Fabio
1beeaab2db
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>
5.3 KiB
5.3 KiB
type, title, classification, fips, state, demographics, districts, sources, confidence, tags, timestamp
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| Jurisdiction | Dallas County, TX | county | 48113 | TX |
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official |
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2026-07-03 |
Dallas County, TX
County jurisdiction — 15 officeholders mapped.
Demographics (ACS 2023)
| Measure | Value |
|---|---|
| Population | 2621179 |
| Under 18 | 658541 |
| 18–64 | 1653583 |
| 65+ | 309055 |
| Median household income | 76547 |
| Poverty rate | 13.92 |
| Homeownership rate | 50.77 |
| Unemployment rate | 5.0 |
| Median home value | 303000 |
| Gini index | 0.4915 |
| Vacancy rate | 7.72 |
| White | 910742 |
| Black | 587065 |
| Asian | 185198 |
| Native | 23770 |
| Hispanic/Latino | 1081496 |
| Bachelor's or higher | 844750 |
Districts
- TX-30 — 39% (congressional)
- TX-05 — 19% (congressional)
- TX-32 — 14% (congressional)
- TX-33 — 12% (congressional)
- TX-24 — 10% (congressional)
- TX-06 — 6% (congressional)
- TX Senate District 23 — 41% (state senate)
- TX Senate District 16 — 29% (state senate)
- TX Senate District 2 — 19% (state senate)
- TX Senate District 12 — 11% (state senate)
- TX House District 109 — 22% (state house)
- TX House District 112 — 9% (state house)
- TX House District 110 — 8% (state house)
- TX House District 111 — 7% (state house)
- TX House District 103 — 7% (state house)
- TX House District 115 — 7% (state house)
- TX House District 104 — 7% (state house)
- TX House District 113 — 6% (state house)
- TX House District 100 — 5% (state house)
- TX House District 105 — 5% (state house)
- TX House District 108 — 5% (state house)
- TX House District 107 — 4% (state house)
- TX House District 102 — 4% (state house)
- TX House District 114 — 4% (state house)
Source
- demographics: Census ACS 2023
- districts: PostGIS area-intersection over Census TIGER 2024 boundaries