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>
4.8 KiB
4.8 KiB
type, title, classification, fips, state, demographics, districts, sources, confidence, tags, timestamp
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| Jurisdiction | Orange County, CA | county | 06059 | CA |
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official |
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2026-07-03 |
Orange County, CA
County jurisdiction — 9 officeholders mapped.
Demographics (ACS 2023)
| Measure | Value |
|---|---|
| Population | 3165820 |
| Under 18 | 665133 |
| 18–64 | 1985863 |
| 65+ | 514824 |
| Median household income | 116289 |
| Poverty rate | 9.5 |
| Homeownership rate | 56.39 |
| Unemployment rate | 5.4 |
| Median home value | 962600 |
| Gini index | 0.4657 |
| Vacancy rate | 5.51 |
| White | 1351830 |
| Black | 51497 |
| Asian | 708219 |
| Native | 27023 |
| Hispanic/Latino | 1082931 |
| Bachelor's or higher | 1340166 |
Districts
- CA-40 — 36% (congressional)
- CA-47 — 18% (congressional)
- CA-49 — 11% (congressional)
- CA-45 — 10% (congressional)
- CA-46 — 8% (congressional)
- CA-38 — 1% (congressional)
- CA Senate District 37 — 40% (state senate)
- CA Senate District 36 — 19% (state senate)
- CA Senate District 38 — 11% (state senate)
- CA Senate District 34 — 10% (state senate)
- CA Senate District 32 — 3% (state senate)
- CA Senate District 30 — 2% (state senate)
- CA House District 71 — 21% (state house)
- CA House District 59 — 16% (state house)
- CA House District 72 — 13% (state house)
- CA House District 73 — 11% (state house)
- CA House District 74 — 6% (state house)
- CA House District 70 — 6% (state house)
- CA House District 68 — 5% (state house)
- CA House District 67 — 5% (state house)
- CA House District 64 — 1% (state house)
Source
- demographics: Census ACS 2023
- districts: PostGIS area-intersection over Census TIGER 2024 boundaries