100+ datasets found
  1. Proportion of population in cities worldwide up to 2050

    • statista.com
    Updated Nov 28, 2025
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    Statista (2025). Proportion of population in cities worldwide up to 2050 [Dataset]. https://www.statista.com/statistics/264651/proportion-of-population-in-cities-worldwide/
    Explore at:
    Dataset updated
    Nov 28, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    World
    Description

    The graph shows the proportion of the population in cities worldwide from 1985 to 2050. **** percent of the world's population lived in cities in the year of 2015. This percentage is forecasted to grow to **** percent in the year 2050.

  2. 2050 NCTCOG Demographic Forecast (City)

    • data-nctcoggis.hub.arcgis.com
    Updated Nov 25, 2024
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    North Central Texas Council of Governments (2024). 2050 NCTCOG Demographic Forecast (City) [Dataset]. https://data-nctcoggis.hub.arcgis.com/datasets/2050-nctcog-demographic-forecast-city
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    Dataset updated
    Nov 25, 2024
    Dataset authored and provided by
    North Central Texas Council of Governments
    Area covered
    Description

    This dataset includes 2019 estimates and 2035 and 2050 projections approximated to city limits for select cities. It is provided as a convenient summary of NCTCOG 2050 Forecast data, having stated limitations. For more information, see NCTCOG 2050 Forecast Methodology.pdf and Data Dictionary 2050 Forecast (city).pdf.

  3. a

    Population Projections (City Area) - RTP 2019

    • data-wfrc.opendata.arcgis.com
    • data.wfrc.utah.gov
    Updated Apr 17, 2019
    + more versions
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    Wasatch Front Regional Council (2019). Population Projections (City Area) - RTP 2019 [Dataset]. https://data-wfrc.opendata.arcgis.com/datasets/wfrc::population-projections-city-area-rtp-2019/about
    Explore at:
    Dataset updated
    Apr 17, 2019
    Dataset authored and provided by
    Wasatch Front Regional Council
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Area covered
    Description

    Important Dataset Update 6/24/2020:Summit and Wasatch Counties updated.Important Dataset Update 6/12/2020:MAG area updated.Important Dataset Update 7/15/2019: This dataset now includes projections for all populated statewide traffic analysis zones (TAZs). Projections within the Wasatch Front urban area ( SUBAREAID = 1) were produced with using the Real Estate Market Model as described below. Socioeconomic forecasts produced for Cache MPO (Cache County, SUBAREAID = 2), Dixie MPO (Washington County, SUBAREAID = 3), Summit County (SUBAREAID = 4), and UDOT (other areas of the state, SUBAREAID = 0) all adhere to the University of Utah Gardner Policy Institute's county-level projection controls, but other modeling methods are used to arrive at the TAZ-level forecasts for these areas.As with any dataset that presents projections into the future, it is important to have a full understanding of the data before using it. Before using this data, you are strongly encouraged to read the metadata description below and direct any questions or feedback about this data to analytics@wfrc.org. Every four years, the Wasatch Front’s two metropolitan planning organizations (MPOs), Wasatch Front Regional Council (WFRC) and Mountainland Association of Governments (MAG), collaborate to update a set of annual small area -- traffic analysis zone and ‘city area’, see descriptions below) -- population and employment projections for the Salt Lake City-West Valley City (WFRC), Ogden-Layton (WFRC), and Provo-Orem (MAG) urbanized areas. These projections are primarily developed for the purpose of informing long-range transportation infrastructure and services planning done as part of the 4 year Regional Transportation Plan update cycle, as well as Utah’s Unified Transportation Plan, 2019-2050. Accordingly, the foundation for these projections is largely data describing existing conditions for a 2015 base year, the first year of the latest RTP process. The projections are included in the official travel models, which are publicly released at the conclusion of the RTP process. As these projections may be a valuable input to other analyses, this dataset is made available at http://data.wfrc.org/search?q=projections as a public service for informational purposes only. It is solely the responsibility of the end user to determine the appropriate use of this dataset for other purposes. Wasatch Front Real Estate Market Model (REMM) ProjectionsWFRC and MAG have developed a spatial statistical model using the UrbanSim modeling platform to assist in producing these annual projections. This model is called the Real Estate Market Model, or REMM for short. REMM is used for the urban portion of Weber, Davis, Salt Lake, and Utah counties. REMM relies on extensive inputs to simulate future development activity across the greater urbanized region. Key inputs to REMM include:Demographic data from the decennial census;County-level population and employment projections -- used as REMM control totals -- are produced by the University of Utah’s Kem C. Gardner Policy Institute (GPI) funded by the Utah State Legislature;Current employment locational patterns derived from the Utah Department of Workforce Services; Land use visioning exercises and feedback, especially in regard to planned urban and local center development, with city and county elected officials and staff;Current land use and valuation GIS-based parcel data stewarded by County Assessors;Traffic patterns and transit service from the regional Travel Demand Model that together form the landscape of regional accessibility to workplaces and other destinations; andCalibration of model variables to balance the fit of current conditions and dynamics at the county and regional level.‘Traffic Analysis Zone’ ProjectionsThe annual projections are forecasted for each of the Wasatch Front’s 2,800+ Traffic Analysis Zone (TAZ) geographic units. TAZ boundaries are set along roads, streams, and other physical features and average about 600 acres (0.94 square miles). TAZ sizes vary, with some TAZs in the densest areas representing only a single city block (25 acres). ‘City Area’ ProjectionsThe TAZ-level output from the model is also available for ‘city areas’ that sum the projections for the TAZ geographies that roughly align with each city’s current boundary. As TAZs do not align perfectly with current city boundaries, the ‘city area’ summaries are not projections specific to a current or future city boundary, but the ‘city area’ summaries may be suitable surrogates or starting points upon which to base city-specific projections.Summary Variables in the DatasetsAnnual projection counts are available for the following variables (please read Key Exclusions note below):DemographicsHousehold Population Count (excludes persons living in group quarters)Household Count (excludes group quarters)EmploymentTypical Job Count (includes job types that exhibit typical commuting and other travel/vehicle use patterns)Retail Job Count (retail, food service, hotels, etc)Office Job Count (office, health care, government, education, etc)Industrial Job Count (manufacturing, wholesale, transport, etc)Non-Typical Job Count* (includes agriculture, construction, mining, and home-based jobs) This can be calculated by subtracting Typical Job Count from All Employment Count.All Employment Count* (all jobs, this sums jobs from typical and non-typical sectors).* These variable includes REMM’s attempt to estimate construction jobs in areas that experience new and re-development activity. Areas may see short-term fluctuations in Non-Typical and All Employment counts due to the temporary location of construction jobs.Population and employment projections for the Wasatch Front area can be combined with those developed by Dixie MPO (St. George area), Cache MPO (Logan area), and the Utah Department of Transportation (for the remainder of the state) into one database for use in the Utah Statewide Travel Model (USTM). While projections for the areas outside of the Wasatch Front use different forecasting methods, they contain the same summary-level population and employment projections making similar TAZ and ‘City Area’ data available statewide. WFRC plans, in the near future, to add additional areas to these projections datasets by including the projections from the USTM model.Key Exclusions from TAZ and ‘City Area’ ProjectionsAs the primary purpose for the development of these population and employment projections is to model future travel in the region, REMM-based projections do not include population or households that reside in group quarters (prisons, senior centers, dormitories, etc), as residents of these facilities typically have a very low impact on regional travel. USTM-based projections also excludes group quarter populations. Group quarters population estimates are available at the county-level from GPI and at various sub-county geographies from the Census Bureau.

  4. g

    GMS database of large urban areas, 1950-2050 population estimates |...

    • gimi9.com
    Updated Mar 23, 2025
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    (2025). GMS database of large urban areas, 1950-2050 population estimates | gimi9.com [Dataset]. https://gimi9.com/dataset/mekong_world-database-of-large-urban-areas-1950-2050-population-estimates
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    Dataset updated
    Mar 23, 2025
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    This database represents the historic, current and future estimates and projections with number of inhabitants for the world's largest urban areas from 1950-2050. The data covers cities and other urban areas with more than 750,000 people.

  5. Countries with the largest increase in population until 2050

    • statista.com
    Updated Nov 28, 2025
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    Statista (2025). Countries with the largest increase in population until 2050 [Dataset]. https://www.statista.com/statistics/875047/top-ten-countries-with-projected-increase-in-urban-population/
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    Dataset updated
    Nov 28, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    Worldwide
    Description

    This statistic shows the ten countries with the largest increase in the size of the population between 2023 and 2050. Based on forecasted population figures, the population of India is projected to be around *** million more in 2050 than it was in 2023.

  6. Populations vulnerable to climate change in cities globally between...

    • statista.com
    Updated Jul 10, 2025
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    Statista (2025). Populations vulnerable to climate change in cities globally between 2020-2050 [Dataset]. https://www.statista.com/statistics/1245965/populations-vulnerable-to-climate-change/
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    Dataset updated
    Jul 10, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2020
    Area covered
    Worldwide
    Description

    Cities are facing climate-related hazards that are becoming ever more frequent and severe. As of 2020, about *** cities globally reported that the elderly population was the most vulnerable to climate change.

  7. D

    Long Range Plan 2050 Planned Centers, Core Cities, Neighborhood Centers

    • catalog.dvrpc.org
    • njogis-newjersey.opendata.arcgis.com
    api, geojson, html +1
    Updated Nov 4, 2025
    + more versions
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    DVRPC (2025). Long Range Plan 2050 Planned Centers, Core Cities, Neighborhood Centers [Dataset]. https://catalog.dvrpc.org/dataset/long-range-plan-2050-planned-centers-core-cities-neighborhood-centers
    Explore at:
    xml, html, geojson, apiAvailable download formats
    Dataset updated
    Nov 4, 2025
    Dataset provided by
    Delaware Valley Regional Planning Commissionhttps://www.dvrpc.org/
    Authors
    DVRPC
    Description

    PLANNED CENTERS, CORE CITIES & NEIGHBORHOOD CENTERS All Connections 2050 Long-Range Plan elements are available online at www.dvrpc.org/plan. The Plan has two primary documents: (1) The Connections 2050 Policy Manual (www.dvrpc.org/Products/21027) identifies the vision, goals, strategies, and a summary of the financial plan. (2) The Connections 2050 Process and Analysis Manual (www.dvrpc.org/Products/21028) provides a more detailed look at the Plan’s outreach, background information, analysis, and financial plan. The concept of Centers is the cornerstone of the Connections 2050 Long-Range Plan. Centers provide a focal point in the regional landscape that recognizes the regional and local significance of places, while reinforcing a sense of community for local residents. Centers serve as a basis for organizing and focusing the development landscape and provide a framework for the most efficient provision of supportive infrastructure systems, including water, sewer, and transportation. By concentrating growth around and within Centers, the region can both preserve open space and reduce infrastructure costs. The densities and mixed uses inherent within Centers can enhance the feasibility of walking, bicycling, and public transit as alternatives to the automobile.

  8. Population of USA (2050-1955)

    • kaggle.com
    zip
    Updated Apr 26, 2022
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    Anandhu H (2022). Population of USA (2050-1955) [Dataset]. https://www.kaggle.com/datasets/anandhuh/population-data-usa
    Explore at:
    zip(2660 bytes)Available download formats
    Dataset updated
    Apr 26, 2022
    Authors
    Anandhu H
    License

    http://opendatacommons.org/licenses/dbcl/1.0/http://opendatacommons.org/licenses/dbcl/1.0/

    Area covered
    United States
    Description

    Content

    The current population of the United States of America is 334,464,117 as of Saturday, April 16, 2022, based on Worldometer elaboration of the latest United Nations data. This three datasets contain population data of USA (2020 and histIndiaorical), population forecast and population in major cities.

    Attribute Information

    • Year - Years from 2020-1955
    • Population - Population in the respective year
    • Yearly % Change - Percentage Yearly Change in Population
    • Yearly Change - Yearly Change in Population
    • Migrants (net) - Total number of migrants
    • Median Age - Median age of the population
    • Fertility Rate - Fertility rate
    • Density (P/Km²)- Population density (population per square km)
    • Urban Pop %- Percentage of urban population
    • Urban Population- Urban population
    • Country's Share of World Pop - Population share
    • World Population - World Population in the respective year
    • India Global Rank - Global Rank in Population

    Source

    Link : https://www.worldometers.info/world-population/us-population/

    Updated Covid 19 and Other Datasets

    Link : https://www.kaggle.com/anandhuh/datasets

    If you find it useful, please support by upvoting ❤️

    Thank You

  9. Population of Indonesia (2050-1955)

    • kaggle.com
    zip
    Updated May 1, 2022
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    Anandhu H (2022). Population of Indonesia (2050-1955) [Dataset]. https://www.kaggle.com/datasets/anandhuh/population-indonasia/code
    Explore at:
    zip(2584 bytes)Available download formats
    Dataset updated
    May 1, 2022
    Authors
    Anandhu H
    License

    http://opendatacommons.org/licenses/dbcl/1.0/http://opendatacommons.org/licenses/dbcl/1.0/

    Area covered
    Indonesia
    Description

    Content

    The current population of Indonesia is 278,799,748 as of Sunday, May 1, 2022, based on Worldometer elaboration of the latest United Nations data.. This three datasets contain population data of Indonesia (2020 and historical), population forecast and population in major cities.

    Attribute Information

    • Year - Years from 2020-1955
    • Population - Population in the respective year
    • Yearly % Change - Percentage Yearly Change in Population
    • Yearly Change - Yearly Change in Population
    • Migrants (net) - Total number of migrants
    • Median Age - Median age of the population
    • Fertility Rate - Fertility rate
    • Density (P/Km²)- Population density (population per square km)
    • Urban Pop %- Percentage of urban population
    • Urban Population- Urban population
    • Country's Share of World Pop - Population share
    • World Population - World Population in the respective year
    • India Global Rank - Global Rank in Population

    Source

    Link : https://www.worldometers.info/world-population/indonesia-population/

    Updated Covid 19 and Other Datasets

    Link : https://www.kaggle.com/anandhuh/datasets

    If you find it useful, please support by upvoting ❤️

    Thank You

  10. H

    Data from: West Africa Coastal Vulnerability Mapping: Population...

    • dataverse.harvard.edu
    • search.dataone.org
    • +3more
    Updated Sep 8, 2025
    + more versions
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    B Jones (2025). West Africa Coastal Vulnerability Mapping: Population Projections, 2030 and 2050 [Dataset]. http://doi.org/10.7910/DVN/FEAVGB
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 8, 2025
    Dataset provided by
    Harvard Dataverse
    Authors
    B Jones
    License

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

    Area covered
    West Africa, Africa
    Description

    The West Africa Coastal Vulnerability Mapping: Population Projections, 2030 and 2050 data set is based on an unreleased working version of the Gridded Population of the World (GPW), Version 4, year 2010 population count raster but at a coarser 5 arc-minute resolution. Bryan Jones of Baruch College produced country-level projections based on the Shared Socioeconomic Pathway 4 (SSP4). SSP4 reflects a divided world where cities that have relatively high standards of living, are attractive to internal and international migrants. In low income countries, rapidly growing rural populations live on shrinking areas of arable land due to both high population pressure and expansion of large-scale mechanized farming by international agricultural firms. This pressure induces large migration flow to the cities, contributing to fast urbanization, although urban areas do not provide many opportunities for the poor and there is a massive expansion of slums and squatter settlements. This scenario may not be the most likely for the West Africa region, but it has internal coherence and is at least plausible. To provide areas in West Africa that may be particularly exposed to climate stressors owing to future high population growth.

  11. o

    2050 Cross Street Data in Brigham City, UT

    • ownerly.com
    Updated Dec 8, 2021
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    Ownerly (2021). 2050 Cross Street Data in Brigham City, UT [Dataset]. https://www.ownerly.com/ut/brigham-city/2050-home-details
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    Dataset updated
    Dec 8, 2021
    Dataset authored and provided by
    Ownerly
    Area covered
    Brigham City, Utah
    Description

    This dataset provides information about the number of properties, residents, and average property values for 2050 cross streets in Brigham City, UT.

  12. Climate risks in cities globally between 2020-2050

    • statista.com
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    Statista, Climate risks in cities globally between 2020-2050 [Dataset]. https://www.statista.com/statistics/1244662/cities-climate-hazards/
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    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2020
    Area covered
    Worldwide
    Description

    Cities are facing climate-related hazards that are becoming ever more frequent and severe. As of 2020, about ** cities globally reported that they experienced heat waves, and about ** cities expected to experience this particular climate risk between 2022 and 2025.

  13. T

    Vital Signs: Population – by city (2022)

    • data.bayareametro.gov
    Updated Dec 19, 2022
    + more versions
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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
    Explore at:
    kmz, xml, csv, kml, application/geo+json, xlsxAvailable 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

  14. Population of India (2050-1955)

    • kaggle.com
    zip
    Updated Jan 16, 2023
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    Anandhu H (2023). Population of India (2050-1955) [Dataset]. https://www.kaggle.com/datasets/anandhuh/population-data-india
    Explore at:
    zip(2642 bytes)Available download formats
    Dataset updated
    Jan 16, 2023
    Authors
    Anandhu H
    License

    http://opendatacommons.org/licenses/dbcl/1.0/http://opendatacommons.org/licenses/dbcl/1.0/

    Area covered
    India
    Description

    Content

    The current population of India is 1,403,717,340 as of Sunday, April 3, 2022, based on Worldometer elaboration of the latest United Nations data. This three datasets contain population data of India (2020 and historical), population forecast and population in major cities.

    Attribute Information

    • Year - Years from 2020-1955
    • Population - Population in the respective year
    • Yearly % Change - Percentage Yearly Change in Population
    • Yearly Change - Yearly Change in Population
    • Migrants (net) - Total number of migrants
    • Median Age - Median age of the population
    • Fertility Rate - Fertility rate
    • Density (P/Km²)- Population density (population per square km)
    • Urban Pop %- Percentage of urban population
    • Urban Population- Urban population
    • Country's Share of World Pop - Population share
    • World Population - World Population in the respective year
    • India Global Rank - Global Rank in Population

    Source

    Link : https://www.worldometers.info/world-population/india-population/

    Updated Covid 19 and Other Datasets

    Link : https://www.kaggle.com/anandhuh/datasets

    If you find it useful, please support by upvoting ❤️

    Thank You

  15. D

    Long Range Plan 2050 Planning Areas

    • catalog.dvrpc.org
    • njogis-newjersey.opendata.arcgis.com
    • +1more
    api, geojson, html +1
    Updated Nov 4, 2025
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    DVRPC (2025). Long Range Plan 2050 Planning Areas [Dataset]. https://catalog.dvrpc.org/dataset/long-range-plan-2050-planning-areas
    Explore at:
    geojson, api, xml, htmlAvailable download formats
    Dataset updated
    Nov 4, 2025
    Dataset provided by
    Delaware Valley Regional Planning Commissionhttps://www.dvrpc.org/
    Authors
    DVRPC
    Description

    PLANNING AREAS All Connections 2050 Long-Range Plan elements are available online at www.dvrpc.org/plan. The Plan has two primary documents: (1) The Connections 2050 Policy Manual (www.dvrpc.org/Products/21027) identifies the vision, goals, strategies, and a summary of the financial plan. (2) The Connections 2050 Process and Analysis Manual (www.dvrpc.org/Products/21028) provides a more detailed look at the Plan’s outreach, background information, analysis, and financial plan. Greater Philadelphia is a complex mosaic of 352 diverse cities, boroughs, and townships. The Connections 2045 Long-Range Plan characterizes each of the region’s municipalities as either a Core City, Developed Community, Growing Suburb, or Rural Area, as a means of categorizing the types of communities and defining the corresponding long-range planning policies most appropriate for each type. This categorization is shown on the Planning Areas and Centers dataset. Many municipalities have areas within their boundaries that fit the characteristics of more than one of these Planning Area types. Gloucester Township (in Camden County, New Jersey), for example, has neighborhoods that are fully developed, but it also has a significant number of undeveloped acres and forecasted population and employment growth more characteristic of a Growing Suburb. The intent of the Plan is to assign to each municipality the planning area type associated with the long-range planning policies that will be most beneficial to the entire community. While the Planning Areas and Centers map is a guide for policy direction at the regional scale, actual approaches should always be guided by local conditions. The region’s four Core Cities are Philadelphia, Trenton, Camden, and Chester. Targeted infrastructure investment, maintenance and rehabilitation, comprehensive neighborhood revitalization, and efforts focused on reinforcing a network of social and educational programs will help to rebuild and revitalize the region’s cities. Developed Communities are places that have already experienced most of their population and employment growth, and include inner ring communities adjacent to the Core Cities; railroad boroughs and trolley car communities; and mature suburban townships. Many of these communities are stable and thriving, offering affordable housing opportunities; access to transit; safe pedestrian and bicycling environments; and a strong community identity. Others, however, are experiencing population and employment losses; have deteriorating infrastructure systems; have aging resident populations living on limited incomes; and have stagnant or declining tax bases that cannot keep pace with rising service demands. Rehabilitation and maintenance of infrastructure systems and the housing stock, and local economic and community development can help to reinforce location advantages, while stabilizing neighborhoods and stemming decline. Growing Suburbs are communities that have a significant number of developable acres remaining and are experiencing or are forecast to experience significant population and/or employment growth. Key planning policies in these communities focus on the need to improve the form of development, reduce congestion, and mitigate the negative consequences of unmanaged growth, and include growth management and enhanced community design. Smart growth techniques that support a more concentrated development pattern (such as clustering, mixed uses, transit-oriented development, and transfer of development rights) can provide the critical mass necessary to support new transit services and other alternatives to the automobile. The quality of design and architectural character of the built environment, open space preservation, and the creation of an integrated system of open space and recreation are all priorities in these communities. Rural Areas include the region’s agricultural communities and communities with large remaining natural areas. Key policy approaches for these communities focus on preservation and limiting development, and include limited expansion of infrastructure systems, preservation of a rural lifestyle and village character, support for continued farming, and enhanced natural resource protection. Livable communities in these areas include rural centers that have an identi fi able main street, a mix of uses, higher densities than their surrounding uses, and a true sense of place.

  16. d

    Future Floodplain 2050s

    • catalog.data.gov
    • data.cityofnewyork.us
    Updated Sep 20, 2025
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    data.cityofnewyork.us (2025). Future Floodplain 2050s [Dataset]. https://catalog.data.gov/dataset/2050-1p-258r-shp-selection-final-elim50k-unionfema
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    Dataset updated
    Sep 20, 2025
    Dataset provided by
    data.cityofnewyork.us
    Description

    This is the 100-Year Floodplain for the 2050s based on FEMA's Preliminary Work Map data and the New York Panel on Climate Change's 90th Percentile Projects for Sea-Level Rise (31 inches). Please see the Disclaimer PDF for more information. Data Provided by the Mayor's Office of Long-Term Planning and Sustainability (OLTPS) on behalf of CUNY Institute for Sustainable Cities (CISC) and the New York Panel on Climate Change (NPCC).

  17. a

    European heat waves: 2021-2050

    • hub.arcgis.com
    Updated Sep 7, 2012
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    ArcGIS StoryMaps (2012). European heat waves: 2021-2050 [Dataset]. https://hub.arcgis.com/maps/Story::european-heat-waves-2021-2050/about
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    Dataset updated
    Sep 7, 2012
    Dataset authored and provided by
    ArcGIS StoryMaps
    Area covered
    Description

    Climate change projections suggest that European summer heat waves will become more frequent and severe during this century, continuing the trend of the past decades. The most severe impacts arise from multi-day heat waves associated with warm night-time temperatures and high relative humidity. Heat waves include tropical nights (with minimum temperatures above 20°C) and hot days (with maximum temperatures exceeding 35°C). The temperature map is the result of climatic modelling and represents the number of combined tropical nights and hot days. These projections are just one set of many different modelled scenarios that were produced by the ENSEMBLES project and featured in CLIMATE-ADAPT. For methodology see Fischer, E. M. and Schär, C., 2010, 'Consistent geographical patterns of changes in high-impact European heatwaves', Nature Geoscience, 3(6), pp.398–403. The original map is presented in the EEA report 2/2012 'Urban adaptation to climate change in Europe': map 2.4; data table and further methodological explanations in Annex II.

  18. u

    Population Projections (City Area) - RTP 2023

    • data.wfrc.utah.gov
    Updated May 16, 2024
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    Wasatch Front Regional Council (2024). Population Projections (City Area) - RTP 2023 [Dataset]. https://data.wfrc.utah.gov/datasets/population-projections-city-area-rtp-2023
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    Dataset updated
    May 16, 2024
    Dataset authored and provided by
    Wasatch Front Regional Council
    Area covered
    Description

    Every four years, the Wasatch Front’s two metropolitan planning organizations (MPOs), Wasatch Front Regional Council (WFRC) and Mountainland Association of Governments (MAG), collaborate to update a set of annual small area -- traffic analysis zone and ‘city area’, see descriptions below) -- population and employment projections for the Salt Lake City-West Valley City (WFRC), Ogden-Layton (WFRC), and Provo-Orem (MAG) urbanized areas.

    These projections are primarily developed for the purpose of informing long-range transportation infrastructure and services planning done as part of the 4 year Regional Transportation Plan update cycle, as well as Utah’s Unified Transportation Plan, 2023-2050. Accordingly, the foundation for these projections is largely data describing existing conditions for a 2019 base year, the first year of the latest RTP process. The projections are included in the official travel models, which are publicly released at the conclusion of the RTP process.

    Projections within the Wasatch Front urban area ( SUBAREAID = 1) were produced with using the Real Estate Market Model as described below. Socioeconomic forecasts produced for Cache MPO (Cache County, SUBAREAID = 2), Dixie MPO (Washington County, SUBAREAID = 3), Summit County (SUBAREAID = 4), and UDOT (other areas of the state, SUBAREAID = 0) all adhere to the University of Utah Gardner Policy Institute's county-level projection controls, but other modeling methods are used to arrive at the TAZ-level forecasts for these areas.

    As these projections may be a valuable input to other analyses, this dataset is made available here as a public service for informational purposes only. It is solely the responsibility of the end user to determine the appropriate use of this dataset for other purposes.

    Wasatch Front Real Estate Market Model (REMM) Projections

    WFRC and MAG have developed a spatial statistical model using the UrbanSim modeling platform to assist in producing these annual projections. This model is called the Real Estate Market Model, or REMM for short. REMM is used for the urban portion of Weber, Davis, Salt Lake, and Utah counties. REMM relies on extensive inputs to simulate future development activity across the greater urbanized region. Key inputs to REMM include:

    Demographic data from the decennial census
    County-level population and employment projections -- used as REMM control totals -- are produced by the University of Utah’s Kem C. Gardner Policy Institute (GPI) funded by the Utah State Legislature
    Current employment locational patterns derived from the Utah Department of Workforce Services
    Land use visioning exercises and feedback, especially in regard to planned urban and local center development, with city and county elected officials and staff
    Current land use and valuation GIS-based parcel data stewarded by County Assessors
    Traffic patterns and transit service from the regional Travel Demand Model that together form the landscape of regional accessibility to workplaces and other destinations
    Calibration of model variables to balance the fit of current conditions and dynamics at the county and regional level
    

    ‘Traffic Analysis Zone’ Projections

    The annual projections are forecasted for each of the Wasatch Front’s 3,546 Traffic Analysis Zone (TAZ) geographic units. TAZ boundaries are set along roads, streams, and other physical features and average about 600 acres (0.94 square miles). TAZ sizes vary, with some TAZs in the densest areas representing only a single city block (25 acres).

    ‘City Area’ Projections

    The TAZ-level output from the model is also available for ‘city areas’ that sum the projections for the TAZ geographies that roughly align with each city’s current boundary. As TAZs do not align perfectly with current city boundaries, the ‘city area’ summaries are not projections specific to a current or future city boundary, but the ‘city area’ summaries may be suitable surrogates or starting points upon which to base city-specific projections.

    Summary Variables in the Datasets

    Annual projection counts are available for the following variables (please read Key Exclusions note below):

    Demographics

    Household Population Count (excludes persons living in group quarters) 
    Household Count (excludes group quarters) 
    

    Employment

    Typical Job Count (includes job types that exhibit typical commuting and other travel/vehicle use patterns)
    Retail Job Count (retail, food service, hotels, etc)
    Office Job Count (office, health care, government, education, etc)
    Industrial Job Count (manufacturing, wholesale, transport, etc)
    Non-Typical Job Count* (includes agriculture, construction, mining, and home-based jobs) This can be calculated by subtracting Typical Job Count from All Employment Count 
    All Employment Count* (all jobs, this sums jobs from typical and non-typical sectors).
    
    • These variables includes REMM’s attempt to estimate construction jobs in areas that experience new and re-development activity. Areas may see short-term fluctuations in Non-Typical and All Employment counts due to the temporary location of construction jobs.

    Key Exclusions from TAZ and ‘City Area’ Projections

    As the primary purpose for the development of these population and employment projections is to model future travel in the region, REMM-based projections do not include population or households that reside in group quarters (prisons, senior centers, dormitories, etc), as residents of these facilities typically have a very low impact on regional travel. USTM-based projections also excludes group quarter populations. Group quarters population estimates are available at the county-level from GPI and at various sub-county geographies from the Census Bureau.

    Statewide Projections

    Population and employment projections for the Wasatch Front area can be combined with those developed by Dixie MPO (St. George area), Cache MPO (Logan area), and the Utah Department of Transportation (for the remainder of the state) into one database for use in the Utah Statewide Travel Model (USTM). While projections for the areas outside of the Wasatch Front use different forecasting methods, they contain the same summary-level population and employment projections making similar TAZ and ‘City Area’ data available statewide. WFRC plans, in the near future, to add additional areas to these projections datasets by including the projections from the USTM model.

  19. Populations exposed to riverine floods in selected cities 2022-2050, by...

    • statista.com
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    Statista, Populations exposed to riverine floods in selected cities 2022-2050, by scenario [Dataset]. https://www.statista.com/statistics/1415023/global-populations-exposed-riverine-floods-selected-cities-by-scenario/
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    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2022
    Area covered
    Worldwide
    Description

    The share of the population exposed to riverine floods in Dhaka, India, is forecast to more than double by 2050, in both a medium and high emissions scenario, to more than **** percent. In Ho Chi Minh City, the share of the population exposed to flooding could also double, to **** percent by 2050. South and east Asian populations are expected to be some of the most affected by riverine floods by 2050.

  20. Urbanization in the United States 1790 to 2050

    • akomarchitects.com
    • statista.com
    Updated Jul 31, 2025
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    Aaron O'Neill (2025). Urbanization in the United States 1790 to 2050 [Dataset]. https://www.akomarchitects.com/?p=2437241
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    Dataset updated
    Jul 31, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Aaron O'Neill
    Area covered
    United States
    Description

    In 2020, about 82.66 percent of the total population in the United States lived in cities and urban areas. As the United States was one of the earliest nations to industrialize, it has had a comparatively high rate of urbanization over the past two centuries. The urban population became larger than the rural population during the 1910s, and by the middle of the century it is expected that almost 90 percent of the population will live in an urban setting. Regional development of urbanization in the U.S. The United States began to urbanize on a larger scale in the 1830s, as technological advancements reduced the labor demand in agriculture, and as European migration began to rise. One major difference between early urbanization in the U.S. and other industrializing economies, such as the UK or Germany, was population distribution. Throughout the 1800s, the Northeastern U.S. became the most industrious and urban region of the country, as this was the main point of arrival for migrants. Disparities in industrialization and urbanization was a key contributor to the Union's victory in the Civil War, not only due to population sizes, but also through production capabilities and transport infrastructure. The Northeast's population reached an urban majority in the 1870s, whereas this did not occur in the South until the 1950s. As more people moved westward in the late 1800s, not only did their population growth increase, but the share of the urban population also rose, with an urban majority established in both the West and Midwest regions in the 1910s. The West would eventually become the most urbanized region in the 1960s, and over 90 percent of the West's population is urbanized today. Urbanization today New York City is the most populous city in the United States, with a population of 8.3 million, while California has the largest urban population of any state. California also has the highest urbanization rate, although the District of Columbia is considered 100 percent urban. Only four U.S. states still have a rural majority, these are Maine, Mississippi, Montana, and West Virginia.

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Statista (2025). Proportion of population in cities worldwide up to 2050 [Dataset]. https://www.statista.com/statistics/264651/proportion-of-population-in-cities-worldwide/
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Proportion of population in cities worldwide up to 2050

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7 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Nov 28, 2025
Dataset authored and provided by
Statistahttp://statista.com/
Area covered
World
Description

The graph shows the proportion of the population in cities worldwide from 1985 to 2050. **** percent of the world's population lived in cities in the year of 2015. This percentage is forecasted to grow to **** percent in the year 2050.

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