No geosure

Makefile.data

@@ -37,8 +37,6 @@ NAPTAN          := $(DATA_DIR)/naptan.parquet
 BROADBAND       := $(DATA_DIR)/broadband.parquet
 SCHOOL_PROX     := $(DATA_DIR)/school_proximity.parquet
 RENTAL          := $(DATA_DIR)/rental_prices.parquet
-GEOSURE_DIR     := $(DATA_DIR)/geosure
-GEOSURE         := $(DATA_DIR)/geosure.parquet
 INSPIRE_DIR     := $(DATA_DIR)/inspire
 OA_BOUNDARIES   := $(DATA_DIR)/oa_boundaries.gpkg
 UPRN_LOOKUP     := $(DATA_DIR)/uprn_lookup.parquet
@@ -55,7 +53,6 @@ ENGLAND_BOUNDARY := $(DATA_DIR)/england_boundary.geojson
 RM_OUTCODES     := frontend/src/lib/rightmove-outcodes.json
 
 # Sentinel files for directory targets (Make doesn't track directories well)
-GEOSURE_STAMP   := $(GEOSURE_DIR)/.done
 INSPIRE_STAMP   := $(INSPIRE_DIR)/.done
 
 PMTILES_VERSION := 1.22.3

pipeline/transform/merge.py

@@ -58,14 +58,8 @@ _AREA_COLUMNS = [
     # Schools
     "Good+ primary schools within 5km",
     "Good+ secondary schools within 5km",
-    # GeoSure
-    "Environmental risk",
-    "Collapsible deposits risk",
-    "Compressible ground risk",
-    "Landslide risk",
-    "Running sand risk",
-    "Shrink-swell risk",
-    "Soluble rocks risk",
+    # Demographics
+    "Median age",
 ]
 
 

pipeline/transform/transform_geosure.py

@@ -1,130 +0,0 @@
-"""Spatial-join GeoSure 5km hex grid risk data to postcode centroids.
-
-Reads six ESRI Shapefiles (one per hazard type), converts polygons from
-BNG to WGS84, and queries postcode centroids against them using an STRtree.
-Outputs a postcode-level parquet with six risk columns plus an overall max.
-
-Source: Ordnance Survey GeoSure (Open Data)
-"""
-
-import argparse
-from pathlib import Path
-
-import numpy as np
-import polars as pl
-import shapefile as shp
-from pyproj import Transformer
-from shapely import STRtree, points
-from shapely.geometry import shape as to_shapely
-
-_GEOSURE_RISKS = [
-    ("CollapsibleDeposits", "collapsible_deposits_risk"),
-    ("CompressibleGround", "compressible_ground_risk"),
-    ("Landslides", "landslide_risk"),
-    ("RunningSand", "running_sand_risk"),
-    ("ShrinkSwell", "shrink_swell_risk"),
-    ("SolubleRocks", "soluble_rocks_risk"),
-]
-
-
-def transform(geosure_dir: Path, arcgis_path: Path) -> pl.DataFrame:
-    """Spatial-join GeoSure 5km hex grid risk data to postcode centroids."""
-    to_wgs84 = Transformer.from_crs("EPSG:27700", "EPSG:4326", always_xy=True)
-
-    print("Loading postcode centroids for GeoSure join...")
-    postcodes_df = pl.read_parquet(arcgis_path, columns=["pcds", "lat", "long"])
-    lats = postcodes_df["lat"].to_numpy()
-    lons = postcodes_df["long"].to_numpy()
-    pts = points(np.column_stack([lons, lats]))
-
-    result = pl.DataFrame({"postcode": postcodes_df["pcds"]})
-    risk_cols = []
-
-    for risk_key, col_name in _GEOSURE_RISKS:
-        shp_files = list(geosure_dir.rglob(f"*_GS_{risk_key}_*.shp"))
-        if not shp_files:
-            print(f"  Warning: No shapefile found for {risk_key}")
-            result = result.with_columns(pl.lit(None).cast(pl.UInt8).alias(col_name))
-            risk_cols.append(col_name)
-            continue
-
-        print(f"  Reading {shp_files[0].name}...")
-        reader = shp.Reader(str(shp_files[0]))
-
-        hex_polys = []
-        hex_classes = []
-        for sr in reader.shapeRecords():
-            geo = sr.shape.__geo_interface__
-            new_coords = []
-            for ring in geo["coordinates"]:
-                xs, ys = zip(*ring)
-                t_lons, t_lats = to_wgs84.transform(list(xs), list(ys))
-                new_coords.append(list(zip(t_lons, t_lats)))
-            geo["coordinates"] = new_coords
-            hex_polys.append(to_shapely(geo))
-            hex_classes.append(int(sr.record["CLASS"]))
-
-        classes_arr = np.array(hex_classes, dtype=np.uint8)
-
-        print(f"  Querying {len(pts)} postcodes against {len(hex_polys)} hexagons...")
-        tree = STRtree(hex_polys)
-        pt_idx, hex_idx = tree.query(pts, predicate="intersects")
-
-        risk_values = np.zeros(len(pts), dtype=np.uint8)
-        np.maximum.at(risk_values, pt_idx, classes_arr[hex_idx])
-
-        result = result.with_columns(pl.Series(col_name, risk_values))
-        risk_cols.append(col_name)
-
-    # Overall environmental risk = max across all 6
-    result = result.with_columns(
-        pl.max_horizontal(*risk_cols).alias("environmental_risk")
-    )
-
-    # Convert 0 → null, 1/2/3 → Low/Moderate/Significant
-    label_map = {1: "Low", 2: "Moderate", 3: "Significant"}
-    for col in risk_cols + ["environmental_risk"]:
-        result = result.with_columns(
-            pl.col(col)
-            .replace_strict(label_map, default=None, return_dtype=pl.Utf8)
-            .alias(col)
-        )
-
-    print(f"  GeoSure join complete: {result.height} postcodes")
-    return result
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(
-        description="Spatial-join GeoSure ground stability data to postcode centroids"
-    )
-    parser.add_argument(
-        "--geosure",
-        type=Path,
-        required=True,
-        help="GeoSure shapefile directory",
-    )
-    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()
-
-    result = transform(args.geosure, args.arcgis)
-
-    args.output.parent.mkdir(parents=True, exist_ok=True)
-    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()