Add council house

pipeline/transform/merge.py

@@ -52,6 +52,7 @@ _AREA_COLUMNS = [
     "Number of parks within 2km",
     "Train or tube stations within 1km",
     "Distance to nearest train or tube station (km)",
+    "Distance to nearest park (km)",
     # Environment
     "Noise (dB)",
     "Max available download speed (Mbps)",
@@ -73,9 +74,9 @@ def _build(
     noise_path: Path,
     school_proximity_path: Path,
     broadband_path: Path,
-    geosure_path: Path,
     rental_prices_path: Path,
     lsoa_population_path: Path,
+    median_age_path: Path,
 ) -> tuple[pl.DataFrame, pl.DataFrame]:
     """Build postcode and properties dataframes from epc_pp + auxiliary data.
 
@@ -188,6 +189,9 @@ def _build(
         .alias("minor_crime_per_1k"),
     ).drop("population")
 
+    median_age = pl.scan_parquet(median_age_path)
+    wide = wide.join(median_age, on="lsoa21", how="left")
+
     poi_counts = pl.scan_parquet(poi_proximity_path)
     wide = wide.join(poi_counts, on="postcode", how="left")
 
@@ -233,9 +237,6 @@ def _build(
     )
     wide = wide.join(broadband, left_on="postcode", right_on="bb_postcode", how="left")
 
-    geosure = pl.scan_parquet(geosure_path)
-    wide = wide.join(geosure, on="postcode", how="left")
-
     # Derive property_type: prefer EPC data, fall back to price-paid.
     # For Houses, use built_form (e.g. Semi-Detached, Mid-Terrace) for finer detail.
     bad_built_form = pl.col("built_form").is_null() | pl.col("built_form").is_in(
@@ -324,6 +325,7 @@ def _build(
                 "parks_2km": "Number of parks within 2km",
                 "train_tube_1km": "Train or tube stations within 1km",
                 "train_tube_nearest_km": "Distance to nearest train or tube station (km)",
+                "parks_nearest_km": "Distance to nearest park (km)",
                 "latest_price": "Last known price",
                 "number_habitable_rooms": "Number of bedrooms & living rooms",
                 "noise_lden_db": "Noise (dB)",
@@ -334,15 +336,10 @@ def _build(
                 "minor_crime_avg_yr": "Minor crime (avg/yr)",
                 "serious_crime_per_1k": "Serious crime per 1k residents (avg/yr)",
                 "minor_crime_per_1k": "Minor crime per 1k residents (avg/yr)",
-                "environmental_risk": "Environmental risk",
-                "collapsible_deposits_risk": "Collapsible deposits risk",
-                "compressible_ground_risk": "Compressible ground risk",
-                "landslide_risk": "Landslide risk",
-                "running_sand_risk": "Running sand risk",
-                "shrink_swell_risk": "Shrink-swell risk",
-                "soluble_rocks_risk": "Soluble rocks risk",
                 "median_monthly_rent": "Estimated monthly rent",
                 "floor_height": "Interior height (m)",
+                "was_council_house": "Former council house",
+                "median_age": "Median age",
             }
         )
     )
@@ -410,12 +407,6 @@ def main():
         required=True,
         help="Broadband performance by output area parquet file",
     )
-    parser.add_argument(
-        "--geosure",
-        type=Path,
-        required=True,
-        help="GeoSure ground stability parquet file",
-    )
     parser.add_argument(
         "--rental-prices",
         type=Path,
@@ -428,6 +419,12 @@ def main():
         required=True,
         help="Census 2021 population by LSOA parquet file",
     )
+    parser.add_argument(
+        "--median-age",
+        type=Path,
+        required=True,
+        help="Census 2021 median age by LSOA parquet file",
+    )
     parser.add_argument(
         "--output-postcodes",
         type=Path,
@@ -452,9 +449,9 @@ def main():
         noise_path=args.noise,
         school_proximity_path=args.school_proximity,
         broadband_path=args.broadband,
-        geosure_path=args.geosure,
         rental_prices_path=args.rental_prices,
         lsoa_population_path=args.lsoa_population,
+        median_age_path=args.median_age,
     )
 
     print(f"\nPostcode columns: {postcode_df.columns}")