carry out least-cost electrification studies for Africa and SSA respectively 15, 25. They use NTL and population maps in order to assess where electrified people live and what the electricity consumption of these people are 36. produced and published datasets to assess electrification in SSA. Knowing the spatial characteristics of population distribution is important in many applications such as, electrification planning 2, 3, 16, 25, 26, 27, urban planning 27, 28, 29, 30 and risk management 27, 31, 32, 33, 34, 35. Previous studies highlight the relationship between the presence of NTL and electricity access and consumption 18, 19, 20, 21, 22, 23, 24. NTL maps detect mostly anthropogenic lights, hence providing valuable insight into where there is electricity consumption during night-time hours. One example of this is night-time lights (NTL). Furthermore, GIS and new high resolution satellite imagery can mitigate data gaps that often hamper energy planning in industrializing countries 16. This is possible due to the spatial and temporal dimensions of GIS, which describe how different characteristics change across a study area based on location and time 16, 17. Energy modelling tools utilizing GIS can tailor solutions and actions to different parts of a study area more heterogeneously than traditional modelling frameworks. Geographic Information Systems (GIS) can inform the planning of future energy systems and facilitate rural electrification 12, 13, 14, 15. Extending the grid to rural communities might not be economically attractive and therefore (as budgets are limited) these settlements often remain un-electrified 2, 3. Electricity access inequality is present within the countries of the region, as urban electrification rates tend to be significantly higher than the rural ones 6, 7, 9, 10, 11.
The increase in electrification rate is unevenly distributed, and more than half of the population in Sub-Saharan Africa (SSA) still do not have access to electricity 10. Scholarly 2, 3, 4, 5, 6, 7, 8 and policy literature 9, 10 has indicated that this is a significant challenge, especially for rural communities of industrializing countries. The 2030 Agenda for Sustainable Development has set the target of universal energy access 1 (SDG 7.1). By delineating settlements, this dataset can complement existing raster population data in studies such as energy planning, urban planning and disease response. We find that our modeled national electrification rates are consistent with the rates reported by the World Bank, while the modeled urban/rural classification has 88% accuracy. Results are validated against national electrification rates provided by the World Bank and data from selected Demographic and Health Surveys (DHS). Each cluster includes unique characteristics indicating population, electrification rate and urban-rural categorization. We use open-source data and develop an open-access algorithm tailored for low and middle-income countries with data scarcity issues. Here we suggest a methodology that translates high-resolution raster population data into vector-based population clusters. Rasters indicate population density at predefined spatial resolutions, but are unable to capture the actual shape or size of settlements. However, population distribution in geo-sciences is often represented in the form of pixelated rasters. Human settlements are usually nucleated around manmade central points or distinctive natural features, forming clusters that vary in shape and size.
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