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The impact of data analysis is enormous virtually in every business sector. The usage so far has been more focused on e-commerce and marketing. But the wide reach of data analysis can provide much more innovative, and beneficial solutions for many perennial problems faced by rural communituies.

Data for Rural Analysis 


     ERS produces and maintains a number of data sets that are used by policymakers and researchers to identify and describe rural and urban areas. Measures of rurality such as the Rural-Urban Continuum Codes, Urban Influence Codes classify counties based on population size, adjacency to a metropolitan area, and commuting flows. These codes have been used to determine program eligibility criteria for various Federal programs. Other ERS data products classify counties in both metro and nonmetro areas based on key social and economic characteristics (County Typology Codes); physical characteristics (Natural Amenities Scale); and prevalence of creative occupations (Creative Class Codes).


ERS has resources to help you:

Determine rural status;

Describe the socioeconomic conditions in an area with county-level data;

Support your analysis with other relevant data.


Tools To Determine Rural Status and Degree of Rurality


Rural Definitions—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. Socioeconomic indicators (population, education, poverty, etc.) that are commonly used to highlight differences between urban and rural areas are included.

Rural-Urban Continuum Codes—The Rural-Urban Continuum Codes classify all U.S. counties by the degree of urbanization and adjacency to a metropolitan area. These codes are used in determining eligibility for several federal programs, and allow researchers to break county-level data into finer residential groups than the standard dichotomous metro/nonmetro. They are based on the February 2013 definition of metropolitan and nonmetropolitan counties as determined by the Office of Management and Budget.

Urban Influence Codes—These codes are similar to the Rural-Urban Continuum Codes. Counties are classified, however, by the population size of the cities within each county, rather than the degree of urbanization, and adjacency to a metropolitan or micropolitan area.

Rural-Urban Commuting Area Codes (RUCA)—The Rural-Urban Commuting Area Codes classify U.S. census tracts using measures of urbanization, population density, and daily commuting from the 2010 decennial census and 2006-10 American Community Survey.

Frontier and Remote Area Codes—The 2010 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.

Commuting Zone and Labor Market Areas Codes—Labor Market Areas and Commuting Zones are county aggregations that are intended to be used as spatial measures of local labour markets.

Population-Interaction Zones for Agriculture (PIZA)—The PIZA codes index small geographic areas (the contiguous 48 States divided up into five-kilometre grid cells) according to the size and proximity of population concentrations.

Socioeconomic Data Based on County Delineations

Atlas of Rural and Small-Town America—The Atlas is a web-based interactive mapping tool that displays a broad range of data at the county level to visualize how social and economic conditions vary in rural areas across the United States. Users can create and download maps and download data.

County Typology Codes—This typology classifies metro and nonmetro counties based on primary economic activity and social characteristics.


The six nonoverlapping economic types are:

Farming dependent

Mining dependent

Manufacturing dependent

Federal/State government-dependent



Also, counties are classified based on seven overlapping policy types:

Low education

Low employment

Persistent poverty 

Persistent child poverty

Population loss

Retirement destination

County-Level Population Data—Population data from the U.S. Census Bureau for 1990, 2000, and the latest estimate available. View maps showing population change and download data.

County-Level Poverty Estimates—Poverty estimates from the U.S. Census Bureau.


County-Level Unemployment and Median Household Income Estimates—Unemployment rates from the Bureau of Labor Statistics, and median household income estimates from the U.S. Census Bureau.


County-level Education Data—Education data from 1970, 1980, 1990, 2000, and the American Community Survey pooled 5-year county data.


Natural Amenities Scale—The Natural Amenities Scale is a measure of the physical characteristics of a county area that enhance the location as a place to live. The scale was constructed by combining measures of warm winter, winter sun, temperate summer, low summer humidity, topographic variation, and water area. The data are available for counties in the lower 48 States.

Creative Class County Codes—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. The ERS creative class codes indicate a county's share of the population employed in occupations that require "thinking creatively." Data are provided for all counties in the U.S. for 1990, 2000, and 2007-11.

County-level Oil and Gas Production Data—Oil and/or natural gas onshore production in the lower 48 States. Data were compiled annually for 2000 through 2011 and include annual gross withdrawals, by county.

Other Data

ERS State Fact Sheets provide State-level summaries and links to county data for population, employment, income, farm characteristics, farm financial conditions, and more.

Major Sources of Data Providing Statistics on U.S. Rural Areas—This page describes data available from ERS and other Federal government sources.

Rural Classifications


What is Rural?

Data for Rural Analysis


Last updated: Monday, April 05, 2021

For more information, contact: John Cromartie

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