This web map references the live tiled map service from the OpenStreetMap project. OpenStreetMap (OSM) is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information such as free satellite imagery, and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap server: http://www.OpenStreetMap.org. See that website for additional information about OpenStreetMap. It is made available as a basemap for GIS work in Esri products under a Creative Commons Attribution-ShareAlike license.Tip: This service is one of the basemaps used in the ArcGIS.com map viewer and ArcGIS Explorer Online. Simply click one of those links to launch the interactive application of your choice, and then choose Open Street Map from the Basemap control to start using this service. You'll also find this service in the Basemap gallery in ArcGIS Explorer Desktop and ArcGIS Desktop 10.

DATASET: Alpha version 2010 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/). REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available. DATE OF PRODUCTION: January 2013

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.

This map features satellite imagery for the world and high-resolution aerial imagery for many areas. The map is intended to support the ArcGIS Online basemap gallery. For more details on the map, please visit the World Imagery map service description.

The map of potential natural vegetation of eastern Africa (V4A) gives the distribution of potential natural vegetation in Ethiopia, Kenya, Tanzania, Uganda, Rwanda, Burundi, Malawi and Zambia.

The map is based on national and local vegetation maps constructed from botanical field surveys - mainly carried out in the two decades after 1950 - in combination with input from national botanical experts. Potential natural vegetation (PNV) is defined as “vegetation that would persist under the current conditions without human interventions”. As such, it can be considered a baseline or null model to assess the vegetation that could be present in a landscape under the current climate and edaphic conditions and used as an input to model vegetation distribution under changing climate.

Vegetation types are defined by their tree species composition, and the documentation of the maps thus includes the potential distribution for more than a thousand tree and shrub species, see the documentation (https://vegetationmap4africa.org/species.html)

The map distinguishes 48 vegetation types, divided in four main vegetation groups: 16 forest types, 15 woodland and wooded grassland types, 5 bushland and thicket types and 12 other types. The map is available in various formats. The online version (https://vegetationmap4africa.org/vegetation_map.html) and for PDF versions of the map, see the documentation (https://vegetationmap4africa.org/documentation.html). Version 2.0 of the potential natural vegetation map and the woody species selection tool was published in 2015 (https://vegetationmap4africa.org/docs/versionhistory/). The original data layers include country-specific vegetation types to maintain the maximum level of information available. This map might be most suitable when carrying out analysis at the national or sub-national level.

When using V4A in your work, cite the publication: Lillesø, J-P.B., van Breugel, P., Kindt, R., Bingham, M., Demissew, S., Dudley, C., Friis, I., Gachathi, F., Kalema, J., Mbago, F., Minani, V., Moshi, H., Mulumba, J., Namaganda, M., Ndangalasi, H., Ruffo, C., Jamnadass, R. & Graudal, L. 2011, Potential Natural Vegetation of Eastern Africa (Ethiopia, Kenya, Malawi, Rwanda, Tanzania, Uganda and Zambia). Volume 1: The Atlas. 61 ed. Forest & Landscape, University of Copenhagen. 155 p. (Forest & Landscape Working Papers; 61 - as well as this repository using the DOI .

The development of V4A was mainly funded by the Rockefeller Foundation and supported by University of Copenhagen

If you want to use the potential natural vegetation map of eastern Africa for your analysis, you can download the spatial data layers in raster format as well as in vector format from this repository

A simplified version of the map can be found on Figshare . That version aggregates country specific vegetation types into regional types. This might be the better option when doing regional-level assessments.

This data was produced using Targeting Tools – a web-based GIS tool, which matches a suitability criteria that include climate and environmental requirements for each of the forage varieties with a spatial database that’s comprises organic carbon, soil PH, annual precipitation, mean temperature, growing days and elevation data to characterize the suitability.

This dataset contains GIS data and LIB files from an initial wind resource assessment for Ethiopia. For more information please visit the country webpage: https://www.esmap.org/node/55920

Shapefiles for Ethiopia's Administrative boundaries: Regions, Zones and Woredas

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.

Land degradation from gully erosion poses a significant threat to the Erer watershed in Eastern Ethiopia, particularly due to agricultural activities and resource exploitation. Identifying erosion-prone areas and underlying factors using advanced machine learning algorithms (MLAs) and geospatial analysis is crucial for addressing this problem and prioritizing adaptive and mitigating strategies. However, previous studies have not leveraged machine learning (ML) and GIS-based approaches to generate susceptibility maps identifying these areas and conditioning factors, hindering sustainable watershed management solutions. This study aimed to predict gully erosion susceptibility (GES) and identify underlying areas and factors in the Erer watershed. Four ML models, namely, XGBoost, random forest (RF), support vector machine (SVM), and artificial neural network (ANN), were integrated with geospatial analysis using 22 geoenvironmental predictors and 1,200 inventory points (70% used for training and 30% for testing). Model performance and robustness were validated through the area under the curve (AUC), accuracy, precision, sensitivity, specificity, kappa coefficient, F1 score, and logarithmic loss. The relative slope position is most influential, with 100% importance in SVM and RF and 95% importance in XGBoost, while annual rainfall (AR) dominated ANN (100% importance). Notably, XGBoost demonstrated robustness and superior prediction/mapping, achieving an AUC of 0.97, 91% accuracy, 92% precision, and 81% kappa while maintaining a low logloss (0.0394). However, SVM excelled in classifying gully resistant/susceptible areas (97% sensitivity, 98% specificity, and 91% F1 score). The ANN model predicted the most areas with very high gully susceptibility (13.74%), followed by the SVM (11.69%), XGBoost (10.65%), and RF (7.85%) models, while XGBoost identified the most areas with very low susceptibility (70.19%). The ensemble technique was employed to further enhance GES modeling, and it outperformed the individual models, achieving an AUC of 0.99, 93.5% accuracy, 92.5% precision, 97.5% sensitivity, 95.4% specificity, 85.8% kappa, and 94.9% F1 score. This technique also classified the GES of the watershed as 36.48% very low, 26.51% low, 16.24% moderate, 11.55% high, and 9.22% very high. Furthermore, district-level analyses revealed the most susceptible areas, including the Babile, Fedis, Harar, and Meyumuluke districts, with high GES areas of 32.4%, 21.3%, 14.3%, and 13.6%, respectively. This study offers robust and flexible ML models with comprehensive validation metrics to enhance GES modeling and identify gully prone areas and factors, thereby supporting decision-making for sustainable watershed conservation and land degradation prevention.

This data set rerpresents land cover map for the year 2016. This layer was clipped from Sentinel-2 global land cover data

IRC’s GIS unit created this database to allow for a more sustained approach to producing relevant mapping products and geospatial analysis for regions undergoing emergencies, and will fill gaps in information sharing and management between OFDA, IRC, and other implementing partners during emergency response. IRC’s GIS services provide OFDA/Ethiopia with the information and analysis required to monitor the evolution of project / program results and to track project and program impacts across implementing agencies and geographic locations. This dataset contains boundaries of Ethiopian administrative Woredas which are roughly consistent with the actual administrative boundaries for the year 2019. The Woreda boundary contains attribute data (implementing Partners, Situation and Sector Fields)for Hotspot woredas in the nation by the different sectors as of 30 June 2020.

This data set contains land use types for 246 fields in Gera district in Southwestern Ethiopia. I collected this data in 2011 as part of a PhD project. Twenty-one smallholding farmers selected for the study used 213 of these fields while the remaining (33 fields) were fields adjacent to some of the fields used by the sample farmers. In addition to recording the land use in 2011 for all fields, through interview with the land users the land use types in 1991 and 1974 were identified for 151 and 117 fields, respectively. Major events occurred in 1974 (the socialist military government overthrew the last feudal monarch) and 1991 (the socialist military government was itself overthrown) in Ethiopia have helped trigger the interviewed farmers’ memory of past events. Farmers were able to identify the land use types during the three reference years (2011, 1991 and 1974) for 115 fields. Of the 213 fields used by the 21 farmers, I recorded the coordinates of the boundaries of 208 fields with a hand-held GPS. Similarly, I recorded the coordinates of the boundaries of the 33 fields adjacent to some of the studied fields. Using the coordinates and field notes, I have built two shape files in ESRI ArcMap in 2018 showing these fields (208 and 33). Data set also includes some biophysical features, e.g. location of the fields in relation to forest edges, of the fields. Moreover, the data set provides also the family size as well as the gender, age and educational status of the head of the household of the 21 studied farmers. I have reported detail descriptions of the purpose of this study and methods used to select village and household, and the results of the study in my PhD thesis (http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-128537).

Dataset contains land use types for 246 areas in the Gera district in southwestern Ethiopia. Data consists of GIS files with additional data in excel format.

Download high-quality, up-to-date Ethiopia shapefile boundaries (SHP, projection system SRID 4326). Our Ethiopia Shapefile Database offers comprehensive boundary data for spatial analysis, including administrative areas and geographic boundaries. This dataset contains accurate and up-to-date information on all administrative divisions, zip codes, cities, and geographic boundaries, making it an invaluable resource for various applications such as geographic analysis, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including Shapefile, GeoJSON, KML, ASC, DAT, CSV, and GML, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

This dataset has also been updated in the World Database on Protected Areas (WDPA) is the most comprehensive global database on terrestrial and marine protected areas.

IRC Directly implemented Projects intervened in the different part of Ethiopia and refugee camps. The data shows the interventions for the period of June 2024 by detail activities, Sectors and Donors. The programs are emergency responses, refugee portfolios and local community development programs. At June 2024 updates there are 11 sector of interventions, whereby Health & nutrition sector has got the highest (39.6.%) followed by EH/WASH (15.9%), CWI (12.2%) & CP (9.8%). From donors perspective, 13 donors granted the local community projects, while GAVI BHA has got the highest (38.7%) and followed by BHA and USAID-WV (CORE GROUP), 16.2% and 8.1%, respectively. IRC’s GIS team created this database to allow for a more sustained approach to producing relevant mapping products and geospatial analysis for undergoing humanitarian responses and will fill gaps in information sharing and management between Donors. IRC’s GIS services provides information and analysis required to monitor the evolution of project / program results and to track project and program impacts across geographic locations. This dataset contains boundaries of Ethiopian administrative Woredas which are roughly consistent with the actual & updated administrative boundaries which bases CSA 2007 Census survey out puts. The Woreda boundary contains attribute data (, activities, Sector Donors Fields) for IRC Ethiopia for IRC Direct Projects- June 2024

This map is designed to be used as a basemap by marine GIS professionals and as a reference map by anyone interested in ocean data. The map is intended to support the ArcGIS Online basemap gallery. For more details on the map, please visit the Ocean Basemap.

Social distancing is a public health measure intended to reduce infectious disease transmission, by maintaining physical distance between individuals or households. In the context of the COVID-19 pandemic, populations in many countries around the world have been advised to maintain social distance (also referred to as physical distance), with distances of 6 feet or 2 metres commonly advised. Feasibility of social distancing is dependent on the availability of space and the number of people, which varies geographically. In locations where social distancing is difficult, a focus on alternative measures to reduce disease transmission may be needed. To help identify locations where social distancing is difficult, we have developed an ease of social distancing index. By index, we mean a composite measure, intended to highlight variations in ease of social distancing in urban settings, calculated based on the space available around buildings and estimated population density. Index values were calculated for small spatial units (vector polygons), typically bounded by roads, rivers or other features. This dataset provides index values for small spatial units within urban areas in Ethiopia. Measures of population density were calculated from high-resolution gridded population datasets from WorldPop, and the space available around buildings was calculated using building footprint polygons derived from satellite imagery (Ecopia.AI and Maxar Technologies. 2020). These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 project with funding from the Bill and Melinda Gates Foundation and the United Kingdom’s Department for International Development. Project partners included the United Nations Population Fund (UNFPA), Center for International Earth Science Information Network (CIESIN) in the Earth Institute at Columbia University, and the Flowminder Foundation.

The harmonized geological and hydrogeological of the Horn of Africa integrates layers initially provided by the World Bank, which were developed from regional and national maps published by the British Geological Survey (BGS). These foundational layers, including detailed geological and aquifer type and productivity maps, were adapted to align cross-border geological formations and hydrogeological units. Through this harmonization, geological formations were reclassified by stratigraphic age and lithological properties, ensuring consistency in representation across Ethiopia, Djibouti, Kenya, Somalia, and South Sudan. Aquifer types and productivity levels were systematically standardized to reflect groundwater flow mechanisms, such as intergranular or fracture flow, and productivity classifications from very low to very high. The harmonized map employs consistent color schemes and attribute codes, allowing for streamlined GIS integration, cross-border assessments, and enhanced water resource management in the Horn of Africa.

IRC’s GIS unit created this database to allow for a more sustained approach to producing relevant mapping products and geospatial analysis for regions undergoing emergencies, and will fill gaps in information sharing and management between OFDA, IRC, and other implementing partners during emergency response. IRC’s GIS services provide OFDA/Ethiopia with the information and analysis required to monitor the evolution of project / program results and to track project and program impacts across implementing agencies and geographic locations. This dataset contains boundaries of Ethiopian administrative Woredas which are roughly consistent with the actual administrative boundaries for the year 2017.The Woreda boundary contains attribute data (implementing Partners, Situation and Sector Fields) for IRC Ethiopia OFDA Funded Emergency Responses as of 30 Sep 2018.