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Fabio 1beeaab2db Graph: national county↔district↔legislator authority layer

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>

2026-07-04 22:28:08 -04:00

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Raw Blame History

type, title, classification, state, district, demographics, sources, confidence, tags, timestamp

type

title

classification

state

district

demographics

sources

confidence

CA-30

Congressional district CA-30.

Demographics (ACS 2023)

Measure	Value
Population	741397
Under 18	115753
18–64	311338
65+	314306
Median household income	89846
Poverty rate	11.92
Homeownership rate	30.27
Unemployment rate	7.24
Median home value	1178900
Gini index	0.512
Vacancy rate	7.81
White	427670
Black	28322
Asian	90883
Hispanic/Latino	168988
Bachelor's or higher	322655

Source

demographics: Census ACS 2023

1.2 KiB Raw Blame History Unescape Escape

CA-30

Demographics (ACS 2023)

Source

1.2 KiB

Raw Blame History