feat: add analysis pages and raster risk map
Ship a new app version with broader analytics, restructured dashboards, and a server-rendered risk map. Frontend: - Add Overview, Demographic, Disease, and Environmental Health analysis pages - Add AnomalyMarkers, CalendarHeatmap, and MetricHeatmapTable components - Rebuild Alerts map onto server-rendered raster risk tiles; expand Monitoring, Trend, and District Comparison views - Extend API client, stores, and TypeScript types Backend: - Add environment router (pollutants, lag correlations) - Add risk_raster util serving XYZ 100m risk tiles - Expand cases endpoints (demographics, seasonality, diagnoses) and insights; harden auth and file-based loaders Data & tooling: - Add processed outpatient/inpatient/combined case parquet (LFS) - Add nested CLAUDE.md guides, pyrightconfig, and test updates
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@@ -5,11 +5,13 @@ Time series trends, district aggregation, and weather-health correlations
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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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from functools import lru_cache
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import random
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import pandas as pd
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from pydantic import BaseModel, Field
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from config import DATA_DIR, RISK_HIGH
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from config import DATA_DIR, RISK_HIGH, PROJECT_ROOT, WUHAN_BOUNDS, LAT_STEP, LON_STEP
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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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@@ -27,11 +29,13 @@ class TrendResponse(BaseModel):
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class DistrictRisk(BaseModel):
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"""District-level risk aggregation"""
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name: str = Field(..., description="District name")
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district: str = Field(..., description="District name")
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avg_risk: float = Field(..., description="Average risk value")
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avg_aqi: float = Field(..., description="Average AQI from weather stations in this district")
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population: int = Field(..., description="Population (sum of 100m LandScan cells in district)")
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high_risk_count: int = Field(..., description="Count of high risk grids")
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total_grids: int = Field(..., description="Total grids in district")
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total_cases: int = Field(..., description="Estimated total cases")
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total_cases: int = Field(..., description="Total recorded cases (real, from cases_by_district_daily)")
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class DistrictsResponse(BaseModel):
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@@ -91,12 +95,9 @@ async def get_trend(days: int = Query(default=7, ge=1, le=30)):
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values.append(0)
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dates.append(date.strftime("%Y-%m-%d"))
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# Filter out zero values
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valid_data = [(d, v) for d, v in zip(dates, values) if v > 0]
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if valid_data:
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dates, values = zip(*valid_data)
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dates, values = list(dates), list(values)
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# Preserve the full requested date range: a "7天" request must return 7
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# contiguous points. Days with no geojson (or empty grids) stay 0 rather
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# than being dropped, which previously produced fewer, non-contiguous points.
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trend_direction = calculate_trend(values)
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return TrendResponse(
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@@ -106,101 +107,153 @@ async def get_trend(days: int = Query(default=7, ge=1, le=30)):
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)
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@lru_cache(maxsize=1)
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def _grid_district_lookup() -> dict:
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"""Map precomputed r{row}_c{col} grid id -> district name (loaded once)."""
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path = PROJECT_ROOT / "processed" / "grid_district_mapping.parquet"
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if not path.exists():
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return {}
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df = pd.read_parquet(path)
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# Some grids have a null district_name; drop them so the lookup only ever
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# returns valid strings (missing keys fall back to "其他").
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df = df.dropna(subset=["district_name"])
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return dict(zip(df["grid_id"].astype(str), df["district_name"].astype(str)))
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@lru_cache(maxsize=1)
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def _district_population() -> dict:
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"""Real population per district.
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Sums the LandScan-derived population_density of every 100m cell
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(grid_100m_with_dem_pop.parquet) grouped by district via the
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grid->district mapping. Returns {district_name: total_population}.
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"""
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pop_path = PROJECT_ROOT / "processed" / "grid_100m_with_dem_pop.parquet"
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map_path = PROJECT_ROOT / "processed" / "grid_district_mapping.parquet"
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if not pop_path.exists() or not map_path.exists():
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return {}
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pop = pd.read_parquet(pop_path, columns=["grid_id", "population_density"])
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mapping = pd.read_parquet(map_path).dropna(subset=["district_name"])
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joined = pop.merge(mapping, on="grid_id", how="inner")
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by_d = joined.groupby("district_name")["population_density"].sum()
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return {str(k): int(round(v)) for k, v in by_d.items()}
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@lru_cache(maxsize=1)
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def _district_avg_aqi() -> dict:
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"""Real average AQI per district from weather station daily data.
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Each station (with lat/lon) is assigned to a district using the same
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grid->district mapping (100m grid spacing of 1/1110 deg, the convention
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the mapping was built with), then AQI is averaged per district across
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all daily observations. Returns {district_name: avg_aqi}. Districts with
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no station fall back to the city-wide mean in the caller.
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"""
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map_path = PROJECT_ROOT / "processed" / "grid_district_mapping.parquet"
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station_path = PROJECT_ROOT / "processed" / "weather" / "station_daily_2022.parquet"
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if not map_path.exists() or not station_path.exists():
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return {}
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mapping = pd.read_parquet(map_path).dropna(subset=["district_name"])
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lookup = dict(zip(mapping["grid_id"].astype(str), mapping["district_name"].astype(str)))
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station = pd.read_parquet(station_path, columns=["station_id", "lat", "lon", "AQI"])
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step = 1.0 / 1110.0 # mapping grid spacing in degrees
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min_lat = WUHAN_BOUNDS["min_lat"]
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min_lon = WUHAN_BOUNDS["min_lon"]
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coords = station[["station_id", "lat", "lon"]].drop_duplicates()
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station_to_district = {}
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for _, r in coords.iterrows():
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row = int((r["lat"] - min_lat) / step)
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col = int((r["lon"] - min_lon) / step)
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station_to_district[r["station_id"]] = lookup.get(f"r{row}_c{col}", "其他")
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station = station.copy()
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station["district"] = station["station_id"].map(station_to_district)
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in_district = station[station["district"] != "其他"]
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by_d = in_district.groupby("district")["AQI"].mean()
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return {str(k): round(float(v), 1) for k, v in by_d.items()}
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@lru_cache(maxsize=1)
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def _district_total_cases() -> dict:
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"""Real total recorded cases per district from cases_by_district_daily.
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District labels in the case file are inconsistent ("武昌" vs "武昌区"),
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so names are normalized by stripping the "区" suffix and summed, then
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keyed by the canonical mapping name (with "区"). Returns {district: cases}.
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"""
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path = PROJECT_ROOT / "processed" / "cases_by_district_daily.parquet"
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if not path.exists():
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return {}
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df = pd.read_parquet(path, columns=["district", "total_cases"])
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df = df.copy()
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df["base"] = df["district"].str.replace("区", "", regex=False)
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by_base = df.groupby("base")["total_cases"].sum()
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return {f"{base}区": int(v) for base, v in by_base.items()}
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@lru_cache(maxsize=8)
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def _aggregate_districts(date: str) -> list:
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"""Aggregate per-district risk for a date.
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Assigns each 100m risk grid to a district via the precomputed
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grid->district mapping (O(1) dict lookup per grid) instead of per-grid
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point-in-polygon (which is ~100x slower over 140k grids). Cached by date.
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"""
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grids = parse_geojson_file(DATA_DIR / f"risk_{date}.geojson")
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lookup = _grid_district_lookup()
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agg: dict = {}
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for g in grids:
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row = int((g["latitude"] - WUHAN_BOUNDS["min_lat"]) / LAT_STEP)
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col = int((g["longitude"] - WUHAN_BOUNDS["min_lon"]) / LON_STEP)
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name = lookup.get(f"r{row}_c{col}", "其他")
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a = agg.setdefault(name, {"sum": 0.0, "count": 0, "high": 0})
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risk = g["risk_value"]
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a["sum"] += risk
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a["count"] += 1
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if risk >= RISK_HIGH:
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a["high"] += 1
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pop_by_district = _district_population()
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aqi_by_district = _district_avg_aqi()
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cases_by_district = _district_total_cases()
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# City-wide mean AQI as fallback for districts without a weather station.
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city_avg_aqi = round(sum(aqi_by_district.values()) / len(aqi_by_district), 1) if aqi_by_district else 0.0
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result = []
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for name, a in agg.items():
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if a["count"] == 0:
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continue
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avg = a["sum"] / a["count"]
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result.append({
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"district": name,
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"avg_risk": round(avg, 4),
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"avg_aqi": aqi_by_district.get(name, city_avg_aqi),
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"population": pop_by_district.get(name, 0),
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"high_risk_count": a["high"],
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"total_grids": a["count"],
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"total_cases": cases_by_district.get(name, 0),
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})
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# '其他' (unassigned) last, otherwise by descending risk
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result.sort(key=lambda d: (d["district"] == "其他", -d["avg_risk"]))
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return result
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@router.get("/districts", response_model=DistrictsResponse)
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async def get_districts():
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"""
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Get district-level risk aggregation
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Returns:
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District-level risk data with averages and counts
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"""
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"""Get district-level risk aggregation (cached per date)."""
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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 districts:
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# Fallback: return city-wide aggregation
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avg_risk = sum(g["risk_value"] for g in grids) / len(grids) if grids else 0
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high_risk_count = sum(1 for g in grids if g["risk_value"] >= RISK_HIGH)
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return DistrictsResponse(
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districts=[
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DistrictRisk(
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name="武汉市",
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avg_risk=round(avg_risk, 4),
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high_risk_count=high_risk_count,
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total_grids=len(grids),
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total_cases=int(len(grids) * avg_risk * 0.1) # Mock case rate
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)
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],
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timestamp=datetime.now().isoformat()
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)
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# Aggregate grids by district using point-in-polygon
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district_data = {d["name"]: {"grids": [], "high_risk": 0} for d in districts}
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unassigned = {"grids": [], "high_risk": 0}
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for grid in grids:
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assigned = False
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for district in districts:
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if point_in_polygon(grid["latitude"], grid["longitude"], district["coordinates"]):
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district_data[district["name"]]["grids"].append(grid)
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if grid["risk_value"] >= RISK_HIGH:
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district_data[district["name"]]["high_risk"] += 1
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assigned = True
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break
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if not assigned:
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unassigned["grids"].append(grid)
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if grid["risk_value"] >= RISK_HIGH:
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unassigned["high_risk"] += 1
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# Build response
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result = []
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for district in districts:
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name = district["name"]
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grids_in_district = district_data[name]["grids"]
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if not grids_in_district:
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continue
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avg_risk = sum(g["risk_value"] for g in grids_in_district) / len(grids_in_district)
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high_risk_count = district_data[name]["high_risk"]
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# Mock total cases based on risk and grid count
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total_cases = int(len(grids_in_district) * avg_risk * 0.1)
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result.append(
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DistrictRisk(
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name=name,
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avg_risk=round(avg_risk, 4),
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high_risk_count=high_risk_count,
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total_grids=len(grids_in_district),
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total_cases=total_cases
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)
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)
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# Add unassigned as "其他" if significant
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if unassigned["grids"]:
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avg_risk = sum(g["risk_value"] for g in unassigned["grids"]) / len(unassigned["grids"])
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result.append(
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DistrictRisk(
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name="其他",
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avg_risk=round(avg_risk, 4),
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high_risk_count=unassigned["high_risk"],
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total_grids=len(unassigned["grids"]),
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total_cases=int(len(unassigned["grids"]) * avg_risk * 0.1)
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)
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)
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districts = [DistrictRisk(**d) for d in _aggregate_districts(latest_date)]
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return DistrictsResponse(
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districts=result,
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timestamp=datetime.now().isoformat()
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districts=districts,
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timestamp=datetime.now().isoformat(),
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)
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