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/geocoded.py

@@ -17,7 +17,7 @@ PROJECT_ROOT = Path(__file__).parent.parent.parent
 DATA_DIR = PROJECT_ROOT / "outputs"
 
 
-@lru_cache(maxsize=1)
+@lru_cache(maxsize=4)
 def _load_csv(path: Path) -> pd.DataFrame:
     return pd.read_csv(path)
 
@@ -109,19 +109,21 @@ async def get_grid_cases():
 async def get_geocoded_cases(
     limit: int = 1000,
     district: Optional[str] = None,
+    date: Optional[str] = Query(None, description="Filter by date (YYYY-MM-DD)"),
 ):
     """
     Get individual geocoded case data.
-    
+
     Args:
         limit: Maximum number of cases to return (for performance)
         district: Filter by district name
+        date: Filter by specific date
     """
     cases_file = DATA_DIR / "geocoded_all_cases.csv"
-    
+
     if not cases_file.exists():
         raise HTTPException(status_code=404, detail="Geocoded data not found")
-    
+
     try:
         df = _load_csv(cases_file)
 
@@ -132,6 +134,11 @@ async def get_geocoded_cases(
         swapped = df['latitude'] > 50  # longitude values are >113
         df.loc[swapped, ['latitude', 'longitude']] = df.loc[swapped, ['longitude', 'latitude']].values
 
+        # Filter by date if specified
+        if date and 'date' in df.columns:
+            df['date_str'] = pd.to_datetime(df['date']).dt.strftime('%Y-%m-%d')
+            df = df[df['date_str'] == date]
+
         # Filter by district if specified
         if district:
             df = df[df['district'] == district]