The 2013 Rural-Urban Continuum Codes form a classification scheme that distinguishes metropolitan counties by the population size of their metro area, and nonmetropolitan counties by degree of urbanization and adjacency to a metro area. The official Office of Management and Budget (OMB) metro and nonmetro categories have been subdivided into three metro and six nonmetro categories. Each county in the U.S. is assigned one of the 9 codes. This scheme allows researchers to break county data into finer residential groups, beyond metro and nonmetro, particularly for the analysis of trends in nonmetro areas that are related to population density and metro influence. The Rural-Urban Continuum Codes were originally developed in 1974. They have been updated each decennial since (1983, 1993, 2003, 2013), and slightly revised in 1988. Note that the 2013 Rural-Urban Continuum Codes are not directly comparable with the codes prior to 2000 because of the new methodology used in developing the 2000 metropolitan areas. See the Documentation for details and a map of the codes. An update of the Rural-Urban Continuum Codes is planned for mid-2023.

Abstract

Rural Urban Continuum Codes (RUCC) Source: U.S. Department of Agriculture, Economic Research Service. (January 2024). Rural-Urban Continuum Codes.

The 2013 data and the 2023 data were downloaded from the USDA website on October 28, 2025 and are available here: https://www.ers.usda.gov/data-products/rural-urban-continuum-codes

The USDA provides RUCC codes at the county.

Rural Urban Commuting Area Codes (RUCA) Source: U.S. Department of Agriculture, Economic Research Service. 2020 Rural-Urban Commuting Area Codes, July 2025.

The 2010 data were downloaded from the USDA website on August 23, 2023.
The 2020 data were downloaded from the USDA website on October 20, 2025.

All tables can be accessed directly from the website: https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes/

The USDA provides RUCA values for zip codes as well as Census Tracts. See website for additional documentation.

Methodology

The following table was taken from the USDA website: https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes/documentation

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View the diversity of challenges and opportunities across America's counties within different types of rural regions and communities. Get statistics on people, jobs, and agriculture.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Data file GIS API Services Interactive map Zip of CSV files For complete information, please visit https://data.gov.

Poverty Area MeasuresThis data product provides poverty area measures for counties across 50 States and Washington DC. The measures include indicators of high poverty areas, extreme poverty areas, persistent poverty areas, and enduring poverty areas for Decennial Census years 1960–2000 and for American Community Survey (ACS) 5-year periods spanning both 2007–11 and 2015–19.HighlightsThis data product uniquely provides poverty area measures at the census-tract level for decennial years 1970 through 2000 and 5-year periods spanning 2007–11 and 2015–19.The poverty area measure—enduring poverty—is introduced, which captures the entrenchment of high poverty in counties for Decennial Census years 1960–2000 and for ACS 5-year periods spanning 2007–11 and 2015–19. The same is available for census tracts beginning in 1970.High and extreme poverty area measures are provided for various data years, offering end-users the flexibility to adjust persistent poverty area measures to meet their unique needs.All measures are geographically standardized to allow for direct comparison over time and for census tracts within county analysis.Diverse geocoding is provided, which can be used for mapping/GIS applications, to link to supplemental data (e.g., USDA, Economic Research Service’s Atlas of Rural and Small-Town America), and to explore various spatial categories (e.g., regions and metro/nonmetro status). DefinitionsHigh poverty: areas with a poverty rate of 20.0 percent or more in a single time period.Extreme poverty: areas with a poverty rate of 40.0 percent or more in a single time period.Persistent poverty: areas with a poverty rate of 20.0 percent or more for 4 consecutive time periods, about 10 years apart, spanning approximately 30 years (baseline time period plus 3 evaluation time periods).Enduring poverty: areas with a poverty rate of 20.0 percent or more for at least 5 consecutive time periods, about 10 years apart, spanning approximately 40 years or more (baseline time period plus four or more evaluation time periods).Additional information about the measures can be found in the downloadable Excel file, which includes the documentation, data, and codebook for the poverty area measures (county and tract).The next update to this data product—planned for early 2023—is expected to include the addition of poverty area measures for the 5-year period 2017–21.Data SetLast UpdatedNext UpdatePoverty area measures (in CSV format)11/10/2022Poverty area measures11/10/2022Poverty Area MeasuresOverviewBackground and UsesERS's Legacy of Poverty Area MeasurementDocumentationDescriptions and MapsLast updated: Thursday, November 10, 2022For more information, contact: Tracey Farrigan and Austin SandersRecommended CitationU.S. Department of Agriculture, Economic Research Service. Poverty Area Measures, November 2022.

State fact sheets provide information on population, income, education, employment, federal funds, organic agriculture, farm characteristics, farm financial indicators, top commodities, and exports, for each State in the United States. Links to county-level data are included when available.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Query tool For complete information, please visit https://data.gov.

Note: Updates to this data product are discontinued. Dozens of definitions are currently used by Federal and State agencies, researchers, and policymakers. The ERS Rural Definitions data product allows users to make comparisons among nine representative rural definitions. Methods of designating the urban periphery range from the use of municipal boundaries to definitions based on counties. Definitions based on municipal boundaries may classify as rural much of what would typically be considered suburban. Definitions that delineate the urban periphery based on counties may include extensive segments of a county that many would consider rural. We have selected a representative set of nine alternative rural definitions and compare social and economic indicators from the 2000 decennial census across the nine definitions. We chose socioeconomic indicators (population, education, poverty, etc.) that are commonly used to highlight differences between urban and rural areas.

This dataset was created for the Appalachia Ohio GIS Collaborative Hub by taking the 2020 Census TIGER/Line Tract boundaries and ZIP Code boundaries, filtered for Ohio, and joining them to the 2020 USDA Rural Urban Commuting Area (RUCA) Codes tables downloaded from the USDA. RUCA codes are a classification scheme allowing for flexible, census tract and ZIP code delineation of rural and urban areas throughout the United States and its territories. There are two layers in this dataset, census tracts and ZIP codes. By default they are symbolized by the Primary RUCA code. Both layers include Primary and Secondary RUCA codes. The census tract layer additionally includes the Urban Area Cluster associated with a tract, the Urban Core Type, primary and secondary commuting destinations, population, and population density. More detail about attributes can be found in the description for each layer.2020 Rural-Urban Commuting Area (RUCA) CodesThe USDA, Economic Research Service’s (ERS) Rural-Urban Commuting Area (RUCA) codes are a classification scheme allowing for flexible, census tract delineation of rural and urban areas throughout the United States and its territories. RUCA codes were designed to address a major limitation associated with county-based classifications; they are often too large to accurately delineate boundaries between rural and urban areas. The more geographically-detailed information provided by RUCA codes can be used to improve rural research and policy—such as addressing concerns that remote, rural communities in large metropolitan counties are not eligible for some rural assistance programs.The RUCA codes consist of two levels. The primary RUCA codes establish urban cores and the census tracts that are the most economically integrated with those cores through commuting. The secondary RUCA codes indicate whether a census tract has a strong secondary connection (through commuting) to an even larger urban core. This two-level structure provides flexibility in combining levels to meet varying definitional needs and preferences. The RUCA codes were created using census tract data and were subsequently adapted to ZIP codes.The tables used for the joins were the USDA 2020 Rural-Urban Commuting Area Codes, census tracts table and the 2020 Rural-Urban Commuting Area Codes, ZIP codes table. Both were marked as last updated 7/31/2025, and are available for download from https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes. Tables used for join were downloaded 9/25/2025.

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.

A collection of over 75 charts and maps presenting key statistics on the farm sector, food spending and prices, food security, rural communities, the interaction of agriculture and natural resources, and more.

How much do you know about food and agriculture? What about rural America or conservation? ERS has assembled more than 75 charts and maps covering key information about the farm and food sectors, including agricultural markets and trade, farm income, food prices and consumption, food security, rural economies, and the interaction of agriculture and natural resources.

How much, for example, do agriculture and related industries contribute to U.S. gross domestic product? Which commodities are the leading agricultural exports? How much of the food dollar goes to farmers? How do job earnings in rural areas compare with metro areas? How much of the Nation’s water is used by agriculture? These are among the statistics covered in this collection of charts and maps—with accompanying text—divided into the nine section titles.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Ag and Food Sectors and the Economy Land and Natural Resources Farming and Farm Income Rural Economy Agricultural Production and Prices Agricultural Trade Food Availability and Consumption Food Prices and Spending Food Security and Nutrition Assistance For complete information, please visit https://data.gov.

The 2013 Urban Influence Codes form a classification scheme that distinguishes metropolitan counties by population size of their metro area, and nonmetropolitan counties by size of the largest city or town and proximity to metro and micropolitan areas. The standard Office of Management and Budget (OMB) metro and nonmetro categories have been subdivided into two metro and 10 nonmetro categories, resulting in a 12-part county classification. This scheme was originally developed in 1993. This scheme allows researchers to break county data into finer residential groups, beyond metro and nonmetro, particularly for the analysis of trends in nonmetro areas that are related to population density and metro influence.

An update of the Urban Influence Codes is planned for mid-2023.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Webpage with links to Excel files For complete information, please visit https://data.gov.

The goal is to predict the rate of heart disease (per 100,000 individuals) across the United States at the county-level from other socioeconomic indicators. The data is compiled from a wide range of sources and made publicly available by the United States Department of Agriculture Economic Research Service (USDA ERS).

There are 33 variables in this dataset. Each row in the dataset represents a United States county, and the dataset we are working with covers two particular years, denoted a, and b We don't provide a unique identifier for an individual county, just a row_id for each row.

The variables in the dataset have names that of the form category_variable, where category is the high level category of the variable (e.g. econ or health). variable is what the specific column contains.

We're trying to predict the variable heart_disease_mortality_per_100k (a positive integer) for each row of the test data set.

Columns

area — information about the county

area_rucc — Rural-Urban Continuum Codes "form a classification scheme that distinguishes metropolitan counties by the population size of their metro area, and nonmetropolitan counties by degree of urbanization and adjacency to a metro area. The official Office of Management and Budget (OMB) metro and nonmetro categories have been subdivided into three metro and six nonmetro categories. Each county in the U.S. is assigned one of the 9 codes." (USDA Economic Research Service, https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/)

area_urban_influence — Urban Influence Codes "form a classification scheme that distinguishes metropolitan counties by population size of their metro area, and nonmetropolitan counties by size of the largest city or town and proximity to metro and micropolitan areas." (USDA Economic Research Service, https://www.ers.usda.gov/data-products/urban-influence-codes/)

econ — economic indicators

econ_economic_typology — County Typology Codes "classify all U.S. counties according to six mutually exclusive categories of economic dependence and six overlapping categories of policy-relevant themes. The economic dependence types include farming, mining, manufacturing, Federal/State government, recreation, and nonspecialized counties. The policy-relevant types include low education, low employment, persistent poverty, persistent child poverty, population loss, and retirement destination." (USDA Economic Research Service, https://www.ers.usda.gov/data-products/county-typology-codes.aspx)

econ_pct_civilian_labor — Civilian labor force, annual average, as percent of population (Bureau of Labor Statistics, http://www.bls.gov/lau/)

econ_pct_unemployment — Unemployment, annual average, as percent of population (Bureau of Labor Statistics, http://www.bls.gov/lau/)

econ_pct_uninsured_adults — Percent of adults without health insurance (Bureau of Labor Statistics, http://www.bls.gov/lau/) econ_pct_uninsured_children — Percent of children without health insurance (Bureau of Labor Statistics, http://www.bls.gov/lau/)

health — health indicators

health_pct_adult_obesity — Percent of adults who meet clinical definition of obese (National Center for Chronic Disease Prevention and Health Promotion)

health_pct_adult_smoking — Percent of adults who smoke (Behavioral Risk Factor Surveillance System)

health_pct_diabetes — Percent of population with diabetes (National Center for Chronic Disease Prevention and Health Promotion, Division of Diabetes Translation)

health_pct_low_birthweight — Percent of babies born with low birth weight (National Center for Health Statistics)

health_pct_excessive_drinking — Percent of adult population that engages in excessive consumption of alcohol (Behavioral Risk Factor Surveillance System, )

health_pct_physical_inacticity — Percent of adult population that is physically inactive (National Center for Chronic Disease Prevention and Health Promotion)

health_air_pollution_particulate_matter — Fine particulate matter in µg/m³ (CDC WONDER, https://wonder.cdc.gov/wonder/help/pm.html)

health_homicides_per_100k — Deaths by homicide per 100,000 population (National Center for Health Statistics)

health_motor_vehicle_crash_deaths_per_100k — Deaths by motor vehicle crash per 100,000 population (National Center for Health Statistics)

health_pop_per_dentist — Population per dentist (HRSA Area Resource File)

health_pop_per_primary_care_physician — Population per Primary Care Physician (HRSA Area Resource File)

demo — demographics information

demo_pct_female — Percent of population that is female (US Census Population Estimates)

demo_pct_below_18_years_of_age — Percent of population that is below 18 years of age (US Census Population Estimates)

demo_pct_aged_65_years_and_older — Percent of population that is aged 65 years or older (US Census Population Estimates)

dem...

Note: Updates to this data product are discontinued. The PIZA codes index small geographic areas (the contiguous 48 States divided up into five-kilometer grid cells) according to the size and proximity of population concentrations.

Widespread conversion of rural lands to urban uses has drawn attention at all levels of government. To provide information useful for projections of future changes in land use, ERS has created a system to classify remaining farmland into "population-interaction zones for agriculture" (PIZA). These zones represent areas of agricultural land use in which urban-related activities (residential, commercial, and industrial) affect the economic and social environment of agriculture. In these zones, interactions between urban-related population and farm production activities tend to increase the value of farmland, change the production practices and enterprises of farm operators, and elevate the probability that farmland will be converted to urban-related uses.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Web page with links to Excel files For complete information, please visit https://data.gov.

This study was undertaken to enable cross-community analysis of gang trends in all areas of the United States. It was also designed to provide a comparative analysis of social, economic, and demographic differences among non-metropolitan jurisdictions in which gangs were reported to have been persistent problems, those in which gangs had been more transitory, and those that reported no gang problems. Data were collected from four separate sources and then merged into a single dataset using the county Federal Information Processing Standards (FIPS) code as the attribute of common identification. The data sources included: (1) local police agency responses to three waves (1996, 1997, and 1998) of the National Youth Gang Survey (NYGS), (2) rural-urban classification and county-level measures of primary economic activity from the Economic Research Service (ERS) of the United States Department of Agriculture, (3) county-level economic and demographic data from the County and City Data Book, 1994, and from USA Counties, 1998, produced by the United States Department of Commerce, and (4) county-level data on access to interstate highways provided by Tom Ricketts and Randy Randolph of the University of North Carolina at Chapel Hill. Variables include the FIPS codes for state, county, county subdivision, and sub-county, population in the agency jurisdiction, type of jurisdiction, and whether the county was dependent on farming, mining, manufacturing, or government. Other variables categorizing counties include retirement destination, federal lands, commuting, persistent poverty, and transfer payments. The year gang problems began in that jurisdiction, number of youth groups, number of active gangs, number of active gang members, percent of gang members who migrated, and the number of gangs in 1996, 1997, and 1998 are also available. Rounding out the variables are unemployment rates, median household income, percent of persons in county below poverty level, percent of family households that were one-parent households, percent of housing units in the county that were vacant, had no telephone, or were renter-occupied, resident population of the county in 1990 and 1997, change in unemployment rates, land area of county, percent of persons in the county speaking Spanish at home, and whether an interstate highway intersected the county.

The creative class thesis—that towns need to attract engineers, architects, artists, and people in other creative occupations to compete in today's economy—may be particularly relevant to rural communities, which tend to lose much of their talent when young adults leave. The ERS creative class codes indicate a county's share of population employed in occupations that require "thinking creatively." Variables used to construct the ERS creative class measure include number and percent employed in creative class occupations and a metro/nonmetro indicator for all counties, 1990, 2000, and 2007-11. A break-out of employment in the arts is included.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Creative Class County Codes Creative Class County Codes For complete information, please visit https://data.gov.

The Quarterly Food-at-Home Price Database (QFAHPD), created by the U.S. Department of Agriculture’s Economic Research Service (ERS), provides quarterly retail price data for 52–54 food groups (e.g., fruits, vegetables, meats) across U.S. metropolitan and rural areas. It was designed to support research on how economic factors influence food consumption, diet quality, and health outcomes. The dataset draws from retail scanner data, including both packaged (UPC-coded) and bulk (random-weight) food purchases, ensuring comprehensive price tracking. Initially covering 1999–2010, the QFAHPD was later superseded by the Food-at-Home Monthly Area Prices (F-MAP) in 2024, which expanded to monthly updates, 90 food groups, and 15 geographic areas. Key features of the original QFAHPD include its granular classification of foods (aligned with ERS Food Purchase Groups) and methodological transparency, detailed in ERS reports. Its unique value lies in enabling longitudinal analysis of price trends and regional disparities, critical for policy and public health studies. Though archived, historical QFAHPD data remain accessible for research, with F-MAP offering updated, extended coverage. Word count: 150

Frontier and Remote Area (FAR) codes provide a statistically-based, nationally-consistent, and adjustable definition of territory in the U.S. characterized by low population density and high geographic remoteness.

To assist in providing policy-relevant information about conditions in sparsely settled, remote areas of the U.S. to public officials, researchers, and the general public, ERS has developed ZIP-code-level frontier and remote (FAR) area codes. The aim is not to provide a single definition. Instead, it is to meet the demand for a delineation that is both geographically detailed and adjustable within reasonable ranges, in order to be usefully applied in diverse research and policy contexts. This initial set, based on urban-rural data from the 2000 decennial census, provides four separate FAR definition levels, ranging from one that is relatively inclusive (18 million FAR residents) to one that is more restrictive (4.8 million FAR residents).This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: State and ZIP code level tables For complete information, please visit https://data.gov.

Note: Updates to this data product are discontinued. Over 1 in 4 rural children are living in families that are poor, according to the official poverty measure, up from 1 in 5 in 1999, but this change was uneven across the rural landscape. Counties with high vulnerability to child poverty, those with both low young adult education levels and high proportions of children in single-parent families, were generally the most hard-hit by the recession of the past decade and experienced substantial increases in their already high child poverty rates. Along with the recession, an increase in rural children in single-parent households, continuing from the 1990s, was a major contributor to the rise in child poverty after 2000. Three factors that shape the geography of high and increasing rural child poverty are explored below: economic conditions, young adult education levels, and family structure. This collection of maps complements the July 2015 Amber Waves feature, Understanding the Geography of Growth in Rural Child Poverty.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Rural Child Poverty Chart Gallery For complete information, please visit https://data.gov.

A web application that displays food access measurements, grocery stores, SNAP-approved stores, WIC-approved stores, fast food restaurants, gas stations, and convenience stores.Limited access to supermarkets, supercenters, grocery stores, or other sources of healthy and affordable food may make it harder for some people to eat a healthy diet. Expanding the availability of nutritious and affordable food by developing and equipping grocery stores, small retailers, corner markets and farmers’ markets in communities with limited access is an important part of food access. There are many ways to define which areas are considered low income and low access (formerly referred to at the Economic Research Service as "food deserts") and many ways to measure food store access for individuals and for neighborhoods. Most measures and definitions consider at least some of the following indicators of access:Accessibility to sources of healthy food, as measured by distance to a store or by the number of stores in an area;Individual-level resources that may affect accessibility, such as family income or vehicle availability; andNeighborhood-level indicators of resources, such as the average income of the neighborhood and the availability of public transportation. The food access measurements uses census tracts that use ½-mile and 1-mile demarcations to the nearest supermarket for urban areas, 10-mile and 20-mile demarcations to the nearest supermarket for rural areas, and vehicle availability for all tracts. Last Updated: April 2023Update Frequency: TBA

This data publication contains multiple maps of Puerto Rico scanned at 600 dots per inch: full map scans, scans clipped to mapped areas only, and georeferenced scans of 1:10,000-scale land-use maps from 1950-1951 that were produced by the Rural Land Classification Program of Puerto Rico, a project led by Dr. Clarence F. Jones of Northwestern University. These historical maps classified land use and land cover into 20 different classes, including 13 different types of crops, two classes of forests, four classes of grasslands and other areas, which is a general class for non-rural areas. This package includes maps from 76 out of the 78 municipalities of Puerto Rico, covering 422 quadrangles of a 443-quadrangle grid for mainland Puerto Rico. It excludes the island municipalities of Vieques and Culebra, Mona Island and minor outlying islands.The Rural Land Classification Program of Puerto Rico produced 430 1:10,000-scale maps. That program also produced one island-wide land-use map with more generalized delineations of land use. Previously, Kennaway and Helmer (2007) scanned and georeferenced the island-wide map, and they converted it to vector and raster formats with embedded georeferencing and classification. This data publication contains the higher-resolution maps, which will provide more precise historical context for forests. It will better inform management efforts for the sustainable use of forest lands and to build resilience and resistance to various future disturbances for these and other tropical forest landscapes.

The maps were scanned and georeferenced to help with the planning and application process for the USDA Forest Service (USDA) Forest Legacy Program, a competition-based program administered by the USDA Forest Service in partnership with State agencies to encourage the protection of privately owned forest lands through conservation easements or land purchases. Geospatial products and maps will also be used by personnel at the Department of Natural and Environmental Resources and partners in Non-Governmental Organizations working with the Forest Stewardship Program. This latter program provides technical assistance and forest management plans to private landowners for the conservation and effective management of private forests across the US. The information will provide local historical context on forest change patterns that will enhance the recommendations of forest management practices for private forest landowners. These data will also be useful for urban forest professionals to understand the land legacies as a basis for planning green infrastructure interventions.

Data depict the rural areas of Puerto Rico around 1951 and how they were classified by geographers then. Having it georeferenced allows managers, teachers, students, the public and scientists to compare how these classifications have changed throughout the years. It will allow more precise identification and mapping of the past land use of present forests, forest stand age, and the past juxtaposition of different land uses relative to each other. These factors can affect forest species composition, biodiversity and ecosystem services. Forest stand age, past land-use type and past disturbance type, forest example, help gauge current forest structure, carbon storage, or rates of carbon accumulation. Another example of how the maps are important is for understanding how watersheds have changed through time, which helps assess how forest ecosystem services related to hydrology evolve. These maps will also help gauge how the forests of Puerto Rico are responding to recent disturbances, and how past disturbances over a range of scales relate to these responses.For more information on the Rural Land Classification Program of Puerto Rico, generated maps, and the island-wide land-use map, please see Jones (1952), Jones and Berrios (1956), as well as Kennaway and Helmer (2007).