feat: Phase 2 — leadership 大屏 (/overview) + district normalization + drawer a11y

Phase 2 of the UX modernization. Three conflict-free workstreams.

Leadership 驾驶舱 (/overview):
- Wuhan 13-district Leaflet choropleth (public/wuhan_districts.geojson, keyed
  on name, darker=higher per 高风险高亮), legend, hover/click-zoom
- 全部/门诊/住院 Segmented toggle drives choropleth + Top-5 district bar
- literal "数据截至2023-12" as-of badge (D3 honesty); raw spinner → LoadingState
- decompose OverviewDashboard 501→273; 6 components + 2 helpers under components/overview/

District normalization (backend data boundary):
- case_loader.normalize_district + load_cases_by_district_daily collapse the
  26 dirty labels (武昌/武昌区…) → 13 canonical; analysis/grid/insights repointed
  (fixes a grid-merge row-drop bug as a bonus); in-memory, schema unchanged

Shell a11y (code-review carryover):
- drawer is now a proper modal: ESC, body scroll-lock, focus-in + focus-trap
  cycle + focus-restore, role=dialog/aria-modal/aria-label, hamburger aria-expanded
- SideNav expanded state lifted to AppShell so rail+drawer stay in sync
- RouteErrorBoundary around <Outlet/> keeps shell chrome on page/chunk failure

Gates: tsc 0 · vitest 64 · e2e 19/19 (17 user-flows + 2 overview) · build ok
· backend pytest 6 new + 48 regression green

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

backend/routers/analysis.py

@@ -12,6 +12,7 @@ import pandas as pd
 from pydantic import BaseModel, Field
 
 from config import DATA_DIR, RISK_HIGH, PROJECT_ROOT, WUHAN_BOUNDS, LAT_STEP, LON_STEP
+from data.case_loader import load_cases_by_district_daily
 from utils.date_helpers import get_latest_date
 from utils.geojson import parse_geojson_file, load_districts
 from utils.geo import point_in_polygon
@@ -179,18 +180,16 @@ def _district_avg_aqi() -> dict:
 def _district_total_cases() -> dict:
     """Real total recorded cases per district from cases_by_district_daily.
 
-    District labels in the case file are inconsistent ("武昌" vs "武昌区"),
-    so names are normalized by stripping the "区" suffix and summed, then
-    keyed by the canonical mapping name (with "区"). Returns {district: cases}.
+    District labels are normalized to the canonical 13 区-suffixed names at the
+    data-access boundary (data.case_loader), so this is a plain per-district
+    sum. Returns {district: cases}.
     """
-    path = PROJECT_ROOT / "processed" / "cases_by_district_daily.parquet"
-    if not path.exists():
+    try:
+        df = load_cases_by_district_daily()
+    except FileNotFoundError:
         return {}
-    df = pd.read_parquet(path, columns=["district", "total_cases"])
-    df = df.copy()
-    df["base"] = df["district"].str.replace("区", "", regex=False)
-    by_base = df.groupby("base")["total_cases"].sum()
-    return {f"{base}区": int(v) for base, v in by_base.items()}
+    by_district = df.groupby("district")["total_cases"].sum()
+    return {str(d): int(v) for d, v in by_district.items()}
 
 
 @lru_cache(maxsize=8)

backend/routers/grid.py

@@ -21,6 +21,7 @@ from models import (
     MultiDayPredictionRequest,
     MultiDayPredictionResponse,
 )
+from data.case_loader import load_cases_by_district_daily
 
 router = APIRouter(prefix="/api", tags=["grid"])
 
@@ -43,14 +44,13 @@ def _compute_historical_aggregation(
 ) -> HistoricalAggregationResponse:
     """Run the full pandas aggregation pipeline (called in thread pool)."""
     try:
-        cases_df = _load_parquet(PROJECT_ROOT / "processed" / "cases_by_district_daily.parquet")
+        cases_df = load_cases_by_district_daily()
     except FileNotFoundError:
         return HistoricalAggregationResponse(
             aggregations=[], total_records=0,
             date_range=(start.strftime("%Y-%m-%d"), end.strftime("%Y-%m-%d")),
             timestamp=datetime.now().isoformat(),
         )
-    cases_df = cases_df.copy()
     cases_df['date'] = pd.to_datetime(cases_df['date'])
 
     filtered_cases = cases_df[
@@ -196,7 +196,7 @@ def _grids_geojson_body(date: str, district: Optional[str], risk_level: Optional
     if district:
         merged = merged[merged['district_name'].str.contains(district.replace('区', ''), na=False, regex=False)]
 
-    cases_df = _load_parquet(PROJECT_ROOT / "processed" / "cases_by_district_daily.parquet").copy()
+    cases_df = load_cases_by_district_daily()
     cases_df['date'] = pd.to_datetime(cases_df['date']).dt.strftime('%Y-%m-%d')
     cases_df = cases_df[cases_df['date'] == date]
 
@@ -364,7 +364,7 @@ def _compute_grid_history(grid_id: str, days: int) -> dict:
 
     district = grid_info.iloc[0]['district_name']
 
-    cases_df = _load_parquet(PROJECT_ROOT / "processed" / "cases_by_district_daily.parquet")
+    cases_df = load_cases_by_district_daily()
     cases_df['date'] = pd.to_datetime(cases_df['date'])
 
     end_date = datetime.now()

backend/routers/insights.py

@@ -11,6 +11,7 @@ from pydantic import BaseModel, Field
 from typing import Dict, List, Literal
 
 from config import DATA_DIR, RISK_HIGH, PROJECT_ROOT
+from data.case_loader import load_cases_by_district_daily
 from models import (
     InsightsResponse,
     InsightTrend,
@@ -491,22 +492,22 @@ async def get_insights_cards():
 
         cases_path = PROJECT_ROOT / "processed" / "cases_by_district_daily.parquet"
         if cases_path.exists():
-            cases_df = _cached_parquet(str(cases_path))
+            # Districts already normalized to the canonical 13 区-suffixed names.
+            cases_df = load_cases_by_district_daily()
+            cases_df["date"] = pd.to_datetime(cases_df["date"])
             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", "")
-            district_daily = latest_cases.groupby("base_district")["total_cases"].sum().sort_values(ascending=False)
+            latest_cases = cases_df[cases_df["date"] == latest_case_date]
+            district_daily = latest_cases.groupby("district")["total_cases"].sum().sort_values(ascending=False)
             total_daily = int(district_daily.sum())
             top_name = district_daily.index[0]
             top_val = int(district_daily.iloc[0])
             num_districts = len(district_daily)
 
             week_ago = latest_case_date - pd.Timedelta(days=6)
-            week_cases = cases_df[cases_df["date"] >= week_ago].copy()
-            week_cases["base_district"] = week_cases["district"].str.replace("区", "")
+            week_cases = cases_df[cases_df["date"] >= week_ago]
             daily_totals = week_cases.groupby("date")["total_cases"].sum()
             avg_daily = int(daily_totals.mean())
-            week_district = week_cases.groupby("base_district")["total_cases"].sum().sort_values(ascending=False)
+            week_district = week_cases.groupby("district")["total_cases"].sum().sort_values(ascending=False)
             week_top_val = int(week_district.iloc[0])
 
             date_str = latest_case_date.strftime("%m月%d日")
@@ -515,8 +516,8 @@ async def get_insights_cards():
                 title=f"日病例统计 ({date_str})",
                 description=(
                     f"最近统计日({date_str})全市{num_districts}个区共记录{total_daily}例儿童呼吸道疾病病例，"
-                    f"{top_name}区{top_val}例为当日最高。近7日日均{avg_daily}例，"
-                    f"{week_district.index[0]}区累计{week_top_val}例居首。"
+                    f"{top_name}{top_val}例为当日最高。近7日日均{avg_daily}例，"
+                    f"{week_district.index[0]}累计{week_top_val}例居首。"
                 ),
                 type="warning",
                 metric="日病例",