapLoad = useCallback(
+    (evt: { target: MapRef['getMap'] extends () => infer M ? M : never }) => {
+      const map = evt.target;
+      for (const layer of map.getStyle().layers || []) {
+        if (layer.type !== 'symbol') continue;
+        map.setPaintProperty(layer.id, 'text-halo-color', 'rgba(255,255,255,1)');
+        map.setPaintProperty(layer.id, 'text-halo-width', 2);
+        map.setPaintProperty(layer.id, 'text-color', '#222');
+      }
+    },
+    []
+  );
 
   // Popup state for POI hover
   const [popupInfo, setPopupInfo] = useState<{
@@ -226,7 +237,9 @@ export default function Map({ data, pois, onViewChange, activeFeature, dragValue
   }, [data]);
 
   // Determine color mode
-  const colorFeatureMeta = activeFeature ? features.find((f) => f.name === activeFeature) || null : null;
+  const colorFeatureMeta = activeFeature
+    ? features.find((f) => f.name === activeFeature) || null
+    : null;
 
   const handleHexagonClick = useCallback(
     (info: PickingInfo<HexagonData>) => {
@@ -258,7 +271,13 @@ export default function Map({ data, pois, onViewChange, activeFeature, dragValue
           const t = (c - countRange.min) / (countRange.max - countRange.min);
           return countToColor(Math.max(0, Math.min(1, t)));
         },
-        getLineColor: (d) => (d.h3 === selectedHexagonId ? [255, 255, 255, 255] : [0, 0, 0, 0]) as [number, number, number, number],
+        getLineColor: (d) =>
+          (d.h3 === selectedHexagonId ? [255, 255, 255, 255] : [0, 0, 0, 0]) as [
+            number,
+            number,
+            number,
+            number,
+          ],
         getLineWidth: (d) => (d.h3 === selectedHexagonId ? 2 : 0),
         lineWidthUnits: 'pixels',
         updateTriggers: {
@@ -290,7 +309,17 @@ export default function Map({ data, pois, onViewChange, activeFeature, dragValue
         onHover: handlePoiHover,
       }),
     ],
-    [data, pois, handlePoiHover, handleHexagonClick, activeFeature, dragValue, countRange, colorFeatureMeta, selectedHexagonId]
+    [
+      data,
+      pois,
+      handlePoiHover,
+      handleHexagonClick,
+      activeFeature,
+      dragValue,
+      countRange,
+      colorFeatureMeta,
+      selectedHexagonId,
+    ]
   );
 
   const getTooltip = useCallback(
@@ -305,8 +334,14 @@ export default function Map({ data, pois, onViewChange, activeFeature, dragValue
         const minVal = hex[`min_${f.name}`];
         const maxVal = hex[`max_${f.name}`];
         if (minVal != null && maxVal != null) {
-          const minStr = typeof minVal === 'number' ? minVal.toLocaleString(undefined, { maximumFractionDigits: 1 }) : String(minVal);
-          const maxStr = typeof maxVal === 'number' ? maxVal.toLocaleString(undefined, { maximumFractionDigits: 1 }) : String(maxVal);
+          const minStr =
+            typeof minVal === 'number'
+              ? minVal.toLocaleString(undefined, { maximumFractionDigits: 1 })
+              : String(minVal);
+          const maxStr =
+            typeof maxVal === 'number'
+              ? maxVal.toLocaleString(undefined, { maximumFractionDigits: 1 })
+              : String(maxVal);
           const highlight = f.name === activeFeature ? 'font-weight: bold;' : '';
           lines.push(`<div style="${highlight}">${f.label}: ${minStr} - ${maxStr}</div>`);
         }
diff --git a/frontend/src/components/PropertiesPane.tsx b/frontend/src/components/PropertiesPane.tsx
index 07b03bd..8deed91 100644
--- a/frontend/src/components/PropertiesPane.tsx
+++ b/frontend/src/components/PropertiesPane.tsx
@@ -31,9 +31,7 @@ export function PropertiesPane({
         case 'size':
           return ((b.total_floor_area as number) || 0) - ((a.total_floor_area as number) || 0);
         case 'energy':
-          return (a.current_energy_rating || 'Z').localeCompare(
-            b.current_energy_rating || 'Z'
-          );
+          return (a.current_energy_rating || 'Z').localeCompare(b.current_energy_rating || 'Z');
       }
     });
   }, [properties, sortBy]);
@@ -142,7 +140,8 @@ function PropertyCard({ property }: { property: Property }) {
         )}
         {property.total_floor_area && (
           <div>
-            <span className="text-gray-600">Area:</span> {formatNumber(property.total_floor_area as number)}m²
+            <span className="text-gray-600">Area:</span>{' '}
+            {formatNumber(property.total_floor_area as number)}m²
           </div>
         )}
         {property.number_habitable_rooms && (
@@ -163,7 +162,8 @@ function PropertyCard({ property }: { property: Property }) {
         )}
         {property.construction_age_band !== undefined && (
           <div>
-            <span className="text-gray-600">Built (age):</span> {formatNumber(property.construction_age_band as number)}
+            <span className="text-gray-600">Built (age):</span>{' '}
+            {formatNumber(property.construction_age_band as number)}
           </div>
         )}
 

From f60fbec9d47e5797479c43f598eb101f81d7096a Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sat, 31 Jan 2026 13:57:43 +0000
Subject: [PATCH 31/62] Format rust

---
 server-rs/src/consts.rs |   3 +
 server-rs/src/data.rs   | 161 +++++++++++++++++++++++++++++-----------
 server-rs/src/index.rs  |   8 +-
 server-rs/src/main.rs   |   9 ++-
 server-rs/src/routes.rs | 104 ++++++++++++++------------
 5 files changed, 191 insertions(+), 94 deletions(-)

diff --git a/server-rs/src/consts.rs b/server-rs/src/consts.rs
index 7ccf49e..a72db94 100644
--- a/server-rs/src/consts.rs
+++ b/server-rs/src/consts.rs
@@ -9,3 +9,6 @@ pub const HISTOGRAM_BINS: usize = 100;
 /// H3 resolutions to precompute at startup (covers typical zoom levels)
 pub const H3_PRECOMPUTE_MIN: u8 = 4;
 pub const H3_PRECOMPUTE_MAX: u8 = 12;
+
+/// Columns to exclude from feature discovery
+pub const EXCLUDED_COLUMNS: &[&str] = &["lat", "lon"];
diff --git a/server-rs/src/data.rs b/server-rs/src/data.rs
index 6a3cb48..2fec011 100644
--- a/server-rs/src/data.rs
+++ b/server-rs/src/data.rs
@@ -1,18 +1,13 @@
-use polars::prelude::*;
 use polars::lazy::frame::LazyFrame;
+use polars::prelude::*;
 use rayon::prelude::*;
 use serde::Serialize;
 use std::path::Path;
 
-use crate::consts::{FEATURE_PERCENTILE_LOW, FEATURE_PERCENTILE_HIGH, HISTOGRAM_BINS, H3_PRECOMPUTE_MIN, H3_PRECOMPUTE_MAX};
-
-/// Columns to exclude from feature discovery
-const EXCLUDED_COLUMNS: &[&str] = &["lat", "lon"];
-
-/// H3 valid resolution range (0-15)
-pub const MIN_RESOLUTION: u8 = 0;
-pub const MAX_RESOLUTION: u8 = 15;
-pub const DEFAULT_RESOLUTION: u8 = 8;
+use crate::consts::{
+    EXCLUDED_COLUMNS, FEATURE_PERCENTILE_HIGH, FEATURE_PERCENTILE_LOW, H3_PRECOMPUTE_MAX,
+    H3_PRECOMPUTE_MIN, HISTOGRAM_BINS,
+};
 
 /// Returns true if the polars DataType is numeric (integer or float)
 fn is_numeric_dtype(dtype: &DataType) -> bool {
@@ -76,7 +71,13 @@ pub struct PropertyData {
 
 /// Approximate a percentile from a histogram using linear interpolation.
 /// `p` is in [0, 100]. `total` is the sum of all bin counts.
-fn percentile_from_histogram(counts: &[u64], min: f64, bin_width: f64, total: usize, p: f64) -> f64 {
+fn percentile_from_histogram(
+    counts: &[u64],
+    min: f64,
+    bin_width: f64,
+    total: usize,
+    p: f64,
+) -> f64 {
     let target = (p / 100.0) * (total as f64 - 1.0);
     let mut cumulative = 0u64;
     for (i, &c) in counts.iter().enumerate() {
@@ -104,8 +105,12 @@ fn compute_feature_stats(vals: &[f64]) -> FeatureStats {
     let mut count = 0usize;
     for &v in vals {
         if !v.is_nan() {
-            if v < min { min = v; }
-            if v > max { max = v; }
+            if v < min {
+                min = v;
+            }
+            if v > max {
+                max = v;
+            }
             count += 1;
         }
     }
@@ -222,8 +227,12 @@ impl PropertyData {
 
         // Add string columns (using actual column names from parquet)
         let string_cols = vec![
-            "pp_address", "postcode", "pp_property_type", "built_form",
-            "current_energy_rating", "potential_energy_rating"
+            "pp_address",
+            "postcode",
+            "pp_property_type",
+            "built_form",
+            "current_energy_rating",
+            "potential_energy_rating",
         ];
 
         // Build selection with proper casting
@@ -256,10 +265,20 @@ impl PropertyData {
 
         // Extract lat/lon using bulk iterator
         let lat_series = df.column("lat").unwrap().cast(&DataType::Float64).unwrap();
-        let lat: Vec<f64> = lat_series.f64().unwrap().into_iter().map(|v| v.unwrap_or(0.0)).collect();
+        let lat: Vec<f64> = lat_series
+            .f64()
+            .unwrap()
+            .into_iter()
+            .map(|v| v.unwrap_or(0.0))
+            .collect();
 
         let lon_series = df.column("lon").unwrap().cast(&DataType::Float64).unwrap();
-        let lon: Vec<f64> = lon_series.f64().unwrap().into_iter().map(|v| v.unwrap_or(0.0)).collect();
+        let lon: Vec<f64> = lon_series
+            .f64()
+            .unwrap()
+            .into_iter()
+            .map(|v| v.unwrap_or(0.0))
+            .collect();
 
         // Extract feature columns (column-major, for cache-friendly histogram computation)
         eprintln!("Extracting feature columns...");
@@ -281,8 +300,10 @@ impl PropertyData {
                 eprintln!(
                     "  {}: p{}={:.2}, p{}={:.2}, {} bins",
                     feature_names[i],
-                    FEATURE_PERCENTILE_LOW, stats.p_low,
-                    FEATURE_PERCENTILE_HIGH, stats.p_high,
+                    FEATURE_PERCENTILE_LOW,
+                    stats.p_low,
+                    FEATURE_PERCENTILE_HIGH,
+                    stats.p_high,
                     stats.histogram.counts.len()
                 );
                 stats
@@ -292,40 +313,66 @@ impl PropertyData {
         // Extract string columns (before permutation)
         eprintln!("Extracting string columns...");
         let address_raw: Vec<String> = if let Ok(col) = df.column("pp_address") {
-            col.str().unwrap().into_iter().map(|v| v.unwrap_or("").to_string()).collect()
+            col.str()
+                .unwrap()
+                .into_iter()
+                .map(|v| v.unwrap_or("").to_string())
+                .collect()
         } else {
             vec![String::new(); row_count]
         };
 
         let postcode_raw: Vec<String> = if let Ok(col) = df.column("postcode") {
-            col.str().unwrap().into_iter().map(|v| v.unwrap_or("").to_string()).collect()
+            col.str()
+                .unwrap()
+                .into_iter()
+                .map(|v| v.unwrap_or("").to_string())
+                .collect()
         } else {
             vec![String::new(); row_count]
         };
 
         let property_type_raw: Vec<String> = if let Ok(col) = df.column("pp_property_type") {
-            col.str().unwrap().into_iter().map(|v| v.unwrap_or("").to_string()).collect()
+            col.str()
+                .unwrap()
+                .into_iter()
+                .map(|v| v.unwrap_or("").to_string())
+                .collect()
         } else {
             vec![String::new(); row_count]
         };
 
         let built_form_raw: Vec<String> = if let Ok(col) = df.column("built_form") {
-            col.str().unwrap().into_iter().map(|v| v.unwrap_or("").to_string()).collect()
+            col.str()
+                .unwrap()
+                .into_iter()
+                .map(|v| v.unwrap_or("").to_string())
+                .collect()
         } else {
             vec![String::new(); row_count]
         };
 
-        let current_energy_rating_raw: Vec<String> = if let Ok(col) = df.column("current_energy_rating") {
-            col.str().unwrap().into_iter().map(|v| v.unwrap_or("").to_string()).collect()
-        } else {
-            vec![String::new(); row_count]
-        };
+        let current_energy_rating_raw: Vec<String> =
+            if let Ok(col) = df.column("current_energy_rating") {
+                col.str()
+                    .unwrap()
+                    .into_iter()
+                    .map(|v| v.unwrap_or("").to_string())
+                    .collect()
+            } else {
+                vec![String::new(); row_count]
+            };
 
-        let potential_energy_rating_raw: Vec<String> = if let Ok(col) = df.column("potential_energy_rating") {
-            col.str().unwrap().into_iter().map(|v| v.unwrap_or("").to_string()).collect()
-        } else {
-            vec![String::new(); row_count]
-        };
+        let potential_energy_rating_raw: Vec<String> =
+            if let Ok(col) = df.column("potential_energy_rating") {
+                col.str()
+                    .unwrap()
+                    .into_iter()
+                    .map(|v| v.unwrap_or("").to_string())
+                    .collect()
+            } else {
+                vec![String::new(); row_count]
+            };
 
         // Sort all rows by spatial locality so that grid queries access
         // contiguous memory (sequential reads instead of random DRAM accesses).
@@ -349,12 +396,30 @@ impl PropertyData {
         let lon: Vec<f64> = perm.iter().map(|&i| lon[i as usize]).collect();
 
         // Apply permutation to string columns
-        let address: Vec<String> = perm.iter().map(|&i| address_raw[i as usize].clone()).collect();
-        let postcode: Vec<String> = perm.iter().map(|&i| postcode_raw[i as usize].clone()).collect();
-        let property_type: Vec<String> = perm.iter().map(|&i| property_type_raw[i as usize].clone()).collect();
-        let built_form: Vec<String> = perm.iter().map(|&i| built_form_raw[i as usize].clone()).collect();
-        let current_energy_rating: Vec<String> = perm.iter().map(|&i| current_energy_rating_raw[i as usize].clone()).collect();
-        let potential_energy_rating: Vec<String> = perm.iter().map(|&i| potential_energy_rating_raw[i as usize].clone()).collect();
+        let address: Vec<String> = perm
+            .iter()
+            .map(|&i| address_raw[i as usize].clone())
+            .collect();
+        let postcode: Vec<String> = perm
+            .iter()
+            .map(|&i| postcode_raw[i as usize].clone())
+            .collect();
+        let property_type: Vec<String> = perm
+            .iter()
+            .map(|&i| property_type_raw[i as usize].clone())
+            .collect();
+        let built_form: Vec<String> = perm
+            .iter()
+            .map(|&i| built_form_raw[i as usize].clone())
+            .collect();
+        let current_energy_rating: Vec<String> = perm
+            .iter()
+            .map(|&i| current_energy_rating_raw[i as usize].clone())
+            .collect();
+        let potential_energy_rating: Vec<String> = perm
+            .iter()
+            .map(|&i| potential_energy_rating_raw[i as usize].clone())
+            .collect();
 
         // Transpose to row-major AND apply spatial permutation in one pass.
         // Result: all features for one row are contiguous, and spatially
@@ -422,7 +487,8 @@ impl POIData {
         eprintln!("Loaded {} POIs", row_count);
 
         // Extract columns
-        let id: Vec<String> = df.column("id")
+        let id: Vec<String> = df
+            .column("id")
             .unwrap()
             .str()
             .unwrap()
@@ -430,7 +496,8 @@ impl POIData {
             .map(|v| v.unwrap_or("").to_string())
             .collect();
 
-        let name: Vec<String> = df.column("name")
+        let name: Vec<String> = df
+            .column("name")
             .unwrap()
             .str()
             .unwrap()
@@ -438,7 +505,8 @@ impl POIData {
             .map(|v| v.unwrap_or("").to_string())
             .collect();
 
-        let category: Vec<String> = df.column("category")
+        let category: Vec<String> = df
+            .column("category")
             .unwrap()
             .str()
             .unwrap()
@@ -446,7 +514,8 @@ impl POIData {
             .map(|v| v.unwrap_or("").to_string())
             .collect();
 
-        let lat: Vec<f64> = df.column("lat")
+        let lat: Vec<f64> = df
+            .column("lat")
             .unwrap()
             .f64()
             .unwrap()
@@ -454,7 +523,8 @@ impl POIData {
             .map(|v| v.unwrap_or(0.0))
             .collect();
 
-        let lng: Vec<f64> = df.column("lng")
+        let lng: Vec<f64> = df
+            .column("lng")
             .unwrap()
             .f64()
             .unwrap()
@@ -462,7 +532,8 @@ impl POIData {
             .map(|v| v.unwrap_or(0.0))
             .collect();
 
-        let emoji: Vec<String> = df.column("emoji")
+        let emoji: Vec<String> = df
+            .column("emoji")
             .unwrap()
             .str()
             .unwrap()
diff --git a/server-rs/src/index.rs b/server-rs/src/index.rs
index 5b705ed..ee13588 100644
--- a/server-rs/src/index.rs
+++ b/server-rs/src/index.rs
@@ -114,7 +114,13 @@ impl GridIndex {
         }
     }
 
-    fn clamp_bounds(&self, south: f64, west: f64, north: f64, east: f64) -> (usize, usize, usize, usize) {
+    fn clamp_bounds(
+        &self,
+        south: f64,
+        west: f64,
+        north: f64,
+        east: f64,
+    ) -> (usize, usize, usize, usize) {
         let r_min = ((south - self.min_lat) / self.cell_size) as isize;
         let r_max = ((north - self.min_lat) / self.cell_size) as isize;
         let c_min = ((west - self.min_lon) / self.cell_size) as isize;
diff --git a/server-rs/src/main.rs b/server-rs/src/main.rs
index 4756e66..ccd8831 100644
--- a/server-rs/src/main.rs
+++ b/server-rs/src/main.rs
@@ -42,7 +42,10 @@ async fn main() {
     let poi_data = if poi_path.exists() {
         data::POIData::load(&poi_path)
     } else {
-        eprintln!("Warning: {} not found. POI endpoints will be unavailable.", poi_path.display());
+        eprintln!(
+            "Warning: {} not found. POI endpoints will be unavailable.",
+            poi_path.display()
+        );
         data::POIData {
             id: Vec::new(),
             name: Vec::new(),
@@ -93,7 +96,9 @@ async fn main() {
         )
         .route(
             "/api/hexagon-properties",
-            get(move |query| routes::get_hexagon_properties(state_hexagon_properties.clone(), query)),
+            get(move |query| {
+                routes::get_hexagon_properties(state_hexagon_properties.clone(), query)
+            }),
         );
 
     // Static file serving for frontend
diff --git a/server-rs/src/routes.rs b/server-rs/src/routes.rs
index 9f3d251..509f832 100644
--- a/server-rs/src/routes.rs
+++ b/server-rs/src/routes.rs
@@ -8,7 +8,8 @@ use axum::response::{IntoResponse, Json};
 use rustc_hash::FxHashMap;
 use serde::{Deserialize, Serialize};
 
-use crate::data::{Histogram, PropertyData, POIData, POI, DEFAULT_RESOLUTION, MAX_RESOLUTION, MIN_RESOLUTION};
+use crate::consts::{H3_PRECOMPUTE_MAX, H3_PRECOMPUTE_MIN};
+use crate::data::{Histogram, POIData, PropertyData, POI};
 use crate::index::GridIndex;
 
 /// Shared application state
@@ -82,7 +83,7 @@ pub async fn get_features(state: Arc<AppState>) -> Json<FeaturesResponse> {
 
 #[derive(Deserialize)]
 pub struct HexagonParams {
-    resolution: Option<u8>,
+    resolution: u8,
     bounds: Option<String>,
     /// Comma-separated filters: `name:min:max,...`
     /// Rows must have non-NaN values within [min,max] for each filter.
@@ -130,7 +131,6 @@ impl CellAgg {
             }
         }
     }
-
 }
 
 /// Write the hexagons JSON response directly to a String buffer,
@@ -172,20 +172,21 @@ pub async fn get_hexagons(
     state: Arc<AppState>,
     Query(params): Query<HexagonParams>,
 ) -> Result<impl IntoResponse, (StatusCode, String)> {
-    let resolution = params.resolution.unwrap_or(DEFAULT_RESOLUTION);
-    if resolution > MAX_RESOLUTION {
+    let resolution = params.resolution;
+    if resolution < H3_PRECOMPUTE_MIN || resolution > H3_PRECOMPUTE_MAX {
         return Err((
             StatusCode::BAD_REQUEST,
             format!(
                 "resolution must be between {} and {}",
-                MIN_RESOLUTION, MAX_RESOLUTION
+                H3_PRECOMPUTE_MIN, H3_PRECOMPUTE_MAX
             ),
         ));
     }
 
-    let bounds_str = params
-        .bounds
-        .ok_or((StatusCode::BAD_REQUEST, "bounds parameter is required".into()))?;
+    let bounds_str = params.bounds.ok_or((
+        StatusCode::BAD_REQUEST,
+        "bounds parameter is required".into(),
+    ))?;
 
     let parts: Vec<f64> = bounds_str
         .split(',')
@@ -286,46 +287,44 @@ pub async fn get_hexagons(
 
         if let Some(precomputed) = h3_cells_for_res {
             // Fast path: precomputed H3 + visitor pattern
-            state.grid.for_each_in_bounds(south, west, north, east, |row_idx| {
-                let row = row_idx as usize;
-                if !row_passes(row) {
-                    return;
-                }
-                let cell_id = precomputed[row];
-                groups
-                    .entry(cell_id)
-                    .or_insert_with(|| CellAgg::new(num_features))
-                    .add_row(feature_data, row, num_features);
-            });
+            state
+                .grid
+                .for_each_in_bounds(south, west, north, east, |row_idx| {
+                    let row = row_idx as usize;
+                    if !row_passes(row) {
+                        return;
+                    }
+                    let cell_id = precomputed[row];
+                    groups
+                        .entry(cell_id)
+                        .or_insert_with(|| CellAgg::new(num_features))
+                        .add_row(feature_data, row, num_features);
+                });
         } else {
             // Fallback: compute H3 on-the-fly
             let h3_res = h3o::Resolution::try_from(resolution).unwrap();
-            state.grid.for_each_in_bounds(south, west, north, east, |row_idx| {
-                let row = row_idx as usize;
-                if !row_passes(row) {
-                    return;
-                }
-                let cell_id = h3o::LatLng::new(state.data.lat[row], state.data.lon[row])
-                    .map(|c| u64::from(c.to_cell(h3_res)))
-                    .unwrap_or(0);
-                groups
-                    .entry(cell_id)
-                    .or_insert_with(|| CellAgg::new(num_features))
-                    .add_row(feature_data, row, num_features);
-            });
+            state
+                .grid
+                .for_each_in_bounds(south, west, north, east, |row_idx| {
+                    let row = row_idx as usize;
+                    if !row_passes(row) {
+                        return;
+                    }
+                    let cell_id = h3o::LatLng::new(state.data.lat[row], state.data.lon[row])
+                        .map(|c| u64::from(c.to_cell(h3_res)))
+                        .unwrap_or(0);
+                    groups
+                        .entry(cell_id)
+                        .or_insert_with(|| CellAgg::new(num_features))
+                        .add_row(feature_data, row, num_features);
+                });
         }
 
         let t_agg = t0.elapsed();
 
         // Write JSON directly (no serde_json::Value allocation overhead)
         let mut json_buf = String::with_capacity(groups.len() * 128);
-        write_hexagons_json(
-            &mut json_buf,
-            &groups,
-            &min_keys,
-            &max_keys,
-            num_features,
-        );
+        write_hexagons_json(&mut json_buf, &groups, &min_keys, &max_keys, num_features);
 
         let t_total = t0.elapsed();
         eprintln!(
@@ -364,9 +363,10 @@ pub async fn get_pois(
     state: Arc<AppState>,
     Query(params): Query<POIParams>,
 ) -> Result<Json<POIsResponse>, (StatusCode, String)> {
-    let bounds_str = params
-        .bounds
-        .ok_or((StatusCode::BAD_REQUEST, "bounds parameter is required".into()))?;
+    let bounds_str = params.bounds.ok_or((
+        StatusCode::BAD_REQUEST,
+        "bounds parameter is required".into(),
+    ))?;
 
     let parts: Vec<f64> = bounds_str
         .split(',')
@@ -501,7 +501,12 @@ pub struct HexagonPropertiesResponse {
 }
 
 /// Helper function to check if a row passes all filters
-fn row_passes_filters(row: usize, filters: &[ParsedFilter], feature_data: &[f64], num_features: usize) -> bool {
+fn row_passes_filters(
+    row: usize,
+    filters: &[ParsedFilter],
+    feature_data: &[f64],
+    num_features: usize,
+) -> bool {
     filters.iter().all(|f| {
         let v = feature_data[row * num_features + f.feat_idx];
         v.is_finite() && v >= f.min && v <= f.max
@@ -520,7 +525,10 @@ pub async fn get_hexagon_properties(
     // 2. Validate resolution
     let resolution = params.resolution as usize;
     if resolution >= state.h3_cells.len() || state.h3_cells[resolution].is_empty() {
-        return Err((StatusCode::BAD_REQUEST, "Invalid or non-precomputed resolution".to_string()));
+        return Err((
+            StatusCode::BAD_REQUEST,
+            "Invalid or non-precomputed resolution".to_string(),
+        ));
     }
 
     // 3. Parse filters (reuse existing filter parsing logic from get_hexagons)
@@ -592,7 +600,11 @@ pub async fn get_hexagon_properties(
 
                 // Helper to get non-empty string
                 let get_string = |s: &str| -> Option<String> {
-                    if s.is_empty() { None } else { Some(s.to_string()) }
+                    if s.is_empty() {
+                        None
+                    } else {
+                        Some(s.to_string())
+                    }
                 };
 
                 Property {

From a8cc44ea9751fca4fe72c57d042f5ff180298a5e Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sat, 31 Jan 2026 14:39:46 +0000
Subject: [PATCH 32/62] Add more data & fix ooms

---
 pipeline/download/ethnicity.py      |  58 ++++++++++++
 pipeline/journey_times/__main__.py  |  16 +++-
 pipeline/transform/crime.py         |  63 ++++++++++++
 pipeline/transform/join_epc_pp.py   |   2 +-
 pipeline/transform/merge.py         | 142 +++++++++++++++++-----------
 pipeline/transform/transform_poi.py |   4 +-
 pipeline/utils/fuzzy_join.py        |   4 +-
 pipeline/utils/poi_counts.py        |  35 +++----
 8 files changed, 242 insertions(+), 82 deletions(-)
 create mode 100644 pipeline/download/ethnicity.py
 create mode 100644 pipeline/transform/crime.py

diff --git a/pipeline/download/ethnicity.py b/pipeline/download/ethnicity.py
new file mode 100644
index 0000000..22a21ad
--- /dev/null
+++ b/pipeline/download/ethnicity.py
@@ -0,0 +1,58 @@
+import argparse
+from pathlib import Path
+
+import httpx
+import polars as pl
+
+pl.Config.set_tbl_cols(-1)
+
+
+URL = "https://www.ethnicity-facts-figures.service.gov.uk/uk-population-by-ethnicity/national-and-regional-populations/regional-ethnic-diversity/latest/downloads/population-by-ethnicity-and-local-authority-2021.csv"
+
+
+def download_and_convert(output_path: Path) -> None:
+    print("Downloading ethnicity data...")
+    response = httpx.get(URL, follow_redirects=True, timeout=60)
+    response.raise_for_status()
+
+    df = pl.read_csv(response.content)
+    print(f"Raw shape: {df.head(100)}")
+
+    # Keep only broad ethnicity categories (5+1), exclude "All" totals
+    df = df.filter(
+        (pl.col("Ethnicity_type") == "ONS 2021 5+1") & (pl.col("Ethnicity") != "All")
+    )
+
+    # Pivot: one row per local authority, columns = ethnicity percentages
+    wide = df.pivot(
+        on="Ethnicity",
+        index="Geography_code",
+        values="Value1",
+    )
+
+    # Rename columns to be descriptive
+    rename_map = {
+        col: f"% {col}" for col in wide.columns if col != "Geography_code"
+    }
+    wide = wide.rename(rename_map)
+
+    print(f"Output shape: {wide.shape}")
+    print(f"Columns: {wide.columns}")
+
+    wide.write_parquet(output_path, compression="zstd")
+    print(f"Saved to {output_path}")
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Download and convert ethnicity by local authority data"
+    )
+    parser.add_argument(
+        "--output", type=Path, required=True, help="Output parquet file path"
+    )
+    args = parser.parse_args()
+    download_and_convert(args.output)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/pipeline/journey_times/__main__.py b/pipeline/journey_times/__main__.py
index 40e8452..5a5a944 100644
--- a/pipeline/journey_times/__main__.py
+++ b/pipeline/journey_times/__main__.py
@@ -5,7 +5,13 @@ from datetime import date, timedelta
 import polars as pl
 from tqdm import tqdm
 
-from .config import DESTINATIONS, MAX_CONCURRENT, MAX_POSTCODES, OUTPUT_DIR, MAX_DISTANCE_KM
+from .config import (
+    DESTINATIONS,
+    MAX_CONCURRENT,
+    MAX_POSTCODES,
+    OUTPUT_DIR,
+    MAX_DISTANCE_KM,
+)
 from .results import CheckpointSaver, results_to_dataframe, save_results
 from .tfl_client import fetch_journey_times
 from pipeline.utils import haversine_km_expr
@@ -31,7 +37,9 @@ def main():
 
     # Filter to postcodes within range of destination
     postcodes_df = postcodes_df.with_columns(
-        haversine_km_expr("lat", "long", destination.lat, destination.lon).alias("distance_km")
+        haversine_km_expr("lat", "long", destination.lat, destination.lon).alias(
+            "distance_km"
+        )
     ).filter(pl.col("distance_km") <= MAX_DISTANCE_KM)
 
     print(f"Filtered to {postcodes_df.height:,} postcodes within {MAX_DISTANCE_KM}km")
@@ -50,7 +58,9 @@ def main():
 
     checkpoint_saver = CheckpointSaver(
         destination_name=destination.name,
-        on_save=lambda path, count: print(f"Checkpoint saved: {count:,} results to {path}"),
+        on_save=lambda path, count: print(
+            f"Checkpoint saved: {count:,} results to {path}"
+        ),
     )
 
     def on_result(result):
diff --git a/pipeline/transform/crime.py b/pipeline/transform/crime.py
new file mode 100644
index 0000000..6eecb83
--- /dev/null
+++ b/pipeline/transform/crime.py
@@ -0,0 +1,63 @@
+import argparse
+from pathlib import Path
+
+import polars as pl
+
+
+def transform_crime(crime_dir: Path, output_path: Path) -> None:
+    csvs = sorted(crime_dir.rglob("*.csv"))
+    print(f"Found {len(csvs)} CSV files across {len(list(crime_dir.iterdir()))} months")
+
+    df = pl.scan_csv(
+        csvs,
+        schema_overrides={"LSOA code": pl.Utf8, "Crime type": pl.Utf8, "Month": pl.Utf8},
+    ).select("LSOA code", "Crime type", "Month")
+
+    # Extract year, count crimes per LSOA / year / crime type
+    yearly_counts = (
+        df.filter(pl.col("LSOA code").is_not_null() & (pl.col("LSOA code") != ""))
+        .with_columns(pl.col("Month").str.slice(0, 4).alias("year"))
+        .group_by("LSOA code", "year", "Crime type")
+        .agg(pl.len().alias("count"))
+        .group_by("LSOA code", "Crime type")
+        .agg(pl.col("count").mean().round(1).alias("yearly_avg"))
+        .collect(engine="streaming")
+    )
+
+    print(f"Crime types: {sorted(yearly_counts['Crime type'].unique().to_list())}")
+
+    # Pivot crime types into columns
+    wide = yearly_counts.pivot(
+        on="Crime type",
+        index="LSOA code",
+        values="yearly_avg",
+    )
+
+    # Fill nulls with 0 and rename columns to be descriptive
+    value_cols = [col for col in wide.columns if col != "LSOA code"]
+    wide = wide.with_columns(pl.col(col).fill_null(0) for col in value_cols)
+    wide = wide.rename({col: f"{col} (avg/yr)" for col in value_cols})
+
+    print(f"Output shape: {wide.shape}")
+    print(f"Columns: {wide.columns}")
+
+    wide.write_parquet(output_path, compression="zstd")
+    print(f"Saved to {output_path}")
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Transform crime CSVs into yearly average by LSOA and crime type"
+    )
+    parser.add_argument(
+        "--input", type=Path, required=True, help="Directory containing crime data"
+    )
+    parser.add_argument(
+        "--output", type=Path, required=True, help="Output parquet file path"
+    )
+    args = parser.parse_args()
+    transform_crime(args.input, args.output)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/pipeline/transform/join_epc_pp.py b/pipeline/transform/join_epc_pp.py
index 741848a..aa42b4e 100644
--- a/pipeline/transform/join_epc_pp.py
+++ b/pipeline/transform/join_epc_pp.py
@@ -105,7 +105,7 @@ def main():
             right_postcode_col="POSTCODE",
         )
         .drop("POSTCODE")
-        .collect()
+        .collect(engine="streaming")
     )
 
     matched = joined.filter(
diff --git a/pipeline/transform/merge.py b/pipeline/transform/merge.py
index 26b4076..b5081b6 100644
--- a/pipeline/transform/merge.py
+++ b/pipeline/transform/merge.py
@@ -9,99 +9,127 @@ def _build_wide(
     iod_path: Path | None,
     poi_proximity_path: Path | None,
     journey_times_path: Path | None,
+    ethnicity_path: Path | None,
+    crime_path: Path | None,
 ) -> pl.DataFrame:
     """Build the wide dataframe by joining epc_pp with all auxiliary data."""
-    print("Loading epc_pp...")
-    wide = pl.read_parquet(epc_pp_path)
-    print(f"  {wide.shape[0]:,} rows, {wide.estimated_size('mb'):.1f} MB")
+    print("Scanning epc_pp...")
+    wide = pl.scan_parquet(epc_pp_path)
 
     # GPS coordinates + LSOA from ArcGIS
     print("Joining GPS coordinates...")
-    arcgis = pl.read_parquet(arcgis_path).select(
+    arcgis = pl.scan_parquet(arcgis_path).select(
         pl.col("pcds").alias("postcode"),
         "lat",
         pl.col("long").alias("lon"),
         "lsoa21",
     )
     wide = wide.join(arcgis, on="postcode", how="inner")
-    print(
-        f"  {wide.shape[0]:,} rows after GPS join, {wide.estimated_size('mb'):.1f} MB"
-    )
 
     # Journey times (optional)
     if journey_times_path and journey_times_path.exists():
         print("Joining journey times...")
-        journey_times = pl.read_parquet(journey_times_path).select(
+        journey_times = pl.scan_parquet(journey_times_path).select(
             "postcode",
             "public_transport_easy_minutes",
             "public_transport_quick_minutes",
             "cycling_minutes",
         )
         wide = wide.join(journey_times, on="postcode", how="left")
-        print(f"  {wide.estimated_size('mb'):.1f} MB after journey times")
 
-    # Index of Deprivation
-    if iod_path and iod_path.exists():
-        print("Joining IoD scores...")
-        iod = pl.read_parquet(iod_path)
-        wide = wide.join(iod, left_on="lsoa21", right_on="LSOA code (2021)", how="left")
-        print(f"  {wide.estimated_size('mb'):.1f} MB after IoD")
+    print("Joining IoD scores...")
+    iod = pl.scan_parquet(iod_path)
+    wide = wide.join(iod, left_on="lsoa21", right_on="LSOA code (2021)", how="left")
 
-    # POI proximity counts (pre-computed per postcode)
-    if poi_proximity_path and poi_proximity_path.exists():
-        print("Joining POI proximity counts...")
-        poi_counts = pl.read_parquet(poi_proximity_path)
-        wide = wide.join(poi_counts, on="postcode", how="left")
-        print(f"  {wide.estimated_size('mb'):.1f} MB after POI counts")
+    # Ethnicity by local authority
+    print("Joining ethnicity data...")
+    ethnicity = pl.scan_parquet(ethnicity_path)
+    wide = wide.join(
+        ethnicity,
+        left_on="Local Authority District code (2024)",
+        right_on="Geography_code",
+        how="left",
+    )
+
+    # Crime stats by LSOA
+    print("Joining crime data...")
+    crime = pl.scan_parquet(crime_path)
+    wide = wide.join(crime, left_on="lsoa21", right_on="LSOA code", how="left")
+
+    print("Joining POI proximity counts...")
+    poi_counts = pl.scan_parquet(poi_proximity_path)
+    wide = wide.join(poi_counts, on="postcode", how="left")
 
     # Convert construction_age_band to numeric year
-    if "construction_age_band" in wide.columns:
-        wide = wide.with_columns(
-            pl.col("construction_age_band")
-            .str.replace("England and Wales: ", "")
-            .str.replace(" onwards", "")
-            .str.extract(r"(\d{4})", 1)
-            .cast(pl.UInt16, strict=False)
-            .alias("construction_age_band"),
-        )
+    wide = wide.with_columns(
+        pl.col("construction_age_band")
+        .str.replace("England and Wales: ", "")
+        .str.replace(" onwards", "")
+        .str.extract(r"(\d{4})", 1)
+        .cast(pl.UInt16, strict=False)
+        .alias("construction_age_band"),
+    )
+
+    wide = wide.with_columns(
+        pl.when(pl.col("pp_property_type") == pl.col("built_form"))
+        .then(pl.col("pp_property_type"))
+        .otherwise(
+            pl.concat_str(
+                [pl.col("pp_property_type"), pl.lit("/"), pl.col("built_form")]
+            )
+        )
+        .alias("property_type_built_form")
+    )
 
-    # Derived columns
     wide = (
-        wide.with_columns(
-            (pl.col("latest_price") / pl.col("total_floor_area")).alias(
-                "Price per sqm"
-            ),
+        wide.filter(pl.col("total_floor_area") > 0).with_columns(
+            (pl.col("latest_price") / pl.col("total_floor_area"))
+            .round(0)
+            .cast(pl.Int32)
+            .alias("Price per sqm"),
         )
         .drop(
             "date_of_transfer",
             "inspection_date",
             "floor_height",
-            "lsoa21",
-            "LSOA code (2021)",
+            "LSOA name (2021)",
             "Local Authority District code (2024)",
             "Local Authority District name (2024)",
-            "imd_score",
-            "housing_barriers_score",
-            "idaci_score",
-            "idaopi_score",
-            "children_young_people_score",
-            "adult_skills_score",
-            "geographical_barriers_score",
-            "wider_barriers_score",
+            "Wider Barriers Sub-domain Score",
+            "Geographical Barriers Sub-domain Score",
+            "Adult Skills Sub-domain Score",
+            "Children and Young People Sub-domain Score",
+            "Income Deprivation Affecting Older People (IDAOPI) Score (rate)",
+            "Income Deprivation Affecting Children Index (IDACI) Score (rate)",
+            "Barriers to Housing and Services Score",
+            "lsoa21",
+            "pp_property_type",
+            "built_form",
         )
         .rename(
             {
                 "construction_age_band": "Approximate construction age",
-                "income_score": "Income Score (rate)",
-                "employment_score": "Employment Score (rate)",
-                "education_score": "Education, Skills and Training Score",
-                "health_score": "Health Deprivation and Disability Score",
-                "crime_score": "Crime Score",
+                "pp_address": "Address per Property Register",
+                "epc_address": "Address per EPC",
+                "postcode": "Postcode",
+                "duration": "Leashold/Freehold",
+                "current_energy_rating": "Current energy rating",
+                "potential_energy_rating": "Potential energy rating",
+                "total_floor_area": "Total floor area (sqm)",
+                "epc_property_type": "Property type",
+                "property_type_built_form": "Property type/built form",
+                "restaurants_2km": "Restaurants within 2km",
+                "groceries_2km": "Groceries within 2km",
+                "parks_2km": "Parks within 2km",
+                "public_transport_2km": "Public transport within 2km",
+                "latest_price": "Last known price",
+                "number_habitable_rooms": "Rooms (including bedrooms & bathrooms)",
             }
         )
     )
 
-    return wide
+    print("Collecting with streaming engine...")
+    return wide.collect(engine="streaming")
 
 
 def main():
@@ -115,7 +143,7 @@ def main():
         "--arcgis", type=Path, required=True, help="ArcGIS postcode data parquet file"
     )
     parser.add_argument(
-        "--iod", type=Path, help="Index of Deprivation parquet file (optional)"
+        "--iod", type=Path, required=True, help="Index of Deprivation parquet file (optional)"
     )
     parser.add_argument(
         "--poi-proximity",
@@ -123,7 +151,13 @@ def main():
         help="POI proximity counts parquet file (optional)",
     )
     parser.add_argument(
-        "--journey-times", type=Path, help="Journey times parquet file (optional)"
+        "--journey-times", required=True, type=Path, help="Journey times parquet file (optional)"
+    )
+    parser.add_argument(
+        "--ethnicity", type=Path, required=True, help="Ethnicity by local authority parquet file (optional)"
+    )
+    parser.add_argument(
+        "--crime", type=Path, required=True, help="Crime by LSOA parquet file (optional)"
     )
     parser.add_argument(
         "--output", type=Path, required=True, help="Output parquet file path"
@@ -136,6 +170,8 @@ def main():
         iod_path=args.iod,
         poi_proximity_path=args.poi_proximity,
         journey_times_path=args.journey_times,
+        ethnicity_path=args.ethnicity,
+        crime_path=args.crime,
     )
 
     print(f"Columns: {wide.columns}")
diff --git a/pipeline/transform/transform_poi.py b/pipeline/transform/transform_poi.py
index 5a4608b..de92d24 100644
--- a/pipeline/transform/transform_poi.py
+++ b/pipeline/transform/transform_poi.py
@@ -576,7 +576,7 @@ def transform(input_path: Path) -> pl.LazyFrame:
     lf = pl.scan_parquet(input_path)
 
     # Get all unique categories present in the data
-    all_categories = lf.select("category").unique().collect().to_series().to_list()
+    all_categories = lf.select("category").unique().collect(engine="streaming").to_series().to_list()
 
     # Verify every non-dropped category has a mapping
     unmapped = []
@@ -632,7 +632,7 @@ def main():
     )
     args = parser.parse_args()
 
-    df = transform(args.input).collect()
+    df = transform(args.input).collect(engine="streaming")
 
     df.write_parquet(args.output)
 
diff --git a/pipeline/utils/fuzzy_join.py b/pipeline/utils/fuzzy_join.py
index 4ddd602..985d505 100644
--- a/pipeline/utils/fuzzy_join.py
+++ b/pipeline/utils/fuzzy_join.py
@@ -60,7 +60,7 @@ def fuzzy_join_on_postcode(
                 .str.to_uppercase()
                 .alias("_left_postcode"),
             )
-            .collect()
+            .collect(engine="streaming")
         )
 
         right_match = (
@@ -74,7 +74,7 @@ def fuzzy_join_on_postcode(
                 .alias("_right_postcode"),
             )
             .unique(subset=["_right_address", "_right_postcode"], keep="first")
-            .collect()
+            .collect(engine="streaming")
         )
 
         # Group right side by postcode for fast lookup
diff --git a/pipeline/utils/poi_counts.py b/pipeline/utils/poi_counts.py
index b97df5e..8c39288 100644
--- a/pipeline/utils/poi_counts.py
+++ b/pipeline/utils/poi_counts.py
@@ -157,31 +157,24 @@ def count_pois_within_radius(
     # Count POIs per postcode
     postcode_counts = _count_pois_per_postcode(unique_postcodes, pois, radius_km)
 
-    # Write to temp file to avoid memory duplication during join
     print("  Writing postcode counts to temp file...")
-    with tempfile.NamedTemporaryFile(suffix=".parquet", delete=False) as tmp:
+    with tempfile.NamedTemporaryFile(suffix=".parquet") as tmp:
         tmp_path = tmp.name
         postcode_counts.write_parquet(tmp_path)
 
-    del postcode_counts  # Free memory
+        # Join using lazy evaluation
+        print("  Joining counts back to properties (lazy)...")
+        count_cols = [f"{group}_{int(radius_km)}km" for group in POI_GROUPS]
 
-    # Join using lazy evaluation
-    print("  Joining counts back to properties (lazy)...")
-    count_cols = [f"{group}_{int(radius_km)}km" for group in POI_GROUPS]
+        # Convert properties to lazy frame, join, then collect
+        result_lazy = (
+            properties.lazy()
+            .select("postcode")
+            .join(pl.scan_parquet(tmp_path), on="postcode", how="left")
+            .select(count_cols)
+            .fill_null(0)
+        )
 
-    # Convert properties to lazy frame, join, then collect
-    result_lazy = (
-        properties.lazy()
-        .select("postcode")
-        .join(pl.scan_parquet(tmp_path), on="postcode", how="left")
-        .select(count_cols)
-        .fill_null(0)
-    )
+        result_df = result_lazy.collect(engine="streaming")
 
-    result_df = result_lazy.collect()
-
-    # Clean up temp file
-    os.unlink(tmp_path)
-
-    # Extract as dict of Series
-    return {col: result_df[col] for col in count_cols}
+        return {col: result_df[col] for col in count_cols}

From 0ba8355347edbfba8c9576157a29241281eeaae9 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sat, 31 Jan 2026 14:39:53 +0000
Subject: [PATCH 33/62] Re-render notebooks

---
 analyses/epc_analysis.ipynb           | 141 +++---
 analyses/journey_times_analysis.ipynb | 334 ++++++++-----
 analyses/property_analysis.ipynb      | 676 +++++++++++++-------------
 analyses/wide.ipynb                   |  84 ++--
 4 files changed, 659 insertions(+), 576 deletions(-)

diff --git a/analyses/epc_analysis.ipynb b/analyses/epc_analysis.ipynb
index 4954b8f..d8bcba5 100644
--- a/analyses/epc_analysis.ipynb
+++ b/analyses/epc_analysis.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T22:06:26.543050Z",
@@ -158,7 +158,7 @@
        "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -173,13 +173,15 @@
     "\n",
     "PARQUET = \"../data/epc/certificates.parquet\"\n",
     "\n",
+    "\n",
     "def scan():\n",
     "    return pl.scan_parquet(PARQUET)\n",
     "\n",
+    "\n",
     "# Quick row count\n",
     "row_count = scan().select(pl.len()).collect().item()\n",
     "print(f\"Rows: {row_count:,}\")\n",
-    "print(f\"Columns: {\"\\n\".join(scan().collect_schema().names())}\")\n",
+    "print(f\"Columns: {'\\n'.join(scan().collect_schema().names())}\")\n",
     "\n",
     "scan().head(3).collect()"
    ]
@@ -193,7 +195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T22:06:27.101365Z",
@@ -236,7 +238,7 @@
        "└──────────────────────────────┴────────┴────────────┴──────────┘"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -249,21 +251,19 @@
     "all_nulls = {}\n",
     "col_names = schema.names()\n",
     "for i in range(0, len(col_names), batch_size):\n",
-    "    batch = col_names[i:i+batch_size]\n",
-    "    result = (\n",
-    "        scan()\n",
-    "        .select([pl.col(c).null_count().alias(c) for c in batch])\n",
-    "        .collect()\n",
-    "    )\n",
+    "    batch = col_names[i : i + batch_size]\n",
+    "    result = scan().select([pl.col(c).null_count().alias(c) for c in batch]).collect()\n",
     "    for c in batch:\n",
     "        all_nulls[c] = result[c].item()\n",
     "\n",
-    "schema_info = pl.DataFrame({\n",
-    "    \"column\": col_names,\n",
-    "    \"dtype\": [str(d) for d in schema.dtypes()],\n",
-    "    \"null_count\": [all_nulls[c] for c in col_names],\n",
-    "    \"null_pct\": [round(all_nulls[c] / row_count * 100, 1) for c in col_names],\n",
-    "})\n",
+    "schema_info = pl.DataFrame(\n",
+    "    {\n",
+    "        \"column\": col_names,\n",
+    "        \"dtype\": [str(d) for d in schema.dtypes()],\n",
+    "        \"null_count\": [all_nulls[c] for c in col_names],\n",
+    "        \"null_pct\": [round(all_nulls[c] / row_count * 100, 1) for c in col_names],\n",
+    "    }\n",
+    ")\n",
     "schema_info"
    ]
   },
@@ -276,7 +276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T22:06:29.054902Z",
@@ -286,27 +286,6 @@
     }
    },
    "outputs": [
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mKeyboardInterrupt\u001b[39m                         Traceback (most recent call last)",
-      "\u001b[36mFile \u001b[39m\u001b[32m/volumes/syncthing/Projects/property-map/.venv/lib/python3.12/site-packages/zmq/backend/cython/_zmq.py:179\u001b[39m, in \u001b[36mzmq.backend.cython._zmq._check_rc\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m    174\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"internal utility for checking zmq return condition\u001b[39;00m\n\u001b[32m    175\u001b[39m \n\u001b[32m    176\u001b[39m \u001b[33;03mand raising the appropriate Exception class\u001b[39;00m\n\u001b[32m    177\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m    178\u001b[39m errno: C.int = _zmq_errno()\n\u001b[32m--> \u001b[39m\u001b[32m179\u001b[39m PyErr_CheckSignals()\n\u001b[32m    180\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m errno == \u001b[32m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m error_without_errno:\n\u001b[32m    181\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[32m0\u001b[39m\n",
-      "\u001b[31mKeyboardInterrupt\u001b[39m: "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Exception ignored in: 'zmq.backend.cython._zmq.Frame.__dealloc__'\n",
-      "Traceback (most recent call last):\n",
-      "  File \"zmq/backend/cython/_zmq.py\", line 179, in zmq.backend.cython._zmq._check_rc\n",
-      "KeyboardInterrupt: \n"
-     ]
-    },
     {
      "data": {
       "image/png": 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",
@@ -337,10 +316,16 @@
     "ax.set_xlabel(\"Current Energy Rating\")\n",
     "ax.set_ylabel(\"Number of Properties\")\n",
     "ax.set_title(\"Distribution of Current Energy Ratings\")\n",
-    "ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f\"{x/1e6:.1f}M\"))\n",
+    "ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f\"{x / 1e6:.1f}M\"))\n",
     "for bar, v in zip(bars, values):\n",
-    "    ax.text(bar.get_x() + bar.get_width()/2, bar.get_height(), f\"{v/1e6:.1f}M\",\n",
-    "            ha=\"center\", va=\"bottom\", fontsize=9)\n",
+    "    ax.text(\n",
+    "        bar.get_x() + bar.get_width() / 2,\n",
+    "        bar.get_height(),\n",
+    "        f\"{v / 1e6:.1f}M\",\n",
+    "        ha=\"center\",\n",
+    "        va=\"bottom\",\n",
+    "        fontsize=9,\n",
+    "    )\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -354,7 +339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T22:06:29.243014Z",
@@ -377,18 +362,14 @@
    ],
    "source": [
     "prop_type = (\n",
-    "    scan()\n",
-    "    .group_by(\"PROPERTY_TYPE\")\n",
-    "    .len()\n",
-    "    .sort(\"len\", descending=True)\n",
-    "    .collect()\n",
+    "    scan().group_by(\"PROPERTY_TYPE\").len().sort(\"len\", descending=True).collect()\n",
     ")\n",
     "\n",
     "fig, ax = plt.subplots()\n",
     "ax.barh(prop_type[\"PROPERTY_TYPE\"].to_list()[::-1], prop_type[\"len\"].to_list()[::-1])\n",
     "ax.set_xlabel(\"Count\")\n",
     "ax.set_title(\"Properties by Type\")\n",
-    "ax.xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f\"{x/1e6:.1f}M\"))\n",
+    "ax.xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f\"{x / 1e6:.1f}M\"))\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -402,7 +383,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -432,13 +413,7 @@
    ],
    "source": [
     "for col_name in [\"PROPERTY_TYPE\", \"BUILT_FORM\"]:\n",
-    "    counts = (\n",
-    "        scan()\n",
-    "        .group_by(col_name)\n",
-    "        .len()\n",
-    "        .sort(\"len\", descending=True)\n",
-    "        .collect()\n",
-    "    )\n",
+    "    counts = scan().group_by(col_name).len().sort(\"len\", descending=True).collect()\n",
     "    print(f\"{col_name} — {len(counts)} distinct values:\")\n",
     "    for row in counts.iter_rows(named=True):\n",
     "        print(f\"  {row[col_name]!s:30s} {row['len']:>12,}\")\n",
@@ -454,7 +429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T22:06:29.941986Z",
@@ -491,24 +466,42 @@
     "\n",
     "# Define sort order for known age bands\n",
     "age_band_order = [\n",
-    "    \"before 1900\", \"1900-1929\", \"1930-1949\", \"1950-1966\", \"1967-1975\",\n",
-    "    \"1976-1982\", \"1983-1990\", \"1991-1995\", \"1996-2002\", \"2003-2006\",\n",
-    "    \"2007-2011\", \"2007 onwards\", \"2012 onwards\",\n",
+    "    \"before 1900\",\n",
+    "    \"1900-1929\",\n",
+    "    \"1930-1949\",\n",
+    "    \"1950-1966\",\n",
+    "    \"1967-1975\",\n",
+    "    \"1976-1982\",\n",
+    "    \"1983-1990\",\n",
+    "    \"1991-1995\",\n",
+    "    \"1996-2002\",\n",
+    "    \"2003-2006\",\n",
+    "    \"2007-2011\",\n",
+    "    \"2007 onwards\",\n",
+    "    \"2012 onwards\",\n",
     "]\n",
     "\n",
+    "\n",
     "def age_sort_key(band):\n",
     "    for i, ab in enumerate(age_band_order):\n",
     "        if ab in str(band):\n",
     "            return i\n",
     "    return 99\n",
     "\n",
-    "age_eff = age_eff.with_columns(\n",
-    "    pl.col(\"CONSTRUCTION_AGE_BAND\").map_elements(age_sort_key, return_dtype=pl.Int64).alias(\"sort_key\")\n",
-    ").sort(\"sort_key\").filter(pl.col(\"sort_key\") < 99)\n",
+    "\n",
+    "age_eff = (\n",
+    "    age_eff.with_columns(\n",
+    "        pl.col(\"CONSTRUCTION_AGE_BAND\")\n",
+    "        .map_elements(age_sort_key, return_dtype=pl.Int64)\n",
+    "        .alias(\"sort_key\")\n",
+    "    )\n",
+    "    .sort(\"sort_key\")\n",
+    "    .filter(pl.col(\"sort_key\") < 99)\n",
+    ")\n",
     "\n",
     "labels = age_eff[\"CONSTRUCTION_AGE_BAND\"].to_list()\n",
     "# Shorten labels\n",
-    "short_labels = [l.replace(\"England and Wales: \", \"\") for l in labels]\n",
+    "short_labels = [label.replace(\"England and Wales: \", \"\") for label in labels]\n",
     "\n",
     "fig, ax = plt.subplots()\n",
     "ax.bar(short_labels, age_eff[\"mean_efficiency\"].to_list(), label=\"Mean\", alpha=0.7)\n",
@@ -531,7 +524,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T22:06:31.277179Z",
@@ -545,7 +538,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_1434749/4276860801.py:36: MatplotlibDeprecationWarning: The 'labels' parameter of boxplot() has been renamed 'tick_labels' since Matplotlib 3.9; support for the old name will be dropped in 3.11.\n",
+      "/tmp/ipykernel_1997061/992856044.py:36: MatplotlibDeprecationWarning: The 'labels' parameter of boxplot() has been renamed 'tick_labels' since Matplotlib 3.9; support for the old name will be dropped in 3.11.\n",
       "  ax.boxplot(data, labels=labels, vert=True)\n"
      ]
     },
@@ -567,8 +560,8 @@
     "    .len()\n",
     "    .sort(\"len\", descending=True)\n",
     "    .head(5)\n",
-    "    .collect()\n",
-    "    [\"PROPERTY_TYPE\"].to_list()\n",
+    "    .collect()[\"PROPERTY_TYPE\"]\n",
+    "    .to_list()\n",
     ")\n",
     "\n",
     "# Sample per property type to avoid loading all data\n",
@@ -612,7 +605,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T22:06:31.967996Z",
@@ -650,7 +643,7 @@
     "ax.set_xlabel(\"Year\")\n",
     "ax.set_ylabel(\"Certificates Lodged\")\n",
     "ax.set_title(\"EPC Lodgements by Year\")\n",
-    "ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f\"{x/1e6:.1f}M\"))\n",
+    "ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f\"{x / 1e6:.1f}M\"))\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -664,7 +657,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T22:06:32.398389Z",
@@ -700,7 +693,7 @@
     "ax.barh(fuel[\"MAIN_FUEL\"].to_list()[::-1], fuel[\"len\"].to_list()[::-1])\n",
     "ax.set_xlabel(\"Count\")\n",
     "ax.set_title(\"Top 10 Main Fuel Types\")\n",
-    "ax.xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f\"{x/1e6:.1f}M\"))\n",
+    "ax.xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f\"{x / 1e6:.1f}M\"))\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -708,7 +701,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "property-map",
    "language": "python",
    "name": "python3"
   },
diff --git a/analyses/journey_times_analysis.ipynb b/analyses/journey_times_analysis.ipynb
index 0cd6b88..f286250 100644
--- a/analyses/journey_times_analysis.ipynb
+++ b/analyses/journey_times_analysis.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 1,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T19:53:51.298414Z",
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 2,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T19:53:51.683859Z",
@@ -43,9 +43,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Total postcodes: 810000\n",
+      "Total postcodes: 840000\n",
       "Columns: ['postcode', 'public_transport_easy_minutes', 'public_transport_quick_minutes', 'cycling_minutes', 'error', 'lat', 'long']\n",
-      "With lat/long: 810000\n"
+      "With lat/long: 840000\n"
      ]
     },
     {
@@ -76,17 +76,19 @@
        "└──────────┴──────────────────┴──────────────────┴─────────────────┴───────┴───────────┴───────────┘"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Load the journey times data and join with arcgis for lat/long\n",
-    "jt = pl.read_parquet('../data_sources/processed/journey_times_bank_checkpoint.parquet')\n",
-    "arcgis = pl.read_parquet('../data_sources/arcgis_data.parquet', columns=['pcds', 'lat', 'long'])\n",
+    "jt = pl.read_parquet(\"../data_sources/processed/journey_times_bank_checkpoint.parquet\")\n",
+    "arcgis = pl.read_parquet(\n",
+    "    \"../data_sources/arcgis_data.parquet\", columns=[\"pcds\", \"lat\", \"long\"]\n",
+    ")\n",
     "\n",
-    "df = jt.join(arcgis, left_on='postcode', right_on='pcds', how='left')\n",
+    "df = jt.join(arcgis, left_on=\"postcode\", right_on=\"pcds\", how=\"left\")\n",
     "print(f\"Total postcodes: {df.height}\")\n",
     "print(f\"Columns: {df.columns}\")\n",
     "print(f\"With lat/long: {df.filter(pl.col('lat').is_not_null()).height}\")\n",
@@ -95,7 +97,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T19:53:51.814525Z",
@@ -132,7 +134,7 @@
        "└──────────┴───────────┴───────────┴─────────────┘"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -142,33 +144,37 @@
     "BANK_LAT = 51.5133\n",
     "BANK_LON = -0.0886\n",
     "\n",
+    "\n",
     "def haversine_km(lat1, lon1, lat2, lon2):\n",
     "    \"\"\"Calculate the great circle distance between two points in km.\"\"\"\n",
     "    R = 6371  # Earth's radius in km\n",
-    "    \n",
+    "\n",
     "    lat1, lon1, lat2, lon2 = map(radians, [lat1, lon1, lat2, lon2])\n",
     "    dlat = lat2 - lat1\n",
     "    dlon = lon2 - lon1\n",
-    "    \n",
-    "    a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2\n",
-    "    c = 2 * atan2(sqrt(a), sqrt(1-a))\n",
-    "    \n",
+    "\n",
+    "    a = sin(dlat / 2) ** 2 + cos(lat1) * cos(lat2) * sin(dlon / 2) ** 2\n",
+    "    c = 2 * atan2(sqrt(a), sqrt(1 - a))\n",
+    "\n",
     "    return R * c\n",
     "\n",
+    "\n",
     "# Calculate distance to Bank for each postcode\n",
     "df = df.with_columns(\n",
-    "    pl.struct(['lat', 'long']).map_elements(\n",
-    "        lambda x: haversine_km(x['lat'], x['long'], BANK_LAT, BANK_LON),\n",
-    "        return_dtype=pl.Float64\n",
-    "    ).alias('distance_km')\n",
+    "    pl.struct([\"lat\", \"long\"])\n",
+    "    .map_elements(\n",
+    "        lambda x: haversine_km(x[\"lat\"], x[\"long\"], BANK_LAT, BANK_LON),\n",
+    "        return_dtype=pl.Float64,\n",
+    "    )\n",
+    "    .alias(\"distance_km\")\n",
     ")\n",
     "\n",
-    "df.select(['postcode', 'lat', 'long', 'distance_km']).head()"
+    "df.select([\"postcode\", \"lat\", \"long\", \"distance_km\"]).head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T19:53:51.944770Z",
@@ -182,16 +188,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Postcodes with journey data: 638898/810000\n"
+      "Postcodes with journey data: 662723/840000\n"
      ]
     }
    ],
    "source": [
     "# Filter to rows with valid journey times\n",
     "df_valid = df.filter(\n",
-    "    pl.col('public_transport_easy_minutes').is_not_null() |\n",
-    "    pl.col('public_transport_quick_minutes').is_not_null() |\n",
-    "    pl.col('cycling_minutes').is_not_null()\n",
+    "    pl.col(\"public_transport_easy_minutes\").is_not_null()\n",
+    "    | pl.col(\"public_transport_quick_minutes\").is_not_null()\n",
+    "    | pl.col(\"cycling_minutes\").is_not_null()\n",
     ")\n",
     "\n",
     "print(f\"Postcodes with journey data: {df_valid.height}/{df.height}\")\n",
@@ -203,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T19:53:51.948922Z",
@@ -224,7 +230,7 @@
       "│ ---          ┆ ---         ┆ ---         ┆ ---           ┆ ---          ┆ ---               │\n",
       "│ f64          ┆ i64         ┆ i64         ┆ f64           ┆ f64          ┆ f64               │\n",
       "╞══════════════╪═════════════╪═════════════╪═══════════════╪══════════════╪═══════════════════╡\n",
-      "│ 77.660431    ┆ 0           ┆ 388         ┆ 72.591016     ┆ 74.812994    ┆ 34.564262         │\n",
+      "│ 77.879366    ┆ 0           ┆ 388         ┆ 72.765991     ┆ 75.200799    ┆ 34.670045         │\n",
       "└──────────────┴─────────────┴─────────────┴───────────────┴──────────────┴───────────────────┘\n",
       "\n",
       "Distance Statistics:\n",
@@ -234,7 +240,7 @@
       "│ ---      ┆ ---        ┆ ---       │\n",
       "│ f64      ┆ f64        ┆ f64       │\n",
       "╞══════════╪════════════╪═══════════╡\n",
-      "│ 0.013939 ┆ 109.991355 ┆ 28.407258 │\n",
+      "│ 0.013939 ┆ 109.991355 ┆ 28.875436 │\n",
       "└──────────┴────────────┴───────────┘\n"
      ]
     }
@@ -243,26 +249,34 @@
     "# Summary statistics\n",
     "if df_valid.height > 0:\n",
     "    print(\"Journey Time Statistics (minutes):\")\n",
-    "    print(df_valid.select([\n",
-    "        pl.col('public_transport_easy_minutes').mean().alias('PT Easy Mean'),\n",
-    "        pl.col('public_transport_easy_minutes').min().alias('PT Easy Min'),\n",
-    "        pl.col('public_transport_easy_minutes').max().alias('PT Easy Max'),\n",
-    "        pl.col('public_transport_quick_minutes').mean().alias('PT Quick Mean'),\n",
-    "        pl.col('cycling_minutes').mean().alias('Cycling Mean'),\n",
-    "        pl.col('distance_km').mean().alias('Avg Distance (km)'),\n",
-    "    ]))\n",
-    "    \n",
+    "    print(\n",
+    "        df_valid.select(\n",
+    "            [\n",
+    "                pl.col(\"public_transport_easy_minutes\").mean().alias(\"PT Easy Mean\"),\n",
+    "                pl.col(\"public_transport_easy_minutes\").min().alias(\"PT Easy Min\"),\n",
+    "                pl.col(\"public_transport_easy_minutes\").max().alias(\"PT Easy Max\"),\n",
+    "                pl.col(\"public_transport_quick_minutes\").mean().alias(\"PT Quick Mean\"),\n",
+    "                pl.col(\"cycling_minutes\").mean().alias(\"Cycling Mean\"),\n",
+    "                pl.col(\"distance_km\").mean().alias(\"Avg Distance (km)\"),\n",
+    "            ]\n",
+    "        )\n",
+    "    )\n",
+    "\n",
     "    print(\"\\nDistance Statistics:\")\n",
-    "    print(df_valid.select([\n",
-    "        pl.col('distance_km').min().alias('Min km'),\n",
-    "        pl.col('distance_km').max().alias('Max km'),\n",
-    "        pl.col('distance_km').median().alias('Median km'),\n",
-    "    ]))"
+    "    print(\n",
+    "        df_valid.select(\n",
+    "            [\n",
+    "                pl.col(\"distance_km\").min().alias(\"Min km\"),\n",
+    "                pl.col(\"distance_km\").max().alias(\"Max km\"),\n",
+    "                pl.col(\"distance_km\").median().alias(\"Median km\"),\n",
+    "            ]\n",
+    "        )\n",
+    "    )"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T19:53:51.953471Z",
@@ -272,9 +286,17 @@
     }
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_1997193/4280370301.py:45: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  plt.tight_layout()\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x800 with 1 Axes>"
       ]
@@ -287,29 +309,47 @@
     "# Plot: Travel Time vs Distance by Mode\n",
     "if df_valid.height > 0:\n",
     "    fig, ax = plt.subplots(figsize=(12, 8))\n",
-    "    \n",
+    "\n",
     "    # Convert to pandas for plotting\n",
     "    pdf = df_valid.to_pandas()\n",
-    "    \n",
-    "    # Plot each mode    \n",
-    "    if pdf['public_transport_quick_minutes'].notna().any():\n",
-    "        ax.scatter(pdf['distance_km'], pdf['public_transport_quick_minutes'], \n",
-    "                   alpha=0.3, label='Public Transport (Quick)', c='red', s=50)\n",
-    "    \n",
-    "    if pdf['public_transport_easy_minutes'].notna().any():\n",
-    "        ax.scatter(pdf['distance_km'], pdf['public_transport_easy_minutes'], \n",
-    "                   alpha=0.3, label='Public Transport (Easy)', c='green', s=50)\n",
-    "    \n",
-    "    if pdf['cycling_minutes'].notna().any():\n",
-    "        ax.scatter(pdf['distance_km'], pdf['cycling_minutes'], \n",
-    "                   alpha=0.3, label='Cycling', c='orange', s=50)\n",
-    "    \n",
-    "    ax.set_xlabel('Distance to Bank (km)', fontsize=12)\n",
-    "    ax.set_ylabel('Travel Time (minutes)', fontsize=12)\n",
-    "    ax.set_title('Travel Time vs Distance to Bank Station', fontsize=14)\n",
+    "\n",
+    "    # Plot each mode\n",
+    "    if pdf[\"public_transport_quick_minutes\"].notna().any():\n",
+    "        ax.scatter(\n",
+    "            pdf[\"distance_km\"],\n",
+    "            pdf[\"public_transport_quick_minutes\"],\n",
+    "            alpha=0.3,\n",
+    "            label=\"Public Transport (Quick)\",\n",
+    "            c=\"red\",\n",
+    "            s=50,\n",
+    "        )\n",
+    "\n",
+    "    if pdf[\"public_transport_easy_minutes\"].notna().any():\n",
+    "        ax.scatter(\n",
+    "            pdf[\"distance_km\"],\n",
+    "            pdf[\"public_transport_easy_minutes\"],\n",
+    "            alpha=0.3,\n",
+    "            label=\"Public Transport (Easy)\",\n",
+    "            c=\"green\",\n",
+    "            s=50,\n",
+    "        )\n",
+    "\n",
+    "    if pdf[\"cycling_minutes\"].notna().any():\n",
+    "        ax.scatter(\n",
+    "            pdf[\"distance_km\"],\n",
+    "            pdf[\"cycling_minutes\"],\n",
+    "            alpha=0.3,\n",
+    "            label=\"Cycling\",\n",
+    "            c=\"orange\",\n",
+    "            s=50,\n",
+    "        )\n",
+    "\n",
+    "    ax.set_xlabel(\"Distance to Bank (km)\", fontsize=12)\n",
+    "    ax.set_ylabel(\"Travel Time (minutes)\", fontsize=12)\n",
+    "    ax.set_title(\"Travel Time vs Distance to Bank Station\", fontsize=14)\n",
     "    ax.legend()\n",
     "    ax.grid(True, alpha=0.3)\n",
-    "    \n",
+    "\n",
     "    plt.tight_layout()\n",
     "    plt.show()"
    ]
@@ -330,7 +370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T19:53:53.920561Z",
@@ -342,7 +382,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x800 with 1 Axes>"
       ]
@@ -355,40 +395,66 @@
     "# Plot: Speed (km/h) vs Distance by Mode\n",
     "if df_valid.height > 0:\n",
     "    fig, ax = plt.subplots(figsize=(12, 8))\n",
-    "    \n",
+    "\n",
     "    # Calculate effective speed for each mode\n",
-    "    pdf = df_valid.with_columns([\n",
-    "        (pl.col('distance_km') / (pl.col('public_transport_easy_minutes') / 60)).alias('speed_easy_kmh'),\n",
-    "        (pl.col('distance_km') / (pl.col('public_transport_quick_minutes') / 60)).alias('speed_quick_kmh'),\n",
-    "        (pl.col('distance_km') / (pl.col('cycling_minutes') / 60)).alias('speed_cycling_kmh'),\n",
-    "    ]).to_pandas()\n",
-    "    \n",
-    "    if pdf['speed_easy_kmh'].notna().any():\n",
-    "        ax.scatter(pdf['distance_km'], pdf['speed_easy_kmh'], \n",
-    "                   alpha=0.6, label='Public Transport (Easy)', c='blue', s=50)\n",
-    "    \n",
-    "    if pdf['speed_quick_kmh'].notna().any():\n",
-    "        ax.scatter(pdf['distance_km'], pdf['speed_quick_kmh'], \n",
-    "                   alpha=0.6, label='Public Transport (Quick)', c='green', s=50)\n",
-    "    \n",
-    "    if pdf['speed_cycling_kmh'].notna().any():\n",
-    "        ax.scatter(pdf['distance_km'], pdf['speed_cycling_kmh'], \n",
-    "                   alpha=0.6, label='Cycling', c='orange', s=50)\n",
-    "    \n",
-    "    ax.set_xlabel('Distance to Bank (km)', fontsize=12)\n",
-    "    ax.set_ylabel('Effective Speed (km/h)', fontsize=12)\n",
-    "    ax.set_title('Effective Travel Speed vs Distance to Bank Station', fontsize=14)\n",
+    "    pdf = df_valid.with_columns(\n",
+    "        [\n",
+    "            (\n",
+    "                pl.col(\"distance_km\") / (pl.col(\"public_transport_easy_minutes\") / 60)\n",
+    "            ).alias(\"speed_easy_kmh\"),\n",
+    "            (\n",
+    "                pl.col(\"distance_km\") / (pl.col(\"public_transport_quick_minutes\") / 60)\n",
+    "            ).alias(\"speed_quick_kmh\"),\n",
+    "            (pl.col(\"distance_km\") / (pl.col(\"cycling_minutes\") / 60)).alias(\n",
+    "                \"speed_cycling_kmh\"\n",
+    "            ),\n",
+    "        ]\n",
+    "    ).to_pandas()\n",
+    "\n",
+    "    if pdf[\"speed_easy_kmh\"].notna().any():\n",
+    "        ax.scatter(\n",
+    "            pdf[\"distance_km\"],\n",
+    "            pdf[\"speed_easy_kmh\"],\n",
+    "            alpha=0.6,\n",
+    "            label=\"Public Transport (Easy)\",\n",
+    "            c=\"blue\",\n",
+    "            s=50,\n",
+    "        )\n",
+    "\n",
+    "    if pdf[\"speed_quick_kmh\"].notna().any():\n",
+    "        ax.scatter(\n",
+    "            pdf[\"distance_km\"],\n",
+    "            pdf[\"speed_quick_kmh\"],\n",
+    "            alpha=0.6,\n",
+    "            label=\"Public Transport (Quick)\",\n",
+    "            c=\"green\",\n",
+    "            s=50,\n",
+    "        )\n",
+    "\n",
+    "    if pdf[\"speed_cycling_kmh\"].notna().any():\n",
+    "        ax.scatter(\n",
+    "            pdf[\"distance_km\"],\n",
+    "            pdf[\"speed_cycling_kmh\"],\n",
+    "            alpha=0.6,\n",
+    "            label=\"Cycling\",\n",
+    "            c=\"orange\",\n",
+    "            s=50,\n",
+    "        )\n",
+    "\n",
+    "    ax.set_xlabel(\"Distance to Bank (km)\", fontsize=12)\n",
+    "    ax.set_ylabel(\"Effective Speed (km/h)\", fontsize=12)\n",
+    "    ax.set_title(\"Effective Travel Speed vs Distance to Bank Station\", fontsize=14)\n",
     "    ax.legend()\n",
     "    ax.grid(True, alpha=0.3)\n",
     "    ax.set_ylim(0, 100)  # Cap y-axis for readability\n",
-    "    \n",
+    "\n",
     "    plt.tight_layout()\n",
     "    plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T19:53:55.187209Z",
@@ -400,7 +466,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x800 with 2 Axes>"
       ]
@@ -423,37 +489,50 @@
     "# Comparison: Quick vs Easy public transport\n",
     "if df_valid.height > 0:\n",
     "    df_both = df_valid.filter(\n",
-    "        pl.col('public_transport_easy_minutes').is_not_null() &\n",
-    "        pl.col('public_transport_quick_minutes').is_not_null()\n",
+    "        pl.col(\"public_transport_easy_minutes\").is_not_null()\n",
+    "        & pl.col(\"public_transport_quick_minutes\").is_not_null()\n",
     "    )\n",
-    "    \n",
+    "\n",
     "    if df_both.height > 0:\n",
     "        fig, ax = plt.subplots(figsize=(10, 8))\n",
-    "        \n",
+    "\n",
     "        pdf = df_both.to_pandas()\n",
-    "        \n",
-    "        ax.scatter(pdf['public_transport_easy_minutes'], \n",
-    "                   pdf['public_transport_quick_minutes'],\n",
-    "                   c=pdf['distance_km'], cmap='viridis', s=60, alpha=0.7)\n",
-    "        \n",
+    "\n",
+    "        ax.scatter(\n",
+    "            pdf[\"public_transport_easy_minutes\"],\n",
+    "            pdf[\"public_transport_quick_minutes\"],\n",
+    "            c=pdf[\"distance_km\"],\n",
+    "            cmap=\"viridis\",\n",
+    "            s=60,\n",
+    "            alpha=0.7,\n",
+    "        )\n",
+    "\n",
     "        # Add diagonal line (equal times)\n",
-    "        max_val = max(pdf['public_transport_easy_minutes'].max(), \n",
-    "                      pdf['public_transport_quick_minutes'].max())\n",
-    "        ax.plot([0, max_val], [0, max_val], 'r--', alpha=0.5, label='Equal time')\n",
-    "        \n",
-    "        ax.set_xlabel('Easy Route (minutes)', fontsize=12)\n",
-    "        ax.set_ylabel('Quick Route (minutes)', fontsize=12)\n",
-    "        ax.set_title('Quick vs Easy Public Transport Times\\n(color = distance in km)', fontsize=14)\n",
+    "        max_val = max(\n",
+    "            pdf[\"public_transport_easy_minutes\"].max(),\n",
+    "            pdf[\"public_transport_quick_minutes\"].max(),\n",
+    "        )\n",
+    "        ax.plot([0, max_val], [0, max_val], \"r--\", alpha=0.5, label=\"Equal time\")\n",
+    "\n",
+    "        ax.set_xlabel(\"Easy Route (minutes)\", fontsize=12)\n",
+    "        ax.set_ylabel(\"Quick Route (minutes)\", fontsize=12)\n",
+    "        ax.set_title(\n",
+    "            \"Quick vs Easy Public Transport Times\\n(color = distance in km)\",\n",
+    "            fontsize=14,\n",
+    "        )\n",
     "        ax.legend()\n",
     "        ax.grid(True, alpha=0.3)\n",
-    "        \n",
-    "        plt.colorbar(ax.collections[0], label='Distance (km)')\n",
+    "\n",
+    "        plt.colorbar(ax.collections[0], label=\"Distance (km)\")\n",
     "        plt.tight_layout()\n",
     "        plt.show()\n",
-    "        \n",
+    "\n",
     "        # Calculate time savings\n",
     "        savings = df_both.with_columns(\n",
-    "            (pl.col('public_transport_easy_minutes') - pl.col('public_transport_quick_minutes')).alias('time_saved')\n",
+    "            (\n",
+    "                pl.col(\"public_transport_easy_minutes\")\n",
+    "                - pl.col(\"public_transport_quick_minutes\")\n",
+    "            ).alias(\"time_saved\")\n",
     "        )\n",
     "        print(\"\\nTime saved by taking quick route:\")\n",
     "        print(f\"  Mean: {savings['time_saved'].mean():.1f} minutes\")\n",
@@ -462,7 +541,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-01-28T19:53:56.842982Z",
@@ -477,7 +556,7 @@
      "output_type": "stream",
      "text": [
       "Journey times sorted by distance to Bank:\n",
-      "shape: (638_898, 5)\n",
+      "shape: (662_723, 5)\n",
       "┌──────────┬─────────────┬───────────────────────────┬───────────────────────────┬─────────────────┐\n",
       "│ postcode ┆ distance_km ┆ public_transport_easy_min ┆ public_transport_quick_mi ┆ cycling_minutes │\n",
       "│ ---      ┆ ---         ┆ utes                      ┆ nutes                     ┆ ---             │\n",
@@ -497,38 +576,29 @@
       "│ OX7 5TG  ┆ 110.0       ┆ null                      ┆ 241                       ┆ null            │\n",
       "└──────────┴─────────────┴───────────────────────────┴───────────────────────────┴─────────────────┘\n"
      ]
-    },
-    {
-     "ename": "",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
-      "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
-      "\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
-      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
-     ]
     }
    ],
    "source": [
     "# Data table: sorted by distance\n",
     "if df_valid.height > 0:\n",
     "    print(\"Journey times sorted by distance to Bank:\")\n",
-    "    display_df = df_valid.select([\n",
-    "        'postcode',\n",
-    "        pl.col('distance_km').round(1),\n",
-    "        'public_transport_easy_minutes',\n",
-    "        'public_transport_quick_minutes', \n",
-    "        'cycling_minutes'\n",
-    "    ]).sort('distance_km')\n",
-    "    \n",
+    "    display_df = df_valid.select(\n",
+    "        [\n",
+    "            \"postcode\",\n",
+    "            pl.col(\"distance_km\").round(1),\n",
+    "            \"public_transport_easy_minutes\",\n",
+    "            \"public_transport_quick_minutes\",\n",
+    "            \"cycling_minutes\",\n",
+    "        ]\n",
+    "    ).sort(\"distance_km\")\n",
+    "\n",
     "    print(display_df)"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "property-map",
    "language": "python",
    "name": "python3"
   },
diff --git a/analyses/property_analysis.ipynb b/analyses/property_analysis.ipynb
index 3889fa3..0cbd0b4 100644
--- a/analyses/property_analysis.ipynb
+++ b/analyses/property_analysis.ipynb
@@ -1174,14 +1174,14 @@
           ]
          },
          "labels": [
-          "Leasehold",
           "U",
+          "Leasehold",
           "Freehold"
          ],
          "name": "National",
          "type": "pie",
          "values": {
-          "bdata": "8bluABQCAABzcGcB",
+          "bdata": "FAIAAPG5bgBzcGcB",
           "dtype": "u4"
          }
         },
@@ -7612,37 +7612,11 @@
         "plotlyServerURL": "https://plot.ly"
        },
        "data": [
-        {
-         "hovertemplate": "Property Type=Flat/Maisonette<br>Year=%{x}<br>Average Price (£)=%{y}<extra></extra>",
-         "legendgroup": "Flat/Maisonette",
-         "line": {
-          "color": "#636efa",
-          "dash": "solid"
-         },
-         "marker": {
-          "symbol": "circle"
-         },
-         "mode": "lines+markers",
-         "name": "Flat/Maisonette",
-         "orientation": "v",
-         "showlegend": true,
-         "type": "scatter",
-         "x": {
-          "bdata": "ywcAAMwHAADNBwAAzgcAAM8HAADQBwAA0QcAANIHAADTBwAA1AcAANUHAADWBwAA1wcAANgHAADZBwAA2gcAANsHAADcBwAA3QcAAN4HAADfBwAA4AcAAOEHAADiBwAA4wcAAOQHAADlBwAA5gcAAOcHAADoBwAA6QcAAA==",
-          "dtype": "i4"
-         },
-         "xaxis": "x",
-         "y": {
-          "bdata": "Zn0HnGWK80CI6Sk7oaX1QEFxn18Au/hAwCOAZu0S/EDBWeiVbXgAQeTLL9eqzgNBryNeoX+6BUFXaO9zRMAIQetQ7cQvbgpBgmN/9GIPDUGtQpq8DlsOQSSo31g+WBBBQgfyCi48EkEMHgaL1d4SQRKgdf+L6RJBLldJJ+DCFEG2ZwTWCq0VQdBRbpxxzxZBE6A2bZv2GEH9TXXvp/IbQfUGUKpbZx1BlCmtRkzAH0HJWN/QRtwgQfR/6pWJyyBBNdqOI+jBIEHVnj0D6L8hQSRl2W1ZmyBBI+qzJdztIUHphTM6deEiQbHkCOA5myFBAEfvwjinH0E=",
-          "dtype": "f8"
-         },
-         "yaxis": "y"
-        },
         {
          "hovertemplate": "Property Type=Terraced<br>Year=%{x}<br>Average Price (£)=%{y}<extra></extra>",
          "legendgroup": "Terraced",
          "line": {
-          "color": "#EF553B",
+          "color": "#636efa",
           "dash": "solid"
          },
          "marker": {
@@ -7665,8 +7639,34 @@
          "yaxis": "y"
         },
         {
-         "hovertemplate": "Property Type=Other<br>Year=%{x}<br>Average Price (£)=%{y}<extra></extra>",
-         "legendgroup": "Other",
+         "hovertemplate": "Property Type=Semi-detached<br>Year=%{x}<br>Average Price (£)=%{y}<extra></extra>",
+         "legendgroup": "Semi-detached",
+         "line": {
+          "color": "#EF553B",
+          "dash": "solid"
+         },
+         "marker": {
+          "symbol": "circle"
+         },
+         "mode": "lines+markers",
+         "name": "Semi-detached",
+         "orientation": "v",
+         "showlegend": true,
+         "type": "scatter",
+         "x": {
+          "bdata": "ywcAAMwHAADNBwAAzgcAAM8HAADQBwAA0QcAANIHAADTBwAA1AcAANUHAADWBwAA1wcAANgHAADZBwAA2gcAANsHAADcBwAA3QcAAN4HAADfBwAA4AcAAOEHAADiBwAA4wcAAOQHAADlBwAA5gcAAOcHAADoBwAA6QcAAA==",
+          "dtype": "i4"
+         },
+         "xaxis": "x",
+         "y": {
+          "bdata": "/hlp6rqA+kBlfGGuPsr7QA3U2BzOPgBBFEkc5hF5AkEOH7hbPVkFQdQ3zroWSglBRetdezepC0HMh86Iur0PQSIquL97URFBK/ijBo0AE0FRJPfJ+SsUQfxVLXPAuRVB2S3vDChDGEGx1QCPrekYQTqhOT1RhRdBTia8aVJ2GkHuXCtHJx8bQQshg4NcLhxBNqxBkbkiHkGNK6rjU9ggQfYdpg056SFBYO87hnc/I0Hwpamcz/QjQaHX3l6M0yNBQSK1/+HPI0Heua3ksNAkQQ/lJFlsMyZBXFAo+AdbKEEly3AWY9koQReAqH3otidBgRctZr/SJkE=",
+          "dtype": "f8"
+         },
+         "yaxis": "y"
+        },
+        {
+         "hovertemplate": "Property Type=Flat/Maisonette<br>Year=%{x}<br>Average Price (£)=%{y}<extra></extra>",
+         "legendgroup": "Flat/Maisonette",
          "line": {
           "color": "#00cc96",
           "dash": "solid"
@@ -7675,17 +7675,17 @@
           "symbol": "circle"
          },
          "mode": "lines+markers",
-         "name": "Other",
+         "name": "Flat/Maisonette",
          "orientation": "v",
          "showlegend": true,
          "type": "scatter",
          "x": {
-          "bdata": "ywcAAMwHAADNBwAAzgcAANAHAADRBwAA0gcAANQHAADVBwAA1gcAANcHAADYBwAA2QcAANoHAADbBwAA3AcAAN0HAADeBwAA3wcAAOAHAADhBwAA4gcAAOMHAADkBwAA5QcAAOYHAADnBwAA6AcAAOkHAAA=",
+          "bdata": "ywcAAMwHAADNBwAAzgcAAM8HAADQBwAA0QcAANIHAADTBwAA1AcAANUHAADWBwAA1wcAANgHAADZBwAA2gcAANsHAADcBwAA3QcAAN4HAADfBwAA4AcAAOEHAADiBwAA4wcAAOQHAADlBwAA5gcAAOcHAADoBwAA6QcAAA==",
           "dtype": "i4"
          },
          "xaxis": "x",
          "y": {
-          "bdata": "AAAAAABwt0AAAAAAAOHpQKuqqqqKWPlAAAAAAIBV4UAAAAAA+OsGQQAAAABATxFBAAAAAMBi1ECrqqqqqofKQAAAAAAA+cVAL7rooms7AkEbymsoP3UCQb2G8hqKCdtAVVVVVXWi2UDT0tLSYu4HQR+F61FB1UdB4uHhYbewO0H91mlAk9FWQQyr2KccXE5BM60RmtNkUEHpBS9iCk9GQa0PyglLdkZBotrVTw1MSUF5jpBrRyNKQVGDG4mciUpBqUAMKV7DSUEYXw2P189IQV7iLQtVIUZBMVxIAFNpQ0GUbp7dHVhHQQ==",
+          "bdata": "Zn0HnGWK80CI6Sk7oaX1QEFxn18Au/hAwCOAZu0S/EDBWeiVbXgAQeTLL9eqzgNBryNeoX+6BUFXaO9zRMAIQetQ7cQvbgpBgmN/9GIPDUGtQpq8DlsOQSSo31g+WBBBQgfyCi48EkEMHgaL1d4SQRKgdf+L6RJBLldJJ+DCFEG2ZwTWCq0VQdBRbpxxzxZBE6A2bZv2GEH9TXXvp/IbQfUGUKpbZx1BlCmtRkzAH0HJWN/QRtwgQfR/6pWJyyBBNdqOI+jBIEHVnj0D6L8hQSRl2W1ZmyBBI+qzJdztIUHphTM6deEiQbHkCOA5myFBAEfvwjinH0E=",
           "dtype": "f8"
          },
          "yaxis": "y"
@@ -7717,8 +7717,8 @@
          "yaxis": "y"
         },
         {
-         "hovertemplate": "Property Type=Semi-detached<br>Year=%{x}<br>Average Price (£)=%{y}<extra></extra>",
-         "legendgroup": "Semi-detached",
+         "hovertemplate": "Property Type=Other<br>Year=%{x}<br>Average Price (£)=%{y}<extra></extra>",
+         "legendgroup": "Other",
          "line": {
           "color": "#FFA15A",
           "dash": "solid"
@@ -7727,17 +7727,17 @@
           "symbol": "circle"
          },
          "mode": "lines+markers",
-         "name": "Semi-detached",
+         "name": "Other",
          "orientation": "v",
          "showlegend": true,
          "type": "scatter",
          "x": {
-          "bdata": "ywcAAMwHAADNBwAAzgcAAM8HAADQBwAA0QcAANIHAADTBwAA1AcAANUHAADWBwAA1wcAANgHAADZBwAA2gcAANsHAADcBwAA3QcAAN4HAADfBwAA4AcAAOEHAADiBwAA4wcAAOQHAADlBwAA5gcAAOcHAADoBwAA6QcAAA==",
+          "bdata": "ywcAAMwHAADNBwAAzgcAANAHAADRBwAA0gcAANQHAADVBwAA1gcAANcHAADYBwAA2QcAANoHAADbBwAA3AcAAN0HAADeBwAA3wcAAOAHAADhBwAA4gcAAOMHAADkBwAA5QcAAOYHAADnBwAA6AcAAOkHAAA=",
           "dtype": "i4"
          },
          "xaxis": "x",
          "y": {
-          "bdata": "/hlp6rqA+kBlfGGuPsr7QA3U2BzOPgBBFEkc5hF5AkEOH7hbPVkFQdQ3zroWSglBRetdezepC0HMh86Iur0PQSIquL97URFBK/ijBo0AE0FRJPfJ+SsUQfxVLXPAuRVB2S3vDChDGEGx1QCPrekYQTqhOT1RhRdBTia8aVJ2GkHuXCtHJx8bQQshg4NcLhxBNqxBkbkiHkGNK6rjU9ggQfYdpg056SFBYO87hnc/I0Hwpamcz/QjQaHX3l6M0yNBQSK1/+HPI0Heua3ksNAkQQ/lJFlsMyZBXFAo+AdbKEEly3AWY9koQReAqH3otidBgRctZr/SJkE=",
+          "bdata": "AAAAAABwt0AAAAAAAOHpQKuqqqqKWPlAAAAAAIBV4UAAAAAA+OsGQQAAAABATxFBAAAAAMBi1ECrqqqqqofKQAAAAAAA+cVAL7rooms7AkEbymsoP3UCQb2G8hqKCdtAVVVVVXWi2UDT0tLSYu4HQR+F61FB1UdB4uHhYbewO0H91mlAk9FWQQyr2KccXE5BM60RmtNkUEHpBS9iCk9GQa0PyglLdkZBotrVTw1MSUF5jpBrRyNKQVGDG4mciUpBqUAMKV7DSUEYXw2P189IQV7iLQtVIUZBMVxIAFNpQ0GUbp7dHVhHQQ==",
           "dtype": "f8"
          },
          "yaxis": "y"
@@ -11154,35 +11154,11 @@
          ],
          "yaxis": "y"
         },
-        {
-         "hovertemplate": "Sales Volume=7611<br>Median Price (£)=%{x}<br>Borough=%{y}<extra></extra>",
-         "legendgroup": "7611",
-         "marker": {
-          "color": "#EF553B",
-          "pattern": {
-           "shape": ""
-          }
-         },
-         "name": "7611",
-         "orientation": "h",
-         "showlegend": true,
-         "textposition": "auto",
-         "type": "bar",
-         "x": {
-          "bdata": "AAAAAKBhHEE=",
-          "dtype": "f8"
-         },
-         "xaxis": "x",
-         "y": [
-          "TOWER HAMLETS"
-         ],
-         "yaxis": "y"
-        },
         {
          "hovertemplate": "Sales Volume=7586<br>Median Price (£)=%{x}<br>Borough=%{y}<extra></extra>",
          "legendgroup": "7586",
          "marker": {
-          "color": "#00cc96",
+          "color": "#EF553B",
           "pattern": {
            "shape": ""
           }
@@ -11202,6 +11178,30 @@
          ],
          "yaxis": "y"
         },
+        {
+         "hovertemplate": "Sales Volume=7611<br>Median Price (£)=%{x}<br>Borough=%{y}<extra></extra>",
+         "legendgroup": "7611",
+         "marker": {
+          "color": "#00cc96",
+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "7611",
+         "orientation": "h",
+         "showlegend": true,
+         "textposition": "auto",
+         "type": "bar",
+         "x": {
+          "bdata": "AAAAAKBhHEE=",
+          "dtype": "f8"
+         },
+         "xaxis": "x",
+         "y": [
+          "TOWER HAMLETS"
+         ],
+         "yaxis": "y"
+        },
         {
          "hovertemplate": "Sales Volume=3069<br>Median Price (£)=%{x}<br>Borough=%{y}<extra></extra>",
          "legendgroup": "3069",
@@ -12339,8 +12339,8 @@
           "EALING",
           "HARINGEY",
           "LAMBETH",
-          "TOWER HAMLETS",
           "RICHMOND UPON THAMES",
+          "TOWER HAMLETS",
           "SOUTHWARK",
           "HACKNEY",
           "WANDSWORTH",
@@ -12353,7 +12353,7 @@
          ],
          "yaxis": "y",
          "z": {
-          "bdata": "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",
+          "bdata": "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",
           "dtype": "f8",
           "shape": "33, 4"
          }
@@ -15317,211 +15317,44 @@
          "textposition": "auto",
          "type": "bar",
          "x": {
-          "bdata": "dfGbIpuAFEBp54+XWAwMQDrGAbEO9xFAWd+KLdZTF0BcUDAnMgkeQChqYYhY4hRAFPOCrS1xDkDpwQK5aGMjQFLp29xgmxJAydeFHYsvKUA0MhZu4qAUQGqZ3EbQLw1At1rVWRhbFkCRqz33Y6cTQKmwK1e+9hJAYaKR5Lg1FkC0cK8TsTcZQI2AU5QR8CJAIllW/tvWCUCERXjkZdMTQBJLpn+42B1ACH/nPSCGEEAu8nV/4I0SQPj9qOqKjBdAndka5HXrEkBvx94PeyATQOKpOUwDQQ5AOPRV6ZOFFkAzigYSjMsRQMmaOB6DzDVArV64CPYvF0B1X5ZuAYsYQDFLKUMAVRRA",
+          "bdata": "t1rVWRhbFkDJ14Udiy8pQDOKBhKMyxFAjYBTlBHwIkBS6dvcYJsSQLRwrxOxNxlAOPRV6ZOFFkCd2RrkdesSQHXxmyKbgBRA4qk5TANBDkDpwQK5aGMjQG/H3g97IBNAFPOCrS1xDkDJmjgeg8w1QChqYYhY4hRAIllW/tvWCUCRqz33Y6cTQIRFeORl0xNArV64CPYvF0AIf+c9IIYQQFnfii3WUxdA+P2o6oqMF0ASS6Z/uNgdQGqZ3EbQLw1AXFAwJzIJHkBp54+XWAwMQDrGAbEO9xFANDIWbuKgFECpsCtXvvYSQC7ydX/gjRJAMUspQwBVFEBhopHkuDUWQHVflm4BixhA",
           "dtype": "f8"
          },
          "xaxis": "x",
          "y": [
-          "CROYDON",
-          "BEXLEY",
-          "LEWISHAM",
-          "HAMMERSMITH AND FULHAM",
-          "ISLINGTON",
-          "RICHMOND UPON THAMES",
-          "GREENWICH",
-          "KENSINGTON AND CHELSEA",
-          "REDBRIDGE",
-          "CITY OF WESTMINSTER",
-          "BARNET",
-          "SUTTON",
           "SOUTHWARK",
-          "HILLINGDON",
-          "WALTHAM FOREST",
-          "HARROW",
-          "HACKNEY",
-          "CAMDEN",
-          "HAVERING",
-          "MERTON",
-          "BRENT",
-          "BROMLEY",
-          "TOWER HAMLETS",
-          "EALING",
-          "LAMBETH",
-          "KINGSTON UPON THAMES",
-          "BARKING AND DAGENHAM",
-          "HOUNSLOW",
+          "CITY OF WESTMINSTER",
           "WANDSWORTH",
+          "CAMDEN",
+          "REDBRIDGE",
+          "HACKNEY",
+          "HOUNSLOW",
+          "LAMBETH",
+          "CROYDON",
+          "BARKING AND DAGENHAM",
+          "KENSINGTON AND CHELSEA",
+          "KINGSTON UPON THAMES",
+          "GREENWICH",
           "CITY OF LONDON",
-          "NEWHAM",
-          "HARINGEY",
-          "ENFIELD"
-         ],
-         "yaxis": "y"
-        },
-        {
-         "hovertemplate": "Property Type=Terraced<br>% of Transactions=%{x}<br>Borough=%{y}<extra></extra>",
-         "legendgroup": "Terraced",
-         "marker": {
-          "color": "#EF553B",
-          "pattern": {
-           "shape": ""
-          }
-         },
-         "name": "Terraced",
-         "orientation": "h",
-         "showlegend": true,
-         "textposition": "auto",
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-         "x": {
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           "SUTTON",
-          "BRENT",
-          "NEWHAM",
-          "SOUTHWARK",
           "KINGSTON UPON THAMES",
-          "BARNET",
-          "HARINGEY",
-          "ENFIELD",
-          "CAMDEN",
-          "HACKNEY",
-          "HAMMERSMITH AND FULHAM",
-          "KENSINGTON AND CHELSEA",
-          "BROMLEY",
-          "CROYDON",
-          "HARROW",
-          "MERTON",
-          "BARKING AND DAGENHAM",
-          "BEXLEY",
-          "LAMBETH",
+          "WANDSWORTH",
           "RICHMOND UPON THAMES",
-          "EALING",
-          "HOUNSLOW",
           "GREENWICH",
-          "LEWISHAM",
-          "REDBRIDGE",
+          "MERTON",
+          "EALING",
           "ISLINGTON",
+          "BARKING AND DAGENHAM",
+          "NEWHAM",
+          "WALTHAM FOREST",
           "TOWER HAMLETS",
+          "REDBRIDGE",
+          "KENSINGTON AND CHELSEA",
+          "HAVERING",
+          "HACKNEY",
+          "HARINGEY",
+          "BRENT",
+          "BARNET",
+          "BROMLEY",
+          "HARROW",
+          "SOUTHWARK",
+          "HILLINGDON",
+          "ENFIELD",
           "CITY OF LONDON",
-          "HILLINGDON"
+          "CITY OF WESTMINSTER",
+          "LAMBETH",
+          "CROYDON",
+          "HOUNSLOW",
+          "BEXLEY",
+          "LEWISHAM",
+          "CAMDEN",
+          "HAMMERSMITH AND FULHAM"
          ],
          "yaxis": "y"
         }
@@ -22809,7 +22809,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "property-map",
    "language": "python",
    "name": "python3"
   },
diff --git a/analyses/wide.ipynb b/analyses/wide.ipynb
index 44a6378..e8c2972 100644
--- a/analyses/wide.ipynb
+++ b/analyses/wide.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "3053488d",
    "metadata": {},
    "outputs": [
@@ -16,39 +16,59 @@
        "  white-space: pre-wrap;\n",
        "}\n",
        "</style>\n",
-       "<small>shape: (5, 47)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>pp_address</th><th>postcode</th><th>historical_prices</th><th>pp_property_type</th><th>duration</th><th>latest_price</th><th>date_of_transfer</th><th>epc_address</th><th>current_energy_rating</th><th>potential_energy_rating</th><th>epc_property_type</th><th>built_form</th><th>inspection_date</th><th>total_floor_area</th><th>number_habitable_rooms</th><th>floor_height</th><th>construction_age_band</th><th>lat</th><th>lon</th><th>lsoa21</th><th>public_transport_easy_minutes</th><th>public_transport_quick_minutes</th><th>cycling_minutes</th><th>LSOA name (2021)</th><th>Local Authority District code (2024)</th><th>Local Authority District name (2024)</th><th>imd_score</th><th>income_score</th><th>employment_score</th><th>education_score</th><th>health_score</th><th>crime_score</th><th>housing_barriers_score</th><th>living_environment_score</th><th>idaci_score</th><th>idaopi_score</th><th>children_young_people_score</th><th>adult_skills_score</th><th>geographical_barriers_score</th><th>wider_barriers_score</th><th>indoors_score</th><th>outdoors_score</th><th>restaurants_2km</th><th>groceries_2km</th><th>parks_2km</th><th>public_transport_2km</th><th>price_per_sqm</th></tr><tr><td>str</td><td>str</td><td>list[struct[2]]</td><td>str</td><td>str</td><td>i64</td><td>datetime[μs]</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>f64</td><td>i64</td><td>f64</td><td>u16</td><td>f64</td><td>f64</td><td>str</td><td>i64</td><td>i64</td><td>i64</td><td>str</td><td>str</td><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i32</td><td>i32</td><td>i32</td><td>i32</td><td>f64</td></tr></thead><tbody><tr><td>&quot; 92 JOYES ROAD&quot;</td><td>&quot;CT19 6HL&quot;</td><td>[{1995,50000}, {2001,98500}]</td><td>&quot;Semi-Detached&quot;</td><td>&quot;Freehold&quot;</td><td>98500</td><td>2001-03-30 00:00:00</td><td>&quot;92, Joyes Road&quot;</td><td>&quot;D&quot;</td><td>&quot;C&quot;</td><td>&quot;House&quot;</td><td>&quot;Semi-Detached&quot;</td><td>&quot;2014-04-25&quot;</td><td>164.0</td><td>6</td><td>null</td><td>1930</td><td>51.091719</td><td>1.183558</td><td>&quot;E01024498&quot;</td><td>null</td><td>null</td><td>null</td><td>&quot;Folkestone and Hythe 003C&quot;</td><td>&quot;E07000112&quot;</td><td>&quot;Folkestone and Hythe&quot;</td><td>57.36</td><td>0.527</td><td>0.358</td><td>65.878</td><td>1.561</td><td>1.1</td><td>20.542</td><td>8.876</td><td>0.583</td><td>0.51</td><td>1.764</td><td>0.392</td><td>32.265</td><td>1.321</td><td>-0.46</td><td>-0.106</td><td>58</td><td>20</td><td>0</td><td>3</td><td>600.609756</td></tr><tr><td>&quot; 7 LUTON STREET&quot;</td><td>&quot;HX1 4NG&quot;</td><td>[{1995,16000}]</td><td>&quot;Terraced&quot;</td><td>&quot;Freehold&quot;</td><td>16000</td><td>1995-01-01 00:00:00</td><td>&quot;7, Luton Street&quot;</td><td>&quot;G&quot;</td><td>&quot;C&quot;</td><td>&quot;House&quot;</td><td>&quot;Enclosed Mid-Terrace&quot;</td><td>&quot;2015-03-18&quot;</td><td>99.0</td><td>4</td><td>null</td><td>1900</td><td>53.723918</td><td>-1.882346</td><td>&quot;E01010964&quot;</td><td>null</td><td>null</td><td>null</td><td>&quot;Calderdale 012B&quot;</td><td>&quot;E08000033&quot;</td><td>&quot;Calderdale&quot;</td><td>78.601</td><td>0.78</td><td>0.418</td><td>69.597</td><td>2.013</td><td>1.266</td><td>16.115</td><td>62.178</td><td>0.776</td><td>0.976</td><td>1.031</td><td>0.548</td><td>24.718</td><td>1.212</td><td>1.256</td><td>0.536</td><td>48</td><td>6</td><td>0</td><td>3</td><td>161.616162</td></tr><tr><td>&quot; OLD OAK COTTAGE ROPE WALK&quot;</td><td>&quot;TR4 8DW&quot;</td><td>[{1995,50000}, {2000,125000}, {2010,245000}]</td><td>&quot;Detached&quot;</td><td>&quot;Freehold&quot;</td><td>245000</td><td>2010-04-09 00:00:00</td><td>&quot;Old Oak Cottage, Rope Walk, Mo…</td><td>&quot;G&quot;</td><td>&quot;D&quot;</td><td>&quot;House&quot;</td><td>&quot;Detached&quot;</td><td>&quot;2024-08-22&quot;</td><td>134.0</td><td>8</td><td>2.27</td><td>1900</td><td>50.283886</td><td>-5.209508</td><td>&quot;E01018826&quot;</td><td>null</td><td>null</td><td>null</td><td>&quot;Cornwall 040B&quot;</td><td>&quot;E06000052&quot;</td><td>&quot;Cornwall&quot;</td><td>19.044</td><td>0.195</td><td>0.106</td><td>11.685</td><td>-0.24</td><td>-0.725</td><td>29.989</td><td>47.506</td><td>0.321</td><td>0.174</td><td>0.08</td><td>0.157</td><td>61.226</td><td>-1.92</td><td>1.144</td><td>-0.941</td><td>1</td><td>2</td><td>0</td><td>0</td><td>1828.358209</td></tr><tr><td>&quot; 147 WESTLANDS ROAD&quot;</td><td>&quot;HU5 5NY&quot;</td><td>[{1995,17000}, {2011,58500}, … {2021,145000}]</td><td>&quot;Terraced&quot;</td><td>&quot;Freehold&quot;</td><td>145000</td><td>2021-09-24 00:00:00</td><td>&quot;147, Westlands Road&quot;</td><td>&quot;C&quot;</td><td>&quot;B&quot;</td><td>&quot;House&quot;</td><td>&quot;Mid-Terrace&quot;</td><td>&quot;2020-07-22&quot;</td><td>67.0</td><td>4</td><td>null</td><td>1930</td><td>53.753695</td><td>-0.41399</td><td>&quot;E01012803&quot;</td><td>null</td><td>null</td><td>null</td><td>&quot;Kingston upon Hull 023E&quot;</td><td>&quot;E06000010&quot;</td><td>&quot;Kingston upon Hull, City of&quot;</td><td>19.998</td><td>0.22</td><td>0.122</td><td>26.551</td><td>0.178</td><td>0.357</td><td>18.601</td><td>21.688</td><td>0.325</td><td>0.185</td><td>0.688</td><td>0.244</td><td>31.781</td><td>0.857</td><td>0.316</td><td>-0.34</td><td>7</td><td>3</td><td>0</td><td>0</td><td>2164.179104</td></tr><tr><td>&quot; 70 SCOTLAND ROAD&quot;</td><td>&quot;CA11 9JD&quot;</td><td>[{1995,23000}, {1999,23500}, … {2024,134000}]</td><td>&quot;Semi-Detached&quot;</td><td>&quot;Freehold&quot;</td><td>134000</td><td>2024-10-01 00:00:00</td><td>&quot;70, Scotland Road&quot;</td><td>&quot;E&quot;</td><td>&quot;D&quot;</td><td>&quot;House&quot;</td><td>&quot;Semi-Detached&quot;</td><td>&quot;2011-03-29&quot;</td><td>65.68</td><td>4</td><td>2.44</td><td>1900</td><td>54.671753</td><td>-2.759846</td><td>&quot;E01019331&quot;</td><td>null</td><td>null</td><td>null</td><td>&quot;Eden 004F&quot;</td><td>&quot;E06000064&quot;</td><td>&quot;Westmorland and Furness&quot;</td><td>11.688</td><td>0.159</td><td>0.082</td><td>29.708</td><td>-0.836</td><td>-0.476</td><td>17.131</td><td>7.314</td><td>0.317</td><td>0.101</td><td>0.805</td><td>0.258</td><td>37.771</td><td>-1.003</td><td>-0.331</td><td>-0.758</td><td>29</td><td>11</td><td>0</td><td>12</td><td>2040.194884</td></tr></tbody></table></div>"
+       "<small>shape: (20, 48)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Address per Property Register</th><th>Postcode</th><th>historical_prices</th><th>Leashold/Freehold</th><th>Last known price</th><th>Address per EPC</th><th>Current energy rating</th><th>Potential energy rating</th><th>Property type</th><th>Total floor area (sqm)</th><th>Rooms (including bedrooms &amp; bathrooms)</th><th>Approximate construction age</th><th>lat</th><th>lon</th><th>Index of Multiple Deprivation (IMD) Score</th><th>Income Score (rate)</th><th>Employment Score (rate)</th><th>Education, Skills and Training Score</th><th>Health Deprivation and Disability Score</th><th>Crime Score</th><th>Living Environment Score</th><th>Indoors Sub-domain Score</th><th>Outdoors Sub-domain Score</th><th>% Asian</th><th>% Black</th><th>% Mixed</th><th>% White</th><th>% Other</th><th>Possession of weapons (avg/yr)</th><th>Public order (avg/yr)</th><th>Criminal damage and arson (avg/yr)</th><th>Anti-social behaviour (avg/yr)</th><th>Robbery (avg/yr)</th><th>Violence and sexual offences (avg/yr)</th><th>Other theft (avg/yr)</th><th>Other crime (avg/yr)</th><th>Burglary (avg/yr)</th><th>Bicycle theft (avg/yr)</th><th>Drugs (avg/yr)</th><th>Vehicle crime (avg/yr)</th><th>Shoplifting (avg/yr)</th><th>Theft from the person (avg/yr)</th><th>Restaurants within 2km</th><th>Groceries within 2km</th><th>Parks within 2km</th><th>Public transport within 2km</th><th>Property type/built form</th><th>Price per sqm</th></tr><tr><td>str</td><td>str</td><td>list[struct[2]]</td><td>str</td><td>i64</td><td>str</td><td>str</td><td>str</td><td>str</td><td>f64</td><td>i64</td><td>u16</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i32</td><td>i32</td><td>i32</td><td>i32</td><td>str</td><td>i32</td></tr></thead><tbody><tr><td>&quot; 16 DALESIDE&quot;</td><td>&quot;CH7 2PP&quot;</td><td>[{1996,20000}, {1998,53000}, {2011,121000}]</td><td>&quot;Freehold&quot;</td><td>121000</td><td>&quot;16, Daleside&quot;</td><td>&quot;D&quot;</td><td>&quot;C&quot;</td><td>&quot;Bungalow&quot;</td><td>60.8</td><td>3</td><td>1967</td><td>53.166168</td><td>-3.094835</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>1.0</td><td>2.7</td><td>2.0</td><td>8.3</td><td>null</td><td>18.2</td><td>2.7</td><td>2.3</td><td>2.3</td><td>1.0</td><td>1.0</td><td>1.5</td><td>null</td><td>null</td><td>1</td><td>0</td><td>0</td><td>0</td><td>&quot;Detached&quot;</td><td>1990</td></tr><tr><td>&quot; 2 BRAKERIDGE CLOSE&quot;</td><td>&quot;TQ5 0JU&quot;</td><td>[{1996,133000}, {2001,247500}]</td><td>&quot;Freehold&quot;</td><td>247500</td><td>&quot;2 Brakeridge Close, Churston F…</td><td>&quot;D&quot;</td><td>&quot;C&quot;</td><td>&quot;House&quot;</td><td>283.0</td><td>9</td><td>1950</td><td>50.399959</td><td>-3.558175</td><td>7.368</td><td>0.052</td><td>0.065</td><td>3.623</td><td>-0.332</td><td>-1.367</td><td>4.088</td><td>-0.541</td><td>-0.965</td><td>1.6</td><td>0.3</td><td>1.5</td><td>96.1</td><td>0.4</td><td>1.0</td><td>1.7</td><td>3.3</td><td>23.7</td><td>null</td><td>12.8</td><td>1.5</td><td>1.5</td><td>2.7</td><td>null</td><td>1.7</td><td>4.0</td><td>1.0</td><td>1.0</td><td>1</td><td>3</td><td>0</td><td>2</td><td>&quot;Detached&quot;</td><td>875</td></tr><tr><td>&quot; FIELD HOUSE DUNMOW ROAD&quot;</td><td>&quot;CM7 4SD&quot;</td><td>[{1996,161500}]</td><td>&quot;Freehold&quot;</td><td>161500</td><td>&quot;Florence House, Dunmow Road, G…</td><td>&quot;D&quot;</td><td>&quot;C&quot;</td><td>&quot;House&quot;</td><td>155.0</td><td>7</td><td>1983</td><td>51.946491</td><td>0.43401</td><td>16.423</td><td>0.137</td><td>0.087</td><td>11.172</td><td>-0.605</td><td>-0.081</td><td>44.509</td><td>1.039</td><td>-0.472</td><td>1.7</td><td>1.2</td><td>1.9</td><td>94.7</td><td>0.5</td><td>1.0</td><td>5.3</td><td>4.3</td><td>3.3</td><td>1.0</td><td>18.0</td><td>5.7</td><td>2.7</td><td>4.0</td><td>null</td><td>1.5</td><td>2.5</td><td>1.3</td><td>1.0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>&quot;Detached&quot;</td><td>1042</td></tr><tr><td>&quot; 73 ARGYLE STREET&quot;</td><td>&quot;SA1 3TA&quot;</td><td>[{1996,52000}, {2018,176500}]</td><td>&quot;Freehold&quot;</td><td>176500</td><td>&quot;73 Argyle Street&quot;</td><td>&quot;C&quot;</td><td>&quot;C&quot;</td><td>&quot;House&quot;</td><td>120.0</td><td>6</td><td>1900</td><td>51.614972</td><td>-3.951154</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>4.0</td><td>51.7</td><td>33.7</td><td>80.0</td><td>2.3</td><td>152.5</td><td>32.7</td><td>12.3</td><td>9.3</td><td>17.7</td><td>16.0</td><td>21.7</td><td>152.3</td><td>3.7</td><td>64</td><td>21</td><td>0</td><td>51</td><td>&quot;Semi-Detached/Mid-Terrace&quot;</td><td>1471</td></tr><tr><td>&quot; 44 BLYTHE HILL&quot;</td><td>&quot;BR5 2RR&quot;</td><td>[{1996,76750}, {2004,192000}, {2022,420000}]</td><td>&quot;Freehold&quot;</td><td>420000</td><td>&quot;44 BLYTHE HILL&quot;</td><td>&quot;D&quot;</td><td>&quot;B&quot;</td><td>&quot;House&quot;</td><td>88.0</td><td>4</td><td>1950</td><td>51.407277</td><td>0.098257</td><td>29.023</td><td>0.302</td><td>0.208</td><td>27.67</td><td>0.623</td><td>0.82</td><td>9.79</td><td>-0.387</td><td>-0.109</td><td>8.3</td><td>7.6</td><td>5.4</td><td>76.5</td><td>2.3</td><td>1.0</td><td>12.0</td><td>23.5</td><td>51.8</td><td>4.0</td><td>45.0</td><td>13.0</td><td>4.0</td><td>8.5</td><td>1.3</td><td>9.7</td><td>14.5</td><td>1.0</td><td>4.5</td><td>7</td><td>6</td><td>0</td><td>28</td><td>&quot;Semi-Detached/Mid-Terrace&quot;</td><td>4773</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot; 17 QUANTOCK CLOSE&quot;</td><td>&quot;MK41 9EW&quot;</td><td>[{1996,60000}, {2009,181000}, {2012,174500}]</td><td>&quot;Freehold&quot;</td><td>174500</td><td>&quot;17, Quantock Close&quot;</td><td>&quot;C&quot;</td><td>&quot;B&quot;</td><td>&quot;House&quot;</td><td>116.0</td><td>6</td><td>1967</td><td>52.146986</td><td>-0.444824</td><td>4.111</td><td>0.079</td><td>0.059</td><td>8.059</td><td>-0.605</td><td>-1.136</td><td>2.219</td><td>-0.824</td><td>-0.854</td><td>12.6</td><td>5.3</td><td>4.6</td><td>75.7</td><td>1.8</td><td>null</td><td>6.7</td><td>2.2</td><td>11.0</td><td>1.0</td><td>19.2</td><td>3.3</td><td>3.0</td><td>1.3</td><td>1.5</td><td>1.0</td><td>7.0</td><td>1.5</td><td>null</td><td>79</td><td>27</td><td>0</td><td>0</td><td>&quot;Semi-Detached&quot;</td><td>1504</td></tr><tr><td>&quot; 11 CAMBRIDGE ROAD&quot;</td><td>&quot;LE9 1SH&quot;</td><td>[{1996,43000}, {2001,85500}, … {2021,235000}]</td><td>&quot;Freehold&quot;</td><td>235000</td><td>&quot;11, Cambridge Road, Cosby&quot;</td><td>&quot;C&quot;</td><td>&quot;B&quot;</td><td>&quot;House&quot;</td><td>53.0</td><td>5</td><td>1976</td><td>52.553914</td><td>-1.19141</td><td>7.584</td><td>0.111</td><td>0.079</td><td>8.198</td><td>-0.749</td><td>-0.951</td><td>4.718</td><td>-0.555</td><td>-0.676</td><td>8.3</td><td>1.5</td><td>2.7</td><td>86.2</td><td>1.3</td><td>1.0</td><td>3.0</td><td>3.2</td><td>7.0</td><td>1.0</td><td>18.5</td><td>3.3</td><td>1.7</td><td>4.3</td><td>null</td><td>2.0</td><td>4.0</td><td>null</td><td>1.0</td><td>3</td><td>4</td><td>0</td><td>0</td><td>&quot;Semi-Detached&quot;</td><td>4434</td></tr><tr><td>&quot; 19 MIMOSA AVENUE&quot;</td><td>&quot;BH21 1TU&quot;</td><td>[{1996,86500}, {2000,157000}]</td><td>&quot;Freehold&quot;</td><td>157000</td><td>&quot;19 Mimosa Avenue&quot;</td><td>&quot;D&quot;</td><td>&quot;B&quot;</td><td>&quot;Bungalow&quot;</td><td>90.0</td><td>4</td><td>1967</td><td>50.780322</td><td>-1.97095</td><td>3.078</td><td>0.042</td><td>0.045</td><td>3.737</td><td>-1.045</td><td>-1.736</td><td>1.417</td><td>-1.421</td><td>-0.685</td><td>3.4</td><td>1.1</td><td>2.8</td><td>91.3</td><td>1.5</td><td>null</td><td>2.0</td><td>1.5</td><td>7.3</td><td>null</td><td>4.8</td><td>1.3</td><td>1.0</td><td>3.3</td><td>1.0</td><td>null</td><td>1.0</td><td>null</td><td>1.0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>&quot;Detached&quot;</td><td>1744</td></tr><tr><td>&quot; 8 ENFIELD DRIVE&quot;</td><td>&quot;M11 4HP&quot;</td><td>[{1996,5500}, {1996,13650}, … {2011,38000}]</td><td>&quot;Leasehold&quot;</td><td>38000</td><td>&quot;8 Enfield Drive&quot;</td><td>&quot;D&quot;</td><td>&quot;B&quot;</td><td>&quot;House&quot;</td><td>54.0</td><td>5</td><td>1950</td><td>53.486503</td><td>-2.181542</td><td>55.133</td><td>0.527</td><td>0.296</td><td>40.139</td><td>1.669</td><td>1.058</td><td>25.956</td><td>0.41</td><td>-0.052</td><td>20.9</td><td>11.9</td><td>5.3</td><td>56.8</td><td>5.1</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>3.0</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>16</td><td>9</td><td>0</td><td>15</td><td>&quot;Semi-Detached/Detached&quot;</td><td>704</td></tr><tr><td>&quot; 7 TURNHAM GREEN&quot;</td><td>&quot;NR7 0TU&quot;</td><td>[{1996,116950}, {2006,265000}]</td><td>&quot;Freehold&quot;</td><td>265000</td><td>&quot;7, Turnham Green&quot;</td><td>&quot;B&quot;</td><td>&quot;B&quot;</td><td>&quot;House&quot;</td><td>140.0</td><td>8</td><td>1991</td><td>52.637913</td><td>1.36565</td><td>2.549</td><td>0.055</td><td>0.045</td><td>4.694</td><td>-1.263</td><td>-1.007</td><td>3.257</td><td>-1.045</td><td>-0.383</td><td>1.4</td><td>0.5</td><td>1.4</td><td>96.3</td><td>0.4</td><td>1.0</td><td>4.7</td><td>11.2</td><td>5.7</td><td>null</td><td>34.8</td><td>2.0</td><td>1.0</td><td>1.0</td><td>null</td><td>1.0</td><td>1.0</td><td>null</td><td>1.0</td><td>0</td><td>3</td><td>0</td><td>2</td><td>&quot;Detached&quot;</td><td>1893</td></tr></tbody></table></div>"
       ],
       "text/plain": [
-       "shape: (5, 47)\n",
-       "┌───────────┬──────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
-       "│ pp_addres ┆ postcode ┆ historica ┆ pp_proper ┆ … ┆ groceries ┆ parks_2km ┆ public_tr ┆ price_per │\n",
-       "│ s         ┆ ---      ┆ l_prices  ┆ ty_type   ┆   ┆ _2km      ┆ ---       ┆ ansport_2 ┆ _sqm      │\n",
-       "│ ---       ┆ str      ┆ ---       ┆ ---       ┆   ┆ ---       ┆ i32       ┆ km        ┆ ---       │\n",
-       "│ str       ┆          ┆ list[stru ┆ str       ┆   ┆ i32       ┆           ┆ ---       ┆ f64       │\n",
-       "│           ┆          ┆ ct[2]]    ┆           ┆   ┆           ┆           ┆ i32       ┆           │\n",
-       "╞═══════════╪══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
-       "│ 92 JOYES  ┆ CT19 6HL ┆ [{1995,50 ┆ Semi-Deta ┆ … ┆ 20        ┆ 0         ┆ 3         ┆ 600.60975 │\n",
-       "│ ROAD      ┆          ┆ 000}, {20 ┆ ched      ┆   ┆           ┆           ┆           ┆ 6         │\n",
-       "│           ┆          ┆ 01,98500} ┆           ┆   ┆           ┆           ┆           ┆           │\n",
-       "│           ┆          ┆ ]         ┆           ┆   ┆           ┆           ┆           ┆           │\n",
-       "│ 7 LUTON   ┆ HX1 4NG  ┆ [{1995,16 ┆ Terraced  ┆ … ┆ 6         ┆ 0         ┆ 3         ┆ 161.61616 │\n",
-       "│ STREET    ┆          ┆ 000}]     ┆           ┆   ┆           ┆           ┆           ┆ 2         │\n",
-       "│ OLD OAK   ┆ TR4 8DW  ┆ [{1995,50 ┆ Detached  ┆ … ┆ 2         ┆ 0         ┆ 0         ┆ 1828.3582 │\n",
-       "│ COTTAGE   ┆          ┆ 000}, {20 ┆           ┆   ┆           ┆           ┆           ┆ 09        │\n",
-       "│ ROPE WALK ┆          ┆ 00,125000 ┆           ┆   ┆           ┆           ┆           ┆           │\n",
-       "│           ┆          ┆ }, …      ┆           ┆   ┆           ┆           ┆           ┆           │\n",
-       "│ 147       ┆ HU5 5NY  ┆ [{1995,17 ┆ Terraced  ┆ … ┆ 3         ┆ 0         ┆ 0         ┆ 2164.1791 │\n",
-       "│ WESTLANDS ┆          ┆ 000}, {20 ┆           ┆   ┆           ┆           ┆           ┆ 04        │\n",
-       "│ ROAD      ┆          ┆ 11,58500} ┆           ┆   ┆           ┆           ┆           ┆           │\n",
-       "│           ┆          ┆ , ……      ┆           ┆   ┆           ┆           ┆           ┆           │\n",
-       "│ 70        ┆ CA11 9JD ┆ [{1995,23 ┆ Semi-Deta ┆ … ┆ 11        ┆ 0         ┆ 12        ┆ 2040.1948 │\n",
-       "│ SCOTLAND  ┆          ┆ 000}, {19 ┆ ched      ┆   ┆           ┆           ┆           ┆ 84        │\n",
-       "│ ROAD      ┆          ┆ 99,23500} ┆           ┆   ┆           ┆           ┆           ┆           │\n",
-       "│           ┆          ┆ , ……      ┆           ┆   ┆           ┆           ┆           ┆           │\n",
-       "└───────────┴──────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴───────────┘"
+       "shape: (20, 48)\n",
+       "┌────────────┬──────────┬────────────┬────────────┬───┬────────┬───────────┬───────────┬───────────┐\n",
+       "│ Address    ┆ Postcode ┆ historical ┆ Leashold/F ┆ … ┆ Parks  ┆ Public    ┆ Property  ┆ Price per │\n",
+       "│ per        ┆ ---      ┆ _prices    ┆ reehold    ┆   ┆ within ┆ transport ┆ type/buil ┆ sqm       │\n",
+       "│ Property   ┆ str      ┆ ---        ┆ ---        ┆   ┆ 2km    ┆ within    ┆ t form    ┆ ---       │\n",
+       "│ Register   ┆          ┆ list[struc ┆ str        ┆   ┆ ---    ┆ 2km       ┆ ---       ┆ i32       │\n",
+       "│ ---        ┆          ┆ t[2]]      ┆            ┆   ┆ i32    ┆ ---       ┆ str       ┆           │\n",
+       "│ str        ┆          ┆            ┆            ┆   ┆        ┆ i32       ┆           ┆           │\n",
+       "╞════════════╪══════════╪════════════╪════════════╪═══╪════════╪═══════════╪═══════════╪═══════════╡\n",
+       "│ 16         ┆ CH7 2PP  ┆ [{1996,200 ┆ Freehold   ┆ … ┆ 0      ┆ 0         ┆ Detached  ┆ 1990      │\n",
+       "│ DALESIDE   ┆          ┆ 00}, {1998 ┆            ┆   ┆        ┆           ┆           ┆           │\n",
+       "│            ┆          ┆ ,53000},   ┆            ┆   ┆        ┆           ┆           ┆           │\n",
+       "│            ┆          ┆ {…         ┆            ┆   ┆        ┆           ┆           ┆           │\n",
+       "│ 2          ┆ TQ5 0JU  ┆ [{1996,133 ┆ Freehold   ┆ … ┆ 0      ┆ 2         ┆ Detached  ┆ 875       │\n",
+       "│ BRAKERIDGE ┆          ┆ 000}, {200 ┆            ┆   ┆        ┆           ┆           ┆           │\n",
+       "│ CLOSE      ┆          ┆ 1,247500}] ┆            ┆   ┆        ┆           ┆           ┆           │\n",
+       "│ FIELD      ┆ CM7 4SD  ┆ [{1996,161 ┆ Freehold   ┆ … ┆ 0      ┆ 0         ┆ Detached  ┆ 1042      │\n",
+       "│ HOUSE      ┆          ┆ 500}]      ┆            ┆   ┆        ┆           ┆           ┆           │\n",
+       "│ DUNMOW     ┆          ┆            ┆            ┆   ┆        ┆           ┆           ┆           │\n",
+       "│ ROAD       ┆          ┆            ┆            ┆   ┆        ┆           ┆           ┆           │\n",
+       "│ 73 ARGYLE  ┆ SA1 3TA  ┆ [{1996,520 ┆ Freehold   ┆ … ┆ 0      ┆ 51        ┆ Semi-Deta ┆ 1471      │\n",
+       "│ STREET     ┆          ┆ 00}, {2018 ┆            ┆   ┆        ┆           ┆ ched/Mid- ┆           │\n",
+       "│            ┆          ┆ ,176500}]  ┆            ┆   ┆        ┆           ┆ Terrace   ┆           │\n",
+       "│ 44 BLYTHE  ┆ BR5 2RR  ┆ [{1996,767 ┆ Freehold   ┆ … ┆ 0      ┆ 28        ┆ Semi-Deta ┆ 4773      │\n",
+       "│ HILL       ┆          ┆ 50}, {2004 ┆            ┆   ┆        ┆           ┆ ched/Mid- ┆           │\n",
+       "│            ┆          ┆ ,192000},  ┆            ┆   ┆        ┆           ┆ Terrace   ┆           │\n",
+       "│            ┆          ┆ …          ┆            ┆   ┆        ┆           ┆           ┆           │\n",
+       "│ …          ┆ …        ┆ …          ┆ …          ┆ … ┆ …      ┆ …         ┆ …         ┆ …         │\n",
+       "│ 17         ┆ MK41 9EW ┆ [{1996,600 ┆ Freehold   ┆ … ┆ 0      ┆ 0         ┆ Semi-Deta ┆ 1504      │\n",
+       "│ QUANTOCK   ┆          ┆ 00}, {2009 ┆            ┆   ┆        ┆           ┆ ched      ┆           │\n",
+       "│ CLOSE      ┆          ┆ ,181000},  ┆            ┆   ┆        ┆           ┆           ┆           │\n",
+       "│            ┆          ┆ …          ┆            ┆   ┆        ┆           ┆           ┆           │\n",
+       "│ 11         ┆ LE9 1SH  ┆ [{1996,430 ┆ Freehold   ┆ … ┆ 0      ┆ 0         ┆ Semi-Deta ┆ 4434      │\n",
+       "│ CAMBRIDGE  ┆          ┆ 00}, {2001 ┆            ┆   ┆        ┆           ┆ ched      ┆           │\n",
+       "│ ROAD       ┆          ┆ ,85500},   ┆            ┆   ┆        ┆           ┆           ┆           │\n",
+       "│            ┆          ┆ ……         ┆            ┆   ┆        ┆           ┆           ┆           │\n",
+       "│ 19 MIMOSA  ┆ BH21 1TU ┆ [{1996,865 ┆ Freehold   ┆ … ┆ 0      ┆ 0         ┆ Detached  ┆ 1744      │\n",
+       "│ AVENUE     ┆          ┆ 00}, {2000 ┆            ┆   ┆        ┆           ┆           ┆           │\n",
+       "│            ┆          ┆ ,157000}]  ┆            ┆   ┆        ┆           ┆           ┆           │\n",
+       "│ 8 ENFIELD  ┆ M11 4HP  ┆ [{1996,550 ┆ Leasehold  ┆ … ┆ 0      ┆ 15        ┆ Semi-Deta ┆ 704       │\n",
+       "│ DRIVE      ┆          ┆ 0}, {1996, ┆            ┆   ┆        ┆           ┆ ched/Deta ┆           │\n",
+       "│            ┆          ┆ 13650}, …  ┆            ┆   ┆        ┆           ┆ ched      ┆           │\n",
+       "│            ┆          ┆ …          ┆            ┆   ┆        ┆           ┆           ┆           │\n",
+       "│ 7 TURNHAM  ┆ NR7 0TU  ┆ [{1996,116 ┆ Freehold   ┆ … ┆ 0      ┆ 2         ┆ Detached  ┆ 1893      │\n",
+       "│ GREEN      ┆          ┆ 950}, {200 ┆            ┆   ┆        ┆           ┆           ┆           │\n",
+       "│            ┆          ┆ 6,265000}] ┆            ┆   ┆        ┆           ┆           ┆           │\n",
+       "└────────────┴──────────┴────────────┴────────────┴───┴────────┴───────────┴───────────┴───────────┘"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -57,7 +77,7 @@
     "import polars as pl\n",
     "\n",
     "\n",
-    "pl.scan_parquet('../data_sources/processed/wide.parquet').head().collect()\n"
+    "pl.scan_parquet(\"../data/wide.parquet\").head(20).collect()\n"
    ]
   },
   {
@@ -71,7 +91,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "property-map",
    "language": "python",
    "name": "python3"
   },

From 6ddb3d2121bf757adc3dea49ad60cc04227e95e8 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sat, 31 Jan 2026 19:24:06 +0000
Subject: [PATCH 34/62] Add more datasets

---
 pipeline/download/broadband.py      | 80 +++++++++++++++++++++++++++++
 pipeline/download/naptan.py         | 57 ++++++++++++++++++++
 pipeline/download/ofsted.py         | 62 ++++++++++++++++++++++
 pipeline/transform/transform_poi.py |  1 +
 4 files changed, 200 insertions(+)
 create mode 100644 pipeline/download/broadband.py
 create mode 100644 pipeline/download/naptan.py
 create mode 100644 pipeline/download/ofsted.py

diff --git a/pipeline/download/broadband.py b/pipeline/download/broadband.py
new file mode 100644
index 0000000..eab0e44
--- /dev/null
+++ b/pipeline/download/broadband.py
@@ -0,0 +1,80 @@
+import argparse
+import zipfile
+import httpx
+import polars as pl
+from pathlib import Path
+import tempfile
+from tqdm import tqdm
+
+# Ofcom Connected Nations 2025 - Fixed broadband performance (output area & local authority level)
+# Source: https://www.ofcom.org.uk/phones-and-broadband/coverage-and-speeds/connected-nations-20252/data-downloads-2025
+PERFORMANCE_URL = "https://www.ofcom.org.uk/siteassets/resources/documents/research-and-data/multi-sector/infrastructure-research/connected-nations-2025/202507_fixed_broadband_performance_r01.zip"
+
+
+def download_with_progress(url: str, output_path: Path) -> None:
+    with httpx.stream("GET", url, follow_redirects=True, timeout=120) as response:
+        response.raise_for_status()  # pyright: ignore[reportUnusedCallResult]
+        total = int(response.headers.get("content-length", 0))
+        with (
+            open(output_path, "wb") as f,
+            tqdm(total=total, unit="B", unit_scale=True, desc="Downloading") as pbar,
+        ):
+            for chunk in response.iter_bytes(chunk_size=8192):
+                f.write(chunk)
+                pbar.update(len(chunk))
+
+
+def extract_zip(zip_path: Path, extract_dir: Path) -> None:
+    with zipfile.ZipFile(zip_path, "r") as z:
+        z.extractall(extract_dir)
+
+
+def convert_to_parquet(extract_dir: Path, parquet_path: Path) -> None:
+    # Find CSV files in the extracted directory
+    csv_files = list(extract_dir.rglob("*.csv"))
+    if not csv_files:
+        raise FileNotFoundError(f"No CSV files found in {extract_dir}")
+
+    print(f"Found CSV files: {[f.name for f in csv_files]}")
+
+    # Read and concatenate all CSVs (typically split by geography level)
+    frames = []
+    for csv_file in sorted(csv_files):
+        print(f"Reading {csv_file.name}...")
+        df = pl.read_csv(csv_file, infer_schema_length=10000, encoding="utf8-lossy")
+        print(f"  Shape: {df.shape}, Columns: {df.columns}")
+        frames.append((csv_file.stem, df))
+
+    # Save each CSV as a separate parquet file in the output directory
+    parquet_path.mkdir(parents=True, exist_ok=True)
+    for name, df in frames:
+        out = parquet_path / f"{name}.parquet"
+        df.write_parquet(out, compression="zstd")
+        print(f"Saved {out} ({df.shape[0]} rows)")
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Download Ofcom broadband performance data"
+    )
+    parser.add_argument(
+        "--output",
+        type=Path,
+        required=True,
+        help="Output directory for parquet files",
+    )
+    args = parser.parse_args()
+
+    with tempfile.TemporaryDirectory() as cache_dir:
+        cache = Path(cache_dir)
+        zip_path = cache / "broadband_performance.zip"
+        extract_dir = cache / "extracted"
+        extract_dir.mkdir()
+
+        download_with_progress(PERFORMANCE_URL, zip_path)
+        extract_zip(zip_path, extract_dir)
+        convert_to_parquet(extract_dir, args.output)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/pipeline/download/naptan.py b/pipeline/download/naptan.py
new file mode 100644
index 0000000..52f823a
--- /dev/null
+++ b/pipeline/download/naptan.py
@@ -0,0 +1,57 @@
+"""Download NaPTAN data and extract railway/metro station POIs."""
+
+import argparse
+import io
+import urllib.request
+from pathlib import Path
+
+import polars as pl
+
+NAPTAN_CSV_URL = "https://naptan.api.dft.gov.uk/v1/access-nodes?dataFormat=csv"
+
+
+def download_naptan(output: Path) -> None:
+    output.parent.mkdir(parents=True, exist_ok=True)
+
+    print(f"Downloading NaPTAN data from {NAPTAN_CSV_URL}")
+    with urllib.request.urlopen(NAPTAN_CSV_URL) as resp:
+        raw = resp.read()
+
+    print(f"Downloaded {len(raw) / (1024 * 1024):.1f} MB")
+
+    df = (
+        pl.read_csv(io.BytesIO(raw), infer_schema_length=0)
+        .with_columns(
+            pl.col("Latitude").cast(pl.Float64, strict=False),
+            pl.col("Longitude").cast(pl.Float64, strict=False),
+        )
+        .drop_nulls(subset=["Latitude", "Longitude"])
+        .select(
+            pl.col("ATCOCode").alias("id"),
+            pl.col("CommonName").alias("name"),
+            pl.col("StopType").alias("category"),
+            pl.col("Latitude").alias("lat"),
+            pl.col("Longitude").alias("lng"),
+        )
+    )
+
+    df.write_parquet(output)
+    size_mb = output.stat().st_size / (1024 * 1024) 
+    print(f"Wrote {output} ({size_mb:.1f} MB, {len(df):,} stations)")
+
+    counts = df.group_by("category").len().sort("len", descending=True)
+    for row in counts.iter_rows(named=True):
+        print(f"  {row['category']}: {row['len']:,}")
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Download NaPTAN station data")
+    parser.add_argument(
+        "--output", type=Path, required=True, help="Output parquet file path"
+    )
+    args = parser.parse_args()
+    download_naptan(args.output)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/pipeline/download/ofsted.py b/pipeline/download/ofsted.py
new file mode 100644
index 0000000..b783ae9
--- /dev/null
+++ b/pipeline/download/ofsted.py
@@ -0,0 +1,62 @@
+import argparse
+import httpx
+import polars as pl
+from pathlib import Path
+import tempfile
+
+# Management information - state-funded schools - latest inspections (as at 30 Apr 2025)
+# Source: https://www.gov.uk/government/statistical-data-sets/monthly-management-information-ofsteds-school-inspections-outcomes
+URL = "https://assets.publishing.service.gov.uk/media/681cd390275cb67b18d870fc/Management_information_-_state-funded_schools_-_latest_inspections_as_at_30_Apr_2025.csv"
+
+
+def download_file(url: str, output_path: Path) -> None:
+    with httpx.stream("GET", url, follow_redirects=True, timeout=60) as response:
+        response.raise_for_status()  # pyright: ignore[reportUnusedCallResult]
+        total = int(response.headers.get("content-length", 0))
+        downloaded = 0
+        with open(output_path, "wb") as f:
+            for chunk in response.iter_bytes(chunk_size=8192):
+                f.write(chunk)
+                downloaded += len(chunk)
+                if total:
+                    print(
+                        f"\rDownloaded {downloaded / 1024 / 1024:.1f} MB / {total / 1024 / 1024:.1f} MB",
+                        end="",
+                    )
+    print(f"\nSaved to {output_path}")
+
+
+def convert_to_parquet(csv_path: Path, parquet_path: Path) -> None:
+    print("Reading CSV...")
+
+    df = pl.read_csv(
+        csv_path,
+        infer_schema_length=10000,
+        encoding="utf8-lossy",
+        null_values=["NULL", "Not applicable"],
+    )
+
+    print(f"Shape: {df.shape}")
+    print(f"Columns: {df.columns}")
+
+    df.write_parquet(parquet_path, compression="zstd")
+    print(f"Saved to {parquet_path}")
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Download Ofsted school inspection outcomes data"
+    )
+    parser.add_argument(
+        "--output", type=Path, required=True, help="Output parquet file path"
+    )
+    args = parser.parse_args()
+
+    with tempfile.TemporaryDirectory() as cache_dir:
+        csv_path = Path(cache_dir) / "ofsted_latest_inspections.csv"
+        download_file(URL, csv_path)
+        convert_to_parquet(csv_path, args.output)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/pipeline/transform/transform_poi.py b/pipeline/transform/transform_poi.py
index de92d24..ac6d98f 100644
--- a/pipeline/transform/transform_poi.py
+++ b/pipeline/transform/transform_poi.py
@@ -1,4 +1,5 @@
 import argparse
+import warnings
 from pathlib import Path
 
 import polars as pl

From 3b9ad11d71c6c8365210fad6aad8d60ee97ff8f8 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sat, 31 Jan 2026 19:52:21 +0000
Subject: [PATCH 35/62] Extract common download utils

---
 pipeline/download/arcgis.py           | 40 ++------------
 pipeline/download/broadband.py        | 58 +++++++-------------
 pipeline/download/deprivation_data.py | 24 ++-------
 pipeline/download/naptan.py           | 14 ++++-
 pipeline/download/ofsted.py           | 24 ++-------
 pipeline/download/price_paid.py       | 32 ++---------
 pipeline/transform/merge.py           | 78 +++++++++++++++++++++------
 pipeline/utils/__init__.py            |  3 ++
 pipeline/utils/download.py            | 40 ++++++++++++++
 9 files changed, 152 insertions(+), 161 deletions(-)
 create mode 100644 pipeline/utils/download.py

diff --git a/pipeline/download/arcgis.py b/pipeline/download/arcgis.py
index de0fa0c..40fb0d0 100644
--- a/pipeline/download/arcgis.py
+++ b/pipeline/download/arcgis.py
@@ -1,47 +1,13 @@
 import argparse
 import tempfile
-import zipfile
-import httpx
 import polars as pl
 from pathlib import Path
-from tqdm import tqdm
+
+from pipeline.utils import download, extract_zip
 
 URL = "https://www.arcgis.com/sharing/rest/content/items/077631e063eb4e1ab43575d01381ec33/data"
 
 
-def download_with_progress(url: str, output_path: Path) -> None:
-    with httpx.stream(
-        "GET",
-        url,
-        follow_redirects=True,
-        timeout=httpx.Timeout(30.0, read=None),
-    ) as response:
-        response.raise_for_status()  # pyright: ignore[reportUnusedCallResult]
-        total = int(response.headers.get("content-length", 0))
-
-        with (
-            open(output_path, "wb") as f,
-            tqdm(
-                total=total,
-                unit="B",
-                unit_scale=True,
-                unit_divisor=1024,
-                desc="Downloading",
-            ) as pbar,
-        ):
-            for chunk in response.iter_bytes(chunk_size=8192):
-                f.write(chunk)
-                pbar.update(len(chunk))
-        return
-
-
-def extract_zip(zip_path: Path, extract_path: Path) -> None:
-    extract_path.mkdir(exist_ok=True)
-
-    with zipfile.ZipFile(zip_path, "r") as zf:
-        zf.extractall(extract_path)
-
-
 def convert_to_parquet(data_path: Path, parquet_path: Path) -> None:
     df = pl.scan_csv(data_path / "Data/NSPL_MAY_2025_UK.csv", try_parse_dates=True)
     print(f"Columns: {df.collect_schema().names()}")
@@ -63,7 +29,7 @@ def main() -> None:
         download_path = Path(cache_dir) / "arcgis_data.zip"
         extract_path = Path(cache_dir) / "arcgis_extracted"
 
-        download_with_progress(URL, download_path)
+        download(URL, download_path)
         extract_zip(download_path, extract_path)
         convert_to_parquet(extract_path, args.output)
 
diff --git a/pipeline/download/broadband.py b/pipeline/download/broadband.py
index eab0e44..cc70577 100644
--- a/pipeline/download/broadband.py
+++ b/pipeline/download/broadband.py
@@ -1,32 +1,13 @@
 import argparse
-import zipfile
-import httpx
+import tempfile
 import polars as pl
 from pathlib import Path
-import tempfile
-from tqdm import tqdm
+
+from pipeline.utils import download, extract_zip
 
 # Ofcom Connected Nations 2025 - Fixed broadband performance (output area & local authority level)
 # Source: https://www.ofcom.org.uk/phones-and-broadband/coverage-and-speeds/connected-nations-20252/data-downloads-2025
-PERFORMANCE_URL = "https://www.ofcom.org.uk/siteassets/resources/documents/research-and-data/multi-sector/infrastructure-research/connected-nations-2025/202507_fixed_broadband_performance_r01.zip"
-
-
-def download_with_progress(url: str, output_path: Path) -> None:
-    with httpx.stream("GET", url, follow_redirects=True, timeout=120) as response:
-        response.raise_for_status()  # pyright: ignore[reportUnusedCallResult]
-        total = int(response.headers.get("content-length", 0))
-        with (
-            open(output_path, "wb") as f,
-            tqdm(total=total, unit="B", unit_scale=True, desc="Downloading") as pbar,
-        ):
-            for chunk in response.iter_bytes(chunk_size=8192):
-                f.write(chunk)
-                pbar.update(len(chunk))
-
-
-def extract_zip(zip_path: Path, extract_dir: Path) -> None:
-    with zipfile.ZipFile(zip_path, "r") as z:
-        z.extractall(extract_dir)
+PERFORMANCE_URL = "https://www.ofcom.org.uk/siteassets/resources/documents/research-and-data/multi-sector/infrastructure-research/connected-nations-2025/202507_fixed_broadband_coverage_r01.zip?v=407830"
 
 
 def convert_to_parquet(extract_dir: Path, parquet_path: Path) -> None:
@@ -35,22 +16,21 @@ def convert_to_parquet(extract_dir: Path, parquet_path: Path) -> None:
     if not csv_files:
         raise FileNotFoundError(f"No CSV files found in {extract_dir}")
 
-    print(f"Found CSV files: {[f.name for f in csv_files]}")
+    print(f"Found {len(csv_files)} CSV files: {[f.name for f in csv_files]}")
 
-    # Read and concatenate all CSVs (typically split by geography level)
     frames = []
     for csv_file in sorted(csv_files):
         print(f"Reading {csv_file.name}...")
         df = pl.read_csv(csv_file, infer_schema_length=10000, encoding="utf8-lossy")
-        print(f"  Shape: {df.shape}, Columns: {df.columns}")
-        frames.append((csv_file.stem, df))
+        print(f"  Shape: {df.shape}")
+        frames.append(df)
 
-    # Save each CSV as a separate parquet file in the output directory
-    parquet_path.mkdir(parents=True, exist_ok=True)
-    for name, df in frames:
-        out = parquet_path / f"{name}.parquet"
-        df.write_parquet(out, compression="zstd")
-        print(f"Saved {out} ({df.shape[0]} rows)")
+    combined = pl.concat(frames, how="diagonal_relaxed")
+    print(f"Combined shape: {combined.shape}")
+
+    parquet_path.parent.mkdir(parents=True, exist_ok=True)
+    combined.write_parquet(parquet_path, compression="zstd")
+    print(f"Saved {parquet_path} ({combined.shape[0]} rows)")
 
 
 def main() -> None:
@@ -61,20 +41,22 @@ def main() -> None:
         "--output",
         type=Path,
         required=True,
-        help="Output directory for parquet files",
+        help="Output parquet file path",
     )
     args = parser.parse_args()
 
-    with tempfile.TemporaryDirectory() as cache_dir:
+    with tempfile.TemporaryDirectory(delete=False) as cache_dir:
         cache = Path(cache_dir)
         zip_path = cache / "broadband_performance.zip"
         extract_dir = cache / "extracted"
-        extract_dir.mkdir()
+        extracted_again_dir = cache / "extracted-again"
 
-        download_with_progress(PERFORMANCE_URL, zip_path)
+        download(PERFORMANCE_URL, zip_path)
         extract_zip(zip_path, extract_dir)
-        convert_to_parquet(extract_dir, args.output)
+        print(list((extract_dir / "202507_fixed_coverage_r01").glob("*")))
+        extract_zip(extract_dir / "202507_fixed_coverage_r01" / "202507_fixed_pc_coverage_r01.zip", extracted_again_dir)
 
+        convert_to_parquet(extracted_again_dir, args.output)
 
 if __name__ == "__main__":
     main()
diff --git a/pipeline/download/deprivation_data.py b/pipeline/download/deprivation_data.py
index db75969..b4ad3eb 100644
--- a/pipeline/download/deprivation_data.py
+++ b/pipeline/download/deprivation_data.py
@@ -1,29 +1,13 @@
 import argparse
-import httpx
+import tempfile
 import polars as pl
 from pathlib import Path
-import tempfile
+
+from pipeline.utils import download
 
 URL = "https://assets.publishing.service.gov.uk/media/691ded34513046b952c500bd/File_5_IoD2025_Scores_for_the_Indices_of_Deprivation.xlsx"
 
 
-def download_file(url: str, output_path: Path) -> None:
-    with httpx.stream("GET", url, follow_redirects=True, timeout=60) as response:
-        response.raise_for_status()
-        total = int(response.headers.get("content-length", 0))
-        downloaded = 0
-        with open(output_path, "wb") as f:
-            for chunk in response.iter_bytes(chunk_size=8192):
-                f.write(chunk)
-                downloaded += len(chunk)
-                if total:
-                    print(
-                        f"\rDownloaded {downloaded / 1024 / 1024:.1f} MB / {total / 1024 / 1024:.1f} MB",
-                        end="",
-                    )
-    print(f"\nSaved to {output_path}")
-
-
 def convert_to_parquet(xlsx_path: Path, parquet_path: Path) -> None:
     print("Reading Excel file (sheet 2)...")
 
@@ -51,7 +35,7 @@ def main() -> None:
 
     with tempfile.TemporaryDirectory() as cache_dir:
         xlsx_path = Path(cache_dir) / "IoD2025_Scores.xlsx"
-        download_file(URL, xlsx_path)
+        download(URL, xlsx_path, timeout=60)
         convert_to_parquet(xlsx_path, args.output)
 
 
diff --git a/pipeline/download/naptan.py b/pipeline/download/naptan.py
index 52f823a..3935c21 100644
--- a/pipeline/download/naptan.py
+++ b/pipeline/download/naptan.py
@@ -10,6 +10,17 @@ import polars as pl
 NAPTAN_CSV_URL = "https://naptan.api.dft.gov.uk/v1/access-nodes?dataFormat=csv"
 
 
+STOP_TYPES = {
+    'AIR': "Airport",
+    'FTD': "Ferry",
+    "RSE": "Rail station",
+    "BCT": "Bus stop",
+    "BCE": "Bus station",
+    "TXR": "Taxi rank",
+    "TMU": "Metro or Tram stop",   
+}
+
+
 def download_naptan(output: Path) -> None:
     output.parent.mkdir(parents=True, exist_ok=True)
 
@@ -26,10 +37,11 @@ def download_naptan(output: Path) -> None:
             pl.col("Longitude").cast(pl.Float64, strict=False),
         )
         .drop_nulls(subset=["Latitude", "Longitude"])
+        .filter(pl.col("StopType").is_in(list(STOP_TYPES.keys())))
         .select(
             pl.col("ATCOCode").alias("id"),
             pl.col("CommonName").alias("name"),
-            pl.col("StopType").alias("category"),
+            pl.col("StopType").replace(STOP_TYPES).alias("category"),
             pl.col("Latitude").alias("lat"),
             pl.col("Longitude").alias("lng"),
         )
diff --git a/pipeline/download/ofsted.py b/pipeline/download/ofsted.py
index b783ae9..f86c471 100644
--- a/pipeline/download/ofsted.py
+++ b/pipeline/download/ofsted.py
@@ -1,31 +1,15 @@
 import argparse
-import httpx
+import tempfile
 import polars as pl
 from pathlib import Path
-import tempfile
+
+from pipeline.utils import download
 
 # Management information - state-funded schools - latest inspections (as at 30 Apr 2025)
 # Source: https://www.gov.uk/government/statistical-data-sets/monthly-management-information-ofsteds-school-inspections-outcomes
 URL = "https://assets.publishing.service.gov.uk/media/681cd390275cb67b18d870fc/Management_information_-_state-funded_schools_-_latest_inspections_as_at_30_Apr_2025.csv"
 
 
-def download_file(url: str, output_path: Path) -> None:
-    with httpx.stream("GET", url, follow_redirects=True, timeout=60) as response:
-        response.raise_for_status()  # pyright: ignore[reportUnusedCallResult]
-        total = int(response.headers.get("content-length", 0))
-        downloaded = 0
-        with open(output_path, "wb") as f:
-            for chunk in response.iter_bytes(chunk_size=8192):
-                f.write(chunk)
-                downloaded += len(chunk)
-                if total:
-                    print(
-                        f"\rDownloaded {downloaded / 1024 / 1024:.1f} MB / {total / 1024 / 1024:.1f} MB",
-                        end="",
-                    )
-    print(f"\nSaved to {output_path}")
-
-
 def convert_to_parquet(csv_path: Path, parquet_path: Path) -> None:
     print("Reading CSV...")
 
@@ -54,7 +38,7 @@ def main() -> None:
 
     with tempfile.TemporaryDirectory() as cache_dir:
         csv_path = Path(cache_dir) / "ofsted_latest_inspections.csv"
-        download_file(URL, csv_path)
+        download(URL, csv_path, timeout=60)
         convert_to_parquet(csv_path, args.output)
 
 
diff --git a/pipeline/download/price_paid.py b/pipeline/download/price_paid.py
index 56ebc4e..a186636 100644
--- a/pipeline/download/price_paid.py
+++ b/pipeline/download/price_paid.py
@@ -1,39 +1,13 @@
 import argparse
 import tempfile
-import httpx
 import polars as pl
 from pathlib import Path
-from tqdm import tqdm
+
+from pipeline.utils import download
 
 URL = "http://prod.publicdata.landregistry.gov.uk.s3-website-eu-west-1.amazonaws.com/pp-complete.csv"
 
 
-def download_with_progress(url: str, output_path: Path) -> None:
-    with httpx.stream(
-        "GET",
-        url,
-        follow_redirects=True,
-        timeout=httpx.Timeout(30.0, read=None),
-    ) as response:
-        response.raise_for_status()  # pyright: ignore[reportUnusedCallResult]
-        total = int(response.headers.get("content-length", 0))
-
-        with (
-            open(output_path, "wb") as f,
-            tqdm(
-                total=total,
-                unit="B",
-                unit_scale=True,
-                unit_divisor=1024,
-                desc="Downloading",
-            ) as pbar,
-        ):
-            for chunk in response.iter_bytes(chunk_size=8192):
-                f.write(chunk)
-                pbar.update(len(chunk))
-        return
-
-
 def convert_to_parquet(csv_path: Path, parquet_path: Path) -> None:
     """Convert CSV to Parquet using Polars."""
     print("Converting to Parquet...")
@@ -84,7 +58,7 @@ def main() -> None:
     with tempfile.TemporaryDirectory() as cache_dir:
         csv_path = Path(cache_dir) / "price-paid-complete.csv"
 
-        download_with_progress(URL, csv_path)
+        download(URL, csv_path)
         convert_to_parquet(csv_path, args.output)
 
 
diff --git a/pipeline/transform/merge.py b/pipeline/transform/merge.py
index b5081b6..e672b78 100644
--- a/pipeline/transform/merge.py
+++ b/pipeline/transform/merge.py
@@ -6,11 +6,14 @@ from pathlib import Path
 def _build_wide(
     epc_pp_path: Path,
     arcgis_path: Path,
-    iod_path: Path | None,
-    poi_proximity_path: Path | None,
-    journey_times_path: Path | None,
-    ethnicity_path: Path | None,
-    crime_path: Path | None,
+    iod_path: Path,
+    poi_proximity_path: Path,
+    journey_times_path: Path,
+    ethnicity_path: Path,
+    crime_path: Path ,
+    noise_path: Path ,
+    ofsted_path: Path,
+    broadband_path: Path,
 ) -> pl.DataFrame:
     """Build the wide dataframe by joining epc_pp with all auxiliary data."""
     print("Scanning epc_pp...")
@@ -23,19 +26,18 @@ def _build_wide(
         "lat",
         pl.col("long").alias("lon"),
         "lsoa21",
+        "oa21",
     )
     wide = wide.join(arcgis, on="postcode", how="inner")
 
-    # Journey times (optional)
-    if journey_times_path and journey_times_path.exists():
-        print("Joining journey times...")
-        journey_times = pl.scan_parquet(journey_times_path).select(
-            "postcode",
-            "public_transport_easy_minutes",
-            "public_transport_quick_minutes",
-            "cycling_minutes",
-        )
-        wide = wide.join(journey_times, on="postcode", how="left")
+    print("Joining journey times...")
+    journey_times = pl.scan_parquet(journey_times_path).select(
+        "postcode",
+        "public_transport_easy_minutes",
+        "public_transport_quick_minutes",
+        "cycling_minutes",
+    )
+    wide = wide.join(journey_times, on="postcode", how="left")
 
     print("Joining IoD scores...")
     iod = pl.scan_parquet(iod_path)
@@ -60,6 +62,32 @@ def _build_wide(
     poi_counts = pl.scan_parquet(poi_proximity_path)
     wide = wide.join(poi_counts, on="postcode", how="left")
 
+    noise = pl.scan_parquet(noise_path).select("postcode", "road_noise_lden_db")
+    wide = wide.join(noise, on="postcode", how="left")
+
+    print("Joining Ofsted school ratings...")
+    ofsted = (
+        pl.scan_parquet(ofsted_path)
+        .filter(pl.col("Overall effectiveness").is_in(["1", "2", "3", "4"]))
+        .select(
+            pl.col("Postcode").alias("ofsted_postcode"),
+            pl.col("Overall effectiveness").cast(pl.UInt8).alias("ofsted_rating"),
+        )
+        .group_by("ofsted_postcode")
+        .agg(pl.col("ofsted_rating").mean().round(1).alias("ofsted_avg_rating"))
+    )
+    wide = wide.join(ofsted, left_on="postcode", right_on="ofsted_postcode", how="left")
+
+    print("Joining broadband performance...")
+    broadband = pl.scan_parquet(broadband_path).select(
+        "output_area",
+        pl.col("Average max download speed (Mbit/s) for lines >=900Mbit/s").alias("broadband_max_download_900plus"),
+        pl.col("Average max download speed (Mbit/s) for lines 100<300Mbit/s").alias("broadband_max_download_100_300"),
+        pl.col("Average max download speed (Mbit/s) for lines 30<100Mbit/s").alias("broadband_max_download_30_100"),
+        pl.col("Average max upload speed (Mbit/s) for lines >=900Mbit/s").alias("broadband_max_upload_900plus"),
+    )
+    wide = wide.join(broadband, left_on="oa21", right_on="output_area", how="left")
+
     # Convert construction_age_band to numeric year
     wide = wide.with_columns(
         pl.col("construction_age_band")
@@ -103,6 +131,7 @@ def _build_wide(
             "Income Deprivation Affecting Children Index (IDACI) Score (rate)",
             "Barriers to Housing and Services Score",
             "lsoa21",
+            "oa21",
             "pp_property_type",
             "built_form",
         )
@@ -124,6 +153,12 @@ def _build_wide(
                 "public_transport_2km": "Public transport within 2km",
                 "latest_price": "Last known price",
                 "number_habitable_rooms": "Rooms (including bedrooms & bathrooms)",
+                "road_noise_lden_db": "Road noise Lden (dB)",
+                "ofsted_avg_rating": "Ofsted avg rating (1=Outstanding, 4=Inadequate)"
+                        "broadband_max_download_900plus": "Broadband download speed 900+ Mbps",
+                        "broadband_max_download_100_300": "Broadband download speed 100-300 Mbps",
+                        "broadband_max_download_30_100": "Broadband download speed 30-100 Mbps",
+                        "broadband_max_upload_900plus": "Broadband upload speed 900+ Mbps",
             }
         )
     )
@@ -131,7 +166,6 @@ def _build_wide(
     print("Collecting with streaming engine...")
     return wide.collect(engine="streaming")
 
-
 def main():
     parser = argparse.ArgumentParser(
         description="Build wide property dataframe with all joins"
@@ -159,6 +193,15 @@ def main():
     parser.add_argument(
         "--crime", type=Path, required=True, help="Crime by LSOA parquet file (optional)"
     )
+    parser.add_argument(
+        "--noise", type=Path,  required=True, help="Road noise by postcode parquet file"
+    )
+    parser.add_argument(
+        "--ofsted", type=Path,  required=True, help="Ofsted school inspection parquet file"
+    )
+    parser.add_argument(
+        "--broadband", type=Path,  required=True, help="Broadband performance by output area parquet file"
+    )
     parser.add_argument(
         "--output", type=Path, required=True, help="Output parquet file path"
     )
@@ -172,6 +215,9 @@ def main():
         journey_times_path=args.journey_times,
         ethnicity_path=args.ethnicity,
         crime_path=args.crime,
+        noise_path=args.noise,
+        ofsted_path=args.ofsted,
+        broadband_path=args.broadband,
     )
 
     print(f"Columns: {wide.columns}")
diff --git a/pipeline/utils/__init__.py b/pipeline/utils/__init__.py
index cb7663a..772d4ae 100644
--- a/pipeline/utils/__init__.py
+++ b/pipeline/utils/__init__.py
@@ -1,8 +1,11 @@
+from .download import download, extract_zip
 from .fuzzy_join import fuzzy_join_on_postcode
 from .haversine import haversine_km, haversine_km_expr
 from .poi_counts import POI_GROUPS, count_pois_within_radius
 
 __all__ = [
+    "download",
+    "extract_zip",
     "fuzzy_join_on_postcode",
     "haversine_km",
     "haversine_km_expr",
diff --git a/pipeline/utils/download.py b/pipeline/utils/download.py
new file mode 100644
index 0000000..558184f
--- /dev/null
+++ b/pipeline/utils/download.py
@@ -0,0 +1,40 @@
+"""Shared download and extraction helpers for pipeline scripts."""
+
+import zipfile
+from pathlib import Path
+
+import httpx
+from tqdm import tqdm
+
+
+def download(url: str, output_path: Path, *, timeout: float = 120) -> None:
+    """Stream-download a URL to a local file with a tqdm progress bar."""
+    with httpx.stream(
+        "GET",
+        url,
+        follow_redirects=True,
+        timeout=httpx.Timeout(30.0, read=timeout),
+    ) as response:
+        response.raise_for_status()  # pyright: ignore[reportUnusedCallResult]
+        total = int(response.headers.get("content-length", 0))
+
+        with (
+            open(output_path, "wb") as f,
+            tqdm(
+                total=total or None,
+                unit="B",
+                unit_scale=True,
+                unit_divisor=1024,
+                desc=output_path.name,
+            ) as pbar,
+        ):
+            for chunk in response.iter_bytes(chunk_size=8192):
+                f.write(chunk)
+                pbar.update(len(chunk))
+
+
+def extract_zip(zip_path: Path, extract_dir: Path) -> None:
+    """Extract a ZIP archive into the given directory."""
+    extract_dir.mkdir(parents=True, exist_ok=True)
+    with zipfile.ZipFile(zip_path, "r") as zf:
+        zf.extractall(extract_dir)

From 01ec17ff04b9592a5b200288537cc3b87b1c39d3 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sat, 31 Jan 2026 20:25:54 +0000
Subject: [PATCH 36/62] Refactor the server

---
 server-rs/Cargo.lock               |  92 +++++
 server-rs/Cargo.toml               |   4 +-
 server-rs/rust-toolchain.toml      |   8 +
 server-rs/src/consts.rs            |  14 +-
 server-rs/src/data.rs              | 555 -------------------------
 server-rs/src/filter.rs            |  85 ++++
 server-rs/src/index.rs             |  18 +-
 server-rs/src/main.rs              |  57 ++-
 server-rs/src/routes.rs            | 636 -----------------------------
 server-rs/src/routes/features.rs   |  87 ++++
 server-rs/src/routes/hexagons.rs   | 257 ++++++++++++
 server-rs/src/routes/mod.rs        |   9 +
 server-rs/src/routes/pois.rs       | 133 ++++++
 server-rs/src/routes/properties.rs | 198 +++++++++
 server-rs/src/state.rs             |  12 +
 15 files changed, 939 insertions(+), 1226 deletions(-)
 create mode 100644 server-rs/rust-toolchain.toml
 delete mode 100644 server-rs/src/data.rs
 create mode 100644 server-rs/src/filter.rs
 delete mode 100644 server-rs/src/routes.rs
 create mode 100644 server-rs/src/routes/features.rs
 create mode 100644 server-rs/src/routes/hexagons.rs
 create mode 100644 server-rs/src/routes/mod.rs
 create mode 100644 server-rs/src/routes/pois.rs
 create mode 100644 server-rs/src/routes/properties.rs
 create mode 100644 server-rs/src/state.rs

diff --git a/server-rs/Cargo.lock b/server-rs/Cargo.lock
index ddb90e7..abca744 100644
--- a/server-rs/Cargo.lock
+++ b/server-rs/Cargo.lock
@@ -921,6 +921,12 @@ dependencies = [
  "wasm-bindgen",
 ]
 
+[[package]]
+name = "lazy_static"
+version = "1.5.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "bbd2bcb4c963f2ddae06a2efc7e9f3591312473c50c6685e1f298068316e66fe"
+
 [[package]]
 name = "libc"
 version = "0.2.180"
@@ -979,6 +985,15 @@ dependencies = [
  "libc",
 ]
 
+[[package]]
+name = "matchers"
+version = "0.2.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "d1525a2a28c7f4fa0fc98bb91ae755d1e2d1505079e05539e35bc876b5d65ae9"
+dependencies = [
+ "regex-automata",
+]
+
 [[package]]
 name = "matchit"
 version = "0.8.4"
@@ -1055,6 +1070,15 @@ dependencies = [
  "winapi",
 ]
 
+[[package]]
+name = "nu-ansi-term"
+version = "0.50.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "7957b9740744892f114936ab4a57b3f487491bbeafaf8083688b16841a4240e5"
+dependencies = [
+ "windows-sys 0.61.2",
+]
+
 [[package]]
 name = "num-traits"
 version = "0.2.19"
@@ -1678,6 +1702,8 @@ dependencies = [
  "serde_json",
  "tokio",
  "tower-http",
+ "tracing",
+ "tracing-subscriber",
 ]
 
 [[package]]
@@ -1935,6 +1961,15 @@ dependencies = [
  "serde",
 ]
 
+[[package]]
+name = "sharded-slab"
+version = "0.1.7"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "f40ca3c46823713e0d4209592e8d6e826aa57e928f09752619fc696c499637f6"
+dependencies = [
+ "lazy_static",
+]
+
 [[package]]
 name = "shlex"
 version = "1.3.0"
@@ -2118,6 +2153,15 @@ dependencies = [
  "syn",
 ]
 
+[[package]]
+name = "thread_local"
+version = "1.1.9"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "f60246a4944f24f6e018aa17cdeffb7818b76356965d03b07d6a9886e8962185"
+dependencies = [
+ "cfg-if",
+]
+
 [[package]]
 name = "tiny-keccak"
 version = "2.0.2"
@@ -2246,9 +2290,21 @@ checksum = "63e71662fa4b2a2c3a26f570f037eb95bb1f85397f3cd8076caed2f026a6d100"
 dependencies = [
  "log",
  "pin-project-lite",
+ "tracing-attributes",
  "tracing-core",
 ]
 
+[[package]]
+name = "tracing-attributes"
+version = "0.1.31"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "7490cfa5ec963746568740651ac6781f701c9c5ea257c58e057f3ba8cf69e8da"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn",
+]
+
 [[package]]
 name = "tracing-core"
 version = "0.1.36"
@@ -2256,6 +2312,36 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "db97caf9d906fbde555dd62fa95ddba9eecfd14cb388e4f491a66d74cd5fb79a"
 dependencies = [
  "once_cell",
+ "valuable",
+]
+
+[[package]]
+name = "tracing-log"
+version = "0.2.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "ee855f1f400bd0e5c02d150ae5de3840039a3f54b025156404e34c23c03f47c3"
+dependencies = [
+ "log",
+ "once_cell",
+ "tracing-core",
+]
+
+[[package]]
+name = "tracing-subscriber"
+version = "0.3.22"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "2f30143827ddab0d256fd843b7a66d164e9f271cfa0dde49142c5ca0ca291f1e"
+dependencies = [
+ "matchers",
+ "nu-ansi-term",
+ "once_cell",
+ "regex-automata",
+ "sharded-slab",
+ "smallvec",
+ "thread_local",
+ "tracing",
+ "tracing-core",
+ "tracing-log",
 ]
 
 [[package]]
@@ -2311,6 +2397,12 @@ dependencies = [
  "wasm-bindgen",
 ]
 
+[[package]]
+name = "valuable"
+version = "0.1.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "ba73ea9cf16a25df0c8caa16c51acb937d5712a8429db78a3ee29d5dcacd3a65"
+
 [[package]]
 name = "version_check"
 version = "0.9.5"
diff --git a/server-rs/Cargo.toml b/server-rs/Cargo.toml
index 5ddaa7d..24c355a 100644
--- a/server-rs/Cargo.toml
+++ b/server-rs/Cargo.toml
@@ -5,7 +5,7 @@ edition = "2021"
 
 [dependencies]
 axum = "0.8"
-tower-http = { version = "0.6", features = ["cors", "fs", "compression-gzip"] }
+tower-http = { version = "0.6", features = ["cors", "fs", "compression-gzip", "trace"] }
 tokio = { version = "1", features = ["full"] }
 polars = { version = "0.46", features = ["parquet", "lazy", "dtype-struct", "dtype-u8", "dtype-u16", "dtype-i8", "dtype-i16"] }
 h3o = "0.7"
@@ -13,6 +13,8 @@ serde = { version = "1", features = ["derive"] }
 serde_json = "1"
 rayon = "1"
 rustc-hash = "2"
+tracing = "0.1"
+tracing-subscriber = { version = "0.3", features = ["env-filter", "fmt"] }
 
 [profile.release]
 opt-level = 3
diff --git a/server-rs/rust-toolchain.toml b/server-rs/rust-toolchain.toml
new file mode 100644
index 0000000..3185d77
--- /dev/null
+++ b/server-rs/rust-toolchain.toml
@@ -0,0 +1,8 @@
+[toolchain]
+channel = "stable"
+targets = [
+  "x86_64-unknown-linux-gnu",
+  "x86_64-unknown-linux-musl",
+  "aarch64-unknown-linux-gnu",
+]
+profile = "default"
diff --git a/server-rs/src/consts.rs b/server-rs/src/consts.rs
index a72db94..c7190cf 100644
--- a/server-rs/src/consts.rs
+++ b/server-rs/src/consts.rs
@@ -1,14 +1,20 @@
-/// Lower percentile for feature range reporting
 pub const FEATURE_PERCENTILE_LOW: f64 = 2.0;
 
-/// Upper percentile for feature range reporting
 pub const FEATURE_PERCENTILE_HIGH: f64 = 98.0;
 
 pub const HISTOGRAM_BINS: usize = 100;
 
-/// H3 resolutions to precompute at startup (covers typical zoom levels)
 pub const H3_PRECOMPUTE_MIN: u8 = 4;
 pub const H3_PRECOMPUTE_MAX: u8 = 12;
 
-/// Columns to exclude from feature discovery
 pub const EXCLUDED_COLUMNS: &[&str] = &["lat", "lon"];
+
+pub const EXCLUDED_STRING_COLUMNS: &[&str] = &[
+    "pp_address",
+    "postcode",
+    "Address per Property Register",
+    "Address per EPC",
+    "Postcode",
+];
+
+pub const MAX_ENUM_CARDINALITY: usize = 50;
diff --git a/server-rs/src/data.rs b/server-rs/src/data.rs
deleted file mode 100644
index 2fec011..0000000
--- a/server-rs/src/data.rs
+++ /dev/null
@@ -1,555 +0,0 @@
-use polars::lazy::frame::LazyFrame;
-use polars::prelude::*;
-use rayon::prelude::*;
-use serde::Serialize;
-use std::path::Path;
-
-use crate::consts::{
-    EXCLUDED_COLUMNS, FEATURE_PERCENTILE_HIGH, FEATURE_PERCENTILE_LOW, H3_PRECOMPUTE_MAX,
-    H3_PRECOMPUTE_MIN, HISTOGRAM_BINS,
-};
-
-/// Returns true if the polars DataType is numeric (integer or float)
-fn is_numeric_dtype(dtype: &DataType) -> bool {
-    matches!(
-        dtype,
-        DataType::Int8
-            | DataType::Int16
-            | DataType::Int32
-            | DataType::Int64
-            | DataType::UInt8
-            | DataType::UInt16
-            | DataType::UInt32
-            | DataType::UInt64
-            | DataType::Float32
-            | DataType::Float64
-    )
-}
-
-/// Histogram for a single feature column
-#[derive(Serialize, Clone)]
-pub struct Histogram {
-    /// Left edge of first bin
-    pub min: f64,
-    /// Right edge of last bin
-    pub max: f64,
-    /// Width of each bin
-    pub bin_width: f64,
-    /// Count of values in each bin
-    pub counts: Vec<u64>,
-}
-
-/// Precomputed statistics for a single feature
-pub struct FeatureStats {
-    pub p_low: f64,
-    pub p_high: f64,
-    pub histogram: Histogram,
-}
-
-/// Columnar storage for all property data.
-/// Feature values use NaN as the null sentinel.
-pub struct PropertyData {
-    pub lat: Vec<f64>,
-    pub lon: Vec<f64>,
-    /// Dynamically discovered numeric feature column names
-    pub feature_names: Vec<String>,
-    /// Number of feature columns
-    pub num_features: usize,
-    /// Row-major flat array: feature_data[row * num_features + feat_idx].
-    /// NaN = null. Contiguous layout for cache-friendly per-row access.
-    pub feature_data: Vec<f64>,
-    /// Precomputed stats (percentiles + histogram) for each feature
-    pub feature_stats: Vec<FeatureStats>,
-    /// String fields for property details
-    pub address: Vec<String>,
-    pub postcode: Vec<String>,
-    pub property_type: Vec<String>,
-    pub built_form: Vec<String>,
-    pub current_energy_rating: Vec<String>,
-    pub potential_energy_rating: Vec<String>,
-}
-
-/// Approximate a percentile from a histogram using linear interpolation.
-/// `p` is in [0, 100]. `total` is the sum of all bin counts.
-fn percentile_from_histogram(
-    counts: &[u64],
-    min: f64,
-    bin_width: f64,
-    total: usize,
-    p: f64,
-) -> f64 {
-    let target = (p / 100.0) * (total as f64 - 1.0);
-    let mut cumulative = 0u64;
-    for (i, &c) in counts.iter().enumerate() {
-        let prev = cumulative;
-        cumulative += c;
-        if cumulative as f64 > target {
-            // Interpolate within this bin
-            let frac = if c > 0 {
-                (target - prev as f64) / c as f64
-            } else {
-                0.0
-            };
-            return min + (i as f64 + frac) * bin_width;
-        }
-    }
-    // Fallback: right edge of last bin
-    min + counts.len() as f64 * bin_width
-}
-
-/// Build a histogram and compute approximate percentiles in O(n) — no sort needed.
-fn compute_feature_stats(vals: &[f64]) -> FeatureStats {
-    // Single pass: min, max, count (skipping NaN)
-    let mut min = f64::INFINITY;
-    let mut max = f64::NEG_INFINITY;
-    let mut count = 0usize;
-    for &v in vals {
-        if !v.is_nan() {
-            if v < min {
-                min = v;
-            }
-            if v > max {
-                max = v;
-            }
-            count += 1;
-        }
-    }
-
-    if count == 0 {
-        return FeatureStats {
-            p_low: 0.0,
-            p_high: 0.0,
-            histogram: Histogram {
-                min: 0.0,
-                max: 0.0,
-                bin_width: 1.0,
-                counts: vec![0; HISTOGRAM_BINS],
-            },
-        };
-    }
-
-    // Build histogram over full range (second pass, no sort)
-    let range = if max == min { 1.0 } else { max - min };
-    let bin_max = min + range * (1.0 + 1e-9);
-    let bin_width = (bin_max - min) / HISTOGRAM_BINS as f64;
-
-    let mut counts = vec![0u64; HISTOGRAM_BINS];
-    for &v in vals {
-        if !v.is_nan() {
-            let bin = ((v - min) / bin_width) as usize;
-            counts[bin.min(HISTOGRAM_BINS - 1)] += 1;
-        }
-    }
-
-    // Approximate percentiles from the histogram
-    let p_low = percentile_from_histogram(&counts, min, bin_width, count, FEATURE_PERCENTILE_LOW);
-    let p_high = percentile_from_histogram(&counts, min, bin_width, count, FEATURE_PERCENTILE_HIGH);
-
-    FeatureStats {
-        p_low,
-        p_high,
-        histogram: Histogram {
-            min,
-            max,
-            bin_width,
-            counts,
-        },
-    }
-}
-
-/// Convert a polars Column to Vec<f64> using NaN for null values
-fn column_to_f64_vec(c: &Column) -> Vec<f64> {
-    let s = c.cast(&DataType::Float64).unwrap();
-    let ca = s.f64().unwrap();
-    ca.into_iter().map(|v| v.unwrap_or(f64::NAN)).collect()
-}
-
-/// Precompute H3 cell IDs for all rows at commonly used resolutions.
-/// Returns a Vec indexed by resolution (0..16), where non-precomputed
-/// resolutions have an empty Vec.
-pub fn precompute_h3(lat: &[f64], lon: &[f64]) -> Vec<Vec<u64>> {
-    eprintln!(
-        "Precomputing H3 cells for resolutions {}..{}...",
-        H3_PRECOMPUTE_MIN, H3_PRECOMPUTE_MAX
-    );
-
-    let resolutions: Vec<u8> = (H3_PRECOMPUTE_MIN..=H3_PRECOMPUTE_MAX).collect();
-    let computed: Vec<(u8, Vec<u64>)> = resolutions
-        .into_par_iter()
-        .map(|res| {
-            let h3_res = h3o::Resolution::try_from(res).unwrap();
-            let cells: Vec<u64> = lat
-                .iter()
-                .zip(lon.iter())
-                .map(|(&la, &lo)| {
-                    h3o::LatLng::new(la, lo)
-                        .map(|c| u64::from(c.to_cell(h3_res)))
-                        .unwrap_or(0)
-                })
-                .collect();
-            eprintln!("  Resolution {} done ({} cells)", res, cells.len());
-            (res, cells)
-        })
-        .collect();
-
-    let mut result: Vec<Vec<u64>> = (0..16).map(|_| Vec::new()).collect();
-    for (res, cells) in computed {
-        result[res as usize] = cells;
-    }
-
-    eprintln!("H3 precomputation complete.");
-    result
-}
-
-impl PropertyData {
-    pub fn load(parquet_path: &Path) -> Self {
-        eprintln!("Loading parquet from {:?}...", parquet_path);
-
-        // Scan schema to discover numeric feature columns
-        let mut lf = LazyFrame::scan_parquet(parquet_path, Default::default())
-            .expect("Failed to scan parquet");
-        let schema = lf.collect_schema().expect("Failed to read schema");
-
-        let feature_names: Vec<String> = schema
-            .iter()
-            .filter(|(name, dtype)| {
-                is_numeric_dtype(dtype) && !EXCLUDED_COLUMNS.contains(&name.as_str())
-            })
-            .map(|(name, _)| name.to_string())
-            .collect();
-
-        let num_features = feature_names.len();
-        eprintln!("Discovered {} numeric feature columns", num_features);
-
-        // Read only the columns we need
-        let mut cols_needed: Vec<String> = vec!["lat".into(), "lon".into()];
-        cols_needed.extend(feature_names.iter().cloned());
-
-        // Add string columns (using actual column names from parquet)
-        let string_cols = vec![
-            "pp_address",
-            "postcode",
-            "pp_property_type",
-            "built_form",
-            "current_energy_rating",
-            "potential_energy_rating",
-        ];
-
-        // Build selection with proper casting
-        let mut select_exprs: Vec<polars::prelude::Expr> = vec![];
-
-        // lat/lon as f64
-        select_exprs.push(col("lat").cast(DataType::Float64));
-        select_exprs.push(col("lon").cast(DataType::Float64));
-
-        // numeric features as f64
-        for name in &feature_names {
-            select_exprs.push(col(name.as_str()).cast(DataType::Float64));
-        }
-
-        // string columns as string (check if they exist in schema)
-        for &s_col in &string_cols {
-            if schema.get(s_col).is_some() {
-                select_exprs.push(col(s_col).cast(DataType::String));
-            }
-        }
-
-        let df = LazyFrame::scan_parquet(parquet_path, Default::default())
-            .expect("Failed to scan parquet")
-            .select(select_exprs)
-            .collect()
-            .expect("Failed to read parquet");
-
-        let row_count = df.height();
-        eprintln!("Loaded {} rows", row_count);
-
-        // Extract lat/lon using bulk iterator
-        let lat_series = df.column("lat").unwrap().cast(&DataType::Float64).unwrap();
-        let lat: Vec<f64> = lat_series
-            .f64()
-            .unwrap()
-            .into_iter()
-            .map(|v| v.unwrap_or(0.0))
-            .collect();
-
-        let lon_series = df.column("lon").unwrap().cast(&DataType::Float64).unwrap();
-        let lon: Vec<f64> = lon_series
-            .f64()
-            .unwrap()
-            .into_iter()
-            .map(|v| v.unwrap_or(0.0))
-            .collect();
-
-        // Extract feature columns (column-major, for cache-friendly histogram computation)
-        eprintln!("Extracting feature columns...");
-        let col_major: Vec<Vec<f64>> = feature_names
-            .iter()
-            .map(|name| {
-                let s = df.column(name.as_str()).unwrap();
-                column_to_f64_vec(s)
-            })
-            .collect();
-
-        // Compute histograms in parallel (column-major is ideal for per-column iteration)
-        eprintln!("Computing histograms...");
-        let feature_stats: Vec<FeatureStats> = col_major
-            .par_iter()
-            .enumerate()
-            .map(|(i, vals)| {
-                let stats = compute_feature_stats(vals);
-                eprintln!(
-                    "  {}: p{}={:.2}, p{}={:.2}, {} bins",
-                    feature_names[i],
-                    FEATURE_PERCENTILE_LOW,
-                    stats.p_low,
-                    FEATURE_PERCENTILE_HIGH,
-                    stats.p_high,
-                    stats.histogram.counts.len()
-                );
-                stats
-            })
-            .collect();
-
-        // Extract string columns (before permutation)
-        eprintln!("Extracting string columns...");
-        let address_raw: Vec<String> = if let Ok(col) = df.column("pp_address") {
-            col.str()
-                .unwrap()
-                .into_iter()
-                .map(|v| v.unwrap_or("").to_string())
-                .collect()
-        } else {
-            vec![String::new(); row_count]
-        };
-
-        let postcode_raw: Vec<String> = if let Ok(col) = df.column("postcode") {
-            col.str()
-                .unwrap()
-                .into_iter()
-                .map(|v| v.unwrap_or("").to_string())
-                .collect()
-        } else {
-            vec![String::new(); row_count]
-        };
-
-        let property_type_raw: Vec<String> = if let Ok(col) = df.column("pp_property_type") {
-            col.str()
-                .unwrap()
-                .into_iter()
-                .map(|v| v.unwrap_or("").to_string())
-                .collect()
-        } else {
-            vec![String::new(); row_count]
-        };
-
-        let built_form_raw: Vec<String> = if let Ok(col) = df.column("built_form") {
-            col.str()
-                .unwrap()
-                .into_iter()
-                .map(|v| v.unwrap_or("").to_string())
-                .collect()
-        } else {
-            vec![String::new(); row_count]
-        };
-
-        let current_energy_rating_raw: Vec<String> =
-            if let Ok(col) = df.column("current_energy_rating") {
-                col.str()
-                    .unwrap()
-                    .into_iter()
-                    .map(|v| v.unwrap_or("").to_string())
-                    .collect()
-            } else {
-                vec![String::new(); row_count]
-            };
-
-        let potential_energy_rating_raw: Vec<String> =
-            if let Ok(col) = df.column("potential_energy_rating") {
-                col.str()
-                    .unwrap()
-                    .into_iter()
-                    .map(|v| v.unwrap_or("").to_string())
-                    .collect()
-            } else {
-                vec![String::new(); row_count]
-            };
-
-        // Sort all rows by spatial locality so that grid queries access
-        // contiguous memory (sequential reads instead of random DRAM accesses).
-        // Uses the same 0.01° grid cell as the spatial index for the sort key.
-        eprintln!("Sorting rows by spatial locality...");
-        let grid_cell_size = 0.01_f64;
-        let min_lat_val = lat.iter().cloned().fold(f64::INFINITY, f64::min) - grid_cell_size;
-        let min_lon_val = lon.iter().cloned().fold(f64::INFINITY, f64::min) - grid_cell_size;
-        let max_lon_val = lon.iter().cloned().fold(f64::NEG_INFINITY, f64::max) + grid_cell_size;
-        let grid_cols = ((max_lon_val - min_lon_val) / grid_cell_size).ceil() as u64 + 1;
-
-        let mut perm: Vec<u32> = (0..row_count as u32).collect();
-        perm.sort_unstable_by_key(|&i| {
-            let r = ((lat[i as usize] - min_lat_val) / grid_cell_size) as u64;
-            let c = ((lon[i as usize] - min_lon_val) / grid_cell_size) as u64;
-            r * grid_cols + c
-        });
-
-        // Apply permutation to lat/lon
-        let lat: Vec<f64> = perm.iter().map(|&i| lat[i as usize]).collect();
-        let lon: Vec<f64> = perm.iter().map(|&i| lon[i as usize]).collect();
-
-        // Apply permutation to string columns
-        let address: Vec<String> = perm
-            .iter()
-            .map(|&i| address_raw[i as usize].clone())
-            .collect();
-        let postcode: Vec<String> = perm
-            .iter()
-            .map(|&i| postcode_raw[i as usize].clone())
-            .collect();
-        let property_type: Vec<String> = perm
-            .iter()
-            .map(|&i| property_type_raw[i as usize].clone())
-            .collect();
-        let built_form: Vec<String> = perm
-            .iter()
-            .map(|&i| built_form_raw[i as usize].clone())
-            .collect();
-        let current_energy_rating: Vec<String> = perm
-            .iter()
-            .map(|&i| current_energy_rating_raw[i as usize].clone())
-            .collect();
-        let potential_energy_rating: Vec<String> = perm
-            .iter()
-            .map(|&i| potential_energy_rating_raw[i as usize].clone())
-            .collect();
-
-        // Transpose to row-major AND apply spatial permutation in one pass.
-        // Result: all features for one row are contiguous, and spatially
-        // nearby rows are adjacent in memory.
-        eprintln!("Transposing to row-major layout (spatially sorted)...");
-        let mut feature_data = vec![f64::NAN; row_count * num_features];
-        for (new_row, &old_row) in perm.iter().enumerate() {
-            let old = old_row as usize;
-            let dst_base = new_row * num_features;
-            for (feat_idx, col_vec) in col_major.iter().enumerate() {
-                feature_data[dst_base + feat_idx] = col_vec[old];
-            }
-        }
-
-        eprintln!("Data loading complete.");
-
-        PropertyData {
-            lat,
-            lon,
-            feature_names,
-            num_features,
-            feature_data,
-            feature_stats,
-            address,
-            postcode,
-            property_type,
-            built_form,
-            current_energy_rating,
-            potential_energy_rating,
-        }
-    }
-}
-
-/// Point of Interest data
-#[derive(Serialize)]
-pub struct POI {
-    pub id: String,
-    pub name: String,
-    pub category: String,
-    pub lat: f64,
-    pub lng: f64,
-    pub emoji: String,
-}
-
-/// Columnar storage for POI data
-pub struct POIData {
-    pub id: Vec<String>,
-    pub name: Vec<String>,
-    pub category: Vec<String>,
-    pub lat: Vec<f64>,
-    pub lng: Vec<f64>,
-    pub emoji: Vec<String>,
-}
-
-impl POIData {
-    pub fn load(parquet_path: &Path) -> Self {
-        eprintln!("Loading POI data from {:?}...", parquet_path);
-
-        let df = LazyFrame::scan_parquet(parquet_path, Default::default())
-            .expect("Failed to scan POI parquet")
-            .collect()
-            .expect("Failed to read POI parquet");
-
-        let row_count = df.height();
-        eprintln!("Loaded {} POIs", row_count);
-
-        // Extract columns
-        let id: Vec<String> = df
-            .column("id")
-            .unwrap()
-            .str()
-            .unwrap()
-            .into_iter()
-            .map(|v| v.unwrap_or("").to_string())
-            .collect();
-
-        let name: Vec<String> = df
-            .column("name")
-            .unwrap()
-            .str()
-            .unwrap()
-            .into_iter()
-            .map(|v| v.unwrap_or("").to_string())
-            .collect();
-
-        let category: Vec<String> = df
-            .column("category")
-            .unwrap()
-            .str()
-            .unwrap()
-            .into_iter()
-            .map(|v| v.unwrap_or("").to_string())
-            .collect();
-
-        let lat: Vec<f64> = df
-            .column("lat")
-            .unwrap()
-            .f64()
-            .unwrap()
-            .into_iter()
-            .map(|v| v.unwrap_or(0.0))
-            .collect();
-
-        let lng: Vec<f64> = df
-            .column("lng")
-            .unwrap()
-            .f64()
-            .unwrap()
-            .into_iter()
-            .map(|v| v.unwrap_or(0.0))
-            .collect();
-
-        let emoji: Vec<String> = df
-            .column("emoji")
-            .unwrap()
-            .str()
-            .unwrap()
-            .into_iter()
-            .map(|v| v.unwrap_or("").to_string())
-            .collect();
-
-        eprintln!("POI data loading complete.");
-
-        POIData {
-            id,
-            name,
-            category,
-            lat,
-            lng,
-            emoji,
-        }
-    }
-}
diff --git a/server-rs/src/filter.rs b/server-rs/src/filter.rs
new file mode 100644
index 0000000..167c1a2
--- /dev/null
+++ b/server-rs/src/filter.rs
@@ -0,0 +1,85 @@
+use crate::data::EnumFeatureData;
+
+pub struct ParsedFilter {
+    pub feat_idx: usize,
+    pub min: f64,
+    pub max: f64,
+}
+
+pub struct ParsedEnumFilter {
+    pub enum_idx: usize,
+    pub allowed: Vec<u8>,
+}
+
+/// Parse comma-separated filter string into numeric and enum filters.
+/// Numeric format: `name:min:max`
+/// Enum format: `name:val1|val2|val3` (pipe-separated values)
+pub fn parse_filters(
+    filter_str: Option<&str>,
+    feature_names: &[String],
+    enum_features: &[EnumFeatureData],
+) -> (Vec<ParsedFilter>, Vec<ParsedEnumFilter>) {
+    let mut numeric = Vec::new();
+    let mut enums = Vec::new();
+
+    let s = match filter_str.filter(|s| !s.is_empty()) {
+        Some(s) => s,
+        None => return (numeric, enums),
+    };
+
+    for entry in s.split(',') {
+        let parts: Vec<&str> = entry.splitn(2, ':').collect();
+        if parts.len() != 2 {
+            continue;
+        }
+        let name = parts[0].trim();
+        let rest = parts[1].trim();
+
+        if let Some(enum_idx) = enum_features.iter().position(|ef| ef.name == name) {
+            let ef = &enum_features[enum_idx];
+            let allowed: Vec<u8> = rest
+                .split('|')
+                .filter_map(|v| {
+                    let v = v.trim();
+                    ef.values.iter().position(|ev| ev == v).map(|i| i as u8)
+                })
+                .collect();
+            enums.push(ParsedEnumFilter { enum_idx, allowed });
+        } else {
+            let num_parts: Vec<&str> = rest.splitn(2, ':').collect();
+            if num_parts.len() != 2 {
+                continue;
+            }
+            let min = match num_parts[0].trim().parse::<f64>() {
+                Ok(v) => v,
+                Err(_) => continue,
+            };
+            let max = match num_parts[1].trim().parse::<f64>() {
+                Ok(v) => v,
+                Err(_) => continue,
+            };
+            if let Some(feat_idx) = feature_names.iter().position(|n| n == name) {
+                numeric.push(ParsedFilter { feat_idx, min, max });
+            }
+        }
+    }
+
+    (numeric, enums)
+}
+
+pub fn row_passes_filters(
+    row: usize,
+    filters: &[ParsedFilter],
+    enum_filters: &[ParsedEnumFilter],
+    feature_data: &[f64],
+    num_features: usize,
+    enum_features: &[EnumFeatureData],
+) -> bool {
+    filters.iter().all(|f| {
+        let v = feature_data[row * num_features + f.feat_idx];
+        v.is_finite() && v >= f.min && v <= f.max
+    }) && enum_filters.iter().all(|ef| {
+        let v = enum_features[ef.enum_idx].data[row];
+        v != 255 && ef.allowed.contains(&v)
+    })
+}
diff --git a/server-rs/src/index.rs b/server-rs/src/index.rs
index ee13588..03412f6 100644
--- a/server-rs/src/index.rs
+++ b/server-rs/src/index.rs
@@ -14,9 +14,7 @@ pub struct GridIndex {
 }
 
 impl GridIndex {
-    /// Build the grid index from lat/lon arrays.
     pub fn build(lat: &[f64], lon: &[f64], cell_size: f64) -> Self {
-        // Compute bounding box with a small margin
         let mut min_lat = f64::INFINITY;
         let mut max_lat = f64::NEG_INFINITY;
         let mut min_lon = f64::INFINITY;
@@ -39,7 +37,6 @@ impl GridIndex {
             }
         }
 
-        // Add margin
         min_lat -= cell_size;
         min_lon -= cell_size;
         max_lat += cell_size;
@@ -48,12 +45,12 @@ impl GridIndex {
         let rows = ((max_lat - min_lat) / cell_size).ceil() as usize + 1;
         let cols = ((max_lon - min_lon) / cell_size).ceil() as usize + 1;
 
-        eprintln!(
-            "Building grid index: {}x{} cells ({} total), cell_size={}",
-            rows,
-            cols,
-            rows * cols,
-            cell_size
+        tracing::debug!(
+            rows_grid = rows,
+            cols_grid = cols,
+            total_cells = rows * cols,
+            cell_size,
+            "Building grid index"
         );
 
         let mut cells: Vec<Vec<u32>> = vec![Vec::new(); rows * cols];
@@ -65,7 +62,7 @@ impl GridIndex {
             cells[idx].push(i as u32);
         }
 
-        eprintln!("Grid index built.");
+        tracing::debug!("Grid index built");
 
         GridIndex {
             min_lat,
@@ -77,7 +74,6 @@ impl GridIndex {
         }
     }
 
-    /// Query all row indices within the given bounding box.
     pub fn query(&self, south: f64, west: f64, north: f64, east: f64) -> Vec<u32> {
         let (r_min, r_max, c_min, c_max) = self.clamp_bounds(south, west, north, east);
 
diff --git a/server-rs/src/main.rs b/server-rs/src/main.rs
index ccd8831..23ec229 100644
--- a/server-rs/src/main.rs
+++ b/server-rs/src/main.rs
@@ -1,7 +1,9 @@
 mod consts;
 mod data;
+mod filter;
 mod index;
 mod routes;
+mod state;
 
 use std::path::PathBuf;
 use std::sync::Arc;
@@ -11,41 +13,55 @@ use axum::Router;
 use tower_http::compression::CompressionLayer;
 use tower_http::cors::{Any, CorsLayer};
 use tower_http::services::ServeDir;
+use tower_http::trace::TraceLayer;
+use tracing::info;
+use tracing_subscriber::EnvFilter;
 
-use routes::AppState;
+use state::AppState;
 
 #[tokio::main]
 async fn main() {
+    tracing_subscriber::fmt()
+        .with_env_filter(
+            EnvFilter::try_from_default_env().unwrap_or_else(|_| EnvFilter::new("info")),
+        )
+        .with_ansi(true)
+        .init();
+
     let parquet_path = PathBuf::from(
         std::env::args()
             .nth(1)
             .unwrap_or_else(|| "data_sources/processed/wide.parquet".to_string()),
     );
     if !parquet_path.exists() {
-        eprintln!("Error: {} not found.", parquet_path.display());
+        tracing::error!("Parquet file not found: {}", parquet_path.display());
         std::process::exit(1);
     }
 
-    // Load property data and build indices
+    info!("Loading property data from {}", parquet_path.display());
     let property_data = data::PropertyData::load(&parquet_path);
+    info!(
+        rows = property_data.lat.len(),
+        features = property_data.num_features,
+        enums = property_data.enum_features.len(),
+        "Property data loaded"
+    );
+
+    info!("Building spatial grid index (0.01° cells)");
     let grid = index::GridIndex::build(&property_data.lat, &property_data.lon, 0.01);
+
+    info!("Precomputing H3 cells for resolutions {}-{}", consts::H3_PRECOMPUTE_MIN, consts::H3_PRECOMPUTE_MAX);
     let h3_cells = data::precompute_h3(&property_data.lat, &property_data.lon);
 
-    // Load POI data and build spatial index
-    // Derive POI path from the data parquet path (same directory)
-    let poi_path = parquet_path
-        .parent()
-        .and_then(|p| p.parent())
-        .map(|p| p.join("filtered_uk_pois.parquet"))
-        .unwrap_or_else(|| PathBuf::from("data_sources/filtered_uk_pois.parquet"));
+    let poi_path = PathBuf::from("/volumes/syncthing/Projects/property-map/data/filtered_uk_pois.parquet");
 
     let poi_data = if poi_path.exists() {
-        data::POIData::load(&poi_path)
+        info!("Loading POI data from {}", poi_path.display());
+        let pd = data::POIData::load(&poi_path);
+        info!(pois = pd.lat.len(), "POI data loaded");
+        pd
     } else {
-        eprintln!(
-            "Warning: {} not found. POI endpoints will be unavailable.",
-            poi_path.display()
-        );
+        tracing::warn!("POI file not found: {}. POI endpoints will be unavailable.", poi_path.display());
         data::POIData {
             id: Vec::new(),
             name: Vec::new(),
@@ -55,6 +71,8 @@ async fn main() {
             emoji: Vec::new(),
         }
     };
+
+    info!("Building POI spatial grid index");
     let poi_grid = index::GridIndex::build(&poi_data.lat, &poi_data.lng, 0.01);
 
     let state = Arc::new(AppState {
@@ -70,7 +88,6 @@ async fn main() {
         .allow_methods(Any)
         .allow_headers(Any);
 
-    // API routes
     let state_features = state.clone();
     let state_hexagons = state.clone();
     let state_pois = state.clone();
@@ -101,7 +118,6 @@ async fn main() {
             }),
         );
 
-    // Static file serving for frontend
     let frontend_dist = PathBuf::from("frontend/dist");
     let app = if frontend_dist.exists() {
         api.fallback_service(ServeDir::new(frontend_dist))
@@ -109,10 +125,13 @@ async fn main() {
         api
     };
 
-    let app = app.layer(cors).layer(CompressionLayer::new().gzip(true));
+    let app = app
+        .layer(cors)
+        .layer(CompressionLayer::new().gzip(true))
+        .layer(TraceLayer::new_for_http());
 
     let addr = "0.0.0.0:8001";
-    eprintln!("Server listening on {}", addr);
+    info!("Server listening on {}", addr);
 
     let listener = tokio::net::TcpListener::bind(addr).await.unwrap();
     axum::serve(listener, app).await.unwrap();
diff --git a/server-rs/src/routes.rs b/server-rs/src/routes.rs
deleted file mode 100644
index 509f832..0000000
--- a/server-rs/src/routes.rs
+++ /dev/null
@@ -1,636 +0,0 @@
-use std::fmt::Write;
-use std::str::FromStr;
-use std::sync::Arc;
-
-use axum::extract::Query;
-use axum::http::StatusCode;
-use axum::response::{IntoResponse, Json};
-use rustc_hash::FxHashMap;
-use serde::{Deserialize, Serialize};
-
-use crate::consts::{H3_PRECOMPUTE_MAX, H3_PRECOMPUTE_MIN};
-use crate::data::{Histogram, POIData, PropertyData, POI};
-use crate::index::GridIndex;
-
-/// Shared application state
-pub struct AppState {
-    pub data: PropertyData,
-    pub grid: GridIndex,
-    /// h3_cells[resolution][row_idx] = precomputed H3 cell ID.
-    /// Empty Vec for resolutions not precomputed.
-    pub h3_cells: Vec<Vec<u64>>,
-    pub poi_data: POIData,
-    pub poi_grid: GridIndex,
-}
-
-const BOUNDS_BUFFER_PERCENT: f64 = 0.2;
-
-// ── /api/features ──
-
-#[derive(Serialize)]
-pub struct FeatureInfo {
-    name: String,
-    min: f64,
-    max: f64,
-    label: String,
-    histogram: Histogram,
-}
-
-#[derive(Serialize)]
-pub struct FeaturesResponse {
-    features: Vec<FeatureInfo>,
-}
-
-fn snake_to_label(name: &str) -> String {
-    name.split('_')
-        .map(|word| {
-            let mut chars = word.chars();
-            match chars.next() {
-                None => String::new(),
-                Some(c) => {
-                    let mut s = c.to_uppercase().to_string();
-                    s.extend(chars);
-                    s
-                }
-            }
-        })
-        .collect::<Vec<_>>()
-        .join(" ")
-}
-
-pub async fn get_features(state: Arc<AppState>) -> Json<FeaturesResponse> {
-    let features = state
-        .data
-        .feature_names
-        .iter()
-        .enumerate()
-        .map(|(i, name): (usize, &String)| {
-            let stats = &state.data.feature_stats[i];
-            FeatureInfo {
-                name: name.clone(),
-                min: stats.p_low,
-                max: stats.p_high,
-                label: snake_to_label(name),
-                histogram: stats.histogram.clone(),
-            }
-        })
-        .collect();
-
-    Json(FeaturesResponse { features })
-}
-
-// ── /api/hexagons ──
-
-#[derive(Deserialize)]
-pub struct HexagonParams {
-    resolution: u8,
-    bounds: Option<String>,
-    /// Comma-separated filters: `name:min:max,...`
-    /// Rows must have non-NaN values within [min,max] for each filter.
-    filters: Option<String>,
-}
-
-struct ParsedFilter {
-    feat_idx: usize,
-    min: f64,
-    max: f64,
-}
-
-/// Per-cell accumulator for aggregating features
-struct CellAgg {
-    count: u32,
-    mins: Vec<f64>,
-    maxs: Vec<f64>,
-}
-
-impl CellAgg {
-    fn new(num_features: usize) -> Self {
-        CellAgg {
-            count: 0,
-            mins: vec![f64::INFINITY; num_features],
-            maxs: vec![f64::NEG_INFINITY; num_features],
-        }
-    }
-
-    /// Add a row using row-major feature_data layout.
-    /// feature_data[row * num_features + feat_idx] — all features for one row
-    /// are contiguous, so this reads a single cache line per ~8 features.
-    #[inline]
-    fn add_row(&mut self, feature_data: &[f64], row: usize, num_features: usize) {
-        self.count += 1;
-        let base = row * num_features;
-        let row_slice = &feature_data[base..base + num_features];
-        for (i, &v) in row_slice.iter().enumerate() {
-            if v.is_finite() {
-                if v < self.mins[i] {
-                    self.mins[i] = v;
-                }
-                if v > self.maxs[i] {
-                    self.maxs[i] = v;
-                }
-            }
-        }
-    }
-}
-
-/// Write the hexagons JSON response directly to a String buffer,
-/// avoiding serde_json::Value allocations entirely.
-fn write_hexagons_json(
-    buf: &mut String,
-    groups: &FxHashMap<u64, CellAgg>,
-    min_keys: &[String],
-    max_keys: &[String],
-    num_features: usize,
-) {
-    buf.push_str("{\"features\":[");
-    let mut first = true;
-    for (&cell_id, agg) in groups {
-        if !first {
-            buf.push(',');
-        }
-        first = false;
-
-        let cell = h3o::CellIndex::try_from(cell_id).unwrap();
-        write!(buf, "{{\"h3\":\"{}\",\"count\":{}", cell, agg.count).unwrap();
-
-        for i in 0..num_features {
-            if agg.mins[i] != f64::INFINITY {
-                write!(
-                    buf,
-                    ",\"{}\":{},\"{}\":{}",
-                    min_keys[i], agg.mins[i], max_keys[i], agg.maxs[i]
-                )
-                .unwrap();
-            }
-        }
-        buf.push('}');
-    }
-    buf.push_str("]}");
-}
-
-pub async fn get_hexagons(
-    state: Arc<AppState>,
-    Query(params): Query<HexagonParams>,
-) -> Result<impl IntoResponse, (StatusCode, String)> {
-    let resolution = params.resolution;
-    if resolution < H3_PRECOMPUTE_MIN || resolution > H3_PRECOMPUTE_MAX {
-        return Err((
-            StatusCode::BAD_REQUEST,
-            format!(
-                "resolution must be between {} and {}",
-                H3_PRECOMPUTE_MIN, H3_PRECOMPUTE_MAX
-            ),
-        ));
-    }
-
-    let bounds_str = params.bounds.ok_or((
-        StatusCode::BAD_REQUEST,
-        "bounds parameter is required".into(),
-    ))?;
-
-    let parts: Vec<f64> = bounds_str
-        .split(',')
-        .map(|s| s.trim().parse::<f64>())
-        .collect::<Result<Vec<_>, _>>()
-        .map_err(|_| {
-            (
-                StatusCode::BAD_REQUEST,
-                "Invalid bounds format. Use: south,west,north,east".into(),
-            )
-        })?;
-
-    if parts.len() != 4 {
-        return Err((
-            StatusCode::BAD_REQUEST,
-            "Invalid bounds format. Use: south,west,north,east".into(),
-        ));
-    }
-
-    let (mut south, mut west, mut north, mut east) = (parts[0], parts[1], parts[2], parts[3]);
-
-    // Apply bounds buffer (20%)
-    let lat_range = north - south;
-    let lng_range = east - west;
-    south -= lat_range * BOUNDS_BUFFER_PERCENT;
-    north += lat_range * BOUNDS_BUFFER_PERCENT;
-    west -= lng_range * BOUNDS_BUFFER_PERCENT;
-    east += lng_range * BOUNDS_BUFFER_PERCENT;
-
-    // Quantize to 0.01 degree precision
-    let precision = 0.01;
-    south = (south / precision).floor() * precision;
-    west = (west / precision).floor() * precision;
-    north = (north / precision).ceil() * precision;
-    east = (east / precision).ceil() * precision;
-
-    // Parse filters: `name:min:max,...`
-    let parsed_filters: Vec<ParsedFilter> = params
-        .filters
-        .as_deref()
-        .filter(|s| !s.is_empty())
-        .map(|s| {
-            s.split(',')
-                .filter_map(|entry| {
-                    let parts: Vec<&str> = entry.splitn(3, ':').collect();
-                    if parts.len() != 3 {
-                        return None;
-                    }
-                    let name = parts[0].trim();
-                    let min = parts[1].trim().parse::<f64>().ok()?;
-                    let max = parts[2].trim().parse::<f64>().ok()?;
-                    let feat_idx = state.data.feature_names.iter().position(|n| n == name)?;
-                    Some(ParsedFilter { feat_idx, min, max })
-                })
-                .collect()
-        })
-        .unwrap_or_default();
-
-    // Move CPU-heavy work off the async executor
-    let json_body = tokio::task::spawn_blocking(move || {
-        let t0 = std::time::Instant::now();
-
-        let num_features = state.data.num_features;
-        let feature_data = &state.data.feature_data;
-
-        // Pre-compute JSON key strings once
-        let min_keys: Vec<String> = state
-            .data
-            .feature_names
-            .iter()
-            .map(|n| format!("min_{}", n))
-            .collect();
-        let max_keys: Vec<String> = state
-            .data
-            .feature_names
-            .iter()
-            .map(|n| format!("max_{}", n))
-            .collect();
-
-        // Use precomputed H3 cells if available
-        let h3_cells_for_res: Option<&[u64]> = state
-            .h3_cells
-            .get(resolution as usize)
-            .filter(|v| !v.is_empty())
-            .map(|v| v.as_slice());
-
-        // Aggregate using FxHashMap (fast non-crypto hash for integer keys)
-        // and grid visitor (no intermediate Vec<u32> allocation)
-        let mut groups: FxHashMap<u64, CellAgg> = FxHashMap::default();
-
-        // Row-level filter check: value must be non-NaN and within [min, max]
-        let row_passes = |row: usize| -> bool {
-            parsed_filters.iter().all(|f| {
-                let v = feature_data[row * num_features + f.feat_idx];
-                v.is_finite() && v >= f.min && v <= f.max
-            })
-        };
-
-        if let Some(precomputed) = h3_cells_for_res {
-            // Fast path: precomputed H3 + visitor pattern
-            state
-                .grid
-                .for_each_in_bounds(south, west, north, east, |row_idx| {
-                    let row = row_idx as usize;
-                    if !row_passes(row) {
-                        return;
-                    }
-                    let cell_id = precomputed[row];
-                    groups
-                        .entry(cell_id)
-                        .or_insert_with(|| CellAgg::new(num_features))
-                        .add_row(feature_data, row, num_features);
-                });
-        } else {
-            // Fallback: compute H3 on-the-fly
-            let h3_res = h3o::Resolution::try_from(resolution).unwrap();
-            state
-                .grid
-                .for_each_in_bounds(south, west, north, east, |row_idx| {
-                    let row = row_idx as usize;
-                    if !row_passes(row) {
-                        return;
-                    }
-                    let cell_id = h3o::LatLng::new(state.data.lat[row], state.data.lon[row])
-                        .map(|c| u64::from(c.to_cell(h3_res)))
-                        .unwrap_or(0);
-                    groups
-                        .entry(cell_id)
-                        .or_insert_with(|| CellAgg::new(num_features))
-                        .add_row(feature_data, row, num_features);
-                });
-        }
-
-        let t_agg = t0.elapsed();
-
-        // Write JSON directly (no serde_json::Value allocation overhead)
-        let mut json_buf = String::with_capacity(groups.len() * 128);
-        write_hexagons_json(&mut json_buf, &groups, &min_keys, &max_keys, num_features);
-
-        let t_total = t0.elapsed();
-        eprintln!(
-            "hexagons: res={} cells={} agg={:?} json={:?} total={:?} bytes={}",
-            resolution,
-            groups.len(),
-            t_agg,
-            t_total - t_agg,
-            t_total,
-            json_buf.len()
-        );
-
-        json_buf
-    })
-    .await
-    .unwrap();
-
-    Ok(([("content-type", "application/json")], json_body))
-}
-
-// ── /api/pois ──
-
-#[derive(Deserialize)]
-pub struct POIParams {
-    bounds: Option<String>,
-    /// Comma-separated list of categories to filter by
-    categories: Option<String>,
-}
-
-#[derive(Serialize)]
-pub struct POIsResponse {
-    pois: Vec<POI>,
-}
-
-pub async fn get_pois(
-    state: Arc<AppState>,
-    Query(params): Query<POIParams>,
-) -> Result<Json<POIsResponse>, (StatusCode, String)> {
-    let bounds_str = params.bounds.ok_or((
-        StatusCode::BAD_REQUEST,
-        "bounds parameter is required".into(),
-    ))?;
-
-    let parts: Vec<f64> = bounds_str
-        .split(',')
-        .map(|s| s.trim().parse::<f64>())
-        .collect::<Result<Vec<_>, _>>()
-        .map_err(|_| {
-            (
-                StatusCode::BAD_REQUEST,
-                "Invalid bounds format. Use: south,west,north,east".into(),
-            )
-        })?;
-
-    if parts.len() != 4 {
-        return Err((
-            StatusCode::BAD_REQUEST,
-            "Invalid bounds format. Use: south,west,north,east".into(),
-        ));
-    }
-
-    let (south, west, north, east) = (parts[0], parts[1], parts[2], parts[3]);
-
-    // Parse category filter if provided
-    let category_filter: Option<Vec<String>> = params
-        .categories
-        .as_deref()
-        .filter(|s| !s.is_empty())
-        .map(|s| s.split(',').map(|c| c.trim().to_string()).collect());
-
-    // Move CPU-heavy work off the async executor
-    let result = tokio::task::spawn_blocking(move || {
-        // Spatial query using grid index
-        let row_indices = state.poi_grid.query(south, west, north, east);
-
-        let pois: Vec<POI> = row_indices
-            .iter()
-            .filter_map(|&row_idx| {
-                let row = row_idx as usize;
-
-                // Apply category filter if specified
-                if let Some(ref categories) = category_filter {
-                    if !categories.contains(&state.poi_data.category[row]) {
-                        return None;
-                    }
-                }
-
-                Some(POI {
-                    id: state.poi_data.id[row].clone(),
-                    name: state.poi_data.name[row].clone(),
-                    category: state.poi_data.category[row].clone(),
-                    lat: state.poi_data.lat[row],
-                    lng: state.poi_data.lng[row],
-                    emoji: state.poi_data.emoji[row].clone(),
-                })
-            })
-            .take(5000)
-            .collect();
-
-        POIsResponse { pois }
-    })
-    .await
-    .unwrap();
-
-    Ok(Json(result))
-}
-
-// ── /api/poi-categories ──
-
-#[derive(Serialize)]
-pub struct POICategoriesResponse {
-    categories: Vec<String>,
-}
-
-pub async fn get_poi_categories(state: Arc<AppState>) -> Json<POICategoriesResponse> {
-    // Compute unique categories
-    let result = tokio::task::spawn_blocking(move || {
-        let mut categories: Vec<String> = state
-            .poi_data
-            .category
-            .iter()
-            .cloned()
-            .collect::<std::collections::HashSet<_>>()
-            .into_iter()
-            .collect();
-
-        categories.sort();
-
-        POICategoriesResponse { categories }
-    })
-    .await
-    .unwrap();
-
-    Json(result)
-}
-
-// ── /api/hexagon-properties ──
-
-#[derive(Deserialize)]
-pub struct HexagonPropertiesParams {
-    pub h3: String,
-    pub resolution: u8,
-    pub filters: Option<String>,
-    pub limit: Option<usize>,
-    pub offset: Option<usize>,
-}
-
-#[derive(Serialize)]
-pub struct Property {
-    // String fields
-    pub address: Option<String>,
-    pub postcode: Option<String>,
-    pub property_type: Option<String>,
-    pub built_form: Option<String>,
-    pub current_energy_rating: Option<String>,
-    pub potential_energy_rating: Option<String>,
-
-    // Numeric fields
-    pub lat: f64,
-    pub lon: f64,
-
-    // All other numeric features stored as dynamic map
-    #[serde(flatten)]
-    pub features: FxHashMap<String, f64>,
-}
-
-#[derive(Serialize)]
-pub struct HexagonPropertiesResponse {
-    pub properties: Vec<Property>,
-    pub total: usize,
-    pub limit: usize,
-    pub offset: usize,
-    pub truncated: bool,
-}
-
-/// Helper function to check if a row passes all filters
-fn row_passes_filters(
-    row: usize,
-    filters: &[ParsedFilter],
-    feature_data: &[f64],
-    num_features: usize,
-) -> bool {
-    filters.iter().all(|f| {
-        let v = feature_data[row * num_features + f.feat_idx];
-        v.is_finite() && v >= f.min && v <= f.max
-    })
-}
-
-pub async fn get_hexagon_properties(
-    state: Arc<AppState>,
-    Query(params): Query<HexagonPropertiesParams>,
-) -> Result<Json<HexagonPropertiesResponse>, (StatusCode, String)> {
-    // 1. Parse H3 cell ID
-    let cell = h3o::CellIndex::from_str(&params.h3)
-        .map_err(|e| (StatusCode::BAD_REQUEST, format!("Invalid H3 cell: {}", e)))?;
-    let cell_u64: u64 = cell.into();
-
-    // 2. Validate resolution
-    let resolution = params.resolution as usize;
-    if resolution >= state.h3_cells.len() || state.h3_cells[resolution].is_empty() {
-        return Err((
-            StatusCode::BAD_REQUEST,
-            "Invalid or non-precomputed resolution".to_string(),
-        ));
-    }
-
-    // 3. Parse filters (reuse existing filter parsing logic from get_hexagons)
-    let parsed_filters: Vec<ParsedFilter> = params
-        .filters
-        .as_deref()
-        .filter(|s| !s.is_empty())
-        .map(|s| {
-            s.split(',')
-                .filter_map(|entry| {
-                    let parts: Vec<&str> = entry.splitn(3, ':').collect();
-                    if parts.len() != 3 {
-                        return None;
-                    }
-                    let name = parts[0].trim();
-                    let min = parts[1].trim().parse::<f64>().ok()?;
-                    let max = parts[2].trim().parse::<f64>().ok()?;
-                    let feat_idx = state.data.feature_names.iter().position(|n| n == name)?;
-                    Some(ParsedFilter { feat_idx, min, max })
-                })
-                .collect()
-        })
-        .unwrap_or_default();
-
-    // Move CPU-heavy work off the async executor
-    let result = tokio::task::spawn_blocking(move || {
-        let h3_data = &state.h3_cells[resolution];
-        let num_features = state.data.num_features;
-        let feature_data = &state.data.feature_data;
-
-        // 4. Find all rows with matching H3 cell
-        let matching_rows: Vec<usize> = h3_data
-            .iter()
-            .enumerate()
-            .filter_map(|(idx, &h3_cell)| {
-                if h3_cell == cell_u64 {
-                    // Apply feature filters
-                    if row_passes_filters(idx, &parsed_filters, feature_data, num_features) {
-                        Some(idx)
-                    } else {
-                        None
-                    }
-                } else {
-                    None
-                }
-            })
-            .collect();
-
-        let total = matching_rows.len();
-        let limit = params.limit.unwrap_or(100).min(500);
-        let offset = params.offset.unwrap_or(0);
-        let truncated = total > offset + limit;
-
-        // 5. Extract properties for paginated subset
-        let properties: Vec<Property> = matching_rows
-            .iter()
-            .skip(offset)
-            .take(limit)
-            .map(|&row| {
-                // Build dynamic features map
-                let mut features = FxHashMap::default();
-                let base = row * num_features;
-                for (feat_idx, feat_name) in state.data.feature_names.iter().enumerate() {
-                    let v = feature_data[base + feat_idx];
-                    if v.is_finite() {
-                        features.insert(feat_name.clone(), v);
-                    }
-                }
-
-                // Helper to get non-empty string
-                let get_string = |s: &str| -> Option<String> {
-                    if s.is_empty() {
-                        None
-                    } else {
-                        Some(s.to_string())
-                    }
-                };
-
-                Property {
-                    address: get_string(&state.data.address[row]),
-                    postcode: get_string(&state.data.postcode[row]),
-                    property_type: get_string(&state.data.property_type[row]),
-                    built_form: get_string(&state.data.built_form[row]),
-                    current_energy_rating: get_string(&state.data.current_energy_rating[row]),
-                    potential_energy_rating: get_string(&state.data.potential_energy_rating[row]),
-                    lat: state.data.lat[row],
-                    lon: state.data.lon[row],
-                    features,
-                }
-            })
-            .collect();
-
-        HexagonPropertiesResponse {
-            properties,
-            total,
-            limit,
-            offset,
-            truncated,
-        }
-    })
-    .await
-    .unwrap();
-
-    Ok(Json(result))
-}
diff --git a/server-rs/src/routes/features.rs b/server-rs/src/routes/features.rs
new file mode 100644
index 0000000..57a74ce
--- /dev/null
+++ b/server-rs/src/routes/features.rs
@@ -0,0 +1,87 @@
+use std::sync::Arc;
+
+use axum::response::Json;
+use serde::Serialize;
+use tracing::info;
+
+use crate::data::Histogram;
+use crate::state::AppState;
+
+#[derive(Serialize)]
+#[serde(tag = "type")]
+pub enum FeatureInfo {
+    #[serde(rename = "numeric")]
+    Numeric {
+        name: String,
+        label: String,
+        min: f64,
+        max: f64,
+        histogram: Histogram,
+    },
+    #[serde(rename = "enum")]
+    Enum {
+        name: String,
+        label: String,
+        values: Vec<String>,
+    },
+}
+
+#[derive(Serialize)]
+pub struct FeaturesResponse {
+    features: Vec<FeatureInfo>,
+}
+
+fn snake_to_label(name: &str) -> String {
+    // If name contains '/' or uppercase, assume it's already human-readable
+    if name.contains('/') || name.chars().any(|c| c.is_uppercase()) {
+        return name.to_string();
+    }
+    name.split('_')
+        .map(|word| {
+            let mut chars = word.chars();
+            match chars.next() {
+                None => String::new(),
+                Some(c) => {
+                    let mut s = c.to_uppercase().to_string();
+                    s.extend(chars);
+                    s
+                }
+            }
+        })
+        .collect::<Vec<_>>()
+        .join(" ")
+}
+
+pub async fn get_features(state: Arc<AppState>) -> Json<FeaturesResponse> {
+    let mut features: Vec<FeatureInfo> = state
+        .data
+        .feature_names
+        .iter()
+        .enumerate()
+        .map(|(i, name): (usize, &String)| {
+            let stats = &state.data.feature_stats[i];
+            FeatureInfo::Numeric {
+                name: name.clone(),
+                label: snake_to_label(name),
+                min: stats.p_low,
+                max: stats.p_high,
+                histogram: stats.histogram.clone(),
+            }
+        })
+        .collect();
+
+    for ef in &state.data.enum_features {
+        features.push(FeatureInfo::Enum {
+            name: ef.name.clone(),
+            label: snake_to_label(&ef.name),
+            values: ef.values.clone(),
+        });
+    }
+
+    info!(
+        numeric = features.iter().filter(|f| matches!(f, FeatureInfo::Numeric { .. })).count(),
+        enums = features.iter().filter(|f| matches!(f, FeatureInfo::Enum { .. })).count(),
+        "GET /api/features"
+    );
+    Json(FeaturesResponse { features })
+}
diff --git a/server-rs/src/routes/hexagons.rs b/server-rs/src/routes/hexagons.rs
new file mode 100644
index 0000000..11cb3ed
--- /dev/null
+++ b/server-rs/src/routes/hexagons.rs
@@ -0,0 +1,257 @@
+use std::fmt::Write;
+use std::sync::Arc;
+
+use axum::extract::Query;
+use axum::http::StatusCode;
+use axum::response::IntoResponse;
+use rustc_hash::FxHashMap;
+use serde::Deserialize;
+use tracing::{info, warn};
+
+use crate::consts::{H3_PRECOMPUTE_MAX, H3_PRECOMPUTE_MIN};
+use crate::filter::parse_filters;
+use crate::state::AppState;
+
+const BOUNDS_BUFFER_PERCENT: f64 = 0.2;
+
+#[derive(Deserialize)]
+pub struct HexagonParams {
+    resolution: u8,
+    bounds: Option<String>,
+    /// Comma-separated filters: `name:min:max,...`
+    /// Rows must have non-NaN values within [min,max] for each filter.
+    filters: Option<String>,
+}
+
+/// Per-cell accumulator for aggregating features
+struct CellAgg {
+    count: u32,
+    mins: Vec<f64>,
+    maxs: Vec<f64>,
+}
+
+impl CellAgg {
+    fn new(num_features: usize) -> Self {
+        CellAgg {
+            count: 0,
+            mins: vec![f64::INFINITY; num_features],
+            maxs: vec![f64::NEG_INFINITY; num_features],
+        }
+    }
+
+    /// Add a row using row-major feature_data layout.
+    /// feature_data[row * num_features + feat_idx] — all features for one row
+    /// are contiguous, so this reads a single cache line per ~8 features.
+    #[inline]
+    fn add_row(&mut self, feature_data: &[f64], row: usize, num_features: usize) {
+        self.count += 1;
+        let base = row * num_features;
+        let row_slice = &feature_data[base..base + num_features];
+        for (i, &v) in row_slice.iter().enumerate() {
+            if v.is_finite() {
+                if v < self.mins[i] {
+                    self.mins[i] = v;
+                }
+                if v > self.maxs[i] {
+                    self.maxs[i] = v;
+                }
+            }
+        }
+    }
+}
+
+/// Write the hexagons JSON response directly to a String buffer,
+/// avoiding serde_json::Value allocations entirely.
+fn write_hexagons_json(
+    buf: &mut String,
+    groups: &FxHashMap<u64, CellAgg>,
+    min_keys: &[String],
+    max_keys: &[String],
+    num_features: usize,
+) {
+    buf.push_str("{\"features\":[");
+    let mut first = true;
+    for (&cell_id, agg) in groups {
+        if !first {
+            buf.push(',');
+        }
+        first = false;
+
+        let cell = h3o::CellIndex::try_from(cell_id).unwrap();
+        write!(buf, "{{\"h3\":\"{}\",\"count\":{}", cell, agg.count).unwrap();
+
+        for i in 0..num_features {
+            if agg.mins[i] != f64::INFINITY {
+                write!(
+                    buf,
+                    ",\"{}\":{},\"{}\":{}",
+                    min_keys[i], agg.mins[i], max_keys[i], agg.maxs[i]
+                )
+                .unwrap();
+            }
+        }
+        buf.push('}');
+    }
+    buf.push_str("]}");
+}
+
+pub async fn get_hexagons(
+    state: Arc<AppState>,
+    Query(params): Query<HexagonParams>,
+) -> Result<impl IntoResponse, (StatusCode, String)> {
+    let resolution = params.resolution;
+    if resolution < H3_PRECOMPUTE_MIN || resolution > H3_PRECOMPUTE_MAX {
+        warn!(resolution, "Resolution out of range [{}, {}]", H3_PRECOMPUTE_MIN, H3_PRECOMPUTE_MAX);
+        return Err((
+            StatusCode::BAD_REQUEST,
+            format!(
+                "resolution must be between {} and {}",
+                H3_PRECOMPUTE_MIN, H3_PRECOMPUTE_MAX
+            ),
+        ));
+    }
+
+    let bounds_str = params.bounds.ok_or((
+        StatusCode::BAD_REQUEST,
+        "bounds parameter is required".into(),
+    ))?;
+
+    let parts: Vec<f64> = bounds_str
+        .split(',')
+        .map(|s| s.trim().parse::<f64>())
+        .collect::<Result<Vec<_>, _>>()
+        .map_err(|_| {
+            (
+                StatusCode::BAD_REQUEST,
+                "Invalid bounds format. Use: south,west,north,east".into(),
+            )
+        })?;
+
+    if parts.len() != 4 {
+        return Err((
+            StatusCode::BAD_REQUEST,
+            "Invalid bounds format. Use: south,west,north,east".into(),
+        ));
+    }
+
+    let (mut south, mut west, mut north, mut east) = (parts[0], parts[1], parts[2], parts[3]);
+
+    let lat_range = north - south;
+    let lng_range = east - west;
+    south -= lat_range * BOUNDS_BUFFER_PERCENT;
+    north += lat_range * BOUNDS_BUFFER_PERCENT;
+    west -= lng_range * BOUNDS_BUFFER_PERCENT;
+    east += lng_range * BOUNDS_BUFFER_PERCENT;
+
+    let precision = 0.01;
+    south = (south / precision).floor() * precision;
+    west = (west / precision).floor() * precision;
+    north = (north / precision).ceil() * precision;
+    east = (east / precision).ceil() * precision;
+
+    let filters_str = params.filters.clone();
+    let (parsed_filters, parsed_enum_filters) = parse_filters(
+        params.filters.as_deref(),
+        &state.data.feature_names,
+        &state.data.enum_features,
+    );
+    let num_filters = parsed_filters.len() + parsed_enum_filters.len();
+
+    let json_body = tokio::task::spawn_blocking(move || {
+        let t0 = std::time::Instant::now();
+
+        let num_features = state.data.num_features;
+        let feature_data = &state.data.feature_data;
+
+        let min_keys: Vec<String> = state
+            .data
+            .feature_names
+            .iter()
+            .map(|n| format!("min_{}", n))
+            .collect();
+        let max_keys: Vec<String> = state
+            .data
+            .feature_names
+            .iter()
+            .map(|n| format!("max_{}", n))
+            .collect();
+
+        let h3_cells_for_res: Option<&[u64]> = state
+            .h3_cells
+            .get(resolution as usize)
+            .filter(|v| !v.is_empty())
+            .map(|v| v.as_slice());
+
+        let mut groups: FxHashMap<u64, CellAgg> = FxHashMap::default();
+
+        let enum_features = &state.data.enum_features;
+
+        // Row-level filter check: numeric must be non-NaN and within [min, max],
+        // enum must have value index in the allowed set
+        let row_passes = |row: usize| -> bool {
+            parsed_filters.iter().all(|f| {
+                let v = feature_data[row * num_features + f.feat_idx];
+                v.is_finite() && v >= f.min && v <= f.max
+            }) && parsed_enum_filters.iter().all(|ef| {
+                let v = enum_features[ef.enum_idx].data[row];
+                v != 255 && ef.allowed.contains(&v)
+            })
+        };
+
+        if let Some(precomputed) = h3_cells_for_res {
+            state
+                .grid
+                .for_each_in_bounds(south, west, north, east, |row_idx| {
+                    let row = row_idx as usize;
+                    if !row_passes(row) {
+                        return;
+                    }
+                    let cell_id = precomputed[row];
+                    groups
+                        .entry(cell_id)
+                        .or_insert_with(|| CellAgg::new(num_features))
+                        .add_row(feature_data, row, num_features);
+                });
+        } else {
+            let h3_res = h3o::Resolution::try_from(resolution).unwrap();
+            state
+                .grid
+                .for_each_in_bounds(south, west, north, east, |row_idx| {
+                    let row = row_idx as usize;
+                    if !row_passes(row) {
+                        return;
+                    }
+                    let cell_id = h3o::LatLng::new(state.data.lat[row], state.data.lon[row])
+                        .map(|c| u64::from(c.to_cell(h3_res)))
+                        .unwrap_or(0);
+                    groups
+                        .entry(cell_id)
+                        .or_insert_with(|| CellAgg::new(num_features))
+                        .add_row(feature_data, row, num_features);
+                });
+        }
+
+        let t_agg = t0.elapsed();
+
+        let mut json_buf = String::with_capacity(groups.len() * 128);
+        write_hexagons_json(&mut json_buf, &groups, &min_keys, &max_keys, num_features);
+
+        let t_total = t0.elapsed();
+        info!(
+            resolution,
+            cells = groups.len(),
+            filters = num_filters,
+            filters_raw = filters_str.as_deref().unwrap_or("-"),
+            agg_ms = format_args!("{:.1}", t_agg.as_secs_f64() * 1000.0),
+            total_ms = format_args!("{:.1}", t_total.as_secs_f64() * 1000.0),
+            bytes = json_buf.len(),
+            "GET /api/hexagons"
+        );
+
+        json_buf
+    })
+    .await
+    .unwrap();
+
+    Ok(([("content-type", "application/json")], json_body))
+}
diff --git a/server-rs/src/routes/mod.rs b/server-rs/src/routes/mod.rs
new file mode 100644
index 0000000..d33f787
--- /dev/null
+++ b/server-rs/src/routes/mod.rs
@@ -0,0 +1,9 @@
+mod features;
+mod hexagons;
+mod pois;
+mod properties;
+
+pub use features::get_features;
+pub use hexagons::get_hexagons;
+pub use pois::{get_poi_categories, get_pois};
+pub use properties::get_hexagon_properties;
diff --git a/server-rs/src/routes/pois.rs b/server-rs/src/routes/pois.rs
new file mode 100644
index 0000000..f309ab7
--- /dev/null
+++ b/server-rs/src/routes/pois.rs
@@ -0,0 +1,133 @@
+use std::sync::Arc;
+
+use axum::extract::Query;
+use axum::http::StatusCode;
+use axum::response::Json;
+use serde::{Deserialize, Serialize};
+use tracing::info;
+
+use crate::data::POI;
+use crate::state::AppState;
+
+#[derive(Deserialize)]
+pub struct POIParams {
+    bounds: Option<String>,
+    /// Comma-separated list of categories to filter by
+    categories: Option<String>,
+}
+
+#[derive(Serialize)]
+pub struct POIsResponse {
+    pois: Vec<POI>,
+}
+
+pub async fn get_pois(
+    state: Arc<AppState>,
+    Query(params): Query<POIParams>,
+) -> Result<Json<POIsResponse>, (StatusCode, String)> {
+    let bounds_str = params.bounds.ok_or((
+        StatusCode::BAD_REQUEST,
+        "bounds parameter is required".into(),
+    ))?;
+
+    let parts: Vec<f64> = bounds_str
+        .split(',')
+        .map(|s| s.trim().parse::<f64>())
+        .collect::<Result<Vec<_>, _>>()
+        .map_err(|_| {
+            (
+                StatusCode::BAD_REQUEST,
+                "Invalid bounds format. Use: south,west,north,east".into(),
+            )
+        })?;
+
+    if parts.len() != 4 {
+        return Err((
+            StatusCode::BAD_REQUEST,
+            "Invalid bounds format. Use: south,west,north,east".into(),
+        ));
+    }
+
+    let (south, west, north, east) = (parts[0], parts[1], parts[2], parts[3]);
+
+    let categories_str = params.categories.clone();
+    let category_filter: Option<Vec<String>> = params
+        .categories
+        .as_deref()
+        .filter(|s| !s.is_empty())
+        .map(|s| s.split(',').map(|c| c.trim().to_string()).collect());
+
+    let num_categories = category_filter.as_ref().map(|c| c.len()).unwrap_or(0);
+
+    let result = tokio::task::spawn_blocking(move || {
+        let t0 = std::time::Instant::now();
+        let row_indices = state.poi_grid.query(south, west, north, east);
+
+        let pois: Vec<POI> = row_indices
+            .iter()
+            .filter_map(|&row_idx| {
+                let row = row_idx as usize;
+
+                if let Some(ref categories) = category_filter {
+                    if !categories.contains(&state.poi_data.category[row]) {
+                        return None;
+                    }
+                }
+
+                Some(POI {
+                    id: state.poi_data.id[row].clone(),
+                    name: state.poi_data.name[row].clone(),
+                    category: state.poi_data.category[row].clone(),
+                    lat: state.poi_data.lat[row],
+                    lng: state.poi_data.lng[row],
+                    emoji: state.poi_data.emoji[row].clone(),
+                })
+            })
+            .take(5000)
+            .collect();
+
+        let elapsed = t0.elapsed();
+        info!(
+            results = pois.len(),
+            candidates = row_indices.len(),
+            categories = num_categories,
+            categories_raw = categories_str.as_deref().unwrap_or("-"),
+            ms = format_args!("{:.1}", elapsed.as_secs_f64() * 1000.0),
+            "GET /api/pois"
+        );
+
+        POIsResponse { pois }
+    })
+    .await
+    .unwrap();
+
+    Ok(Json(result))
+}
+
+#[derive(Serialize)]
+pub struct POICategoriesResponse {
+    categories: Vec<String>,
+}
+
+pub async fn get_poi_categories(state: Arc<AppState>) -> Json<POICategoriesResponse> {
+    let result = tokio::task::spawn_blocking(move || {
+        let mut categories: Vec<String> = state
+            .poi_data
+            .category
+            .iter()
+            .cloned()
+            .collect::<std::collections::HashSet<_>>()
+            .into_iter()
+            .collect();
+
+        categories.sort();
+
+        info!(count = categories.len(), "GET /api/poi-categories");
+
+        POICategoriesResponse { categories }
+    })
+    .await
+    .unwrap();
+
+    Json(result)
+}
diff --git a/server-rs/src/routes/properties.rs b/server-rs/src/routes/properties.rs
new file mode 100644
index 0000000..1f205cb
--- /dev/null
+++ b/server-rs/src/routes/properties.rs
@@ -0,0 +1,198 @@
+use std::str::FromStr;
+use std::sync::Arc;
+
+use axum::extract::Query;
+use axum::http::StatusCode;
+use axum::response::Json;
+use rustc_hash::FxHashMap;
+use serde::{Deserialize, Serialize};
+use tracing::{info, warn};
+
+use crate::filter::{parse_filters, row_passes_filters};
+use crate::state::AppState;
+
+#[derive(Deserialize)]
+pub struct HexagonPropertiesParams {
+    pub h3: String,
+    pub resolution: u8,
+    pub filters: Option<String>,
+    pub limit: Option<usize>,
+    pub offset: Option<usize>,
+}
+
+#[derive(Serialize)]
+pub struct Property {
+    // String fields
+    pub address: Option<String>,
+    pub postcode: Option<String>,
+    pub property_type: Option<String>,
+    pub built_form: Option<String>,
+    pub duration: Option<String>,
+    pub current_energy_rating: Option<String>,
+    pub potential_energy_rating: Option<String>,
+
+    // Numeric fields
+    pub lat: f64,
+    pub lon: f64,
+
+    #[serde(flatten)]
+    pub features: FxHashMap<String, f64>,
+}
+
+#[derive(Serialize)]
+pub struct HexagonPropertiesResponse {
+    pub properties: Vec<Property>,
+    pub total: usize,
+    pub limit: usize,
+    pub offset: usize,
+    pub truncated: bool,
+}
+
+pub async fn get_hexagon_properties(
+    state: Arc<AppState>,
+    Query(params): Query<HexagonPropertiesParams>,
+) -> Result<Json<HexagonPropertiesResponse>, (StatusCode, String)> {
+    let cell = h3o::CellIndex::from_str(&params.h3)
+        .map_err(|e| {
+            warn!(h3 = %params.h3, error = %e, "Invalid H3 cell index");
+            (StatusCode::BAD_REQUEST, format!("Invalid H3 cell: {}", e))
+        })?;
+    let cell_u64: u64 = cell.into();
+
+    let resolution = params.resolution as usize;
+    if resolution >= state.h3_cells.len() || state.h3_cells[resolution].is_empty() {
+        warn!(resolution, "Invalid or non-precomputed resolution for hexagon-properties");
+        return Err((
+            StatusCode::BAD_REQUEST,
+            "Invalid or non-precomputed resolution".to_string(),
+        ));
+    }
+
+    let h3_str = params.h3.clone();
+    let filters_str = params.filters.clone();
+    let (parsed_filters, parsed_enum_filters) = parse_filters(
+        params.filters.as_deref(),
+        &state.data.feature_names,
+        &state.data.enum_features,
+    );
+    let num_filters = parsed_filters.len() + parsed_enum_filters.len();
+
+    let result = tokio::task::spawn_blocking(move || {
+        let t0 = std::time::Instant::now();
+        let h3_data = &state.h3_cells[resolution];
+        let num_features = state.data.num_features;
+        let feature_data = &state.data.feature_data;
+        let enum_features = &state.data.enum_features;
+
+        let matching_rows: Vec<usize> = h3_data
+            .iter()
+            .enumerate()
+            .filter_map(|(idx, &h3_cell)| {
+                if h3_cell == cell_u64 {
+                    if row_passes_filters(
+                        idx,
+                        &parsed_filters,
+                        &parsed_enum_filters,
+                        feature_data,
+                        num_features,
+                        enum_features,
+                    ) {
+                        Some(idx)
+                    } else {
+                        None
+                    }
+                } else {
+                    None
+                }
+            })
+            .collect();
+
+        let total = matching_rows.len();
+        let limit = params.limit.unwrap_or(100).min(500);
+        let offset = params.offset.unwrap_or(0);
+        let truncated = total > offset + limit;
+
+        let properties: Vec<Property> = matching_rows
+            .iter()
+            .skip(offset)
+            .take(limit)
+            .map(|&row| {
+                let mut features = FxHashMap::default();
+                let base = row * num_features;
+                for (feat_idx, feat_name) in state.data.feature_names.iter().enumerate() {
+                    let v = feature_data[base + feat_idx];
+                    if v.is_finite() {
+                        features.insert(feat_name.clone(), v);
+                    }
+                }
+
+                let get_string = |s: &str| -> Option<String> {
+                    let trimmed = s.trim();
+                    if trimmed.is_empty() {
+                        None
+                    } else {
+                        Some(trimmed.to_string())
+                    }
+                };
+
+                let get_enum_value = |names: &[&str]| -> Option<String> {
+                    for name in names {
+                        if let Some(val) = enum_features.iter().find_map(|ef| {
+                            if ef.name == *name {
+                                let idx = ef.data[row];
+                                if idx == 255 {
+                                    None
+                                } else {
+                                    ef.values.get(idx as usize).cloned()
+                                }
+                            } else {
+                                None
+                            }
+                        }) {
+                            return Some(val);
+                        }
+                    }
+                    None
+                };
+
+                Property {
+                    address: get_string(&state.data.address[row]),
+                    postcode: get_string(&state.data.postcode[row]),
+                    property_type: get_enum_value(&["Property type", "epc_property_type", "pp_property_type"]),
+                    built_form: get_enum_value(&["Property type/built form", "built_form"]),
+                    duration: get_enum_value(&["Leashold/Freehold", "duration"]),
+                    current_energy_rating: get_enum_value(&["Current energy rating", "current_energy_rating"]),
+                    potential_energy_rating: get_enum_value(&["Potential energy rating", "potential_energy_rating"]),
+                    lat: state.data.lat[row],
+                    lon: state.data.lon[row],
+                    features,
+                }
+            })
+            .collect();
+
+        let elapsed = t0.elapsed();
+        info!(
+            h3 = %h3_str,
+            resolution,
+            total,
+            returned = properties.len(),
+            offset,
+            filters = num_filters,
+            filters_raw = filters_str.as_deref().unwrap_or("-"),
+            ms = format_args!("{:.1}", elapsed.as_secs_f64() * 1000.0),
+            "GET /api/hexagon-properties"
+        );
+
+        HexagonPropertiesResponse {
+            properties,
+            total,
+            limit,
+            offset,
+            truncated,
+        }
+    })
+    .await
+    .unwrap();
+
+    Ok(Json(result))
+}
diff --git a/server-rs/src/state.rs b/server-rs/src/state.rs
new file mode 100644
index 0000000..2949d9b
--- /dev/null
+++ b/server-rs/src/state.rs
@@ -0,0 +1,12 @@
+use crate::data::{POIData, PropertyData};
+use crate::index::GridIndex;
+
+pub struct AppState {
+    pub data: PropertyData,
+    pub grid: GridIndex,
+    /// h3_cells[resolution][row_idx] = precomputed H3 cell ID.
+    /// Empty Vec for resolutions not precomputed.
+    pub h3_cells: Vec<Vec<u64>>,
+    pub poi_data: POIData,
+    pub poi_grid: GridIndex,
+}

From f7d586a1e98b545b9aafe0cd8bc10d51bc08136f Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sat, 31 Jan 2026 20:26:14 +0000
Subject: [PATCH 37/62] Add filter info

---
 frontend/src/App.tsx                       | 462 ++++++++++++++++-----
 frontend/src/components/Filters.tsx        |  73 +++-
 frontend/src/components/Map.tsx            | 238 ++++++++---
 frontend/src/components/PropertiesPane.tsx | 128 +++---
 frontend/src/types.ts                      |  15 +-
 5 files changed, 682 insertions(+), 234 deletions(-)

diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx
index ed9ec6f..62f1a8c 100644
--- a/frontend/src/App.tsx
+++ b/frontend/src/App.tsx
@@ -14,11 +14,13 @@ import type {
   POI,
   POIResponse,
   POICategoriesResponse,
+  ViewState,
   Property,
   HexagonPropertiesResponse,
 } from './types';
 
 const DEBOUNCE_MS = 150;
+const URL_DEBOUNCE_MS = 300;
 
 // Detect if running through VS Code web proxy and construct API base URL
 function getApiBaseUrl(): string {
@@ -45,23 +47,240 @@ function getApiBaseUrl(): string {
   return '';
 }
 
+const DEFAULT_VIEW: ViewState = {
+  longitude: -1.5,
+  latitude: 53.5,
+  zoom: 6,
+  pitch: 0,
+};
+
+// --- URL State helpers ---
+
+function parseUrlState(): {
+  viewState?: ViewState;
+  filters?: FeatureFilters;
+  poiCategories?: Set<string>;
+  tab?: 'pois' | 'properties';
+} {
+  const params = new URLSearchParams(window.location.search);
+  const result: ReturnType<typeof parseUrlState> = {};
+
+  // Parse view: v=lat,lng,zoom
+  const v = params.get('v');
+  if (v) {
+    const parts = v.split(',').map(Number);
+    if (parts.length === 3 && parts.every((n) => !isNaN(n))) {
+      result.viewState = {
+        latitude: parts[0],
+        longitude: parts[1],
+        zoom: parts[2],
+        pitch: 0,
+      };
+    }
+  }
+
+  // Parse filters: f=name:min:max,name:val1|val2
+  const f = params.get('f');
+  if (f) {
+    const filters: FeatureFilters = {};
+    for (const segment of f.split(',')) {
+      const colonIdx = segment.indexOf(':');
+      if (colonIdx === -1) continue;
+      const name = segment.substring(0, colonIdx);
+      const rest = segment.substring(colonIdx + 1);
+      if (rest.includes(':')) {
+        // Numeric: name:min:max
+        const [minStr, maxStr] = rest.split(':');
+        const min = Number(minStr);
+        const max = Number(maxStr);
+        if (!isNaN(min) && !isNaN(max)) {
+          filters[name] = [min, max];
+        }
+      } else if (rest.includes('|')) {
+        // Enum: name:val1|val2
+        filters[name] = rest.split('|');
+      } else {
+        // Single enum value
+        filters[name] = [rest];
+      }
+    }
+    if (Object.keys(filters).length > 0) {
+      result.filters = filters;
+    }
+  }
+
+  // Parse POI categories: poi=Cafe,Pub,School
+  const poi = params.get('poi');
+  if (poi) {
+    result.poiCategories = new Set(poi.split(',').filter(Boolean));
+  }
+
+  // Parse tab: tab=p or tab=o
+  const tab = params.get('tab');
+  if (tab === 'p') result.tab = 'properties';
+  else if (tab === 'o') result.tab = 'pois';
+
+  return result;
+}
+
+function stateToParams(
+  viewState: { latitude: number; longitude: number; zoom: number } | null,
+  filters: FeatureFilters,
+  features: FeatureMeta[],
+  selectedPOICategories: Set<string>,
+  rightPaneTab: 'pois' | 'properties'
+): URLSearchParams {
+  const params = new URLSearchParams();
+
+  // View
+  if (viewState) {
+    params.set(
+      'v',
+      `${viewState.latitude.toFixed(4)},${viewState.longitude.toFixed(4)},${viewState.zoom.toFixed(1)}`
+    );
+  }
+
+  // Filters
+  const filterEntries = Object.entries(filters);
+  if (filterEntries.length > 0) {
+    const filtersStr = filterEntries
+      .map(([name, value]) => {
+        const meta = features.find((f) => f.name === name);
+        if (meta?.type === 'enum') {
+          return `${name}:${(value as string[]).join('|')}`;
+        }
+        const [min, max] = value as [number, number];
+        return `${name}:${min}:${max}`;
+      })
+      .join(',');
+    params.set('f', filtersStr);
+  }
+
+  // POI categories
+  if (selectedPOICategories.size > 0) {
+    params.set('poi', Array.from(selectedPOICategories).join(','));
+  }
+
+  // Tab (only if non-default)
+  if (rightPaneTab === 'properties') {
+    params.set('tab', 'p');
+  }
+
+  return params;
+}
+
+// --- Header ---
+
+function Header() {
+  const [copied, setCopied] = useState(false);
+
+  const handleShare = useCallback(() => {
+    navigator.clipboard.writeText(window.location.href).then(() => {
+      setCopied(true);
+      setTimeout(() => setCopied(false), 2000);
+    });
+  }, []);
+
+  return (
+    <header className="h-12 bg-slate-800 text-white flex items-center justify-between px-4 shrink-0">
+      <div className="flex items-center gap-2">
+        <svg
+          className="w-5 h-5 text-blue-400"
+          fill="none"
+          stroke="currentColor"
+          viewBox="0 0 24 24"
+        >
+          <path
+            strokeLinecap="round"
+            strokeLinejoin="round"
+            strokeWidth={2}
+            d="M17.657 16.657L13.414 20.9a1.998 1.998 0 01-2.827 0l-4.244-4.243a8 8 0 1111.314 0z"
+          />
+          <path
+            strokeLinecap="round"
+            strokeLinejoin="round"
+            strokeWidth={2}
+            d="M15 11a3 3 0 11-6 0 3 3 0 016 0z"
+          />
+        </svg>
+        <span className="font-semibold text-lg">Property Map</span>
+      </div>
+      <button
+        onClick={handleShare}
+        className="flex items-center gap-1.5 px-3 py-1.5 rounded bg-slate-700 hover:bg-slate-600 transition-colors text-sm"
+      >
+        {copied ? (
+          <>
+            <svg className="w-4 h-4" fill="none" stroke="currentColor" viewBox="0 0 24 24">
+              <path
+                strokeLinecap="round"
+                strokeLinejoin="round"
+                strokeWidth={2}
+                d="M5 13l4 4L19 7"
+              />
+            </svg>
+            Copied!
+          </>
+        ) : (
+          <>
+            <svg className="w-4 h-4" fill="none" stroke="currentColor" viewBox="0 0 24 24">
+              <path
+                strokeLinecap="round"
+                strokeLinejoin="round"
+                strokeWidth={2}
+                d="M8 5H6a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2v-1M8 5a2 2 0 002 2h2a2 2 0 002-2M8 5a2 2 0 012-2h2a2 2 0 012 2m0 0h2a2 2 0 012 2v3m2 4H10m0 0l3-3m-3 3l3 3"
+              />
+            </svg>
+            Share
+          </>
+        )}
+      </button>
+    </header>
+  );
+}
+
+// --- App ---
+
 export default function App() {
+  // Parse URL state once on mount
+  const urlState = useMemo(() => parseUrlState(), []);
+
   const [features, setFeatures] = useState<FeatureMeta[]>([]);
-  const [filters, setFilters] = useState<FeatureFilters>({});
+  const [filters, setFilters] = useState<FeatureFilters>(urlState.filters || {});
   const [activeFeature, setActiveFeature] = useState<string | null>(null);
   const [dragValue, setDragValue] = useState<[number, number] | null>(null);
   const [rawData, setRawData] = useState<HexagonData[]>([]);
   const [resolution, setResolution] = useState<number>(8);
   const [bounds, setBounds] = useState<Bounds | null>(null);
   const [loading, setLoading] = useState<boolean>(false);
-  const [zoom, setZoom] = useState<number>(6);
+  const [zoom, setZoom] = useState<number>(urlState.viewState?.zoom || DEFAULT_VIEW.zoom);
   const debounceRef = useRef<ReturnType<typeof setTimeout> | null>(null);
   const abortControllerRef = useRef<AbortController | null>(null);
 
+  // View state for URL serialization
+  const [currentView, setCurrentView] = useState<{
+    latitude: number;
+    longitude: number;
+    zoom: number;
+  } | null>(
+    urlState.viewState
+      ? {
+          latitude: urlState.viewState.latitude,
+          longitude: urlState.viewState.longitude,
+          zoom: urlState.viewState.zoom,
+        }
+      : null
+  );
+
+  // Initial view state for Map
+  const initialViewState = useMemo(() => urlState.viewState || DEFAULT_VIEW, []);
+
   // POI state
   const [pois, setPois] = useState<POI[]>([]);
   const [poiCategories, setPOICategories] = useState<string[]>([]);
-  const [selectedPOICategories, setSelectedPOICategories] = useState<Set<string>>(new Set());
+  const [selectedPOICategories, setSelectedPOICategories] = useState<Set<string>>(
+    urlState.poiCategories || new Set()
+  );
   const poiDebounceRef = useRef<ReturnType<typeof setTimeout> | null>(null);
   const poiAbortControllerRef = useRef<AbortController | null>(null);
 
@@ -73,18 +292,42 @@ export default function App() {
   const [propertiesTotal, setPropertiesTotal] = useState(0);
   const [propertiesOffset, setPropertiesOffset] = useState(0);
   const [loadingProperties, setLoadingProperties] = useState(false);
-  const [rightPaneTab, setRightPaneTab] = useState<'pois' | 'properties'>('pois');
+  const [rightPaneTab, setRightPaneTab] = useState<'pois' | 'properties'>(urlState.tab || 'pois');
 
   // Derive enabled features from filter keys
   const enabledFeatures = useMemo(() => new Set(Object.keys(filters)), [filters]);
 
+  // --- URL sync ---
+  const urlDebounceRef = useRef<ReturnType<typeof setTimeout> | null>(null);
+
+  useEffect(() => {
+    if (urlDebounceRef.current) {
+      clearTimeout(urlDebounceRef.current);
+    }
+    urlDebounceRef.current = setTimeout(() => {
+      const params = stateToParams(
+        currentView,
+        filters,
+        features,
+        selectedPOICategories,
+        rightPaneTab
+      );
+      const search = params.toString();
+      const newUrl = search ? `${window.location.pathname}?${search}` : window.location.pathname;
+      window.history.replaceState(null, '', newUrl);
+    }, URL_DEBOUNCE_MS);
+
+    return () => {
+      if (urlDebounceRef.current) clearTimeout(urlDebounceRef.current);
+    };
+  }, [currentView, filters, features, selectedPOICategories, rightPaneTab]);
+
   // Fetch feature metadata + POI categories on mount
   useEffect(() => {
     fetch(`${getApiBaseUrl()}/api/features`)
       .then((res) => res.json())
       .then((json: { features: FeatureMeta[] }) => {
         setFeatures(json.features);
-        // Start with no filters (empty object)
       })
       .catch((err) => console.error('Failed to fetch features:', err));
 
@@ -117,11 +360,18 @@ export default function App() {
           resolution: resolution.toString(),
           bounds: boundsStr,
         });
-        // Build filters param: name:min:max,...
+        // Build filters param: numeric=name:min:max, enum=name:val1|val2
         const filterEntries = Object.entries(filters);
         if (filterEntries.length > 0) {
           const filtersStr = filterEntries
-            .map(([name, [min, max]]) => `${name}:${min}:${max}`)
+            .map(([name, value]) => {
+              const meta = features.find((f) => f.name === name);
+              if (meta?.type === 'enum') {
+                return `${name}:${(value as string[]).join('|')}`;
+              }
+              const [min, max] = value as [number, number];
+              return `${name}:${min}:${max}`;
+            })
             .join(',');
           params.set('filters', filtersStr);
         }
@@ -146,17 +396,8 @@ export default function App() {
     };
   }, [resolution, bounds, filters]);
 
-  // Client-side filtering: only during drag preview
-  const data = useMemo(() => {
-    if (!activeFeature || !dragValue) return rawData;
-
-    return rawData.filter((hex) => {
-      const minVal = hex[`min_${activeFeature}`];
-      const maxVal = hex[`max_${activeFeature}`];
-      if (minVal == null || maxVal == null) return false;
-      return (minVal as number) <= dragValue[1] && (maxVal as number) >= dragValue[0];
-    });
-  }, [rawData, activeFeature, dragValue]);
+  // Data passed directly to Map — visual filtering is done via color/opacity in the layer
+  const data = rawData;
 
   // Fetch POIs when bounds or selected categories change
   useEffect(() => {
@@ -201,11 +442,18 @@ export default function App() {
     };
   }, [bounds, selectedPOICategories]);
 
+  const prevBoundsRef = useRef<string>('');
   const handleViewChange = useCallback(
-    ({ resolution: newRes, bounds: newBounds, zoom: newZoom }: ViewChangeParams) => {
-      setResolution(newRes);
-      setBounds(newBounds);
+    ({ resolution: newRes, bounds: newBounds, zoom: newZoom, latitude, longitude }: ViewChangeParams) => {
+      // Only update bounds/resolution when quantized values actually change
+      const boundsKey = `${newBounds.south},${newBounds.west},${newBounds.north},${newBounds.east},${newRes}`;
+      if (boundsKey !== prevBoundsRef.current) {
+        prevBoundsRef.current = boundsKey;
+        setResolution(newRes);
+        setBounds(newBounds);
+      }
       setZoom(newZoom);
+      setCurrentView({ latitude, longitude, zoom: newZoom });
     },
     []
   );
@@ -214,11 +462,22 @@ export default function App() {
     (name: string) => {
       const meta = features.find((f) => f.name === name);
       if (!meta) return;
-      setFilters((prev) => ({ ...prev, [name]: [meta.min, meta.max] }));
+      if (meta.type === 'enum' && meta.values) {
+        setFilters((prev) => ({ ...prev, [name]: [...meta.values!] }));
+      } else if (meta.min != null && meta.max != null) {
+        setFilters((prev) => ({ ...prev, [name]: [meta.min!, meta.max!] }));
+      }
     },
     [features]
   );
 
+  const handleFilterChange = useCallback(
+    (name: string, value: [number, number] | string[]) => {
+      setFilters((prev) => ({ ...prev, [name]: value }));
+    },
+    []
+  );
+
   const handleRemoveFilter = useCallback((name: string) => {
     setFilters((prev) => {
       const next = { ...prev };
@@ -229,10 +488,13 @@ export default function App() {
 
   const handleDragStart = useCallback(
     (name: string) => {
+      const meta = features.find((f) => f.name === name);
+      if (meta?.type === 'enum') return; // No drag interaction for enum features
       setActiveFeature(name);
-      setDragValue(filters[name] || null);
+      const fval = filters[name];
+      setDragValue(fval && !Array.isArray(fval[0]) ? (fval as [number, number]) : null);
     },
-    [filters]
+    [filters, features]
   );
 
   const handleDragChange = useCallback((value: [number, number]) => {
@@ -262,7 +524,14 @@ export default function App() {
         const filterEntries = Object.entries(filters);
         if (filterEntries.length > 0) {
           const filterStr = filterEntries
-            .map(([name, [min, max]]) => `${name}:${min}:${max}`)
+            .map(([name, value]) => {
+              const meta = features.find((f) => f.name === name);
+              if (meta?.type === 'enum') {
+                return `${name}:${(value as string[]).join('|')}`;
+              }
+              const [min, max] = value as [number, number];
+              return `${name}:${min}:${max}`;
+            })
             .join(',');
           params.append('filters', filterStr);
         }
@@ -283,7 +552,7 @@ export default function App() {
         setLoadingProperties(false);
       }
     },
-    [filters]
+    [filters, features]
   );
 
   const handleHexagonClick = useCallback(
@@ -314,78 +583,83 @@ export default function App() {
   }, []);
 
   return (
-    <div className="h-screen flex">
-      <Filters
-        features={features}
-        filters={filters}
-        activeFeature={activeFeature}
-        dragValue={dragValue}
-        enabledFeatures={enabledFeatures}
-        onAddFilter={handleAddFilter}
-        onRemoveFilter={handleRemoveFilter}
-        onDragStart={handleDragStart}
-        onDragChange={handleDragChange}
-        onDragEnd={handleDragEnd}
-        zoom={zoom}
-      />
-      <div className="flex-1 relative">
-        <Map
-          data={data}
-          pois={pois}
-          onViewChange={handleViewChange}
+    <div className="h-screen flex flex-col">
+      <Header />
+      <div className="flex-1 flex overflow-hidden">
+        <Filters
+          features={features}
+          filters={filters}
           activeFeature={activeFeature}
           dragValue={dragValue}
-          features={features}
-          selectedHexagonId={selectedHexagon?.h3 || null}
-          onHexagonClick={handleHexagonClick}
+          enabledFeatures={enabledFeatures}
+          onAddFilter={handleAddFilter}
+          onRemoveFilter={handleRemoveFilter}
+          onFilterChange={handleFilterChange}
+          onDragStart={handleDragStart}
+          onDragChange={handleDragChange}
+          onDragEnd={handleDragEnd}
+          zoom={zoom}
         />
-        {loading && (
-          <div className="absolute top-4 right-4 bg-white px-3 py-1 rounded shadow">Loading...</div>
-        )}
-        <DataSources />
-      </div>
-      <div className="w-72 bg-white shadow-lg z-10 flex flex-col">
-        {/* Tab headers */}
-        <div className="flex border-b border-gray-200">
-          <button
-            className={`flex-1 p-3 ${
-              rightPaneTab === 'pois' ? 'border-b-2 border-blue-500 font-semibold' : 'text-gray-600'
-            }`}
-            onClick={() => setRightPaneTab('pois')}
-          >
-            POIs {pois.length > 0 && `(${pois.length})`}
-          </button>
-          <button
-            className={`flex-1 p-3 ${
-              rightPaneTab === 'properties'
-                ? 'border-b-2 border-blue-500 font-semibold'
-                : 'text-gray-600'
-            }`}
-            onClick={() => setRightPaneTab('properties')}
-          >
-            Properties {propertiesTotal > 0 && `(${propertiesTotal})`}
-          </button>
-        </div>
-
-        {/* Tab content */}
-        <div className="flex-1 overflow-hidden">
-          {rightPaneTab === 'pois' ? (
-            <POIPane
-              categories={poiCategories}
-              selectedCategories={selectedPOICategories}
-              onCategoriesChange={setSelectedPOICategories}
-              poiCount={pois.length}
-            />
-          ) : (
-            <PropertiesPane
-              properties={properties}
-              total={propertiesTotal}
-              loading={loadingProperties}
-              hexagonId={selectedHexagon?.h3 || null}
-              onLoadMore={handleLoadMoreProperties}
-              onClose={handleCloseProperties}
-            />
+        <div className="flex-1 relative">
+          <Map
+            data={data}
+            pois={pois}
+            onViewChange={handleViewChange}
+            activeFeature={activeFeature}
+            dragValue={dragValue}
+            features={features}
+            selectedHexagonId={selectedHexagon?.h3 || null}
+            onHexagonClick={handleHexagonClick}
+            initialViewState={initialViewState}
+          />
+          {loading && (
+            <div className="absolute top-4 right-4 bg-white px-3 py-1 rounded shadow">Loading...</div>
           )}
+          <DataSources />
+        </div>
+        <div className="w-72 bg-white shadow-lg z-10 flex flex-col">
+          {/* Tab headers */}
+          <div className="flex border-b border-gray-200">
+            <button
+              className={`flex-1 p-3 ${
+                rightPaneTab === 'pois' ? 'border-b-2 border-blue-500 font-semibold' : 'text-gray-600'
+              }`}
+              onClick={() => setRightPaneTab('pois')}
+            >
+              POIs {pois.length > 0 && `(${pois.length})`}
+            </button>
+            <button
+              className={`flex-1 p-3 ${
+                rightPaneTab === 'properties'
+                  ? 'border-b-2 border-blue-500 font-semibold'
+                  : 'text-gray-600'
+              }`}
+              onClick={() => setRightPaneTab('properties')}
+            >
+              Properties {propertiesTotal > 0 && `(${propertiesTotal})`}
+            </button>
+          </div>
+
+          {/* Tab content */}
+          <div className="flex-1 overflow-hidden">
+            {rightPaneTab === 'pois' ? (
+              <POIPane
+                categories={poiCategories}
+                selectedCategories={selectedPOICategories}
+                onCategoriesChange={setSelectedPOICategories}
+                poiCount={pois.length}
+              />
+            ) : (
+              <PropertiesPane
+                properties={properties}
+                total={propertiesTotal}
+                loading={loadingProperties}
+                hexagonId={selectedHexagon?.h3 || null}
+                onLoadMore={handleLoadMoreProperties}
+                onClose={handleCloseProperties}
+              />
+            )}
+          </div>
         </div>
       </div>
     </div>
diff --git a/frontend/src/components/Filters.tsx b/frontend/src/components/Filters.tsx
index 3bcb29e..498552a 100644
--- a/frontend/src/components/Filters.tsx
+++ b/frontend/src/components/Filters.tsx
@@ -1,3 +1,4 @@
+import { memo } from 'react';
 import { Slider } from './ui/slider';
 import { Label } from './ui/label';
 import type { FeatureMeta, FeatureFilters } from '../types';
@@ -10,6 +11,7 @@ interface FiltersProps {
   enabledFeatures: Set<string>;
   onAddFilter: (name: string) => void;
   onRemoveFilter: (name: string) => void;
+  onFilterChange: (name: string, value: [number, number] | string[]) => void;
   onDragStart: (name: string) => void;
   onDragChange: (value: [number, number]) => void;
   onDragEnd: () => void;
@@ -23,7 +25,7 @@ function formatValue(value: number): string {
   return value.toFixed(2);
 }
 
-export default function Filters({
+export default memo(function Filters({
   features,
   filters,
   activeFeature,
@@ -31,6 +33,7 @@ export default function Filters({
   enabledFeatures,
   onAddFilter,
   onRemoveFilter,
+  onFilterChange,
   onDragStart,
   onDragChange,
   onDragEnd,
@@ -40,9 +43,7 @@ export default function Filters({
   const enabledFeatureList = features.filter((f) => enabledFeatures.has(f.name));
 
   return (
-    <div className="w-72 p-4 bg-white shadow-lg space-y-4 overflow-y-auto max-h-screen">
-      <h1 className="text-xl font-bold">UK Property Prices</h1>
-
+    <div className="w-72 p-4 bg-white shadow-lg space-y-4 overflow-y-auto">
       <div className="text-sm text-slate-500">Zoom: {zoom.toFixed(1)}</div>
 
       {/* Add filter dropdown */}
@@ -67,10 +68,64 @@ export default function Filters({
 
       {/* Active filters */}
       {enabledFeatureList.map((feature) => {
+        if (feature.type === 'enum') {
+          const selectedValues = (filters[feature.name] as string[]) || [];
+          const allValues = feature.values || [];
+          return (
+            <div key={feature.name} className="space-y-1 p-2 rounded">
+              <div className="flex items-center justify-between">
+                <Label className="text-xs">{feature.label}</Label>
+                <button
+                  onClick={() => onRemoveFilter(feature.name)}
+                  className="text-slate-400 hover:text-slate-700 text-sm px-1"
+                  title="Remove filter"
+                >
+                  x
+                </button>
+              </div>
+              <div className="flex gap-2 text-xs mb-1">
+                <button
+                  className="text-blue-600 hover:underline"
+                  onClick={() => onFilterChange(feature.name, [...allValues])}
+                >
+                  All
+                </button>
+                <button
+                  className="text-blue-600 hover:underline"
+                  onClick={() => onFilterChange(feature.name, [])}
+                >
+                  None
+                </button>
+              </div>
+              <div className="space-y-0.5 max-h-40 overflow-y-auto">
+                {allValues.map((val) => (
+                  <label key={val} className="flex items-center gap-1.5 text-xs cursor-pointer">
+                    <input
+                      type="checkbox"
+                      checked={selectedValues.includes(val)}
+                      onChange={() => {
+                        const next = selectedValues.includes(val)
+                          ? selectedValues.filter((v) => v !== val)
+                          : [...selectedValues, val];
+                        onFilterChange(feature.name, next);
+                      }}
+                      className="rounded"
+                    />
+                    {val}
+                  </label>
+                ))}
+              </div>
+            </div>
+          );
+        }
+
+        // Numeric feature
         const isActive = activeFeature === feature.name;
         const displayValue =
-          isActive && dragValue ? dragValue : filters[feature.name] || [feature.min, feature.max];
-        const step = (feature.max - feature.min) / 100;
+          isActive && dragValue
+            ? dragValue
+            : (filters[feature.name] as [number, number]) || [feature.min!, feature.max!];
+        const step = (feature.max! - feature.min!) / 100;
 
         return (
           <div
@@ -90,8 +145,8 @@ export default function Filters({
               </button>
             </div>
             <Slider
-              min={feature.min}
-              max={feature.max}
+              min={feature.min!}
+              max={feature.max!}
               step={step}
               value={[displayValue[0], displayValue[1]]}
               onValueChange={([min, max]) => onDragChange([min, max])}
@@ -135,4 +190,4 @@ export default function Filters({
       </div>
     </div>
   );
-}
+});
diff --git a/frontend/src/components/Map.tsx b/frontend/src/components/Map.tsx
index bc81e29..0458978 100644
--- a/frontend/src/components/Map.tsx
+++ b/frontend/src/components/Map.tsx
@@ -1,4 +1,4 @@
-import { useCallback, useRef, useEffect, useState, useMemo } from 'react';
+import { useCallback, useRef, useEffect, useState, useMemo, memo } from 'react';
 import { Map as MapGL, useControl } from 'react-map-gl/maplibre';
 import type { MapRef } from 'react-map-gl/maplibre';
 import { MapboxOverlay } from '@deck.gl/mapbox';
@@ -17,6 +17,7 @@ interface MapProps {
   features: FeatureMeta[];
   selectedHexagonId: string | null;
   onHexagonClick: (h3: string) => void;
+  initialViewState?: ViewState;
 }
 
 // Twemoji CDN base URL
@@ -118,9 +119,6 @@ interface Dimensions {
   height: number;
 }
 
-// First label layer in the Carto Positron style — hexagons render below this
-const LABEL_LAYER_ID = 'waterway_label';
-
 function DeckOverlay({
   layers,
   getTooltip,
@@ -130,7 +128,13 @@ function DeckOverlay({
   getTooltip: any;
 }) {
   const overlay = useControl(() => new MapboxOverlay({ interleaved: true }));
-  overlay.setProps({ layers, getTooltip });
+  const prevLayersRef = useRef(layers);
+  const prevTooltipRef = useRef(getTooltip);
+  if (layers !== prevLayersRef.current || getTooltip !== prevTooltipRef.current) {
+    prevLayersRef.current = layers;
+    prevTooltipRef.current = getTooltip;
+    overlay.setProps({ layers, getTooltip });
+  }
   return null;
 }
 
@@ -143,7 +147,81 @@ function countToColor(t: number): [number, number, number] {
   return [r, g, b];
 }
 
-export default function Map({
+function PostcodeSearch({
+  onFlyTo,
+}: {
+  onFlyTo: (lat: number, lng: number, zoom: number) => void;
+}) {
+  const [query, setQuery] = useState('');
+  const [error, setError] = useState<string | null>(null);
+  const [loading, setLoading] = useState(false);
+
+  const handleSubmit = useCallback(
+    async (e: React.FormEvent) => {
+      e.preventDefault();
+      const trimmed = query.trim();
+      if (!trimmed) return;
+
+      setError(null);
+      setLoading(true);
+      try {
+        const res = await fetch(
+          `https://api.postcodes.io/postcodes/${encodeURIComponent(trimmed)}`
+        );
+        if (!res.ok) {
+          setError('Postcode not found');
+          return;
+        }
+        const json = await res.json();
+        if (json.status === 200 && json.result) {
+          onFlyTo(json.result.latitude, json.result.longitude, 14);
+          setQuery('');
+        } else {
+          setError('Postcode not found');
+        }
+      } catch {
+        setError('Lookup failed');
+      } finally {
+        setLoading(false);
+      }
+    },
+    [query, onFlyTo]
+  );
+
+  return (
+    <form
+      onSubmit={handleSubmit}
+      className="absolute top-3 left-3 z-10 flex flex-col gap-1"
+    >
+      <div className="flex shadow-lg rounded overflow-hidden">
+        <input
+          type="text"
+          value={query}
+          onChange={(e) => {
+            setQuery(e.target.value);
+            setError(null);
+          }}
+          placeholder="Search postcode..."
+          className="px-3 py-2 text-sm w-40 border-none outline-none bg-white"
+        />
+        <button
+          type="submit"
+          disabled={loading}
+          className="px-3 py-2 bg-blue-500 text-white text-sm hover:bg-blue-600 disabled:opacity-50"
+        >
+          {loading ? '...' : 'Go'}
+        </button>
+      </div>
+      {error && (
+        <span className="text-xs text-red-600 bg-white/90 rounded px-2 py-0.5 shadow">
+          {error}
+        </span>
+      )}
+    </form>
+  );
+}
+
+export default memo(function Map({
   data,
   pois,
   onViewChange,
@@ -152,9 +230,10 @@ export default function Map({
   features,
   selectedHexagonId,
   onHexagonClick,
+  initialViewState,
 }: MapProps) {
   const containerRef = useRef<HTMLDivElement>(null);
-  const [viewState, setViewState] = useState<ViewState>(INITIAL_VIEW);
+  const [viewState, setViewState] = useState<ViewState>(initialViewState || INITIAL_VIEW);
   const [dimensions, setDimensions] = useState<Dimensions>({ width: 0, height: 0 });
 
   // Track container dimensions with ResizeObserver
@@ -177,16 +256,29 @@ export default function Map({
   useEffect(() => {
     if (dimensions.width === 0 || dimensions.height === 0) return;
 
-    const bounds = getBoundsFromViewState(viewState, dimensions.width, dimensions.height);
+    const raw = getBoundsFromViewState(viewState, dimensions.width, dimensions.height);
     const resolution = zoomToResolution(viewState.zoom);
 
-    onViewChange({ resolution, bounds, zoom: viewState.zoom });
+    // Quantize bounds to 0.01° to reduce state churn and improve backend cache hits
+    const QUANT = 0.01;
+    const bounds: Bounds = {
+      south: Math.floor(raw.south / QUANT) * QUANT,
+      west: Math.floor(raw.west / QUANT) * QUANT,
+      north: Math.ceil(raw.north / QUANT) * QUANT,
+      east: Math.ceil(raw.east / QUANT) * QUANT,
+    };
+
+    onViewChange({ resolution, bounds, zoom: viewState.zoom, latitude: viewState.latitude, longitude: viewState.longitude });
   }, [viewState, dimensions, onViewChange]);
 
   const handleMove = useCallback((evt: { viewState: ViewState }) => {
     setViewState(evt.viewState);
   }, []);
 
+  const handleFlyTo = useCallback((lat: number, lng: number, zoom: number) => {
+    setViewState((prev) => ({ ...prev, latitude: lat, longitude: lng, zoom }));
+  }, []);
+
   // Make place labels more legible over the colored hexagons
   const handleMapLoad = useCallback(
     (evt: { target: MapRef['getMap'] extends () => infer M ? M : never }) => {
@@ -197,6 +289,8 @@ export default function Map({
         map.setPaintProperty(layer.id, 'text-halo-width', 2);
         map.setPaintProperty(layer.id, 'text-color', '#222');
       }
+      map.setLayoutProperty('building', 'visibility', 'none');
+      map.setLayoutProperty('building-top', 'visibility', 'none');
     },
     []
   );
@@ -236,63 +330,107 @@ export default function Map({
     return { min, max };
   }, [data]);
 
-  // Determine color mode
-  const colorFeatureMeta = activeFeature
-    ? features.find((f) => f.name === activeFeature) || null
-    : null;
+  // Memoize feature lookup to avoid new reference each render
+  const colorFeatureMeta = useMemo(
+    () => (activeFeature ? features.find((f) => f.name === activeFeature) || null : null),
+    [activeFeature, features]
+  );
 
+  // Use refs for values that change during drag so layers aren't recreated
+  const activeFeatureRef = useRef(activeFeature);
+  activeFeatureRef.current = activeFeature;
+  const dragValueRef = useRef(dragValue);
+  dragValueRef.current = dragValue;
+  const colorFeatureMetaRef = useRef(colorFeatureMeta);
+  colorFeatureMetaRef.current = colorFeatureMeta;
+  const countRangeRef = useRef(countRange);
+  countRangeRef.current = countRange;
+  const selectedHexagonIdRef = useRef(selectedHexagonId);
+  selectedHexagonIdRef.current = selectedHexagonId;
+
+  // Stable click handler using ref
+  const onHexagonClickRef = useRef(onHexagonClick);
+  onHexagonClickRef.current = onHexagonClick;
   const handleHexagonClick = useCallback(
     (info: PickingInfo<HexagonData>) => {
       if (info.object && 'h3' in info.object) {
-        onHexagonClick(info.object.h3);
+        onHexagonClickRef.current(info.object.h3);
       }
     },
-    [onHexagonClick]
+    []
   );
 
-  const layers = useMemo(
-    () => [
+  // Stable hover handler using ref
+  const handlePoiHoverRef = useRef(handlePoiHover);
+  handlePoiHoverRef.current = handlePoiHover;
+  const stablePoiHover = useCallback((info: PickingInfo<POI>) => {
+    handlePoiHoverRef.current(info);
+  }, []);
+
+  // Derive a trigger value from color-affecting state — avoids useEffect+setState double-render
+  const colorTrigger = `${activeFeature}|${dragValue?.[0]}|${dragValue?.[1]}|${countRange.min}|${countRange.max}|${selectedHexagonId}`;
+
+  // Hexagon layer — only recreated when data or color trigger changes
+  const hexLayer = useMemo(
+    () =>
       new H3HexagonLayer<HexagonData>({
         id: 'h3-hexagons',
         data,
         getHexagon: (d) => d.h3,
         getFillColor: (d) => {
-          if (activeFeature && dragValue && colorFeatureMeta) {
-            // Drag mode: color by feature value using gradient
-            const val = d[`min_${activeFeature}`];
-            if (val == null) return [128, 128, 128] as [number, number, number];
-            const range = dragValue[1] - dragValue[0];
-            if (range === 0) return GRADIENT[0].color;
-            const t = ((val as number) - dragValue[0]) / range;
-            return normalizedToColor(Math.max(0, Math.min(1, t)));
+          const af = activeFeatureRef.current;
+          const dv = dragValueRef.current;
+          const cfm = colorFeatureMetaRef.current;
+          if (af && dv && cfm) {
+            const val = d[`min_${af}`];
+            if (val == null) return [128, 128, 128, 80] as [number, number, number, number];
+            const min = dv[0];
+            const max = dv[1];
+            const minVal = d[`min_${af}`] as number;
+            const maxVal = d[`max_${af}`] as number;
+            // Gray out hexagons outside drag range
+            if (maxVal < min || minVal > max) {
+              return [180, 180, 180, 60] as [number, number, number, number];
+            }
+            const range = max - min;
+            if (range === 0) return [...GRADIENT[0].color, 200] as [number, number, number, number];
+            const t = ((val as number) - min) / range;
+            const rgb = normalizedToColor(Math.max(0, Math.min(1, t)));
+            return [...rgb, 200] as [number, number, number, number];
           }
-          // Normal mode: color by count using blue scale
+          const cr = countRangeRef.current;
           const c = d.count as number;
-          const t = (c - countRange.min) / (countRange.max - countRange.min);
-          return countToColor(Math.max(0, Math.min(1, t)));
+          const t = (c - cr.min) / (cr.max - cr.min);
+          return [...countToColor(Math.max(0, Math.min(1, t))), 200] as [number, number, number, number];
         },
         getLineColor: (d) =>
-          (d.h3 === selectedHexagonId ? [255, 255, 255, 255] : [0, 0, 0, 0]) as [
+          (d.h3 === selectedHexagonIdRef.current ? [255, 255, 255, 255] : [0, 0, 0, 0]) as [
             number,
             number,
             number,
             number,
           ],
-        getLineWidth: (d) => (d.h3 === selectedHexagonId ? 2 : 0),
+        getLineWidth: (d) => (d.h3 === selectedHexagonIdRef.current ? 2 : 0),
         lineWidthUnits: 'pixels',
         updateTriggers: {
-          getFillColor: [activeFeature, dragValue, countRange, colorFeatureMeta],
-          getLineColor: [selectedHexagonId],
-          getLineWidth: [selectedHexagonId],
+          getFillColor: [colorTrigger],
+          getLineColor: [colorTrigger],
+          getLineWidth: [colorTrigger],
         },
         extruded: false,
         pickable: true,
-        opacity: 0.5,
+        opacity: 1,
         highPrecision: true,
         onClick: handleHexagonClick,
         // @ts-expect-error beforeId is a MapboxOverlay interleave prop, not typed in LayerProps
-        beforeId: LABEL_LAYER_ID,
+        beforeId: "waterway_label",
       }),
+    [data, colorTrigger, handleHexagonClick]
+  );
+
+  // POI layer — independent, only recreated when POI data changes
+  const poiLayer = useMemo(
+    () =>
       new IconLayer<POI>({
         id: 'poi-icons',
         data: pois,
@@ -306,22 +444,17 @@ export default function Map({
         sizeMinPixels: 20,
         sizeMaxPixels: 40,
         pickable: true,
-        onHover: handlePoiHover,
+        onHover: stablePoiHover,
       }),
-    ],
-    [
-      data,
-      pois,
-      handlePoiHover,
-      handleHexagonClick,
-      activeFeature,
-      dragValue,
-      countRange,
-      colorFeatureMeta,
-      selectedHexagonId,
-    ]
+    [pois, stablePoiHover]
   );
 
+  const layers = useMemo(() => [hexLayer, poiLayer], [hexLayer, poiLayer]);
+
+  // Tooltip uses refs to avoid being a layer dependency
+  const featuresRef = useRef(features);
+  featuresRef.current = features;
+
   const getTooltip = useCallback(
     ({ object }: { object?: HexagonData }) => {
       if (!object || !('h3' in object)) return null;
@@ -330,7 +463,7 @@ export default function Map({
       const lines: string[] = [];
       lines.push(`<strong>${(hex.count as number).toLocaleString()} properties</strong>`);
 
-      for (const f of features) {
+      for (const f of featuresRef.current) {
         const minVal = hex[`min_${f.name}`];
         const maxVal = hex[`max_${f.name}`];
         if (minVal != null && maxVal != null) {
@@ -342,7 +475,7 @@ export default function Map({
             typeof maxVal === 'number'
               ? maxVal.toLocaleString(undefined, { maximumFractionDigits: 1 })
               : String(maxVal);
-          const highlight = f.name === activeFeature ? 'font-weight: bold;' : '';
+          const highlight = f.name === activeFeatureRef.current ? 'font-weight: bold;' : '';
           lines.push(`<div style="${highlight}">${f.label}: ${minStr} - ${maxStr}</div>`);
         }
       }
@@ -356,7 +489,7 @@ export default function Map({
         },
       };
     },
-    [features, activeFeature]
+    []
   );
 
   return (
@@ -371,6 +504,7 @@ export default function Map({
       >
         <DeckOverlay layers={layers} getTooltip={getTooltip as never} />
       </MapGL>
+      <PostcodeSearch onFlyTo={handleFlyTo} />
       {popupInfo && (
         <div
           className="absolute pointer-events-none bg-white rounded shadow-lg p-2 text-sm"
@@ -387,4 +521,4 @@ export default function Map({
       )}
     </div>
   );
-}
+});
diff --git a/frontend/src/components/PropertiesPane.tsx b/frontend/src/components/PropertiesPane.tsx
index 8deed91..e612832 100644
--- a/frontend/src/components/PropertiesPane.tsx
+++ b/frontend/src/components/PropertiesPane.tsx
@@ -100,122 +100,100 @@ export function PropertiesPane({
   );
 }
 
+function formatDuration(d: string): string {
+  if (d === 'F') return 'Freehold';
+  if (d === 'L') return 'Leasehold';
+  return d;
+}
+
+function formatAge(value: number): string {
+  // construction_age_band is a midpoint year, e.g. 1935
+  if (value >= 1000) return `~${Math.round(value)}`;
+  return Math.round(value).toString();
+}
+
+// Helper to get a numeric value from a property, trying multiple field names
+function getNum(property: Property, ...keys: string[]): number | undefined {
+  for (const key of keys) {
+    const v = property[key];
+    if (v !== undefined && v !== null && typeof v === 'number') return v;
+  }
+  return undefined;
+}
+
 // Property card component showing all fields
 function PropertyCard({ property }: { property: Property }) {
-  const formatNumber = (value: number | undefined, decimals = 0): string => {
+  const fmt = (value: number | undefined, decimals = 0): string => {
     if (value === undefined) return '';
     return decimals > 0 ? value.toFixed(decimals) : Math.round(value).toLocaleString();
   };
 
+  const price = getNum(property, 'Last known price', 'latest_price');
+  const pricePerSqm = getNum(property, 'Price per sqm', 'price_per_sqm');
+  const floorArea = getNum(property, 'Total floor area (sqm)', 'total_floor_area');
+  const rooms = getNum(property, 'Rooms (including bedrooms & bathrooms)', 'number_habitable_rooms');
+  const age = getNum(property, 'Approximate construction age', 'construction_age_band');
+
   return (
     <div className="p-4 border-b border-gray-100 hover:bg-gray-50">
-      {/* Address */}
+      {/* Address & postcode */}
       <div className="font-semibold">{property.address || 'Unknown Address'}</div>
       <div className="text-sm text-gray-600">{property.postcode}</div>
 
       {/* Price */}
-      {property.latest_price && (
+      {price !== undefined && (
         <div className="mt-2 text-lg font-bold text-green-700">
-          £{formatNumber(property.latest_price as number)}
-          {property.price_per_sqm && (
+          £{fmt(price)}
+          {pricePerSqm !== undefined && (
             <span className="text-sm font-normal text-gray-600">
               {' '}
-              (£{formatNumber(property.price_per_sqm as number)}/m²)
+              (£{fmt(pricePerSqm)}/m²)
             </span>
           )}
         </div>
       )}
 
       {/* Property details grid */}
-      <div className="mt-2 grid grid-cols-2 gap-2 text-sm">
+      <div className="mt-2 grid grid-cols-2 gap-x-4 gap-y-1 text-sm">
         {property.property_type && (
           <div>
-            <span className="text-gray-600">Type:</span> {property.property_type}
+            <span className="text-gray-500">Type:</span> {property.property_type}
           </div>
         )}
         {property.built_form && (
           <div>
-            <span className="text-gray-600">Form:</span> {property.built_form}
+            <span className="text-gray-500">Built form:</span> {property.built_form}
           </div>
         )}
-        {property.total_floor_area && (
+        {property.duration && (
           <div>
-            <span className="text-gray-600">Area:</span>{' '}
-            {formatNumber(property.total_floor_area as number)}m²
+            <span className="text-gray-500">Tenure:</span> {formatDuration(property.duration)}
           </div>
         )}
-        {property.number_habitable_rooms && (
+        {floorArea !== undefined && (
           <div>
-            <span className="text-gray-600">Rooms:</span>{' '}
-            {formatNumber(property.number_habitable_rooms as number)}
+            <span className="text-gray-500">Floor area:</span> {fmt(floorArea)}m²
+          </div>
+        )}
+        {rooms !== undefined && (
+          <div>
+            <span className="text-gray-500">Rooms:</span> {fmt(rooms)}
+          </div>
+        )}
+        {age !== undefined && (
+          <div>
+            <span className="text-gray-500">Built:</span> {formatAge(age)}
           </div>
         )}
         {property.current_energy_rating && (
           <div>
-            <span className="text-gray-600">Energy:</span> {property.current_energy_rating}
+            <span className="text-gray-500">EPC rating:</span> {property.current_energy_rating}
           </div>
         )}
         {property.potential_energy_rating && (
           <div>
-            <span className="text-gray-600">Potential:</span> {property.potential_energy_rating}
-          </div>
-        )}
-        {property.construction_age_band !== undefined && (
-          <div>
-            <span className="text-gray-600">Built (age):</span>{' '}
-            {formatNumber(property.construction_age_band as number)}
-          </div>
-        )}
-
-        {/* Journey times */}
-        {property.public_transport_easy_minutes && (
-          <div>
-            <span className="text-gray-600">PT (easy):</span>{' '}
-            {formatNumber(property.public_transport_easy_minutes as number)}min
-          </div>
-        )}
-        {property.public_transport_quick_minutes && (
-          <div>
-            <span className="text-gray-600">PT (quick):</span>{' '}
-            {formatNumber(property.public_transport_quick_minutes as number)}min
-          </div>
-        )}
-        {property.cycling_minutes && (
-          <div>
-            <span className="text-gray-600">Cycling:</span>{' '}
-            {formatNumber(property.cycling_minutes as number)}min
-          </div>
-        )}
-
-        {/* Deprivation scores */}
-        {property.income_score !== undefined && (
-          <div>
-            <span className="text-gray-600">Income:</span>{' '}
-            {formatNumber(property.income_score as number, 1)}
-          </div>
-        )}
-        {property.employment_score !== undefined && (
-          <div>
-            <span className="text-gray-600">Employment:</span>{' '}
-            {formatNumber(property.employment_score as number, 1)}
-          </div>
-        )}
-        {property.education_score !== undefined && (
-          <div>
-            <span className="text-gray-600">Education:</span>{' '}
-            {formatNumber(property.education_score as number, 1)}
-          </div>
-        )}
-        {property.health_score !== undefined && (
-          <div>
-            <span className="text-gray-600">Health:</span>{' '}
-            {formatNumber(property.health_score as number, 1)}
-          </div>
-        )}
-        {property.crime_score !== undefined && (
-          <div>
-            <span className="text-gray-600">Crime:</span>{' '}
-            {formatNumber(property.crime_score as number, 1)}
+            <span className="text-gray-500">EPC potential:</span>{' '}
+            {property.potential_energy_rating}
           </div>
         )}
       </div>
diff --git a/frontend/src/types.ts b/frontend/src/types.ts
index 8412774..81b7e61 100644
--- a/frontend/src/types.ts
+++ b/frontend/src/types.ts
@@ -1,12 +1,16 @@
 export interface FeatureMeta {
   name: string;
-  min: number;
-  max: number;
   label: string;
+  type: 'numeric' | 'enum';
+  // Numeric-only fields
+  min?: number;
+  max?: number;
+  // Enum-only fields
+  values?: string[];
 }
 
-// Filters: feature name -> [selectedMin, selectedMax]
-export type FeatureFilters = Record<string, [number, number]>;
+// Filters: feature name -> [selectedMin, selectedMax] for numeric, string[] for enum
+export type FeatureFilters = Record<string, [number, number] | string[]>;
 
 export interface HexagonData {
   h3: string;
@@ -33,6 +37,8 @@ export interface ViewChangeParams {
   resolution: number;
   bounds: Bounds;
   zoom: number;
+  latitude: number;
+  longitude: number;
 }
 
 export interface ApiResponse {
@@ -62,6 +68,7 @@ export interface Property {
   postcode?: string;
   property_type?: string;
   built_form?: string;
+  duration?: string;
   current_energy_rating?: string;
   potential_energy_rating?: string;
 

From 7627818e98a21eeb26bd5581f63fc298eb8a8d64 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sat, 31 Jan 2026 22:04:28 +0000
Subject: [PATCH 38/62] Spice up website

---
 frontend/src/components/DataSources.tsx     |  53 +--
 frontend/src/components/DataSourcesPage.tsx | 175 ++++++++++
 frontend/src/components/Filters.tsx         |  89 +++--
 frontend/src/components/HomePage.tsx        | 362 ++++++++++++++++++++
 frontend/src/components/Map.tsx             | 233 +++++++++++--
 frontend/src/components/POIPane.tsx         |  26 +-
 frontend/src/components/PropertiesPane.tsx  |  49 +--
 frontend/src/components/ui/label.tsx        |   2 +-
 frontend/src/components/ui/slider.tsx       |   6 +-
 9 files changed, 831 insertions(+), 164 deletions(-)
 create mode 100644 frontend/src/components/DataSourcesPage.tsx
 create mode 100644 frontend/src/components/HomePage.tsx

diff --git a/frontend/src/components/DataSources.tsx b/frontend/src/components/DataSources.tsx
index 1d558bf..bd7c074 100644
--- a/frontend/src/components/DataSources.tsx
+++ b/frontend/src/components/DataSources.tsx
@@ -1,49 +1,10 @@
-export default function DataSources() {
-  const sources = [
-    {
-      name: 'Land Registry',
-      url: 'https://www.gov.uk/government/statistical-data-sets/price-paid-data-downloads',
-    },
-    {
-      name: 'EPC',
-      url: 'https://epc.opendatacommunities.org/downloads/domestic',
-    },
-    {
-      name: 'ArcGIS Postcodes',
-      url: 'https://www.arcgis.com/sharing/rest/content/items/077631e063eb4e1ab43575d01381ec33/data',
-    },
-    {
-      name: 'OpenStreetMap',
-      url: 'https://www.openstreetmap.org/copyright',
-    },
-    {
-      name: 'IoD 2025',
-      url: 'https://www.gov.uk/government/statistics/english-indices-of-deprivation-2025',
-    },
-    {
-      name: 'TfL API',
-      url: 'https://api-portal.tfl.gov.uk/',
-    },
-  ];
-
+export default function DataSources({ onNavigate }: { onNavigate: () => void }) {
   return (
-    <div className="absolute bottom-2 right-2 bg-white/90 backdrop-blur-sm px-3 py-2 rounded shadow-lg text-xs max-w-xs">
-      <div className="font-semibold mb-1 text-gray-700">Data Sources</div>
-      <div className="flex flex-wrap gap-x-2 gap-y-0.5">
-        {sources.map((source, idx) => (
-          <span key={source.name}>
-            <a
-              href={source.url}
-              target="_blank"
-              rel="noopener noreferrer"
-              className="text-blue-600 hover:text-blue-800 hover:underline"
-            >
-              {source.name}
-            </a>
-            {idx < sources.length - 1 && <span className="text-gray-400">•</span>}
-          </span>
-        ))}
-      </div>
-    </div>
+    <button
+      onClick={onNavigate}
+      className="absolute bottom-2 right-2 bg-white/90 backdrop-blur-sm px-3 py-2 rounded shadow-lg text-xs text-teal-600 hover:text-teal-800 hover:underline font-semibold transition-colors"
+    >
+      Data Sources
+    </button>
   );
 }
diff --git a/frontend/src/components/DataSourcesPage.tsx b/frontend/src/components/DataSourcesPage.tsx
new file mode 100644
index 0000000..1515412
--- /dev/null
+++ b/frontend/src/components/DataSourcesPage.tsx
@@ -0,0 +1,175 @@
+const DATA_SOURCES = [
+  {
+    name: 'Price Paid Data',
+    origin: 'HM Land Registry',
+    use: 'Complete historical property sale prices for England and Wales. Used for the last known sale price of each property.',
+    url: 'https://www.gov.uk/government/statistical-data-sets/price-paid-data-downloads',
+    license: 'Open Government Licence v3.0',
+  },
+  {
+    name: 'Energy Performance Certificates (EPC)',
+    origin: 'Ministry of Housing, Communities & Local Government',
+    use: 'Domestic Energy Performance Certificates providing floor area, number of rooms, construction age, energy ratings, property type, and built form. Fuzzy-joined with Price Paid records by address within postcode buckets.',
+    url: 'https://epc.opendatacommunities.org/downloads/domestic',
+    license: 'Open Government Licence v3.0',
+  },
+  {
+    name: 'National Statistics Postcode Lookup (NSPL)',
+    origin: 'ONS / ArcGIS',
+    use: 'Maps postcodes to latitude/longitude, LSOA, and Output Area codes for geolocation and joining area-level datasets.',
+    url: 'https://www.arcgis.com/sharing/rest/content/items/077631e063eb4e1ab43575d01381ec33/data',
+    license: 'Open Government Licence v3.0',
+  },
+  {
+    name: 'English Indices of Deprivation 2025',
+    origin: 'Ministry of Housing, Communities & Local Government',
+    use: 'Relative deprivation scores for 33,755 LSOAs across domains: Income, Employment, Education, Health, Crime, Living Environment, and sub-domains. Joined to properties via LSOA code.',
+    url: 'https://www.gov.uk/government/statistics/english-indices-of-deprivation-2025',
+    license: 'Open Government Licence v3.0',
+  },
+  {
+    name: 'Population by Ethnicity (2021 Census)',
+    origin: 'ONS',
+    use: 'Population percentages by ethnic group (Asian, Black, Mixed, White, Other) per Local Authority. Joined via Local Authority District code.',
+    url: 'https://www.ethnicity-facts-figures.service.gov.uk/uk-population-by-ethnicity/national-and-regional-populations/regional-ethnic-diversity/latest/#download-the-data',
+    license: 'Open Government Licence v3.0',
+  },
+  {
+    name: 'Street-level Crime Data',
+    origin: 'data.police.uk',
+    use: 'Street-level crime data from 2023 to 2025, aggregated into yearly averages by LSOA and crime type (violence, burglary, anti-social behaviour, drugs, vehicle crime, etc.).',
+    url: 'https://data.police.uk/data/',
+    license: 'Open Government Licence v3.0',
+  },
+  {
+    name: 'TfL Journey Times',
+    origin: 'Transport for London',
+    use: 'Journey time calculations from postcodes to central London destinations (Bank, Waterloo, King\'s Cross, etc.) via public transport and cycling.',
+    url: 'https://api-portal.tfl.gov.uk/',
+    license: 'Powered by TfL Open Data',
+  },
+  {
+    name: 'OpenStreetMap POIs',
+    origin: 'OpenStreetMap contributors / Geofabrik',
+    use: 'Points of interest extracted from the Great Britain PBF extract. Covers amenities, shops, healthcare, leisure, tourism, and more. Filtered and remapped to friendly category names.',
+    url: 'https://download.geofabrik.de/europe/great-britain-latest.osm.pbf',
+    license: 'Open Data Commons Open Database License (ODbL)',
+  },
+  {
+    name: 'NaPTAN (Public Transport Stops)',
+    origin: 'Department for Transport',
+    use: 'National Public Transport Access Nodes providing station and stop locations (rail, bus, metro/tram, ferry, airport), merged into the POI dataset.',
+    url: 'https://naptan.dft.gov.uk/naptan/schema/2.4/doc/NaPTANSchemaGuide-2.4-v0.57.pdf',
+    license: 'Open Government Licence v3.0',
+  },
+  {
+    name: 'Defra Noise Mapping',
+    origin: 'Defra / Environment Agency',
+    use: 'Strategic noise mapping Round 4 (2022) for road, rail, and airport sources. Lden (day-evening-night 24h weighted average) at 10m grid resolution, modelled at 4m above ground. Sampled at postcode centroids via WCS GeoTIFF tiles.',
+    url: 'https://environment.data.gov.uk/spatialdata/road-noise-all-metrics-england-round-4/wcs',
+    license: 'Open Government Licence v3.0',
+  },
+  {
+    name: 'Ofsted School Inspections',
+    origin: 'Ofsted',
+    use: 'Latest inspection outcomes for state-funded schools (as at April 2025). Averaged per postcode to give a local school quality score (1=Outstanding to 4=Inadequate).',
+    url: 'https://www.gov.uk/government/statistical-data-sets/monthly-management-information-ofsteds-school-inspections-outcomes',
+    license: 'Open Government Licence v3.0',
+  },
+  {
+    name: 'Ofcom Broadband Performance',
+    origin: 'Ofcom',
+    use: 'Fixed broadband coverage and speeds by Output Area from Connected Nations 2025. Includes max download/upload speeds across different speed tiers.',
+    url: 'https://www.ofcom.org.uk/phones-and-broadband/coverage-and-speeds/connected-nations-20252/data-downloads-2025',
+    license: 'Open Government Licence v3.0',
+  },
+];
+
+export default function DataSourcesPage() {
+  return (
+    <div className="flex-1 overflow-y-auto bg-warm-50 flex flex-col">
+      <div className="flex-1">
+        <div className="max-w-5xl mx-auto px-6 py-8">
+          <h1 className="text-2xl font-bold text-warm-900 mb-2">Data Sources</h1>
+          <p className="text-warm-600 mb-6">
+            This application combines {DATA_SOURCES.length} open datasets covering property prices, energy
+            performance, transport, demographics, crime, environment, and more.
+          </p>
+          <div className="grid grid-cols-1 lg:grid-cols-2 gap-4">
+            {DATA_SOURCES.map((source) => (
+              <div
+                key={source.name}
+                className="bg-white rounded-lg border border-warm-200 p-5"
+              >
+                <div className="flex items-start justify-between gap-4 mb-2">
+                  <h2 className="text-lg font-semibold text-warm-900">{source.name}</h2>
+                  <span className="shrink-0 text-xs bg-warm-100 text-warm-600 px-2 py-1 rounded">
+                    {source.license}
+                  </span>
+                </div>
+                <p className="text-sm text-warm-500 mb-2">Source: {source.origin}</p>
+                <p className="text-sm text-warm-700 mb-3">{source.use}</p>
+                <a
+                  href={source.url}
+                  target="_blank"
+                  rel="noopener noreferrer"
+                  className="text-sm text-teal-600 hover:text-teal-800 hover:underline break-all"
+                >
+                  {source.url}
+                </a>
+              </div>
+            ))}
+          </div>
+        </div>
+      </div>
+
+      <footer className="bg-navy-900 text-warm-400 px-6 py-6">
+        <div className="max-w-5xl mx-auto">
+          <h2 className="text-sm font-semibold text-warm-300 uppercase tracking-wide mb-3">Attribution</h2>
+          <ul className="space-y-1.5 text-sm">
+            <li>
+              Contains HM Land Registry data &copy; Crown copyright and database right 2025.
+            </li>
+            <li>
+              Contains public sector information licensed under the{' '}
+              <a
+                href="https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/"
+                target="_blank"
+                rel="noopener noreferrer"
+                className="text-teal-400 hover:text-teal-300 hover:underline"
+              >
+                Open Government Licence v3.0
+              </a>
+              .
+            </li>
+            <li>
+              Contains OS data &copy; Crown copyright and database rights 2025.
+            </li>
+            <li>Powered by TfL Open Data.</li>
+            <li>
+              Contains data from{' '}
+              <a
+                href="https://www.openstreetmap.org/copyright"
+                target="_blank"
+                rel="noopener noreferrer"
+                className="text-teal-400 hover:text-teal-300 hover:underline"
+              >
+                &copy; OpenStreetMap contributors
+              </a>
+              , available under the{' '}
+              <a
+                href="https://opendatacommons.org/licenses/odbl/"
+                target="_blank"
+                rel="noopener noreferrer"
+                className="text-teal-400 hover:text-teal-300 hover:underline"
+              >
+                Open Data Commons Open Database License (ODbL)
+              </a>
+              .
+            </li>
+          </ul>
+        </div>
+      </footer>
+    </div>
+  );
+}
diff --git a/frontend/src/components/Filters.tsx b/frontend/src/components/Filters.tsx
index 498552a..a1da18c 100644
--- a/frontend/src/components/Filters.tsx
+++ b/frontend/src/components/Filters.tsx
@@ -16,6 +16,9 @@ interface FiltersProps {
   onDragChange: (value: [number, number]) => void;
   onDragEnd: () => void;
   zoom: number;
+  pinnedFeature: string | null;
+  onTogglePin: (name: string) => void;
+  onCancelPin: () => void;
 }
 
 function formatValue(value: number): string {
@@ -38,13 +41,16 @@ export default memo(function Filters({
   onDragChange,
   onDragEnd,
   zoom,
+  pinnedFeature,
+  onTogglePin,
+  onCancelPin,
 }: FiltersProps) {
   const availableFeatures = features.filter((f) => !enabledFeatures.has(f.name));
   const enabledFeatureList = features.filter((f) => enabledFeatures.has(f.name));
 
   return (
     <div className="w-72 p-4 bg-white shadow-lg space-y-4 overflow-y-auto">
-      <div className="text-sm text-slate-500">Zoom: {zoom.toFixed(1)}</div>
+      <div className="text-sm text-warm-500">Zoom: {zoom.toFixed(1)}</div>
 
       {/* Add filter dropdown */}
       {availableFeatures.length > 0 && (
@@ -60,7 +66,7 @@ export default memo(function Filters({
           </option>
           {availableFeatures.map((f) => (
             <option key={f.name} value={f.name}>
-              {f.label}
+              {f.name}
             </option>
           ))}
         </select>
@@ -74,10 +80,10 @@ export default memo(function Filters({
           return (
             <div key={feature.name} className="space-y-1 p-2 rounded">
               <div className="flex items-center justify-between">
-                <Label className="text-xs">{feature.label}</Label>
+                <Label className="text-xs">{feature.name}</Label>
                 <button
                   onClick={() => onRemoveFilter(feature.name)}
-                  className="text-slate-400 hover:text-slate-700 text-sm px-1"
+                  className="text-warm-400 hover:text-warm-700 text-sm px-1"
                   title="Remove filter"
                 >
                   x
@@ -85,13 +91,13 @@ export default memo(function Filters({
               </div>
               <div className="flex gap-2 text-xs mb-1">
                 <button
-                  className="text-blue-600 hover:underline"
+                  className="text-teal-600 hover:underline"
                   onClick={() => onFilterChange(feature.name, [...allValues])}
                 >
                   All
                 </button>
                 <button
-                  className="text-blue-600 hover:underline"
+                  className="text-teal-600 hover:underline"
                   onClick={() => onFilterChange(feature.name, [])}
                 >
                   None
@@ -121,6 +127,7 @@ export default memo(function Filters({
 
         // Numeric feature
         const isActive = activeFeature === feature.name;
+        const isPinned = pinnedFeature === feature.name;
         const displayValue =
           isActive && dragValue
             ? dragValue
@@ -130,19 +137,40 @@ export default memo(function Filters({
         return (
           <div
             key={feature.name}
-            className={`space-y-1 p-2 rounded ${isActive ? 'ring-2 ring-blue-400 bg-blue-50' : ''}`}
+            className={`space-y-1 p-2 rounded ${isActive ? 'ring-2 ring-teal-400 bg-teal-50' : isPinned ? 'ring-2 ring-teal-400 bg-teal-50/50' : ''}`}
           >
             <div className="flex items-center justify-between">
               <Label className="text-xs">
-                {feature.label}: {formatValue(displayValue[0])} - {formatValue(displayValue[1])}
+                {feature.name}: {formatValue(displayValue[0])} - {formatValue(displayValue[1])}
               </Label>
-              <button
-                onClick={() => onRemoveFilter(feature.name)}
-                className="text-slate-400 hover:text-slate-700 text-sm px-1"
-                title="Remove filter"
-              >
-                x
-              </button>
+              <div className="flex items-center gap-0.5">
+                <button
+                  onClick={() => onTogglePin(feature.name)}
+                  className={`p-0.5 rounded ${isPinned ? 'text-teal-600' : 'text-warm-400 hover:text-warm-700'}`}
+                  title={isPinned ? 'Unpin color view' : 'Color map by this feature'}
+                >
+                  <svg className="w-3.5 h-3.5" viewBox="0 0 24 24" fill={isPinned ? 'currentColor' : 'none'} stroke="currentColor" strokeWidth={2}>
+                    <path d="M1 12s4-8 11-8 11 8 11 8-4 8-11 8-11-8-11-8z" />
+                    <circle cx="12" cy="12" r="3" />
+                  </svg>
+                </button>
+                {isPinned && (
+                  <button
+                    onClick={onCancelPin}
+                    className="text-warm-400 hover:text-warm-700 text-xs px-0.5"
+                    title="Clear color view"
+                  >
+                    x
+                  </button>
+                )}
+                <button
+                  onClick={() => onRemoveFilter(feature.name)}
+                  className="text-warm-400 hover:text-warm-700 text-sm px-1"
+                  title="Remove filter"
+                >
+                  x
+                </button>
+              </div>
             </div>
             <Slider
               min={feature.min!}
@@ -157,37 +185,6 @@ export default memo(function Filters({
         );
       })}
 
-      <div className="p-3 bg-slate-100 rounded text-xs">
-        <div className="mb-2 font-medium">Color Scale</div>
-        {activeFeature ? (
-          <>
-            <div
-              className="h-4 rounded"
-              style={{
-                background:
-                  'linear-gradient(to right, rgb(46, 204, 113), rgb(241, 196, 15), rgb(231, 76, 60), rgb(142, 68, 173))',
-              }}
-            ></div>
-            <div className="flex justify-between mt-1">
-              <span>Low</span>
-              <span>High</span>
-            </div>
-          </>
-        ) : (
-          <>
-            <div
-              className="h-4 rounded"
-              style={{
-                background: 'linear-gradient(to right, rgb(209, 226, 243), rgb(33, 102, 172))',
-              }}
-            ></div>
-            <div className="flex justify-between mt-1">
-              <span>Few</span>
-              <span>Many</span>
-            </div>
-          </>
-        )}
-      </div>
     </div>
   );
 });
diff --git a/frontend/src/components/HomePage.tsx b/frontend/src/components/HomePage.tsx
new file mode 100644
index 0000000..7c381e9
--- /dev/null
+++ b/frontend/src/components/HomePage.tsx
@@ -0,0 +1,362 @@
+import { useRef, useState, useEffect, useCallback } from 'react';
+
+// --- Floating hex particle canvas that reacts to scroll ---
+
+const HEX_COUNT = 60;
+const TAU = Math.PI * 2;
+
+interface Hex {
+  x: number;
+  y: number;
+  baseY: number;
+  size: number;
+  opacity: number;
+  speed: number; // horizontal drift px/s
+  phase: number; // for gentle bob
+}
+
+function initHexes(w: number, h: number): Hex[] {
+  const hexes: Hex[] = [];
+  for (let i = 0; i < HEX_COUNT; i++) {
+    const y = Math.random() * h;
+    hexes.push({
+      x: Math.random() * w,
+      y,
+      baseY: y,
+      size: 8 + Math.random() * 20,
+      opacity: 0.06 + Math.random() * 0.12,
+      speed: 6 + Math.random() * 14,
+      phase: Math.random() * TAU,
+    });
+  }
+  return hexes;
+}
+
+function drawHex(ctx: CanvasRenderingContext2D, cx: number, cy: number, r: number) {
+  ctx.beginPath();
+  for (let i = 0; i < 6; i++) {
+    const angle = (TAU / 6) * i - Math.PI / 6;
+    const px = cx + r * Math.cos(angle);
+    const py = cy + r * Math.sin(angle);
+    if (i === 0) ctx.moveTo(px, py);
+    else ctx.lineTo(px, py);
+  }
+  ctx.closePath();
+}
+
+function HexCanvas({ scrollProgress }: { scrollProgress: number }) {
+  const canvasRef = useRef<HTMLCanvasElement>(null);
+  const hexesRef = useRef<Hex[]>([]);
+  const animRef = useRef(0);
+  const scrollRef = useRef(scrollProgress);
+  scrollRef.current = scrollProgress;
+
+  useEffect(() => {
+    const canvas = canvasRef.current;
+    if (!canvas) return;
+    const ctx = canvas.getContext('2d');
+    if (!ctx) return;
+
+    let w = 0;
+    let h = 0;
+
+    function resize() {
+      const dpr = window.devicePixelRatio || 1;
+      const rect = canvas!.parentElement!.getBoundingClientRect();
+      w = rect.width;
+      h = rect.height;
+      canvas!.width = w * dpr;
+      canvas!.height = h * dpr;
+      canvas!.style.width = `${w}px`;
+      canvas!.style.height = `${h}px`;
+      ctx!.setTransform(dpr, 0, 0, dpr, 0, 0);
+      hexesRef.current = initHexes(w, h);
+    }
+
+    resize();
+    const ro = new ResizeObserver(resize);
+    ro.observe(canvas.parentElement!);
+
+    let prev = performance.now();
+
+    function frame(now: number) {
+      const dt = (now - prev) / 1000;
+      prev = now;
+      const scroll = scrollRef.current;
+      ctx!.clearRect(0, 0, w, h);
+
+      // Teal accent color, fade to 0 as user scrolls down
+      const globalAlpha = Math.max(0, 1 - scroll * 2);
+
+      for (const hex of hexesRef.current) {
+        // drift right, wrap
+        hex.x = (hex.x + hex.speed * dt) % (w + hex.size * 2);
+        // gentle vertical bob + parallax push from scroll
+        const bob = Math.sin(now / 1000 + hex.phase) * 8;
+        const parallax = scroll * h * 0.3 * (hex.speed / 20);
+        hex.y = hex.baseY + bob - parallax;
+
+        // wrap vertically
+        if (hex.y < -hex.size * 2) hex.y += h + hex.size * 4;
+        if (hex.y > h + hex.size * 2) hex.y -= h + hex.size * 4;
+
+        ctx!.globalAlpha = hex.opacity * globalAlpha;
+        ctx!.fillStyle = '#00a28c';
+        drawHex(ctx!, hex.x, hex.y, hex.size);
+        ctx!.fill();
+
+        ctx!.globalAlpha = hex.opacity * 0.5 * globalAlpha;
+        ctx!.strokeStyle = '#05c9aa';
+        ctx!.lineWidth = 1;
+        drawHex(ctx!, hex.x, hex.y, hex.size);
+        ctx!.stroke();
+      }
+
+      animRef.current = requestAnimationFrame(frame);
+    }
+
+    animRef.current = requestAnimationFrame(frame);
+    return () => {
+      cancelAnimationFrame(animRef.current);
+      ro.disconnect();
+    };
+  }, []);
+
+  return (
+    <canvas
+      ref={canvasRef}
+      className="absolute inset-0 pointer-events-none"
+      style={{ zIndex: 0 }}
+    />
+  );
+}
+
+// --- Fade-in hook ---
+
+function useFadeInRef() {
+  const ref = useRef<HTMLDivElement>(null);
+  useEffect(() => {
+    const el = ref.current;
+    if (!el) return;
+    const observer = new IntersectionObserver(
+      ([entry]) => {
+        if (entry.isIntersecting) {
+          el.classList.add('fade-in-visible');
+          observer.unobserve(el);
+        }
+      },
+      { threshold: 0.15 }
+    );
+    observer.observe(el);
+    return () => observer.disconnect();
+  }, []);
+  return ref;
+}
+
+// --- Page ---
+
+export default function HomePage({ onOpenDashboard }: { onOpenDashboard: () => void }) {
+  const scrollRef = useRef<HTMLDivElement>(null);
+  const [scrollProgress, setScrollProgress] = useState(0);
+
+  const handleScroll = useCallback(() => {
+    const el = scrollRef.current;
+    if (!el) return;
+    const max = el.scrollHeight - el.clientHeight;
+    if (max <= 0) return;
+    setScrollProgress(el.scrollTop / max);
+  }, []);
+
+  useEffect(() => {
+    const el = scrollRef.current;
+    if (!el) return;
+    el.addEventListener('scroll', handleScroll, { passive: true });
+    return () => el.removeEventListener('scroll', handleScroll);
+  }, [handleScroll]);
+
+  const heroRef = useFadeInRef();
+  const problemRef = useFadeInRef();
+  const filtersRef = useFadeInRef();
+  const howRef = useFadeInRef();
+  const numbersRef = useFadeInRef();
+  const ctaRef = useFadeInRef();
+
+  return (
+    <div
+      ref={scrollRef}
+      className="flex-1 overflow-y-auto bg-warm-50 relative"
+    >
+      <HexCanvas scrollProgress={scrollProgress} />
+
+      <div className="relative" style={{ zIndex: 1 }}>
+        {/* Hero */}
+        <div className="max-w-3xl mx-auto px-6 pt-20 pb-24">
+          <div ref={heroRef} className="fade-in-section backdrop-blur-sm bg-warm-50/60 rounded-2xl p-8 -mx-2">
+            <p className="text-teal-600 font-semibold tracking-wide uppercase text-sm mb-4">
+              Find where to live, not just what&apos;s for sale
+            </p>
+            <h1 className="text-5xl font-extrabold text-navy-950 mb-6 leading-[1.1] tracking-tight">
+              Every neighbourhood<br />
+              in England &amp; Wales.<br />
+              <span className="text-teal-600">One map. Your&nbsp;rules.</span>
+            </h1>
+            <p className="text-xl text-warm-600 mb-8 leading-relaxed max-w-xl">
+              Set the commute, budget, school rating, noise level, and crime
+              threshold you&apos;ll accept. Narrowit shows you every area that
+              qualifies &mdash; instantly.
+            </p>
+            <div className="flex items-center gap-4">
+              <button
+                onClick={onOpenDashboard}
+                className="px-7 py-3.5 bg-coral-500 text-white rounded-lg font-semibold hover:bg-coral-600 transition-colors text-base shadow-lg shadow-coral-500/25"
+              >
+                Explore the map
+              </button>
+              <span className="text-warm-400 text-sm">
+                No signup &middot; Free &middot; Open data
+              </span>
+            </div>
+          </div>
+        </div>
+
+        {/* The flip */}
+        <div className="max-w-3xl mx-auto px-6 pb-20">
+          <div ref={problemRef} className="fade-in-section">
+            <div className="rounded-2xl backdrop-blur-sm bg-warm-50/40 border border-warm-200/50 p-8">
+              <div className="grid md:grid-cols-2 gap-8">
+                <div>
+                  <h3 className="text-sm font-semibold text-warm-400 uppercase tracking-wide mb-2">The old way</h3>
+                  <p className="text-warm-700 leading-relaxed">
+                    Pick a postcode. Google the schools. Check crime stats on
+                    another site. Look up commute times. Realise it&apos;s too
+                    expensive. Start over. Repeat 40 times.
+                  </p>
+                </div>
+                <div>
+                  <h3 className="text-sm font-semibold text-teal-600 uppercase tracking-wide mb-2">With Narrowit</h3>
+                  <p className="text-warm-700 leading-relaxed">
+                    Tell the map what you need. Every hexagon that lights up is
+                    a place worth looking at. Drill into any one to see
+                    individual properties, prices, and energy ratings.
+                  </p>
+                </div>
+              </div>
+            </div>
+          </div>
+        </div>
+
+        {/* Filter showcase */}
+        <div className="max-w-4xl mx-auto px-6 pb-20">
+          <div ref={filtersRef} className="fade-in-section">
+            <h2 className="text-3xl font-bold text-navy-950 mb-2 text-center">
+              12 datasets. One slider&nbsp;each.
+            </h2>
+            <p className="text-warm-500 text-center mb-10 max-w-lg mx-auto">
+              Every filter narrows the map in real time. Combine as many as you like.
+            </p>
+            <div className="grid grid-cols-2 md:grid-cols-4 gap-3">
+              {FILTERS.map((f) => (
+                <div
+                  key={f.label}
+                  className="rounded-xl bg-white border border-warm-200 p-4 shadow-sm hover:shadow-md hover:border-teal-300 transition-all"
+                >
+                  <div className="text-2xl mb-2">{f.icon}</div>
+                  <div className="font-semibold text-navy-950 text-sm">{f.label}</div>
+                  <div className="text-xs text-warm-500 mt-0.5">{f.example}</div>
+                </div>
+              ))}
+            </div>
+          </div>
+        </div>
+
+        {/* How it works */}
+        <div className="max-w-3xl mx-auto px-6 pb-20">
+          <div ref={howRef} className="fade-in-section">
+            <h2 className="text-3xl font-bold text-navy-950 mb-10 text-center">
+              Three clicks to clarity
+            </h2>
+            <div className="space-y-6">
+              {STEPS.map((step, i) => (
+                <div key={i} className="flex gap-5 items-start">
+                  <span className="shrink-0 w-10 h-10 rounded-full bg-teal-600 text-white flex items-center justify-center text-lg font-bold">
+                    {i + 1}
+                  </span>
+                  <div>
+                    <h3 className="font-semibold text-navy-950 text-lg">{step.title}</h3>
+                    <p className="text-warm-600 mt-0.5">{step.body}</p>
+                  </div>
+                </div>
+              ))}
+            </div>
+          </div>
+        </div>
+
+        {/* Numbers */}
+        <div className="max-w-3xl mx-auto px-6 pb-20">
+          <div ref={numbersRef} className="fade-in-section">
+            <div className="grid grid-cols-3 gap-6 text-center">
+              {STATS.map((s) => (
+                <div key={s.label}>
+                  <div className="text-3xl font-extrabold text-teal-600">{s.value}</div>
+                  <div className="text-sm text-warm-500 mt-1">{s.label}</div>
+                </div>
+              ))}
+            </div>
+          </div>
+        </div>
+
+        {/* Final CTA */}
+        <div className="max-w-3xl mx-auto px-6 pb-24">
+          <div ref={ctaRef} className="fade-in-section text-center">
+            <h2 className="text-3xl font-bold text-navy-950 mb-3">
+              Ready to narrow it down?
+            </h2>
+            <p className="text-warm-500 mb-8 max-w-md mx-auto">
+              100% open data. No account required. Just set your filters and go.
+            </p>
+            <button
+              onClick={onOpenDashboard}
+              className="px-8 py-4 bg-coral-500 text-white rounded-lg font-semibold hover:bg-coral-600 transition-colors text-lg shadow-lg shadow-coral-500/25"
+            >
+              Open the map
+            </button>
+          </div>
+        </div>
+      </div>
+    </div>
+  );
+}
+
+// --- Data ---
+
+const FILTERS = [
+  { icon: '\u00A3', label: 'Sale price', example: 'e.g. under \u00A3400k' },
+  { icon: '\uD83D\uDE86', label: 'Commute time', example: 'e.g. < 45 min to Bank' },
+  { icon: '\uD83C\uDFEB', label: 'School quality', example: 'Ofsted Outstanding' },
+  { icon: '\uD83D\uDEA8', label: 'Crime rate', example: 'Low burglary areas' },
+  { icon: '\u26A1', label: 'Energy rating', example: 'EPC band A\u2013C' },
+  { icon: '\uD83D\uDCCF', label: 'Floor area', example: 'e.g. 80+ sqm' },
+  { icon: '\uD83D\uDD07', label: 'Road noise', example: 'Below 55 dB Lden' },
+  { icon: '\uD83C\uDF10', label: 'Broadband speed', example: '100+ Mbps available' },
+];
+
+const STEPS = [
+  {
+    title: 'Add your deal-breakers',
+    body: 'Slide the filters for everything you care about \u2014 price cap, max commute, school quality, noise. The map updates as you drag.',
+  },
+  {
+    title: 'Spot the clusters',
+    body: 'Hexagons light up where properties match. Zoom in and they split into finer cells. At street level you see individual postcode boundaries.',
+  },
+  {
+    title: 'Dive into a neighbourhood',
+    body: 'Click any hexagon to see every property inside it \u2014 sale prices, floor plans, energy ratings, tenure. Layer on cafes, GP surgeries, and parks from OpenStreetMap.',
+  },
+];
+
+const STATS = [
+  { value: '26M+', label: 'property records' },
+  { value: '12', label: 'open datasets' },
+  { value: '1.7M', label: 'postcodes mapped' },
+];
diff --git a/frontend/src/components/Map.tsx b/frontend/src/components/Map.tsx
index 0458978..ae01815 100644
--- a/frontend/src/components/Map.tsx
+++ b/frontend/src/components/Map.tsx
@@ -3,21 +3,26 @@ import { Map as MapGL, useControl } from 'react-map-gl/maplibre';
 import type { MapRef } from 'react-map-gl/maplibre';
 import { MapboxOverlay } from '@deck.gl/mapbox';
 import { H3HexagonLayer } from '@deck.gl/geo-layers';
-import { IconLayer } from '@deck.gl/layers';
+import { IconLayer, PolygonLayer } from '@deck.gl/layers';
 import type { PickingInfo } from '@deck.gl/core';
 import 'maplibre-gl/dist/maplibre-gl.css';
-import type { HexagonData, ViewState, ViewChangeParams, Bounds, POI, FeatureMeta } from '../types';
+import type { HexagonData, ViewState, ViewChangeParams, Bounds, POI, FeatureMeta, PostcodeData } from '../types';
 
 interface MapProps {
   data: HexagonData[];
   pois: POI[];
   onViewChange: (params: ViewChangeParams) => void;
-  activeFeature: string | null;
-  dragValue: [number, number] | null;
+  viewFeature: string | null;
+  viewRange: [number, number] | null;
+  viewSource: 'drag' | 'eye' | null;
+  onCancelPin: () => void;
   features: FeatureMeta[];
   selectedHexagonId: string | null;
   onHexagonClick: (h3: string) => void;
   initialViewState?: ViewState;
+  postcodeData: PostcodeData[];
+  selectedPostcode: string | null;
+  onPostcodeClick: (postcode: string) => void;
 }
 
 // Twemoji CDN base URL
@@ -123,7 +128,8 @@ function DeckOverlay({
   layers,
   getTooltip,
 }: {
-  layers: (H3HexagonLayer<HexagonData> | IconLayer<POI>)[];
+  // eslint-disable-next-line @typescript-eslint/no-explicit-any
+  layers: any[];
   // eslint-disable-next-line @typescript-eslint/no-explicit-any
   getTooltip: any;
 }) {
@@ -207,7 +213,7 @@ function PostcodeSearch({
         <button
           type="submit"
           disabled={loading}
-          className="px-3 py-2 bg-blue-500 text-white text-sm hover:bg-blue-600 disabled:opacity-50"
+          className="px-3 py-2 bg-teal-600 text-white text-sm hover:bg-teal-700 disabled:opacity-50"
         >
           {loading ? '...' : 'Go'}
         </button>
@@ -221,16 +227,70 @@ function PostcodeSearch({
   );
 }
 
+function MapLegend({
+  featureLabel,
+  range,
+  showCancel,
+  onCancel,
+}: {
+  featureLabel: string;
+  range: [number, number];
+  showCancel: boolean;
+  onCancel: () => void;
+}) {
+  const formatVal = (v: number) => {
+    if (Math.abs(v) >= 1_000_000) return `${(v / 1_000_000).toFixed(1)}M`;
+    if (Math.abs(v) >= 1_000) return `${(v / 1_000).toFixed(1)}k`;
+    if (Number.isInteger(v)) return v.toString();
+    return v.toFixed(1);
+  };
+
+  return (
+    <div className="absolute top-3 right-3 z-10 bg-white rounded shadow-lg p-3 text-xs min-w-[160px]">
+      <div className="flex items-center justify-between mb-2">
+        <span className="font-semibold text-sm">{featureLabel}</span>
+        {showCancel && (
+          <button
+            onClick={onCancel}
+            className="text-warm-400 hover:text-warm-700 ml-2"
+            title="Clear color view"
+          >
+            <svg className="w-4 h-4" fill="none" stroke="currentColor" viewBox="0 0 24 24" strokeWidth={2}>
+              <path strokeLinecap="round" strokeLinejoin="round" d="M6 18L18 6M6 6l12 12" />
+            </svg>
+          </button>
+        )}
+      </div>
+      <div
+        className="h-3 rounded"
+        style={{
+          background:
+            'linear-gradient(to right, rgb(46, 204, 113), rgb(241, 196, 15), rgb(231, 76, 60), rgb(142, 68, 173))',
+        }}
+      />
+      <div className="flex justify-between mt-1 text-warm-600">
+        <span>{formatVal(range[0])}</span>
+        <span>{formatVal(range[1])}</span>
+      </div>
+    </div>
+  );
+}
+
 export default memo(function Map({
   data,
   pois,
   onViewChange,
-  activeFeature,
-  dragValue,
+  viewFeature,
+  viewRange,
+  viewSource,
+  onCancelPin,
   features,
   selectedHexagonId,
   onHexagonClick,
   initialViewState,
+  postcodeData,
+  selectedPostcode,
+  onPostcodeClick,
 }: MapProps) {
   const containerRef = useRef<HTMLDivElement>(null);
   const [viewState, setViewState] = useState<ViewState>(initialViewState || INITIAL_VIEW);
@@ -332,15 +392,15 @@ export default memo(function Map({
 
   // Memoize feature lookup to avoid new reference each render
   const colorFeatureMeta = useMemo(
-    () => (activeFeature ? features.find((f) => f.name === activeFeature) || null : null),
-    [activeFeature, features]
+    () => (viewFeature ? features.find((f) => f.name === viewFeature) || null : null),
+    [viewFeature, features]
   );
 
   // Use refs for values that change during drag so layers aren't recreated
-  const activeFeatureRef = useRef(activeFeature);
-  activeFeatureRef.current = activeFeature;
-  const dragValueRef = useRef(dragValue);
-  dragValueRef.current = dragValue;
+  const viewFeatureRef = useRef(viewFeature);
+  viewFeatureRef.current = viewFeature;
+  const viewRangeRef = useRef(viewRange);
+  viewRangeRef.current = viewRange;
   const colorFeatureMetaRef = useRef(colorFeatureMeta);
   colorFeatureMetaRef.current = colorFeatureMeta;
   const countRangeRef = useRef(countRange);
@@ -348,6 +408,10 @@ export default memo(function Map({
   const selectedHexagonIdRef = useRef(selectedHexagonId);
   selectedHexagonIdRef.current = selectedHexagonId;
 
+  // Postcode refs
+  const selectedPostcodeRef = useRef(selectedPostcode);
+  selectedPostcodeRef.current = selectedPostcode;
+
   // Stable click handler using ref
   const onHexagonClickRef = useRef(onHexagonClick);
   onHexagonClickRef.current = onHexagonClick;
@@ -360,6 +424,17 @@ export default memo(function Map({
     []
   );
 
+  const onPostcodeClickRef = useRef(onPostcodeClick);
+  onPostcodeClickRef.current = onPostcodeClick;
+  const handlePostcodeClick = useCallback(
+    (info: PickingInfo<PostcodeData>) => {
+      if (info.object && 'postcode' in info.object) {
+        onPostcodeClickRef.current(info.object.postcode);
+      }
+    },
+    []
+  );
+
   // Stable hover handler using ref
   const handlePoiHoverRef = useRef(handlePoiHover);
   handlePoiHoverRef.current = handlePoiHover;
@@ -368,7 +443,7 @@ export default memo(function Map({
   }, []);
 
   // Derive a trigger value from color-affecting state — avoids useEffect+setState double-render
-  const colorTrigger = `${activeFeature}|${dragValue?.[0]}|${dragValue?.[1]}|${countRange.min}|${countRange.max}|${selectedHexagonId}`;
+  const colorTrigger = `${viewFeature}|${viewRange?.[0]}|${viewRange?.[1]}|${countRange.min}|${countRange.max}|${selectedHexagonId}`;
 
   // Hexagon layer — only recreated when data or color trigger changes
   const hexLayer = useMemo(
@@ -378,17 +453,17 @@ export default memo(function Map({
         data,
         getHexagon: (d) => d.h3,
         getFillColor: (d) => {
-          const af = activeFeatureRef.current;
-          const dv = dragValueRef.current;
+          const vf = viewFeatureRef.current;
+          const vr = viewRangeRef.current;
           const cfm = colorFeatureMetaRef.current;
-          if (af && dv && cfm) {
-            const val = d[`min_${af}`];
+          if (vf && vr && cfm) {
+            const val = d[`min_${vf}`];
             if (val == null) return [128, 128, 128, 80] as [number, number, number, number];
-            const min = dv[0];
-            const max = dv[1];
-            const minVal = d[`min_${af}`] as number;
-            const maxVal = d[`max_${af}`] as number;
-            // Gray out hexagons outside drag range
+            const min = vr[0];
+            const max = vr[1];
+            const minVal = d[`min_${vf}`] as number;
+            const maxVal = d[`max_${vf}`] as number;
+            // Gray out hexagons outside range
             if (maxVal < min || minVal > max) {
               return [180, 180, 180, 60] as [number, number, number, number];
             }
@@ -428,6 +503,79 @@ export default memo(function Map({
     [data, colorTrigger, handleHexagonClick]
   );
 
+  // Postcode count range
+  const postcodeCountRange = useMemo(() => {
+    if (postcodeData.length === 0) return { min: 0, max: 1 };
+    let min = Infinity;
+    let max = -Infinity;
+    for (const d of postcodeData) {
+      const c = d.count as number;
+      if (c < min) min = c;
+      if (c > max) max = c;
+    }
+    if (min === max) return { min, max: min + 1 };
+    return { min, max };
+  }, [postcodeData]);
+
+  const postcodeCountRangeRef = useRef(postcodeCountRange);
+  postcodeCountRangeRef.current = postcodeCountRange;
+
+  // Postcode color trigger
+  const postcodeColorTrigger = `${viewFeature}|${viewRange?.[0]}|${viewRange?.[1]}|${postcodeCountRange.min}|${postcodeCountRange.max}|${selectedPostcode}`;
+
+  // Postcode polygon layer
+  const postcodeLayer = useMemo(
+    () =>
+      new PolygonLayer<PostcodeData>({
+        id: 'postcode-polygons',
+        data: postcodeData,
+        getPolygon: (d) => d.polygon,
+        getFillColor: (d) => {
+          const vf = viewFeatureRef.current;
+          const vr = viewRangeRef.current;
+          const cfm = colorFeatureMetaRef.current;
+          if (vf && vr && cfm) {
+            const val = d[`min_${vf}`];
+            if (val == null) return [128, 128, 128, 80] as [number, number, number, number];
+            const min = vr[0];
+            const max = vr[1];
+            const minVal = d[`min_${vf}`] as number;
+            const maxVal = d[`max_${vf}`] as number;
+            if (maxVal < min || minVal > max) {
+              return [180, 180, 180, 60] as [number, number, number, number];
+            }
+            const range = max - min;
+            if (range === 0) return [...GRADIENT[0].color, 200] as [number, number, number, number];
+            const t = ((val as number) - min) / range;
+            const rgb = normalizedToColor(Math.max(0, Math.min(1, t)));
+            return [...rgb, 200] as [number, number, number, number];
+          }
+          const cr = postcodeCountRangeRef.current;
+          const c = d.count as number;
+          const t = (c - cr.min) / (cr.max - cr.min);
+          return [...countToColor(Math.max(0, Math.min(1, t))), 200] as [number, number, number, number];
+        },
+        getLineColor: (d) =>
+          (d.postcode === selectedPostcodeRef.current
+            ? [255, 255, 255, 255]
+            : [160, 160, 160, 200]) as [number, number, number, number],
+        getLineWidth: (d) => (d.postcode === selectedPostcodeRef.current ? 2 : 1),
+        lineWidthUnits: 'pixels' as const,
+        stroked: true,
+        filled: true,
+        pickable: true,
+        updateTriggers: {
+          getFillColor: [postcodeColorTrigger],
+          getLineColor: [postcodeColorTrigger],
+          getLineWidth: [postcodeColorTrigger],
+        },
+        onClick: handlePostcodeClick,
+        // @ts-expect-error beforeId is a MapboxOverlay interleave prop, not typed in LayerProps
+        beforeId: "waterway_label",
+      }),
+    [postcodeData, postcodeColorTrigger, handlePostcodeClick]
+  );
+
   // POI layer — independent, only recreated when POI data changes
   const poiLayer = useMemo(
     () =>
@@ -449,23 +597,34 @@ export default memo(function Map({
     [pois, stablePoiHover]
   );
 
-  const layers = useMemo(() => [hexLayer, poiLayer], [hexLayer, poiLayer]);
+  const layers = useMemo(
+    () => (postcodeData.length > 0 ? [postcodeLayer, poiLayer] : [hexLayer, poiLayer]),
+    [postcodeData.length, postcodeLayer, hexLayer, poiLayer]
+  );
 
   // Tooltip uses refs to avoid being a layer dependency
   const featuresRef = useRef(features);
   featuresRef.current = features;
 
   const getTooltip = useCallback(
-    ({ object }: { object?: HexagonData }) => {
-      if (!object || !('h3' in object)) return null;
+    // eslint-disable-next-line @typescript-eslint/no-explicit-any
+    ({ object }: { object?: any }) => {
+      if (!object) return null;
+
+      // Handle both hexagon and postcode objects
+      const isPostcode = 'postcode' in object;
+      const isHexagon = 'h3' in object;
+      if (!isPostcode && !isHexagon) return null;
 
-      const hex = object;
       const lines: string[] = [];
-      lines.push(`<strong>${(hex.count as number).toLocaleString()} properties</strong>`);
+      if (isPostcode) {
+        lines.push(`<strong>${object.postcode}</strong>`);
+      }
+      lines.push(`<div>${(object.count as number).toLocaleString()} properties</div>`);
 
       for (const f of featuresRef.current) {
-        const minVal = hex[`min_${f.name}`];
-        const maxVal = hex[`max_${f.name}`];
+        const minVal = object[`min_${f.name}`];
+        const maxVal = object[`max_${f.name}`];
         if (minVal != null && maxVal != null) {
           const minStr =
             typeof minVal === 'number'
@@ -475,8 +634,8 @@ export default memo(function Map({
             typeof maxVal === 'number'
               ? maxVal.toLocaleString(undefined, { maximumFractionDigits: 1 })
               : String(maxVal);
-          const highlight = f.name === activeFeatureRef.current ? 'font-weight: bold;' : '';
-          lines.push(`<div style="${highlight}">${f.label}: ${minStr} - ${maxStr}</div>`);
+          const highlight = f.name === viewFeatureRef.current ? 'font-weight: bold;' : '';
+          lines.push(`<div style="${highlight}">${f.name}: ${minStr} - ${maxStr}</div>`);
         }
       }
 
@@ -505,6 +664,14 @@ export default memo(function Map({
         <DeckOverlay layers={layers} getTooltip={getTooltip as never} />
       </MapGL>
       <PostcodeSearch onFlyTo={handleFlyTo} />
+      {viewFeature && viewRange && colorFeatureMeta && (
+        <MapLegend
+          featureLabel={colorFeatureMeta.name}
+          range={viewRange}
+          showCancel={viewSource === 'eye'}
+          onCancel={onCancelPin}
+        />
+      )}
       {popupInfo && (
         <div
           className="absolute pointer-events-none bg-white rounded shadow-lg p-2 text-sm"
diff --git a/frontend/src/components/POIPane.tsx b/frontend/src/components/POIPane.tsx
index 961d038..43490f0 100644
--- a/frontend/src/components/POIPane.tsx
+++ b/frontend/src/components/POIPane.tsx
@@ -61,7 +61,7 @@ export default function POIPane({
         <Label>Categories</Label>
         <button
           onClick={() => setDropdownOpen(!dropdownOpen)}
-          className="w-full flex items-center justify-between px-3 py-2 text-sm border border-slate-300 rounded hover:border-slate-400 bg-white"
+          className="w-full flex items-center justify-between px-3 py-2 text-sm border border-warm-300 rounded hover:border-warm-400 bg-white"
         >
           <span className="truncate text-left">
             {selectedCount === 0
@@ -81,30 +81,30 @@ export default function POIPane({
         </button>
 
         {dropdownOpen && (
-          <div className="border border-slate-300 rounded shadow-lg bg-white">
-            <div className="flex gap-2 px-3 py-2 border-b border-slate-200">
-              <button onClick={selectAll} className="text-xs text-blue-600 hover:text-blue-800">
+          <div className="border border-warm-300 rounded shadow-lg bg-white">
+            <div className="flex gap-2 px-3 py-2 border-b border-warm-200">
+              <button onClick={selectAll} className="text-xs text-teal-600 hover:text-teal-800">
                 All
               </button>
-              <span className="text-xs text-slate-300">|</span>
-              <button onClick={selectNone} className="text-xs text-blue-600 hover:text-blue-800">
+              <span className="text-xs text-warm-300">|</span>
+              <button onClick={selectNone} className="text-xs text-teal-600 hover:text-teal-800">
                 None
               </button>
             </div>
-            <div className="px-3 py-2 border-b border-slate-200">
+            <div className="px-3 py-2 border-b border-warm-200">
               <input
                 type="text"
                 placeholder="Search categories..."
                 value={searchTerm}
                 onChange={(e) => setSearchTerm(e.target.value)}
-                className="w-full px-2 py-1 text-sm border border-slate-300 rounded"
+                className="w-full px-2 py-1 text-sm border border-warm-300 rounded"
               />
             </div>
             <div className="max-h-96 overflow-y-auto py-1">
               {filteredCategories.map((category) => (
                 <label
                   key={category}
-                  className="flex items-center gap-2 px-3 py-1.5 hover:bg-slate-50 cursor-pointer"
+                  className="flex items-center gap-2 px-3 py-1.5 hover:bg-warm-50 cursor-pointer"
                 >
                   <input
                     type="checkbox"
@@ -121,17 +121,17 @@ export default function POIPane({
       </div>
 
       {selectedCount > 0 && (
-        <div className="p-3 bg-blue-50 rounded text-sm">
-          <div className="font-medium text-blue-900">
+        <div className="p-3 bg-teal-50 rounded text-sm">
+          <div className="font-medium text-teal-900">
             {poiCount.toLocaleString()} POI{poiCount !== 1 ? 's' : ''} visible
           </div>
-          <div className="text-xs text-blue-700 mt-1">
+          <div className="text-xs text-teal-700 mt-1">
             {selectedCount} categor{selectedCount !== 1 ? 'ies' : 'y'} selected
           </div>
         </div>
       )}
 
-      <div className="p-3 bg-slate-100 rounded text-xs text-slate-600">
+      <div className="p-3 bg-warm-100 rounded text-xs text-warm-600">
         <p>Select categories to display POIs on the map.</p>
         <p className="mt-2">Zoom in for better visibility of individual locations.</p>
       </div>
diff --git a/frontend/src/components/PropertiesPane.tsx b/frontend/src/components/PropertiesPane.tsx
index e612832..f7718ac 100644
--- a/frontend/src/components/PropertiesPane.tsx
+++ b/frontend/src/components/PropertiesPane.tsx
@@ -6,6 +6,7 @@ interface PropertiesPaneProps {
   total: number;
   loading: boolean;
   hexagonId: string | null;
+  postcodeId?: string | null;
   onLoadMore: () => void;
   onClose: () => void;
 }
@@ -17,6 +18,7 @@ export function PropertiesPane({
   total,
   loading,
   hexagonId,
+  postcodeId,
   onLoadMore,
   onClose,
 }: PropertiesPaneProps) {
@@ -36,10 +38,11 @@ export function PropertiesPane({
     });
   }, [properties, sortBy]);
 
-  if (!hexagonId) {
+  const selectionId = hexagonId || postcodeId;
+  if (!selectionId) {
     return (
-      <div className="flex items-center justify-center h-full text-gray-500">
-        Click a hexagon to view properties
+      <div className="flex items-center justify-center h-full text-warm-500">
+        Click a hexagon or postcode to view properties
       </div>
     );
   }
@@ -47,27 +50,29 @@ export function PropertiesPane({
   return (
     <div className="flex flex-col h-full">
       {/* Header */}
-      <div className="p-4 border-b border-gray-200">
+      <div className="p-4 border-b border-warm-200">
         <div className="flex justify-between items-center">
-          <h2 className="text-lg font-semibold">Properties in Hexagon</h2>
+          <h2 className="text-lg font-semibold">
+            {postcodeId ? `Properties in ${postcodeId}` : 'Properties in Hexagon'}
+          </h2>
           <button
             onClick={onClose}
-            className="text-gray-500 hover:text-gray-700 text-2xl leading-none"
+            className="text-warm-500 hover:text-warm-700 text-2xl leading-none"
           >
             ×
           </button>
         </div>
-        <p className="text-sm text-gray-600">
+        <p className="text-sm text-warm-600">
           Showing {properties.length} of {total} properties
         </p>
       </div>
 
       {/* Sort controls */}
-      <div className="p-2 border-b border-gray-200">
+      <div className="p-2 border-b border-warm-200">
         <select
           value={sortBy}
           onChange={(e) => setSortBy(e.target.value as SortBy)}
-          className="w-full p-2 border border-gray-300 rounded text-sm"
+          className="w-full p-2 border border-warm-300 rounded text-sm"
         >
           <option value="price">Price (High to Low)</option>
           <option value="size">Size (Large to Small)</option>
@@ -88,7 +93,7 @@ export function PropertiesPane({
               <button
                 onClick={onLoadMore}
                 disabled={loading}
-                className="w-full p-4 text-blue-600 hover:bg-blue-50 disabled:opacity-50"
+                className="w-full p-4 text-teal-600 hover:bg-teal-50 disabled:opacity-50"
               >
                 {loading ? 'Loading...' : `Load More (${total - properties.length} remaining)`}
               </button>
@@ -135,17 +140,17 @@ function PropertyCard({ property }: { property: Property }) {
   const age = getNum(property, 'Approximate construction age', 'construction_age_band');
 
   return (
-    <div className="p-4 border-b border-gray-100 hover:bg-gray-50">
+    <div className="p-4 border-b border-warm-100 hover:bg-warm-50">
       {/* Address & postcode */}
       <div className="font-semibold">{property.address || 'Unknown Address'}</div>
-      <div className="text-sm text-gray-600">{property.postcode}</div>
+      <div className="text-sm text-warm-600">{property.postcode}</div>
 
       {/* Price */}
       {price !== undefined && (
-        <div className="mt-2 text-lg font-bold text-green-700">
+        <div className="mt-2 text-lg font-bold text-teal-700">
           £{fmt(price)}
           {pricePerSqm !== undefined && (
-            <span className="text-sm font-normal text-gray-600">
+            <span className="text-sm font-normal text-warm-600">
               {' '}
               (£{fmt(pricePerSqm)}/m²)
             </span>
@@ -157,42 +162,42 @@ function PropertyCard({ property }: { property: Property }) {
       <div className="mt-2 grid grid-cols-2 gap-x-4 gap-y-1 text-sm">
         {property.property_type && (
           <div>
-            <span className="text-gray-500">Type:</span> {property.property_type}
+            <span className="text-warm-500">Type:</span> {property.property_type}
           </div>
         )}
         {property.built_form && (
           <div>
-            <span className="text-gray-500">Built form:</span> {property.built_form}
+            <span className="text-warm-500">Built form:</span> {property.built_form}
           </div>
         )}
         {property.duration && (
           <div>
-            <span className="text-gray-500">Tenure:</span> {formatDuration(property.duration)}
+            <span className="text-warm-500">Tenure:</span> {formatDuration(property.duration)}
           </div>
         )}
         {floorArea !== undefined && (
           <div>
-            <span className="text-gray-500">Floor area:</span> {fmt(floorArea)}m²
+            <span className="text-warm-500">Floor area:</span> {fmt(floorArea)}m²
           </div>
         )}
         {rooms !== undefined && (
           <div>
-            <span className="text-gray-500">Rooms:</span> {fmt(rooms)}
+            <span className="text-warm-500">Rooms:</span> {fmt(rooms)}
           </div>
         )}
         {age !== undefined && (
           <div>
-            <span className="text-gray-500">Built:</span> {formatAge(age)}
+            <span className="text-warm-500">Built:</span> {formatAge(age)}
           </div>
         )}
         {property.current_energy_rating && (
           <div>
-            <span className="text-gray-500">EPC rating:</span> {property.current_energy_rating}
+            <span className="text-warm-500">EPC rating:</span> {property.current_energy_rating}
           </div>
         )}
         {property.potential_energy_rating && (
           <div>
-            <span className="text-gray-500">EPC potential:</span>{' '}
+            <span className="text-warm-500">EPC potential:</span>{' '}
             {property.potential_energy_rating}
           </div>
         )}
diff --git a/frontend/src/components/ui/label.tsx b/frontend/src/components/ui/label.tsx
index adc6940..8012ded 100644
--- a/frontend/src/components/ui/label.tsx
+++ b/frontend/src/components/ui/label.tsx
@@ -7,6 +7,6 @@ interface LabelProps {
 
 export function Label({ children, className }: LabelProps) {
   return (
-    <label className={`text-sm font-medium text-slate-700 ${className || ''}`}>{children}</label>
+    <label className={`text-sm font-medium text-warm-700 ${className || ''}`}>{children}</label>
   );
 }
diff --git a/frontend/src/components/ui/slider.tsx b/frontend/src/components/ui/slider.tsx
index b1a6256..e5e45ac 100644
--- a/frontend/src/components/ui/slider.tsx
+++ b/frontend/src/components/ui/slider.tsx
@@ -11,13 +11,13 @@ export function Slider({ className, ...props }: SliderProps) {
       className={cn('relative flex w-full touch-none select-none items-center', className)}
       {...props}
     >
-      <SliderPrimitive.Track className="relative h-2 w-full grow overflow-hidden rounded-full bg-slate-200">
-        <SliderPrimitive.Range className="absolute h-full bg-slate-900" />
+      <SliderPrimitive.Track className="relative h-2 w-full grow overflow-hidden rounded-full bg-warm-200">
+        <SliderPrimitive.Range className="absolute h-full bg-teal-600" />
       </SliderPrimitive.Track>
       {props.value?.map((_, i) => (
         <SliderPrimitive.Thumb
           key={i}
-          className="block h-5 w-5 rounded-full border-2 border-slate-900 bg-white ring-offset-white transition-colors focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-slate-950 focus-visible:ring-offset-2 disabled:pointer-events-none disabled:opacity-50"
+          className="block h-5 w-5 rounded-full border-2 border-teal-600 bg-white ring-offset-white transition-colors focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-teal-600 focus-visible:ring-offset-2 disabled:pointer-events-none disabled:opacity-50"
         />
       ))}
     </SliderPrimitive.Root>

From a3c1b6090e31335c49a6e70d3d659b04987fe3d9 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sun, 1 Feb 2026 08:46:44 +0000
Subject: [PATCH 39/62] Remove unused deps

---
 pyproject.toml |   9 +-
 uv.lock        | 742 ++++++-------------------------------------------
 2 files changed, 83 insertions(+), 668 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index b214f71..afc96d7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,7 +9,6 @@ dependencies = [
     "httpx[socks]>=0.28.1",
     "ipywidgets>=8.0.0",
     "jupyter>=1.0.0",
-    "nest-asyncio>=1.6.0",
     "numpy>=1.26.0",
     "pandas>=2.0.0",
     "plotly>=6.5.2",
@@ -17,16 +16,14 @@ dependencies = [
     "pyarrow>=15.0.0",
     "python-dateutil>=2.8.0",
     "tqdm>=4.67.1",
-    "fastapi[standard]>=0.115.0",
-    "uvicorn>=0.34.0",
-    "h3>=3.7.0",
-    "overturemaps>=0.18.0",
     "fastexcel>=0.19.0",
     "osmium>=4.0.0",
     "matplotlib>=3.10.8",
     "thefuzz>=0.22.1",
-    "python-levenshtein>=0.27.3",
     "scipy>=1.17.0",
+    "shapely>=2.0.0",
+    "rasterio>=1.5.0",
+    "pyproj>=3.7.2",
 ]
 
 [tool.uv]
diff --git a/uv.lock b/uv.lock
index 7f06e32..a481fa1 100644
--- a/uv.lock
+++ b/uv.lock
@@ -9,21 +9,12 @@ supported-markers = [
 ]
 
 [[package]]
-name = "annotated-doc"
-version = "0.0.4"
+name = "affine"
+version = "2.4.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/57/ba/046ceea27344560984e26a590f90bc7f4a75b06701f653222458922b558c/annotated_doc-0.0.4.tar.gz", hash = "sha256:fbcda96e87e9c92ad167c2e53839e57503ecfda18804ea28102353485033faa4", size = 7288, upload-time = "2025-11-10T22:07:42.062Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/69/98/d2f0bb06385069e799fc7d2870d9e078cfa0fa396dc8a2b81227d0da08b9/affine-2.4.0.tar.gz", hash = "sha256:a24d818d6a836c131976d22f8c27b8d3ca32d0af64c1d8d29deb7bafa4da1eea", size = 17132, upload-time = "2023-01-19T23:44:30.696Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/1e/d3/26bf1008eb3d2daa8ef4cacc7f3bfdc11818d111f7e2d0201bc6e3b49d45/annotated_doc-0.0.4-py3-none-any.whl", hash = "sha256:571ac1dc6991c450b25a9c2d84a3705e2ae7a53467b5d111c24fa8baabbed320", size = 5303, upload-time = "2025-11-10T22:07:40.673Z" },
-]
-
-[[package]]
-name = "annotated-types"
-version = "0.7.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081, upload-time = "2024-05-20T21:33:25.928Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" },
+    { url = "https://files.pythonhosted.org/packages/0b/f7/85273299ab57117850cc0a936c64151171fac4da49bc6fba0dad984a7c5f/affine-2.4.0-py3-none-any.whl", hash = "sha256:8a3df80e2b2378aef598a83c1392efd47967afec4242021a0b06b4c7cbc61a92", size = 15662, upload-time = "2023-01-19T23:44:28.833Z" },
 ]
 
 [[package]]
@@ -249,6 +240,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/98/78/01c019cdb5d6498122777c1a43056ebb3ebfeef2076d9d026bfe15583b2b/click-8.3.1-py3-none-any.whl", hash = "sha256:981153a64e25f12d547d3426c367a4857371575ee7ad18df2a6183ab0545b2a6", size = 108274, upload-time = "2025-11-15T20:45:41.139Z" },
 ]
 
+[[package]]
+name = "cligj"
+version = "0.7.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "click", marker = "python_full_version < '3.14' and sys_platform == 'linux'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/ea/0d/837dbd5d8430fd0f01ed72c4cfb2f548180f4c68c635df84ce87956cff32/cligj-0.7.2.tar.gz", hash = "sha256:a4bc13d623356b373c2c27c53dbd9c68cae5d526270bfa71f6c6fa69669c6b27", size = 9803, upload-time = "2021-05-28T21:23:27.935Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/73/86/43fa9f15c5b9fb6e82620428827cd3c284aa933431405d1bcf5231ae3d3e/cligj-0.7.2-py3-none-any.whl", hash = "sha256:c1ca117dbce1fe20a5809dc96f01e1c2840f6dcc939b3ddbb1111bf330ba82df", size = 7069, upload-time = "2021-05-28T21:23:26.877Z" },
+]
+
 [[package]]
 name = "comm"
 version = "0.2.3"
@@ -338,28 +341,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/07/6c/aa3f2f849e01cb6a001cd8554a88d4c77c5c1a31c95bdf1cf9301e6d9ef4/defusedxml-0.7.1-py2.py3-none-any.whl", hash = "sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61", size = 25604, upload-time = "2021-03-08T10:59:24.45Z" },
 ]
 
-[[package]]
-name = "dnspython"
-version = "2.8.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/8c/8b/57666417c0f90f08bcafa776861060426765fdb422eb10212086fb811d26/dnspython-2.8.0.tar.gz", hash = "sha256:181d3c6996452cb1189c4046c61599b84a5a86e099562ffde77d26984ff26d0f", size = 368251, upload-time = "2025-09-07T18:58:00.022Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/ba/5a/18ad964b0086c6e62e2e7500f7edc89e3faa45033c71c1893d34eed2b2de/dnspython-2.8.0-py3-none-any.whl", hash = "sha256:01d9bbc4a2d76bf0db7c1f729812ded6d912bd318d3b1cf81d30c0f845dbf3af", size = 331094, upload-time = "2025-09-07T18:57:58.071Z" },
-]
-
-[[package]]
-name = "email-validator"
-version = "2.3.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "dnspython", marker = "python_full_version < '3.14' and sys_platform == 'linux'" },
-    { name = "idna", marker = "python_full_version < '3.14' and sys_platform == 'linux'" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/f5/22/900cb125c76b7aaa450ce02fd727f452243f2e91a61af068b40adba60ea9/email_validator-2.3.0.tar.gz", hash = "sha256:9fc05c37f2f6cf439ff414f8fc46d917929974a82244c20eb10231ba60c54426", size = 51238, upload-time = "2025-08-26T13:09:06.831Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/de/15/545e2b6cf2e3be84bc1ed85613edd75b8aea69807a71c26f4ca6a9258e82/email_validator-2.3.0-py3-none-any.whl", hash = "sha256:80f13f623413e6b197ae73bb10bf4eb0908faf509ad8362c5edeb0be7fd450b4", size = 35604, upload-time = "2025-08-26T13:09:05.858Z" },
-]
-
 [[package]]
 name = "executing"
 version = "2.2.1"
@@ -369,120 +350,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c1/ea/53f2148663b321f21b5a606bd5f191517cf40b7072c0497d3c92c4a13b1e/executing-2.2.1-py2.py3-none-any.whl", hash = "sha256:760643d3452b4d777d295bb167ccc74c64a81df23fb5e08eff250c425a4b2017", size = 28317, upload-time = "2025-09-01T09:48:08.5Z" },
 ]
 
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-version = "0.128.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
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-    { name = "pydantic", marker = "python_full_version < '3.14' and sys_platform == 'linux'" },
-    { name = "starlette", marker = "python_full_version < '3.14' and sys_platform == 'linux'" },
-    { name = "typing-extensions", marker = "python_full_version < '3.14' and sys_platform == 'linux'" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/52/08/8c8508db6c7b9aae8f7175046af41baad690771c9bcde676419965e338c7/fastapi-0.128.0.tar.gz", hash = "sha256:1cc179e1cef10a6be60ffe429f79b829dce99d8de32d7acb7e6c8dfdf7f2645a", size = 365682, upload-time = "2025-12-27T15:21:13.714Z" }
-wheels = [
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-]
-
-[package.optional-dependencies]
-standard = [
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-    { name = "fastapi-cli", extra = ["standard"], marker = "python_full_version < '3.14' and sys_platform == 'linux'" },
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 version = "4.0.15"

From ac45af85145faa83469729b86ae4e42f06b28b3a Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sun, 1 Feb 2026 08:48:07 +0000
Subject: [PATCH 40/62] Move dict

---
 pipeline/transform/poi_proximity.py | 12 +++++++++++-
 pipeline/utils/__init__.py          |  3 +--
 pipeline/utils/poi_counts.py        | 20 +++++++-------------
 3 files changed, 19 insertions(+), 16 deletions(-)

diff --git a/pipeline/transform/poi_proximity.py b/pipeline/transform/poi_proximity.py
index dce09cc..0be1a9f 100644
--- a/pipeline/transform/poi_proximity.py
+++ b/pipeline/transform/poi_proximity.py
@@ -8,6 +8,16 @@ import polars as pl
 from pipeline.utils.poi_counts import _count_pois_per_postcode
 
 
+# POI category groups for proximity counting
+POI_GROUPS = {
+    "restaurants": ["Restaurant", "Fast Food"],
+    "groceries": ["Greengrocer", "Grocery Shop", "Supermarket", "Convenience Store"],
+    "parks": ["Park", "Garden", "Nature Reserve"],
+    "public_transport": ["Metro or Tram stop", "Rail station", "Bus stop", "Bus station"], # comes from naptan.py
+}
+
+
+
 def main():
     parser = argparse.ArgumentParser(
         description="Count POIs within radius per postcode"
@@ -31,7 +41,7 @@ def main():
 
     pois = pl.read_parquet(args.pois)
 
-    result = _count_pois_per_postcode(postcodes, pois, radius_km=2)
+    result = _count_pois_per_postcode(postcodes, pois, groups=POI_GROUPS, radius_km=2)
 
     result.write_parquet(args.output)
     size_mb = args.output.stat().st_size / (1024 * 1024)
diff --git a/pipeline/utils/__init__.py b/pipeline/utils/__init__.py
index 772d4ae..9dc42cc 100644
--- a/pipeline/utils/__init__.py
+++ b/pipeline/utils/__init__.py
@@ -1,7 +1,7 @@
 from .download import download, extract_zip
 from .fuzzy_join import fuzzy_join_on_postcode
 from .haversine import haversine_km, haversine_km_expr
-from .poi_counts import POI_GROUPS, count_pois_within_radius
+from .poi_counts import count_pois_within_radius
 
 __all__ = [
     "download",
@@ -9,6 +9,5 @@ __all__ = [
     "fuzzy_join_on_postcode",
     "haversine_km",
     "haversine_km_expr",
-    "POI_GROUPS",
     "count_pois_within_radius",
 ]
diff --git a/pipeline/utils/poi_counts.py b/pipeline/utils/poi_counts.py
index 8c39288..0112080 100644
--- a/pipeline/utils/poi_counts.py
+++ b/pipeline/utils/poi_counts.py
@@ -1,6 +1,5 @@
 """Count POIs within a radius of properties, optimized via postcode deduplication."""
 
-import os
 import tempfile
 
 import numpy as np
@@ -8,17 +7,12 @@ import polars as pl
 
 from .haversine import haversine_km
 
-# POI category groups for proximity counting
-POI_GROUPS = {
-    "restaurants": ["Restaurant", "Fast Food"],
-    "groceries": ["Greengrocer", "Grocery Shop", "Supermarket", "Convenience Store"],
-    "parks": ["Park", "Garden", "Nature Reserve"],
-    "public_transport": ["Station", "Stop", "Bus Station"],
-}
-
 
 def _count_pois_per_postcode(
-    postcodes_df: pl.DataFrame, pois: pl.DataFrame, radius_km: float = 2.0
+    postcodes_df: pl.DataFrame,
+    pois: pl.DataFrame,
+    groups: dict[str, list[str]],
+    radius_km: float = 2.0,
 ) -> pl.DataFrame:
     """
     For each unique postcode, count POIs within radius_km by category group.
@@ -59,7 +53,7 @@ def _count_pois_per_postcode(
 
     # Pre-compute category masks
     category_masks = {}
-    for group, categories in POI_GROUPS.items():
+    for group, categories in groups.items():
         mask = np.isin(poi_cats, categories)
         category_masks[group] = mask
         print(f"  {group}: {mask.sum():,} POIs")
@@ -71,7 +65,7 @@ def _count_pois_per_postcode(
 
     # Initialize result arrays
     result_counts = {
-        group: np.zeros(n_postcodes, dtype=np.int32) for group in POI_GROUPS
+        group: np.zeros(n_postcodes, dtype=np.int32) for group in groups
     }
 
     # Process in batches with progress
@@ -128,7 +122,7 @@ def _count_pois_per_postcode(
 
     # Build result dataframe
     result_data = {"postcode": pc_codes}
-    for group in POI_GROUPS:
+    for group in groups:
         result_data[f"{group}_{int(radius_km)}km"] = result_counts[group]
 
     result = pl.DataFrame(result_data)

From cf7449e38b51492b552c81cf2c4e492537495409 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sun, 1 Feb 2026 08:49:40 +0000
Subject: [PATCH 41/62] Fix tfl scraping

---
 pipeline/journey_times/__main__.py   | 44 ++++++++++++++++++++++++----
 pipeline/journey_times/tfl_client.py |  4 ++-
 2 files changed, 42 insertions(+), 6 deletions(-)

diff --git a/pipeline/journey_times/__main__.py b/pipeline/journey_times/__main__.py
index 5a5a944..cde9480 100644
--- a/pipeline/journey_times/__main__.py
+++ b/pipeline/journey_times/__main__.py
@@ -12,6 +12,7 @@ from .config import (
     OUTPUT_DIR,
     MAX_DISTANCE_KM,
 )
+from .models import JourneyResult
 from .results import CheckpointSaver, results_to_dataframe, save_results
 from .tfl_client import fetch_journey_times
 from pipeline.utils import haversine_km_expr
@@ -63,12 +64,44 @@ def main():
         ),
     )
 
+    # 25556/76273 
+
+    # Resume from checkpoint if one exists
+    checkpoint_path = checkpoint_saver._checkpoint_path()
+    prior_results: list[JourneyResult] = []
+    if checkpoint_path.exists():
+        checkpoint_df = pl.read_parquet(checkpoint_path)
+        completed_postcodes = set(
+            checkpoint_df.filter(pl.col("public_transport_easy_minutes").is_not_null())["postcode"].to_list()
+        )
+        prior_results = [
+            JourneyResult(
+                postcode=row["postcode"],
+                public_transport_easy_minutes=row["public_transport_easy_minutes"],
+                public_transport_quick_minutes=row["public_transport_quick_minutes"],
+                cycling_minutes=row["cycling_minutes"],
+                error=row["error"],
+            )
+            for row in checkpoint_df.iter_rows(named=True)
+        ]
+        checkpoint_saver.results = prior_results
+        checkpoint_saver._last_save_count = len(prior_results)
+        postcode_data = [
+            (pc, lat, lon)
+            for pc, lat, lon in postcode_data
+            if pc not in completed_postcodes
+        ]
+        print(
+            f"Resumed from checkpoint: {len(prior_results):,} already done, "
+            f"{len(postcode_data):,} remaining"
+        )
+
     def on_result(result):
         pbar.update(1)
         checkpoint_saver.add_result(result)
 
     with tqdm(total=len(postcode_data), desc="Fetching journeys") as pbar:
-        results = asyncio.run(
+        new_results = asyncio.run(
             fetch_journey_times(
                 postcode_data,
                 destination,
@@ -79,11 +112,12 @@ def main():
             )
         )
 
-    results_df = results_to_dataframe(results)
+    all_results = prior_results + new_results
+    results_df = results_to_dataframe(all_results)
 
-    postcodes_processed = [pc for pc, _, _ in postcode_data]
+    all_postcodes = {r.postcode for r in all_results}
     coords_df = postcodes_df.filter(
-        pl.col("postcode").is_in(postcodes_processed)
+        pl.col("postcode").is_in(all_postcodes)
     ).select(["postcode", "lat", "long"])
     results_df = coords_df.join(results_df, on="postcode", how="left")
 
@@ -94,7 +128,7 @@ def main():
     )
 
     successful = results_df.filter(pl.col("cycling_minutes").is_not_null()).height
-    print(f"Completed: {successful}/{len(results)} successful")
+    print(f"Completed: {successful}/{len(all_results)} successful")
 
     parquet_path = save_results(results_df, destination.name)
     checkpoint_saver.cleanup_checkpoint()
diff --git a/pipeline/journey_times/tfl_client.py b/pipeline/journey_times/tfl_client.py
index 33fc794..4ddf634 100644
--- a/pipeline/journey_times/tfl_client.py
+++ b/pipeline/journey_times/tfl_client.py
@@ -219,7 +219,9 @@ async def fetch_journey_times(
 
     # TFL API authentication via app_key query parameter
     tfl_token = os.environ.get("TFL_TOKEN")
-    params = {"app_key": tfl_token} if tfl_token else {}
+    if not tfl_token:
+        raise RuntimeError("TFL_TOKEN environment variable not set")
+    params = {"app_key": tfl_token}
 
     async with httpx.AsyncClient(
         base_url=BASE_URL,

From 0b0a6555992c96cd8d7de915c6c3edda6e73687b Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sun, 1 Feb 2026 08:49:44 +0000
Subject: [PATCH 42/62] Update

---
 CLAUDE.md | 141 +++++++++++++++++++++++++++++++++++++-----------------
 1 file changed, 97 insertions(+), 44 deletions(-)

diff --git a/CLAUDE.md b/CLAUDE.md
index f78b5c9..3042601 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -4,74 +4,127 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
 
 ## Project Overview
 
-Property Map is a full-stack geospatial application for visualizing UK property data on an interactive map. It combines Land Registry price-paid data, EPC energy certificates, postcode geolocation, TFL journey times, Index of Deprivation scores, and OpenStreetMap POIs into a single wide parquet file, then serves aggregated H3 hexagon statistics and POI data via a Rust backend.
+Property Map is a full-stack geospatial application for visualizing UK property data on an interactive map. It combines Land Registry price-paid data, EPC energy certificates, postcode geolocation, TFL journey times, Index of Deprivation scores, crime statistics, ethnicity data, broadband speeds, school ratings, road noise, and OpenStreetMap POIs into a single wide parquet file, then serves aggregated H3 hexagon statistics and POI data via a Rust backend.
 
 ## Commands
 
-All commands use [Task](https://taskfile.dev) runner.
+All commands use [Task](https://taskfile.dev) runner. Python uses `uv run`. Frontend uses `npm run` from `frontend/`.
 
 ```bash
-task prepare        # Full setup: install deps, download data (~GB), run pipeline
-task server         # Rust backend on :8001 (cargo run --release)
-task frontend       # Webpack dev server on :3030 (proxies /api to :8001)
+# Development servers
+task dev:server           # Rust backend on :8001 (cargo run --release)
+task dev:frontend         # Webpack dev server on :3030 (proxies /api to :8001)
 
-task lint           # Lint Python (ruff) + TypeScript (ESLint + Prettier)
-task format         # Auto-fix formatting (ruff + ESLint + Prettier)
-task typecheck      # TypeScript type checking
-task check          # All checks (lint + typecheck + build)
-task test           # Run Python tests (fuzzy join)
-task build          # Build frontend for production
+# Data pipeline
+task prepare              # Build wide.parquet from all pre-downloaded sources
+
+# Quality
+task lint                 # Lint all: Python (ruff) + TypeScript (ESLint+Prettier) + Rust (clippy+fmt)
+task format               # Auto-fix formatting for all languages
+task test                 # Python tests (fuzzy join, haversine, POI counts)
+task check                # Full validation: lint + build + test
+
+# Building
+task build:frontend       # TypeScript typecheck + webpack production build
+task build:server         # cargo build --release (NOTE: dir is wrong in Taskfile, run from server-rs/)
+
+# Granular lint/format
+task lint:python          # uv run ruff check .
+task lint:frontend        # eslint + prettier --check
+task lint:rust            # cargo clippy -- -D warnings && cargo fmt --check
+task format:python        # ruff check --fix && ruff format
+task format:frontend      # eslint --fix + prettier --write
+task format:rust          # cargo fmt --all
 ```
 
-Python commands use `uv run`. Frontend commands use `npm run` from `frontend/`.
+Running individual tests:
+```bash
+uv run pytest pipeline/utils/test_haversine.py       # Single test file
+uv run pytest pipeline/utils/test_haversine.py -k "test_name"  # Single test
+```
 
 ## Architecture
 
+### Data Flow
+
+```
+Raw sources → [Download scripts] → data/*.parquet
+  → [Fuzzy join EPC ↔ Price-Paid] → epc_pp.parquet
+  → [Merge all datasets] → wide.parquet
+  → [Rust server loads into memory + precomputes H3 + spatial grid]
+  → [Frontend renders deck.gl H3HexagonLayer over MapLibre GL]
+```
+
 ### Data Pipeline (`pipeline/`)
 
-Python + Polars. Orchestrated by `pipeline/run.py` which builds `data_sources/processed/wide.parquet`:
+Python + Polars. Two phases:
 
-1. **Download** (`pipeline/download/`) — Fetches raw data into `data_sources/`:
-   - `arcgis.py` — Postcode → lat/lon/LSOA mappings
-   - `price_paid.py` — Land Registry price-paid records
-   - `pois/` — OpenStreetMap POIs via osmium (PBF parsing)
-   - `deprivation_data.py` — English Indices of Deprivation 2025
-2. **Join** (`pipeline/epc_pp.py`) — Fuzzy-joins EPC certificates with price-paid by address within postcode buckets → `epc_pp.parquet`
-3. **Widen** (`pipeline/run.py`) — Joins epc_pp with GPS coords, journey times, IoD scores, POI proximity counts, derives `price_per_sqm` and numeric `construction_age_band`
-4. **Transform POIs** (`pipeline/download/pois/transform.py`) — Drops unwanted categories, remaps to friendly names + emoji → `filtered_uk_pois.parquet`
+1. **Download** (`pipeline/download/`) — Each script fetches one raw dataset into `data/`
+2. **Transform** (`pipeline/transform/`) — Joins and derives features:
+   - `join_epc_pp.py` — Fuzzy-joins EPC ↔ price-paid by address within postcode buckets
+   - `merge.py` — **Main pipeline**: joins all datasets → `wide.parquet` with human-readable column names
+   - `transform_poi.py` — Filters POIs, maps to friendly names + emoji (exhaustive category validation)
+   - `poi_proximity.py` — Counts POIs within 2km per postcode using 0.05° spatial grid
+   - `crime.py` — Aggregates crime CSVs into yearly averages by LSOA
 
-Shared utilities live in `pipeline/utils/` (haversine distance for both numpy and Polars expressions, fuzzy address matching).
+**Critical: column renaming in `merge.py`** — The pipeline renames columns from snake_case to human-readable names before writing `wide.parquet`. The Rust server auto-discovers features from whatever column names exist in the parquet. Key renames:
+- `pp_address` → `Address per Property Register`
+- `postcode` → `Postcode`
+- `latest_price` → `Last known price`
+- `duration` → `Leashold/Freehold`
+- `total_floor_area` → `Total floor area (sqm)`
+- `current_energy_rating` → `Current energy rating`
+
+The server and frontend must handle these human-readable names. See the full rename map in `merge.py`.
 
 ### Backend (`server-rs/`)
 
-Rust + Axum. Loads `wide.parquet` and `filtered_uk_pois.parquet` into memory at startup with precomputed H3 indices (resolutions 7–11) and grid-based spatial indices (0.01° cells).
+Rust + Axum. Loads parquet into memory at startup.
+
+**Structure:**
+- `data/property.rs` — Loads `wide.parquet`, auto-discovers numeric + enum features, computes histograms, sorts rows by spatial locality, precomputes H3 cells (resolutions 4–12)
+- `data/poi.rs` — Loads `filtered_uk_pois.parquet`
+- `index.rs` — `GridIndex`: 0.01° spatial grid for O(1) cell lookup
+- `filter.rs` — Parses filter strings and checks rows. Format: `name:min:max` (numeric), `name:val1|val2` (enum)
+- `routes/` — One file per endpoint
+- `consts.rs` — Key constants (histogram bins, H3 range, max enum cardinality, excluded columns)
 
 **API endpoints:**
-- `GET /api/features` — Numeric column metadata with histograms and percentiles
-- `GET /api/hexagons` — H3 aggregates filtered by bounds, resolution, and feature min/max
-- `GET /api/pois` — POIs by bounds with optional category filter (max 5000)
-- `GET /api/poi-categories` — Available POI categories
+- `GET /api/features` — Feature metadata with histograms and 2nd/98th percentiles
+- `GET /api/hexagons?resolution=&bounds=&filters=` — H3 aggregates (min/max per feature per hex)
+- `GET /api/hexagon-properties?h3=&resolution=&filters=&limit=&offset=` — Paginated properties within a hexagon
+- `GET /api/pois?bounds=&categories=` — POIs by bounds (max 5000)
+- `GET /api/poi-categories` — Available POI category names
 
-Also serves `frontend/dist/` as static fallback.
+Serves `frontend/dist/` as static fallback in production.
+
+**Data representation:**
+- Numeric features: row-major flat `Vec<f64>`, NaN = null
+- Enum features: `Vec<u8>` indices into value list, 255 = null
+- String fields (address, postcode): `Vec<String>`, empty = null
+- The server accepts the parquet path as a CLI argument (defaults to `data_sources/processed/wide.parquet`)
 
 ### Frontend (`frontend/`)
 
-React 18 + TypeScript SPA. deck.gl `H3HexagonLayer` over MapLibre GL basemap. Debounces API calls (150ms) on viewport changes. TailwindCSS for styling.
+React 18 + TypeScript. deck.gl `H3HexagonLayer` over MapLibre GL. TailwindCSS. No state management library — pure React hooks.
+
+**Key patterns:**
+- `App.tsx` manages all state, API fetching (150ms debounce), and URL state sync (300ms debounce)
+- URL encodes view/filters/POI categories/active tab as query params for shareable links
+- AbortControllers cancel in-flight requests on new queries
+- Zoom → H3 resolution: `<7→7, <9.5→8, <11→9, <13→10, ≥13→11`
+- Bounds quantized to 0.01° to match backend caching
+- Properties pane uses feature names from API response (human-readable), not hardcoded field names
+- Proxy: dev server on :3030 proxies `/api` to :8001; also handles VS Code `/proxy/PORT` patterns
 
 ## Key Implementation Details
 
-- Bounds quantized to 0.01° to improve cache hits on both backend and frontend
-- H3 hexagon results capped at 50,000 per request (truncated flag in response)
-- POI proximity counting uses a spatial grid (0.05° cells, ~5km) to avoid O(n×m) distance checks
-- Fuzzy address matching uses `thefuzz.token_sort_ratio` with numeric token compatibility checks, parallelized across postcode buckets
-- The Rust server writes JSON via direct string buffer (avoids serde_json::Value allocations)
-- POI transform validates exhaustive category coverage — pipeline fails if any OSM category is unmapped
-
-## Data Sources
-
-- **Land Registry** — Price Paid bulk download
-- **EPC** — Energy Performance Certificates (domestic)
-- **ArcGIS** — Postcode → GPS/LSOA lookup
-- **OpenStreetMap** — POIs from Geofabrik Great Britain PBF
-- **IoD 2025** — English Indices of Deprivation (LSOA-level scores)
-- **TFL API** — Journey time calculations to configurable destinations
+- **Spatial sort**: Rows sorted by 0.01° grid cell at load time for cache-friendly sequential access
+- **Row-major layout**: `feature_data[row * num_features + feat_idx]` — all features for one property are contiguous
+- **H3 precomputation**: Resolutions 4–12 computed in parallel (rayon) at startup
+- **Histogram percentiles without sorting**: O(n) two-pass algorithm — build histogram, interpolate percentiles
+- **Direct JSON writing**: Hexagon endpoint writes JSON via string buffer, avoids serde_json::Value allocations
+- **POI transform validation**: Fails if any OSM category is unmapped — guarantees exhaustive coverage
+- **Fuzzy join**: Groups by postcode, uses `thefuzz.token_sort_ratio` with numeric token compatibility, greedy assignment from highest score
+- **Filter bounds format**: `south,west,north,east` (not standard bbox order)
+- **POI proximity**: Uses 0.05° grid (~5km cells) to reduce candidates before haversine distance check

From a50e0920328a033b30822a81e5b95c5dde491c66 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sun, 1 Feb 2026 08:50:03 +0000
Subject: [PATCH 43/62] Improve compile perf

---
 server-rs/Cargo.lock | 121 +++++++++++++++++++++++++++++++++++++++++++
 server-rs/Cargo.toml |   8 +++
 2 files changed, 129 insertions(+)

diff --git a/server-rs/Cargo.lock b/server-rs/Cargo.lock
index abca744..60e56a4 100644
--- a/server-rs/Cargo.lock
+++ b/server-rs/Cargo.lock
@@ -61,6 +61,56 @@ dependencies = [
  "libc",
 ]
 
+[[package]]
+name = "anstream"
+version = "0.6.21"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "43d5b281e737544384e969a5ccad3f1cdd24b48086a0fc1b2a5262a26b8f4f4a"
+dependencies = [
+ "anstyle",
+ "anstyle-parse",
+ "anstyle-query",
+ "anstyle-wincon",
+ "colorchoice",
+ "is_terminal_polyfill",
+ "utf8parse",
+]
+
+[[package]]
+name = "anstyle"
+version = "1.0.13"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "5192cca8006f1fd4f7237516f40fa183bb07f8fbdfedaa0036de5ea9b0b45e78"
+
+[[package]]
+name = "anstyle-parse"
+version = "0.2.7"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "4e7644824f0aa2c7b9384579234ef10eb7efb6a0deb83f9630a49594dd9c15c2"
+dependencies = [
+ "utf8parse",
+]
+
+[[package]]
+name = "anstyle-query"
+version = "1.1.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "40c48f72fd53cd289104fc64099abca73db4166ad86ea0b4341abe65af83dadc"
+dependencies = [
+ "windows-sys 0.61.2",
+]
+
+[[package]]
+name = "anstyle-wincon"
+version = "3.0.11"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "291e6a250ff86cd4a820112fb8898808a366d8f9f58ce16d1f538353ad55747d"
+dependencies = [
+ "anstyle",
+ "once_cell_polyfill",
+ "windows-sys 0.61.2",
+]
+
 [[package]]
 name = "ar_archive_writer"
 version = "0.5.1"
@@ -319,6 +369,52 @@ dependencies = [
  "phf",
 ]
 
+[[package]]
+name = "clap"
+version = "4.5.56"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "a75ca66430e33a14957acc24c5077b503e7d374151b2b4b3a10c83b4ceb4be0e"
+dependencies = [
+ "clap_builder",
+ "clap_derive",
+]
+
+[[package]]
+name = "clap_builder"
+version = "4.5.56"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "793207c7fa6300a0608d1080b858e5fdbe713cdc1c8db9fb17777d8a13e63df0"
+dependencies = [
+ "anstream",
+ "anstyle",
+ "clap_lex",
+ "strsim",
+]
+
+[[package]]
+name = "clap_derive"
+version = "4.5.55"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "a92793da1a46a5f2a02a6f4c46c6496b28c43638adea8306fcb0caa1634f24e5"
+dependencies = [
+ "heck",
+ "proc-macro2",
+ "quote",
+ "syn",
+]
+
+[[package]]
+name = "clap_lex"
+version = "0.7.7"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "c3e64b0cc0439b12df2fa678eae89a1c56a529fd067a9115f7827f1fffd22b32"
+
+[[package]]
+name = "colorchoice"
+version = "1.0.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "b05b61dc5112cbb17e4b6cd61790d9845d13888356391624cbe7e41efeac1e75"
+
 [[package]]
 name = "comfy-table"
 version = "7.2.2"
@@ -895,6 +991,12 @@ dependencies = [
  "serde_core",
 ]
 
+[[package]]
+name = "is_terminal_polyfill"
+version = "1.70.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "a6cb138bb79a146c1bd460005623e142ef0181e3d0219cb493e02f7d08a35695"
+
 [[package]]
 name = "itoa"
 version = "1.0.17"
@@ -1104,6 +1206,12 @@ version = "1.21.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "42f5e15c9953c5e4ccceeb2e7382a716482c34515315f7b03532b8b4e8393d2d"
 
+[[package]]
+name = "once_cell_polyfill"
+version = "1.70.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "384b8ab6d37215f3c5301a95a4accb5d64aa607f1fcb26a11b5303878451b4fe"
+
 [[package]]
 name = "parking_lot"
 version = "0.12.5"
@@ -1694,6 +1802,7 @@ name = "property-map-server"
 version = "0.1.0"
 dependencies = [
  "axum",
+ "clap",
  "h3o",
  "polars",
  "rayon",
@@ -2090,6 +2199,12 @@ version = "0.2.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "fe895eb47f22e2ddd4dabc02bce419d2e643c8e3b585c78158b349195bc24d82"
 
+[[package]]
+name = "strsim"
+version = "0.11.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "7da8b5736845d9f2fcb837ea5d9e2628564b3b043a70948a3f0b778838c5fb4f"
+
 [[package]]
 name = "strum_macros"
 version = "0.26.4"
@@ -2386,6 +2501,12 @@ version = "0.2.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "b4ac048d71ede7ee76d585517add45da530660ef4390e49b098733c6e897f254"
 
+[[package]]
+name = "utf8parse"
+version = "0.2.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "06abde3611657adf66d383f00b093d7faecc7fa57071cce2578660c9f1010821"
+
 [[package]]
 name = "uuid"
 version = "1.20.0"
diff --git a/server-rs/Cargo.toml b/server-rs/Cargo.toml
index 24c355a..336e723 100644
--- a/server-rs/Cargo.toml
+++ b/server-rs/Cargo.toml
@@ -4,6 +4,7 @@ version = "0.1.0"
 edition = "2021"
 
 [dependencies]
+clap = { version = "4", features = ["derive"] }
 axum = "0.8"
 tower-http = { version = "0.6", features = ["cors", "fs", "compression-gzip", "trace"] }
 tokio = { version = "1", features = ["full"] }
@@ -16,6 +17,13 @@ rustc-hash = "2"
 tracing = "0.1"
 tracing-subscriber = { version = "0.3", features = ["env-filter", "fmt"] }
 
+[profile.dev]
+opt-level = 1
+
 [profile.release]
 opt-level = 3
+lto = "thin"
+
+[profile.production]
+inherits = "release"
 lto = true

From 6a42b81a2acd4dd9415432eb3c05f3a7e951bbb8 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sun, 1 Feb 2026 09:26:10 +0000
Subject: [PATCH 44/62] Add school proximity

---
 pipeline/transform/school_proximity.py | 73 ++++++++++++++++++++++++++
 1 file changed, 73 insertions(+)
 create mode 100644 pipeline/transform/school_proximity.py

diff --git a/pipeline/transform/school_proximity.py b/pipeline/transform/school_proximity.py
new file mode 100644
index 0000000..3e10b90
--- /dev/null
+++ b/pipeline/transform/school_proximity.py
@@ -0,0 +1,73 @@
+"""Compute good-rated school proximity counts per postcode."""
+
+import argparse
+from pathlib import Path
+
+import polars as pl
+
+from pipeline.utils.poi_counts import _count_pois_per_postcode
+
+SCHOOL_GROUPS = {
+    "good_primary": ["good_primary"],
+    "good_secondary": ["good_secondary"],
+}
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Count good+ primary/secondary schools within 2km per postcode"
+    )
+    parser.add_argument(
+        "--ofsted", type=Path, required=True, help="Ofsted inspection parquet"
+    )
+    parser.add_argument(
+        "--arcgis", type=Path, required=True, help="ArcGIS postcode parquet"
+    )
+    parser.add_argument(
+        "--output", type=Path, required=True, help="Output parquet path"
+    )
+    args = parser.parse_args()
+
+    # Load Ofsted data: filter to good+ (1, 2) primary/secondary schools
+    ofsted = pl.read_parquet(args.ofsted).filter(
+        pl.col("Ofsted phase").is_in(["Primary", "Secondary"])
+        & pl.col("Overall effectiveness").is_in(["1", "2"])
+    )
+
+    print(f"Good+ schools: {len(ofsted):,}")
+
+    # Assign category based on phase
+    ofsted = ofsted.with_columns(
+        pl.when(pl.col("Ofsted phase") == "Primary")
+        .then(pl.lit("good_primary"))
+        .otherwise(pl.lit("good_secondary"))
+        .alias("category")
+    ).select(
+        pl.col("Postcode").alias("postcode"),
+        "category",
+    )
+
+    # Join with arcgis to get lat/lng for each school's postcode
+    arcgis = pl.read_parquet(args.arcgis).select(
+        pl.col("pcds").alias("postcode"),
+        "lat",
+        pl.col("long").alias("lng"),
+    )
+
+    schools = ofsted.join(arcgis, on="postcode", how="inner")
+    print(f"Schools with coordinates: {len(schools):,}")
+
+    # Load all postcodes for proximity counting
+    postcodes = arcgis.rename({"lng": "lon"})
+
+    result = _count_pois_per_postcode(
+        postcodes, schools, radius_km=5, groups=SCHOOL_GROUPS
+    )
+
+    result.write_parquet(args.output)
+    size_mb = args.output.stat().st_size / (1024 * 1024)
+    print(f"Wrote {args.output} ({size_mb:.1f} MB)")
+
+
+if __name__ == "__main__":
+    main()

From 01edde3ebd1bf2988d81208c002135a2ca7272fe Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sun, 1 Feb 2026 09:26:22 +0000
Subject: [PATCH 45/62] Use naptan pois

---
 pipeline/transform/transform_poi.py | 33 ++++++++++++++++++++++-------
 1 file changed, 25 insertions(+), 8 deletions(-)

diff --git a/pipeline/transform/transform_poi.py b/pipeline/transform/transform_poi.py
index ac6d98f..d44b877 100644
--- a/pipeline/transform/transform_poi.py
+++ b/pipeline/transform/transform_poi.py
@@ -75,6 +75,11 @@ DROP_CATEGORIES = {
     "tourism/information",
     "tourism/village_sign",
     "tourism/yes",
+    # public transport comes from naptan
+    "public_transport/entrance",
+    "public_transport/platform",
+    "public_transport/station",
+    "public_transport/stop_position",
 }
 
 # (friendly_name, emoji) for every category we keep
@@ -368,11 +373,6 @@ CATEGORY_MAP: dict[str, tuple[str, str]] = {
     "office/university": ("University Office", "🎓"),
     "office/vacant": ("Vacant Office", "🏚️"),
     "office/web_design": ("Web Design", "🌐"),
-    # public_transport
-    "public_transport/entrance": ("Transport Entrance", "🚪"),
-    "public_transport/platform": ("Platform", "🚉"),
-    "public_transport/station": ("Station", "🚉"),
-    "public_transport/stop_position": ("Stop", "🚏"),
     # shop
     "shop/accessories": ("Accessories Shop", "👜"),
     "shop/agrarian": ("Farm Supply Shop", "🌾"),
@@ -573,7 +573,18 @@ CATEGORY_MAP: dict[str, tuple[str, str]] = {
 }
 
 
-def transform(input_path: Path) -> pl.LazyFrame:
+NAPTAN_EMOJIS: dict[str, str] = {
+    "Airport": "✈️",
+    "Ferry": "⛴️",
+    "Rail station": "🚆",
+    "Bus stop": "🚏",
+    "Bus station": "🚌",
+    "Taxi rank": "🚕",
+    "Metro or Tram stop": "🚊",
+}
+
+
+def transform(input_path: Path, naptan_path: Path | None = None) -> pl.LazyFrame:
     lf = pl.scan_parquet(input_path)
 
     # Get all unique categories present in the data
@@ -618,7 +629,10 @@ def transform(input_path: Path) -> pl.LazyFrame:
         pl.col("category").replace_strict(emoji_mapping).alias("emoji"),
     )
 
-    return lf
+    naptan = pl.scan_parquet(naptan_path).with_columns(
+        pl.col("category").replace_strict(NAPTAN_EMOJIS).alias("emoji"),
+    )
+    return pl.concat([lf, naptan], how="diagonal_relaxed")
 
 
 def main():
@@ -628,12 +642,15 @@ def main():
     parser.add_argument(
         "--input", type=Path, required=True, help="Raw POIs parquet file"
     )
+    parser.add_argument(
+        "--naptan", type=Path, required=True, help="NaPTAN stations parquet file"
+    )
     parser.add_argument(
         "--output", type=Path, required=True, help="Output filtered POIs parquet file"
     )
     args = parser.parse_args()
 
-    df = transform(args.input).collect(engine="streaming")
+    df = transform(args.input, args.naptan).collect(engine="streaming")
 
     df.write_parquet(args.output)
 

From 646d8bdc46addbd1bc7df1bc3b9261b34aaaef4b Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sun, 1 Feb 2026 09:27:05 +0000
Subject: [PATCH 46/62] Add more data

---
 pipeline/transform/merge.py | 61 ++++++++++++++++++-------------------
 1 file changed, 30 insertions(+), 31 deletions(-)

diff --git a/pipeline/transform/merge.py b/pipeline/transform/merge.py
index e672b78..ffbb046 100644
--- a/pipeline/transform/merge.py
+++ b/pipeline/transform/merge.py
@@ -11,8 +11,8 @@ def _build_wide(
     journey_times_path: Path,
     ethnicity_path: Path,
     crime_path: Path ,
-    noise_path: Path ,
-    ofsted_path: Path,
+    noise_path: Path,
+    school_proximity_path: Path,
     broadband_path: Path,
 ) -> pl.DataFrame:
     """Build the wide dataframe by joining epc_pp with all auxiliary data."""
@@ -62,31 +62,30 @@ def _build_wide(
     poi_counts = pl.scan_parquet(poi_proximity_path)
     wide = wide.join(poi_counts, on="postcode", how="left")
 
-    noise = pl.scan_parquet(noise_path).select("postcode", "road_noise_lden_db")
-    wide = wide.join(noise, on="postcode", how="left")
+    # noise = pl.scan_parquet(noise_path).select(
+    #     "postcode", "road_noise_lden_db", "rail_noise_lden_db", "airport_noise_lden_db"
+    # )
+    # wide = wide.join(noise, on="postcode", how="left")
 
-    print("Joining Ofsted school ratings...")
-    ofsted = (
-        pl.scan_parquet(ofsted_path)
-        .filter(pl.col("Overall effectiveness").is_in(["1", "2", "3", "4"]))
-        .select(
-            pl.col("Postcode").alias("ofsted_postcode"),
-            pl.col("Overall effectiveness").cast(pl.UInt8).alias("ofsted_rating"),
-        )
-        .group_by("ofsted_postcode")
-        .agg(pl.col("ofsted_rating").mean().round(1).alias("ofsted_avg_rating"))
-    )
-    wide = wide.join(ofsted, left_on="postcode", right_on="ofsted_postcode", how="left")
+    print("Joining school proximity counts...")
+    school_proximity = pl.scan_parquet(school_proximity_path)
+    wide = wide.join(school_proximity, on="postcode", how="left")
 
-    print("Joining broadband performance...")
+    # Broadband: derive max available download speed tier per postcode from
+    # Ofcom availability percentages.  Tiers: Gigabit ≥1000, UFBB ≥300,
+    # UFBB(100) ≥100, SFBB ≥30 Mbps.
+    print("Joining broadband availability...")
     broadband = pl.scan_parquet(broadband_path).select(
-        "output_area",
-        pl.col("Average max download speed (Mbit/s) for lines >=900Mbit/s").alias("broadband_max_download_900plus"),
-        pl.col("Average max download speed (Mbit/s) for lines 100<300Mbit/s").alias("broadband_max_download_100_300"),
-        pl.col("Average max download speed (Mbit/s) for lines 30<100Mbit/s").alias("broadband_max_download_30_100"),
-        pl.col("Average max upload speed (Mbit/s) for lines >=900Mbit/s").alias("broadband_max_upload_900plus"),
+        pl.col("postcode_space").alias("bb_postcode"),
+        pl.when(pl.col("Gigabit availability (% premises)") > 0).then(1000)
+          .when(pl.col("UFBB availability (% premises)") > 0).then(300)
+          .when(pl.col("UFBB (100Mbit/s) availability (% premises)") > 0).then(100)
+          .when(pl.col("SFBB availability (% premises)") > 0).then(30)
+          .otherwise(10)
+          .cast(pl.UInt16)
+          .alias("max_download_speed"),
     )
-    wide = wide.join(broadband, left_on="oa21", right_on="output_area", how="left")
+    wide = wide.join(broadband, left_on="postcode", right_on="bb_postcode", how="left")
 
     # Convert construction_age_band to numeric year
     wide = wide.with_columns(
@@ -153,12 +152,12 @@ def _build_wide(
                 "public_transport_2km": "Public transport within 2km",
                 "latest_price": "Last known price",
                 "number_habitable_rooms": "Rooms (including bedrooms & bathrooms)",
-                "road_noise_lden_db": "Road noise Lden (dB)",
-                "ofsted_avg_rating": "Ofsted avg rating (1=Outstanding, 4=Inadequate)"
-                        "broadband_max_download_900plus": "Broadband download speed 900+ Mbps",
-                        "broadband_max_download_100_300": "Broadband download speed 100-300 Mbps",
-                        "broadband_max_download_30_100": "Broadband download speed 30-100 Mbps",
-                        "broadband_max_upload_900plus": "Broadband upload speed 900+ Mbps",
+                # "road_noise_lden_db": "Road noise Lden (dB)",
+                # "rail_noise_lden_db": "Rail noise Lden (dB)",
+                # "airport_noise_lden_db": "Airport noise Lden (dB)",
+                "good_primary_5km": "Good+ primary schools within 5km",
+                "good_secondary_5km": "Good+ secondary schools within 5km",
+                "max_download_speed": "Max available download speed (Mbps)",
             }
         )
     )
@@ -197,7 +196,7 @@ def main():
         "--noise", type=Path,  required=True, help="Road noise by postcode parquet file"
     )
     parser.add_argument(
-        "--ofsted", type=Path,  required=True, help="Ofsted school inspection parquet file"
+        "--school-proximity", type=Path, required=True, help="School proximity counts parquet file"
     )
     parser.add_argument(
         "--broadband", type=Path,  required=True, help="Broadband performance by output area parquet file"
@@ -216,7 +215,7 @@ def main():
         ethnicity_path=args.ethnicity,
         crime_path=args.crime,
         noise_path=args.noise,
-        ofsted_path=args.ofsted,
+        school_proximity_path=args.school_proximity,
         broadband_path=args.broadband,
     )
 

From 806213ce3c381b88e98a3f67df7394a82706e097 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sun, 1 Feb 2026 11:05:46 +0000
Subject: [PATCH 47/62] Add actual construction age

---
 pipeline/transform/join_epc_pp.py | 32 +++++++++++++++++++++++++++++++
 1 file changed, 32 insertions(+)

diff --git a/pipeline/transform/join_epc_pp.py b/pipeline/transform/join_epc_pp.py
index aa42b4e..630813a 100644
--- a/pipeline/transform/join_epc_pp.py
+++ b/pipeline/transform/join_epc_pp.py
@@ -4,6 +4,8 @@ from pathlib import Path
 from ..utils import fuzzy_join_on_postcode
 
 
+MIN_FLOOR_AREA_M2 = 10
+
 pl.Config.set_tbl_cols(-1)
 
 
@@ -69,6 +71,7 @@ def main():
             "locality",
             "town_city",
             pl.col("duration").replace(duration_map),
+            "old_new",
         )
         .filter(pl.col("pp_property_type") != "Other")
         .with_columns(
@@ -89,6 +92,8 @@ def main():
             pl.col("duration").last(),
             pl.col("price").last().alias("latest_price"),
             pl.col("date_of_transfer").last(),
+            pl.col("date_of_transfer").first().alias("first_transfer_date"),
+            pl.col("old_new").first(),
         )
     ).filter(pl.col("pp_address").is_not_null())
 
@@ -116,6 +121,33 @@ def main():
     print(f"Matched: {matched.height} ({100 * matched.height / total:.1f}%)")
     print(f"Unmatched: {total - matched.height}")
 
+    matched = matched.filter(pl.col("TOTAL_FLOOR_AREA") >= MIN_FLOOR_AREA_M2)
+
+    # For new-builds (old_new == "Y"), use the first transaction date year as
+    # the exact construction date; otherwise fall back to the EPC age band.
+    epc_band_year = (
+        pl.col("CONSTRUCTION_AGE_BAND")
+        .str.replace("England and Wales: ", "")
+        .str.replace(" onwards", "")
+        .str.extract(r"(\d{4})", 1)
+        .cast(pl.UInt16, strict=False)
+    )
+    transfer_year = pl.col("first_transfer_date").dt.year().cast(pl.UInt16, strict=False)
+    is_new_build = pl.col("old_new") == "Y"
+
+    matched = matched.with_columns(
+        pl.when(is_new_build & transfer_year.is_not_null())
+        .then(transfer_year)
+        .otherwise(epc_band_year)
+        .alias("CONSTRUCTION_AGE_BAND"),
+        pl.when(is_new_build & transfer_year.is_not_null())
+        .then(pl.lit(0, dtype=pl.UInt8))
+        .when(epc_band_year.is_not_null())
+        .then(pl.lit(1, dtype=pl.UInt8))
+        .otherwise(pl.lit(None, dtype=pl.UInt8))
+        .alias("is_construction_date_approximate"),
+    ).drop("old_new", "first_transfer_date")
+
     matched = matched.rename({col: col.lower() for col in joined.columns})
 
     print(matched.head())

From e15ab7dfef6230f498e8605521d1efdf80d13266 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sun, 1 Feb 2026 11:06:50 +0000
Subject: [PATCH 48/62] Add noise data

---
 pipeline/transform/merge.py | 106 ++++++++++++++++++++----------------
 1 file changed, 59 insertions(+), 47 deletions(-)

diff --git a/pipeline/transform/merge.py b/pipeline/transform/merge.py
index ffbb046..b7aa516 100644
--- a/pipeline/transform/merge.py
+++ b/pipeline/transform/merge.py
@@ -10,17 +10,14 @@ def _build_wide(
     poi_proximity_path: Path,
     journey_times_path: Path,
     ethnicity_path: Path,
-    crime_path: Path ,
+    crime_path: Path,
     noise_path: Path,
     school_proximity_path: Path,
     broadband_path: Path,
 ) -> pl.DataFrame:
     """Build the wide dataframe by joining epc_pp with all auxiliary data."""
-    print("Scanning epc_pp...")
     wide = pl.scan_parquet(epc_pp_path)
 
-    # GPS coordinates + LSOA from ArcGIS
-    print("Joining GPS coordinates...")
     arcgis = pl.scan_parquet(arcgis_path).select(
         pl.col("pcds").alias("postcode"),
         "lat",
@@ -30,7 +27,6 @@ def _build_wide(
     )
     wide = wide.join(arcgis, on="postcode", how="inner")
 
-    print("Joining journey times...")
     journey_times = pl.scan_parquet(journey_times_path).select(
         "postcode",
         "public_transport_easy_minutes",
@@ -39,12 +35,9 @@ def _build_wide(
     )
     wide = wide.join(journey_times, on="postcode", how="left")
 
-    print("Joining IoD scores...")
     iod = pl.scan_parquet(iod_path)
     wide = wide.join(iod, left_on="lsoa21", right_on="LSOA code (2021)", how="left")
 
-    # Ethnicity by local authority
-    print("Joining ethnicity data...")
     ethnicity = pl.scan_parquet(ethnicity_path)
     wide = wide.join(
         ethnicity,
@@ -53,50 +46,50 @@ def _build_wide(
         how="left",
     )
 
-    # Crime stats by LSOA
-    print("Joining crime data...")
     crime = pl.scan_parquet(crime_path)
     wide = wide.join(crime, left_on="lsoa21", right_on="LSOA code", how="left")
 
-    print("Joining POI proximity counts...")
     poi_counts = pl.scan_parquet(poi_proximity_path)
     wide = wide.join(poi_counts, on="postcode", how="left")
 
-    # noise = pl.scan_parquet(noise_path).select(
-    #     "postcode", "road_noise_lden_db", "rail_noise_lden_db", "airport_noise_lden_db"
-    # )
-    # wide = wide.join(noise, on="postcode", how="left")
+    noise = (
+        pl.scan_parquet(noise_path)
+        .with_columns(
+            pl.max_horizontal(
+                "road_noise_lden_db", "rail_noise_lden_db", "airport_noise_lden_db"
+            ).alias("noise_lden_db"),
+        )
+        .select("postcode", "noise_lden_db")
+    )
+    wide = wide.join(noise, on="postcode", how="left")
 
-    print("Joining school proximity counts...")
     school_proximity = pl.scan_parquet(school_proximity_path)
     wide = wide.join(school_proximity, on="postcode", how="left")
 
     # Broadband: derive max available download speed tier per postcode from
     # Ofcom availability percentages.  Tiers: Gigabit ≥1000, UFBB ≥300,
     # UFBB(100) ≥100, SFBB ≥30 Mbps.
-    print("Joining broadband availability...")
-    broadband = pl.scan_parquet(broadband_path).select(
-        pl.col("postcode_space").alias("bb_postcode"),
-        pl.when(pl.col("Gigabit availability (% premises)") > 0).then(1000)
-          .when(pl.col("UFBB availability (% premises)") > 0).then(300)
-          .when(pl.col("UFBB (100Mbit/s) availability (% premises)") > 0).then(100)
-          .when(pl.col("SFBB availability (% premises)") > 0).then(30)
-          .otherwise(10)
-          .cast(pl.UInt16)
-          .alias("max_download_speed"),
+    broadband = (
+        pl.scan_parquet(broadband_path)
+        .select(
+            pl.col("postcode_space").alias("bb_postcode"),
+            pl.when(pl.col("Gigabit availability (% premises)") > 0)
+            .then(1000)
+            .when(pl.col("UFBB availability (% premises)") > 0)
+            .then(300)
+            .when(pl.col("UFBB (100Mbit/s) availability (% premises)") > 0)
+            .then(100)
+            .when(pl.col("SFBB availability (% premises)") > 0)
+            .then(30)
+            .otherwise(10)
+            .cast(pl.UInt16)
+            .alias("max_download_speed"),
+        )
+        .group_by("bb_postcode")
+        .agg(pl.col("max_download_speed").max())
     )
     wide = wide.join(broadband, left_on="postcode", right_on="bb_postcode", how="left")
 
-    # Convert construction_age_band to numeric year
-    wide = wide.with_columns(
-        pl.col("construction_age_band")
-        .str.replace("England and Wales: ", "")
-        .str.replace(" onwards", "")
-        .str.extract(r"(\d{4})", 1)
-        .cast(pl.UInt16, strict=False)
-        .alias("construction_age_band"),
-    )
-
     wide = wide.with_columns(
         pl.when(pl.col("pp_property_type") == pl.col("built_form"))
         .then(pl.col("pp_property_type"))
@@ -109,7 +102,8 @@ def _build_wide(
     )
 
     wide = (
-        wide.filter(pl.col("total_floor_area") > 0).with_columns(
+        wide.filter(pl.col("total_floor_area") > 0)
+        .with_columns(
             (pl.col("latest_price") / pl.col("total_floor_area"))
             .round(0)
             .cast(pl.Int32)
@@ -137,6 +131,7 @@ def _build_wide(
         .rename(
             {
                 "construction_age_band": "Approximate construction age",
+                "is_construction_date_approximate": "Is construction date approximate",
                 "pp_address": "Address per Property Register",
                 "epc_address": "Address per EPC",
                 "postcode": "Postcode",
@@ -152,9 +147,7 @@ def _build_wide(
                 "public_transport_2km": "Public transport within 2km",
                 "latest_price": "Last known price",
                 "number_habitable_rooms": "Rooms (including bedrooms & bathrooms)",
-                # "road_noise_lden_db": "Road noise Lden (dB)",
-                # "rail_noise_lden_db": "Rail noise Lden (dB)",
-                # "airport_noise_lden_db": "Airport noise Lden (dB)",
+                "noise_lden_db": "Noise (dB)",
                 "good_primary_5km": "Good+ primary schools within 5km",
                 "good_secondary_5km": "Good+ secondary schools within 5km",
                 "max_download_speed": "Max available download speed (Mbps)",
@@ -165,6 +158,7 @@ def _build_wide(
     print("Collecting with streaming engine...")
     return wide.collect(engine="streaming")
 
+
 def main():
     parser = argparse.ArgumentParser(
         description="Build wide property dataframe with all joins"
@@ -176,7 +170,10 @@ def main():
         "--arcgis", type=Path, required=True, help="ArcGIS postcode data parquet file"
     )
     parser.add_argument(
-        "--iod", type=Path, required=True, help="Index of Deprivation parquet file (optional)"
+        "--iod",
+        type=Path,
+        required=True,
+        help="Index of Deprivation parquet file (optional)",
     )
     parser.add_argument(
         "--poi-proximity",
@@ -184,22 +181,37 @@ def main():
         help="POI proximity counts parquet file (optional)",
     )
     parser.add_argument(
-        "--journey-times", required=True, type=Path, help="Journey times parquet file (optional)"
+        "--journey-times",
+        required=True,
+        type=Path,
+        help="Journey times parquet file (optional)",
     )
     parser.add_argument(
-        "--ethnicity", type=Path, required=True, help="Ethnicity by local authority parquet file (optional)"
+        "--ethnicity",
+        type=Path,
+        required=True,
+        help="Ethnicity by local authority parquet file (optional)",
     )
     parser.add_argument(
-        "--crime", type=Path, required=True, help="Crime by LSOA parquet file (optional)"
+        "--crime",
+        type=Path,
+        required=True,
+        help="Crime by LSOA parquet file (optional)",
     )
     parser.add_argument(
-        "--noise", type=Path,  required=True, help="Road noise by postcode parquet file"
+        "--noise", type=Path, required=True, help="Road noise by postcode parquet file"
     )
     parser.add_argument(
-        "--school-proximity", type=Path, required=True, help="School proximity counts parquet file"
+        "--school-proximity",
+        type=Path,
+        required=True,
+        help="School proximity counts parquet file",
     )
     parser.add_argument(
-        "--broadband", type=Path,  required=True, help="Broadband performance by output area parquet file"
+        "--broadband",
+        type=Path,
+        required=True,
+        help="Broadband performance by output area parquet file",
     )
     parser.add_argument(
         "--output", type=Path, required=True, help="Output parquet file path"

From 07a5fdc31fdf2df35963667c03297b14da657362 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sun, 1 Feb 2026 11:07:14 +0000
Subject: [PATCH 49/62] Remove unused deps

---
 pyproject.toml |  8 ++++++++
 uv.lock        | 31 +++++++++++++++++++++++++++++++
 2 files changed, 39 insertions(+)

diff --git a/pyproject.toml b/pyproject.toml
index afc96d7..2bb412e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -31,6 +31,14 @@ environments = ["sys_platform == 'linux' and python_version < '3.14'"]
 
 [dependency-groups]
 dev = [
+    "deptry>=0.22.0",
     "pytest>=9.0.2",
     "ruff>=0.8.0",
 ]
+
+[tool.deptry.per_rule_ignores]
+# pyarrow/fastexcel: runtime backends for polars parquet/Excel I/O
+# jupyter/ipywidgets/pandas: needed to run analysis notebooks
+DEP002 = ["pyarrow", "fastexcel", "jupyter", "ipywidgets", "pandas"]
+# pytest is a dev dependency, not a missing one
+DEP004 = ["pytest"]
diff --git a/uv.lock b/uv.lock
index a481fa1..f01b56b 100644
--- a/uv.lock
+++ b/uv.lock
@@ -341,6 +341,23 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/07/6c/aa3f2f849e01cb6a001cd8554a88d4c77c5c1a31c95bdf1cf9301e6d9ef4/defusedxml-0.7.1-py2.py3-none-any.whl", hash = "sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61", size = 25604, upload-time = "2021-03-08T10:59:24.45Z" },
 ]
 
+[[package]]
+name = "deptry"
+version = "0.24.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "click", marker = "python_full_version < '3.14' and sys_platform == 'linux'" },
+    { name = "packaging", marker = "python_full_version < '3.14' and sys_platform == 'linux'" },
+    { name = "requirements-parser", marker = "python_full_version < '3.14' and sys_platform == 'linux'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/58/aa/5cae0f25a2ac5334d5bd2782a6bcd80eecf184f433ff74b2fb0387cfbbb6/deptry-0.24.0.tar.gz", hash = "sha256:852e88af2087e03cdf9ece6916f3f58b74191ab51cc8074897951bd496ee7dbb", size = 440158, upload-time = "2025-11-09T00:31:44.637Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/4a/e2/af81dfd46b457be9e8ded9472872141777fbda8af661f5d509157b165359/deptry-0.24.0-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6ddfedafafe5cbfce31a50d4ea99d7b9074edcd08b9b94350dc739e2fb6ed7f9", size = 1782740, upload-time = "2025-11-09T00:31:28.302Z" },
+    { url = "https://files.pythonhosted.org/packages/ab/28/960c311aae084deef57ece41aac13cb359b06ce31b7771139e79c394a1b7/deptry-0.24.0-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dd22fa2dbbdf4b38061ca9504f2a6ce41ec14fa5c9fe9b0b763ccc1275efebd5", size = 1845477, upload-time = "2025-11-09T00:31:33.452Z" },
+    { url = "https://files.pythonhosted.org/packages/f5/6c/4b972b011a06611e0cf8f4bb6bc04a3d0f9c651950ad9abe320fcbac6983/deptry-0.24.0-cp39-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:0fbe50a2122d79cec53fdfd73a7092c05f316555a1139bcbacf3432572675977", size = 1960410, upload-time = "2025-11-09T00:31:31.174Z" },
+    { url = "https://files.pythonhosted.org/packages/1b/08/0eac3c72a9fd79a043cc492f3ba350c47a7be2160288353218b2c8c1bf3a/deptry-0.24.0-cp39-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:92bd8d331a5a6f8e6247436bc6fe384bcf86a8d69fe33442d195996fb9b20547", size = 2023832, upload-time = "2025-11-09T00:31:36.381Z" },
+]
+
 [[package]]
 name = "executing"
 version = "2.2.1"
@@ -1345,6 +1362,7 @@ dependencies = [
 
 [package.dev-dependencies]
 dev = [
+    { name = "deptry", marker = "python_full_version < '3.14' and sys_platform == 'linux'" },
     { name = "pytest", marker = "python_full_version < '3.14' and sys_platform == 'linux'" },
     { name = "ruff", marker = "python_full_version < '3.14' and sys_platform == 'linux'" },
 ]
@@ -1374,6 +1392,7 @@ requires-dist = [
 
 [package.metadata.requires-dev]
 dev = [
+    { name = "deptry", specifier = ">=0.22.0" },
     { name = "pytest", specifier = ">=9.0.2" },
     { name = "ruff", specifier = ">=0.8.0" },
 ]
@@ -1685,6 +1704,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/1e/db/4254e3eabe8020b458f1a747140d32277ec7a271daf1d235b70dc0b4e6e3/requests-2.32.5-py3-none-any.whl", hash = "sha256:2462f94637a34fd532264295e186976db0f5d453d1cdd31473c85a6a161affb6", size = 64738, upload-time = "2025-08-18T20:46:00.542Z" },
 ]
 
+[[package]]
+name = "requirements-parser"
+version = "0.13.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "packaging", marker = "python_full_version < '3.14' and sys_platform == 'linux'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/95/96/fb6dbfebb524d5601d359a47c78fe7ba1eef90fc4096404aa60c9a906fbb/requirements_parser-0.13.0.tar.gz", hash = "sha256:0843119ca2cb2331de4eb31b10d70462e39ace698fd660a915c247d2301a4418", size = 22630, upload-time = "2025-05-21T13:42:05.464Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/bd/60/50fbb6ffb35f733654466f1a90d162bcbea358adc3b0871339254fbc37b2/requirements_parser-0.13.0-py3-none-any.whl", hash = "sha256:2b3173faecf19ec5501971b7222d38f04cb45bb9d87d0ad629ca71e2e62ded14", size = 14782, upload-time = "2025-05-21T13:42:04.007Z" },
+]
+
 [[package]]
 name = "rfc3339-validator"
 version = "0.1.4"

From 2ac37ece974be349cdaa0c21c839fdcb59a89e23 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sun, 1 Feb 2026 11:07:34 +0000
Subject: [PATCH 50/62] Add extensions

---
 .vscode/extensions.json | 31 +++++++++++++++++++++++++++++++
 .vscode/settings.json   |  5 +++--
 2 files changed, 34 insertions(+), 2 deletions(-)
 create mode 100644 .vscode/extensions.json

diff --git a/.vscode/extensions.json b/.vscode/extensions.json
new file mode 100644
index 0000000..cc402e4
--- /dev/null
+++ b/.vscode/extensions.json
@@ -0,0 +1,31 @@
+{
+    "recommendations": [
+        "esbenp.prettier-vscode",
+        "dbaeumer.vscode-eslint",
+        "ms-toolsai.jupyter",
+        "ms-python.python",
+        "GitHub.copilot",
+        "ms-azuretools.vscode-docker",
+        "redhat.vscode-yaml",
+        "1yib.rust-bundle",
+        "alexcvzz.vscode-sqlite",
+        "esbenp.prettier-vscode",
+        "dbaeumer.vscode-eslint",
+        "ms-python.python",
+        "ms-toolsai.jupyter",
+        "ms-azuretools.vscode-docker",
+        "redhat.vscode-yaml",
+        "tomoki1207.pdf",
+        "qwtel.sqlite-viewer",
+        "alexcvzz.vscode-sqlite",
+        "rust-lang.rust-analyzer",
+        "pkief.material-icon-theme",
+        "detachhead.basedpyright",
+        "editorconfig.editorconfig",
+        "davidanson.vscode-markdownlint",
+        "charliermarsh.ruff",
+        "timonwong.shellcheck",
+        "tonybaloney.vscode-pets",
+        "vadimcn.vscode-lldb"
+    ]
+}
\ No newline at end of file
diff --git a/.vscode/settings.json b/.vscode/settings.json
index f99b994..3c9d529 100644
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@@ -4,6 +4,7 @@
         "**/__pycache__": true,
         "**/node_modules": true,
         "**/.ruff_cache":true,
-        "**/.pytest_cache":true
+        "**/.pytest_cache":true,
+        "**/target":true
     }
-}
\ No newline at end of file
+}

From 5f311233e4b086feac3db25305a6db5a43b8e8c5 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sun, 1 Feb 2026 11:07:58 +0000
Subject: [PATCH 51/62] Update UI

---
 frontend/src/App.tsx                        | 446 +++++++++++++-------
 frontend/src/components/DataSourcesPage.tsx |  23 +-
 frontend/src/components/Filters.tsx         | 114 +++--
 frontend/src/components/HomePage.tsx        |  44 +-
 frontend/src/components/Map.tsx             | 242 +++++------
 frontend/src/components/POIPane.tsx         | 135 +++++-
 frontend/src/components/PropertiesPane.tsx  |  32 +-
 frontend/src/index.css                      |  14 +
 frontend/src/index.html                     |   2 +-
 frontend/src/types.ts                       |  19 +-
 10 files changed, 663 insertions(+), 408 deletions(-)

diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx
index 62f1a8c..ce7963c 100644
--- a/frontend/src/App.tsx
+++ b/frontend/src/App.tsx
@@ -4,8 +4,11 @@ import Filters from './components/Filters';
 import POIPane from './components/POIPane';
 import { PropertiesPane } from './components/PropertiesPane';
 import DataSources from './components/DataSources';
+import DataSourcesPage from './components/DataSourcesPage';
+import HomePage from './components/HomePage';
 import type {
   FeatureMeta,
+  FeatureGroup,
   FeatureFilters,
   Bounds,
   HexagonData,
@@ -14,6 +17,7 @@ import type {
   POI,
   POIResponse,
   POICategoriesResponse,
+  POICategoryGroup,
   ViewState,
   Property,
   HexagonPropertiesResponse,
@@ -171,7 +175,15 @@ function stateToParams(
 
 // --- Header ---
 
-function Header() {
+type Page = 'home' | 'dashboard' | 'data-sources';
+
+function Header({
+  activePage,
+  onPageChange,
+}: {
+  activePage: Page;
+  onPageChange: (page: Page) => void;
+}) {
   const [copied, setCopied] = useState(false);
 
   const handleShare = useCallback(() => {
@@ -181,60 +193,85 @@ function Header() {
     });
   }, []);
 
+  const tabClass = (page: Page) =>
+    `px-3 py-1.5 rounded text-sm transition-colors ${
+      activePage === page
+        ? 'bg-navy-700 font-semibold'
+        : 'text-warm-300 hover:bg-navy-800 hover:text-white'
+    }`;
+
   return (
-    <header className="h-12 bg-slate-800 text-white flex items-center justify-between px-4 shrink-0">
-      <div className="flex items-center gap-2">
-        <svg
-          className="w-5 h-5 text-blue-400"
-          fill="none"
-          stroke="currentColor"
-          viewBox="0 0 24 24"
+    <header className="h-12 bg-navy-900 text-white flex items-center justify-between px-4 shrink-0">
+      <div className="flex items-center gap-4">
+        <button
+          className="flex items-center gap-2 hover:opacity-80 transition-opacity"
+          onClick={() => onPageChange('home')}
         >
-          <path
-            strokeLinecap="round"
-            strokeLinejoin="round"
-            strokeWidth={2}
-            d="M17.657 16.657L13.414 20.9a1.998 1.998 0 01-2.827 0l-4.244-4.243a8 8 0 1111.314 0z"
-          />
-          <path
-            strokeLinecap="round"
-            strokeLinejoin="round"
-            strokeWidth={2}
-            d="M15 11a3 3 0 11-6 0 3 3 0 016 0z"
-          />
-        </svg>
-        <span className="font-semibold text-lg">Property Map</span>
+          <svg
+            className="w-5 h-5 text-teal-400"
+            fill="none"
+            stroke="currentColor"
+            viewBox="0 0 24 24"
+          >
+            <path
+              strokeLinecap="round"
+              strokeLinejoin="round"
+              strokeWidth={2}
+              d="M17.657 16.657L13.414 20.9a1.998 1.998 0 01-2.827 0l-4.244-4.243a8 8 0 1111.314 0z"
+            />
+            <path
+              strokeLinecap="round"
+              strokeLinejoin="round"
+              strokeWidth={2}
+              d="M15 11a3 3 0 11-6 0 3 3 0 016 0z"
+            />
+          </svg>
+          <span className="font-semibold text-lg">Narrowit</span>
+        </button>
+        <nav className="flex items-center gap-1">
+          <button className={tabClass('home')} onClick={() => onPageChange('home')}>
+            Home
+          </button>
+          <button className={tabClass('dashboard')} onClick={() => onPageChange('dashboard')}>
+            Dashboard
+          </button>
+          <button className={tabClass('data-sources')} onClick={() => onPageChange('data-sources')}>
+            Data Sources
+          </button>
+        </nav>
       </div>
-      <button
-        onClick={handleShare}
-        className="flex items-center gap-1.5 px-3 py-1.5 rounded bg-slate-700 hover:bg-slate-600 transition-colors text-sm"
-      >
-        {copied ? (
-          <>
-            <svg className="w-4 h-4" fill="none" stroke="currentColor" viewBox="0 0 24 24">
-              <path
-                strokeLinecap="round"
-                strokeLinejoin="round"
-                strokeWidth={2}
-                d="M5 13l4 4L19 7"
-              />
-            </svg>
-            Copied!
-          </>
-        ) : (
-          <>
-            <svg className="w-4 h-4" fill="none" stroke="currentColor" viewBox="0 0 24 24">
-              <path
-                strokeLinecap="round"
-                strokeLinejoin="round"
-                strokeWidth={2}
-                d="M8 5H6a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2v-1M8 5a2 2 0 002 2h2a2 2 0 002-2M8 5a2 2 0 012-2h2a2 2 0 012 2m0 0h2a2 2 0 012 2v3m2 4H10m0 0l3-3m-3 3l3 3"
-              />
-            </svg>
-            Share
-          </>
-        )}
-      </button>
+      {activePage === 'dashboard' && (
+        <button
+          onClick={handleShare}
+          className="flex items-center gap-1.5 px-3 py-1.5 rounded bg-navy-800 hover:bg-navy-700 transition-colors text-sm"
+        >
+          {copied ? (
+            <>
+              <svg className="w-4 h-4" fill="none" stroke="currentColor" viewBox="0 0 24 24">
+                <path
+                  strokeLinecap="round"
+                  strokeLinejoin="round"
+                  strokeWidth={2}
+                  d="M5 13l4 4L19 7"
+                />
+              </svg>
+              Copied!
+            </>
+          ) : (
+            <>
+              <svg className="w-4 h-4" fill="none" stroke="currentColor" viewBox="0 0 24 24">
+                <path
+                  strokeLinecap="round"
+                  strokeLinejoin="round"
+                  strokeWidth={2}
+                  d="M8 5H6a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2v-1M8 5a2 2 0 002 2h2a2 2 0 002-2M8 5a2 2 0 012-2h2a2 2 0 012 2m0 0h2a2 2 0 012 2v3m2 4H10m0 0l3-3m-3 3l3 3"
+                />
+              </svg>
+              Share
+            </>
+          )}
+        </button>
+      )}
     </header>
   );
 }
@@ -249,13 +286,16 @@ export default function App() {
   const [filters, setFilters] = useState<FeatureFilters>(urlState.filters || {});
   const [activeFeature, setActiveFeature] = useState<string | null>(null);
   const [dragValue, setDragValue] = useState<[number, number] | null>(null);
+  const [pinnedFeature, setPinnedFeature] = useState<string | null>(null);
   const [rawData, setRawData] = useState<HexagonData[]>([]);
+  const [dragData, setDragData] = useState<HexagonData[] | null>(null);
   const [resolution, setResolution] = useState<number>(8);
   const [bounds, setBounds] = useState<Bounds | null>(null);
   const [loading, setLoading] = useState<boolean>(false);
   const [zoom, setZoom] = useState<number>(urlState.viewState?.zoom || DEFAULT_VIEW.zoom);
   const debounceRef = useRef<ReturnType<typeof setTimeout> | null>(null);
   const abortControllerRef = useRef<AbortController | null>(null);
+  const dragAbortRef = useRef<AbortController | null>(null);
 
   // View state for URL serialization
   const [currentView, setCurrentView] = useState<{
@@ -277,7 +317,7 @@ export default function App() {
 
   // POI state
   const [pois, setPois] = useState<POI[]>([]);
-  const [poiCategories, setPOICategories] = useState<string[]>([]);
+  const [poiCategoryGroups, setPOICategoryGroups] = useState<POICategoryGroup[]>([]);
   const [selectedPOICategories, setSelectedPOICategories] = useState<Set<string>>(
     urlState.poiCategories || new Set()
   );
@@ -293,10 +333,31 @@ export default function App() {
   const [propertiesOffset, setPropertiesOffset] = useState(0);
   const [loadingProperties, setLoadingProperties] = useState(false);
   const [rightPaneTab, setRightPaneTab] = useState<'pois' | 'properties'>(urlState.tab || 'pois');
+  const [activePage, setActivePage] = useState<Page>('home');
 
   // Derive enabled features from filter keys
   const enabledFeatures = useMemo(() => new Set(Object.keys(filters)), [filters]);
 
+  // Derive view feature: active drag takes priority over pinned
+  const viewFeature = activeFeature || pinnedFeature;
+  const viewSource: 'drag' | 'eye' | null = activeFeature ? 'drag' : pinnedFeature ? 'eye' : null;
+  // Color range: always the feature's full slider range from metadata
+  const colorRange = useMemo((): [number, number] | null => {
+    if (!viewFeature) return null;
+    const meta = features.find((f) => f.name === viewFeature);
+    if (meta?.min != null && meta?.max != null) return [meta.min, meta.max];
+    return null;
+  }, [viewFeature, features]);
+
+  // Filter range: current drag or committed filter values, used for gray-out
+  const filterRange = useMemo((): [number, number] | null => {
+    if (!viewFeature) return null;
+    if (activeFeature && dragValue) return dragValue;
+    const filterVal = filters[viewFeature];
+    if (filterVal && typeof filterVal[0] === 'number') return filterVal as [number, number];
+    return null;
+  }, [viewFeature, activeFeature, dragValue, filters]);
+
   // --- URL sync ---
   const urlDebounceRef = useRef<ReturnType<typeof setTimeout> | null>(null);
 
@@ -326,20 +387,40 @@ export default function App() {
   useEffect(() => {
     fetch(`${getApiBaseUrl()}/api/features`)
       .then((res) => res.json())
-      .then((json: { features: FeatureMeta[] }) => {
-        setFeatures(json.features);
+      .then((json: { groups: FeatureGroup[] }) => {
+        // Flatten grouped response into a flat feature list with group annotation
+        const flat: FeatureMeta[] = json.groups.flatMap((g) =>
+          g.features.map((f) => ({ ...f, group: g.name }))
+        );
+        setFeatures(flat);
       })
       .catch((err) => console.error('Failed to fetch features:', err));
 
     fetch(`${getApiBaseUrl()}/api/poi-categories`)
       .then((res) => res.json())
       .then((json: POICategoriesResponse) => {
-        setPOICategories(json.categories);
+        setPOICategoryGroups(json.groups);
       })
       .catch((err) => console.error('Failed to fetch POI categories:', err));
   }, []);
 
-  // Debounced fetch when resolution/bounds/filters change
+  // Build filter query string helper
+  const buildFilterParam = useCallback((): string => {
+    const filterEntries = Object.entries(filters);
+    if (filterEntries.length === 0) return '';
+    return filterEntries
+      .map(([name, value]) => {
+        const meta = features.find((f) => f.name === name);
+        if (meta?.type === 'enum') {
+          return `${name}:${(value as string[]).join('|')}`;
+        }
+        const [min, max] = value as [number, number];
+        return `${name}:${min}:${max}`;
+      })
+      .join(',');
+  }, [filters, features]);
+
+  // Debounced fetch when resolution/bounds/filters change — always fetch hexagons
   useEffect(() => {
     if (!bounds) return;
 
@@ -356,25 +437,13 @@ export default function App() {
       setLoading(true);
       try {
         const boundsStr = `${bounds.south},${bounds.west},${bounds.north},${bounds.east}`;
+        const filtersStr = buildFilterParam();
+
         const params = new URLSearchParams({
           resolution: resolution.toString(),
           bounds: boundsStr,
         });
-        // Build filters param: numeric=name:min:max, enum=name:val1|val2
-        const filterEntries = Object.entries(filters);
-        if (filterEntries.length > 0) {
-          const filtersStr = filterEntries
-            .map(([name, value]) => {
-              const meta = features.find((f) => f.name === name);
-              if (meta?.type === 'enum') {
-                return `${name}:${(value as string[]).join('|')}`;
-              }
-              const [min, max] = value as [number, number];
-              return `${name}:${min}:${max}`;
-            })
-            .join(',');
-          params.set('filters', filtersStr);
-        }
+        if (filtersStr) params.set('filters', filtersStr);
         const res = await fetch(`${getApiBaseUrl()}/api/hexagons?${params}`, {
           signal: abortControllerRef.current.signal,
         });
@@ -394,10 +463,11 @@ export default function App() {
         clearTimeout(debounceRef.current);
       }
     };
-  }, [resolution, bounds, filters]);
+  }, [resolution, bounds, filters, buildFilterParam]);
 
-  // Data passed directly to Map — visual filtering is done via color/opacity in the layer
-  const data = rawData;
+  // During slider drag, use the expanded dataset (without active feature filter)
+  // so both narrowing and expanding are visible. Otherwise use server-filtered data.
+  const data = dragData ?? rawData;
 
   // Fetch POIs when bounds or selected categories change
   useEffect(() => {
@@ -444,7 +514,13 @@ export default function App() {
 
   const prevBoundsRef = useRef<string>('');
   const handleViewChange = useCallback(
-    ({ resolution: newRes, bounds: newBounds, zoom: newZoom, latitude, longitude }: ViewChangeParams) => {
+    ({
+      resolution: newRes,
+      bounds: newBounds,
+      zoom: newZoom,
+      latitude,
+      longitude,
+    }: ViewChangeParams) => {
       // Only update bounds/resolution when quantized values actually change
       const boundsKey = `${newBounds.south},${newBounds.west},${newBounds.north},${newBounds.east},${newRes}`;
       if (boundsKey !== prevBoundsRef.current) {
@@ -471,12 +547,9 @@ export default function App() {
     [features]
   );
 
-  const handleFilterChange = useCallback(
-    (name: string, value: [number, number] | string[]) => {
-      setFilters((prev) => ({ ...prev, [name]: value }));
-    },
-    []
-  );
+  const handleFilterChange = useCallback((name: string, value: [number, number] | string[]) => {
+    setFilters((prev) => ({ ...prev, [name]: value }));
+  }, []);
 
   const handleRemoveFilter = useCallback((name: string) => {
     setFilters((prev) => {
@@ -484,6 +557,7 @@ export default function App() {
       delete next[name];
       return next;
     });
+    setPinnedFeature((prev) => (prev === name ? null : prev));
   }, []);
 
   const handleDragStart = useCallback(
@@ -493,8 +567,41 @@ export default function App() {
       setActiveFeature(name);
       const fval = filters[name];
       setDragValue(fval && !Array.isArray(fval[0]) ? (fval as [number, number]) : null);
+
+      // Fetch hexagons without this feature's filter so we can expand the range
+      if (!bounds) return;
+      if (dragAbortRef.current) dragAbortRef.current.abort();
+      dragAbortRef.current = new AbortController();
+
+      const otherFilters = Object.entries(filters).filter(([k]) => k !== name);
+      let filtersStr = '';
+      if (otherFilters.length > 0) {
+        filtersStr = otherFilters
+          .map(([n, value]) => {
+            const m = features.find((f) => f.name === n);
+            if (m?.type === 'enum') return `${n}:${(value as string[]).join('|')}`;
+            const [min, max] = value as [number, number];
+            return `${n}:${min}:${max}`;
+          })
+          .join(',');
+      }
+
+      const boundsStr = `${bounds.south},${bounds.west},${bounds.north},${bounds.east}`;
+      const params = new URLSearchParams({ resolution: resolution.toString(), bounds: boundsStr });
+      if (filtersStr) params.set('filters', filtersStr);
+
+      fetch(`${getApiBaseUrl()}/api/hexagons?${params}`, {
+        signal: dragAbortRef.current.signal,
+      })
+        .then((res) => res.json())
+        .then((json: ApiResponse) => setDragData(json.features || []))
+        .catch((err) => {
+          if (err instanceof Error && err.name !== 'AbortError') {
+            console.error('Failed to fetch drag data:', err);
+          }
+        });
     },
-    [filters, features]
+    [filters, features, bounds, resolution]
   );
 
   const handleDragChange = useCallback((value: [number, number]) => {
@@ -507,8 +614,21 @@ export default function App() {
     }
     setActiveFeature(null);
     setDragValue(null);
+    setDragData(null);
+    if (dragAbortRef.current) {
+      dragAbortRef.current.abort();
+      dragAbortRef.current = null;
+    }
   }, [activeFeature, dragValue]);
 
+  const handleTogglePin = useCallback((name: string) => {
+    setPinnedFeature((prev) => (prev === name ? null : name));
+  }, []);
+
+  const handleCancelPin = useCallback(() => {
+    setPinnedFeature(null);
+  }, []);
+
   const fetchHexagonProperties = useCallback(
     async (h3: string, res: number, offset = 0) => {
       setLoadingProperties(true);
@@ -584,84 +704,100 @@ export default function App() {
 
   return (
     <div className="h-screen flex flex-col">
-      <Header />
-      <div className="flex-1 flex overflow-hidden">
-        <Filters
-          features={features}
-          filters={filters}
-          activeFeature={activeFeature}
-          dragValue={dragValue}
-          enabledFeatures={enabledFeatures}
-          onAddFilter={handleAddFilter}
-          onRemoveFilter={handleRemoveFilter}
-          onFilterChange={handleFilterChange}
-          onDragStart={handleDragStart}
-          onDragChange={handleDragChange}
-          onDragEnd={handleDragEnd}
-          zoom={zoom}
-        />
-        <div className="flex-1 relative">
-          <Map
-            data={data}
-            pois={pois}
-            onViewChange={handleViewChange}
+      <Header activePage={activePage} onPageChange={setActivePage} />
+      {activePage === 'home' ? (
+        <HomePage onOpenDashboard={() => setActivePage('dashboard')} />
+      ) : activePage === 'data-sources' ? (
+        <DataSourcesPage />
+      ) : (
+        <div className="flex-1 flex overflow-hidden">
+          <Filters
+            features={features}
+            filters={filters}
             activeFeature={activeFeature}
             dragValue={dragValue}
-            features={features}
-            selectedHexagonId={selectedHexagon?.h3 || null}
-            onHexagonClick={handleHexagonClick}
-            initialViewState={initialViewState}
+            enabledFeatures={enabledFeatures}
+            onAddFilter={handleAddFilter}
+            onRemoveFilter={handleRemoveFilter}
+            onFilterChange={handleFilterChange}
+            onDragStart={handleDragStart}
+            onDragChange={handleDragChange}
+            onDragEnd={handleDragEnd}
+            zoom={zoom}
+            pinnedFeature={pinnedFeature}
+            onTogglePin={handleTogglePin}
+            onCancelPin={handleCancelPin}
           />
-          {loading && (
-            <div className="absolute top-4 right-4 bg-white px-3 py-1 rounded shadow">Loading...</div>
-          )}
-          <DataSources />
-        </div>
-        <div className="w-72 bg-white shadow-lg z-10 flex flex-col">
-          {/* Tab headers */}
-          <div className="flex border-b border-gray-200">
-            <button
-              className={`flex-1 p-3 ${
-                rightPaneTab === 'pois' ? 'border-b-2 border-blue-500 font-semibold' : 'text-gray-600'
-              }`}
-              onClick={() => setRightPaneTab('pois')}
-            >
-              POIs {pois.length > 0 && `(${pois.length})`}
-            </button>
-            <button
-              className={`flex-1 p-3 ${
-                rightPaneTab === 'properties'
-                  ? 'border-b-2 border-blue-500 font-semibold'
-                  : 'text-gray-600'
-              }`}
-              onClick={() => setRightPaneTab('properties')}
-            >
-              Properties {propertiesTotal > 0 && `(${propertiesTotal})`}
-            </button>
-          </div>
-
-          {/* Tab content */}
-          <div className="flex-1 overflow-hidden">
-            {rightPaneTab === 'pois' ? (
-              <POIPane
-                categories={poiCategories}
-                selectedCategories={selectedPOICategories}
-                onCategoriesChange={setSelectedPOICategories}
-                poiCount={pois.length}
-              />
-            ) : (
-              <PropertiesPane
-                properties={properties}
-                total={propertiesTotal}
-                loading={loadingProperties}
-                hexagonId={selectedHexagon?.h3 || null}
-                onLoadMore={handleLoadMoreProperties}
-                onClose={handleCloseProperties}
-              />
+          <div className="flex-1 relative">
+            <Map
+              data={data}
+              pois={pois}
+              onViewChange={handleViewChange}
+              viewFeature={viewFeature}
+              colorRange={colorRange}
+              filterRange={filterRange}
+              viewSource={viewSource}
+              onCancelPin={handleCancelPin}
+              features={features}
+              selectedHexagonId={selectedHexagon?.h3 || null}
+              onHexagonClick={handleHexagonClick}
+              initialViewState={initialViewState}
+            />
+            {loading && (
+              <div className="absolute top-4 right-4 bg-white px-3 py-1 rounded shadow">
+                Loading...
+              </div>
             )}
+            <DataSources onNavigate={() => setActivePage('data-sources')} />
+          </div>
+          <div className="w-72 bg-white shadow-lg z-10 flex flex-col">
+            {/* Tab headers */}
+            <div className="flex border-b border-warm-200">
+              <button
+                className={`flex-1 p-3 ${
+                  rightPaneTab === 'pois'
+                    ? 'border-b-2 border-teal-500 font-semibold'
+                    : 'text-warm-600'
+                }`}
+                onClick={() => setRightPaneTab('pois')}
+              >
+                POIs {pois.length > 0 && `(${pois.length})`}
+              </button>
+              <button
+                className={`flex-1 p-3 ${
+                  rightPaneTab === 'properties'
+                    ? 'border-b-2 border-teal-500 font-semibold'
+                    : 'text-warm-600'
+                }`}
+                onClick={() => setRightPaneTab('properties')}
+              >
+                Properties {propertiesTotal > 0 && `(${propertiesTotal})`}
+              </button>
+            </div>
+
+            {/* Tab content */}
+            <div className="flex-1 overflow-hidden">
+              {rightPaneTab === 'pois' ? (
+                <POIPane
+                  groups={poiCategoryGroups}
+                  selectedCategories={selectedPOICategories}
+                  onCategoriesChange={setSelectedPOICategories}
+                  poiCount={pois.length}
+                />
+              ) : (
+                <PropertiesPane
+                  properties={properties}
+                  total={propertiesTotal}
+                  loading={loadingProperties}
+                  hexagonId={selectedHexagon?.h3 || null}
+                  onLoadMore={handleLoadMoreProperties}
+                  onClose={handleCloseProperties}
+                />
+              )}
+            </div>
           </div>
         </div>
-      </div>
+      )}
     </div>
   );
 }
diff --git a/frontend/src/components/DataSourcesPage.tsx b/frontend/src/components/DataSourcesPage.tsx
index 1515412..67a533a 100644
--- a/frontend/src/components/DataSourcesPage.tsx
+++ b/frontend/src/components/DataSourcesPage.tsx
@@ -44,7 +44,7 @@ const DATA_SOURCES = [
   {
     name: 'TfL Journey Times',
     origin: 'Transport for London',
-    use: 'Journey time calculations from postcodes to central London destinations (Bank, Waterloo, King\'s Cross, etc.) via public transport and cycling.',
+    use: "Journey time calculations from postcodes to central London destinations (Bank, Waterloo, King's Cross, etc.) via public transport and cycling.",
     url: 'https://api-portal.tfl.gov.uk/',
     license: 'Powered by TfL Open Data',
   },
@@ -92,15 +92,12 @@ export default function DataSourcesPage() {
         <div className="max-w-5xl mx-auto px-6 py-8">
           <h1 className="text-2xl font-bold text-warm-900 mb-2">Data Sources</h1>
           <p className="text-warm-600 mb-6">
-            This application combines {DATA_SOURCES.length} open datasets covering property prices, energy
-            performance, transport, demographics, crime, environment, and more.
+            This application combines {DATA_SOURCES.length} open datasets covering property prices,
+            energy performance, transport, demographics, crime, environment, and more.
           </p>
           <div className="grid grid-cols-1 lg:grid-cols-2 gap-4">
             {DATA_SOURCES.map((source) => (
-              <div
-                key={source.name}
-                className="bg-white rounded-lg border border-warm-200 p-5"
-              >
+              <div key={source.name} className="bg-white rounded-lg border border-warm-200 p-5">
                 <div className="flex items-start justify-between gap-4 mb-2">
                   <h2 className="text-lg font-semibold text-warm-900">{source.name}</h2>
                   <span className="shrink-0 text-xs bg-warm-100 text-warm-600 px-2 py-1 rounded">
@@ -125,11 +122,11 @@ export default function DataSourcesPage() {
 
       <footer className="bg-navy-900 text-warm-400 px-6 py-6">
         <div className="max-w-5xl mx-auto">
-          <h2 className="text-sm font-semibold text-warm-300 uppercase tracking-wide mb-3">Attribution</h2>
+          <h2 className="text-sm font-semibold text-warm-300 uppercase tracking-wide mb-3">
+            Attribution
+          </h2>
           <ul className="space-y-1.5 text-sm">
-            <li>
-              Contains HM Land Registry data &copy; Crown copyright and database right 2025.
-            </li>
+            <li>Contains HM Land Registry data &copy; Crown copyright and database right 2025.</li>
             <li>
               Contains public sector information licensed under the{' '}
               <a
@@ -142,9 +139,7 @@ export default function DataSourcesPage() {
               </a>
               .
             </li>
-            <li>
-              Contains OS data &copy; Crown copyright and database rights 2025.
-            </li>
+            <li>Contains OS data &copy; Crown copyright and database rights 2025.</li>
             <li>Powered by TfL Open Data.</li>
             <li>
               Contains data from{' '}
diff --git a/frontend/src/components/Filters.tsx b/frontend/src/components/Filters.tsx
index a1da18c..544f64c 100644
--- a/frontend/src/components/Filters.tsx
+++ b/frontend/src/components/Filters.tsx
@@ -1,4 +1,4 @@
-import { memo } from 'react';
+import { memo, useState, useRef, useEffect, useMemo } from 'react';
 import { Slider } from './ui/slider';
 import { Label } from './ui/label';
 import type { FeatureMeta, FeatureFilters } from '../types';
@@ -21,6 +21,83 @@ interface FiltersProps {
   onCancelPin: () => void;
 }
 
+function FilterDropdown({
+  availableFeatures,
+  onAddFilter,
+}: {
+  availableFeatures: FeatureMeta[];
+  onAddFilter: (name: string) => void;
+}) {
+  const [open, setOpen] = useState(false);
+  const ref = useRef<HTMLDivElement>(null);
+
+  useEffect(() => {
+    if (!open) return;
+    const handler = (e: MouseEvent) => {
+      if (ref.current && !ref.current.contains(e.target as Node)) setOpen(false);
+    };
+    const keyHandler = (e: KeyboardEvent) => {
+      if (e.key === 'Escape') setOpen(false);
+    };
+    document.addEventListener('mousedown', handler);
+    document.addEventListener('keydown', keyHandler);
+    return () => {
+      document.removeEventListener('mousedown', handler);
+      document.removeEventListener('keydown', keyHandler);
+    };
+  }, [open]);
+
+  const grouped = useMemo(() => {
+    const groups: { name: string; features: FeatureMeta[] }[] = [];
+    const seen = new Map<string, FeatureMeta[]>();
+    for (const f of availableFeatures) {
+      const g = f.group || 'Other';
+      let arr = seen.get(g);
+      if (!arr) {
+        arr = [];
+        seen.set(g, arr);
+        groups.push({ name: g, features: arr });
+      }
+      arr.push(f);
+    }
+    return groups;
+  }, [availableFeatures]);
+
+  return (
+    <div ref={ref} className="relative">
+      <button
+        className="w-full p-2 border rounded text-sm bg-white text-left text-warm-500 hover:border-warm-400"
+        onClick={() => setOpen(!open)}
+      >
+        + Add filter...
+      </button>
+      {open && (
+        <div className="absolute z-50 mt-1 w-full bg-white border rounded shadow-lg max-h-80 overflow-y-auto">
+          {grouped.map((group) => (
+            <div key={group.name}>
+              <div className="px-3 py-1.5 text-xs font-bold text-warm-500 bg-warm-50 sticky top-0">
+                {group.name}
+              </div>
+              {group.features.map((f) => (
+                <button
+                  key={f.name}
+                  className="w-full text-left px-3 py-1.5 text-sm hover:bg-teal-50 hover:text-teal-700"
+                  onClick={() => {
+                    onAddFilter(f.name);
+                    setOpen(false);
+                  }}
+                >
+                  {f.name}
+                </button>
+              ))}
+            </div>
+          ))}
+        </div>
+      )}
+    </div>
+  );
+}
+
 function formatValue(value: number): string {
   if (Math.abs(value) >= 1_000_000) return `${(value / 1_000_000).toFixed(1)}M`;
   if (Math.abs(value) >= 1_000) return `${(value / 1_000).toFixed(1)}k`;
@@ -54,22 +131,7 @@ export default memo(function Filters({
 
       {/* Add filter dropdown */}
       {availableFeatures.length > 0 && (
-        <select
-          className="w-full p-2 border rounded text-sm bg-white"
-          value=""
-          onChange={(e) => {
-            if (e.target.value) onAddFilter(e.target.value);
-          }}
-        >
-          <option value="" disabled>
-            + Add filter...
-          </option>
-          {availableFeatures.map((f) => (
-            <option key={f.name} value={f.name}>
-              {f.name}
-            </option>
-          ))}
-        </select>
+        <FilterDropdown availableFeatures={availableFeatures} onAddFilter={onAddFilter} />
       )}
 
       {/* Active filters */}
@@ -149,20 +211,17 @@ export default memo(function Filters({
                   className={`p-0.5 rounded ${isPinned ? 'text-teal-600' : 'text-warm-400 hover:text-warm-700'}`}
                   title={isPinned ? 'Unpin color view' : 'Color map by this feature'}
                 >
-                  <svg className="w-3.5 h-3.5" viewBox="0 0 24 24" fill={isPinned ? 'currentColor' : 'none'} stroke="currentColor" strokeWidth={2}>
+                  <svg
+                    className="w-3.5 h-3.5"
+                    viewBox="0 0 24 24"
+                    fill={isPinned ? 'currentColor' : 'none'}
+                    stroke="currentColor"
+                    strokeWidth={2}
+                  >
                     <path d="M1 12s4-8 11-8 11 8 11 8-4 8-11 8-11-8-11-8z" />
                     <circle cx="12" cy="12" r="3" />
                   </svg>
                 </button>
-                {isPinned && (
-                  <button
-                    onClick={onCancelPin}
-                    className="text-warm-400 hover:text-warm-700 text-xs px-0.5"
-                    title="Clear color view"
-                  >
-                    x
-                  </button>
-                )}
                 <button
                   onClick={() => onRemoveFilter(feature.name)}
                   className="text-warm-400 hover:text-warm-700 text-sm px-1"
@@ -184,7 +243,6 @@ export default memo(function Filters({
           </div>
         );
       })}
-
     </div>
   );
 });
diff --git a/frontend/src/components/HomePage.tsx b/frontend/src/components/HomePage.tsx
index 7c381e9..05f0289 100644
--- a/frontend/src/components/HomePage.tsx
+++ b/frontend/src/components/HomePage.tsx
@@ -182,28 +182,29 @@ export default function HomePage({ onOpenDashboard }: { onOpenDashboard: () => v
   const ctaRef = useFadeInRef();
 
   return (
-    <div
-      ref={scrollRef}
-      className="flex-1 overflow-y-auto bg-warm-50 relative"
-    >
+    <div ref={scrollRef} className="flex-1 overflow-y-auto bg-warm-50 relative">
       <HexCanvas scrollProgress={scrollProgress} />
 
       <div className="relative" style={{ zIndex: 1 }}>
         {/* Hero */}
         <div className="max-w-3xl mx-auto px-6 pt-20 pb-24">
-          <div ref={heroRef} className="fade-in-section backdrop-blur-sm bg-warm-50/60 rounded-2xl p-8 -mx-2">
+          <div
+            ref={heroRef}
+            className="fade-in-section backdrop-blur-sm bg-warm-50/60 rounded-2xl p-8 -mx-2"
+          >
             <p className="text-teal-600 font-semibold tracking-wide uppercase text-sm mb-4">
               Find where to live, not just what&apos;s for sale
             </p>
             <h1 className="text-5xl font-extrabold text-navy-950 mb-6 leading-[1.1] tracking-tight">
-              Every neighbourhood<br />
-              in England &amp; Wales.<br />
+              Every neighbourhood
+              <br />
+              in England &amp; Wales.
+              <br />
               <span className="text-teal-600">One map. Your&nbsp;rules.</span>
             </h1>
             <p className="text-xl text-warm-600 mb-8 leading-relaxed max-w-xl">
-              Set the commute, budget, school rating, noise level, and crime
-              threshold you&apos;ll accept. Narrowit shows you every area that
-              qualifies &mdash; instantly.
+              Set the commute, budget, school rating, noise level, and crime threshold you&apos;ll
+              accept. Narrowit shows you every area that qualifies &mdash; instantly.
             </p>
             <div className="flex items-center gap-4">
               <button
@@ -225,19 +226,22 @@ export default function HomePage({ onOpenDashboard }: { onOpenDashboard: () => v
             <div className="rounded-2xl backdrop-blur-sm bg-warm-50/40 border border-warm-200/50 p-8">
               <div className="grid md:grid-cols-2 gap-8">
                 <div>
-                  <h3 className="text-sm font-semibold text-warm-400 uppercase tracking-wide mb-2">The old way</h3>
+                  <h3 className="text-sm font-semibold text-warm-400 uppercase tracking-wide mb-2">
+                    The old way
+                  </h3>
                   <p className="text-warm-700 leading-relaxed">
-                    Pick a postcode. Google the schools. Check crime stats on
-                    another site. Look up commute times. Realise it&apos;s too
-                    expensive. Start over. Repeat 40 times.
+                    Pick a postcode. Google the schools. Check crime stats on another site. Look up
+                    commute times. Realise it&apos;s too expensive. Start over. Repeat 40 times.
                   </p>
                 </div>
                 <div>
-                  <h3 className="text-sm font-semibold text-teal-600 uppercase tracking-wide mb-2">With Narrowit</h3>
+                  <h3 className="text-sm font-semibold text-teal-600 uppercase tracking-wide mb-2">
+                    With Narrowit
+                  </h3>
                   <p className="text-warm-700 leading-relaxed">
-                    Tell the map what you need. Every hexagon that lights up is
-                    a place worth looking at. Drill into any one to see
-                    individual properties, prices, and energy ratings.
+                    Tell the map what you need. Every hexagon that lights up is a place worth
+                    looking at. Drill into any one to see individual properties, prices, and energy
+                    ratings.
                   </p>
                 </div>
               </div>
@@ -308,9 +312,7 @@ export default function HomePage({ onOpenDashboard }: { onOpenDashboard: () => v
         {/* Final CTA */}
         <div className="max-w-3xl mx-auto px-6 pb-24">
           <div ref={ctaRef} className="fade-in-section text-center">
-            <h2 className="text-3xl font-bold text-navy-950 mb-3">
-              Ready to narrow it down?
-            </h2>
+            <h2 className="text-3xl font-bold text-navy-950 mb-3">Ready to narrow it down?</h2>
             <p className="text-warm-500 mb-8 max-w-md mx-auto">
               100% open data. No account required. Just set your filters and go.
             </p>
diff --git a/frontend/src/components/Map.tsx b/frontend/src/components/Map.tsx
index ae01815..898ce38 100644
--- a/frontend/src/components/Map.tsx
+++ b/frontend/src/components/Map.tsx
@@ -3,26 +3,24 @@ import { Map as MapGL, useControl } from 'react-map-gl/maplibre';
 import type { MapRef } from 'react-map-gl/maplibre';
 import { MapboxOverlay } from '@deck.gl/mapbox';
 import { H3HexagonLayer } from '@deck.gl/geo-layers';
-import { IconLayer, PolygonLayer } from '@deck.gl/layers';
+import { IconLayer, TextLayer } from '@deck.gl/layers';
 import type { PickingInfo } from '@deck.gl/core';
 import 'maplibre-gl/dist/maplibre-gl.css';
-import type { HexagonData, ViewState, ViewChangeParams, Bounds, POI, FeatureMeta, PostcodeData } from '../types';
+import type { HexagonData, ViewState, ViewChangeParams, Bounds, POI, FeatureMeta } from '../types';
 
 interface MapProps {
   data: HexagonData[];
   pois: POI[];
   onViewChange: (params: ViewChangeParams) => void;
   viewFeature: string | null;
-  viewRange: [number, number] | null;
+  colorRange: [number, number] | null;
+  filterRange: [number, number] | null;
   viewSource: 'drag' | 'eye' | null;
   onCancelPin: () => void;
   features: FeatureMeta[];
   selectedHexagonId: string | null;
   onHexagonClick: (h3: string) => void;
   initialViewState?: ViewState;
-  postcodeData: PostcodeData[];
-  selectedPostcode: string | null;
-  onPostcodeClick: (postcode: string) => void;
 }
 
 // Twemoji CDN base URL
@@ -44,7 +42,7 @@ const INITIAL_VIEW: ViewState = {
   pitch: 0,
 };
 
-const MAP_STYLE = 'https://basemaps.cartocdn.com/gl/positron-gl-style/style.json';
+const MAP_STYLE = 'https://basemaps.cartocdn.com/gl/voyager-gl-style/style.json';
 
 // Gradient stops for normalized [0,1] values
 const GRADIENT: { t: number; color: [number, number, number] }[] = [
@@ -78,7 +76,8 @@ function zoomToResolution(zoom: number): number {
   if (zoom < 9.5) return 8;
   if (zoom < 11) return 9;
   if (zoom < 13) return 10;
-  return 11;
+  if (zoom < 15) return 11;
+  return 12;
 }
 
 function getBoundsFromViewState(viewState: ViewState, width: number, height: number): Bounds {
@@ -195,10 +194,7 @@ function PostcodeSearch({
   );
 
   return (
-    <form
-      onSubmit={handleSubmit}
-      className="absolute top-3 left-3 z-10 flex flex-col gap-1"
-    >
+    <form onSubmit={handleSubmit} className="absolute top-3 left-3 z-10 flex flex-col gap-1">
       <div className="flex shadow-lg rounded overflow-hidden">
         <input
           type="text"
@@ -219,9 +215,7 @@ function PostcodeSearch({
         </button>
       </div>
       {error && (
-        <span className="text-xs text-red-600 bg-white/90 rounded px-2 py-0.5 shadow">
-          {error}
-        </span>
+        <span className="text-xs text-red-600 bg-white/90 rounded px-2 py-0.5 shadow">{error}</span>
       )}
     </form>
   );
@@ -255,7 +249,13 @@ function MapLegend({
             className="text-warm-400 hover:text-warm-700 ml-2"
             title="Clear color view"
           >
-            <svg className="w-4 h-4" fill="none" stroke="currentColor" viewBox="0 0 24 24" strokeWidth={2}>
+            <svg
+              className="w-4 h-4"
+              fill="none"
+              stroke="currentColor"
+              viewBox="0 0 24 24"
+              strokeWidth={2}
+            >
               <path strokeLinecap="round" strokeLinejoin="round" d="M6 18L18 6M6 6l12 12" />
             </svg>
           </button>
@@ -281,16 +281,14 @@ export default memo(function Map({
   pois,
   onViewChange,
   viewFeature,
-  viewRange,
+  colorRange,
+  filterRange,
   viewSource,
   onCancelPin,
   features,
   selectedHexagonId,
   onHexagonClick,
   initialViewState,
-  postcodeData,
-  selectedPostcode,
-  onPostcodeClick,
 }: MapProps) {
   const containerRef = useRef<HTMLDivElement>(null);
   const [viewState, setViewState] = useState<ViewState>(initialViewState || INITIAL_VIEW);
@@ -328,7 +326,13 @@ export default memo(function Map({
       east: Math.ceil(raw.east / QUANT) * QUANT,
     };
 
-    onViewChange({ resolution, bounds, zoom: viewState.zoom, latitude: viewState.latitude, longitude: viewState.longitude });
+    onViewChange({
+      resolution,
+      bounds,
+      zoom: viewState.zoom,
+      latitude: viewState.latitude,
+      longitude: viewState.longitude,
+    });
   }, [viewState, dimensions, onViewChange]);
 
   const handleMove = useCallback((evt: { viewState: ViewState }) => {
@@ -349,6 +353,12 @@ export default memo(function Map({
         map.setPaintProperty(layer.id, 'text-halo-width', 2);
         map.setPaintProperty(layer.id, 'text-color', '#222');
       }
+      // Make water more prominent
+      for (const layer of map.getStyle().layers || []) {
+        if (layer.id === 'water' || layer.id.startsWith('water')) {
+          map.setPaintProperty(layer.id, 'fill-color', '#6baed6');
+        }
+      }
       map.setLayoutProperty('building', 'visibility', 'none');
       map.setLayoutProperty('building-top', 'visibility', 'none');
     },
@@ -399,8 +409,10 @@ export default memo(function Map({
   // Use refs for values that change during drag so layers aren't recreated
   const viewFeatureRef = useRef(viewFeature);
   viewFeatureRef.current = viewFeature;
-  const viewRangeRef = useRef(viewRange);
-  viewRangeRef.current = viewRange;
+  const colorRangeRef = useRef(colorRange);
+  colorRangeRef.current = colorRange;
+  const filterRangeRef = useRef(filterRange);
+  filterRangeRef.current = filterRange;
   const colorFeatureMetaRef = useRef(colorFeatureMeta);
   colorFeatureMetaRef.current = colorFeatureMeta;
   const countRangeRef = useRef(countRange);
@@ -408,32 +420,14 @@ export default memo(function Map({
   const selectedHexagonIdRef = useRef(selectedHexagonId);
   selectedHexagonIdRef.current = selectedHexagonId;
 
-  // Postcode refs
-  const selectedPostcodeRef = useRef(selectedPostcode);
-  selectedPostcodeRef.current = selectedPostcode;
-
   // Stable click handler using ref
   const onHexagonClickRef = useRef(onHexagonClick);
   onHexagonClickRef.current = onHexagonClick;
-  const handleHexagonClick = useCallback(
-    (info: PickingInfo<HexagonData>) => {
-      if (info.object && 'h3' in info.object) {
-        onHexagonClickRef.current(info.object.h3);
-      }
-    },
-    []
-  );
-
-  const onPostcodeClickRef = useRef(onPostcodeClick);
-  onPostcodeClickRef.current = onPostcodeClick;
-  const handlePostcodeClick = useCallback(
-    (info: PickingInfo<PostcodeData>) => {
-      if (info.object && 'postcode' in info.object) {
-        onPostcodeClickRef.current(info.object.postcode);
-      }
-    },
-    []
-  );
+  const handleHexagonClick = useCallback((info: PickingInfo<HexagonData>) => {
+    if (info.object && 'h3' in info.object) {
+      onHexagonClickRef.current(info.object.h3);
+    }
+  }, []);
 
   // Stable hover handler using ref
   const handlePoiHoverRef = useRef(handlePoiHover);
@@ -443,7 +437,7 @@ export default memo(function Map({
   }, []);
 
   // Derive a trigger value from color-affecting state — avoids useEffect+setState double-render
-  const colorTrigger = `${viewFeature}|${viewRange?.[0]}|${viewRange?.[1]}|${countRange.min}|${countRange.max}|${selectedHexagonId}`;
+  const colorTrigger = `${viewFeature}|${colorRange?.[0]}|${colorRange?.[1]}|${filterRange?.[0]}|${filterRange?.[1]}|${countRange.min}|${countRange.max}|${selectedHexagonId}`;
 
   // Hexagon layer — only recreated when data or color trigger changes
   const hexLayer = useMemo(
@@ -454,29 +448,36 @@ export default memo(function Map({
         getHexagon: (d) => d.h3,
         getFillColor: (d) => {
           const vf = viewFeatureRef.current;
-          const vr = viewRangeRef.current;
+          const clr = colorRangeRef.current;
+          const fr = filterRangeRef.current;
           const cfm = colorFeatureMetaRef.current;
-          if (vf && vr && cfm) {
+          if (vf && clr && cfm) {
             const val = d[`min_${vf}`];
             if (val == null) return [128, 128, 128, 80] as [number, number, number, number];
-            const min = vr[0];
-            const max = vr[1];
-            const minVal = d[`min_${vf}`] as number;
-            const maxVal = d[`max_${vf}`] as number;
-            // Gray out hexagons outside range
-            if (maxVal < min || minVal > max) {
-              return [180, 180, 180, 60] as [number, number, number, number];
+            // Gray out hexagons outside filter range
+            if (fr) {
+              const minVal = d[`min_${vf}`] as number;
+              const maxVal = d[`max_${vf}`] as number;
+              if (maxVal < fr[0] || minVal > fr[1]) {
+                return [180, 180, 180, 60] as [number, number, number, number];
+              }
             }
-            const range = max - min;
+            // Color using full slider range
+            const range = clr[1] - clr[0];
             if (range === 0) return [...GRADIENT[0].color, 200] as [number, number, number, number];
-            const t = ((val as number) - min) / range;
+            const t = ((val as number) - clr[0]) / range;
             const rgb = normalizedToColor(Math.max(0, Math.min(1, t)));
             return [...rgb, 200] as [number, number, number, number];
           }
           const cr = countRangeRef.current;
           const c = d.count as number;
           const t = (c - cr.min) / (cr.max - cr.min);
-          return [...countToColor(Math.max(0, Math.min(1, t))), 200] as [number, number, number, number];
+          return [...countToColor(Math.max(0, Math.min(1, t))), 200] as [
+            number,
+            number,
+            number,
+            number,
+          ];
         },
         getLineColor: (d) =>
           (d.h3 === selectedHexagonIdRef.current ? [255, 255, 255, 255] : [0, 0, 0, 0]) as [
@@ -498,84 +499,11 @@ export default memo(function Map({
         highPrecision: true,
         onClick: handleHexagonClick,
         // @ts-expect-error beforeId is a MapboxOverlay interleave prop, not typed in LayerProps
-        beforeId: "waterway_label",
+        beforeId: 'waterway_label',
       }),
     [data, colorTrigger, handleHexagonClick]
   );
 
-  // Postcode count range
-  const postcodeCountRange = useMemo(() => {
-    if (postcodeData.length === 0) return { min: 0, max: 1 };
-    let min = Infinity;
-    let max = -Infinity;
-    for (const d of postcodeData) {
-      const c = d.count as number;
-      if (c < min) min = c;
-      if (c > max) max = c;
-    }
-    if (min === max) return { min, max: min + 1 };
-    return { min, max };
-  }, [postcodeData]);
-
-  const postcodeCountRangeRef = useRef(postcodeCountRange);
-  postcodeCountRangeRef.current = postcodeCountRange;
-
-  // Postcode color trigger
-  const postcodeColorTrigger = `${viewFeature}|${viewRange?.[0]}|${viewRange?.[1]}|${postcodeCountRange.min}|${postcodeCountRange.max}|${selectedPostcode}`;
-
-  // Postcode polygon layer
-  const postcodeLayer = useMemo(
-    () =>
-      new PolygonLayer<PostcodeData>({
-        id: 'postcode-polygons',
-        data: postcodeData,
-        getPolygon: (d) => d.polygon,
-        getFillColor: (d) => {
-          const vf = viewFeatureRef.current;
-          const vr = viewRangeRef.current;
-          const cfm = colorFeatureMetaRef.current;
-          if (vf && vr && cfm) {
-            const val = d[`min_${vf}`];
-            if (val == null) return [128, 128, 128, 80] as [number, number, number, number];
-            const min = vr[0];
-            const max = vr[1];
-            const minVal = d[`min_${vf}`] as number;
-            const maxVal = d[`max_${vf}`] as number;
-            if (maxVal < min || minVal > max) {
-              return [180, 180, 180, 60] as [number, number, number, number];
-            }
-            const range = max - min;
-            if (range === 0) return [...GRADIENT[0].color, 200] as [number, number, number, number];
-            const t = ((val as number) - min) / range;
-            const rgb = normalizedToColor(Math.max(0, Math.min(1, t)));
-            return [...rgb, 200] as [number, number, number, number];
-          }
-          const cr = postcodeCountRangeRef.current;
-          const c = d.count as number;
-          const t = (c - cr.min) / (cr.max - cr.min);
-          return [...countToColor(Math.max(0, Math.min(1, t))), 200] as [number, number, number, number];
-        },
-        getLineColor: (d) =>
-          (d.postcode === selectedPostcodeRef.current
-            ? [255, 255, 255, 255]
-            : [160, 160, 160, 200]) as [number, number, number, number],
-        getLineWidth: (d) => (d.postcode === selectedPostcodeRef.current ? 2 : 1),
-        lineWidthUnits: 'pixels' as const,
-        stroked: true,
-        filled: true,
-        pickable: true,
-        updateTriggers: {
-          getFillColor: [postcodeColorTrigger],
-          getLineColor: [postcodeColorTrigger],
-          getLineWidth: [postcodeColorTrigger],
-        },
-        onClick: handlePostcodeClick,
-        // @ts-expect-error beforeId is a MapboxOverlay interleave prop, not typed in LayerProps
-        beforeId: "waterway_label",
-      }),
-    [postcodeData, postcodeColorTrigger, handlePostcodeClick]
-  );
-
   // POI layer — independent, only recreated when POI data changes
   const poiLayer = useMemo(
     () =>
@@ -597,9 +525,41 @@ export default memo(function Map({
     [pois, stablePoiHover]
   );
 
+  // Postcode labels on high-res hexagons (resolution 11+, zoom >= 13)
+  const postcodeData = useMemo(
+    () => data.filter((d) => d.postcode && d.lat != null && d.lon != null),
+    [data]
+  );
+
+  const showPostcodes = viewState.zoom >= 13;
+  const postcodeLayer = useMemo(
+    () =>
+      showPostcodes
+        ? new TextLayer<HexagonData>({
+            id: 'postcode-labels',
+            data: postcodeData,
+            getPosition: (d) => [d.lon as number, d.lat as number],
+            getText: (d) => d.postcode as string,
+            getSize: 11,
+            getColor: [30, 30, 30, 220],
+            getTextAnchor: 'middle',
+            getAlignmentBaseline: 'center',
+            fontFamily: 'Inter, system-ui, sans-serif',
+            fontWeight: 600,
+            outlineWidth: 2,
+            outlineColor: [255, 255, 255, 200],
+            billboard: false,
+            sizeUnits: 'pixels',
+            sizeMinPixels: 10,
+            sizeMaxPixels: 14,
+          })
+        : null,
+    [postcodeData, showPostcodes]
+  );
+
   const layers = useMemo(
-    () => (postcodeData.length > 0 ? [postcodeLayer, poiLayer] : [hexLayer, poiLayer]),
-    [postcodeData.length, postcodeLayer, hexLayer, poiLayer]
+    () => [hexLayer, poiLayer, ...(postcodeLayer ? [postcodeLayer] : [])],
+    [hexLayer, poiLayer, postcodeLayer]
   );
 
   // Tooltip uses refs to avoid being a layer dependency
@@ -611,15 +571,9 @@ export default memo(function Map({
     ({ object }: { object?: any }) => {
       if (!object) return null;
 
-      // Handle both hexagon and postcode objects
-      const isPostcode = 'postcode' in object;
-      const isHexagon = 'h3' in object;
-      if (!isPostcode && !isHexagon) return null;
+      if (!('h3' in object)) return null;
 
       const lines: string[] = [];
-      if (isPostcode) {
-        lines.push(`<strong>${object.postcode}</strong>`);
-      }
       lines.push(`<div>${(object.count as number).toLocaleString()} properties</div>`);
 
       for (const f of featuresRef.current) {
@@ -664,10 +618,10 @@ export default memo(function Map({
         <DeckOverlay layers={layers} getTooltip={getTooltip as never} />
       </MapGL>
       <PostcodeSearch onFlyTo={handleFlyTo} />
-      {viewFeature && viewRange && colorFeatureMeta && (
+      {viewFeature && colorRange && colorFeatureMeta && (
         <MapLegend
           featureLabel={colorFeatureMeta.name}
-          range={viewRange}
+          range={colorRange}
           showCancel={viewSource === 'eye'}
           onCancel={onCancelPin}
         />
diff --git a/frontend/src/components/POIPane.tsx b/frontend/src/components/POIPane.tsx
index 43490f0..2e41a6b 100644
--- a/frontend/src/components/POIPane.tsx
+++ b/frontend/src/components/POIPane.tsx
@@ -1,21 +1,22 @@
-import { useState, useRef, useEffect } from 'react';
-import { Label } from './ui/label';
+import { useState, useRef, useEffect, useCallback } from 'react';
+import type { POICategoryGroup } from '../types';
 
 interface POIPaneProps {
-  categories: string[];
+  groups: POICategoryGroup[];
   selectedCategories: Set<string>;
   onCategoriesChange: (categories: Set<string>) => void;
   poiCount: number;
 }
 
 export default function POIPane({
-  categories,
+  groups,
   selectedCategories,
   onCategoriesChange,
   poiCount,
 }: POIPaneProps) {
   const [dropdownOpen, setDropdownOpen] = useState(false);
   const [searchTerm, setSearchTerm] = useState('');
+  const [collapsedGroups, setCollapsedGroups] = useState<Set<string>>(new Set());
   const dropdownRef = useRef<HTMLDivElement>(null);
 
   // Close dropdown when clicking outside
@@ -29,6 +30,8 @@ export default function POIPane({
     return () => document.removeEventListener('mousedown', handleClickOutside);
   }, []);
 
+  const allCategories = groups.flatMap((g) => g.categories);
+
   const toggleCategory = (category: string) => {
     const newSet = new Set(selectedCategories);
     if (newSet.has(category)) {
@@ -40,17 +43,55 @@ export default function POIPane({
   };
 
   const selectAll = () => {
-    onCategoriesChange(new Set(categories));
+    onCategoriesChange(new Set(allCategories));
   };
 
   const selectNone = () => {
     onCategoriesChange(new Set());
   };
 
-  const filteredCategories = categories.filter((cat) =>
-    cat.toLowerCase().includes(searchTerm.toLowerCase())
+  const toggleGroup = useCallback(
+    (groupName: string) => {
+      const group = groups.find((g) => g.name === groupName);
+      if (!group) return;
+      const allSelected = group.categories.every((c) => selectedCategories.has(c));
+      const newSet = new Set(selectedCategories);
+      if (allSelected) {
+        group.categories.forEach((c) => newSet.delete(c));
+      } else {
+        group.categories.forEach((c) => newSet.add(c));
+      }
+      onCategoriesChange(newSet);
+    },
+    [groups, selectedCategories, onCategoriesChange]
   );
 
+  const toggleCollapse = (groupName: string) => {
+    setCollapsedGroups((prev) => {
+      const next = new Set(prev);
+      if (next.has(groupName)) {
+        next.delete(groupName);
+      } else {
+        next.add(groupName);
+      }
+      return next;
+    });
+  };
+
+  const lowerSearch = searchTerm.toLowerCase();
+
+  // Filter groups and categories by search term
+  const filteredGroups = groups
+    .map((group) => {
+      if (!searchTerm) return group;
+      const matchingCats = group.categories.filter((c) => c.toLowerCase().includes(lowerSearch));
+      const groupMatches = group.name.toLowerCase().includes(lowerSearch);
+      if (groupMatches) return group;
+      if (matchingCats.length === 0) return null;
+      return { ...group, categories: matchingCats };
+    })
+    .filter(Boolean) as POICategoryGroup[];
+
   const selectedCount = selectedCategories.size;
 
   return (
@@ -58,7 +99,6 @@ export default function POIPane({
       <h2 className="text-xl font-bold">Points of Interest</h2>
 
       <div className="space-y-2" ref={dropdownRef}>
-        <Label>Categories</Label>
         <button
           onClick={() => setDropdownOpen(!dropdownOpen)}
           className="w-full flex items-center justify-between px-3 py-2 text-sm border border-warm-300 rounded hover:border-warm-400 bg-white"
@@ -66,7 +106,7 @@ export default function POIPane({
           <span className="truncate text-left">
             {selectedCount === 0
               ? 'Select categories...'
-              : selectedCount === categories.length
+              : selectedCount === allCategories.length
                 ? 'All categories'
                 : `${selectedCount} selected`}
           </span>
@@ -101,20 +141,69 @@ export default function POIPane({
               />
             </div>
             <div className="max-h-96 overflow-y-auto py-1">
-              {filteredCategories.map((category) => (
-                <label
-                  key={category}
-                  className="flex items-center gap-2 px-3 py-1.5 hover:bg-warm-50 cursor-pointer"
-                >
-                  <input
-                    type="checkbox"
-                    checked={selectedCategories.has(category)}
-                    onChange={() => toggleCategory(category)}
-                    className="rounded"
-                  />
-                  <span className="text-sm flex-1">{category}</span>
-                </label>
-              ))}
+              {filteredGroups.map((group) => {
+                const groupSelected = group.categories.filter((c) =>
+                  selectedCategories.has(c)
+                ).length;
+                const allInGroupSelected = groupSelected === group.categories.length;
+                const someInGroupSelected = groupSelected > 0 && !allInGroupSelected;
+                const isCollapsed = collapsedGroups.has(group.name) && !searchTerm;
+
+                return (
+                  <div key={group.name}>
+                    <div className="flex items-center gap-1 px-3 py-1.5 bg-warm-50 border-y border-warm-100">
+                      <button
+                        onClick={() => toggleCollapse(group.name)}
+                        className="p-0.5 text-warm-400 hover:text-warm-600"
+                      >
+                        <svg
+                          className={`w-3 h-3 transition-transform ${isCollapsed ? '' : 'rotate-90'}`}
+                          fill="none"
+                          stroke="currentColor"
+                          viewBox="0 0 24 24"
+                        >
+                          <path
+                            strokeLinecap="round"
+                            strokeLinejoin="round"
+                            strokeWidth={2}
+                            d="M9 5l7 7-7 7"
+                          />
+                        </svg>
+                      </button>
+                      <label className="flex items-center gap-2 flex-1 cursor-pointer">
+                        <input
+                          type="checkbox"
+                          checked={allInGroupSelected}
+                          ref={(el) => {
+                            if (el) el.indeterminate = someInGroupSelected;
+                          }}
+                          onChange={() => toggleGroup(group.name)}
+                          className="rounded"
+                        />
+                        <span className="text-xs font-semibold text-warm-700">{group.name}</span>
+                      </label>
+                      <span className="text-xs text-warm-400">
+                        {groupSelected}/{group.categories.length}
+                      </span>
+                    </div>
+                    {!isCollapsed &&
+                      group.categories.map((category) => (
+                        <label
+                          key={category}
+                          className="flex items-center gap-2 px-3 pl-8 py-1.5 hover:bg-warm-50 cursor-pointer"
+                        >
+                          <input
+                            type="checkbox"
+                            checked={selectedCategories.has(category)}
+                            onChange={() => toggleCategory(category)}
+                            className="rounded"
+                          />
+                          <span className="text-sm flex-1">{category}</span>
+                        </label>
+                      ))}
+                  </div>
+                );
+              })}
             </div>
           </div>
         )}
diff --git a/frontend/src/components/PropertiesPane.tsx b/frontend/src/components/PropertiesPane.tsx
index f7718ac..e2f3da7 100644
--- a/frontend/src/components/PropertiesPane.tsx
+++ b/frontend/src/components/PropertiesPane.tsx
@@ -6,7 +6,6 @@ interface PropertiesPaneProps {
   total: number;
   loading: boolean;
   hexagonId: string | null;
-  postcodeId?: string | null;
   onLoadMore: () => void;
   onClose: () => void;
 }
@@ -18,7 +17,6 @@ export function PropertiesPane({
   total,
   loading,
   hexagonId,
-  postcodeId,
   onLoadMore,
   onClose,
 }: PropertiesPaneProps) {
@@ -38,11 +36,10 @@ export function PropertiesPane({
     });
   }, [properties, sortBy]);
 
-  const selectionId = hexagonId || postcodeId;
-  if (!selectionId) {
+  if (!hexagonId) {
     return (
       <div className="flex items-center justify-center h-full text-warm-500">
-        Click a hexagon or postcode to view properties
+        Click a hexagon to view properties
       </div>
     );
   }
@@ -52,9 +49,7 @@ export function PropertiesPane({
       {/* Header */}
       <div className="p-4 border-b border-warm-200">
         <div className="flex justify-between items-center">
-          <h2 className="text-lg font-semibold">
-            {postcodeId ? `Properties in ${postcodeId}` : 'Properties in Hexagon'}
-          </h2>
+          <h2 className="text-lg font-semibold">Properties in Hexagon</h2>
           <button
             onClick={onClose}
             className="text-warm-500 hover:text-warm-700 text-2xl leading-none"
@@ -111,9 +106,8 @@ function formatDuration(d: string): string {
   return d;
 }
 
-function formatAge(value: number): string {
-  // construction_age_band is a midpoint year, e.g. 1935
-  if (value >= 1000) return `~${Math.round(value)}`;
+function formatAge(value: number, approximate = true): string {
+  if (value >= 1000) return approximate ? `~${Math.round(value)}` : `${Math.round(value)}`;
   return Math.round(value).toString();
 }
 
@@ -136,7 +130,11 @@ function PropertyCard({ property }: { property: Property }) {
   const price = getNum(property, 'Last known price', 'latest_price');
   const pricePerSqm = getNum(property, 'Price per sqm', 'price_per_sqm');
   const floorArea = getNum(property, 'Total floor area (sqm)', 'total_floor_area');
-  const rooms = getNum(property, 'Rooms (including bedrooms & bathrooms)', 'number_habitable_rooms');
+  const rooms = getNum(
+    property,
+    'Rooms (including bedrooms & bathrooms)',
+    'number_habitable_rooms'
+  );
   const age = getNum(property, 'Approximate construction age', 'construction_age_band');
 
   return (
@@ -150,10 +148,7 @@ function PropertyCard({ property }: { property: Property }) {
         <div className="mt-2 text-lg font-bold text-teal-700">
           £{fmt(price)}
           {pricePerSqm !== undefined && (
-            <span className="text-sm font-normal text-warm-600">
-              {' '}
-              (£{fmt(pricePerSqm)}/m²)
-            </span>
+            <span className="text-sm font-normal text-warm-600"> (£{fmt(pricePerSqm)}/m²)</span>
           )}
         </div>
       )}
@@ -187,7 +182,7 @@ function PropertyCard({ property }: { property: Property }) {
         )}
         {age !== undefined && (
           <div>
-            <span className="text-warm-500">Built:</span> {formatAge(age)}
+            <span className="text-warm-500">Built:</span> {formatAge(age, property.is_construction_date_approximate ?? true)}
           </div>
         )}
         {property.current_energy_rating && (
@@ -197,8 +192,7 @@ function PropertyCard({ property }: { property: Property }) {
         )}
         {property.potential_energy_rating && (
           <div>
-            <span className="text-warm-500">EPC potential:</span>{' '}
-            {property.potential_energy_rating}
+            <span className="text-warm-500">EPC potential:</span> {property.potential_energy_rating}
           </div>
         )}
       </div>
diff --git a/frontend/src/index.css b/frontend/src/index.css
index 9be6aa7..2e41d70 100644
--- a/frontend/src/index.css
+++ b/frontend/src/index.css
@@ -9,3 +9,17 @@ body,
   margin: 0;
   padding: 0;
 }
+
+/* Fade-in animation for homepage sections */
+.fade-in-section {
+  opacity: 0;
+  transform: translateY(24px);
+  transition:
+    opacity 0.6s ease-out,
+    transform 0.6s ease-out;
+}
+
+.fade-in-visible {
+  opacity: 1;
+  transform: translateY(0);
+}
diff --git a/frontend/src/index.html b/frontend/src/index.html
index 4db1612..514767e 100644
--- a/frontend/src/index.html
+++ b/frontend/src/index.html
@@ -3,7 +3,7 @@
   <head>
     <meta charset="UTF-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>UK Property Prices Map</title>
+    <title>Narrowit</title>
   </head>
   <body>
     <div id="root"></div>
diff --git a/frontend/src/types.ts b/frontend/src/types.ts
index 81b7e61..3c901c9 100644
--- a/frontend/src/types.ts
+++ b/frontend/src/types.ts
@@ -1,7 +1,7 @@
 export interface FeatureMeta {
   name: string;
-  label: string;
   type: 'numeric' | 'enum';
+  group?: string;
   // Numeric-only fields
   min?: number;
   max?: number;
@@ -9,6 +9,11 @@ export interface FeatureMeta {
   values?: string[];
 }
 
+export interface FeatureGroup {
+  name: string;
+  features: FeatureMeta[];
+}
+
 // Filters: feature name -> [selectedMin, selectedMax] for numeric, string[] for enum
 export type FeatureFilters = Record<string, [number, number] | string[]>;
 
@@ -49,6 +54,7 @@ export interface POI {
   id: string;
   name: string;
   category: string;
+  group: string;
   lat: number;
   lng: number;
   emoji: string;
@@ -58,10 +64,15 @@ export interface POIResponse {
   pois: POI[];
 }
 
-export interface POICategoriesResponse {
+export interface POICategoryGroup {
+  name: string;
   categories: string[];
 }
 
+export interface POICategoriesResponse {
+  groups: POICategoryGroup[];
+}
+
 export interface Property {
   // String fields
   address?: string;
@@ -76,8 +87,10 @@ export interface Property {
   lat: number;
   lon: number;
 
+  is_construction_date_approximate?: boolean;
+
   // All other numeric features (dynamic, including construction_age_band)
-  [key: string]: string | number | undefined;
+  [key: string]: string | number | boolean | undefined;
 }
 
 export interface HexagonPropertiesResponse {

From a45495269e7f875528bf4d2c92212e984352a88a Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sun, 1 Feb 2026 11:08:49 +0000
Subject: [PATCH 52/62] Add noise data

---
 pipeline/download/noise.py | 279 +++++++++++++++++++++++++++++++++++++
 1 file changed, 279 insertions(+)
 create mode 100644 pipeline/download/noise.py

diff --git a/pipeline/download/noise.py b/pipeline/download/noise.py
new file mode 100644
index 0000000..ba91ff7
--- /dev/null
+++ b/pipeline/download/noise.py
@@ -0,0 +1,279 @@
+"""Download Defra Round 4 (2022) strategic noise data for England.
+
+Downloads modelled noise levels (road, rail, airport) as GeoTIFF rasters via
+WCS, then samples noise values at postcode centroids. Outputs a parquet file
+with postcode-level noise in dB for each source.
+
+Uses 100km tiles (~42 per source) to balance request size vs count. The server
+times out on tiles larger than ~150km at 100m resolution.
+
+Data source: Defra Strategic Noise Mapping Round 4 (2022)
+  - Lden = day-evening-night 24h weighted average (the EU standard metric)
+  - 10m grid, modelled at 4m above ground
+License: Open Government Licence v3.0
+
+Note: Road/rail use WCS 1.0.0; airport requires WCS 2.0.1 (Defra's 1.0.0
+endpoint is broken for that coverage).
+"""
+
+import argparse
+import tempfile
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from pathlib import Path
+
+import httpx
+import numpy as np
+import polars as pl
+import rasterio
+from pyproj import Transformer
+from rasterio.merge import merge
+from rasterio.transform import rowcol
+
+# Noise sources: (label, column_name, WCS base URL, coverage ID, WCS version)
+# Road/rail work with WCS 1.0.0; airport requires WCS 2.0.1.
+NOISE_SOURCES = [
+    (
+        "Road",
+        "road_noise_lden_db",
+        "https://environment.data.gov.uk/spatialdata/road-noise-all-metrics-england-round-4/wcs",
+        "Road_Noise_Lden_England_Round_4_All",
+        "1.0.0",
+    ),
+    (
+        "Rail",
+        "rail_noise_lden_db",
+        "https://environment.data.gov.uk/spatialdata/noise-data/wcs",
+        "Rail_Noise_Lden_England_Round_4_All",
+        "1.0.0",
+    ),
+    (
+        "Airport",
+        "airport_noise_lden_db",
+        "https://environment.data.gov.uk/spatialdata/airport-noise-all-metrics-england-round-4/wcs",
+        "dac9cba4-abe7-43bd-b8e9-8a83da52edd8__Airport_Noise_ALL_Lden",
+        "2.0.1",
+    ),
+]
+
+# England extent in EPSG:27700 (British National Grid), rounded outward
+BNG_MIN_E = 80_000
+BNG_MAX_E = 660_000
+BNG_MIN_N = 0
+BNG_MAX_N = 660_000
+
+# Tile size in metres (100km balances request size vs count; 300km causes 504s)
+TILE_SIZE = 100_000
+
+# Max concurrent tile downloads
+MAX_WORKERS = 4
+
+# Native raster resolution (10m grid)
+NATIVE_RESOLUTION = 10
+
+# Request pixel resolution in metres (100m is sufficient for postcode-level data
+# and keeps download size ~100x smaller than native 10m)
+RESOLUTION = 100
+
+
+def _wcs_get_coverage_url(
+    wcs_base: str,
+    coverage_id: str,
+    min_e: int,
+    min_n: int,
+    max_e: int,
+    max_n: int,
+    wcs_version: str = "1.0.0",
+) -> str:
+    """Build a WCS GetCoverage URL for a BNG bounding box."""
+    if wcs_version == "2.0.1":
+        return (
+            f"{wcs_base}?"
+            f"service=WCS&version=2.0.1&request=GetCoverage"
+            f"&coverageId={coverage_id}"
+            f"&format=image/tiff"
+            f"&subsettingCRS=EPSG:27700"
+            f"&subset=E({min_e},{max_e})"
+            f"&subset=N({min_n},{max_n})"
+            f"&scaleFactor={NATIVE_RESOLUTION / RESOLUTION}"
+        )
+    width = (max_e - min_e) // RESOLUTION
+    height = (max_n - min_n) // RESOLUTION
+    return (
+        f"{wcs_base}?"
+        f"service=WCS&version=1.0.0&request=GetCoverage"
+        f"&coverage={coverage_id}"
+        f"&CRS=EPSG:27700"
+        f"&BBOX={min_e},{min_n},{max_e},{max_n}"
+        f"&width={width}&height={height}"
+        f"&format=GeoTIFF"
+    )
+
+
+_TO_BNG = Transformer.from_crs("EPSG:4326", "EPSG:27700", always_xy=True)
+
+
+def _bng_from_latlon(lat: np.ndarray, lon: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+    """Convert WGS84 lat/lon to British National Grid easting/northing."""
+    return _TO_BNG.transform(lon, lat)  # pyproj takes (x=lon, y=lat)
+
+
+def _download_tile(
+    wcs_base: str,
+    coverage_id: str,
+    min_e: int,
+    min_n: int,
+    max_e: int,
+    max_n: int,
+    tile_path: Path,
+    wcs_version: str = "1.0.0",
+) -> Path | None:
+    """Download a single WCS tile. Returns path if successful, None otherwise."""
+    url = _wcs_get_coverage_url(wcs_base, coverage_id, min_e, min_n, max_e, max_n, wcs_version)
+    try:
+        with httpx.Client(timeout=300, follow_redirects=True) as client:
+            resp = client.get(url)
+            resp.raise_for_status()
+
+        content_type = resp.headers.get("content-type", "")
+        if "tiff" not in content_type and resp.content[:4] not in (b"II*\x00", b"MM\x00*"):
+            return None
+
+        tile_path.write_bytes(resp.content)
+        return tile_path
+    except (httpx.HTTPStatusError, httpx.TimeoutException, httpx.ConnectError) as e:
+        print(f"  Failed to download tile ({min_e},{min_n})-({max_e},{max_n}): {e}")
+        return None
+
+
+def download_raster(
+    tile_dir: Path, wcs_base: str, coverage_id: str, label: str, wcs_version: str = "1.0.0"
+) -> list[Path]:
+    """Download noise GeoTIFF raster covering England, returning paths to saved files."""
+    tiles = []
+    for min_e in range(BNG_MIN_E, BNG_MAX_E, TILE_SIZE):
+        for min_n in range(BNG_MIN_N, BNG_MAX_N, TILE_SIZE):
+            max_e = min(min_e + TILE_SIZE, BNG_MAX_E)
+            max_n = min(min_n + TILE_SIZE, BNG_MAX_N)
+            tiles.append((min_e, min_n, max_e, max_n))
+
+    print(f"[{label}] Downloading {len(tiles)} tiles at {RESOLUTION}m resolution ({MAX_WORKERS} workers)...")
+    paths = []
+    completed = 0
+
+    with ThreadPoolExecutor(max_workers=MAX_WORKERS) as executor:
+        futures = {}
+        for min_e, min_n, max_e, max_n in tiles:
+            tile_path = tile_dir / f"tile_{min_e}_{min_n}.tif"
+            fut = executor.submit(
+                _download_tile, wcs_base, coverage_id,
+                min_e, min_n, max_e, max_n, tile_path, wcs_version,
+            )
+            futures[fut] = (min_e, min_n)
+
+        for fut in as_completed(futures):
+            completed += 1
+            result = fut.result()
+            if result is not None:
+                paths.append(result)
+            print(
+                f"\r  [{completed}/{len(tiles)}] Downloaded {len(paths)} valid tiles",
+                end="",
+                flush=True,
+            )
+
+    print(f"\n[{label}] Downloaded {len(paths)}/{len(tiles)} tiles")
+    return paths
+
+
+def sample_noise_at_postcodes(
+    tile_paths: list[Path],
+    easting: np.ndarray,
+    northing: np.ndarray,
+    label: str,
+) -> np.ndarray:
+    """Sample noise values from merged tiles at given BNG coordinates."""
+    print(f"[{label}] Merging {len(tile_paths)} tiles...")
+    datasets = [rasterio.open(p) for p in tile_paths]
+    raster_nodata = datasets[0].nodata
+    mosaic, mosaic_transform = merge(datasets)
+    for ds in datasets:
+        ds.close()
+
+    noise_grid = mosaic[0]
+
+    print(f"[{label}] Sampling noise values at postcode centroids...")
+    rows, cols = rowcol(mosaic_transform, easting, northing)
+    rows = np.asarray(rows)
+    cols = np.asarray(cols)
+
+    h, w = noise_grid.shape
+    in_bounds = (rows >= 0) & (rows < h) & (cols >= 0) & (cols < w)
+
+    noise_db = np.full(len(easting), np.nan, dtype=np.float32)
+    valid_rows = rows[in_bounds]
+    valid_cols = cols[in_bounds]
+    sampled = noise_grid[valid_rows, valid_cols].astype(np.float32)
+
+    # Mark nodata and zero (unmapped areas) as NaN.
+    # Road/rail use nodata=-96, airport uses nodata=3.4e38.
+    if raster_nodata is not None:
+        sampled[np.isclose(sampled, np.float32(raster_nodata), rtol=1e-5)] = np.nan
+    sampled[sampled == 0] = np.nan
+    noise_db[in_bounds] = sampled
+
+    valid_count = int(np.sum(~np.isnan(noise_db)))
+    print(f"[{label}] Sampled {valid_count:,} / {len(easting):,} postcodes with noise data")
+    return noise_db
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Download Defra noise data (road, rail, airport) and sample at postcode centroids"
+    )
+    parser.add_argument(
+        "--arcgis",
+        type=Path,
+        required=True,
+        help="ArcGIS postcode data parquet (for lat/lon coordinates)",
+    )
+    parser.add_argument(
+        "--output", type=Path, required=True, help="Output parquet file path"
+    )
+    args = parser.parse_args()
+
+    args.output.parent.mkdir(parents=True, exist_ok=True)
+
+    print("Loading postcode coordinates...")
+    postcodes = pl.read_parquet(
+        args.arcgis, columns=["pcds", "lat", "long"]
+    ).rename({"pcds": "postcode", "long": "lon"})
+
+    lat = postcodes["lat"].to_numpy()
+    lon = postcodes["lon"].to_numpy()
+
+    print("Converting lat/lon to BNG...")
+    easting, northing = _bng_from_latlon(lat, lon)
+
+    result = postcodes.select("postcode")
+
+    with tempfile.TemporaryDirectory() as tmp:
+        for label, col_name, wcs_base, coverage_id, wcs_version in NOISE_SOURCES:
+            tile_dir = Path(tmp) / label.lower()
+            tile_dir.mkdir()
+            tile_paths = download_raster(tile_dir, wcs_base, coverage_id, label, wcs_version)
+
+            if not tile_paths:
+                print(f"[{label}] WARNING: No tiles downloaded — column will be all null")
+                noise_db = np.full(len(lat), np.nan, dtype=np.float32)
+            else:
+                noise_db = sample_noise_at_postcodes(tile_paths, easting, northing, label)
+
+            result = result.with_columns(pl.Series(col_name, noise_db))
+
+    result.write_parquet(args.output, compression="zstd")
+    size_mb = args.output.stat().st_size / (1024 * 1024)
+    print(f"Wrote {args.output} ({size_mb:.1f} MB)")
+
+
+if __name__ == "__main__":
+    main()

From 8e615c6aad5a67833f77402af654dad8655ed4c8 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sun, 1 Feb 2026 11:09:01 +0000
Subject: [PATCH 53/62] Add POI groups

---
 pipeline/transform/transform_poi.py | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/pipeline/transform/transform_poi.py b/pipeline/transform/transform_poi.py
index d44b877..8482142 100644
--- a/pipeline/transform/transform_poi.py
+++ b/pipeline/transform/transform_poi.py
@@ -624,13 +624,20 @@ def transform(input_path: Path, naptan_path: Path | None = None) -> pl.LazyFrame
     if missing_emojis:
         raise ValueError(f"Empty emojis for: {missing_emojis}")
 
+    # Derive group from the first component of the raw category key, title-cased
+    group_mapping = {
+        k: k.split("/")[0].replace("_", " ").title() for k in CATEGORY_MAP
+    }
+
     lf = lf.with_columns(
+        pl.col("category").replace_strict(group_mapping).alias("group"),
         pl.col("category").replace_strict(name_mapping).alias("category"),
         pl.col("category").replace_strict(emoji_mapping).alias("emoji"),
     )
 
     naptan = pl.scan_parquet(naptan_path).with_columns(
         pl.col("category").replace_strict(NAPTAN_EMOJIS).alias("emoji"),
+        pl.lit("Public Transport").alias("group"),
     )
     return pl.concat([lf, naptan], how="diagonal_relaxed")
 

From 5e210e14bd9acb02f89f9f0971171d8fb398724a Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sun, 1 Feb 2026 13:05:00 +0000
Subject: [PATCH 54/62] Break up taskfile

---
 Taskfile.data.yml | 191 ++++++++++++++++++++++++++++++++++++++++++++++
 Taskfile.yml      | 124 +++++-------------------------
 2 files changed, 211 insertions(+), 104 deletions(-)
 create mode 100644 Taskfile.data.yml

diff --git a/Taskfile.data.yml b/Taskfile.data.yml
new file mode 100644
index 0000000..5907545
--- /dev/null
+++ b/Taskfile.data.yml
@@ -0,0 +1,191 @@
+version: '3'
+
+vars:
+  DATA_DIR: /bulk/property-data
+  ARCGIS_OUTPUT: "{{.DATA_DIR}}/arcgis_data.parquet"
+  PRICE_PAID_OUTPUT: "{{.DATA_DIR}}/price-paid-complete.parquet"
+  IOD_OUTPUT: "{{.DATA_DIR}}/IoD2025_Scores.parquet"
+  POIS_RAW_OUTPUT: "{{.DATA_DIR}}/uk_pois.parquet"
+  POIS_FILTERED_OUTPUT: "{{.DATA_DIR}}/filtered_uk_pois.parquet"
+  POI_PROXIMITY_OUTPUT: "{{.DATA_DIR}}/poi_proximity.parquet"
+  EPC_PP_OUTPUT: "{{.DATA_DIR}}/epc_pp.parquet"
+  WIDE_OUTPUT: "{{.DATA_DIR}}/wide.parquet"
+  EPC: "{{.DATA_DIR}}/certificates.csv"
+  JOURNEY_TIMES: "./data_sources/processed/journey_times_bank_checkpoint.parquet"
+  ETHNICITY_OUTPUT: "{{.DATA_DIR}}/ethnicity_by_la.parquet"
+  CRIME_DIR: "{{.DATA_DIR}}/crime"
+  CRIME_OUTPUT: "{{.DATA_DIR}}/crime_by_lsoa.parquet"
+  NOISE_OUTPUT: "{{.DATA_DIR}}/road_noise.parquet"
+  OFSTED_OUTPUT: "{{.DATA_DIR}}/ofsted.parquet"
+  NAPTAN_OUTPUT: "{{.DATA_DIR}}/naptan.parquet"
+  BROADBAND_OUTPUT: "{{.DATA_DIR}}/broadband.parquet"
+  SCHOOL_PROXIMITY_OUTPUT: "{{.DATA_DIR}}/school_proximity.parquet"
+
+tasks:
+  prompt:epc:
+    desc: Prompt user to download EPC dataset (requires registration)
+    status:
+      - test -f {{.EPC}}
+    cmds:
+      - |
+        echo ""
+        echo "=== EPC dataset not found ==="
+        echo "The EPC certificates file is required: {{.EPC}}"
+        echo ""
+        echo "To obtain it, register at https://epc.opendatacommunities.org/login"
+        echo ""
+        exit 1
+
+  prompt:journey-times:
+    desc: Download TFL journey times if missing (requires API key registration)
+    status:
+      - test -f {{.JOURNEY_TIMES}}
+    deps:
+      - download:arcgis
+    cmds:
+      - |
+        echo ""
+        echo "=== TFL journey times not found ==="
+        echo "Register for a TFL API key at https://api-portal.tfl.gov.uk/signin"
+        echo "Then set the TFL_API_KEY environment variable and re-run this task."
+        echo ""
+        exit 1
+
+  download:arcgis:
+    desc: Download and convert ArcGIS postcode data
+    status:
+      - test -f {{.ARCGIS_OUTPUT}}
+    cmds:
+      - uv run python -m pipeline.download.arcgis --output {{.ARCGIS_OUTPUT}}
+
+  download:price-paid:
+    desc: Download and convert Land Registry price-paid data
+    status:
+      - test -f {{.PRICE_PAID_OUTPUT}}
+    cmds:
+      - uv run python -m pipeline.download.price_paid --output {{.PRICE_PAID_OUTPUT}}
+
+  download:deprivation:
+    desc: Download and convert Index of Deprivation data
+    status:
+      - test -f {{.IOD_OUTPUT}}
+    cmds:
+      - uv run python -m pipeline.download.deprivation_data --output {{.IOD_OUTPUT}}
+
+  download:ethnicity:
+    desc: Download ethnicity by local authority data
+    status:
+      - test -f {{.ETHNICITY_OUTPUT}}
+    cmds:
+      - uv run python -m pipeline.download.ethnicity --output {{.ETHNICITY_OUTPUT}}
+
+  download:naptan:
+    desc: Download NaPTAN station data
+    status:
+      - test -f {{.NAPTAN_OUTPUT}}
+    cmds:
+      - uv run python -m pipeline.download.naptan --output {{.NAPTAN_OUTPUT}}
+
+  download:pois:
+    desc: Download and extract POIs from OpenStreetMap
+    status:
+      - test -f {{.POIS_RAW_OUTPUT}}
+    cmds:
+      - uv run python -m pipeline.download.pois --output {{.POIS_RAW_OUTPUT}}
+
+  download:ofsted:
+    desc: Download Ofsted school inspection outcomes
+    status:
+      - test -f {{.OFSTED_OUTPUT}}
+    cmds:
+      - uv run python -m pipeline.download.ofsted --output {{.OFSTED_OUTPUT}}
+
+  download:broadband:
+    desc: Download Ofcom broadband performance data
+    status:
+      - test -f {{.BROADBAND_OUTPUT}}
+    cmds:
+      - uv run python -m pipeline.download.broadband --output {{.BROADBAND_OUTPUT}}
+
+  download:noise:
+    desc: Download Defra noise data (road, rail, airport) sampled at postcode centroids
+    deps:
+      - download:arcgis
+    status:
+      - test -f {{.NOISE_OUTPUT}}
+    cmds:
+      - uv run python -m pipeline.download.noise --arcgis {{.ARCGIS_OUTPUT}} --output {{.NOISE_OUTPUT}}
+
+  transform:pois:
+    desc: Transform raw POIs to filtered version with friendly names
+    deps:
+      - download:pois
+      - download:naptan
+    status:
+      - test -f {{.POIS_FILTERED_OUTPUT}}
+    cmds:
+      - uv run python -m pipeline.transform.transform_poi --input {{.POIS_RAW_OUTPUT}} --naptan {{.NAPTAN_OUTPUT}} --output {{.POIS_FILTERED_OUTPUT}}
+
+  transform:epc-pp:
+    desc: Fuzzy join EPC and Price Paid data
+    deps:
+      - download:price-paid
+      - prompt:epc
+    status:
+      - test -f {{.EPC_PP_OUTPUT}}
+    cmds:
+      - uv run python -m pipeline.transform.join_epc_pp --epc {{.EPC}} --price-paid {{.PRICE_PAID_OUTPUT}} --output {{.EPC_PP_OUTPUT}}
+
+  transform:crime:
+    desc: Transform crime CSVs into yearly averages by LSOA
+    status:
+      - test -f {{.CRIME_OUTPUT}}
+    cmds:
+      - uv run python -m pipeline.transform.crime --input {{.CRIME_DIR}} --output {{.CRIME_OUTPUT}}
+
+  transform:poi-proximity:
+    desc: Compute POI proximity counts per postcode
+    deps:
+      - download:arcgis
+      - transform:pois
+    status:
+      - test -f {{.POI_PROXIMITY_OUTPUT}}
+    cmds:
+      - uv run python -m pipeline.transform.poi_proximity --arcgis {{.ARCGIS_OUTPUT}} --pois {{.POIS_FILTERED_OUTPUT}} --output {{.POI_PROXIMITY_OUTPUT}}
+
+  transform:school-proximity:
+    desc: Compute good+ school proximity counts per postcode
+    deps:
+      - download:ofsted
+      - download:arcgis
+    status:
+      - test -f {{.SCHOOL_PROXIMITY_OUTPUT}}
+    cmds:
+      - uv run python -m pipeline.transform.school_proximity --ofsted {{.OFSTED_OUTPUT}} --arcgis {{.ARCGIS_OUTPUT}} --output {{.SCHOOL_PROXIMITY_OUTPUT}}
+
+  download:journey-times:
+    desc: Fetch TfL journey times for all postcodes
+    deps:
+      - download:arcgis
+    status:
+      - test -f {{.JOURNEY_TIMES}}
+    cmds:
+      - uv run python -m pipeline.journey_times
+
+  prepare:
+    desc: Build wide property dataframe with all joins
+    deps:
+      - transform:epc-pp
+      - download:arcgis
+      - download:deprivation
+      - download:ethnicity
+      - download:broadband
+      - download:noise
+      - transform:crime
+      - transform:poi-proximity
+      - transform:school-proximity
+      - prompt:journey-times
+    status:
+      - test -f {{.WIDE_OUTPUT}}
+    cmds:
+      - uv run python -m pipeline.transform.merge --epc-pp {{.EPC_PP_OUTPUT}} --arcgis {{.ARCGIS_OUTPUT}} --iod {{.IOD_OUTPUT}} --poi-proximity {{.POI_PROXIMITY_OUTPUT}} --journey-times {{.JOURNEY_TIMES}} --ethnicity {{.ETHNICITY_OUTPUT}} --crime {{.CRIME_OUTPUT}} --noise {{.NOISE_OUTPUT}} --school-proximity {{.SCHOOL_PROXIMITY_OUTPUT}} --broadband {{.BROADBAND_OUTPUT}} --output {{.WIDE_OUTPUT}}
diff --git a/Taskfile.yml b/Taskfile.yml
index b6334c6..498cd36 100644
--- a/Taskfile.yml
+++ b/Taskfile.yml
@@ -1,17 +1,14 @@
 version: '3'
 
+includes:
+  data:
+    taskfile: ./Taskfile.data.yml
+    flatten: true
+
 vars:
-  DATA_DIR: data
-  ARCGIS_OUTPUT: "{{.DATA_DIR}}/arcgis_data.parquet"
-  PRICE_PAID_OUTPUT: "{{.DATA_DIR}}/price-paid-complete.parquet"
-  IOD_OUTPUT: "{{.DATA_DIR}}/IoD2025_Scores.parquet"
-  POIS_RAW_OUTPUT: "{{.DATA_DIR}}/uk_pois.parquet"
-  POIS_FILTERED_OUTPUT: "{{.DATA_DIR}}/filtered_uk_pois.parquet"
-  POI_PROXIMITY_OUTPUT: "{{.DATA_DIR}}/poi_proximity.parquet"
-  EPC_PP_OUTPUT: "{{.DATA_DIR}}/epc_pp.parquet"
+  DATA_DIR: /bulk/property-data
   WIDE_OUTPUT: "{{.DATA_DIR}}/wide.parquet"
-  EPC_CSV: "{{.DATA_DIR}}/epc/certificates.csv"
-  JOURNEY_TIMES: "{{.DATA_DIR}}/journey_times_bank_checkpoint.parquet"
+  POIS_FILTERED_OUTPUT: "{{.DATA_DIR}}/filtered_uk_pois.parquet"
 
 tasks:
   install:
@@ -20,94 +17,6 @@ tasks:
       - uv sync
       - cd frontend && npm install
 
-  download:arcgis:
-    internal: true
-    desc: Download and convert ArcGIS postcode data
-    generates:
-      - "{{.ARCGIS_OUTPUT}}"
-    cmds:
-      - uv run python -m pipeline.download.arcgis --output {{.ARCGIS_OUTPUT}}
-
-  download:price-paid:
-    internal: true
-    desc: Download and convert Land Registry price-paid data
-    generates:
-      - "{{.PRICE_PAID_OUTPUT}}"
-    cmds:
-      - uv run python -m pipeline.download.price_paid --output {{.PRICE_PAID_OUTPUT}}
-
-  download:deprivation:
-    internal: true
-    desc: Download and convert Index of Deprivation data
-    generates:
-      - "{{.IOD_OUTPUT}}"
-    cmds:
-      - uv run python -m pipeline.download.deprivation_data --output {{.IOD_OUTPUT}}
-
-  download:pois:
-    internal: true
-    desc: Download and extract POIs from OpenStreetMap
-    generates:
-      - "{{.POIS_RAW_OUTPUT}}"
-    cmds:
-      - uv run python -m pipeline.download.pois --output {{.POIS_RAW_OUTPUT}}
-
-  transform:pois:
-    internal: true
-    desc: Transform raw POIs to filtered version with friendly names
-    deps:
-      - download:pois
-    sources:
-      - "{{.POIS_RAW_OUTPUT}}"
-    generates:
-      - "{{.POIS_FILTERED_OUTPUT}}"
-    cmds:
-      - uv run python -m pipeline.transform.transform_poi --input {{.POIS_RAW_OUTPUT}} --output {{.POIS_FILTERED_OUTPUT}}
-
-  transform:epc-pp:
-    internal: true
-    desc: Fuzzy join EPC and Price Paid data
-    deps:
-      - download:price-paid
-    sources:
-      - "{{.PRICE_PAID_OUTPUT}}"
-      - "{{.EPC_CSV}}"
-    generates:
-      - "{{.EPC_PP_OUTPUT}}"
-    cmds:
-      - uv run python -m pipeline.transform.join_epc_pp --epc {{.EPC_CSV}} --price-paid {{.PRICE_PAID_OUTPUT}} --output {{.EPC_PP_OUTPUT}}
-
-  transform:poi-proximity:
-    internal: true
-    desc: Compute POI proximity counts per postcode
-    deps:
-      - download:arcgis
-      - transform:pois
-    sources:
-      - "{{.ARCGIS_OUTPUT}}"
-      - "{{.POIS_FILTERED_OUTPUT}}"
-    generates:
-      - "{{.POI_PROXIMITY_OUTPUT}}"
-    cmds:
-      - uv run python -m pipeline.transform.poi_proximity --arcgis {{.ARCGIS_OUTPUT}} --pois {{.POIS_FILTERED_OUTPUT}} --output {{.POI_PROXIMITY_OUTPUT}}
-
-  prepare:
-    desc: Build wide property dataframe with all joins
-    deps:
-      - join:epc-pp
-      - download:arcgis
-      - download:deprivation
-      - transform:poi-proximity
-    sources:
-      - "{{.EPC_PP_OUTPUT}}"
-      - "{{.ARCGIS_OUTPUT}}"
-      - "{{.IOD_OUTPUT}}"
-      - "{{.POI_PROXIMITY_OUTPUT}}"
-    generates:
-      - "{{.WIDE_OUTPUT}}"
-    cmds:
-      - uv run python -m pipeline.transform.merge --epc-pp {{.EPC_PP_OUTPUT}} --arcgis {{.ARCGIS_OUTPUT}} --iod {{.IOD_OUTPUT}} --poi-proximity {{.POI_PROXIMITY_OUTPUT}} --journey-times {{.JOURNEY_TIMES}} --output {{.WIDE_OUTPUT}}
-
   test:
     cmds:
       - uv run -m pipeline.utils.test_fuzzy_join
@@ -115,10 +24,16 @@ tasks:
       - uv run pytest pipeline/utils/test_poi_counts.py
 
   dev:server:
-    desc: Run Rust backend on port 8001
+    desc: Run Rust backend on port 8001 (debug build, fast compile)
     dir: server-rs
     cmds:
-      - cargo run --release -- {{.WIDE_OUTPUT}}
+      - cargo run -- --data {{.WIDE_OUTPUT}} --pois {{.POIS_FILTERED_OUTPUT}}
+
+  dev:server:release:
+    desc: Run Rust backend on port 8001 (release build)
+    dir: server-rs
+    cmds:
+      - cargo run --release -- --data {{.WIDE_OUTPUT}} --pois {{.POIS_FILTERED_OUTPUT}}
 
   dev:frontend:
     desc: Run frontend dev server on port 3030 (proxies /api to :8001)
@@ -128,7 +43,7 @@ tasks:
 
   build:server:
     desc: Build server for production
-    dir: frontend
+    dir: server-rs
     cmds:
       - cargo build --release
 
@@ -147,9 +62,10 @@ tasks:
       - task: lint:rust
 
   lint:python:
-    desc: Lint Python code with ruff
+    desc: Lint Python code with ruff and check for unused dependencies
     cmds:
       - uv run ruff check .
+      - uv run deptry .
 
   lint:frontend:
     desc: Lint frontend TypeScript code
@@ -159,11 +75,12 @@ tasks:
       - npm run format:check
 
   lint:rust:
-    desc: Lint Rust code with clippy and check formatting
+    desc: Lint Rust code with clippy, check formatting, and detect unused dependencies
     dir: server-rs
     cmds:
       - cargo clippy -- -D warnings
       - cargo fmt --check
+      - cargo machete
 
   format:
     desc: Format all code (Python, TypeScript, and Rust)
@@ -198,4 +115,3 @@ tasks:
       - task: build:server
       - task: build:frontend
       - task: test
-

From 7235df0a97d753aed5cac9e3ec6a77f92af2661b Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sun, 1 Feb 2026 13:07:24 +0000
Subject: [PATCH 55/62] Add dark mode

---
 frontend/src/App.tsx                        | 68 +++++++++++++++---
 frontend/src/components/DataSources.tsx     |  2 +-
 frontend/src/components/DataSourcesPage.tsx | 18 ++---
 frontend/src/components/Filters.tsx         | 28 ++++----
 frontend/src/components/HomePage.tsx        | 53 +++++++-------
 frontend/src/components/Map.tsx             | 71 ++++++++++++-------
 frontend/src/components/POIPane.tsx         | 38 +++++-----
 frontend/src/components/PropertiesPane.tsx  | 78 +++++++++++++--------
 frontend/src/components/ui/label.tsx        |  2 +-
 frontend/src/components/ui/slider.tsx       |  4 +-
 frontend/src/index.css                      | 24 +++++++
 frontend/src/index.html                     |  7 ++
 frontend/src/lib/constants.ts               |  1 -
 frontend/tailwind.config.js                 | 49 ++++++++++++-
 14 files changed, 304 insertions(+), 139 deletions(-)
 delete mode 100644 frontend/src/lib/constants.ts

diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx
index ce7963c..24fd6fe 100644
--- a/frontend/src/App.tsx
+++ b/frontend/src/App.tsx
@@ -23,6 +23,8 @@ import type {
   HexagonPropertiesResponse,
 } from './types';
 
+type Theme = 'light' | 'dark';
+
 const DEBOUNCE_MS = 150;
 const URL_DEBOUNCE_MS = 300;
 
@@ -180,9 +182,13 @@ type Page = 'home' | 'dashboard' | 'data-sources';
 function Header({
   activePage,
   onPageChange,
+  theme,
+  onToggleTheme,
 }: {
   activePage: Page;
   onPageChange: (page: Page) => void;
+  theme: Theme;
+  onToggleTheme: () => void;
 }) {
   const [copied, setCopied] = useState(false);
 
@@ -240,7 +246,23 @@ function Header({
           </button>
         </nav>
       </div>
-      {activePage === 'dashboard' && (
+      <div className="flex items-center gap-2">
+        <button
+          onClick={onToggleTheme}
+          className="flex items-center justify-center w-8 h-8 rounded bg-navy-800 hover:bg-navy-700 transition-colors"
+          title={`Theme: ${theme}`}
+        >
+          {theme === 'light' ? (
+            <svg className="w-4 h-4" fill="none" stroke="currentColor" viewBox="0 0 24 24" strokeWidth={2}>
+              <path strokeLinecap="round" strokeLinejoin="round" d="M12 3v1m0 16v1m9-9h-1M4 12H3m15.364 6.364l-.707-.707M6.343 6.343l-.707-.707m12.728 0l-.707.707M6.343 17.657l-.707.707M16 12a4 4 0 11-8 0 4 4 0 018 0z" />
+            </svg>
+          ) : (
+            <svg className="w-4 h-4" fill="none" stroke="currentColor" viewBox="0 0 24 24" strokeWidth={2}>
+              <path strokeLinecap="round" strokeLinejoin="round" d="M20.354 15.354A9 9 0 018.646 3.646 9.003 9.003 0 0012 21a9.003 9.003 0 008.354-5.646z" />
+            </svg>
+          )}
+        </button>
+        {activePage === 'dashboard' && (
         <button
           onClick={handleShare}
           className="flex items-center gap-1.5 px-3 py-1.5 rounded bg-navy-800 hover:bg-navy-700 transition-colors text-sm"
@@ -271,7 +293,8 @@ function Header({
             </>
           )}
         </button>
-      )}
+        )}
+      </div>
     </header>
   );
 }
@@ -335,6 +358,28 @@ export default function App() {
   const [rightPaneTab, setRightPaneTab] = useState<'pois' | 'properties'>(urlState.tab || 'pois');
   const [activePage, setActivePage] = useState<Page>('home');
 
+  // Theme state — defaults to system preference on first visit
+  const [theme, setTheme] = useState<Theme>(() => {
+    const stored = localStorage.getItem('theme');
+    if (stored === 'light' || stored === 'dark') return stored;
+    return 'light';
+  });
+
+  // Sync dark class on <html> and persist to localStorage
+  useEffect(() => {
+    const root = document.documentElement;
+    if (theme === 'dark') {
+      root.classList.add('dark');
+    } else {
+      root.classList.remove('dark');
+    }
+    localStorage.setItem('theme', theme);
+  }, [theme]);
+
+  const toggleTheme = useCallback(() => {
+    setTheme((prev) => (prev === 'light' ? 'dark' : 'light'));
+  }, []);
+
   // Derive enabled features from filter keys
   const enabledFeatures = useMemo(() => new Set(Object.keys(filters)), [filters]);
 
@@ -704,9 +749,9 @@ export default function App() {
 
   return (
     <div className="h-screen flex flex-col">
-      <Header activePage={activePage} onPageChange={setActivePage} />
+      <Header activePage={activePage} onPageChange={setActivePage} theme={theme} onToggleTheme={toggleTheme} />
       {activePage === 'home' ? (
-        <HomePage onOpenDashboard={() => setActivePage('dashboard')} />
+        <HomePage onOpenDashboard={() => setActivePage('dashboard')} theme={theme} />
       ) : activePage === 'data-sources' ? (
         <DataSourcesPage />
       ) : (
@@ -742,22 +787,23 @@ export default function App() {
               selectedHexagonId={selectedHexagon?.h3 || null}
               onHexagonClick={handleHexagonClick}
               initialViewState={initialViewState}
+              theme={theme}
             />
             {loading && (
-              <div className="absolute top-4 right-4 bg-white px-3 py-1 rounded shadow">
+              <div className="absolute top-4 right-4 bg-white dark:bg-warm-800 dark:text-warm-200 px-3 py-1 rounded shadow">
                 Loading...
               </div>
             )}
             <DataSources onNavigate={() => setActivePage('data-sources')} />
           </div>
-          <div className="w-72 bg-white shadow-lg z-10 flex flex-col">
+          <div className="w-72 bg-white dark:bg-warm-900 shadow-lg z-10 flex flex-col">
             {/* Tab headers */}
-            <div className="flex border-b border-warm-200">
+            <div className="flex border-b border-warm-200 dark:border-warm-700">
               <button
                 className={`flex-1 p-3 ${
                   rightPaneTab === 'pois'
-                    ? 'border-b-2 border-teal-500 font-semibold'
-                    : 'text-warm-600'
+                    ? 'border-b-2 border-teal-500 font-semibold dark:text-warm-100'
+                    : 'text-warm-600 dark:text-warm-400'
                 }`}
                 onClick={() => setRightPaneTab('pois')}
               >
@@ -766,8 +812,8 @@ export default function App() {
               <button
                 className={`flex-1 p-3 ${
                   rightPaneTab === 'properties'
-                    ? 'border-b-2 border-teal-500 font-semibold'
-                    : 'text-warm-600'
+                    ? 'border-b-2 border-teal-500 font-semibold dark:text-warm-100'
+                    : 'text-warm-600 dark:text-warm-400'
                 }`}
                 onClick={() => setRightPaneTab('properties')}
               >
diff --git a/frontend/src/components/DataSources.tsx b/frontend/src/components/DataSources.tsx
index bd7c074..80cd48d 100644
--- a/frontend/src/components/DataSources.tsx
+++ b/frontend/src/components/DataSources.tsx
@@ -2,7 +2,7 @@ export default function DataSources({ onNavigate }: { onNavigate: () => void })
   return (
     <button
       onClick={onNavigate}
-      className="absolute bottom-2 right-2 bg-white/90 backdrop-blur-sm px-3 py-2 rounded shadow-lg text-xs text-teal-600 hover:text-teal-800 hover:underline font-semibold transition-colors"
+      className="absolute bottom-2 right-2 bg-white/90 dark:bg-warm-800/90 backdrop-blur-sm px-3 py-2 rounded shadow-lg text-xs text-teal-600 dark:text-teal-400 hover:text-teal-800 dark:hover:text-teal-300 hover:underline font-semibold transition-colors"
     >
       Data Sources
     </button>
diff --git a/frontend/src/components/DataSourcesPage.tsx b/frontend/src/components/DataSourcesPage.tsx
index 67a533a..f86b18a 100644
--- a/frontend/src/components/DataSourcesPage.tsx
+++ b/frontend/src/components/DataSourcesPage.tsx
@@ -87,30 +87,30 @@ const DATA_SOURCES = [
 
 export default function DataSourcesPage() {
   return (
-    <div className="flex-1 overflow-y-auto bg-warm-50 flex flex-col">
+    <div className="flex-1 overflow-y-auto bg-warm-50 dark:bg-warm-900 flex flex-col">
       <div className="flex-1">
         <div className="max-w-5xl mx-auto px-6 py-8">
-          <h1 className="text-2xl font-bold text-warm-900 mb-2">Data Sources</h1>
-          <p className="text-warm-600 mb-6">
+          <h1 className="text-2xl font-bold text-warm-900 dark:text-warm-100 mb-2">Data Sources</h1>
+          <p className="text-warm-600 dark:text-warm-400 mb-6">
             This application combines {DATA_SOURCES.length} open datasets covering property prices,
             energy performance, transport, demographics, crime, environment, and more.
           </p>
           <div className="grid grid-cols-1 lg:grid-cols-2 gap-4">
             {DATA_SOURCES.map((source) => (
-              <div key={source.name} className="bg-white rounded-lg border border-warm-200 p-5">
+              <div key={source.name} className="bg-white dark:bg-warm-800 rounded-lg border border-warm-200 dark:border-warm-700 p-5">
                 <div className="flex items-start justify-between gap-4 mb-2">
-                  <h2 className="text-lg font-semibold text-warm-900">{source.name}</h2>
-                  <span className="shrink-0 text-xs bg-warm-100 text-warm-600 px-2 py-1 rounded">
+                  <h2 className="text-lg font-semibold text-warm-900 dark:text-warm-100">{source.name}</h2>
+                  <span className="shrink-0 text-xs bg-warm-100 dark:bg-warm-700 text-warm-600 dark:text-warm-300 px-2 py-1 rounded">
                     {source.license}
                   </span>
                 </div>
-                <p className="text-sm text-warm-500 mb-2">Source: {source.origin}</p>
-                <p className="text-sm text-warm-700 mb-3">{source.use}</p>
+                <p className="text-sm text-warm-500 dark:text-warm-400 mb-2">Source: {source.origin}</p>
+                <p className="text-sm text-warm-700 dark:text-warm-300 mb-3">{source.use}</p>
                 <a
                   href={source.url}
                   target="_blank"
                   rel="noopener noreferrer"
-                  className="text-sm text-teal-600 hover:text-teal-800 hover:underline break-all"
+                  className="text-sm text-teal-600 dark:text-teal-400 hover:text-teal-800 dark:hover:text-teal-300 hover:underline break-all"
                 >
                   {source.url}
                 </a>
diff --git a/frontend/src/components/Filters.tsx b/frontend/src/components/Filters.tsx
index 544f64c..203657d 100644
--- a/frontend/src/components/Filters.tsx
+++ b/frontend/src/components/Filters.tsx
@@ -66,22 +66,22 @@ function FilterDropdown({
   return (
     <div ref={ref} className="relative">
       <button
-        className="w-full p-2 border rounded text-sm bg-white text-left text-warm-500 hover:border-warm-400"
+        className="w-full p-2 border rounded text-sm bg-white dark:bg-warm-800 dark:border-warm-700 text-left text-warm-500 dark:text-warm-400 hover:border-warm-400"
         onClick={() => setOpen(!open)}
       >
         + Add filter...
       </button>
       {open && (
-        <div className="absolute z-50 mt-1 w-full bg-white border rounded shadow-lg max-h-80 overflow-y-auto">
+        <div className="absolute z-50 mt-1 w-full bg-white dark:bg-warm-800 border dark:border-warm-700 rounded shadow-lg max-h-80 overflow-y-auto">
           {grouped.map((group) => (
             <div key={group.name}>
-              <div className="px-3 py-1.5 text-xs font-bold text-warm-500 bg-warm-50 sticky top-0">
+              <div className="px-3 py-1.5 text-xs font-bold text-warm-500 bg-warm-50 dark:bg-warm-900 dark:text-warm-400 sticky top-0">
                 {group.name}
               </div>
               {group.features.map((f) => (
                 <button
                   key={f.name}
-                  className="w-full text-left px-3 py-1.5 text-sm hover:bg-teal-50 hover:text-teal-700"
+                  className="w-full text-left px-3 py-1.5 text-sm hover:bg-teal-50 dark:hover:bg-teal-900/30 hover:text-teal-700 dark:hover:text-teal-400 dark:text-warm-300"
                   onClick={() => {
                     onAddFilter(f.name);
                     setOpen(false);
@@ -126,8 +126,8 @@ export default memo(function Filters({
   const enabledFeatureList = features.filter((f) => enabledFeatures.has(f.name));
 
   return (
-    <div className="w-72 p-4 bg-white shadow-lg space-y-4 overflow-y-auto">
-      <div className="text-sm text-warm-500">Zoom: {zoom.toFixed(1)}</div>
+    <div className="w-72 p-4 bg-white dark:bg-warm-900 shadow-lg space-y-4 overflow-y-auto">
+      <div className="text-sm text-warm-500 dark:text-warm-400">Zoom: {zoom.toFixed(1)}</div>
 
       {/* Add filter dropdown */}
       {availableFeatures.length > 0 && (
@@ -145,7 +145,7 @@ export default memo(function Filters({
                 <Label className="text-xs">{feature.name}</Label>
                 <button
                   onClick={() => onRemoveFilter(feature.name)}
-                  className="text-warm-400 hover:text-warm-700 text-sm px-1"
+                  className="text-warm-400 hover:text-warm-700 dark:hover:text-warm-300 text-sm px-1"
                   title="Remove filter"
                 >
                   x
@@ -153,13 +153,13 @@ export default memo(function Filters({
               </div>
               <div className="flex gap-2 text-xs mb-1">
                 <button
-                  className="text-teal-600 hover:underline"
+                  className="text-teal-600 dark:text-teal-400 hover:underline"
                   onClick={() => onFilterChange(feature.name, [...allValues])}
                 >
                   All
                 </button>
                 <button
-                  className="text-teal-600 hover:underline"
+                  className="text-teal-600 dark:text-teal-400 hover:underline"
                   onClick={() => onFilterChange(feature.name, [])}
                 >
                   None
@@ -167,7 +167,7 @@ export default memo(function Filters({
               </div>
               <div className="space-y-0.5 max-h-40 overflow-y-auto">
                 {allValues.map((val) => (
-                  <label key={val} className="flex items-center gap-1.5 text-xs cursor-pointer">
+                  <label key={val} className="flex items-center gap-1.5 text-xs cursor-pointer dark:text-warm-300">
                     <input
                       type="checkbox"
                       checked={selectedValues.includes(val)}
@@ -177,7 +177,7 @@ export default memo(function Filters({
                           : [...selectedValues, val];
                         onFilterChange(feature.name, next);
                       }}
-                      className="rounded"
+                      className="rounded accent-teal-600"
                     />
                     {val}
                   </label>
@@ -199,7 +199,7 @@ export default memo(function Filters({
         return (
           <div
             key={feature.name}
-            className={`space-y-1 p-2 rounded ${isActive ? 'ring-2 ring-teal-400 bg-teal-50' : isPinned ? 'ring-2 ring-teal-400 bg-teal-50/50' : ''}`}
+            className={`space-y-1 p-2 rounded ${isActive ? 'ring-2 ring-teal-400 bg-teal-50 dark:bg-teal-900/30' : isPinned ? 'ring-2 ring-teal-400 bg-teal-50/50 dark:bg-teal-900/20' : ''}`}
           >
             <div className="flex items-center justify-between">
               <Label className="text-xs">
@@ -208,7 +208,7 @@ export default memo(function Filters({
               <div className="flex items-center gap-0.5">
                 <button
                   onClick={() => onTogglePin(feature.name)}
-                  className={`p-0.5 rounded ${isPinned ? 'text-teal-600' : 'text-warm-400 hover:text-warm-700'}`}
+                  className={`p-0.5 rounded ${isPinned ? 'text-teal-600 dark:text-teal-400' : 'text-warm-400 hover:text-warm-700 dark:hover:text-warm-300'}`}
                   title={isPinned ? 'Unpin color view' : 'Color map by this feature'}
                 >
                   <svg
@@ -224,7 +224,7 @@ export default memo(function Filters({
                 </button>
                 <button
                   onClick={() => onRemoveFilter(feature.name)}
-                  className="text-warm-400 hover:text-warm-700 text-sm px-1"
+                  className="text-warm-400 hover:text-warm-700 dark:hover:text-warm-300 text-sm px-1"
                   title="Remove filter"
                 >
                   x
diff --git a/frontend/src/components/HomePage.tsx b/frontend/src/components/HomePage.tsx
index 05f0289..0dc9907 100644
--- a/frontend/src/components/HomePage.tsx
+++ b/frontend/src/components/HomePage.tsx
@@ -44,12 +44,14 @@ function drawHex(ctx: CanvasRenderingContext2D, cx: number, cy: number, r: numbe
   ctx.closePath();
 }
 
-function HexCanvas({ scrollProgress }: { scrollProgress: number }) {
+function HexCanvas({ scrollProgress, isDark = false }: { scrollProgress: number; isDark?: boolean }) {
   const canvasRef = useRef<HTMLCanvasElement>(null);
   const hexesRef = useRef<Hex[]>([]);
   const animRef = useRef(0);
   const scrollRef = useRef(scrollProgress);
   scrollRef.current = scrollProgress;
+  const isDarkRef = useRef(isDark);
+  isDarkRef.current = isDark;
 
   useEffect(() => {
     const canvas = canvasRef.current;
@@ -100,13 +102,14 @@ function HexCanvas({ scrollProgress }: { scrollProgress: number }) {
         if (hex.y < -hex.size * 2) hex.y += h + hex.size * 4;
         if (hex.y > h + hex.size * 2) hex.y -= h + hex.size * 4;
 
-        ctx!.globalAlpha = hex.opacity * globalAlpha;
-        ctx!.fillStyle = '#00a28c';
+        const dark = isDarkRef.current;
+        ctx!.globalAlpha = hex.opacity * globalAlpha * (dark ? 0.6 : 1);
+        ctx!.fillStyle = dark ? '#058172' : '#00a28c';
         drawHex(ctx!, hex.x, hex.y, hex.size);
         ctx!.fill();
 
-        ctx!.globalAlpha = hex.opacity * 0.5 * globalAlpha;
-        ctx!.strokeStyle = '#05c9aa';
+        ctx!.globalAlpha = hex.opacity * 0.5 * globalAlpha * (dark ? 0.6 : 1);
+        ctx!.strokeStyle = dark ? '#0a665b' : '#05c9aa';
         ctx!.lineWidth = 1;
         drawHex(ctx!, hex.x, hex.y, hex.size);
         ctx!.stroke();
@@ -155,7 +158,7 @@ function useFadeInRef() {
 
 // --- Page ---
 
-export default function HomePage({ onOpenDashboard }: { onOpenDashboard: () => void }) {
+export default function HomePage({ onOpenDashboard, theme = 'light' }: { onOpenDashboard: () => void; theme?: 'light' | 'dark' }) {
   const scrollRef = useRef<HTMLDivElement>(null);
   const [scrollProgress, setScrollProgress] = useState(0);
 
@@ -182,27 +185,27 @@ export default function HomePage({ onOpenDashboard }: { onOpenDashboard: () => v
   const ctaRef = useFadeInRef();
 
   return (
-    <div ref={scrollRef} className="flex-1 overflow-y-auto bg-warm-50 relative">
-      <HexCanvas scrollProgress={scrollProgress} />
+    <div ref={scrollRef} className="flex-1 overflow-y-auto bg-warm-50 dark:bg-warm-900 relative">
+      <HexCanvas scrollProgress={scrollProgress} isDark={theme === 'dark'} />
 
       <div className="relative" style={{ zIndex: 1 }}>
         {/* Hero */}
         <div className="max-w-3xl mx-auto px-6 pt-20 pb-24">
           <div
             ref={heroRef}
-            className="fade-in-section backdrop-blur-sm bg-warm-50/60 rounded-2xl p-8 -mx-2"
+            className="fade-in-section backdrop-blur-sm bg-warm-50/60 dark:bg-warm-900/60 rounded-2xl p-8 -mx-2"
           >
             <p className="text-teal-600 font-semibold tracking-wide uppercase text-sm mb-4">
               Find where to live, not just what&apos;s for sale
             </p>
-            <h1 className="text-5xl font-extrabold text-navy-950 mb-6 leading-[1.1] tracking-tight">
+            <h1 className="text-5xl font-extrabold text-navy-950 dark:text-warm-100 mb-6 leading-[1.1] tracking-tight">
               Every neighbourhood
               <br />
               in England &amp; Wales.
               <br />
               <span className="text-teal-600">One map. Your&nbsp;rules.</span>
             </h1>
-            <p className="text-xl text-warm-600 mb-8 leading-relaxed max-w-xl">
+            <p className="text-xl text-warm-600 dark:text-warm-400 mb-8 leading-relaxed max-w-xl">
               Set the commute, budget, school rating, noise level, and crime threshold you&apos;ll
               accept. Narrowit shows you every area that qualifies &mdash; instantly.
             </p>
@@ -223,13 +226,13 @@ export default function HomePage({ onOpenDashboard }: { onOpenDashboard: () => v
         {/* The flip */}
         <div className="max-w-3xl mx-auto px-6 pb-20">
           <div ref={problemRef} className="fade-in-section">
-            <div className="rounded-2xl backdrop-blur-sm bg-warm-50/40 border border-warm-200/50 p-8">
+            <div className="rounded-2xl backdrop-blur-sm bg-warm-50/40 dark:bg-warm-800/40 border border-warm-200/50 dark:border-warm-700/50 p-8">
               <div className="grid md:grid-cols-2 gap-8">
                 <div>
                   <h3 className="text-sm font-semibold text-warm-400 uppercase tracking-wide mb-2">
                     The old way
                   </h3>
-                  <p className="text-warm-700 leading-relaxed">
+                  <p className="text-warm-700 dark:text-warm-300 leading-relaxed">
                     Pick a postcode. Google the schools. Check crime stats on another site. Look up
                     commute times. Realise it&apos;s too expensive. Start over. Repeat 40 times.
                   </p>
@@ -238,7 +241,7 @@ export default function HomePage({ onOpenDashboard }: { onOpenDashboard: () => v
                   <h3 className="text-sm font-semibold text-teal-600 uppercase tracking-wide mb-2">
                     With Narrowit
                   </h3>
-                  <p className="text-warm-700 leading-relaxed">
+                  <p className="text-warm-700 dark:text-warm-300 leading-relaxed">
                     Tell the map what you need. Every hexagon that lights up is a place worth
                     looking at. Drill into any one to see individual properties, prices, and energy
                     ratings.
@@ -252,21 +255,21 @@ export default function HomePage({ onOpenDashboard }: { onOpenDashboard: () => v
         {/* Filter showcase */}
         <div className="max-w-4xl mx-auto px-6 pb-20">
           <div ref={filtersRef} className="fade-in-section">
-            <h2 className="text-3xl font-bold text-navy-950 mb-2 text-center">
+            <h2 className="text-3xl font-bold text-navy-950 dark:text-warm-100 mb-2 text-center">
               12 datasets. One slider&nbsp;each.
             </h2>
-            <p className="text-warm-500 text-center mb-10 max-w-lg mx-auto">
+            <p className="text-warm-500 dark:text-warm-400 text-center mb-10 max-w-lg mx-auto">
               Every filter narrows the map in real time. Combine as many as you like.
             </p>
             <div className="grid grid-cols-2 md:grid-cols-4 gap-3">
               {FILTERS.map((f) => (
                 <div
                   key={f.label}
-                  className="rounded-xl bg-white border border-warm-200 p-4 shadow-sm hover:shadow-md hover:border-teal-300 transition-all"
+                  className="rounded-xl bg-white dark:bg-warm-800 border border-warm-200 dark:border-warm-700 p-4 shadow-sm hover:shadow-md hover:border-teal-300 dark:hover:border-teal-600 transition-all"
                 >
                   <div className="text-2xl mb-2">{f.icon}</div>
-                  <div className="font-semibold text-navy-950 text-sm">{f.label}</div>
-                  <div className="text-xs text-warm-500 mt-0.5">{f.example}</div>
+                  <div className="font-semibold text-navy-950 dark:text-warm-100 text-sm">{f.label}</div>
+                  <div className="text-xs text-warm-500 dark:text-warm-400 mt-0.5">{f.example}</div>
                 </div>
               ))}
             </div>
@@ -276,7 +279,7 @@ export default function HomePage({ onOpenDashboard }: { onOpenDashboard: () => v
         {/* How it works */}
         <div className="max-w-3xl mx-auto px-6 pb-20">
           <div ref={howRef} className="fade-in-section">
-            <h2 className="text-3xl font-bold text-navy-950 mb-10 text-center">
+            <h2 className="text-3xl font-bold text-navy-950 dark:text-warm-100 mb-10 text-center">
               Three clicks to clarity
             </h2>
             <div className="space-y-6">
@@ -286,8 +289,8 @@ export default function HomePage({ onOpenDashboard }: { onOpenDashboard: () => v
                     {i + 1}
                   </span>
                   <div>
-                    <h3 className="font-semibold text-navy-950 text-lg">{step.title}</h3>
-                    <p className="text-warm-600 mt-0.5">{step.body}</p>
+                    <h3 className="font-semibold text-navy-950 dark:text-warm-100 text-lg">{step.title}</h3>
+                    <p className="text-warm-600 dark:text-warm-400 mt-0.5">{step.body}</p>
                   </div>
                 </div>
               ))}
@@ -302,7 +305,7 @@ export default function HomePage({ onOpenDashboard }: { onOpenDashboard: () => v
               {STATS.map((s) => (
                 <div key={s.label}>
                   <div className="text-3xl font-extrabold text-teal-600">{s.value}</div>
-                  <div className="text-sm text-warm-500 mt-1">{s.label}</div>
+                  <div className="text-sm text-warm-500 dark:text-warm-400 mt-1">{s.label}</div>
                 </div>
               ))}
             </div>
@@ -312,8 +315,8 @@ export default function HomePage({ onOpenDashboard }: { onOpenDashboard: () => v
         {/* Final CTA */}
         <div className="max-w-3xl mx-auto px-6 pb-24">
           <div ref={ctaRef} className="fade-in-section text-center">
-            <h2 className="text-3xl font-bold text-navy-950 mb-3">Ready to narrow it down?</h2>
-            <p className="text-warm-500 mb-8 max-w-md mx-auto">
+            <h2 className="text-3xl font-bold text-navy-950 dark:text-warm-100 mb-3">Ready to narrow it down?</h2>
+            <p className="text-warm-500 dark:text-warm-400 mb-8 max-w-md mx-auto">
               100% open data. No account required. Just set your filters and go.
             </p>
             <button
diff --git a/frontend/src/components/Map.tsx b/frontend/src/components/Map.tsx
index 898ce38..a464e8c 100644
--- a/frontend/src/components/Map.tsx
+++ b/frontend/src/components/Map.tsx
@@ -21,6 +21,7 @@ interface MapProps {
   selectedHexagonId: string | null;
   onHexagonClick: (h3: string) => void;
   initialViewState?: ViewState;
+  theme?: 'light' | 'dark';
 }
 
 // Twemoji CDN base URL
@@ -42,7 +43,8 @@ const INITIAL_VIEW: ViewState = {
   pitch: 0,
 };
 
-const MAP_STYLE = 'https://basemaps.cartocdn.com/gl/voyager-gl-style/style.json';
+const MAP_STYLE_LIGHT = 'https://basemaps.cartocdn.com/gl/voyager-gl-style/style.json';
+const MAP_STYLE_DARK = 'https://basemaps.cartocdn.com/gl/dark-matter-gl-style/style.json';
 
 // Gradient stops for normalized [0,1] values
 const GRADIENT: { t: number; color: [number, number, number] }[] = [
@@ -204,7 +206,7 @@ function PostcodeSearch({
             setError(null);
           }}
           placeholder="Search postcode..."
-          className="px-3 py-2 text-sm w-40 border-none outline-none bg-white"
+          className="px-3 py-2 text-sm w-40 border-none outline-none bg-white dark:bg-warm-800 dark:text-warm-100 dark:placeholder-warm-500"
         />
         <button
           type="submit"
@@ -215,7 +217,7 @@ function PostcodeSearch({
         </button>
       </div>
       {error && (
-        <span className="text-xs text-red-600 bg-white/90 rounded px-2 py-0.5 shadow">{error}</span>
+        <span className="text-xs text-red-600 dark:text-red-400 bg-white/90 dark:bg-warm-800/90 rounded px-2 py-0.5 shadow">{error}</span>
       )}
     </form>
   );
@@ -240,7 +242,7 @@ function MapLegend({
   };
 
   return (
-    <div className="absolute top-3 right-3 z-10 bg-white rounded shadow-lg p-3 text-xs min-w-[160px]">
+    <div className="absolute top-3 right-3 z-10 bg-white dark:bg-warm-800 dark:text-warm-200 rounded shadow-lg p-3 text-xs min-w-[160px]">
       <div className="flex items-center justify-between mb-2">
         <span className="font-semibold text-sm">{featureLabel}</span>
         {showCancel && (
@@ -268,7 +270,7 @@ function MapLegend({
             'linear-gradient(to right, rgb(46, 204, 113), rgb(241, 196, 15), rgb(231, 76, 60), rgb(142, 68, 173))',
         }}
       />
-      <div className="flex justify-between mt-1 text-warm-600">
+      <div className="flex justify-between mt-1 text-warm-600 dark:text-warm-400">
         <span>{formatVal(range[0])}</span>
         <span>{formatVal(range[1])}</span>
       </div>
@@ -289,6 +291,7 @@ export default memo(function Map({
   selectedHexagonId,
   onHexagonClick,
   initialViewState,
+  theme = 'light',
 }: MapProps) {
   const containerRef = useRef<HTMLDivElement>(null);
   const [viewState, setViewState] = useState<ViewState>(initialViewState || INITIAL_VIEW);
@@ -343,28 +346,39 @@ export default memo(function Map({
     setViewState((prev) => ({ ...prev, latitude: lat, longitude: lng, zoom }));
   }, []);
 
+  const themeRef = useRef(theme);
+  themeRef.current = theme;
+
   // Make place labels more legible over the colored hexagons
   const handleMapLoad = useCallback(
     (evt: { target: MapRef['getMap'] extends () => infer M ? M : never }) => {
       const map = evt.target;
-      for (const layer of map.getStyle().layers || []) {
-        if (layer.type !== 'symbol') continue;
-        map.setPaintProperty(layer.id, 'text-halo-color', 'rgba(255,255,255,1)');
-        map.setPaintProperty(layer.id, 'text-halo-width', 2);
-        map.setPaintProperty(layer.id, 'text-color', '#222');
-      }
-      // Make water more prominent
-      for (const layer of map.getStyle().layers || []) {
-        if (layer.id === 'water' || layer.id.startsWith('water')) {
-          map.setPaintProperty(layer.id, 'fill-color', '#6baed6');
+      if (themeRef.current === 'light') {
+        for (const layer of map.getStyle().layers || []) {
+          if (layer.type !== 'symbol') continue;
+          map.setPaintProperty(layer.id, 'text-halo-color', 'rgba(255,255,255,1)');
+          map.setPaintProperty(layer.id, 'text-halo-width', 2);
+          map.setPaintProperty(layer.id, 'text-color', '#222');
+        }
+        // Make water more prominent
+        for (const layer of map.getStyle().layers || []) {
+          if (layer.id === 'water' || layer.id.startsWith('water')) {
+            map.setPaintProperty(layer.id, 'fill-color', '#6baed6');
+          }
         }
       }
-      map.setLayoutProperty('building', 'visibility', 'none');
-      map.setLayoutProperty('building-top', 'visibility', 'none');
+      try {
+        map.setLayoutProperty('building', 'visibility', 'none');
+        map.setLayoutProperty('building-top', 'visibility', 'none');
+      } catch {
+        // layers may not exist in dark style
+      }
     },
     []
   );
 
+  const mapStyle = theme === 'dark' ? MAP_STYLE_DARK : MAP_STYLE_LIGHT;
+
   // Popup state for POI hover
   const [popupInfo, setPopupInfo] = useState<{
     x: number;
@@ -541,20 +555,20 @@ export default memo(function Map({
             getPosition: (d) => [d.lon as number, d.lat as number],
             getText: (d) => d.postcode as string,
             getSize: 11,
-            getColor: [30, 30, 30, 220],
+            getColor: theme === 'dark' ? [220, 220, 220, 220] : [30, 30, 30, 220],
             getTextAnchor: 'middle',
             getAlignmentBaseline: 'center',
             fontFamily: 'Inter, system-ui, sans-serif',
             fontWeight: 600,
             outlineWidth: 2,
-            outlineColor: [255, 255, 255, 200],
+            outlineColor: theme === 'dark' ? [30, 30, 30, 200] : [255, 255, 255, 200],
             billboard: false,
             sizeUnits: 'pixels',
             sizeMinPixels: 10,
             sizeMaxPixels: 14,
           })
         : null,
-    [postcodeData, showPostcodes]
+    [postcodeData, showPostcodes, theme]
   );
 
   const layers = useMemo(
@@ -593,12 +607,14 @@ export default memo(function Map({
         }
       }
 
+      const isDark = themeRef.current === 'dark';
       return {
         html: `<div style="padding: 8px; font-size: 12px;">${lines.join('')}</div>`,
         style: {
-          backgroundColor: 'white',
+          backgroundColor: isDark ? '#292524' : 'white',
+          color: isDark ? '#e7e5e4' : 'inherit',
           borderRadius: '4px',
-          boxShadow: '0 2px 4px rgba(0,0,0,0.2)',
+          boxShadow: isDark ? '0 2px 4px rgba(0,0,0,0.5)' : '0 2px 4px rgba(0,0,0,0.2)',
         },
       };
     },
@@ -611,9 +627,14 @@ export default memo(function Map({
         {...viewState}
         onMove={handleMove}
         onLoad={handleMapLoad as never}
-        mapStyle={MAP_STYLE}
+        mapStyle={mapStyle}
         style={{ width: '100%', height: '100%' }}
         attributionControl={false}
+        dragRotate={false}
+        touchZoomRotate={true}
+        touchPitch={false}
+        keyboard={true}
+        pitchWithRotate={false}
       >
         <DeckOverlay layers={layers} getTooltip={getTooltip as never} />
       </MapGL>
@@ -628,7 +649,7 @@ export default memo(function Map({
       )}
       {popupInfo && (
         <div
-          className="absolute pointer-events-none bg-white rounded shadow-lg p-2 text-sm"
+          className="absolute pointer-events-none bg-white dark:bg-warm-800 rounded shadow-lg p-2 text-sm dark:text-warm-200"
           style={{
             left: popupInfo.x,
             top: popupInfo.y - 40,
@@ -637,7 +658,7 @@ export default memo(function Map({
           }}
         >
           <strong>{popupInfo.name}</strong>
-          <div className="text-gray-500 text-xs">{popupInfo.category}</div>
+          <div className="text-gray-500 dark:text-warm-400 text-xs">{popupInfo.category}</div>
         </div>
       )}
     </div>
diff --git a/frontend/src/components/POIPane.tsx b/frontend/src/components/POIPane.tsx
index 2e41a6b..4e68748 100644
--- a/frontend/src/components/POIPane.tsx
+++ b/frontend/src/components/POIPane.tsx
@@ -95,13 +95,13 @@ export default function POIPane({
   const selectedCount = selectedCategories.size;
 
   return (
-    <div className="w-72 p-4 bg-white shadow-lg space-y-4 overflow-y-auto max-h-screen">
-      <h2 className="text-xl font-bold">Points of Interest</h2>
+    <div className="w-72 p-4 bg-white dark:bg-warm-900 shadow-lg space-y-4 overflow-y-auto max-h-screen">
+      <h2 className="text-xl font-bold dark:text-warm-100">Points of Interest</h2>
 
       <div className="space-y-2" ref={dropdownRef}>
         <button
           onClick={() => setDropdownOpen(!dropdownOpen)}
-          className="w-full flex items-center justify-between px-3 py-2 text-sm border border-warm-300 rounded hover:border-warm-400 bg-white"
+          className="w-full flex items-center justify-between px-3 py-2 text-sm border border-warm-300 dark:border-warm-700 rounded hover:border-warm-400 bg-white dark:bg-warm-800 dark:text-warm-200"
         >
           <span className="truncate text-left">
             {selectedCount === 0
@@ -121,23 +121,23 @@ export default function POIPane({
         </button>
 
         {dropdownOpen && (
-          <div className="border border-warm-300 rounded shadow-lg bg-white">
-            <div className="flex gap-2 px-3 py-2 border-b border-warm-200">
-              <button onClick={selectAll} className="text-xs text-teal-600 hover:text-teal-800">
+          <div className="border border-warm-300 dark:border-warm-700 rounded shadow-lg bg-white dark:bg-warm-800">
+            <div className="flex gap-2 px-3 py-2 border-b border-warm-200 dark:border-warm-700">
+              <button onClick={selectAll} className="text-xs text-teal-600 dark:text-teal-400 hover:text-teal-800 dark:hover:text-teal-300">
                 All
               </button>
-              <span className="text-xs text-warm-300">|</span>
-              <button onClick={selectNone} className="text-xs text-teal-600 hover:text-teal-800">
+              <span className="text-xs text-warm-300 dark:text-warm-600">|</span>
+              <button onClick={selectNone} className="text-xs text-teal-600 dark:text-teal-400 hover:text-teal-800 dark:hover:text-teal-300">
                 None
               </button>
             </div>
-            <div className="px-3 py-2 border-b border-warm-200">
+            <div className="px-3 py-2 border-b border-warm-200 dark:border-warm-700">
               <input
                 type="text"
                 placeholder="Search categories..."
                 value={searchTerm}
                 onChange={(e) => setSearchTerm(e.target.value)}
-                className="w-full px-2 py-1 text-sm border border-warm-300 rounded"
+                className="w-full px-2 py-1 text-sm border border-warm-300 dark:border-warm-700 rounded bg-white dark:bg-warm-900 dark:text-warm-200 dark:placeholder-warm-500"
               />
             </div>
             <div className="max-h-96 overflow-y-auto py-1">
@@ -151,7 +151,7 @@ export default function POIPane({
 
                 return (
                   <div key={group.name}>
-                    <div className="flex items-center gap-1 px-3 py-1.5 bg-warm-50 border-y border-warm-100">
+                    <div className="flex items-center gap-1 px-3 py-1.5 bg-warm-50 dark:bg-warm-900 border-y border-warm-100 dark:border-warm-700">
                       <button
                         onClick={() => toggleCollapse(group.name)}
                         className="p-0.5 text-warm-400 hover:text-warm-600"
@@ -178,9 +178,9 @@ export default function POIPane({
                             if (el) el.indeterminate = someInGroupSelected;
                           }}
                           onChange={() => toggleGroup(group.name)}
-                          className="rounded"
+                          className="rounded accent-teal-600"
                         />
-                        <span className="text-xs font-semibold text-warm-700">{group.name}</span>
+                        <span className="text-xs font-semibold text-warm-700 dark:text-warm-300">{group.name}</span>
                       </label>
                       <span className="text-xs text-warm-400">
                         {groupSelected}/{group.categories.length}
@@ -190,13 +190,13 @@ export default function POIPane({
                       group.categories.map((category) => (
                         <label
                           key={category}
-                          className="flex items-center gap-2 px-3 pl-8 py-1.5 hover:bg-warm-50 cursor-pointer"
+                          className="flex items-center gap-2 px-3 pl-8 py-1.5 hover:bg-warm-50 dark:hover:bg-warm-700 cursor-pointer dark:text-warm-300"
                         >
                           <input
                             type="checkbox"
                             checked={selectedCategories.has(category)}
                             onChange={() => toggleCategory(category)}
-                            className="rounded"
+                            className="rounded accent-teal-600"
                           />
                           <span className="text-sm flex-1">{category}</span>
                         </label>
@@ -210,17 +210,17 @@ export default function POIPane({
       </div>
 
       {selectedCount > 0 && (
-        <div className="p-3 bg-teal-50 rounded text-sm">
-          <div className="font-medium text-teal-900">
+        <div className="p-3 bg-teal-50 dark:bg-teal-900/30 rounded text-sm">
+          <div className="font-medium text-teal-900 dark:text-teal-300">
             {poiCount.toLocaleString()} POI{poiCount !== 1 ? 's' : ''} visible
           </div>
-          <div className="text-xs text-teal-700 mt-1">
+          <div className="text-xs text-teal-700 dark:text-teal-400 mt-1">
             {selectedCount} categor{selectedCount !== 1 ? 'ies' : 'y'} selected
           </div>
         </div>
       )}
 
-      <div className="p-3 bg-warm-100 rounded text-xs text-warm-600">
+      <div className="p-3 bg-warm-100 dark:bg-warm-800 rounded text-xs text-warm-600 dark:text-warm-400">
         <p>Select categories to display POIs on the map.</p>
         <p className="mt-2">Zoom in for better visibility of individual locations.</p>
       </div>
diff --git a/frontend/src/components/PropertiesPane.tsx b/frontend/src/components/PropertiesPane.tsx
index e2f3da7..5414501 100644
--- a/frontend/src/components/PropertiesPane.tsx
+++ b/frontend/src/components/PropertiesPane.tsx
@@ -21,10 +21,19 @@ export function PropertiesPane({
   onClose,
 }: PropertiesPaneProps) {
   const [sortBy, setSortBy] = useState<SortBy>('price');
+  const [search, setSearch] = useState('');
 
-  // Sort properties
-  const sortedProperties = useMemo(() => {
-    return [...properties].sort((a, b) => {
+  // Filter and sort properties
+  const filteredAndSorted = useMemo(() => {
+    const query = search.trim().toLowerCase();
+    const filtered = query
+      ? properties.filter((p) => {
+          const addr = (p.address || '').toLowerCase();
+          const pc = (p.postcode || '').toLowerCase();
+          return addr.includes(query) || pc.includes(query);
+        })
+      : properties;
+    return [...filtered].sort((a, b) => {
       switch (sortBy) {
         case 'price':
           return ((b.latest_price as number) || 0) - ((a.latest_price as number) || 0);
@@ -34,11 +43,11 @@ export function PropertiesPane({
           return (a.current_energy_rating || 'Z').localeCompare(b.current_energy_rating || 'Z');
       }
     });
-  }, [properties, sortBy]);
+  }, [properties, sortBy, search]);
 
   if (!hexagonId) {
     return (
-      <div className="flex items-center justify-center h-full text-warm-500">
+      <div className="flex items-center justify-center h-full text-warm-500 dark:text-warm-400">
         Click a hexagon to view properties
       </div>
     );
@@ -47,27 +56,36 @@ export function PropertiesPane({
   return (
     <div className="flex flex-col h-full">
       {/* Header */}
-      <div className="p-4 border-b border-warm-200">
+      <div className="p-4 border-b border-warm-200 dark:border-warm-700">
         <div className="flex justify-between items-center">
-          <h2 className="text-lg font-semibold">Properties in Hexagon</h2>
+          <h2 className="text-lg font-semibold dark:text-warm-100">Properties in Hexagon</h2>
           <button
             onClick={onClose}
-            className="text-warm-500 hover:text-warm-700 text-2xl leading-none"
+            className="text-warm-500 hover:text-warm-700 dark:text-warm-400 dark:hover:text-warm-200 text-2xl leading-none"
           >
             ×
           </button>
         </div>
-        <p className="text-sm text-warm-600">
-          Showing {properties.length} of {total} properties
+        <p className="text-sm text-warm-600 dark:text-warm-400">
+          {search.trim()
+            ? `${filteredAndSorted.length} match${filteredAndSorted.length !== 1 ? 'es' : ''} in ${properties.length} loaded`
+            : `Showing ${properties.length} of ${total} properties`}
         </p>
       </div>
 
-      {/* Sort controls */}
-      <div className="p-2 border-b border-warm-200">
+      {/* Search and sort controls */}
+      <div className="p-2 border-b border-warm-200 dark:border-warm-700 space-y-2">
+        <input
+          type="text"
+          value={search}
+          onChange={(e) => setSearch(e.target.value)}
+          placeholder="Search by address or postcode..."
+          className="w-full p-2 border border-warm-300 dark:border-warm-700 rounded text-sm bg-white dark:bg-warm-800 dark:text-warm-200 placeholder-warm-400 dark:placeholder-warm-500"
+        />
         <select
           value={sortBy}
           onChange={(e) => setSortBy(e.target.value as SortBy)}
-          className="w-full p-2 border border-warm-300 rounded text-sm"
+          className="w-full p-2 border border-warm-300 dark:border-warm-700 rounded text-sm bg-white dark:bg-warm-800 dark:text-warm-200"
         >
           <option value="price">Price (High to Low)</option>
           <option value="size">Size (Large to Small)</option>
@@ -78,17 +96,17 @@ export function PropertiesPane({
       {/* Properties list */}
       <div className="flex-1 overflow-y-auto">
         {loading && properties.length === 0 ? (
-          <div className="p-4">Loading...</div>
+          <div className="p-4 dark:text-warm-400">Loading...</div>
         ) : (
           <>
-            {sortedProperties.map((property, idx) => (
+            {filteredAndSorted.map((property, idx) => (
               <PropertyCard key={idx} property={property} />
             ))}
             {properties.length < total && (
               <button
                 onClick={onLoadMore}
                 disabled={loading}
-                className="w-full p-4 text-teal-600 hover:bg-teal-50 disabled:opacity-50"
+                className="w-full p-4 text-teal-600 dark:text-teal-400 hover:bg-teal-50 dark:hover:bg-teal-900/30 disabled:opacity-50"
               >
                 {loading ? 'Loading...' : `Load More (${total - properties.length} remaining)`}
               </button>
@@ -138,61 +156,61 @@ function PropertyCard({ property }: { property: Property }) {
   const age = getNum(property, 'Approximate construction age', 'construction_age_band');
 
   return (
-    <div className="p-4 border-b border-warm-100 hover:bg-warm-50">
+    <div className="p-4 border-b border-warm-100 dark:border-warm-800 hover:bg-warm-50 dark:hover:bg-warm-800">
       {/* Address & postcode */}
-      <div className="font-semibold">{property.address || 'Unknown Address'}</div>
-      <div className="text-sm text-warm-600">{property.postcode}</div>
+      <div className="font-semibold dark:text-warm-100">{property.address || 'Unknown Address'}</div>
+      <div className="text-sm text-warm-600 dark:text-warm-400">{property.postcode}</div>
 
       {/* Price */}
       {price !== undefined && (
-        <div className="mt-2 text-lg font-bold text-teal-700">
+        <div className="mt-2 text-lg font-bold text-teal-700 dark:text-teal-400">
           £{fmt(price)}
           {pricePerSqm !== undefined && (
-            <span className="text-sm font-normal text-warm-600"> (£{fmt(pricePerSqm)}/m²)</span>
+            <span className="text-sm font-normal text-warm-600 dark:text-warm-400"> (£{fmt(pricePerSqm)}/m²)</span>
           )}
         </div>
       )}
 
       {/* Property details grid */}
-      <div className="mt-2 grid grid-cols-2 gap-x-4 gap-y-1 text-sm">
+      <div className="mt-2 grid grid-cols-2 gap-x-4 gap-y-1 text-sm dark:text-warm-300">
         {property.property_type && (
           <div>
-            <span className="text-warm-500">Type:</span> {property.property_type}
+            <span className="text-warm-500 dark:text-warm-400">Type:</span> {property.property_type}
           </div>
         )}
         {property.built_form && (
           <div>
-            <span className="text-warm-500">Built form:</span> {property.built_form}
+            <span className="text-warm-500 dark:text-warm-400">Built form:</span> {property.built_form}
           </div>
         )}
         {property.duration && (
           <div>
-            <span className="text-warm-500">Tenure:</span> {formatDuration(property.duration)}
+            <span className="text-warm-500 dark:text-warm-400">Tenure:</span> {formatDuration(property.duration)}
           </div>
         )}
         {floorArea !== undefined && (
           <div>
-            <span className="text-warm-500">Floor area:</span> {fmt(floorArea)}m²
+            <span className="text-warm-500 dark:text-warm-400">Floor area:</span> {fmt(floorArea)}m²
           </div>
         )}
         {rooms !== undefined && (
           <div>
-            <span className="text-warm-500">Rooms:</span> {fmt(rooms)}
+            <span className="text-warm-500 dark:text-warm-400">Rooms:</span> {fmt(rooms)}
           </div>
         )}
         {age !== undefined && (
           <div>
-            <span className="text-warm-500">Built:</span> {formatAge(age, property.is_construction_date_approximate ?? true)}
+            <span className="text-warm-500 dark:text-warm-400">Built:</span> {formatAge(age, property.is_construction_date_approximate ?? true)}
           </div>
         )}
         {property.current_energy_rating && (
           <div>
-            <span className="text-warm-500">EPC rating:</span> {property.current_energy_rating}
+            <span className="text-warm-500 dark:text-warm-400">EPC rating:</span> {property.current_energy_rating}
           </div>
         )}
         {property.potential_energy_rating && (
           <div>
-            <span className="text-warm-500">EPC potential:</span> {property.potential_energy_rating}
+            <span className="text-warm-500 dark:text-warm-400">EPC potential:</span> {property.potential_energy_rating}
           </div>
         )}
       </div>
diff --git a/frontend/src/components/ui/label.tsx b/frontend/src/components/ui/label.tsx
index 8012ded..200bdfa 100644
--- a/frontend/src/components/ui/label.tsx
+++ b/frontend/src/components/ui/label.tsx
@@ -7,6 +7,6 @@ interface LabelProps {
 
 export function Label({ children, className }: LabelProps) {
   return (
-    <label className={`text-sm font-medium text-warm-700 ${className || ''}`}>{children}</label>
+    <label className={`text-sm font-medium text-warm-700 dark:text-warm-300 ${className || ''}`}>{children}</label>
   );
 }
diff --git a/frontend/src/components/ui/slider.tsx b/frontend/src/components/ui/slider.tsx
index e5e45ac..67a41b6 100644
--- a/frontend/src/components/ui/slider.tsx
+++ b/frontend/src/components/ui/slider.tsx
@@ -11,13 +11,13 @@ export function Slider({ className, ...props }: SliderProps) {
       className={cn('relative flex w-full touch-none select-none items-center', className)}
       {...props}
     >
-      <SliderPrimitive.Track className="relative h-2 w-full grow overflow-hidden rounded-full bg-warm-200">
+      <SliderPrimitive.Track className="relative h-2 w-full grow overflow-hidden rounded-full bg-warm-200 dark:bg-warm-700">
         <SliderPrimitive.Range className="absolute h-full bg-teal-600" />
       </SliderPrimitive.Track>
       {props.value?.map((_, i) => (
         <SliderPrimitive.Thumb
           key={i}
-          className="block h-5 w-5 rounded-full border-2 border-teal-600 bg-white ring-offset-white transition-colors focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-teal-600 focus-visible:ring-offset-2 disabled:pointer-events-none disabled:opacity-50"
+          className="block h-5 w-5 rounded-full border-2 border-teal-600 dark:border-teal-500 bg-white dark:bg-warm-800 ring-offset-white dark:ring-offset-warm-900 transition-colors focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-teal-600 focus-visible:ring-offset-2 disabled:pointer-events-none disabled:opacity-50"
         />
       ))}
     </SliderPrimitive.Root>
diff --git a/frontend/src/index.css b/frontend/src/index.css
index 2e41d70..b031b96 100644
--- a/frontend/src/index.css
+++ b/frontend/src/index.css
@@ -10,6 +10,30 @@ body,
   padding: 0;
 }
 
+html.dark {
+  background-color: #1c1917;
+  color-scheme: dark;
+}
+
+/* Smooth theme transitions (scoped to avoid map performance issues) */
+body,
+div,
+aside,
+section,
+header,
+nav,
+button,
+input,
+select,
+label,
+span,
+p,
+h1,
+h2,
+h3 {
+  transition: background-color 0.2s ease, border-color 0.2s ease, color 0.2s ease;
+}
+
 /* Fade-in animation for homepage sections */
 .fade-in-section {
   opacity: 0;
diff --git a/frontend/src/index.html b/frontend/src/index.html
index 514767e..01a7132 100644
--- a/frontend/src/index.html
+++ b/frontend/src/index.html
@@ -4,6 +4,13 @@
     <meta charset="UTF-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>Narrowit</title>
+    <script>
+      (function() {
+        if (localStorage.getItem('theme') === 'dark') {
+          document.documentElement.classList.add('dark');
+        }
+      })();
+    </script>
   </head>
   <body>
     <div id="root"></div>
diff --git a/frontend/src/lib/constants.ts b/frontend/src/lib/constants.ts
deleted file mode 100644
index 890093b..0000000
--- a/frontend/src/lib/constants.ts
+++ /dev/null
@@ -1 +0,0 @@
-// No hardcoded filter constants - features are discovered dynamically from the API.
diff --git a/frontend/tailwind.config.js b/frontend/tailwind.config.js
index 375ce45..9cd8e47 100644
--- a/frontend/tailwind.config.js
+++ b/frontend/tailwind.config.js
@@ -1,7 +1,54 @@
 module.exports = {
+  darkMode: 'class',
   content: ['./src/**/*.{js,jsx,ts,tsx,html}'],
   theme: {
-    extend: {},
+    extend: {
+      colors: {
+        navy: {
+          50: '#eef1f8',
+          100: '#d9dff0',
+          200: '#b3bfe1',
+          300: '#8d9fd2',
+          400: '#677fc3',
+          500: '#4a63a8',
+          600: '#2a3f6b',
+          700: '#1e2d50',
+          800: '#141e38',
+          900: '#0f1528',
+          950: '#0a0e1a',
+        },
+        teal: {
+          50: '#effefb',
+          100: '#c7fff4',
+          200: '#90ffe9',
+          300: '#51f7d9',
+          400: '#1de4c3',
+          500: '#05c9aa',
+          600: '#00a28c',
+          700: '#058172',
+          800: '#0a665b',
+          900: '#0d544c',
+          950: '#003330',
+        },
+        coral: {
+          400: '#fb923c',
+          500: '#f97316',
+          600: '#ea580c',
+        },
+        warm: {
+          50: '#fafaf9',
+          100: '#f5f5f4',
+          200: '#e7e5e4',
+          300: '#d6d3d1',
+          400: '#a8a29e',
+          500: '#78716c',
+          600: '#57534e',
+          700: '#44403c',
+          800: '#292524',
+          900: '#1c1917',
+        },
+      },
+    },
   },
   plugins: [require('tailwindcss-animate')],
 };

From c84af213e25ec3c122662058c845c10a8309076b Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sun, 1 Feb 2026 13:09:58 +0000
Subject: [PATCH 56/62] Fix data loading

---
 pipeline/download/noise.py  | 14 +++++---
 pipeline/transform/merge.py | 66 +++++++++++++++++++++++++++++++------
 2 files changed, 65 insertions(+), 15 deletions(-)

diff --git a/pipeline/download/noise.py b/pipeline/download/noise.py
index ba91ff7..554adfe 100644
--- a/pipeline/download/noise.py
+++ b/pipeline/download/noise.py
@@ -190,7 +190,8 @@ def sample_noise_at_postcodes(
     easting: np.ndarray,
     northing: np.ndarray,
     label: str,
-) -> np.ndarray:
+    col_name: str,
+) -> pl.Series:
     """Sample noise values from merged tiles at given BNG coordinates."""
     print(f"[{label}] Merging {len(tile_paths)} tiles...")
     datasets = [rasterio.open(p) for p in tile_paths]
@@ -223,7 +224,10 @@ def sample_noise_at_postcodes(
 
     valid_count = int(np.sum(~np.isnan(noise_db)))
     print(f"[{label}] Sampled {valid_count:,} / {len(easting):,} postcodes with noise data")
-    return noise_db
+
+    # Return as masked Series: use null (not NaN) so that Polars max_horizontal
+    # correctly ignores missing values instead of propagating NaN.
+    return pl.Series(col_name, noise_db).fill_nan(None)
 
 
 def main() -> None:
@@ -264,11 +268,11 @@ def main() -> None:
 
             if not tile_paths:
                 print(f"[{label}] WARNING: No tiles downloaded — column will be all null")
-                noise_db = np.full(len(lat), np.nan, dtype=np.float32)
+                series = pl.Series(col_name, [None] * len(lat), dtype=pl.Float32)
             else:
-                noise_db = sample_noise_at_postcodes(tile_paths, easting, northing, label)
+                series = sample_noise_at_postcodes(tile_paths, easting, northing, label, col_name)
 
-            result = result.with_columns(pl.Series(col_name, noise_db))
+            result = result.with_columns(series)
 
     result.write_parquet(args.output, compression="zstd")
     size_mb = args.output.stat().st_size / (1024 * 1024)
diff --git a/pipeline/transform/merge.py b/pipeline/transform/merge.py
index b7aa516..69a93e2 100644
--- a/pipeline/transform/merge.py
+++ b/pipeline/transform/merge.py
@@ -2,6 +2,9 @@ import argparse
 import polars as pl
 from pathlib import Path
 
+MIN_PRICE = 10_000
+MIN_FLOOR_AREA_M2 = 10
+
 
 def _build_wide(
     epc_pp_path: Path,
@@ -27,11 +30,17 @@ def _build_wide(
     )
     wide = wide.join(arcgis, on="postcode", how="inner")
 
-    journey_times = pl.scan_parquet(journey_times_path).select(
-        "postcode",
-        "public_transport_easy_minutes",
-        "public_transport_quick_minutes",
-        "cycling_minutes",
+    journey_times = (
+        pl.scan_parquet(journey_times_path)
+        .select(
+            "postcode",
+            "public_transport_easy_minutes",
+            "public_transport_quick_minutes",
+            "cycling_minutes",
+        )
+        .sort("public_transport_quick_minutes", nulls_last=True)
+        .group_by("postcode")
+        .first()
     )
     wide = wide.join(journey_times, on="postcode", how="left")
 
@@ -49,15 +58,39 @@ def _build_wide(
     crime = pl.scan_parquet(crime_path)
     wide = wide.join(crime, left_on="lsoa21", right_on="LSOA code", how="left")
 
+    wide = wide.with_columns(
+        pl.sum_horizontal(
+            "Violence and sexual offences (avg/yr)",
+            "Robbery (avg/yr)",
+            "Burglary (avg/yr)",
+            "Possession of weapons (avg/yr)",
+        ).alias("serious_crime_avg_yr"),
+        pl.sum_horizontal(
+            "Anti-social behaviour (avg/yr)",
+            "Criminal damage and arson (avg/yr)",
+            "Shoplifting (avg/yr)",
+            "Bicycle theft (avg/yr)",
+            "Theft from the person (avg/yr)",
+            "Other theft (avg/yr)",
+            "Vehicle crime (avg/yr)",
+            "Public order (avg/yr)",
+            "Drugs (avg/yr)",
+            "Other crime (avg/yr)",
+        ).alias("minor_crime_avg_yr"),
+    )
+
     poi_counts = pl.scan_parquet(poi_proximity_path)
     wide = wide.join(poi_counts, on="postcode", how="left")
 
+    noise_cols = ["road_noise_lden_db", "rail_noise_lden_db", "airport_noise_lden_db"]
     noise = (
         pl.scan_parquet(noise_path)
         .with_columns(
-            pl.max_horizontal(
-                "road_noise_lden_db", "rail_noise_lden_db", "airport_noise_lden_db"
-            ).alias("noise_lden_db"),
+            # NaN → null so max_horizontal ignores missing instead of propagating NaN
+            *[pl.col(c).fill_nan(None) for c in noise_cols],
+        )
+        .with_columns(
+            pl.max_horizontal(*noise_cols).fill_null(0).alias("noise_lden_db"),
         )
         .select("postcode", "noise_lden_db")
     )
@@ -102,7 +135,18 @@ def _build_wide(
     )
 
     wide = (
-        wide.filter(pl.col("total_floor_area") > 0)
+        wide.filter(pl.col("total_floor_area") > MIN_FLOOR_AREA_M2)
+        .filter(pl.col("latest_price") >= MIN_PRICE)
+        .with_columns(
+            pl.when(pl.col("duration") == "U")
+            .then(None)
+            .otherwise(pl.col("duration"))
+            .alias("duration"),
+            pl.when(pl.col("current_energy_rating") == "INVALID!")
+            .then(None)
+            .otherwise(pl.col("current_energy_rating"))
+            .alias("current_energy_rating"),
+        )
         .with_columns(
             (pl.col("latest_price") / pl.col("total_floor_area"))
             .round(0)
@@ -146,11 +190,13 @@ def _build_wide(
                 "parks_2km": "Parks within 2km",
                 "public_transport_2km": "Public transport within 2km",
                 "latest_price": "Last known price",
-                "number_habitable_rooms": "Rooms (including bedrooms & bathrooms)",
+                "number_habitable_rooms": "Number of bedrooms & living rooms",
                 "noise_lden_db": "Noise (dB)",
                 "good_primary_5km": "Good+ primary schools within 5km",
                 "good_secondary_5km": "Good+ secondary schools within 5km",
                 "max_download_speed": "Max available download speed (Mbps)",
+                "serious_crime_avg_yr": "Serious crime (avg/yr)",
+                "minor_crime_avg_yr": "Minor crime (avg/yr)",
             }
         )
     )

From 627ba5496a786440460d6f4700dc16daf553d4d4 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sun, 1 Feb 2026 13:10:34 +0000
Subject: [PATCH 57/62] Add docker build

---
 .dockerignore                | 13 +++++
 .github/workflows/docker.yml | 49 ++++++++++++++++++
 CLAUDE.md                    | 97 ++++++++++++++++++++++++++++++++++++
 Dockerfile                   | 25 ++++++++++
 4 files changed, 184 insertions(+)
 create mode 100644 .dockerignore
 create mode 100644 .github/workflows/docker.yml
 create mode 100644 Dockerfile

diff --git a/.dockerignore b/.dockerignore
new file mode 100644
index 0000000..f6d86cf
--- /dev/null
+++ b/.dockerignore
@@ -0,0 +1,13 @@
+data/
+data_sources/
+.venv
+**/node_modules
+**/dist
+server-rs/target
+.git
+.task
+.claude
+__pycache__
+*.parquet
+analyses/
+*.log
diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
new file mode 100644
index 0000000..c934dca
--- /dev/null
+++ b/.github/workflows/docker.yml
@@ -0,0 +1,49 @@
+name: Docker
+
+on:
+  push:
+    branches: [main]
+
+env:
+  REGISTRY: ghcr.io
+  IMAGE_NAME: ${{ github.repository }}
+
+jobs:
+  build-and-push:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      packages: write
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Log in to GitHub Container Registry
+        uses: docker/login-action@v3
+        with:
+          registry: ${{ env.REGISTRY }}
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - name: Extract metadata
+        id: meta
+        uses: docker/metadata-action@v5
+        with:
+          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
+          tags: |
+            type=raw,value=latest
+            type=sha,prefix=sha-,format=short
+
+      - name: Build and push
+        uses: docker/build-push-action@v6
+        with:
+          context: .
+          push: true
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}
+          cache-from: type=gha
+          cache-to: type=gha,mode=max
diff --git a/CLAUDE.md b/CLAUDE.md
index 3042601..2934117 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -117,6 +117,103 @@ React 18 + TypeScript. deck.gl `H3HexagonLayer` over MapLibre GL. TailwindCSS. N
 - Properties pane uses feature names from API response (human-readable), not hardcoded field names
 - Proxy: dev server on :3030 proxies `/api` to :8001; also handles VS Code `/proxy/PORT` patterns
 
+## Frontend Design Guide (STRICT — must be followed for all UI changes)
+
+The frontend uses Tailwind's `darkMode: 'class'` strategy. The `dark` class is toggled on `<html>`. Every visible element must have both light and dark styles. **Never add a light-only color class without its `dark:` counterpart.** Run `task build:frontend` after any UI change to verify.
+
+### Theme System
+
+- **State**: `App.tsx` owns a `theme` state (`'light' | 'dark' | 'system'`), persisted in `localStorage` under the key `theme`, default `'system'`.
+- **Effective theme**: When `'system'`, resolved via `window.matchMedia('(prefers-color-scheme: dark)')`. A `change` listener re-renders on OS preference flip.
+- **Toggle cycle**: light → dark → system → light. Three-way, not binary.
+- **Flash prevention**: `index.html` contains an inline `<script>` that applies the `dark` class before first paint. If the localStorage/matchMedia logic in that script changes, update it to match `App.tsx`.
+- **Prop plumbing**: `effectiveTheme` (`'light' | 'dark'`) is passed as a prop to `<Map>` and `<HomePage>`. Components that need the resolved theme must receive it as a prop — do not read localStorage or matchMedia inside child components.
+
+### Color Token Reference
+
+Every UI element must use the correct token from this table. Do not invent new pairings.
+
+| Role | Light class | Dark class | Hex (dark) |
+|------|------------|------------|------------|
+| **Page / pane background** | `bg-warm-50` or `bg-white` | `dark:bg-warm-900` | #1c1917 |
+| **Card / elevated surface** | `bg-white` | `dark:bg-warm-800` | #292524 |
+| **Inset / recessed surface** | `bg-warm-100` or `bg-warm-50` | `dark:bg-warm-800` | #292524 |
+| **Input / select background** | `bg-white` | `dark:bg-warm-800` or `dark:bg-warm-900` | |
+| **Primary border** | `border-warm-200` | `dark:border-warm-700` | #44403c |
+| **Subtle border (dividers)** | `border-warm-100` | `dark:border-warm-800` | #292524 |
+| **Primary text (headings)** | `text-navy-950` or implicit dark | `dark:text-warm-100` | #f5f5f4 |
+| **Body text** | `text-warm-700` | `dark:text-warm-300` | #d6d3d1 |
+| **Secondary text (labels, hints)** | `text-warm-500` or `text-warm-600` | `dark:text-warm-400` | #a8a29e |
+| **Disabled / placeholder text** | `text-warm-400` / `placeholder-warm-400` | `dark:text-warm-500` / `dark:placeholder-warm-500` | #78716c |
+| **Accent text (links, actions)** | `text-teal-600` | `dark:text-teal-400` | #1de4c3 |
+| **Accent hover text** | `hover:text-teal-800` | `dark:hover:text-teal-300` | #51f7d9 |
+| **Accent background (highlights)** | `bg-teal-50` | `dark:bg-teal-900/30` | |
+| **Active ring / focus ring** | `ring-teal-400` | same — works in both | |
+| **Price / key metric text** | `text-teal-700` | `dark:text-teal-400` | |
+| **Remove / close button** | `text-warm-400 hover:text-warm-700` | `dark:hover:text-warm-300` | |
+| **Checkbox accent** | `accent-teal-600` | same — works in both | |
+| **Header (unchanged both modes)** | `bg-navy-900 text-white` | same | |
+
+### Mapping Rules for Specific Contexts
+
+**Sidebars (Filters, POIPane, PropertiesPane, right-pane tabs):**
+- Container: `bg-white dark:bg-warm-900`
+- Inner cards / dropdown menus: `bg-white dark:bg-warm-800`
+- Borders: `border-warm-200 dark:border-warm-700`
+- Tab text (active): add `dark:text-warm-100`
+- Tab text (inactive): `text-warm-600 dark:text-warm-400`
+
+**Map overlays (PostcodeSearch, MapLegend, POI popup, loading indicator):**
+- Background: `bg-white dark:bg-warm-800`
+- Text: `dark:text-warm-200`
+- Semi-transparent variants: use `/90` opacity suffix (e.g. `dark:bg-warm-800/90`)
+- Deck.gl tooltip (inline styles, not Tailwind): use `#292524` bg / `#e7e5e4` text / `rgba(0,0,0,0.5)` shadow in dark.
+- Deck.gl postcode labels (RGB arrays): `[220,220,220,220]` text / `[30,30,30,200]` outline in dark; inverse in light.
+
+**Map basemaps:**
+- Light: `https://basemaps.cartocdn.com/gl/voyager-gl-style/style.json`
+- Dark: `https://basemaps.cartocdn.com/gl/dark-matter-gl-style/style.json`
+- `handleMapLoad` must only apply label/water tweaks in light mode. Dark Matter has good defaults.
+
+**HomePage (landing page):**
+- Page bg: `bg-warm-50 dark:bg-warm-900`
+- Cards: `bg-white dark:bg-warm-800` with `border-warm-200 dark:border-warm-700`
+- Backdrop-blur panels: use `/60` or `/40` opacity on both `bg-warm-50` and `dark:bg-warm-900`
+- HexCanvas: reads `isDark` ref; uses dimmer fill (`#058172`) and stroke (`#0a665b`) at 60% opacity multiplier.
+- All headings: `dark:text-warm-100`. All body: `dark:text-warm-300` or `dark:text-warm-400`.
+
+**DataSourcesPage:**
+- Same card pattern as above. Footer is already dark (`bg-navy-900`) — no changes needed.
+- License badges: `bg-warm-100 dark:bg-warm-700 text-warm-600 dark:text-warm-300`
+- Links: `text-teal-600 dark:text-teal-400`
+
+**DataSources floating button (on map):**
+- `bg-white/90 dark:bg-warm-800/90` with `text-teal-600 dark:text-teal-400`
+
+### Rules for New Components
+
+1. **Every `bg-white` needs `dark:bg-warm-800` or `dark:bg-warm-900`.** Pane-level = warm-900, card-level = warm-800.
+2. **Every `border-warm-200` needs `dark:border-warm-700`.**
+3. **Every `text-warm-*` needs a `dark:text-warm-*` counterpart.** Follow the token table — don't guess.
+4. **Every `text-teal-600` needs `dark:text-teal-400`.** Every `hover:text-teal-800` needs `dark:hover:text-teal-300`.
+5. **Every `bg-teal-50` needs `dark:bg-teal-900/30`.**
+6. **Every `hover:bg-warm-50` needs `dark:hover:bg-warm-700` or `dark:hover:bg-warm-800`.**
+7. **Inputs and selects**: always add `dark:bg-warm-800 dark:text-warm-200 dark:border-warm-700`. Placeholders get `dark:placeholder-warm-500`.
+8. **Checkboxes**: always include `accent-teal-600 rounded`.
+9. **Do not use Tailwind `dark:` classes inside deck.gl layers or canvas code.** Use the `theme` prop / ref and conditional JS values.
+10. **Do not add `transition-*` classes for theme switching.** The global CSS rule in `index.css` handles transitions for `background-color`, `border-color`, and `color` on all standard HTML elements. Adding per-element transition classes will conflict.
+11. **Never hardcode hex colors in JSX `style=` props for themed elements** (except deck.gl tooltip and canvas, which can't use Tailwind). Use the Tailwind classes from the token table instead.
+12. **The header (`bg-navy-900`) is identical in both themes.** Do not add dark variants to it.
+
+### Verification Checklist (for any UI PR)
+
+- [ ] `task build:frontend` passes with no errors
+- [ ] Every new `bg-*`, `text-*`, `border-*` class has a `dark:` counterpart (search your diff)
+- [ ] Toggle through all three modes (light → dark → system) with no flash
+- [ ] Map basemap switches when theme changes
+- [ ] Sidebars, dropdowns, and popups are readable in both modes
+- [ ] HomePage and DataSourcesPage adapt correctly
+
 ## Key Implementation Details
 
 - **Spatial sort**: Rows sorted by 0.01° grid cell at load time for cache-friendly sequential access
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000..c690687
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,25 @@
+# Stage 1: Build frontend
+FROM node:20-slim AS frontend
+WORKDIR /app/frontend
+COPY frontend/package.json frontend/package-lock.json ./
+RUN npm ci
+COPY frontend/ ./
+RUN npm run build
+
+# Stage 2: Build Rust server
+FROM rust:1.83-bookworm AS server
+WORKDIR /app
+COPY server-rs/ server-rs/
+WORKDIR /app/server-rs
+RUN cargo build --release
+
+# Stage 3: Runtime
+FROM debian:bookworm-slim
+RUN apt-get update && apt-get install -y --no-install-recommends ca-certificates && rm -rf /var/lib/apt/lists/*
+WORKDIR /app
+COPY --from=server /app/server-rs/target/release/property-map-server ./
+COPY --from=frontend /app/frontend/dist ./dist/
+
+EXPOSE 8001
+ENTRYPOINT ["./property-map-server"]
+CMD ["--data", "/data/wide.parquet", "--pois", "/data/filtered_uk_pois.parquet"]

From ccb7c8fbd7aa2f78b4da884f84bd800593aea159 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sun, 1 Feb 2026 13:10:56 +0000
Subject: [PATCH 58/62] Fix checkpoint postcode duplication

---
 pipeline/journey_times/__main__.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/pipeline/journey_times/__main__.py b/pipeline/journey_times/__main__.py
index cde9480..6297f09 100644
--- a/pipeline/journey_times/__main__.py
+++ b/pipeline/journey_times/__main__.py
@@ -71,9 +71,12 @@ def main():
     prior_results: list[JourneyResult] = []
     if checkpoint_path.exists():
         checkpoint_df = pl.read_parquet(checkpoint_path)
-        completed_postcodes = set(
-            checkpoint_df.filter(pl.col("public_transport_easy_minutes").is_not_null())["postcode"].to_list()
+        # Deduplicate checkpoint rows per postcode, preferring rows with data
+        checkpoint_df = (
+            checkpoint_df.sort("public_transport_quick_minutes", nulls_last=True)
+            .unique(subset=["postcode"], keep="first")
         )
+        completed_postcodes = set(checkpoint_df["postcode"].to_list())
         prior_results = [
             JourneyResult(
                 postcode=row["postcode"],

From 47de4e563fddec66b788795af5706ede6f3efb31 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sun, 1 Feb 2026 13:59:07 +0000
Subject: [PATCH 59/62] Refactor parts of the server

---
 Taskfile.yml                              |   7 +
 server-rs/src/consts.rs                   |  34 +--
 server-rs/src/{index.rs => grid_index.rs} |  85 +++++---
 server-rs/src/routes/parse.rs             |  23 ++
 server-rs/src/routes/pois.rs              |  60 ++----
 server-rs/src/tests.rs                    | 250 ++++++++++++++++++++++
 6 files changed, 369 insertions(+), 90 deletions(-)
 rename server-rs/src/{index.rs => grid_index.rs} (51%)
 create mode 100644 server-rs/src/routes/parse.rs
 create mode 100644 server-rs/src/tests.rs

diff --git a/Taskfile.yml b/Taskfile.yml
index 498cd36..dfaf773 100644
--- a/Taskfile.yml
+++ b/Taskfile.yml
@@ -22,6 +22,12 @@ tasks:
       - uv run -m pipeline.utils.test_fuzzy_join
       - uv run pytest pipeline/utils/test_haversine.py
       - uv run pytest pipeline/utils/test_poi_counts.py
+  
+  test:server:
+    desc: Run Rust backend tests
+    dir: server-rs
+    cmds:
+      - cargo test
 
   dev:server:
     desc: Run Rust backend on port 8001 (debug build, fast compile)
@@ -115,3 +121,4 @@ tasks:
       - task: build:server
       - task: build:frontend
       - task: test
+      - task: test:server
diff --git a/server-rs/src/consts.rs b/server-rs/src/consts.rs
index c7190cf..28b096b 100644
--- a/server-rs/src/consts.rs
+++ b/server-rs/src/consts.rs
@@ -1,20 +1,28 @@
-pub const FEATURE_PERCENTILE_LOW: f64 = 2.0;
-
-pub const FEATURE_PERCENTILE_HIGH: f64 = 98.0;
-
 pub const HISTOGRAM_BINS: usize = 100;
 
 pub const H3_PRECOMPUTE_MIN: u8 = 4;
 pub const H3_PRECOMPUTE_MAX: u8 = 12;
 
-pub const EXCLUDED_COLUMNS: &[&str] = &["lat", "lon"];
+pub const SERVER_ADDRESS: &str = "0.0.0.0:8001";
 
-pub const EXCLUDED_STRING_COLUMNS: &[&str] = &[
-    "pp_address",
-    "postcode",
-    "Address per Property Register",
-    "Address per EPC",
-    "Postcode",
+pub const BOUNDS_QUANTIZATION: f64 = 0.01;
+pub const BOUNDS_BUFFER_PERCENT: f64 = 0.1;
+pub const POSTCODE_MIN_RESOLUTION: u8 = 11;
+pub const MAX_POIS_PER_REQUEST: usize = 5000;
+pub const DEFAULT_PROPERTIES_LIMIT: usize = 100;
+pub const MAX_PROPERTIES_LIMIT: usize = 500;
+pub const ENUM_NULL: u8 = 255;
+
+/// Canonical display order for POI category groups.
+/// The server will panic at startup if the data contains groups not in this list or vice versa.
+pub const POI_GROUP_ORDER: &[&str] = &[
+    "Public Transport",
+    "Amenity",
+    "Building",
+    "Craft",
+    "Healthcare",
+    "Leisure",
+    "Office",
+    "Shop",
+    "Tourism",
 ];
-
-pub const MAX_ENUM_CARDINALITY: usize = 50;
diff --git a/server-rs/src/index.rs b/server-rs/src/grid_index.rs
similarity index 51%
rename from server-rs/src/index.rs
rename to server-rs/src/grid_index.rs
index 03412f6..4743d84 100644
--- a/server-rs/src/index.rs
+++ b/server-rs/src/grid_index.rs
@@ -2,7 +2,6 @@
 ///
 /// Divides the UK bounding box into cells of ~0.01 degrees (~1km),
 /// each storing indices of rows whose lat/lon falls within that cell.
-
 pub struct GridIndex {
     min_lat: f64,
     min_lon: f64,
@@ -21,19 +20,17 @@ impl GridIndex {
         let mut max_lon = f64::NEG_INFINITY;
 
         for i in 0..lat.len() {
-            let la = lat[i];
-            let lo = lon[i];
-            if la < min_lat {
-                min_lat = la;
+            if lat[i] < min_lat {
+                min_lat = lat[i];
             }
-            if la > max_lat {
-                max_lat = la;
+            if lat[i] > max_lat {
+                max_lat = lat[i];
             }
-            if lo < min_lon {
-                min_lon = lo;
+            if lon[i] < min_lon {
+                min_lon = lon[i];
             }
-            if lo > max_lon {
-                max_lon = lo;
+            if lon[i] > max_lon {
+                max_lon = lon[i];
             }
         }
 
@@ -56,9 +53,9 @@ impl GridIndex {
         let mut cells: Vec<Vec<u32>> = vec![Vec::new(); rows * cols];
 
         for i in 0..lat.len() {
-            let r = ((lat[i] - min_lat) / cell_size) as usize;
-            let c = ((lon[i] - min_lon) / cell_size) as usize;
-            let idx = r * cols + c;
+            let grid_row = ((lat[i] - min_lat) / cell_size) as usize;
+            let grid_col = ((lon[i] - min_lon) / cell_size) as usize;
+            let idx = grid_row * cols + grid_col;
             cells[idx].push(i as u32);
         }
 
@@ -75,20 +72,23 @@ impl GridIndex {
     }
 
     pub fn query(&self, south: f64, west: f64, north: f64, east: f64) -> Vec<u32> {
-        let (r_min, r_max, c_min, c_max) = self.clamp_bounds(south, west, north, east);
+        let Some((row_min, row_max, col_min, col_max)) =
+            self.clamp_bounds(south, west, north, east)
+        else {
+            return Vec::new();
+        };
 
         let mut result = Vec::new();
-        for r in r_min..=r_max {
-            let row_start = r * self.cols;
-            for c in c_min..=c_max {
-                result.extend_from_slice(&self.cells[row_start + c]);
+        for row in row_min..=row_max {
+            let row_start = row * self.cols;
+            for col in col_min..=col_max {
+                result.extend_from_slice(&self.cells[row_start + col]);
             }
         }
 
         result
     }
 
-    /// Iterate all row indices in bounds without allocating a Vec.
     #[inline]
     pub fn for_each_in_bounds(
         &self,
@@ -98,12 +98,16 @@ impl GridIndex {
         east: f64,
         mut f: impl FnMut(u32),
     ) {
-        let (r_min, r_max, c_min, c_max) = self.clamp_bounds(south, west, north, east);
+        let Some((row_min, row_max, col_min, col_max)) =
+            self.clamp_bounds(south, west, north, east)
+        else {
+            return;
+        };
 
-        for r in r_min..=r_max {
-            let row_start = r * self.cols;
-            for c in c_min..=c_max {
-                for &row_idx in &self.cells[row_start + c] {
+        for row in row_min..=row_max {
+            let row_start = row * self.cols;
+            for col in col_min..=col_max {
+                for &row_idx in &self.cells[row_start + col] {
                     f(row_idx);
                 }
             }
@@ -116,17 +120,28 @@ impl GridIndex {
         west: f64,
         north: f64,
         east: f64,
-    ) -> (usize, usize, usize, usize) {
-        let r_min = ((south - self.min_lat) / self.cell_size) as isize;
-        let r_max = ((north - self.min_lat) / self.cell_size) as isize;
-        let c_min = ((west - self.min_lon) / self.cell_size) as isize;
-        let c_max = ((east - self.min_lon) / self.cell_size) as isize;
+    ) -> Option<(usize, usize, usize, usize)> {
+        let row_min_raw = ((south - self.min_lat) / self.cell_size) as isize;
+        let row_max_raw = ((north - self.min_lat) / self.cell_size) as isize;
+        let col_min_raw = ((west - self.min_lon) / self.cell_size) as isize;
+        let col_max_raw = ((east - self.min_lon) / self.cell_size) as isize;
 
-        let r_min = r_min.max(0) as usize;
-        let r_max = (r_max.min(self.rows as isize - 1)).max(0) as usize;
-        let c_min = c_min.max(0) as usize;
-        let c_max = (c_max.min(self.cols as isize - 1)).max(0) as usize;
+        let row_min = row_min_raw.max(0) as usize;
+        let row_max_clamped = row_max_raw.min(self.rows as isize - 1);
+        let col_min = col_min_raw.max(0) as usize;
+        let col_max_clamped = col_max_raw.min(self.cols as isize - 1);
 
-        (r_min, r_max, c_min, c_max)
+        if row_max_clamped < 0 || col_max_clamped < 0 {
+            return None;
+        }
+
+        let row_max = row_max_clamped as usize;
+        let col_max = col_max_clamped as usize;
+
+        if row_min > row_max || col_min > col_max {
+            return None;
+        }
+
+        Some((row_min, row_max, col_min, col_max))
     }
 }
diff --git a/server-rs/src/routes/parse.rs b/server-rs/src/routes/parse.rs
new file mode 100644
index 0000000..5bcd04f
--- /dev/null
+++ b/server-rs/src/routes/parse.rs
@@ -0,0 +1,23 @@
+use axum::http::StatusCode;
+
+pub fn parse_bounds(bounds_str: &str) -> Result<(f64, f64, f64, f64), (StatusCode, String)> {
+    let parts: Vec<f64> = bounds_str
+        .split(',')
+        .map(|s| s.trim().parse::<f64>())
+        .collect::<Result<Vec<_>, _>>()
+        .map_err(|_| {
+            (
+                StatusCode::BAD_REQUEST,
+                "Invalid bounds format. Use: south,west,north,east".into(),
+            )
+        })?;
+
+    if parts.len() != 4 {
+        return Err((
+            StatusCode::BAD_REQUEST,
+            "Invalid bounds format. Use: south,west,north,east".into(),
+        ));
+    }
+
+    Ok((parts[0], parts[1], parts[2], parts[3]))
+}
diff --git a/server-rs/src/routes/pois.rs b/server-rs/src/routes/pois.rs
index f309ab7..40d5557 100644
--- a/server-rs/src/routes/pois.rs
+++ b/server-rs/src/routes/pois.rs
@@ -6,8 +6,11 @@ use axum::response::Json;
 use serde::{Deserialize, Serialize};
 use tracing::info;
 
+use crate::consts::MAX_POIS_PER_REQUEST;
 use crate::data::POI;
-use crate::state::AppState;
+use crate::state::{AppState, POICategoryGroup};
+
+use super::parse::parse_bounds;
 
 #[derive(Deserialize)]
 pub struct POIParams {
@@ -30,28 +33,10 @@ pub async fn get_pois(
         "bounds parameter is required".into(),
     ))?;
 
-    let parts: Vec<f64> = bounds_str
-        .split(',')
-        .map(|s| s.trim().parse::<f64>())
-        .collect::<Result<Vec<_>, _>>()
-        .map_err(|_| {
-            (
-                StatusCode::BAD_REQUEST,
-                "Invalid bounds format. Use: south,west,north,east".into(),
-            )
-        })?;
-
-    if parts.len() != 4 {
-        return Err((
-            StatusCode::BAD_REQUEST,
-            "Invalid bounds format. Use: south,west,north,east".into(),
-        ));
-    }
-
-    let (south, west, north, east) = (parts[0], parts[1], parts[2], parts[3]);
+    let (south, west, north, east) = parse_bounds(&bounds_str)?;
 
     let categories_str = params.categories.clone();
-    let category_filter: Option<Vec<String>> = params
+    let category_filter: Option<rustc_hash::FxHashSet<String>> = params
         .categories
         .as_deref()
         .filter(|s| !s.is_empty())
@@ -78,12 +63,13 @@ pub async fn get_pois(
                     id: state.poi_data.id[row].clone(),
                     name: state.poi_data.name[row].clone(),
                     category: state.poi_data.category[row].clone(),
+                    group: state.poi_data.group[row].clone(),
                     lat: state.poi_data.lat[row],
                     lng: state.poi_data.lng[row],
                     emoji: state.poi_data.emoji[row].clone(),
                 })
             })
-            .take(5000)
+            .take(MAX_POIS_PER_REQUEST)
             .collect();
 
         let elapsed = t0.elapsed();
@@ -99,35 +85,25 @@ pub async fn get_pois(
         POIsResponse { pois }
     })
     .await
-    .unwrap();
+    .map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;
 
     Ok(Json(result))
 }
 
 #[derive(Serialize)]
 pub struct POICategoriesResponse {
-    categories: Vec<String>,
+    groups: Vec<POICategoryGroup>,
 }
 
 pub async fn get_poi_categories(state: Arc<AppState>) -> Json<POICategoriesResponse> {
-    let result = tokio::task::spawn_blocking(move || {
-        let mut categories: Vec<String> = state
-            .poi_data
-            .category
-            .iter()
-            .cloned()
-            .collect::<std::collections::HashSet<_>>()
-            .into_iter()
-            .collect();
+    let groups: Vec<POICategoryGroup> = state.poi_category_groups.clone();
 
-        categories.sort();
+    let total: usize = groups.iter().map(|g| g.categories.len()).sum();
+    info!(
+        count = total,
+        groups = groups.len(),
+        "GET /api/poi-categories"
+    );
 
-        info!(count = categories.len(), "GET /api/poi-categories");
-
-        POICategoriesResponse { categories }
-    })
-    .await
-    .unwrap();
-
-    Json(result)
+    Json(POICategoriesResponse { groups })
 }
diff --git a/server-rs/src/tests.rs b/server-rs/src/tests.rs
new file mode 100644
index 0000000..a6bd035
--- /dev/null
+++ b/server-rs/src/tests.rs
@@ -0,0 +1,250 @@
+#[cfg(test)]
+mod grid_index_tests {
+    use crate::grid_index::GridIndex;
+
+    #[test]
+    fn query_bounds_fully_below_grid_returns_empty() {
+        let lat = vec![50.0, 50.5, 51.0];
+        let lon = vec![0.0, 0.5, 1.0];
+        let grid = GridIndex::build(&lat, &lon, 0.01);
+
+        let results = grid.query(10.0, -10.0, 20.0, -5.0);
+        assert!(
+            results.is_empty(),
+            "Should return empty for bounds fully below grid"
+        );
+    }
+
+    #[test]
+    fn query_bounds_fully_above_grid_returns_empty() {
+        let lat = vec![50.0, 50.5, 51.0];
+        let lon = vec![0.0, 0.5, 1.0];
+        let grid = GridIndex::build(&lat, &lon, 0.01);
+
+        let results = grid.query(80.0, 50.0, 90.0, 60.0);
+        assert!(
+            results.is_empty(),
+            "Should return empty for bounds fully above grid"
+        );
+    }
+
+    #[test]
+    fn query_inverted_bounds_returns_empty() {
+        let lat = vec![50.0, 50.5, 51.0];
+        let lon = vec![0.0, 0.5, 1.0];
+        let grid = GridIndex::build(&lat, &lon, 0.01);
+
+        // south > north
+        let results = grid.query(52.0, 0.0, 49.0, 1.0);
+        assert!(
+            results.is_empty(),
+            "Should return empty for inverted bounds"
+        );
+    }
+
+    #[test]
+    fn for_each_bounds_fully_outside_yields_nothing() {
+        let lat = vec![50.0, 50.5, 51.0];
+        let lon = vec![0.0, 0.5, 1.0];
+        let grid = GridIndex::build(&lat, &lon, 0.01);
+
+        let mut count = 0;
+        grid.for_each_in_bounds(10.0, -10.0, 20.0, -5.0, |_| count += 1);
+        assert_eq!(
+            count, 0,
+            "for_each should yield nothing for out-of-bounds query"
+        );
+    }
+
+    #[test]
+    fn query_with_large_cells_outside_returns_empty() {
+        // Previously, out-of-bounds queries with large cell sizes would
+        // scan cell (0,0) which could contain data. Now returns empty.
+        let lat = vec![50.0];
+        let lon = vec![0.0];
+        let grid = GridIndex::build(&lat, &lon, 1.0);
+
+        let results = grid.query(0.0, -50.0, 10.0, -40.0);
+        assert!(
+            results.is_empty(),
+            "Should return empty even with large cell size"
+        );
+    }
+
+    #[test]
+    fn query_within_bounds_returns_correct_results() {
+        let lat = vec![50.0, 50.5, 51.0];
+        let lon = vec![0.0, 0.5, 1.0];
+        let grid = GridIndex::build(&lat, &lon, 0.01);
+
+        let results = grid.query(49.9, -0.1, 51.1, 1.1);
+        assert_eq!(results.len(), 3, "Should return all 3 points within bounds");
+    }
+
+    #[test]
+    fn query_partial_bounds_returns_subset() {
+        let lat = vec![50.0, 51.0, 52.0];
+        let lon = vec![0.0, 0.0, 0.0];
+        let grid = GridIndex::build(&lat, &lon, 0.01);
+
+        let results = grid.query(49.9, -0.1, 50.1, 0.1);
+        assert_eq!(results.len(), 1, "Should return only the point at lat=50");
+    }
+}
+
+#[cfg(test)]
+mod filter_tests {
+    use crate::data::EnumFeatureData;
+    use crate::filter::{parse_filters, row_passes_filters};
+
+    #[test]
+    fn nan_rows_fail_numeric_filter_even_with_infinite_range() {
+        let feature_names = vec!["price".to_string()];
+        let feature_data = vec![f64::NAN];
+        let enum_features: Vec<EnumFeatureData> = vec![];
+
+        let (numeric, enums) =
+            parse_filters(Some("price:-inf:inf"), &feature_names, &enum_features);
+        assert_eq!(numeric.len(), 1, "Should parse -inf:inf as valid filter");
+
+        let passes = row_passes_filters(0, &numeric, &enums, &feature_data, 1, &enum_features);
+        assert!(!passes, "NaN should fail filter even with infinite range");
+    }
+
+    #[test]
+    fn empty_enum_filter_value_rejects_all() {
+        let enum_features = vec![EnumFeatureData {
+            name: "rating".to_string(),
+            values: vec!["A".to_string(), "B".to_string()],
+            data: vec![0],
+        }];
+        let feature_names: Vec<String> = vec![];
+
+        let (numeric, enums) = parse_filters(Some("rating:"), &feature_names, &enum_features);
+        assert_eq!(enums.len(), 1);
+        assert!(enums[0].allowed.is_empty());
+
+        let passes = row_passes_filters(0, &numeric, &enums, &[], 0, &enum_features);
+        assert!(!passes, "Empty allowed set should reject all rows");
+    }
+
+    #[test]
+    fn enum_filter_with_nonexistent_values_produces_empty_allowed() {
+        let enum_features = vec![EnumFeatureData {
+            name: "rating".to_string(),
+            values: vec!["A".to_string(), "B".to_string()],
+            data: vec![0],
+        }];
+        let feature_names: Vec<String> = vec![];
+
+        let (_, enums) = parse_filters(Some("rating:X|Y|Z"), &feature_names, &enum_features);
+        assert_eq!(enums.len(), 1);
+        assert!(enums[0].allowed.is_empty());
+    }
+
+    #[test]
+    fn malformed_numeric_min_is_silently_skipped() {
+        let feature_names = vec!["price".to_string()];
+        let enum_features: Vec<EnumFeatureData> = vec![];
+
+        let (numeric, enums) = parse_filters(
+            Some("price:not_a_number:200"),
+            &feature_names,
+            &enum_features,
+        );
+        assert_eq!(numeric.len(), 0);
+        assert_eq!(enums.len(), 0);
+    }
+}
+
+#[cfg(test)]
+mod json_tests {
+    use std::fmt::Write;
+
+    #[test]
+    fn json_escaped_postcode_with_quotes_is_valid() {
+        use crate::routes::hexagons::write_json_escaped;
+
+        let mut buf = String::new();
+        buf.push_str("{\"postcode\":\"");
+        write_json_escaped(&mut buf, "SW1A \"test");
+        buf.push_str("\"}");
+
+        let result: Result<serde_json::Value, _> = serde_json::from_str(&buf);
+        assert!(
+            result.is_ok(),
+            "Escaped quote should produce valid JSON: {}",
+            buf
+        );
+        assert_eq!(result.unwrap()["postcode"].as_str().unwrap(), "SW1A \"test");
+    }
+
+    #[test]
+    fn json_escaped_postcode_with_backslash_is_valid() {
+        use crate::routes::hexagons::write_json_escaped;
+
+        let mut buf = String::new();
+        buf.push_str("{\"postcode\":\"");
+        write_json_escaped(&mut buf, "SW1A\\2AA");
+        buf.push_str("\"}");
+
+        let result: Result<serde_json::Value, _> = serde_json::from_str(&buf);
+        assert!(
+            result.is_ok(),
+            "Escaped backslash should produce valid JSON: {}",
+            buf
+        );
+        assert_eq!(result.unwrap()["postcode"].as_str().unwrap(), "SW1A\\2AA");
+    }
+
+    #[test]
+    fn nan_is_not_valid_json() {
+        // Verify that raw NaN in write! is still invalid JSON (documenting the risk
+        // that the is_finite() guard in write_hexagons_json protects against).
+        let mut buf = String::new();
+        write!(buf, "{{\"min_price\":{}}}", f64::NAN).unwrap();
+
+        let result: Result<serde_json::Value, _> = serde_json::from_str(&buf);
+        assert!(result.is_err(), "Raw NaN should produce invalid JSON");
+    }
+
+    #[test]
+    fn infinity_is_not_valid_json() {
+        let mut buf = String::new();
+        write!(buf, "{{\"min_price\":{}}}", f64::INFINITY).unwrap();
+
+        let result: Result<serde_json::Value, _> = serde_json::from_str(&buf);
+        assert!(result.is_err(), "Raw Infinity should produce invalid JSON");
+    }
+}
+
+#[cfg(test)]
+mod enum_encoding_tests {
+    #[test]
+    fn u8_cast_wraps_around_beyond_255() {
+        // Documents the underlying u8 wrapping behavior that the truncation
+        // guard in property.rs now prevents.
+        let num_values = 300usize;
+        let indices: Vec<u8> = (0..num_values).map(|i| i as u8).collect();
+
+        assert_eq!(indices[0], indices[256], "u8 wraps: 0 == 256");
+        assert_eq!(indices[1], indices[257], "u8 wraps: 1 == 257");
+
+        use std::collections::HashMap;
+        let values: Vec<String> = (0..num_values).map(|i| format!("val_{}", i)).collect();
+        let value_to_idx: HashMap<&str, u8> = values
+            .iter()
+            .enumerate()
+            .map(|(i, v)| (v.as_str(), i as u8))
+            .collect();
+
+        let unique_indices: std::collections::HashSet<u8> =
+            value_to_idx.values().cloned().collect();
+        assert!(
+            unique_indices.len() < num_values,
+            "Without the truncation guard, {} values produce only {} unique u8 indices",
+            num_values,
+            unique_indices.len()
+        );
+    }
+}

From 63fae87f888f4d2d0525c9c9b326fe2a828b2bad Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sun, 1 Feb 2026 16:54:54 +0000
Subject: [PATCH 60/62] Add rules

---
 server-rs/Cargo.toml  | 6 +++++-
 server-rs/clippy.toml | 1 +
 2 files changed, 6 insertions(+), 1 deletion(-)
 create mode 100644 server-rs/clippy.toml

diff --git a/server-rs/Cargo.toml b/server-rs/Cargo.toml
index 336e723..fd0d22b 100644
--- a/server-rs/Cargo.toml
+++ b/server-rs/Cargo.toml
@@ -4,9 +4,10 @@ version = "0.1.0"
 edition = "2021"
 
 [dependencies]
+anyhow = "1"
 clap = { version = "4", features = ["derive"] }
 axum = "0.8"
-tower-http = { version = "0.6", features = ["cors", "fs", "compression-gzip", "trace"] }
+tower-http = { version = "0.6", features = ["cors", "fs", "compression-gzip", "compression-zstd", "trace"] }
 tokio = { version = "1", features = ["full"] }
 polars = { version = "0.46", features = ["parquet", "lazy", "dtype-struct", "dtype-u8", "dtype-u16", "dtype-i8", "dtype-i16"] }
 h3o = "0.7"
@@ -17,6 +18,9 @@ rustc-hash = "2"
 tracing = "0.1"
 tracing-subscriber = { version = "0.3", features = ["env-filter", "fmt"] }
 
+[lints.clippy]
+min_ident_chars = "warn"
+
 [profile.dev]
 opt-level = 1
 
diff --git a/server-rs/clippy.toml b/server-rs/clippy.toml
new file mode 100644
index 0000000..5f216f8
--- /dev/null
+++ b/server-rs/clippy.toml
@@ -0,0 +1 @@
+allowed-idents-below-min-chars = ["i", "j", "k", "_"]

From 65877acf95f454a78501b9ae4b88b60ac86152aa Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sun, 1 Feb 2026 19:29:07 +0000
Subject: [PATCH 61/62] Small changes

---
 CLAUDE.md            | 2 ++
 Taskfile.yml         | 2 ++
 server-rs/Cargo.lock | 9 +++++++++
 3 files changed, 13 insertions(+)

diff --git a/CLAUDE.md b/CLAUDE.md
index 2934117..cd0a5df 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -2,6 +2,8 @@
 
 This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
 
+NEVER EVER RUN GIT COMMANDS!!
+
 ## Project Overview
 
 Property Map is a full-stack geospatial application for visualizing UK property data on an interactive map. It combines Land Registry price-paid data, EPC energy certificates, postcode geolocation, TFL journey times, Index of Deprivation scores, crime statistics, ethnicity data, broadband speeds, school ratings, road noise, and OpenStreetMap POIs into a single wide parquet file, then serves aggregated H3 hexagon statistics and POI data via a Rust backend.
diff --git a/Taskfile.yml b/Taskfile.yml
index dfaf773..fceba56 100644
--- a/Taskfile.yml
+++ b/Taskfile.yml
@@ -44,6 +44,8 @@ tasks:
   dev:frontend:
     desc: Run frontend dev server on port 3030 (proxies /api to :8001)
     dir: frontend
+    deps:
+      - install
     cmds:
       - npm run dev
 
diff --git a/server-rs/Cargo.lock b/server-rs/Cargo.lock
index 60e56a4..89c3585 100644
--- a/server-rs/Cargo.lock
+++ b/server-rs/Cargo.lock
@@ -111,6 +111,12 @@ dependencies = [
  "windows-sys 0.61.2",
 ]
 
+[[package]]
+name = "anyhow"
+version = "1.0.100"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "a23eb6b1614318a8071c9b2521f36b424b2c83db5eb3a0fead4a6c0809af6e61"
+
 [[package]]
 name = "ar_archive_writer"
 version = "0.5.1"
@@ -450,6 +456,8 @@ dependencies = [
  "compression-core",
  "flate2",
  "memchr",
+ "zstd",
+ "zstd-safe",
 ]
 
 [[package]]
@@ -1801,6 +1809,7 @@ dependencies = [
 name = "property-map-server"
 version = "0.1.0"
 dependencies = [
+ "anyhow",
  "axum",
  "clap",
  "h3o",

From 66c2a25457f2e16e92ce9489b53dbb94d93d47e3 Mon Sep 17 00:00:00 2001
From: Andras Schmelczer <andras@schmelczer.dev>
Date: Sun, 1 Feb 2026 19:30:33 +0000
Subject: [PATCH 62/62] Checkpoint all changes

---
 frontend/src/App.tsx                        | 384 +++++++++--
 frontend/src/components/AreaPane.tsx        | 243 +++++++
 frontend/src/components/DataSources.tsx     |   2 +-
 frontend/src/components/DataSourcesPage.tsx |  50 +-
 frontend/src/components/FAQPage.tsx         | 119 ++++
 frontend/src/components/Filters.tsx         | 556 +++++++++++-----
 frontend/src/components/HomePage.tsx        |   8 +-
 frontend/src/components/Map.tsx             | 175 ++---
 frontend/src/components/POIPane.tsx         |  88 ++-
 frontend/src/components/PropertiesPane.tsx  | 123 +++-
 frontend/src/components/ui/slider.tsx       |   4 +-
 frontend/src/index.css                      |   2 +-
 frontend/src/types.ts                       |  25 +
 pipeline/transform/join_epc_pp.py           |   4 +-
 server-rs/src/consts.rs                     |   2 +-
 server-rs/src/features.rs                   | 676 ++++++++++++++++++++
 server-rs/src/filter.rs                     |  35 +-
 server-rs/src/grid_index.rs                 |  32 +-
 server-rs/src/main.rs                       | 178 ++++--
 server-rs/src/routes/features.rs            | 145 +++--
 server-rs/src/routes/hexagon_stats.rs       | 251 ++++++++
 server-rs/src/routes/hexagons.rs            | 272 +++++---
 server-rs/src/routes/mod.rs                 |   7 +-
 server-rs/src/routes/parse.rs               |  31 +-
 server-rs/src/routes/pois.rs                |  55 +-
 server-rs/src/routes/properties.rs          | 158 +++--
 server-rs/src/state.rs                      |  23 +-
 server-rs/src/tests.rs                      |   8 +-
 28 files changed, 3035 insertions(+), 621 deletions(-)
 create mode 100644 frontend/src/components/AreaPane.tsx
 create mode 100644 frontend/src/components/FAQPage.tsx
 create mode 100644 server-rs/src/features.rs
 create mode 100644 server-rs/src/routes/hexagon_stats.rs

diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx
index 24fd6fe..0de2505 100644
--- a/frontend/src/App.tsx
+++ b/frontend/src/App.tsx
@@ -3,8 +3,10 @@ import Map from './components/Map';
 import Filters from './components/Filters';
 import POIPane from './components/POIPane';
 import { PropertiesPane } from './components/PropertiesPane';
+import AreaPane from './components/AreaPane';
 import DataSources from './components/DataSources';
 import DataSourcesPage from './components/DataSourcesPage';
+import FAQPage from './components/FAQPage';
 import HomePage from './components/HomePage';
 import type {
   FeatureMeta,
@@ -21,18 +23,43 @@ import type {
   ViewState,
   Property,
   HexagonPropertiesResponse,
+  HexagonStatsResponse,
 } from './types';
 
 type Theme = 'light' | 'dark';
 
 const DEBOUNCE_MS = 150;
 const URL_DEBOUNCE_MS = 300;
+const INITIAL_RETRY_MS = 1000;
+const MAX_RETRY_MS = 10000;
+
+async function fetchWithRetry<T>(
+  url: string,
+  onSuccess: (data: T) => void,
+  signal: AbortSignal
+): Promise<void> {
+  let delay = INITIAL_RETRY_MS;
+  while (!signal.aborted) {
+    try {
+      const res = await fetch(url, { signal });
+      if (!res.ok) throw new Error(`HTTP ${res.status}`);
+      const json = await res.json();
+      onSuccess(json);
+      return;
+    } catch (err) {
+      if (signal.aborted) return;
+      console.error(`Failed to fetch ${url}, retrying in ${delay}ms:`, err);
+      await new Promise((resolve) => setTimeout(resolve, delay));
+      delay = Math.min(delay * 2, MAX_RETRY_MS);
+    }
+  }
+}
 
 // Detect if running through VS Code web proxy and construct API base URL
 function getApiBaseUrl(): string {
-  const { hostname, pathname, href } = window.location;
+  const { pathname, href } = window.location;
 
-  // Check pathname for /proxy/PORT pattern
+  // Check pathname for /proxy/PORT pattern (VS Code web proxy)
   const pathMatch = pathname.match(/^(\/proxy\/)(\d+)/);
   if (pathMatch) {
     return `${pathMatch[1]}8001`;
@@ -44,12 +71,7 @@ function getApiBaseUrl(): string {
     return `${hrefMatch[1]}8001`;
   }
 
-  // If not localhost, assume we're behind a proxy and need explicit backend port
-  if (hostname !== 'localhost' && hostname !== '127.0.0.1') {
-    return '/proxy/8001';
-  }
-
-  // Local development - webpack proxies /api to :8001
+  // Default: same origin (works for both local dev with webpack proxy and production)
   return '';
 }
 
@@ -66,7 +88,7 @@ function parseUrlState(): {
   viewState?: ViewState;
   filters?: FeatureFilters;
   poiCategories?: Set<string>;
-  tab?: 'pois' | 'properties';
+  tab?: 'pois' | 'properties' | 'area';
 } {
   const params = new URLSearchParams(window.location.search);
   const result: ReturnType<typeof parseUrlState> = {};
@@ -121,10 +143,11 @@ function parseUrlState(): {
     result.poiCategories = new Set(poi.split(',').filter(Boolean));
   }
 
-  // Parse tab: tab=p or tab=o
+  // Parse tab: tab=p or tab=o or tab=a
   const tab = params.get('tab');
   if (tab === 'p') result.tab = 'properties';
   else if (tab === 'o') result.tab = 'pois';
+  else if (tab === 'a') result.tab = 'area';
 
   return result;
 }
@@ -134,7 +157,7 @@ function stateToParams(
   filters: FeatureFilters,
   features: FeatureMeta[],
   selectedPOICategories: Set<string>,
-  rightPaneTab: 'pois' | 'properties'
+  rightPaneTab: 'pois' | 'properties' | 'area'
 ): URLSearchParams {
   const params = new URLSearchParams();
 
@@ -170,6 +193,8 @@ function stateToParams(
   // Tab (only if non-default)
   if (rightPaneTab === 'properties') {
     params.set('tab', 'p');
+  } else if (rightPaneTab === 'area') {
+    params.set('tab', 'a');
   }
 
   return params;
@@ -177,7 +202,7 @@ function stateToParams(
 
 // --- Header ---
 
-type Page = 'home' | 'dashboard' | 'data-sources';
+type Page = 'home' | 'dashboard' | 'data-sources' | 'faq';
 
 function Header({
   activePage,
@@ -200,9 +225,9 @@ function Header({
   }, []);
 
   const tabClass = (page: Page) =>
-    `px-3 py-1.5 rounded text-sm transition-colors ${
+    `px-3 py-1.5 rounded text-sm font-medium transition-colors ${
       activePage === page
-        ? 'bg-navy-700 font-semibold'
+        ? 'bg-navy-700 text-white'
         : 'text-warm-300 hover:bg-navy-800 hover:text-white'
     }`;
 
@@ -234,16 +259,16 @@ function Header({
           </svg>
           <span className="font-semibold text-lg">Narrowit</span>
         </button>
-        <nav className="flex items-center gap-1">
-          <button className={tabClass('home')} onClick={() => onPageChange('home')}>
-            Home
-          </button>
+        <nav className="flex items-center gap-2">
           <button className={tabClass('dashboard')} onClick={() => onPageChange('dashboard')}>
             Dashboard
           </button>
           <button className={tabClass('data-sources')} onClick={() => onPageChange('data-sources')}>
             Data Sources
           </button>
+          <button className={tabClass('faq')} onClick={() => onPageChange('faq')}>
+            FAQ
+          </button>
         </nav>
       </div>
       <div className="flex items-center gap-2">
@@ -355,8 +380,62 @@ export default function App() {
   const [propertiesTotal, setPropertiesTotal] = useState(0);
   const [propertiesOffset, setPropertiesOffset] = useState(0);
   const [loadingProperties, setLoadingProperties] = useState(false);
-  const [rightPaneTab, setRightPaneTab] = useState<'pois' | 'properties'>(urlState.tab || 'pois');
-  const [activePage, setActivePage] = useState<Page>('home');
+  const [rightPaneTab, setRightPaneTab] = useState<'pois' | 'properties' | 'area'>(urlState.tab || 'pois');
+
+  // Area stats state
+  const [areaStats, setAreaStats] = useState<HexagonStatsResponse | null>(null);
+  const [loadingAreaStats, setLoadingAreaStats] = useState(false);
+
+  // Hover state
+  const [hoveredHexagon, setHoveredHexagon] = useState<string | null>(null);
+  const [hoveredAreaStats, setHoveredAreaStats] = useState<HexagonStatsResponse | null>(null);
+  const [hoveredProperties, setHoveredProperties] = useState<Property[] | null>(null);
+  const [hoveredPropertiesTotal, setHoveredPropertiesTotal] = useState(0);
+  const [loadingHoveredAreaStats, setLoadingHoveredAreaStats] = useState(false);
+  const [hoverMode, setHoverMode] = useState(true);
+  const hoverAbortRef = useRef<AbortController | null>(null);
+  const hoverDebounceRef = useRef<ReturnType<typeof setTimeout> | null>(null);
+  const [initialLoading, setInitialLoading] = useState(true);
+  const [activePage, setActivePage] = useState<Page>(() => {
+    // Restore from history state if available (e.g. back/forward navigation)
+    if (window.history.state?.page) return window.history.state.page;
+    const params = new URLSearchParams(window.location.search);
+    return params.has('v') || params.has('f') || params.has('poi') || params.has('tab')
+      ? 'dashboard'
+      : 'home';
+  });
+
+  // Feature name to auto-open in the info popup after back navigation
+  const [pendingInfoFeature, setPendingInfoFeature] = useState<string | null>(null);
+
+  // Navigate between pages with history support
+  const navigateTo = useCallback((page: Page, hash?: string, infoFeature?: string) => {
+    // Before pushing, tag the current state with the info feature so back restores it
+    if (infoFeature) {
+      window.history.replaceState({ ...window.history.state, infoFeature }, '');
+    }
+    const url = hash ? `${window.location.pathname}${window.location.search}#${hash}` : `${window.location.pathname}${window.location.search}`;
+    window.history.pushState({ page }, '', url);
+    setActivePage(page);
+  }, []);
+
+  // Handle browser back/forward
+  useEffect(() => {
+    // Tag the initial state so popstate can restore it
+    if (!window.history.state?.page) {
+      window.history.replaceState({ page: activePage }, '');
+    }
+    const handlePopState = (e: PopStateEvent) => {
+      if (e.state?.page) {
+        setActivePage(e.state.page);
+        if (e.state.infoFeature) {
+          setPendingInfoFeature(e.state.infoFeature);
+        }
+      }
+    };
+    window.addEventListener('popstate', handlePopState);
+    return () => window.removeEventListener('popstate', handlePopState);
+  }, []); // eslint-disable-line react-hooks/exhaustive-deps
 
   // Theme state — defaults to system preference on first visit
   const [theme, setTheme] = useState<Theme>(() => {
@@ -387,10 +466,15 @@ export default function App() {
   const viewFeature = activeFeature || pinnedFeature;
   const viewSource: 'drag' | 'eye' | null = activeFeature ? 'drag' : pinnedFeature ? 'eye' : null;
   // Color range: always the feature's full slider range from metadata
+  // For enum features, use ordinal index range [0, values.length - 1]
   const colorRange = useMemo((): [number, number] | null => {
     if (!viewFeature) return null;
     const meta = features.find((f) => f.name === viewFeature);
-    if (meta?.min != null && meta?.max != null) return [meta.min, meta.max];
+    if (!meta) return null;
+    if (meta.type === 'enum' && meta.values && meta.values.length > 0) {
+      return [0, meta.values.length - 1];
+    }
+    if (meta.min != null && meta.max != null) return [meta.min, meta.max];
     return null;
   }, [viewFeature, features]);
 
@@ -420,7 +504,7 @@ export default function App() {
       );
       const search = params.toString();
       const newUrl = search ? `${window.location.pathname}?${search}` : window.location.pathname;
-      window.history.replaceState(null, '', newUrl);
+      window.history.replaceState({ ...window.history.state }, '', newUrl);
     }, URL_DEBOUNCE_MS);
 
     return () => {
@@ -428,25 +512,40 @@ export default function App() {
     };
   }, [currentView, filters, features, selectedPOICategories, rightPaneTab]);
 
-  // Fetch feature metadata + POI categories on mount
+  // Fetch feature metadata + POI categories on mount with exponential backoff
   useEffect(() => {
-    fetch(`${getApiBaseUrl()}/api/features`)
-      .then((res) => res.json())
-      .then((json: { groups: FeatureGroup[] }) => {
-        // Flatten grouped response into a flat feature list with group annotation
+    const controller = new AbortController();
+    let featuresLoaded = false;
+    let poisLoaded = false;
+
+    const checkDone = () => {
+      if (featuresLoaded && poisLoaded) setInitialLoading(false);
+    };
+
+    fetchWithRetry<{ groups: FeatureGroup[] }>(
+      `${getApiBaseUrl()}/api/features`,
+      (json) => {
         const flat: FeatureMeta[] = json.groups.flatMap((g) =>
           g.features.map((f) => ({ ...f, group: g.name }))
         );
         setFeatures(flat);
-      })
-      .catch((err) => console.error('Failed to fetch features:', err));
+        featuresLoaded = true;
+        checkDone();
+      },
+      controller.signal
+    );
 
-    fetch(`${getApiBaseUrl()}/api/poi-categories`)
-      .then((res) => res.json())
-      .then((json: POICategoriesResponse) => {
+    fetchWithRetry<POICategoriesResponse>(
+      `${getApiBaseUrl()}/api/poi-categories`,
+      (json) => {
         setPOICategoryGroups(json.groups);
-      })
-      .catch((err) => console.error('Failed to fetch POI categories:', err));
+        poisLoaded = true;
+        checkDone();
+      },
+      controller.signal
+    );
+
+    return () => controller.abort();
   }, []);
 
   // Build filter query string helper
@@ -674,6 +773,32 @@ export default function App() {
     setPinnedFeature(null);
   }, []);
 
+  const fetchHexagonStats = useCallback(
+    async (h3: string, res: number, signal?: AbortSignal) => {
+      const params = new URLSearchParams({
+        h3,
+        resolution: res.toString(),
+      });
+      const filterEntries = Object.entries(filters);
+      if (filterEntries.length > 0) {
+        const filterStr = filterEntries
+          .map(([name, value]) => {
+            const meta = features.find((feature) => feature.name === name);
+            if (meta?.type === 'enum') {
+              return `${name}:${(value as string[]).join('|')}`;
+            }
+            const [min, max] = value as [number, number];
+            return `${name}:${min}:${max}`;
+          })
+          .join(',');
+        params.append('filters', filterStr);
+      }
+      const response = await fetch(`${getApiBaseUrl()}/api/hexagon-stats?${params}`, { signal });
+      return (await response.json()) as HexagonStatsResponse;
+    },
+    [filters, features]
+  );
+
   const fetchHexagonProperties = useCallback(
     async (h3: string, res: number, offset = 0) => {
       setLoadingProperties(true);
@@ -726,16 +851,96 @@ export default function App() {
         // Deselect if clicking same hexagon
         setSelectedHexagon(null);
         setProperties([]);
+        setAreaStats(null);
       } else {
         setSelectedHexagon({ h3, resolution });
         setPropertiesOffset(0);
-        setRightPaneTab('properties'); // Auto-switch to properties tab
-        fetchHexagonProperties(h3, resolution, 0);
+        setRightPaneTab('area'); // Auto-switch to area tab
+        setLoadingAreaStats(true);
+        fetchHexagonStats(h3, resolution)
+          .then((stats) => setAreaStats(stats))
+          .catch((error) => {
+            if (error instanceof Error && error.name !== 'AbortError') {
+              console.error('Failed to fetch area stats:', error);
+            }
+          })
+          .finally(() => setLoadingAreaStats(false));
       }
     },
-    [selectedHexagon, resolution, fetchHexagonProperties]
+    [selectedHexagon, resolution, fetchHexagonStats]
   );
 
+  const handleHexagonHover = useCallback(
+    (h3: string | null) => {
+      setHoveredHexagon(h3);
+      if (!hoverMode || !h3 || h3 === selectedHexagon?.h3) {
+        if (hoverDebounceRef.current) clearTimeout(hoverDebounceRef.current);
+        if (hoverAbortRef.current) hoverAbortRef.current.abort();
+        setHoveredAreaStats(null);
+        setHoveredProperties(null);
+        setHoveredPropertiesTotal(0);
+        return;
+      }
+
+      if (hoverDebounceRef.current) clearTimeout(hoverDebounceRef.current);
+      hoverDebounceRef.current = setTimeout(async () => {
+        if (hoverAbortRef.current) hoverAbortRef.current.abort();
+        hoverAbortRef.current = new AbortController();
+        const signal = hoverAbortRef.current.signal;
+
+        try {
+          if (rightPaneTab === 'area') {
+            setLoadingHoveredAreaStats(true);
+            const stats = await fetchHexagonStats(h3, resolution, signal);
+            if (!signal.aborted) setHoveredAreaStats(stats);
+          } else if (rightPaneTab === 'properties') {
+            const params = new URLSearchParams({
+              h3,
+              resolution: resolution.toString(),
+              limit: '3',
+              offset: '0',
+            });
+            const filterEntries = Object.entries(filters);
+            if (filterEntries.length > 0) {
+              const filterStr = filterEntries
+                .map(([name, value]) => {
+                  const meta = features.find((feature) => feature.name === name);
+                  if (meta?.type === 'enum') return `${name}:${(value as string[]).join('|')}`;
+                  const [min, max] = value as [number, number];
+                  return `${name}:${min}:${max}`;
+                })
+                .join(',');
+              params.append('filters', filterStr);
+            }
+            const response = await fetch(`${getApiBaseUrl()}/api/hexagon-properties?${params}`, {
+              signal,
+            });
+            const data: HexagonPropertiesResponse = await response.json();
+            if (!signal.aborted) {
+              setHoveredProperties(data.properties);
+              setHoveredPropertiesTotal(data.total);
+            }
+          }
+        } catch (error) {
+          if (error instanceof Error && error.name !== 'AbortError') {
+            console.error('Failed to fetch hover data:', error);
+          }
+        } finally {
+          if (!signal.aborted) setLoadingHoveredAreaStats(false);
+        }
+      }, DEBOUNCE_MS);
+    },
+    [hoverMode, selectedHexagon, rightPaneTab, resolution, filters, features, fetchHexagonStats]
+  );
+
+  const handleViewPropertiesFromArea = useCallback(() => {
+    if (selectedHexagon) {
+      setRightPaneTab('properties');
+      setPropertiesOffset(0);
+      fetchHexagonProperties(selectedHexagon.h3, selectedHexagon.resolution, 0);
+    }
+  }, [selectedHexagon, fetchHexagonProperties]);
+
   const handleLoadMoreProperties = useCallback(() => {
     if (selectedHexagon) {
       fetchHexagonProperties(selectedHexagon.h3, selectedHexagon.resolution, propertiesOffset);
@@ -745,17 +950,48 @@ export default function App() {
   const handleCloseProperties = useCallback(() => {
     setSelectedHexagon(null);
     setProperties([]);
+    setAreaStats(null);
   }, []);
 
   return (
     <div className="h-screen flex flex-col">
-      <Header activePage={activePage} onPageChange={setActivePage} theme={theme} onToggleTheme={toggleTheme} />
+      <Header activePage={activePage} onPageChange={navigateTo} theme={theme} onToggleTheme={toggleTheme} />
       {activePage === 'home' ? (
-        <HomePage onOpenDashboard={() => setActivePage('dashboard')} theme={theme} />
+        <HomePage onOpenDashboard={() => navigateTo('dashboard')} theme={theme} />
       ) : activePage === 'data-sources' ? (
         <DataSourcesPage />
+      ) : activePage === 'faq' ? (
+        <FAQPage />
       ) : (
-        <div className="flex-1 flex overflow-hidden">
+        <div className="flex-1 flex overflow-hidden relative">
+          {initialLoading && (
+            <div className="absolute inset-0 z-50 flex items-center justify-center bg-warm-50/80 dark:bg-navy-950/80 backdrop-blur-sm">
+              <div className="flex flex-col items-center gap-4">
+                <svg
+                  className="w-12 h-12 text-teal-600 dark:text-teal-400 animate-spin"
+                  fill="none"
+                  viewBox="0 0 24 24"
+                >
+                  <circle
+                    className="opacity-25"
+                    cx="12"
+                    cy="12"
+                    r="10"
+                    stroke="currentColor"
+                    strokeWidth="4"
+                  />
+                  <path
+                    className="opacity-75"
+                    fill="currentColor"
+                    d="M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4z"
+                  />
+                </svg>
+                <p className="text-warm-600 dark:text-warm-300 text-sm font-medium">
+                  Connecting to server...
+                </p>
+              </div>
+            </div>
+          )}
           <Filters
             features={features}
             filters={filters}
@@ -772,6 +1008,11 @@ export default function App() {
             pinnedFeature={pinnedFeature}
             onTogglePin={handleTogglePin}
             onCancelPin={handleCancelPin}
+            onNavigateToSource={(slug, featureName) => {
+              navigateTo('data-sources', slug, featureName);
+            }}
+            openInfoFeature={pendingInfoFeature}
+            onClearOpenInfoFeature={() => setPendingInfoFeature(null)}
           />
           <div className="flex-1 relative">
             <Map
@@ -785,29 +1026,31 @@ export default function App() {
               onCancelPin={handleCancelPin}
               features={features}
               selectedHexagonId={selectedHexagon?.h3 || null}
+              hoveredHexagonId={hoveredHexagon}
               onHexagonClick={handleHexagonClick}
+              onHexagonHover={handleHexagonHover}
               initialViewState={initialViewState}
               theme={theme}
             />
             {loading && (
-              <div className="absolute top-4 right-4 bg-white dark:bg-warm-800 dark:text-warm-200 px-3 py-1 rounded shadow">
+              <div className="absolute bottom-4 left-4 bg-white dark:bg-navy-800 dark:text-warm-200 px-3 py-1 rounded shadow">
                 Loading...
               </div>
             )}
-            <DataSources onNavigate={() => setActivePage('data-sources')} />
+            <DataSources onNavigate={() => navigateTo('data-sources')} />
           </div>
-          <div className="w-72 bg-white dark:bg-warm-900 shadow-lg z-10 flex flex-col">
+          <div className="w-72 bg-white dark:bg-navy-950 shadow-lg z-10 flex flex-col">
             {/* Tab headers */}
-            <div className="flex border-b border-warm-200 dark:border-warm-700">
+            <div className="flex border-b border-warm-200 dark:border-navy-700 text-sm">
               <button
                 className={`flex-1 p-3 ${
-                  rightPaneTab === 'pois'
+                  rightPaneTab === 'area'
                     ? 'border-b-2 border-teal-500 font-semibold dark:text-warm-100'
                     : 'text-warm-600 dark:text-warm-400'
                 }`}
-                onClick={() => setRightPaneTab('pois')}
+                onClick={() => setRightPaneTab('area')}
               >
-                POIs {pois.length > 0 && `(${pois.length})`}
+                Area {areaStats ? `(${areaStats.count})` : ''}
               </button>
               <button
                 className={`flex-1 p-3 ${
@@ -819,25 +1062,52 @@ export default function App() {
               >
                 Properties {propertiesTotal > 0 && `(${propertiesTotal})`}
               </button>
+              <button
+                className={`flex-1 p-3 ${
+                  rightPaneTab === 'pois'
+                    ? 'border-b-2 border-teal-500 font-semibold dark:text-warm-100'
+                    : 'text-warm-600 dark:text-warm-400'
+                }`}
+                onClick={() => setRightPaneTab('pois')}
+              >
+                POIs {pois.length > 0 && `(${pois.length})`}
+              </button>
             </div>
 
             {/* Tab content */}
             <div className="flex-1 overflow-hidden">
-              {rightPaneTab === 'pois' ? (
+              {rightPaneTab === 'area' ? (
+                <AreaPane
+                  stats={hoverMode && hoveredAreaStats ? hoveredAreaStats : areaStats}
+                  globalFeatures={features}
+                  loading={hoverMode && hoveredHexagon && hoveredHexagon !== selectedHexagon?.h3 ? loadingHoveredAreaStats : loadingAreaStats}
+                  hexagonId={hoverMode && hoveredHexagon && hoveredHexagon !== selectedHexagon?.h3 ? hoveredHexagon : selectedHexagon?.h3 || null}
+                  isHoveredPreview={!!(hoverMode && hoveredAreaStats && hoveredHexagon && hoveredHexagon !== selectedHexagon?.h3)}
+                  hoverMode={hoverMode}
+                  onHoverModeChange={setHoverMode}
+                  onViewProperties={handleViewPropertiesFromArea}
+                  onClose={handleCloseProperties}
+                />
+              ) : rightPaneTab === 'properties' ? (
+                <PropertiesPane
+                  properties={hoverMode && hoveredProperties ? hoveredProperties : properties}
+                  total={hoverMode && hoveredProperties ? hoveredPropertiesTotal : propertiesTotal}
+                  loading={loadingProperties}
+                  hexagonId={hoverMode && hoveredProperties ? hoveredHexagon : selectedHexagon?.h3 || null}
+                  onLoadMore={handleLoadMoreProperties}
+                  onClose={handleCloseProperties}
+                  onNavigateToSource={(slug) => navigateTo('data-sources', slug)}
+                  isHoveredPreview={!!(hoverMode && hoveredProperties && hoveredHexagon && hoveredHexagon !== selectedHexagon?.h3)}
+                  hoverMode={hoverMode}
+                  onHoverModeChange={setHoverMode}
+                />
+              ) : (
                 <POIPane
                   groups={poiCategoryGroups}
                   selectedCategories={selectedPOICategories}
                   onCategoriesChange={setSelectedPOICategories}
                   poiCount={pois.length}
-                />
-              ) : (
-                <PropertiesPane
-                  properties={properties}
-                  total={propertiesTotal}
-                  loading={loadingProperties}
-                  hexagonId={selectedHexagon?.h3 || null}
-                  onLoadMore={handleLoadMoreProperties}
-                  onClose={handleCloseProperties}
+                  onNavigateToSource={(slug) => navigateTo('data-sources', slug)}
                 />
               )}
             </div>
diff --git a/frontend/src/components/AreaPane.tsx b/frontend/src/components/AreaPane.tsx
new file mode 100644
index 0000000..dda8d6c
--- /dev/null
+++ b/frontend/src/components/AreaPane.tsx
@@ -0,0 +1,243 @@
+import { useMemo } from 'react';
+import type { FeatureMeta, HexagonStatsResponse } from '../types';
+
+interface AreaPaneProps {
+  stats: HexagonStatsResponse | null;
+  globalFeatures: FeatureMeta[];
+  loading: boolean;
+  hexagonId: string | null;
+  isHoveredPreview: boolean;
+  hoverMode: boolean;
+  onHoverModeChange: (enabled: boolean) => void;
+  onViewProperties: () => void;
+  onClose: () => void;
+}
+
+function formatValue(value: number): string {
+  if (Math.abs(value) >= 1_000_000) return `${(value / 1_000_000).toFixed(1)}M`;
+  if (Math.abs(value) >= 1_000) return `${(value / 1_000).toFixed(0)}k`;
+  if (Number.isInteger(value)) return value.toLocaleString();
+  return value.toFixed(1);
+}
+
+// Group features by their group field from globalFeatures
+function groupFeatures(
+  globalFeatures: FeatureMeta[]
+): { name: string; features: FeatureMeta[] }[] {
+  const groups: { name: string; features: FeatureMeta[] }[] = [];
+  const seen = new Set<string>();
+  for (const feature of globalFeatures) {
+    const groupName = feature.group || 'Other';
+    if (!seen.has(groupName)) {
+      seen.add(groupName);
+      groups.push({ name: groupName, features: [] });
+    }
+    groups.find((group) => group.name === groupName)!.features.push(feature);
+  }
+  return groups;
+}
+
+function MiniHistogram({ counts, maxCount }: { counts: number[]; maxCount: number }) {
+  if (maxCount === 0) return null;
+  // Downsample to ~20 bars for display
+  const targetBars = 20;
+  const step = Math.max(1, Math.floor(counts.length / targetBars));
+  const bars: number[] = [];
+  for (let index = 0; index < counts.length; index += step) {
+    let sum = 0;
+    for (let offset = 0; offset < step && index + offset < counts.length; offset++) {
+      sum += counts[index + offset];
+    }
+    bars.push(sum);
+  }
+  const barMax = Math.max(...bars, 1);
+
+  return (
+    <div className="flex items-end gap-px h-8 mt-1">
+      {bars.map((count, index) => (
+        <div
+          key={index}
+          className="flex-1 bg-teal-500 dark:bg-teal-400 rounded-t-sm min-w-[2px]"
+          style={{ height: `${(count / barMax) * 100}%`, opacity: count > 0 ? 1 : 0.1 }}
+        />
+      ))}
+    </div>
+  );
+}
+
+function EnumBarChart({ counts }: { counts: Record<string, number> }) {
+  const entries = Object.entries(counts).sort(([, countA], [, countB]) => countB - countA);
+  const maxCount = Math.max(...entries.map(([, count]) => count), 1);
+
+  return (
+    <div className="space-y-1 mt-1">
+      {entries.map(([label, count]) => (
+        <div key={label} className="flex items-center gap-2 text-xs">
+          <span className="w-16 truncate text-warm-500 dark:text-warm-400 text-right shrink-0">
+            {label}
+          </span>
+          <div className="flex-1 h-3 bg-warm-100 dark:bg-navy-700 rounded overflow-hidden">
+            <div
+              className="h-full bg-teal-500 dark:bg-teal-400 rounded"
+              style={{ width: `${(count / maxCount) * 100}%` }}
+            />
+          </div>
+          <span className="w-8 text-warm-500 dark:text-warm-400 text-right shrink-0">{count}</span>
+        </div>
+      ))}
+    </div>
+  );
+}
+
+export default function AreaPane({
+  stats,
+  globalFeatures,
+  loading,
+  hexagonId,
+  isHoveredPreview,
+  hoverMode,
+  onHoverModeChange,
+  onViewProperties,
+  onClose,
+}: AreaPaneProps) {
+  const featureGroups = useMemo(() => groupFeatures(globalFeatures), [globalFeatures]);
+
+  // Build lookup maps from stats
+  const numericByName = useMemo(() => {
+    if (!stats) return new Map();
+    return new Map(stats.numeric_features.map((feature) => [feature.name, feature]));
+  }, [stats]);
+
+  const enumByName = useMemo(() => {
+    if (!stats) return new Map();
+    return new Map(stats.enum_features.map((feature) => [feature.name, feature]));
+  }, [stats]);
+
+  if (!hexagonId) {
+    return (
+      <div className="flex items-center justify-center h-full text-warm-500 dark:text-warm-400 px-4 text-center text-sm">
+        Click a hexagon to view area statistics
+      </div>
+    );
+  }
+
+  return (
+    <div className="flex flex-col h-full">
+      {/* Header */}
+      <div className="p-3 border-b border-warm-200 dark:border-navy-700">
+        <div className="flex justify-between items-center">
+          <div className="flex items-center gap-2">
+            <h2 className="text-sm font-semibold dark:text-warm-100">Area Statistics</h2>
+            {isHoveredPreview && (
+              <span className="text-xs px-1.5 py-0.5 rounded bg-teal-50 dark:bg-teal-900/30 text-teal-600 dark:text-teal-400">
+                Preview
+              </span>
+            )}
+          </div>
+          <div className="flex items-center gap-1">
+            <button
+              onClick={() => onHoverModeChange(!hoverMode)}
+              className={`p-1 rounded ${
+                hoverMode
+                  ? 'text-teal-600 dark:text-teal-400 bg-teal-50 dark:bg-teal-900/30'
+                  : 'text-warm-400 hover:text-warm-700 dark:hover:text-warm-300'
+              }`}
+              title={hoverMode ? 'Live preview on (click to lock)' : 'Live preview off (click to enable)'}
+            >
+              <svg className="w-4 h-4" viewBox="0 0 24 24" fill="none" stroke="currentColor" strokeWidth={2}>
+                <path strokeLinecap="round" strokeLinejoin="round" d="M15 12a3 3 0 11-6 0 3 3 0 016 0z" />
+                <path strokeLinecap="round" strokeLinejoin="round" d="M2.458 12C3.732 7.943 7.523 5 12 5c4.478 0 8.268 2.943 9.542 7-1.274 4.057-5.064 7-9.542 7-4.477 0-8.268-2.943-9.542-7z" />
+              </svg>
+            </button>
+            <button
+              onClick={onClose}
+              className="text-warm-400 hover:text-warm-700 dark:hover:text-warm-300 p-1"
+            >
+              <svg className="w-4 h-4" viewBox="0 0 24 24" fill="none" stroke="currentColor" strokeWidth={2}>
+                <path strokeLinecap="round" strokeLinejoin="round" d="M6 18L18 6M6 6l12 12" />
+              </svg>
+            </button>
+          </div>
+        </div>
+        {stats && (
+          <p className="text-sm text-warm-600 dark:text-warm-400 mt-1">
+            {stats.count.toLocaleString()} properties
+          </p>
+        )}
+        {stats && (
+          <button
+            onClick={onViewProperties}
+            className="mt-2 w-full text-sm py-1.5 rounded bg-teal-600 hover:bg-teal-700 text-white font-medium"
+          >
+            View {stats.count.toLocaleString()} Properties
+          </button>
+        )}
+      </div>
+
+      {/* Stats content */}
+      <div className="flex-1 overflow-y-auto">
+        {loading && !stats ? (
+          <div className="p-4 text-warm-500 dark:text-warm-400 text-sm">Loading...</div>
+        ) : stats ? (
+          <div className="p-3 space-y-4">
+            {featureGroups.map((group) => {
+              // Check if any feature in this group has data
+              const hasData = group.features.some(
+                (feature) => numericByName.has(feature.name) || enumByName.has(feature.name)
+              );
+              if (!hasData) return null;
+
+              return (
+                <div key={group.name}>
+                  <h3 className="text-xs font-semibold text-warm-500 dark:text-warm-400 uppercase tracking-wider mb-2">
+                    {group.name}
+                  </h3>
+                  <div className="space-y-3">
+                    {group.features.map((feature) => {
+                      const numericStats = numericByName.get(feature.name);
+                      const enumStats = enumByName.get(feature.name);
+
+                      if (numericStats) {
+                        const maxCount = Math.max(...numericStats.histogram.counts);
+                        return (
+                          <div key={feature.name} className="bg-warm-50 dark:bg-navy-800 rounded p-2">
+                            <div className="flex justify-between items-baseline">
+                              <span className="text-xs text-warm-700 dark:text-warm-300 truncate mr-2">
+                                {feature.name}
+                              </span>
+                              <span className="text-xs font-semibold text-teal-700 dark:text-teal-400 whitespace-nowrap">
+                                {formatValue(numericStats.mean)}
+                              </span>
+                            </div>
+                            <div className="flex justify-between text-[10px] text-warm-400 dark:text-warm-500 mt-0.5">
+                              <span>{formatValue(numericStats.min)}</span>
+                              <span>{formatValue(numericStats.max)}</span>
+                            </div>
+                            <MiniHistogram counts={numericStats.histogram.counts} maxCount={maxCount} />
+                          </div>
+                        );
+                      }
+
+                      if (enumStats) {
+                        return (
+                          <div key={feature.name} className="bg-warm-50 dark:bg-navy-800 rounded p-2">
+                            <span className="text-xs text-warm-700 dark:text-warm-300">
+                              {feature.name}
+                            </span>
+                            <EnumBarChart counts={enumStats.counts} />
+                          </div>
+                        );
+                      }
+
+                      return null;
+                    })}
+                  </div>
+                </div>
+              );
+            })}
+          </div>
+        ) : null}
+      </div>
+    </div>
+  );
+}
diff --git a/frontend/src/components/DataSources.tsx b/frontend/src/components/DataSources.tsx
index 80cd48d..386fb35 100644
--- a/frontend/src/components/DataSources.tsx
+++ b/frontend/src/components/DataSources.tsx
@@ -2,7 +2,7 @@ export default function DataSources({ onNavigate }: { onNavigate: () => void })
   return (
     <button
       onClick={onNavigate}
-      className="absolute bottom-2 right-2 bg-white/90 dark:bg-warm-800/90 backdrop-blur-sm px-3 py-2 rounded shadow-lg text-xs text-teal-600 dark:text-teal-400 hover:text-teal-800 dark:hover:text-teal-300 hover:underline font-semibold transition-colors"
+      className="absolute bottom-2 right-2 bg-white/90 dark:bg-navy-800/90 backdrop-blur-sm px-3 py-2 rounded shadow-lg text-xs text-teal-600 dark:text-teal-400 hover:text-teal-800 dark:hover:text-teal-300 hover:underline font-semibold transition-colors"
     >
       Data Sources
     </button>
diff --git a/frontend/src/components/DataSourcesPage.tsx b/frontend/src/components/DataSourcesPage.tsx
index f86b18a..f836a26 100644
--- a/frontend/src/components/DataSourcesPage.tsx
+++ b/frontend/src/components/DataSourcesPage.tsx
@@ -1,5 +1,8 @@
+import { useEffect, useState, useRef } from 'react';
+
 const DATA_SOURCES = [
   {
+    id: 'price-paid',
     name: 'Price Paid Data',
     origin: 'HM Land Registry',
     use: 'Complete historical property sale prices for England and Wales. Used for the last known sale price of each property.',
@@ -7,6 +10,7 @@ const DATA_SOURCES = [
     license: 'Open Government Licence v3.0',
   },
   {
+    id: 'epc',
     name: 'Energy Performance Certificates (EPC)',
     origin: 'Ministry of Housing, Communities & Local Government',
     use: 'Domestic Energy Performance Certificates providing floor area, number of rooms, construction age, energy ratings, property type, and built form. Fuzzy-joined with Price Paid records by address within postcode buckets.',
@@ -14,6 +18,7 @@ const DATA_SOURCES = [
     license: 'Open Government Licence v3.0',
   },
   {
+    id: 'nspl',
     name: 'National Statistics Postcode Lookup (NSPL)',
     origin: 'ONS / ArcGIS',
     use: 'Maps postcodes to latitude/longitude, LSOA, and Output Area codes for geolocation and joining area-level datasets.',
@@ -21,6 +26,7 @@ const DATA_SOURCES = [
     license: 'Open Government Licence v3.0',
   },
   {
+    id: 'iod',
     name: 'English Indices of Deprivation 2025',
     origin: 'Ministry of Housing, Communities & Local Government',
     use: 'Relative deprivation scores for 33,755 LSOAs across domains: Income, Employment, Education, Health, Crime, Living Environment, and sub-domains. Joined to properties via LSOA code.',
@@ -28,6 +34,7 @@ const DATA_SOURCES = [
     license: 'Open Government Licence v3.0',
   },
   {
+    id: 'ethnicity',
     name: 'Population by Ethnicity (2021 Census)',
     origin: 'ONS',
     use: 'Population percentages by ethnic group (Asian, Black, Mixed, White, Other) per Local Authority. Joined via Local Authority District code.',
@@ -35,6 +42,7 @@ const DATA_SOURCES = [
     license: 'Open Government Licence v3.0',
   },
   {
+    id: 'crime',
     name: 'Street-level Crime Data',
     origin: 'data.police.uk',
     use: 'Street-level crime data from 2023 to 2025, aggregated into yearly averages by LSOA and crime type (violence, burglary, anti-social behaviour, drugs, vehicle crime, etc.).',
@@ -42,6 +50,7 @@ const DATA_SOURCES = [
     license: 'Open Government Licence v3.0',
   },
   {
+    id: 'tfl-journey-times',
     name: 'TfL Journey Times',
     origin: 'Transport for London',
     use: "Journey time calculations from postcodes to central London destinations (Bank, Waterloo, King's Cross, etc.) via public transport and cycling.",
@@ -49,6 +58,7 @@ const DATA_SOURCES = [
     license: 'Powered by TfL Open Data',
   },
   {
+    id: 'osm-pois',
     name: 'OpenStreetMap POIs',
     origin: 'OpenStreetMap contributors / Geofabrik',
     use: 'Points of interest extracted from the Great Britain PBF extract. Covers amenities, shops, healthcare, leisure, tourism, and more. Filtered and remapped to friendly category names.',
@@ -56,6 +66,7 @@ const DATA_SOURCES = [
     license: 'Open Data Commons Open Database License (ODbL)',
   },
   {
+    id: 'naptan',
     name: 'NaPTAN (Public Transport Stops)',
     origin: 'Department for Transport',
     use: 'National Public Transport Access Nodes providing station and stop locations (rail, bus, metro/tram, ferry, airport), merged into the POI dataset.',
@@ -63,6 +74,7 @@ const DATA_SOURCES = [
     license: 'Open Government Licence v3.0',
   },
   {
+    id: 'noise',
     name: 'Defra Noise Mapping',
     origin: 'Defra / Environment Agency',
     use: 'Strategic noise mapping Round 4 (2022) for road, rail, and airport sources. Lden (day-evening-night 24h weighted average) at 10m grid resolution, modelled at 4m above ground. Sampled at postcode centroids via WCS GeoTIFF tiles.',
@@ -70,6 +82,7 @@ const DATA_SOURCES = [
     license: 'Open Government Licence v3.0',
   },
   {
+    id: 'ofsted',
     name: 'Ofsted School Inspections',
     origin: 'Ofsted',
     use: 'Latest inspection outcomes for state-funded schools (as at April 2025). Averaged per postcode to give a local school quality score (1=Outstanding to 4=Inadequate).',
@@ -77,6 +90,7 @@ const DATA_SOURCES = [
     license: 'Open Government Licence v3.0',
   },
   {
+    id: 'broadband',
     name: 'Ofcom Broadband Performance',
     origin: 'Ofcom',
     use: 'Fixed broadband coverage and speeds by Output Area from Connected Nations 2025. Includes max download/upload speeds across different speed tiers.',
@@ -86,8 +100,29 @@ const DATA_SOURCES = [
 ];
 
 export default function DataSourcesPage() {
+  const [highlightedId, setHighlightedId] = useState<string | null>(null);
+  const cardRefs = useRef<Record<string, HTMLDivElement | null>>({});
+
+  useEffect(() => {
+    function handleHash() {
+      const hash = window.location.hash.replace('#', '');
+      if (hash && DATA_SOURCES.some((s) => s.id === hash)) {
+        setHighlightedId(hash);
+        // Scroll after a brief delay to allow render
+        setTimeout(() => {
+          cardRefs.current[hash]?.scrollIntoView({ behavior: 'smooth', block: 'center' });
+        }, 100);
+      } else {
+        setHighlightedId(null);
+      }
+    }
+    handleHash();
+    window.addEventListener('hashchange', handleHash);
+    return () => window.removeEventListener('hashchange', handleHash);
+  }, []);
+
   return (
-    <div className="flex-1 overflow-y-auto bg-warm-50 dark:bg-warm-900 flex flex-col">
+    <div className="flex-1 overflow-y-auto bg-warm-50 dark:bg-navy-950 flex flex-col">
       <div className="flex-1">
         <div className="max-w-5xl mx-auto px-6 py-8">
           <h1 className="text-2xl font-bold text-warm-900 dark:text-warm-100 mb-2">Data Sources</h1>
@@ -97,10 +132,19 @@ export default function DataSourcesPage() {
           </p>
           <div className="grid grid-cols-1 lg:grid-cols-2 gap-4">
             {DATA_SOURCES.map((source) => (
-              <div key={source.name} className="bg-white dark:bg-warm-800 rounded-lg border border-warm-200 dark:border-warm-700 p-5">
+              <div
+                key={source.id}
+                id={source.id}
+                ref={(el) => { cardRefs.current[source.id] = el; }}
+                className={`bg-white dark:bg-navy-800 rounded-lg border p-5 ${
+                  highlightedId === source.id
+                    ? 'border-teal-400 ring-2 ring-teal-400'
+                    : 'border-warm-200 dark:border-navy-700'
+                }`}
+              >
                 <div className="flex items-start justify-between gap-4 mb-2">
                   <h2 className="text-lg font-semibold text-warm-900 dark:text-warm-100">{source.name}</h2>
-                  <span className="shrink-0 text-xs bg-warm-100 dark:bg-warm-700 text-warm-600 dark:text-warm-300 px-2 py-1 rounded">
+                  <span className="shrink-0 text-xs bg-warm-100 dark:bg-navy-700 text-warm-600 dark:text-warm-300 px-2 py-1 rounded">
                     {source.license}
                   </span>
                 </div>
diff --git a/frontend/src/components/FAQPage.tsx b/frontend/src/components/FAQPage.tsx
new file mode 100644
index 0000000..36bd032
--- /dev/null
+++ b/frontend/src/components/FAQPage.tsx
@@ -0,0 +1,119 @@
+import { useState } from 'react';
+
+interface FAQItem {
+  question: string;
+  answer: string;
+}
+
+const FAQ_ITEMS: FAQItem[] = [
+  {
+    question: 'What is this application?',
+    answer:
+      'Narrowit is an interactive map that visualises property-level data across England and Wales. It combines Land Registry sale prices, EPC energy certificates, TfL journey times, deprivation indices, crime statistics, broadband speeds, school ratings, road noise levels, ethnicity demographics, and OpenStreetMap points of interest into a single explorable view.',
+  },
+  {
+    question: 'Where does the data come from?',
+    answer:
+      'All data comes from open government and community sources. Property prices are from HM Land Registry, energy certificates from MHCLG, transport times from TfL, deprivation scores from the English Indices of Deprivation 2025, crime data from data.police.uk, school ratings from Ofsted, broadband from Ofcom, noise from Defra, ethnicity from the 2021 Census, and points of interest from OpenStreetMap. See the Data Sources page for full details and links.',
+  },
+  {
+    question: 'What are the coloured hexagons on the map?',
+    answer:
+      'The map uses H3 hexagons to aggregate property data at different zoom levels. Each hexagon summarises the properties within it. The colour represents the value of whichever feature you have pinned or are actively filtering — for example, average price or energy rating. Zoom in to see smaller, more detailed hexagons; zoom out for a broader overview.',
+  },
+  {
+    question: 'How do filters work?',
+    answer:
+      'Use the Filters panel on the left to narrow down properties. Add a filter by clicking a feature name, then drag the range slider to set minimum and maximum values. For categorical features like property type, select or deselect individual values. Only hexagons containing properties that match all active filters are shown. Filters are combined with AND logic — every property must satisfy every filter.',
+  },
+  {
+    question: 'What does the eye icon do on a filter?',
+    answer:
+      'The eye icon pins a feature as the colour source for the hexagon layer. When pinned, hexagons are coloured by that feature\'s value range even when you are not actively dragging its slider. This lets you visualise one feature while filtering on others. Click the eye icon again to unpin.',
+  },
+  {
+    question: 'How fresh is the data?',
+    answer:
+      'Property prices cover all Land Registry transactions up to the most recent quarterly release. EPC data includes certificates issued up to the latest available download. Crime data spans 2023–2025 as yearly averages. TfL journey times are computed from current timetables. Deprivation indices are from the 2025 release. School ratings reflect the latest Ofsted inspections as at April 2025. Broadband data is from Ofcom Connected Nations 2025.',
+  },
+  {
+    question: 'How are EPC records matched to Land Registry sales?',
+    answer:
+      'EPC and Land Registry records don\'t share a common identifier, so they are fuzzy-joined by address within each postcode bucket. The pipeline uses token-sorted string similarity with special handling for numeric tokens (house numbers, flat numbers). Matches are assigned greedily from highest similarity score downward so each record is used at most once.',
+  },
+  {
+    question: 'What are Points of Interest (POIs)?',
+    answer:
+      'POIs are places like cafes, schools, supermarkets, GP surgeries, parks, and train stations extracted from OpenStreetMap and the NaPTAN public transport dataset. Use the POI panel on the right to toggle categories on and off. POIs appear as markers on the map when you are zoomed in far enough.',
+  },
+  {
+    question: 'Can I share a specific view with someone?',
+    answer:
+      'Yes. The URL updates automatically as you pan, zoom, and change filters. Click the Share button in the header to copy the current URL to your clipboard. Anyone who opens that link will see the same view, filters, and active POI categories.',
+  },
+  {
+    question: 'How do I see individual properties?',
+    answer:
+      'Click on a hexagon to open the Properties panel on the right. It lists all matching properties within that hexagon, showing address, price, and key features. Use "Load more" at the bottom to paginate through large hexagons.',
+  },
+  {
+    question: 'Why are some hexagons grey?',
+    answer:
+      'Grey hexagons contain properties that have data but fall outside the range of your currently pinned or active feature. This gives you a sense of where properties exist even when their values are outside your selected range.',
+  },
+  {
+    question: 'Does this work on mobile?',
+    answer:
+      'The app is designed for desktop browsers where you have enough screen space for the map, filter panel, and POI/properties panel side by side. It will load on mobile but the experience is best on a larger screen.',
+  },
+];
+
+function FAQItemCard({ item }: { item: FAQItem }) {
+  const [open, setOpen] = useState(false);
+
+  return (
+    <div className="bg-white dark:bg-navy-800 rounded-lg border border-warm-200 dark:border-navy-700">
+      <button
+        className="w-full text-left px-5 py-4 flex items-center justify-between gap-4"
+        onClick={() => setOpen(!open)}
+      >
+        <span className="font-medium text-warm-900 dark:text-warm-100">{item.question}</span>
+        <svg
+          className={`w-5 h-5 shrink-0 text-warm-400 dark:text-warm-500 transform ${open ? 'rotate-180' : ''}`}
+          fill="none"
+          stroke="currentColor"
+          viewBox="0 0 24 24"
+          strokeWidth={2}
+        >
+          <path strokeLinecap="round" strokeLinejoin="round" d="M19 9l-7 7-7-7" />
+        </svg>
+      </button>
+      {open && (
+        <div className="px-5 pb-4">
+          <p className="text-sm text-warm-700 dark:text-warm-300 leading-relaxed">{item.answer}</p>
+        </div>
+      )}
+    </div>
+  );
+}
+
+export default function FAQPage() {
+  return (
+    <div className="flex-1 overflow-y-auto bg-warm-50 dark:bg-navy-950">
+      <div className="max-w-3xl mx-auto px-6 py-8">
+        <h1 className="text-2xl font-bold text-warm-900 dark:text-warm-100 mb-2">
+          Frequently Asked Questions
+        </h1>
+        <p className="text-warm-600 dark:text-warm-400 mb-6">
+          Common questions about how Narrowit works, where the data comes from, and how to use the
+          map.
+        </p>
+        <div className="space-y-3">
+          {FAQ_ITEMS.map((item, index) => (
+            <FAQItemCard key={index} item={item} />
+          ))}
+        </div>
+      </div>
+    </div>
+  );
+}
diff --git a/frontend/src/components/Filters.tsx b/frontend/src/components/Filters.tsx
index 203657d..6c8e396 100644
--- a/frontend/src/components/Filters.tsx
+++ b/frontend/src/components/Filters.tsx
@@ -1,4 +1,4 @@
-import { memo, useState, useRef, useEffect, useMemo } from 'react';
+import { memo, useState, useRef, useCallback, useMemo, useEffect } from 'react';
 import { Slider } from './ui/slider';
 import { Label } from './ui/label';
 import type { FeatureMeta, FeatureFilters } from '../types';
@@ -19,38 +19,129 @@ interface FiltersProps {
   pinnedFeature: string | null;
   onTogglePin: (name: string) => void;
   onCancelPin: () => void;
+  onNavigateToSource?: (slug: string, featureName: string) => void;
+  openInfoFeature?: string | null;
+  onClearOpenInfoFeature?: () => void;
 }
 
-function FilterDropdown({
-  availableFeatures,
-  onAddFilter,
+function EyeIcon({ filled, className }: { filled: boolean; className?: string }) {
+  return (
+    <svg
+      className={className || 'w-3.5 h-3.5'}
+      viewBox="0 0 24 24"
+      fill={filled ? 'currentColor' : 'none'}
+      stroke="currentColor"
+      strokeWidth={2}
+    >
+      <path d="M1 12s4-8 11-8 11 8 11 8-4 8-11 8-11-8-11-8z" />
+      <circle cx="12" cy="12" r="3" />
+    </svg>
+  );
+}
+
+function InfoPopup({
+  feature,
+  onClose,
+  onNavigateToSource,
 }: {
-  availableFeatures: FeatureMeta[];
-  onAddFilter: (name: string) => void;
+  feature: FeatureMeta;
+  onClose: () => void;
+  onNavigateToSource?: (slug: string, featureName: string) => void;
 }) {
-  const [open, setOpen] = useState(false);
-  const ref = useRef<HTMLDivElement>(null);
+  const popupRef = useRef<HTMLDivElement>(null);
 
   useEffect(() => {
-    if (!open) return;
-    const handler = (e: MouseEvent) => {
-      if (ref.current && !ref.current.contains(e.target as Node)) setOpen(false);
-    };
-    const keyHandler = (e: KeyboardEvent) => {
-      if (e.key === 'Escape') setOpen(false);
-    };
-    document.addEventListener('mousedown', handler);
-    document.addEventListener('keydown', keyHandler);
-    return () => {
-      document.removeEventListener('mousedown', handler);
-      document.removeEventListener('keydown', keyHandler);
-    };
-  }, [open]);
+    function handleClickOutside(e: MouseEvent) {
+      if (popupRef.current && !popupRef.current.contains(e.target as Node)) {
+        onClose();
+      }
+    }
+    document.addEventListener('mousedown', handleClickOutside);
+    return () => document.removeEventListener('mousedown', handleClickOutside);
+  }, [onClose]);
+
+  return (
+    <div className="fixed inset-0 z-50 flex items-center justify-center bg-black/30">
+      <div
+        ref={popupRef}
+        className="bg-white dark:bg-navy-800 border border-warm-200 dark:border-navy-700 rounded-lg shadow-xl max-w-md w-full mx-4 p-5"
+      >
+        <div className="flex items-start justify-between mb-3">
+          <h3 className="text-sm font-semibold text-warm-900 dark:text-warm-100 pr-4">
+            {feature.name}
+          </h3>
+          <button
+            onClick={onClose}
+            className="text-warm-400 hover:text-warm-700 dark:hover:text-warm-300 shrink-0"
+          >
+            <svg className="w-4 h-4" viewBox="0 0 24 24" fill="none" stroke="currentColor" strokeWidth={2}>
+              <path strokeLinecap="round" strokeLinejoin="round" d="M6 18L18 6M6 6l12 12" />
+            </svg>
+          </button>
+        </div>
+        {feature.description && (
+          <p className="text-xs text-warm-500 dark:text-warm-400 mb-2">{feature.description}</p>
+        )}
+        {feature.detail && (
+          <p className="text-sm text-warm-700 dark:text-warm-300 mb-4 leading-relaxed">{feature.detail}</p>
+        )}
+        {feature.source && onNavigateToSource && (
+          <button
+            onClick={() => {
+              onNavigateToSource(feature.source!, feature.name);
+              onClose();
+            }}
+            className="text-sm text-teal-600 dark:text-teal-400 hover:text-teal-800 dark:hover:text-teal-300 hover:underline"
+          >
+            View data source
+          </button>
+        )}
+      </div>
+    </div>
+  );
+}
+
+function FeatureBrowser({
+  availableFeatures,
+  allFeatures,
+  pinnedFeature,
+  onAddFilter,
+  onTogglePin,
+  onNavigateToSource,
+  openInfoFeature,
+  onClearOpenInfoFeature,
+}: {
+  availableFeatures: FeatureMeta[];
+  allFeatures: FeatureMeta[];
+  pinnedFeature: string | null;
+  onAddFilter: (name: string) => void;
+  onTogglePin: (name: string) => void;
+  onNavigateToSource?: (slug: string, featureName: string) => void;
+  openInfoFeature?: string | null;
+  onClearOpenInfoFeature?: () => void;
+}) {
+  const [search, setSearch] = useState('');
+  const [infoFeature, setInfoFeature] = useState<FeatureMeta | null>(null);
+
+  // Auto-open info popup when navigating back
+  useEffect(() => {
+    if (openInfoFeature) {
+      const feat = allFeatures.find((f) => f.name === openInfoFeature);
+      if (feat) setInfoFeature(feat);
+      onClearOpenInfoFeature?.();
+    }
+  }, [openInfoFeature, allFeatures, onClearOpenInfoFeature]);
+
+  const filtered = useMemo(() => {
+    if (!search) return availableFeatures;
+    const lower = search.toLowerCase();
+    return availableFeatures.filter((f) => f.name.toLowerCase().includes(lower));
+  }, [availableFeatures, search]);
 
   const grouped = useMemo(() => {
     const groups: { name: string; features: FeatureMeta[] }[] = [];
     const seen = new Map<string, FeatureMeta[]>();
-    for (const f of availableFeatures) {
+    for (const f of filtered) {
       const g = f.group || 'Other';
       let arr = seen.get(g);
       if (!arr) {
@@ -61,40 +152,87 @@ function FilterDropdown({
       arr.push(f);
     }
     return groups;
-  }, [availableFeatures]);
+  }, [filtered]);
 
   return (
-    <div ref={ref} className="relative">
-      <button
-        className="w-full p-2 border rounded text-sm bg-white dark:bg-warm-800 dark:border-warm-700 text-left text-warm-500 dark:text-warm-400 hover:border-warm-400"
-        onClick={() => setOpen(!open)}
-      >
-        + Add filter...
-      </button>
-      {open && (
-        <div className="absolute z-50 mt-1 w-full bg-white dark:bg-warm-800 border dark:border-warm-700 rounded shadow-lg max-h-80 overflow-y-auto">
-          {grouped.map((group) => (
-            <div key={group.name}>
-              <div className="px-3 py-1.5 text-xs font-bold text-warm-500 bg-warm-50 dark:bg-warm-900 dark:text-warm-400 sticky top-0">
-                {group.name}
-              </div>
-              {group.features.map((f) => (
-                <button
-                  key={f.name}
-                  className="w-full text-left px-3 py-1.5 text-sm hover:bg-teal-50 dark:hover:bg-teal-900/30 hover:text-teal-700 dark:hover:text-teal-400 dark:text-warm-300"
-                  onClick={() => {
-                    onAddFilter(f.name);
-                    setOpen(false);
-                  }}
-                >
-                  {f.name}
-                </button>
-              ))}
+    <>
+      <div className="p-2 border-b border-warm-200 dark:border-navy-700">
+        <input
+          type="text"
+          placeholder="Search features..."
+          value={search}
+          onChange={(e) => setSearch(e.target.value)}
+          className="w-full px-2 py-1 text-sm border rounded bg-white dark:bg-navy-800 dark:text-warm-200 border-warm-200 dark:border-navy-700 placeholder-warm-400 dark:placeholder-warm-500 focus:outline-none focus:ring-1 focus:ring-teal-400"
+        />
+      </div>
+      <div className="flex-1 overflow-y-auto">
+        {grouped.map((group) => (
+          <div key={group.name}>
+            <div className="px-3 py-1.5 text-xs font-bold text-warm-500 bg-warm-50 dark:bg-navy-950 dark:text-warm-400 sticky top-0">
+              {group.name}
             </div>
-          ))}
-        </div>
+            {group.features.map((f) => {
+              const isPinned = pinnedFeature === f.name;
+              return (
+                <div
+                  key={f.name}
+                  className="flex items-start justify-between px-3 py-1.5 hover:bg-teal-50 dark:hover:bg-teal-900/30 dark:text-warm-300"
+                >
+                  <div className="min-w-0 mr-2">
+                    <span className="text-sm truncate block">{f.name}</span>
+                    {f.description && (
+                      <span className="text-xs text-warm-400 dark:text-warm-500 truncate block">{f.description}</span>
+                    )}
+                  </div>
+                  <div className="flex items-center gap-1 shrink-0 mt-0.5">
+                    {f.detail && (
+                      <button
+                        onClick={() => setInfoFeature(f)}
+                        className="text-warm-400 hover:text-warm-700 dark:hover:text-warm-300 p-0.5 rounded"
+                        title="Feature info"
+                      >
+                        <svg className="w-3.5 h-3.5" viewBox="0 0 24 24" fill="none" stroke="currentColor" strokeWidth={2}>
+                          <circle cx="12" cy="12" r="10" />
+                          <path strokeLinecap="round" d="M12 16v-4m0-4h.01" />
+                        </svg>
+                      </button>
+                    )}
+                    <button
+                      onClick={() => onTogglePin(f.name)}
+                      className={`p-0.5 rounded ${isPinned ? 'text-teal-600 dark:text-teal-400' : 'text-warm-400 hover:text-warm-700 dark:hover:text-warm-300'}`}
+                      title={isPinned ? 'Unpin color view' : 'Color map by this feature'}
+                    >
+                      <EyeIcon filled={isPinned} />
+                    </button>
+                    <button
+                      onClick={() => onAddFilter(f.name)}
+                      className="text-warm-400 hover:text-warm-700 dark:hover:text-warm-300 p-0.5 rounded"
+                      title="Add filter"
+                    >
+                      <svg className="w-3.5 h-3.5" viewBox="0 0 24 24" fill="none" stroke="currentColor" strokeWidth={2}>
+                        <path strokeLinecap="round" strokeLinejoin="round" d="M12 5v14m-7-7h14" />
+                      </svg>
+                    </button>
+                  </div>
+                </div>
+              );
+            })}
+          </div>
+        ))}
+        {grouped.length === 0 && (
+          <div className="px-3 py-4 text-sm text-warm-400 dark:text-warm-500 text-center">
+            {search ? 'No matching features' : 'All features are active'}
+          </div>
+        )}
+      </div>
+      {infoFeature && (
+        <InfoPopup
+          feature={infoFeature}
+          onClose={() => setInfoFeature(null)}
+          onNavigateToSource={onNavigateToSource}
+        />
       )}
-    </div>
+    </>
   );
 }
 
@@ -121,128 +259,208 @@ export default memo(function Filters({
   pinnedFeature,
   onTogglePin,
   onCancelPin,
+  onNavigateToSource,
+  openInfoFeature,
+  onClearOpenInfoFeature,
 }: FiltersProps) {
   const availableFeatures = features.filter((f) => !enabledFeatures.has(f.name));
   const enabledFeatureList = features.filter((f) => enabledFeatures.has(f.name));
 
+  const containerRef = useRef<HTMLDivElement>(null);
+  const [splitFraction, setSplitFraction] = useState(0.65);
+  const draggingRef = useRef(false);
+
+  const handleSeparatorPointerDown = useCallback(
+    (e: React.PointerEvent) => {
+      e.preventDefault();
+      (e.target as HTMLElement).setPointerCapture(e.pointerId);
+      draggingRef.current = true;
+    },
+    []
+  );
+
+  const handleSeparatorPointerMove = useCallback(
+    (e: React.PointerEvent) => {
+      if (!draggingRef.current || !containerRef.current) return;
+      const rect = containerRef.current.getBoundingClientRect();
+      const y = e.clientY - rect.top;
+      const fraction = Math.min(0.8, Math.max(0.15, y / rect.height));
+      setSplitFraction(fraction);
+    },
+    []
+  );
+
+  const handleSeparatorPointerUp = useCallback(() => {
+    draggingRef.current = false;
+  }, []);
+
   return (
-    <div className="w-72 p-4 bg-white dark:bg-warm-900 shadow-lg space-y-4 overflow-y-auto">
-      <div className="text-sm text-warm-500 dark:text-warm-400">Zoom: {zoom.toFixed(1)}</div>
-
-      {/* Add filter dropdown */}
-      {availableFeatures.length > 0 && (
-        <FilterDropdown availableFeatures={availableFeatures} onAddFilter={onAddFilter} />
-      )}
-
-      {/* Active filters */}
-      {enabledFeatureList.map((feature) => {
-        if (feature.type === 'enum') {
-          const selectedValues = (filters[feature.name] as string[]) || [];
-          const allValues = feature.values || [];
-          return (
-            <div key={feature.name} className="space-y-1 p-2 rounded">
-              <div className="flex items-center justify-between">
-                <Label className="text-xs">{feature.name}</Label>
-                <button
-                  onClick={() => onRemoveFilter(feature.name)}
-                  className="text-warm-400 hover:text-warm-700 dark:hover:text-warm-300 text-sm px-1"
-                  title="Remove filter"
-                >
-                  x
-                </button>
-              </div>
-              <div className="flex gap-2 text-xs mb-1">
-                <button
-                  className="text-teal-600 dark:text-teal-400 hover:underline"
-                  onClick={() => onFilterChange(feature.name, [...allValues])}
-                >
-                  All
-                </button>
-                <button
-                  className="text-teal-600 dark:text-teal-400 hover:underline"
-                  onClick={() => onFilterChange(feature.name, [])}
-                >
-                  None
-                </button>
-              </div>
-              <div className="space-y-0.5 max-h-40 overflow-y-auto">
-                {allValues.map((val) => (
-                  <label key={val} className="flex items-center gap-1.5 text-xs cursor-pointer dark:text-warm-300">
-                    <input
-                      type="checkbox"
-                      checked={selectedValues.includes(val)}
-                      onChange={() => {
-                        const next = selectedValues.includes(val)
-                          ? selectedValues.filter((v) => v !== val)
-                          : [...selectedValues, val];
-                        onFilterChange(feature.name, next);
-                      }}
-                      className="rounded accent-teal-600"
-                    />
-                    {val}
-                  </label>
-                ))}
-              </div>
-            </div>
-          );
-        }
-
-        // Numeric feature
-        const isActive = activeFeature === feature.name;
-        const isPinned = pinnedFeature === feature.name;
-        const displayValue =
-          isActive && dragValue
-            ? dragValue
-            : (filters[feature.name] as [number, number]) || [feature.min!, feature.max!];
-        const step = (feature.max! - feature.min!) / 100;
-
-        return (
-          <div
-            key={feature.name}
-            className={`space-y-1 p-2 rounded ${isActive ? 'ring-2 ring-teal-400 bg-teal-50 dark:bg-teal-900/30' : isPinned ? 'ring-2 ring-teal-400 bg-teal-50/50 dark:bg-teal-900/20' : ''}`}
-          >
-            <div className="flex items-center justify-between">
-              <Label className="text-xs">
-                {feature.name}: {formatValue(displayValue[0])} - {formatValue(displayValue[1])}
-              </Label>
-              <div className="flex items-center gap-0.5">
-                <button
-                  onClick={() => onTogglePin(feature.name)}
-                  className={`p-0.5 rounded ${isPinned ? 'text-teal-600 dark:text-teal-400' : 'text-warm-400 hover:text-warm-700 dark:hover:text-warm-300'}`}
-                  title={isPinned ? 'Unpin color view' : 'Color map by this feature'}
-                >
-                  <svg
-                    className="w-3.5 h-3.5"
-                    viewBox="0 0 24 24"
-                    fill={isPinned ? 'currentColor' : 'none'}
-                    stroke="currentColor"
-                    strokeWidth={2}
-                  >
-                    <path d="M1 12s4-8 11-8 11 8 11 8-4 8-11 8-11-8-11-8z" />
-                    <circle cx="12" cy="12" r="3" />
-                  </svg>
-                </button>
-                <button
-                  onClick={() => onRemoveFilter(feature.name)}
-                  className="text-warm-400 hover:text-warm-700 dark:hover:text-warm-300 text-sm px-1"
-                  title="Remove filter"
-                >
-                  x
-                </button>
-              </div>
-            </div>
-            <Slider
-              min={feature.min!}
-              max={feature.max!}
-              step={step}
-              value={[displayValue[0], displayValue[1]]}
-              onValueChange={([min, max]) => onDragChange([min, max])}
-              onPointerDown={() => onDragStart(feature.name)}
-              onPointerUp={() => onDragEnd()}
-            />
+    <div ref={containerRef} className="w-80 flex flex-col bg-white dark:bg-navy-950 shadow-lg overflow-hidden">
+      {/* Top: Active filters — user-resizable, scrollable */}
+      <div className="min-h-0 flex flex-col" style={{ height: `${splitFraction * 100}%` }}>
+        {/* Active Filters header */}
+        <div className="shrink-0 flex items-center justify-between px-3 py-2 border-b border-warm-200 dark:border-navy-700">
+          <div className="flex items-center gap-2">
+            <span className="text-sm font-semibold text-navy-950 dark:text-warm-100">Active Filters</span>
+            {enabledFeatureList.length > 0 && (
+              <span className="text-xs font-medium px-1.5 py-0.5 rounded-full bg-teal-50 dark:bg-teal-900/30 text-teal-600 dark:text-teal-400">
+                {enabledFeatureList.length}
+              </span>
+            )}
           </div>
-        );
-      })}
+          <span className="text-xs text-warm-500 dark:text-warm-400">Zoom {zoom.toFixed(1)}</span>
+        </div>
+
+        <div className="flex-1 overflow-y-auto p-3 space-y-3">
+          {enabledFeatureList.length === 0 && (
+            <div className="flex flex-col items-center justify-center py-8 text-center">
+              <svg className="w-8 h-8 text-warm-300 dark:text-warm-600 mb-2" viewBox="0 0 24 24" fill="none" stroke="currentColor" strokeWidth={1.5}>
+                <path strokeLinecap="round" strokeLinejoin="round" d="M12 3c2.755 0 5.455.232 8.083.678.533.09.917.556.917 1.096v1.044a2.25 2.25 0 01-.659 1.591l-5.432 5.432a2.25 2.25 0 00-.659 1.591v2.927a2.25 2.25 0 01-1.244 2.013L9.75 21v-6.568a2.25 2.25 0 00-.659-1.591L3.659 7.409A2.25 2.25 0 013 5.818V4.774c0-.54.384-1.006.917-1.096A48.32 48.32 0 0112 3z" />
+              </svg>
+              <span className="text-sm font-medium text-warm-400 dark:text-warm-500">No active filters</span>
+              <span className="text-xs text-warm-400 dark:text-warm-500 mt-1">Browse features below and click + to add a filter</span>
+            </div>
+          )}
+
+          {enabledFeatureList.map((feature) => {
+            if (feature.type === 'enum') {
+              const selectedValues = (filters[feature.name] as string[]) || [];
+              const allValues = feature.values || [];
+              return (
+                <div key={feature.name} className={`space-y-1 p-3 rounded ${pinnedFeature === feature.name ? 'ring-2 ring-teal-400 bg-teal-50/50 dark:bg-teal-900/20' : ''}`}>
+                  <div className="flex items-center justify-between">
+                    <Label>{feature.name}</Label>
+                    <div className="flex items-center gap-0.5">
+                      <button
+                        onClick={() => onTogglePin(feature.name)}
+                        className={`p-0.5 rounded ${pinnedFeature === feature.name ? 'text-teal-600 dark:text-teal-400' : 'text-warm-400 hover:text-warm-700 dark:hover:text-warm-300'}`}
+                        title={pinnedFeature === feature.name ? 'Unpin color view' : 'Color map by this feature'}
+                      >
+                        <EyeIcon filled={pinnedFeature === feature.name} />
+                      </button>
+                      <button
+                        onClick={() => onRemoveFilter(feature.name)}
+                        className="text-warm-400 hover:text-warm-700 dark:hover:text-warm-300 text-sm px-1"
+                        title="Remove filter"
+                      >
+                        x
+                      </button>
+                    </div>
+                  </div>
+                  <div className="flex gap-2 text-sm mb-1">
+                    <button
+                      className="text-teal-600 dark:text-teal-400 hover:underline"
+                      onClick={() => onFilterChange(feature.name, [...allValues])}
+                    >
+                      All
+                    </button>
+                    <button
+                      className="text-teal-600 dark:text-teal-400 hover:underline"
+                      onClick={() => onFilterChange(feature.name, [])}
+                    >
+                      None
+                    </button>
+                  </div>
+                  <div className="space-y-0.5 max-h-40 overflow-y-auto">
+                    {allValues.map((val) => (
+                      <label key={val} className="flex items-center gap-1.5 text-sm cursor-pointer dark:text-warm-300">
+                        <input
+                          type="checkbox"
+                          checked={selectedValues.includes(val)}
+                          onChange={() => {
+                            const next = selectedValues.includes(val)
+                              ? selectedValues.filter((v) => v !== val)
+                              : [...selectedValues, val];
+                            onFilterChange(feature.name, next);
+                          }}
+                          className="rounded accent-teal-600"
+                        />
+                        {val}
+                      </label>
+                    ))}
+                  </div>
+                </div>
+              );
+            }
+
+            // Numeric feature
+            const isActive = activeFeature === feature.name;
+            const isPinned = pinnedFeature === feature.name;
+            const displayValue =
+              isActive && dragValue
+                ? dragValue
+                : (filters[feature.name] as [number, number]) || [feature.min!, feature.max!];
+            const step = feature.step ?? (feature.max! - feature.min!) / 100;
+
+            return (
+              <div
+                key={feature.name}
+                className={`space-y-1 p-3 rounded ${isActive ? 'ring-2 ring-teal-400 bg-teal-50 dark:bg-teal-900/30' : isPinned ? 'ring-2 ring-teal-400 bg-teal-50/50 dark:bg-teal-900/20' : ''}`}
+              >
+                <div className="flex items-center justify-between">
+                  <Label>
+                    {feature.name}: {formatValue(displayValue[0])} - {formatValue(displayValue[1])}
+                  </Label>
+                  <div className="flex items-center gap-0.5">
+                    <button
+                      onClick={() => onTogglePin(feature.name)}
+                      className={`p-0.5 rounded ${isPinned ? 'text-teal-600 dark:text-teal-400' : 'text-warm-400 hover:text-warm-700 dark:hover:text-warm-300'}`}
+                      title={isPinned ? 'Unpin color view' : 'Color map by this feature'}
+                    >
+                      <EyeIcon filled={isPinned} />
+                    </button>
+                    <button
+                      onClick={() => onRemoveFilter(feature.name)}
+                      className="text-warm-400 hover:text-warm-700 dark:hover:text-warm-300 text-sm px-1"
+                      title="Remove filter"
+                    >
+                      x
+                    </button>
+                  </div>
+                </div>
+                <Slider
+                  min={feature.min!}
+                  max={feature.max!}
+                  step={step}
+                  value={[displayValue[0], displayValue[1]]}
+                  onValueChange={([min, max]) => onDragChange([min, max])}
+                  onPointerDown={() => onDragStart(feature.name)}
+                  onPointerUp={() => onDragEnd()}
+                />
+              </div>
+            );
+          })}
+        </div>
+      </div>
+
+      {/* Draggable separator */}
+      <div
+        className="shrink-0 h-1.5 cursor-row-resize flex items-center justify-center bg-warm-100 dark:bg-navy-800 hover:bg-warm-200 dark:hover:bg-navy-700 border-y border-warm-200 dark:border-navy-700"
+        onPointerDown={handleSeparatorPointerDown}
+        onPointerMove={handleSeparatorPointerMove}
+        onPointerUp={handleSeparatorPointerUp}
+      >
+        <div className="w-8 h-0.5 rounded bg-warm-300 dark:bg-navy-600" />
+      </div>
+
+      {/* Bottom: Feature browser — fills remaining space */}
+      <div className="min-h-0 flex-1 flex flex-col">
+        <div className="shrink-0 px-3 py-2 border-b border-warm-200 dark:border-navy-700">
+          <span className="text-sm font-semibold text-navy-950 dark:text-warm-100">Add Filter</span>
+        </div>
+        <div className="min-h-0 flex-1 flex flex-col">
+          <FeatureBrowser
+            availableFeatures={availableFeatures}
+            allFeatures={features}
+            pinnedFeature={pinnedFeature}
+            onAddFilter={onAddFilter}
+            onTogglePin={onTogglePin}
+            onNavigateToSource={onNavigateToSource}
+            openInfoFeature={openInfoFeature}
+            onClearOpenInfoFeature={onClearOpenInfoFeature}
+          />
+        </div>
+      </div>
     </div>
   );
 });
diff --git a/frontend/src/components/HomePage.tsx b/frontend/src/components/HomePage.tsx
index 0dc9907..86110f1 100644
--- a/frontend/src/components/HomePage.tsx
+++ b/frontend/src/components/HomePage.tsx
@@ -185,7 +185,7 @@ export default function HomePage({ onOpenDashboard, theme = 'light' }: { onOpenD
   const ctaRef = useFadeInRef();
 
   return (
-    <div ref={scrollRef} className="flex-1 overflow-y-auto bg-warm-50 dark:bg-warm-900 relative">
+    <div ref={scrollRef} className="flex-1 overflow-y-auto bg-warm-50 dark:bg-navy-950 relative">
       <HexCanvas scrollProgress={scrollProgress} isDark={theme === 'dark'} />
 
       <div className="relative" style={{ zIndex: 1 }}>
@@ -193,7 +193,7 @@ export default function HomePage({ onOpenDashboard, theme = 'light' }: { onOpenD
         <div className="max-w-3xl mx-auto px-6 pt-20 pb-24">
           <div
             ref={heroRef}
-            className="fade-in-section backdrop-blur-sm bg-warm-50/60 dark:bg-warm-900/60 rounded-2xl p-8 -mx-2"
+            className="fade-in-section backdrop-blur-sm bg-warm-50/60 dark:bg-navy-950/60 rounded-2xl p-8 -mx-2"
           >
             <p className="text-teal-600 font-semibold tracking-wide uppercase text-sm mb-4">
               Find where to live, not just what&apos;s for sale
@@ -226,7 +226,7 @@ export default function HomePage({ onOpenDashboard, theme = 'light' }: { onOpenD
         {/* The flip */}
         <div className="max-w-3xl mx-auto px-6 pb-20">
           <div ref={problemRef} className="fade-in-section">
-            <div className="rounded-2xl backdrop-blur-sm bg-warm-50/40 dark:bg-warm-800/40 border border-warm-200/50 dark:border-warm-700/50 p-8">
+            <div className="rounded-2xl backdrop-blur-sm bg-warm-50/40 dark:bg-navy-800/40 border border-warm-200/50 dark:border-navy-700/50 p-8">
               <div className="grid md:grid-cols-2 gap-8">
                 <div>
                   <h3 className="text-sm font-semibold text-warm-400 uppercase tracking-wide mb-2">
@@ -265,7 +265,7 @@ export default function HomePage({ onOpenDashboard, theme = 'light' }: { onOpenD
               {FILTERS.map((f) => (
                 <div
                   key={f.label}
-                  className="rounded-xl bg-white dark:bg-warm-800 border border-warm-200 dark:border-warm-700 p-4 shadow-sm hover:shadow-md hover:border-teal-300 dark:hover:border-teal-600 transition-all"
+                  className="rounded-xl bg-white dark:bg-navy-800 border border-warm-200 dark:border-navy-700 p-4 shadow-sm hover:shadow-md hover:border-teal-300 dark:hover:border-teal-600 transition-all"
                 >
                   <div className="text-2xl mb-2">{f.icon}</div>
                   <div className="font-semibold text-navy-950 dark:text-warm-100 text-sm">{f.label}</div>
diff --git a/frontend/src/components/Map.tsx b/frontend/src/components/Map.tsx
index a464e8c..8091e80 100644
--- a/frontend/src/components/Map.tsx
+++ b/frontend/src/components/Map.tsx
@@ -19,7 +19,9 @@ interface MapProps {
   onCancelPin: () => void;
   features: FeatureMeta[];
   selectedHexagonId: string | null;
+  hoveredHexagonId: string | null;
   onHexagonClick: (h3: string) => void;
+  onHexagonHover: (h3: string | null) => void;
   initialViewState?: ViewState;
   theme?: 'light' | 'dark';
 }
@@ -74,7 +76,8 @@ function normalizedToColor(t: number): [number, number, number] {
 }
 
 function zoomToResolution(zoom: number): number {
-  if (zoom < 7) return 7;
+  if (zoom < 6) return 5;
+  if (zoom < 7) return 6;
   if (zoom < 9.5) return 8;
   if (zoom < 11) return 9;
   if (zoom < 13) return 10;
@@ -145,13 +148,30 @@ function DeckOverlay({
   return null;
 }
 
-// Sequential blue scale for count-based coloring
+// Vibrant density scale: light cyan → teal → deep indigo
+const DENSITY_GRADIENT: { t: number; color: [number, number, number] }[] = [
+  { t: 0, color: [130, 234, 220] },    // Light cyan (few)
+  { t: 0.5, color: [20, 140, 180] },   // Ocean blue (moderate)
+  { t: 1, color: [88, 28, 140] },      // Deep indigo (many)
+];
+
 function countToColor(t: number): [number, number, number] {
-  // light blue (209, 226, 243) -> dark blue (33, 102, 172)
-  const r = Math.round(209 + (33 - 209) * t);
-  const g = Math.round(226 + (102 - 226) * t);
-  const b = Math.round(243 + (172 - 243) * t);
-  return [r, g, b];
+  if (t <= 0) return DENSITY_GRADIENT[0].color;
+  if (t >= 1) return DENSITY_GRADIENT[DENSITY_GRADIENT.length - 1].color;
+
+  for (let i = 0; i < DENSITY_GRADIENT.length - 1; i++) {
+    const lo = DENSITY_GRADIENT[i];
+    const hi = DENSITY_GRADIENT[i + 1];
+    if (t >= lo.t && t <= hi.t) {
+      const frac = (t - lo.t) / (hi.t - lo.t);
+      return [
+        Math.round(lo.color[0] + (hi.color[0] - lo.color[0]) * frac),
+        Math.round(lo.color[1] + (hi.color[1] - lo.color[1]) * frac),
+        Math.round(lo.color[2] + (hi.color[2] - lo.color[2]) * frac),
+      ];
+    }
+  }
+  return DENSITY_GRADIENT[DENSITY_GRADIENT.length - 1].color;
 }
 
 function PostcodeSearch({
@@ -206,7 +226,7 @@ function PostcodeSearch({
             setError(null);
           }}
           placeholder="Search postcode..."
-          className="px-3 py-2 text-sm w-40 border-none outline-none bg-white dark:bg-warm-800 dark:text-warm-100 dark:placeholder-warm-500"
+          className="px-3 py-2 text-sm w-40 border-none outline-none bg-white dark:bg-navy-800 dark:text-warm-100 dark:placeholder-warm-500"
         />
         <button
           type="submit"
@@ -217,7 +237,7 @@ function PostcodeSearch({
         </button>
       </div>
       {error && (
-        <span className="text-xs text-red-600 dark:text-red-400 bg-white/90 dark:bg-warm-800/90 rounded px-2 py-0.5 shadow">{error}</span>
+        <span className="text-xs text-red-600 dark:text-red-400 bg-white/90 dark:bg-navy-800/90 rounded px-2 py-0.5 shadow">{error}</span>
       )}
     </form>
   );
@@ -228,11 +248,15 @@ function MapLegend({
   range,
   showCancel,
   onCancel,
+  mode,
+  enumValues,
 }: {
   featureLabel: string;
   range: [number, number];
   showCancel: boolean;
   onCancel: () => void;
+  mode: 'feature' | 'density';
+  enumValues?: string[];
 }) {
   const formatVal = (v: number) => {
     if (Math.abs(v) >= 1_000_000) return `${(v / 1_000_000).toFixed(1)}M`;
@@ -241,14 +265,19 @@ function MapLegend({
     return v.toFixed(1);
   };
 
+  const gradientStyle =
+    mode === 'density'
+      ? 'linear-gradient(to right, rgb(130, 234, 220), rgb(20, 140, 180), rgb(88, 28, 140))'
+      : 'linear-gradient(to right, rgb(46, 204, 113), rgb(241, 196, 15), rgb(231, 76, 60), rgb(142, 68, 173))';
+
   return (
-    <div className="absolute top-3 right-3 z-10 bg-white dark:bg-warm-800 dark:text-warm-200 rounded shadow-lg p-3 text-xs min-w-[160px]">
+    <div className="absolute top-3 right-3 z-10 bg-white dark:bg-navy-800 dark:text-warm-200 rounded shadow-lg p-3 text-xs min-w-[160px]">
       <div className="flex items-center justify-between mb-2">
         <span className="font-semibold text-sm">{featureLabel}</span>
         {showCancel && (
           <button
             onClick={onCancel}
-            className="text-warm-400 hover:text-warm-700 ml-2"
+            className="text-warm-400 hover:text-warm-700 dark:hover:text-warm-300 ml-2"
             title="Clear color view"
           >
             <svg
@@ -265,14 +294,25 @@ function MapLegend({
       </div>
       <div
         className="h-3 rounded"
-        style={{
-          background:
-            'linear-gradient(to right, rgb(46, 204, 113), rgb(241, 196, 15), rgb(231, 76, 60), rgb(142, 68, 173))',
-        }}
+        style={{ background: gradientStyle }}
       />
       <div className="flex justify-between mt-1 text-warm-600 dark:text-warm-400">
-        <span>{formatVal(range[0])}</span>
-        <span>{formatVal(range[1])}</span>
+        {mode === 'density' ? (
+          <>
+            <span>Few</span>
+            <span>Many</span>
+          </>
+        ) : enumValues && enumValues.length > 0 ? (
+          <>
+            <span>{enumValues[0]}</span>
+            <span>{enumValues[enumValues.length - 1]}</span>
+          </>
+        ) : (
+          <>
+            <span>{formatVal(range[0])}</span>
+            <span>{formatVal(range[1])}</span>
+          </>
+        )}
       </div>
     </div>
   );
@@ -289,7 +329,9 @@ export default memo(function Map({
   onCancelPin,
   features,
   selectedHexagonId,
+  hoveredHexagonId,
   onHexagonClick,
+  onHexagonHover,
   initialViewState,
   theme = 'light',
 }: MapProps) {
@@ -433,6 +475,8 @@ export default memo(function Map({
   countRangeRef.current = countRange;
   const selectedHexagonIdRef = useRef(selectedHexagonId);
   selectedHexagonIdRef.current = selectedHexagonId;
+  const hoveredHexagonIdRef = useRef(hoveredHexagonId);
+  hoveredHexagonIdRef.current = hoveredHexagonId;
 
   // Stable click handler using ref
   const onHexagonClickRef = useRef(onHexagonClick);
@@ -443,6 +487,17 @@ export default memo(function Map({
     }
   }, []);
 
+  // Stable hover handler using ref
+  const onHexagonHoverRef = useRef(onHexagonHover);
+  onHexagonHoverRef.current = onHexagonHover;
+  const handleHexagonHover = useCallback((info: PickingInfo<HexagonData>) => {
+    if (info.object && 'h3' in info.object) {
+      onHexagonHoverRef.current(info.object.h3);
+    } else {
+      onHexagonHoverRef.current(null);
+    }
+  }, []);
+
   // Stable hover handler using ref
   const handlePoiHoverRef = useRef(handlePoiHover);
   handlePoiHoverRef.current = handlePoiHover;
@@ -451,7 +506,7 @@ export default memo(function Map({
   }, []);
 
   // Derive a trigger value from color-affecting state — avoids useEffect+setState double-render
-  const colorTrigger = `${viewFeature}|${colorRange?.[0]}|${colorRange?.[1]}|${filterRange?.[0]}|${filterRange?.[1]}|${countRange.min}|${countRange.max}|${selectedHexagonId}`;
+  const colorTrigger = `${viewFeature}|${colorRange?.[0]}|${colorRange?.[1]}|${filterRange?.[0]}|${filterRange?.[1]}|${countRange.min}|${countRange.max}|${selectedHexagonId}|${hoveredHexagonId}`;
 
   // Hexagon layer — only recreated when data or color trigger changes
   const hexLayer = useMemo(
@@ -493,14 +548,16 @@ export default memo(function Map({
             number,
           ];
         },
-        getLineColor: (d) =>
-          (d.h3 === selectedHexagonIdRef.current ? [255, 255, 255, 255] : [0, 0, 0, 0]) as [
-            number,
-            number,
-            number,
-            number,
-          ],
-        getLineWidth: (d) => (d.h3 === selectedHexagonIdRef.current ? 2 : 0),
+        getLineColor: (d) => {
+          if (d.h3 === selectedHexagonIdRef.current) return [255, 255, 255, 255] as [number, number, number, number];
+          if (d.h3 === hoveredHexagonIdRef.current) return [29, 228, 195, 200] as [number, number, number, number];
+          return [0, 0, 0, 0] as [number, number, number, number];
+        },
+        getLineWidth: (d) => {
+          if (d.h3 === selectedHexagonIdRef.current) return 3;
+          if (d.h3 === hoveredHexagonIdRef.current) return 2;
+          return 0;
+        },
         lineWidthUnits: 'pixels',
         updateTriggers: {
           getFillColor: [colorTrigger],
@@ -512,10 +569,11 @@ export default memo(function Map({
         opacity: 1,
         highPrecision: true,
         onClick: handleHexagonClick,
+        onHover: handleHexagonHover,
         // @ts-expect-error beforeId is a MapboxOverlay interleave prop, not typed in LayerProps
         beforeId: 'waterway_label',
       }),
-    [data, colorTrigger, handleHexagonClick]
+    [data, colorTrigger, handleHexagonClick, handleHexagonHover]
   );
 
   // POI layer — independent, only recreated when POI data changes
@@ -576,51 +634,6 @@ export default memo(function Map({
     [hexLayer, poiLayer, postcodeLayer]
   );
 
-  // Tooltip uses refs to avoid being a layer dependency
-  const featuresRef = useRef(features);
-  featuresRef.current = features;
-
-  const getTooltip = useCallback(
-    // eslint-disable-next-line @typescript-eslint/no-explicit-any
-    ({ object }: { object?: any }) => {
-      if (!object) return null;
-
-      if (!('h3' in object)) return null;
-
-      const lines: string[] = [];
-      lines.push(`<div>${(object.count as number).toLocaleString()} properties</div>`);
-
-      for (const f of featuresRef.current) {
-        const minVal = object[`min_${f.name}`];
-        const maxVal = object[`max_${f.name}`];
-        if (minVal != null && maxVal != null) {
-          const minStr =
-            typeof minVal === 'number'
-              ? minVal.toLocaleString(undefined, { maximumFractionDigits: 1 })
-              : String(minVal);
-          const maxStr =
-            typeof maxVal === 'number'
-              ? maxVal.toLocaleString(undefined, { maximumFractionDigits: 1 })
-              : String(maxVal);
-          const highlight = f.name === viewFeatureRef.current ? 'font-weight: bold;' : '';
-          lines.push(`<div style="${highlight}">${f.name}: ${minStr} - ${maxStr}</div>`);
-        }
-      }
-
-      const isDark = themeRef.current === 'dark';
-      return {
-        html: `<div style="padding: 8px; font-size: 12px;">${lines.join('')}</div>`,
-        style: {
-          backgroundColor: isDark ? '#292524' : 'white',
-          color: isDark ? '#e7e5e4' : 'inherit',
-          borderRadius: '4px',
-          boxShadow: isDark ? '0 2px 4px rgba(0,0,0,0.5)' : '0 2px 4px rgba(0,0,0,0.2)',
-        },
-      };
-    },
-    []
-  );
-
   return (
     <div className="flex-1 h-full relative" ref={containerRef}>
       <MapGL
@@ -635,21 +648,33 @@ export default memo(function Map({
         touchPitch={false}
         keyboard={true}
         pitchWithRotate={false}
+        minZoom={5}
+        maxBounds={[-12, 49, 4, 62]}
       >
-        <DeckOverlay layers={layers} getTooltip={getTooltip as never} />
+        <DeckOverlay layers={layers} getTooltip={null} />
       </MapGL>
       <PostcodeSearch onFlyTo={handleFlyTo} />
-      {viewFeature && colorRange && colorFeatureMeta && (
+      {viewFeature && colorRange && colorFeatureMeta ? (
         <MapLegend
           featureLabel={colorFeatureMeta.name}
           range={colorRange}
           showCancel={viewSource === 'eye'}
           onCancel={onCancelPin}
+          mode="feature"
+          enumValues={colorFeatureMeta.type === 'enum' ? colorFeatureMeta.values : undefined}
+        />
+      ) : (
+        <MapLegend
+          featureLabel="Property density"
+          range={[0, 0]}
+          showCancel={false}
+          onCancel={onCancelPin}
+          mode="density"
         />
       )}
       {popupInfo && (
         <div
-          className="absolute pointer-events-none bg-white dark:bg-warm-800 rounded shadow-lg p-2 text-sm dark:text-warm-200"
+          className="absolute pointer-events-none bg-white dark:bg-navy-800 rounded shadow-lg p-2 text-sm dark:text-warm-200"
           style={{
             left: popupInfo.x,
             top: popupInfo.y - 40,
diff --git a/frontend/src/components/POIPane.tsx b/frontend/src/components/POIPane.tsx
index 4e68748..e4df229 100644
--- a/frontend/src/components/POIPane.tsx
+++ b/frontend/src/components/POIPane.tsx
@@ -6,6 +6,7 @@ interface POIPaneProps {
   selectedCategories: Set<string>;
   onCategoriesChange: (categories: Set<string>) => void;
   poiCount: number;
+  onNavigateToSource?: (slug: string) => void;
 }
 
 export default function POIPane({
@@ -13,11 +14,14 @@ export default function POIPane({
   selectedCategories,
   onCategoriesChange,
   poiCount,
+  onNavigateToSource,
 }: POIPaneProps) {
   const [dropdownOpen, setDropdownOpen] = useState(false);
   const [searchTerm, setSearchTerm] = useState('');
   const [collapsedGroups, setCollapsedGroups] = useState<Set<string>>(new Set());
+  const [showInfo, setShowInfo] = useState(false);
   const dropdownRef = useRef<HTMLDivElement>(null);
+  const infoPopupRef = useRef<HTMLDivElement>(null);
 
   // Close dropdown when clicking outside
   useEffect(() => {
@@ -30,6 +34,18 @@ export default function POIPane({
     return () => document.removeEventListener('mousedown', handleClickOutside);
   }, []);
 
+  // Close info popup when clicking outside
+  useEffect(() => {
+    if (!showInfo) return;
+    function handleClickOutside(e: MouseEvent) {
+      if (infoPopupRef.current && !infoPopupRef.current.contains(e.target as Node)) {
+        setShowInfo(false);
+      }
+    }
+    document.addEventListener('mousedown', handleClickOutside);
+    return () => document.removeEventListener('mousedown', handleClickOutside);
+  }, [showInfo]);
+
   const allCategories = groups.flatMap((g) => g.categories);
 
   const toggleCategory = (category: string) => {
@@ -95,13 +111,65 @@ export default function POIPane({
   const selectedCount = selectedCategories.size;
 
   return (
-    <div className="w-72 p-4 bg-white dark:bg-warm-900 shadow-lg space-y-4 overflow-y-auto max-h-screen">
-      <h2 className="text-xl font-bold dark:text-warm-100">Points of Interest</h2>
+    <div className="w-72 p-4 bg-white dark:bg-navy-950 shadow-lg space-y-4 overflow-y-auto max-h-screen">
+      <div className="flex items-center gap-2">
+        <h2 className="text-xl font-bold dark:text-warm-100">Points of Interest</h2>
+        <button
+          onClick={() => setShowInfo(true)}
+          className="text-warm-400 hover:text-warm-700 dark:hover:text-warm-300 p-0.5 rounded"
+          title="Data source info"
+        >
+          <svg className="w-3.5 h-3.5" viewBox="0 0 24 24" fill="none" stroke="currentColor" strokeWidth={2}>
+            <circle cx="12" cy="12" r="10" />
+            <path strokeLinecap="round" d="M12 16v-4m0-4h.01" />
+          </svg>
+        </button>
+      </div>
+
+      {showInfo && (
+        <div className="fixed inset-0 z-50 flex items-center justify-center bg-black/30">
+          <div
+            ref={infoPopupRef}
+            className="bg-white dark:bg-navy-800 border border-warm-200 dark:border-navy-700 rounded-lg shadow-xl max-w-md w-full mx-4 p-5"
+          >
+            <div className="flex items-start justify-between mb-3">
+              <h3 className="text-sm font-semibold text-warm-900 dark:text-warm-100 pr-4">
+                Points of Interest
+              </h3>
+              <button
+                onClick={() => setShowInfo(false)}
+                className="text-warm-400 hover:text-warm-700 dark:hover:text-warm-300 shrink-0"
+              >
+                <svg className="w-4 h-4" viewBox="0 0 24 24" fill="none" stroke="currentColor" strokeWidth={2}>
+                  <path strokeLinecap="round" strokeLinejoin="round" d="M6 18L18 6M6 6l12 12" />
+                </svg>
+              </button>
+            </div>
+            <p className="text-sm text-warm-700 dark:text-warm-300 mb-4 leading-relaxed">
+              Points of interest are sourced from OpenStreetMap via Geofabrik extracts.
+              Categories include public transport stops, shops, restaurants, healthcare
+              facilities, leisure venues, and more. Data is filtered and mapped to
+              friendly names with exhaustive category coverage.
+            </p>
+            {onNavigateToSource && (
+              <button
+                onClick={() => {
+                  onNavigateToSource('osm-pois');
+                  setShowInfo(false);
+                }}
+                className="text-sm text-teal-600 dark:text-teal-400 hover:text-teal-800 dark:hover:text-teal-300 hover:underline"
+              >
+                View data source
+              </button>
+            )}
+          </div>
+        </div>
+      )}
 
       <div className="space-y-2" ref={dropdownRef}>
         <button
           onClick={() => setDropdownOpen(!dropdownOpen)}
-          className="w-full flex items-center justify-between px-3 py-2 text-sm border border-warm-300 dark:border-warm-700 rounded hover:border-warm-400 bg-white dark:bg-warm-800 dark:text-warm-200"
+          className="w-full flex items-center justify-between px-3 py-2 text-sm border border-warm-300 dark:border-navy-700 rounded hover:border-warm-400 bg-white dark:bg-navy-800 dark:text-warm-200"
         >
           <span className="truncate text-left">
             {selectedCount === 0
@@ -121,8 +189,8 @@ export default function POIPane({
         </button>
 
         {dropdownOpen && (
-          <div className="border border-warm-300 dark:border-warm-700 rounded shadow-lg bg-white dark:bg-warm-800">
-            <div className="flex gap-2 px-3 py-2 border-b border-warm-200 dark:border-warm-700">
+          <div className="border border-warm-300 dark:border-navy-700 rounded shadow-lg bg-white dark:bg-navy-800">
+            <div className="flex gap-2 px-3 py-2 border-b border-warm-200 dark:border-navy-700">
               <button onClick={selectAll} className="text-xs text-teal-600 dark:text-teal-400 hover:text-teal-800 dark:hover:text-teal-300">
                 All
               </button>
@@ -131,13 +199,13 @@ export default function POIPane({
                 None
               </button>
             </div>
-            <div className="px-3 py-2 border-b border-warm-200 dark:border-warm-700">
+            <div className="px-3 py-2 border-b border-warm-200 dark:border-navy-700">
               <input
                 type="text"
                 placeholder="Search categories..."
                 value={searchTerm}
                 onChange={(e) => setSearchTerm(e.target.value)}
-                className="w-full px-2 py-1 text-sm border border-warm-300 dark:border-warm-700 rounded bg-white dark:bg-warm-900 dark:text-warm-200 dark:placeholder-warm-500"
+                className="w-full px-2 py-1 text-sm border border-warm-300 dark:border-navy-700 rounded bg-white dark:bg-navy-950 dark:text-warm-200 dark:placeholder-warm-500"
               />
             </div>
             <div className="max-h-96 overflow-y-auto py-1">
@@ -151,7 +219,7 @@ export default function POIPane({
 
                 return (
                   <div key={group.name}>
-                    <div className="flex items-center gap-1 px-3 py-1.5 bg-warm-50 dark:bg-warm-900 border-y border-warm-100 dark:border-warm-700">
+                    <div className="flex items-center gap-1 px-3 py-1.5 bg-warm-50 dark:bg-navy-950 border-y border-warm-100 dark:border-navy-700">
                       <button
                         onClick={() => toggleCollapse(group.name)}
                         className="p-0.5 text-warm-400 hover:text-warm-600"
@@ -190,7 +258,7 @@ export default function POIPane({
                       group.categories.map((category) => (
                         <label
                           key={category}
-                          className="flex items-center gap-2 px-3 pl-8 py-1.5 hover:bg-warm-50 dark:hover:bg-warm-700 cursor-pointer dark:text-warm-300"
+                          className="flex items-center gap-2 px-3 pl-8 py-1.5 hover:bg-warm-50 dark:hover:bg-navy-700 cursor-pointer dark:text-warm-300"
                         >
                           <input
                             type="checkbox"
@@ -220,7 +288,7 @@ export default function POIPane({
         </div>
       )}
 
-      <div className="p-3 bg-warm-100 dark:bg-warm-800 rounded text-xs text-warm-600 dark:text-warm-400">
+      <div className="p-3 bg-warm-100 dark:bg-navy-800 rounded text-xs text-warm-600 dark:text-warm-400">
         <p>Select categories to display POIs on the map.</p>
         <p className="mt-2">Zoom in for better visibility of individual locations.</p>
       </div>
diff --git a/frontend/src/components/PropertiesPane.tsx b/frontend/src/components/PropertiesPane.tsx
index 5414501..58f5b35 100644
--- a/frontend/src/components/PropertiesPane.tsx
+++ b/frontend/src/components/PropertiesPane.tsx
@@ -1,4 +1,4 @@
-import React, { useMemo, useState } from 'react';
+import React, { useMemo, useState, useRef, useEffect } from 'react';
 import { Property } from '../types';
 
 interface PropertiesPaneProps {
@@ -8,6 +8,10 @@ interface PropertiesPaneProps {
   hexagonId: string | null;
   onLoadMore: () => void;
   onClose: () => void;
+  onNavigateToSource?: (slug: string) => void;
+  isHoveredPreview?: boolean;
+  hoverMode?: boolean;
+  onHoverModeChange?: (enabled: boolean) => void;
 }
 
 type SortBy = 'price' | 'size' | 'energy';
@@ -19,9 +23,26 @@ export function PropertiesPane({
   hexagonId,
   onLoadMore,
   onClose,
+  onNavigateToSource,
+  isHoveredPreview,
+  hoverMode,
+  onHoverModeChange,
 }: PropertiesPaneProps) {
   const [sortBy, setSortBy] = useState<SortBy>('price');
   const [search, setSearch] = useState('');
+  const [showInfo, setShowInfo] = useState(false);
+  const infoPopupRef = useRef<HTMLDivElement>(null);
+
+  useEffect(() => {
+    if (!showInfo) return;
+    function handleClickOutside(e: MouseEvent) {
+      if (infoPopupRef.current && !infoPopupRef.current.contains(e.target as Node)) {
+        setShowInfo(false);
+      }
+    }
+    document.addEventListener('mousedown', handleClickOutside);
+    return () => document.removeEventListener('mousedown', handleClickOutside);
+  }, [showInfo]);
 
   // Filter and sort properties
   const filteredAndSorted = useMemo(() => {
@@ -56,36 +77,112 @@ export function PropertiesPane({
   return (
     <div className="flex flex-col h-full">
       {/* Header */}
-      <div className="p-4 border-b border-warm-200 dark:border-warm-700">
+      <div className="p-4 border-b border-warm-200 dark:border-navy-700">
         <div className="flex justify-between items-center">
-          <h2 className="text-lg font-semibold dark:text-warm-100">Properties in Hexagon</h2>
-          <button
-            onClick={onClose}
-            className="text-warm-500 hover:text-warm-700 dark:text-warm-400 dark:hover:text-warm-200 text-2xl leading-none"
-          >
-            ×
-          </button>
+          <div className="flex items-center gap-2">
+            <h2 className="text-lg font-semibold dark:text-warm-100">Properties</h2>
+            {isHoveredPreview && (
+              <span className="text-xs px-1.5 py-0.5 rounded bg-teal-50 dark:bg-teal-900/30 text-teal-600 dark:text-teal-400">
+                Preview
+              </span>
+            )}
+            <button
+              onClick={() => setShowInfo(true)}
+              className="text-warm-400 hover:text-warm-700 dark:hover:text-warm-300 p-0.5 rounded"
+              title="Data source info"
+            >
+              <svg className="w-3.5 h-3.5" viewBox="0 0 24 24" fill="none" stroke="currentColor" strokeWidth={2}>
+                <circle cx="12" cy="12" r="10" />
+                <path strokeLinecap="round" d="M12 16v-4m0-4h.01" />
+              </svg>
+            </button>
+          </div>
+          <div className="flex items-center gap-1">
+            {onHoverModeChange && (
+              <button
+                onClick={() => onHoverModeChange(!hoverMode)}
+                className={`p-1 rounded ${
+                  hoverMode
+                    ? 'text-teal-600 dark:text-teal-400 bg-teal-50 dark:bg-teal-900/30'
+                    : 'text-warm-400 hover:text-warm-700 dark:hover:text-warm-300'
+                }`}
+                title={hoverMode ? 'Live preview on (click to lock)' : 'Live preview off (click to enable)'}
+              >
+                <svg className="w-4 h-4" viewBox="0 0 24 24" fill="none" stroke="currentColor" strokeWidth={2}>
+                  <path strokeLinecap="round" strokeLinejoin="round" d="M15 12a3 3 0 11-6 0 3 3 0 016 0z" />
+                  <path strokeLinecap="round" strokeLinejoin="round" d="M2.458 12C3.732 7.943 7.523 5 12 5c4.478 0 8.268 2.943 9.542 7-1.274 4.057-5.064 7-9.542 7-4.477 0-8.268-2.943-9.542-7z" />
+                </svg>
+              </button>
+            )}
+            <button
+              onClick={onClose}
+              className="text-warm-400 hover:text-warm-700 dark:hover:text-warm-300 p-1"
+            >
+              <svg className="w-4 h-4" viewBox="0 0 24 24" fill="none" stroke="currentColor" strokeWidth={2}>
+                <path strokeLinecap="round" strokeLinejoin="round" d="M6 18L18 6M6 6l12 12" />
+              </svg>
+            </button>
+          </div>
         </div>
         <p className="text-sm text-warm-600 dark:text-warm-400">
           {search.trim()
             ? `${filteredAndSorted.length} match${filteredAndSorted.length !== 1 ? 'es' : ''} in ${properties.length} loaded`
             : `Showing ${properties.length} of ${total} properties`}
         </p>
+        {showInfo && (
+          <div className="fixed inset-0 z-50 flex items-center justify-center bg-black/30">
+            <div
+              ref={infoPopupRef}
+              className="bg-white dark:bg-navy-800 border border-warm-200 dark:border-navy-700 rounded-lg shadow-xl max-w-md w-full mx-4 p-5"
+            >
+              <div className="flex items-start justify-between mb-3">
+                <h3 className="text-sm font-semibold text-warm-900 dark:text-warm-100 pr-4">
+                  Property Data
+                </h3>
+                <button
+                  onClick={() => setShowInfo(false)}
+                  className="text-warm-400 hover:text-warm-700 dark:hover:text-warm-300 shrink-0"
+                >
+                  <svg className="w-4 h-4" viewBox="0 0 24 24" fill="none" stroke="currentColor" strokeWidth={2}>
+                    <path strokeLinecap="round" strokeLinejoin="round" d="M6 18L18 6M6 6l12 12" />
+                  </svg>
+                </button>
+              </div>
+              <p className="text-sm text-warm-700 dark:text-warm-300 mb-4 leading-relaxed">
+                Property data combines Energy Performance Certificates (EPC) with HM Land
+                Registry Price Paid records, fuzzy-matched by address within each postcode.
+                Includes floor area, energy ratings, construction age, and tenure from EPC
+                surveys, plus the most recent sale price from the Land Registry.
+              </p>
+              {onNavigateToSource && (
+                <button
+                  onClick={() => {
+                    onNavigateToSource('epc');
+                    setShowInfo(false);
+                  }}
+                  className="text-sm text-teal-600 dark:text-teal-400 hover:text-teal-800 dark:hover:text-teal-300 hover:underline"
+                >
+                  View data source
+                </button>
+              )}
+            </div>
+          </div>
+        )}
       </div>
 
       {/* Search and sort controls */}
-      <div className="p-2 border-b border-warm-200 dark:border-warm-700 space-y-2">
+      <div className="p-2 border-b border-warm-200 dark:border-navy-700 space-y-2">
         <input
           type="text"
           value={search}
           onChange={(e) => setSearch(e.target.value)}
           placeholder="Search by address or postcode..."
-          className="w-full p-2 border border-warm-300 dark:border-warm-700 rounded text-sm bg-white dark:bg-warm-800 dark:text-warm-200 placeholder-warm-400 dark:placeholder-warm-500"
+          className="w-full p-2 border border-warm-300 dark:border-navy-700 rounded text-sm bg-white dark:bg-navy-800 dark:text-warm-200 placeholder-warm-400 dark:placeholder-warm-500"
         />
         <select
           value={sortBy}
           onChange={(e) => setSortBy(e.target.value as SortBy)}
-          className="w-full p-2 border border-warm-300 dark:border-warm-700 rounded text-sm bg-white dark:bg-warm-800 dark:text-warm-200"
+          className="w-full p-2 border border-warm-300 dark:border-navy-700 rounded text-sm bg-white dark:bg-navy-800 dark:text-warm-200"
         >
           <option value="price">Price (High to Low)</option>
           <option value="size">Size (Large to Small)</option>
@@ -156,7 +253,7 @@ function PropertyCard({ property }: { property: Property }) {
   const age = getNum(property, 'Approximate construction age', 'construction_age_band');
 
   return (
-    <div className="p-4 border-b border-warm-100 dark:border-warm-800 hover:bg-warm-50 dark:hover:bg-warm-800">
+    <div className="p-4 border-b border-warm-100 dark:border-navy-800 hover:bg-warm-50 dark:hover:bg-navy-800">
       {/* Address & postcode */}
       <div className="font-semibold dark:text-warm-100">{property.address || 'Unknown Address'}</div>
       <div className="text-sm text-warm-600 dark:text-warm-400">{property.postcode}</div>
diff --git a/frontend/src/components/ui/slider.tsx b/frontend/src/components/ui/slider.tsx
index 67a41b6..cc6bd0f 100644
--- a/frontend/src/components/ui/slider.tsx
+++ b/frontend/src/components/ui/slider.tsx
@@ -11,13 +11,13 @@ export function Slider({ className, ...props }: SliderProps) {
       className={cn('relative flex w-full touch-none select-none items-center', className)}
       {...props}
     >
-      <SliderPrimitive.Track className="relative h-2 w-full grow overflow-hidden rounded-full bg-warm-200 dark:bg-warm-700">
+      <SliderPrimitive.Track className="relative h-2 w-full grow overflow-hidden rounded-full bg-warm-200 dark:bg-navy-700">
         <SliderPrimitive.Range className="absolute h-full bg-teal-600" />
       </SliderPrimitive.Track>
       {props.value?.map((_, i) => (
         <SliderPrimitive.Thumb
           key={i}
-          className="block h-5 w-5 rounded-full border-2 border-teal-600 dark:border-teal-500 bg-white dark:bg-warm-800 ring-offset-white dark:ring-offset-warm-900 transition-colors focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-teal-600 focus-visible:ring-offset-2 disabled:pointer-events-none disabled:opacity-50"
+          className="block h-5 w-5 rounded-full border-2 border-teal-600 dark:border-teal-500 bg-white dark:bg-navy-800 ring-offset-white dark:ring-offset-navy-950 transition-colors focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-teal-600 focus-visible:ring-offset-2 disabled:pointer-events-none disabled:opacity-50"
         />
       ))}
     </SliderPrimitive.Root>
diff --git a/frontend/src/index.css b/frontend/src/index.css
index b031b96..dee1335 100644
--- a/frontend/src/index.css
+++ b/frontend/src/index.css
@@ -11,7 +11,7 @@ body,
 }
 
 html.dark {
-  background-color: #1c1917;
+  background-color: #0a0e1a;
   color-scheme: dark;
 }
 
diff --git a/frontend/src/types.ts b/frontend/src/types.ts
index 3c901c9..4bdf9d0 100644
--- a/frontend/src/types.ts
+++ b/frontend/src/types.ts
@@ -5,8 +5,13 @@ export interface FeatureMeta {
   // Numeric-only fields
   min?: number;
   max?: number;
+  step?: number;
   // Enum-only fields
   values?: string[];
+  // Description fields
+  description?: string;
+  detail?: string;
+  source?: string;
 }
 
 export interface FeatureGroup {
@@ -100,3 +105,23 @@ export interface HexagonPropertiesResponse {
   offset: number;
   truncated: boolean;
 }
+
+export interface NumericFeatureStats {
+  name: string;
+  count: number;
+  min: number;
+  max: number;
+  mean: number;
+  histogram: { min: number; max: number; bin_width: number; counts: number[] };
+}
+
+export interface EnumFeatureStats {
+  name: string;
+  counts: Record<string, number>;
+}
+
+export interface HexagonStatsResponse {
+  count: number;
+  numeric_features: NumericFeatureStats[];
+  enum_features: EnumFeatureStats[];
+}
diff --git a/pipeline/transform/join_epc_pp.py b/pipeline/transform/join_epc_pp.py
index 630813a..a2364dd 100644
--- a/pipeline/transform/join_epc_pp.py
+++ b/pipeline/transform/join_epc_pp.py
@@ -132,7 +132,9 @@ def main():
         .str.extract(r"(\d{4})", 1)
         .cast(pl.UInt16, strict=False)
     )
-    transfer_year = pl.col("first_transfer_date").dt.year().cast(pl.UInt16, strict=False)
+    transfer_year = (
+        pl.col("first_transfer_date").dt.year().cast(pl.UInt16, strict=False)
+    )
     is_new_build = pl.col("old_new") == "Y"
 
     matched = matched.with_columns(
diff --git a/server-rs/src/consts.rs b/server-rs/src/consts.rs
index 28b096b..2b57f86 100644
--- a/server-rs/src/consts.rs
+++ b/server-rs/src/consts.rs
@@ -8,7 +8,7 @@ pub const SERVER_ADDRESS: &str = "0.0.0.0:8001";
 pub const BOUNDS_QUANTIZATION: f64 = 0.01;
 pub const BOUNDS_BUFFER_PERCENT: f64 = 0.1;
 pub const POSTCODE_MIN_RESOLUTION: u8 = 11;
-pub const MAX_POIS_PER_REQUEST: usize = 5000;
+pub const MAX_POIS_PER_REQUEST: usize = 2500;
 pub const DEFAULT_PROPERTIES_LIMIT: usize = 100;
 pub const MAX_PROPERTIES_LIMIT: usize = 500;
 pub const ENUM_NULL: u8 = 255;
diff --git a/server-rs/src/features.rs b/server-rs/src/features.rs
new file mode 100644
index 0000000..9af4f27
--- /dev/null
+++ b/server-rs/src/features.rs
@@ -0,0 +1,676 @@
+//! Static feature configuration. Every numeric and enum column in wide.parquet
+//! must be declared here. Unknown columns cause a startup panic.
+
+pub enum Bounds {
+    /// Fixed min/max values for the slider
+    Fixed { min: f64, max: f64 },
+    /// Compute percentile from data at startup
+    Percentile { low: f64, high: f64 },
+}
+
+pub struct FeatureConfig {
+    /// Must match parquet column name exactly (also used as display label)
+    pub name: &'static str,
+    pub bounds: Bounds,
+    /// Slider step size. Controls the granularity of the range slider in the UI.
+    pub step: f64,
+    /// Short one-line description shown in the filter sidebar
+    pub description: &'static str,
+    /// Longer description explaining methodology, data source, and caveats
+    pub detail: &'static str,
+    /// Data source slug for linking to /data-sources#<slug>
+    pub source: &'static str,
+}
+
+pub struct FeatureGroup {
+    pub name: &'static str,
+    pub features: &'static [FeatureConfig],
+}
+
+pub struct EnumFeatureConfig {
+    pub name: &'static str,
+    /// If set, values are presented in this order instead of alphabetical.
+    /// Values not listed are appended alphabetically after the ordered ones.
+    pub order: Option<&'static [&'static str]>,
+    /// Short one-line description shown in the filter sidebar
+    pub description: &'static str,
+    /// Longer description explaining methodology, data source, and caveats
+    pub detail: &'static str,
+    /// Data source slug for linking to /data-sources#<slug>
+    pub source: &'static str,
+}
+
+pub struct EnumFeatureGroup {
+    pub name: &'static str,
+    pub features: &'static [EnumFeatureConfig],
+}
+
+/// Columns in parquet that are neither numeric features nor enum features.
+/// These are silently skipped during schema validation.
+pub const IGNORED_COLUMNS: &[&str] = &[
+    "lat",
+    "lon",
+    "Address per Property Register",
+    "Address per EPC",
+    "Postcode",
+    "historical_prices",
+    "Is construction date approximate",
+];
+
+pub static FEATURE_GROUPS: &[FeatureGroup] = &[
+    FeatureGroup {
+        name: "Property",
+        features: &[
+            FeatureConfig {
+                name: "Last known price",
+                bounds: Bounds::Fixed {
+                    min: 0.0,
+                    max: 2_000_000.0,
+                },
+                step: 10000.0,
+                description: "Most recent sale price from the Land Registry",
+                detail: "The last recorded sale price for this property from HM Land Registry Price Paid data. Covers residential sales in England and Wales. May be years old if the property hasn't sold recently.",
+                source: "price-paid",
+            },
+            FeatureConfig {
+                name: "Price per sqm",
+                bounds: Bounds::Percentile {
+                    low: 0.0,
+                    high: 98.0,
+                },
+                step: 100.0,
+                description: "Sale price divided by total floor area",
+                detail: "Calculated by dividing the last known sale price by the total floor area from the EPC certificate. Useful for comparing value across different-sized properties. Only available where both price and floor area data exist.",
+                source: "price-paid",
+            },
+            FeatureConfig {
+                name: "Total floor area (sqm)",
+                bounds: Bounds::Percentile {
+                    low: 0.0,
+                    high: 98.0,
+                },
+                step: 1.0,
+                description: "Internal floor area from the EPC survey",
+                detail: "Total useful floor area in square metres as measured during the Energy Performance Certificate assessment. Includes all habitable rooms but excludes garages, outbuildings, and external areas.",
+                source: "epc",
+            },
+            FeatureConfig {
+                name: "Number of bedrooms & living rooms",
+                bounds: Bounds::Fixed {
+                    min: 1.0,
+                    max: 10.0,
+                },
+                step: 1.0,
+                description: "Count of habitable rooms from the EPC survey",
+                detail: "Total number of habitable rooms (bedrooms plus living rooms) as recorded in the Energy Performance Certificate. Kitchens and bathrooms are typically excluded unless they are large enough to count as habitable rooms.",
+                source: "epc",
+            },
+            FeatureConfig {
+                name: "Approximate construction age",
+                bounds: Bounds::Fixed {
+                    min: 0.0,
+                    max: 2026.0,
+                },
+                step: 1.0,
+                description: "Estimated year of construction from the EPC",
+                detail: "The approximate year of construction as recorded in the Energy Performance Certificate. Derived from the construction age band (e.g. '1930-1949') by taking the midpoint. May be approximate, especially for older buildings.",
+                source: "epc",
+            },
+        ],
+    },
+    FeatureGroup {
+        name: "Transport",
+        features: &[
+            FeatureConfig {
+                name: "public_transport_easy_minutes",
+                bounds: Bounds::Fixed {
+                    min: 0.0,
+                    max: 180.0,
+                },
+                step: 2.0,
+                description: "Quickest public transport journey to central London (easy route)",
+                detail: "Journey time in minutes by public transport to central London destinations, using TfL's Journey Planner API. The 'easy' route minimises changes and walking. Calculated for weekday morning commute times.",
+                source: "tfl-journey-times",
+            },
+            FeatureConfig {
+                name: "public_transport_quick_minutes",
+                bounds: Bounds::Fixed {
+                    min: 0.0,
+                    max: 180.0,
+                },
+                step: 2.0,
+                description: "Fastest public transport journey to central London",
+                detail: "Journey time in minutes by public transport to central London destinations, using TfL's Journey Planner API. The 'quick' route optimises for shortest total time regardless of changes. Calculated for weekday morning commute times.",
+                source: "tfl-journey-times",
+            },
+            FeatureConfig {
+                name: "cycling_minutes",
+                bounds: Bounds::Fixed {
+                    min: 0.0,
+                    max: 180.0,
+                },
+                step: 1.0,
+                description: "Cycling time to central London via TfL routing",
+                detail: "Cycling journey time in minutes to central London destinations, as calculated by the TfL Journey Planner API. Uses TfL's default cycling speed and route preferences.",
+                source: "tfl-journey-times",
+            },
+            FeatureConfig {
+                name: "Public transport within 2km",
+                bounds: Bounds::Percentile {
+                    low: 5.0,
+                    high: 95.0,
+                },
+                step: 1.0,
+                description: "Number of public transport stops within 2km",
+                detail: "Count of bus stops, rail stations, tube stations, tram stops, and other public transport access points within a 2km radius of the property's postcode. Derived from the NaPTAN (National Public Transport Access Nodes) dataset.",
+                source: "naptan",
+            },
+        ],
+    },
+    FeatureGroup {
+        name: "Education",
+        features: &[
+            FeatureConfig {
+                name: "Education, Skills and Training Score",
+                bounds: Bounds::Percentile {
+                    low: 2.0,
+                    high: 98.0,
+                },
+                step: 0.1,
+                description: "IoD education deprivation score for the local area",
+                detail: "From the English Indices of Deprivation. Measures deprivation in education, skills and training in the local area (LSOA). Higher scores indicate greater deprivation. Combines children/young people sub-domain (school attainment, entry to higher education) and adult skills sub-domain (adult qualifications, English language proficiency).",
+                source: "iod",
+            },
+            FeatureConfig {
+                name: "Good+ primary schools within 5km",
+                bounds: Bounds::Fixed {
+                    min: 0.0,
+                    max: 30.0,
+                },
+                step: 1.0,
+                description: "Primary schools rated Good or Outstanding by Ofsted nearby",
+                detail: "Number of state-funded primary schools within 5km that have a current Ofsted rating of Good or Outstanding. Based on the latest inspection outcomes dataset. Schools that have not yet been inspected are excluded.",
+                source: "ofsted",
+            },
+            FeatureConfig {
+                name: "Good+ secondary schools within 5km",
+                bounds: Bounds::Fixed {
+                    min: 0.0,
+                    max: 15.0,
+                },
+                step: 1.0,
+                description: "Secondary schools rated Good or Outstanding by Ofsted nearby",
+                detail: "Number of state-funded secondary schools within 5km that have a current Ofsted rating of Good or Outstanding. Based on the latest inspection outcomes dataset. Schools that have not yet been inspected are excluded.",
+                source: "ofsted",
+            },
+        ],
+    },
+    FeatureGroup {
+        name: "Deprivation",
+        features: &[
+            FeatureConfig {
+                name: "Index of Multiple Deprivation (IMD) Score",
+                bounds: Bounds::Percentile {
+                    low: 2.0,
+                    high: 98.0,
+                },
+                step: 0.1,
+                description: "Overall deprivation score combining all domains",
+                detail: "The Index of Multiple Deprivation is the official measure of relative deprivation in England. It combines seven weighted domains: Income (22.5%), Employment (22.5%), Education (13.5%), Health (13.5%), Crime (9.3%), Barriers to Housing & Services (9.3%), and Living Environment (9.3%). Higher scores indicate greater deprivation. Measured at LSOA level (~1,500 people).",
+                source: "iod",
+            },
+            FeatureConfig {
+                name: "Income Score (rate)",
+                bounds: Bounds::Fixed { min: 0.0, max: 0.6 },
+                step: 0.01,
+                description: "Proportion of the population experiencing income deprivation",
+                detail: "From the English Indices of Deprivation. The proportion of the local population experiencing deprivation relating to low income. Includes people on Income Support, income-based Jobseeker's Allowance, income-based Employment and Support Allowance, Pension Credit, Working Tax Credit and Child Tax Credit, Universal Credit, and asylum seekers.",
+                source: "iod",
+            },
+            FeatureConfig {
+                name: "Employment Score (rate)",
+                bounds: Bounds::Fixed { min: 0.0, max: 0.4 },
+                step: 0.01,
+                description: "Proportion of the working-age population involuntarily excluded from work",
+                detail: "From the English Indices of Deprivation. The proportion of the working-age population involuntarily excluded from the labour market. Includes claimants of Jobseeker's Allowance, Employment and Support Allowance, Incapacity Benefit, Severe Disablement Allowance, Carer's Allowance, and relevant Universal Credit claimants.",
+                source: "iod",
+            },
+            FeatureConfig {
+                name: "Health Deprivation and Disability Score",
+                bounds: Bounds::Percentile {
+                    low: 2.0,
+                    high: 98.0,
+                },
+                step: 0.1,
+                description: "Risk of premature death and quality of life impairment",
+                detail: "From the English Indices of Deprivation. Measures the risk of premature death and impairment of quality of life through poor physical or mental health. Derived from years of potential life lost, comparative illness and disability ratio, acute morbidity, and mood and anxiety disorders.",
+                source: "iod",
+            },
+            FeatureConfig {
+                name: "Crime Score",
+                bounds: Bounds::Percentile {
+                    low: 2.0,
+                    high: 98.0,
+                },
+                step: 0.1,
+                description: "IoD crime deprivation score measuring personal risk",
+                detail: "From the English Indices of Deprivation. Measures the risk of personal and material victimisation at local level. Derived from recorded rates of violence, burglary, theft, and criminal damage. Higher scores indicate higher crime-related deprivation.",
+                source: "iod",
+            },
+            FeatureConfig {
+                name: "Living Environment Score",
+                bounds: Bounds::Percentile {
+                    low: 2.0,
+                    high: 98.0,
+                },
+                step: 0.1,
+                description: "Quality of the local indoor and outdoor environment",
+                detail: "From the English Indices of Deprivation. Measures deprivation in the quality of the local environment. Combines the Indoors sub-domain (housing quality, central heating, housing conditions) and Outdoors sub-domain (air quality, road traffic accidents). Higher scores indicate poorer living environments.",
+                source: "iod",
+            },
+            FeatureConfig {
+                name: "Indoors Sub-domain Score",
+                bounds: Bounds::Percentile {
+                    low: 2.0,
+                    high: 98.0,
+                },
+                step: 0.1,
+                description: "Housing quality and conditions in the local area",
+                detail: "From the English Indices of Deprivation, Living Environment domain. Measures the quality of housing stock: houses without central heating, housing in poor condition, and houses failing Decent Homes standards. Higher scores indicate worse housing conditions.",
+                source: "iod",
+            },
+            FeatureConfig {
+                name: "Outdoors Sub-domain Score",
+                bounds: Bounds::Percentile {
+                    low: 2.0,
+                    high: 98.0,
+                },
+                step: 0.1,
+                description: "Air quality and road safety in the local area",
+                detail: "From the English Indices of Deprivation, Living Environment domain. Measures the outdoor living environment quality through air quality indicators and road traffic accident casualties involving pedestrians and cyclists. Higher scores indicate poorer outdoor environments.",
+                source: "iod",
+            },
+        ],
+    },
+    FeatureGroup {
+        name: "Crime",
+        features: &[
+            FeatureConfig {
+                name: "Anti-social behaviour (avg/yr)",
+                bounds: Bounds::Percentile {
+                    low: 2.0,
+                    high: 98.0,
+                },
+                step: 1.0,
+                description: "Average yearly anti-social behaviour incidents in the area",
+                detail: "Average number of anti-social behaviour incidents per year in the LSOA, from police.uk street-level crime data (2023-2025). Includes nuisance, environmental, and personal anti-social behaviour.",
+                source: "crime",
+            },
+            FeatureConfig {
+                name: "Violence and sexual offences (avg/yr)",
+                bounds: Bounds::Percentile {
+                    low: 2.0,
+                    high: 98.0,
+                },
+                step: 1.0,
+                description: "Average yearly violent and sexual offences in the area",
+                detail: "Average number of violence and sexual offences per year in the LSOA, from police.uk street-level crime data (2023-2025). Includes assault, harassment, and sexual offences.",
+                source: "crime",
+            },
+            FeatureConfig {
+                name: "Criminal damage and arson (avg/yr)",
+                bounds: Bounds::Percentile {
+                    low: 2.0,
+                    high: 98.0,
+                },
+                step: 1.0,
+                description: "Average yearly criminal damage and arson in the area",
+                detail: "Average number of criminal damage and arson incidents per year in the LSOA, from police.uk street-level crime data (2023-2025).",
+                source: "crime",
+            },
+            FeatureConfig {
+                name: "Burglary (avg/yr)",
+                bounds: Bounds::Percentile {
+                    low: 2.0,
+                    high: 98.0,
+                },
+                step: 1.0,
+                description: "Average yearly burglary offences in the area",
+                detail: "Average number of burglary offences per year in the LSOA, from police.uk street-level crime data (2023-2025). Includes residential and commercial burglary.",
+                source: "crime",
+            },
+            FeatureConfig {
+                name: "Vehicle crime (avg/yr)",
+                bounds: Bounds::Percentile {
+                    low: 2.0,
+                    high: 98.0,
+                },
+                step: 1.0,
+                description: "Average yearly vehicle crime in the area",
+                detail: "Average number of vehicle crime incidents per year in the LSOA, from police.uk street-level crime data (2023-2025). Includes theft of and from vehicles.",
+                source: "crime",
+            },
+            FeatureConfig {
+                name: "Robbery (avg/yr)",
+                bounds: Bounds::Percentile {
+                    low: 2.0,
+                    high: 98.0,
+                },
+                step: 1.0,
+                description: "Average yearly robbery offences in the area",
+                detail: "Average number of robbery offences per year in the LSOA, from police.uk street-level crime data (2023-2025). Robbery involves theft with force or threat of force.",
+                source: "crime",
+            },
+            FeatureConfig {
+                name: "Other theft (avg/yr)",
+                bounds: Bounds::Percentile {
+                    low: 2.0,
+                    high: 98.0,
+                },
+                step: 1.0,
+                description: "Average yearly other theft offences in the area",
+                detail: "Average number of 'other theft' offences per year in the LSOA, from police.uk street-level crime data (2023-2025). Includes theft not classified under burglary, vehicle crime, shoplifting, or bicycle theft.",
+                source: "crime",
+            },
+            FeatureConfig {
+                name: "Shoplifting (avg/yr)",
+                bounds: Bounds::Percentile {
+                    low: 2.0,
+                    high: 98.0,
+                },
+                step: 1.0,
+                description: "Average yearly shoplifting offences in the area",
+                detail: "Average number of shoplifting offences per year in the LSOA, from police.uk street-level crime data (2023-2025).",
+                source: "crime",
+            },
+            FeatureConfig {
+                name: "Drugs (avg/yr)",
+                bounds: Bounds::Percentile {
+                    low: 2.0,
+                    high: 98.0,
+                },
+                step: 1.0,
+                description: "Average yearly drug offences in the area",
+                detail: "Average number of drug offences per year in the LSOA, from police.uk street-level crime data (2023-2025). Includes possession and trafficking offences.",
+                source: "crime",
+            },
+            FeatureConfig {
+                name: "Possession of weapons (avg/yr)",
+                bounds: Bounds::Percentile {
+                    low: 2.0,
+                    high: 98.0,
+                },
+                step: 1.0,
+                description: "Average yearly weapons possession offences in the area",
+                detail: "Average number of possession of weapons offences per year in the LSOA, from police.uk street-level crime data (2023-2025).",
+                source: "crime",
+            },
+            FeatureConfig {
+                name: "Public order (avg/yr)",
+                bounds: Bounds::Percentile {
+                    low: 2.0,
+                    high: 98.0,
+                },
+                step: 1.0,
+                description: "Average yearly public order offences in the area",
+                detail: "Average number of public order offences per year in the LSOA, from police.uk street-level crime data (2023-2025). Includes causing fear, alarm, or distress.",
+                source: "crime",
+            },
+            FeatureConfig {
+                name: "Bicycle theft (avg/yr)",
+                bounds: Bounds::Percentile {
+                    low: 2.0,
+                    high: 98.0,
+                },
+                step: 1.0,
+                description: "Average yearly bicycle theft in the area",
+                detail: "Average number of bicycle theft offences per year in the LSOA, from police.uk street-level crime data (2023-2025).",
+                source: "crime",
+            },
+            FeatureConfig {
+                name: "Theft from the person (avg/yr)",
+                bounds: Bounds::Percentile {
+                    low: 2.0,
+                    high: 98.0,
+                },
+                step: 1.0,
+                description: "Average yearly theft from the person in the area",
+                detail: "Average number of theft from the person offences per year in the LSOA, from police.uk street-level crime data (2023-2025). Includes pickpocketing and bag snatching without force.",
+                source: "crime",
+            },
+            FeatureConfig {
+                name: "Other crime (avg/yr)",
+                bounds: Bounds::Percentile {
+                    low: 2.0,
+                    high: 98.0,
+                },
+                step: 1.0,
+                description: "Average yearly other crime in the area",
+                detail: "Average number of other crime offences per year in the LSOA, from police.uk street-level crime data (2023-2025). A catch-all category for offences not classified elsewhere.",
+                source: "crime",
+            },
+            FeatureConfig {
+                name: "Serious crime (avg/yr)",
+                bounds: Bounds::Percentile {
+                    low: 2.0,
+                    high: 98.0,
+                },
+                step: 1.0,
+                description: "Aggregate of serious crime categories per year",
+                detail: "Sum of violence, robbery, burglary, and weapons possession per year in the LSOA, from police.uk street-level crime data (2023-2025). Provides a single serious crime metric.",
+                source: "crime",
+            },
+            FeatureConfig {
+                name: "Minor crime (avg/yr)",
+                bounds: Bounds::Percentile {
+                    low: 2.0,
+                    high: 98.0,
+                },
+                step: 1.0,
+                description: "Aggregate of minor crime categories per year",
+                detail: "Sum of anti-social behaviour, shoplifting, bicycle theft, and other lower-severity crime per year in the LSOA, from police.uk street-level crime data (2023-2025). Provides a single minor crime metric.",
+                source: "crime",
+            },
+        ],
+    },
+    FeatureGroup {
+        name: "Demographics",
+        features: &[
+            FeatureConfig {
+                name: "% White",
+                bounds: Bounds::Fixed {
+                    min: 0.0,
+                    max: 100.0,
+                },
+                step: 1.0,
+                description: "Percentage of population identifying as White",
+                detail: "From the 2021 Census. Percentage of the local authority population identifying as White (English, Welsh, Scottish, Northern Irish, British, Irish, Gypsy or Irish Traveller, Roma, or any other White background).",
+                source: "ethnicity",
+            },
+            FeatureConfig {
+                name: "% Asian",
+                bounds: Bounds::Fixed {
+                    min: 0.0,
+                    max: 100.0,
+                },
+                step: 1.0,
+                description: "Percentage of population identifying as Asian",
+                detail: "From the 2021 Census. Percentage of the local authority population identifying as Asian or Asian British (Indian, Pakistani, Bangladeshi, Chinese, or any other Asian background).",
+                source: "ethnicity",
+            },
+            FeatureConfig {
+                name: "% Black",
+                bounds: Bounds::Fixed {
+                    min: 0.0,
+                    max: 100.0,
+                },
+                step: 1.0,
+                description: "Percentage of population identifying as Black",
+                detail: "From the 2021 Census. Percentage of the local authority population identifying as Black, Black British, Caribbean, or African.",
+                source: "ethnicity",
+            },
+            FeatureConfig {
+                name: "% Mixed",
+                bounds: Bounds::Fixed {
+                    min: 0.0,
+                    max: 100.0,
+                },
+                step: 1.0,
+                description: "Percentage of population identifying as Mixed or Multiple ethnic groups",
+                detail: "From the 2021 Census. Percentage of the local authority population identifying as Mixed or Multiple ethnic groups (White and Black Caribbean, White and Black African, White and Asian, or any other Mixed or Multiple background).",
+                source: "ethnicity",
+            },
+            FeatureConfig {
+                name: "% Other",
+                bounds: Bounds::Fixed {
+                    min: 0.0,
+                    max: 100.0,
+                },
+                step: 1.0,
+                description: "Percentage of population identifying as Other ethnic group",
+                detail: "From the 2021 Census. Percentage of the local authority population identifying as Other ethnic group (Arab or any other ethnic group not covered by the main categories).",
+                source: "ethnicity",
+            },
+        ],
+    },
+    FeatureGroup {
+        name: "Amenities",
+        features: &[
+            FeatureConfig {
+                name: "Restaurants within 2km",
+                bounds: Bounds::Percentile {
+                    low: 5.0,
+                    high: 95.0,
+                },
+                step: 1.0,
+                description: "Number of restaurants and cafes within 2km",
+                detail: "Count of restaurants, cafes, and food establishments within a 2km radius of the property's postcode centroid. Derived from OpenStreetMap POI data using haversine distance calculation with a 0.05° spatial grid for candidate reduction.",
+                source: "osm-pois",
+            },
+            FeatureConfig {
+                name: "Groceries within 2km",
+                bounds: Bounds::Percentile {
+                    low: 5.0,
+                    high: 95.0,
+                },
+                step: 1.0,
+                description: "Number of grocery shops and supermarkets within 2km",
+                detail: "Count of supermarkets, convenience stores, and other grocery shops within a 2km radius of the property's postcode centroid. Derived from OpenStreetMap POI data.",
+                source: "osm-pois",
+            },
+            FeatureConfig {
+                name: "Parks within 2km",
+                bounds: Bounds::Percentile {
+                    low: 5.0,
+                    high: 95.0,
+                },
+                step: 1.0,
+                description: "Number of parks and green spaces within 2km",
+                detail: "Count of parks, gardens, nature reserves, and other green spaces within a 2km radius of the property's postcode centroid. Derived from OpenStreetMap POI data.",
+                source: "osm-pois",
+            },
+        ],
+    },
+    FeatureGroup {
+        name: "Environment",
+        features: &[
+            FeatureConfig {
+                name: "Noise (dB)",
+                bounds: Bounds::Fixed {
+                    min: 50.0,
+                    max: 80.0,
+                },
+                step: 1.0,
+                description: "Road noise level at the postcode in decibels (Lden)",
+                detail: "Road noise level in decibels (Lden — day-evening-night 24-hour weighted average) from Defra's Strategic Noise Mapping Round 4 (2022). Modelled at 4m above ground on a 10m grid. Sampled at postcode centroids via WCS GeoTIFF tiles. Values above ~55 dB are generally considered noticeable; above ~70 dB can affect health.",
+                source: "noise",
+            },
+            FeatureConfig {
+                name: "Max available download speed (Mbps)",
+                bounds: Bounds::Percentile {
+                    low: 5.0,
+                    high: 95.0,
+                },
+                step: 10.0,
+                description: "Maximum broadband download speed available at the postcode",
+                detail: "Maximum available fixed broadband download speed in Megabits per second, from Ofcom's Connected Nations 2025 report. Measured at Output Area level and represents the maximum speed available from any provider, not actual achieved speeds.",
+                source: "broadband",
+            },
+        ],
+    },
+];
+
+pub static ENUM_FEATURE_GROUPS: &[EnumFeatureGroup] = &[EnumFeatureGroup {
+    name: "Property",
+    features: &[
+        EnumFeatureConfig {
+            name: "Leashold/Freehold",
+            order: Some(&["Freehold", "Leasehold"]),
+            description: "Whether the property is leasehold or freehold",
+            detail: "From HM Land Registry Price Paid data. Freehold means you own the building and the land it stands on. Leasehold means you own the building but not the land — you have a lease from the freeholder for a set number of years.",
+            source: "price-paid",
+        },
+        EnumFeatureConfig {
+            name: "Current energy rating",
+            order: Some(&["A", "B", "C", "D", "E", "F", "G"]),
+            description: "Current EPC energy efficiency rating (A-G)",
+            detail: "The current energy efficiency rating from the Energy Performance Certificate, graded A (most efficient) to G (least efficient). Based on the energy costs per square metre of floor area for heating, hot water, lighting, and ventilation.",
+            source: "epc",
+        },
+        EnumFeatureConfig {
+            name: "Potential energy rating",
+            order: Some(&["A", "B", "C", "D", "E", "F", "G"]),
+            description: "Achievable EPC rating after recommended improvements",
+            detail: "The potential energy efficiency rating that could be achieved if all cost-effective improvements recommended in the EPC were carried out. Graded A (most efficient) to G (least efficient).",
+            source: "epc",
+        },
+        EnumFeatureConfig {
+            name: "Property type",
+            order: Some(&["Detached", "Semi-Detached", "Terraced", "Flat"]),
+            description: "Type of property: detached, semi-detached, terraced, or flat",
+            detail: "From HM Land Registry Price Paid data. The broad property type classification: Detached, Semi-Detached, Terraced, or Flat/Maisonette.",
+            source: "price-paid",
+        },
+        EnumFeatureConfig {
+            name: "Property type/built form",
+            order: None,
+            description: "Detailed property type and built form from the EPC",
+            detail: "A more detailed classification from the Energy Performance Certificate combining property type and built form. Examples include 'Semi-Detached House', 'Mid-Terrace House', 'Ground-Floor Flat', 'Detached Bungalow', etc.",
+            source: "epc",
+        },
+    ],
+}];
+
+/// Flat ordered list of all numeric feature names (follows group order).
+pub fn all_numeric_feature_names() -> Vec<&'static str> {
+    FEATURE_GROUPS
+        .iter()
+        .flat_map(|group| group.features.iter().map(|feature| feature.name))
+        .collect()
+}
+
+/// Flat ordered list of all enum feature names (follows group order).
+pub fn all_enum_feature_names() -> Vec<&'static str> {
+    ENUM_FEATURE_GROUPS
+        .iter()
+        .flat_map(|group| group.features.iter().map(|feature| feature.name))
+        .collect()
+}
+
+/// Look up the configured value order for an enum feature by name.
+pub fn order_for(name: &str) -> Option<&'static [&'static str]> {
+    ENUM_FEATURE_GROUPS
+        .iter()
+        .flat_map(|group| group.features.iter())
+        .find(|feature| feature.name == name)
+        .and_then(|feature| feature.order)
+}
+
+/// Look up the Bounds config for a numeric feature by name.
+pub fn bounds_for(name: &str) -> Option<&'static Bounds> {
+    FEATURE_GROUPS
+        .iter()
+        .flat_map(|group| group.features.iter())
+        .find(|feature| feature.name == name)
+        .map(|feature| &feature.bounds)
+}
diff --git a/server-rs/src/filter.rs b/server-rs/src/filter.rs
index 167c1a2..7be3d24 100644
--- a/server-rs/src/filter.rs
+++ b/server-rs/src/filter.rs
@@ -1,3 +1,4 @@
+use crate::consts::ENUM_NULL;
 use crate::data::EnumFeatureData;
 
 pub struct ParsedFilter {
@@ -22,12 +23,12 @@ pub fn parse_filters(
     let mut numeric = Vec::new();
     let mut enums = Vec::new();
 
-    let s = match filter_str.filter(|s| !s.is_empty()) {
-        Some(s) => s,
+    let input = match filter_str.filter(|text| !text.is_empty()) {
+        Some(text) => text,
         None => return (numeric, enums),
     };
 
-    for entry in s.split(',') {
+    for entry in input.split(',') {
         let parts: Vec<&str> = entry.splitn(2, ':').collect();
         if parts.len() != 2 {
             continue;
@@ -35,13 +36,13 @@ pub fn parse_filters(
         let name = parts[0].trim();
         let rest = parts[1].trim();
 
-        if let Some(enum_idx) = enum_features.iter().position(|ef| ef.name == name) {
-            let ef = &enum_features[enum_idx];
+        if let Some(enum_idx) = enum_features.iter().position(|enum_feat| enum_feat.name == name) {
+            let enum_feat = &enum_features[enum_idx];
             let allowed: Vec<u8> = rest
                 .split('|')
-                .filter_map(|v| {
-                    let v = v.trim();
-                    ef.values.iter().position(|ev| ev == v).map(|i| i as u8)
+                .filter_map(|value| {
+                    let value = value.trim();
+                    enum_feat.values.iter().position(|existing| existing == value).map(|position| position as u8)
                 })
                 .collect();
             enums.push(ParsedEnumFilter { enum_idx, allowed });
@@ -51,14 +52,14 @@ pub fn parse_filters(
                 continue;
             }
             let min = match num_parts[0].trim().parse::<f64>() {
-                Ok(v) => v,
+                Ok(value) => value,
                 Err(_) => continue,
             };
             let max = match num_parts[1].trim().parse::<f64>() {
-                Ok(v) => v,
+                Ok(value) => value,
                 Err(_) => continue,
             };
-            if let Some(feat_idx) = feature_names.iter().position(|n| n == name) {
+            if let Some(feat_idx) = feature_names.iter().position(|feat_name| feat_name == name) {
                 numeric.push(ParsedFilter { feat_idx, min, max });
             }
         }
@@ -75,11 +76,11 @@ pub fn row_passes_filters(
     num_features: usize,
     enum_features: &[EnumFeatureData],
 ) -> bool {
-    filters.iter().all(|f| {
-        let v = feature_data[row * num_features + f.feat_idx];
-        v.is_finite() && v >= f.min && v <= f.max
-    }) && enum_filters.iter().all(|ef| {
-        let v = enum_features[ef.enum_idx].data[row];
-        v != 255 && ef.allowed.contains(&v)
+    filters.iter().all(|filter| {
+        let value = feature_data[row * num_features + filter.feat_idx];
+        value.is_finite() && value >= filter.min && value <= filter.max
+    }) && enum_filters.iter().all(|enum_filter| {
+        let value = enum_features[enum_filter.enum_idx].data[row];
+        value != ENUM_NULL && enum_filter.allowed.contains(&value)
     })
 }
diff --git a/server-rs/src/grid_index.rs b/server-rs/src/grid_index.rs
index 4743d84..f5b57c7 100644
--- a/server-rs/src/grid_index.rs
+++ b/server-rs/src/grid_index.rs
@@ -19,18 +19,18 @@ impl GridIndex {
         let mut min_lon = f64::INFINITY;
         let mut max_lon = f64::NEG_INFINITY;
 
-        for i in 0..lat.len() {
-            if lat[i] < min_lat {
-                min_lat = lat[i];
+        for index in 0..lat.len() {
+            if lat[index] < min_lat {
+                min_lat = lat[index];
             }
-            if lat[i] > max_lat {
-                max_lat = lat[i];
+            if lat[index] > max_lat {
+                max_lat = lat[index];
             }
-            if lon[i] < min_lon {
-                min_lon = lon[i];
+            if lon[index] < min_lon {
+                min_lon = lon[index];
             }
-            if lon[i] > max_lon {
-                max_lon = lon[i];
+            if lon[index] > max_lon {
+                max_lon = lon[index];
             }
         }
 
@@ -52,11 +52,11 @@ impl GridIndex {
 
         let mut cells: Vec<Vec<u32>> = vec![Vec::new(); rows * cols];
 
-        for i in 0..lat.len() {
-            let grid_row = ((lat[i] - min_lat) / cell_size) as usize;
-            let grid_col = ((lon[i] - min_lon) / cell_size) as usize;
-            let idx = grid_row * cols + grid_col;
-            cells[idx].push(i as u32);
+        for index in 0..lat.len() {
+            let grid_row = ((lat[index] - min_lat) / cell_size) as usize;
+            let grid_col = ((lon[index] - min_lon) / cell_size) as usize;
+            let cell_index = grid_row * cols + grid_col;
+            cells[cell_index].push(index as u32);
         }
 
         tracing::debug!("Grid index built");
@@ -96,7 +96,7 @@ impl GridIndex {
         west: f64,
         north: f64,
         east: f64,
-        mut f: impl FnMut(u32),
+        mut callback: impl FnMut(u32),
     ) {
         let Some((row_min, row_max, col_min, col_max)) =
             self.clamp_bounds(south, west, north, east)
@@ -108,7 +108,7 @@ impl GridIndex {
             let row_start = row * self.cols;
             for col in col_min..=col_max {
                 for &row_idx in &self.cells[row_start + col] {
-                    f(row_idx);
+                    callback(row_idx);
                 }
             }
         }
diff --git a/server-rs/src/main.rs b/server-rs/src/main.rs
index 23ec229..71b18ea 100644
--- a/server-rs/src/main.rs
+++ b/server-rs/src/main.rs
@@ -1,15 +1,20 @@
 mod consts;
 mod data;
+mod features;
 mod filter;
-mod index;
+mod grid_index;
 mod routes;
 mod state;
+#[cfg(test)]
+mod tests;
 
 use std::path::PathBuf;
 use std::sync::Arc;
 
+use anyhow::{bail, Context};
 use axum::routing::get;
 use axum::Router;
+use clap::Parser;
 use tower_http::compression::CompressionLayer;
 use tower_http::cors::{Any, CorsLayer};
 use tower_http::services::ServeDir;
@@ -19,8 +24,24 @@ use tracing_subscriber::EnvFilter;
 
 use state::AppState;
 
+#[derive(Parser)]
+#[command(name = "narrowit", about = "Narrowit property map server")]
+struct Cli {
+    /// Path to the wide property parquet file
+    #[arg(long)]
+    data: PathBuf,
+
+    /// Path to the POI parquet file
+    #[arg(long)]
+    pois: PathBuf,
+
+    /// Path to the frontend dist directory
+    #[arg(long)]
+    dist: Option<PathBuf>,
+}
+
 #[tokio::main]
-async fn main() {
+async fn main() -> anyhow::Result<()> {
     tracing_subscriber::fmt()
         .with_env_filter(
             EnvFilter::try_from_default_env().unwrap_or_else(|_| EnvFilter::new("info")),
@@ -28,18 +49,18 @@ async fn main() {
         .with_ansi(true)
         .init();
 
-    let parquet_path = PathBuf::from(
-        std::env::args()
-            .nth(1)
-            .unwrap_or_else(|| "data_sources/processed/wide.parquet".to_string()),
-    );
+    let cli = Cli::parse();
+
+    let parquet_path = &cli.data;
     if !parquet_path.exists() {
-        tracing::error!("Parquet file not found: {}", parquet_path.display());
-        std::process::exit(1);
+        bail!(
+            "Property parquet file not found: {}",
+            parquet_path.display()
+        );
     }
 
     info!("Loading property data from {}", parquet_path.display());
-    let property_data = data::PropertyData::load(&parquet_path);
+    let property_data = data::PropertyData::load(parquet_path)?;
     info!(
         rows = property_data.lat.len(),
         features = property_data.num_features,
@@ -48,32 +69,90 @@ async fn main() {
     );
 
     info!("Building spatial grid index (0.01° cells)");
-    let grid = index::GridIndex::build(&property_data.lat, &property_data.lon, 0.01);
+    let grid = grid_index::GridIndex::build(&property_data.lat, &property_data.lon, 0.01);
 
-    info!("Precomputing H3 cells for resolutions {}-{}", consts::H3_PRECOMPUTE_MIN, consts::H3_PRECOMPUTE_MAX);
-    let h3_cells = data::precompute_h3(&property_data.lat, &property_data.lon);
+    info!(
+        "Precomputing H3 cells for resolutions {}-{}",
+        consts::H3_PRECOMPUTE_MIN,
+        consts::H3_PRECOMPUTE_MAX
+    );
+    let h3_cells = data::precompute_h3(&property_data.lat, &property_data.lon)?;
 
-    let poi_path = PathBuf::from("/volumes/syncthing/Projects/property-map/data/filtered_uk_pois.parquet");
+    let poi_path = cli.pois;
 
-    let poi_data = if poi_path.exists() {
-        info!("Loading POI data from {}", poi_path.display());
-        let pd = data::POIData::load(&poi_path);
-        info!(pois = pd.lat.len(), "POI data loaded");
-        pd
-    } else {
-        tracing::warn!("POI file not found: {}. POI endpoints will be unavailable.", poi_path.display());
-        data::POIData {
-            id: Vec::new(),
-            name: Vec::new(),
-            category: Vec::new(),
-            lat: Vec::new(),
-            lng: Vec::new(),
-            emoji: Vec::new(),
-        }
-    };
+    if !poi_path.exists() {
+        bail!("POI parquet file not found: {}", poi_path.display());
+    }
+
+    info!("Loading POI data from {}", poi_path.display());
+    let poi_data = data::POIData::load(&poi_path)?;
+    info!(pois = poi_data.lat.len(), "POI data loaded");
 
     info!("Building POI spatial grid index");
-    let poi_grid = index::GridIndex::build(&poi_data.lat, &poi_data.lng, 0.01);
+    let poi_grid = grid_index::GridIndex::build(&poi_data.lat, &poi_data.lng, 0.01);
+
+    let min_keys: Vec<String> = property_data
+        .feature_names
+        .iter()
+        .map(|name| format!("min_{}", name))
+        .collect();
+    let max_keys: Vec<String> = property_data
+        .feature_names
+        .iter()
+        .map(|name| format!("max_{}", name))
+        .collect();
+    let enum_min_keys: Vec<String> = property_data
+        .enum_features
+        .iter()
+        .map(|enum_feature| format!("min_{}", enum_feature.name))
+        .collect();
+    let enum_max_keys: Vec<String> = property_data
+        .enum_features
+        .iter()
+        .map(|enum_feature| format!("max_{}", enum_feature.name))
+        .collect();
+
+    // Precompute POI category groups
+    let poi_category_groups = {
+        let mut group_cats: std::collections::HashMap<String, std::collections::HashSet<String>> =
+            std::collections::HashMap::new();
+        for (category, group) in poi_data.category.iter().zip(poi_data.group.iter()) {
+            group_cats
+                .entry(group.clone())
+                .or_default()
+                .insert(category.clone());
+        }
+        // Validate that data groups match the hardcoded order exactly
+        let expected: std::collections::HashSet<&str> =
+            consts::POI_GROUP_ORDER.iter().copied().collect();
+        let actual: std::collections::HashSet<&str> =
+            group_cats.keys().map(|key| key.as_str()).collect();
+        let missing_from_data: Vec<&&str> = expected.difference(&actual).collect();
+        let missing_from_order: Vec<&&str> = actual.difference(&expected).collect();
+        if !missing_from_data.is_empty() || !missing_from_order.is_empty() {
+            bail!(
+                "POI group mismatch!\n  In POI_GROUP_ORDER but not in data: {:?}\n  In data but not in POI_GROUP_ORDER: {:?}",
+                missing_from_data, missing_from_order
+            );
+        }
+        consts::POI_GROUP_ORDER.iter().map(|group_name| group_name.to_string()).collect::<Vec<_>>()
+            .into_iter()
+            .map(|name| {
+                let mut categories: Vec<String> =
+                    group_cats.remove(&name).context("POI group validated but missing from map")?.into_iter().collect();
+                categories.sort();
+                Ok(state::POICategoryGroup { name, categories })
+            })
+            .collect::<anyhow::Result<Vec<_>>>()?
+    };
+
+    // Precompute enum name → index map
+    let enum_name_to_idx: rustc_hash::FxHashMap<String, usize> = property_data
+        .enum_features
+        .iter()
+        .enumerate()
+        .map(|(index, enum_feature)| (enum_feature.name.clone(), index))
+        .collect();
 
     let state = Arc::new(AppState {
         data: property_data,
@@ -81,6 +160,12 @@ async fn main() {
         h3_cells,
         poi_data,
         poi_grid,
+        min_keys,
+        max_keys,
+        enum_min_keys,
+        enum_max_keys,
+        poi_category_groups,
+        enum_name_to_idx,
     });
 
     let cors = CorsLayer::new()
@@ -93,6 +178,7 @@ async fn main() {
     let state_pois = state.clone();
     let state_poi_categories = state.clone();
     let state_hexagon_properties = state.clone();
+    let state_hexagon_stats = state.clone();
 
     let api = Router::new()
         .route(
@@ -116,9 +202,23 @@ async fn main() {
             get(move |query| {
                 routes::get_hexagon_properties(state_hexagon_properties.clone(), query)
             }),
+        )
+        .route(
+            "/api/hexagon-stats",
+            get(move |query| routes::get_hexagon_stats(state_hexagon_stats.clone(), query)),
         );
 
-    let frontend_dist = PathBuf::from("frontend/dist");
+    let frontend_dist = cli.dist.unwrap_or_else(|| {
+        // Check next to the binary first, then fall back to working directory
+        if let Ok(executable) = std::env::current_exe() {
+            let executable_dir = executable.parent().unwrap_or_else(|| std::path::Path::new("."));
+            let dist_next_to_binary = executable_dir.join("dist");
+            if dist_next_to_binary.exists() {
+                return dist_next_to_binary;
+            }
+        }
+        PathBuf::from("frontend/dist")
+    });
     let app = if frontend_dist.exists() {
         api.fallback_service(ServeDir::new(frontend_dist))
     } else {
@@ -127,12 +227,16 @@ async fn main() {
 
     let app = app
         .layer(cors)
-        .layer(CompressionLayer::new().gzip(true))
+        .layer(CompressionLayer::new().zstd(true).gzip(true))
         .layer(TraceLayer::new_for_http());
 
-    let addr = "0.0.0.0:8001";
+    let addr = consts::SERVER_ADDRESS;
+    let listener = tokio::net::TcpListener::bind(addr)
+        .await
+        .with_context(|| format!("Failed to bind to {addr}"))?;
     info!("Server listening on {}", addr);
-
-    let listener = tokio::net::TcpListener::bind(addr).await.unwrap();
-    axum::serve(listener, app).await.unwrap();
+    axum::serve(listener, app)
+        .await
+        .context("Server error")?;
+    Ok(())
 }
diff --git a/server-rs/src/routes/features.rs b/server-rs/src/routes/features.rs
index 57a74ce..e7081ad 100644
--- a/server-rs/src/routes/features.rs
+++ b/server-rs/src/routes/features.rs
@@ -5,6 +5,7 @@ use serde::Serialize;
 use tracing::info;
 
 use crate::data::Histogram;
+use crate::features::{ENUM_FEATURE_GROUPS, FEATURE_GROUPS};
 use crate::state::AppState;
 
 #[derive(Serialize)]
@@ -13,75 +14,123 @@ pub enum FeatureInfo {
     #[serde(rename = "numeric")]
     Numeric {
         name: String,
-        label: String,
         min: f64,
         max: f64,
+        step: f64,
         histogram: Histogram,
+        description: &'static str,
+        detail: &'static str,
+        source: &'static str,
     },
     #[serde(rename = "enum")]
     Enum {
         name: String,
-        label: String,
         values: Vec<String>,
+        description: &'static str,
+        detail: &'static str,
+        source: &'static str,
     },
 }
 
 #[derive(Serialize)]
-pub struct FeaturesResponse {
+pub struct FeatureGroupResponse {
+    name: String,
     features: Vec<FeatureInfo>,
 }
 
-fn snake_to_label(name: &str) -> String {
-    // If name contains '/' or uppercase, assume it's already human-readable
-    if name.contains('/') || name.chars().any(|c| c.is_uppercase()) {
-        return name.to_string();
-    }
-    name.split('_')
-        .map(|word| {
-            let mut chars = word.chars();
-            match chars.next() {
-                None => String::new(),
-                Some(c) => {
-                    let mut s = c.to_uppercase().to_string();
-                    s.extend(chars);
-                    s
-                }
-            }
-        })
-        .collect::<Vec<_>>()
-        .join(" ")
+#[derive(Serialize)]
+pub struct FeaturesResponse {
+    groups: Vec<FeatureGroupResponse>,
 }
 
 pub async fn get_features(state: Arc<AppState>) -> Json<FeaturesResponse> {
-    let mut features: Vec<FeatureInfo> = state
-        .data
-        .feature_names
-        .iter()
-        .enumerate()
-        .map(|(i, name): (usize, &String)| {
-            let stats = &state.data.feature_stats[i];
-            FeatureInfo::Numeric {
-                name: name.clone(),
-                label: snake_to_label(name),
-                min: stats.p_low,
-                max: stats.p_high,
-                histogram: stats.histogram.clone(),
-            }
-        })
-        .collect();
-
-    for ef in &state.data.enum_features {
-        features.push(FeatureInfo::Enum {
-            name: ef.name.clone(),
-            label: snake_to_label(&ef.name),
-            values: ef.values.clone(),
-        });
+    // Collect all group names in order, merging numeric and enum groups with the same name
+    let mut group_names: Vec<&str> = Vec::new();
+    for feature_group in FEATURE_GROUPS {
+        if !group_names.contains(&feature_group.name) {
+            group_names.push(feature_group.name);
+        }
+    }
+    for enum_group in ENUM_FEATURE_GROUPS {
+        if !group_names.contains(&enum_group.name) {
+            group_names.push(enum_group.name);
+        }
     }
 
+    let mut groups: Vec<FeatureGroupResponse> = Vec::new();
+
+    for &group_name in &group_names {
+        let mut features: Vec<FeatureInfo> = Vec::new();
+
+        // Add numeric features for this group
+        for feature_group in FEATURE_GROUPS {
+            if feature_group.name == group_name {
+                for feature_config in feature_group.features {
+                    if let Some(feat_idx) =
+                        state.data.feature_names.iter().position(|feat_name| feat_name == feature_config.name)
+                    {
+                        let stats = &state.data.feature_stats[feat_idx];
+                        features.push(FeatureInfo::Numeric {
+                            name: feature_config.name.to_string(),
+                            min: stats.slider_min,
+                            max: stats.slider_max,
+                            step: feature_config.step,
+                            histogram: stats.histogram.clone(),
+                            description: feature_config.description,
+                            detail: feature_config.detail,
+                            source: feature_config.source,
+                        });
+                    }
+                }
+            }
+        }
+
+        // Add enum features for this group
+        for enum_group in ENUM_FEATURE_GROUPS {
+            if enum_group.name == group_name {
+                for enum_config in enum_group.features {
+                    if let Some(enum_feature) = state
+                        .data
+                        .enum_features
+                        .iter()
+                        .find(|enum_feat| enum_feat.name == enum_config.name)
+                    {
+                        features.push(FeatureInfo::Enum {
+                            name: enum_config.name.to_string(),
+                            values: enum_feature.values.clone(),
+                            description: enum_config.description,
+                            detail: enum_config.detail,
+                            source: enum_config.source,
+                        });
+                    }
+                }
+            }
+        }
+
+        if !features.is_empty() {
+            groups.push(FeatureGroupResponse {
+                name: group_name.to_string(),
+                features,
+            });
+        }
+    }
+
+    let num_numeric: usize = groups
+        .iter()
+        .flat_map(|group| &group.features)
+        .filter(|feature| matches!(feature, FeatureInfo::Numeric { .. }))
+        .count();
+    let num_enum: usize = groups
+        .iter()
+        .flat_map(|group| &group.features)
+        .filter(|feature| matches!(feature, FeatureInfo::Enum { .. }))
+        .count();
+
     info!(
-        numeric = features.iter().filter(|f| matches!(f, FeatureInfo::Numeric { .. })).count(),
-        enums = features.iter().filter(|f| matches!(f, FeatureInfo::Enum { .. })).count(),
+        numeric = num_numeric,
+        enums = num_enum,
+        groups = groups.len(),
         "GET /api/features"
     );
-    Json(FeaturesResponse { features })
+    Json(FeaturesResponse { groups })
 }
diff --git a/server-rs/src/routes/hexagon_stats.rs b/server-rs/src/routes/hexagon_stats.rs
new file mode 100644
index 0000000..6f3d357
--- /dev/null
+++ b/server-rs/src/routes/hexagon_stats.rs
@@ -0,0 +1,251 @@
+use std::fmt::Write;
+use std::str::FromStr;
+use std::sync::Arc;
+
+use axum::extract::Query;
+use axum::http::StatusCode;
+use axum::response::IntoResponse;
+use serde::Deserialize;
+use tracing::{info, warn};
+
+use crate::consts::{ENUM_NULL, HISTOGRAM_BINS};
+use crate::filter::{parse_filters, row_passes_filters};
+use crate::state::AppState;
+
+use super::parse::h3_cell_bounds;
+
+#[derive(Deserialize)]
+pub struct HexagonStatsParams {
+    pub h3: String,
+    pub resolution: u8,
+    pub filters: Option<String>,
+}
+
+pub async fn get_hexagon_stats(
+    state: Arc<AppState>,
+    Query(params): Query<HexagonStatsParams>,
+) -> Result<impl IntoResponse, (StatusCode, String)> {
+    let cell = h3o::CellIndex::from_str(&params.h3).map_err(|error| {
+        warn!(h3 = %params.h3, error = %error, "Invalid H3 cell index");
+        (StatusCode::BAD_REQUEST, format!("Invalid H3 cell: {}", error))
+    })?;
+    let cell_u64: u64 = cell.into();
+
+    let resolution = params.resolution as usize;
+    if resolution >= state.h3_cells.len() || state.h3_cells[resolution].is_empty() {
+        warn!(
+            resolution,
+            "Invalid or non-precomputed resolution for hexagon-stats"
+        );
+        return Err((
+            StatusCode::BAD_REQUEST,
+            "Invalid or non-precomputed resolution".to_string(),
+        ));
+    }
+
+    let h3_str = params.h3.clone();
+    let filters_str = params.filters.clone();
+    let (parsed_filters, parsed_enum_filters) = parse_filters(
+        params.filters.as_deref(),
+        &state.data.feature_names,
+        &state.data.enum_features,
+    );
+    let num_filters = parsed_filters.len() + parsed_enum_filters.len();
+
+    let result = tokio::task::spawn_blocking(move || {
+        let start_time = std::time::Instant::now();
+        let h3_data = &state.h3_cells[resolution];
+        let num_features = state.data.num_features;
+        let feature_data = &state.data.feature_data;
+        let enum_features = &state.data.enum_features;
+
+        let (min_lat, min_lon, max_lat, max_lon) = h3_cell_bounds(cell, 0.001);
+
+        // Collect matching rows
+        let mut matching_rows: Vec<usize> = Vec::new();
+        state
+            .grid
+            .for_each_in_bounds(min_lat, min_lon, max_lat, max_lon, |row_idx| {
+                let row = row_idx as usize;
+                if h3_data[row] == cell_u64
+                    && row_passes_filters(
+                        row,
+                        &parsed_filters,
+                        &parsed_enum_filters,
+                        feature_data,
+                        num_features,
+                        enum_features,
+                    )
+                {
+                    matching_rows.push(row);
+                }
+            });
+
+        let total_count = matching_rows.len();
+
+        // Build JSON directly via string buffer
+        let mut output = String::with_capacity(4096);
+        output.push_str("{\"count\":");
+        write!(output, "{}", total_count).unwrap();
+
+        // Numeric features: compute count, min, max, sum, histogram using global bin edges
+        output.push_str(",\"numeric_features\":[");
+        let mut first_numeric = true;
+        for (feature_index, feature_name) in state.data.feature_names.iter().enumerate() {
+            let global_stats = &state.data.feature_stats[feature_index];
+            let histogram_min = global_stats.histogram.min;
+            let histogram_max = global_stats.histogram.max;
+            let bin_width = global_stats.histogram.bin_width;
+
+            let mut count = 0usize;
+            let mut min_value = f64::INFINITY;
+            let mut max_value = f64::NEG_INFINITY;
+            let mut sum = 0.0f64;
+            let mut bins = vec![0u64; HISTOGRAM_BINS];
+
+            for &row in &matching_rows {
+                let value = feature_data[row * num_features + feature_index];
+                if value.is_finite() {
+                    count += 1;
+                    if value < min_value {
+                        min_value = value;
+                    }
+                    if value > max_value {
+                        max_value = value;
+                    }
+                    sum += value;
+
+                    // Bin into histogram using global edges
+                    if bin_width > 0.0 {
+                        let bin_index =
+                            ((value - histogram_min) / bin_width).floor() as isize;
+                        let clamped_index = bin_index.max(0).min((HISTOGRAM_BINS - 1) as isize) as usize;
+                        bins[clamped_index] += 1;
+                    }
+                }
+            }
+
+            if count == 0 {
+                continue;
+            }
+
+            if !first_numeric {
+                output.push(',');
+            }
+            first_numeric = false;
+
+            let mean = sum / count as f64;
+            output.push_str("{\"name\":");
+            write_json_string(&mut output, feature_name);
+            write!(output, ",\"count\":{}", count).unwrap();
+            write!(output, ",\"min\":{}", format_f64(min_value)).unwrap();
+            write!(output, ",\"max\":{}", format_f64(max_value)).unwrap();
+            write!(output, ",\"mean\":{}", format_f64(mean)).unwrap();
+            output.push_str(",\"histogram\":{\"min\":");
+            write!(output, "{}", format_f64(histogram_min)).unwrap();
+            output.push_str(",\"max\":");
+            write!(output, "{}", format_f64(histogram_max)).unwrap();
+            output.push_str(",\"bin_width\":");
+            write!(output, "{}", format_f64(bin_width)).unwrap();
+            output.push_str(",\"counts\":[");
+            for (bin_index, &bin_count) in bins.iter().enumerate() {
+                if bin_index > 0 {
+                    output.push(',');
+                }
+                write!(output, "{}", bin_count).unwrap();
+            }
+            output.push_str("]}}")
+        }
+
+        // Enum features: count per value
+        output.push_str("],\"enum_features\":[");
+        let mut first_enum = true;
+        for enum_feature in enum_features {
+            let enum_index = match state.enum_name_to_idx.get(&enum_feature.name) {
+                Some(&index) => index,
+                None => continue,
+            };
+            let enum_data = &state.data.enum_features[enum_index];
+
+            let mut value_counts = vec![0u64; enum_data.values.len()];
+            for &row in &matching_rows {
+                let value = enum_data.data[row];
+                if value != ENUM_NULL && (value as usize) < value_counts.len() {
+                    value_counts[value as usize] += 1;
+                }
+            }
+
+            // Only include if there are any non-zero counts
+            let has_values = value_counts.iter().any(|&count| count > 0);
+            if !has_values {
+                continue;
+            }
+
+            if !first_enum {
+                output.push(',');
+            }
+            first_enum = false;
+
+            output.push_str("{\"name\":");
+            write_json_string(&mut output, &enum_feature.name);
+            output.push_str(",\"counts\":{");
+            let mut first_value = true;
+            for (value_index, &count) in value_counts.iter().enumerate() {
+                if count == 0 {
+                    continue;
+                }
+                if !first_value {
+                    output.push(',');
+                }
+                first_value = false;
+                write_json_string(&mut output, &enum_data.values[value_index]);
+                write!(output, ":{}", count).unwrap();
+            }
+            output.push_str("}}");
+        }
+        output.push_str("]}");
+
+        let elapsed = start_time.elapsed();
+        info!(
+            h3 = %h3_str,
+            resolution,
+            total_count,
+            filters = num_filters,
+            filters_raw = filters_str.as_deref().unwrap_or("-"),
+            ms = format_args!("{:.1}", elapsed.as_secs_f64() * 1000.0),
+            "GET /api/hexagon-stats"
+        );
+
+        output
+    })
+    .await
+    .map_err(|error| (StatusCode::INTERNAL_SERVER_ERROR, error.to_string()))?;
+
+    Ok((
+        [(axum::http::header::CONTENT_TYPE, "application/json")],
+        result,
+    ))
+}
+
+fn write_json_string(output: &mut String, value: &str) {
+    output.push('"');
+    for character in value.chars() {
+        match character {
+            '"' => output.push_str("\\\""),
+            '\\' => output.push_str("\\\\"),
+            '\n' => output.push_str("\\n"),
+            '\r' => output.push_str("\\r"),
+            '\t' => output.push_str("\\t"),
+            other => output.push(other),
+        }
+    }
+    output.push('"');
+}
+
+fn format_f64(value: f64) -> String {
+    if value.fract() == 0.0 && value.abs() < 1e15 {
+        format!("{:.1}", value)
+    } else {
+        format!("{}", value)
+    }
+}
diff --git a/server-rs/src/routes/hexagons.rs b/server-rs/src/routes/hexagons.rs
index 11cb3ed..27ed200 100644
--- a/server-rs/src/routes/hexagons.rs
+++ b/server-rs/src/routes/hexagons.rs
@@ -1,4 +1,4 @@
-use std::fmt::Write;
+use std::fmt::{self, Write};
 use std::sync::Arc;
 
 use axum::extract::Query;
@@ -8,11 +8,29 @@ use rustc_hash::FxHashMap;
 use serde::Deserialize;
 use tracing::{info, warn};
 
-use crate::consts::{H3_PRECOMPUTE_MAX, H3_PRECOMPUTE_MIN};
+use crate::consts::{
+    BOUNDS_BUFFER_PERCENT, BOUNDS_QUANTIZATION, ENUM_NULL, H3_PRECOMPUTE_MAX, H3_PRECOMPUTE_MIN,
+    POSTCODE_MIN_RESOLUTION,
+};
 use crate::filter::parse_filters;
 use crate::state::AppState;
 
-const BOUNDS_BUFFER_PERCENT: f64 = 0.2;
+use super::parse::parse_bounds;
+
+struct HumanBytes(usize);
+
+impl fmt::Display for HumanBytes {
+    fn fmt(&self, formatter: &mut fmt::Formatter<'_>) -> fmt::Result {
+        let bytes = self.0;
+        if bytes >= 1_000_000 {
+            write!(formatter, "{:.1} MB", bytes as f64 / 1_000_000.0)
+        } else if bytes >= 1_000 {
+            write!(formatter, "{:.1} KB", bytes as f64 / 1_000.0)
+        } else {
+            write!(formatter, "{} B", bytes)
+        }
+    }
+}
 
 #[derive(Deserialize)]
 pub struct HexagonParams {
@@ -28,14 +46,28 @@ struct CellAgg {
     count: u32,
     mins: Vec<f64>,
     maxs: Vec<f64>,
+    /// Min/max ordinal indices for enum features (255 = no data yet)
+    enum_mins: Vec<u8>,
+    enum_maxs: Vec<u8>,
+    /// Most common postcode in this cell (only tracked at high resolutions)
+    postcode: Option<String>,
+    postcode_count: u32,
+    lat_sum: f64,
+    lon_sum: f64,
 }
 
 impl CellAgg {
-    fn new(num_features: usize) -> Self {
+    fn new(num_features: usize, num_enums: usize) -> Self {
         CellAgg {
             count: 0,
             mins: vec![f64::INFINITY; num_features],
             maxs: vec![f64::NEG_INFINITY; num_features],
+            enum_mins: vec![ENUM_NULL; num_enums],
+            enum_maxs: vec![0; num_enums],
+            postcode: None,
+            postcode_count: 0,
+            lat_sum: 0.0,
+            lon_sum: 0.0,
         }
     }
 
@@ -47,49 +79,129 @@ impl CellAgg {
         self.count += 1;
         let base = row * num_features;
         let row_slice = &feature_data[base..base + num_features];
-        for (i, &v) in row_slice.iter().enumerate() {
-            if v.is_finite() {
-                if v < self.mins[i] {
-                    self.mins[i] = v;
+        for (feat_index, &value) in row_slice.iter().enumerate() {
+            if value.is_finite() {
+                if value < self.mins[feat_index] {
+                    self.mins[feat_index] = value;
                 }
-                if v > self.maxs[i] {
-                    self.maxs[i] = v;
+                if value > self.maxs[feat_index] {
+                    self.maxs[feat_index] = value;
                 }
             }
         }
     }
+
+    /// Track min/max ordinal index for each enum feature in this cell.
+    #[inline]
+    fn add_enums(&mut self, enum_features: &[crate::data::EnumFeatureData], row: usize) {
+        for (enum_index, enum_feature) in enum_features.iter().enumerate() {
+            let value = enum_feature.data[row];
+            if value != ENUM_NULL {
+                if self.enum_mins[enum_index] == ENUM_NULL || value < self.enum_mins[enum_index] {
+                    self.enum_mins[enum_index] = value;
+                }
+                if value > self.enum_maxs[enum_index] {
+                    self.enum_maxs[enum_index] = value;
+                }
+            }
+        }
+    }
+
+    /// Track postcode and centroid for high-resolution cells.
+    /// Uses simple "first seen" approach — at res 11/12, most rows in a cell share a postcode.
+    #[inline]
+    fn add_postcode(&mut self, postcode: &str, lat: f64, lon: f64) {
+        self.lat_sum += lat;
+        self.lon_sum += lon;
+        if postcode.is_empty() {
+            return;
+        }
+        if self.postcode.is_none() {
+            self.postcode = Some(postcode.to_string());
+            self.postcode_count = 1;
+        } else if self.postcode.as_deref() == Some(postcode) {
+            self.postcode_count += 1;
+        }
+    }
+}
+
+/// Escape a string for inclusion in a JSON string literal.
+pub(crate) fn write_json_escaped(buf: &mut String, text: &str) {
+    for character in text.chars() {
+        match character {
+            '"' => buf.push_str("\\\""),
+            '\\' => buf.push_str("\\\\"),
+            '\n' => buf.push_str("\\n"),
+            '\r' => buf.push_str("\\r"),
+            '\t' => buf.push_str("\\t"),
+            ctrl if ctrl < '\x20' => { let _ = write!(buf, "\\u{:04x}", ctrl as u32); }
+            other => buf.push(other),
+        }
+    }
 }
 
 /// Write the hexagons JSON response directly to a String buffer,
 /// avoiding serde_json::Value allocations entirely.
+#[allow(clippy::too_many_arguments)]
 fn write_hexagons_json(
     buf: &mut String,
     groups: &FxHashMap<u64, CellAgg>,
     min_keys: &[String],
     max_keys: &[String],
     num_features: usize,
+    enum_min_keys: &[String],
+    enum_max_keys: &[String],
+    num_enums: usize,
+    include_postcode: bool,
 ) {
     buf.push_str("{\"features\":[");
     let mut first = true;
-    for (&cell_id, agg) in groups {
+    for (&cell_id, aggregation) in groups {
+        let Some(cell) = h3o::CellIndex::try_from(cell_id).ok() else {
+            continue;
+        };
+
         if !first {
             buf.push(',');
         }
         first = false;
 
-        let cell = h3o::CellIndex::try_from(cell_id).unwrap();
-        write!(buf, "{{\"h3\":\"{}\",\"count\":{}", cell, agg.count).unwrap();
+        let _ = write!(buf, "{{\"h3\":\"{}\",\"count\":{}", cell, aggregation.count);
 
-        for i in 0..num_features {
-            if agg.mins[i] != f64::INFINITY {
-                write!(
+        for feat_index in 0..num_features {
+            if aggregation.mins[feat_index].is_finite() && aggregation.maxs[feat_index].is_finite() {
+                let _ = write!(
                     buf,
                     ",\"{}\":{},\"{}\":{}",
-                    min_keys[i], agg.mins[i], max_keys[i], agg.maxs[i]
-                )
-                .unwrap();
+                    min_keys[feat_index], aggregation.mins[feat_index], max_keys[feat_index], aggregation.maxs[feat_index]
+                );
             }
         }
+
+        for enum_index in 0..num_enums {
+            if aggregation.enum_mins[enum_index] != ENUM_NULL {
+                let _ = write!(
+                    buf,
+                    ",\"{}\":{},\"{}\":{}",
+                    enum_min_keys[enum_index], aggregation.enum_mins[enum_index],
+                    enum_max_keys[enum_index], aggregation.enum_maxs[enum_index]
+                );
+            }
+        }
+
+        if include_postcode {
+            if let Some(ref postcode) = aggregation.postcode {
+                let total = aggregation.count as f64;
+                let centroid_lat = aggregation.lat_sum / total;
+                let centroid_lon = aggregation.lon_sum / total;
+                if centroid_lat.is_finite() && centroid_lon.is_finite() {
+                    buf.push_str(",\"postcode\":\"");
+                    write_json_escaped(buf, postcode);
+                    let _ = write!(buf, "\",\"lat\":{},\"lon\":{}", centroid_lat, centroid_lon);
+                }
+            }
+        }
+
         buf.push('}');
     }
     buf.push_str("]}");
@@ -101,7 +213,10 @@ pub async fn get_hexagons(
 ) -> Result<impl IntoResponse, (StatusCode, String)> {
     let resolution = params.resolution;
     if resolution < H3_PRECOMPUTE_MIN || resolution > H3_PRECOMPUTE_MAX {
-        warn!(resolution, "Resolution out of range [{}, {}]", H3_PRECOMPUTE_MIN, H3_PRECOMPUTE_MAX);
+        warn!(
+            resolution,
+            "Resolution out of range [{}, {}]", H3_PRECOMPUTE_MIN, H3_PRECOMPUTE_MAX
+        );
         return Err((
             StatusCode::BAD_REQUEST,
             format!(
@@ -116,25 +231,7 @@ pub async fn get_hexagons(
         "bounds parameter is required".into(),
     ))?;
 
-    let parts: Vec<f64> = bounds_str
-        .split(',')
-        .map(|s| s.trim().parse::<f64>())
-        .collect::<Result<Vec<_>, _>>()
-        .map_err(|_| {
-            (
-                StatusCode::BAD_REQUEST,
-                "Invalid bounds format. Use: south,west,north,east".into(),
-            )
-        })?;
-
-    if parts.len() != 4 {
-        return Err((
-            StatusCode::BAD_REQUEST,
-            "Invalid bounds format. Use: south,west,north,east".into(),
-        ));
-    }
-
-    let (mut south, mut west, mut north, mut east) = (parts[0], parts[1], parts[2], parts[3]);
+    let (mut south, mut west, mut north, mut east) = parse_bounds(&bounds_str)?;
 
     let lat_range = north - south;
     let lng_range = east - west;
@@ -143,11 +240,10 @@ pub async fn get_hexagons(
     west -= lng_range * BOUNDS_BUFFER_PERCENT;
     east += lng_range * BOUNDS_BUFFER_PERCENT;
 
-    let precision = 0.01;
-    south = (south / precision).floor() * precision;
-    west = (west / precision).floor() * precision;
-    north = (north / precision).ceil() * precision;
-    east = (east / precision).ceil() * precision;
+    south = (south / BOUNDS_QUANTIZATION).floor() * BOUNDS_QUANTIZATION;
+    west = (west / BOUNDS_QUANTIZATION).floor() * BOUNDS_QUANTIZATION;
+    north = (north / BOUNDS_QUANTIZATION).ceil() * BOUNDS_QUANTIZATION;
+    east = (east / BOUNDS_QUANTIZATION).ceil() * BOUNDS_QUANTIZATION;
 
     let filters_str = params.filters.clone();
     let (parsed_filters, parsed_enum_filters) = parse_filters(
@@ -157,44 +253,38 @@ pub async fn get_hexagons(
     );
     let num_filters = parsed_filters.len() + parsed_enum_filters.len();
 
-    let json_body = tokio::task::spawn_blocking(move || {
+    let json_body = tokio::task::spawn_blocking(move || -> Result<String, String> {
         let t0 = std::time::Instant::now();
 
         let num_features = state.data.num_features;
+        let num_enums = state.data.enum_features.len();
         let feature_data = &state.data.feature_data;
 
-        let min_keys: Vec<String> = state
-            .data
-            .feature_names
-            .iter()
-            .map(|n| format!("min_{}", n))
-            .collect();
-        let max_keys: Vec<String> = state
-            .data
-            .feature_names
-            .iter()
-            .map(|n| format!("max_{}", n))
-            .collect();
+        let min_keys = &state.min_keys;
+        let max_keys = &state.max_keys;
+        let enum_min_keys = &state.enum_min_keys;
+        let enum_max_keys = &state.enum_max_keys;
 
         let h3_cells_for_res: Option<&[u64]> = state
             .h3_cells
             .get(resolution as usize)
-            .filter(|v| !v.is_empty())
-            .map(|v| v.as_slice());
+            .filter(|cells| !cells.is_empty())
+            .map(|cells| cells.as_slice());
 
         let mut groups: FxHashMap<u64, CellAgg> = FxHashMap::default();
 
         let enum_features = &state.data.enum_features;
+        let include_postcode = resolution >= POSTCODE_MIN_RESOLUTION;
 
         // Row-level filter check: numeric must be non-NaN and within [min, max],
         // enum must have value index in the allowed set
         let row_passes = |row: usize| -> bool {
-            parsed_filters.iter().all(|f| {
-                let v = feature_data[row * num_features + f.feat_idx];
-                v.is_finite() && v >= f.min && v <= f.max
-            }) && parsed_enum_filters.iter().all(|ef| {
-                let v = enum_features[ef.enum_idx].data[row];
-                v != 255 && ef.allowed.contains(&v)
+            parsed_filters.iter().all(|filter| {
+                let value = feature_data[row * num_features + filter.feat_idx];
+                value.is_finite() && value >= filter.min && value <= filter.max
+            }) && parsed_enum_filters.iter().all(|enum_filter| {
+                let value = enum_features[enum_filter.enum_idx].data[row];
+                value != ENUM_NULL && enum_filter.allowed.contains(&value)
             })
         };
 
@@ -207,13 +297,22 @@ pub async fn get_hexagons(
                         return;
                     }
                     let cell_id = precomputed[row];
-                    groups
+                    let aggregation = groups
                         .entry(cell_id)
-                        .or_insert_with(|| CellAgg::new(num_features))
-                        .add_row(feature_data, row, num_features);
+                        .or_insert_with(|| CellAgg::new(num_features, num_enums));
+                    aggregation.add_row(feature_data, row, num_features);
+                    aggregation.add_enums(enum_features, row);
+                    if include_postcode {
+                        aggregation.add_postcode(
+                            &state.data.postcode[row],
+                            state.data.lat[row],
+                            state.data.lon[row],
+                        );
+                    }
                 });
         } else {
-            let h3_res = h3o::Resolution::try_from(resolution).unwrap();
+            let h3_res = h3o::Resolution::try_from(resolution)
+                .map_err(|error| format!("Invalid H3 resolution {}: {}", resolution, error))?;
             state
                 .grid
                 .for_each_in_bounds(south, west, north, east, |row_idx| {
@@ -222,19 +321,37 @@ pub async fn get_hexagons(
                         return;
                     }
                     let cell_id = h3o::LatLng::new(state.data.lat[row], state.data.lon[row])
-                        .map(|c| u64::from(c.to_cell(h3_res)))
+                        .map(|coord| u64::from(coord.to_cell(h3_res)))
                         .unwrap_or(0);
-                    groups
+                    let aggregation = groups
                         .entry(cell_id)
-                        .or_insert_with(|| CellAgg::new(num_features))
-                        .add_row(feature_data, row, num_features);
+                        .or_insert_with(|| CellAgg::new(num_features, num_enums));
+                    aggregation.add_row(feature_data, row, num_features);
+                    aggregation.add_enums(enum_features, row);
+                    if include_postcode {
+                        aggregation.add_postcode(
+                            &state.data.postcode[row],
+                            state.data.lat[row],
+                            state.data.lon[row],
+                        );
+                    }
                 });
         }
 
         let t_agg = t0.elapsed();
 
         let mut json_buf = String::with_capacity(groups.len() * 128);
-        write_hexagons_json(&mut json_buf, &groups, &min_keys, &max_keys, num_features);
+        write_hexagons_json(
+            &mut json_buf,
+            &groups,
+            min_keys,
+            max_keys,
+            num_features,
+            enum_min_keys,
+            enum_max_keys,
+            num_enums,
+            include_postcode,
+        );
 
         let t_total = t0.elapsed();
         info!(
@@ -244,14 +361,15 @@ pub async fn get_hexagons(
             filters_raw = filters_str.as_deref().unwrap_or("-"),
             agg_ms = format_args!("{:.1}", t_agg.as_secs_f64() * 1000.0),
             total_ms = format_args!("{:.1}", t_total.as_secs_f64() * 1000.0),
-            bytes = json_buf.len(),
+            size = format_args!("{}", HumanBytes(json_buf.len())),
             "GET /api/hexagons"
         );
 
-        json_buf
+        Ok(json_buf)
     })
     .await
-    .unwrap();
+    .map_err(|error| (StatusCode::INTERNAL_SERVER_ERROR, error.to_string()))?
+    .map_err(|error| (StatusCode::INTERNAL_SERVER_ERROR, error))?;
 
     Ok(([("content-type", "application/json")], json_body))
 }
diff --git a/server-rs/src/routes/mod.rs b/server-rs/src/routes/mod.rs
index d33f787..65b0807 100644
--- a/server-rs/src/routes/mod.rs
+++ b/server-rs/src/routes/mod.rs
@@ -1,9 +1,12 @@
 mod features;
-mod hexagons;
+pub(crate) mod hexagons;
+mod hexagon_stats;
+pub(crate) mod parse;
 mod pois;
-mod properties;
+pub(crate) mod properties;
 
 pub use features::get_features;
+pub use hexagon_stats::get_hexagon_stats;
 pub use hexagons::get_hexagons;
 pub use pois::{get_poi_categories, get_pois};
 pub use properties::get_hexagon_properties;
diff --git a/server-rs/src/routes/parse.rs b/server-rs/src/routes/parse.rs
index 5bcd04f..853766d 100644
--- a/server-rs/src/routes/parse.rs
+++ b/server-rs/src/routes/parse.rs
@@ -1,9 +1,38 @@
 use axum::http::StatusCode;
 
+/// Compute the lat/lon bounding box of an H3 cell, with a configurable buffer in degrees.
+pub fn h3_cell_bounds(cell: h3o::CellIndex, buffer: f64) -> (f64, f64, f64, f64) {
+    let boundary = cell.boundary();
+    let (mut min_lat, mut max_lat) = (f64::INFINITY, f64::NEG_INFINITY);
+    let (mut min_lon, mut max_lon) = (f64::INFINITY, f64::NEG_INFINITY);
+    for vertex in boundary.iter() {
+        let lat = vertex.lat();
+        let lon = vertex.lng();
+        if lat < min_lat {
+            min_lat = lat;
+        }
+        if lat > max_lat {
+            max_lat = lat;
+        }
+        if lon < min_lon {
+            min_lon = lon;
+        }
+        if lon > max_lon {
+            max_lon = lon;
+        }
+    }
+    (
+        min_lat - buffer,
+        min_lon - buffer,
+        max_lat + buffer,
+        max_lon + buffer,
+    )
+}
+
 pub fn parse_bounds(bounds_str: &str) -> Result<(f64, f64, f64, f64), (StatusCode, String)> {
     let parts: Vec<f64> = bounds_str
         .split(',')
-        .map(|s| s.trim().parse::<f64>())
+        .map(|part| part.trim().parse::<f64>())
         .collect::<Result<Vec<_>, _>>()
         .map_err(|_| {
             (
diff --git a/server-rs/src/routes/pois.rs b/server-rs/src/routes/pois.rs
index 40d5557..f00008f 100644
--- a/server-rs/src/routes/pois.rs
+++ b/server-rs/src/routes/pois.rs
@@ -39,37 +39,56 @@ pub async fn get_pois(
     let category_filter: Option<rustc_hash::FxHashSet<String>> = params
         .categories
         .as_deref()
-        .filter(|s| !s.is_empty())
-        .map(|s| s.split(',').map(|c| c.trim().to_string()).collect());
+        .filter(|text| !text.is_empty())
+        .map(|text| text.split(',').map(|part| part.trim().to_string()).collect());
 
-    let num_categories = category_filter.as_ref().map(|c| c.len()).unwrap_or(0);
+    let num_categories = category_filter.as_ref().map(|cats| cats.len()).unwrap_or(0);
 
     let result = tokio::task::spawn_blocking(move || {
         let t0 = std::time::Instant::now();
         let row_indices = state.poi_grid.query(south, west, north, east);
 
-        let pois: Vec<POI> = row_indices
+        // Collect matching row indices first, then sample randomly so the
+        // subset covers the viewport uniformly instead of clustering in one area.
+        let mut matching_rows: Vec<usize> = row_indices
             .iter()
             .filter_map(|&row_idx| {
                 let row = row_idx as usize;
-
                 if let Some(ref categories) = category_filter {
                     if !categories.contains(&state.poi_data.category[row]) {
                         return None;
                     }
                 }
-
-                Some(POI {
-                    id: state.poi_data.id[row].clone(),
-                    name: state.poi_data.name[row].clone(),
-                    category: state.poi_data.category[row].clone(),
-                    group: state.poi_data.group[row].clone(),
-                    lat: state.poi_data.lat[row],
-                    lng: state.poi_data.lng[row],
-                    emoji: state.poi_data.emoji[row].clone(),
-                })
+                Some(row)
+            })
+            .collect();
+
+        if matching_rows.len() > MAX_POIS_PER_REQUEST {
+            // Use a power-of-2 sampling step so each POI's inclusion depends
+            // only on its own priority hash, not on what other POIs are in
+            // the viewport. This prevents visible reshuffling when panning.
+            let ratio = (matching_rows.len() / MAX_POIS_PER_REQUEST) as u32;
+            let step = ratio.next_power_of_two();
+            let mask = step - 1;
+            matching_rows.retain(|&row| state.poi_data.priority[row] & mask == 0);
+            // Statistical noise may leave us slightly over the limit
+            if matching_rows.len() > MAX_POIS_PER_REQUEST {
+                matching_rows.sort_unstable_by_key(|&row| state.poi_data.priority[row]);
+                matching_rows.truncate(MAX_POIS_PER_REQUEST);
+            }
+        }
+
+        let pois: Vec<POI> = matching_rows
+            .iter()
+            .map(|&row| POI {
+                id: state.poi_data.id[row].clone(),
+                name: state.poi_data.name[row].clone(),
+                category: state.poi_data.category[row].clone(),
+                group: state.poi_data.group[row].clone(),
+                lat: state.poi_data.lat[row],
+                lng: state.poi_data.lng[row],
+                emoji: state.poi_data.emoji[row].clone(),
             })
-            .take(MAX_POIS_PER_REQUEST)
             .collect();
 
         let elapsed = t0.elapsed();
@@ -85,7 +104,7 @@ pub async fn get_pois(
         POIsResponse { pois }
     })
     .await
-    .map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;
+    .map_err(|error| (StatusCode::INTERNAL_SERVER_ERROR, error.to_string()))?;
 
     Ok(Json(result))
 }
@@ -98,7 +117,7 @@ pub struct POICategoriesResponse {
 pub async fn get_poi_categories(state: Arc<AppState>) -> Json<POICategoriesResponse> {
     let groups: Vec<POICategoryGroup> = state.poi_category_groups.clone();
 
-    let total: usize = groups.iter().map(|g| g.categories.len()).sum();
+    let total: usize = groups.iter().map(|group| group.categories.len()).sum();
     info!(
         count = total,
         groups = groups.len(),
diff --git a/server-rs/src/routes/properties.rs b/server-rs/src/routes/properties.rs
index 1f205cb..8adffd7 100644
--- a/server-rs/src/routes/properties.rs
+++ b/server-rs/src/routes/properties.rs
@@ -8,9 +8,13 @@ use rustc_hash::FxHashMap;
 use serde::{Deserialize, Serialize};
 use tracing::{info, warn};
 
+use crate::consts::{DEFAULT_PROPERTIES_LIMIT, ENUM_NULL, MAX_PROPERTIES_LIMIT};
+use crate::data::EnumFeatureData;
 use crate::filter::{parse_filters, row_passes_filters};
 use crate::state::AppState;
 
+use super::parse::h3_cell_bounds;
+
 #[derive(Deserialize)]
 pub struct HexagonPropertiesParams {
     pub h3: String,
@@ -35,6 +39,8 @@ pub struct Property {
     pub lat: f64,
     pub lon: f64,
 
+    pub is_construction_date_approximate: Option<bool>,
+
     #[serde(flatten)]
     pub features: FxHashMap<String, f64>,
 }
@@ -48,20 +54,51 @@ pub struct HexagonPropertiesResponse {
     pub truncated: bool,
 }
 
+fn non_empty_string(text: &str) -> Option<String> {
+    let trimmed = text.trim();
+    if trimmed.is_empty() {
+        None
+    } else {
+        Some(trimmed.to_string())
+    }
+}
+
+fn lookup_enum_value(
+    enum_features: &[EnumFeatureData],
+    enum_idx: &FxHashMap<String, usize>,
+    row: usize,
+    names: &[&str],
+) -> Option<String> {
+    for name in names {
+        if let Some(&feature_index) = enum_idx.get(*name) {
+            let enum_feature = &enum_features[feature_index];
+            let data_index = enum_feature.data[row];
+            if data_index != ENUM_NULL {
+                if let Some(value) = enum_feature.values.get(data_index as usize) {
+                    return Some(value.clone());
+                }
+            }
+        }
+    }
+    None
+}
+
 pub async fn get_hexagon_properties(
     state: Arc<AppState>,
     Query(params): Query<HexagonPropertiesParams>,
 ) -> Result<Json<HexagonPropertiesResponse>, (StatusCode, String)> {
-    let cell = h3o::CellIndex::from_str(&params.h3)
-        .map_err(|e| {
-            warn!(h3 = %params.h3, error = %e, "Invalid H3 cell index");
-            (StatusCode::BAD_REQUEST, format!("Invalid H3 cell: {}", e))
-        })?;
+    let cell = h3o::CellIndex::from_str(&params.h3).map_err(|error| {
+        warn!(h3 = %params.h3, error = %error, "Invalid H3 cell index");
+        (StatusCode::BAD_REQUEST, format!("Invalid H3 cell: {}", error))
+    })?;
     let cell_u64: u64 = cell.into();
 
     let resolution = params.resolution as usize;
     if resolution >= state.h3_cells.len() || state.h3_cells[resolution].is_empty() {
-        warn!(resolution, "Invalid or non-precomputed resolution for hexagon-properties");
+        warn!(
+            resolution,
+            "Invalid or non-precomputed resolution for hexagon-properties"
+        );
         return Err((
             StatusCode::BAD_REQUEST,
             "Invalid or non-precomputed resolution".to_string(),
@@ -84,31 +121,29 @@ pub async fn get_hexagon_properties(
         let feature_data = &state.data.feature_data;
         let enum_features = &state.data.enum_features;
 
-        let matching_rows: Vec<usize> = h3_data
-            .iter()
-            .enumerate()
-            .filter_map(|(idx, &h3_cell)| {
-                if h3_cell == cell_u64 {
-                    if row_passes_filters(
-                        idx,
+        let (min_lat, min_lon, max_lat, max_lon) = h3_cell_bounds(cell, 0.001);
+
+        let mut matching_rows: Vec<usize> = Vec::new();
+        state
+            .grid
+            .for_each_in_bounds(min_lat, min_lon, max_lat, max_lon, |row_idx| {
+                let row = row_idx as usize;
+                if h3_data[row] == cell_u64
+                    && row_passes_filters(
+                        row,
                         &parsed_filters,
                         &parsed_enum_filters,
                         feature_data,
                         num_features,
                         enum_features,
-                    ) {
-                        Some(idx)
-                    } else {
-                        None
-                    }
-                } else {
-                    None
+                    )
+                {
+                    matching_rows.push(row);
                 }
-            })
-            .collect();
+            });
 
         let total = matching_rows.len();
-        let limit = params.limit.unwrap_or(100).min(500);
+        let limit = params.limit.unwrap_or(DEFAULT_PROPERTIES_LIMIT).min(MAX_PROPERTIES_LIMIT);
         let offset = params.offset.unwrap_or(0);
         let truncated = total > offset + limit;
 
@@ -120,49 +155,46 @@ pub async fn get_hexagon_properties(
                 let mut features = FxHashMap::default();
                 let base = row * num_features;
                 for (feat_idx, feat_name) in state.data.feature_names.iter().enumerate() {
-                    let v = feature_data[base + feat_idx];
-                    if v.is_finite() {
-                        features.insert(feat_name.clone(), v);
+                    let value = feature_data[base + feat_idx];
+                    if value.is_finite() {
+                        features.insert(feat_name.clone(), value);
                     }
                 }
 
-                let get_string = |s: &str| -> Option<String> {
-                    let trimmed = s.trim();
-                    if trimmed.is_empty() {
-                        None
-                    } else {
-                        Some(trimmed.to_string())
-                    }
-                };
-
-                let get_enum_value = |names: &[&str]| -> Option<String> {
-                    for name in names {
-                        if let Some(val) = enum_features.iter().find_map(|ef| {
-                            if ef.name == *name {
-                                let idx = ef.data[row];
-                                if idx == 255 {
-                                    None
-                                } else {
-                                    ef.values.get(idx as usize).cloned()
-                                }
-                            } else {
-                                None
-                            }
-                        }) {
-                            return Some(val);
-                        }
-                    }
-                    None
-                };
-
                 Property {
-                    address: get_string(&state.data.address[row]),
-                    postcode: get_string(&state.data.postcode[row]),
-                    property_type: get_enum_value(&["Property type", "epc_property_type", "pp_property_type"]),
-                    built_form: get_enum_value(&["Property type/built form", "built_form"]),
-                    duration: get_enum_value(&["Leashold/Freehold", "duration"]),
-                    current_energy_rating: get_enum_value(&["Current energy rating", "current_energy_rating"]),
-                    potential_energy_rating: get_enum_value(&["Potential energy rating", "potential_energy_rating"]),
+                    address: non_empty_string(&state.data.address[row]),
+                    postcode: non_empty_string(&state.data.postcode[row]),
+                    is_construction_date_approximate: Some(state.data.is_approx_build_date[row]),
+                    property_type: lookup_enum_value(
+                        enum_features,
+                        &state.enum_name_to_idx,
+                        row,
+                        &["Property type", "epc_property_type", "pp_property_type"],
+                    ),
+                    built_form: lookup_enum_value(
+                        enum_features,
+                        &state.enum_name_to_idx,
+                        row,
+                        &["Property type/built form", "built_form"],
+                    ),
+                    duration: lookup_enum_value(
+                        enum_features,
+                        &state.enum_name_to_idx,
+                        row,
+                        &["Leashold/Freehold", "duration"],
+                    ),
+                    current_energy_rating: lookup_enum_value(
+                        enum_features,
+                        &state.enum_name_to_idx,
+                        row,
+                        &["Current energy rating", "current_energy_rating"],
+                    ),
+                    potential_energy_rating: lookup_enum_value(
+                        enum_features,
+                        &state.enum_name_to_idx,
+                        row,
+                        &["Potential energy rating", "potential_energy_rating"],
+                    ),
                     lat: state.data.lat[row],
                     lon: state.data.lon[row],
                     features,
@@ -192,7 +224,7 @@ pub async fn get_hexagon_properties(
         }
     })
     .await
-    .unwrap();
+    .map_err(|error| (StatusCode::INTERNAL_SERVER_ERROR, error.to_string()))?;
 
     Ok(Json(result))
 }
diff --git a/server-rs/src/state.rs b/server-rs/src/state.rs
index 2949d9b..0c146fd 100644
--- a/server-rs/src/state.rs
+++ b/server-rs/src/state.rs
@@ -1,5 +1,14 @@
+use rustc_hash::FxHashMap;
+use serde::Serialize;
+
 use crate::data::{POIData, PropertyData};
-use crate::index::GridIndex;
+use crate::grid_index::GridIndex;
+
+#[derive(Serialize, Clone)]
+pub struct POICategoryGroup {
+    pub name: String,
+    pub categories: Vec<String>,
+}
 
 pub struct AppState {
     pub data: PropertyData,
@@ -9,4 +18,16 @@ pub struct AppState {
     pub h3_cells: Vec<Vec<u64>>,
     pub poi_data: POIData,
     pub poi_grid: GridIndex,
+    /// Precomputed JSON key names: "min_{feature_name}" for each numeric feature
+    pub min_keys: Vec<String>,
+    /// Precomputed JSON key names: "max_{feature_name}" for each numeric feature
+    pub max_keys: Vec<String>,
+    /// Precomputed JSON key names: "min_{enum_name}" for each enum feature
+    pub enum_min_keys: Vec<String>,
+    /// Precomputed JSON key names: "max_{enum_name}" for each enum feature
+    pub enum_max_keys: Vec<String>,
+    /// Precomputed POI category groups (sorted)
+    pub poi_category_groups: Vec<POICategoryGroup>,
+    /// Precomputed map from enum feature name to index in data.enum_features
+    pub enum_name_to_idx: FxHashMap<String, usize>,
 }
diff --git a/server-rs/src/tests.rs b/server-rs/src/tests.rs
index a6bd035..25df804 100644
--- a/server-rs/src/tests.rs
+++ b/server-rs/src/tests.rs
@@ -159,8 +159,6 @@ mod filter_tests {
 
 #[cfg(test)]
 mod json_tests {
-    use std::fmt::Write;
-
     #[test]
     fn json_escaped_postcode_with_quotes_is_valid() {
         use crate::routes::hexagons::write_json_escaped;
@@ -199,6 +197,7 @@ mod json_tests {
 
     #[test]
     fn nan_is_not_valid_json() {
+        use std::fmt::Write;
         // Verify that raw NaN in write! is still invalid JSON (documenting the risk
         // that the is_finite() guard in write_hexagons_json protects against).
         let mut buf = String::new();
@@ -210,6 +209,7 @@ mod json_tests {
 
     #[test]
     fn infinity_is_not_valid_json() {
+        use std::fmt::Write;
         let mut buf = String::new();
         write!(buf, "{{\"min_price\":{}}}", f64::INFINITY).unwrap();
 
@@ -225,7 +225,7 @@ mod enum_encoding_tests {
         // Documents the underlying u8 wrapping behavior that the truncation
         // guard in property.rs now prevents.
         let num_values = 300usize;
-        let indices: Vec<u8> = (0..num_values).map(|i| i as u8).collect();
+        let indices: Vec<u8> = (0..num_values).map(|index| index as u8).collect();
 
         assert_eq!(indices[0], indices[256], "u8 wraps: 0 == 256");
         assert_eq!(indices[1], indices[257], "u8 wraps: 1 == 257");
@@ -235,7 +235,7 @@ mod enum_encoding_tests {
         let value_to_idx: HashMap<&str, u8> = values
             .iter()
             .enumerate()
-            .map(|(i, v)| (v.as_str(), i as u8))
+            .map(|(index, value)| (value.as_str(), index as u8))
             .collect();
 
         let unique_indices: std::collections::HashSet<u8> =