159 lines
4.8 KiB
Python
159 lines
4.8 KiB
Python
"""Build PMTiles point tiles for the crime heatmap overlay.
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The output intentionally keeps point features rather than H3/grid aggregates so
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MapLibre can render a true client-side heatmap. Police.uk coordinates are
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published anonymous map points, not exact offence locations.
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"""
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from __future__ import annotations
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import argparse
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import json
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import shutil
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import subprocess
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import tempfile
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from pathlib import Path
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import polars as pl
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from pipeline.local_temp import local_tmp_dir
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from pipeline.transform.crime import find_street_crime_csvs
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def _latest_months(crime_dir: Path, month_count: int) -> list[str]:
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csvs, _ignored = find_street_crime_csvs(crime_dir)
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months = sorted({path.parent.name for path in csvs})
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if not months:
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raise FileNotFoundError(f"No street crime CSVs found in {crime_dir}")
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return months[-month_count:]
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def _street_csvs_for_months(crime_dir: Path, months: set[str]) -> list[Path]:
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csvs, _ignored = find_street_crime_csvs(crime_dir)
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selected = [path for path in csvs if path.parent.name in months]
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if not selected:
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raise FileNotFoundError(f"No street crime CSVs found for {sorted(months)}")
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return selected
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def _require_tippecanoe() -> str:
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executable = shutil.which("tippecanoe")
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if executable is None:
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raise RuntimeError(
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"tippecanoe is required to build crime hotspot PMTiles. "
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"Install tippecanoe and rerun this target."
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)
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return executable
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def _write_geojsonseq(csvs: list[Path], output_path: Path) -> int:
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df = (
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pl.scan_csv(
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csvs,
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schema_overrides={
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"Longitude": pl.Float64,
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"Latitude": pl.Float64,
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"Month": pl.Utf8,
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"Crime type": pl.Utf8,
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},
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ignore_errors=True,
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)
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.select(
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pl.col("Longitude").alias("lon"),
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pl.col("Latitude").alias("lat"),
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pl.col("Month").alias("month"),
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pl.col("Crime type").alias("crime_type"),
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)
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.drop_nulls(["lon", "lat"])
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.filter(pl.col("lon").is_between(-9.5, 5.0))
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.filter(pl.col("lat").is_between(49.0, 57.0))
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.collect(engine="streaming")
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)
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with output_path.open("w") as file:
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for row in df.iter_rows(named=True):
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feature = {
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"type": "Feature",
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"geometry": {
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"type": "Point",
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"coordinates": [row["lon"], row["lat"]],
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},
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"properties": {
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"count": 1,
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"weight": 1,
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"month": row["month"],
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"crime_type": row["crime_type"],
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},
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}
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file.write(json.dumps(feature, separators=(",", ":")) + "\n")
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return df.height
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def build_crime_hotspot_tiles(
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crime_dir: Path,
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output_path: Path,
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months: int,
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min_zoom: int,
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max_zoom: int,
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) -> None:
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tippecanoe = _require_tippecanoe()
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selected_months = set(_latest_months(crime_dir, months))
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csvs = _street_csvs_for_months(crime_dir, selected_months)
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output_path.parent.mkdir(parents=True, exist_ok=True)
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with tempfile.TemporaryDirectory(dir=local_tmp_dir()) as tmp:
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ndjson_path = Path(tmp) / "crime_hotspots.geojsonseq"
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feature_count = _write_geojsonseq(csvs, ndjson_path)
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print(
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f"Writing {feature_count:,} approximate crime heatmap points "
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f"from {min(selected_months)} to {max(selected_months)}"
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)
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subprocess.run(
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[
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tippecanoe,
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"--force",
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"--output",
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str(output_path),
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"--layer",
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"crime_hotspots",
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"--minimum-zoom",
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str(min_zoom),
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"--maximum-zoom",
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str(max_zoom),
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"--drop-densest-as-needed",
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"--extend-zooms-if-still-dropping",
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str(ndjson_path),
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],
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check=True,
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)
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def main() -> None:
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("--input", type=Path, required=True, help="Crime CSV directory")
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parser.add_argument(
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"--output", type=Path, required=True, help="Output .pmtiles path"
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)
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parser.add_argument(
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"--months",
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type=int,
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default=12,
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help="Latest complete months to include in the heatmap",
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)
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parser.add_argument("--min-zoom", type=int, default=12)
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parser.add_argument("--max-zoom", type=int, default=16)
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args = parser.parse_args()
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build_crime_hotspot_tiles(
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args.input,
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args.output,
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args.months,
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args.min_zoom,
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args.max_zoom,
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)
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if __name__ == "__main__":
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main()
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