This layer shows the percentage of children under 5 years of age (in 2019) who are underweight, based on low weight-for-age. Underweight is a composite indicator capturing both chronic and acute undernutrition. It is useful for identifying vulnerable populations and informing multisectoral strategies to improve child health, food access, and caregiving practices.

Priority areas are identified using local coefficients estimated through Geographically Weighted Regression (GWR), which measures the spatially varying relationship between access to electricity and four nutrition indicators among children under five: stunting, wasting, severe wasting, and underweight.

The tool highlights locations where the local association between electricity access and the selected nutrition indicator is both negative and statistically significant. In this application, priority areas are further restricted to locations where electricity access is below 80%, ensuring that the results remain relevant for policy intervention.

The identified areas are classified as having strong, moderate, or mild associations according to the magnitude of the local GWR coefficient. This classification helps users determine the appropriate intensity of intervention. The spatially varying coefficients therefore allow users to identify areas where limited electricity access is most strongly associated with poorer nutritional outcomes among children under five, supporting targeted interventions, programme planning, and place-based development strategies.

Priority areas are identified using local coefficients estimated through Geographically Weighted Regression (GWR), which measures the spatially varying relationship between access to sanitation and four nutrition indicators among children under five: stunting, wasting, severe wasting, and underweight.

The tool highlights locations where the local association between sanitation access and the selected nutrition indicator is both negative and statistically significant. In this application, priority areas are further restricted to locations where sanitation access is below 80%, ensuring that the results remain relevant for policy intervention.

The identified areas are classified as having strong, moderate, or mild associations according to the magnitude of the local GWR coefficient. This classification helps users determine the appropriate intensity of intervention. The spatially varying coefficients therefore allow users to identify areas where limited sanitation access is most strongly associated with poorer child nutrition outcomes, supporting targeted interventions, programme planning and place-based development strategies.

Priority areas are identified using local coefficients estimated through Geographically Weighted Regression (GWR), which measures the spatially varying relationship between access to piped water and four nutrition indicators among children under five: stunting, wasting, severe wasting, and underweight.

The tool highlights locations where the local association between piped water access and the selected nutrition indicator is both negative and statistically significant. In this application, priority areas are further restricted to locations where piped water access is below 80%, ensuring that the results remain relevant for policy intervention.

The identified areas are classified as having strong, moderate, or mild associations according to the magnitude of the local GWR coefficient. This classification helps users determine the appropriate intensity of intervention. The spatially varying coefficients therefore allow users to identify areas where limited piped water access is most strongly associated with poorer nutritional outcomes among children under five, supporting targeted interventions, programme planning, and place-based development strategies.

This layer shows the percentage of children under 5 experiencing severe wasting (in 2019) — the most critical form of acute malnutrition. These children face a significantly higher risk of mortality and require immediate therapeutic support. This indicator helps prioritize emergency nutrition interventions and monitor high-risk areas in need of health and food system strengthening.

This layer shows the prevalence of wasting among children under 5 years of age (in 2019), a sign of acute undernutrition indicated by low weight-for-height. Wasting reflects recent and severe weight loss, often caused by food shortage, infection, or crisis conditions. Mapping areas with high wasting rates can help target urgent humanitarian and nutrition assistance.

This layer shows the percentage of stunting among children under 5 years of age, a form of chronic undernutrition reflected in low height-for-age. Stunting can result from prolonged food insecurity, poor maternal health, or inadequate early childhood care. Areas with high stunting rates indicate long-term development challenges and can help guide interventions aimed at improving nutrition, health services, and water and sanitation infrastructure.

Malaria, a life-threatening disease transmitted by mosquitoes, affects millions of people worldwide. Treatment and prevention efforts such as insecticide-treated mosquito nets and rapid diagnostic tests significantly decreased the number of malaria cases in Africa. This layer displays the change in malaria rates (%) from 2000 to 2015 among children in Sub-Saharan Africa.

This layer provides estimates of the percentage of children aged 2 to 10 years (PfPR2–10) with detectable Plasmodium falciparum parasites in 2022. The estimates are generated using geostatistical models based on point prevalence surveys, routine surveillance data, and a wide range of geospatial covariates representing mosquito habitat and environmental conditions. The data are available annually from 2000 onward, covering all malaria-endemic countries, at a spatial resolution of 5 × 5 km.

This dataset provides estimates of the total number of under-5 children per grid cell, for Uganda. The data come from the WorldPop R2025A, version v1 release. The dataset is available in GeoTIFF format at a spatial resolution of 30 arc-seconds (approximately 1 km at the equator). All layers use the WGS84 Geographic Coordinate System.

Prevalence of underweight (weight-for-age <-2 standard deviation from the median of the World Health Organization (WHO) Child Growth Standards) among children under 5 years of age. Survey estimates are based on standardized methodology using the WHO Child Growth Standards. Global and regional estimates are based on methodology outlined in UNICEF-WHO-The World Bank: Joint child malnutrition estimates - Levels and trends (UNICEF/WHO/WB 2012).

Percentage of children 24-35 months who had received all age appropriate vaccinations. The lower the number of vaccinated children, the more beneficial decentralised renewable energy solutions may be in providing electricity to store vaccines in proper refrigerators.

Whether you’re monitoring crops, modelling green energy installations or soil sealing, combatting loss of natural resources or just helping countries meet their Sustainable Development Goals, chances are high that you’ll need an accurate and spatially detailed map on land cover and land use. Earth Observation satellites, like those from EU’s flagship programme Copernicus, are key to providing such maps, at a global scale, with free and open access. Land cover maps represent spatial information on different types (classes) of physical coverage of the Earth's surface, e.g. forests, grasslands, croplands, lakes, wetlands. Dynamic land cover maps include transitions of land cover classes over time and hence captures land cover changes. This dataset shows the land cover for the baseline year 2019 with a discrete classification in 23 classes aligned with UN-FAO's Land Cover Classification System.

Malaria, a life-threatening disease transmitted by mosquitoes, affects millions of people worldwide. This layer highlights malaria rates among children age 2 to 10 in Sub-Saharan Africa in 2015.

This layer shows the percentage of children under 6 months who are exclusively breastfed, meaning they receive only breast milk without any additional food or drink. Exclusive breastfeeding is a key determinant of child survival, growth, and development.

Percentage of children in a school attendance age (approximately 3-17 years old depending on the country) that have internet connection at home. Also in this case the indicator relates to the potential educational impact of electrification on children and young people.

This layer shows the estimated number of deaths per 1,000 live births among children under 5 years of age (in 2017) — a key indicator of child survival and overall health system performance. High under-5 mortality rates often reflect limited access to essential health services, clean water, sanitation, and adequate nutrition. Expanding decentralized systems can play a key role in improving child survival outcomes in underserved areas.

This dataset compiles geophysical models detailing the thickness of the Earth's outer layers, ranging from surface sedimentary basins down to the base of the lithosphere. The collection serves a dual purpose: it provides broad, global baseline data for sediment distribution, while also offering high-resolution, integrated regional models specifically focused on the African plate.

Dataset Contents:

I. Global Baselines

• Global Sediment Thickness (Laske & Masters, 2013): The global sediment thickness baseline extracted from the CRUST1.0 model. This provides broad, foundational coverage across both continental and oceanic domains. (Values expressed in km).
• Global Oceanic Sediment Thickness (Straume et al., 2019): A high-resolution, 5-arc-minute grid detailing total sediment thickness specifically for the world's oceans and marginal seas, compiled from extensive new data and previous work. (Values expressed in m).

II. African Regional Models

• Combined Sediment Thickness (Africa): A synthesized, optimized sedimentary thickness map for Africa and its surrounding regions. This layer intelligently merges the Laske & Masters (2013) and Straume et al. (2019) maps, prioritizing the Straume data wherever overlaps occur to maximize resolution. (Values expressed in m).
• Crustal Thickness (Globig et al., 2016): An Africa-specific crustal thickness map derived from the integrated modeling of elevation, geoid data, and rigorous thermal analysis. (Values expressed in km).
• Lithosphere Thickness (Globig et al., 2016): An Africa-specific lithospheric thickness map, complementing the crustal layer, derived using the same integrated elevation, geoid, and thermal modeling approach. (Values expressed in km).

These layers are part of the Geothermal Atlas for Africa developed within the LEAP-RE project

The INFORM Risk Index 2021 is a composite model structured into a hierarchy of dimensions, categories, and components. All scores are normalized on a scale of 0 to 10 (10 being the highest risk).

Below is the clear breakdown of the index's architecture based on your descriptions:

1. The Composite Index

The top-level score representing a country's overall risk of humanitarian crisis. It is used to assign countries into Risk Classes:

• Low: ≤3.5
• Medium: 3.5−5.0
• High: 5.0−6.5
• Very High: ≥6.5

2. Dimension: Vulnerability

This dimension measures the predisposition of a population to be affected by a hazard based on economic, political, and social characteristics.

Sub-Index: Socio-Economic Vulnerability

An aggregate index covering three main components of systemic instability:

• Development Deprivation: Measures poverty using the Human Development Index (HDI) and Multidimensional Poverty Index (MPI).
• Inequality: Captures the dispersion of human development, including the Gender Inequality Index and the Gini Coefficient.
• Aid Dependency: Measures economic instability through reliance on public aid, development assistance, and remittances.

Sub-Index: Vulnerable Groups

Captures social groups with limited access to care and heightened susceptibility:

• Uprooted People: Tracks refugees, IDPs, and returnees.
• Children Under 5: Specifically monitors child malnutrition and mortality rates.
• Recent Shocks: Factors in populations already affected by natural disasters in the last 3 years.
• Food Security: Captures trends in chronic hunger and nutritional balance.
• Health Conditions: Monitors the prevalence of pandemics (HIV, TB, Malaria) and neglected tropical diseases.

3. Dimension: Lack of Coping Capacity

This dimension measures the ability of a country to manage and recover from disasters through its infrastructure and government effort.

Sub-Index: Institutional Capacity

Measures the "soft" infrastructure of a country's disaster management:

• Governance: Captures government effectiveness, resilience-building, and political integrity (lack of corruption).
• DRR Implementation: Quantifies how well Disaster Risk Reduction activities are actually put into practice.

Sub-Index: Infrastructure

Measures the "hard" assets and systems available during a crisis:

• Communication: Evaluates the efficiency of early warning dissemination and the literacy/education of the recipients.
• Physical Connectivity: Assesses the accessibility of transport and the quality/availability of water and sanitation services.
• Access to Healthcare: Captures health system strength, including physician density and maternal/infant health services.

This dataset compiles global and regional models of the Mohorovičić discontinuity (Moho) depth from 2019 to 2022, providing key insights into crustal thickness and lithospheric boundaries. All layer values are expressed in kilometers (km). It includes the Moho Depth (Finger et al., 2022) map, which details the boundary derived from S-wave seismic tomography data, alongside its corresponding Moho Depth Uncertainty layer to highlight spatial confidence and data reliability. Additionally, it features the Global Moho Depth (Szwillus et al., 2019) map, derived using a nonstationary kriging algorithm (Risser & Calder, 2017), which serves as an excellent comparative baseline for structural, geophysical, and tectonic analysis.

These layers are part of the Geothermal Atlas for Africa developed within the LEAP-RE project

• Basins (greater than 2km): Boundaries of sedimentary basins in Africa with sedimentary thicknesses of at least 2 km. Contains geological information about the sedimentary basins such as basin name, class/type and age. Created by extracting data from open sources into a series of shapefiles and rasters containing information on culture, geology, geothermal and geophysical data.
• Basins Nyberg & Howell (2015): Boundaries active sedimentary basin/depositional centers of Africa categorised by basin structure and climate-zone.
• Basins Robertson (2019): Boundaries of sedimentary basins in Africa. Part of the Robertson new ventures product suite. Includes information on the name, geometry, type and the hydrocarbon exploration status of a given basin.
• Basins Evenick (2021): Boundaries of sedimentary basins in Africa compiled from previous work. The sedimentary basins have been revised to simplify the borders of sedimentary basins, as the definition of a basin (boundary) is often contested. Includes the name, geometry, type, age and evaporite content of a given basin.

Priority areas are identified using local coefficients estimated through Geographically Weighted Regression (GWR), which measures the spatially varying relationship between access to water infrastructure and health using the indicator of under 5 mortality. The tool highlights locations where the local association between access to piped water and the selected health indicator is both negative and statistically significant. The identified areas are classified as having strong, moderate, or mild associations according to the magnitude of the local GWR coefficient. This classification helps users determine the appropriate intensity of intervention. The spatially varying coefficients therefore allow users to identify areas where limited access to piped water is most strongly associated with higher child mortality, supporting targeted interventions, programme planning, and place-based development strategies.