fix(places): address review, and merge places GIAS spells more than one way

Two findings from review on #121, plus a third the review prompted.

The cap at three authorities silently dropped the fourth in exactly the case
where the information matters most — a genuinely fragmented place — and
contradicted the stated goal of naming every authority a place sits in. It is
gone. The share rule was always the real limit and already bounds the list at
ten. Measured against the live corpus, one town would have been truncated
today: LONDON, split evenly between Hackney, Lambeth, Westminster and
Lewisham.

parent_authority used mode() while authorities used value_counts(), and on an
exact tie pandas does not guarantee the two pick the same name, so the 301
could have pointed somewhere other than the authority named first on the page.
The parent is now derived from authorities[0]: one computation, one answer.
It also inherits the sentinel filter, so a place can no longer redirect to
/schools/authority/does-not-apply.

Chasing the truncation case surfaced a worse bug. Places were grouped by raw
town value, but the registry is keyed by slug, and GIAS spells the same place
several ways. Five town slugs come from more than one spelling: "London"
(1,819 schools) and "LONDON" (12) both slugify to `london`, so the later group
simply overwrote the earlier one — /schools/london could have shown twelve
schools, silently, depending on row order. Weston-super-Mare was split 14/19
across two spellings and Newcastle-under-Lyme across three. Grouping is now by
slug, and the display name is the most common spelling.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015mWQnpye9F299NVRCCSRvj

backend/places.py

@@ -81,9 +81,12 @@ def _phase_urns(group, publishable: set[int]) -> dict[str, tuple[int, ...]]:
 # schools, and at least two. GIAS carries occasional postcode errors — EN6
 # lists two Shropshire schools among fourteen in Hertfordshire — and a bare
 # "any authority present" rule would print those as though they were real.
+# There is deliberately no cap on how many are named. An earlier cut stopped
+# at three, which silently dropped the fourth in exactly the case where the
+# information matters most — a genuinely fragmented place. The share rule is
+# the only limit, and it already bounds the list at ten.
 _AUTHORITY_MIN_SHARE = 0.10
 _AUTHORITY_MIN_SCHOOLS = 2
-_AUTHORITY_MAX_SHOWN = 3
 
 
 def _authorities(group) -> tuple[tuple[str, int], ...]:
@@ -110,43 +113,64 @@ def _authorities(group) -> tuple[tuple[str, int], ...]:
             if str(name) not in EXCLUDED_FILTER_VALUES:
                 return ((str(name), int(n)),)
         return ()
-    return tuple(kept[:_AUTHORITY_MAX_SHOWN])
+    return tuple(kept)
 
 
-def _parent_authority(group) -> str | None:
-    """The most common authority in a group — the useful 301 target.
+def _parent_authority(authorities: tuple[tuple[str, int], ...]) -> str | None:
+    """The 301 target: the largest authority a place sits in.
 
-    A town spanning several authorities has no single parent, so the mode is
-    the honest answer rather than an arbitrary first row.
+    Derived from `authorities` rather than computed separately. The first cut
+    used `mode()` here while `authorities` used `value_counts()`, and on an
+    exact tie pandas does not guarantee the two pick the same name — so the
+    redirect could have pointed somewhere other than the authority the page
+    named first. One computation, one answer.
+
+    Deriving it also inherits the sentinel filter, so a place can no longer
+    redirect to /schools/authority/does-not-apply.
     """
-    if "local_authority" not in group.columns:
-        return None
-    top = group["local_authority"].dropna()
-    return str(top.mode().iloc[0]) if not top.empty else None
+    return authorities[0][0] if authorities else None
 
 
 def _group(df, column: str, kind: str, publishable: set[int]) -> dict[str, Place]:
-    """One Place per distinct value of `column` that clears the threshold."""
+    """One Place per distinct SLUG in `column` that clears the threshold.
+
+    Grouped by slug, not by raw value, because GIAS spells the same place
+    several ways and they all resolve to one URL. Five town slugs come from
+    more than one spelling: "London" (1,819 schools) and "LONDON" (12) both
+    slugify to `london`; Weston-super-Mare is split 14/19 across two
+    spellings; Newcastle-under-Lyme across three.
+
+    Grouping by raw value meant the later group simply overwrote the earlier
+    one in this dict — so /schools/london could have shown twelve schools
+    instead of 1,819, silently and depending on row order.
+
+    The display name is the most common spelling, which is the one a reader
+    expects to see.
+    """
     from backend.app import _slugify
 
     if column not in df.columns:
         return {}
 
+    working = df.assign(_slug=df[column].map(
+        lambda v: _slugify(str(v).strip()) if isinstance(v, str) and v.strip() else None))
+    working = working[working["_slug"].notna() & (working["_slug"] != "")]
+
     out: dict[str, Place] = {}
-    for name, group in df.groupby(column, dropna=True):
-        name = str(name).strip()
-        if not name:
-            continue
+    for slug, group in working.groupby("_slug"):
+        slug = str(slug)
         urns = tuple(sorted({int(u) for u in group["urn"]} & publishable))
         if len(urns) < MIN_SCHOOLS:
             continue
-        slug = _slugify(name)
-        if not slug:
+        spellings = group[column].dropna().value_counts()
+        if spellings.empty:
             continue
+        name = str(spellings.index[0]).strip()
+        authorities = () if kind == "authority" else _authorities(group)
         place = Place(
             kind=kind, slug=slug, name=name, urns=urns,
-            parent_authority=_parent_authority(group) if kind == "town" else None,
-            authorities=() if kind == "authority" else _authorities(group),
+            parent_authority=_parent_authority(authorities),
+            authorities=authorities,
             phase_urns=_phase_urns(group, publishable),
         )
         out[place.key] = place
@@ -179,9 +203,10 @@ def _outcode_places(df, publishable: set[int]) -> dict[str, Place]:
         urns = tuple(sorted({int(u) for u in group["urn"]} & publishable))
         if len(urns) < MIN_SCHOOLS:
             continue
+        authorities = _authorities(group)
         place = Place(kind="outcode", slug=str(oc).lower(), name=str(oc),
-                      urns=urns, parent_authority=_parent_authority(group),
-                      authorities=_authorities(group),
+                      urns=urns, parent_authority=_parent_authority(authorities),
+                      authorities=authorities,
                       phase_urns=_phase_urns(group, publishable))
         out[place.key] = place
     return out
@@ -223,9 +248,10 @@ def _locality_places(df, publishable: set[int],
                 "threshold of %d - not published",
                 slug, ", ".join(outcodes), len(urns), MIN_SCHOOLS)
             continue
+        authorities = _authorities(group)
         place = Place(kind="locality", slug=slug, name=name, urns=urns,
-                      parent_authority=_parent_authority(group),
-                      authorities=_authorities(group),
+                      parent_authority=_parent_authority(authorities),
+                      authorities=authorities,
                       phase_urns=_phase_urns(group, publishable))
         out[place.key] = place
     return out

backend/tests/test_places.py

@@ -290,3 +290,56 @@ def test_a_place_always_names_at_least_one_authority():
     place = reg.get("town:fragmented")
     assert place is not None
     assert len(place.authorities) == 1
+
+
+def test_every_qualifying_authority_is_named_with_no_cap():
+    """An earlier cut stopped at three, dropping the fourth silently.
+
+    That truncation bit exactly where the information matters most — a
+    genuinely fragmented place — and nothing recorded it.
+    """
+    rows = []
+    for i, la in enumerate(["Hackney", "Lambeth", "Westminster", "Lewisham"]):
+        rows += _town(3, "Fourway", la, start=300000 + i * 100)
+    reg = build_place_registry(_df(rows))
+    assert len(reg["town:fourway"].authorities) == 4
+
+
+def test_the_redirect_target_is_the_authority_named_first():
+    """They were computed separately — mode() against value_counts() — and on
+    an exact tie pandas does not guarantee the two agree."""
+    rows = (_town(26, "London", "Merton", start=300000)
+            + _town(7, "London", "Wandsworth", start=400000))
+    for r in rows:
+        r["postcode"] = "SW19 1AA"
+    place = build_place_registry(_df(rows))["outcode:sw19"]
+    assert place.parent_authority == place.authorities[0][0]
+
+
+def test_a_place_never_redirects_to_a_sentinel_authority():
+    # Deriving the parent from `authorities` inherits its sentinel filter.
+    rows = (_town(6, "Someplace", "Does not apply", start=300000)
+            + _town(5, "Someplace", "Essex", start=400000))
+    reg = build_place_registry(_df(rows))
+    assert reg["town:someplace"].parent_authority == "Essex"
+
+
+def test_spellings_of_one_place_are_merged_not_overwritten():
+    """GIAS spells the same place several ways, and they share a URL.
+
+    "London" (1,819 schools) and "LONDON" (12) both slugify to `london`.
+    Grouping by raw value let the later group overwrite the earlier one, so
+    the page could have shown twelve schools instead of 1,819 — silently, and
+    depending on row order.
+    """
+    rows = (_town(6, "Weston-super-Mare", "North Somerset", start=300000)
+            + _town(5, "Weston-Super-Mare", "North Somerset", start=400000))
+    reg = build_place_registry(_df(rows))
+    assert len(reg["town:weston-super-mare"].urns) == 11
+
+
+def test_the_merged_place_takes_its_most_common_spelling():
+    rows = (_town(9, "Newcastle-under-Lyme", "Staffordshire", start=300000)
+            + _town(5, "NEWCASTLE-UNDER-LYME", "Staffordshire", start=400000))
+    reg = build_place_registry(_df(rows))
+    assert reg["town:newcastle-under-lyme"].name == "Newcastle-under-Lyme"