The rural-urban commuting area codes (RUCA) classify U.S. census tracts using measures of urbanization, population density, and daily commuting from the decennial census. The most recent RUCA codes are based on data from the 2000 decennial census. The classification contains two levels. Whole numbers (1-10) delineate metropolitan, micropolitan, small town, and rural commuting areas based on the size and direction of the primary (largest) commuting flows. These 10 codes are further subdivided to permit stricter or looser delimitation of commuting areas, based on secondary (second largest) commuting flows. The approach errs in the direction of more codes, providing flexibility in combining levels to meet varying definitional needs and preferences. The 1990 codes are similarly defined. However, the Census Bureau's methods of defining urban cores and clusters changed between the two censuses. And, census tracts changed in number and shapes. The 2000 rural-urban commuting codes are not directly comparable with the 1990 codes because of these differences. An update of the Rural-Urban Commuting Area Codes is planned for late 2013.

The Census Bureau's urban-rural classification is fundamentally a classification of areas, identifying individual urban areas and, as a residual, the rural area of the nation. The Census Bureau's urban area definitions represent densely developed territory, encompassing residential, commercial, and other non-residential urban land uses. The Census Bureau defines urban areas after each decennial census by applying specified criteria with decennial census and other data. The Census Bureau classifies as urban all territory, population, and housing units located within urbanized areas (UAs) and urban clusters (UCs), both defined using the same criteria. The Census Bureau delineates UA and UC boundaries to encompass densely settled territory. Urban Areas (UAs) consist of densely settled territory that contains 50,000 or more people. The Census Bureau delineates UAs to provide a better separation of urban and rural territory, population, and housing in the vicinity of large places.

Definitions of “urban” and “rural” are abundant in government, academic literature, and data-driven journalism. Equally abundant are debates about what is urban or rural and which factors should be used to define these terms. Absent from most of this discussion is evidence about how people perceive or describe their neighborhood. Moreover, as several housing and demographic researchers have noted, the lack of an official or unofficial definition of suburban obscures the stylized fact that a majority of Americans live in a suburban setting. In 2017, the U.S. Department of Housing and Urban Development added a simple question to the 2017 American Housing Survey (AHS) asking respondents to describe their neighborhood as urban, suburban, or rural. This service provides a tract-level dataset illustrating the outcome of analysis techniques applied to neighborhood classification reported by the American Housing Survey (AHS) as either urban, suburban, or rural.

To create this data, analysts first applied machine learning techniques to the AHS neighborhood description question to build a model that predicts how out-of-sample households would describe their neighborhood (urban, suburban, or rural), given regional and neighborhood characteristics. Analysts then applied the model to the American Community Survey (ACS) aggregate tract-level regional and neighborhood measures, thereby creating a predicted likelihood the average household in a census tract would describe their neighborhood as urban, suburban, and rural. This last step is commonly referred to as small area estimation. The approach is an example of the use of existing federal data to create innovative new data products of substantial interest to researchers and policy makers alike.

If aggregating tract-level probabilities to larger areas, users are strongly encouraged to use occupied household counts as weights.

We recommend users read Section 7 of the working paper before using the raw probabilities. Likewise, we recognize that some users may:

prefer to use an uncontrolled classification, or

prefer to create more than three categories.

To accommodate these uses, our final tract-level output dataset includes the "raw" probability an average household would describe their neighborhood as urban, suburban, and rural. These probability values can be used to create an uncontrolled classification or additional categories.

The final classification is controlled to AHS national estimates (26.9% urban; 52.1% suburban, 21.0% rural).

  For more information about the 2017 AHS Neighborhood Description Study click on the following visit: https://www.hud.gov/program_offices/comm_planning/communitydevelopment/programs/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. 

Data Dictionary: DD_Urbanization Perceptions Small Area Index.

FHFA's Duty to Serve regulation defines "rural area" as: (i) A census tract outside of an MSA as designated by the Office of Management and Budget (OMB); or (ii) A census tract in an MSA as designated by OMB that is: (A) Outside of the MSA’s Urbanized Areas as designated by the U.S. Department of Agriculture’s (USDA) Rural-Urban Commuting Area (RUCA) Code #1, and outside of tracts with a housing density of over 64 housing units per square mile for USDA’s RUCA Code #2; or (B) A colonia census tract that does not satisfy paragraphs (i) or (ii)(A) of this definition. This data contains both the specific geographies which meet the Rural Areas definition and also the areas defined as “high-needs rural regions”.