The first edges in the repo. Every US county now links to the districts it
sits in, weighted by area overlap, and every legislator links to the district
they represent — county → district → representative, traversable.
Data (PostGIS, no Atlas dependency — computed directly from geometry):
- data/county_district_edges.jsonl — 16,328 weighted edges. area_weight =
ST_Area(ST_Intersection(county, district))/ST_Area(county) over ST_MakeValid
Census TIGER 2024 geometries, overlaps <0.5% dropped. Uses area-intersection,
NOT centroid-in-polygon, so urban districts that carve through counties
(e.g. GA CD-5 / Atlanta across Clayton+DeKalb+Fulton) are captured, not
silently dropped. Per-county weights sum to ~1.0 per district type.
build.py:
- ~6,896 new district nodes: 435 CD (ACS demographics where available; sparse
for the 8 states missing from acs_cd) + 4,927 state-house + 1,897 state-senate
(geometry-only). GEOID/number-keyed, stable.
- county index.md gains a `districts:` block (frontmatter) + a ## Districts
section (body links, so link-integrity checks them).
- legislators + US House members gain `represents:` pointing at their district
node; edge stored once on the person, inverse left to the view.
- schemas/jurisdiction: classification enum + chamber/districts fields.
Honest gaps (logged, not hidden): ID + NH have no state-house geometry, so
their house districts get nodes (represents resolves) but no county edges;
CD nodes for acs_cd-missing states are demographically sparse.
Deterministic (byte-identical rebuild); validate.py passes 26,617 records,
link-integrity clean (every district/represents link resolves).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
build_viz.py was resolving officials to counties without the place→county
crosswalk, so only FL's 67 counties had rosters. Factor the resolver out of
build.py into a shared build.place_resolver() and use it in both, so the
Board and the tree agree by construction.
- scripts/build.py — extract place_resolver(); main() now calls it (tree
output byte-identical, refactor is behavior-preserving)
- scripts/build_viz.py — resolve via the shared resolver
- viz/county_data.json, viz/county_detail.json — regenerated
County drill-downs: 67 → 1049 counties with rosters; officials mapped
5184/5226 (42 county-unresolvable, tree-only). Deterministic.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Pull Atlas's place→county crosswalk (Census TIGER 2024 places × county
geometries, 32,041 places) into the build as a committed raw input, and
use it to resolve municipal officeholders whose county was unknown.
- data/place_county_crosswalk.jsonl — new raw input (place_geoid → county_geoid)
- build.py — place-name+state → county_geoid → ACS county slug as a fallback
in county_slug(); city_slug() generalized to non-FL bare place labels
- data/sources/sources.yaml — provenance entry for the crosswalk
_unresolved municipals: 1919 → 58. The remainder are genuine: CT planning
regions (post-county), county-level rows mislabeled municipal upstream,
consolidated city-counties (null label), Wikidata-QID placeholder names,
and ambiguous same-name places — left unresolved rather than guessed.
Join key is county_geoid (5-digit FIPS); the directory slug is taken from
the ACS county node so municipals nest under the exact existing county dir.
Deterministic (byte-identical rebuild); validate.py passes 19,721 records.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Re-exported directly from Postgres (10.0.0.116), not Atlas's introspection,
which under-reported the roster (its v3 export had 11,473, missing the
restored Florida League of Cities municipal set). Live DB current tenures:
state 7,561 · municipal 4,009 · county 1,217 · federal 542.
- build.py now reads officeholders-v3.jsonl (was v2); dataset vintage 2026-07-04
- fixed one corrupt start_date at source-export (21021-12-03 -> 2021-12-03,
Lorraine Borowski, Mayor of Decorah IA)
- scripts/build_viz.py: committed, deterministic generator for the Board's
county_data.json / county_detail.json (previously built ad-hoc)
- generate_changelog.py: ignore files whose only change is the vintage stamp,
so the diff reflects government change, not re-export bookkeeping
Person files 11,285 -> 13,329. Non-FL municipal officials (1,903) are in the
tree but not yet county-mappable (place->county crosswalk still NULL).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Two new entity types, built deterministically from the raw exports:
Candidate (2,494) — 2026 federal candidates as files under
us/states/<st>/candidates/, with office,
district, party, incumbent/challenger stance,
campaign committee, FEC id.
Jurisdiction (3,494) — demographic nodes carrying ACS 2023 data:
3,131 counties (deduped from 3,231 mixed-format
fips keys) + 363 congressional districts.
This completes the county skeleton nationwide: every US county is now a
browsable node with population, income, poverty, race, education and
housing — even where no officeholders are mapped yet. In Florida the
county index.md merges into the existing dir and reports its officeholder
count alongside demographics (St. Lucie: 33 officeholders + ACS profile).
build.py now reads seven raw inputs in one deterministic pass
(byte-identical on rerun). The mirror now answers: who holds power, who
runs this institution, who is running, and what each place is made of.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The seat data answered 'who represents this place?' but not 'who runs
this institution?' — the Speaker of the House existed only as the rep
for LA-4. This adds the missing axis of institutional power.
New Body entities (233): the U.S. House, U.S. Senate, Executive Office,
49 committees, 181 subcommittees — each with its leadership.
Federal people (532/542) enriched with bioguide IDs, leadership roles,
and committee seats, reconciled from congress-legislators by name.
Mike Johnson is now Speaker of the House, not just LA-4.
Sources ingested directly into the repo (repo-canonical): Atlas
supplied bodies/leadership/committee_memberships via congress-legislators,
committed raw in data/. build.py supersedes convert_officeholders.py and
reads all four inputs in one deterministic pass (byte-identical on rerun).
The 10 unenriched federal records are a finding, not a bug: vacancies,
the President, and ~5 seats where Atlas's roster disagrees with the
authoritative current roster — model-vs-reality discrepancies to surface.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Fabio