Kenya population data

Open and free data for assessing the human presence on the planet. The Global Human Settlement Layer (GHSL) project produces global spatial information, evidence-based analytics, and knowledge describing the human presence on the planet. The GHSL relies on the design and implementation of spatial data processing technologies that allow automatic data analytics and information extraction from large amounts of heterogeneous geospatial data including global, fine-scale satellite image data streams, census data, and crowd sourced or volunteered geographic information sources. The JRC, together with the Directorate-General for Regional and Urban Policy (DG REGIO) and Directorate-General for Defence Industry and Space (DG DEFIS) are working towards a regular and operational monitoring of global built-up and population based on the processing of Sentinel Earth Observation data produced by European Copernicus space program. In addition, the EU Agency for the Space Programme (EUSPA) undertakes activities related to user uptake of data, information and services.

2016 Kenya population density (per square km)

Kenya Population Distribution - spatial resolution is approximately 1 sq km at the equator. The population distribution is for 2016 but can be projected to future years.

These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 project with funding from the United Nations Children's Fund ( UNICEF ) - Population Modelling for use in Routine Health Planning and Monitoring project (contract no. 43335861). Projects partners included the Kenya Unicef Regional and Country Offices, WorldPop research group at the University of Southampton and the Center for International Earth Science Information Network CIESIN in the Columbia Climate School at Columbia University. Assane Gadiaga (WorldPop) led the input processing and the modelling work following the Random Forest (RF)-based dasymetric mapping approach developed by Stevens et al. (2015). Thomas Abbott supported the covariates processing work, as well as Christopher Lloyd, particularly for the processing of residential/non-residential building footprints. In-country engagements were done by Benard Mitto, Justine Dowden (CIESIN) and Maria Muniz (Unicef). Using the 2009 and 2019 census data from the Kenya’s National Bureau of Statistics (KNBS), the US Census Bureau released the census-based total population projections, population by age and gender and digital sub-counties boundaries. Duygu Cihan helped in the preparation of these input population data. Attila N Lazar, Edith Darin and Heather Chamberlain advised on the modelling procedure. The work was overseen by Attila N Lazar and Andy J Tatem. For further details, please, read the Release Statement. **Release content** 1. KEN_population_v1_0_gridded.tif 2. KEN_population_v1_0_agesex.zip 3. KEN_population_v1_0_mastergrid.tif **Recommended citations** Gadiaga A. N., Abbott T. J., Chamberlain H., Lloyd C. T., Lazar A. N., Darin E., Tatem A. J. 2022. *Census disaggregated gridded population estimates for Kenya (2021), version 1.0* University of Southampton. doi:10.5258/SOTON/WP00747 **License** *These data may be distributed using a Creative Commons Attribution 4.0 International (CC BY 4.0) License, specified in legal code. Contact release[at]worldpop.org for more information*. The authors followed rigorous procedures designed to ensure that the used data, the applied method and thus the results are appropriate and of reasonable quality. If users encounter apparent errors or misstatements, they should contact WorldPop at release[at]worldpop.org. WorldPop, University of Southampton, and their sponsors offer these data on a "where is, as is" basis; do not offer an express or implied warranty of any kind; do not guarantee the quality, applicability, accuracy, reliability or completeness of any data provided; and shall not be liable for incidental, consequential, or special damages arising out of the use of any data that they offer.

V1.5 The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Kenya: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 project with funding from the United Nations Children's Fund ( UNICEF ) - Population Modelling for use in Routine Health Planning and Monitoring project (contract no. 43335861). Projects partners included the Kenya Unicef Regional and Country Offices, WorldPop research group at the University of Southampton and the Center for International Earth Science Information Network in the Columbia Climate School at Columbia University. Assane Gadiaga (WorldPop) led the input processing and the modelling work following the Random Forest (RF)-based dasymetric mapping approach developed by Stevens et al. (2015). Thomas Abbott supported the covariates processing work. In-country engagements were done by David Kyalo, Olena Borkovska ( GRID3 , Maria Muniz (Unicef). Using the 2009 and 2019 census data from the Kenya’s National Bureau of Statistics (KNBS), the US Census Bureau released the census-based total population projections, population by age and gender and digital sub-counties boundaries. Duygu Cihan helped in the preparation of these input population data. Attila N Lazar, Edith Darin and Heather Chamberlain advised on the modelling procedure. The work was overseen by Attila N Lazar and Andy J Tatem. **Recommended citations** Gadiaga A. N., Abbott T. J., Chamberlain H., Lazar A. N., Darin E., Tatem A. J. 2023. Census disaggregated gridded population estimates for Kenya (2022), version 2.0. University of Southampton. doi:10.5258/SOTON/WP00762 **License** *These data may be distributed using a Creative Commons Attribution 4.0 International (CC BY 4.0) License, specified in legal code. Contact release[at]worldpop.org for more information*. The authors followed rigorous procedures designed to ensure that the used data, the applied method and thus the results are appropriate and of reasonable quality. If users encounter apparent errors or misstatements, they should contact WorldPop at release[at]worldpop.org. WorldPop, University of Southampton, and their sponsors offer these data on a "where is, as is" basis; do not offer an express or implied warranty of any kind; do not guarantee the quality, applicability, accuracy, reliability or completeness of any data provided; and shall not be liable for incidental, consequential, or special damages arising out of the use of any data that they offer.

The two layers contained within the GRID3 KEN - Settlement Extents v3.0 include: GRID3_KEN_settlement_extents_v3.gpkg: a spatial layer representing settlement polygons. GRID3_KEN_settlement_grid_v3.gpkg: a spatial layer representing the centroids of settled grid cells. The current version supersedes GRID3 KEN - Settlement Extents v2.0; the following changes were made: Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes. Recommended Citation: Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 KEN - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/6hnz-fr54. Accessed [DAY MONTH YEAR]. Terms of use: Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use. Copyright 2024. The Trustees of Columbia University in the City of New York. Data license: The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode). Contacts and data queries: The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu

The two layers contained within the GRID3 KEN - Settlement Extents v3.0 include: GRID3_KEN_settlement_extents_v3.gpkg: a spatial layer representing settlement polygons. GRID3_KEN_settlement_grid_v3.gpkg: a spatial layer representing the centroids of settled grid cells. This is the settlement extents layer. The current version supersedes GRID3 KEN - Settlement Extents v2.0; the following changes were made: Development of a new methodology to derive settlement polygons with open data. Estimate a probability value for settlement polygons For more information on data inputs, methodology, and codebooks please see the Data Release Notes. Recommended Citation: Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 KEN - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/6hnz-fr54. Accessed [DAY MONTH YEAR]. Terms of use: Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use. Copyright 2024. The Trustees of Columbia University in the City of New York. Data license: The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode). Contacts and data queries: The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu

The two layers contained within the GRID3 KEN - Settlement Extents v3.0 include: GRID3_KEN_settlement_extents_v3.gpkg: a spatial layer representing settlement polygons. GRID3_KEN_settlement_grid_v3.gpkg: a spatial layer representing the centroids of settled grid cells. This is the settlement extents layer. The current version supersedes GRID3 KEN - Settlement Extents v2.0; the following changes were made: Development of a new methodology to derive settlement polygons with open data. Estimate a probability value for settlement polygons For more information on data inputs, methodology, and codebooks please see the Data Release Notes. Recommended Citation: Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 KEN - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/6hnz-fr54. Accessed [DAY MONTH YEAR]. Terms of use: Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use. Copyright 2024. The Trustees of Columbia University in the City of New York. Data license: The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode). Contacts and data queries: The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu

Settlements and residential areas: named cities, towns, villages, suburbs, and residential land (`place=*` and `landuse=residential`). Useful for demographic context, settlement-level targeting, and population analysis. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: place, landuse - Part of the country exports on HDX by HOT

This dataset represents the estimated population density of Kenya in the year 2020 , expressed as the number of people per square kilometer. It is intended to support spatial analysis and decision-making in areas such as disaster risk reduction, resource allocation, development planning, and vulnerability assessment Source - WORLDPOP Relationship to Exposure- (Positive)-   The higher the population, the higher the exposure

Gridded population density of Kenya for the year 2020 at 1km resolution, adjusted to match UN national population estimates. Each grid cell shows the estimated number of people per square kilometre. Use this layer for settlement pattern analysis, infrastructure planning, service delivery optimisation, and identifying high and low density areas.

The zip files contain the following files: KEN_population_v1_0_gridded.tif This geotiff raster, at a spatial resolution of 3 arc-seconds (approximately 100m at the equator), contains estimates of the total population size per grid cell across Kenya. NA values represent areas that were mapped as unsettled based on gridded building patterns derived from building footprints (Dooley and Tatem, 2020). These data are stored as floating-point numbers rather than integers to avoid rounding errors in aggregated population totals for larger areas. KEN_population_v1_0_agesex.zip This zip file contains 40 GeoTIFF rasters representing estimated population counts for specific age and sex groups within grid cells of approximately 100m. We provide 36 rasters for the commonly reported age-sex groupings of sequential age classes for males and females separately. These are labelled with either an “m” (male) or an “f” (female) followed by the number of the first year of the age class represented by the data. “f0” and “m0” are population counts of under 1-year olds for females and males, respectively. “f1” and “m1” are population counts of 1 to 4 year olds for females and males, respectively. Over 4 years old, the age groups are in five year bins labelled with a “5”, “10”, etc. Eighty year olds and over are represented in the groups “f80” and “m80”. We provide four additional rasters that represent demographic groups often targeted by programmes and interventions. These are “under1” (all females and males under the age of 1), “under5” (all females and males under the age of 5), “under15” (all females and males under the age of 15) and “f1549” (all females between the ages of 15 and 49, inclusive). These data were produced post-hoc by multiplying the total population counts provided in the KEN_population_v1_0_gridded.tif raster and age and sex proportions derived the US Census bureau age-sex projections for each sub-county. While this data represents population counts, values contain decimals, i.e. fractions of people. This is because both the input population data and age-sex proportions contain decimals. For this reason, it is advised to aggregate the rasters at a coarser scale. For example, if four grid cells next to each other have values of 0.25 this indicates that there is 1 person of that age group somewhere in those four grid cells. Data Citation Gadiaga A. N., Abbott T. J., Chamberlain H., Lloyd C. T., Lazar A. N., Darin E., Tatem A. J. 2022. Census-disaggregated gridded population estimates for Kenya (2021), version 1.0. University of Southampton. doi:10.5258/SOTON/WP00747. The downloadableMetadataprovides more information about Source Data, Methods Overview, Assumptions & Limitations and Works and Data Cited. Contact release@worldpop.org for more information or gohere.

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This dataset contains administrative polygons grouped by country (admin-0) with the following subdivisions according to Who's On First placetypes: - macroregion (admin-1 including region) - region (admin-2 including state, province, department, governorate) - macrocounty (admin-3 including arrondissement) - county (admin-4 including prefecture, sub-prefecture, regency, canton, commune) - localadmin (admin-5 including municipality, local government area, unitary authority, commune, suburb) The dataset also contains human settlement points and polygons for: - localities (city, town, and village) - neighbourhoods (borough, macrohood, neighbourhood, microhood) The dataset covers activities carried out by Who's On First (WOF) since 2015. Global administrative boundaries and human settlements are aggregated and standardized from hundreds of sources and available with an open CC-BY license. Who's On First data is updated on an as-need basis for individual places with annual sprints focused on improving specific countries or placetypes. Please refer to the README.md file for complete data source metadata. Refer to our blog post for explanation of field names. Data corrections can be proposed using Write Field, an web app for making quick data edits. You’ll need a Github.com account to login and propose edits, which are then reviewed by the Who's On First community using the Github pull request process. Approved changes are available for download within 24-hours. Please contact WOF admin about bulk edits.