Kenya GIS data

The raster dataset consists of a 1km score grid for beans storage sites achieved by processing sub-model outputs that characterize logistical factors for crop warehouse location: • Supply: Crop. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + ("Port Accessibility" * 0.2) + (“Major Cities Weighted Accessibility” * 0.1) + (”Regional Cities Weighted Accessibility” * 0.1) This 1km resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location). **Data publication:** 2021-11-01 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Nelson Ribeiro](mailto:Nelson.RosasRibeiroFilho@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. OpenStreetMap. 4. FAO Data – Rivers of Africa, Inland Waters of Africa, Airports, Ports. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for Kenya beans storage/warehouse location score.

The raster dataset consists of a 1km score grid for cassava storage sites achieved by processing sub-model outputs that characterize logistical factors for crop warehouse location: • Supply: Crop. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + ("Port Accessibility" * 0.2) + (“Major Cities Weighted Accessibility” * 0.1) + (”Regional Cities Weighted Accessibility” * 0.1) This 1km resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location). **Data publication:** 2021-11-01 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Nelson Ribeiro](mailto:Nelson.RosasRibeiroFilho@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. OpenStreetMap. 4. FAO Data – Rivers of Africa, Inland Waters of Africa, Airports, Ports. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for Kenya cassava storage/warehouse location score.

The raster dataset consists of a 1km score grid for sweet potato storage sites achieved by processing sub-model outputs that characterize logistical factors for crop warehouse location: • Supply: Crop. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + ("Port Accessibility" * 0.2) + (“Major Cities Weighted Accessibility” * 0.1) + (”Regional Cities Weighted Accessibility” * 0.1) This 1km resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location). **Data publication:** 2021-11-01 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Nelson Ribeiro](mailto:Nelson.RosasRibeiroFilho@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. OpenStreetMap. 4. FAO Data – Rivers of Africa, Inland Waters of Africa, Airports, Ports. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for Kenya sweet potato storage/warehouse location score.

The raster dataset consists of a 1 km score grid for slaughterhouse industry facilities, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The analysis is based on livestock production potential using proximity to cities, crop production, and aggregated average weighted density of cattle, goats, sheep and pigs. Animal weight: Goat 30kg (X0.09); Sheep 30kg (X0.09); Cattle 210kg (X0.63), Pig 63kg (X0.19). The score is achieved by processing sub-model outputs that characterize logistical factors: 1. Supply - Feed and livestock distribution. 2. Infrastructure - Transportation network (accessibility). 3. Atlas AI Asset Wealth Index. It consists of an arithmetic weighted sum of normalized grids (0 to 100): (”Livestock Density” * 0.4) + ("Asset Wealth" * 0.3) + (“Major Cities Accessibility” * 0.2) + ("Crop Production" * 0.1). **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Nelson Ribeiro](mailto:nelson.rosasribeirofilho@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2). 3. IFPRI MapSPAM crop production. 4. OpenStreetMap. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Download Kenya location score TIF (zip) dataset

Travel time/cost surface (raster grid), port accessibility map for Kenya's Mombasa port. Outputs produced in the sub-Saharan African Corridor, Mobile Warehouse Location pilot project. The calculation of cost/time distance surfaces is based on some assumptions: A. Road travel time/cost is computed for large trucks only, it is assumed that the accessibility is related to large cargo freight vehicles, tertiary and local traffic roads are not included; B. Lake and river navigation are treated as a surface (polygons) not taking into consideration navigation infrastructure (points). The steps to produce travel time surfaces are: rasterization of transportation network vector layers and surfaces, production of cost/time layer summing different grids cell values, and computation of a cumulative cost/time layer (Ports Accessibility Map). **Data publication:** 2020-07-13 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Nelson Ribeiro](mailto:Nelson.RosasRibeiroFilho@fao.org) **Data lineage:** lineage statement lineage statement Produced using OpenStreetMap data for road, railways; GAUL country border; FAO Inland Waters in Africa; FAO Major Rivers in Africa; FAO Ports; **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0). **Online resources:**

The raster dataset consists of a 1km score grid for potato storage sites achieved by processing sub-model outputs that characterize logistical factors for crop warehouse location: • Supply: Crop. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + ("Port Accessibility" * 0.2) + (“Major Cities Weighted Accessibility” * 0.1) + (”Regional Cities Weighted Accessibility” * 0.1) This 1km resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location). **Data publication:** 2021-11-01 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Nelson Ribeiro](mailto:Nelson.RosasRibeiroFilho@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. OpenStreetMap. 4. FAO Data – Rivers of Africa, Inland Waters of Africa, Airports, Ports. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for Kenya potato storage/warehouse location score.

Point locations of agricultural crops in Kirinyaga, Kenya, with each feature representing a crop recorded by name. Published by Orion Terrametrics.

Point features representing livestock-related locations in Kajiado, Kenya, with a name field for each type. Published by Orion Terrametrics.

Line features representing rivers in Kenya, with a name field that is null in the sample. Published by Orion Terrametrics.

This Maps Viewer shows all the Agro-Ecological Zones in Kenya dataset

Point features representing agricultural crop locations in Embu, Kenya, with a crop_name field indicating the crop type. Published by Orion Terrametrics.

Point locations of named places in Kenya, including populated places, mountains and streams, with coordinates, administrative divisions, population, elevation and timezone. Each feature represents one named place. Published by Orion Terrametrics.

Administrative boundary lines for Kenya, published by Orion Terrametrics. Each line represents a boundary segment with attributes for administrative level, validity dates, and codes for the areas on either side. The dataset covers Kenya and includes 891 features.

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.

This Maps Viewer shows all the Agro-Ecological Zones in Kenya dataset

Ward-level boundaries of Kenya (1,442 wards) from GADM 4.1. Used as zonal units for sub-county analysis, e.g. ward-level summaries within Kisumu Central.

uav imagery by HOTOSM East and Southern Africa Hub, acquired 2026-04-01 to 2026-04-01, 5.0 cm per pixel, published on OpenAerialMap.

Rivers, streams and drainage network of Kenya showing the extent and hierarchy of surface water features. Use this layer for watershed analysis, flood modelling, irrigation planning, water resource mapping, and environmental impact assessment.

Point locations representing administrative centroids in Kenya, published by Orion Terrametrics. Each feature includes coordinates and administrative names at various levels. The dataset covers Kenya and contains 338 features.

Line features representing administrative boundaries in Kenya, with fields for administrative level, name, validity dates, and codes. Published by Orion Terrametrics.

Polygon features representing matched administrative units in Kenya, each with a unit name, a count of matched records, and an average match score. Sample rows cover units such as Mombasa, Kwale, and Kilifi. Published by Orion Terrametrics.

Population counts by age range and gender for administrative units in Kenya, with each row giving the population of one age-gender group in a location such as Mombasa. The reference period is 2019. Published by Orion Terrametrics.

Polygon features representing matched administrative units in Kenya, each with a unit name, a count of matched records, and an average match score. Sample rows cover units such as Mombasa, Kwale, and Kilifi. Published by Orion Terrametrics.

Records matched administrative units in Kenya, with each row giving a unit name, a count of records and an average match score. The sample rows cover Mombasa, Kwale and Kilifi.

Polygon features representing matched administrative units in Kenya, with record counts and average match scores. Published by Orion Terrametrics.

Estimated total number of people per grid-cell for Kenya, published by WorldPop, University of Southampton, UK. The records cover annual population counts from 2000 to 2002 at 100m resolution.

Estimated total number of people per grid-cell for Kenya, published by WorldPop, University of Southampton, UK. The records cover annual population counts from 2000 to 2002 at 100m resolution.

The Open Geographical Regional Reference Vector Database at 1:1 million scale for water and agro-ecological zoning entailed: the development of a consistent and comprehensive common and shared geographical database at regional level using open data sources, creating agro-ecological zoning using weighted overlay and dissemination of these products using web GIS, geoportals and other available tools

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.

geoBoundaries ADM3 boundaries for Kenya: 1452 units, representing 2020. Source: Benedict Aboki Omare and George Omare.

Retirement Notice: This item is in mature support as of October 2025 and will retire in December 2027.  A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Kenya Wilaya Boundaries provides a 2023 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online. The 2023 boundaries are provided by Michael Bauer Research GmbH. These were published in October 2023. Other administrative boundaries for this country are also available: Country

47 county boundaries of Kenya from the IEBC 2019 administrative boundaries (Common Operational Datasets, HDX / OCHA RCO).

Polygon features representing matched administrative units in Kenya, each with a unit name, a count of matched records, and an average match score. Sample rows cover units such as Mombasa, Kwale, and Kilifi. Published by Orion Terrametrics.

Point records describing gridded population datasets for Kenya, with fields for population year, data format, source, and citation. Sample rows cover the years 2000, 2001, and 2002. The source is WorldPop, University of Southampton, UK.

Records WorldPop population count data for Kenya, with each row describing an estimated total number of people per grid-cell for a given year. The sample rows cover the years 2000, 2001 and 2002. Published by WorldPop, University of Southampton, UK.

Records WorldPop population count data for Kenya, with each row describing an estimated total number of people per grid-cell for a given year. The sample rows cover the years 2000, 2001 and 2002. Published by WorldPop, University of Southampton, UK.