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.

The Rural Definition was introduced in 2004 as a joint project between the Commission for Rural Communities (CRC - formerly the Countryside Agency), the Department for Environment, Food and Rural Affairs (Defra), the Office for National Statistics (ONS), the Office of the Deputy Prime Minister (ODPM) and the Welsh Assembly. It was delivered by the Rural Evidence Research Centre at Birkbeck College (RERC).A) This new 'spectrum', or graded system, replaces the earlier Oxford/CA binary ward classification and adopts a settlement-based approach.B) It is available for England and Wales at:Census Output Area (COA or OA)Census Super Output Area (CSOA or SOA)Ward[OAs consist of ~125 households and have a population of ~300. SOAs are built of OAs, typically 5, and so contain ~625 households or a mean population of ~1500. OAs therefore vary greatly in size and shape between urban and rural regions, for example a single tower block may consist of more than one OA, whereas a large area of remote moorland may be covered by a single OA.] More information on OAs and SOAs.C) Output areas are classified by morphology and context:MorphologyUrban (over 10,000)Rural townVillageDispersed (hamlets and isolated dwellings)And contextSparseLess sparseThis gives 8 Urban/Rural Classification (1 urban and 6 rural):Urban (Sparse)Urban (Less Sparse)Town (Less Sparse)Town (Sparse)Village (Less Sparse)Village (Sparse)Dispersed (Less Sparse)Dispersed (Sparse)In April 2009 significant changes in the structure of local government came into force. These changes, especially the creation of 9 new unitary authorities, have necessitated an update to the Local Authority Classification. The Government Statistical Service Regional and Geography Group (GSSRG) commissioned a working group to look at this issue, and the outcome of this working group is a revised LA Classification. Detailed information about the changes can be found here, with guidance on how to use the Definition and Classification here.

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.

The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Land and Geographic Unit Area Grids measure land areas in square kilometers and the mean Unit size (population-weighted) in square kilometers. The land area grid permits the summation of areas (net of permanent ice and water) at the same resolution as the population density, count, and urban-rural grids. The mean Unit size grids provide a quantitative surface that indicates the size of the input Unit(s) from which population count and density grids are derived. Additional global grids are created from the 30 arc-second grid at 1/4, 1/2, and 1 degree resolutions. 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).

Urban studies often rely on urban boundaries that have been defined by administrative units or by land use or land cover classification of satellite images. The final results of those boundaries is the categorization of urban/non-urban units in the form of a binary layer used to extract additional information (e.g., zonal statistic) from other geographical layers (e.g., land surface temperature or population density). Given the heterogeneous and continuous nature of the built-up area, binary representations contain a mixture of urban/non-urban areas that influence the results of following analyses. Here we present a way to move beyond the limitations of the binary urban/non-urban representations with a hierarchical watershed-based thresholding and segmentation approach that partitions the built-up area into more homogeneous units. The proposed algorithm, applied to the Global Human Settlement Layer, enables researchers and planners to define urban computational units in three ways - bin-unit, watershed-unit, and agglomeration-unit - depending on need and scale of analyses. We provide suggested terminology and notation style for this cross-over application of a specialized watershed algorithm. Among other possible applications, the resulting segmented, binned and agglomeration units offer alternatives to existing patch analysis methods for drawing relationships between patterns of urban development and ecological or environmental attributes.

Abstract This study aimed to assess the association between oral health and rurality in an older Brazilian population. Population-based samples of 1,451 urban and 411 rural elders were obtained from two databases. Several oral health and related measures, including the number of teeth lost, use of dental prostheses, dental visits, self-reported oral health, and perceived need for a dental prosthesis, were compared. Oral health-related information was obtained by a trained research team with interviews conducted in the individuals’ homes. Regression models were used to verify the association between living in rural areas and oral health outcomes after adjusting for possible confounding factors. The elderly population mostly comprised of women in rural or urban areas, and the mean age was 70 years in both locations. Less-educated individuals (without or with complete elementary schooling) were more common in rural regions than in urban areas. After adjustment for socioeconomic characteristics, living in rural areas was associated with a lower perceived need for dental prostheses (PR 0.68, 95% CI 0.56–0.84), poor self-reported oral health (OR 1.24; 95% CI 1.05–1.46), and having fewer teeth (β -1.31; 95% CI -2.18 to -0.45). The place of residence had a significant impact on oral health indicators, with rurality negatively influencing oral health. These findings suggest that preventive and curative strategies for dental services may be needed for the Brazilian rural population.

Refer to the current geographies boundaries table for a list of all current geographies and recent updates. This dataset is the definitive version of the annually released statistical area 2 (SA2) boundaries as at 1 January 2025 as defined by Stats NZ. This version contains 2,395 SA2s (2,379 digitised and 16 with empty or null geometries (non-digitised)). SA2 is an output geography that provides higher aggregations of population data than can be provided at the statistical area 1 (SA1) level. The SA2 geography aims to reflect communities that interact together socially and economically. In populated areas, SA2s generally contain similar sized populations. The SA2 should: form a contiguous cluster of one or more SA1s, excluding exceptions below, allow the release of multivariate statistics with minimal data suppression, capture a similar type of area, such as a high-density urban area, farmland, wilderness area, and water area, be socially homogeneous and capture a community of interest. It may have, for example: a shared road network, shared community facilities, shared historical or social links, or socio-economic similarity, ​ form a nested hierarchy with statistical output geographies and administrative boundaries. It must: be built from SA1s, either define or aggregate to define SA3s, urban areas, territorial authorities, and regional councils. SA2s in city council areas generally have a population of 2,000–4,000 residents while SA2s in district council areas generally have a population of 1,000–3,000 residents. In major urban areas, an SA2 or a group of SA2s often approximates a single suburb. In rural areas, rural settlements are included in their respective SA2 with the surrounding rural area. SA2s in urban areas where there is significant business and industrial activity, for example ports, airports, industrial, commercial, and retail areas, often have fewer than 1,000 residents. These SA2s are useful for analysing business demographics, labour markets, and commuting patterns. In rural areas, some SA2s have fewer than 1,000 residents because they are in conservation areas or contain sparse populations that cover a large area. To minimise suppression of population data, small islands with zero or low populations close to the mainland, and marinas are generally included in their adjacent land-based SA2. Zero or nominal population SA2s To ensure that the SA2 geography covers all of New Zealand and aligns with New Zealand’s topography and local government boundaries, some SA2s have zero or nominal populations. These include: SA2s where territorial authority boundaries straddle regional council boundaries. These SA2s each have fewer than 200 residents and are: Arahiwi, Tiroa, Rangataiki, Kaimanawa, Taharua, Te More, Ngamatea, Whangamomona, and Mara. SA2s created for single islands or groups of islands that are some distance from the mainland or to separate large unpopulated islands from urban areas SA2s that represent inland water, inlets or oceanic areas including: inland lakes larger than 50 square kilometres, harbours larger than 40 square kilometres, major ports, other non-contiguous inlets and harbours defined by territorial authority, and contiguous oceanic areas defined by regional council. SA2s for non-digitised oceanic areas, offshore oil rigs, islands, and the Ross Dependency. Each SA2 is represented by a single meshblock. The following 16 SA2s are held in non-digitised form (SA2 code; SA2 name): 400001; New Zealand Economic Zone, 400002; Oceanic Kermadec Islands, 400003; Kermadec Islands, 400004; Oceanic Oil Rig Taranaki, 400005; Oceanic Campbell Island, 400006; Campbell Island, 400007; Oceanic Oil Rig Southland, 400008; Oceanic Auckland Islands, 400009; Auckland Islands, 400010 ; Oceanic Bounty Islands, 400011; Bounty Islands, 400012; Oceanic Snares Islands, 400013; Snares Islands, 400014; Oceanic Antipodes Islands, 400015; Antipodes Islands, 400016; Ross Dependency. SA2 numbering and naming Each SA2 is a single geographic entity with a name and a numeric code. The name refers to a geographic feature or a recognised place name or suburb. In some instances where place names are the same or very similar, the SA2s are differentiated by their territorial authority name, for example, Gladstone (Carterton District) and Gladstone (Invercargill City). SA2 codes have six digits. North Island SA2 codes start with a 1 or 2, South Island SA2 codes start with a 3 and non-digitised SA2 codes start with a 4. They are numbered approximately north to south within their respective territorial authorities. To ensure the north–south code pattern is maintained, the SA2 codes were given 00 for the last two digits when the geography was created in 2018. When SA2 names or boundaries change only the last two digits of the code will change. ​ High-definition version This high definition (HD) version is the most detailed geometry, suitable for use in GIS for geometric analysis operations and for the computation of areas, centroids and other metrics. The HD version is aligned to the LINZ cadastre. ​ Macrons Names are provided with and without tohutō/macrons. The column name for those without macrons is suffixed ‘ascii’. ​ Digital data Digital boundary data became freely available on 1 July 2007. ​ Further information To download geographic classifications in table formats such as CSV please use Ariā For more information please refer to the Statistical standard for geographic areas 2023. Contact: geography@stats.govt.nz

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”.

The Scottish Government (SG) Urban Rural Classification provides a consistent way of defining urban and rural areas across Scotland. The classification aids policy development and the understanding of issues facing urban, rural and remote communities. It is based upon two main criteria:

(i) population as defined by National Records of Scotland (NRS), and (ii) accessibility based on drive time analysis to differentiate between accessible and remote areas in Scotland.

The classification can be analysed in a two, three, six or eight fold form. The two-fold classification simply distinguishes between urban and rural areas through two categories, urban and rural, while the three-fold classification splits the rural category between accessible and remote. Most commonly used is the 6-fold classification which distinguishes between urban, rural, and remote areas through six categories. The 8-fold classification further distinguishes between remote and very remote regions. The Classification is normally updated on a biennial basis, with the current dataset reflective of Census Day 2022. Data for previous versions are available for download in ESRI Shapefile format.

Changes in NZ Garden Bird counts for 14 common garden birds for urban rural areas for the last 10 years (2008 to 2018) and last 5 years (2013 to 2018). Barplots are only provided for locations where there were at least 20 garden records available for the 5-year period. Spatial boundaries are defined by the Statistics NZ 2018 high definition meshblock spatial layer. Citation: MacLeod CJ, Howard S, Gormley AM, Spurr EB. 2019. State of NZ Garden Birds 2018 | Te Ahua o nga Manu o te Ka i Aotearoa. Manaaki Wheuna - Landcare Research, Lincoln. ISBN 978-0-947525-63-7.

This dataset classifies statistical areas (lower super output areas or LSOAs) in Cheshire East on either a two level classification - rural or urban - or a six level classification; rural, predominantly rural, more rural than urban, more urban than rural, predominantly urban and urban. A methodology document explains how the classifications were created. A map of the classifications is also available.Six variables are used to create the classification, four of these come from the census:1. Proportion (aged 16-74) of employment in agriculture 2. Average number of cars per household 3. Population density - people per hectare 4. Proportion (aged 16-74) self-employed of those economically active 5. Access to services – this includes road distances to; a GP surgery, a supermarket or convenience store, a primary school and distance to a Post Office6. Buildings as a proportion of all land useThe classification will be updated following the release of the 2021 Census in 2022-23.There are many definitions of areas within Cheshire East classifying them into varying degrees of rural or urban. Organisations such as the Countryside Agency, DEFRA, the Office for National Statistics and central government each produced their own classification. The indicators used and available geographies are different. Several local definitions also existed. To remedy this, a local classification was developed.

This dataset contains sex by ethnic group (grouped total responses), for the census usually resident population count, 2006, 2013, and 2018 Censuses for urban rural areas. The dataset uses geographic boundaries as at 1 January 2018. For explanation of the urban rural classification see Statistical standard for geographic areas 2018. Definitions The census usually resident population count (CURP) of an area (subnational count) is a count of all people who usually live in that area and were present in New Zealand on census night. It excludes visitors from overseas, visitors from elsewhere in New Zealand, and residents temporarily overseas on census night. For example, a person who usually lives in Christchurch city and is visiting Wellington city on census night will be included in the census usually resident population count of Christchurch city. The CURP variable is rated as high quality. Ethnic group includes all people who stated each ethnic group, whether as their only ethnic group or as one of several ethnic groups. Where a person reported more than one ethnic group, they have been counted in each applicable group. The Ethnicity variable is rated as high quality. Quality For information on quality ratings by variable, please see Data quality ratings for 2018 census variables. Due to changes in the 2018 Census methodology and lower than anticipated response rates, time series data should be interpreted with care. Confidentiality The 2018 Census confidentiality rules have been applied to 2006, 2013, and 2018 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2018 Census data. Counts are calculated using Fixed Random Rounding to base 3 (FRR3), and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. For more information on the most recent 2018 Census confidentiality rules see Applying confidentiality rules to 2018 Census data and summary of changes since 2013

Indication of the rural or urban character of the intercommunalities of Île-de-France according to the criteria of the Île-de-France Region.

The definition of the rural character vs urban intercommunalities was approved by the General Directorate of Services in mid-2016.

The Rural Access Index (RAI) is a measure of access, developed by the World Bank in 2006. It was adopted as Sustainable Development Goal (SDG) indicator 9.1.1 in 2015, to measure the accessibility of rural populations. It is currently the only indicator for the SDGs that directly measures rural access.The RAI measures the proportion of the rural population that lives within 2 km of an all-season road. An all-season road is one that is motorable all year, but may be temporarily unavailable during inclement weather (Roberts, Shyam, & Rastogi, 2006). This dataset implements and expands on the most recent official methodology put forward by the World Bank, ReCAP's 2019 RAI Supplemental Guidelines. This is, to date, the only publicly available application of this method at a global scale.MethodologyReCAP's methodology provided new insight on what makes a road all-season and how this data should be handled: instead of removing unpaved roads from the network, the ones that are classified as unpaved are to be intersected with topographic and climatic conditions and, whenever there’s an overlap with excess precipitation and slope, a multiplying factor ranging from 0% to 100% is applied to the population that would access to that road. This present dataset developed by SDSN's SDG Transformation Centre proposes that authorities ability to maintain and remediate road conditions also be taken into account.Data sourcesThe indicator relies on four major items of geospatial data: land cover (rural or urban), population distribution, road network extent and the “all-season” status of those roads.Land cover data (urban/rural distinction)Since the indicator measures the acess rural populations, it's necessary to define what is and what isn't rural. This dataset uses the DegUrba Methodology, proposed by the United Nations Expert Group on Statistical Methodology for Delineating Cities and Rural Areas (United Nations Expert Group, 2019). This approach has been developed by the European Commission Global Human Settlement Layer (GHSL-SMOD) project, and is designed to instil some consistency into the definitions based on population density on a 1-km grid, but adjusted for local situations.Population distributionThe source for population distribution data is WorldPop. This uses national census data, projections and other ancillary data from countries to produce aggregated, 100 m2 population data. Road extentTwo widely recognized road datasets are used: the real-time updated crowd-sourced OpenStreetMap (OSM) or the GLOBIO’s 2018 GRIP database, which draws data from official national sources. The reasons for picking the latter are mostly related to its ability to provide information on the surface (pavement) of these roads, to the detriment of the timeliness of the data, which is restrained to the year 2018. Additionally, data from Microsoft Bing's recent Road Detection project is used to ensure completeness. This dataset is completely derived from machine learning methods applied over satellite imagery, and detected 1,165 km of roads missing from OSM.Roads’ all-season statusThe World Bank's original 2006 methodology defines the term all-season as “… a road that is motorable all year round by the prevailing means of rural transport, allowing for occasional interruptions of short duration”. ReCAP's 2019 methodology makes a case for passability equating to the all-season status of a road, along with the assumption that typically the wet season is when roads become impassable, especially so in steep roads that are more exposed to landslides.This dataset follows the ReCAP methodology by creating an passability index. The proposed use of passability factors relies on the following three aspects:• Surface type. Many rural roads in LICs (and even in large high-income countries including the USA and Australia) are unpaved. As mentioned before, unpaved roads deteriorate rapidly and in a different way to paved roads. They are very susceptible to water ingress to the surface, which softens the materials and makes them very vulnerable to the action of traffic. So, when a road surface becomes saturated and is subject to traffic, the deterioration is accelerated. • Climate. Precipitation has a significant effect on the condition of a road, especially on unpaved roads, which predominate in LICs and provide much of the extended connectivity to rural and poor areas. As mentioned above, the rainfall on a road is a significant factor in its deterioration, but the extent depends on the type of rainfall in terms of duration and intensity, and how well the roadside drainage copes with this. While ReCAP suggested the use of general climate zones, we argue that better spatial and temporal resolutions can be acquired through the Copernicus Programme precipitation data, which is made available freely at ~30km pixel size for each month of the year.• Terrain. The gradient and altitude of roads also has an effect on their accessibility. Steep roads become impassable more easily due to the potential for scour during heavy rainfall, and also due to slipperiness as a result of the road surface materials used. Here this is drawn from slope calculated from SRTM Digital Terrain data.• Road maintenance. The ability of local authorities to remediate damaged caused by precipitation and landslides is proposed as a correcting factor to the previous ones. Ideally this would be measured by the % of GDP invested in road construction and maintenance, but this isn't available for all countries. For this reason, GDP per capita is adopted as a proxy instead. The data range is normalized in such a way that a road maxed out in terms of precipitation and slope (accessibility score of 0.25) in a country at the top of the GDP per capita range is brought back at to the higher end of the accessibility score (0.95), while the accessibility score of a road meeting the same passability conditions in a country which GDP per capita is towards the lower end is kept unchanged.Data processingThe roads from the three aforementioned datasets (Bing, GRIP and OSM) are merged together to them is applied a 2km buffer. The populations falling exclusively on unpaved road buffers are multiplied by the resulting passability index, which is defined as the normalized sum of the aforementioned components, ranging from 0.25 to. 0.9, with 0.95 meaning 95% probability that the road is all-season. The index applied to the population data, so, when calculated, the RAI includes the probability that the roads which people are using in each area will be all-season or not. For example, an unpaved road in a flat area with low rainfall would have an accessibility factor of 0.95, as this road is designed to be accessible all year round and the environmental effects on its impassability are minimal.The code for generating this dataset is available on Github at: https://github.com/sdsna/rai

Reporting of Aggregate Case and Death Count data was discontinued on May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. Although these data will continue to be publicly available, this dataset will no longer be updated.

The surveillance case definition for COVID-19, a nationally notifiable disease, was first described in a position statement from the Council for State and Territorial Epidemiologists, which was later revised. However, there is some variation in how jurisdictions implemented these case definitions. More information on how CDC collects COVID-19 case surveillance data can be found at FAQ: COVID-19 Data and Surveillance.

Aggregate Data Collection Process Since the beginning of the COVID-19 pandemic, data were reported from state and local health departments through a robust process with the following steps:

• Aggregate county-level counts were obtained indirectly, via automated overnight web collection, or directly, via a data submission process.
• If more than one official county data source existed, CDC used a comprehensive data selection process comparing each official county data source to retrieve the highest case and death counts, unless otherwise specified by the state.
• A CDC data team reviewed counts for congruency prior to integration and set up alerts to monitor for discrepancies in the data.
• CDC routinely compiled these data and post the finalized information on COVID Data Tracker.
• County level data were aggregated to obtain state- and territory- specific totals.
• Counting of cases and deaths is based on date of report and not on the date of symptom onset. CDC calculates rates in these data by using population estimates provided by the US Census Bureau Population Estimates Program (2019 Vintage).
• COVID-19 aggregate case and death data are organized in a time series that includes cumulative number of cases and deaths as reported by a jurisdiction on a given date. New case and death counts are calculated as the week-to-week change in cumulative counts of cases and deaths reported (i.e., newly reported cases and deaths = cumulative number of cases/deaths reported this week minus the cumulative total reported the prior week.

This process was collaborative, with CDC and jurisdictions working together to ensure the accuracy of COVID-19 case and death numbers. County counts provided the most up-to-date numbers on cases and deaths by report date. Throughout data collection, CDC retrospectively updated counts to correct known data quality issues.

Description This archived public use dataset focuses on the cumulative and weekly case and death rates per 100,000 persons within various sociodemographic factors across all states and their counties. All resulting data are expressed as rates calculated as the number of cases or deaths per 100,000 persons in counties meeting various classification criteria using the US Census Bureau Population Estimates Program (2019 Vintage).

Each county within jurisdictions is classified into multiple categories for each factor. All rates in this dataset are based on classification of counties by the characteristics of their population, not individual-level factors. This applies to each of the available factors observed in this dataset. Specific factors and their corresponding categories are detailed below.

Population-level factors Each unique population factor is detailed below. Please note that the “Classification” column describes each of the 12 factors in the dataset, including a data dictionary describing what each numeric digit means within each classification. The “Category” column uses numeric digits (2-6, depending on the factor) defined in the “Classification” column.

Metro vs. Non-Metro – “Metro_Rural” Metro vs. Non-Metro classification type is an aggregation of the 6 National Center for Health Statistics (NCHS) Urban-Rural classifications, where “Metro” counties include Large Central Metro, Large Fringe Metro, Medium Metro, and Small Metro areas and “Non-Metro” counties include Micropolitan and Non-Core (Rural) areas. 1 – Metro, including “Large Central Metro, Large Fringe Metro, Medium Metro, and Small Metro” areas 2 – Non-Metro, including “Micropolitan, and Non-Core” areas

Urban/rural - “NCHS_Class” Urban/rural classification type is based on the 2013 National Center for Health Statistics Urban-Rural Classification Scheme for Counties. Levels consist of:

1 Large Central Metro
2 Large Fringe Metro 3 Medium Metro 4 Small Metro 5 Micropolitan 6 Non-Core (Rural)

American Community Survey (ACS) data were used to classify counties based on their age, race/ethnicity, household size, poverty level, and health insurance status distributions. Cut points were generated by using tertiles and categorized as High, Moderate, and Low percentages. The classification “Percent non-Hispanic, Native Hawaiian/Pacific Islander” is only available for “Hawaii” due to low numbers in this category for other available locations. This limitation also applies to other race/ethnicity categories within certain jurisdictions, where 0 counties fall into the certain category. The cut points for each ACS category are further detailed below:

Age 65 - “Age65”

1 Low (0-24.4%) 2 Moderate (>24.4%-28.6%) 3 High (>28.6%)

Non-Hispanic, Asian - “NHAA”

1 Low (<=5.7%) 2 Moderate (>5.7%-17.4%) 3 High (>17.4%)

Non-Hispanic, American Indian/Alaskan Native - “NHIA”

1 Low (<=0.7%) 2 Moderate (>0.7%-30.1%) 3 High (>30.1%)

Non-Hispanic, Black - “NHBA”

1 Low (<=2.5%) 2 Moderate (>2.5%-37%) 3 High (>37%)

Hispanic - “HISP”

1 Low (<=18.3%) 2 Moderate (>18.3%-45.5%) 3 High (>45.5%)

Population in Poverty - “Pov”

1 Low (0-12.3%) 2 Moderate (>12.3%-17.3%) 3 High (>17.3%)

Population Uninsured- “Unins”

1 Low (0-7.1%) 2 Moderate (>7.1%-11.4%) 3 High (>11.4%)

Average Household Size - “HH”

1 Low (1-2.4) 2 Moderate (>2.4-2.6) 3 High (>2.6)

Community Vulnerability Index Value - “CCVI” COVID-19 Community Vulnerability Index (CCVI) scores are from Surgo Ventures, which range from 0 to 1, were generated based on tertiles and categorized as:

1 Low Vulnerability (0.0-0.4) 2 Moderate Vulnerability (0.4-0.6) 3 High Vulnerability (0.6-1.0)

Social Vulnerability Index Value – “SVI" Social Vulnerability Index (SVI) scores (vintage 2020), which also range from 0 to 1, are from CDC/ASTDR’s Geospatial Research, Analysis & Service Program. Cut points for CCVI and SVI scores were generated based on tertiles and categorized as:

1 Low Vulnerability (0-0.333) 2 Moderate Vulnerability (0.334-0.666) 3 High Vulnerability (0.667-1)

The Global Population Count Grid Time Series Estimates provide a back-cast time series of population grids based on the year 2000 population grid from SEDAC's Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) data set. The grids were created by using rates of population change between decades from the coarser resolution History Database of the Global Environment (HYDE) database to back-cast the GRUMPv1 population count grids. Mismatches between the spatial extent of the HYDE calculated rates and GRUMPv1 population data were resolved via infilling rate cells based on a focal mean of values. Finally, the grids were adjusted so that the population totals for each country equaled the UN World Population Prospects (2008 Revision) estimates for that country for the respective year (1970, 1980, 1990, and 2000). These data do not represent census observations for the years prior to 2000, and therefore can at best be thought of as estimations of the populations in given locations. The population grids are consistent internally within the time series, but are not recommended for use in creating longer time series with any other population grids, including GRUMPv1, Gridded Population of the World, Version 4 (GPWv4), or non-SEDAC developed population grids. These population grids served as an input to SEDAC's Global Estimated Net Migration Grids by Decade: 1970-2000 data set.

Data Sources, including links; Data Dictionary; 2009-2013 American Community Survey, Block group-level Population Data; 2010 Decennial Census, Block group-level Population Data; 2008 National Emissions Inventory, Facility-level Data; 2011 National Emissions Inventory, Facility-level Data; 2014 National Emissions Inventory, Facility-level Data; 2010 Rural-Urban Commuting Area Codes, Tract-level Data; 2011 PM 2.5 Daily Average Fused Air Quality Surface Using Downscaling (FAQSD) Output, mean Tract-level Data, CONUS. This dataset is associated with the following publication: Mikati, I., A. Benson, T. Luben, J. Sacks, and J. Richmond-Bryant. Disparities in Distribution of Particulate Matter Emission Sources by Race and Poverty Status. American Journal of Public Health. American Public Health Association, Washington, DC, USA, 108(4): 480-485, (2018).

Abstract copyright UK Data Service and data collection copyright owner.

This is a mixed method data collection. The study is part of the Rural Economy and Land Use (RELU) programme. The data result from two RELU projects carried out by the same research team:
• Social and environmental conditions in rural areas (SECRA), 01/10/2004 - 30/09/2005
• Social and environmental inequalities in rural areas (SEIRA), 01/08/2007 -31/07/2009

Both SECRA and SEIRA consist of a series of social and environmental variables for the same 6,027 rural Lower Super Output Areas in England. SECRA is the base dataset produced during the pilot project. The SEIRA dataset contains additional variables. In addition, SEIRA also contains interviews with rural residents on perceptions of inequality and inequity. Interview results revealed that people recognise that rural areas offer limited opportunities for recreation and local services, and a lack of affordable housing.

SECRA: The dataset on social and environmental conditions in rural areas was intended to encourage and enable researchers and policy makers to include both social and environmental perspectives in their consideration of rural problems.

The original objectives of the one-year scoping study to produce the dataset were:
1. to compile a rural sustainability dataset incorporating both socio-economic and
environmental characteristics of rural census output areas in England;
2. to highlight and address the methodological difficulties in working with spatial and
survey data from sources in the social and environmental science domains;
3. to identify the limitations of currently available data for rural areas;
4. to pilot the use of the rural sustainability dataset for classifying rural areas according to socio-economic and environmental conditions and hence allowing the construction of typologies to provide sampling frames for further research and to inform policies for sustainable rural development;
5. to explore the possibilities of extending dataset coverage to Scotland and Northern
Ireland given differences in census data infrastructures and output design processes.

The SECRA dataset has been compiled at the level of the new Super Output Areas (SOAs) for England. The rural extent has been identified from the new Office of the Deputy Prime Minister (ODPM) definition of urban and rural areas which relies primarily on the morphology and context of settlements.

Further information and documentation for this study may be found through the ESRC Research Catalogue: Developing spatial data for the classification of rural areas.

SEIRA: This research project has investigated the nature and extent of social and environmental inequalities and injustice in rural England addressing the questions:
1. How can we measure rural spatial inequalities in (a) socio-economic and (b) environmental-ecological characteristics of small-scale areas of England?
2. How can inequality measures inform our understanding of the distributions of social and environmental deprivation in rural England?
3. How do rural residents experience the kinds of inequality identified by the research, and what types of inequalities do they perceive as inequitable?
4. Are there identifiable areas of rural England where the potential for environmental and social inequity suggests a need for policy intervention?

Inequality in social, economic and environmental conditions has important implications for individuals or groups of people experiencing its negative effects, but also for society as a whole. In urban areas, poor environments are associated frequently with deprivation and social exclusion. Where the unequal distribution of social and environmental goods is considered unfair, it constitutes social or environmental injustice. This project has quantified inequalities in social and environmental conditions throughout rural England and identified those areas where inequalities are greatest. It has also enhanced understanding of perceptions of inequality and injustice in rural areas. The work shows how rural policy can be refined and targeted to tackle these multi-faceted problems in the most appropriate way for the benefit of society.

Further information for this study may be found through the ESRC Research Catalogue webpage: Social and environmental inequalities in rural areas.