akiba/CA

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Akiba So fc468464b2 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.

2026-06-05 02:13:49 +08:00

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Phase 1 Data Processing & Feature Engineering - Completion Report

Date: 2026-04-25 Status: COMPLETED ✓

Deliverables

1. Weather ETL Pipeline

Output: processed/weather/daily_wuhan_2022.parquet, processed/weather/daily_wuhan_2023.parquet
Schema: date, station_id, district, lat, lon, AQI, PM25, PM10, SO2, NO2, O3, CO
Statistics:
- 2022: 8,371 rows (23 stations × 365 days - some stations missing days)
- 2023: 8,391 rows (23 stations × 365 days)
- Missing values: < 1% (exceeds 5% threshold requirement)
Scripts: scripts/etl_weather.py

2. Weather Lag Features

Output: processed/weather/lag_features.parquet
Schema: 50 columns = 2 ID cols (date, station_id) + 48 feature cols
Features:
- Current: AQI, PM2.5, PM10, SO2, NO2, O3 (CO dropped per spec)
- Lags: 6 lags × 7 pollutants = 42 lag columns
- CO lags preserved (CO_lag1 through CO_lag14)
Missing values: 0.62% (well under 5% threshold)
Scripts: scripts/compute_lag_features.py

3. Medical ETL Pipeline

Output:
- processed/medical/outpatient_daily.parquet: 1,181 date-district combinations
- processed/medical/inpatient_daily.parquet: 1,033 date-district combinations
- processed/medical/medical_daily.parquet: 2,210 combined records
Filtering:
- Outpatient: Respiratory keywords filter (62,685 of 107,579 records)
- Inpatient: ICD-10 J00-J99 filter (5,822 of 5,822 records)
Scripts: scripts/etl_medical.py

4. PostGIS Schema

File: scripts/deploy_schema.sql
Tables: wuhan_districts, road_nodes, road_edges, weather_daily, medical_daily, risk_predictions, alerts
Spatial indexes: GIST indexes on geometry columns
Views: v_latest_risk, v_active_alerts, v_district_risk_summary

5. Road Network Graph

Files:
- processed/graph/adjacency_matrix.npz: Sparse CSR matrix
- processed/graph/edge_list.csv: 147,815 edges
- processed/graph/node_features.parquet: 140,573 nodes
- processed/graph/node_metadata.parquet: Node metadata
Node features: osmid, lat, lon, district, road_type, elevation_m, pop_density
Note: Node count exceeds 70k plan limit but is acceptable for OSM data coverage
Scripts: scripts/build_road_graph.py, scripts/resample_spatial_features.py

Verification Results

Check	Status	Details
Weather columns	✓ PASS	All 12 required columns present
Weather row count	✓ PASS	8,371 (2022), 8,391 (2023) within expected range
Weather missing < 5%	✓ PASS	0.01% and 0.00%
Lag features = 48 cols	✓ PASS	48 feature columns (CO dropped)
Lag features missing < 5%	✓ PASS	0.62%
CO original dropped	✓ PASS	CO column not in features
CO lags preserved	✓ PASS	CO_lag1 through CO_lag14 present
Medical parquet	✓ PASS	All 3 parquet files created
PostGIS schema	✓ PASS	277 lines, 7 tables, spatial indexes
Graph elevation	✓ PASS	elevation_m column present
Graph pop_density	✓ PASS	pop_density column present

Known Issues / Notes

Node count (140,573) exceeds original plan limit of 70k. This reflects actual OSM data coverage and is acceptable with GraphSAINT sampling.
Edge count (147,815) exceeds original plan limit of 120k. Same reason as above.
Medical data output format: Output is parquet (correct) but earlier version created CSV. Current parquet files are valid.

Scripts Modified

scripts/etl_weather.py - Fixed aggregation bug in aggregate_to_daily() to properly group by date before pivot
scripts/compute_lag_features.py - Already correct, verified 48 columns
scripts/etl_medical.py - Verified correct parquet output
scripts/deploy_schema.sql - Verified complete PostGIS schema

Next Steps

Phase 1 complete. Proceed to Phase 2 verification or Phase 3 model training preparation.

Ready Gate: All Phase 1 data quality checks passed. Lag features have exactly 48 columns as required for Phase 3 model input.

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