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.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.

PLURAL (Place-level urban-rural indices) is a framework to create continuous classifications of "rurality" or "urbanness" based on the spatial configuration of populated places. PLURAL makes use of the concept of "remoteness" to characterize the level of spatial isolation of a populated place with respect to its neighbors. There are two implementations of PLURAL, including (a) PLURAL-1, based on distances to the nearest places of user-specified population classes, and (b) PLURAL-2, based on neighborhood characterization derived from spatial networks. PLURAL requires simplistic input data, i.e., the coordinates (x,y) and population p of populated places (villages, towns, cities) in a given point in time. Due to its simplistic input, the PLURAL rural-urban classification scheme can be applied to historical data, as well as to data from data-scarce settings. Using the PLURAL framework, we created place-level rural-urban indices for the conterminous United States from 1930 to 2018. Rural-urban classifications are essential for analyzing geographic, demographic, environmental, and social processes across the rural-urban continuum. Most existing classifications are, however, only available at relatively aggregated spatial scales, such as at the county scale in the United States. The absence of rurality or urbanness measures at high spatial resolution poses significant problems when the process of interest is highly localized, as with the incorporation of rural towns and villages into encroaching metropolitan areas. Moreover, existing rural-urban classifications are often inconsistent over time, or require complex, multi-source input data (e.g., remote sensing observations or road network data), thus, prohibiting the longitudinal analysis of rural-urban dynamics. We developed a set of distance- and spatial-network-based methods for consistently estimating the remoteness and rurality of places at fine spatial resolution, over long periods of time. Based on these methods, we constructed indices of urbanness for 30,000 places in the United States from 1930 to 2018. We call these indices the place-level urban-rural index (PLURAL), enabling long-term, fine-grained analyses of urban and rural change in the United States. The method paper has been peer-reviewed and is published in "Landscape and Urban Planning". The PLURAL indices from 1930 to 2018 are available as CSV files, and as point-based geospatial vector data (.SHP). Moreover, we provide animated GIF files illustrating the spatio-temporal variation of the different variants of the PLURAL indices, illustrating the dynamics of the rural-urban continuum in the United States from 1930 to 2018. Apply the PLURAL rural-urban classification to your own data: Python code is fully open source and available at https://github.com/johannesuhl/plural. Data sources: Place-level population counts (1980-2010) and place locations 1930 - 2018 were obtained from IPUMS NHGIS, (University of Minnesota, www.nhgis.org; Manson et al. 2022). Place-level population counts 1930-1970 were digitized from historical census records (U.S. Census Bureau 1942, 1964). References: Uhl, J.H., Hunter, L.M., Leyk, S., Connor, D.S., Nieves, J.J., Hester, C., Talbot, C. and Gutmann, M., 2023. Place-level urban–rural indices for the United States from 1930 to 2018. Landscape and Urban Planning, 236, p.104762. DOI: https://doi.org/10.1016/j.landurbplan.2023.104762 Steven Manson, Jonathan Schroeder, David Van Riper, Tracy Kugler, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 16.0 [dataset]. Minneapolis, MN: IPUMS. 2021. http://doi.org/10.18128/D050.V16.0 U.S. Census Bureau (1942). U.S. Census of Population: 1940. Vol. I, Number of Inhabitants. U.S. Government Printing Office, Washington, D.C. U.S. Census Bureau (1964). U.S. Census of Population: 1960. Vol. I, Characteristics of the Population. Part I, United States Summary. U.S. Government Printing Office, Washington, D.C.

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.

The urban–rural continuum classifies the global population, allocating rural populations around differently-sized cities. The classification is based on four dimensions: population distribution, population density, urban center location, and travel time to urban centers, all of which can be mapped globally and consistently and then aggregated as administrative unit statistics.Using spatial data, we matched all rural locations to their urban center of reference based on the time needed to reach these urban centers. A hierarchy of urban centers by population size (largest to smallest) is used to determine which center is the point of “reference” for a given rural location: proximity to a larger center “dominates” over a smaller one in the same travel time category. This was done for 7 urban categories and then aggregated, for presentation purposes, into “large cities” (over 1 million people), “intermediate cities” (250,000 –1 million), and “small cities and towns” (20,000–250,000).Finally, to reflect the diversity of population density across the urban–rural continuum, we distinguished between high-density rural areas with over 1,500 inhabitants per km2 and lower density areas. Unlike traditional functional area approaches, our approach does not define urban catchment areas by using thresholds, such as proportion of people commuting; instead, these emerge endogenously from our urban hierarchy and by calculating the shortest travel time.Urban-Rural Catchment Areas (URCA).tif is a raster dataset of the 30 urban–rural continuum categories for the urban–rural continuum showing the catchment areas around cities and towns of different sizes. Each rural pixel is assigned to one defined travel time category: less than one hour, one to two hours, and two to three hours travel time to one of seven urban agglomeration sizes. The agglomerations range from large cities with i) populations greater than 5 million and ii) between 1 to 5 million; intermediate cities with iii) 500,000 to 1 million and iv) 250,000 to 500,000 inhabitants; small cities with populations v) between 100,000 and 250,000 and vi) between 50,000 and 100,000; and vii) towns of between 20,000 and 50,000 people. The remaining pixels that are more than 3 hours away from any urban agglomeration of at least 20,000 people are considered as either hinterland or dispersed towns being that they are not gravitating around any urban agglomeration. The raster also allows for visualizing a simplified continuum created by grouping the seven urban agglomerations into 4 categories.Urban-Rural Catchment Areas (URCA).tif is in GeoTIFF format, band interleaved with LZW compression, suitable for use in Geographic Information Systems and statistical packages. The data type is byte, with pixel values ranging from 1 to 30. The no data value is 128. It has a spatial resolution of 30 arc seconds, which is approximately 1km at the equator. The spatial reference system (projection) is EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long). The geographic extent is 83.6N - 60S / 180E - 180W. The same tif file is also available as an ESRI ArcMap MapPackage Urban-Rural Catchment Areas.mpkFurther details are in the ReadMe_data_description.docx

Urban rural 2023 update

UR 2023 is the first major update of the geography since it was first created in 2018. The update is to ensure UR geographies are relevant and meet criteria before each five-yearly population and dwelling census. UR 2023 contains 13 new rural settlements and 7 new small urban areas. Updates were made to reflect real world change including new subdivisions and motorways, and to improve delineation of urban areas and rural settlements. The Wānaka urban area, whose population has grown to be more than 10,000 based on population estimates, has been reclassified to a medium urban area in the 2023 urban rural indicator.

​

In the 2023 classification there are:

• 7 major urban areas
• 13 large urban areas
• 23 medium urban areas
• 152 small urban areas
• 402 rural settlements.

​

This dataset is the definitive version of the annually released urban rural (UR) boundaries as at 1 January 2023 as defined by Stats NZ. This version contains 745 UR areas, including 195 urban areas and 402 rural settlements.

​

Urban rural (UR) is an output geography that classifies New Zealand into areas that share common urban or rural characteristics and is used to disseminate a broad range of Stats NZ’s social, demographic and economic statistics.

The UR separately identifies urban areas, rural settlements, other rural areas, and water areas. Urban areas and rural settlements are form-based geographies delineated by the inspection of aerial imagery, local government land designations on district plan maps, address registers, property title data, and any other available information. However, because the underlying meshblock pattern is used to define the geographies, boundaries may not align exactly with local government land designations or what can be seen in aerial images. Other rural areas, and bodies of water represent areas not included within an urban area.

Urban areas are built from the statistical area 2 (SA2) geography, while rural and water areas are built from the statistical area 1 (SA1) geography.

​

Non-digitised

The following 4 non-digitised UR areas have been aggregated from the 16 non-digitised meshblocks/SA2s.

6901; Oceanic outside region, 6902; Oceanic oil rigs, 6903; Islands outside region, 6904; Ross Dependency outside region.

​

UR numbering and naming

Each urban area and rural settlement is a single geographic entity with a name and a numeric code.

Other rural areas, inland water areas, and inlets are defined by territorial authority; oceanic areas are defined by regional council; and each have a name and a numeric code.

Urban rural codes have four digits. North Island locations start with a 1, South Island codes start with a 2, oceanic codes start with a 6 and non-digitised codes start with 69.

​

Urban rural indicator (IUR)

The accompanying urban rural indicator (IUR) classifies the urban, rural, and water areas by type. Urban areas are further classified by the size of their estimated resident population:

• major urban area – 100,000 or more residents,
• large urban area – 30,000–99,999 residents,
• medium urban area – 10,000–29,999 residents,
• small urban area – 1,000–9,999 residents.

​

This was based on 2018 Census data and 2021 population estimates. Their IUR status (urban area size/rural settlement) may change if the 2023 Census population count moves them up or down a category.

​

The indicators, by name, with their codes in brackets, are:

urban area – major urban (11), large urban (12), medium urban (13), small urban (14),

rural area – rural settlement (21), rural other (22),

water – inland water (31), inlet (32), oceanic (33).

​

The urban rural indicator complements the urban rural geography and is an attribute in this dataset. Further information on the urban rural indicator is available on the Stats NZ classification and coding tool ARIA.

​

For more information please refer to the Statistical standard for geographic areas 2023.

​

Generalised version

This generalised version has been simplified for rapid drawing and is designed for thematic or web mapping purposes.

​

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.

To download geographic classifications in table formats such as CSV please use Ariā

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 the year 2020. Data for previous versions are available for download in ESRI Shapefile format.

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

These datasets are concordance files that link the Geographic Classification for Health (GCH) to statistical geographies and geographic units commonly used in health research and analysis in Aotearoa New Zealand (NZ). More information about the develppment of the GCH is available in our Open Access publication. Our long-term aim is the comprehensive and accurate understanding of urban-rural variation in health outcomes and healthcare utilization at both national and regional levels. This is best achieved by the widespread uptake of the GCH by health researchers and health policy makers. The GCH is straightforward to use and most users will only need the relevant concordance file.
Statistical Area 1s (SA1s, small statistical areas which are the output geography for population data) were used as the building blocks for the Geographic Classification for Health (GCH) and are the preferred small areas when undertaking the analysis of health data using the GCH. It is however appreciated that a lot of health data is not available at the SA1 level and GCH concordance files are also available for Domicile (Census Area Units, CAU) and Statistical Area 2s (SA2) and Meshblock. The following concordance files are available in excel format:

SA12018_to_GCH2018.csv This concordance file applies a GCH category to each SA1 in NZ SA22018_to_GCH2018.csv This concordance file applies a GCH category to each SA2 in NZ MoH_HDOM_to_GCH2018.csv This concordance file applies a GCH category to each Domicile in NZ. Please read the additional information below if you plan to use this concordance file. MoH_MB_to_GCH2018.csv This concordance file applies a GCH category to each Meshblock in NZ. Please read the additional information below if you plan to use this concordance file.

Additional information relating to geographic units used by the Ministry of Health:

MoH_HDOM_to_GCH2018.csv This file has been designed specifically to add GCH to the Ministry of Health (MoH) datasets containing Domicile codes. Use this file if your dataset contains only Domicile codes. If your dataset also contains Meshblock codes, then use the MoH Meshblock to GCH concordance file. This file includes 2006 and 2013 domicile codes. The 2013 domiciles are still current as of 2022, and this file will still work well with data outside those years. Domicile boundaries do not align well with SA1 boundaries, and longitudinal health data usually contains some older Domiciles which have been phased out and replaced with multiple smaller Domiciles. These deprecated Domiciles may overlap multiple SA1s. Usually, all such SA1s belong to the same GCH category. Occasionally, a Domicile will overlap more than one GCH category. When this happens, we have assigned the GCH category to which the majority of people living in that Domicile belong. By necessity, this will allocate a minority of people in those Domiciles to a GCH category to which they do not belong.
MoH_MB_to_GCH2018.csv This file has been designed specifically to add GCH to Ministry of Health (MoH) datasets containing Meshblock codes. This file includes 2018, 2013, 2006, and 2001 Meshblock codes, but will still work well with data outside those years. Meshblock boundaries from census 2018 fit perfectly and completely within the Statistics New Zealand Statistical Area 1s (SA1) boundaries on which GCH is based. However, longitudinal health data usually contains some older Meshblocks which have been phased out and replaced by multiple smaller Meshblocks. These deprecated Meshblocks may overlap multiple SA1s. Usually, all such SA1s belong to the same GCH category. Occasionally, a Meshblock will overlap more than one GCH category. When this happens, we have assigned the GCH category to which the majority of people living in that Meshblock belong. By necessity, this will allocate a minority of people in those Meshblocks to a GCH category to which they do not belong.

This dataset is published as Open DataThe 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 the year 2020. Data for previous versions are available for download in ESRI Shapefile format.

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 urban rural (UR) boundaries as at 1 January 2025 as defined by Stats NZ. This version contains 689 UR areas, including 195 urban areas and 402 rural settlements.

Urban rural (UR) is an output geography that classifies New Zealand into areas that share common urban or rural characteristics and is used to disseminate a broad range of Stats NZ’s social, demographic and economic statistics.

The UR separately identifies urban areas, rural settlements, other rural areas, and water areas. Urban areas and rural settlements are form-based geographies delineated by the inspection of aerial imagery, local government land designations on district plan maps, address registers, property title data, and any other available information. However, because the underlying meshblock pattern is used to define the geographies, boundaries may not align exactly with local government land designations or what can be seen in aerial images. Other rural areas, and bodies of water represent areas not included within an urban area.

Urban areas are built from the statistical area 2 (SA2) geography, while rural and water areas are built from the statistical area 1 (SA1) geography.

Urban areas

Urban areas are statistically defined areas with no administrative or legal basis. They are characterised by high population density with many built environment features where people and buildings are located close together for residential, cultural, productive, trade and social purposes.

Urban areas are delineated using the following criteria. They:

form a contiguous cluster of one or more SA2s,

contain an estimated resident population of more than 1,000 people and usually have a population density of more than 400 residents or 200 address points per square kilometre,

have a high coverage of built physical structures and artificial landscapes such as:

• residential dwellings and apartments,
• commercial structures, such as factories, office complexes, and shopping centres,
• transport and communication facilities, such as airports, ports and port facilities, railway stations, bus stations and similar transport hubs, and communications infrastructure,
• medical, education, and community facilities,
• tourist attractions and accommodation facilities,
• waste disposal and sewerage facilities,
• cemeteries,
• sports and recreation facilities, such as stadiums, golf courses, racecourses, showgrounds, and fitness centres,
• green spaces, such as community parks, gardens, and reserves,

have strong economic ties where people gather together to work, and for social, cultural, and recreational interaction,

have planned development within the next 5–8 years.

Urban boundaries are independent of local government and other administrative boundaries. However, the Richmond urban area, which is mainly in the Tasman District, is the only urban area that crosses territorial authority boundaries

Rural areas

Rural areas are classified as rural settlements or other rural.

Rural settlements

Rural settlements are statistically defined areas with no administrative or legal basis. A rural settlement is a cluster of residential dwellings about a place that usually contains at least one community or public building.

Rural settlements are delineated using the following criteria. They:

form a contiguous cluster of one or more SA1s,

contain an estimated resident population of 200–1,000, or at least 40 residential dwellings,

represent a reasonably compact area or have a visible centre of population with a population density of at least 200 residents per square kilometre or 100 address points per square kilometre,

contain at least one community or public building, such as a church, school, or shop.

To reach the target SA2 population size of more than 1,000 residents, rural settlements are usually included with other rural SA1s to form an SA2. In some instances, the settlement and the SA2 have the same name, for example, Kirwee rural settlement is part of the Kirwee SA2.

Some rural settlements whose populations are just under 1,000 are a single SA2. Creating separate SA2s for these rural settlements allows for easy reclassification to urban areas if their populations grow beyond 1,000.

Other rural

Other rural areas are the mainland areas and islands located outside urban areas or rural settlements. Other rural areas include land used for agriculture and forestry, conservation areas, and regional and national parks. Other rural areas are defined by territorial authority.

Water

Bodies of water are classified separately, using the land/water demarcation classification described in the Statistical standard for meshblock. These water areas are not named and are defined by territorial authority or regional council.

The water classes include:

inland water – non-contiguous, defined by territorial authority,

inlets (which also includes tidal areas and harbours) – non-contiguous, defined by territorial authority,

oceanic – non-contiguous, defined by regional council.

To minimise suppression of population data, separate meshblocks have been created for marinas. These meshblocks are attached to adjacent land in the UR geography.

Non-digitised

The following 4 non-digitised UR areas have been aggregated from the 16 non-digitised meshblocks/SA2s.

6901; Oceanic outside region, 6902; Oceanic oil rigs, 6903; Islands outside region, 6904; Ross Dependency outside region.

UR numbering and naming

Each urban area and rural settlement is a single geographic entity with a name and a numeric code.

Other rural areas, inland water areas, and inlets are defined by territorial authority; oceanic areas are defined by regional council; and each have a name and a numeric code.

Urban rural codes have four digits. North Island locations start with a 1, South Island codes start with a 2, oceanic codes start with a 6 and non-digitised codes start with 69.

Urban rural indicator (IUR)

The accompanying urban rural indicator (IUR) classifies the urban, rural, and water areas by type. Urban areas are further classified by the size of their estimated resident population:

• major urban area – 100,000 or more residents,
• large urban area – 30,000–99,999 residents,
• medium urban area – 10,000–29,999 residents,
• small urban area – 1,000–9,999 residents.

This was based on 2018 Census data and 2021 population estimates. Their IUR status (urban area size/rural settlement) may change if the 2025 Census population count moves them up or down a category.

The indicators, by name, with their codes in brackets, are:

urban area – major urban (11), large urban (12), medium urban (13), small urban (14),

rural area – rural settlement (21), rural other (22),

water – inland water (31), inlet (32), oceanic (33).

​

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

Rural Urban ClassificationThe 2021 RUC is a statistical classification to provide a consistent and standardised method for classifying geographies as rural or urban. This is based on address density, physical settlement form, population size, and Relative Access to Major towns and cities (populations of over 75,000 people). The classification is produced by the Office for National Statistics (ONS) with advice from the Department for Environment, Food and Rural Affairs (Defra), the Welsh Government and colleagues from the Government Geography Profession (GGP).This is 2021 rural-urban classification (RUC) of 2024 Local Authority Districts in England and Wales. This means that the 2021 RUC methodology has been applied to the 2024 LAD boundaries. LAD classifications are divided into four categories based on their populations:1. Majority Rural: had at least 50% of their population residing in Rural OAs2. Intermediate Rural: 35-50% rural population3. Intermediate Urban: 20-35% rural population4. Urban: 20% or less of the population lived in rural OAs.Each 2024 LAD category is split into one of two Relative Access categories, using the same data as the 2021 Output Area RUC. If more than 50% of a LAD population lives in ‘Nearer a major town or city’ OAs, it is deemed ‘nearer a major town or city’; otherwise, it is classified as ‘further from a major town or city’.

Where data is unavailable for Super Output Area geographies, it may be appropriate for users to undertake analysis at the LAD level. At this level, the categorisation works slightly differently in that most areas will include a mix of both rural and urban areas - so the LA RUC categorisation is a reflection of this. A statistical geography may contain substantial portions of open countryside but still be given an ‘Urban’ classification if the majority of the population within the area live in settlements that are urban in nature. Users should take this into consideration to ensure correct interpretations of any analysis of RUC LAD categories.

(File Size - 5 KB). The 2011 rural-urban classification (RUC) of counties in England is based on the 2011 RUC of Output Areas (OA) published in August 2013, and allows users to create a rural/urban view of county level products. The classification was produced by the University of Sheffield and was sponsored by a cross-Government working group comprising Department for Environment, Food and Rural Affairs, Department of the Communities and Local Government and Office for National Statistics.

This dataset collection provides information on fuelwood usage in small-scale housing, categorized according to the urban-rural classification and fireplace type. The collection consists of one or more dataset tables sourced from the website of Luke, the Natural Resources Institute Finland, in the country of Finland.

The 2011 rural-urban classification (RUC) of lower layer super output areas in England and Wales is based on the 2011 RUC of output areas published in August 2013, and allows users to create a rural/urban view of LSOA level products. This product was sponsored by a cross-Government working group comprising Department for Environment, Food and Rural Affairs, Department of the Communities and Local Government, Office for National Statistics and the Welsh Government. The classification at LSOA level is built from the RUC at OA level (the most detailed version of the classification). Assignments of LSOA to urban or rural categories are made by reference to the category to which the majority of their constituent OA are assigned (File Size 1.8MB).REST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Rural Urban Classification (2011) of Lower Layer Super Output Areas in England and Wales_new/FeatureServer

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 the year 2016. Data for previous versions are available for download in ESRI Shapefile format.

This dataset has been clipped to Stirling Council's Planning Policy BoundaryThe 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 the year 2022.

This file provides a rural-urban view of 2011 Output Areas (OA) in England and Wales. The 2011 rural-urban classification (RUC) of OAs was released in August 2013. The product was sponsored by a cross-Government working group comprising Department for Environment, Food and Rural Affairs, Department for Communities and Local Government, Office for National Statistics and the Welsh Government. OAs are treated as ‘urban’ if they were allocated to a 2011 built-up area with a population of 10,000 or more. The urban domain is then further sub-divided into three broad morphological types based on the predominant settlement component. As with the previous version of the classification, the remaining ‘rural’ OAs are grouped into three broad morphological types based on the predominant settlement component. The classification also categorises OAs based on context – i.e. whether the wider surrounding area of a given OA is sparsely populated or less sparsely populated.

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.

Click on the title for more details and to download the file. (File Size - 368 KB)