Kenya counties boundary data in .shp format

Context

The dataset contains the Kenya Counties ShapeFiles

Content

The dataset contains the Kenya Counties ShapeFiles

Acknowledgements

The data is from OpenAfrica. The image is jacob brogdon on Unsplash

Inspiration

The data can be used effectively in geospatial analysis of Kenya. An example would be to use it with the very popular Kiva dataset

Kenya counties boundary data in .shp format

Kenya Sub Counties matched to former provinces and DHIS2 IDs. The files have been corrected for topological errors and are perfectly aligned to the wards. If you need the wards, go to https://data.humdata.org/dataset/administrative-wards-in-kenya-1450

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

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.

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)

This data shows 47 administrative units or counties in Kenya as per IEBC demarcation

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.

Impacts of Prosopis invasion and grassland degradation and restoration on soil organic carbon

To understand the impacts of Prosopis invasion, land degradation, and grassland restoration on SOC, LULC maps for the years 1995, 2002, 2009, and 2016 were analysed, along with a detailed map of Prosopis fractional cover in 2016. The LULC maps were generated earlier for another study (Mbaabu et al., 2019). For the present analysis, the relevant original LULC classes were regrouped into the following five categories: (1) degraded grassland, (2) pristine grassland, (3) restored grassland, (4) sparse Prosopis (Ps) (<50% coverage), and (5) dense Prosopis (Pd) (>50% coverage). A number of assumptions were made, based on the study area’s LULC and land degradation history. Thus, areas originally classified as “bare” were considered to be degraded grassland. Areas classified as “grassland” were considered pristine grassland if they had been “grassland” since 1995. Restored grassland comprises ar...

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 shapefile shows wet and dry seasons grazing patterns in Samburu County, Kenya. It was derived from resource persons from Samburu and published on June 1, 2011.

The population desnity per Km2 in each County in Kenya

This map illustrates a landcover classification over Kalobeyei and Kakuma areas, Turkana Province, Kenya as derived from Landsat-8 multispectral imagery acquired the 08 March 2015 at 30m of pixel resolution. The classification is divided into 5 main classes: Savana and Sparse Vegetation, Sandy and alluvial soils with few vegetation, Baresoils and vegetated areas and riparian. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.

The population desnity per Km2 in each County in Kenya

The shapefile shows wet and dry seasons grazing patterns in Turkana county. It was derived from resource persons from Turkana and was published on June 1, 2011.