100+ datasets found
  1. Cities with the highest population density globally 2023

    • statista.com
    • ai-chatbox.pro
    Updated May 27, 2025
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    Statista (2025). Cities with the highest population density globally 2023 [Dataset]. https://www.statista.com/statistics/1237290/cities-highest-population-density/
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    Dataset updated
    May 27, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2022
    Area covered
    World
    Description

    Mogadishu in Somalia led the ranking of cities with the highest population density in 2023, with ****** residents per square kilometer. When it comes to countries, Monaco is the most densely populated state worldwide.

  2. Cities with the highest population density in Latin America 2023

    • statista.com
    Updated Aug 15, 2023
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    Statista (2023). Cities with the highest population density in Latin America 2023 [Dataset]. https://www.statista.com/statistics/1473796/cities-highest-population-density-latam/
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    Dataset updated
    Aug 15, 2023
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    Latin America, LAC
    Description

    As of 2023, the top five most densely populated cities in Latin America and the Caribbean were in Colombia. The capital, Bogotá, ranked first with over ****** inhabitants per square kilometer.

  3. Cities with the highest population density in Mexico 2023

    • statista.com
    Updated Jul 9, 2025
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    Statista (2025). Cities with the highest population density in Mexico 2023 [Dataset]. https://www.statista.com/statistics/1473797/cities-highest-population-density-mexico/
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    Dataset updated
    Jul 9, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    Mexico
    Description

    Mexico City ranked as the most densely populated city in Mexico as of 2023. The capital recorded ***** inhabitants per square kilometer. Xalapa and Acapulco followed with ***** and ***** inhabitants per square kilometer, respectively.

  4. Covid-19 Highest City Population Density

    • kaggle.com
    Updated Mar 25, 2020
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    lookfwd (2020). Covid-19 Highest City Population Density [Dataset]. https://www.kaggle.com/lookfwd/covid19highestcitypopulationdensity/tasks
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 25, 2020
    Dataset provided by
    Kaggle
    Authors
    lookfwd
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Context

    This is a dataset of the most highly populated city (if applicable) in a form easy to join with the COVID19 Global Forecasting (Week 1) dataset. You can see how to use it in this kernel

    Content

    There are four columns. The first two correspond to the columns from the original COVID19 Global Forecasting (Week 1) dataset. The other two is the highest population density, at city level, for the given country/state. Note that some countries are very small and in those cases the population density reflects the entire country. Since the original dataset has a few cruise ships as well, I've added them there.

    Acknowledgements

    Thanks a lot to Kaggle for this competition that gave me the opportunity to look closely at some data and understand this problem better.

    Inspiration

    Summary: I believe that the square root of the population density should relate to the logistic growth factor of the SIR model. I think the SEIR model isn't applicable due to any intervention being too late for a fast-spreading virus like this, especially in places with dense populations.

    After playing with the data provided in COVID19 Global Forecasting (Week 1) (and everything else online or media) a bit, one thing becomes clear. They have nothing to do with epidemiology. They reflect sociopolitical characteristics of a country/state and, more specifically, the reactivity and attitude towards testing.

    The testing method used (PCR tests) means that what we measure could potentially be a proxy for the number of people infected during the last 3 weeks, i.e the growth (with lag). It's not how many people have been infected and recovered. Antibody or serology tests would measure that, and by using them, we could go back to normality faster... but those will arrive too late. Way earlier, China will have experimentally shown that it's safe to go back to normal as soon as your number of newly infected per day is close to zero.

    https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F197482%2F429e0fdd7f1ce86eba882857ac7a735e%2Fcovid-summary.png?generation=1585072438685236&alt=media" alt="">

    My view, as a person living in NYC, about this virus, is that by the time governments react to media pressure, to lockdown or even test, it's too late. In dense areas, everyone susceptible has already amble opportunities to be infected. Especially for a virus with 5-14 days lag between infections and symptoms, a period during which hosts spread it all over on subway, the conditions are hopeless. Active populations have already been exposed, mostly asymptomatic and recovered. Sensitive/older populations are more self-isolated/careful in affluent societies (maybe this isn't the case in North Italy). As the virus finishes exploring the active population, it starts penetrating the more isolated ones. At this point in time, the first fatalities happen. Then testing starts. Then the media and the lockdown. Lockdown seems overly effective because it coincides with the tail of the disease spread. It helps slow down the virus exploring the long-tail of sensitive population, and we should all contribute by doing it, but it doesn't cause the end of the disease. If it did, then as soon as people were back in the streets (see China), there would be repeated outbreaks.

    Smart politicians will test a lot because it will make their condition look worse. It helps them demand more resources. At the same time, they will have a low rate of fatalities due to large denominator. They can take credit for managing well a disproportionally major crisis - in contrast to people who didn't test.

    We were lucky this time. We, Westerners, have woken up to the potential of a pandemic. I'm sure we will give further resources for prevention. Additionally, we will be more open-minded, helping politicians to have more direct responses. We will also require them to be more responsible in their messages and reactions.

  5. Italian cities with the highest population density 2024

    • statista.com
    Updated Feb 14, 2025
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    Statista (2025). Italian cities with the highest population density 2024 [Dataset]. https://www.statista.com/statistics/1128344/italian-cities-with-the-highest-population-density/
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    Dataset updated
    Feb 14, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Italy
    Description

    Naples is the Italian city with the highest population density. As of 2024, the largest south Italian city counts 7,800 inhabitants per square kilometer. Milan followed with 7,600 residents per square kilometer, whereas Rome, the largest Italian city, registered a population density of only 2,100 people, 5,700 inhabitants per square kilometer less than Naples.

  6. Highest population density by country 2024

    • statista.com
    Updated May 27, 2025
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    Statista (2025). Highest population density by country 2024 [Dataset]. https://www.statista.com/statistics/264683/top-fifty-countries-with-the-highest-population-density/
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    Dataset updated
    May 27, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2024
    Area covered
    World
    Description

    Monaco led the ranking for countries with the highest population density in 2024, with nearly 26,000 residents per square kilometer. The Special Administrative Region of Macao came in second, followed by Singapore. The world’s second smallest country Monaco is the world’s second-smallest country, with an area of about two square kilometers and a population of only around 40,000. It is a constitutional monarchy located by the Mediterranean Sea, and while Monaco is not part of the European Union, it does participate in some EU policies. The country is perhaps most famous for the Monte Carlo casino and for hosting the Monaco Grand Prix, the world's most prestigious Formula One race. The global population Globally, the population density per square kilometer is about 60 inhabitants, and Asia is the most densely populated region in the world. The global population is increasing rapidly, so population density is only expected to increase. In 1950, for example, the global population stood at about 2.54 billion people, and it reached over eight billion during 2023.

  7. T

    Vital Signs: Population – by city (2022)

    • data.bayareametro.gov
    Updated Dec 19, 2022
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    (2022). Vital Signs: Population – by city (2022) [Dataset]. https://data.bayareametro.gov/dataset/Vital-Signs-Population-by-city-2022-/gnyn-e3uh
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    kml, csv, xml, application/geo+json, kmz, application/rssxml, application/rdfxml, tsvAvailable download formats
    Dataset updated
    Dec 19, 2022
    Description

    VITAL SIGNS INDICATOR Population (LU1)

    FULL MEASURE NAME
    Population estimates

    LAST UPDATED
    February 2023

    DESCRIPTION
    Population is a measurement of the number of residents that live in a given geographical area, be it a neighborhood, city, county or region.

    DATA SOURCE
    California Department of Finance: Population and Housing Estimates - http://www.dof.ca.gov/Forecasting/Demographics/Estimates/
    Table E-6: County Population Estimates (1960-1970)
    Table E-4: Population Estimates for Counties and State (1970-2021)
    Table E-8: Historical Population and Housing Estimates (1990-2010)
    Table E-5: Population and Housing Estimates (2010-2021)

    Bay Area Jurisdiction Centroids (2020) - https://data.bayareametro.gov/Boundaries/Bay-Area-Jurisdiction-Centroids-2020-/56ar-t6bs
    Computed using 2020 US Census TIGER boundaries

    U.S. Census Bureau: Decennial Census Population Estimates - http://www.s4.brown.edu/us2010/index.htm- via Longitudinal Tract Database Spatial Structures in the Social Sciences, Brown University
    1970-2020

    U.S. Census Bureau: American Community Survey (5-year rolling average; tract) - https://data.census.gov/
    2011-2021
    Form B01003

    Priority Development Areas (Plan Bay Area 2050) - https://opendata.mtc.ca.gov/datasets/MTC::priority-development-areas-plan-bay-area-2050/about

    CONTACT INFORMATION
    vitalsigns.info@bayareametro.gov

    METHODOLOGY NOTES (across all datasets for this indicator)
    All historical data reported for Census geographies (metropolitan areas, county, city and tract) use current legal boundaries and names. A Priority Development Area (PDA) is a locally-designated area with frequent transit service, where a jurisdiction has decided to concentrate most of its housing and jobs growth for development in the foreseeable future. PDA boundaries are current as of December 2022.

    Population estimates for Bay Area counties and cities are from the California Department of Finance, which are as of January 1st of each year. Population estimates for non-Bay Area regions are from the U.S. Census Bureau. Decennial Census years reflect population as of April 1st of each year whereas population estimates for intercensal estimates are as of July 1st of each year. Population estimates for Bay Area tracts are from the decennial Census (1970-2020) and the American Community Survey (2011-2021 5-year rolling average). Estimates of population density for tracts use gross acres as the denominator.

    Population estimates for Bay Area tracts and PDAs are from the decennial Census (1970-2020) and the American Community Survey (2011-2021 5-year rolling average). Population estimates for PDAs are allocated from tract-level Census population counts using an area ratio. For example, if a quarter of a Census tract lies with in a PDA, a quarter of its population will be allocated to that PDA. Estimates of population density for PDAs use gross acres as the denominator. Note that the population densities between PDAs reported in previous iterations of Vital Signs are mostly not comparable due to minor differences and an updated set of PDAs (previous iterations reported Plan Bay Area 2040 PDAs, whereas current iterations report Plan Bay Area 2050 PDAs).

    The following is a list of cities and towns by geographical area:

    Big Three: San Jose, San Francisco, Oakland

    Bayside: Alameda, Albany, Atherton, Belmont, Belvedere, Berkeley, Brisbane, Burlingame, Campbell, Colma, Corte Madera, Cupertino, Daly City, East Palo Alto, El Cerrito, Emeryville, Fairfax, Foster City, Fremont, Hayward, Hercules, Hillsborough, Larkspur, Los Altos, Los Altos Hills, Los Gatos, Menlo Park, Mill Valley, Millbrae, Milpitas, Monte Sereno, Mountain View, Newark, Pacifica, Palo Alto, Piedmont, Pinole, Portola Valley, Redwood City, Richmond, Ross, San Anselmo, San Bruno, San Carlos, San Leandro, San Mateo, San Pablo, San Rafael, Santa Clara, Saratoga, Sausalito, South San Francisco, Sunnyvale, Tiburon, Union City, Vallejo, Woodside

    Inland, Delta and Coastal: American Canyon, Antioch, Benicia, Brentwood, Calistoga, Clayton, Cloverdale, Concord, Cotati, Danville, Dixon, Dublin, Fairfield, Gilroy, Half Moon Bay, Healdsburg, Lafayette, Livermore, Martinez, Moraga, Morgan Hill, Napa, Novato, Oakley, Orinda, Petaluma, Pittsburg, Pleasant Hill, Pleasanton, Rio Vista, Rohnert Park, San Ramon, Santa Rosa, Sebastopol, Sonoma, St. Helena, Suisun City, Vacaville, Walnut Creek, Windsor, Yountville

    Unincorporated: all unincorporated towns

  8. T

    Vital Signs: Population – by city

    • data.bayareametro.gov
    Updated Oct 6, 2021
    + more versions
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    California Department of Finance (2021). Vital Signs: Population – by city [Dataset]. https://data.bayareametro.gov/dataset/Vital-Signs-Population-by-city/2jwr-z36f
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    application/rssxml, tsv, csv, application/rdfxml, xml, kmz, application/geo+json, kmlAvailable download formats
    Dataset updated
    Oct 6, 2021
    Dataset authored and provided by
    California Department of Finance
    Description

    VITAL SIGNS INDICATOR Population (LU1)

    FULL MEASURE NAME Population estimates

    LAST UPDATED October 2019

    DESCRIPTION Population is a measurement of the number of residents that live in a given geographical area, be it a neighborhood, city, county or region.

    DATA SOURCES U.S Census Bureau: Decennial Census No link available (1960-1990) http://factfinder.census.gov (2000-2010)

    California Department of Finance: Population and Housing Estimates Table E-6: County Population Estimates (1961-1969) Table E-4: Population Estimates for Counties and State (1971-1989) Table E-8: Historical Population and Housing Estimates (2001-2018) Table E-5: Population and Housing Estimates (2011-2019) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

    U.S. Census Bureau: Decennial Census - via Longitudinal Tract Database Spatial Structures in the Social Sciences, Brown University Population Estimates (1970 - 2010) http://www.s4.brown.edu/us2010/index.htm

    U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2011-2017) http://factfinder.census.gov

    U.S. Census Bureau: Intercensal Estimates Estimates of the Intercensal Population of Counties (1970-1979) Intercensal Estimates of the Resident Population (1980-1989) Population Estimates (1990-1999) Annual Estimates of the Population (2000-2009) Annual Estimates of the Population (2010-2017) No link available (1970-1989) http://www.census.gov/popest/data/metro/totals/1990s/tables/MA-99-03b.txt http://www.census.gov/popest/data/historical/2000s/vintage_2009/metro.html https://www.census.gov/data/datasets/time-series/demo/popest/2010s-total-metro-and-micro-statistical-areas.html

    CONTACT INFORMATION vitalsigns.info@bayareametro.gov

    METHODOLOGY NOTES (across all datasets for this indicator) All legal boundaries and names for Census geography (metropolitan statistical area, county, city, and tract) are as of January 1, 2010, released beginning November 30, 2010, by the U.S. Census Bureau. A Priority Development Area (PDA) is a locally-designated area with frequent transit service, where a jurisdiction has decided to concentrate most of its housing and jobs growth for development in the foreseeable future. PDA boundaries are current as of August 2019. For more information on PDA designation see http://gis.abag.ca.gov/website/PDAShowcase/.

    Population estimates for Bay Area counties and cities are from the California Department of Finance, which are as of January 1st of each year. Population estimates for non-Bay Area regions are from the U.S. Census Bureau. Decennial Census years reflect population as of April 1st of each year whereas population estimates for intercensal estimates are as of July 1st of each year. Population estimates for Bay Area tracts are from the decennial Census (1970 -2010) and the American Community Survey (2008-2012 5-year rolling average; 2010-2014 5-year rolling average; 2013-2017 5-year rolling average). Estimates of population density for tracts use gross acres as the denominator.

    Population estimates for Bay Area PDAs are from the decennial Census (1970 - 2010) and the American Community Survey (2006-2010 5 year rolling average; 2010-2014 5-year rolling average; 2013-2017 5-year rolling average). Population estimates for PDAs are derived from Census population counts at the tract level for 1970-1990 and at the block group level for 2000-2017. Population from either tracts or block groups are allocated to a PDA using an area ratio. For example, if a quarter of a Census block group lies with in a PDA, a quarter of its population will be allocated to that PDA. Tract-to-PDA and block group-to-PDA area ratios are calculated using gross acres. Estimates of population density for PDAs use gross acres as the denominator.

    Annual population estimates for metropolitan areas outside the Bay Area are from the Census and are benchmarked to each decennial Census. The annual estimates in the 1990s were not updated to match the 2000 benchmark.

    The following is a list of cities and towns by geographical area: Big Three: San Jose, San Francisco, Oakland Bayside: Alameda, Albany, Atherton, Belmont, Belvedere, Berkeley, Brisbane, Burlingame, Campbell, Colma, Corte Madera, Cupertino, Daly City, East Palo Alto, El Cerrito, Emeryville, Fairfax, Foster City, Fremont, Hayward, Hercules, Hillsborough, Larkspur, Los Altos, Los Altos Hills, Los Gatos, Menlo Park, Mill Valley, Millbrae, Milpitas, Monte Sereno, Mountain View, Newark, Pacifica, Palo Alto, Piedmont, Pinole, Portola Valley, Redwood City, Richmond, Ross, San Anselmo, San Bruno, San Carlos, San Leandro, San Mateo, San Pablo, San Rafael, Santa Clara, Saratoga, Sausalito, South San Francisco, Sunnyvale, Tiburon, Union City, Vallejo, Woodside Inland, Delta and Coastal: American Canyon, Antioch, Benicia, Brentwood, Calistoga, Clayton, Cloverdale, Concord, Cotati, Danville, Dixon, Dublin, Fairfield, Gilroy, Half Moon Bay, Healdsburg, Lafayette, Livermore, Martinez, Moraga, Morgan Hill, Napa, Novato, Oakley, Orinda, Petaluma, Pittsburg, Pleasant Hill, Pleasanton, Rio Vista, Rohnert Park, San Ramon, Santa Rosa, Sebastopol, Sonoma, St. Helena, Suisun City, Vacaville, Walnut Creek, Windsor, Yountville Unincorporated: all unincorporated towns

  9. a

    Boston Population Density

    • boston-harbor-resources-bsumaps.hub.arcgis.com
    Updated Apr 29, 2021
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    Ball State University ArcGIS Online (2021). Boston Population Density [Dataset]. https://boston-harbor-resources-bsumaps.hub.arcgis.com/maps/c41b6b075d5d4a87a1788bc21f30d38a
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    Dataset updated
    Apr 29, 2021
    Dataset authored and provided by
    Ball State University ArcGIS Online
    Description

    The population density picture of Boston is generally a story of two Bostons: the high density central and northern neighborhoods, and the low density southern neighborhoods.The highest density areas of Boston are particularly concentrated in Brighton, Allston, and the Fenway area, areas of the city with large numbers of college students and young adults. There is also high population density in areas such as the Back Bay, the South End, Charlestown, the North End, and South Boston. These are all relatively small areas geographically, but have housing stock conducive to population density (e.g. multi-family dwelling units, row housing, large apartment buildings). The southern neighborhoods, specifically Hyde Park and West Roxbury, have significant numbers of people living in them, but lots sizes tend to be much larger. These areas of the city also tend to have more single family dwelling units. In that, there are fewer people per square mile than places north in the city. Census data reveals that population density varies noticeably from area to area. Small area census data do a better job depicting where the crowded neighborhoods are. In this map, areas of highest density exceed 30,000 persons per square kilometer. Very high density areas exceed 7,000 persons per square kilometer. High density areas exceed 5,200 persons per square kilometer. The last categories break at 3,330 persons per square kilometer, and 1,500 persons per square kilometer.How to make this map for your city

  10. o

    Geonames - All Cities with a population > 1000

    • public.opendatasoft.com
    • data.smartidf.services
    • +2more
    csv, excel, geojson +1
    Updated Mar 10, 2024
    + more versions
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    (2024). Geonames - All Cities with a population > 1000 [Dataset]. https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/
    Explore at:
    csv, json, geojson, excelAvailable download formats
    Dataset updated
    Mar 10, 2024
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

  11. f

    Data from: Population Density in Nineteenth-Century American Urbanism

    • tandf.figshare.com
    7z
    Updated Oct 11, 2024
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    Celia Arsen; Gergely Baics; Leah Meisterlin (2024). Population Density in Nineteenth-Century American Urbanism [Dataset]. http://doi.org/10.6084/m9.figshare.26366998.v1
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    7zAvailable download formats
    Dataset updated
    Oct 11, 2024
    Dataset provided by
    Taylor & Francis
    Authors
    Celia Arsen; Gergely Baics; Leah Meisterlin
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    United States
    Description

    Population density and size are the most commonly used metrics for defining modern cities and urbanism. Yet unlike size, density has been overlooked in systematic analyses of the historical development of the U.S. urban system. Deploying large-scale geocoded census microdata on forty major cities in 1880, this article contributes to a systematic understanding of density in late-nineteenth-century U.S. urbanism. Methodologically, we make the case for a block-level, population-weighted density measure that reflects the experience of density and is transferable to other urban contexts. Thematically, we use this measure to compare density across cities, outlining regionally distinct patterns in density and identifying the built environment as a contributing factor to high- versus low-density urban development, and to explore density within cities across population subgroups, finding that immigrants, racial minorities, and lower class residents experienced higher densities at a time when high density increased exposure to health risks. Additionally, throughout the article we draw out preliminary findings worthy of future research about density conditions for particular cities, places, and demographic subgroups.

  12. M

    Mexico Population Density

    • macrotrends.net
    csv
    Updated May 31, 2025
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    MACROTRENDS (2025). Mexico Population Density [Dataset]. https://www.macrotrends.net/global-metrics/countries/mex/mexico/population-density
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    csvAvailable download formats
    Dataset updated
    May 31, 2025
    Dataset authored and provided by
    MACROTRENDS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Mexico
    Description
    Mexico population density for 2022 was 66.16, a 0.76% increase from 2021.
    <ul style='margin-top:20px;'>
    
    <li>Mexico population density for 2021 was <strong>65.66</strong>, a <strong>0.67% increase</strong> from 2020.</li>
    <li>Mexico population density for 2020 was <strong>65.23</strong>, a <strong>0.82% increase</strong> from 2019.</li>
    <li>Mexico population density for 2019 was <strong>64.69</strong>, a <strong>0.95% increase</strong> from 2018.</li>
    </ul>Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.
    
  13. Biggest metropolitan areas in Italy 2024

    • ai-chatbox.pro
    • statista.com
    Updated Feb 14, 2025
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    Statista (2025). Biggest metropolitan areas in Italy 2024 [Dataset]. https://www.ai-chatbox.pro/?_=%2Fstatistics%2F1246028%2Fbiggest-metropolitan-areas-in-italy%2F%23XgboD02vawLYpGJjSPEePEUG%2FVFd%2Bik%3D
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    Dataset updated
    Feb 14, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Italy
    Description

    Rome is the largest Italian metropolitan area. As of 2024, the urban area of the capital city has a population of around 4.23 million people. Milan and Naples follow with 3.25 million and 2.97 million people, respectively. In terms of inhabitants per square kilometer, Naples, located in the south, has the highest population density. Rome, Milan, and Naples are also Italy's largest cities.

  14. G

    Canada's Population Density

    • open.canada.ca
    • gimi9.com
    • +2more
    jpg, pdf
    Updated Mar 14, 2022
    + more versions
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    Natural Resources Canada (2022). Canada's Population Density [Dataset]. https://open.canada.ca/data/en/dataset/11325935-3af3-543e-80d4-8cf6cb4900e2
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    jpg, pdfAvailable download formats
    Dataset updated
    Mar 14, 2022
    Dataset provided by
    Natural Resources Canada
    License

    Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
    License information was derived automatically

    Area covered
    Canada
    Description

    Contained within the Atlas of Canada Poster Map Series, is a poster showing population density across Canada. There is a relief base to the map on top of which is shown all populated areas of Canada where the population density is great than 0.4 persons per square kilometer. This area is then divided into five colour classes of population density based on Statistics Canada's census divisions.

  15. o

    US Cities: Demographics

    • public.opendatasoft.com
    • data.smartidf.services
    • +3more
    csv, excel, json
    Updated Jul 27, 2017
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    (2017). US Cities: Demographics [Dataset]. https://public.opendatasoft.com/explore/dataset/us-cities-demographics/
    Explore at:
    excel, csv, jsonAvailable download formats
    Dataset updated
    Jul 27, 2017
    License

    https://en.wikipedia.org/wiki/Public_domainhttps://en.wikipedia.org/wiki/Public_domain

    Area covered
    United States
    Description

    This dataset contains information about the demographics of all US cities and census-designated places with a population greater or equal to 65,000. This data comes from the US Census Bureau's 2015 American Community Survey. This product uses the Census Bureau Data API but is not endorsed or certified by the Census Bureau.

  16. Most densely populated cities in the Netherlands 2019

    • statista.com
    Updated Aug 9, 2024
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    Statista (2024). Most densely populated cities in the Netherlands 2019 [Dataset]. https://www.statista.com/statistics/1095505/most-densely-populated-cities-in-the-netherlands/
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    Dataset updated
    Aug 9, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2019
    Area covered
    Netherlands
    Description

    Government city The Hague was the most densely populated city in the Netherlands in 2019, with a population density of nearly 6,500 people per square kilometer. Perhaps surprisingly, Amsterdam is not the most densely populated city in the country, ranking fourth on the list of most populous cities in the Netherlands in 2019.

  17. a

    Population Density (1 kilometer)

    • hub.arcgis.com
    Updated Jun 20, 2023
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    MapMaker (2023). Population Density (1 kilometer) [Dataset]. https://hub.arcgis.com/maps/a0f3ad34d5ac48d1aa6a2c7fcfcefbbc
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    Dataset updated
    Jun 20, 2023
    Dataset authored and provided by
    MapMaker
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    In the last century, the global population has increased by billions of people. And it is still growing. Job opportunities in large cities have caused an influx of people to these already packed locations. This has resulted in an increase in population density for these cities, which are now forced to expand in order to accommodate the growing population. Population density is the average number of people per unit, usually miles or kilometers, of land area. Understanding and mapping population density is important. Experts can use this information to inform decisions around resource allocation, natural disaster relief, and new infrastructure projects. Infectious disease scientists use these maps to understand the spread of infectious disease, a topic that has become critical after the COVID-19 global pandemic.While a useful tool for decision and policymakers, it is important to understand the limitations of population density. Population density is most effective in small scale places—cities or neighborhoods—where people are evenly distributed. Whereas at a larger scale, such as the state, region, or province level, population density could vary widely as it includes a mix of urban, suburban, and rural places. All of these areas have a vastly different population density, but they are averaged together. This means urban areas could appear to have fewer people than they really do, while rural areas would seem to have more. Use this map to explore the estimated global population density (people per square kilometer) in 2020. Where do people tend to live? Why might they choose those places? Do you live in a place with a high population density or a low one?

  18. Population density in urban areas of China 2023, by region

    • statista.com
    Updated Jul 11, 2025
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    Statista (2025). Population density in urban areas of China 2023, by region [Dataset]. https://www.statista.com/statistics/279040/population-density-in-urban-areas-of-china-by-region/
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    Dataset updated
    Jul 11, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    China
    Description

    This statistic shows the population density in urban areas of China in 2023, by region. In 2023, cities in Heilongjiang province had the highest population density in China with around ***** people living on one square kilometer on average. However, as the administrative areas of many Chinese cities reach beyond their contiguous built-up urban areas - and this by varying degree, the statistical significance of the given figures may be limited. By comparison, the Chinese province with the highest overall population density is Jiangsu province in Eastern China reaching about 7956 people per square kilometer in 2023.

  19. m

    Maine Cities by Population

    • maine-demographics.com
    Updated Jun 20, 2024
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    Kristen Carney (2024). Maine Cities by Population [Dataset]. https://www.maine-demographics.com/cities_by_population
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    Dataset updated
    Jun 20, 2024
    Dataset provided by
    Cubit Planning, Inc.
    Authors
    Kristen Carney
    License

    https://www.maine-demographics.com/terms_and_conditionshttps://www.maine-demographics.com/terms_and_conditions

    Area covered
    Maine, Portland
    Description

    A dataset listing Maine cities by population for 2024.

  20. n

    New York Cities by Population

    • newyork-demographics.com
    Updated Jun 20, 2024
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    Kristen Carney (2024). New York Cities by Population [Dataset]. https://www.newyork-demographics.com/cities_by_population
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    Dataset updated
    Jun 20, 2024
    Dataset provided by
    Cubit Planning, Inc.
    Authors
    Kristen Carney
    License

    https://www.newyork-demographics.com/terms_and_conditionshttps://www.newyork-demographics.com/terms_and_conditions

    Area covered
    New York
    Description

    A dataset listing New York cities by population for 2024.

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Statista (2025). Cities with the highest population density globally 2023 [Dataset]. https://www.statista.com/statistics/1237290/cities-highest-population-density/
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Cities with the highest population density globally 2023

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9 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
May 27, 2025
Dataset authored and provided by
Statistahttp://statista.com/
Time period covered
2022
Area covered
World
Description

Mogadishu in Somalia led the ranking of cities with the highest population density in 2023, with ****** residents per square kilometer. When it comes to countries, Monaco is the most densely populated state worldwide.

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