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/generate_grid.py

@@ -0,0 +1,101 @@
+#!/usr/bin/env python3
+"""Generate 100x100m resolution grid index for Wuhan city, China."""
+
+import geopandas as gpd
+import pandas as pd
+import numpy as np
+from shapely.geometry import box
+from pathlib import Path
+
+
+def generate_wuhan_grid(
+    boundary_path: str = "Datas/武汉市.geojson",
+    output_dir: str = "processed",
+    grid_size: float = 100,
+) -> tuple[gpd.GeoDataFrame, pd.DataFrame]:
+    """Generate 100m resolution grid covering Wuhan boundary."""
+    print(f"Loading Wuhan boundary from {boundary_path}...")
+    wuhan = gpd.read_file(boundary_path)
+
+    bounds = wuhan.total_bounds
+    print(f"Wuhan bounds: minx={bounds[0]:.4f}, miny={bounds[1]:.4f}, maxx={bounds[2]:.4f}, maxy={bounds[3]:.4f}")
+
+    minx, miny, maxx, maxy = bounds
+    cell_size_deg = grid_size / 111000.0
+
+    print(f"Creating grid with {grid_size}m cells (vectorized)...")
+
+    x_coords = np.arange(minx, maxx, cell_size_deg)
+    y_coords = np.arange(miny, maxy, cell_size_deg)
+    print(f"  Grid dimensions: {len(x_coords)} x {len(y_coords)}")
+
+    x_grid, y_grid = np.meshgrid(x_coords, y_coords)
+    x_flat = x_grid.flatten()
+    y_flat = y_grid.flatten()
+
+    print(f"  Total cells in bounding box: {len(x_flat)}")
+
+    minxs = x_flat
+    minys = y_flat
+    maxxs = minxs + cell_size_deg
+    maxys = minys + cell_size_deg
+
+    geometries = [box(mx, my, Mx, My) for mx, my, Mx, My in zip(minxs, minys, maxxs, maxys)]
+
+    cells = np.arange(len(geometries))
+    rows = cells // len(x_coords)
+    cols = cells % len(x_coords)
+
+    print("  Building GeoDataFrame...")
+    grid_gdf = gpd.GeoDataFrame({
+        'row': rows,
+        'col': cols,
+        'geometry': geometries
+    }, crs="EPSG:4326")
+
+    print("Filtering to cells intersecting Wuhan boundary...")
+    wuhan_union = wuhan.unary_union
+    mask = grid_gdf.intersects(wuhan_union)
+    grid_gdf = grid_gdf[mask].copy().reset_index(drop=True)
+
+    print(f"Cells within Wuhan boundary: {len(grid_gdf)}")
+
+    grid_gdf['grid_id'] = [f"r{r}_c{c}" for r, c in zip(grid_gdf['row'], grid_gdf['col'])]
+
+    centroids = grid_gdf.geometry.centroid
+    grid_gdf['center_lon'] = centroids.x
+    grid_gdf['center_lat'] = centroids.y
+    grid_gdf['polygon'] = grid_gdf.geometry.apply(lambda g: g.wkt)
+
+    parquet_df = grid_gdf[['grid_id', 'center_lon', 'center_lat', 'row', 'col', 'polygon']].copy()
+
+    return grid_gdf, parquet_df
+
+
+def main():
+    output_dir = Path("processed")
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    grid_gdf, parquet_df = generate_wuhan_grid()
+
+    geojson_path = output_dir / "grid_100m_index.geojson"
+    print(f"Exporting to GeoJSON: {geojson_path}")
+    grid_gdf.to_file(geojson_path, driver="GeoJSON")
+    print(f"  Exported {len(grid_gdf)} features")
+
+    parquet_path = output_dir / "grid_100m_index.parquet"
+    print(f"Exporting to Parquet: {parquet_path}")
+    parquet_df.to_parquet(parquet_path, index=False)
+    print(f"  Exported {len(parquet_df)} rows")
+
+    print("\n=== Grid Summary ===")
+    print(f"Total grid cells: {len(grid_gdf)}")
+    print(f"Bounds: {grid_gdf.total_bounds}")
+    print(f"Grid ID format example: {grid_gdf['grid_id'].iloc[0]}")
+    print(f"Center coordinate range:")
+    print(f"  Lon: {parquet_df['center_lon'].min():.4f} to {parquet_df['center_lon'].max():.4f}")
+    print(f"  Lat: {parquet_df['center_lat'].min():.4f} to {parquet_df['center_lat'].max():.4f}")
+
+
+if __name__ == "__main__":
+    main()