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Fabio 5b0712c31f Add elections + demographics layer: candidates and jurisdictions

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>

2026-07-03 23:35:00 -04:00

1.2 KiB

Raw Blame History

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

type

title

classification

state

district

demographics

sources

confidence

NC-04

Congressional district NC-04.

Demographics (ACS 2023)

Measure	Value
Population	804357
Under 18	200744
18–64	288829
65+	314784
Median household income	102410
Poverty rate	8.46
Homeownership rate	63.55
Unemployment rate	3.31
Median home value	531100
Gini index	0.4549
Vacancy rate	6.78
White	429279
Black	146068
Asian	97803
Hispanic/Latino	95625
Bachelor's or higher	344872

Source

demographics: Census ACS 2023

1.2 KiB Raw Blame History Unescape Escape

NC-04

Demographics (ACS 2023)

Source

1.2 KiB

Raw Blame History