{"id":1130259,"name":"Population density of the top 100 most populous cities (projected)","unit":"people per km²","createdAt":"2025-12-12T10:04:16.000Z","updatedAt":"2026-06-08T21:41:17.000Z","coverage":"","timespan":"2020-2100","datasetId":7291,"shortUnit":"per km²","columnOrder":0,"shortName":"urban_density_top_100_projections","catalogPath":"grapher/urbanization/2025-12-10/ghsl_urban_centers/ghsl_urban_centers#urban_density_top_100_projections","descriptionShort":"Projected number of people per km² of land area for cities ranked among the top 100 most populous in 2020. [City boundaries](#dod:cities-degurba) are defined using a consistent global approach based on satellite imagery and population data.","descriptionProcessing":"Population density was calculated by dividing the population of the urban centre by its total land area.","type":"float","dataChecksum":"2212427828113269505","metadataChecksum":"4009001626363875942","datasetName":"GHSL Urban Centre Database 2025 (GHS-UCDB R2024A)","updatePeriodDays":365,"datasetVersion":"2025-12-10","nonRedistributable":false,"display":{"unit":"people per km²","shortUnit":"per km²","isProjection":true,"numDecimalPlaces":0,"entityAnnotationsMap":"Abidjan: Côte d'Ivoire\nAccra: Ghana\nAddis Ababa: Ethiopia\nAhmedabad: India\nAlexandria: Egypt\nAmman: Jordan\nBaghdad: Iraq\nBandung: Indonesia\nBangkok: Thailand\nBeijing: China\nBelo Horizonte: Brazil\nBengaluru: India\nBogota: Colombia\nBuenos Aires: Argentina\nCairo: Egypt\nCape Town: South Africa\nCasablanca: Morocco\nChattogram: Bangladesh\nChennai: India\nChengdu: China\nChaozhou: China\nChongqing: China\nColombo: Sri Lanka\nDar es Salaam: Tanzania\nDhaka: Bangladesh\nDubai: United Arab Emirates\nFaisalabad: Pakistan\nGuangzhou: China\nHajipur: India\nHangzhou: China\nHanoi: Vietnam\nHarbin: China\nHefei: China\nHo Chi Minh City: Vietnam\nHong Kong: China\nHyderabad: India\nIslamabad: Pakistan\nIstanbul: Turkey\nJakarta: Indonesia\nJohannesburg: South Africa\nKabul: Afghanistan\nKampala: Uganda\nKano: Nigeria\nKanpur: India\nKarachi: Pakistan\nKhartoum: Sudan\nKinshasa: Democratic Republic of Congo\nKochi: India\nKolkata: India\nKozhikode: India\nKuala Lumpur: Malaysia\nLagos: Nigeria\nLahore: Pakistan\nLima: Peru\nLondon: United Kingdom\nLos Angeles: United States\nLuanda: Angola\nLucknow: India\nMadrid: Spain\nManila: Philippines\nMashhad: Iran\nMedan: Indonesia\nMexico City: Mexico\nMoscow: Russia\nMumbai: India\nNagoya: Japan\nNairobi: Kenya\nNanjing: China\nNew Delhi: India\nNew York City: United States\nOnitsha: Nigeria\nOsaka: Japan\nParis: France\nPune: India\nRiyadh: Saudi Arabia\nRio de Janeiro: Brazil\nSaint Petersburg: Russia\nSantiago: Chile\nSanto Domingo: Dominican Republic\nSao Paulo: Brazil\nSeoul: South Korea\nShanghai: China\nShenyang: China\nShenzhen: China\nSingapore: Singapore\nSurat: India\nSurabaya: Indonesia\nSuzhou: China\nSydney: Australia\nTaipei: Taiwan\nTehran: Iran\nTianjin: China\nTokyo: Japan\nToronto: Canada\nWenzhou: China\nWuhan: China\nXi'an: China\nYangon: Myanmar\nYaounde: Cameroon\nZhengzhou: China"},"schemaVersion":2,"processingLevel":"minor","presentation":{"topicTagsLinks":["Urbanization"]},"descriptionKey":["Projected population density of each of the world's 100 most populous cities (as ranked in 2020), based on the GHSL modelling framework anchored to UN World Population Prospects 2022.","[Cities](#dod:cities-degurba) are defined as areas with at least 1,500 people per km² and a total population of at least 50,000, identified using the [Degree of Urbanization](https://human-settlement.emergency.copernicus.eu/degurba.php) framework based on satellite imagery and census data.","City boundaries are fixed at their 2025 extent across all years, so historical values reflect conditions within today's boundaries. This can make fast-growing cities appear less dense in earlier periods.","Cities are also split at country borders, so a city that straddles two countries will appear as two separate entries.","City boundaries are model-derived and may not match official administrative limits. Data quality varies by region and tends to be lower where census data is sparse or outdated.","The underlying population figures have been rescaled to match UN World Population Prospects 2022 national totals, so country-level numbers are consistent with UN estimates.","Projections use the [CRISP spatial model](https://www.researchgate.net/publication/384062691_Calibration_of_the_CRISP_model_A_global_assessment_of_local_built-up_area_presence) at 1 km² resolution, following the UN World Population Prospects 2024 medium scenario.","The ranking of the top 100 cities is fixed based on their population in 2020. Projected values show the density of those same cities in future years."],"dimensions":{"years":{"values":[{"id":2020},{"id":2025},{"id":2030},{"id":2035},{"id":2040},{"id":2045},{"id":2050},{"id":2055},{"id":2060},{"id":2065},{"id":2070},{"id":2075},{"id":2080},{"id":2085},{"id":2090},{"id":2095},{"id":2100}]},"entities":{"values":[{"id":37992,"name":"Abidjan","code":null},{"id":37971,"name":"Accra","code":null},{"id":38075,"name":"Addis Ababa","code":null},{"id":37988,"name":"Ahmedabad","code":null},{"id":37991,"name":"Alexandria","code":null},{"id":37955,"name":"Amman","code":null},{"id":37999,"name":"Baghdad","code":null},{"id":37995,"name":"Bandung","code":null},{"id":37975,"name":"Bangkok","code":null},{"id":38029,"name":"Beijing","code":null},{"id":38035,"name":"Belo Horizonte","code":null},{"id":372354,"name":"Bengaluru","code":null},{"id":37990,"name":"Bogota","code":null},{"id":37957,"name":"Buenos Aires","code":null},{"id":38002,"name":"Cairo","code":null},{"id":37958,"name":"Cape Town","code":null},{"id":38068,"name":"Casablanca","code":null},{"id":372349,"name":"Chaozhou","code":null},{"id":372353,"name":"Chattogram","code":null},{"id":38007,"name":"Chengdu","code":null},{"id":37974,"name":"Chennai","code":null},{"id":38008,"name":"Chongqing","code":null},{"id":368927,"name":"Colombo","code":null},{"id":38013,"name":"Dar es Salaam","code":null},{"id":37985,"name":"Dhaka","code":null},{"id":35435,"name":"Dubai","code":null},{"id":38065,"name":"Faisalabad","code":null},{"id":38028,"name":"Guangzhou","code":null},{"id":372352,"name":"Hajipur","code":null},{"id":38024,"name":"Hangzhou","code":null},{"id":372351,"name":"Hanoi","code":null},{"id":38001,"name":"Harbin","code":null},{"id":38072,"name":"Hefei","code":null},{"id":38016,"name":"Ho Chi Minh City","code":null},{"id":144,"name":"Hong Kong","code":"HKG"},{"id":35194,"name":"Hyderabad","code":null},{"id":368966,"name":"Islamabad","code":null},{"id":37972,"name":"Istanbul","code":null},{"id":38000,"name":"Jakarta","code":null},{"id":37960,"name":"Johannesburg","code":null},{"id":38047,"name":"Kabul","code":null},{"id":368854,"name":"Kampala","code":null},{"id":38064,"name":"Kano","code":null},{"id":38085,"name":"Kanpur","code":null},{"id":38022,"name":"Karachi","code":null},{"id":38025,"name":"Khartoum","code":null},{"id":37989,"name":"Kinshasa","code":null},{"id":36764,"name":"Kochi","code":null},{"id":38040,"name":"Kolkata","code":null},{"id":372348,"name":"Kozhikode","code":null},{"id":38039,"name":"Kuala Lumpur","code":null},{"id":37973,"name":"Lagos","code":null},{"id":37996,"name":"Lahore","code":null},{"id":37966,"name":"Lima","code":null},{"id":34609,"name":"London","code":null},{"id":36884,"name":"Los Angeles","code":null},{"id":38019,"name":"Luanda","code":null},{"id":38077,"name":"Lucknow","code":null},{"id":36790,"name":"Madrid","code":null},{"id":37993,"name":"Manila","code":null},{"id":38086,"name":"Mashhad","code":null},{"id":38005,"name":"Medan","code":null},{"id":36907,"name":"Mexico City","code":null},{"id":35249,"name":"Moscow","code":null},{"id":37986,"name":"Mumbai","code":null},{"id":36745,"name":"Nagoya","code":null},{"id":38004,"name":"Nairobi","code":null},{"id":38032,"name":"Nanjing","code":null},{"id":372347,"name":"New Delhi","code":null},{"id":37962,"name":"New York City","code":null},{"id":372350,"name":"Onitsha","code":null},{"id":36747,"name":"Osaka","code":null},{"id":36686,"name":"Paris","code":null},{"id":37997,"name":"Pune","code":null},{"id":38023,"name":"Rio de Janeiro","code":null},{"id":38038,"name":"Riyadh","code":null},{"id":38045,"name":"Saint Petersburg","code":null},{"id":36678,"name":"Santiago","code":null},{"id":368772,"name":"Santo Domingo","code":null},{"id":38011,"name":"Sao Paulo","code":null},{"id":37965,"name":"Seoul","code":null},{"id":38020,"name":"Shanghai","code":null},{"id":38021,"name":"Shenyang","code":null},{"id":38010,"name":"Shenzhen","code":null},{"id":86,"name":"Singapore","code":"SGP"},{"id":38009,"name":"Surabaya","code":null},{"id":37987,"name":"Surat","code":null},{"id":38036,"name":"Suzhou","code":null},{"id":36930,"name":"Sydney","code":null},{"id":38017,"name":"Taipei","code":null},{"id":37998,"name":"Tehran","code":null},{"id":38030,"name":"Tianjin","code":null},{"id":36743,"name":"Tokyo","code":null},{"id":36675,"name":"Toronto","code":null},{"id":38078,"name":"Wenzhou","code":null},{"id":38012,"name":"Wuhan","code":null},{"id":38018,"name":"Xi'an","code":null},{"id":38003,"name":"Yangon","code":null},{"id":38081,"name":"Yaounde","code":null},{"id":38014,"name":"Zhengzhou","code":null}]}},"origins":[{"id":9488,"titleSnapshot":"Global Human Settlement Layer Dataset - Urban centers","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; Dijkstra, Lewis (2025): GHS-WUP-MTUC R2025A – GHS-WUP multitemporal urban centres, obtained from the Degree of Urbanisation grids (GHS-WUP-DEGURBA R2025A) and linked across epochs, multitemporal (1950-2100). European Commission, Joint Research Centre (JRC). PID: http://data.europa.eu/89h/1ea967e5-bedc-4cf3-a0b0-3851742ee7e2 , doi: 10.2905/1ea967e5-bedc-4cf3-a0b0-3851742ee7e2\n\nPesaresi, Martino, Marcello Schiavina, Panagiotis Politis, Sergio Freire, Katarzyna Krasnodębska, Johannes H. Uhl, Alessandra Carioli, et al. (2024). Advances on the Global Human Settlement Layer by Joint Assessment of Earth Observation and Population Survey Data. International Journal of Digital Earth 17 (1). doi:10.1080/17538947.2024.2390454\n\nJacobs-Crisioni, Chris et al (2025). Population projections by degree of urbanisation for the UN World Urbanization Prospects: introducing the CRISP model, Publications Office of the European Union, Luxembourg, 2025, doi:10.2760/7163875","urlMain":"https://human-settlement.emergency.copernicus.eu/ghs_wup2025.php","urlDownload":"https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/GHSL/GHS_WUP_MTUC_GLOBE_R2025A/V1-0/GHS_WUP_MTUC_GLOBE_R2025A_V1_0_statistics.zip","dateAccessed":"2025-12-10","datePublished":"2025","license":{"url":"https://human-settlement.emergency.copernicus.eu/GHSLhowToCite.php","name":"European Union, 1995-2025"}}]}