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

backend/routers/insights.py

@@ -4,6 +4,7 @@ Provides comprehensive analytics, trends, hotspots, and correlations
 """
 from fastapi import APIRouter, HTTPException, Query
 from datetime import datetime, timedelta
+from functools import lru_cache
 import random
 
 from pydantic import BaseModel, Field
@@ -20,6 +21,13 @@ from models import (
 )
 from utils.date_helpers import get_latest_date
 from utils.geojson import parse_geojson_file, load_districts
+
+
+@lru_cache(maxsize=8)
+def _cached_parquet(path_str: str):
+    """Load a parquet file once and reuse it (read-only) across requests."""
+    import pandas as pd
+    return pd.read_parquet(path_str)
 from utils.geo import point_in_polygon
 from utils.risk import calculate_trend as calculate_trend_direction
 
@@ -41,6 +49,7 @@ class InsightCardResponse(BaseModel):
     warning_count: int
     info_count: int
     success_count: int
+    danger_count: int
     cards: list[InsightCardItem]
 
 
@@ -482,7 +491,7 @@ async def get_insights_cards():
 
         cases_path = PROJECT_ROOT / "processed" / "cases_by_district_daily.parquet"
         if cases_path.exists():
-            cases_df = pd.read_parquet(cases_path)
+            cases_df = _cached_parquet(str(cases_path))
             latest_case_date = cases_df["date"].max()
             latest_cases = cases_df[cases_df["date"] == latest_case_date].copy()
             latest_cases["base_district"] = latest_cases["district"].str.replace("区", "")
@@ -533,7 +542,7 @@ async def get_insights_cards():
             grids_df["col"] = ((grids_df["longitude"] - MIN_LON) / STEP).astype(int)
             grids_df["grid_id"] = "r" + grids_df["row"].astype(str) + "_c" + grids_df["col"].astype(str)
 
-            mapping = pd.read_parquet(mapping_path)
+            mapping = _cached_parquet(str(mapping_path))
             merged = grids_df.merge(mapping, on="grid_id", how="inner")
 
             if len(merged) > 0:
@@ -574,7 +583,7 @@ async def get_insights_cards():
 
         weather_path = PROJECT_ROOT / "processed" / "weather" / "station_daily_2022.parquet"
         if weather_path.exists():
-            weather_df = pd.read_parquet(weather_path)
+            weather_df = _cached_parquet(str(weather_path))
             daily_wx = weather_df.groupby("date").agg(
                 AQI=("AQI", "mean"), PM25=("PM25", "mean"), PM10=("PM10", "mean"),
             ).reset_index()
@@ -664,11 +673,13 @@ async def get_insights_cards():
     warning_count = sum(1 for c in cards if c.type == "warning")
     info_count = sum(1 for c in cards if c.type == "info")
     success_count = sum(1 for c in cards if c.type == "success")
+    danger_count = sum(1 for c in cards if c.type == "danger")
 
     return InsightCardResponse(
         total_insights=len(cards),
         warning_count=warning_count,
         info_count=info_count,
         success_count=success_count,
+        danger_count=danger_count,
         cards=cards,
     )