This dataset demarcates the boundaries of the education districts across South Africa. Education districts differ greatly in terms of their geographical size, number of schools served and the poverty profile of communities they encompass. There are 86 districts at present, most of which are based on district municipalities or sub-divisions thereof.

The Free State and the Northern Cape have used district municipalities as the basis for education district demarcation, so they are purposely limited by the number of district municipalities defined in their respective provinces. Therefore, these two provinces have perfect alignment between education districts and district municipality boundaries. Likewise, the North West has named their education districts after district municipalities but adopted slightly different boundaries to them, creating an overlap between the education districts and district municipalities. The remaining provinces have split some of there district municipalities to create unique education districts. Mpumalanga has split one of its three district municipalities into two education districts (creating four eductaion disstricts for the province) while Limpopo split each of its five district municipalities into two education districts to reach a total of 10 education districts. Provinces with large metropolitan areas such as Gauteng, Western Cape and KwaZulu-Natal have tended to split their metropolitan areas into two or more districts. The Eastern Cape province diverted from the norm and adopted local municipalities as the basis for demarcation of education districts, making it the province with the most education districts as local municipality boundaries are far more numerous than district municipalities. The education districts of the Eastern Cape perfectly align with the local municipality boundaries of the province.

This data was originally sourced from the Department of Basic Education (DBE) https://www.education.gov.za/Programmes/EMIS/EGISInteractiveData.aspx accessed on 10 July 2020.

The tabular and visual dataset focuses on South African basic education and provides insights into the distribution of schools and basic population statistics across the country. This tabular and visual data are stratified across different quintiles for each provincial and district boundary. The quintile system is used by the South African government to classify schools based on their level of socio-economic disadvantage, with quintile 1 being the most disadvantaged and quintile 5 being the least disadvantaged. The data was joined by extracting information from the debarment of basic education with StatsSA population census data. Thereafter, all tabular data and geo located data were transformed to maps using GIS software and the Python integrated development environment. The dataset includes information on the number of schools and students in each quintile, as well as the population density in each area. The data is displayed through a combination of charts, maps and tables, allowing for easy analysis and interpretation of the information.

MAPS OverviewThe MAPS experiment measures the global distribution of carbon monoxide (CO) mixing ratios in the free troposphere. Because of MAPS' previous flights on board the Space Shuttle, Earth system scientists now know that carbon monoxide concentrations in the troposphere are highly variable around the planet, and that widespread burning in the South American Amazon Basin and southern cerrados, the African savannahs,and the Australian grasslands and ranches are major sources of carbon monoxide in the southern hemisphere and tropical troposphere. The 1994 flights of the MAPS experiment provided CO measurements that show seasonal changes in CO emissions, sources, transports, and chemistry. InstrumentThe MAPS instrument is based on a technique called gas filter radiometry. Thermal energy from the Earth passes through the atmosphere and enters the viewport of the downlooking MAPS instrument. Carbon monoxide and nitrous oxide (N2O) in the atmosphere produce unique absorption lines in the transmitted energy. The energy which enters the MAPS instrument is split into three beams. One beam passes through a cell containing CO and falls onto a detector. This CO gas cell acts as a filter for the effects of CO present in the middle troposphere. A second beam falls directly onto a detector without passing through any gas filter. The difference in the voltage of the signals from these two detectors can be used to determine the amount of CO present in the atmosphere at an altitude of 7-8 km. During the dedicated Earth-Observing Space Shuttle mission in 1994, MAPS measured the distribution of carbon monoxide in the middle troposphere to evaluate CO sources and chemistry, and to evaluate the seasonal and interannual variation of this key atmospheric trace gas. Interpretation of these measurements will help us to better understand the atmosphere and the consequences that human activities initiate in global climate change. A third beam of the incident energy passes through a cell containing N2O and falls onto a detector. This N2O gas cell acts as a filter for the effects of N2O present in the atmosphere. The global distribution of N2O is well known, so the N2O signal can be used to detect the presence of clouds in the field of view and to correct the simultaneous CO measurement for systematic errors in the data. SRL2 GoalsThe MAPS SRL-2 mission took place during the Northern Hemisphere summer when global biomass burning is nearing its maximum. The southern hemispheric burning of savanna and agricultural grasslands can be extensive in central and southern South America and in nearly all of Africa, south of the equator. The tundra regions of the northern boreal zone also are approaching the peak burning season. Other regions may experience scattered fire events as a result of lightning strikes during severe thunderstorms. The primary goal of the MAPS experiment on SRL-2 is to provide a near global survey of the distribution of tropospheric carbon monoxide during northern hemisphere summer. The secondary goal is to determine how the global distribution of carbon monoxide changes over the course of the mission.SL2 SummaryThe high values of carbon monoxide are associated with extensive areas of smoke and haze that have been observed by the Endeavour (STS-68) flight crew. The smoke results from fires that are burning in the continental regions. The carbon monoxide is carried by tropical thunderstorms to the altitudes (2 to 10 miles above the surface) at which it is measured by the MAPS instrument. The data that are available from MAPS SRL2 include a 5 by 5 degree gridded box (MAPS_SRL2_5X5_HDF) and a second by second data product (MAPS_SRL2_COSEC_HDF). These data sets are available from the Langley DAAC.

The dataset contains the point locations of South Africa Social Security Agency (SASSA) pay points. The dataset is a third party entity published by the South African National Treasury (NT) through the NT's Vulekamali Data Portal (https://geo.vulekamali.gov.za/#category-2). Vulekamali is an easily accessible online data portal that was developed by NT in conjunction with Imali Yethu to promote budget transparency and public participation.

The physical location of the SASSA pay points are deemed as a service point and form part of the service point consortium that includes clinics, schools, post offices and police stations. The South African constitution mandates national, provincial and local governments to provide accessible services to the public through these service points. SASSA paypointss offer eligible South Africans social security services in the form of various grants.

The data was originally accessed and downloaded as a spreadsheet on 01 November 2020 by SAEON. The spreadsheet was then modified into a tidy table, alphabetically arranging the SASSA pay point locations by name in ascending order. This tidy dataset, containing the centroids (latitude and longitude coordinates) of the pay points was then used to create a shapefile of the SASSA pay point locations.

This data set contains a subset for southern Africa of Wilson and Henderson-Sellers' Global Vegetation & Soils 1-degree data. The data are available in both ASCII GRID and binary image files formats. The Wilson, Henderson-Sellers' Global Vegetation and Soils data set is an archive of soil type and land cover data derived for use in general circulation models (GCMs). The data were collated from natural vegetation, forestry, agriculture, land use, and soil maps. The data are archived at 1 degree latitude x 1 degree longitude resolution and include data for soil, soil reliability, primary vegetation, secondary vegetation, and land cover reliability. There are approximately fifty land cover classifications which include categories for agricultural and urban uses. The inclusion of secondary vegetation type is particularly useful is areas with cover types which may have a fragmented distribution, such as urban development. The soil type data are classified using climatically important properties for CGMs and provide color (light, medium, or dark), texture, and drainage quality of the soil. The land cover data are compatible with the soils data forming a coherent and consistent data set. Reliability data rank the land cover data on a 1 to 5 scale from high to low reliability. The soil reliability is ranked as one of the following: high, good, moderate, fair, or poor. Recommendations for the use of these data as well as more detailed information can be found in: Wilson, M.F. and A. Henderson-Sellers, 1985. A Global Archive of Land Cover and Soils Data for Use in General Circulation Climate Models. Journal of Climatology, Vol.5, 119-143. More data set information can be found at: ftp://daac.ornl.gov/data/safari2k/vegetation_wetlands/vegsoils_wilhend/comp/wilhend_readme.pdf.