US county population-component intelligence (annual)
Census Bureau Vintage 2025 county population estimates (keyless official CSV, U.S. public domain): the wide county file reshaped to a county x estimate-year panel (3,144 county-equivalents x 2020-2025) with births, deaths, natural change, domestic and international migration, net migration and residual components, all identity-gated exactly (natural == births - deaths, net == domestic + international, residual == total - natural - net, county sums == state rows) and per-1,000 midyear-population rates cross-checked against the file's own R columns. A documented 0-100 growth-momentum score (50% total rate + 30% net-migration rate + 20% natural rate, winsorized, min-maxed within each estimate year) is ranked nationally (growth_rank) and within each state, tiered g1-g4, with a closed migration-driver taxonomy (migration-led/natural-increase-led/mixed growth and decline, stable) and top-100, bottom-100, migration-reversal, record-high/low-population and growth-streak flags. Primary key: (county_fips, estimate_year). Cadence: yearly; the upstream file is a static annual vintage, so as_of is the vintage year and identical input produces an identical content hash. Caveats: estimates are modeled and revised each vintage (single-vintage consistent series); Puerto Rico municipios are absent upstream; rates use midyear population. Sample use: order by growth_rank within an estimate year for the fastest-growing counties, or filter state_code for within-state migration geography.
- Rows
- 18,864
- Columns
- 37
- Source cadence
- Yearly
- Last refreshed
- Sep 27, 2026
- Theme
- demographics
| Column | Type | Description |
|---|---|---|
| as_of | string | Fixed vintage stamp: the vintage year of the estimates file (identical input yields an identical content hash). (unit: year) |
| vintage | integer | Census population-estimates vintage year. (unit: year) |
| estimate_year | integer | Estimate reference year (July-1 estimates). (unit: year) |
| county_fips | string | 5-digit FIPS (state 2 + county 3); stable primary key with estimate_year. (unit: id) |
| county_name | string | County-equivalent name from the file (includes independent cities). |
| state_code | string | USPS two-letter state/District code. (unit: code) |
| state_name | string | State/District name for the state_code. |
| country_code | string | Constant USA join key. (unit: code) |
| pop_estimate | integer | July-1 resident population estimate. (unit: persons) |
| pop_change | integer | Year-over-year population change (persons). (unit: persons) |
| pop_change_rate_1k | float | Population change per 1,000 midyear population. (unit: per 1,000) |
| births | integer | Births during the estimate year. (unit: persons) |
| deaths | integer | Deaths during the estimate year. (unit: persons) |
| natural_chg | integer | Natural change = births - deaths (identity-gated exact). (unit: persons) |
| natural_rate_1k | float | Natural change per 1,000 midyear population. (unit: per 1,000) |
| domestic_mig | integer | Net domestic migration. (unit: persons) |
| intl_mig | integer | Net international migration. (unit: persons) |
| net_mig | integer | Net migration = domestic + international (identity-gated exact). (unit: persons) |
| net_mig_rate_1k | float | Net migration per 1,000 midyear population. (unit: per 1,000) |
| domestic_mig_rate_1k | float | Net domestic migration per 1,000 midyear population. (unit: per 1,000) |
| intl_mig_rate_1k | float | Net international migration per 1,000 midyear population. (unit: per 1,000) |
| residual | integer | Residual = pop_change - natural_chg - net_mig (identity-gated exact; absorbs vintage revisions). (unit: persons) |
| residual_share_abs_pct | float | |residual| as a share of |pop_change|; null when pop_change is 0. (unit: percent) |
| mig_driver | string | Closed 7-value taxonomy: migration-led / natural-increase-led / mixed growth, migration-driven / natural-decline-led / mixed decline, stable. (unit: category) |
| mig_share_of_change_pct | float | net_mig / pop_change * 100; null when pop_change is 0. (unit: percent) |
| growth_score | float | Documented 0-100 score = 100*(0.50*min-max clip(total_rate_1k,+-20) + 0.30*min-max clip(net_mig_rate_1k,+-15) + 0.20*min-max clip(natural_rate_1k,+-10)), min-maxed within each estimate year. (unit: score) |
| growth_rank | integer | National growth rank within the estimate year (1 = fastest-growing; ties broken by county_fips asc). (unit: rank) |
| growth_tier | string | Rank-quartile tier: g1 (top 25%) .. g4. (unit: tier) |
| state_growth_rank | integer | Growth rank within the county's state and estimate year. (unit: rank) |
| top100_growth_flag | integer | 1 when growth_rank <= 100. (unit: flag) |
| bottom100_decline_flag | integer | 1 when growth_rank is in the bottom 100 of the year. (unit: flag) |
| migration_reversal_flag | integer | 1 when net_mig changes sign vs the prior estimate year. (unit: flag) |
| record_high_pop_flag | integer | 1 when pop_estimate is a new within-vintage high (year > 2020). (unit: flag) |
| record_low_pop_flag | integer | 1 when pop_estimate is a new within-vintage low (year > 2020). (unit: flag) |
| growth_streak_years | integer | Trailing run of estimate years with pop_change > 0 ending at this year. (unit: years) |
| source_url | string | Canonical URL of the official Vintage 2025 county-estimates CSV. (unit: URL) |
| row_hash | string | SHA-256 (16 hex) over the row's content fields; identical input yields an identical hash. (unit: hash) |
First 10 sample rows — a preview, not the complete dataset.
| as_of | vintage | estimate_year | county_fips | county_name | state_code | state_name | country_code | pop_estimate | pop_change | pop_change_rate_1k | births | deaths | natural_chg | natural_rate_1k | domestic_mig | intl_mig | net_mig | net_mig_rate_1k | domestic_mig_rate_1k | intl_mig_rate_1k | residual | residual_share_abs_pct | mig_driver | mig_share_of_change_pct | growth_score | growth_rank | growth_tier | state_growth_rank | top100_growth_flag | bottom100_decline_flag | migration_reversal_flag | record_high_pop_flag | record_low_pop_flag | growth_streak_years | source_url | row_hash |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2025 | 2,025 | 2,020 | 48269 | King County | TX | Texas | USA | 267 | 5 | 18.904 | 1 | 0 | 1 | 3.781 | 4 | 0 | 4 | 15.123 | 15.123 | 0 | 0 | 0 | migration-led growth | 80 | 95.5 | 1 | g1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | https://www2.census.gov/programs-surveys/popest/datasets/2020-2025/counties/totals/co-est2025-alldata.csv | 882d212bfd9ba49a |
| 2025 | 2,025 | 2,020 | 02282 | Yakutat City and Borough | AK | Alaska | USA | 682 | 20 | 29.762 | 1 | 0 | 1 | 1.488 | 21 | 0 | 21 | 31.25 | 31.25 | 0 | -2 | 10 | migration-led growth | 105 | 92.97 | 2 | g1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | https://www2.census.gov/programs-surveys/popest/datasets/2020-2025/counties/totals/co-est2025-alldata.csv | 89459e3a9a3dbe02 |
| 2025 | 2,025 | 2,020 | 48033 | Borden County | TX | Texas | USA | 643 | 14 | 22.013 | 1 | 1 | 0 | 0 | 16 | 0 | 16 | 25.157 | 25.157 | 0 | -2 | 14.286 | migration-led growth | 114.29 | 90.43 | 3 | g1 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | https://www2.census.gov/programs-surveys/popest/datasets/2020-2025/counties/totals/co-est2025-alldata.csv | 1dd5a5093b03e94a |
| 2025 | 2,025 | 2,020 | 01125 | Tuscaloosa County | AL | Alabama | USA | 232,604 | 4,471 | 19.408 | 613 | 518 | 95 | 0.412 | 5,141 | 4 | 5,145 | 22.334 | 22.316 | 0.017 | -769 | 17.2 | migration-led growth | 115.07 | 90.39 | 4 | g1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | https://www2.census.gov/programs-surveys/popest/datasets/2020-2025/counties/totals/co-est2025-alldata.csv | 27c1406377d277eb |
| 2025 | 2,025 | 2,020 | 29063 | DeKalb County | MO | Missouri | USA | 11,605 | 576 | 50.897 | 24 | 31 | -7 | -0.619 | 696 | 0 | 696 | 61.5 | 61.5 | 0 | -113 | 19.618 | migration-led growth | 120.83 | 89.38 | 5 | g1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | https://www2.census.gov/programs-surveys/popest/datasets/2020-2025/counties/totals/co-est2025-alldata.csv | ea4d94b7b7cd8e9c |
| 2025 | 2,025 | 2,020 | 48301 | Loving County | TX | Texas | USA | 63 | 1 | 16 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 16 | 16 | 0 | 0 | 0 | migration-led growth | 100 | 85.43 | 6 | g1 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | https://www2.census.gov/programs-surveys/popest/datasets/2020-2025/counties/totals/co-est2025-alldata.csv | 44a75672408025ef |
| 2025 | 2,025 | 2,020 | 49009 | Daggett County | UT | Utah | USA | 951 | 15 | 15.898 | 1 | 1 | 0 | 0 | 18 | 0 | 18 | 19.078 | 19.078 | 0 | -3 | 20 | migration-led growth | 120 | 85.31 | 7 | g1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | https://www2.census.gov/programs-surveys/popest/datasets/2020-2025/counties/totals/co-est2025-alldata.csv | d8dde6bf6ecf55a8 |
| 2025 | 2,025 | 2,020 | 48091 | Comal County | TX | Texas | USA | 163,608 | 2,159 | 13.284 | 389 | 345 | 44 | 0.271 | 2,360 | 2 | 2,362 | 14.533 | 14.521 | 0.012 | -247 | 11.44 | migration-led growth | 109.4 | 82.03 | 8 | g1 | 4 | 1 | 0 | 0 | 0 | 0 | 1 | https://www2.census.gov/programs-surveys/popest/datasets/2020-2025/counties/totals/co-est2025-alldata.csv | 7e7f7eb6ba2118f4 |
| 2025 | 2,025 | 2,020 | 12109 | St. Johns County | FL | Florida | USA | 277,119 | 3,693 | 13.416 | 506 | 560 | -54 | -0.196 | 4,153 | 2 | 4,155 | 15.094 | 15.087 | 0.007 | -408 | 11.048 | migration-led growth | 112.51 | 81.87 | 9 | g1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | https://www2.census.gov/programs-surveys/popest/datasets/2020-2025/counties/totals/co-est2025-alldata.csv | ac26ac6c7f606c95 |
| 2025 | 2,025 | 2,020 | 30007 | Broadwater County | MT | Montana | USA | 6,860 | 85 | 12.468 | 25 | 8 | 17 | 2.494 | 77 | 0 | 77 | 11.294 | 11.294 | 0 | -9 | 10.588 | migration-led growth | 90.59 | 81.56 | 10 | g1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | https://www2.census.gov/programs-surveys/popest/datasets/2020-2025/counties/totals/co-est2025-alldata.csv | 3bef5d71d4fea439 |
- Current
20260927T131244Z-0380dcd0da2b · sha256 0380dcd0da2b…
18,864 rows · first snapshot
Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).
curl "https://datazimuts.com/v1/datasets/census_county_pop_intel/us_county_pop_component_annual" | jq '{title, rows, columns_count, license}'import requests
ds = requests.get("https://datazimuts.com/v1/datasets/census_county_pop_intel/us_county_pop_component_annual").json()
print(ds["title"], ds["rows"], "rows")
# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
print(row)API endpoint: https://datazimuts.com/v1/datasets/census_county_pop_intel/us_county_pop_component_annual
Tip: fetch /llms.txt for the full machine-readable catalog.
Where this data comes from and what was made from it. Other people's work shows as counts; only shared projects are named.
Cite this snapshot
Pinned to snapshot 20260927T131244Z-0380dcd0da2b and its content hash, so readers get exactly the data you used.
Census County Population-Component Intelligence. (2026). US county population-component intelligence (annual) [Data set, snapshot 20260927T131244Z-0380dcd0da2b, sha256 0380dcd0da2b]. Datazimuts. Retrieved 2026-09-27, from https://datazimuts.com/en/datasets/census_county_pop_intel/us_county_pop_component_annual?snapshot=20260927T131244Z-0380dcd0da2b
@misc{dz_census_county_pop_intel_us_county_pop_co_0380dcd0,
title = {{US county population-component intelligence (annual)}},
author = {{Census County Population-Component Intelligence}},
year = {2026},
publisher = {Datazimuts},
howpublished = {\url{https://datazimuts.com/en/datasets/census_county_pop_intel/us_county_pop_component_annual?snapshot=20260927T131244Z-0380dcd0da2b}},
note = {Snapshot 20260927T131244Z-0380dcd0da2b, sha256 0380dcd0da2b7878d61d50df916dce78090c75e74b836e55bae30ab7951bc056; accessed 2026-09-27}
}Embed a table or a chart
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<iframe src="https://datazimuts.com/embed/chart?dataset=census_county_pop_intel%2Fus_county_pop_component_annual&lang=en&theme=auto&snapshot=20260927T131244Z-0380dcd0da2b&x=estimate_year&y=vintage&agg=avg" title="US county population-component intelligence (annual)" width="100%" height="380" style="border:0" loading="lazy"></iframe>
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