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African Waterways extracted from OpenStreetMap on the 15th of October 2021
Modern energy services are crucial to human well-being and to a country’s economic development; and yet 1.2 billion people are without access to electricity. It is recognized that the central grid is unlikely to reach many remote areas in the near future: many of these communities will have low electricity consumption, making the costs of extending the grid unaffordable. Given the evident potential of solar energy for African countries, using stand-alone and mini-grid photovoltaic (PV) systems could be an alternative approach to meet the objective of universal electrification. This layer presents the ratio between the optimized battery size (kWh) and PV array size (kWp) for PV mini-grid using Li-ion batteries to store electricity (instead of the traditional lead-acid batteries) based on a high energy consumption pattern (most energy used during day-time). A higher ratio means that the battery size needed to satisfy the same electricity demand produced by the PV system is larger. Used in combination with other sources, these data can help governments, local authorities and non-governmental organisations to investigate the suitability of PV mini-grids for electrification of regions where access to electricity is lacking.
The Great Green Wall (GGW) Initiative comprises 21 African countries and is implemented under the coordination of the African Union.
Climate - in terms of temperature, precipitation and continentality - is a primary determinant in the distribution of vegetation. Salvador Rivas-Martinez and Salvador Rivas-Saenz (2004) developed a global bioclimatic classification system that quantifies key bioclimatic indices reflective of vegetation distributions. These indices can be used to model thermotypes (i.e. hot-cold gradients) and ombrotypes (i.e. wet-dry gradients). Their model was translated into GIS spatial algorithms during modeling of the US ES bioclimate data (Warner et. al. 2008). These spatial models were used (with minor adaptations) with Worldclim climatological data (Hijmans et. al. 2005) to model/map thermotypes and ombrotypes. These two maps were then combined into an isobioclimate map with a total of 157 composite classes. The African isobioclimate data was developed as a primary input dataset for an African Ecological Footprint mapping project undertaken by the U.S. Geological Survey and The Nature Conservancy. The project used a biophysical stratification approach - combining isobioclimate, surficial lithology, land surface forms, landcover, topographic moisture potential, and biogeographic ecological divisions - to generate ecological footprints. The composition and distribution of these unique footprints of the physical and biological landscape was then reviewed by regional vegetation and landscape ecology experts and attributed (labeled) to an intermediate scale African ecosystem class.
The land surface forms were identified using the method developed by the Missouri Resource Assessment Partnership (MoRAP). The MoRAP method is an automated land surface form classification based on Hammond's (1964a, 1964b) classification. MoRAP made modifications to Hammond's classification, which allowed finer-resolution elevation data to be used as input data and analyses to be made using 1 km2 moving window (True, 2002; True et al., 2000). While Hammond's methodology was based on three variables, slope, local relief, and profile type, MoRAP's methodology uses only slope and local relief (True, 2002). Slope is classified as gently sloping or not gently sloping using a threshold value of 8%. Local relief, the difference between the maximum and minimum elevation in a 1km2 neighborhood for analysis, is classified into five classes (0-15m, 16-30m, 31-90m, 91-150m, and >150m). Slope classes and relief classes were subsequently combined to produce eight land surface form classes (flat plains, smooth plains, irregular plains, escarpments, low hills, hills, breaks/foothills, and low mountains). In the implementation for the contiguous United States, Sayre et al. (2009) further refined the MoRAP methodology to identify a new land surface form class, "high mountains/deep canyons", by using an additional local relief class (>400 m). This method was implemented for Africa using a void-filled 90m SRTM elevation dataset which was created from the 30m SRTM elevation data provided by the National Geospatial-Intelligence Agency. In the preliminary output, which had nine land surface form classes (flat plains, smooth plains, irregular plains, escarpments, low hills, hills, breaks/foothills, and low mountains, and high mountains/deep canyons), artifacts were identified over flat desert areas affecting the classification between the two lowest relief classes, "flat plains" and "smooth plains." Since this problem was especially pronounced in areas where the input SRTM elevation data originally had data-voids, the problem could have been caused by anomalies or artifacts in the input data, which resulted from the void-filling processes. Instead of further investigating causes of the problem, the two land surface form classes were combined. In addition, the "low hills" class which had a very low occurrence was combined with the "hills" class. As a result, seven land surface form classes were identified in the final dataset (smooth plains, irregular plains, escarpments, hills, breaks/foothills, low mountains, and high mountains/deep canyons).
The Africa Topographic Potisiont layer classifies the African landscape (including Madagascar, Comoros Islands, and other coastal Islands near continenal Africa) into uplands and lowlands/depressions at a 100m resolution. Published by the USGS in 2009, this dataset was created for the Africa Terrestrial Ecosystems mapping project. The classification is derived from the Compound Topographic Index (CTI) , which integrates slope (from SRTM elevation data) and flow accumulation (from HydroSHEDS data) to model potential water flow.
Raw materials are essential for the sustainable functioning of modern societies and their industries. The European Commission's Raw Materials Information System (RMIS) is developed by the Joint Research Centre (JRC) in cooperation with the DG for Internal Market, Industry, Entrepreneurship and SMEs (GROWTH). The RMIS is the Commission’s reference web-based knowledge platform on non-fuel, non-agricultural raw materials from primary and secondary sources. From gold to natural rubber, including cobalt, cooking coal, construction aggregates (sand, gravel...) and many more, it focuses on both abiotic and biotic materials, covering the entire value chain. This map shows the amount (in USD) of the main non-food, non-energy raw material commodities imported by each African country in 2017.
Raw materials are essential for the sustainable functioning of modern societies and their industries. The European Commission's Raw Materials Information System (RMIS) is developed by the Joint Research Centre (JRC) in cooperation with the DG for Internal Market, Industry, Entrepreneurship and SMEs (GROWTH). The RMIS is the Commission’s reference web-based knowledge platform on non-fuel, non-agricultural raw materials from primary and secondary sources. From gold to natural rubber, including cobalt, cooking coal, construction aggregates (sand, gravel...) and many more, it focuses on both abiotic and biotic materials, covering the entire value chain. This map shows the amount (in USD) of the main non-food, non-energy raw material commodities exported by each African country in 2017.
This dataset shows the African power plants by energy generation type. It includes thermal plants (coal, gas, oil, nuclear, biomass, waste, geothermal) and renewables (hydro, wind, solar). Each power plant is geolocated and entries contain information on plant capacity and generation type.
This dataset shows the African power plants and their installed capacity in MegaWatt (MW). It includes thermal plants (coal, gas, oil, nuclear, biomass, waste, geothermal) and renewables (hydro, wind, solar). Each power plant is geolocated and entries contain information on plant capacity and generation.
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