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.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 State-Level Maps For complete information, please visit https://data.gov.

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

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 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).

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.

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, clipped to the coastline. This clipped version has been created for cartographic purposes and so does not fully represent the official full extent boundaries. 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).

Clipped Version

This clipped version has been created for cartographic purposes and so does not fully represent the official full extent boundaries.

​

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

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.

This service provides a tract-level dataset illustrating the outcome of machine learning techniques applied to neighborhood classification reported by the American Housing Survey (AHS) as either urban, suburban, or rural. 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.

Frontier and Remote Area (FAR) codes provide a statistically-based, nationally-consistent, and adjustable definition of territory in the U.S. characterized by low population density and high geographic remoteness.

To assist in providing policy-relevant information about conditions in sparsely settled, remote areas of the U.S. to public officials, researchers, and the general public, ERS has developed ZIP-code-level frontier and remote (FAR) area codes. The aim is not to provide a single definition. Instead, it is to meet the demand for a delineation that is both geographically detailed and adjustable within reasonable ranges, in order to be usefully applied in diverse research and policy contexts. This initial set, based on urban-rural data from the 2000 decennial census, provides four separate FAR definition levels, ranging from one that is relatively inclusive (18 million FAR residents) to one that is more restrictive (4.8 million FAR residents).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: State and ZIP code level tables For complete information, please visit https://data.gov.

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.

This dataset contains urban and rural LST, DEM, and NDVI data for annual, summer, and winter daytime and nighttime for all census tracts in US urbanized areas, as well as the mean values for the entire urbanized area.

METADATA DEM: Digital Elevation Model NDVI: Normalized Difference Vegetation Index LST: Land Surface Temperature
_urb: Urban values (all urban pixels within urbanized areas) _rur: Rural reference (Spatial mean of the non-urban, non-water pixels within the region of interest) Regions of Interest: _CT: Spatial mean of pixels intersecting the Census Tract clipped to the urbanized area (one value per census tract). This should be equal to the _CT for census tracts that are completely within the urbanized areas (the census tracts with the green dots in the image below) _all: Spatial mean of all pixels intersecting the urbanized area, as defined by the US census (one value for one urbanized area) _CT_act: Spatial mean of all available pixels intersecting the Census Tract (one value per census tract) [This should be equal to the previous values I calculated]

For the UHI: The ideal configuration is LST_urb_all-LST_rur for the entire urbanized area and LST_urb_CT_act-LST_rur for individual census tracts within the urbanized areas

For the equity analysis: Either _CT or CT_act can be used if we are only concerned with spatial variation. Using CT_act leads to mismatch between census data for the tracts crossing the urban boundary and the remotely sensed data. Using _CT leads to mismatch between the UHI analysis and the equity analysis.

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

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

The regional definition of the rural vs. urban character of municipalities was adopted in 2016. A municipality is considered rural if it has fewer than 10,000 inhabitants and is located outside the Greater Paris metropolitan area.

List of rural municipalities within the meaning of “Eligibility to the GIP”, a global allocation of equipment paid to the department of Saône and Loire. Prefectural Order No. 2017103-001 of 13 April 2017. Article D3334-8-1 of the General Code of Local and Regional Authorities: The following municipalities in metropolitan France are considered to be rural municipalities for the purposes of Articles L. 3334-10 and R. 3334-8: — municipalities whose population does not exceed 2 000 inhabitants; — municipalities whose population exceeds 2 000 inhabitants and does not exceed 5 000 inhabitants, if they do not belong to an urban unit or if they belong to an urban unit whose population does not exceed 5000 inhabitants. The urban reference unit is that defined by the National Institute of Statistics and Economic Studies. The population taken into account is the total population authenticated at the end of the population census.

description: The Global Rural-Urban Mapping Project (GRUMP), Alpha Version consists of estimates of human population for the years 1990, 1995, and 2000 by 30 arc-second (1km) grid cells and associated datasets dated circa 2000. The data products include population count grids (raw counts), population density grids (per square km), land area grids (actual area net of ice and water), mean geographic unit area grids, urban extents grids, centroids, a national identifier grid, national boundaries, coastlines, and settlement points. These products vary in GIS-compatible data formats and geographic extents (global, continent [Antarctica not included], and country levels). A proportional allocation gridding algorithm, utilizing more than 1,000,000 national and sub-national geographic units, is used to assign population values to grid cells. Additional global grids are created from the 30 arc-second grid at 1/4, 1/2, and 1 degree resolutions. The Spatial Reference metadata section information applies only to global extent, 30 arc-second resolution. This dataset 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). (Suggested Usage: To allow analysis of urban and rural population figures based on a consistent global dataset.); abstract: The Global Rural-Urban Mapping Project (GRUMP), Alpha Version consists of estimates of human population for the years 1990, 1995, and 2000 by 30 arc-second (1km) grid cells and associated datasets dated circa 2000. The data products include population count grids (raw counts), population density grids (per square km), land area grids (actual area net of ice and water), mean geographic unit area grids, urban extents grids, centroids, a national identifier grid, national boundaries, coastlines, and settlement points. These products vary in GIS-compatible data formats and geographic extents (global, continent [Antarctica not included], and country levels). A proportional allocation gridding algorithm, utilizing more than 1,000,000 national and sub-national geographic units, is used to assign population values to grid cells. Additional global grids are created from the 30 arc-second grid at 1/4, 1/2, and 1 degree resolutions. The Spatial Reference metadata section information applies only to global extent, 30 arc-second resolution. This dataset 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). (Suggested Usage: To allow analysis of urban and rural population figures based on a consistent global dataset.)

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.

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.

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”.

Contained within the 3rd Edition (1957) of the Atlas of Canada is a map consisting of two condensed maps showing the distribution of rural population according to the 1951 census of Canada. The term 'rural population' embraces all persons residing outside the census metropolitan areas and cities, towns and villages of 1000 inhabitants and over, whether such cities, towns and villages were incorporated or not. The distribution is shown according to the two divisions of rural population commonly made, namely, rural farm and rural non-farm. The rural farm population comprises all people residing on a farm regardless of occupation. A farm for such purposes is defined as a land holding of over three acres in size on which agricultural operations are carried out, or a land holding from one to three acres in size, which in 1950 accounted for an agricultural production amounting to $250 or more. All other persons classed as rural population come under the rural non-farm division. The northern parts of Yukon Territory and the Northwest Territories are not included on the rural non-farm map although there are some rural non-farm dwellers in these areas. In 1951, Canada's rural population was 52.5% rural farm, and 47.5% rural non-farm.

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