South Africa GIS data

uav imagery by RP Agri, acquired 2025-04-27 to 2025-04-28, 3.3 cm per pixel, published on OpenAerialMap.

ArcGIS Image ServiceTime Interval: Annual With Hourly Timesteps Version: 1 Spatial Resolution: 3 km Time Extent: 2005 to 2025 (With 24 Hours) Projection: GCS WGS84 Extent: Regional | South Africa Other Formats: OGC WMS, OGC WCS, REST CollectionThis collection contains South African OT detection climatological statistics averaged Annually with Hourly Timesteps.

South Africa Seasonal Full 2005-2019 IR OT Day Count

Hourly counts of infrared-detected anvil clouds, aggregated into annual climatology with hourly timesteps, for South Africa. Published by mstisdal.

Annual climatology of 99th percentile cloud top height at hourly timesteps, part of a South Africa convection climatology. Published by mstisdal.

saf_ann_full_cloud_top_height_99th_percentile properties

ArcGIS Image ServiceTime Interval: Monthly Version: 1 Spatial Resolution: 3 km Time Extent: 2005-01-15 to 2025-12-15 Projection: GCS WGS84 Extent: Regional | South Africa Other Formats: OGC WMS, OGC WCS, REST CollectionThis service provides monthly gridded overshooting cloud top detection, specifically the Overshooting Top Days/Month over South Africa.

South Africa Climatology Full 2005 2019 IR Anvile Day Count

ArcGIS Image ServiceTime Interval: Full Climatology With Hourly Timesteps Version: 1 Spatial Resolution: 3 km Time Extent: 2005 to 2025 (With 24 Hours) Projection: GCS WGS84 Extent: Regional | South Africa Other Formats: OGC WMS, OGC WCS, REST CollectionThis collection contains South African CTH detection climatological diurnal statistics averaged over 2005-2025

ArcGIS Image ServiceTime Interval: Seasonal Climatology With Hourly Timesteps Version: 1 Spatial Resolution: 3 km Time Extent: 2005 to 2025 (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 CollectionThis collection contains South African OT detection climatological diurnal statistics averaged over: Dec-Feb, Mar-May, Jun-Aug, Sep-Nov 2005-2025

saf_annual_hr_ir_tropopause_difference_99th_percentile

ArcGIS Image ServiceTime Interval: Monthly Version: 1 Spatial Resolution: 3 km Time Extent: 2005-01-15 to 2025-12-15 Projection: GCS WGS84 Extent: Regional | South Africa Other Formats: OGC WMS, OGC WCS, REST CollectionThis service provides monthly gridded overshooting cloud top detection, specifically the Anvil Cloud Days/Month over South Africa.

ArcGIS Image ServiceTime Interval: Seasonal Climatology With Hourly Timesteps Version: 1 Spatial Resolution: 3 km Time Extent: 2005 to 2025 (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 CollectionThis collection contains South African Anvil Cloud Days per Year detection climatological diurnal statistics averaged over: Dec-Feb, Mar-May, Jun-Aug, Sep-Nov 2005-2025

geoBoundaries ADM0 boundaries for South Africa: 1 units, representing 2020. Source: OCHA ROSEA, South African Municipal Demarcation Board.

A 100 m spatial resolution geotiff of motorised travel time in seconds to nearest health facility in South Africa. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming driving speeds of local public transport options on all-weather roads/asphalt roads and walking speeds on all other roads, tracks, paths and land cover types. A full description is available here a video description of the data is also available here Projection system is: GCS_WGS_1984 EPSG 4326 for all

This dataset shows the list of operating health facilities. Attributes included: Name,Nature of Facility, Activities, Lat, Long

# South Africa Floods: Damage Proxy Maps, Jun 2022 The **Earth Observatory of Singapore – Remote Sensing Lab (EOS-RS)** produced these damage proxy maps for South Africa Floods (Jun 2022) from synthetic aperture radar (SAR) satellite images. Each map is a separate ZIP below with its GeoTIFF, a KMZ for Google Earth, the PNG figures and a text file describing how it was made and how to read it. ## Maps in this dataset - **EOS-RS Damage Proxy Map: South Africa, Floods, 4 Jun 2022, v0.9 wbd** (superseded) - **EOS-RS Damage Proxy Map: South Africa, Floods, 4 Jun 2022, v0.9** **More map details and files:** **More map details and files:**

The International Best Track Archive for Climate Stewardship (IBTrACS) project is the most complete global collection of tropical cyclones available. It merges recent and historical tropical cyclone data from multiple agencies to create a unified, publicly available, best-track dataset that improves inter-agency comparisons. Fields available: SID: A unique storm identifier (SID) assigned by IBTrACS algorithm. ISO_TIME: Time of the observation in ISO format (YYYY-MM-DD hh:mm:ss) BASIN: Basin of the current storm position SUBBASIN: Sub-basin of the current storm position NATURE: Type of storm (a combination of the various types from the available sources) NUMBER: Number of the storm for the year (restarts at 1 for each year LAT: Mean position - latitude (a combination of the available positions) LON: Mean position - longitude (a combination of the available positions) WMO_WIND: Maximum sustained wind speed assigned by the responsible WMO agency WMO_PRES: Minimum central pressure assigned by the responsible WMO agency.

Education facilities: schools, kindergartens, colleges, and universities tagged via `amenity` or `building`. Useful for school-access mapping, education planning, and resource allocation. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity, building - Part of the country exports on HDX by HOT

Points of interest: public-facing amenities, shops, tourist attractions, and landmarks tagged via `amenity`, `shop`, `tourism`, or `man_made`. Useful for general POI reference, urban analysis, and basemap detail. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity, shop, tourism, man_made - Part of the country exports on HDX by HOT

Water features across the area: rivers, streams, canals, lakes, ponds, and water bodies tagged with `waterway`, `water`, or `natural=water`. Useful for flood-risk modelling, watershed analysis, and environmental basemap context. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: waterway, water, natural - Part of the country exports on HDX by HOT

Cultural and historic places: museums, galleries, theatres, places of worship, monuments, and historic sites tagged via `tourism`, `amenity`, `historic`, or `heritage`. Useful for heritage protection, cultural mapping, and tourism context. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: tourism, amenity, historic, heritage - Part of the country exports on HDX by HOT

Financial services: banks, ATMs, post offices, money transfer agents, and microfinance branches tagged via `amenity`. Useful for financial-inclusion mapping, cash-distribution planning, and economic analysis. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity - Part of the country exports on HDX by HOT

Building footprints across the area: every feature tagged `building=*`, from residential homes to hospitals, schools, and places of worship. Useful for population estimation, damage assessment, and urban planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: building - Part of the country exports on HDX by HOT

Health facilities: hospitals, clinics, pharmacies, doctors, and dentists tagged via `amenity` or `healthcare`. Useful for service-access analysis, emergency response, and healthcare gap mapping. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity, healthcare - Part of the country exports on HDX by HOT

Settlements and residential areas: named cities, towns, villages, suburbs, and residential land (`place=*` and `landuse=residential`). Useful for demographic context, settlement-level targeting, and population analysis. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: place, landuse - Part of the country exports on HDX by HOT

Railway lines and stations tagged `railway=*`: rail lines, tram tracks, and stations. Useful for transport accessibility analysis and basemap context. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: railway - Part of the country exports on HDX by HOT

Road network across the area tagged `highway=*`: motorways, residential streets, service roads, footways, and trails. Useful for routing, accessibility analysis, and evacuation planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: highway - Part of the country exports on HDX by HOT

Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a biophysical dimensionless quantity (its values range from 0/no absorption to 1/total absorption) used to assess the greenness and health of vegetation. FAPAR anomalies can be used as an indicator to detect and monitor the impacts of agricultural drought on the growth and productivity of vegetation. The data is presented in 10-day time composite of the Visible Infrared Imaging Radiometer Suite (VIIRS).

GLIDEnumbers are GLobal unique IDEntifiers for Disasters. Each disaster is registered in a record that contains a GLIDEnumber that uniquely identifies it and allows information to be interoperable across multiple disaster information sources, both structured and textual. Accessing disaster information can be a time consuming and laborious task. Not only is data scattered but frequently identification of the disaster can be confusing in countries with many disaster events and across borders. To address both of these issues, Asian Disaster Reduction Center (ADRC) proposed a globally common Unique ID code for disasters. Among the institutions using actively GLIDEnumbers are UN-OCHA (ReliefWeb, FSCC, HDX), UNDRR, UNDP, WMO, IFRC, FAO, the European Commission and the Centre for Research on the Epidemiology of Disasters (CRED), among many others. Country datasets are only produced for countries with at least one GLIDE event since 1 January 2022. The global dataset contains all events available from the GLIDE API, including those not assigned to a country.

A 1 km raster of population for South Africa, where each cell value represents the number of people. Published by Orion Terrametrics.

A 100 m raster of population for South Africa for 2015, where each cell value represents the number of people. Published by Orion Terrametrics.

Point locations of health facilities in South Africa including hospitals, health centres, clinics, dispensaries and health posts. Each feature records the facility name, type and ownership. Use this layer to map healthcare coverage, identify service gaps, plan facility distribution, and support health sector analysis.

South Africa Seasonal Hourly 2005-2019 IR Anvil Hour Count

This dataset contains administrative polygons grouped by country (admin-0) with the following subdivisions according to Who's On First placetypes: - macroregion (admin-1 including region) - region (admin-2 including state, province, department, governorate) - macrocounty (admin-3 including arrondissement) - county (admin-4 including prefecture, sub-prefecture, regency, canton, commune) - localadmin (admin-5 including municipality, local government area, unitary authority, commune, suburb) The dataset also contains human settlement points and polygons for: - localities (city, town, and village) - neighbourhoods (borough, macrohood, neighbourhood, microhood) The dataset covers activities carried out by Who's On First (WOF) since 2015. Global administrative boundaries and human settlements are aggregated and standardized from hundreds of sources and available with an open CC-BY license. Who's On First data is updated on an as-need basis for individual places with annual sprints focused on improving specific countries or placetypes. Please refer to the [README.md](https://github.com/whosonfirst/whosonfirst-sources/blob/main/sources/README.md) file for complete data source metadata. Refer to our [blog post](https://whosonfirst.org/blog/2023/05/31/shapefiles/) for explanation of field names. Data corrections can be proposed using [Write Field](https://writefield.nextzen.org/), an web app for making quick data edits. You’ll need a Github.com account to login and propose edits, which are then reviewed by the Who's On First community using the Github pull request process. Approved changes are available for download within 24-hours. Please contact WOF admin about bulk edits.

South Africa Climatology Full IR Tropopause Difference 99th Percentile