Complete Köppen-Geiger filter review with Mixed climate class
Classify each county by area-weighted Köppen class shares: a county is predominantly its top class when that class covers at least 50% of its land and leads the runner-up by at least 5 percentage points; otherwise it is Mixed (133 of 3,143 counties in the 50 states and DC). - Add build_county_koppen_metric.py (writes data/metrics/koppen.csv) and apply_koppen_metric_to_climate_data.py (writes koppenZone plus koppenPrimaryClass/koppenSecondaryClass for Mixed counties). - Move shared helpers into scripts/common/ (county loading, Köppen legend, area-weighted raster shares); fix the 180th-meridian raster window for Aleutians West. - Add check_climate_data.py to validate the app CSV. - Draw Mixed counties in app.js as diagonal stripes of their top two classes, fixed to the ground and following the map at every zoom, with a crossfade only when the stripe size changes. Filtering a class also matches Mixed counties where it is primary or secondary. - Document the rule, display, and pipeline plan in docs/ and update the README and data-source notes. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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Claude Opus 5
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"""Build the county Koppen-Geiger metric file from area-weighted class shares.
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Writes data/metrics/koppen.csv. A county is predominantly its top class when
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that class covers at least 50% of the county's land and leads the runner-up by
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at least 5 percentage points; otherwise it is "Mixed". Counties with no valid
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raster cells are left blank. See docs/filter-calculations.md, section 1.
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Run:
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.venv\\Scripts\\python.exe scripts\\build_county_koppen_metric.py
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"""
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from __future__ import annotations
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import argparse
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import csv
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from collections import Counter
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from pathlib import Path
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from typing import Dict, List, Tuple
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import geopandas as gpd
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import rasterio
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from common.counties import load_counties
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from common.county_zonal_stats import DEFAULT_SUBCELLS, area_weighted_class_weights
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from common.koppen_legend import load_koppen_legend
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PROJECT_ROOT = Path(__file__).resolve().parents[1]
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DEFAULT_COUNTIES_GEOJSON = PROJECT_ROOT / "data" / "geojson-counties-fips.json"
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DEFAULT_KOPPEN_RASTER = PROJECT_ROOT / "data" / "koppen_geiger_tif" / "1991_2020" / "koppen_geiger_0p00833333.tif"
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DEFAULT_KOPPEN_LEGEND = PROJECT_ROOT / "data" / "koppen_geiger_tif" / "legend.txt"
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DEFAULT_OUT = PROJECT_ROOT / "data" / "metrics" / "koppen.csv"
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PREDOMINANT_MIN_SHARE = 0.50
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PREDOMINANT_MIN_GAP = 0.05
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MIXED_CLASS = "Mixed"
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# Keeps shares that sit exactly on a cutoff from failing on floating-point error.
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RULE_TOLERANCE = 1e-9
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FIELDS = [
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"countyFips",
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"countyName",
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"state",
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"koppenZone",
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"koppenTopClass",
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"koppenTopShare",
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"koppenSecondClass",
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"koppenSecondShare",
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]
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def rank_class_shares(weights: Dict[int, float], code_map: Dict[int, str]) -> List[Tuple[str, float]]:
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"""Convert per-code area weights to class shares, largest first.
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Exact ties go to the smaller raster code, matching the previous build.
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"""
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total = sum(weights.values())
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if total <= 0:
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return []
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unknown = sorted(code for code in weights if code not in code_map)
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if unknown:
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raise ValueError(f"Raster codes {unknown} are not in the Koppen legend.")
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ranked = sorted(weights.items(), key=lambda item: (-item[1], item[0]))
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return [(code_map[code], weight / total) for code, weight in ranked]
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def classify(ranked: List[Tuple[str, float]]) -> str:
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"""Return the predominant class, "Mixed", or blank when there is no data."""
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if not ranked:
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return ""
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top_share = ranked[0][1]
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second_share = ranked[1][1] if len(ranked) > 1 else 0.0
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has_majority = top_share >= PREDOMINANT_MIN_SHARE - RULE_TOLERANCE
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has_clear_lead = top_share - second_share >= PREDOMINANT_MIN_GAP - RULE_TOLERANCE
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return ranked[0][0] if has_majority and has_clear_lead else MIXED_CLASS
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def _format_share(ranked: List[Tuple[str, float]], index: int) -> Tuple[str, str]:
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"""Return the class and 4-decimal share at a rank, or blanks."""
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if index >= len(ranked):
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return "", ""
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code, share = ranked[index]
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return code, f"{share:.4f}"
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def build_koppen_records(
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counties: gpd.GeoDataFrame,
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koppen_raster: Path,
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code_map: Dict[int, str],
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subcells: int = DEFAULT_SUBCELLS,
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) -> List[dict]:
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"""Classify every county and return its metric row."""
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records: List[dict] = []
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with rasterio.open(koppen_raster) as source:
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raster_counties = counties
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if source.crs is not None and counties.crs is not None and counties.crs != source.crs:
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raster_counties = counties.to_crs(source.crs)
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geographic = source.crs is None or source.crs.is_geographic
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for county, geometry in zip(counties.itertuples(), raster_counties.geometry):
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weights = area_weighted_class_weights(source, geometry, geographic=geographic, subcells=subcells)
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ranked = rank_class_shares(weights, code_map)
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top_class, top_share = _format_share(ranked, 0)
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second_class, second_share = _format_share(ranked, 1)
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records.append(
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{
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"countyFips": county.county_fips,
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"countyName": county.county_name,
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"state": county.state,
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"koppenZone": classify(ranked),
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"koppenTopClass": top_class,
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"koppenTopShare": top_share,
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"koppenSecondClass": second_class,
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"koppenSecondShare": second_share,
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}
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)
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return records
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def write_records(records: List[dict], out_file: Path) -> None:
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"""Write the Koppen metric rows."""
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out_file.parent.mkdir(parents=True, exist_ok=True)
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with out_file.open("w", encoding="utf-8", newline="") as csv_file:
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writer = csv.DictWriter(csv_file, fieldnames=FIELDS)
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writer.writeheader()
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writer.writerows(records)
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def parse_args() -> argparse.Namespace:
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"""Define and parse command-line options for this builder."""
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parser = argparse.ArgumentParser(description="Build the county Koppen-Geiger metric file.")
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parser.add_argument("--counties-geojson", type=Path, default=DEFAULT_COUNTIES_GEOJSON, help="County polygon GeoJSON path.")
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parser.add_argument("--koppen-raster", type=Path, default=DEFAULT_KOPPEN_RASTER, help="Koppen-Geiger raster TIFF path.")
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parser.add_argument("--koppen-legend", type=Path, default=DEFAULT_KOPPEN_LEGEND, help="legend.txt mapping raster codes.")
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parser.add_argument("--subcells", type=int, default=DEFAULT_SUBCELLS, help="Sub-cells per raster cell edge.")
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parser.add_argument("--out", type=Path, default=DEFAULT_OUT, help="Output metric CSV path.")
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return parser.parse_args()
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def main() -> None:
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args = parse_args()
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counties = load_counties(args.counties_geojson)
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records = build_koppen_records(counties, args.koppen_raster, load_koppen_legend(args.koppen_legend), args.subcells)
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write_records(records, args.out)
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outcomes = Counter(
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"blank" if not r["koppenZone"] else "Mixed" if r["koppenZone"] == MIXED_CLASS else "predominant" for r in records
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)
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print(
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f"Wrote {len(records)} counties to {args.out}: {outcomes['predominant']} predominant, "
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f"{outcomes['Mixed']} Mixed, {outcomes['blank']} blank."
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)
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if __name__ == "__main__":
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main()
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