Ethiopia GIS data

geoBoundaries ADM1 boundaries for Ethiopia: 11 units, representing 2016. Source: Open Africa, Code for Ethiopia. opendefinition.org/licenses/cc-by/

geoBoundaries ADM2 boundaries for Ethiopia: 74 units, representing 2016. Source: Open Africa, Code for Ethiopia. opendefinition.org/licenses/cc-by/

geoBoundaries ADM0 boundaries for Ethiopia: 1 units, representing 2016. Source: Open Africa, Code for Ethiopia. opendefinition.org/licenses/cc-by/

**ADAM ID: FL-20231109-ETH-00** Flood covering 205753.0 sq m on Nov 09 2023 in Ethiopia. It impacted 95847 people.

This data set has been collected from official databases and contains: 1- Youth Population (15-35) for 2018 and 2063 2- Proportion of Youth Members of Parliaments (Lower and Upper house) 3- Ratification of African Youth Charter 4- Existence of Youth Policy

**ADAM ID: FL-20231114-ETH-01** Flood covering 1016365.0 sq m on Nov 14 2023 in Ethiopia. It impacted 534273 people.

**ADAM ID: FL-20231121-ETH-01** Flood covering 418451.0 sq m on Nov 21 2023 in Ethiopia. It impacted 295186 people.

Open and free data for assessing the human presence on the planet. The Global Human Settlement Layer (GHSL) project produces global spatial information, evidence-based analytics, and knowledge describing the human presence on the planet. The GHSL relies on the design and implementation of spatial data processing technologies that allow automatic data analytics and information extraction from large amounts of heterogeneous geospatial data including global, fine-scale satellite image data streams, census data, and crowd sourced or volunteered geographic information sources. The JRC, together with the Directorate-General for Regional and Urban Policy (DG REGIO) and Directorate-General for Defence Industry and Space (DG DEFIS) are working towards a regular and operational monitoring of global built-up and population based on the processing of Sentinel Earth Observation data produced by European Copernicus space program. In addition, the EU Agency for the Space Programme (EUSPA) undertakes activities related to user uptake of data, information and services.

NDVI Anomaly is a geospatial metric used to measure how current vegetation conditions deviate from what is considered "normal" or average for a specific location and time period. It helps identify abnormal vegetation growth, whether better or worse than expected, and is especially valuable in agriculture, drought monitoring, food security, and environmental management. The dataset used for this analysis is the MOD13Q1 V6.1 product, which provides a Vegetation Index (VI) value on a per-pixel basis. For this study, we used MODIS data from July for the past three years, along with historical data spanning from 2010 to 2020.

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.

These datasets provide Ethiopia’s Vegetation Health Index (VHI) values for the past five years, focusing specifically on the growing season from June to September. The Vegetation Health Index (VHI) is a satellite-derived indicator used to monitor the overall health and stress conditions of vegetation, particularly in relation to drought, heat, and moisture availability. It offers an integrated measure by combining data on vegetation greenness and thermal conditions. VHI is a vital remote sensing index for assessing vegetation condition, drought impacts, and agricultural stress. It combines two indices: the Vegetation Condition Index (VCI), which reflects vegetation greenness, biomass, and vigor; and the Temperature Condition Index (TCI), which indicates thermal conditions including heat and water stress. The long-term dataset analyzed for seasonal VHI spans from 2000 to the present, concentrating on the months of June, July, August, and September, corresponding to the Ethiopian growing season. These time series datasets highlight VHI values for the most recent five growing seasons, enabling direct comparison with current year conditions.

These datasets provide Ethiopia’s Vegetation Condition Index (VCI) values for the past five years, specifically covering the June to September growing season. The VCI is a satellite-derived indicator used to evaluate vegetation health and drought conditions. It distinguishes drought-related stress from other factors affecting vegetation growth. The VCI values range from 0 to 100, where 0% indicates the worst vegetation condition—approaching the historical minimum and signifying severe drought—and 100% indicates optimal conditions, near the historical maximum, reflecting healthy and lush vegetation. The VCI data is obtained from the MODIS satellite. The long-term dataset used for seasonal analysis spans from the year 2000 to the present, focusing on the months of June through September, aligning with Ethiopia’s main growing season. These time series datasets enable the comparison of current vegetation conditions with those of recent growing seasons, supporting drought monitoring and vegetation health assessments.

These datasets provide Ethiopia’s Temperature Condition Index (TCI) values for the past five years, focusing specifically on the growing season (June–September). The Temperature Condition Index is a remote sensing-based indicator used to evaluate vegetation stress resulting from temperature extremes, primarily heat stress. TCI is calculated using Land Surface Temperature (LST) data sourced from the MODIS platform. Higher TCI values reflect cooler and more favorable conditions for plant growth, while lower values are associated with increased heat stress and potential vegetation damage. For long-term seasonal analysis, MODIS LST data from 2000 to the present were utilized, with an emphasis on the June–September period. These time series datasets highlight TCI values from the recent five growing seasons, enabling direct comparison with current year conditions.

A 100 m spatial resolution geotiff of walking travel time in seconds to nearest health facility in Ethiopia. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming walking speeds on all roads, tracks, paths and land cover types. A full description is available here a video description of the data is also available here Projection system is: GCS_WGS_1984 EPSG 4326 for all

A 100 m spatial resolution geotiff of motorised travel time in seconds to nearest health facility in Ethiopia. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming driving speeds of local public transport options on all-weather roads/asphalt roads and walking speeds on all other roads, tracks, paths and land cover types. A full description is available here a video description of the data is also available here Projection system is: GCS_WGS_1984 EPSG 4326 for all

Rainfall anomaly refers to the difference between the observed rainfall and the long-term average for a specific location and time period. It indicates whether rainfall conditions are above or below normal. Positive anomalies represent higher-than-average rainfall, while negative anomalies indicate below-average rainfall. These anomalies are essential for monitoring climate variability, including droughts and floods, and for assessing their impacts on agriculture, water resources, and other sectors. For this analysis, CHIRPS daily rainfall data for Ethiopia were utilized, covering the month of March 2024, 2025 and 2025. Rainfall anomalies for month of March were calculated by subtracting the long-term average (2000–2020) from the observed rainfall values for the same period.

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.

Ethiopia administrative level 0-3 boundaries (COD-AB) dataset version 04. This dataset is structured into 3 levels: - Admin 1: 15 Region - Admin 2: 107 Zone - Admin 3: 1148 Woreda Note: Addis Ababa serves as the national capital and admin1 capital for Addis Ababa and Oromia regions. Dates associated with this dataset: - 01 December 2025: dataset reviewed for accuracy and completeness - 01 January 2025: valid for use by the humanitarian community - 01 November 2025: last edit to the dataset before publishing - 01 August 2023: boundaries created by the source Quality assured, configured, and published by OCHA Field Information Services (FIS) and HDX. Part of the dataset: Global Subnational Administrative Boundaries

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Ethiopia Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2019

Ethiopia Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2020

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Ethiopia Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2019

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Ethiopia Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2019

Ethiopia Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2020

Ethiopia Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2021

Ethiopia Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2022

Ethiopia Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2021

Ethiopia Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2020

Ethiopia Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2022

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.

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

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

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

Water features across the area: rivers, streams, canals, lakes, ponds, and water bodies tagged with `waterway`, `water`, or `natural=water`. Useful for flood-risk modelling, watershed analysis, and environmental 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: waterway, water, natural - 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

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