perfect-postcode/pipeline/transform/poi_proximity.py
2026-02-07 19:13:36 +00:00

56 lines
1.5 KiB
Python

"""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
# 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"
)
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, groups=POI_GROUPS, 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()