Fire Frequency (2000 - 2016)
The Fire Frequency 2000 - 2016 dataset was derived from the NASA Fire Information for Resource Management System (FIRMS) The MODIS NRT active fire products (MCD14DL) are processed using the standard MOD14/MYD14 Fire and Thermal Anomalies product. Each MODIS active fire location represents the center of a 1km pixel that is flagged by the algorithm as containing one or more fires within the pixel. The dataset is from MODIS Collection 6. The fire and thermal anomalies for the 2000 - 2016 monitoring period were grouped according to the number of fire anomalies present within each 500m grid cell. This was then used to provide an idication of the number of fire events that occured in each cell during the 2000 - 2016 monitoring period.
   
Population Age Distribution
The data was derived from the 2011 national census. Population age was obtained from the census small area layer data and grouped into the following categories: (i) 0-5 Juvenile, (ii) 6-18 School-going, (iii) 19-30, 31-50, 51-65 Working population, (iv) 66-80 Elderly and (v) 81-102 Frail. A 500m grid was used to map the dwelling frame distribution for South Africa. The resulting dataset was then intersected with the small area layer for the purpose of esmating the area percentages of dwellings within each grid cell. The area percentages for the grid cells were multiplied with the corresponding census count for each small area in order to map population exposure by age group.
   
Percentage of the Workforce Employed in the Informal Sector
The data was derived from the 2011 national census. The number of individuals employed in the informal sector were obtained from the census small area layer data. A 500m grid was used to map the dwelling frame distribution for South Africa. The resulting dataset was then intersected with the small area layer for the purpose of esmating the area percentages of dwellings within each grid cell. The area percentages for the grid cells were multiplied with the corresponding census count for each small area in order to map the workforce employed in the informal sector.
   
Percentage of Households with no Rubbish Disposal
The data was derived from the 2011 national census. The number of households with no rubbish disposal were obtained from the census small area layer data. A 500m grid was used to map the dwelling frame distribution for South Africa. The resulting dataset was then intersected with the small area layer for the purpose of esmating the area percentages of dwellings within each grid cell. The area percentages for the grid cells were multiplied with the corresponding census count for each small area in order to map households with no rubbish disposal.
   
Household Toilet Facilities
The data was derived from the 2011 national census. The number of households using pit or flush were obtained from the census small area layer data. A 500m grid was used to map the dwelling frame distribution for South Africa. The resulting dataset was then intersected with the small area layer for the purpose of esmating the area percentages of dwellings within each grid cell. The area percentages for the grid cells were multiplied with the corresponding census count for each small area in order to map households using pit or flush toilets.
   
Density of Road Infrastructure
The dataset presents the density of road infrastructure by 500m grid cell for South Africa. Data is calculated as length of infrastructure in m, categorised by road type. Input data and road categorisation are both based on OpenStreetMap data, downloaded from the official site. These polylines were intersected with the 500m grid for South Africa, and the length of road per grid cell calculated after conversion to an equal area projection.
   
Number of dwellings in South Africa
The dataset depicts the number of dwellings in South Africa. The count for the number of dwellings per 500m grid cell is presented in a log-10 scale. The dataset was created by intersecting the dwelling frame dataset produced by StatsSA with a 500m grid for South Africa.
   
Seismic Intensities
Data processed by Council for GeoScience to show areas of intensity of seismic activity in South Africa.
   
Seismic Intensity Lines
Data processed by Council for GeoScience to show seismic intensity lines for South Africa.
   
Soils Susceptible to Water Erosion

   
Soils Susceptible to Wind Erosion

   
Soils Susceptible to Acidification

   
SKEP Framework for action
Combination of irreplaceability and vulnerability value per planning units. Irreplaceability captures the biodiversity significance of each planning unit in terms of vegetation types and expert features. Vulnerability reflects the potential of future transformation based on urban, crop, ostrich, and mining potential. The Framework field in the dataset has the following meanings: Combination of irreplaceability and vulnerability. Values from 1 to 6 characterise areas of low irreplaceability values (by increasing order of vulnerability; 1: none, 6: high vulnerability). Values from 11 to 15 characterise areas of medium irreplaceability values (by increasing order of vulnerability). Values from 101 to 105 characterise areas of high irreplaceability values (by increasing order of vulnerability) The purpose of this data was to help in setting priority areas for conservation in the SKEP region. Irreplaceability was calculated using C-Plan. Area-based targets were assigned for each biodiversity feature. Vulnerability was developed using expert knowledge. Topology was checked using ARCINFO software. Additions by Western Cape Nature Conservation Board: 24 January 2003, Glynnis Barodien (Conservation Planning Unit) recalculated areas and perimeters using Xtools in Lambert projection.
   
Wave power atlas for South Africa
The spatial dataset is provided as a point shapefile and indicates the average annual and seasonal wave power (kW/m of wave crest). Model outputs were produced on a 500m resolution (nested numerical grid approach) and written out at the 7m and 15m bathymetry contour lines. False Bay is the exception, with output on a 200m resolution. The numerical code, Simulating WAves in the Nearshore (SWAN) was used for the present study. Averages are based on stationary, coastal, spectral wave model computations. The simulation period is at a three-hourly resolution spanning from 1997 until 2013. Saturated, wind wave computations and swell computations were executed separately. The combined product presented here were thus the result of total wave energy addition. The wave power averages are also presented in terms of coastal wave exposure classifications. Through this classification the South African coastline can thus readily be understood in terms of total and seasonal experienced wave exposure. These range from fully sheltered to fully exposed and is based on widely accepted literature. These data were originally produced during a Research and Development contract between the Council for Scientific and Industrial Research (CSIR) and the Department of Environmental Affairs (DEA). The scope of the project was to determine the coastal wave run-ups for the entire South African coastline (Theron et al., 2014*). These data were then used as input for post-processing and thus producing the wave power atlas data for South Africa, as presented here. *Theron, A., Rossouw, M., Rautenbach, C., Page, P., von Saint Ange, U., van Niekerk, L., & Luck-Vogel, M. (2014). DEA-CSIR Coastal Vulnerability Phase 2 Wave Report.
   
Wave power exposure for South Africa
The spatial dataset is provided as a line shapefile and indicates the average annual and seasonal wave power (kW/m of wave crest). The original model outputs (The wave power atlas for South Africa, doi: 10.15493/DEFF.10000003) were written out at the 7m and 15m bathymetry contour lines using the numerical code, Simulating WAves in the Nearshore (SWAN). Averages are based on stationary, coastal, spectral wave model computations. The simulation period is at a three-hourly resolution spanning from 1997 until 2013. Saturated, wind wave computations and swell computations were executed separately. The combined product presented here were thus the result of total wave energy addition. The wave power averages are presented here in terms of coastal wave exposure classifications. Through this classification the South African coastline can thus readily be understood in terms of total and seasonal experienced wave exposure. These range from fully sheltered to fully exposed and is based on widely accepted literature. Tidal fluctuations were not taken into account.
   
District Health Barometer 2018/19
The data are a reformatted version of the 2018/19 District Health Barometer (DHB) health indicator dataset provided by the HST. The intention of this publication was to provide an overview of the delivery of selected healthcare services in the public health sector across the provinces, districts and local municipalities/sub-districts of South Africa. The DHB has been an annual publication since 2005. The main focus of the 2018/19 publication is the Sustainable Development Goals (SDGs)a and the Universal Health Coverage (UHC)b index. Data are drawn from the electronic District Health Information Software (WebDHIS), the Ideal Clinic Realisation and Maintenance system, Statistics South Africa (Stats SA) surveys, the National Treasury Basic Accounting System (BAS), the Personnel Administration (PERSAL) system, the TIER.Net for Tuberculosis (TB) and antiretroviral (ART) data, the Electronic Drug-resistant Tuberculosis Register (EDRWeb), the National Income Dynamics Study (NiDS), and other National Department of Health (NDoH) information systems. The publication seeks to highlight inequities in health outcomes, health-resource allocation and health delivery, and to track the efficiency of health processes, across all provinces and districts. Modifications were made to the data table provided in line with Hadley Wickem`s `tidy data` standards for statistical research (10.18637/jss.v059.i10). The dates of the indices were also standardised to represent a single year in the YYYY format and a column of municipal codes was allocated to each row in line with the coding from the South African Municipal Demarcation Board. Lastly, centroids of the political areas described (national, provincial, district municipal, and local municipal areas) were added as applied in a final column.
   
Landslide susceptibility of SA

   
Swelling Clays

   
Soils Susceptible to Wind Erosion

   
SDG 1.2.1 Percentage of population living below the national poverty line (FPL <R400 per month), by sex and age from the 2011 census
Sustainable development goal sdg 1.2.1 (% population below national poverty line) for the year 2011 dasymetrically mapped to a 500m grid using the 2008 spot dwelling count. The data provides a percentage of the population per category living below the food poverty line of R400 per month and is further disaggregated according to gender and and age categories "0-5";"6-18";"19-30";"31-50";"51-65";"66-80";"81-120".
   
Sectoral Gross Value Added (GVA) for agriculture, forestry and fishing (SIC1) as a driver from 1995 to 2016
The South African Gross Geographic Value Added (GVA) data for each economic sector per year since 1995 to 2016 was supplied by Quantec to the CSIR built environment. This data was disaggregated to a 500m grid using a dasymetric mapping algorithm (https://gap.csir.co.za/gap/images/documents/developing-a-geo-data-frame-using-dasymetric-mapping-principles-to-facilitate-data-integration). A linear regression was then fitted to the GVA dataset and the Slope of the regression was used as an indicator for exposure, the R2 value was converted to a percentage to provide a measure of probability. An Average GVA value was calculated as was the sample standard deviation as a measure of economic uncertainty.
   
Drought Index
The data was derived from NIR and SWIR bands of the NDWI dataset in Sentinel hub using the equations published by Gao, 1996. For more information, see: Gao, B, 1996. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58 (3), 257-266.
   
Biome Shift 1990-2100
The dataset depicts the expected shift in biome distrubution from 1990 to 2100 due to climate change. Data includes detail on current vegetation distribiution at a relativey fine resolution (500m), as well as the expected biome shift. The distribution of standard biomes were simulated using a dynamic vegetation model – the aDGVM- designed specifically for tropical and subtropical African ecosystems. Biomes distribution is simulated using climate models. Simulations were forced with projected changes in climate given by the Max Planck Institute for Meteorology`s (Hamburg) ECHAM5 IPCC projections with atmospheric CO2 from IPCC (2007) SRES A1B projections. Biomes distribution are output for 1990 and 2100. BOtn datasets were published as part of the South African Carbon Sinks Atlas. vegetation distribution was obtained from the SANBI vegetation map.
   
Percentange of Households in Informal and Traditional Dwellings
The data was derived from the 2011 national census. The household counts for informal and traditional dwellings were obtained from the census small area layer data. A 500m grid was used to map the dwelling frame distribution for South Africa. The resulting dataset was then intersected with the small area layer for the purpose of esmating the area percentages of dwellings within each grid cell. The area percentages for the grid cells were multiplied with the corresponding census count for each small area in order to map households by the type of dwelling.
   
Percentage of Households Using Alternatives to Electricity for Cooking
The data was derived from the 2011 national census. The number of individuals using alternatives sources to electricity for cooking were obtained from the census small area layer data. A 500m grid was used to map the dwelling frame distribution for South Africa. The resulting dataset was then intersected with the small area layer for the purpose of esmating the area percentages of dwellings within each grid cell. The area percentages for the grid cells were multiplied with the corresponding census count for each small area in order to map household energy use for cooking.
   
Terrestrial Ecosystem Threat Status
The mapping of terrestrial ecosystems was based on the South African vegetation map, national forest types or high irreplaceability forest patches or clusters systematically identified by recognized by DWAF. In addition some priority areas identified in a provincial systematic biodiversity plan in Gauteng, KwaZulu Natal and Mpumalanga provincial conservation authorities were also included. This shapefile uses the South African Vegetation Map as a base file. Most of the original attributes of the original vegetation map are retained. For the purpose of the NBA all vegetation types are classified as ecosystems. The “map code” column and vegmap avl can be used to view the South African vegetation map. To view the threatened ecosystems the Eco_Status field together with the “Vegmap_with_threatened_ecosystems” layer file to display threat status. The “TE name” field shows the name of the Threatened ecosystem. Names for the threatened ecosystems come from the South African vegetation map, the DAFF forest types or a provincial conservation plan. The “TE_SOURCE” column will indicate where the ecosystem name and description is from. Six criteria were developed for identifying threatened terrestrial ecosystems. Of these six criteria, four (A, C, D1 and F) were used and the remaining two (B and E) are dormant owing to lack of data. Two of the criteria (A and D) were split into sub-criteria. The six criteria for threatened terrestrial ecosystems are: Criterion A1: Irreversible loss of natural habitat Criterion A2: Ecosystem degradation and loss of integrity Criterion B: Rate of loss of natural habitat Criterion C: Limited extent and imminent threat Criterion D1: Threatened plant species associations Criterion D2: Threatened animal species associations Criterion E: Habitat Fragmentation Criterion F: Priority areas for meeting explicit biodiversity targets as defined in a systematic biodiversity plan The criteria and thresholds for critically endangered, endangered and vulnerable ecosystems and explained in more detail in the document on Threatened Ecosystems in South Africa: General Information. Note: the data represents the original extent of listed ecosystems; in other words, natural areas which have been converted to agriculture, mining and urban areas have been included. It is important to note that while the original extent of each listed ecosystem has been mapped, a basic assessment report in terms of the EIA regulations is only triggered in remaining natural habitat within each ecosystem and not in portions of the ecosystem where natural habitat has already been irreversibly lost. See and refer to the following reports for more information: Threatened Ecosystems in South Africa: General Information (available on BGIS website) Threatened Ecosystems in South Africa: Descriptions and Maps (available on BGIS website) National Biodiversity Assessment 2011 Terrestrial Report. (available on BGIS website) Mucina, L & Rutherford, M.C.(eds) 2006. The vegetation of South Africa, Lesotho and Swaziland. Strelitzia 19. South African National Biodiversity Institute. Pretoria
   
Household Sources of Water
The data was derived from the 2011 national census. Datasets for household sources of water were obtained from the census small area layer data and grouped into the following categories: (i) Municipal, (ii) Dam/Spring/Stream and (iii) Households that do not source water from piped water schemes. A 500m grid was used to map the dwelling frame distribution for South Africa. The resulting dataset was then intersected with the small area layer for the purpose of esmating the area percentages of dwellings within each grid cell. The area percentages for the grid cells were multiplied with the corresponding census count for each small area in order to map household sources of water.
   
Emergency Infrastructure
The dataset shows the location of facilities that can be used as evacuation sites during disaster events. The dataset is based on OpenStreetMap data, downloaded from the official site: https://www.openstreetmap.org.
   
Total Population living below the food poverty line (<R400 per month) by sex and age from the 2011 census
Total population from the 2011 census data, dasymetrically mapped to a 500m grid using the 2008 spot dwelling count. The data provides an indication of the total number of people per category living below the food poverty line of R400 per month and is further disaggregated according to gender and and age categories "0-5";"6-18";"19-30";"31-50";"51-65";"66-80";"81-120".
   
Total population by sex and age from the 2011 census
Total population from the 2011 census data, dasymetrically mapped to a 500m grid using the 2008 spot dwelling count. The data provides an indication of the total number of people per 500m grid cell and is further disaggregated according to gender and and age categories "0-5";"6-18";"19-30";"31-50";"51-65";"66-80";"81-120".