Akiba So
fc468464b2
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.
54 lines
1.7 KiB
Python
54 lines
1.7 KiB
Python
"""
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GeoJSON file parsing utilities.
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"""
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import json
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from pathlib import Path
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from typing import Any
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from config import WUHAN_BOUNDARY_PATH
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from utils.risk import risk_value_to_level
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def parse_geojson_file(filepath: Path) -> list[dict[str, Any]]:
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"""Parse GeoJSON file and extract grid data with standard fields."""
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with open(filepath, "r", encoding="utf-8") as f:
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geojson = json.load(f)
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grids: list[dict[str, Any]] = []
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for feature in geojson.get("features", []):
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props = feature.get("properties", {})
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coords = feature.get("geometry", {}).get("coordinates", [0, 0])
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risk_1d = props.get("risk_1d", 0)
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grids.append({
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"grid_id": str(props.get("node_id", "")),
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"latitude": props.get("lat", coords[1] if len(coords) > 1 else 0),
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"longitude": props.get("lon", coords[0] if len(coords) > 0 else 0),
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"risk_value": risk_1d,
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"risk_3d": props.get("risk_3d", 0),
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"risk_7d": props.get("risk_7d", 0),
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"risk_level": risk_value_to_level(risk_1d),
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})
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return grids
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def load_districts() -> list[dict[str, Any]]:
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"""Load Wuhan district boundaries from GeoJSON."""
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if not WUHAN_BOUNDARY_PATH.exists():
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return []
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with open(WUHAN_BOUNDARY_PATH, "r", encoding="utf-8") as f:
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geojson = json.load(f)
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districts = []
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for feature in geojson.get("features", []):
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props = feature.get("properties", {})
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districts.append({
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"name": props.get("name", ""),
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"adcode": props.get("adcode", ""),
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"coordinates": feature.get("geometry", {}).get("coordinates", []),
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})
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return districts
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