Fast facts about local community statistics
Local community statistics are most useful when they turn a broad area into a readable set of signals: health, demographics, place-level coverage, and comparison tools that let you zoom in fast.
In this case, the dataset is especially strong because it combines CDC PLACES, ACS, LAUS, and County Health Rankings figures. That gives you a practical map of what can be measured, where it can be measured, and how local areas can be compared.
At a glance
- PLACES provides local health data for all 3,144 U.S. counties (PLACES One-Sheet).
- It also covers 29,923 incorporated and census-designated places (PLACES One-Sheet).
- The same system covers 83,522 census tracts and 32,520 ZIP Code Tabulation Areas (PLACES One-Sheet).
- ACS releases new data every year and its tools now include 2020-2024 ACS 5-year estimates (ACS Data; ACS Data Tools).
- LAUS publishes both monthly and annual estimates for labor force, employment, unemployment, and unemployment rate measures (LAUS Quick Facts).
- County Health Rankings uses large regional definitions such as 423 Appalachian counties, 285 Black Belt counties, and 44 U.S.-Mexico Border counties (CHR National Findings Report 2024).
Table of contents
- What local community statistics cover
- PLACES coverage and measures
- ACS data for neighborhood and place analysis
- LAUS labor market signals
- County Health Rankings regional context
- How to use local community statistics together
What local community statistics cover
Local community statistics are not one dataset with one purpose. They are a stack of different systems that answer different questions about the same place.
Some measures describe health outcomes and risks. Others describe population structure, housing, income, and social conditions. Others track employment and unemployment at a local scale. Together, they make it possible to compare a county to a city, a tract, a ZIP Code Tabulation Area, or a broader region without guessing at the baseline.
Key takeaway
The biggest value of local community statistics is coverage. The more geographies a system supports, the easier it becomes to compare a small place with a larger county or state context and keep the comparison meaningful.
That is why the dataset here matters: it shows how different public-data systems divide the local picture into complementary layers rather than a single score.
PLACES coverage and measures
PLACES is the clearest example of broad local health coverage in this dataset. It provides local health data for all 3,144 U.S. counties (PLACES One-Sheet), which means county-level analysis is available nationwide.
But PLACES does not stop at counties. It also covers:
- 29,923 incorporated and census-designated places (PLACES One-Sheet)
- 83,522 census tracts (PLACES One-Sheet)
- 32,520 ZIP Code Tabulation Areas (PLACES One-Sheet)
That range matters because a county often hides sharp variation inside smaller neighborhoods. A tract or ZIP Code Tabulation Area can show a pattern that disappears in the county average.
Big number
83,522 census tracts is a large enough footprint to support neighborhood-scale analysis across the country (PLACES One-Sheet).
PLACES also has a clear internal structure for what it measures:
- 49 chronic disease and other health measures (PLACES One-Sheet)
- Those measures are split into 12 health outcomes (PLACES One-Sheet)
- 7 prevention practices (PLACES One-Sheet)
- 4 health risk behaviors (PLACES One-Sheet)
- 7 disabilities (PLACES One-Sheet)
- 3 health statuses (PLACES One-Sheet)
- 7 non-medical factors affecting health (PLACES One-Sheet)
- 9 community factors (PLACES One-Sheet)
That structure is useful because it prevents a local-health discussion from collapsing into one headline number. A place can be strong in one area and weak in another, and PLACES gives you a way to separate those components.
Why it matters
When a data system includes outcomes, behaviors, status, and community factors, you can move from description to diagnosis. A local statistic is more actionable when it can be grouped into what is happening, what may be driving it, and what context surrounds it.
PLACES inputs and comparison tools
PLACES is also notable because the dataset exposes its inputs and viewing tools.
It uses the 2022 Behavioral Risk Factor Surveillance System as an input (PLACES One-Sheet). It also uses the 5-year American Community Survey, Census 2020 population counts, and annual census county population estimates (PLACES One-Sheet).
That tells you the system is built from multiple public sources rather than a single survey or a single administrative file.
Fast facts
- PLACES is available for 39 states plus the District of Columbia for the seven new non-medical factors (PLACES One-Sheet).
- The interactive comparison report supports up to 3 counties or places at a time (PLACES Comparison Report).
- The PLACES data portal includes 8 datasets for each release (PLACES Data Portal).
- The portal serves data in 2 formats: open data and GIS-friendly (PLACES Data Portal).
Those access details matter as much as the measures themselves. A comparison tool that handles up to three geographies at once is built for practical side-by-side use, not just single-point lookup.
Compact comparison table
| PLACES detail | Number | Source label |
|---|---|---|
| U.S. counties covered | 3,144 | PLACES One-Sheet |
| Incorporated and census-designated places | 29,923 | PLACES One-Sheet |
| Census tracts covered | 83,522 | PLACES One-Sheet |
| ZIP Code Tabulation Areas covered | 32,520 | PLACES One-Sheet |
| Health and other measures | 49 | PLACES One-Sheet |
| Health outcomes | 12 | PLACES One-Sheet |
| Prevention practices | 7 | PLACES One-Sheet |
| Risk behaviors | 4 | PLACES One-Sheet |
The table shows a simple pattern: PLACES is wide in geographic coverage and broad in measure design. That combination makes it a useful backbone for local community statistics because it can support both place-level and county-level comparisons without changing the basic dataset.
ACS data for neighborhood and place analysis
The American Community Survey adds a different kind of local community statistic. ACS is not primarily a health system; it is a general-purpose source for population, economics, housing, and demographics.
The ACS Data Profiles are split into 4 subject areas: social, economic, housing, and demographic (ACS Data Profiles). They provide both estimates and percentages (ACS Data Profiles), which makes them practical for quick scanning and for more detailed comparisons.
What the ACS tools support
The ACS data tools page says 2020-2024 ACS 5-year estimates are now available (ACS Data Tools). It also says data.census.gov provides:
- ACS Comparison Profiles
- Data Profiles
- Detailed Tables
- Subject Tables (ACS Data Tools)
The same page notes that data.census.gov provides ACS 5-year estimates from 2006-2010 to the latest release and covers all areas down to block group level for that ACS 5-year series (ACS Data Tools).
Why that matters for local community statistics
ACS is the bridge between broad population context and local variation. If a county-level health measure suggests a concern, ACS can help contextualize it with social, economic, housing, and demographic detail.
It also helps explain why two places with similar health statistics can still look very different in lived conditions.
Useful ACS availability points
- Selected population tables and American Indian and Alaska Native tables are available for 2006-2010, 2011-2015, and 2017-2021 5-year releases (ACS Data Tools).
- Those selected population tables are available for selected geographies down to tract level (ACS Data Tools).
- ACS Supplemental Estimates run from 2014 ACS 1-year supplemental estimates to the latest standard release (ACS Data Tools).
- ACS Supplemental Estimates are available for areas with populations of 20,000 or more (ACS Data Tools).
- ACS 1-year estimates are available for areas with populations of 65,000 or more (ACS Data Tools).
- Narrative Profiles are available beginning with the 2010-2014 ACS 5-year release (ACS Data Tools).
- Ranking Tables and Geographic Comparison Tables are available on the ACS website as FTP files, not in data.census.gov (ACS Data Tools).
At a glance
ACS gives you annual refreshes plus multiple levels of geography, down to block group level in the 5-year series (ACS Data; ACS Data Tools). That makes it a core source for tracking local community change over time.
LAUS labor market signals
Local community statistics are not complete if they ignore jobs and unemployment. LAUS fills that gap with labor market measures.
The dataset says LAUS publishes monthly estimates and annual estimates (LAUS Quick Facts). It provides labor force, employment, unemployment, and unemployment rate measures (LAUS Quick Facts). It also covers metro areas, states, counties, and many cities (LAUS Quick Facts).
That scope matters because labor market conditions often change faster than demographic conditions. A place can look stable in population terms while still experiencing a weaker labor market signal.
What LAUS contributes to local analysis
- It adds a current labor view to a broader community profile.
- It works across multiple geography types.
- It gives both monthly movement and annual context.
Quick interpretation frame
A practical local report often benefits from pairing:
- a health or community outcome measure,
- a demographic or housing profile,
- a labor market measure.
LAUS covers the third piece with direct, recurring estimates (LAUS Quick Facts).
County Health Rankings regional context
County Health Rankings adds another layer: named regional groupings and report-based health context.
The 2024 National Findings Report defines the Appalachian Region as 423 counties across 13 states (CHR National Findings Report 2024). It defines the Black Belt Region as 285 counties (CHR National Findings Report 2024). It also defines the U.S.-Mexico Border Region as 44 counties and says that region stretches 2,000 miles (CHR National Findings Report 2024).
Those regional definitions matter because they help frame local community statistics as part of a wider pattern, not just a single isolated county result.
Regional comparison table
| Regional grouping | Counties | Extra detail | Source label |
|---|---|---|---|
| Appalachian Region | 423 | Across 13 states | CHR National Findings Report 2024 |
| Black Belt Region | 285 | Noted as a regional definition | CHR National Findings Report 2024 |
| U.S.-Mexico Border Region | 44 | Stretches 2,000 miles | CHR National Findings Report 2024 |
County Health Rankings also describes how it handles health outcomes and related measures in 2024. It uses the 10th percentile counties as the least healthy and the 90th percentile counties as the healthiest for the 2024 Health Outcomes z-score (CHR National Findings Report 2024).
That percentile framing is useful because it makes the distribution explicit. Instead of treating local health as a vague average, it places counties relative to the lower and upper ends of the distribution.
Selected measure timing in the report
The report includes a set of measures drawn from different data years:
- 2019-2021 for premature death (CHR National Findings Report 2024)
- 2016-2022 for low birthweight (CHR National Findings Report 2024)
- 2021 BRFSS for adult smoking (CHR National Findings Report 2024)
- 2021 BRFSS for adult obesity (CHR National Findings Report 2024)
- 2023, 2022, and 2020 for access to exercise opportunities (CHR National Findings Report 2024)
- 2021 BRFSS for excessive drinking (CHR National Findings Report 2024)
- 2017-2021 for alcohol-impaired driving deaths (CHR National Findings Report 2024)
- 2016-2022 for teen births (CHR National Findings Report 2024)
Why it matters
This mix shows that local community statistics often combine indicators with different refresh cycles. That is normal. It also means readers should be careful about comparing a monthly labor estimate with a multi-year health measure as if they described the same time window.
How to use local community statistics together
The strongest use case is not to pick one system and ignore the others. It is to combine them in a sequence.
A practical reading order
- Start with the broad place coverage from PLACES to locate the geography and health measure set (PLACES One-Sheet).
- Add ACS to bring in social, economic, housing, and demographic context (ACS Data Profiles).
- Add LAUS to see labor-force movement and unemployment context (LAUS Quick Facts).
- Use County Health Rankings to understand regional framing and the percentile context of county outcomes (CHR National Findings Report 2024).
That workflow keeps the data layered instead of redundant.
What to look for first
- Geographic fit: county, tract, ZIP Code Tabulation Area, city, metro area, or state.
- Measure type: health outcome, health behavior, community factor, demographic estimate, housing profile, or labor indicator.
- Time window: monthly, annual, 1-year estimate, 5-year estimate, or multi-year health period.
- Comparison scale: single place, up to 3-place comparison, regional grouping, or percentile-based ranking.
A short reading checklist
When you are trying to understand a place, ask:
- Does the dataset cover the geography you need?
- Is the measure current enough for the question?
- Does the system provide percentages, estimates, or both?
- Can you compare the place with a similar peer area?
- Are you looking at a single year or a multi-year average?
Those questions are especially important because the dataset here spans county health, local health outcomes, demographic profiles, and labor measures. Each source is strong on its own terms, but the interpretation changes once you put them side by side.
Local data questions these statistics can answer
- How many geographies does a health dataset actually cover?
- Can a place be measured at county, tract, and ZIP Code Tabulation Area levels?
- Which systems support annual updates versus monthly updates?
- How many counties are included in named regional definitions like Appalachia or the Black Belt?
- Which datasets are better for health, which are better for demographics, and which are better for labor markets?
The answer to those questions is in the coverage numbers themselves. 3,144 counties, 83,522 census tracts, 49 health measures, 4 ACS subject areas, and monthly LAUS estimates together show that local community statistics are best treated as a toolkit, not a single scorecard (PLACES One-Sheet; ACS Data Profiles; LAUS Quick Facts).