feat(reports): occupancy curve, hour×dow heatmap, stay histogram, fraud KPIs

The dashboard had generic BI views but nothing parking-shaped. Added:

- Occupancy step-area over the range with the configured capacity as a
  red reference line. occupancyStart folds the ENTIRE prior ledger
  (voided entries excluded, clamped ≥0); each series point carries
  occupancyEnd. Answers "when are we near full".
- Entries heatmap hour × day-of-week (7×24, row 0 = Monday, site tz) as
  a pure CSS-grid intensity map — weekday-vs-weekend at a glance, the
  direct evidence for tariff windows. Replaces the flat hour histogram
  (strictly contains it).
- Stay-duration histogram at tariff-shaped edges (30m/1h/2h/4h/8h/24h/
  tail): where ladder/up-to breakpoints should sit.
- Voids + anomalies KPIs (accented when >0) — the look-closer counters
  the signed chain exists for; peak-occupancy KPI (peak / capacity).
- Revenue bars stacked cash vs card (the drawer's money vs the bank's);
  CSV export gains cash, card, occupancy_end columns.

Internals: localParts caches its Intl formatter per tz (was one new
formatter per ledger row); @parking/db re-exports lt/gt. 5 new tests.

Claude-Session: https://claude.ai/code/session_01Xcm6ikLgGoCxxHrxtjkk5V

apps/server/src/reports.test.ts

@@ -181,3 +181,68 @@ describe("reportSummary — duration (sessions cache) + subscriptions", () => {
     expect(r.subscriptions.coveredCars).toBe(2);
   });
 });
+
+describe("reportSummary — occupancy, heatmap, stay histogram, look-closer counters (2026-07-05)", () => {
+  it("folds prior ledger into occupancyStart and walks occupancyEnd through the series", async () => {
+    // Before the range: 3 entries, 1 exit → 2 cars inside when June opens.
+    await entry(at("2026-05-20T08:00:00Z"));
+    await entry(at("2026-05-20T09:00:00Z"));
+    await entry(at("2026-05-21T10:00:00Z"));
+    await exit(at("2026-05-21T12:00:00Z"));
+    // In range: +2 on the 10th, −1 on the 11th.
+    await entry(at("2026-06-10T08:00:00Z"));
+    await entry(at("2026-06-10T09:00:00Z"));
+    await exit(at("2026-06-11T09:00:00Z"));
+
+    const r = reportSummary(db, { ...RANGE, bucket: "day" });
+    expect(r.occupancyStart).toBe(2);
+    expect(r.series.map((p) => [p.bucket, p.occupancyEnd])).toEqual([
+      ["2026-06-10", 4],
+      ["2026-06-11", 3],
+    ]);
+  });
+
+  it("a voided pre-range entry does not inflate occupancyStart", async () => {
+    const id = randomUUID();
+    await log.append({ type: "vehicle_entry", direction: "entry", identity: id, occurredAt: at("2026-05-20T08:00:00Z") });
+    await log.append({ type: "void", identity: id, occurredAt: at("2026-05-20T08:05:00Z"), payload: { reason: "misprint" } });
+    const r = reportSummary(db, { ...RANGE, bucket: "day" });
+    expect(r.occupancyStart).toBe(0);
+  });
+
+  it("entriesByDowHour lands on the local weekday/hour (row 0 = Monday)", async () => {
+    // 2026-06-10 is a WEDNESDAY; 08:00Z = 10:00 in Tirane (UTC+2 in June).
+    await entry(at("2026-06-10T08:00:00Z"));
+    const r = reportSummary(db, { ...RANGE, bucket: "day" });
+    expect(r.entriesByDowHour[2]![10]).toBe(1); // Wed row, 10h column
+    expect(r.entriesByDowHour.flat().reduce((a, b) => a + b, 0)).toBe(1);
+  });
+
+  it("stay histogram buckets closed sessions; series carries the cash/card split", async () => {
+    db.insert(sessions).values({ id: "h1", identity: "h1", enteredAt: at("2026-06-10T08:00:00Z"), exitedAt: at("2026-06-10T08:20:00Z"), state: "closed" }).run(); // 20m → ≤30
+    db.insert(sessions).values({ id: "h2", identity: "h2", enteredAt: at("2026-06-10T08:00:00Z"), exitedAt: at("2026-06-10T09:30:00Z"), state: "closed" }).run(); // 90m → ≤120
+    db.insert(sessions).values({ id: "h3", identity: "h3", enteredAt: at("2026-06-08T08:00:00Z"), exitedAt: at("2026-06-10T09:00:00Z"), state: "closed" }).run(); // 2 days → >24h tail
+    await payment(at("2026-06-10T09:00:00Z"), 500, { tender: "cash" });
+    await payment(at("2026-06-10T09:30:00Z"), 700, { tender: "card" });
+
+    const r = reportSummary(db, { ...RANGE, bucket: "day" });
+    const counts = Object.fromEntries(r.stayHistogram.map((b) => [String(b.uptoMin), b.count]));
+    expect(counts["30"]).toBe(1);
+    expect(counts["120"]).toBe(1);
+    expect(counts["null"]).toBe(1);
+    const day = r.series.find((p) => p.bucket === "2026-06-10")!;
+    expect(day.cashMinor).toBe(500);
+    expect(day.cardMinor).toBe(700);
+  });
+
+  it("counts voids and anomalies in range (the look-closer counters)", async () => {
+    const id = randomUUID();
+    await log.append({ type: "vehicle_entry", direction: "entry", identity: id, occurredAt: at("2026-06-10T08:00:00Z") });
+    await log.append({ type: "void", identity: id, occurredAt: at("2026-06-10T08:05:00Z"), payload: { reason: "misprint" } });
+    await log.append({ type: "anomaly", identity: "X", occurredAt: at("2026-06-10T09:00:00Z"), payload: { reason: "test" } });
+    const r = reportSummary(db, { ...RANGE, bucket: "day" });
+    expect(r.totals.voids).toBe(1);
+    expect(r.totals.anomalies).toBe(1);
+    expect(r.totals.entries).toBe(0); // the voided entry stays excluded
+  });
+});