Kenya GIS data

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 Datasets on HDX that refer to the Data Warehouse have been archived.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 2014

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 Datasets on HDX that refer to the Data Warehouse have been archived.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 2015

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 Datasets on HDX that refer to the Data Warehouse have been archived.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 2017

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 Datasets on HDX that refer to the Data Warehouse have been archived.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 2018

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 Datasets on HDX that refer to the Data Warehouse have been archived.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 2012

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 Datasets on HDX that refer to the Data Warehouse have been archived.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 2015

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 Datasets on HDX that refer to the Data Warehouse have been archived.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 2017

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 Datasets on HDX that refer to the Data Warehouse have been archived.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 2013

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 Datasets on HDX that refer to the Data Warehouse have been archived.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 2016

This dataset shows the list of operating health facilities. Attributes included: Name,Nature of Facility, Activities, Lat, Long

This dataset contains annual, 100m-resolution above ground biomass (AGB) estimates from CTrees, a NASA-affiliated forest-carbon monitoring organization, clipped to this country's boundary from CTrees' global AGB raster. Pixel values are stored as int16, scaled x10 relative to the true value in megagrams per hectare (Mg/ha) -- divide by 10 to get Mg/ha. See the caveats field and resource description for details on how this scale factor is recorded and why it isn't applied automatically by most tools.

The International Best Track Archive for Climate Stewardship (IBTrACS) project is the most complete global collection of tropical cyclones available. It merges recent and historical tropical cyclone data from multiple agencies to create a unified, publicly available, best-track dataset that improves inter-agency comparisons. Fields available: SID: A unique storm identifier (SID) assigned by IBTrACS algorithm. ISO_TIME: Time of the observation in ISO format (YYYY-MM-DD hh:mm:ss) BASIN: Basin of the current storm position SUBBASIN: Sub-basin of the current storm position NATURE: Type of storm (a combination of the various types from the available sources) NUMBER: Number of the storm for the year (restarts at 1 for each year LAT: Mean position - latitude (a combination of the available positions) LON: Mean position - longitude (a combination of the available positions) WMO_WIND: Maximum sustained wind speed assigned by the responsible WMO agency WMO_PRES: Minimum central pressure assigned by the responsible WMO agency.

Points of interest: public-facing amenities, shops, tourist attractions, and landmarks tagged via `amenity`, `shop`, `tourism`, or `man_made`. Useful for general POI reference, urban analysis, and basemap detail. 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: amenity, shop, tourism, man_made - Part of the country exports on HDX by HOT

Airports and aviation infrastructure: airports, heliports, helipads, runways, terminals, and emergency landing sites tagged via `aeroway`, `building`, or `emergency`. Useful for logistics, evacuation routing, and aerial-response planning. 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: aeroway, building, emergency - Part of the country exports on HDX by HOT

Cultural and historic places: museums, galleries, theatres, places of worship, monuments, and historic sites tagged via `tourism`, `amenity`, `historic`, or `heritage`. Useful for heritage protection, cultural mapping, and tourism context. 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: tourism, amenity, historic, heritage - Part of the country exports on HDX by HOT

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

Railway lines and stations tagged `railway=*`: rail lines, tram tracks, and stations. Useful for transport accessibility analysis and basemap context. 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: railway - Part of the country exports on HDX by HOT

Health facilities: hospitals, clinics, pharmacies, doctors, and dentists tagged via `amenity` or `healthcare`. Useful for service-access analysis, emergency response, and healthcare gap mapping. 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: amenity, healthcare - Part of the country exports on HDX by HOT

Financial services: banks, ATMs, post offices, money transfer agents, and microfinance branches tagged via `amenity`. Useful for financial-inclusion mapping, cash-distribution planning, and economic 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: amenity - Part of the country exports on HDX by HOT

Building footprints across the area: every feature tagged `building=*`, from residential homes to hospitals, schools, and places of worship. Useful for population estimation, damage assessment, and urban planning. 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: building - Part of the country exports on HDX by HOT

Road network across the area tagged `highway=*`: motorways, residential streets, service roads, footways, and trails. Useful for routing, accessibility analysis, and evacuation planning. 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: highway - Part of the country exports on HDX by HOT

Sea ports, harbours and ferry terminals. Useful for maritime logistics, supply-chain analysis, and coastal access planning. 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: industrial=port, harbour, port, amenity=ferry_terminal, building=ferry_terminal - Part of the country exports on HDX by HOT

Education facilities: schools, kindergartens, colleges, and universities tagged via `amenity` or `building`. Useful for school-access mapping, education planning, and resource allocation. 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: amenity, building - Part of the country exports on HDX by HOT

Kenya Current Situation FEWS NET Acute Food Insecurity Classifications Data from 2011

This is Kenya , livestock ownership with a resolution of 0.05 , approximately 5000 metres  Source : DHS 2022

Anthropogenic biomes, also known as anthromes or human biomes, are ecosystems significantly altered by human activity. They represent the terrestrial biosphere in its contemporary, human-modified form, using global ecosystem units defined by patterns of sustained human interaction. These biomes are categorized based on factors like population density and agricultural practice

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

NASA's nighttime lights imagery has a wide range of uses, including tracking urban growth, assessing electrification, monitoring disasters, and studying the biological impacts of light pollution.

Aridity index (AI) is an effective estimator of drought status, and spatiotemporally continuous long-term AI dataset is critical for drought assessment and applications.This dataset offers valuable support for research on dryland ecosystems, agriculture, and climate change, offering critical insights to address global environmental and sustainability challenges. Resolution ;0.05 × 0.05   in the resolution  ( referenced year 2022)

This dataset is the output from the Global Flood Model intercomparison project undertaken on behalf of the Global Flood Partnership. The methodology is described in Trigg et al., 2016, "The credibility challenge for global fluvial flood risk analysis," published in the Environmental Research Letters Journal. Each file is a geospatial, WGS84, 1/1200 decimal degrees resolution (~90m) GeoTIFF raster with a classified integer value representing how many out of the 6 models agree that a cell is wet (note 0 - dry/no data)

Forward-looking analysis representing the most likely food security outcomes for the near term (April 2023 - May 2023) and medium term (June 2023 - September 2023) periods.

Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a biophysical dimensionless quantity (its values range from 0/no absorption to 1/total absorption) used to assess the greenness and health of vegetation. FAPAR anomalies can be used as an indicator to detect and monitor the impacts of agricultural drought on the growth and productivity of vegetation. The data is presented in 10-day time composite of the Visible Infrared Imaging Radiometer Suite (VIIRS).

GLIDEnumbers are GLobal unique IDEntifiers for Disasters. Each disaster is registered in a record that contains a GLIDEnumber that uniquely identifies it and allows information to be interoperable across multiple disaster information sources, both structured and textual. Accessing disaster information can be a time consuming and laborious task. Not only is data scattered but frequently identification of the disaster can be confusing in countries with many disaster events and across borders. To address both of these issues, Asian Disaster Reduction Center (ADRC) proposed a globally common Unique ID code for disasters. Among the institutions using actively GLIDEnumbers are UN-OCHA (ReliefWeb, FSCC, HDX), UNDRR, UNDP, WMO, IFRC, FAO, the European Commission and the Centre for Research on the Epidemiology of Disasters (CRED), among many others. Country datasets are only produced for countries with at least one GLIDE event since 1 January 2022. The global dataset contains all events available from the GLIDE API, including those not assigned to a country.

The dataset represents an estimated cumulative travel time/cost (raster grid) accessibility map, for Kenya's major cities. The map is an output of the sub-Saharan African Corridor, Mobile Warehouse Location pilot project. Modeled cities are: Nairobi (7,626,752); Mombasa (1,535,899); Nakuru (610,637); Kisumu (567,963) The calculation of cost/time distance surfaces is based on some assumptions: A. Road travel time/cost is computed for large trucks, it is assumed accessibility for 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 production of the travel time surfaces followed the steps: rasterization of transportation network vector layers and surfaces; production of cost/time layer; computation of a cumulative cost/time layer from cities (Major Cities Accessibility Map). **Data publication:** 2020-08-04 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Nelson Ribeiro](mailto:Nelson.RosasRibeiroFilho@fao.org) **Data lineage:** lineage statement Produced using OpenStreetMap data for road, railways, cities; GAUL country border; FAO Inland Waters in Africa; FAO Major Rivers in Africa; **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped raster TIF file for Accessibility: Travel Time-Cost to Major Cities (Kenya - ~1km )

The dataset represents an estimated cumulative travel time/cost (raster grid) accessibility map, for Kenya's Mombasa port. The map is an output of 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, it is assumed accessibility for 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 production of the travel time surfaces followed the steps: rasterization of transportation network vector layers and surfaces; production of cost/time layer; computation of a cumulative cost/time layer from ports (Ports Accessibility Map). **Data publication:** 2020-08-04 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Nelson Ribeiro](mailto:Nelson.RosasRibeiroFilho@fao.org) **Data lineage:** 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 dataset represents an estimated cumulative travel time/cost (raster grid) accessibility map, for Kenya's regional major cities . The map is an output of the sub-Saharan African Corridor, Mobile Warehouse Location pilot project version 2. Modeled cities are: Tanzania: Dar es Salam (5,572,776); Mwanza (830,342); Zanzibar (796,903); Dodoma (571,629); Arusha (466,892); Somalia: Mogadiscio (2,275,976); Merka (480,543); Kismaayo (402,691); Ethiopia: Addis-Abeba (4,567,857); Diré Dawa (453,000); South Sudan: Djouba (917,910); Wau (328,651); Uganda: Kampala (4,101,302); Jinja (589,661); The calculation of cost/time distance surfaces is based on some assumptions: A. Road travel time/cost is computed for large trucks, it is assumed accessibility for 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). Regional travel time surfaces production steps are: rasterization of transportation network and surfaces and definition of cell travel time; creation of countries time/cost layers; combining countries into a regional cost layer; computation of a cumulative cost/time accessibility layer from cities (Regional Major Cities Accessibility Map). **Data publication:** 2020-08-04 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Nelson Ribeiro](mailto:Nelson.RosasRibeiroFilho@fao.org) **Data lineage:** lineage statement Produced using OpenStreetMap data for road, railways, cities; GAUL country border; FAO Inland Waters in Africa; FAO Major Rivers in Africa; **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:**