Rural and urban population 1990 - 2017, FAOSTAT (website: http://www.fao.org/faostat/en/#country/127)

This dataset is about countries per year in Georgia. It has 64 rows. It features 4 columns: country, rural population, and urban population.

This table contains 3 series, with data for years 1871 - 1971 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Unit of measure (1 items: Persons ...) Geography (1 items: Canada ...) Population (3 items: Total population; Urban population; Rural population ...).

The 2013 Rural-Urban Continuum Codes form a classification scheme that distinguishes metropolitan counties by the population size of their metro area, and nonmetropolitan counties by degree of urbanization and adjacency to a metro area. The official Office of Management and Budget (OMB) metro and nonmetro categories have been subdivided into three metro and six nonmetro categories. Each county in the U.S. is assigned one of the 9 codes. This scheme allows researchers to break county data into finer residential groups, beyond metro and nonmetro, particularly for the analysis of trends in nonmetro areas that are related to population density and metro influence. The Rural-Urban Continuum Codes were originally developed in 1974. They have been updated each decennial since (1983, 1993, 2003, 2013), and slightly revised in 1988. Note that the 2013 Rural-Urban Continuum Codes are not directly comparable with the codes prior to 2000 because of the new methodology used in developing the 2000 metropolitan areas. See the Documentation for details and a map of the codes. An update of the Rural-Urban Continuum Codes is planned for mid-2023.

Rural and urban population 1990 - 2017, FAOSTAT (website: http://www.fao.org/faostat/en/#country/180)

This comprehensive dataset, derived from the United Nations World Urbanization Prospects 2018, provides detailed insights into the global demographic shifts from 1950 to 2050. It covers a wide range of data points including total, urban, and rural populations, alongside growth rates and urbanization trends across different regions, subregions, and countries.

Dataset Files WUP2018-F01-Total_Urban_Rural.xls: Population counts for urban and rural areas as of mid-2018, including percentages. WUP2018-F02-Proportion_Urban.xls: Historical and projected percentages of urban populations from 1950 to 2050. WUP2018-F03-Urban_Population.xls: Urban population figures from 1950 to 2050. WUP2018-F04-Rural_Population.xls: Rural population figures from 1950 to 2050. WUP2018-F05-Total_Population.xls: Total population figures from 1950 to 2050. WUP2018-F06-Urban_Growth_Rate.xls: Annual urban population growth rates from 1950 to 2050. WUP2018-F07-Rural_Growth_Rate.xls: Annual rural population growth rates from 1950 to 2050. WUP2018-F08-Total_Growth_Rate.xls: Total population growth rates from 1950 to 2000. WUP2018-F09-Urbanization_Rate.xls: Changes in the rate of urbanization from 1950 to 2050. WUP2018-F10-Rate_Proportion_Rural.xls: Changes in the proportion of rural populations from 1950 to 2050. WUP2018-F18-Total_Population_Annual.xls: Detailed annual total population data from 1950 to 2050. WUP2018-F19-Urban_Population_Annual.xls: Detailed annual urban population data from 1950 to 2050. WUP2018-F20-Rural_Population_Annual.xls: Detailed annual rural population data from 1950 to 2050. WUP2018-F21-Proportion_Urban_Annual.xls: Detailed annual urban population percentages from 1950 to 2050. Potential Uses This dataset is invaluable for researchers, policy makers, urban planners, and sociologists interested in understanding the dynamics of urbanization and its impacts on global development. The data can be used for:

Analyzing trends in urban and rural growth. Forecasting future demographic shifts. Planning for infrastructure, services, and resources in rapidly urbanizing regions. Studying regional differences in development and urbanization.

This table uses U.S. Census data to create a dataset that identifies all Virginia localities as either Mostly Urban, Mostly Rural or Completely Rural. Total population and breakdown between urban and rural populations are included.

For information on the U.S. Census Bureau's use of these designations see https://www.census.gov/programs-surveys/geography/guidance/geo-areas/urban-rural.html. (Source data for this dataset is found at this link and is titled "County Classification Lookup Table [XLS]".)

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

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.

This dataset is about countries per year in Denmark. It has 64 rows. It features 4 columns: country, rural population, and urban population.

United States US: Rural Population: % of Total Population data was reported at 17.942 % in 2017. This records a decrease from the previous number of 18.138 % for 2016. United States US: Rural Population: % of Total Population data is updated yearly, averaging 24.985 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 30.004 % in 1960 and a record low of 17.942 % in 2017. United States US: Rural Population: % of Total Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Population and Urbanization Statistics. Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population.; ; World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2018 Revision.; Weighted average;

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

2009 Census data on male and female populations by core-urban, peri-urban and rural locations.

United States US: Urban Population data was reported at 267,278,643.000 Person in 2017. This records an increase from the previous number of 264,746,567.000 Person for 2016. United States US: Urban Population data is updated yearly, averaging 184,283,180.000 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 267,278,643.000 Person in 2017 and a record low of 126,462,473.000 Person in 1960. United States US: Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Population and Urbanization Statistics. Urban population refers to people living in urban areas as defined by national statistical offices. It is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects. Aggregation of urban and rural population may not add up to total population because of different country coverages.; ; World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2018 Revision.; Sum;

Each year, the Forecasting and Trends Office (FTO) publishes population estimates and future year projections based on the population estimates developed by the Bureau of Economic and Business Research (BEBR) at the University of Florida. This dataset contains boundaries for each county’s 2010 rural (non-urban) area in the State of Florida with 2020 census population and 2021 population estimates. The population estimates can be used for a variety of planning studies including statewide and regional transportation plan updates, subarea and corridor studies, and funding allocations for various planning agencies.Each year, the Forecasting and Trends Office (FTO) publishes population estimates and future year projections. The population estimates can be used for a variety of planning studies including statewide and regional transportation plan updates, subarea and corridor studies, and funding allocations for various planning agencies.The 2020 population estimates reported are based on the US Census Bureau 2020 Decennial Census. The 2021 population estimates are based on the population estimates developed by the Bureau of Economic and Business Research (BEBR) at the University of Florida. BEBR uses the decennial census count for April 1, 2020, as the starting point for state-level projections. More information is available from BEBR here.This dataset contains boundaries for each county’s 2010 rural (non-urban) area in the State of Florida with 2020 census population and 2021 population estimates. All legal boundaries and names in this dataset are from the US Census Bureau’s TIGER/Line Files (2021).For the 2010 Census, urban areas comprised a “densely settled core of census tracts and/or census blocks that meet minimum population density requirements, along with adjacent territory containing non-residential urban land uses as well as territory with low population density included to link outlying densely settled territory with the densely settled core.” “Rural” encompasses all population, housing, and territory not included within an urban area. Please see the Data Dictionary for more information on data fields. Data Sources:US Census Bureau 2020 Decennial CensusUS Census Bureau’s TIGER/Line Files (2021)Bureau of Economic and Business Research (BEBR) – Florida Estimates of Population 2021 Data Coverage: StatewideData Time Period: 2020 – 2021 Date of Publication: July 2022 Point of Contact:Dana Reiding, ManagerForecasting and Trends OfficeFlorida Department of TransportationDana.Reiding@dot.state.fl.us605 Suwannee Street, Tallahassee, Florida 32399850-414-4719

The Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 2 data set consists of country-level estimates of urban population, rural population, total population and land area country-wide and in LECZs for years 1990, 2000, 2010, and 2100. The LECZs were derived from Shuttle Radar Topography Mission (SRTM), 3 arc-second (~90m) data which were post processed by ISciences LLC to include only elevations less than 20m contiguous to coastlines; and to supplement SRTM data in northern and southern latitudes. The population and land area statistics presented herein are summarized at the low coastal elevations of less than or equal to 1m, 3m, 5m, 7m, 9m, 10m, 12m, and 20m. Additionally, estimates are provided for elevations greater than 20m, and nationally. The spatial coverage of this data set includes 202 of the 232 countries and statistical areas delineated in the Gridded Rural-Urban Mapping Project version 1 (GRUMPv1) data set. The 30 omitted areas were not included because they were landlocked, or otherwise lacked coastal features. This data set makes use of the population inputs of GRUMPv1 allocated at 3 arc-seconds to match the SRTM elevations, and at 30 arc-seconds resolution in order to reflect uncertainty levels in the product resulting from the interplay of input population data resolutions (based on census Units) and the elevation data. Urban and rural areas are differentiated by the GRUMPv1 Urban Extents. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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).

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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.

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

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Generalised version

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

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Macrons

Names are provided with and without tohutō/macrons. The column name for those without macrons is suffixed ‘ascii’.

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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): Population Count Grid estimates human population for the years 1990, 1995, and 2000 by 30 arc-second (1 km) grid cells and associated data sets dated circa 2000. A proportional allocation gridding algorithm, utilizing more than 1,000,000 national and sub-national geographic Units, is used to assign population values (counts, in persons) to grid cells. 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).

The Low Elevation Coastal Zone (LECZ) Urban-Rural Population Estimates consists of country-level estimates of urban, rural and total population and land area country-wide and in the LECZ, if applicable. Additionally, the data set provides the number of urban extents, their population and land area that intersect the LECZ, by city-size population classifications of less than 100,000, 100,000 to 500,000, 500,000 to 1,000,000, 1,000,000 to 5,000,000, and more than 5,000,000. All estimates are based on GRUMP Alpha data products. The LECZ was generated using SRTM Digital Elevation Model data and includes all land area that is contiguous with the coast and 10 meters or less in elevation. All grids used for population, land area, urban mask, and LECZ were of 30 arc-second (~1 km ) resolution. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Institute for Environment and Development (IIED).

This dataset is about countries per year in Uzbekistan. It has 1 row and is filtered where the date is 2023. It features 4 columns: country, rural population, and urban population.