Kenya counties boundary data in .json format

The counties of Kenya are geographical units envisioned by the 2010 Constitution of Kenya as the units of devolved government. The powers are provided in Articles 191 and 192, and in the fourth schedule of the Constitution of Kenya and the County Governments Act of 2012. The counties are also single member constituencies for the election of members of parliament to the Senate of Kenya and special women members of parliament to the National Assembly of Kenya. As of 2013 general elections, there are 47 counties whose size and boundaries are based on the 47 legally recognized Districts. Following the re-organisation of Kenya's national administration, counties were integrated into a new national administration with the national government posting county commissioners to represent it at the counties.County governments are responsible for county legislation

A geospatial layer of 295 sub-counties in Kenya created by digitizing sub-county maps available for each county from the county integrated development plans (CIDPs)

Kenya - Subnational Administrative Boundaries
This dataset falls under the category Planning & Policy Planning.
It contains the following data: Kenya administrative level 0 (country), 1 (county), and 2 (sub-county) boundary polygon and line shapefiles, geodatabase, live services, and gazeteer
This dataset was scouted on 2022-02-03 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://data.humdata.org/dataset/cod-ab-ken

This dataset provides a 10m resolution map of cropland in Kenya for the 2019-2020 growing season (kenya_cropland_binary_2019.tif.zip) and a 10m resolution map of cropland in Busia County, Kenya for the 2020-2021 growing season (busia_cropland_binary_2020.tif.zip). Each pixel has a binary value, 0 if it does not contain crops and 1 if it does. These values were obtained by thresholding the predictions of an LSTM classifier trained on multi-spectral time series of Sentinel-2 satellite observations. A thresholding value of 0.5 was used.

This dataset also provides the hand-labelled non-crop points used for training, which were created by labelling high-resolution satellite imagery in QGIS and Google Earth Pro.

For more information, or if you use any part of this dataset, please refer to / cite the following paper: Gabriel Tseng, Hannah Kerner, Catherine Nakalembe and Inbal Becker-Reshef. 2020. Annual and in-season mapping of cropland at field scale with sparse labels. Tackling Climate Change with Machine Learning workshop at NeurIPS ’20: December 11th, 2020

This dataset represents the first-level administrative unit 'counties' of Kenya. The counties of Kenya are geographical units envisioned by the 2010 Constitution of Kenya as the units of devolved government. The dataset was originally produced by the WHO Teams for analysis of Rift valley fever (RVF). FAO-CSI got the data from(?) and validated topology/geometry in January, 2022. International borders were validated against the official borders from the United Nations Geospatial Information Section (UN-Map 2018).

Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item. This layer shows the purchasing power per capita in Kenya in 2023, in a multiscale map (Country and County). Nationally, the purchasing power per capita is 170,615 Kenyan shilling. Purchasing Power describes the disposable income (income without taxes and social security contributions, including received transfer payments) of a certain area's population. The figures are in Kenyan shilling (KES) per capita.The pop-up is configured to show the following information at each geography level:Purchasing power per capitaThe source of this data is Michael Bauer Research. The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.Additional Esri Resources:Esri DemographicsThis item is for visualization purposes only and cannot be exported or used in analysis.We would love to hear from you. If you have any feedback regarding this item or Esri Demographics, please let us know.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

We were required to Georeference topographical maps which had been shared. I digitised a polygon shapefile within the mosaicked image. I used the polygon to clip the raster dataset in which I digitised line, polygon and point features within the clipped raster. The final product was a map of Meru which is shown. We added Kenya counties layer, Kenya schools layer, Kenya health layer and Kenya streets layer to Arcmap. I then clipped my respective county which is Laikipia County,in Kenya. I then clipped the added layers to fit my county so that I could process the required data. I buffered health layer so that it could help me know which schools were within 120 m from the health facilities. Also, i buffered steets to 55m from the schools to know which were closest and their accessibility. This data was to be used by the Ministry of Health to plan for polio vaccination in the county. The finished product was a map as shown below.

This map illustrates topographic features in the planned Kalobeyei site, Turkana County, Kenya using a Digital Elevation Model derived from imagery with 1m resolution. UNITAR-UNOSAT built water levels scenarios that represents the potentially affected areas along the modelled stream network assuming a static raising of waters of 1 meter, 2 meters and 3 meters. Streamlines and static water levels has been extracted from a Hydrologically Conditioned version of the DEM derived from WorldView-2 Imagery with 5 m resolution. The model shows spatial distribution of potential water levels in the basin based on the elevation extracted from the DEM but does not represent a current flood scenario. This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.

This report describes the Soil characterization and Fertility Mapping of Kenya Cereal Enhancement Programme (KCEP) in Njoro and Molo Sub counties, Nakuru County

Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item. This layer shows the average household size in Kenya in 2023, in a multiscale map (Country and County). Nationally, the average household size is 3.8 people per household. It is calculated by dividing the household population by total households.The pop-up is configured to show the following information at each geography level:Average household size (people per household)Total populationTotal householdsCounts of population by 15-year age increments The source of this data is Michael Bauer Research. The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.Additional Esri Resources:Esri DemographicsThis item is for visualization purposes only and cannot be exported or used in analysis.We would love to hear from you. If you have any feedback regarding this item or Esri Demographics, please let us know.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

Y731 (1: 50 000 scale) Topographic Maps represents the main 1: 50 000 scale mapping covering large parts of Kenya. The maps illustrate the key topographic features both natural and man made. There have been multiple versions of the maps published. Not all versions of the maps are held by the Geodata Centre. Those which are currently held (November 2018) are listed. Publishers OSD Government of the United Kingdom (Crown Copyright); OSD(K) Government of the United Kingdom for the Government of Kenya; OSD(T) Government of the United Kingdom for the Government of Tanzania; OSD(U) Government of the United Kingdom; USD Department of Land and Surveys Uganda; ING French National Geographic Institute for the Government of Kenya; JICA Japan International Co-operation Agency for the Government of Kenya.

Y731 (1: 50 000 scale) Topographic Maps represents the main 1: 50 000 scale mapping covering large parts of Kenya. The maps illustrate the key topographic features both natural and man made. There have been multiple versions of the maps published. Not all versions of the maps are held by the Geodata Centre. Those which are currently held (November 2018) are listed. Publishers OSD Government of the United Kingdom (Crown Copyright); OSD(K) Government of the United Kingdom for the Government of Kenya; OSD(T) Government of the United Kingdom for the Government of Tanzania; OSD(U) Government of the United Kingdom; USD Department of Land and Surveys Uganda; ING French National Geographic Institute for the Government of Kenya; JICA Japan International Co-operation Agency for the Government of Kenya.

This map illustrates a landcover classification over the Kalobeyei area, Turkana Province, Kenya as derived from WorldView-2 very high resolution multispectral satellite imagery acquired on 05 March 2015 with a resolution of 1.8m. The classification is divided into 04 main classes: Savana and Sparse Vegetation, Sandy and Alluvial Soils (with little vegetation), Bare Soils and Vegetated/Riparian areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.

The Multimedia University of Kenya is a public university located in Nairobi, Nairobi County, Kenya. The university offers IT & related courses, Mass media, Business, Engineering and Social sciencesMoi University was first established in 1984. In its first year of existence, there was only one department - the Department of Forestry.

This map illustrates satellite-detected flood water extent along Tana River, Galole and Garsen sub counties, Tana River county, Kenya. The analysis was conducted by analyzing Sentinel-1 imagery acquired on the 4 May 2018. The analysis extent is focused on the river bed of Tana, the surrounding land between the primary road and the limit of the Tana River boundary, specifically where the population is concentrated. Within the analysis extent, around 22,700 ha of land appears to be inundated and more than 21,600 people are living inside this flood water extent. It is likely that flood waters have been systematically underestimated along highly vegetated areas along main river banks and within built-up urban areas because of the special characteristics of the satellite data used. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR UNOSAT.

Kenya’s population has nearly tripled in the last 35 years, from 16.3 million in 1980 to about 47 million today yet majority of the population are below the poverty line. poverty in Kenya is a widespread problem concentrated in the rural areas. This data set shows poverty rates within the Kenyan counties.

Y731 (1: 50 000 scale) Topographic Maps represents the main 1: 50 000 scale mapping covering large parts of Kenya. The maps illustrate the key topographic features both natural and man made. There have been multiple versions of the maps published. Not all versions of the maps are held by the Geodata Centre. Those which are currently held (November 2018) are listed. Publishers OSD Government of the United Kingdom (Crown Copyright); OSD(K) Government of the United Kingdom for the Government of Kenya; OSD(T) Government of the United Kingdom for the Government of Tanzania; OSD(U) Government of the United Kingdom; USD Department of Land and Surveys Uganda; ING French National Geographic Institute for the Government of Kenya; JICA Japan International Co-operation Agency for the Government of Kenya.

Abstract

The survey of the Pemba was an attempt to reach all households in Kenya with links to Pemba in Tanzania. It was conducted in the two counties of Kilifi and Kwale on the coast, north and south of Mombasa, respectively. According to information from village elders familiar with the Pemba community in Kenya, most of the Pemba population resides in these two counties. While there are some Pemba residents in Lamu, the security situation prevented data collection there. Further, a few Pemba are believed to live in the city of Mombasa and elsewhere in the country. But due to lack of further information, no data were collected in Mombasa or elsewhere. The objectives of the full survey, conducted in August 2016, were: 1. To establish the number and characteristics of the Pemba living in Kenya, including their arrival period in Kenya, nationality and their problems; 2. To make recommendations for the issuance of the documentation that is required for those who apply for citizenshiop by registration

Geographic coverage

Kwale and Kilifi counties, Kenya.

Analysis unit

Households, individuals

Universe

The total number of households with links to Pemba in Tanzania, in Kilifi and Kwale counties.

Kind of data

Census/enumeration data [cen]

Sampling procedure

A household mapping exercise was conducted in Kilifi and Kwale to identify Pemba households and to make it easier to locate them on the ground. The mapping was done from 4 to 12 August 2016 by a team from UNHCR Kenya office and KNBS. The mapping in each village commenced with a visit to the chief's office, who put the team in touch with the village chair. The team explained the purpose of its visit to the village chair and began the mapping exercise. The importance of involving the chiefs and village chairpersons is that they are well connected, recognised and trusted by residents in their communities. The same procedure is followed by KNBS when they are mapping for sample surveys and censuses. The team established physical boundaries of the area to be mapped, located the boundaries on the map and then identified and listed the Pemba households within the enumeration boundary. A Pemba household, in this context, is one identified by the informants as having at least one person with origins or links to Pemba. The links may include a person's spouse, parents or grandparents, who migrated to Kenya from Pemba or where a person has migrated from Pemba to Kenya. The mapping team was followed by the village chair to the Pemba households, where the UNHCR and Haki Centre staff listed number of persons in each, while the KNBS staff marked the location of the household on the map. The entrances of identified Pemba households were marked in chalk with the letters HCR and a number starting at 001 to make it easier to find the houses during the enumeration. Since it seems to be generally well known where the Pemba live it was not considered stigmatising to mark their doors. During the feedback forums with the Pemba after the survey, there was no mention of stigmatization due to marking the door with chalk. The maps were from the 2009 national housing and population census, purchased from KNBS. The team made lists with information about the location, number and size of each household. The mapping team visited 17 villages in Kilifi and Kwale (see Table 1 in Section 2.7). All villages visited were identified before the mapping exercise by key informants as locations being home to the Pemba of Kenya. The key informants were Pemba elders in different sub-counties previously identified for providing background information on the Pemba arrival and history in Kenya. In each sub-country, the chief, the assistant chief or the village chair also accompanied the team. In Kwale, 358 households were identified with 2,220 persons, and in Kilifi, 86 households with 558 persons.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was developed before the pilot survey and revised during and after the pilot survey, based on the experience gained. The pilot survey was used to test the questions and to check for inconsistences and misinterpretations due to unclear concepts and definitions. The testing process also revealed some important themes that had been left out. The structure of the questionnaire was altered, including the order of the questions and the introductory pages, to facilitate administration of the questionnaire. Finally, the questionnaire was translated into Swahili. Both the English and Swahili versions were used in the survey, even though the English version was preferred by almost all interviewers. The two versions of the questionnaire are attached in Annex 4 and 5. Enumerators used the English questionnaire to frame the questions in the local and less academic version of Swahili.

Cleaning operations

The data were imported into a Statistics Analysis Software (SAS) file and validated. Several errors were identified during the validation process, both on how the data had been recorded by the interviewers in the field and how the data had been entered by the clerks. There were particularly many errors in the entry of the variable “Relation to the household head” (Q.2). There were also many errors in the entry of the age of the household head, which was mostly due to errors in recording the right codes. A substantial amount of time was spent cleaning the data after the data had been entered, which included consulting many paper questionnaires. The quality of the survey data was significantly improved after the data entry revision. The data were analysed using both SAS software and Excel spreadsheets.

Response rate

The rate of non-response was low. Of the 452 households visited, visits to only 23 households can be categorised as non-response. A lot of effort was made to revisit non-responding households, using interviewers living nearby. Out of the 23 non-responsive households, 12 were not at home or there was no adult at home. There were 2 interrupted interviews, 7 refusals and 2 with no links to Pemba. In one household the respondent was not mentally stable enough to be interviewed, according to the enumerator.

This layer contains data showing the various sources of energy in each County in Kenya. By just visualizing, it is evident that residents in Nairobi couty rely on electricity as the main source of energy while residents in Northern Kenya rely more on Solar energy.