26 Aug 2025

Rural-Urban Commuting Zones

The 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. For more information, visit the “RUCA website” here.

18 Nov 2024

National Land Cover Dataset, 2024

The National Land Cover Database (NLCD) serves as the definitive Landsat-based, 30-meter resolution, land cover database for the Nation. NLCD provides spatial reference and descriptive data for characteristics of the land surface such as thematic class (for example, urban, agriculture, and forest), percent impervious surface, and percent tree canopy cover. NLCD supports a wide variety of Federal, State, local, and nongovernmental applications that seek to assess ecosystem status and health, understand the spatial patterns of biodiversity, predict effects of climate change, and develop land management policy. NLCD products are created by the Multi-Resolution Land Characteristics (MRLC) Consortium, a partnership of Federal agencies led by the U.S. Geological Survey.

2 Nov 2023

National Land Cover Dataset, 2021

The National Land Cover Database (NLCD) serves as the definitive Landsat-based, 30-meter resolution, land cover database for the Nation. NLCD provides spatial reference and descriptive data for characteristics of the land surface such as thematic class (for example, urban, agriculture, and forest), percent impervious surface, and percent tree canopy cover. NLCD supports a wide variety of Federal, State, local, and nongovernmental applications that seek to assess ecosystem status and health, understand the spatial patterns of biodiversity, predict effects of climate change, and develop land management policy. NLCD products are created by the Multi-Resolution Land Characteristics (MRLC) Consortium, a partnership of Federal agencies led by the U.S. Geological Survey.

20 Jul 2021

National Land Cover Dataset, 2019

The National Land Cover Database (NLCD) serves as the definitive Landsat-based, 30-meter resolution, land cover database for the Nation. NLCD provides spatial reference and descriptive data for characteristics of the land surface such as thematic class (for example, urban, agriculture, and forest), percent impervious surface, and percent tree canopy cover. NLCD supports a wide variety of Federal, State, local, and nongovernmental applications that seek to assess ecosystem status and health, understand the spatial patterns of biodiversity, predict effects of climate change, and develop land management policy. NLCD products are created by the Multi-Resolution Land Characteristics (MRLC) Consortium, a partnership of Federal agencies led by the U.S. Geological Survey.

12 Mar 2021

Rural vs. Urban Household Poverty

This layer displays information about the difference between rural and urban childhood poverty across the United States. Data are based on census-tract level information from the 2008-12 American Community Survey. Census tracts are defined as urban or rural based on 2010 US Census Bureau definitions; tract level information is then aggregated to and displayed at the Public Use Microdata Area (PUMA) geographic level.

12 Mar 2021

Urban Rural Classification Scheme by County, 2013

This dataset is used to study the associations between urbanization level of residence and health and to monitor the health of urban and rural residents. NCHS has developed a six-level urban-rural classification scheme for U.S. counties and county-equivalent entities.

12 Mar 2021

Rural vs. Urban Childhood Poverty

This layer displays information about the difference between rural and urban childhood poverty across the United States. Data are based on census-tract level information from the 2010-14 American Community Survey. Census tracts are defined as urban or rural based on 2010 US Census Bureau definitions; tract level information is then aggregated to and displayed at the Public Use Microdata Area (PUMA) geographic level.

12 Mar 2021

Rural vs. Urban Educational Attainment

This layer displays information about the difference between rural and urban educational attainment across the United States. Data are based on census-tract level information from the 2010-14 American Community Survey. Census tracts are defined as urban or rural based on 2010 US Census Bureau definitions; tract level information is then aggregated to and displayed at the Public Use Microdata Area (PUMA) geographic level.

12 Mar 2021

National Land Cover Dataset

The National Land Cover Database (NLCD) serves as the definitive Landsat-based, 30-meter resolution, land cover database for the Nation. NLCD provides spatial reference and descriptive data for characteristics of the land surface such as thematic class (for example, urban, agriculture, and forest), percent impervious surface, and percent tree canopy cover. NLCD supports a wide variety of Federal, State, local, and nongovernmental applications that seek to assess ecosystem status and health, understand the spatial patterns of biodiversity, predict effects of climate change, and develop land management policy. NLCD products are created by the Multi-Resolution Land Characteristics (MRLC) Consortium, a partnership of Federal agencies led by the U.S. Geological Survey.