This dataset contains measures of the urban/rural characteristics of each census tract in the United States. These include proportions of urban and rural population, population density, rural/urban commuting area (RUCA) codes, and RUCA-based four- and seven- category urbanicity scales. A curated version of this data is available through ICPSR at https://www.icpsr.umich.edu/web/ICPSR/studies/38606/versions/V1

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.

National provisional drug overdose deaths by month and 2013 NCHS Urban–Rural Classification Scheme for Counties. Drug overdose deaths are identified using underlying cause-of-death codes from the Tenth Revision of ICD (ICD–10): X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), and Y10–Y14 (undetermined). Deaths are based on the county of residence in the United States. Death counts provided are for “12-month ending periods,” defined as the number of deaths occurring in the 12-month period ending in the month indicated. Estimates for 2020 are based on provisional data. Estimates for 2018 and 2019 are based on final data.

For more information on NCHS urban-rural classification, see: https://www.cdc.gov/nchs/data/series/sr_02/sr02_166.pdf

Data for study on urbanicity

The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Urban Extents Grid distinguishes urban and rural areas based on a combination of population counts (persons), settlement points, and the presence of Nighttime Lights. Areas are defined as urban where contiguous lighted cells from the Nighttime Lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT).

In 2023, there were approximately ***** million people living in rural areas in the United States, while about ****** million people were living in urban areas. Within the provided time period, the number of people living in urban U.S. areas has increased significantly since totaling only ****** million in 1960.

Income and Urban vs. Rural Population Dataset

Overview

This dataset provides insights into the population distribution and income levels across counties in the United States, with a classification of counties as either "Urban" or "Rural." The data was sourced from the U.S. Census Bureau's 2023 American Community Survey (ACS).

Methodology

1. Data Source:

   • API Endpoint: U.S. Census Bureau ACS 2023 API
   • Variables:
     • B01003_001E: Total population.
     • B19013_001E: Median household income.

2. Processing:

   • Counties were classified as "Urban" if their population was above the median population; otherwise, they were classified as "Rural."
   • FIPS codes were generated by concatenating State and County FIPS codes.

3. Columns:

   • County: County name.
   • State: State name.
   • FIPS: Combined state and county FIPS code.
   • State FIPS Code: State's Federal Information Processing Standard code.
   • County FIPS Code: County's FIPS code.
   • Total Population: Total population of the county.
   • Median Household Income: Median household income for the county.
   • Urban-Rural: Classification based on population (Urban or Rural).

Usage

This dataset can be used for: - Urban vs. rural demographic and economic analysis. - Income distribution studies. - Data visualization and mapping using FIPS codes.

License

This dataset is provided under the public domain. Proper attribution to the U.S. Census Bureau is appreciated.

USA Census Urban Areas provides the boundaries, and 2020 U.S. Census names, codes, populations, and housing information for the urban areas of the United States. For the 2020 U.S. Census, an urban area comprises a densely settled core of census blocks that meet minimum housing unit density or population density requirements. This includes adjacent territory containing nonresidential urban land uses. To qualify as an urban area, the territory identified according to criteria must encompass at least 2,000 housing units or have a population of at least 5,000. Urban areas represent densely developed territory, and encompass residential, commercial, and other nonresidential urban land uses. The sources for this layer are the U.S. Census Bureau's 2020 Census Urban Areas TIGER/Line data and the corresponding List of 2020 Census Population attribute fields.

The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Urban Extent Polygons, Revision 02 is an update to Revision 01, which included new settlements and represented the first time that SEDAC released polygons (in Esri shapefile format) with the settlement name (or name of the largest city in the case of multi-city agglomerations). The shapefile consists of polygons defined by the extent of the nighttime lights and approximated urban extents (circles) based on buffered settlement points. Revision 01 also included new urban extents identified from multiple sources and corrected georeferencing for some settlements (see separate documentation for Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Settlement Points, Revision 01 for the data and methods). Revision 01 was produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with CUNY Institute for Demographic Research (CIDR). Revision 02 was produced by CIESIN.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the "urban footprint." There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes.

Effective September 27, 2023, this dataset will no longer be updated. Similar data are accessible from wonder.cdc.gov.

Provisional COVID-19 deaths by urbanicity and week. Deaths are based on the county of occurrence in the United States. Urbanicity is defined as metropolitan and non-metropolitan, based on the 2013 National Center for Health Statistics (NCHS) Urban-Rural Classification Scheme for Counties. Counties are classified as “metropolitan” if they are large central metro, large fringe metro, medium metro or small metro; and “non-metropolitan” if micropolitan or non-core.

†Proportion of the population refers to the proportion of the adult population (those aged ≥18 y).∧The average number of years of education has been divided by six so that the total score for the “Education facilities” component is no more than 10 points.

In this dataset are the aggregated urban renewal projects reported by cities, counties, and rural improvement zones through the Annual Urban Renewal Report beginning with FY2012.

Propensity Score Methods Urbanicity

Cortical Thickness by Group and Urbanicity level.

Cortical Thickness as a Function of Group Status and Urbanicity.

In 2020, there were approximately 4.2 overdose deaths from heroin per 100,000 population in urban settings in the United States. In comparison, the overdose death rate from heroin in rural areas of the U.S. was 3.2 per 100,000 population. This statistic shows the death rate from drug overdose in the U.S. in 2020, by urbanicity and drug type

Cortical Thickness as a Function of Group Status, Sex and Urbanicity.

PurposeIn Mexico, 39.5% of adolescents do not meet the World Health Organisation’s physical activity guidelines. Urbanicity is a potential correlate of physical activity. The aim of this study was to examine the associations between different aspects of urbanicity and adolescents’ physical activity.MethodsParticipants were 4,079 Mexican adolescents aged 15–18 from Mexico City and Oaxaca, Mexico. Data was collected between February and June 2016. Multiple imputation of missing data was implemented after confirming values were missing at random. Multivariable regression models examined associations between five domains of self-reported physical activity: 1) moderate-to-vigorous physical activity, 2) sports activities, 3) leisure time activities, 4) Physical Education class at school, 5) active commuting to school; and a composite measure of urbanicity and its seven sub-scores: 1) demographic, 2) economic activity, 3) built environment, 4) communication, 5) education, 6) diversity and 7) health services. Multivariable regression models were adjusted for parents’ education and participants’ age.ResultsUrbanicity was positively associated with activity spent in Physical Education class. The association between urbanicity and sport activities depended on state context. Communication-based urbanicity was negatively associated with leisure physical activity and active commuting. Population density was positively associated with active commuting.ConclusionUrbanicity is associated with adolescents’ physical activity in Mexico. Findings were largely consistent between Mexico City and Oaxaca and highlight the value of examining urbanicity as a multidimensional construct.

The Global Grid of Probabilities of Urban Expansion to 2030 presents spatially explicit probabilistic forecasts of global urban land cover change from 2000 to 2030 at a 2.5 arc-minute resolution. For each grid cell that is non-urban in 2000, a Monte-Carlo model assigned a probability of becoming urban by the year 2030. The authors first extracted urban extent circa 2000 from the NASA MODIS Land Cover Type Product Version 5, which provides a conservative estimate of global urban land cover. The authors then used population densities from the Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) to create the population density driver map. They estimated the amount of new urban land in each United Nations region by 2030 in a Monte-Carlo fashion based on present empirical distribution of regional urban population densities and probability density functions of projected regional population and GDP values for 2030. To facilitate integration with other data products, CIESIN reprojected the data from Goode's Homolosine to Geographic WGS84 projection.