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.

backend/utils/geojson.py

@@ -0,0 +1,53 @@
+"""
+GeoJSON file parsing utilities.
+"""
+import json
+from pathlib import Path
+from typing import Any
+
+from config import WUHAN_BOUNDARY_PATH
+from utils.risk import risk_value_to_level
+
+
+def parse_geojson_file(filepath: Path) -> list[dict[str, Any]]:
+    """Parse GeoJSON file and extract grid data with standard fields."""
+    with open(filepath, "r", encoding="utf-8") as f:
+        geojson = json.load(f)
+
+    grids: list[dict[str, Any]] = []
+    for feature in geojson.get("features", []):
+        props = feature.get("properties", {})
+        coords = feature.get("geometry", {}).get("coordinates", [0, 0])
+
+        risk_1d = props.get("risk_1d", 0)
+        grids.append({
+            "grid_id": str(props.get("node_id", "")),
+            "latitude": props.get("lat", coords[1] if len(coords) > 1 else 0),
+            "longitude": props.get("lon", coords[0] if len(coords) > 0 else 0),
+            "risk_value": risk_1d,
+            "risk_3d": props.get("risk_3d", 0),
+            "risk_7d": props.get("risk_7d", 0),
+            "risk_level": risk_value_to_level(risk_1d),
+        })
+
+    return grids
+
+
+def load_districts() -> list[dict[str, Any]]:
+    """Load Wuhan district boundaries from GeoJSON."""
+    if not WUHAN_BOUNDARY_PATH.exists():
+        return []
+
+    with open(WUHAN_BOUNDARY_PATH, "r", encoding="utf-8") as f:
+        geojson = json.load(f)
+
+    districts = []
+    for feature in geojson.get("features", []):
+        props = feature.get("properties", {})
+        districts.append({
+            "name": props.get("name", ""),
+            "adcode": props.get("adcode", ""),
+            "coordinates": feature.get("geometry", {}).get("coordinates", []),
+        })
+
+    return districts