{"id":1130256,"name":"Population density of the top 100 most populous cities","unit":"people per km²","createdAt":"2025-12-12T10:04:16.000Z","updatedAt":"2025-12-13T10:41:03.000Z","coverage":"","timespan":"1975-2020","datasetId":7291,"shortUnit":"per km²","columnOrder":0,"shortName":"urban_density_top_100_estimates","catalogPath":"grapher/urbanization/2025-12-10/ghsl_urban_centers/ghsl_urban_centers#urban_density_top_100_estimates","descriptionShort":"The number of people per km² of land area for cities ranked among the top 100 most populous in 2020.","descriptionProcessing":"Population density was calculated by dividing the population of the city by the total area it covers, providing a measure of the number of people living in each km².","type":"float","dataChecksum":"11987577063109747802","metadataChecksum":"-1363827153574705861","datasetName":"Global Human Settlement Layer Dataset","updatePeriodDays":365,"datasetVersion":"2025-12-10","nonRedistributable":false,"display":{"unit":"people per km²","shortUnit":"per km²","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":["The European Commission integrates satellite imagery with national census data to delineate the boundaries of capital cities and estimate their populations.\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."],"dimensions":{"years":{"values":[{"id":1975},{"id":1980},{"id":1985},{"id":1990},{"id":1995},{"id":2000},{"id":2005},{"id":2010},{"id":2015},{"id":2020}]},"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":9478,"titleSnapshot":"Global Human Settlement Layer Dataset - Urban centers","title":"Global Human Settlement Layer Dataset","description":"The \"Stats in the City Database\" offers harmonized data on population and population density for urban centres.\n\nThis data, based on the Global Human Settlement Layer Dataset, uses the Degree of Urbanisation framework to delineate spatial entities and integrates geospatial data from a variety of open-source datasets. It represents one of the most comprehensive resources for understanding urban population patterns and densities worldwide.","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_wup_mtuc_r2025a.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"}}]}