Conservation Tracking

RCoE Action

Topic

Policy

Target

Title

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.

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.

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.

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.

This dataset provides mobile (cellular) network performance metrics in zoom level 16 web Mercator tiles (approximately 610.8 meters by 610.8 meters at the equator). Download speed is collected via the Speedtest by Ookla applications for Android and iOS and averaged for each tile. Measurements are filtered to results containing GPS-quality location accuracy. Speedtest data is used today by commercial mobile network operators around the world to inform network buildout, improve global Internet quality, and increase Internet accessibility. This data can be used for rural and urban connectivity development, to help make the internet better, faster, and more accessible for everyone.

This dataset provides the global geographic distribution of key livestock species—cattle, sheep, and goats—for the year 2010, sourced from the Gridded Livestock of the World (GLW 3) database. Expressed as the total number of animals per pixel at a spatial resolution of 5 minutes of arc, these layers are essential for diverse applications in agricultural socio-economics, food security, environmental impact assessments, and epidemiology.

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.

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.