itziarZG
7aa0fb7a89
El usuario queria ver claramente dónde hay viviendas vacantes y cuántas. Hasta ahora solo teníamos el choropleth de consumo_p50, que era abstracto y los numeros de Airbnb en circulos confundian. Nueva variable 'viviendas_vacantes_estimadas' en el dataset: - Viviendas_totales (municipio) * 0.162 (tasa CCAA INE 2021) - Asume distribucion uniforme del 16,2% (proxy: en realidad varia por municipio, mas alta en zonas turisticas) - Replicada a todas las secciones del mismo municipio Capa nueva en el mapa con escala de rojos: < 100 vacants (claro) 100-500 500-1500 1500-5000 > 5000 vacants (oscuro, Palma) Total Balears = 105.632 vacantes (proxy) - Palma: 30.210 - Calvia: 5.912 - Manacor: 4.629 - Inca: 2.570 - Llucmajor: 3.776 - Eivissa: 3.878 i18n: nueva key 'variable_viviendas_vacantes' en ca.json y es.json
88 lines
2.8 KiB
Python
88 lines
2.8 KiB
Python
"""
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Genera tiles pmtiles a partir de data/output/dataset.gpkg
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usando tippecanoe (https://github.com/felt/tippecanoe).
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Salida: data/output/tiles.pmtiles
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"""
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import shutil
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import subprocess
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from pathlib import Path
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import warnings
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import geopandas as gpd
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warnings.filterwarnings('ignore')
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ROOT = Path(__file__).resolve().parent.parent
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RAW = ROOT / "data" / "raw"
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OUT = ROOT / "data" / "output"
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OUT.mkdir(parents=True, exist_ok=True)
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def main() -> None:
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if not shutil.which("tippecanoe"):
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print("ERROR: tippecanoe no esta instalado.")
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print("Instala con: brew install tippecanoe")
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return
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print("Convirtiendo dataset.gpkg a GeoJSON (formato que tippecanoe lee bien)...")
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gdf = gpd.read_file(OUT / "dataset.gpkg")
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cols = [
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"CUSEC", "NMUN", "isla", "granularidad", "fuente",
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"airbnb_listings", "airbnb_entire_homes", "airbnb_price_median",
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"airbnb_accommodates_total", "airbnb_revenue_total",
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"airbnb_hosts_unicos", "airbnb_con_licencia",
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"hut_registros", "hut_plazas", "hut_habitaciones",
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"plazas_turisticas",
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# INE Censo 2021 (viven en dataset tras Fase 3 mejora)
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"viviendas_totales", "viviendas_vacias_pct_ccaa",
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"viviendas_uso_esporadico_pct_ccaa", "viviendas_turisticas_pct_ccaa_2025M05",
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"consumo_p10_kwh", "consumo_p25_kwh", "consumo_p50_kwh",
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"consumo_p75_kwh", "consumo_p90_kwh",
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"airbnb_listings_por_1000_viviendas",
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"ratio_p10_p50_consumo",
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"viviendas_vacantes_estimadas",
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"geometry",
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]
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cols = [c for c in cols if c in gdf.columns]
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geojson_path = OUT / "dataset.geojson"
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gdf[cols].to_file(geojson_path, driver="GeoJSON")
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print(f" -> {geojson_path.name} ({geojson_path.stat().st_size // 1024} KB)")
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print("\nGenerando tiles pmtiles...")
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cmd = [
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"tippecanoe",
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"--output", str(OUT / "tiles.pmtiles"),
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"--name", "cases",
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"--layer", "cases",
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"-zg",
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"--drop-densest-as-needed",
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"--read-parallel",
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"--minimum-zoom", "4",
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"--force",
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str(geojson_path),
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]
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result = subprocess.run(cmd, capture_output=True, text=True)
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if result.returncode != 0:
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print(f"ERROR tippecanoe (codigo {result.returncode}):")
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print(result.stderr[-2000:])
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return
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tiles = OUT / "tiles.pmtiles"
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if tiles.exists():
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size_kb = tiles.stat().st_size // 1024
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print(f"\ntiles.pmtiles generado: {size_kb} KB")
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with open(tiles, "rb") as f:
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magic = f.read(4)
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print(f" Magic: {magic} (esperado: b'PMTi')")
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else:
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print("ERROR: tiles.pmtiles no se genero")
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geojson_path.unlink()
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print(f" Limpiado {geojson_path.name} (intermedio)")
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if __name__ == "__main__":
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main()
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