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/setup_postgis_indexes.py

@@ -0,0 +1,121 @@
+#!/usr/bin/env python3
+"""
+PostGIS index optimization script for grid queries.
+
+Creates spatial indexes on the grids table for efficient bounding box
+and radius queries used by the monitoring and prediction APIs.
+
+Usage:
+    python scripts/setup_postgis_indexes.py --connection postgresql://user:pass@localhost:5432/wuhan_disease
+"""
+
+import sys
+import argparse
+
+def create_indexes(connection_string):
+    import asyncpg
+
+    indexes = [
+        ("idx_grids_geometry", "CREATE INDEX IF NOT EXISTS idx_grids_geometry ON grids USING GIST (geometry)"),
+        ("idx_grids_centroid", "CREATE INDEX IF NOT EXISTS idx_grids_centroid ON grids USING GIST (ST_Transform(geometry, 32650))"),
+        ("idx_grids_grid_id", "CREATE INDEX IF NOT EXISTS idx_grids_grid_id ON grids (grid_id)"),
+        ("idx_grids_district", "CREATE INDEX IF NOT EXISTS idx_grids_district ON grids (district)"),
+    ]
+
+    print("Creating PostGIS spatial indexes...")
+
+    async def run_indexes():
+        conn = await asyncpg.connect(connection_string)
+
+        for idx_name, sql in indexes:
+            try:
+                await conn.execute(sql)
+                print(f"  Created: {idx_name}")
+            except Exception as e:
+                print(f"  Failed: {idx_name} - {e}")
+
+        await conn.close()
+
+    import asyncio
+    asyncio.run(run_indexes())
+    print("Done!")
+
+
+def create_grid_table_sql():
+
+    return """
+-- Create grids table for 100m grid cells
+CREATE TABLE IF NOT EXISTS grids (
+    grid_id VARCHAR(20) PRIMARY KEY,
+    geometry GEOMETRY(POLYGON, 4326) NOT NULL,
+    center_lat DOUBLE PRECISION NOT NULL,
+    center_lon DOUBLE PRECISION NOT NULL,
+    district VARCHAR(50),
+    dem DOUBLE PRECISION,
+    population_density DOUBLE PRECISION,
+    created_at TIMESTAMP DEFAULT NOW()
+);
+
+-- Spatial index for geometry queries
+CREATE INDEX IF NOT EXISTS idx_grids_geometry ON grids USING GIST (geometry);
+
+-- Index for district lookups
+CREATE INDEX IF NOT EXISTS idx_grids_district ON grids (district);
+
+-- Index for grid_id lookups
+CREATE INDEX IF NOT EXISTS idx_grids_grid_id ON grids (grid_id);
+
+-- Index for bounding box queries (UTM projection for meters)
+CREATE INDEX IF NOT EXISTS idx_grids_centroid ON grids USING GIST (ST_Transform(geometry, 32650));
+
+-- Cluster the table by geometry for better spatial query performance
+CLUSTER grids USING idx_grids_geometry;
+
+-- Analyze the table for query planner
+ANALYZE grids;
+
+-- Example queries:
+
+-- 1. Bounding box query (within 114.0-115.0 lon, 29.5-30.5 lat)
+SELECT grid_id, center_lat, center_lon
+FROM grids
+WHERE geometry && ST_MakeEnvelope(113.8, 29.4, 115.2, 30.6, 4326);
+
+-- 2. Radius query (within 10km of point)
+SELECT grid_id, center_lat, center_lon,
+       ST_Distance(geometry, ST_Transform(ST_SetSRID(ST_MakePoint(114.3, 30.6), 4326), 32650)) as distance
+FROM grids
+WHERE ST_DWithin(
+    ST_Transform(geometry, 32650),
+    ST_Transform(ST_SetSRID(ST_MakePoint(114.3, 30.6), 4326), 32650),
+    10000
+)
+ORDER BY distance
+LIMIT 100;
+
+-- 3. District aggregation
+SELECT district, COUNT(*) as grid_count, AVG(population_density) as avg_pop
+FROM grids
+GROUP BY district
+ORDER BY grid_count DESC;
+"""
+
+
+def main():
+    parser = argparse.ArgumentParser(description='PostGIS index setup for grid queries')
+    parser.add_argument('--connection', help='PostgreSQL connection string')
+    parser.add_argument('--sql-only', action='store_true', help='Print SQL only')
+    args = parser.parse_args()
+
+    if args.sql_only:
+        print(create_grid_table_sql())
+    elif args.connection:
+        create_indexes(args.connection)
+    else:
+        print("Usage:")
+        print("  python scripts/setup_postgis_indexes.py --sql-only  # Print SQL")
+        print("  python scripts/setup_postgis_indexes.py --connection postgresql://...")
+
+
+if __name__ == '__main__':
+    main()