Completes the last-distance-offered feature against the mockup: the
year-by-year record, the same numbers drawn over real streets, and the
reader's own address measured against them.
Serving the history
The first cut deliberately served only the latest year, because a plain
series would draw a trend line straight through gaps that are absences of
publication, not of a cut-off. That reasoning is answered rather than
abandoned: cutoffYearRows classifies every year in the span, and the chart
breaks the line rather than interpolating across it.
A missing year is not one fact but three. It may be unpublished; it may be
a year the school was not oversubscribed; or there may be no record at all.
Collapsing them into "no data" throws away the reassuring case and hides
the important caveat, so each is stated in words in the table.
The claim is held to what the data supports. fact_admissions.oversubscribed
compares FIRST PREFERENCES against places, which does not establish that
every applicant was offered one — so the copy says "places available on
first preferences" and a test asserts the stronger claim never appears.
The trend summary is not a verdict
It names both endpoints and their years and lets the reader conclude. It is
withheld below four published points, and a swing under a tenth of the
earlier figure is reported as "broadly the same" rather than dressed up as
a direction.
The postcode check
This is the only place on the site that answers a question about a family
rather than a school, so most of the care went into what it refuses to say.
postcodes.io returns a centroid covering roughly fifteen addresses, which
against a 500 m cut-off is a fifth of the whole distance — so a margin
inside 100 m returns "too close to call" rather than a place a family does
not have. Unpublished years count as unknown, never as a pass. The limits
are stated before the check is used, not revealed with the answer.
The postcode is geocoded in the browser and never stored.
Both templates
Banded and selective secondaries are exactly where this matters most, so
the detail is shared. The primary page gives it a third tab; the secondary
page is one flat panel by design and renders it inline.
Absence is explained rather than reported. A selective school's missing
figure is explained by how it admits; a consistently undersubscribed school
reads as good news.
Also makes the batch loader's test double honour ORDER BY. It was a no-op,
so "latest row per URN" was really "first row in the fixture" and the test
would have passed with the sort reversed or removed.
Verified: 214 frontend tests, 54 backend, 45/53 e2e green against staging
(the 8 cut-off journeys skip until the DAG runs). Rendered offline against
the real compiled CSS in both themes and at 390px; every new surface clears
WCAG AA, measured on composited pixels.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01WDvkyqqHABm4bmth2kjAxE
Adds the cut-off distance a parent actually asks about — "how close do we
need to live?" — end to end: a Singer tap, dbt staging and mart models, an
Airflow DAG, and a tile on both detail templates. 3,597 schools across 57
local authorities carry a figure; the rest are unchanged.
There is no national source for this. Each LA publishes its own cut-offs in
its own format, and the collected CSV is transcribed from PDFs, spreadsheets
and web pages — so most of the work here is deciding what is safe to show.
Data
* tap-uk-school-distance loads the CSV verbatim into raw. Keyed on
(urn, year, school_name), because school_name carries the admission
route: (urn, year) alone collides on 118 keys and a reload would have
silently dropped every band but one.
* stg_school_distance applies a 25 m – 25 km plausibility band. The source
contains 0.0-mile rows (published where a school filled on a higher
criterion), 1-metre cut-offs, and one reading 533 miles — ~4% of rows,
all of which would put a visibly wrong number on a live page.
* fact_admission_distance collapses routes to one row per school per year
using the furthest, and keeps route_count so the page can say the figure
is the widest of several bands rather than the one for a given child.
Serving
* Kept out of fact_admissions: that mart is EES-derived and near-complete
for England, this one covers 57 LAs, and the two refresh independently.
* Latest year only. Coverage is ragged — a school may have 2021 and 2026
and nothing between — so a history array would invite a trend line drawn
through gaps that are absences of publication, not of a cut-off.
* The Admissions section now renders on either source. 3% of the schools
that render have a cut-off and no EES admissions row, and gating on
admissions alone would have hidden the figure on those pages.
Interface
* The year travels with the figure everywhere it appears; a cut-off
detached from its admissions round is not a fact about anything.
* "Not a fixed catchment — it moves every year" sits under every instance,
because that is the inference a parent will otherwise draw.
* Replaces a hardcoded "Historical distance cut-off data is not available
for this school" that appeared on every secondary page, including the
ones whose council does publish it. The absence is now stated only when
it is real, and names the authority that would hold it.
The tint costs the muted tokens their AA margin: measured on the composited
backdrop (not the computed one, which reports the untinted card), --text-muted
falls to 4.09:1 in dark theme. The tile uses --text-secondary instead — 6.50:1
dark, 6.60:1 light.
The DAG is manual, like the other annual ones: councils publish on allocation
day, each on its own timetable, so there is no date worth scheduling against.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01WDvkyqqHABm4bmth2kjAxE
Swept in accidentally by a broad 'git add pipeline'. They embed local
absolute paths and a personal usage-tracking UUID, and a stale committed
manifest causes partial-parse/version-mismatch noise for others.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0146VHeLAWjDVE2B5uU67jCB
