Name the GPS k-means areas by their marquee ride

Replace the "Area N" fallbacks with derived names: Kiddieland
(lowest mean PC2) plus The Woodies / The Grand Midway / Flying
Turns Grove / Giant Wheel Corner, each keyed off the headline
ride that lands in the cluster so the labels survive re-clustering
as the wait history grows.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

app.py

@@ -170,8 +170,10 @@ def compute_pca():
     loadings = loadings.join(meta)
 
     # Spatial "areas" from GPS alone — k-means on standardized lat/lon, the same
-    # 5 clusters the write-up uses. Label the one whose rides lean most negative
+    # 5 clusters the write-up uses. Names are derived, not hard-coded to a
-    # on PC2 as Kiddieland so the colors read in plain English.
+    # cluster index, so they survive re-clustering as the history grows:
+    #  - Kiddieland = the cluster whose rides lean most negative on PC2.
+    #  - the rest are named for the marquee ride that lands in each.
     located = loadings.dropna(subset=["lat", "lon"])
     if len(located) >= 5:
         km = KMeans(n_clusters=5, n_init=10, random_state=0).fit_predict(
@@ -179,6 +181,13 @@ def compute_pca():
         area = pd.Series(km, index=located.index)
         kiddie = loadings.loc[area.index].assign(a=area).groupby("a")["PC2"].mean().idxmin()
         names = {kiddie: "Kiddieland"}
+        anchors = [("phoenix", "The Woodies"),
+                   ("impulse", "The Grand Midway"),
+                   ("flying_turns", "Flying Turns Grove"),
+                   ("giant_wheel", "Giant Wheel Corner")]
+        for ride, label in anchors:
+            if ride in area.index:
+                names.setdefault(area[ride], label)  # don't overwrite Kiddieland
         loadings["area"] = area.map(lambda a: names.get(a, f"Area {a + 1}"))
     else:
         loadings["area"] = "Untagged"