added itb bti comparison.

6-statistics-fusion-order.py

@@ -0,0 +1,252 @@
+"""Step 6: Paired ItB-vs-BtI significance test across the full sample.
+
+Inputs (``data/``, ``{year}`` = ``--evaluation-year``):
+
+- ``metrics/{year}/{site}/metrics.json`` — per-site validation metrics (Step 5)
+
+Outputs (``data/statistics_fusion_order/``):
+
+- ``{year}.json`` — paired Wilcoxon + t-test summary for NSE, RMSE, nRMSE, r
+
+CLI:
+
+- ``--evaluation-year`` (default 2025)
+- ``--alpha`` (default 0.05) — significance threshold for ``better_order``
+
+This step aggregates across all sites with Step 5 output; it does not accept
+``--site`` (a single-site filter would not support a sample-level test).
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+from scipy.stats import ttest_rel, wilcoxon
+
+# ---------------------------------------------------------------------------
+# Constants
+# ---------------------------------------------------------------------------
+
+DATA_DIR = Path("data")
+DEFAULT_YEAR = 2025
+DEFAULT_ALPHA = 0.05
+
+METRICS = ["nse", "rmse", "nrmse", "r"]
+LOWER_IS_BETTER = {"rmse", "nrmse"}
+
+MIN_PAIRS_WARNING = 6
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+def _r4(v: float | None) -> float | None:
+    return round(v, 4) if v is not None else None
+
+
+def _load_site_metrics(year: int) -> list[dict[str, Any]]:
+    """Return parsed ``metrics.json`` payloads for every site under ``{year}``."""
+    metrics_dir = DATA_DIR / "metrics" / str(year)
+    if not metrics_dir.is_dir():
+        return []
+
+    payloads: list[dict[str, Any]] = []
+    for site_dir in sorted(metrics_dir.iterdir()):
+        if not site_dir.is_dir():
+            continue
+        path = site_dir / "metrics.json"
+        if not path.is_file():
+            continue
+        payloads.append(json.loads(path.read_text()))
+    return payloads
+
+
+def collect_pairs(
+    site_metrics: list[dict[str, Any]], metric: str
+) -> tuple[list[float], list[float], int]:
+    """Return paired BtI / ItB values for ``metric`` and count of dropped sites."""
+    bti_vals: list[float] = []
+    itb_vals: list[float] = []
+    n_dropped = 0
+
+    for payload in site_metrics:
+        bti = payload.get("bti")
+        itb = payload.get("itb")
+        if not isinstance(bti, dict) or not isinstance(itb, dict):
+            n_dropped += 1
+            continue
+
+        bti_v = bti.get(metric)
+        itb_v = itb.get(metric)
+        if bti_v is None or itb_v is None:
+            n_dropped += 1
+            continue
+
+        bti_vals.append(float(bti_v))
+        itb_vals.append(float(itb_v))
+
+    return bti_vals, itb_vals, n_dropped
+
+
+def _better_order(
+    bti_vals: list[float],
+    itb_vals: list[float],
+    metric: str,
+    p_value: float | None,
+    alpha: float,
+) -> str:
+    """Name the better fusion order when Wilcoxon p < alpha, else no difference."""
+    if p_value is None or p_value >= alpha:
+        return "no significant difference"
+
+    mean_diff = float(np.mean(itb_vals) - np.mean(bti_vals))
+    if metric in LOWER_IS_BETTER:
+        return "itb" if mean_diff < 0 else "bti"
+    return "itb" if mean_diff > 0 else "bti"
+
+
+def paired_test(
+    bti_vals: list[float],
+    itb_vals: list[float],
+    metric: str,
+    alpha: float,
+) -> dict[str, Any]:
+    """Run paired Wilcoxon (primary) and t-test; return summary dict."""
+    n_pairs = len(bti_vals)
+    bti_arr = np.array(bti_vals, dtype=float)
+    itb_arr = np.array(itb_vals, dtype=float)
+    diffs = itb_arr - bti_arr
+
+    result: dict[str, Any] = {
+        "n_pairs": n_pairs,
+        "bti_mean": _r4(float(bti_arr.mean())) if n_pairs else None,
+        "bti_median": _r4(float(np.median(bti_arr))) if n_pairs else None,
+        "itb_mean": _r4(float(itb_arr.mean())) if n_pairs else None,
+        "itb_median": _r4(float(np.median(itb_arr))) if n_pairs else None,
+        "mean_diff": _r4(float(diffs.mean())) if n_pairs else None,
+        "median_diff": _r4(float(np.median(diffs))) if n_pairs else None,
+        "wilcoxon": {"statistic": None, "p_value": None},
+        "ttest": {"statistic": None, "p_value": None},
+        "better_order": "insufficient data",
+    }
+
+    if n_pairs < 2:
+        return result
+
+    wilcoxon_stat: float | None = None
+    wilcoxon_p: float | None = None
+    if np.any(diffs != 0):
+        try:
+            w_stat, w_p = wilcoxon(itb_arr, bti_arr)
+            wilcoxon_stat = float(w_stat)
+            wilcoxon_p = float(w_p)
+        except ValueError:
+            pass
+
+    t_stat, t_p = ttest_rel(itb_arr, bti_arr)
+
+    result["wilcoxon"] = {
+        "statistic": _r4(wilcoxon_stat),
+        "p_value": _r4(wilcoxon_p),
+    }
+    result["ttest"] = {
+        "statistic": _r4(float(t_stat)),
+        "p_value": _r4(float(t_p)),
+    }
+    result["better_order"] = _better_order(
+        bti_vals, itb_vals, metric, wilcoxon_p, alpha
+    )
+    return result
+
+
+def _print_summary(
+    year: int, alpha: float, n_sites_total: int, metrics_out: dict
+) -> None:
+    print(f"\nPaired ItB vs BtI test — {year} (alpha={alpha}, sites={n_sites_total})")
+    print(
+        f"{'metric':<8} {'n':>4} {'BtI mean':>10} {'ItB mean':>10} "
+        f"{'diff':>10} {'W p':>8} {'t p':>8}  better"
+    )
+    print("-" * 78)
+    for metric in METRICS:
+        m = metrics_out[metric]
+        bti_mean = m["bti_mean"]
+        itb_mean = m["itb_mean"]
+        mean_diff = m["mean_diff"]
+        w_p = m["wilcoxon"]["p_value"]
+        t_p = m["ttest"]["p_value"]
+        better = m["better_order"]
+
+        def _fmt(v: float | None) -> str:
+            return f"{v:10.4f}" if v is not None else f"{'—':>10}"
+
+        print(
+            f"{metric:<8} {m['n_pairs']:>4} {_fmt(bti_mean)} {_fmt(itb_mean)} "
+            f"{_fmt(mean_diff)} "
+            f"{w_p if w_p is not None else '—':>8} "
+            f"{t_p if t_p is not None else '—':>8}  {better}"
+        )
+        if 0 < m["n_pairs"] < MIN_PAIRS_WARNING:
+            print(
+                f"  warning: only {m['n_pairs']} pair(s) for {metric}; "
+                "interpret p-values cautiously"
+            )
+
+
+# ---------------------------------------------------------------------------
+# CLI
+# ---------------------------------------------------------------------------
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--evaluation-year", type=int, default=DEFAULT_YEAR)
+    parser.add_argument(
+        "--alpha",
+        type=float,
+        default=DEFAULT_ALPHA,
+        help="Significance threshold for better_order (default 0.05)",
+    )
+    args = parser.parse_args()
+
+    year = args.evaluation_year
+    alpha = args.alpha
+
+    site_metrics = _load_site_metrics(year)
+    n_sites_total = len(site_metrics)
+    if n_sites_total == 0:
+        raise SystemExit(
+            f"No Step 5 metrics found under {DATA_DIR / 'metrics' / str(year)}"
+        )
+
+    metrics_out: dict[str, Any] = {}
+    for metric in METRICS:
+        bti_vals, itb_vals, n_dropped = collect_pairs(site_metrics, metric)
+        summary = paired_test(bti_vals, itb_vals, metric, alpha)
+        summary["n_dropped"] = n_dropped
+        metrics_out[metric] = summary
+
+    payload = {
+        "year": year,
+        "alpha": alpha,
+        "n_sites_total": n_sites_total,
+        "metrics": metrics_out,
+    }
+
+    out_dir = DATA_DIR / "statistics_fusion_order"
+    out_dir.mkdir(parents=True, exist_ok=True)
+    out_path = out_dir / f"{year}.json"
+    out_path.write_text(json.dumps(payload, separators=(",", ":")))
+
+    _print_summary(year, alpha, n_sites_total, metrics_out)
+    print(f"\nWritten → {out_path}")
+
+
+if __name__ == "__main__":
+    main()

README.md

@@ -1,6 +1,6 @@
 # EFAST fusion with phenocam validation.
 
-End-to-end pipeline from selecting sites from the global [PhenoCam Network](https://phenocam.nau.edu/) to run [EFAST](https://github.com/DHI-GRAS/efast) spatio-temporal fusion with Sentinel-2 / Sentinel-3 and validate GCCs accross sensors. The five numbered steps cover site selection, Sentinel data acquisition, different fusion orders, and accuracy metrics, all feeding a static web QA viewer.
+End-to-end pipeline from selecting sites from the global [PhenoCam Network](https://phenocam.nau.edu/) to run [EFAST](https://github.com/DHI-GRAS/efast) spatio-temporal fusion with Sentinel-2 / Sentinel-3 and validate GCCs accross sensors. The numbered steps cover site selection, Sentinel data acquisition, different fusion orders, accuracy metrics, and sample-level statistics, all feeding a static web QA viewer.
 
 ---
 
@@ -13,6 +13,7 @@ End-to-end pipeline from selecting sites from the global [PhenoCam Network](http
 | 3 | `3-sentinel-data.py` | Acquire S2 (Earth Search COG) and S3 OLCI SYN L2 (CDSE OpenEO); prepare REFL, DIST_CLOUD, and composite GeoTIFFs |
 | 4 | `4-fusion.py` | Run EFAST BtI (fuse reflectance → GCC) and ItB (fuse GCC directly) for each screened site |
 | 5 | `5-metrics.py` | Extract PhenoCam-matched timeseries, compute NSE/RMSE/r baselines and fusion metrics, emit per-site JSON and webapp manifest |
+| 6 | `6-statistics-fusion-order.py` | Paired ItB-vs-BtI significance test (Wilcoxon + t-test) across all sites |
 
 ---
 
@@ -41,9 +42,10 @@ uv run python 2-phenocam-screening.py --evaluation-year 2025
 uv run python 3-sentinel-data.py --evaluation-year 2025
 uv run python 4-fusion.py --evaluation-year 2025
 uv run python 5-metrics.py --evaluation-year 2025
+uv run python 6-statistics-fusion-order.py --evaluation-year 2025
 ```
 
-All steps accept `--evaluation-year` (default `2025`) and `--site` (optional, for single-site runs). Steps 3–5 are resumable — existing output files are skipped.
+Steps 1–5 accept `--evaluation-year` (default `2025`) and `--site` (optional, for single-site runs). Step 6 is a full-sample aggregate and only accepts `--evaluation-year` and `--alpha` (default `0.05`). Steps 3–5 are resumable — existing output files are skipped.
 
 ```bash
 # single site
@@ -70,6 +72,7 @@ Step 3 S3 download uses CDSE OpenEO (`SENTINEL3_SYN_L2_SYN`). Set `CDSE_USER` an
 | `fusion/{year}/{site}/bti/`, `.../itb/` | 4 | BtI fused reflectance + GCC; ItB fused GCC |
 | `metrics/{year}/{site}/` | 5 | Per-site timeseries, metrics, covariates JSON |
 | `metrics/manifest.json` | 5 | Webapp manifest (years + site metadata) |
+| `statistics_fusion_order/{year}.json` | 6 | Paired ItB-vs-BtI test summary (NSE, RMSE, nRMSE, r) |
 
 ---
 

index.html

@@ -147,14 +147,42 @@ body { margin: 0; font: 13px/1.4 system-ui, sans-serif; background: #f5f5f5; col
   display: flex; align-items: center; gap: 12px; padding: 10px 14px;
   background: #1a1a2e; color: #eee; flex-shrink: 0;
 }
-#worldHeader h2 { margin: 0; font-size: 14px; font-weight: 600; color: #7eb8f7; flex: 1; }
+#overlayTabs { display: flex; gap: 4px; flex: 1; }
-#worldHeader .world-meta { font-size: 12px; color: #aaa; }
+.otab {
+  padding: 4px 12px; border-radius: 4px; font-size: 13px; cursor: pointer;
+  border: 1px solid #555; background: transparent; color: #ccc;
+}
+.otab.active { background: #2a3f5f; color: #dceeff; border-color: #4a6fa5; }
+.otab:hover:not(.active) { background: rgba(255, 255, 255, 0.07); }
+#worldHeader .world-meta { font-size: 12px; color: #aaa; flex-shrink: 0; }
 #worldClose {
   font-size: 13px; padding: 4px 12px; border-radius: 4px;
   border: 1px solid #666; background: transparent; color: #ddd; cursor: pointer;
+  flex-shrink: 0;
 }
 #worldClose:hover { background: rgba(255, 255, 255, 0.08); }
 #worldMap { flex: 1; min-height: 0; }
+#statsPanel { flex: 1; min-height: 0; overflow-y: auto; padding: 20px 24px; display: none; background: #f5f5f5; }
+.stat-grid { display: grid; grid-template-columns: repeat(auto-fit, minmax(340px, 1fr)); gap: 14px; margin-top: 4px; }
+.stat-card { background: #fff; border: 1px solid #e0e0e0; border-radius: 6px; padding: 14px 16px; }
+.stat-card h3 { margin: 0 0 10px; font-size: 14px; color: #1a1a2e; display: flex; align-items: center; gap: 8px; flex-wrap: wrap; }
+.stat-badge {
+  font-size: 11px; padding: 2px 7px; border-radius: 10px; font-weight: 600;
+  background: #e8f5e9; color: #1a6e2e;
+}
+.stat-badge.itb { background: #fff3e0; color: #c75c00; }
+.stat-badge.bti { background: #e3f2fd; color: #0d47a1; }
+.stat-badge.none { background: #f5f5f5; color: #777; font-weight: 400; }
+.stat-badge.insuf { background: #fce4ec; color: #b71c1c; font-weight: 400; }
+.stat-row-table { width: 100%; border-collapse: collapse; font-size: 12px; }
+.stat-row-table td { padding: 3px 0; vertical-align: top; }
+.stat-row-table .slabel { color: #888; width: 46%; }
+.stat-row-table .sval { color: #222; font-variant-numeric: tabular-nums; }
+.stat-pval { display: inline-block; font-family: monospace; }
+.stat-pval.sig { color: #1a6e2e; font-weight: 600; }
+.stat-divider { border: none; border-top: 1px solid #f0f0f0; margin: 8px 0; }
+.stat-summary { font-size: 12px; color: #666; margin-bottom: 14px; }
+.stat-nodata { color: #999; padding: 40px; text-align: center; font-size: 13px; }
 .world-popup { font-size: 12px; line-height: 1.35; }
 .world-popup b { display: block; margin-bottom: 2px; }
 .world-popup .veg { color: #2e7d32; font-size: 11px; }
@@ -173,11 +201,15 @@ body { margin: 0; font: 13px/1.4 system-ui, sans-serif; background: #f5f5f5; col
 <div id="worldOverlay" aria-hidden="true">
   <div id="worldPanel">
     <div id="worldHeader">
-      <h2>Worldwide sites</h2>
+      <div id="overlayTabs">
+        <button type="button" class="otab active" data-tab="map">Worldwide sites</button>
+        <button type="button" class="otab" data-tab="stats">Statistics</button>
+      </div>
       <span class="world-meta" id="worldMeta"></span>
       <button type="button" id="worldClose">Close</button>
     </div>
     <div id="worldMap"></div>
+    <div id="statsPanel"></div>
   </div>
 </div>
 
@@ -270,6 +302,9 @@ let fusionMode = "bti";   // bti | itb
 let miniMapInst = null, miniMarker = null;
 let worldMapInst = null, worldCluster = null;
 let worldOverlayOpen = false;
+let overlayTab = "map";
+let statsData = null;
+let statsYear = null;
 const maps3 = {};         // { s2, fusion, s3 } Leaflet instances
 const overlays3 = {};     // current ImageOverlay per map
 const markers3 = {};      // site dot markers per map
@@ -298,6 +333,22 @@ const SERIES_LABELS = {
   s2: "S2 raw", s3: "S3 raw",
 };
 
+const METRIC_META = {
+  nse:   { label: "NSE",   full: "Nash–Sutcliffe Efficiency", better: "higher" },
+  rmse:  { label: "RMSE",  full: "Root Mean Square Error",    better: "lower"  },
+  nrmse: { label: "nRMSE", full: "Normalised RMSE",           better: "lower"  },
+  r:     { label: "r",     full: "Pearson correlation",       better: "higher" },
+};
+
+const STAT_METRICS = ["nse", "rmse", "nrmse", "r"];
+
+const BADGE_CLASS = {
+  itb: "itb",
+  bti: "bti",
+  "no significant difference": "none",
+  "insufficient data": "insuf",
+};
+
 const INSPECTOR_SERIES = [
   { key: "phenocam",     label: "PhenoCam",       cols: [{ h: "gcc_90", k: "gcc_90" }] },
   { key: "bands_s2",     label: "S2 reflectance", cols: ["B02","B03","B04"].map(b => ({ h: b, k: b })) },
@@ -503,6 +554,118 @@ function renderSitePanel(meta, cov) {
     `<table class="site-meta-table"><tbody>${rows.join("")}</tbody></table>${species}`;
 }
 
+function fmtStat(v, decimals = 4) {
+  return v != null ? v.toFixed(decimals) : "—";
+}
+
+function fmtPval(p, alpha) {
+  if (p == null) return "—";
+  const cls = p < alpha ? "stat-pval sig" : "stat-pval";
+  return `<span class="${cls}">${p.toFixed(4)}</span>`;
+}
+
+function betterOrderLabel(order) {
+  if (order === "itb") return "ItB better";
+  if (order === "bti") return "BtI better";
+  if (order === "no significant difference") return "No significant difference";
+  if (order === "insufficient data") return "Insufficient data";
+  return order;
+}
+
+function updateWorldMeta() {
+  const sites = manifest?.sites?.[currentYear] || {};
+  const n = Object.values(sites).filter(m => m.has_fusion).length;
+  if (overlayTab === "stats") {
+    const nPairs = statsData?.metrics?.nse?.n_pairs;
+    qs("#worldMeta").textContent = nPairs != null
+      ? `${nPairs} paired site${nPairs === 1 ? "" : "s"} · α=${statsData.alpha ?? 0.05} · ${currentYear}`
+      : `${n} fusion site${n === 1 ? "" : "s"} · ${currentYear}`;
+  } else {
+    qs("#worldMeta").textContent =
+      `${n} fusion site${n === 1 ? "" : "s"} · ${currentYear}`;
+  }
+}
+
+function renderStatsPanel(data) {
+  const panel = qs("#statsPanel");
+  const alpha = data.alpha ?? 0.05;
+  const cards = STAT_METRICS.map(key => {
+    const meta = METRIC_META[key];
+    const m = data.metrics?.[key] || {};
+    const badgeCls = BADGE_CLASS[m.better_order] || "none";
+    const badge = `<span class="stat-badge ${badgeCls}">${betterOrderLabel(m.better_order)}</span>`;
+    const row = (label, val) =>
+      `<tr><td class="slabel">${label}</td><td class="sval">${val}</td></tr>`;
+
+    return `<div class="stat-card">
+      <h3>${meta.label} <span style="font-weight:400;color:#888;font-size:12px">${meta.full}</span> ${badge}</h3>
+      <div style="font-size:11px;color:#999;margin-bottom:8px">${meta.better} is better</div>
+      <table class="stat-row-table">
+        ${row("BtI mean", fmtStat(m.bti_mean))}
+        ${row("BtI median", fmtStat(m.bti_median))}
+        ${row("ItB mean", fmtStat(m.itb_mean))}
+        ${row("ItB median", fmtStat(m.itb_median))}
+        ${row("Diff (ItB − BtI) mean", fmtStat(m.mean_diff))}
+        ${row("Diff (ItB − BtI) median", fmtStat(m.median_diff))}
+      </table>
+      <hr class="stat-divider">
+      <table class="stat-row-table">
+        ${row("Wilcoxon W", m.wilcoxon?.statistic ?? "—")}
+        ${row("Wilcoxon p", fmtPval(m.wilcoxon?.p_value, alpha))}
+        ${row("Paired t", m.ttest?.statistic ?? "—")}
+        ${row("Paired t p", fmtPval(m.ttest?.p_value, alpha))}
+      </table>
+      <hr class="stat-divider">
+      <table class="stat-row-table">
+        ${row("Paired sites", m.n_pairs ?? "—")}
+        ${row("Dropped sites", m.n_dropped ?? "—")}
+      </table>
+    </div>`;
+  }).join("");
+
+  panel.innerHTML =
+    `<div class="stat-summary">Paired ItB vs BtI test across ${data.n_sites_total ?? "—"} site(s) with Step 5 metrics · significance α=${alpha}</div>` +
+    `<div class="stat-grid">${cards}</div>`;
+  updateWorldMeta();
+}
+
+async function loadStatsPanel() {
+  const panel = qs("#statsPanel");
+  panel.innerHTML = '<div class="stat-nodata">Loading…</div>';
+  try {
+    const data = await fetch(`data/statistics_fusion_order/${currentYear}.json`)
+      .then(r => { if (!r.ok) throw new Error(); return r.json(); });
+    statsData = data;
+    statsYear = currentYear;
+    renderStatsPanel(data);
+  } catch {
+    statsData = null;
+    statsYear = null;
+    panel.innerHTML =
+      '<div class="stat-nodata">No statistics file found — run 6-statistics-fusion-order.py first.</div>';
+    updateWorldMeta();
+  }
+}
+
+function switchOverlayTab(tab, updateHash = true) {
+  overlayTab = tab;
+  document.querySelectorAll(".otab").forEach(btn =>
+    btn.classList.toggle("active", btn.dataset.tab === tab));
+  qs("#worldMap").style.display = tab === "map" ? "block" : "none";
+  qs("#statsPanel").style.display = tab === "stats" ? "block" : "none";
+
+  if (tab === "stats") {
+    if (statsYear !== currentYear || !statsData) loadStatsPanel();
+    else updateWorldMeta();
+    if (updateHash) setHash("statistics");
+    return;
+  }
+
+  buildWorldMap();
+  requestAnimationFrame(() => worldMapInst?.invalidateSize());
+  if (updateHash) setHash("worldwide");
+}
+
 // ── init ──
 async function init() {
   try {
@@ -519,11 +682,18 @@ async function init() {
 
   yearSel.addEventListener("change", () => {
     currentYear = +yearSel.value;
+    statsData = null;
+    statsYear = null;
     buildSiteList();
-    if (worldOverlayOpen) buildWorldMap();
+    if (worldOverlayOpen) {
+      if (overlayTab === "stats") loadStatsPanel();
+      else buildWorldMap();
+    }
   });
 
   qs("#worldMapBtn").addEventListener("click", () => openWorldOverlay());
+  document.querySelectorAll(".otab").forEach(btn =>
+    btn.addEventListener("click", () => switchOverlayTab(btn.dataset.tab)));
   qs("#worldClose").addEventListener("click", () => closeWorldOverlay());
   qs("#worldOverlay").addEventListener("click", e => {
     if (e.target === qs("#worldOverlay")) closeWorldOverlay();
@@ -551,11 +721,12 @@ async function init() {
   onHashChange();
 }
 
-// ── hash routing (#worldwide, #2025/sitename) ──
+// ── hash routing (#worldwide, #statistics, #2025/sitename) ──
 function parseHash() {
   const raw = location.hash.replace(/^#/, "").trim();
   if (!raw) return { view: null, year: null, site: null };
   if (raw === "worldwide") return { view: "worldwide", year: null, site: null };
+  if (raw === "statistics") return { view: "statistics", year: null, site: null };
   const parts = raw.split("/");
   if (parts.length === 2 && /^\d{4}$/.test(parts[0]))
     return { view: "site", year: +parts[0], site: decodeURIComponent(parts[1]) };
@@ -565,6 +736,7 @@ function parseHash() {
 function setHash(view, year, site) {
   let hash = "";
   if (view === "worldwide") hash = "worldwide";
+  else if (view === "statistics") hash = "statistics";
   else if (view === "site" && year && site)
     hash = `${year}/${encodeURIComponent(site)}`;
   const next = hash ? `#${hash}` : "";
@@ -574,7 +746,11 @@ function setHash(view, year, site) {
 function onHashChange() {
   const { view, year, site } = parseHash();
   if (view === "worldwide") {
-    openWorldOverlay(false);
+    openWorldOverlay(false, "map");
+    return;
+  }
+  if (view === "statistics") {
+    openWorldOverlay(false, "stats");
     return;
   }
   if (view === "site" && year && site && manifest?.sites?.[year]?.[site]?.has_fusion) {
@@ -592,15 +768,13 @@ function onHashChange() {
 }
 
 // ── worldwide map overlay ──
-function openWorldOverlay(updateHash = true) {
+function openWorldOverlay(updateHash = true, tab = "map") {
   if (!manifest) return;
   worldOverlayOpen = true;
   const overlay = qs("#worldOverlay");
   overlay.classList.add("open");
   overlay.setAttribute("aria-hidden", "false");
-  if (updateHash) setHash("worldwide");
+  switchOverlayTab(tab, updateHash);
-  buildWorldMap();
-  requestAnimationFrame(() => worldMapInst?.invalidateSize());
 }
 
 function closeWorldOverlay(updateHash = true) {
@@ -608,7 +782,8 @@ function closeWorldOverlay(updateHash = true) {
   const overlay = qs("#worldOverlay");
   overlay.classList.remove("open");
   overlay.setAttribute("aria-hidden", "true");
-  if (updateHash && parseHash().view === "worldwide") {
+  const view = parseHash().view;
+  if (updateHash && (view === "worldwide" || view === "statistics")) {
     if (currentSite) setHash("site", currentYear, currentSite);
     else history.replaceState(null, "", location.pathname + location.search);
   }
@@ -712,7 +887,7 @@ function buildSiteList() {
     list.appendChild(li);
   }
   const h = parseHash();
-  if (h.view === "worldwide") return;
+  if (h.view === "worldwide" || h.view === "statistics") return;
   if (h.view === "site" && h.year === currentYear && sites[h.site]?.has_fusion) {
     selectSite(h.site);
     return;