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.

Percentage of schools in Africa reporting to have no electricity. The lower the education facilities with access to electricity the greater the potential for decentralised renewable energies to improve electricity access in these facilities and thus educational outcomes.

This layer represents the predicted likelihood that a given settlement is electrified, with values ranging from 0 (no electricity) to 1 (fully electrified). The data are derived from the High Resolution Electricity Access (HREA) dataset, which combines satellite observations of night-time lights with population settlement layers to estimate electricity access. Areas with low predicted electrification can help identify where decentralized renewable energy solutions may be most impactful in closing electricity access gaps.

Percentage of healthcare facilities with electricity access in selected countries. Information on electricity access for healthcare facilities has been collected in the electricity access health facility database (EHFDB). The lower the healthcare facilities with access to electricity the greater the potential for decentralised renewable energies to improve electricity access in these facilities and thus healthcare outcomes.

This layer shows the percentage of people with access to any improved sanitation facility, including both piped systems (such as sewer or septic tanks) and non-piped improved options (like ventilated improved pit latrines or composting toilets), in 2017. Improved sanitation is essential for public health, reducing the spread of waterborne diseases and supporting safe and dignified living conditions. Areas with low access highlight the need for expanded basic services and targeted investments in infrastructure.

Despite a high total population, most parts of the African continent are sparsely populated, with almost 60% living in non-urban areas. Diesel generators have long been the traditional solution to decentralized electrification needs. For off-grid applications, they present lower up-front capital costs per kilowatt installed; however, the dramatic increase of fuel costs in recent years and the cost of transport to remote areas greatly diminish the low capital cost advantage of the diesel option. Even in the cases when the initial investments were subsidized, the high yearly fuel cost are born by the users which often results in early termination of its use. The map shows the spatial variance of the electricity costs per kWh delivered by an off-grid diesel generator.

Priority areas are identified using local coefficients estimated through Geographically Weighted Regression (GWR), which measures the spatially varying relationship between access to electricity and education using the indicator of percentage of population with secondary education .
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 higher education, 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 electricity and gender using the indicator of proportion of women with secondary school. 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 lower proportion of women at secondary school, 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 electricity and health using the indicator of under-5 mortality.

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 higher mortality among children under five, supporting targeted interventions, programme planning, and place-based development strategies.

In Sub-Saharan Africa, medium- and low-voltage data are often non-existent, uncompleted, or unavailable. This is a challenge for practitioners working on the electricity access agenda, power sector resilience or climate change adaptation. This layer presents the spatial extent of the existing and planned electricity grid (high, medium, low voltage level) compiled using multiple sources that enumerate elements of the existing transmission and distribution network.

Distance from existing or planned electric grid lines (LV; MV; HV).
Unit: km

• Binary ORC Power Plant;
• Flash Power Plant;

• base case scenario;
• upside scenario, modelling a better fluid flow;
• up up side, modelling both better fluid flow and higher underground temperatures.

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.

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.