Glossary.
Every domain term used on Akashic, defined. Geography acronyms, the typology, the demographic shorthand, and the methodology vocabulary — all in one place, all linkable.
On this page
- Geography — The legal and statistical units the federal government uses to describe American places.
- Politics & elections — The vocabulary of US presidential election analysis as we use it.
- Demographics — The statistical vocabulary we borrow from the Census Bureau and ASARB.
- Methodology — Terms specific to how Akashic is built.
Geography
The legal and statistical units the federal government uses to describe American places.
- FIPS code
Federal Information Processing Standards code identifying a US geographic area.
A five-digit FIPS code uniquely identifies a county (first two digits = state, last three = county). We use FIPS as the stable URL key for every county page (akashic.app/county/{FIPS}).
- CBSA
Core-Based Statistical Area — a metropolitan or micropolitan statistical area.
CBSAs are defined by the federal Office of Management and Budget as one or more counties sharing a labor market around an urban core of at least 10,000 people. The US has 918 CBSAs (387 Metropolitan, 531 Micropolitan as of 2023).
- MSA
Metropolitan Statistical Area — a CBSA with an urban core of 50,000+.
MSAs are the larger subset of CBSAs. "Pittsburgh, PA Metro Area" is an MSA; the smaller "Indiana, PA Micropolitan Area" is also a CBSA but not an MSA.
- DMA
Designated Market Area — a Nielsen-defined media market.
DMAs are Nielsen's 208 mutually exclusive television markets, used for advertising buys and political-ad targeting. Counties are assigned to a single DMA based on the dominant over-the-air signal.
- CD
Congressional District — one of the 435 US House districts (plus DC, PR, etc., as non-voting).
Congressional districts are redrawn each decade after the decennial census. Akashic currently serves the 118th Congress (2023–2025) boundaries; historical-boundary pages are on the roadmap.
- SLD-upper
State Legislative District, upper chamber (state senate).
Each state divides itself into legislative districts. The "upper" chamber is the state senate (or, in Nebraska, the unicameral legislature). District counts vary by state.
- SLD-lower
State Legislative District, lower chamber (state house or assembly).
The "lower" chamber in every bicameral state legislature. Typically smaller and more numerous than upper-chamber districts.
- Precinct
The smallest unit at which votes are reported — usually a single polling place.
A precinct (also "election district" in some states) is the level at which county election officials tabulate ballots. Precinct boundaries change frequently. Akashic uses 2024 precinct boundaries on its precinct maps, with vote totals disaggregated to the precinct level.
- Planning region
Connecticut's post-2022 replacement for counties.
In 2022 Connecticut formally replaced its eight legacy counties with nine Council of Government planning regions as its primary subdivision. Akashic uses planning-region geography for Connecticut and apportions pre-2022 county-level election totals to planning regions by 2020 town-level population.
- CDP
Census Designated Place — an unincorporated populated area the Census tracks as if it were a place.
CDPs let the Census report demographics for communities (typically suburban or unincorporated towns) that lack a municipal government. They appear in the long-tail /place/ surface alongside incorporated cities and towns.
Politics & elections
The vocabulary of US presidential election analysis as we use it.
- Margin
The two-party margin: (Democratic vote − Republican vote) ÷ total vote.
Expressed as a number in [−1, +1] or as a percentage. A margin of +0.10 means the Democratic candidate won by 10 percentage points. Third-party and write-in votes are included in the denominator.
- PVI
Partisan Voting Index — how a place votes relative to the nation.
PVI compares a place's presidential vote margin to the national popular-vote margin over recent cycles. "D+8" means the place is roughly eight points more Democratic than the country as a whole. The term originated with the Cook Political Report.
- Cook PVI
The original Cook Political Report PVI methodology.
Cook PVI uses a weighted average of the last two presidential elections (recent cycle weighted more heavily). Our methodology page documents how our equivalent metric is computed.
- Typology
A data-driven cluster that groups each place with the American communities it most resembles.
Akashic places every US geography in one of thirteen typologies, built by unsupervised clustering over seven feature families: vote share, vote swing, race and ethnicity, income, language spoken at home, religion, and ancestry. See the methodology page for the full procedure.
- New American
Heavily non-English-speaking communities — the hardest-swinging bloc toward the Republican Party in 2024.
The top decile of places by share of households speaking a language other than English at home. Democratic-leaning overall, but 97% of them moved toward the Republican Party between 2020 and 2024 — regardless of which language — which is why Akashic treats them as a single bloc. Los Angeles County, CA is the largest.
- Florida Surge
Florida coastal and retiree counties that moved sharply Republican across the Trump era.
Fast-growing, retiree-heavy Florida counties — Volusia, Pasco, Marion — whose mix of older white and working-class voters produced some of the steepest rightward swings in the country.
- Black Belt
Black-majority counties of the rural South — long Democratic, now trending Republican.
Named for the dark, fertile soil of the historic plantation South, the Black Belt has been among the most Democratic ground in its region for generations, with a modest recent drift toward the Republican Party. Russell County, AL is a representative example.
- Heartland Swing
Industrial Midwest swing counties drifting steadily Republican.
Mid-size, mostly white counties of Ohio, Indiana, Iowa, and Michigan that once split their tickets and have moved toward the Republican Party cycle by cycle.
- Appalachian Realigners
Lower-income Greater Appalachia — a long, deep Democratic-to-Republican arc.
Counties of the Appalachian coalfields and uplands whose mid-century Democratic loyalty, rooted in industrial and extractive labor, gave way to some of the widest Republican margins in the country. McDowell County, WV (Democratic 1932–1996, Republican since 2000) is the canonical case.
- Evangelical Deep South
Deeply Republican, heavily Baptist counties of the Deep South.
The conservative core of the Deep South — predominantly white, heavily evangelical Protestant, and Republican by wide margins.
- Texan Right
Solidly Republican East Texas and the Piney Woods, with a Latino presence.
A band of solidly Republican Texas counties — Nacogdoches, Taylor, Anderson — where a meaningful Latino population votes, here, with the Republican grain.
- Industrial Catholic Metro
Mid-size industrial metros with Catholic ethnic roots — the most competitive ground.
Mid-size metropolitan counties shaped by Italian, Irish, and Polish Catholic settlement — Peoria, Erie, Scranton — whose close margins make them among the most swing-prone places in the country.
- Sunbelt Conservative
Affluent, predominantly white Sunbelt counties — reliably Republican.
Higher-income, mostly white counties across the Sunbelt that vote Republican by consistent margins.
- Diversifying Metro
Mid-size metros growing more diverse and trending Democratic.
Mid-size metropolitan counties — Omaha, Louisville, Virginia Beach — growing more diverse and college-educated, and moving toward the Democratic Party.
- Farm Belt
Rural Plains and Midwest farm counties — Republican, with modest movement.
Agricultural counties of the Plains and rural Midwest — predominantly white, German and Scandinavian in ancestry — that vote Republican with only modest recent movement.
- Stable Rural Right
Mountain and Plains Republican counties already near their ceiling.
Rural Republican counties of the Mountain West and Plains whose margins are already so wide they have little room left to swing further right.
- Realigning Affluent Suburb
College-educated, affluent suburbs — the one bloc moving Democratic.
Higher-income, college-educated suburban counties — Johnson County, KS; the Denver and Indianapolis collar — that have trended toward the Democratic Party even as their states have not.
Demographics
The statistical vocabulary we borrow from the Census Bureau and ASARB.
- ACS
American Community Survey — the Census Bureau's rolling demographic survey.
ACS replaces the long-form decennial Census. The 5-year estimates (which we use) pool five years of survey responses to produce reliable estimates even for small geographies. Our data uses ACS 2024 5-year (reference period 2020–2024).
- ACS 5-year estimates
Five years of ACS responses pooled into one estimate.
The 5-year file is the only ACS product available for every county, no matter how small. Estimates carry sampling error; the Census Bureau publishes margins of error which we honor in our data validation pipeline.
- Median household income
The midpoint of household-income distribution for the place.
Reported in inflation-adjusted dollars for the ACS reference period. Half of households earn less; half earn more. Sensitive to ACS year — comparing across vintages requires adjusting for inflation.
- ASARB
Association of Statisticians of American Religious Bodies.
ASARB compiles the decennial US Religion Census, the most comprehensive county-level religious-adherence dataset. We use their 2020 release, bucketed into seven traditions for display.
- US Religion Census
Decennial county-level religious-adherence dataset compiled by ASARB.
The Religion Census reports the number of adherents per religious body per US county. We aggregate the ~250 reporting bodies into seven traditions: Baptist; Methodist; Pentecostal & Holiness; Catholic & Orthodox; Mainline Protestant; Other Christian; Non-Christian.
Methodology
Terms specific to how Akashic is built.
- Similar counties
The set of counties whose recent voting pattern most resembles a given county.
Computed as cosine similarity over the last-ten-election two-party margin vector. The result reflects political similarity over recent decades — not demographic or geographic similarity. Two counties with similar margin trajectories will appear similar even if they are on opposite coasts.
- Narrative
The deterministic prose paragraphs generated for each place page.
Every county page carries an editor-curated lead paragraph followed by paragraphs deterministically templated from the underlying election and demographic data. Same input, same output — no LLM in the runtime path.
- TIGER/Line
The Census Bureau's geographic-boundary shapefiles.
TIGER/Line files are the federal authoritative source for political and statistical boundary geometry. Akashic uses TIGER/Line 2024 county and precinct shapefiles, simplified with topojson-simplify for web delivery.
- ICPSR
Inter-university Consortium for Political and Social Research — historical election archive.
ICPSR (housed at the University of Michigan) maintains the canonical historical archive of US election results back to the early Republic. We use their county-level presidential series for pre-1916 cycles.
- MIT Election Lab
The contemporary county-level election results dataset, 1916–present.
The MIT Election Data and Science Lab publishes the most-cited modern county-level presidential election dataset. We use their series for 1916–2020 and the official state-certified returns for 2024.
- VEST
Voting and Election Science Team — precinct-level election results.
VEST is a project that assembles precinct-level vote totals from state and county sources. We use VEST's precinct data, aggregated to modern county boundaries, to produce the precinct maps on county pages.
- Cosine similarity
A measure of similarity between two vectors based on the angle between them.
For two vectors A and B, cosine similarity is (A · B) / (||A|| × ||B||), a value in [−1, +1]. Two vectors that point in the same direction score 1.0. We use it to find counties whose last-ten-election margin patterns most resemble each other.
See also
For the long-form discussion of how every term above is operationalized, see the methodology paper. For the project overview, see about. For what we’re building next, see the roadmap.