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Emirates","code":"ARE"},{"id":1,"name":"United Kingdom","code":"GBR"},{"id":13,"name":"United States","code":"USA"},{"id":256,"name":"United States Virgin Islands","code":"VIR"},{"id":459,"name":"Upper-middle-income countries","code":null},{"id":63,"name":"Uruguay","code":"URY"},{"id":62,"name":"Uzbekistan","code":"UZB"},{"id":221,"name":"Vanuatu","code":"VUT"},{"id":257,"name":"Vatican","code":"VAT"},{"id":238,"name":"Venezuela","code":"VEN"},{"id":84,"name":"Vietnam","code":"VNM"},{"id":255,"name":"Wallis and Futuna","code":"WLF"},{"id":248,"name":"Western Sahara","code":"ESH"},{"id":355,"name":"World","code":"OWID_WRL"},{"id":61,"name":"Yemen","code":"YEM"},{"id":60,"name":"Zambia","code":"ZMB"},{"id":80,"name":"Zimbabwe","code":"ZWE"}]}},"descriptionShort":"The European Commission combines satellite imagery with national census data to identify [cities](#dod:cities-degurba), [towns](#dod:towns-degurba), and [villages](#dod:villages-degurba) and estimate their respective populations.","descriptionProcessing":"The share of total area or population for each urbanization level was calculated by dividing the area or population of each level (cities, towns, villages) by the overall total, providing a percentage representation for each category.","type":"float","dataChecksum":"11837304392116593802","metadataChecksum":"-2625436421389190377","datasetName":"Global Human Settlement Layer Dataset","updatePeriodDays":365,"datasetVersion":"2025-12-10","nonRedistributable":false,"display":{"unit":"%","shortUnit":"%","numDecimalPlaces":1},"schemaVersion":2,"processingLevel":"major","presentation":{"topicTagsLinks":["Urbanization"]},"descriptionKey":["**The Degree of Urbanisation (DEGURBA)**  is a method for capturing the urban-rural divide, designed for international comparisons. Developed by six organizations and endorsed by the UN, it uses a two-level classification.\n\nThe first level divides areas into cities, towns, and villages, distinguishing between urban (cities, towns, suburbs) and rural regions. The second level adds detail, splitting towns and villages further.\n\nThis classification is based on 1 km² grid cells, grouped into urban centers, urban clusters, and rural cells. These grids are then used to classify smaller areas, typically using residential population grids from censuses or registers. If detailed data isn't available, a disaggregation grid estimates population distribution.\n\nTo predict future urbanization (2025 to 2100), both static (land features) and dynamic (past satellite images) components are used to project growth. DEGURBA defines cities by population, not administrative borders, aligning with UN guidelines, though fixed thresholds may not always capture local differences."],"origins":[{"id":9487,"titleSnapshot":"Global Human Settlement Layer Dataset - Countries","title":"Global Human Settlement Layer Dataset","description":"The dataset includes population projections by degree of urbanisation and at the city level.\n\nFor every country and territory in the world, the authors estimated their population from 1950 to 2100 in cities, towns and semi-dense areas, and rural areas. It relies on the UN-endorsed Degree of Urbanisation methodology. As a result, the definitions used in each country are fully harmonised; while national definitions vary considerably.\n\nThe long time series consists of three parts:\n\n- From 1950 to 1970, it is based on backcasting by blending data using national definitions of urban and rural areas with data using the Degree of Urbanisation.\n- From 1975 to 2020, it is based on the Global Human Settlement Layer (GHSL), because it has the longest time series and uses a transparent and reproducible method.\n- From 2020 to 2100, it relies on a new model, \"Cities and Rural Integrated Spatial Projections\" (CRISP).\n\nThe CRISP model estimates population and built-up area change for a global grid of 1 km2 cells in an evidence-based, three-step process. First, the authors estimate population and built-up area change for roughly 1000 functional areas based on past trends and national population projections. Second, they allocate new built-up area to grid cells considering distance to settlements, roads, water, current share of built-up area and other characteristics. Finally, they add population to newly built-up areas and more suitable locations and reduce it in less suitable locations to capture internal migration and natural population decline.\n\nBeyond population, the dataset also delivers maps showing the evolving spatial extent of cities, towns and rural areas. For every city in the world, it also provides updated boundaries, land area and built-up area at five-year intervals from 1975 to 2100.","producer":"European Commission, Joint Research Centre (JRC)","citationFull":"Schiavina, Marcello; Alessandrini, Alfredo; Melchiorri, Michele; Jacobs-Crisioni, Chris; Dijkstra Lewis, (2025): GHS-WUP-COUNTRY-STATS R2025A – GHS-WUP country statistics by Degree of Urbanisation classes, multitemporal (1950-2100). European Commission, Joint Research Centre (JRC). PID: http://data.europa.eu/89h/ee52f334-aaee-4b9e-aa00-d38ecf6c11a9 , doi: 10.2905/ee52f334-aaee-4b9e-aa00-d38ecf6c11a9\n\nJacobs-Crisioni, C., Schiavina, M., Alessandrini, A., Dijkstra, L. (2025) Population by degree of urbanization and by urban agglomeration from 1950 to 2100: Analyses supporting the United Nations World Urbanization Prospects report. Publications Office of the European Union, Luxembourg, 2025, JRC144219","urlMain":"https://human-settlement.emergency.copernicus.eu/ghs_wup2025.php","urlDownload":"https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/GHSL/GHS_WUP_COUNTRY_STATS_GLOBE_R2025A/V1-0/GHS_WUP_COUNTRY_STATS_GLOBE_R2025A_V1_0.zip","dateAccessed":"2025-12-10","datePublished":"2025","license":{"url":"https://human-settlement.emergency.copernicus.eu/GHSLhowToCite.php","name":"European Union, 1995-2025"}}]}