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.

CLAUDE.md

@@ -0,0 +1,57 @@
+# CBPOA — 武汉儿童呼吸疾病风险评估系统
+
+FastAPI + React + PyTorch GCN pipeline. 预测空气质量对儿童健康的空间风险。
+
+## Development
+
+```bash
+# Frontend (pnpm)
+cd frontend && pnpm dev          # localhost:5173 → proxies /api to :8000
+
+# Backend (Python venv)
+cd backend && uvicorn main:app --reload   # localhost:8000
+
+# ML pipeline
+cd scripts && python train_model.py       # PyTorch + MLflow
+```
+
+## Where to Look
+
+| Task | Location |
+|------|----------|
+| API endpoint | `backend/routers/` |
+| Database / PostGIS | `backend/database.py` |
+| UI component | `frontend/src/components/` |
+| Page view | `frontend/src/pages/` |
+| API client / cache | `frontend/src/services/api.ts` |
+| State management | `frontend/src/stores/` |
+| TypeScript types | `frontend/src/types/` |
+| ETL / data processing | `scripts/` |
+| ML model architecture | `models/spatiotemporal_gcn/` |
+| Trained weights | `models/spatiotemporal_gcn/best_model.pt` |
+| Processed features | `processed/` |
+| Raw data sources | `Datas/` |
+| Docker / deploy | `deploy/` |
+
+## Data Sources
+
+| Data | Path | Notes |
+|------|------|-------|
+| 气象+空气 | `Datas/气象+空气/站点_*.csv` | 3yr, 2192 files, ~2.37M rows |
+| 门诊 | `Datas/view_门诊.xlsx` | 107,579 rows |
+| 住院 | `Datas/view_住院.xlsx` | 5,822 rows |
+| DEM高程 | `Datas/DEM/CJJJD_DEM.TIF` | 3.1GB raster |
+| 人口密度 | `Datas/landscan-hd-china-v1-assets/*.tif` | 284MB |
+| 行政边界 | `Datas/武汉市.geojson` | Wuhan boundary |
+
+## ML Pipeline
+
+```
+气象(时间序列) + 站点坐标 + DEM高程 + 人口密度 → SpatialTemporalGCN → 风险预测 [1d, 3d, 7d]
+```
+
+## Agent Workflow
+
+Explore finds → Librarian reads → You plan → Worker implements → Validator checks
+
+Context-specific guidance lives in nested CLAUDE.md files — they load automatically when you work in those directories. Closest CLAUDE.md to the file being edited takes precedence.