Ethiopia GIS data

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

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

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

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

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

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

The household income index of the different cluster numbers in Ethiopia from the Demographic Health Survey , 2016 -    from Low income per household to High household income per cluster - at a resolution of about 5000 metres

The computed dataset represents the credit services , in the different clusters and households which was computed  as a result of the mode per cluster at a resolution of about 5000 metres , from the DHS , 2016

This is the mode of the households who have electricity accesibility to their homes - There are 645 Clusters in the Standard Demographic and Health Survey (DHS) 2016 , the resolution is about 5,000 metres

This dataset contains the Ethiopian dataset on the Irrigated Lands , from the Waterdata , Global Irrigated Area for Mapping for the nominaL year of 2000 using satellite images at the resolution of about 5000 metres

The dataset is the level of literacy rates and education in Ethiopia , from DHS which the CSA was the provider under the 2016 Standard Demographic Health Survey (DHS) with a resolution of 0.05 pixels

Gridded datasets used in Jones et al. (2021) paper 'Agrobiodiversity Index scores show agrobiodiversity is underutilized in national food systems'. Details of how datasets were made and underlying sources are provided in Jones et al. (2021) Supplementary Information - with a resolution of 0.05* 0.05 pixels approximately 5000 metres and referenced 2022

Ethiopia household members per cluster entails the average household members per cluster (645 Clusters According to the 2016 DHS data - with a Spatial  Resolution of 0.05 pixels about 5000 metres

Sex of the Household Head in Ethiopia - Computed from the different clusters in Ethiopia - from the Demographic Health Survey (DHS) standard datasets of 2016 - This is the average Household head per cluster which has the (Male and Female )  with a resolution of 0.05pixels approximately 5000 metres  referenced (2016)

Crop production is among the most extensive human activities on the planet – with critical importance for global food security, land use, environmental burden, and climate. Yet despite the key role that croplands play in global land use and Earth systems, there remains little understanding of how spatial patterns of global crop cultivation have recently evolved and which crops have contributed most to these changes. Here we construct a new data library of subnational crop-specific irrigated and rainfed harvested area statistics and combine it with global gridded land cover products to develop a global gridded (5-arcminute) irrigated and rainfed cropped area (MIRCA-OS) dataset for the years 2000 to 2015 for 23 crop classes. These global data products support critical insights into the spatially detailed patterns of irrigated and rainfed cropland change since the start of the century and provide an improved foundation for a wide array of global assessments spanning agriculture, water resource management, land use change, climate impact, and sustainable development

This dataset measures relative water demand. Higher values indicate more competition among users.The dataset has a resolution of 0.05 pixels about 5000 metres ( referenced 2023) The baseline water stress (BWS) layer, developed as part of WRI's Aqueduct Water Risk Atlas, measures the ratio of total water withdrawals relative to the annual available renewable surface water supplies. BWS serves as a good proxy for water-related challenges more broadly, given that areas of higher water stress will likely be subject to higher depletion of surface and groundwater resources and more competition amongst users, as well as the associated impacts on water quality and other ecosystem services. Watersheds with high baseline water stress may warrant greater need to take appropriate action to respond to watershed risks. A long time series of supply (1950–2010) was used to reduce the effect of multi-year climate cycles and ignore complexities of short-term water storage (e.g., dams, floodplains) for which global operational data are nonexistent. Baseline water stress thus measures chronic stress rather than drought stress. Watersheds with less than 0.012 m/m2 /year of withdrawal and 0.03 m/m2 /year of available blue water were masked as “arid and low water use” since watersheds with low values were more prone to error in the estimates of baseline water stress. Additionally, although current use in such catchments is low, any new withdrawals could easily push them into higher stress categories.

This dataset presents high-resolution satellite-derived Nighttime Lights (NTL) imagery of Ethiopia,a resolution of approximately 5000 mettres ,  acquired from NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS). NTL data captures the intensity and distribution of artificial lighting, serving as a proxy for human settlements, infrastructure development, and economic activity.

Livelihood zone maps define geographic areas of a country where people generally share similar options for obtaining food and income and similar access to markets. An understanding of geographic livelihood systems is a key component in Vulnerability Assesment for both Drought and Floods - The livelihood zones of resolution approximately 5000 metres ( 0.05×0.05)   Referenced  September 2019

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 provides a spatial mask of Ethiopia rangelands, identifying areas dominated by grasslands, shrublands, and sparse vegetation that are primarily used for grazing and pastoral livelihoods. The rangeland mask for Ethiopia delineates regions where pastoralism and agro-pastoralism are prevalent, helping distinguish these land systems from croplands, forests, or urban areas. The dataset is derived from a combination of remote sensing data and land cover classifications, ensuring consistency across ecological zones-

This dataset represents the estimated population density of Ethiopia 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

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 (March 2023 - May 2023) and medium term (June 2023 - September 2023) periods.

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

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.

This map represents the Ethiopia Land Cover map for the development of Green House Gas Inventory. These products were derived from LandSat Imagery Data through a supervised classification method. The Ancillary data was provided by Ethiopia Mapping Agency. **Contact points:** Resource Contact: Ethiopia Mapping Agency Resource Contact: [USAID](mailto:https://www.usaid.gov/) Resource Contact: [RCMRD](mailto:https://www.rcmrd.org/en/) Resource Contact: [NASA](mailto:https://www.nasa.gov/) Resource Contact: [US EPA](mailto:https://www.epa.gov/) Metadata Contact: [Patrick Kabatha. ](mailto:pkabatha@rcmrd.org) **Data lineage:** Data quality information not available **Resource constraints:** This Ethiopia Land Cover 2003 Scheme I is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/ **Online resources:** Ethiopia Land Cover 2013 Scheme I

Awash river is derived from the HydroRIVERS product, which was obtained by delineating river networks from hydrologically corrected elevation data (WWF HydroSHEDS, Lehner et al. 2008; Lehner and Grill 2013). The Awash river, as well as any other river within the HydroRIVERS database, is co-registered to the sub-basin of the HydroBASINS database in which it resides (via a shared ID). Source: The HydroRIVERS product has been developed on behalf of World Wildlife Fund US (WWF), Washington DC, USA, in collaboration with McGill University, Montreal, Canada. Major funding for the underpinning HydroSHEDS project was provided to WWF by Sealed Air Corporation. Citations and acknowledgements of the HydroRIVERS data should be made as follows: Lehner, B., Grill G. (2013): Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. Hydrological Processes, 27(15): 2171–2186. Data is available at www.hydrosheds.org. **Data publication:** 2020-01-01 **Contact points:** Metadata Contact: [Solomon Seyoum](mailto:s.seyoum@un-ihe.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Resource constraints:** HydroRIVERS data are covered by the same License Agreement as the HydroSHEDS database, which is available at https://www.hydrosheds.org/products/hydrorivers. HydroRIVERS data are free for non-commercial and commercial use. For all regulations regarding license grants, copyright, redistribution restrictions, required attributions, disclaimer of warranty, indemnification, liability, waiver of damages, and a precise definition of licensed materials, please refer to the License Agreement. By downloading and using the data the user agrees to the terms and conditions of the License Agreement. **Online resources:** HydroRIVERS Version 1.0 HydroRIVERS Technical Documentation Version 1.0 HydroSHEDS License Agreement

Awash river basin boundary is derived from the HydroBASINS product, which was obtained by delineating drainage basin boundaries from hydrologically corrected elevation data (WWF HydroSHEDS, Lehner et al. 2008; Lehner and Grill 2013) and supported by the topological concept of the Pfafstetter coding system (Verdin & Verdin 1999). Citations and acknowledgements of the HydroBASINS data should be made as follows: Lehner, B., Grill G. (2013): Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. Hydrological Processes, 27(15): 2171–2186. Data is available at www.hydrosheds.org. **Data publication:** 2020-01-01 **Contact points:** Metadata Contact: [Solomon Seyoum](mailto:s.seyoum@un-ihe.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** Source: The HydroBASINS product has been developed on behalf of World Wildlife Fund US (WWF), with support and in collaboration with the EU BioFresh project, Berlin, Germany; the International Union for Conservation of Nature (IUCN), Cambridge, UK; and McGill University, Montreal, Canada. Major funding for this project was provided to WWF by Sealed Air Corporation; additional funding was provided by BioFresh and McGill University. **Resource constraints:** HydroRIVERS data are covered by the same License Agreement as the HydroSHEDS database, which is available at https://www.hydrosheds.org/products/hydrorivers. HydroRIVERS data are free for non-commercial and commercial use. For all regulations regarding license grants, copyright, redistribution restrictions, required attributions, disclaimer of warranty, indemnification, liability, waiver of damages, and a precise definition of licensed materials, please refer to the License Agreement. By downloading and using the data the user agrees to the terms and conditions of the License Agreement. **Online resources:** HydroBASINS Version 1.0 HydroSHEDS License Agreement HydroBASINS Technical Documentation Version 1.c

Accessibility to major cities dataset is modelled as raster-based travel time/cost analysis, computed for the largest cities (>50k habitants) in the country. 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 (or optimal location). **Data publication:** 2021-02-01 **Contact points:** Metadata Contact: [Food and Agriculture Organization of the United Nations](mailto:fao-data@fao.org) Resource Contact: [Maribel Elias](mailto:Maribel.Elias@fao.org) Maintainer: [Maribel Elias](mailto:Maribel.Elias@fao.org) **Data lineage:** lineage statement Produced using GADM country border; the OpenStreetMap data for roads, railways, waterways, the HydroSHEDS 15' resolution GRID for the DEM, the JRC Big Data Analytics Platform for the urban areas, The Hand-in-Hand Initiative, 2020 **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Accessibility: Travel time-cost to major cities (Ethiopia - ~ 1Km)

The raster dataset consists of a 1km score grid for cereal storage location, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Cereal. • 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) + (”Dry ports accessibility” * 0.3) + ("Major Cities Accessibility" * 0.2) + ("Human Population Density" * 0.1) **Data publication:** 2021-10-18 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Maribel Elias](mailto:maribel.elias@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. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Crop Storage Location Score: Cereal (Ethiopia - ~ 1Km)

The raster dataset consists of a 1km score grid for coffee storage location, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Coffee. • 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) + (”Dry ports accessibility” * 0.3) + ("Major Cities Accessibility" * 0.2) + ("Human Population Density" * 0.1) **Data publication:** 2021-10-18 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Maribel Elias](mailto:maribel.elias@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. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Crop Storage Location Score: Coffee (Ethiopia - ~ 1Km)

The raster dataset consists of a 1km score grid for tropical fruits storage location, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse siting: • Supply: Tropical fruit. • 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) + (”Dry ports accessibility” * 0.3) + ("Major Cities Accessibility" * 0.2) + ("Human Population Density" * 0.1) **Data publication:** 2021-10-18 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Maribel Elias](mailto:maribel.elias@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. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Crop Storage Location Score: Tropical fruit (Ethiopia - ~ 1Km)

The raster dataset consists of a 1km score grid for vegetables storage location, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Vegetables. • 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) + (”Dry ports accessibility” * 0.3) + ("Major Cities Accessibility" * 0.2) + ("Human Population Density" * 0.1) **Data publication:** 2021-10-18 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Maribel Elias](mailto:maribel.elias@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. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Crop Storage Location Score: Tropical fruit (Ethiopia - ~ 1Km)

The raster dataset consists of a 1km score grid for wheat storage location, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Wheat. • 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) + (”Dry ports accessibility” * 0.3) + ("Major Cities Accessibility" * 0.2) + ("Human Population Density" * 0.1). **Data publication:** 2021-10-18 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Maribel Elias](mailto:maribel.elias@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. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Crop Storage Location Score: Wheat (Ethiopia - ~1Km)

The raster dataset consists of a 1km score grid for dairy processing industry facilities siting, 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 cattle dairy production intensification potential, defined using crop production, livestock production systems and cattle distribution. The score is achieved by processing sub-model outputs that characterize logistical factors: 1. Supply - Feed, livestock production systems, dairy distribution. 2. Demand - Human population density, large cities, urban areas. 3. Infrastructure - Transportation network (accessibility) It consists of an arithmetic weighted sum of normalized grids (0 to 100): (”dairyIntensification” * 0.4) + ("Crop Production" * 0.3) + ("Cost to dry ports" * 0.2) + (“Major Cities Accessibility” * 0.1) **Data publication:** 2021-10-18 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Maribel Elias](mailto:maribel.elias@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. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2 4. Global Livestock Production Systems v.5 2011. 5. OpenStreetMap. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Dairy Processing Location Score: Cattle (Ethiopia - ~ 1Km)