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American Community Survey (ACS)

Esri provides demographics data from the American Community Survey (ACS) for the U.S. and Puerto Rico.

Esri's ACS data provides much of the information previously available through the decennial census. ACS uses a continuous measurement or "rolling" sample, in which a small percent of the population is sampled every month. The ACS is updated and released more frequently than the decennial census—every year instead of every ten years. Smaller sample sizes and variable collection times have introduced a margin of error into their estimates. ACS data categories offered by Esri include the following:

  • Population—Total population, language, poverty
  • School and work—School enrollment, travel time and means of transportation to work
  • Households—Total households, tenure, families, poverty status, rent, vehicles available, mortgage status
  • Housing—Total housing units, number of units in structure, year built
  • Health Insurance, public assistance income

Read about Understanding Margin of Error.



Available geographies

See Available geographies.

Update frequency



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 Esri's Apps for Everyone.

For information on how many credits are needed to run reports, see Credits by capability.

Variable lists

How to get it

Esri's ACS demographics data is available in various products including:

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

Understanding Margin of Error

The Census 2000 sample, with data collected using the "long form", represented approximately 1-in-6 households and one point in time, April 1, 2000. ACS represents approximately 1-in-40 households on a rolling sample basis, but the smaller sample sizes can produce larger sampling errors.

With each ACS estimate, the Census Bureau reports a Margin of Error (MOE), or measure of the variability of the estimate due to sampling error. The MOE enables data users to measure the range of uncertainty around each estimate. For example, if the ACS estimate is 100 and has a MOE of +/- 20, then you can be 90 percent certain the value for the whole population falls between 80 and 120. The larger the MOE, the lower the accuracy of the estimate-and the less confidence one should have that the estimate is close to the true value.

Esri Improves Confidence in Using ACS

Decisions about the quality of an estimate based on the MOE are difficult to make. Esri has simplified this process by adding symbols to flag reliability of data based on sample size. Symbols are based on thresholds of reliability Esri established using an estimate's Coefficient of Variation (CV).

green flagHigh Reliability: 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.

yellow flagMedium Reliability: Estimates with CVs between 12 and 40 are flagged yellow—use with caution.

red flagLow Reliability: Large CVs, over 40 percent, are flagged red to indicate that the sampling error is large relative to the estimate. The estimate is considered very unreliable.

The CV is a measure of relative error in the estimate, calculated as the ratio of the standard error to the estimate itself.

Read an in-depth explanation of Margin of Error from Esri's Data Team.

Understanding Suppression

The American Community Survey data are derived from a sample of housing units. Estimates are provided along with margins of errors to assess estimate quality. Moreover, 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 will display the variable's value as N/A (Not Applicable). This not only applies to standard and nonstandard geographic areas, but also for any user defined polygon like rings and drive times when one or more component block groups includes a missing value for a variable.