.

finder/test_rightmove_concurrency.py

@@ -14,7 +14,7 @@ def _prop(pid, pin=None):
 
 
 # ---------------------------------------------------------------------------
-# _needs_detail_fetch — accurate-pin skip
+# _needs_detail_fetch: accurate-pin skip
 # ---------------------------------------------------------------------------
 
 
@@ -32,7 +32,7 @@ def test_needs_detail_fetch_disabled_always_fetches(monkeypatch):
 
 
 # ---------------------------------------------------------------------------
-# _prime_detail_postcodes — worklist selection + concurrent fetch
+# _prime_detail_postcodes: worklist selection + concurrent fetch
 # ---------------------------------------------------------------------------
 
 
@@ -97,7 +97,7 @@ def test_prime_is_a_noop_when_disabled_or_cap_zero(monkeypatch):
 
 
 # ---------------------------------------------------------------------------
-# _paginate — end-to-end (network stubbed): accurate pins fall back to
+# _paginate end-to-end (network stubbed): accurate pins fall back to
 # coordinates, approximate pins use the detail postcode.
 # ---------------------------------------------------------------------------
 

finder/test_scraper_concurrency.py

@@ -9,7 +9,7 @@ import zoopla
 
 
 # ---------------------------------------------------------------------------
-# _run_sources — Zoopla inline, others in threads, failures isolated
+# _run_sources: Zoopla inline, others in threads, failures isolated
 # ---------------------------------------------------------------------------
 
 
@@ -85,13 +85,13 @@ def test_seed_and_save_detail_caches_round_trip(tmp_path):
 
 def test_seed_detail_caches_tolerates_missing_files(tmp_path):
     rightmove._detail_postcode_cache.clear()
-    # No file written yet — seeding must not raise and must leave cache empty.
+    # No file written yet: seeding must not raise and must leave cache empty.
     scraper._seed_detail_caches(["rightmove"], tmp_path)
     assert rightmove._detail_postcode_cache == {}
 
 
 # ---------------------------------------------------------------------------
-# run_scrape — full orchestration wiring (sources stubbed, no network)
+# run_scrape: full orchestration wiring (sources stubbed, no network)
 # ---------------------------------------------------------------------------
 
 

finder/test_transform.py

@@ -1,4 +1,5 @@
 from transform import (
+    build_listing_url,
     build_register_address,
     clean_listing_address,
     extract_full_postcode,
@@ -173,3 +174,33 @@ def test_rightmove_transform_without_detail_keeps_coordinate_logic() -> None:
     assert result is not None
     assert result["Postcode"] == "SW9 7AA"
     assert result["Postcode source"] == "coordinates"
+
+
+def test_build_listing_url_stamps_channel_from_new_build_flag() -> None:
+    # Resale gets RES_BUY; new builds get RES_NEW.
+    assert build_listing_url("/properties/200", False) == (
+        "https://www.rightmove.co.uk/properties/200#/?channel=RES_BUY"
+    )
+    assert build_listing_url("/properties/200", True) == (
+        "https://www.rightmove.co.uk/properties/200#/?channel=RES_NEW"
+    )
+    # An existing channel/fragment on the source URL is stripped and re-stamped.
+    assert build_listing_url("/properties/200#/?channel=RES_BUY", True) == (
+        "https://www.rightmove.co.uk/properties/200#/?channel=RES_NEW"
+    )
+    # Missing URL stays empty.
+    assert build_listing_url("", True) == ""
+
+
+def test_rightmove_transform_tags_new_builds_res_new() -> None:
+    # The Rightmove search response marks new-build developments with
+    # development=True; transform_property must stamp the listing URL RES_NEW.
+    new_build = {**_rightmove_prop(), "development": True}
+    result = transform_property(new_build, "SW9", StubPostcodeIndex("SW9 7AA"))
+    assert result is not None
+    assert result["Listing URL"].endswith("#/?channel=RES_NEW")
+
+    # Ordinary resale (development absent/false) stays RES_BUY.
+    resale = transform_property(_rightmove_prop(), "SW9", StubPostcodeIndex("SW9 7AA"))
+    assert resale is not None
+    assert resale["Listing URL"].endswith("#/?channel=RES_BUY")

finder/test_zoopla.py

@@ -103,7 +103,7 @@ def test_parse_detail_geo_merges_location_uprn_with_address_full_address() -> No
 def test_parse_detail_geo_does_not_borrow_comparable_full_address() -> None:
     # The only `address` twin on the page belongs to a different uprn (a
     # comparable listing). With a uprn to match on, an unrelated twin is never
-    # borrowed — full_address stays None rather than grabbing the wrong street.
+    # borrowed: full_address stays None rather than grabbing the wrong street.
     html = (
         '"location":{"outcode":"NR29",'
         '"coordinates":{"latitude":52.716014,"longitude":1.614495},'
@@ -185,7 +185,7 @@ def test_parse_detail_geo_returns_none_for_garbage() -> None:
     assert parse_detail_geo("<html><body>no data here</body></html>") is None
     assert parse_detail_geo("") is None
     # Coordinates that are not inside a property location/address wrapper (e.g.
-    # only an unwrapped POI) yield nothing — safe degradation to the outcode.
+    # only an unwrapped POI) yield nothing, safe degradation to the outcode.
     assert parse_detail_geo('"name":"X","coordinates":{"latitude":51.5,"longitude":-0.1}') is None
 
 

finder/transform.py

@@ -149,7 +149,7 @@ def map_property_type(sub_type: str | None) -> str:
         return "Terraced"
     if "house" in lower or "cottage" in lower:
         return "Detached"
-    log.warning("Unknown propertySubType: %r — mapping to Other", sub_type)
+    log.warning("Unknown propertySubType: %r, mapping to Other", sub_type)
     return "Other"
 
 
@@ -267,7 +267,7 @@ def build_register_address(
     property's own number or name (e.g. Zoopla detail pages expose
     ``propertyNumberOrName`` = "12" or "Martham Mill"), prepend it so the address
     carries the house identifier that the EPC/Price-Paid register addresses also
-    use — turning a fuzzy street match into a near-exact one. Falls back to the
+    use, turning a fuzzy street match into a near-exact one. Falls back to the
     plain cleaned address when no number/name is available.
     """
     cleaned = clean_listing_address(raw_address)
@@ -282,6 +282,23 @@ def build_register_address(
     return f"{number_or_name}, {cleaned}" if cleaned else number_or_name
 
 
+def build_listing_url(property_url: str | None, is_new_build: bool) -> str:
+    """Build the canonical Rightmove listing URL with an explicit channel marker.
+
+    The search API always echoes ``?channel=RES_BUY`` in ``propertyUrl`` even for
+    new-build developments (the request channel stays BUY), so the channel is
+    re-stamped here from the per-listing ``development`` flag: ``RES_NEW`` for new
+    builds, ``RES_BUY`` for ordinary resale. The map UI reads this marker to split
+    new vs non-new listings. Any channel/fragment already on ``propertyUrl`` is
+    stripped first so the result is deterministic.
+    """
+    if not property_url:
+        return ""
+    base = property_url.split("#", 1)[0].split("?", 1)[0]
+    channel = "RES_NEW" if is_new_build else "RES_BUY"
+    return f"{RIGHTMOVE_BASE}{base}#/?channel={channel}"
+
+
 def transform_property(
     prop: dict,
     outcode: str,
@@ -337,7 +354,7 @@ def transform_property(
 
     inferred_postcode = pc_index.nearest(lat, lng)
     if not inferred_postcode:
-        log.debug("No England postcode for property at %.4f, %.4f — skipping", lat, lng)
+        log.debug("No England postcode for property at %.4f, %.4f; skipping", lat, lng)
         return None
     raw_address = prop.get("displayAddress", "") or ""
     extracted_postcode = extract_full_postcode(raw_address)
@@ -391,7 +408,7 @@ def transform_property(
         "price_frequency": "",
         "Price qualifier": price_qualifier,
         "Total floor area (sqm)": parse_display_size(prop.get("displaySize")),
-        "Listing URL": RIGHTMOVE_BASE + property_url if property_url else "",
+        "Listing URL": build_listing_url(property_url, bool(prop.get("development"))),
         "Listing features": key_features,
         "first_visible_date": prop.get("firstVisibleDate", ""),
     }

finder/zoopla_flaresolverr.py

@@ -1,7 +1,7 @@
 """Zoopla scraping via FlareSolverr (no browser/VNC needed).
 
 FlareSolverr solves Zoopla's Cloudflare and returns the rendered HTML, which
-still contains the React Server Components flight stream — so the existing pure
+still contains the React Server Components flight stream, so the existing pure
 parsers work unchanged:
   - the search page yields the outcode's listing detail URLs, and
   - each detail page's flight stream carries the property's location object

frontend/src/components/home/ProductShowcase.tsx

@@ -12,6 +12,7 @@ import type { TFunction } from 'i18next';
 import { useTranslation } from 'react-i18next';
 import { cellToLatLng, polygonToCells } from 'h3-js';
 import PriceHistoryChart from '../map/PriceHistoryChart';
+import { MapErrorBoundary } from '../map/MapErrorBoundary';
 import StackedBarChart from '../map/StackedBarChart';
 import JourneyInstructions, { type JourneyInstructionPreset } from '../map/JourneyInstructions';
 import { DualHistogram } from '../map/DualHistogram';
@@ -214,10 +215,13 @@ const SHOWCASE_MAP_START_VIEW: ViewState = {
   bearing: 0,
 };
 
+// End the Match fly-in over Greater London so the "match" step lands on the same
+// place the Inspect (SW5 9AA) and Scout (SW5/SE22/N4) steps then drill into. The
+// single-search narrative used to break by zooming to Birmingham then showing London.
 const SHOWCASE_MAP_END_VIEW: ViewState = {
-  longitude: -1.89,
+  longitude: -0.12,
-  latitude: 52.49,
+  latitude: 51.51,
-  zoom: 8.72,
+  zoom: 9.05,
   pitch: 0,
   bearing: 0,
 };
@@ -260,7 +264,12 @@ function buildShowcaseMapData(): HexagonData[] {
 }
 
 const SHOWCASE_MAP_DATA = buildShowcaseMapData();
-const SHOWCASE_MAP_TOTAL_COUNT = SHOWCASE_MAP_DATA.reduce((sum, item) => sum + item.count, 0);
+// Count only the hexes around Greater London so the Match card's figure matches the
+// area it has zoomed to, instead of mislabelling the England-wide total as one city.
+const SHOWCASE_LONDON_COUNT = SHOWCASE_MAP_DATA.reduce((sum, item) => {
+  const nearLondon = Math.abs(item.lat - 51.5072) < 0.55 && Math.abs(item.lon + 0.1276) < 0.85;
+  return nearLondon ? sum + item.count : sum;
+}, 0);
 const EMPTY_SHOWCASE_POSTCODES: PostcodeFeature[] = [];
 const EMPTY_SHOWCASE_POIS: POI[] = [];
 
@@ -601,37 +610,42 @@ function EnglandHexMapScreen({ isActive }: { isActive: boolean }) {
   return (
     <div className="pointer-events-none relative h-full overflow-hidden bg-warm-100 dark:bg-navy-950/50">
       {shouldRenderMap && (
-        <Suspense fallback={<div className="h-full bg-navy-950/40" aria-hidden="true" />}>
+        // The full deck.gl/maplibre map sits above the fold; contain any WebGL
-          <ProductMap
+        // context-loss crash to this pane (and auto-recover) instead of letting it
-            data={SHOWCASE_MAP_DATA}
+        // bubble to the top-level boundary and blank the hero.
-            postcodeData={EMPTY_SHOWCASE_POSTCODES}
+        <MapErrorBoundary>
-            usePostcodeView={false}
+          <Suspense fallback={<div className="h-full bg-navy-950/40" aria-hidden="true" />}>
-            pois={EMPTY_SHOWCASE_POIS}
+            <ProductMap
-            onViewChange={noopViewChange}
+              data={SHOWCASE_MAP_DATA}
-            viewFeature={null}
+              postcodeData={EMPTY_SHOWCASE_POSTCODES}
-            colorRange={null}
+              usePostcodeView={false}
-            filterRange={null}
+              pois={EMPTY_SHOWCASE_POIS}
-            viewSource={null}
+              onViewChange={noopViewChange}
-            onCancelPin={() => {}}
+              viewFeature={null}
-            features={DEMO_FEATURES}
+              colorRange={null}
-            selectedHexagonId={null}
+              filterRange={null}
-            hoveredHexagonId={null}
+              viewSource={null}
-            onHexagonClick={noopHexagonClick}
+              onCancelPin={() => {}}
-            onHexagonHover={noopHexagonHover}
+              features={DEMO_FEATURES}
-            initialViewState={viewState}
+              selectedHexagonId={null}
-            theme="dark"
+              hoveredHexagonId={null}
-            screenshotMode
+              onHexagonClick={noopHexagonClick}
-            hideLegend
+              onHexagonHover={noopHexagonHover}
-            densityLabel={t('home.showcaseMatchingHomesLabel')}
+              initialViewState={viewState}
-            totalCount={SHOWCASE_MAP_TOTAL_COUNT}
+              theme="dark"
-          />
+              screenshotMode
-        </Suspense>
+              hideLegend
+              densityLabel={t('home.showcaseMatchingHomesLabel')}
+              totalCount={SHOWCASE_LONDON_COUNT}
+            />
+          </Suspense>
+        </MapErrorBoundary>
       )}
       <div className="pointer-events-none absolute bottom-4 left-4 max-w-[16rem] rounded-md border border-white/15 bg-navy-950/95 px-4 py-3 text-white shadow-2xl shadow-navy-950/35">
-        <div className="text-sm font-black leading-none">Birmingham</div>
+        <div className="text-sm font-black leading-none">{t('home.showcaseStep2Region')}</div>
         <div className="mt-1 text-xs font-bold leading-tight text-warm-200">
           {t('home.showcaseMatchingHomes', {
-            value: SHOWCASE_MAP_TOTAL_COUNT.toLocaleString(),
+            value: SHOWCASE_LONDON_COUNT.toLocaleString(),
           })}
         </div>
         <div className="mt-2 text-[10px] font-bold uppercase tracking-wide text-warm-400">
@@ -789,19 +803,22 @@ function ScoutScreen({ isActive }: { isActive: boolean }) {
       postcode: 'SW5 9AA',
       score: '94%',
       commute: t('home.showcaseMinutes', { count: 23 }),
-      price: '£492k',
+      perSqm: '£8,200',
+      delta: '−16%',
     },
     {
       postcode: 'SE22 8EF',
       score: '91%',
       commute: t('home.showcaseMinutes', { count: 28 }),
-      price: '£518k',
+      perSqm: '£5,900',
+      delta: '−24%',
     },
     {
       postcode: 'N4 2AB',
       score: '88%',
       commute: t('home.showcaseMinutes', { count: 31 }),
-      price: '£476k',
+      perSqm: '£6,400',
+      delta: '−21%',
     },
   ];
 
@@ -934,7 +951,12 @@ function ScoutScreen({ isActive }: { isActive: boolean }) {
                 <div className="truncate border-r border-warm-200 px-2 py-1.5 dark:border-navy-700 sm:px-3 sm:py-2.5">
                   {row.commute}
                 </div>
-                <div className="truncate px-2 py-1.5 font-black sm:px-3 sm:py-2.5">{row.price}</div>
+                <div className="px-2 py-1.5 sm:px-3 sm:py-2.5">
+                  <div className="truncate font-black tabular-nums">{row.perSqm}</div>
+                  <div className="text-[10px] font-bold tabular-nums text-emerald-600 dark:text-emerald-300">
+                    {row.delta}
+                  </div>
+                </div>
               </div>
             ))}
           </div>
@@ -1031,6 +1053,7 @@ export default function ProductShowcase({ className = '' }: ProductShowcaseProps
   const [isStagePaused, setIsStagePaused] = useState(false);
   const [hasStarted, setHasStarted] = useState(false);
   const [canPauseOnHover, setCanPauseOnHover] = useState(false);
+  const [prefersReducedMotion, setPrefersReducedMotion] = useState(false);
   const showcaseRef = useRef<HTMLDivElement | null>(null);
   const inspectUserScrolledRef = useRef(false);
 
@@ -1101,6 +1124,27 @@ export default function ProductShowcase({ className = '' }: ProductShowcaseProps
     return () => mediaQuery.removeEventListener('change', updateCanPause);
   }, []);
 
+  useEffect(() => {
+    if (typeof window.matchMedia !== 'function') return;
+    const mediaQuery = window.matchMedia('(prefers-reduced-motion: reduce)');
+    const update = () => setPrefersReducedMotion(mediaQuery.matches);
+    update();
+    mediaQuery.addEventListener('change', update);
+    return () => mediaQuery.removeEventListener('change', update);
+  }, []);
+
+  // Under prefers-reduced-motion the progress-bar animation is disabled in CSS, so
+  // its onAnimationEnd (which normally advances the carousel) never fires and the
+  // demo would freeze on step 1. Drive the advance from a timer in that case.
+  useEffect(() => {
+    if (!prefersReducedMotion || !isProgressRunning) return;
+    const timer = window.setTimeout(
+      () => setActiveStep((step) => (step + 1) % SHOWCASE_STEP_COUNT),
+      activeStepIntervalMs
+    );
+    return () => window.clearTimeout(timer);
+  }, [prefersReducedMotion, isProgressRunning, activeStep, activeStepIntervalMs]);
+
   const pauseForHover = () => {
     if (canPauseOnHover) setIsStagePaused(true);
   };

frontend/src/types.ts

@@ -235,6 +235,7 @@ export interface HistoricalPrice {
   year: number;
   month: number;
   price: number;
+  is_new: boolean;
 }
 
 export interface Property {
@@ -386,7 +387,7 @@ export interface HexagonStatsResponse {
    * sector, shown alongside the national average for each crime metric. */
   crime_area_averages?: CrimeAreaAverage[];
   /** Total individual crime records (last 7 years) across the selection's
-   * postcodes — the count behind the "individual crimes" list. */
+   * postcodes, the count behind the "individual crimes" list. */
   crime_total_records?: number;
   central_postcode?: string;
   /** Total usual residents (ONS Census 2021) across the postcodes in this

pipeline/download/median_age.py

@@ -3,7 +3,7 @@
 Downloads five-year age band counts (TS007A) from the NOMIS API, then computes
 the median age per LSOA using linear interpolation within the median class.
 
-Source: NOMIS (ONS Census 2021 — TS007A dataset, NM_2020_1)
+Source: NOMIS (ONS Census 2021, TS007A dataset, NM_2020_1)
 License: Open Government Licence v3.0
 """
 
@@ -20,7 +20,7 @@ from pipeline.utils import ENGLAND_LSOA_COUNT_2021, download_nomis_csv
 BASE_URL = "https://www.nomisweb.co.uk/api/v01/dataset/NM_2020_1.data.csv?date=latest&geography=TYPE151&c2021_age_19=1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18&measures=20100&select=GEOGRAPHY_CODE,C2021_AGE_19_NAME,OBS_VALUE"
 
 # Five-year age bands in order, with lower bounds for interpolation.
-# The last band (85+) is open-ended — we treat it as 85-89 for median purposes.
+# The last band (85+) is open-ended. We treat it as 85-89 for median purposes.
 AGE_BANDS = [
     (0, 5),  # Aged 0 to 4 years
     (5, 5),  # Aged 5 to 9 years

pipeline/download/naptan.py

@@ -28,7 +28,7 @@ LONDON_UNDERGROUND_ATCO_PATTERN = r"(?i)^\d{3}[0G]ZZLU"
 # The Docklands Light Railway uses the analogous "ZZDL" system code (e.g.
 # "9400ZZDLBEC" for Beckton). Like ZZLU it is unique to one network, so a
 # TMU/MET stop carrying a ZZDL code is reclassified from the tram/metro family
-# to its own "DLR station" category — restoring DLR to the train/tube station
+# to its own "DLR station" category, restoring DLR to the train/tube station
 # list and giving it the DLR roundel rather than a generic tram icon.
 LONDON_DLR_ATCO_PATTERN = r"(?i)^\d{3}[0G]ZZDL"
 
@@ -337,7 +337,7 @@ class StationAccumulator:
         # A single node is never both (ZZLU vs ZZDL), but a co-located
         # interchange (Bank, Stratford, Canning Town, West Ham) merges its LU
         # and DLR halves into one group carrying both flags; Tube is checked
-        # first so these resolve to "Tube station" — their primary identity —
+        # first so these resolve to "Tube station" (their primary identity),
         # leaving "DLR station" for the DLR-only stops the fix targets.
         if self.category == TRAM_METRO_CATEGORY and self.is_lu:
             return TUBE_STATION_CATEGORY

pipeline/download/noise.py

@@ -85,24 +85,24 @@ RESOLUTION = NATIVE_RESOLUTION
 
 # Defra encodes TRUE "no data" with this sentinel (NOT 0.0). A 0.0 cell that is
 # otherwise inside the raster means "modelled below the lowest reporting band",
-# i.e. genuinely quiet — see noise_overlay_tiles.py:167.
+# i.e. genuinely quiet (see noise_overlay_tiles.py:167).
 NOISE_NODATA_SENTINEL = np.float32(-96.0)
 
 # Lowest modelled Defra Lden reporting band (dB). Verified against the actual
 # rasters: the minimum positive in-coverage value is 40.0 dB with NO values in
-# (0, 40) — below the band, cells are encoded as 0.0 (genuinely quiet). We floor
+# (0, 40). Below the band, cells are encoded as 0.0 (genuinely quiet). We floor
 # in-coverage cells to 40.0 so a below-band 0.0 surfaces as "we know it's quiet"
 # (~40 dB) instead of collapsing to null ("we don't know"), WITHOUT inflating the
 # ~35% of genuine 40-44.99 dB readings that a 45.0 floor would wrongly bump to 45.
 # NB: 45.0 is the overlay's lowest *paint* stop (noise_overlay_tiles.
-# NOISE_COLOR_STOPS[0]) — a rendering threshold, not the data's reporting floor.
+# NOISE_COLOR_STOPS[0]), a rendering threshold, not the data's reporting floor.
 NOISE_QUIET_FLOOR_DB = np.float32(40.0)
 
 # Sample noise at the postcode representative point itself (no neighbourhood
 # window). A 50m MAX-of-window grabbed the single loudest 10m cell within ~1.2 ha
 # of every postcode; because Defra road contours hug every modelled road and
 # representative points sit on/near streets, that inflated postcode noise by
-# roughly +9 dB (log scale) — making ~94% of England read >=55 dB Lden and
+# roughly +9 dB (log scale), making ~94% of England read >=55 dB Lden and
 # collapsing the metric's discrimination at the quiet end. Radius 0 ->
 # filter_size 1 -> the maximum_filter is skipped and each postcode reads the
 # 10m cell it actually sits in.
@@ -110,8 +110,8 @@ POSTCODE_NOISE_RADIUS_M = 0
 
 # Adjacent download tiles overlap by the sampling radius so every postcode's
 # sampling footprint is fully contained in at least one tile. With point
-# sampling (radius 0) this is 0 — a representative point falls inside exactly
+# sampling (radius 0) this is 0 (a representative point falls inside exactly
-# one tile — but the relationship is kept so any future non-zero radius keeps
+# one tile), but the relationship is kept so any future non-zero radius keeps
 # its window seam-safe.
 TILE_OVERLAP_M = POSTCODE_NOISE_RADIUS_M
 
@@ -273,7 +273,7 @@ def _download_tile(
             NoGeoTiffError,
             httpx.HTTPStatusError,
             # TransportError is the superset of TimeoutException, ConnectError,
-            # ReadError and ProtocolError — including RemoteProtocolError, raised
+            # ReadError and ProtocolError, including RemoteProtocolError, raised
             # when the WCS server closes the connection mid-stream ("incomplete
             # chunked read"). All are transient; retry/split rather than letting
             # one flaky tile crash the whole raster download.
@@ -446,7 +446,7 @@ def sample_noise_at_postcodes(
             # Defra rasters encode TRUE nodata as the -96.0 sentinel (and
             # occasionally non-finite / dataset.nodata); genuinely quiet ground
             # below the model's lowest reporting band is encoded as 0.0. Only
-            # the former is "we don't know" — the latter is a real "we know it's
+            # the former is "we don't know". The latter is a real "we know it's
             # quiet" reading and must not collapse to null. So treat ONLY true
             # nodata as -inf (it never wins a max and never counts as coverage),
             # and clamp every in-coverage cell up to NOISE_QUIET_FLOOR_DB so a
@@ -555,7 +555,7 @@ def main() -> None:
 
             if not tile_paths:
                 print(
-                    f"[{label}] WARNING: No tiles downloaded — column will be all null"
+                    f"[{label}] WARNING: No tiles downloaded; column will be all null"
                 )
                 series = pl.Series(col_name, [None] * len(lat), dtype=pl.Float32)
             else:

pipeline/download/os_greenspace.py

@@ -7,7 +7,7 @@ site id and the site's polygon centroid. Sites without access points fall
 back to polygon centroids.
 
 Using access points rather than polygon centroids gives much more accurate
-distance calculations — a property next to Hyde Park won't show 400m just
+distance calculations: a property next to Hyde Park won't show 400m just
 because the centroid is in the middle of the park. The site id / centroid
 columns let downstream consumers (poi_proximity) collapse the frame back to
 one row per SITE for counting, so a park with 30 gates counts as one park.
@@ -190,7 +190,7 @@ def download_greenspace(output: Path) -> None:
         print(f"Reading {site_shps[0].name} for function types...")
         site_funcs = _read_site_functions(site_shps[0])
 
-        # Step 2: Read access points (primary — park entrances)
+        # Step 2: Read access points (primary: park entrances)
         print(f"Reading {access_shps[0].name}...")
         ap_lats, ap_lngs, ap_cats, ap_site_ids = _read_access_points(
             access_shps[0], site_funcs

pipeline/download/places.py

@@ -930,7 +930,7 @@ def main() -> None:
         df.write_parquet(args.output)
         print(f"Saved to {args.output}")
     else:
-        print("No places found — skipping output")
+        print("No places found, skipping output")
 
 
 if __name__ == "__main__":

pipeline/download/rental_prices.py

@@ -35,7 +35,7 @@ def _data_rows(df: pl.DataFrame) -> pl.DataFrame:
 
     The preamble length varies (title, optional "This worksheet contains..."
     note, then the header row starting with "Time period"), so locate the
-    header by content instead of counting rows — a fixed slice leaves the
+    header by content instead of counting rows: a fixed slice leaves the
     header in the data whenever ONS adds or removes a note line.
     """
     header_marker = (
@@ -78,7 +78,7 @@ def _latest_rents_long(df: pl.DataFrame) -> pl.DataFrame:
     print(f"LAs in latest month: {df.height}")
 
     # Melt to long format: one row per area x bedroom count.
-    # PIPR has no Studio category — one-bed rent used as proxy for bedrooms=0.
+    # PIPR has no Studio category: one-bed rent used as proxy for bedrooms=0.
     frames = []
     for col, bedrooms in [
         ("rent_1bed", 0),  # Studio (proxy)

pipeline/download/tenure.py

@@ -2,8 +2,8 @@
 
 Downloads the household-tenure breakdown (TS054, classification C2021_TENURE_9)
 from the NOMIS API at LSOA 2021 granularity and folds the 8 detailed leaf
-categories into our 3 output buckets — Owner occupied / Social rent /
+categories into our 3 output buckets (Owner occupied / Social rent /
-Private rent — emitting one row per LSOA with the percentage of households in
+Private rent), emitting one row per LSOA with the percentage of households in
 each. The three buckets sum to 100%, so downstream they render as a
 composition/ratio (like the ethnicity and qualifications stacked bars) AND each
 percentage is independently filterable.
@@ -19,7 +19,7 @@ NOTE this table counts HOUSEHOLDS (not usual residents). The join key downstream
 (merge.py) is `lsoa21`, the same key used for ethnicity, qualifications,
 median age, and IoD.
 
-Source: NOMIS (ONS Census 2021 — TS054 dataset, NM_2072_1)
+Source: NOMIS (ONS Census 2021, TS054 dataset, NM_2072_1)
 License: Open Government Licence v3.0
 """
 

pipeline/download/test_ethnicity.py

@@ -72,7 +72,7 @@ def test_ethnicity_routes_chinese_to_east_and_other_asian_to_se():
 
 
 def test_ethnicity_percentages_independent_per_lsoa():
-    """Two LSOAs get independent profiles — the LSOA granularity is the point."""
+    """Two LSOAs get independent profiles: the LSOA granularity is the point."""
     df = pl.concat(
         [
             pl.DataFrame(

pipeline/download/test_noise.py

@@ -281,7 +281,7 @@ def test_sample_noise_preserves_genuine_reading_above_quiet_floor(
 
     # The lowest Defra reporting band is 40.0 dB; genuine readings populate
     # [40, ~80]. A genuine in-coverage reading at or just above the floor must be
-    # PRESERVED, not clamped UP to the floor — only true-quiet 0.0 is floored. A
+    # PRESERVED, not clamped UP to the floor. Only true-quiet 0.0 is floored. A
     # quiet floor set too high (e.g. 45) would inflate the ~35% of real 40-44.99
     # dB readings; this pins that they survive unchanged.
     floor = float(noise.NOISE_QUIET_FLOOR_DB)

pipeline/download/transit_network.py

@@ -50,7 +50,7 @@ ENGLAND_PBF_URL = (
     "https://download.geofabrik.de/europe/united-kingdom/england-latest.osm.pbf"
 )
 
-# Bus Open Data Service — pre-converted GTFS covering all England bus/tram/ferry
+# Bus Open Data Service: pre-converted GTFS covering all England bus/tram/ferry
 BODS_GTFS_URL = "https://data.bus-data.dft.gov.uk/timetable/download/gtfs-file/all/"
 
 # National Rail Open Data API
@@ -597,7 +597,7 @@ def validate_gtfs_feed(
             fail("has neither calendar.txt nor calendar_dates.txt")
         if not _calendar_active_in_window(z, names, window_start, window_end):
             fail(
-                f"no service active between {window_start} and {window_end} — "
+                f"no service active between {window_start} and {window_end}: "
                 "the feed's calendars are stale/expired and it would contribute "
                 "zero service to routing"
             )
@@ -697,7 +697,7 @@ def _iter_stop_time_trips(lines, trip_id_idx: int):
     dtd2mysql currently writes rows grouped by trip and ordered by
     stop_sequence, but neither is guaranteed by GTFS. Grouping is verified (a
     trip_id reappearing later raises instead of silently scrambling trips);
-    within-trip order is NOT assumed — callers sort each group by its original
+    within-trip order is NOT assumed: callers sort each group by its original
     stop_sequence.
     """
     current_trip: str | None = None
@@ -1175,7 +1175,7 @@ def main() -> None:
     cif = download_national_rail_cif(raw_dir)
     if cif is None:
         raise RuntimeError(
-            "National Rail timetable was not downloaded — set "
+            "National Rail timetable was not downloaded: set "
             "NATIONAL_RAIL_EMAIL / NATIONAL_RAIL_PASSWORD (register free at "
             "https://opendata.nationalrail.co.uk/). National Rail heavy rail is "
             "required; without it the transit network models every train journey "

pipeline/transform/postcode_boundaries/geometry.py

@@ -1,8 +1,8 @@
 """Robust GEOS overlay helpers.
 
 Overlay operations (union, difference, intersection) can raise a
-``GEOSException`` — most often ``TopologyException: side location conflict``,
+``GEOSException``, most often ``TopologyException: side location conflict``,
-``Ring edge missing``, or ``found non-noded intersection`` — on geometries that
+``Ring edge missing``, or ``found non-noded intersection``, on geometries that
 contain near-coincident or near-degenerate edges, or that are individually
 invalid. The robust remedy is a *fixed-precision* overlay: GEOS's OverlayNG
 engine, handed a grid size, nodes every edge onto that grid and finishes where
@@ -14,8 +14,8 @@ learned the hard way from a crash:
 1. **Never precision-reduce with the default mode.** ``set_precision``'s default
    ``valid_output`` (and ``keep_collapsed``) mode runs its *own* noding pass that
    re-raises the very ``side location conflict`` we are trying to escape. We push
-   the grid into the overlay via the ``grid_size`` argument instead — where
+   the grid into the overlay via the ``grid_size`` argument instead (where
-   OverlayNG nodes robustly — and only ever call ``set_precision`` in
+   OverlayNG nodes robustly) and only ever call ``set_precision`` in
    ``pointwise`` mode (pure coordinate rounding, which cannot raise).
 2. **Validate first.** ``make_valid`` repairs the self-intersections (bow-ties,
    pinches) that make GEOS choke, so the overlay starts from an OGC-valid shape.
@@ -40,7 +40,7 @@ from shapely import GEOSException, make_valid, set_precision
 from shapely.geometry import Polygon
 from shapely.ops import unary_union
 
-# 0.1 mm in metres — well below MIN_GEOM_AREA (0.01 m^2) and survey resolution.
+# 0.1 mm in metres: well below MIN_GEOM_AREA (0.01 m^2) and survey resolution.
 _SNAP_GRID = 1e-4
 
 _EMPTY = Polygon()

pipeline/transform/postcode_boundaries/output.py

@@ -120,7 +120,7 @@ def _is_pointlike(geom_bng) -> bool:
 def _rescue_footprint(geom_bng) -> dict | None:
     """Fatten a degenerate BNG geometry into a representable footprint and snap.
 
-    A POINTLIKE input (a point, or a near-zero-area/short-perimeter polygon — the
+    A POINTLIKE input (a point, or a near-zero-area/short-perimeter polygon, the
     signature of a tower-block postcode whose UPRNs all share one coordinate)
     gets a building-scale buffer so it is not reduced to an invisible sub-metre
     dot; thin slivers that still carry length keep the minimal buffer.
@@ -263,7 +263,7 @@ def merge_fragments(
         # Close tiny gaps between adjacent OA boundary edges (float mismatches).
         # The closing can erode a tiny MultiPolygon (e.g. a postcode with only a
         # sliver fragment) to nothing, which would leave the postcode with no
-        # geometry at all — keep the un-closed shape if that happens.
+        # geometry at all. Keep the un-closed shape if that happens.
         if combined.geom_type == "MultiPolygon":
             closed = combined.buffer(5.0).buffer(-5.0)
             if not closed.is_valid:
@@ -308,7 +308,7 @@ def _polygonal(geom):
             return None
         # Both callers run on WGS84-degree output geometry, so the robustness
         # fallback snaps on the 1e-6° grid (~0.11 m), not geometry.py's metre
-        # default — a coarse metre grid would obliterate a degree-scale shape.
+        # default. A coarse metre grid would obliterate a degree-scale shape.
         merged = safe_union(polys, grid=_OUTPUT_PRECISION_DEG)
         return merged if not merged.is_empty else None
     return None
@@ -324,7 +324,7 @@ def _resolve_overlaps(
     containment (a postcode fully enclosed by another). Each postcode is trimmed
     by the union of its higher-priority overlapping neighbours, where **priority =
     ascending area**: a smaller postcode wins contested ground. That single rule
-    handles both cases correctly — an enclosed postcode is always smaller than its
+    handles both cases correctly: an enclosed postcode is always smaller than its
     container, so it keeps its area while the container gets a hole (a `overlaps`
     query alone would miss containment entirely). Run last, on the final output
     geometries, so nothing re-introduces overlap afterwards. A postcode that would
@@ -348,7 +348,7 @@ def _resolve_overlaps(
     arr = np.array(geoms, dtype=object)
     pairs: set[tuple[int, int]] = set()
     # "overlaps" gives partial overlaps; "contains" gives containment (which
-    # "overlaps" excludes) — together they cover every 2-D overlap without the
+    # "overlaps" excludes). Together they cover every 2-D overlap without the
     # edge-touch explosion a plain "intersects" query would add.
     for predicate in ("overlaps", "contains"):
         qsrc, qtgt = tree.query(arr, predicate=predicate)
@@ -577,7 +577,7 @@ def _grid_footprint(geom):
     pass can shave a small (e.g. co-located, non-geographic) postcode down to a
     sub-grid sliver that disappears when snapped to output precision. Rather than
     drop it, place a minimal valid footprint at its location. The tiny overlap
-    this re-creates with the neighbour that trimmed it is harmless — the output
+    this re-creates with the neighbour that trimmed it is harmless: the output
     partition is best-effort, a missing boundary is a hard validation failure.
     """
     try:

pipeline/transform/postcode_boundaries/process_oa.py

@@ -11,7 +11,7 @@ from .voronoi import compute_voronoi_regions
 MIN_GEOM_AREA = 0.01
 
 # Minimal footprint (BNG metres) for a postcode whose UPRN seed wins no area in a
-# crowded multi-postcode OA — its Voronoi cell ∩ remaining collapses below
+# crowded multi-postcode OA: its Voronoi cell ∩ remaining collapses below
 # MIN_GEOM_AREA, or its seed sits inside an INSPIRE parcel wholly claimed by a
 # co-located postcode. Every *active* postcode must keep a boundary
 # (validate_outputs is zero-tolerance), so it gets a small disc at its true seed
@@ -77,7 +77,7 @@ def process_oa(
             fragments.append((pc, merged))
 
     # Every postcode with a UPRN seed in this OA must keep at least a minimal
-    # footprint — in a dense OA (a block of flats with hundreds of distinct
+    # footprint: in a dense OA (a block of flats with hundreds of distinct
     # postcodes) a single-seed postcode's cell can collapse below MIN_GEOM_AREA or
     # be fully absorbed by a co-located postcode's INSPIRE parcel, producing no
     # fragment, and an active postcode must never be dropped.
@@ -142,7 +142,7 @@ def _claim_inspire_parcels(
     # UPRNs from a single postcode goes wholly to that postcode. A parcel shared
     # by several postcodes (a block of flats spanning postcodes, or overlapping
     # parcel data) is split between them via a sub-Voronoi over their own UPRNs
-    # clipped to the parcel — so EVERY contained postcode keeps part of the
+    # clipped to the parcel, so EVERY contained postcode keeps part of the
     # parcel. A bare majority vote would hand the whole parcel to one winner and
     # leave the losers' UPRNs trapped inside claimed land, dropping them from
     # both this claim and the `remaining` polygon handed to Voronoi downstream.
@@ -312,7 +312,7 @@ def _extract_polygonal(geom) -> Polygon | MultiPolygon | None:
             return polys[0]
         # Union (not bare MultiPolygon construction): make_valid can emit
         # overlapping polygonal parts, and a MultiPolygon of overlapping parts is
-        # invalid — it double-counts area and makes the next `.difference()` raise
+        # invalid: it double-counts area and makes the next `.difference()` raise
         # a TopologyException that aborts the OA (and, in parallel mode, the
         # worker). safe_union merges them into a valid geometry.
         merged = safe_union(polys)

pipeline/transform/postcode_boundaries/uprn.py

@@ -82,7 +82,7 @@ def load_uprns(
         # Remap terminated postcodes to their nearest active successor. The
         # successor generally lives in a DIFFERENT OA (and at different grid
         # coordinates), so the remapped point must adopt the successor's
-        # authoritative OA/coords — keeping the terminated postcode's original
+        # authoritative OA/coords. Keeping the terminated postcode's original
         # OA would seed the successor into an OA it doesn't belong to, splitting
         # its boundary across OAs. Genuine (non-remapped) UPRN rows keep their
         # own OA, since a live postcode can legitimately span several OAs.
@@ -127,7 +127,7 @@ def load_uprns(
     uprns.sort("OA21CD").sink_parquet(tmp_path)
     release_memory()
 
-    # Read the sorted data — only one copy in memory (~2GB)
+    # Read the sorted data: only one copy in memory (~2GB)
     df = pl.read_parquet(tmp_path)
     tmp_path.unlink()
     n = len(df)

pipeline/transform/price_estimation/index.py

@@ -4,7 +4,7 @@ Stratified by property type and postcode sector, with IRLS Huber regression,
 hierarchical shrinkage (sector → district → area → national → hedonic),
 and KD-tree spatial smoothing for sparse sectors.
 
-Output: price_index.parquet — sector x type_group x year -> log_index
+Output: price_index.parquet (sector x type_group x year -> log_index)
 """
 
 import argparse

pipeline/transform/price_estimation/utils.py

@@ -23,7 +23,7 @@ ESTIMATE_COLUMNS = ["Estimated current price", "Est. price per sqm"]
 # Natural join key from estimates back onto properties: postcode plus the
 # coalesced register/EPC address. This is unique and non-null on the deduped
 # dwelling universe (see property_base._dedupe_collapsed_properties), so it maps
-# estimates 1:1 onto properties regardless of row order — estimates are computed
+# estimates 1:1 onto properties regardless of row order. Estimates are computed
 # from a separate price_inputs.parquet, so a positional key would not line up.
 JOIN_ADDRESS = "_join_address"
 JOIN_KEYS = ["Postcode", JOIN_ADDRESS]

pipeline/transform/test_join_epc_pp.py

@@ -315,7 +315,7 @@ def test_run_tenure_history_tracks_rent_owner_transitions(tmp_path: Path):
 
 def test_run_tenure_history_empty_when_always_owner_occupied(tmp_path: Path):
     # A property only ever observed as owner-occupied has no tenure change worth
-    # surfacing — the timeline column is null (no events), not a noisy baseline.
+    # surfacing: the timeline column is null (no events), not a noisy baseline.
     zip_path = tmp_path / "domestic-csv.zip"
     _write_epc_zip(
         zip_path,
@@ -339,6 +339,37 @@ def test_run_tenure_history_empty_when_always_owner_occupied(tmp_path: Path):
     assert df.get_column("tenure_history").to_list() == [None]
 
 
+def test_run_latest_tenure_status_reflects_most_recent_certificate(tmp_path: Path):
+    # Two certificates for one dwelling: an older social-rented cert and a newer
+    # owner-occupied one. The published latest_tenure_status must carry the
+    # LATEST certificate's normalized tenure ("Owner-occupied"), while
+    # was_council_house stays "Yes" because the dwelling was social at some
+    # point. This also confirms latest_tenure_status reaches the epc_pp parquet.
+    zip_path = tmp_path / "domestic-csv.zip"
+    _write_epc_zip(
+        zip_path,
+        [
+            _row(inspection_date="2016-04-01", tenure="Rented (social)"),
+            _row(inspection_date="2024-04-01", tenure="owner-occupied"),
+        ],
+    )
+
+    price_paid_path = tmp_path / "price-paid.parquet"
+    _price_paid_frame(prices=[250_000], dates=[date(2024, 2, 3)]).write_parquet(
+        price_paid_path
+    )
+
+    output_path = tmp_path / "epc-pp.parquet"
+    _run(zip_path, price_paid_path, output_path, tmp_path)
+
+    df = pl.read_parquet(output_path)
+
+    assert df.height == 1
+    assert df.select("latest_tenure_status", "was_council_house").to_dicts() == [
+        {"latest_tenure_status": "Owner-occupied", "was_council_house": "Yes"}
+    ]
+
+
 def test_run_dedup_prefers_valid_dated_cert_over_garbled_date(tmp_path: Path):
     # Two certificates for the same property. The cert with the garbled,
     # unparseable inspection_date must NOT be chosen as "latest": a string sort
@@ -565,7 +596,7 @@ def test_run_new_build_keeps_early_first_transfer_when_sub_min_price(tmp_path: P
     price_paid_path = tmp_path / "price-paid.parquet"
     pl.DataFrame(
         {
-            # 5_000 is below MIN_PRICE (10_000) — a nominal/junk transfer that
+            # 5_000 is below MIN_PRICE (10_000), a nominal/junk transfer that
             # must still anchor the construction year but stay out of the price
             # aggregations.
             "price": [5_000, 300_000],
@@ -603,7 +634,7 @@ def test_run_caps_band_year_at_first_transfer_year(tmp_path: Path):
     # lands AFTER its first Land Registry sale (1998). A dwelling cannot have
     # been built after it was first sold, so the published build year must be
     # capped at the first transfer year (1998), not the later band estimate.
-    # It stays flagged as an estimate (approximate=1) — it is still EPC-derived.
+    # It stays flagged as an estimate (approximate=1). It is still EPC-derived.
     zip_path = tmp_path / "domestic-csv.zip"
     _write_epc_zip(
         zip_path, [_row(construction_age_band="England and Wales: 2003 onwards")]
@@ -627,7 +658,7 @@ def test_run_caps_band_year_at_first_transfer_year(tmp_path: Path):
 
 def test_run_keeps_band_year_when_earlier_than_first_transfer(tmp_path: Path):
     # The common case: the EPC band (1950-1966 -> 1958) predates the first
-    # recorded sale (2020). The cap must NOT fire — the band estimate stands.
+    # recorded sale (2020). The cap must NOT fire. The band estimate stands.
     zip_path = tmp_path / "domestic-csv.zip"
     _write_epc_zip(zip_path)
 
@@ -649,7 +680,7 @@ def test_run_keeps_band_year_when_earlier_than_first_transfer(tmp_path: Path):
 def test_run_keeps_sale_above_lowered_min_price(tmp_path: Path):
     # A genuine cheap sale of 30_000 sits between the OLD floor (50k) and the
     # NEW floor (10k): it must now be RETAINED in the price aggregations. This
-    # pins the 50k->10k change — it fails on the pre-fix 50k floor (where 30k was
+    # pins the 50k->10k change: it fails on the pre-fix 50k floor (where 30k was
     # excluded, giving historical_prices length 1 / latest_price 250_000).
     zip_path = tmp_path / "domestic-csv.zip"
     with zipfile.ZipFile(zip_path, "w", compression=zipfile.ZIP_DEFLATED) as archive:
@@ -748,8 +779,8 @@ def test_run_collapses_duplicate_transactions(tmp_path: Path):
     # The duplicated 250_000 sale collapses to one entry; two distinct sales.
     assert df.get_column("historical_prices").to_list() == [
         [
-            {"year": 2020, "month": 2, "price": 200_000},
+            {"year": 2020, "month": 2, "price": 200_000, "is_new": False},
-            {"year": 2024, "month": 2, "price": 250_000},
+            {"year": 2024, "month": 2, "price": 250_000, "is_new": False},
         ]
     ]
     assert df.get_column("latest_price").to_list() == [250_000]
@@ -777,7 +808,7 @@ def test_run_excludes_implausible_price_jump_but_keeps_property(tmp_path: Path):
     assert df.height == 1
     assert df.get_column("latest_price").to_list() == [140_000]
     assert df.get_column("historical_prices").to_list() == [
-        [{"year": 2016, "month": 6, "price": 140_000}]
+        [{"year": 2016, "month": 6, "price": 140_000, "is_new": False}]
     ]
 
 

pipeline/transform/test_transform_poi.py

@@ -593,7 +593,7 @@ def test_transform_grocery_dedup_drops_only_grocery_aspect(tmp_path):
     # The _write_transform_inputs fixture seeds 5 GEOLYTIX "Tesco" points at
     # (51.52, -0.14). An OSM object colocated there carrying "Tesco" in its name
     # is the same physical store, so its Convenience Store (Groceries) row is a
-    # duplicate and must be dropped — but its NON-grocery aspect (a Post Office
+    # duplicate and must be dropped, but its NON-grocery aspect (a Post Office
     # sharing the same OSM id) must survive. An independent shop away from the
     # GEOLYTIX point keeps its grocery row.
     raw = pl.DataFrame(

pipeline/transform/transform_poi.py

@@ -34,7 +34,7 @@ DROP_CATEGORIES = {
     "emergency/water_tank",
     "leisure/bleachers",
     "leisure/schoolyard",
-    # Park "furniture" / incidental features — not parks; they massively
+    # Park "furniture" / incidental features, not parks; they massively
     # inflated the Park count (picnic_table ~15k, outdoor_seating ~5.8k).
     "leisure/bandstand",
     "leisure/bird_hide",
@@ -222,7 +222,7 @@ DROP_CATEGORIES = {
     "public_transport/entrance",
     "public_transport/station",
     "public_transport/stop_position",
-    # Education amenities — schools come from GIAS instead. OSM coverage for
+    # Education amenities. Schools come from GIAS instead. OSM coverage for
     # tertiary education, tutoring, and childcare is too noisy/incomplete to be
     # useful on a property-search map.
     "amenity/school",
@@ -398,7 +398,7 @@ _CATEGORIES: list[tuple[str, str, str, list[str]]] = [
             "tourism/theme_park",
             # bicycle_rental/boat_rental/marina/slipway used to live here and
             # made up ~46% of the bucket (cycle-hire docks, boat ramps); they
-            # are infrastructure, not entertainment venues — see DROP_CATEGORIES.
+            # are infrastructure, not entertainment venues. See DROP_CATEGORIES.
             "leisure/hackerspace",
             "leisure/yes",
         ],
@@ -722,7 +722,7 @@ _CATEGORIES: list[tuple[str, str, str, list[str]]] = [
         [
             "leisure/fitness_centre",
             # leisure/fitness_station (outdoor pull-up bars / trim-trail
-            # apparatus, ~2.5k) is not a gym — see DROP_CATEGORIES.
+            # apparatus, ~2.5k) is not a gym. See DROP_CATEGORIES.
             "amenity/dojo",
             "amenity/dancing_school",
         ],
@@ -849,8 +849,8 @@ _CATEGORIES: list[tuple[str, str, str, list[str]]] = [
             "healthcare/pharmacy",
             "shop/chemist",
             # healthcare/alternative, shop/herbalist and shop/health (homeopaths,
-            # herbalists, generic "health" shops) are not dispensing pharmacies
+            # herbalists, generic "health" shops) are not dispensing pharmacies.
-            # — see DROP_CATEGORIES.
+            # See DROP_CATEGORIES.
         ],
     ),
     # "Hospital & Clinic" used to be one bucket; an actual hospital and a small
@@ -878,7 +878,7 @@ _CATEGORIES: list[tuple[str, str, str, list[str]]] = [
             "healthcare/laboratory",
             "healthcare/rehabilitation",
             "healthcare/vaccination_centre",
-            # healthcare/yes (untyped junk rows) is dropped — see DROP_CATEGORIES.
+            # healthcare/yes (untyped junk rows) is dropped. See DROP_CATEGORIES.
         ],
     ),
     (
@@ -950,7 +950,7 @@ _CATEGORIES: list[tuple[str, str, str, list[str]]] = [
         [
             "tourism/gallery",
             # tourism/artwork (statues, murals, village signs) was 93% of this
-            # bucket and is not a visitable gallery — see DROP_CATEGORIES.
+            # bucket and is not a visitable gallery. See DROP_CATEGORIES.
         ],
     ),
     (
@@ -1420,7 +1420,7 @@ def _school_icon_category_expr() -> pl.Expr:
     # GIAS phase mixes casing ("Middle deemed Primary" vs "Middle deemed
     # primary") so we normalise before matching.
     phase = pl.col("phase").str.to_lowercase()
-    # gias._format_age_range emits three shapes: "<low>–<high>" (em-dash),
+    # gias._format_age_range emits three shapes: "<low>–<high>" (en-dash),
     # "up to <high>" (high-only) and "<low>+" (low-only). Extract the leading
     # integer as low and the trailing integer as high, then suppress the wrong
     # end for the one-sided shapes so they don't collapse to a single bound.
@@ -1477,7 +1477,7 @@ def _load_ofsted_ratings(ofsted_path: Path) -> pl.LazyFrame:
     the conventional Ofsted labels; when there is no usable graded result
     (null/"Not judged", e.g. schools last seen under the post-2024 ungraded
     report-card framework) we fall back to "Ungraded inspection overall outcome"
-    so genuinely good/outstanding schools aren't dropped — mirroring
+    so genuinely good/outstanding schools aren't dropped, mirroring
     school_catchments.classify_good_plus_schools. Remaining nulls drop out."""
     grade_col = pl.col("Latest OEIF overall effectiveness")
     # See school_catchments: the ungraded outcome carries "School remains Good"/
@@ -1682,7 +1682,7 @@ def osm_groceries_colocated_with_geolytix(
 
     An OSM Groceries row is a duplicate when a GEOLYTIX point lies within
     ``radius_m`` metres AND that point's brand tokens (its ``category``, e.g.
-    "Tesco", "Co-op", "M&S") are all present in the OSM row's name — i.e. the
+    "Tesco", "Co-op", "M&S") are all present in the OSM row's name, i.e. the
     same physical branded store. Short brands like "M&S" match via
     _GROCERY_SHORT_BRAND_TOKENS; brands that still tokenise to set() are kept.
 
@@ -1703,7 +1703,7 @@ def osm_groceries_colocated_with_geolytix(
     osm_ids = osm_groceries["id"].to_list()
     osm_name_tokens = [_significant_tokens(n) for n in osm_groceries["name"].to_list()]
 
-    # Equirectangular projection to metres around the shared mean latitude — at
+    # Equirectangular projection to metres around the shared mean latitude. At
     # England's scale this is accurate to well under the dedup radius.
     mean_lat = float(np.mean(np.concatenate([glx_lat, osm_lat])))
     cos_lat = float(np.cos(np.radians(mean_lat)))
@@ -1860,7 +1860,7 @@ def transform(
         )
         # Scope the drop to the Groceries group: a single OSM object can also
         # carry a non-grocery aspect (e.g. a convenience store that is also a
-        # Post Office), which must survive — only its duplicate grocery row goes.
+        # Post Office), which must survive. Only its duplicate grocery row goes.
         lf = lf.filter(
             ~((pl.col("group") == "Groceries") & pl.col("id").is_in(duplicate_ids))
         )

pipeline/transform/tree_density.py

@@ -45,7 +45,7 @@ POSTCODE_DENSITY_PERCENTILE_COL = "Tree canopy density percentile within {radius
 POSTCODE_AREA_COL = "Tree canopy area within {radius}m (sqm)"
 POSTCODE_HEIGHT_COL = "Mean TOW height within {radius}m (m)"
 
-# National Forest Inventory (NFI) woodland — the geometric complement of TOW.
+# National Forest Inventory (NFI) woodland: the geometric complement of TOW.
 # NFI ships as a zipped shapefile of woodland parcels (>=0.5 ha) in EPSG:27700.
 # Field names are from the NFI Woodland England 2022 release; re-check on bumps.
 NFI_CATEGORY_COL = "CATEGORY"
@@ -263,7 +263,7 @@ def _postcode_buffers(
     return circles, shapely.STRtree(circles)
 
 
-# 0.1 mm in the BNG working CRS (EPSG:27700) — far below survey resolution; the
+# 0.1 mm in the BNG working CRS (EPSG:27700), far below survey resolution; the
 # same grid the postcode_boundaries overlay uses.
 _OVERLAY_GRID_M = 1e-4
 
@@ -274,13 +274,13 @@ def _robust_intersection_area(a: np.ndarray, b: np.ndarray) -> np.ndarray:
     External Forest Research TOW/NFI polygons are occasionally invalid
     (self-intersections), and a single bad polygon makes the batched
     ``shapely.intersection`` raise ``TopologyException: side location conflict``,
-    aborting the whole run. The fast path is the raw batched overlay — unchanged,
+    aborting the whole run. The fast path is the raw batched overlay (unchanged,
-    full-speed, when the data is clean — and only a failure triggers repair.
+    full-speed, when the data is clean), and only a failure triggers repair.
 
     The repair deliberately uses a *plain* overlay rather than the fixed-precision
     (``grid_size``) one: ``make_valid`` can emit a mixed-dimension
     ``GeometryCollection`` (a polygon plus a dangling line), which OverlayNG
-    rejects with ``Overlay input is mixed-dimension`` — whereas a plain overlay
+    rejects with ``Overlay input is mixed-dimension``, whereas a plain overlay
     accepts it, and its non-polygonal debris has zero area and is dropped by the
     ``clipped_area > 0`` filter downstream anyway. A final pointwise coordinate
     snap (which never raises) collapses the near-coincident edges behind any
@@ -496,7 +496,7 @@ def _finalize_metrics(
     if over_count:
         print(
             f"  note: {over_count:,} postcode(s) exceeded 100% raw canopy and were "
-            "capped — indicates overlapping TOW/NFI canopy within the buffer"
+            "capped, indicating overlapping TOW/NFI canopy within the buffer"
         )
 
     mean_height = np.divide(

pipeline/utils/test_poi_counts.py

@@ -92,7 +92,7 @@ def test_custom_radius(pois):
         }
     )
 
-    # 0.01 km = 10m — only the POI at the exact same location should match
+    # 0.01 km = 10m: only the POI at the exact same location should match
     result = count_pois_per_postcode(postcodes, pois, groups=POI_GROUPS, radius_km=0.01)
     # The Restaurant at (51.5074, -0.1278) is at distance 0
     assert result["restaurants_0km"][0] >= 1
@@ -135,9 +135,9 @@ def test_min_distance_finds_nearest(postcodes, pois):
     assert len(result) == 2
 
     ec1a = result.filter(pl.col("postcode") == "EC1A 1BB")
-    # Rail station is at (51.5073, -0.1277), postcode at (51.5074, -0.1278) — very close
+    # Rail station is at (51.5073, -0.1277), postcode at (51.5074, -0.1278), very close
     assert ec1a["train_tube_nearest_km"][0] < 0.05  # within 50m
-    # Restaurant is co-located — distance ~0
+    # Restaurant is co-located: distance ~0
     assert ec1a["restaurants_nearest_km"][0] < 0.01
 
     # Far-away postcode should still get the global nearest distance.