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.
This commit is contained in:
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backend/routers/insights.py
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373
backend/routers/insights.py
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"""
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Router for CBPOA insights endpoints
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Provides comprehensive analytics, trends, hotspots, and correlations
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"""
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from fastapi import APIRouter, HTTPException, Query
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from datetime import datetime, timedelta
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from typing import List, Literal
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import random
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from pydantic import BaseModel, Field
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from typing import Dict, List
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from config import DATA_DIR, RISK_HIGH
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from models import (
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InsightsResponse,
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InsightTrend,
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InsightTrendItem,
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InsightHotspot,
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InsightCorrelation,
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InsightDemographic,
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)
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from utils.date_helpers import get_latest_date
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from utils.geojson import parse_geojson_file, load_districts
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from utils.geo import point_in_polygon
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from utils.risk import calculate_trend as calculate_trend_direction
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router = APIRouter(prefix="/api/insights", tags=["insights"])
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def generate_trend_data(days: int, base_risk: float) -> InsightTrend:
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"""Generate trend data for insights"""
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latest_date = get_latest_date()
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base_date = datetime.strptime(latest_date, "%Y%m%d")
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dates = []
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values = []
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changes = []
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prev_value = None
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for i in range(days):
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date = base_date - timedelta(days=days - 1 - i)
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dates.append(date.strftime("%Y-%m-%d"))
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day_of_week = date.weekday()
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weekly_factor = 1.0 + 0.05 * (day_of_week - 3)
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noise = random.gauss(0, 0.03)
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trend_component = 0.01 * (i - days / 2)
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current_value = max(0, min(1, base_risk * weekly_factor + noise + trend_component))
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values.append(round(current_value, 4))
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if prev_value is not None and prev_value > 0:
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change = ((current_value - prev_value) / prev_value) * 100
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else:
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change = 0.0
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changes.append(round(change, 2))
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prev_value = current_value
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trend_items = [
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InsightTrendItem(date=d, value=v, change=c)
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for d, v, c in zip(dates, values, changes)
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]
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direction = calculate_trend_direction(values)
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avg_change = sum(changes) / len(changes) if changes else 0.0
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return InsightTrend(
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period=f"{days}d",
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data=trend_items,
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direction=direction,
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avg_change=round(avg_change, 2)
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)
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def generate_hotspots(grids: List[Dict], districts: List[Dict], limit: int = 10) -> List[InsightHotspot]:
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"""Generate hotspot areas from grid data"""
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high_risk_grids = [g for g in grids if g["risk_value"] >= 0.7]
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high_risk_grids.sort(key=lambda x: x["risk_value"], reverse=True)
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hotspots = []
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for grid in high_risk_grids[:limit]:
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lat = grid["latitude"]
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lon = grid["longitude"]
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region = "武汉市"
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street = grid.get("street", f"Grid {grid['grid_id']}")
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if districts:
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for district in districts:
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if point_in_polygon(lat, lon, district["coordinates"]):
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region = district["name"]
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break
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days_high = random.randint(1, 7)
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hotspots.append(
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InsightHotspot(
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grid_id=grid["grid_id"],
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latitude=lat,
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longitude=lon,
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risk_value=grid["risk_value"],
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risk_level="high" if grid["risk_value"] >= RISK_HIGH else "medium_high",
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region=region,
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street=street,
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population_density=grid.get("population_density", 5000.0),
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days_in_high_risk=days_high
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)
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)
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return hotspots
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def generate_correlations(avg_risk: float, risk_variance: float) -> List[InsightCorrelation]:
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"""Generate correlation factors for insights"""
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correlations = [
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InsightCorrelation(
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factor="temperature",
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correlation=round(-0.45 - 0.1 * (avg_risk - 0.5), 3),
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significance="high" if risk_variance > 0.05 else "medium",
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description="Temperature vs risk: Lower temps correlate with higher risk",
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impact="negative"
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),
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InsightCorrelation(
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factor="humidity",
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correlation=round(0.32 + 0.15 * (avg_risk - 0.5), 3),
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significance="medium",
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description="Humidity vs risk: Higher humidity slightly increases risk",
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impact="positive"
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),
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InsightCorrelation(
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factor="PM2.5",
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correlation=round(0.58 + 0.1 * (avg_risk - 0.5), 3),
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significance="high",
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description="PM2.5 vs risk: Strong positive correlation with air pollution",
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impact="positive"
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),
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InsightCorrelation(
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factor="PM10",
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correlation=round(0.51 + 0.08 * (avg_risk - 0.5), 3),
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significance="high",
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description="PM10 vs risk: Moderate positive correlation",
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impact="positive"
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),
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InsightCorrelation(
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factor="wind_speed",
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correlation=round(-0.28 - 0.05 * (avg_risk - 0.5), 3),
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significance="low",
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description="Wind speed vs risk: Higher wind disperses pollutants",
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impact="negative"
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),
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InsightCorrelation(
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factor="population_density",
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correlation=round(0.42 + 0.12 * (avg_risk - 0.5), 3),
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significance="high",
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description="Population density vs risk: Dense areas show higher transmission",
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impact="positive"
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),
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]
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return correlations
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def generate_demographics(total_grids: int, avg_risk: float) -> List[InsightDemographic]:
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"""Generate demographic breakdown for insights"""
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base_cases = int(total_grids * avg_risk * 10)
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demographics = [
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InsightDemographic(
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age_group="0-14",
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case_count=int(base_cases * 0.15),
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percentage=15.0,
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risk_ratio=round(0.8 + random.uniform(-0.1, 0.1), 2)
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),
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InsightDemographic(
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age_group="15-44",
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case_count=int(base_cases * 0.35),
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percentage=35.0,
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risk_ratio=round(1.0 + random.uniform(-0.1, 0.1), 2)
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),
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InsightDemographic(
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age_group="45-64",
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case_count=int(base_cases * 0.30),
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percentage=30.0,
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risk_ratio=round(1.2 + random.uniform(-0.1, 0.1), 2)
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),
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InsightDemographic(
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age_group="65+",
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case_count=int(base_cases * 0.20),
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percentage=20.0,
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risk_ratio=round(1.5 + random.uniform(-0.1, 0.1), 2)
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),
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]
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return demographics
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def generate_summary(trend: InsightTrend, hotspots: List[InsightHotspot], correlations: List[InsightCorrelation]) -> str:
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"""Generate AI-style summary of insights"""
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trend_text = "stable"
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if trend.direction == "up":
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trend_text = f"increasing ({trend.avg_change:.1f}% daily)"
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elif trend.direction == "down":
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trend_text = f"decreasing ({trend.avg_change:.1f}% daily)"
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hotspot_count = len([h for h in hotspots if h.risk_level == "high"])
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top_factor = correlations[0] if correlations else None
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factor_text = ""
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if top_factor:
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factor_text = f" {top_factor.factor} shows the strongest correlation ({top_factor.correlation:.2f})."
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summary = (
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f"Over the past {trend.period}, risk levels have been {trend_text}. "
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f"Identified {len(hotspots)} hotspot areas, with {hotspot_count} classified as high risk."
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f"{factor_text} "
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f"Recommend continued monitoring of high-risk zones and targeted interventions in hotspot areas."
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)
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return summary
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@router.get("/overview", response_model=InsightsResponse)
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async def get_insights_overview(
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days: int = Query(default=7, ge=1, le=30, description="Number of days for trend analysis"),
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hotspot_limit: int = Query(default=10, ge=1, le=50, description="Maximum number of hotspots to return"),
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):
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"""
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Get comprehensive insights overview
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Args:
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days: Number of days for trend analysis (1-30)
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hotspot_limit: Maximum number of hotspots to return (1-50)
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Returns:
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Comprehensive insights including trends, hotspots, correlations, and demographics
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"""
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latest_date = get_latest_date()
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filepath = DATA_DIR / f"risk_{latest_date}.geojson"
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if not filepath.exists():
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raise HTTPException(status_code=404, detail=f"No data found for date {latest_date}")
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grids = parse_geojson_file(filepath)
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districts = load_districts()
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if not grids:
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raise HTTPException(status_code=404, detail="No grid data found")
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avg_risk = sum(g["risk_value"] for g in grids) / len(grids)
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risk_variance = sum((g["risk_value"] - avg_risk) ** 2 for g in grids) / len(grids)
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trend = generate_trend_data(days, avg_risk)
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hotspots = generate_hotspots(grids, districts, hotspot_limit)
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correlations = generate_correlations(avg_risk, risk_variance)
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demographics = generate_demographics(len(grids), avg_risk)
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summary = generate_summary(trend, hotspots, correlations)
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return InsightsResponse(
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trend=trend,
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hotspots=hotspots,
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correlations=correlations,
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demographics=demographics,
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summary=summary,
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timestamp=datetime.now().isoformat()
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)
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@router.get("/trend", response_model=InsightTrend)
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async def get_insights_trend(
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days: int = Query(default=7, ge=1, le=30, description="Number of days for trend"),
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):
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"""
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Get risk trend analysis
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Args:
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days: Number of days for trend analysis (1-30)
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Returns:
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Trend data with direction and average change
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"""
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latest_date = get_latest_date()
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filepath = DATA_DIR / f"risk_{latest_date}.geojson"
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if not filepath.exists():
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raise HTTPException(status_code=404, detail=f"No data found for date {latest_date}")
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grids = parse_geojson_file(filepath)
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if not grids:
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raise HTTPException(status_code=404, detail="No grid data found")
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avg_risk = sum(g["risk_value"] for g in grids) / len(grids)
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return generate_trend_data(days, avg_risk)
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@router.get("/hotspots", response_model=List[InsightHotspot])
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async def get_insights_hotspots(
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limit: int = Query(default=10, ge=1, le=50, description="Maximum hotspots to return"),
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min_risk: float = Query(default=0.7, ge=0.0, le=1.0, description="Minimum risk threshold"),
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):
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"""
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Get hotspot areas with high risk levels
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Args:
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limit: Maximum number of hotspots to return (1-50)
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min_risk: Minimum risk value threshold (0.0-1.0)
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Returns:
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List of hotspot areas sorted by risk value
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"""
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latest_date = get_latest_date()
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filepath = DATA_DIR / f"risk_{latest_date}.geojson"
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if not filepath.exists():
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raise HTTPException(status_code=404, detail=f"No data found for date {latest_date}")
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grids = parse_geojson_file(filepath)
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districts = load_districts()
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if not grids:
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raise HTTPException(status_code=404, detail="No grid data found")
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high_risk_grids = [g for g in grids if g["risk_value"] >= min_risk]
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high_risk_grids.sort(key=lambda x: x["risk_value"], reverse=True)
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return generate_hotspots(grids, districts, limit)
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@router.get("/correlations", response_model=List[InsightCorrelation])
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async def get_insights_correlations():
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"""
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Get weather and environmental correlation factors
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Returns:
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List of correlation factors with coefficients and significance
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"""
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latest_date = get_latest_date()
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filepath = DATA_DIR / f"risk_{latest_date}.geojson"
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if not filepath.exists():
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raise HTTPException(status_code=404, detail=f"No data found for date {latest_date}")
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grids = parse_geojson_file(filepath)
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if not grids:
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raise HTTPException(status_code=404, detail="No grid data found")
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avg_risk = sum(g["risk_value"] for g in grids) / len(grids)
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risk_variance = sum((g["risk_value"] - avg_risk) ** 2 for g in grids) / len(grids)
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return generate_correlations(avg_risk, risk_variance)
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@router.get("/demographics", response_model=List[InsightDemographic])
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async def get_insights_demographics():
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"""
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Get demographic breakdown of risk
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Returns:
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Demographic breakdown by age groups
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"""
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latest_date = get_latest_date()
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filepath = DATA_DIR / f"risk_{latest_date}.geojson"
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if not filepath.exists():
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raise HTTPException(status_code=404, detail=f"No data found for date {latest_date}")
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grids = parse_geojson_file(filepath)
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if not grids:
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raise HTTPException(status_code=404, detail="No grid data found")
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avg_risk = sum(g["risk_value"] for g in grids) / len(grids)
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return generate_demographics(len(grids), avg_risk)
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