3iSolution

Con base en los escenarios de predicción del Instituto de Hidrología, Meteorología y Estudios Ambientales (IDEAM), se identificaron las zonas con susceptibilidad a inundaciones y deslizamientos durante el segundo trimestre de 2025. El archivo de Excel que acompaña esta publicación contiene ocho hojas con información desagregada sobre personas, hogares, viviendas, zonas de cultivos, instituciones educativas y centros de salud localizados en zonas susceptibles. Además, para cada municipio se incluye el nivel de riesgo estimado, calculado mediante el Índice Municipal de Riesgo Ajustado por Capacidades, desarrollado por el Departamento Nacional de Planeación (DNP).

Datos sobre estimación de personas que pueden ser víctima por desplazamiento individual, masivo y confinamientos y probabilidad de presentarse eventos de desplazamiento y confinamiento para el primer semestre del año 2025. El archivo de Excel incluye 3 hojas con información sobre: • Estimación_Desplaza_individual: Estimación de personas que pueden ser víctimas por eventos de desplazamiento individual y probabilidad de presentarse eventos de desplazamiento individual en el primer semestre del año 2025. • Estimación_Desplaza_masivo: Estimación de personas que pueden ser víctimas por eventos de desplazamiento masivo y probabilidad de presentarse eventos de desplazamiento masivos en el primer semestre del año 2025. • Estimación_Confinamiento: Estimación de personas que pueden ser víctimas por eventos de confinamiento y probabilidad de presentarse eventos de confinamientos en el primer semestre del año 2025.

This database contains regional estimates of Facebook users based on data from the Facebook Marketing API. It includes information on the number of individuals aged 18 and older who have accessed Facebook in the past month, with data separated by region. These estimates are intended for trend identification and triangulation purposes and are not designed to match official census data or other government sources. This data can be used as a proxy of internet access. It should be noted that there could be duplicates across different regions, and the data is anonymized by Meta.

This dataset provides insights into early childhood education enrollment trends in Ethiopia by capturing Gross Enrollment Rate (GER) and Net Enrollment Rate (NER) for children aged 4 to 6 years, disaggregated by region and sex. It serves as a crucial resource for policymakers, educators, and researchers working to improve access to early education and assess disparities across demographic groups. Additional information at: Ministry of Education

The HIV Estimates Dataset for Ethiopia (2024) provides a comprehensive analysis of the HIV epidemic at the zonal level, offering critical insights into trends, intervention impacts, and projections. This dataset is a vital resource for policymakers, researchers, and public health professionals seeking to understand and address the HIV/AIDS situation in Ethiopia. Developed through rigorous validation and consultation with key stakeholders—including the Ethiopian Ministry of Health, UNAIDS, USAID, WHO, and the CDC—this dataset serves as a crucial tool in the ongoing fight against HIV/AIDS in Ethiopia. It facilitates targeted interventions, policy formulation, and progress tracking toward epidemic control.

The dataset available at the Ethiopian Health Data Analytics and Visualization Hub provides information on malaria incidence rates across various regions (Adm 1) in Ethiopia from 1990 to 2019.

The dataset available at IGME provides national estimates of neonatal mortality rates in Ethiopia. These estimates are part of the United Nations Inter-agency Group for Child Mortality Estimation's (UN IGME) efforts to assess child mortality trends at more localized levels, offering insights into regional disparities and progress.​

The dataset available at the Ethiopian Health Data Analytics and Visualization Hub provides information on under-five mortality rates across various regions (Adm 1) in Ethiopia from 1990 to 2020.

This dataset provides Palmer Drought Severity Index (PDSI) values for Ethiopia from 2020 to 2024, offering insights into drought conditions across the country. The PDSI is a widely used drought index that measures deviations in soil moisture conditions from the long-term average, helping to identify both drought and wet conditions. Unlike traditional PDSI calculations that rely on the Thornthwaite method for estimating potential evapotranspiration (ET), this dataset utilizes a more physically based Penman-Monteith reference ET, which accounts for: Solar radiation Temperature Humidity Windspeed Interpretation of PDSI Values: PDSI between -0.5 and 0.5 → Near-normal soil moisture conditions PDSI < -4 → Extreme drought PDSI > 4 → Very wet conditions

This dataset contains information collected from a survey of four Rapid Response Mechanisms (RRMs) operating in Ethiopia. The data includes details on the RRM's management, the types of emergencies they address, their operational timelines, donor information, implementation partners, response effectiveness, coordination levels, and information management practices. The dataset aims to provide a comprehensive overview of RRM operations to identify strengths, weaknesses, and areas for improvement.

This dataset represents populated places in Ethiopia at the locality level, including villages, kebeles, towns, and other administrative divisions. Each entry is associated with XY coordinates and is integrated with Ethiopian administrative boundaries. The data originates from the National Geospatial-Intelligence Agency (NGA) and is accessible through their GeoNames platform(https://geonames.nga.mil/geonames/GNSData/).The dataset includes various types of populated areas such as: villages, kebeles and towns. This dataset is crucial for various applications, including urban planning to assist in the development of infrastructure and services based on population distribution, for research, and on disaster management to support humanitarian efforts by identifying populated areas during crises.

This dataset includes the spatial locations of health facilities across Ethiopia, along with their associated attribute information.

HydroBASINS represents a series of vectorized polygon layers that depict sub-basin boundaries at a global scale. The goal of this product is to provide a seamless global coverage of consistently sized and hierarchically nested sub-basins at different scales (from tens to millions of square kilometers), supported by a coding scheme that allows for analysis of catchment topology such as up- and downstream connectivity. HydroBASINS has been extracted from the gridded HydroSHEDS core layers at 15 arc-second resolution. More info: HydroBASINS

This dataset represents the Level 5 delineation of the Tekeze River Basin in Ethiopia, as defined by the HydroBASINS framework. The Level 5 (L5) subdivision provides a mid-scale segmentation of the Tekeze Basin, capturing key hydrological structures while maintaining computational efficiency. More information at Hydrosheds - Hydrobasins

This dataset represents the Level 5 delineation of the Shabelle River Basin in Ethiopia, as defined by the HydroBASINS framework. The Level 5 (L5) subdivision provides a mid-scale segmentation of the Shabelle Basin, capturing key hydrological structures while maintaining computational efficiency. More information at Hydrosheds - Hydrobasins

HydroRIVERS represents a vectorized line network of all global rivers that have a catchment area of at least 10 km² or an average river flow of at least 0.1 m³/sec, or both. HydroRIVERS has been extracted from the gridded HydroSHEDS core layers at 15 arc-second resolution. The global coverage of HydroRIVERS encompasses 8.5 million individual river reaches with an average length of 4.2 km, representing a total of 35.9 million km of rivers globally. HydroRIVERS only includes a limited amount of (mostly geometric) attribute information, such as the river reach length, the distance from upstream headwaters and ocean outlet, the river order, and an estimate of long-term average discharge. Every river reach is also co-registered to the sub-basin of the HydroBASINS database in which it resides (via a shared ID). More information: Hydrosheds - Hydrorivers

This data set includes tree cover extent, aboveground live woody biomass (AGB) stocks and densities, annual tree cover loss, annual forest greenhouse gas (GHG) emissions, average annual forest CO2 removals (sequestration), and average annual net GHG flux at the country and first (state, province) sub-national levels. Tree cover loss and emissions are available as annual data for 2001-2023. Emissions, removals and net flux are available as annual averages for 2001-2023. Tree cover is available for 2000 and 2010. Aboveground biomass stocks and densities are available for 2000. The tree cover data was produced by the University of Maryland's GLAD laboratory in partnership with Google (Hansen et al. 2013). Tree cover loss is defined as “stand replacement disturbance” which is considered to be clearing of at least half of tree cover within a 30-meter pixel. Carbon densities, emissions, removals, and net flux (megagrams CO2e/yr) are from Harris et al. 2021. The emissions data quantifies the amount of carbon dioxide emissions to the atmosphere where forest disturbances have occurred, and includes CO2, CH4, and N2O and multiple carbon pools. Removals includes the average annual carbon captured by aboveground and belowground woody biomass in forests. Net flux is the difference between average annual emissions and average annual removals; negative values are net sinks and positive values are net sources. Tree cover loss, tree cover extent, and AGB stock and density are presented for percent canopy cover levels >10%, 15%, 20%, 25%, 30%, 50% and 75% in 2000. Emissions, removals, and net flux are presented only for percent canopy cover levels >30%, 50%, and 75% in 2000, plus areas with tree cover gain between 2000 and 2020 (Potapov et al. 2022) regardless of percent canopy cover. We recommend that you select your desired percent canopy cover level before your analysis and use it consistently throughout analyses. The Global Forest Watch website uses a >30% canopy cover threshold as a default for all statistics.

This dataset contains the population distribution of primary and secondary schools in Ethiopia, disaggregated by sex.

The NDRMC has identified 150 hotspot woredas (woredas being administrative districts in Ethiopia) as critical areas where humanitarian needs are most acute. These hotspot woredas are selected based on various vulnerability indicators, such as food insecurity, climate-related shocks (e.g., droughts, floods) and other socio-economic factors that contribute to humanitarian challenges. The identification of these hotspot woredas is crucial for planning and resource allocation under the HRP 2025 , which is a comprehensive strategy aimed at addressing the humanitarian needs of populations affected by crises. The Population in Need (PiN) is a key metric used in HRPs to estimate the number of people requiring humanitarian assistance. By focusing on these 150 hotspot woredas, the NDRMC aims to ensure that resources and interventions are targeted effectively to reach the most vulnerable communities.

These datasets and maps show the number of IDP sites per woreda in the Somali region, using DTM Site Assessment Round 36 as a reference. These IDP sites include various settlement types: collective centers, dispersed settlements, host communities, planned camps, and spontaneous camps/sites. More detailed information can be found at the following link: https://dtm.iom.int/ethiopia#producttabs.

This dataset provides aggregate statistics on official flows, which are the sum of official development assistance (ODA) and other official flows (OOF) disbursements to the recipient country shown. More information available at Data Explorer Data

This dataset contains commonly observed spelling variations of woredas and zones as identified in reports from humanitarian partners. These variations have been standardized and aligned with the official P-codes issued by OCHA in 2024 to ensure consistency and accuracy in data reporting and analysis.

[Initial analysis] The database contains information about buildings identified by Google outside of settlements recognized by GRID3. It serves as a proxy for identifying potential new settlements or areas of urban growth. The analysis incorporates two levels of clustering: one at a distance of 500 meters and another at 100 meters. The 500-meter clustering helps identify broader areas where multiple buildings are concentrated, potentially indicating the presence of a new settlement or an expansion of an existing one. This distance threshold allows for the initial identification of potential new settlement areas. The 100-meter clustering provides a more granular view, highlighting tighter clusters of buildings within the broader 500-meter areas. This finer level of clustering aids in distinguishing individual settlement units or neighborhoods within the larger potential settlement areas. By combining these two levels of clustering, the database facilitates a comprehensive analysis of potential new settlements or urban growth areas. It enables researchers, urban planners, and policymakers to identify and prioritize areas for further investigation, resource allocation, or development planning.

Prevalence of acute malnutrition and mortality rate due to acute malnutrition.

Climate data estimated using satellite images using Google Earth Engine (GEE) and Amazon Sagemaker with Geospatial Capabilities.