American Community Survey

Esri provides the U.S. Census Bureau's American Community Survey (ACS) data for the United States and Puerto Rico. ACS uses a continuous measurement, or rolling, sample, in which a small percentage of the population is sampled every month. The ACS is updated and released more frequently than the decennial census—every year instead of every 10 years. Smaller sample sizes and variable collection times have introduced a margin of error into their estimates. See the Understanding margin of error section for more information. ACS data categories provided by Esri include the following:

  • Population—Total Population, Language Spoken, Health Insurance
  • Age
  • Race and Ethnicity
  • School—School Enrollment, Educational Attainment
  • Work—Labor Force, Travel Time, Means of Transportation to Work, Class of Worker, Veteran Status
  • Income—Total Income, Income by Age, Poverty Status, Public Assistance Income
  • Households—Total Households, Tenure, Rent, Vehicles Available, Mortgage Status, Computer/Internet Service
  • Families
  • Housing—Total Housing Units, Number of Units in Structure, Heating Fuel, Year Built


American Community Survey data is available for:

  • ACS 2017–2021 (in 2020 Census geography).
  • ACS 2016–2020 (in 2020 Census geography).
  • ACS 2015-2019 (in 2010 Census geography).

For the February 2023 release, there are four new ACS services:

  • USA_ACS_1721
  • USA_ACS_Tract_1721
  • USA_PRI_ACS_1721
  • USA_PRI_ACS_Tract_1721

In the June 2023 release, two of these services (USA_ACS_1721 and USA_PRI_ACS_1721) will be updated with 2023 boundaries. The data/attributes will not change. They will continue to be the 2017–2021 data vintage, but the congressional district, place, ZIP Code, DMA, and CBSA boundaries will be updated to the 2023 boundaries.

Available geographies

See Available geographies.

Update frequency

ACS data is updated annually.


Esri uses the following methodology:

Sample reports

The following sample ACS reports are available:

For more information about reports and the products that contain them, visit ArcGIS Apps.

For information about the number of credits needed to run reports, see Credits by capability.

Variable lists

The following variable lists are available:

Data availability

The Esri ACS demographics data is available in various products including the following:

For information about purchasing Esri ACS demographics data as a stand-alone dataset, contact

Additional ACS demographics are available in ArcGIS Living Atlas of the World. These hosted feature layers are updated annually to reflect the most current data values and geographical boundaries for state, county, and census tract geographies. For more information, visit the American Community Survey (ACS) Hosted Feature Layers FAQ.

Understanding margin of error

The margin of error (MOE) enables data users to measure the range of uncertainty around each estimate. This range can be calculated with 90 percent confidence by taking the estimate plus or minus the MOE. For example, if the ACS reports an estimate of 100 +/- 20, there is a 90 percent chance that the value for the total population falls between 80 and 120. The larger the MOE, the lower the precision of the estimate and the less confidence one should have that the estimate is close to the true population value.

The MOE measures the variability of an estimate due to sampling error. Sampling error occurs when only part of the population is surveyed to estimate the total population. There will always be differences between the sample and the total. Sampling error is directly related to sample size: the larger the sample size, the smaller the sampling error. Different areas are sampled at different rates to make the sample representative of the total population. Due to these complex sampling techniques, estimates in some areas have more sampling error than estimates in other areas. All MOEs are approximations of the true sampling error in an area and should not be considered exact. In addition, MOEs do not account for nonsampling error in the data and therefore should be thought of as a lower bound of the total error in a survey estimate.

Esri improves confidence in using ACS

Decisions about the quality of an estimate based on the MOE alone are difficult to make. Esri has simplified this process by adding reliability symbols to reports to flag reliability of data based on sample size. Symbols are based on thresholds of reliability that Esri has established using an estimate's coefficient of variation (CV) and are meant to be used as a quick reference to gauge the usability of an ACS estimate.

  • High reliability Green flag—Small CVs (less than or equal to 12 percent) are flagged green to indicate that the sampling error is small relative to the estimate, and the estimate is reasonably reliable.
  • Medium reliability Yellow flag—Estimates with CVs greater than 12 and less than or equal to 40 are flagged yellow. Use with caution.
  • Low reliability Red flag—Large CVs (over 40 percent) are flagged red to indicate that the sampling error is large relative to the estimate. The estimate is considered unreliable.
  • Some estimates do not indicate reliability. In these cases, either the estimate or MOE is missing, or the estimate is zero.

The CV is a measure of relative error in the estimate. It measures the amount of sampling error in the estimate relative to the size of the estimate itself.

Read an in-depth explanation of margin of error from Esri's data team.

For more information about effectively using margins of error in maps, visit the Tutorial series Mapping with Margins of Error.

Understanding suppression

The ACS data is derived from a sample of housing units. Estimates are provided along with margins of error to assess estimate quality. Some values for medians and aggregates will be reported as missing by the Census Bureau due to their suppression rules. Averages are computed from aggregates. If an aggregate value is missing, averages cannot be determined. When this occurs, Esri displays the variable's value as N/A (not applicable). This applies to not only standard and nonstandard geographic areas, but also to any user-defined polygons such as rings and drive times when one or more component block groups include a missing value for a variable.