This scene highlights layers for Johannesburg, South Africa available in ArcGIS to support your work in 3D. Use these layers in conjunction with your own layers to create new scenes focused on a specific topic or area of interest to you.What's in this scene? Terrain: Includes a global 3D terrain layer to provide elevation context. Your layers are placed in relationship to this terrainBasemap: Includes one of the ArcGIS Basemaps regularly used in in your mapping workScene Layers: Includes a layer of 3D buildings to help understand your data within the context of the built environment. The layer is a file type optimized for rendering in 3D.Create your own sceneOpen this item using the Open in Scene Viewer buttonChoose basemap: Select one of the ArcGIS basemaps from the Basemap GalleryAdd your own unique layersCreate slides to direct users to interesting places in your scene - See MoreSave and share the results of your work with others in your organization and the publicFor more see these helpful videosMashup 3D Content Using ArcGIS OnlineAuthor Web Scenes Using ArcGIS Online

In 2005, National Geo-spatial Information noticed a global trend towards digital image acquisition and decided to invest in a digital camera (an Intergraph DMC). Since 2008, all images have been captured digitally with this camera.This meant that the acquisition of the traditional photo-scale had now been replaced with a Ground Sample Distance (GSD). The ground sample distance is the size of 1 pixel on the ground and is influenced by the flying height and focal length.Currently, 12-bit images are captured in RGB (true colour), Near infra-red and Panchromatic, with a GSD of 0.5m They are stored as Tiff files with a JPEG compression of Q=3; with tiles and a full set of overviews. Each DMC image is approximately 7km by 3.8km and they are mosaiced together to fit the 1:10 000 reference sheets after orthorectification. The aim is to capture 40% of the country every 3 years and the remaining areas every 5 years.The current dataset displays imagery captured between 2008 and 2012 and should only be used for reference purposes.

This layer shows the average household size in South Africa in 2023, in a multiscale map (Country, Province, District, Municipality, Main Place, Sub Place, and Small Area). Nationally, the average household size is 3.4 people per household. It is calculated by dividing the household population by total households.The pop-up is configured to show the following information at each geography level:Average household size (people per household)Total populationTotal householdsCount of population by 15-year age incrementsCount of population by marital statusThe source of this data is Michael Bauer Research. The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.Additional Esri Resources:Esri DemographicsThis item is for visualization purposes only and cannot be exported or used in analysis.We would love to hear from you. If you have any feedback regarding this item or Esri Demographics, please let us know.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

This map shows the average household size in South Africa in 2023, in a multiscale map (Country, Province, District, Municipality, Main Place, Sub Place, and Small Area). Nationally, the average household size is 3.4 people per household. It is calculated by dividing the household population by total households.The pop-up is configured to show the following information at each geography level:Average household size (people per household)Total populationTotal householdsCount of population by 15-year age incrementsCount of population by marital statusThe source of this data is Michael Bauer Research. The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.Additional Esri Resources:Esri DemographicsThis item is for visualization purposes only and cannot be exported or used in analysis.We would love to hear from you. If you have any feedback regarding this item or Esri Demographics, please let us know.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

This map shows the purchasing power per capita in South Africa in 2019, in a multiscale map (Country, Province, District, Municipality, Main Place, Sub Place, and Small Area). Nationally, the purchasing power per capita is 54,780 South African Rand. Purchasing Power describes the disposable income (income without taxes and social security contributions, including received transfer payments) of a certain area's population. The figures are in South African Rand (ZAR) per capita.The pop-up is configured to show the following information at each geography level:Purchasing power per capitaPurchasing power per capita by various categoriesCount of households by income quintilesThe source of this data is Michael Bauer Research. The vintage of the data is 2019.Additional Esri Resources:Esri DemographicsPermitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

A hillshade of the 5m resolution Stellenbosch University Digital Elevation Model (SUDEM) accessible online through free Web Mapping Service made available by the University of Stellenbosch.

ArcGIS Image Service

    Time Interval: Seasonal Climatology With Hourly Timesteps


    Version: 1


    Spatial
        Resolution: 3 km


    Time Extent: 2005 to 2019 (Dec-Feb, Mar-May, Jun-Aug, Sep-Nov) (With 24 Hours)


    Projection: GCS WGS84


    Extent: Regional | South Africa


    Other Formats: OGC WMS, OGC WCS, REST







      Collection



          This collection contains South African IR detection climatological diurnal statistics averaged over: Dec-Feb, Mar-May, Jun-Aug, Sep-Nov 2005-2019















  Satellite Mapping and Analysis of Severe Hailstorms (SMASH) Project

This Hailstorm research project seeks to address knowledge gaps in the severe hail climatology using regional
    to global scale satellite observations and provides mechanisms to explore related datasets.

For questions/issues please contact: kristopher.m.bedka@nasa.gov

SMASH AGOL
  Group
| NASA Applied Sciences 
| NASA Disasters Mapping Portal 
| NASA Langley Research Center Science Directorate

ArcGIS Image Service

    Time Interval: Annual Climatology


    Version: 1


    Spatial
        Resolution: 3 km


    Time Extent: 2005 to 2019


    Projection: GCS WGS84


    Extent: Regional | South Africa


    Other Formats: OGC WMS, OGC WCS, REST







      Collection

    This service contains South African IR detection climatological statistics averaged over: Jan-Dec Annually.














  Satellite Mapping and Analysis of Severe Hailstorms (SMASH) Project

This Hailstorm research project seeks to address knowledge gaps in the severe hail climatology using regional
    to global scale satellite observations and provides mechanisms to explore related datasets.

For questions/issues please contact: kristopher.m.bedka@nasa.gov

SMASH AGOL
  Group
| NASA Applied Sciences 
| NASA Disasters Mapping Portal 
| NASA Langley Research Center Science Directorate

South Africa Main Place Boundaries provides a 2023 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online.The 2023 boundaries are provided by Michael Bauer Research GmbH. They are sourced from Statistics South Africa. These were published in October 2023. A new layer will be published in 12-18 months. Other administrative boundaries for this country are also available: Country Province District Municipality SubPlace SmallArea

ArcGIS Image Service

    Time Interval: Full Climatology With Hourly Timesteps


    Version: 1


    Spatial
        Resolution: 3 km


    Time Extent: 2005 to 2019 (With 24 Hours)


    Projection: GCS WGS84


    Extent: Regional | South Africa


    Other Formats: OGC WMS, OGC WCS, REST







      Collection



          This collection contains South African IR detection climatological diurnal statistics averaged over: Jan-Dec 2005-2019















  Satellite Mapping and Analysis of Severe Hailstorms (SMASH) Project

This Hailstorm research project seeks to address knowledge gaps in the severe hail climatology using regional
    to global scale satellite observations and provides mechanisms to explore related datasets.

For questions/issues please contact: kristopher.m.bedka@nasa.gov

SMASH AGOL
  Group
| NASA Applied Sciences 
| NASA Disasters Mapping Portal 
| NASA Langley Research Center Science Directorate

Cadastral Parcels of South Africa including erven, holdings and farm portions

South Africa District Boundaries provides a 2023 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online.The 2023 boundaries are provided by Michael Bauer Research GmbH. They are sourced from Statistics South Africa. These were published in October 2023. A new layer will be published in 12-18 months. Other administrative boundaries for this country are also available: Country Province Municipality MainPlace SubPlace SmallArea

This map shows the purchasing power per capita in South Africa in 2023, in a multiscale map (Country, Province, District, Municipality, Main Place, Sub Place, and Small Area). Nationally, the purchasing power per capita is 62,579 South African rand. Purchasing Power describes the disposable income (income without taxes and social security contributions, including received transfer payments) of a certain area's population. The figures are in South African rand (ZAR) per capita.The pop-up is configured to show the following information at each geography level:Purchasing power per capitaPurchasing power per capita by various categoriesCount of households by income quintilesThe source of this data is Michael Bauer Research. The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.Additional Esri Resources:Esri DemographicsThis item is for visualization purposes only and cannot be exported or used in analysis.We would love to hear from you. If you have any feedback regarding this item or Esri Demographics, please let us know.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

Important Note: This item is in mature support as of October 2023 and will retire in December 2025. A new version of this item is available for your use.This layer shows the Main Place level boundary of South Africa in 2021. The boundaries are optimized to support both visualization and analysis in ArcGIS Online. Each set of boundaries contains name, ID, and/or population counts for context. The layers can be enhanced with additional attributes using data enrichment tools in ArcGIS Online.Additional boundaries for South Africa are available in a hierarchy of geographies that nest into each other. These layers were published in June 2022 and updated every 18 months. South Africa Administrative BoundariesCountryProvinceDistrictMunicipalityMainPlaceSubPlaceSmallArea

Biodiversity is critical for maintaining the function of ecosystems and their services to humans. Using different biodiversity measures can shed light on which areas should be prioritized for biodiversity conservation. Beyond the number of species occurring in an area, rare species that have small range extents should be prioritized in conservation planning, as the conservation opportunities are limited for these range-restricted species especially when comparing them to wide-ranging species. Patterns of species richness and range rarity provide insights about the biogeography of taxa and offer an initial basis for global biodiversity conservation efforts.Species richness is the number of species ranges estimated to overlap in each cell. The data presented in this layer reflect the number of species occurring in each grid cell expressed as a raw value from low (dark blue) to high (yellow).Species ranges were modeled at a 1-km resolution, integrating occurrence data and expert maps, where each distribution was predicted across Africa, and limited to the southern Africa extent. This extent represents the area for which there was complete spatial and taxonomic data coverage for this species group.

Important Note: This item is in mature support as of October 2023 and will retire in December 2025. A new version of this item is available for your use.This layer shows the District level boundary of South Africa in 2021. The boundaries are optimized to support both visualization and analysis in ArcGIS Online. Each set of boundaries contains name, ID, and/or population counts for context. The layers can be enhanced with additional attributes using data enrichment tools in ArcGIS Online.Additional boundaries for South Africa are available in a hierarchy of geographies that nest into each other. These layers were published in June 2022 and updated every 18 months. South Africa Administrative BoundariesCountryProvinceDistrictMunicipalityMainPlaceSubPlaceSmallArea

Important Note: This item is in mature support as of October 2023 and will retire in December 2025. A new version of this item is available for your use.This layer shows the Small Area level boundary of South Africa in 2021. The boundaries are optimized to support both visualization and analysis in ArcGIS Online. Each set of boundaries contains name, ID, and/or population counts for context. The layers can be enhanced with additional attributes using data enrichment tools in ArcGIS Online.Additional boundaries for South Africa are available in a hierarchy of geographies that nest into each other. These layers were published in June 2022 and updated every 18 months. South Africa Administrative BoundariesCountryProvinceDistrictMunicipalityMainPlaceSubPlaceSmallArea

The CRU Time Series 4.05 dataset was developed and has been subsequently updated, improved and maintained with support from a number of funders, principally the UK's Natural Environment Research Council (NERC) and the US Department of Energy. Long-term support is currently provided by the UK National Centre for Atmospheric Science (NCAS), a NERC collaborative centre. Current gridded products (CRU TS) are presented either as ASCII grids, or in NetCDF format. The gridding process used in Brohan et al.. (2006) and earlier publications assigns each station to the 5 degree latitude/longitude box within which it is located. The gridding then simply averages all available station temperatures (as anomalies from 1961-90) within each grid box for each month from 1851. No account is taken of the station's elevation or location within the grid box (anomalies show little consistent dependence on altitude). A more up-to-date location for a station is not important for the gridding, unless a site change were to move the station to an adjacent grid box. In this instance, the data was derived as a subset of the original dataset. CRU publishes the data in NetCDF file format, however for data visualisation purposes the datasets was tranformed into tidy tables, represented in the South African Risk and Vulnerability Atlas (SARVA) by the South African Environmental Observation Network's uLwazi Node. Citation: University of East Anglia Climatic Research Unit; Harris, I.C.; Jones, P.D.; Osborn, T. (2021): CRU TS4.05: Climatic Research Unit (CRU) Time-Series (TS) version 4.05 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2020). NERC EDS Centre for Environmental Data Analysis, 2021. https://catalogue.ceda.ac.uk/uuid/c26a65020a5e4b80b20018f148556681

Biodiversity is critical for maintaining the function of ecosystems and their services to humans. Using different biodiversity measures can shed light on which areas should be prioritized for biodiversity conservation. Beyond the number of species occurring in an area, rare species that have small range extents should be prioritized in conservation planning, as the conservation opportunities are limited for these range-restricted species especially when comparing them to wide-ranging species. Patterns of species richness and range rarity provide insights about the biogeography of taxa and offer an initial basis for global biodiversity conservation efforts.Species richness is the number of species ranges estimated to overlap in each cell. The data presented in this layer reflect the number of species occurring in each grid cell expressed as a raw value from low (dark blue) to high (yellow).Species ranges were modeled at a 1-km resolution, integrating occurrence data and expert maps, where each distribution was predicted across Africa, and limited to the southern Africa extent. This extent represents the area for which there was complete spatial and taxonomic data coverage for this species group.

The Shuttle Radar Topography Mission data at 30meters resolution for South Africa. Referenced tiles were mosaicked and clipped to the extent of the country.