feat: Initial CBPOA commit — 武汉儿童呼吸疾病风险评估系统

Context: Build a spatial risk assessment system correlating air quality
data with children's respiratory disease incidence across Wuhan.

Approach: FastAPI backend serving PostGIS spatial queries, React
frontend with Deck.gl maps, and a PyTorch SpatialTemporalGCN pipeline
for multi-day (1d/3d/7d) risk prediction.

Changes:
- backend/ — FastAPI API with auth (JWT), alerts, risk analysis,
  geocoded case data, grid statistics, and report endpoints
- frontend/ — React dashboard with interactive risk maps, alert
  monitoring, district comparison charts, and timeline player
- models/ — SpatialTemporalGCN model with trained weights and ONNX
  export for inference
- scripts/ — ETL pipeline for weather + medical data, grid generation,
  feature engineering, training, and daily inference
- deploy/ — Docker Compose configs for backend, frontend, and MLflow
- docs/ — API docs, deployment guide, user guide, and code review

Impact: Enables spatial risk visualization, alert monitoring, and
ML-driven health risk forecasting for environmental health teams.

scripts/resample_raster_to_grid.py

@@ -0,0 +1,73 @@
+#!/usr/bin/env python3
+"""Resample DEM and population density to 100m grid."""
+
+import pandas as pd
+import rasterio
+from rasterio.warp import transform
+
+
+def resample_dem(dem_path: str, grid_parquet: str, output_path: str):
+    import numpy as np
+    df = pd.read_parquet(grid_parquet)
+
+    with rasterio.open(dem_path) as src:
+        elevations = []
+        for lon, lat in zip(df['center_lon'], df['center_lat']):
+            py, px = src.index(lon, lat)
+            if 0 <= py < src.height and 0 <= px < src.width:
+                elevations.append(src.read(1)[py, px])
+            else:
+                elevations.append(np.nan)
+
+    result = pd.DataFrame({'grid_id': df['grid_id'], 'elevation_m': elevations})
+    result.to_parquet(output_path, index=False)
+    print(f"DEM saved: {output_path}")
+
+
+def resample_population(pop_dir: str, grid_parquet: str, output_path: str):
+    import numpy as np
+    import glob
+    df = pd.read_parquet(grid_parquet)
+
+    tif_files = glob.glob(f"{pop_dir}/*.tif")
+    if not tif_files:
+        print(f"No TIF files found in {pop_dir}")
+        return
+
+    populations = []
+    for lon, lat in zip(df['center_lon'], df['center_lat']):
+        val = 0
+        for tif_file in tif_files:
+            try:
+                with rasterio.open(tif_file) as src:
+                    py, px = src.index(lon, lat)
+                    if 0 <= py < src.height and 0 <= px < src.width:
+                        val += src.read(1)[py, px]
+            except:
+                pass
+        populations.append(val)
+
+    result = pd.DataFrame({'grid_id': df['grid_id'], 'population_density': populations})
+    result.to_parquet(output_path, index=False)
+    print(f"Population saved: {output_path}")
+
+
+def main():
+    import argparse
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--dem', default='Datas/DEM/CJJJD_DEM.TIF')
+    parser.add_argument('--pop-dir', default='Datas/landscan-hd-china-v1-assets')
+    parser.add_argument('--grid-parquet', default='processed/grid_100m_index.parquet')
+    parser.add_argument('--output-dem', default='processed/grid_dem.parquet')
+    parser.add_argument('--output-pop', default='processed/grid_population.parquet')
+    args = parser.parse_args()
+
+    print("Resampling DEM...")
+    resample_dem(args.dem, args.grid_parquet, args.output_dem)
+
+    print("Resampling population density...")
+    resample_population(args.pop_dir, args.grid_parquet, args.output_pop)
+
+
+if __name__ == '__main__':
+    main()