Kenya GIS data

uav imagery by HOT, acquired 2026-03-26 to 2026-04-03, 7.7 cm per pixel, published on OpenAerialMap.

uav imagery by SDI Kenya-Muungano Wa Wanavijiji, acquired 2026-10-12 to 2026-10-13, 4.0 cm per pixel, published on OpenAerialMap.

uav imagery by SDI Muungano wa wanavijiji , acquired 2026-10-12 to 2026-10-13, 4.0 cm per pixel, published on OpenAerialMap.

uav imagery by Muungano wa wanavijiji - SDI Kenya , acquired 2026-10-12 to 2026-10-13, 4.0 cm per pixel, published on OpenAerialMap.

uav imagery by Muungano wa wanavijiji- SDI Kenya, acquired 2026-10-12 to 2026-10-13, 4.0 cm per pixel, published on OpenAerialMap.

uav imagery by SDI Kenya Muungano Wa Wanavijiji, acquired 2026-10-12 to 2026-10-13, 4.0 cm per pixel, published on OpenAerialMap.

The Kenya Crop Land layer provides information on the extent of cropland, area specific major crop and other crops being grown in the same location. This layer was generated from the Landsat 8 - 30 meters resolution imagery data for September/October 2000 and validated using location points collected from subsequent field visits.

Layer showing changes in Kenya crop land areas from 2000 to 2010

Layer showing changes in Kenya maize land areas from 2000 to 2010.

This data set represents land cover map for the year 2016. This layer was clipped from Sentinel-2 global land cover data.

uav imagery by Muungano Wa Wanavijiji-SDI Kenya, acquired 2026-10-12 to 2026-10-13, 4.0 cm per pixel, published on OpenAerialMap.

uav imagery by SDI Kenya - Muungano wa wanavijiji, acquired 2026-10-12 to 2026-10-13, 4.0 cm per pixel, published on OpenAerialMap.

satellite imagery by Muungano wa Wanavijiji SDI Kenya, acquired 2026-10-14 to 2026-10-15, 4.0 cm per pixel, published on OpenAerialMap.

geoBoundaries ADM0 boundaries for Kenya: 1 units, representing 2020. Source: RCMRD GeoPortal, geoBoundaries.

Point locations of primary and secondary schools in Kenya, published by Orion. Each point represents a school with attributes including name, level, status, county, district, zone, sub-county, ward, and coordinates. The dataset covers Kenya and includes 37,930 features.

geoBoundaries ADM1 boundaries for Kenya: 47 units, representing 2020. Source: RCMRD GeoPortal, Africa GeoPortal.

satellite imagery by SDI kenya-Muungano wa wanavijiji, acquired 2026-10-13 to 2026-10-14, 4.0 cm per pixel, published on OpenAerialMap.

Data represents the Shuttle Radar Topography Mission (SRTM) 30 metres image for Kenya.These SRTM was created through mosaicking tiles and clippingto the extent of the country

The Kenya Crop Land layer provides information on the extent of cropland, area specific major crop and other crops being grown in the same location. This layer was generated from the Landsat 8 - 30 meters resolution imagery data for September/October 2015 and validated using location points collected from subsequent field visits.

This dataset contains the following administrative boundaries: ADM0, ADM1, ADM2, ADM3. Produced and maintained since 2017, the geoBoundaries Global Database of Political Administrative Boundaries Database www.geoboundaries.org is an open license, standardized resource of boundaries (i.e., state, county) for every country in the world.

**ADAM ID: FL-20231121-KEN-03** Flood covering 543793.0 sq m on Nov 21 2023 in Kenya. It impacted 242812 people.

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.

A 100 m spatial resolution geotiff of walking travel time in seconds to nearest health facility in Kenya. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming walking speeds on all roads, tracks, paths and land cover types. A full description is available here a video description of the data is also available here Projection system is: GCS_WGS_1984 EPSG 4326 for all

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Kenya Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2011

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Kenya Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2018

Kenya Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2021

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Kenya Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2013

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Kenya Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2016

Kenya Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2020

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Kenya Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2013

Kenya Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2022