This work is licensed under the Esri Master License Agreement This work is licensed under the Esri Master License Agreement
View Summary | View Terms of Use
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://wb-sdgs.hub.arcgis.com/maps/UrbanObservatory::what-is-the-most-common-place-of-birth-for-naturalized-us-citizens" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=f503dd82450e4fe8824b3416f701df71", "landingPage": "https://wb-sdgs.hub.arcgis.com/maps/UrbanObservatory::what-is-the-most-common-place-of-birth-among-those-who-are-foreign-born", "title": "What is the most common place of birth among those who are foreign-born?", "description": " This work is licensed under the Esri Master License Agreement
View Summary | View Terms of Use
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://wb-sdgs.hub.arcgis.com/maps/UrbanObservatory::what-is-the-most-common-place-of-birth-among-those-who-are-foreign-born" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=6a70a6ee29cb4d5aac801f581ef6aaf2", "landingPage": "https://wb-sdgs.hub.arcgis.com/maps/UrbanObservatory::what-is-the-most-common-place-of-birth-for-naturalized-us-citizens-charts-version", "title": "What is the most common place of birth for naturalized US citizens? (Charts Version)", "description": "This map shows what country naturalized US citizens were born in using the Charts & Size and Predominance mapping styles. The area with the highest amount of foreign born naturalized US citizens is shown by color. Areas are: Africa, Asia, Europe, Latin America, Northern America, and Oceania. This work is licensed under the Esri Master License Agreement
View Summary | View Terms of Use
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://wb-sdgs.hub.arcgis.com/maps/UrbanObservatory::what-is-the-most-common-place-of-birth-for-naturalized-us-citizens-charts-version" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=1ff463dbeac14b619b9edbd7a9437037", "landingPage": "https://wb-sdgs.hub.arcgis.com/content/esri::viewshed", "title": "Viewshed", "description": "By default, this tool currently works worldwide between 60 degrees north and 56 degrees south based on the 3 arc-second (approximately 90 meter) resolution SRTM dataset. Depending upon the DEM resolution pick by the user, different data sources will be used by the tool. For 24m, tool will use global dataset WorldDEM4Ortho (excluding the counties of Azerbaijan, DR Congo and Ukraine) 0.8 arc-second (approximately 24 meter) from Airbus Defence and Space GmbH. For 30m, tool will use 1 arc-second resolution data in North America (Canada, United States, and Mexico) from the USGS National Elevation Dataset (NED), SRTM DEM-S dataset from Geoscience Australia in Australia and SRTM data between 60 degrees north and 56 degrees south in the remaining parts of the world (Africa, South America, most of Europe and continental Asia, the East Indies, New Zealand, and islands of the western Pacific). For 10m, tool will use 1/3 arc-second resolution data in the continental United States from USGS National Elevation Dataset (NED) and approximately 10 meter data covering Sweden, Costa Rica, Netherlands, Norway, Finland, Denmark, Austria, Spain, Japan Estonia, Latvia, Lithuania, Slovakia, Italy, Northern Ireland, Switzerland and Liechtenstein from various authoritative sources.
To learn more, read the developer documentation for Viewshed or follow the Learn ArcGIS exercise called I Can See for Miles and Miles.
Alternatively, you can make a New ArcGIS Server connection to https://elevation.arcgis.com/arcgis using your ArcGIS Online Organizational account credentials.
Once server connection is made, tools can be accessed and run from Catalog pane like any other geoprocessing tool.
This work is licensed under the Esri Master License Agreement. World Elevation layers are compiled from many authoritative data providers, and are updated quarterly. This map shows the extent of the various datasets comprising the World Elevation dynamic (Terrain,TopoBathy) and tiled (Terrain 3D, TopoBathy 3D, World Hillshade, World Hillshade (Dark)) services.
The tiled services (Terrain 3D, TopoBathy 3D, World Hillshade, World Hillshade (Dark)) also include an additional data source from Vantor's Precision3D covering parts of the globe.
Note: ArcGIS Elevation service, Terrain 3D (for Export) and TopoBathy 3D (for Export) does not include Vantor Precision3D and Airbus WorldDEM4Ortho.
To view the all the sources in a table format, check out World Elevation Data Sources Table.
Topography sources listed in the table are part of Terrain, TopoBathy, Terrain 3D, TopoBathy 3D, World Hillshade and World Hillshade (Dark), while bathymetry sources are part of TopoBathy and TopoBathy 3D only.
Check out elevation layers group for all the elevation content available to ArcGIS users.
Disclaimer: Data sources are not to be used for navigation/safety at sea and in air.
This work is licensed under the Esri Master License Agreement.World Elevation layers are compiled from many authoritative data providers, and are updated quarterly. This map shows the extent of the various datasets comprising the World Elevation dynamic (Terrain,TopoBathy) and tiled (Terrain 3D, TopoBathy 3D, World Hillshade, World Hillshade (Dark)) services.
The tiled services (Terrain 3D, TopoBathy 3D, World Hillshade, World Hillshade (Dark)) also include an additional data source from Vantor's Precision3D covering parts of the globe.
Note: ArcGIS Elevation service, Terrain 3D (for Export) and TopoBathy 3D (for Export) does not include Vantor Precision3D and Airbus WorldDEM4Ortho.
To view the all the sources in a table format, check out World Elevation Data Sources Table.
Topography sources listed in the table are part of Terrain, TopoBathy, Terrain 3D, TopoBathy 3D, World Hillshade and World Hillshade (Dark), while bathymetry sources are part of TopoBathy and TopoBathy 3D only.
Check out elevation layers group for all the elevation content available to ArcGIS users.
Disclaimer: Data sources are not to be used for navigation/safety at sea and in air.
This work is licensed under the Esri Master License Agreement.World Elevation layers are compiled from many authoritative data providers, and are updated quarterly. This map shows the extent of the various datasets comprising the World Elevation dynamic (Terrain,TopoBathy) and tiled (Terrain 3D, TopoBathy 3D, World Hillshade, World Hillshade (Dark)) services.
The tiled services (Terrain 3D, TopoBathy 3D, World Hillshade, World Hillshade (Dark)) also include an additional data source from Vantor's Precision3D covering parts of the globe.
Note: ArcGIS Elevation service, Terrain 3D (for Export) and TopoBathy 3D (for Export) does not include Vantor Precision3D and Airbus WorldDEM4Ortho.
To view the all the sources in a table format, check out World Elevation Data Sources Table.
Topography sources listed in the table are part of Terrain, TopoBathy, Terrain 3D, TopoBathy 3D, World Hillshade and World Hillshade (Dark), while bathymetry sources are part of TopoBathy and TopoBathy 3D only.
Check out elevation layers group for all the elevation content available to ArcGIS users.
Disclaimer: Data sources are not to be used for navigation/safety at sea and in air.
This work is licensed under the Esri Master License Agreement.World Elevation layers are compiled from many authoritative data providers, and are updated quarterly. This map shows the extent of the various datasets comprising the World Elevation dynamic (Terrain,TopoBathy) and tiled (Terrain 3D, TopoBathy 3D, World Hillshade, World Hillshade (Dark)) services.
The tiled services (Terrain 3D, TopoBathy 3D, World Hillshade, World Hillshade (Dark)) also include an additional data source from Vantor's Precision3D covering parts of the globe.
Note: ArcGIS Elevation service, Terrain 3D (for Export) and TopoBathy 3D (for Export) does not include Vantor Precision3D and Airbus WorldDEM4Ortho.
To view the all the sources in a table format, check out World Elevation Data Sources Table.
Topography sources listed in the table are part of Terrain, TopoBathy, Terrain 3D, TopoBathy 3D, World Hillshade and World Hillshade (Dark), while bathymetry sources are part of TopoBathy and TopoBathy 3D only.
Check out elevation layers group for all the elevation content available to ArcGIS users.
Disclaimer: Data sources are not to be used for navigation/safety at sea and in air.
This work is licensed under the Esri Master License Agreement.World Elevation layers are compiled from many authoritative data providers, and are updated quarterly. This map shows the extent of the various datasets comprising the World Elevation dynamic (Terrain,TopoBathy) and tiled (Terrain 3D, TopoBathy 3D, World Hillshade, World Hillshade (Dark)) services.
The tiled services (Terrain 3D, TopoBathy 3D, World Hillshade, World Hillshade (Dark)) also include an additional data source from Vantor's Precision3D covering parts of the globe.
Note: ArcGIS Elevation service, Terrain 3D (for Export) and TopoBathy 3D (for Export) does not include Vantor Precision3D and Airbus WorldDEM4Ortho.
To view the all the sources in a table format, check out World Elevation Data Sources Table.
Topography sources listed in the table are part of Terrain, TopoBathy, Terrain 3D, TopoBathy 3D, World Hillshade and World Hillshade (Dark), while bathymetry sources are part of TopoBathy and TopoBathy 3D only.
Check out elevation layers group for all the elevation content available to ArcGIS users.
Disclaimer: Data sources are not to be used for navigation/safety at sea and in air.
This work is licensed under the Esri Master License Agreement.World Elevation layers are compiled from many authoritative data providers, and are updated quarterly. This map shows the extent of the various datasets comprising the World Elevation dynamic (Terrain,TopoBathy) and tiled (Terrain 3D, TopoBathy 3D, World Hillshade, World Hillshade (Dark)) services.
The tiled services (Terrain 3D, TopoBathy 3D, World Hillshade, World Hillshade (Dark)) also include an additional data source from Vantor's Precision3D covering parts of the globe.
Note: ArcGIS Elevation service, Terrain 3D (for Export) and TopoBathy 3D (for Export) does not include Vantor Precision3D and Airbus WorldDEM4Ortho.
To view the all the sources in a table format, check out World Elevation Data Sources Table.
Topography sources listed in the table are part of Terrain, TopoBathy, Terrain 3D, TopoBathy 3D, World Hillshade and World Hillshade (Dark), while bathymetry sources are part of TopoBathy and TopoBathy 3D only.
Check out elevation layers group for all the elevation content available to ArcGIS users.
Disclaimer: Data sources are not to be used for navigation/safety at sea and in air.
This work is licensed under the Esri Master License Agreement.World Elevation layers are compiled from many authoritative data providers, and are updated quarterly. This map shows the extent of the various datasets comprising the World Elevation dynamic (Terrain,TopoBathy) and tiled (Terrain 3D, TopoBathy 3D, World Hillshade, World Hillshade (Dark)) services.
The tiled services (Terrain 3D, TopoBathy 3D, World Hillshade, World Hillshade (Dark)) also include an additional data source from Vantor's Precision3D covering parts of the globe.
Note: ArcGIS Elevation service, Terrain 3D (for Export) and TopoBathy 3D (for Export) does not include Vantor Precision3D and Airbus WorldDEM4Ortho.
To view the all the sources in a table format, check out World Elevation Data Sources Table.
Topography sources listed in the table are part of Terrain, TopoBathy, Terrain 3D, TopoBathy 3D, World Hillshade and World Hillshade (Dark), while bathymetry sources are part of TopoBathy and TopoBathy 3D only.
Check out elevation layers group for all the elevation content available to ArcGIS users.
Disclaimer: Data sources are not to be used for navigation/safety at sea and in air.
This work is licensed under the Esri Master License Agreement.World Elevation layers are compiled from many authoritative data providers, and are updated quarterly. This map shows the extent of the various datasets comprising the World Elevation dynamic (Terrain,TopoBathy) and tiled (Terrain 3D, TopoBathy 3D, World Hillshade, World Hillshade (Dark)) services.
The tiled services (Terrain 3D, TopoBathy 3D, World Hillshade, World Hillshade (Dark)) also include an additional data source from Vantor's Precision3D covering parts of the globe.
Note: ArcGIS Elevation service, Terrain 3D (for Export) and TopoBathy 3D (for Export) does not include Vantor Precision3D and Airbus WorldDEM4Ortho.
To view the all the sources in a table format, check out World Elevation Data Sources Table.
Topography sources listed in the table are part of Terrain, TopoBathy, Terrain 3D, TopoBathy 3D, World Hillshade and World Hillshade (Dark), while bathymetry sources are part of TopoBathy and TopoBathy 3D only.
Check out elevation layers group for all the elevation content available to ArcGIS users.
Disclaimer: Data sources are not to be used for navigation/safety at sea and in air.
This work is licensed under the Esri Master License Agreement.World Elevation layers are compiled from many authoritative data providers, and are updated quarterly. This map shows the extent of the various datasets comprising the World Elevation dynamic (Terrain,TopoBathy) and tiled (Terrain 3D, TopoBathy 3D, World Hillshade, World Hillshade (Dark)) services.
The tiled services (Terrain 3D, TopoBathy 3D, World Hillshade, World Hillshade (Dark)) also include an additional data source from Vantor's Precision3D covering parts of the globe.
Note: ArcGIS Elevation service, Terrain 3D (for Export) and TopoBathy 3D (for Export) does not include Vantor Precision3D and Airbus WorldDEM4Ortho.
To view the all the sources in a table format, check out World Elevation Data Sources Table.
Topography sources listed in the table are part of Terrain, TopoBathy, Terrain 3D, TopoBathy 3D, World Hillshade and World Hillshade (Dark), while bathymetry sources are part of TopoBathy and TopoBathy 3D only.
Check out elevation layers group for all the elevation content available to ArcGIS users.
Disclaimer: Data sources are not to be used for navigation/safety at sea and in air.
This work is licensed under the Esri Master License Agreement.World Elevation layers are compiled from many authoritative data providers, and are updated quarterly. This map shows the extent of the various datasets comprising the World Elevation dynamic (Terrain,TopoBathy) and tiled (Terrain 3D, TopoBathy 3D, World Hillshade, World Hillshade (Dark)) services.
The tiled services (Terrain 3D, TopoBathy 3D, World Hillshade, World Hillshade (Dark)) also include an additional data source from Vantor's Precision3D covering parts of the globe.
Note: ArcGIS Elevation service, Terrain 3D (for Export) and TopoBathy 3D (for Export) does not include Vantor Precision3D and Airbus WorldDEM4Ortho.
To view the all the sources in a table format, check out World Elevation Data Sources Table.
Topography sources listed in the table are part of Terrain, TopoBathy, Terrain 3D, TopoBathy 3D, World Hillshade and World Hillshade (Dark), while bathymetry sources are part of TopoBathy and TopoBathy 3D only.
Check out elevation layers group for all the elevation content available to ArcGIS users.
Disclaimer: Data sources are not to be used for navigation/safety at sea and in air.
This work is licensed under the Esri Master License Agreement.World Elevation layers are compiled from many authoritative data providers, and are updated quarterly. This map shows the extent of the various datasets comprising the World Elevation dynamic (Terrain,TopoBathy) and tiled (Terrain 3D, TopoBathy 3D, World Hillshade, World Hillshade (Dark)) services.
The tiled services (Terrain 3D, TopoBathy 3D, World Hillshade, World Hillshade (Dark)) also include an additional data source from Vantor's Precision3D covering parts of the globe.
Note: ArcGIS Elevation service, Terrain 3D (for Export) and TopoBathy 3D (for Export) does not include Vantor Precision3D and Airbus WorldDEM4Ortho.
To view the all the sources in a table format, check out World Elevation Data Sources Table.
Topography sources listed in the table are part of Terrain, TopoBathy, Terrain 3D, TopoBathy 3D, World Hillshade and World Hillshade (Dark), while bathymetry sources are part of TopoBathy and TopoBathy 3D only.
Check out elevation layers group for all the elevation content available to ArcGIS users.
Disclaimer: Data sources are not to be used for navigation/safety at sea and in air.
This work is licensed under the Esri Master License Agreement.World Elevation layers are compiled from many authoritative data providers, and are updated quarterly. This map shows the extent of the various datasets comprising the World Elevation dynamic (Terrain,TopoBathy) and tiled (Terrain 3D, TopoBathy 3D, World Hillshade, World Hillshade (Dark)) services.
The tiled services (Terrain 3D, TopoBathy 3D, World Hillshade, World Hillshade (Dark)) also include an additional data source from Vantor's Precision3D covering parts of the globe.
Note: ArcGIS Elevation service, Terrain 3D (for Export) and TopoBathy 3D (for Export) does not include Vantor Precision3D and Airbus WorldDEM4Ortho.
To view the all the sources in a table format, check out World Elevation Data Sources Table.
Topography sources listed in the table are part of Terrain, TopoBathy, Terrain 3D, TopoBathy 3D, World Hillshade and World Hillshade (Dark), while bathymetry sources are part of TopoBathy and TopoBathy 3D only.
Check out elevation layers group for all the elevation content available to ArcGIS users.
Disclaimer: Data sources are not to be used for navigation/safety at sea and in air.
This work is licensed under the Esri Master License Agreement.Click anywhere on earth to see how the water balance is changing over time. This app is based on data from GLDAS version 2.1, which uses weather observations like temperature, humidity, and rainfall to run the Noah land surface model. This model estimates how much of the rain becomes runoff, how much evaporates, and how much infiltrates into the soil. These output variables, calculated every three hours, are aggregated into monthly averages, giving us a record of the hydrologic cycle going all the way back to January 2000.
Because the model is run with 0.25 degree spatial resolution (~30 km), these data should only be used for regional analysis. A specific farm or other small area might experience very different conditions than the region around it, especially because human influences like irrigation are not included.
This app can also be seen as a useful template for sharing other climate datasets. If you would like to customize it for your own organization, or use it as a starting point for your own scientific application, the source code is available on github for anyone to use.
", "keyword": [ "GLDAS", "NASA", "hydrology", "earth observations", "landscape", "climate", "weather" ], "issued": "2017-09-28T18:12:28.000Z", "modified": "2026-10-27T17:38:19.000Z", "publisher": { "name": "Esri" }, "contactPoint": { "@type": "vcard:Contact", "fn": "esri_hydro", "hasEmail": "{{orgContactEmail}}" }, "accessLevel": "public", "spatial": "-179.0000,-89.9900,179.0000,89.9900", "license": " This work is licensed under the Esri Master License Agreement.
Elephants are the largest terrestrial living species. They are herbivorous animals and require 100 kilograms to 200 kilograms of food and about 230 liters of water each day. Their home range can expand up to 11,000 square kilometers. Their ability to find food and water sources is gained from traditional knowledge learned over generations. This knowledge, which is important for survival, is lost if elder elephants of the herd perish.
Elephants are endangered due to many reasons. They can be killed by poachers for their tusks, or captured and tamed for social status and for the circus. Changes in environment such as global warming, rain patterns, deforestation, and mining can lead to degradation of their habitat, forcing these animals to move to different areas in search of food and water. This can cause conflicts with humans as elephants move into human settlements and farmlands. They can also run into electrical fences and traps.
To avoid life-threatening incidents, and for their conservation, monitoring the elephants and their movements is of high importance. It is easier to monitor the elephants using aerial imagery, as it does not require human intervention or disturbance in elephants' natural habitat. Elephant Detection using aerial imagery is more efficient when performed over vast areas. This deep learning model helps automate the task of detecting elephants from high-resolution aerial imagery.
Using the model
Follow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.
Fine-tuning the model
This model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.
Input
High resolution RGB imagery (3-13 centimeters spatial resolution).
Output
Feature class containing detected elephants.
Applicable geographies
The model is expected to work well with aerial imagery of southern African forests (South Africa, Botswana, and Namibia) or similar geographies.
Model architecture
This model uses the FasterRCNN model architecture implemented in ArcGIS API for Python.
Accuracy metrics
This model has an average precision score of 0.857 for elephant.
Training data
The model has been trained on the The Aerial Elephant Dataset.
Limitations
This work is licensed under the Esri Master License Agreement
View Summary | View Terms of Use
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://wb-sdgs.hub.arcgis.com/content/esri::elephant-detection" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=59aae5a252404db1b61b66caf8d1fd3d", "landingPage": "https://wb-sdgs.hub.arcgis.com/apps/arcgis-content::copernicus-sentinel-5p", "title": "Copernicus Sentinel-5P", "description": "Six Sentinel-5P data products are now available in ArcGIS Living Atlas. These services provide timely observations of air quality and atmospheric pollutants for the planet, allowing for accurate weather prediction, improved aviation safety, modeling of health factors (UV exposure) a better understanding of the complex and dynamic chemical processes and reactions that occur in the atmosphere.
This powerful imagery also provides insight into current environmental issues from deforestation in the Congo Basin, to air pollution in the Himalayan foothills, or climate change globally. By exposing what cannot be seen with the naked eye in the atmosphere, Sentinel-5P imagery alerts to problems on the ground. These insights can inform land use, conservation, and climate policy decisions to reduce environmental degradation and improve human health.
", "keyword": [ "ESA", "Sentinel-5P", "Copernicus", "Atmosphere", "Monitoring", "Ozone", "Nitrogen Dioxide", "Formaldehyde", "Methane", "Carbon Monoxide", "Sulfur Dioxide", "European Space Agency" ], "issued": "2022-06-07T17:29:36.000Z", "modified": "2026-10-05T20:03:19.000Z", "publisher": { "name": "ArcGIS Living Atlas Team" }, "contactPoint": { "@type": "vcard:Contact", "fn": "cmccabe_content", "hasEmail": "{{orgContactEmail}}" }, "accessLevel": "public", "spatial": "", "license": "https://creativecommons.org/licenses/by/4.0", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://wb-sdgs.hub.arcgis.com/apps/arcgis-content::copernicus-sentinel-5p" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://storymaps.arcgis.com/stories/59aae5a252404db1b61b66caf8d1fd3d" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=de857cf9ae344f58b8f8114a9b108161", "landingPage": "https://wb-sdgs.hub.arcgis.com/apps/westerncapegov::western-cape-department-of-cultural-affairs-and-sports-dcas-facilities-app-", "title": "Western Cape Department of Cultural Affairs and Sport's (DCAS) Facilities App ", "description": "The purpose of this web app is to provide the locations of the cultural and sport facilities under the auspices of the Western Cape government's Department of Cultural Affairs and Sport (DCAS).Seabirds are found near marine habitats, such as sea and wetlands, due to food availability. They mostly feed on fish and insects. The seabird population is declining at a much faster rate compared to other birds as the coastal region is sensitive to pollution, commercial fisheries, habitat degradation, mineral extraction, human disturbance, etc. Seabirds are also endangered by predatory species from both land and water. Apart from the geography of their habitat, they do not have much ability to defend their nest or protect their young ones. Breeding and laying of eggs happen in open habitats, such as bare ground and open sandy or rocky areas, on coasts and islands with little or no nest material.
The Royal tern and Caspian tern are two of the 350 odd seabird species. These adult terns could be of size 45-60 cm weighing 350-750 gm. Their size puts them in the category of small objects and thus we need very high-resolution imagery to detect them. Recent innovations in drones and AI have enabled us to capture high-resolution imagery over a large geographic area and detect objects of different shapes and sizes. Drones also decrease the disturbance to bird population. Drones are easier to deploy and can perform frequent surveys even after disasters like hurricanes, oil spills, etc. This deep learning model helps automate the task of detecting seabirds (Royal and Caspian terns) from high-resolution aerial imagery. This can help in mapping effective site protection areas for seabirds.
Using the model
Follow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.
Fine-tuning the model
This model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.
Input
High resolution RGB imagery (1.0 cm resolution).
Output
Feature class containing detected seabirds.
Applicable geographies
The model is expected to work well with aerial imagery of West African coast or similar geographies.
Model architecture
This model uses the Mask R-CNN model architecture implemented in ArcGIS API for Python.
Accuracy metrics
This model has an average precision score of 0.76 for seabird.
Training data
The model has been trained on the Aerial Seabirds West Africa.
Limitations
Sample results
Here are a few results from the model.
This work is licensed under the Esri Master License Agreement
View Summary | View Terms of Use
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://wb-sdgs.hub.arcgis.com/content/esri::seabird-tern-detection-africa" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=979cb0cf938946bfb8bb2f41cf9f9795", "landingPage": "https://wb-sdgs.hub.arcgis.com/content/esri::building-footprint-extraction-africa", "title": "Building Footprint Extraction - Africa", "description": "This deep learning model is used to extract building footprints from high-resolution (10–40 cm) imagery. Building footprint layers are useful in preparing base maps and analysis workflows for urban planning and development, insurance, taxation, change detection, infrastructure planning, and a variety of other applications.
Digitizing building footprints from imagery is a time-consuming task and is commonly done by digitizing features manually. Deep learning models have a high capacity to learn these complex workflow semantics and can produce superior results. Use this deep learning model to automate this process and reduce the time and effort required for acquiring building footprints.
Using the model
This work is licensed under the Esri Master License Agreement
View Summary | View Terms of Use
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://wb-sdgs.hub.arcgis.com/content/esri::building-footprint-extraction-africa" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=d1598feb7ec64101b53b96d1879f8b22&sublayer=0", "landingPage": "https://wb-sdgs.hub.arcgis.com/datasets/d1598feb7ec64101b53b96d1879f8b22_0", "title": "OpenStreetMap Leisure Areas for Africa", "description": "This feature layer provides access to OpenStreetMap (OSM) leisure areas data for Africa, which is updated every 15 minutes with the latest edits. This hosted feature layer view is referencing a hosted feature layer of OSM polygon (closed way) data in ArcGIS Online that is updated with minutely diffs from the OSM planet file. This feature layer view includes leisure features defined as a query against the hosted feature layer (i.e. leisure is not blank).This historical map from the David Rumsey Map Collection is a Composite of) The World on Mercator's projection. Drawn under the direction of Mr. Pinkerton by L. Hebert. Neele sculpt. 352 Strand. London: published ... 1812, by Cadell & Davies, Strand & Longman, Hurst, Rees, Orme, & Brown, Paternoster Row.
This work is licensed under the Esri Master License Agreement.These data were produced by NOAA and are not subject to copyright protection in the United States. NOAA waives any potential copyright and related rights in these data worldwide through the Creative Commons Zero 1.0 Universal Public Domain Dedication (CC0 1.0)
CC0 1.0 Universal
",
"distribution": [
{
"@type": "dcat:Distribution",
"title": "ArcGIS Hub Dataset",
"format": "Web Page",
"mediaType": "text/html",
"accessURL": "https://wb-sdgs.hub.arcgis.com/maps/4e681ff69e0e4b90866bb6a2e03db24a"
}
],
"theme": ""
},
{
"@type": "dcat:Dataset",
"identifier": "https://www.arcgis.com/home/item.html?id=37a875ff3611496883b7ccca97f0f5f4",
"landingPage": "https://wb-sdgs.hub.arcgis.com/maps/37a875ff3611496883b7ccca97f0f5f4",
"title": "{{name}}",
"description": "{{description}}",
"keyword": [
"NOAA",
"GOES",
"satellite",
"weather",
"disaster",
"easrth observation",
"Himawari",
"infrared",
"imagery"
],
"issued": "2020-09-18T20:15:58.000Z",
"modified": "{{modified:toISO}}",
"publisher": {
"name": "{{source}}"
},
"contactPoint": {
"@type": "vcard:Contact",
"fn": "VizLab_noaa",
"hasEmail": "{{orgContactEmail}}"
},
"accessLevel": "public",
"spatial": "",
"license": "Public Domain These data were produced by NOAA and are not subject to copyright protection in the United States. NOAA waives any potential copyright and related rights in these data worldwide through the Creative Commons Zero 1.0 Universal Public Domain Dedication (CC0 1.0)
CC0 1.0 Universal",
"distribution": [
{
"@type": "dcat:Distribution",
"title": "ArcGIS Hub Dataset",
"format": "Web Page",
"mediaType": "text/html",
"accessURL": "https://wb-sdgs.hub.arcgis.com/maps/37a875ff3611496883b7ccca97f0f5f4"
}
],
"theme": ""
},
{
"@type": "dcat:Dataset",
"identifier": "https://www.arcgis.com/home/item.html?id=8e93e0f942ae4d54a8d089e3cd5d2774",
"landingPage": "https://wb-sdgs.hub.arcgis.com/maps/8e93e0f942ae4d54a8d089e3cd5d2774",
"title": "{{name}}",
"description": "{{description}}",
"keyword": [
"NOAA",
"satellite",
"weather",
"disaster",
"earth observations",
"GOES",
"Himawari",
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"imagery"
],
"issued": "2019-06-26T18:24:55.000Z",
"modified": "{{modified:toISO}}",
"publisher": {
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},
"contactPoint": {
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"fn": "VizLab_noaa",
"hasEmail": "{{orgContactEmail}}"
},
"accessLevel": "public",
"spatial": "",
"license": "Public domain These data were produced by NOAA and are not subject to copyright protection in the United States. NOAA waives any potential copyright and related rights in these data worldwide through the Creative Commons Zero 1.0 Universal Public Domain Dedication (CC0 1.0)
CC0 1.0 Universal",
"distribution": [
{
"@type": "dcat:Distribution",
"title": "ArcGIS Hub Dataset",
"format": "Web Page",
"mediaType": "text/html",
"accessURL": "https://wb-sdgs.hub.arcgis.com/maps/8e93e0f942ae4d54a8d089e3cd5d2774"
}
],
"theme": ""
},
{
"@type": "dcat:Dataset",
"identifier": "https://www.arcgis.com/home/item.html?id=b4a808eba17d4294991880d9e120faee",
"landingPage": "https://wb-sdgs.hub.arcgis.com/maps/africa::africa-land-cover",
"title": "Africa Land Cover",
"description": "
This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement.This layer provides access to a 100m cell size raster derived from SRTM and other data that divides the African landscape into upland and lowland areas. The data covers Africa, Madagascar, the Comoros Islands, and other coastal Islands near Africa. It was published in 2009 by the USGS Rocky Mountain Geographic Science Center.
This layer was used as an input for the Africa Terrestrial Ecosystems mapping project.
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
This work is licensed under the Esri Master License Agreement.
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
Created as part of the USGS’s Africa Ecosystems Mapping project, the Africa Surficial Lithology layer maps the geology of Africa into 20 classes based bedrock type and the distribution of unconsolidated surface material. The data available through this layer map the key geological features that affect the distribution of plants and ecosystems in Africa.
Dataset Summary
This work is licensed under the Esri Master License Agreement.
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
What can you do with this layer?
This work is licensed under the Esri Master License Agreement.
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
\n\n\n\n\n\n\n\n
This work is licensed under the Esri Master License Agreement.
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
Plants play a central role in the carbon cycle by absorbing carbon dioxide from the atmosphere and incorporating it in the structure of the plant. Globally living plants contain 500 billion metric tons of carbon, more than 60 times the amount of carbon released to the atmosphere by humans each year. Understanding the distribution of the carbon stored in living plants, known as biomass, is key to estimating the effects of land use change on the climate.
Dataset Summary
This layer provides access to a 1 km cell sized raster with data on the density of carbon stored in living plants in metric tons per hectare for the year 2000. The data covers Africa, Madagascar, and other islands near Africa. It was published by the Oak Ridge National Laboratory Carbon Dioxide Information Analysis Center in 2008.
For more information, see the Landscape Layers group on ArcGIS Online.
This work is licensed under the Esri Master License Agreement
View Summary | View Terms of Use
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Location Platform account.
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://wb-sdgs.hub.arcgis.com/datasets/esri::africa-carbon-biomass" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://landscape5.arcgis.com/arcgis/rest/services/Africa_Carbon_Biomass/ImageServer" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=57c1ade4fa7c4e2384e6a23f2b3bd254&sublayer=0", "landingPage": "https://wb-sdgs.hub.arcgis.com/datasets/esri::world-continents", "title": "World Continents", "description": "World Continents represents the boundaries for the continents of the world.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
This work is licensed under the Esri Master License Agreement.
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
\n\n\n\n\n\n\n\n
This work is licensed under the Esri Master License Agreement.
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement.
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
\n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This layer shows the average household size in Zambia in 2023, in a multiscale map (Country, Province, and District). Nationally, the average household size is 4.8 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This layer shows the average household size in Uganda in 2023, in a multiscale map (Country, Region, and District). Nationally, the average household size is 4.5 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This layer shows the average household size in Tunisia in 2023, in a multiscale map (Country, Region, and Governorate). Nationally, the average household size is 3.9 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This map shows the total population in Eswatini in 2023, in a multiscale map (Country and Region). Nationally, there are 1,155,107 people in Eswatini.This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://wb-sdgs.hub.arcgis.com/apps/Story::the-human-reach" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://storymaps.esri.com/stories/2018/anthropocene/1-human-reach.html" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=5bdf3d7ca3014db6ba2f3a7fcb373e9d", "landingPage": "https://wb-sdgs.hub.arcgis.com/maps/esri::namibia-average-household-size", "title": "Namibia Average Household Size", "description": "\n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This layer shows the average household size in Namibia in 2023, in a multiscale map (Country, Region, and Constituency). Nationally, the average household size is 4.0 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
\n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This map shows the total population in Cameroon in 2023, in a multiscale map (Country, Region, and Department). Nationally, there are 28,647,293 people in Cameroon.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This map shows the total population in Angola in 2023, in a multiscale map (Country, Province, and Municipality). Nationally, there are 35,345,973 people in Angola.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This map shows the total population in Algeria in 2023, in a multiscale map (Country and Province). Nationally, there are 46,525,636 people in Algeria.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This layer shows the average household size in Tanzania in 2023, in a multiscale map (Country and Region). Nationally, the average household size is 4.6 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This layer shows the average household size in Sudan in 2023, in a multiscale map (Country and State). Nationally, the average household size is 5.5 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
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.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This layer shows the average household size in Reunion in 2023, in a multiscale map (Country, Arrondissement, and Commune). Nationally, the average household size is 2.5 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
The Purchasing Power Index\ncompares the demand for a specific purchasing category in an area, with\nthe national demand for that product or service. The index values at the\nnational level are 100, representing average demand for the country. A\nvalue of more than 100 represents higher demand than the national average,\nand a value of less than 100 represents lower demand than the national\naverage. For example, an index of 120 implies that demand in the area is\n20 percent higher than the national average; an index of 80 implies that\ndemand is 20 percent lower than the national average.
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This layer shows the average household size in Nigeria in 2023, in a multiscale map (Country, State, and Local Government Area). Nationally, the average household size is 4.5 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. Buildings are the foundation of any 3D city; they create a realistic visual context for understanding the built environment. This rule can help you quickly create 3D buildings using your existing 2D building footprint polygons. Create buildings for your whole city or specific areas of interest. Use the buildings for context surrounding higher-detail buildings or proposed future developments.
Already have existing 3D buildings? Check out the Textured Buildings from Mass by Building Type rule.
What you get
A Rule Package file named Building_FromFootprint_Textured_ByLandUse.rpk
Rule works with a polygon layer
Get started
In ArcGIS Pro
Parameters
Building Type:
Display:
Note: You can hook up the rule parameters to attributes in your data by clicking on the database icon to the right of each rule parameter. The database icon will change to blue when the rule parameter is mapped to an attribute field. The rule will automatically connect when field names match rule parameter names. Use layer files to preserve rule configurations unique to your data.
For those who want to know more
This rule is part of a the 3D Rule Library available in the Living Atlas. Discover more 3D rules to help you perform your work.
Learn more about ArcGIS Pro in the Getting to Know ArcGIS Pro lesson
This work is licensed under the Esri Master License Agreement.Kenya's\n health care system is structured in a step-wise manner so that \ncomplicated cases are referred to a higher level. Gaps in the system are\n filled by private and church run units
Basic\n primary care is provided at primary healthcare centres and \ndispensaries. Dispensaries are run and managed by enrolled and \nregistered nurses who are supervised by the nursing officer at the \nrespective health centre. They provide outpatient services for simple \nailments such as the common cold and flu, uncomplicated malaria and skin\n conditions. Those patients who cannot be managed by the nurse are \nreferred to the health centres.
Sub-district,\n district and provincial hospitals provide secondary care, i.e. \nintegrated curative and rehabilitative care. Sub-district hospitals are \nsimilar to health centres with the addition of a surgery unit for \nCaesarean sections and other procedures. District hospitals usually have\n the resources to provide comprehensive medical and surgical services. \nProvincial hospitals are regional centres which provide specialised care\n including intensive care, life support and specialist consultations.
Third\n level care is provided at the general hospitals Moi and Kenyatta, both \nlocated in Nairobi. Gaps, which regularly appear in the system, are \nfilled by private and church run facilities.
Kenya lies across the equator in\r\n east-central Africa, on the coast of the Indian Ocean. It is twice the \r\nsize of Nevada. Kenya borders Somalia to the east, Ethiopia to the \r\nnorth, Tanzania to the south, Uganda to the west, and Sudan to the \r\nnorthwest. In the north, the land is arid; the southwest corner is in \r\nthe fertile Lake Victoria Basin; and a length of the eastern depression \r\nof the Great Rift Valley separates western highlands from those that \r\nrise from the lowland coastal strip
This work is licensed under the Esri Master License Agreement.",
"distribution": [
{
"@type": "dcat:Distribution",
"title": "ArcGIS Hub Dataset",
"format": "Web Page",
"mediaType": "text/html",
"accessURL": "https://wb-sdgs.hub.arcgis.com/maps/africa::africa-landmarks"
}
],
"theme": ""
},
{
"@type": "dcat:Dataset",
"identifier": "https://www.arcgis.com/home/item.html?id=8eb0f1c0c3184659b714888842e01fc5",
"landingPage": "https://wb-sdgs.hub.arcgis.com/maps/esri::mozambique-purchasing-power-per-capita",
"title": "Mozambique Purchasing Power per Capita",
"description": "\n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
The Purchasing Power Index\ncompares the demand for a specific purchasing category in an area, with\nthe national demand for that product or service. The index values at the\nnational level are 100, representing average demand for the country. A\nvalue of more than 100 represents higher demand than the national average,\nand a value of less than 100 represents lower demand than the national\naverage. For example, an index of 120 implies that demand in the area is\n20 percent higher than the national average; an index of 80 implies that\ndemand is 20 percent lower than the national average.
This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement. This map features the World Imagery map, focused on the continent of Africa. World Imagery provides one meter or better satellite and aerial imagery in many parts of the world and lower resolution satellite imagery worldwide. The map includes 15m TerraColor imagery at small and mid-scales (~1:591M down to ~1:72k) and 2.5m SPOT Imagery (~1:288k to ~1:72k) for the world. DigitalGlobe sub-meter imagery is featured in many parts of the world, including Africa. Sub-meter Pléiades imagery is available in select urban areas. Additionally, imagery at different resolutions has been contributed by the GIS User Community.
For more information on this map, view the World Imagery item description.
Metadata: This service is metadata-enabled. With the Identify tool in ArcMap or the World Imagery with Metadata web map, you can see the resolution, collection date, and source of the imagery at the location you click. Values of "99999" mean that metadata is not available for that field. The metadata applies only to the best available imagery at that location. You may need to zoom in to view the best available imagery.
Feedback: Have you ever seen a problem in the Esri World Imagery Map that you wanted to see fixed? You can use the Imagery Map Feedback web map to provide feedback on issues or errors that you see. The feedback will be reviewed by the ArcGIS Online team and considered for one of our updates.
", "keyword": [ "world", "world imagery", "satellite imagery", "imagery", "basemap", "baseimagery", "current", "metadata", "Africa" ], "issued": "2017-12-01T21:49:57.000Z", "modified": "2018-12-06T06:18:51.000Z", "publisher": { "name": "Africa GeoPortal" }, "contactPoint": { "@type": "vcard:Contact", "fn": "esri_africa", "hasEmail": "https://support.esri.com" }, "accessLevel": "public", "spatial": "-30.9243,-24.4595,72.6109,27.4378", "license": "This work is licensed under the Web Services and API Terms of Use.
View Summary | View Terms of Use
This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement.This digital map comprised of four types of data: (A) Distribution of marine protected areas, (B) seven layers biodiversity features, (C) two types of threats, and (D) 16 environmental variables.
(A). Marine protected areas:
Distribution of Marine protected areas in the Coral Triangle. This dataset consists of 678 MPAs polygon boundaries, derived from the World Database of Protected Areas-WDPA (www.protectedplanet.net), Coral Triangle Atlas (ctatlas.reefbase.org) and Indonesian datasets of marine protected areas (MoF-MoMAF, 2010; MoMAF, 2016). The dataset consists of 35% of the total 1,972 MPAs in the region (White et al., 2014). Around 95% of the missing MPAs’ boundaries were associated with small marine managed areas.
The layers' attribute table provides detailed information following its native sources (WDPA, CTAtlas) (e.g. information of Name, Local Name, Designation Type, IUCN Category, coverage etc) with amendment and adjustment from local sources (Indonesian database).
To allow a simple indexing, a new CT MPAs ID format (MPA_Id) introduced. This new ID consists of 10 digits: "A BC DEFG HIJ"
where:
A = Country; 1 = Indonesia, 2 = Malaysia, 3 = Philippines, 4 = Papua New Guinea, 5 = Solomon Islands, and 6 = Timor Leste
BC = IUCN MPAs Category; Strict Nature Reserve (1a = 11, 1b = 12), National Park (20), Habitat and Species Management Areas (40), Protected Landscape/Seascape (50) and Managed Resources Protected Areas (60)
DEFG = Establishment year (e.g., 1980)
HIJ = Number; ordered based on their establishment year
Example : Taka Bonerate National Park ; ID = 1201985035. It means that Taka Bonerate is Indonesian MPAs (1), is a national park (20), established in 1985, and MPAs number 35.
(B). Biodiversity Features:
This web map provides a detailed vector tile basemap for the world featuring a dark background with glowing blue symbology inspired by the ArcGIS.com splash screen. This web map is focused on Africa, with a mask layer added to the basemap.
Use this Map
This work is licensed under the Esri Master License Agreement.",
"distribution": [
{
"@type": "dcat:Distribution",
"title": "ArcGIS Hub Dataset",
"format": "Web Page",
"mediaType": "text/html",
"accessURL": "https://wb-sdgs.hub.arcgis.com/maps/africa::nova-africa"
}
],
"theme": [
"geospatial"
]
},
{
"@type": "dcat:Dataset",
"identifier": "https://www.arcgis.com/home/item.html?id=18c9d78a6a09408a8eb5f9a7715b5ad8",
"landingPage": "https://wb-sdgs.hub.arcgis.com/maps/18c9d78a6a09408a8eb5f9a7715b5ad8",
"title": "Réseau TramWay du Maroc",
"description": "", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://wb-sdgs.hub.arcgis.com/maps/aa88482528574b06a17927ece1b48892" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=fe4e7d6e46254070a612fd99a8854e4a", "landingPage": "https://wb-sdgs.hub.arcgis.com/maps/fe4e7d6e46254070a612fd99a8854e4a", "title": "Routes du Maroc", "description": "
La longueur totale du réseau routier classé est de 57 334 km, dont 43 318 km revêtu, soit 75% de la longueur du réseau classé.
Les autoroutes totalise un linéaire de 1 588 km tandis que le réseau routier revêtu (Hors agglomération) est réparti comme suit :
Routes Nationales : 10.203 km.Routes Régionales : 9.370 km.Routes Provinciales : 23.745 km. This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement.",
"distribution": [
{
"@type": "dcat:Distribution",
"title": "ArcGIS Hub Dataset",
"format": "Web Page",
"mediaType": "text/html",
"accessURL": "https://wb-sdgs.hub.arcgis.com/maps/africa::a-childrens-map-of-africa"
}
],
"theme": [
"geospatial"
]
},
{
"@type": "dcat:Dataset",
"identifier": "https://www.arcgis.com/home/item.html?id=8a49966f54f445e283ff94acb6b981cf",
"landingPage": "https://wb-sdgs.hub.arcgis.com/maps/africa::newspaper-africa",
"title": "Newspaper Africa",
"description": "Use this Map
This work is licensed under the Esri Master License Agreement.",
"distribution": [
{
"@type": "dcat:Distribution",
"title": "ArcGIS Hub Dataset",
"format": "Web Page",
"mediaType": "text/html",
"accessURL": "https://wb-sdgs.hub.arcgis.com/maps/africa::newspaper-africa"
}
],
"theme": [
"geospatial"
]
},
{
"@type": "dcat:Dataset",
"identifier": "https://www.arcgis.com/home/item.html?id=75d281c6af5d48da8bbb743098eec7b8",
"landingPage": "https://wb-sdgs.hub.arcgis.com/maps/africa::colored-pencil-africa",
"title": "Colored Pencil Africa",
"description": "This web map provides a detailed vector basemap for the world symbolized with the appearance of being hand-drawn by colored pencils, with a focus on Africa. This layer is delivered as a vector tile service that provides unique capabilities for customization, high-resolution display, and offline use in mobile devices. This work is licensed under the Esri Master License Agreement.",
"distribution": [
{
"@type": "dcat:Distribution",
"title": "ArcGIS Hub Dataset",
"format": "Web Page",
"mediaType": "text/html",
"accessURL": "https://wb-sdgs.hub.arcgis.com/maps/africa::colored-pencil-africa"
}
],
"theme": [
"geospatial"
]
},
{
"@type": "dcat:Dataset",
"identifier": "https://www.arcgis.com/home/item.html?id=d57a0dac1ff04ee2ba613ac97fc8800d",
"landingPage": "https://wb-sdgs.hub.arcgis.com/maps/africa::modern-antique-africa",
"title": "Modern Antique Africa",
"description": "Use this Map
This work is licensed under the Esri Master License Agreement.",
"distribution": [
{
"@type": "dcat:Distribution",
"title": "ArcGIS Hub Dataset",
"format": "Web Page",
"mediaType": "text/html",
"accessURL": "https://wb-sdgs.hub.arcgis.com/maps/africa::modern-antique-africa"
}
],
"theme": [
"geospatial"
]
},
{
"@type": "dcat:Dataset",
"identifier": "https://www.arcgis.com/home/item.html?id=28dc47d0e37a4bea9592f808de511861",
"landingPage": "https://wb-sdgs.hub.arcgis.com/maps/esri::mozambique-average-household-size",
"title": "Mozambique Average Household Size",
"description": "\n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This layer shows the average household size in Mozambique in 2023, in a multiscale map (Country and Province). Nationally, the average household size is 4.4 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This vector tile layer provides a buffered mask around an area of interest (AOI), in this case Africa. The layer can be overlayed on other basemaps and operational layers to call attention to the AOI within the mask. This layer is designed for a dark basemap.
The layer is a vector tile layer so it is designed to (a) perform well at multiple scale levels and (b) be customized as needed to work well with other basemaps or layers. The vector tile layer can be added to the basemap layer of a web map and set as a reference layer, if needed, to mask other reference layers in the map.
This vector tile layer provides a buffered mask around an area of interest (AOI), in this case Africa. The layer can be overlayed on other basemaps and operational layers to call attention to the AOI within the mask. This layer is opaque and designed for light basemap.
The layer is a vector tile layer so it is designed to (a) perform well at multiple scale levels and (b) be customized as needed to work well with other basemaps or layers. The vector tile layer can be added to the basemap layer of a web map and set as a reference layer, if needed, to mask other reference layers in the map.
This vector tile layer provides a buffered mask around an area of interest (AOI), in this case Africa. The layer can be overlayed on other basemaps and operational layers to call attention to the AOI within the mask. This layer is designed for light basemap.
The layer is a vector tile layer so it is designed to (a) perform well at multiple scale levels and (b) be customized as needed to work well with other basemaps or layers. The vector tile layer can be added to the basemap layer of a web map and set as a reference layer, if needed, to mask other reference layers in the map.
This map contains a number of world-wide dynamic image services providing\naccess to various Landsat scenes covering the landmass of the World for\nvisual interpretation. Landsat 8 collects new scenes for each location on Earth every 16 days, assuming limited cloud coverage. Newest and near cloud-free scenes are displayed by default on top. Most scenes collected since 1st January 2015 are included. The service also includes scenes from the Global Land Survey* (circa 2010, 2005, 2000, 1990, 1975).
The service contains a range of different predefined renderers for Multispectral, Panchromatic as well as Pansharpened scenes. The layers in the service can be time-enabled\nso that the applications can restrict the displayed scenes to a specific date range.
This ArcGIS Server dynamic service can be used in Web Maps and ArcGIS Desktop, Web and Mobile applications using the REST based image services API. Users can also export images, but the exported area is limited to maximum of 2,000 columns x 2,000 rows per request.
Data Source: The imagery in these services is sourced from the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). The data for these services reside on the Landsat Public Datasets hosted on the Amazon Web Service cloud. Users can access full scenes from https://github.com/landsat-pds/landsat_ingestor/wiki/Accessing-Landsat-on-AWS, or alternatively access http://landsatlook.usgs.gov to review and download full scenes from the complete USGS archive.
For more information on Landsat 8 images, see http://landsat.usgs.gov/landsat8.php.
*The Global Land Survey includes images from Landsat 1 through Landsat 7. Band numbers and band combinations differ from those of Landsat 8, but have been mapped to the most appropriate band as in the above table. For more information about the Global Land Survey, visit http://landsat.usgs.gov/science_GLS.php.
For more information on each of the individual layers, see
http://www.arcgis.com/home/item.html?id=d9b466d6a9e647ce8d1dd5fe12eb434b ;
http://www.arcgis.com/home/item.html?id=6b003010cbe64d5d8fd3ce00332593bf ;
http://www.arcgis.com/home/item.html?id=a7412d0c33be4de698ad981c8ba471e6
", "keyword": [ "imagery", "temporal", "multispectral", "landsat", "landsat 8", "GLS", "panchromatic", "pansharpened", "Africa" ], "issued": "2017-12-02T00:49:13.000Z", "modified": "2017-12-13T23:48:55.000Z", "publisher": { "name": "Africa GeoPortal" }, "contactPoint": { "@type": "vcard:Contact", "fn": "esri_africa", "hasEmail": "https://support.esri.com" }, "accessLevel": "public", "spatial": "-45.2637,-30.8645,84.4629,38.1000", "license": " This work is licensed under the Esri Master License Agreement.",
"distribution": [
{
"@type": "dcat:Distribution",
"title": "ArcGIS Hub Dataset",
"format": "Web Page",
"mediaType": "text/html",
"accessURL": "https://wb-sdgs.hub.arcgis.com/maps/africa::africa-landsat-imagery"
}
],
"theme": [
"geospatial"
]
},
{
"@type": "dcat:Dataset",
"identifier": "https://www.arcgis.com/home/item.html?id=f5b235bfbda24fb6a421c82b69f3873b",
"landingPage": "https://wb-sdgs.hub.arcgis.com/maps/africa::africa-mask-dark",
"title": "Africa Mask (Dark)",
"description": "This vector tile layer provides a buffered mask around an area of interest (AOI), in this case Africa. The layer can be overlayed on other basemaps and operational layers to call attention to the AOI within the mask. This layer is designed for a dark basemap.
The layer is a vector tile layer so it is designed to (a) perform well at multiple scale levels and (b) be customized as needed to work well with other basemaps or layers. The vector tile layer can be added to the basemap layer of a web map and set as a reference layer, if needed, to mask other reference layers in the map.
This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement.From the first cautious steps in the Great Rift Valley, humans have adapted to the challenges of Africa. Today it lies divided into more nations than any other continent. But as shown by this satellite-derived map, landscapes that ignore national borders form a mosaic of ecosystems that subtly blend one into the other -- from the temperate Mediterranean through desert and scattered croplands to forest, swamp, and savanna -- each with its own rules for life. Published in September 2005.
", "keyword": [ "Africa", "landscapes", "ecosystems", "historical" ], "issued": "2014-04-16T15:56:12.000Z", "modified": "2014-11-12T22:03:45.000Z", "publisher": { "name": "National Geographic" }, "contactPoint": { "@type": "vcard:Contact", "fn": "NationalGeographic", "hasEmail": "mailto:mapsinfo@natgeo.com" }, "accessLevel": "public", "spatial": "-51.5421,-65.2067,179.7874,52.1322", "license": "All of the map images and vector data (“maps”) published by National Geographic through ArcGIS Online are the exclusive property of National Geographic Society and may not be used for any Commercial, Governmental or Institutional purpose (defined here) without a separate express written license from National Geographic. Visit NatGeoMaps.com/arcgis-online-eula to read the entire End User License Agreement.
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://wb-sdgs.hub.arcgis.com/maps/NGMaps::2005-africa-storied-landscape-map" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=096fd5018785407ea868521f1d124033", "landingPage": "https://wb-sdgs.hub.arcgis.com/maps/esri::morocco-purchasing-power-per-capita", "title": "Morocco Purchasing Power per Capita", "description": "\n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This layer shows the average household size in Morocco in 2023, in a multiscale map (Country, Region, and Province). Nationally, the average household size is 4.2 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
\n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This layer shows the average household size in Mauritius in 2023, in a multiscale map (Country and District). Nationally, the average household size is 3.3 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This layer shows the average household size in Malawi in 2023, in a multiscale map (Country, Region, and District). Nationally, the average household size is 4.3 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This layer shows the average household size in Lesotho in 2023, in a multiscale map (Country and District). Nationally, the average household size is 3.6 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This layer shows the average household size in Kenya in 2023, in a multiscale map (Country and County). Nationally, the average household size is 3.8 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This layer shows the average household size in Ghana in 2023, in a multiscale map (Country, Region, and District). Nationally, the average household size is 3.8 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This layer shows the total population in Eswatini in 2023, in a multiscale map (Country and Region). Nationally, there are 1,155,107 people in Eswatini.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This layer shows the average household size in Egypt in 2023, in a multiscale map (Country and Province). Nationally, the average household size is 3.9 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This layer shows the average household size in Côte d'Ivoire in 2023, in a multiscale map (Country, District, Region, and Department). Nationally, the average household size is 5.2 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This layer shows the total population in Cameroon in 2023, in a multiscale map (Country, Region, and Department). Nationally, there are 28,647,293 people in Cameroon.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This layer shows the average household size in Botswana in 2023, in a multiscale map (Country, District, and Subdistrict). Nationally, the average household size is 3.3 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This map shows the average household size in Zambia in 2023, in a multiscale map (Country, Province, and District). Nationally, the average household size is 4.8 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This map shows the average household size in Uganda in 2023, in a multiscale map (Country, Region, and District). Nationally, the average household size is 4.5 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This map shows the average household size in Tunisia in 2023, in a multiscale map (Country, Region, and Governorate). Nationally, the average household size is 3.9 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This map shows the average household size in Tanzania in 2023, in a multiscale map (Country and Region). Nationally, the average household size is 4.6 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This map shows the average household size in Sudan in 2023, in a multiscale map (Country and State). Nationally, the average household size is 5.5 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
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.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This map shows the average household size in Reunion in 2023, in a multiscale map (Country, Arrondissement, and Commune). Nationally, the average household size is 2.5 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This map shows the average household size in Nigeria in 2023, in a multiscale map (Country, State, and Local Government Area). Nationally, the average household size is 4.5 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This map shows the average household size in Namibia in 2023, in a multiscale map (Country, Region, and Constituency). Nationally, the average household size is 4.0 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This map shows the average household size in Mozambique in 2023, in a multiscale map (Country and Province). Nationally, the average household size is 4.4 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This map shows the average household size in Morocco in 2023, in a multiscale map (Country, Region, and Province). Nationally, the average household size is 4.2 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This map shows the average household size in Mauritius in 2023, in a multiscale map (Country and District). Nationally, the average household size is 3.3 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This map shows the average household size in Malawi in 2023, in a multiscale map (Country, Region, and District). Nationally, the average household size is 4.3 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement.
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Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
\n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This map shows the average household size in Lesotho in 2023, in a multiscale map (Country and District). Nationally, the average household size is 3.6 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This map shows the average household size in Kenya in 2023, in a multiscale map (Country and County). Nationally, the average household size is 3.8 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This map shows the average household size in Ghana in 2023, in a multiscale map (Country, Region, and District). Nationally, the average household size is 3.8 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This map shows the average household size in Egypt in 2023, in a multiscale map (Country and Province). Nationally, the average household size is 3.9 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This map shows the average household size in Côte d'Ivoire in 2023, in a multiscale map (Country, District, Region, and Department). Nationally, the average household size is 5.2 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This map shows the average household size in Botswana in 2023, in a multiscale map (Country, District, and Subdistrict). Nationally, the average household size is 3.3 people per household. It is calculated by dividing the household population by total households.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This layer shows the total population in Angola in 2023, in a multiscale map (Country, Province, and Municipality). Nationally, there are 35,345,973 people in Angola.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This layer shows the total population in Algeria in 2023, in a multiscale map (Country and Province). Nationally, there are 46,525,636 people in Algeria.This work is licensed under the Esri Master License Agreement. \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
If you don't have an account, you can sign up for a free trial of ArcGIS or a free ArcGIS Developer account.
This work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
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Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
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Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
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Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
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Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
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Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
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Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
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Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
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Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
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Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
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Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
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Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
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Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
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Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
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Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
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Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
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Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
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View Summary | View Terms of Use
Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
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Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
Important Note: This item requires an ArcGIS Online organizational subscription or an ArcGIS Developer account and does not consume credits. To access this item, you'll need to do one of the following:
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Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
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Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
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Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
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Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
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Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
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Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
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Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
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Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
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Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
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Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
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Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
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Important Note: This layer is intended to support online visualization and analysis. Users are not permitted to export data for offline use.
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Retirement Notice: This item is in mature support as of June 2025 and will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.
This work is licensed under the Esri Master License Agreement.
HydroSHEDS
This product, the World Hydro Reference Overlay, incorporates data from the HydroSHEDS database which is © World Wildlife Fund, Inc. (2006-2012) and has been used herein under license. WWF has not evaluated the data as altered and incorporated within the World Hydro Reference Overlay, and therefore gives no warranty regarding its accuracy, completeness, currency or suitability for any particular purpose. Portions of the HydroSHEDS database incorporate data which are the intellectual property rights of © USGS (2006-2008) (data available from U.S. Geological Survey, EROS Data Center, SD), NASA (2000-2005), ESRI (1992-1998), CIAT (2004-2006), UNEP-WCMC (1993), WWF (2004), Commonwealth of Australia (2007), and Her Royal Majesty and the British Crown and are used under license. The scientific citation for the HydroSHEDS database is: Lehner, B., Verdin, K., Jarvis, A. (2008): New global hydrography derived from spaceborne elevation data. Eos, Transactions, AGU, 89(10): 93-94.
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://wb-sdgs.hub.arcgis.com/datasets/esri::esri-hydro-reference-overlay" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://tiles.arcgis.com/tiles/P3ePLMYs2RVChkJx/arcgis/rest/services/Esri_Hydro_Reference_Overlay/MapServer/0" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=93ba20b268fb426c9c665a6bcd816da8", "landingPage": "https://wb-sdgs.hub.arcgis.com/apps/esri::sentinel-explorer-classic", "title": "Sentinel Explorer Classic", "description": "This web application highlights some of the capabilities for accessing Sentinel-2 imagery layers, powered by ArcGIS for Server, accessing Landsat Public Datasets running on the Amazon Web Services Cloud. The layers are updated with new Sentinel-2 images on a daily basis.
Created for you to visualize our planet and understand how the Earth has changed over time, the Esri Sentinel-2 Explorer app provides the power of Sentinel-2 satellites, which gather data beyond what the eye can see. Use this app to draw on Sentinel's different bands to better explore the planet's geology, vegetation, agriculture, and cities. Additionally, access the Sentinel-2 archive to visualize how the Earth's surface has changed over the last fourteen months
Quick access to the following band combinations and indices is provided:
Band | Description | Wavelength (µm) | Resolution (m) |
1 | Coastal aerosol | 0.433 - 0.453 | 60 |
2 | Blue | 0.458 - 0.523 | 10 |
3 | Green | 0.543 - 0.578 | 10 |
4 | Red | 0.650 - 0.680 | 10 |
5 | Vegetation Red Edge | 0.698 - 0.713 | 20 |
6 | Vegetation Red Edge | 0.733 - 0.748 | 20 |
7 | Vegetation Red Edge | 0.773 - 0.793 | 20 |
8 | NIR | 0.785 - 0.900 | 10 |
8A | Narrow NIR | 0.855 - 0.875 | 20 |
9 | Water vapour | 0.935 - 0.955 | 60 |
10 | SWIR – Cirrus | 1.365 - 1.385 | 60 |
11 | SWIR-1 | 1.565 - 1.655 | 20 |
12 | SWIR-2 | 2.100 - 2.280 | 20 |
", "keyword": [ "imagery", "Sentinel", "Sentinel-2", "multispectral", "temporal", "multitemporal", "earth" ], "issued": "2018-05-22T22:05:42.000Z", "modified": "2025-08-29T00:50:18.000Z", "publisher": { "name": "Esri" }, "contactPoint": { "@type": "vcard:Contact", "fn": "esri_imagery", "hasEmail": "{{orgContactEmail}}" }, "accessLevel": "public", "spatial": "", "license": "
This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement. This work is licensed under the Esri Master License Agreement.What can you do with this layer?
This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS for Desktop. It is useful for scientific modeling, but only at global scales.
By applying the "Calculate Anomaly" processing template, it is also possible to view these data in terms of deviation from the mean, instead of total evapotranspiration. Mean evapotranspiration for a given month is calculated over the entire period of record - 2000 to present.
Time: This is a time-enabled layer. It shows the total evaporative loss during the map's time extent, or if time animation is disabled, a time range can be set using the layer's multidimensional settings. The map shows the sum of all months in the time extent. Minimum temporal resolution is one month; maximum is eight years.
Variables: This layer has two variables: rainfall and snowfall. By default the two are summed, but you can view either by itself using the multidimensional filter, or by applying the relevant raster function. You must disable time animation on the layer before using its multidimensional filter.
Important: You must switch from the cartographic renderer to the analytic renderer in the processing template tab in the layer properties window before using this layer as an input to geoprocessing tools.
This work is licensed under the Esri Master License Agreement.
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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 (purple) 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.
You may not distribute, modify, transmit, reuse, repost, copy, or use this content, whether in whole or in part, for commercial purposes or for personal gain, without express advance written permission from Map of Life. The data are and will remain the exclusive property of Map of Life, Yale University, and its licensors. These data are protected by copyright, trademark, and other laws of both the United States and foreign countries. Map of Life trademarks and trade dress may not be used in connection with any product or service without the prior written consent of Map of Life, Yale University.
By downloading this data, you agree to defend, indemnify and hold harmless Map of Life, Yale University and its licensee and licensors, and their employees, contractors, agents, officers and directors, from and against any and all claims, damages, obligations, losses, liabilities, costs or debt, and expenses (including but not limited to attorney's fees), resulting from or arising out of a) your use and access of the data, by you or any person using your account and password; b) a breach of these terms; or c) content posted on the platform.
Your use of the data is at your sole risk. The data are provided on an \"AS IS\" and \"AS AVAILABLE\" basis. The data are provided without warranties of any kind, whether express or implied, including, but not limited to, implied warranties of merchantability, fitness for a particular purpose, non-infringement or course of performance.
In no event shall Map of Life, Yale University, nor its directors, employees, partners, agents, suppliers, or affiliates, be liable for any indirect, incidental, special, consequential or punitive damages, including without limitation, loss of profits, data, use, goodwill, or other intangible losses, resulting from (i) your access to or use of or inability to access or use the data; (ii) any conduct or content of any third party on the data; and (iii) unauthorized access, use or alteration of your transmissions or content, whether based on warranty, contract, tort (including negligence) or any other legal theory, whether or not we have been informed of the possibility of such damage, and even if a remedy set forth herein is found to have failed of its essential purpose.
For more information, refer to the Map of Life Terms of Service.
As part of the UN's Convention to Combat Desertification (UNCCD), South Africa committed in 2018 to achieve Land Degradation Neutrality (LDN) by 2030. This commitment presents a pledge to restore vast swathes of degraded land, which, owing to South Africa's history and geography, is complex.
Land degradation can be defined as a negative trend in land conditions caused by both direct and indirect human-induced processes. This results in long term reduction or loss of biological productivity, ecological integrity and/or value to humans (IPCC, 2019).
", "keyword": [ "Economics", "Ecological Restoration", "Degradation" ], "issued": "2022-10-06T05:33:03.000Z", "modified": "2023-07-05T08:16:53.000Z", "publisher": { "name": "ASSET Research" }, "contactPoint": { "@type": "vcard:Contact", "fn": "Curation", "hasEmail": "mailto:datascience@SAEON.ac.za" }, "accessLevel": "public", "spatial": "", "license": "https://creativecommons.org/licenses/by-sa/4.0", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://wb-sdgs.hub.arcgis.com/apps/NRF-SAEON::the-economics-of-restoration" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://storymaps.arcgis.com/stories/7730d19a78c24a79af307bc9a5186ef5" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=556e478754e44688938e4e4fa7585f5b", "landingPage": "https://wb-sdgs.hub.arcgis.com/apps/africageoportal::how-i-discovered-a-career-in-gis-", "title": "HOW I DISCOVERED A CAREER IN GIS.", "description": "I’d love to begin by saying that I have not “arrived” as I believe I am still on a journey of self-discovery. I have heard people say that they find my journey quite interesting and I hope my story inspires someone out there.
I had my first encounter with Geographic Information System (GIS) in the third year of my undergraduate study in Geography at the University of Ibadan, Oyo State Nigeria. I was opportune to be introduced to the essentials of GIS by one of the prominent Environmental and Urban Geographers in person of Dr O.J Taiwo. Even though the whole syllabus and teaching sounded abstract to me due to the little exposure to a practical hands-on approach to GIS software, I developed a keen interest in the theoretical learning and I ended up scoring 70% in my final course exam.
", "keyword": [ "GIS", "Career", "Geographer" ], "issued": "2020-06-04T17:19:00.000Z", "modified": "2023-01-29T15:51:39.000Z", "publisher": { "name": "Africa GeoPortal" }, "contactPoint": { "@type": "vcard:Contact", "fn": "togunwumi55", "hasEmail": "mailto: africageoportal@esri.com" }, "accessLevel": "public", "spatial": "", "license": "", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://wb-sdgs.hub.arcgis.com/apps/africageoportal::how-i-discovered-a-career-in-gis-" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://storymaps.arcgis.com/stories/556e478754e44688938e4e4fa7585f5b" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=1dcd3c200a8b487faefd86aa4c39e3a5&sublayer=0", "landingPage": "https://wb-sdgs.hub.arcgis.com/maps/1dcd3c200a8b487faefd86aa4c39e3a5_0", "title": "2030 Business as Usual (BAU) land cover scenario for Pemba island, Zanzibar", "description": "The aim of the map was to identify areas which should be targeted for future conservation policy. Map layer demonstrates modelling outputs for a 2030 Business as Usual Scenario for the island of Pemba Zanzibar. Two historical Corpencious land cover maps for the years 2015 and 2017 were analyzed in the land change modeler module in TerrSet (2020) to detect land cover transitions. Community-based workshops were held in Macho Mane, Chumbageni, Mji Mpya, Mfikiwa and Pujini in 2019 to investigate perceived land cover transitions and identify drivers. Where possible the explanatory variables which local communities' thought might be associated with such changes were used to help explain such changes using multi-perceptron analysis. The output of this analysis was used to create a projection of land cover for the year 2030, which showed a potential increase in sea water submergence, wetland areas and built-up land, alongside a decrease in permanent waterbodies and forested spaces. More details about this project can be found here: Rebecca Newman - Africa Network at York, University of York.", "keyword": [ "Zanzibar", " East Africa", " Scenarios", " Stakeholders", " Land cover change" ], "issued": "2022-10-18T18:19:53.000Z", "modified": "2022-10-26T17:53:14.000Z", "publisher": { "name": "{{source}}" }, "contactPoint": { "@type": "vcard:Contact", "fn": "rjsn500_uoy", "hasEmail": "{{orgContactEmail}}" }, "accessLevel": "public", "spatial": "39.5427,-5.5149,39.9276,-4.8343", "license": "https://creativecommons.org/licenses/by/4.0", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://wb-sdgs.hub.arcgis.com/maps/1dcd3c200a8b487faefd86aa4c39e3a5_0" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://tiles.arcgis.com/tiles/WN5coiM8DFAUrNQ2/arcgis/rest/services/Pemba2030comp/MapServer/0" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=da6582807bd642a0921501a9db03ead8&sublayer=0", "landingPage": "https://wb-sdgs.hub.arcgis.com/datasets/EsriUkeducation::amenities-in-kibera-nairobi", "title": "Amenities in Kibera, Nairobi", "description": "The data presented was downloaded from OpenStreetmap on April 11th 2017. Much of what is presented is data collected via the Map Kibera project. Once mapped, settlements take on an air of permanence, for places like Kibera are not planned, often on waste or unused land. People have no legal right to be there, so the settlement may be cleared at any time. In the words off the Map Kibera project web site, this is putting 'marginalised communities on the map.'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 work is licensed under the Esri Master License Agreement.
View Summary | View Terms of Use
", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://wb-sdgs.hub.arcgis.com/apps/Story::living-in-the-age-of-humans" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://storymaps.esri.com/stories/2018/anthropocene/index.html" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=cb5f86383b794dbaa0c207186d29fbae&sublayer=0", "landingPage": "https://wb-sdgs.hub.arcgis.com/datasets/africa::geoglam-sub-national-crop-calendars", "title": "GEOGLAM Sub-national Crop Calendars", "description": "The Crop Monitor sub-national crop calendars are based on a combination of the best available data from different international agencies, national ministries, and expert knowledge from country partners. For the Crop Monitor for AMIS sub-national calendars, the USDA Major World Crop Areas and Climatic Profiles was used as a base to create the crop calendars at the national level. The Crop Monitor for Early Warning sub-national calendars used FAO GIEWS, FEWS NET, and national ministries as a base to create the crop calendars at the national level. Then for both Crop Monitors, each country was broken down into sub-national regions through the combination of agro-ecological zones, administrative level 1 FAO GAUL units, production statistics, and national partner inputs. The sub-national regions are each an aggregation of administrative FAO GAUL level 1 units or in some cases the administrative level 1 units themselves.", "keyword": [], "issued": "2022-02-07T18:44:07.000Z", "modified": "2022-02-07T18:45:02.651Z", "publisher": { "name": "Africa GeoPortal" }, "contactPoint": { "@type": "vcard:Contact", "fn": "esri_africa", "hasEmail": "https://support.esri.com" }, "accessLevel": "public", "spatial": "-92.2313,-46.9814,130.6749,55.4318", "license": "", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://wb-sdgs.hub.arcgis.com/datasets/africa::geoglam-sub-national-crop-calendars" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://services8.arcgis.com/zNrTBuYXV2f35M0U/arcgis/rest/services/Sub-national_Crop_Calendars/FeatureServer/0" }, { "@type": "dcat:Distribution", "title": "CSV", "format": "CSV", "mediaType": "text/csv", "accessURL": "https://wb-sdgs.hub.arcgis.com/api/download/v1/items/cb5f86383b794dbaa0c207186d29fbae/csv?layers=0" }, { "@type": "dcat:Distribution", "title": "Shapefile", "format": "ZIP", "mediaType": "application/zip", "accessURL": "https://wb-sdgs.hub.arcgis.com/api/download/v1/items/cb5f86383b794dbaa0c207186d29fbae/shapefile?layers=0" }, { "@type": "dcat:Distribution", "title": "GeoJSON", "format": "GeoJSON", "mediaType": "application/vnd.geo+json", "accessURL": "https://wb-sdgs.hub.arcgis.com/api/download/v1/items/cb5f86383b794dbaa0c207186d29fbae/geojson?layers=0" }, { "@type": "dcat:Distribution", "title": "KML", "format": "KML", "mediaType": "application/vnd.google-earth.kml+xml", "accessURL": "https://wb-sdgs.hub.arcgis.com/api/download/v1/items/cb5f86383b794dbaa0c207186d29fbae/kml?layers=0" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=e04ea4f7ecbb4e7fb0325dfa24af6969", "landingPage": "https://wb-sdgs.hub.arcgis.com/datasets/UNEP-WCMC::above-and-below-ground-biomass-carbon-and-soil-organic-carbon-to-1m-depth-tonnes-ha", "title": "Above and below ground biomass carbon and soil organic carbon to 1m depth (tonnes/ha)", "description": "This dataset represents above- and below-ground terrestrial carbon\nstorage (tonnes (t) of C per hectare (ha)) for circa 2010.
This layer supports analysis but, if needed, a direct download of the data can be accessed here.
The dataset was constructed by combining the most reliable publicly\navailable datasets and overlying them with the ESA CCI landcover map for the\nyear 2010 [ESA, 2017], assigning to each grid cell the corresponding\nabove-ground biomass value from the biomass map that was most appropriate for\nthe grid cell’s landcover type.
Input carbon datasets were identified through a literature review of\nexisting datasets on biomass carbon in terrestrial ecosystems published in\npeer-reviewed literature. To determine which datasets to combine to produce the\nglobal carbon density map, identified datasets were evaluated based on\nresolution, accuracy, biomass definition and reference date (see table 1 for\nfurther information on datasets selected).
| \n Dataset \n | \n \n Scope \n | \n \n Year \n | \n \n Resolution \n | \n \n Definition \n | \n
| \n Santoro et al. 2018 \n | \n \n Global \n | \n \n 2010 \n | \n \n 100 m \n | \n \n Above-ground woody biomass for trees\n that are >10 cm diameter-at-breast-height, masked to Landsat-derived\n canopy cover for 2010; biomass is expressed as oven-dry weight of the woody\n parts (stem, bark, branches and twigs) of all living trees excluding stump\n and roots. \n | \n
| \n Xia et al. 2014 \n | \n \n Global \n | \n \n 1982-2006 \n | \n \n 8 km \n | \n \n Above-ground grassland biomass. \n | \n
| \n Bouvet et al. 2018 \n | \n \n Africa \n | \n \n 2010 \n | \n \n 25 m \n | \n \n Above-ground woodland and savannah\n biomass; low woody biomass areas, which therefore exclude dense forests and\n deserts. \n | \n
| \n Spawn et al. 2017 \n | \n \n Global \n | \n \n 2010 \n | \n \n 300 m \n | \n \n Synthetic, global above- and\n below-ground biomass maps that combine recently released satellite-based data\n of standing forest biomass with novel estimates for non-forest biomass\n stocks. \n | \n
After aggregating each selected dataset to a nominal scale of 300 m\nresolution, forest categories in the CCI ESA 2010 landcover dataset were used\nto extract above-ground biomass from Santoro et al. 2018 for forest areas.\nWoodland and savanna biomass were then incorporated for Africa from Bouvet et\nal. 2018., and from Santoro et al. 2018 for areas outside of Africa and outside\nof forest. Biomass from croplands, sparse vegetation and grassland landcover\nclasses from CCI ESA, in addition to shrubland areas outside Africa missing\nfrom Santoro et al. 2018, were extracted from were extracted from Xia et al.\n2014. and Spawn et al. 2017 averaged by ecological zone for each landcover\ntype.
Below-ground biomass were added using root-to-shoot ratios from the\n2006 IPCC guidelines for National Greenhouse Gas Inventories (IPCC, 2006). No\nbelow-ground values were assigned to croplands as ratios were unavailable.\nAbove- and below-ground biomass were then summed together and multiplied by 0.5\nto convert to carbon, generating a single above-and-below-ground biomass carbon\nlayer.
", "keyword": [ "unep-wcmc", "biomass", "carbon" ], "issued": "2021-05-04T14:55:46.000Z", "modified": "2021-12-08T15:43:25.000Z", "publisher": { "name": "UN Environment World Conservation Monitoring Centre" }, "contactPoint": { "@type": "vcard:Contact", "fn": "UNEP-WCMC-Publisher", "hasEmail": "mailto: info@unep-wcmc.org" }, "accessLevel": "public", "spatial": "-180.0000,-56.0035,180.0000,84.0000", "license": "https://creativecommons.org/licenses/by-sa/4.0", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://wb-sdgs.hub.arcgis.com/datasets/UNEP-WCMC::above-and-below-ground-biomass-carbon-and-soil-organic-carbon-to-1m-depth-tonnes-ha" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://data-gis.unep-wcmc.org/portal/sharing/servers/374a99fc76574f72bb8c71af7b428d0a/rest/services/World_Biomass_and_Soil_Carbon/ImageServer" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=be0086bae48b43968c2b4b87130706a3&sublayer=0", "landingPage": "https://wb-sdgs.hub.arcgis.com/datasets/be0086bae48b43968c2b4b87130706a3_0", "title": "Rarity Patterns for Restio Species of Southern Africa (1 km)", "description": "Overview
This layer shows species rarity patterns for restio species of southern Africa based on data availability. Species rarity is the average range-restrictedness of all species expected to occur in each grid cell. Here rarity is expressed as a raw value from low (purple) to high (yellow). NOTE: raw rarity values are very small (<1).
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.
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.
Data source used to predict species ranges
Occurrences: GBIF (2017). Global Biodiversity Information Facility Data Portal. Accessed on 5th July 2017.
Expert maps: IUCN (2017). International Union for Conservation of Nature - Red List of Threatened Species. Accessed on January 2017.
You may not distribute, modify, transmit, reuse, repost, copy, or use this content, whether in whole or in part, for commercial purposes or for personal gain, without express advance written permission from Map of Life. The data are and will remain the exclusive property of Map of Life, Yale University, and its licensors. These data are protected by copyright, trademark, and other laws of both the United States and foreign countries. Map of Life trademarks and trade dress may not be used in connection with any product or service without the prior written consent of Map of Life, Yale University.
By downloading this data, you agree to defend, indemnify and hold harmless Map of Life, Yale University and its licensee and licensors, and their employees, contractors, agents, officers and directors, from and against any and all claims, damages, obligations, losses, liabilities, costs or debt, and expenses (including but not limited to attorney's fees), resulting from or arising out of a) your use and access of the data, by you or any person using your account and password; b) a breach of these terms; or c) content posted on the platform.
Your use of the data is at your sole risk. The data are provided on an \"AS IS\" and \"AS AVAILABLE\" basis. The data are provided without warranties of any kind, whether express or implied, including, but not limited to, implied warranties of merchantability, fitness for a particular purpose, non-infringement or course of performance.
In no event shall Map of Life, Yale University, nor its directors, employees, partners, agents, suppliers, or affiliates, be liable for any indirect, incidental, special, consequential or punitive damages, including without limitation, loss of profits, data, use, goodwill, or other intangible losses, resulting from (i) your access to or use of or inability to access or use the data; (ii) any conduct or content of any third party on the data; and (iii) unauthorized access, use or alteration of your transmissions or content, whether based on warranty, contract, tort (including negligence) or any other legal theory, whether or not we have been informed of the possibility of such damage, and even if a remedy set forth herein is found to have failed of its essential purpose.
For more information, refer to the Map of Life Terms of Service.
Overview
This layer shows species rarity patterns for dragonfly species of southern Africa based on data availability. Species rarity is the average range-restrictedness of all species expected to occur in each grid cell. Here rarity is expressed as a raw value from low (purple) to high (yellow). NOTE: raw rarity values are very small (<1).
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.
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.
Data source used to predict species ranges
Occurrences: GBIF (2017). Global Biodiversity Information Facility Data Portal. Accessed on 5th July 2017.
Expert maps: IUCN (2017). International Union for Conservation of Nature - Red List of Threatened Species. Accessed on January 2017.
You may not distribute, modify, transmit, reuse, repost, copy, or use this content, whether in whole or in part, for commercial purposes or for personal gain, without express advance written permission from Map of Life. The data are and will remain the exclusive property of Map of Life, Yale University, and its licensors. These data are protected by copyright, trademark, and other laws of both the United States and foreign countries. Map of Life trademarks and trade dress may not be used in connection with any product or service without the prior written consent of Map of Life, Yale University.
By downloading this data, you agree to defend, indemnify and hold harmless Map of Life, Yale University and its licensee and licensors, and their employees, contractors, agents, officers and directors, from and against any and all claims, damages, obligations, losses, liabilities, costs or debt, and expenses (including but not limited to attorney's fees), resulting from or arising out of a) your use and access of the data, by you or any person using your account and password; b) a breach of these terms; or c) content posted on the platform.
Your use of the data is at your sole risk. The data are provided on an \"AS IS\" and \"AS AVAILABLE\" basis. The data are provided without warranties of any kind, whether express or implied, including, but not limited to, implied warranties of merchantability, fitness for a particular purpose, non-infringement or course of performance.
In no event shall Map of Life, Yale University, nor its directors, employees, partners, agents, suppliers, or affiliates, be liable for any indirect, incidental, special, consequential or punitive damages, including without limitation, loss of profits, data, use, goodwill, or other intangible losses, resulting from (i) your access to or use of or inability to access or use the data; (ii) any conduct or content of any third party on the data; and (iii) unauthorized access, use or alteration of your transmissions or content, whether based on warranty, contract, tort (including negligence) or any other legal theory, whether or not we have been informed of the possibility of such damage, and even if a remedy set forth herein is found to have failed of its essential purpose.
For more information, refer to the Map of Life Terms of Service.
Overview
This layer shows species rarity patterns for reptile species of southern Africa based on data availability. Species rarity is the average range-restrictedness of all species expected to occur in each grid cell. Here rarity is expressed as a raw value from low (purple) to high (yellow). NOTE: raw rarity values are very small (<1).
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.
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.
Data source used to predict species ranges
Occurrences: GBIF (2017). Global Biodiversity Information Facility Data Portal. Accessed on 5th July 2017.
Expert maps: IUCN (2017). International Union for Conservation of Nature - Red List of Threatened Species. Accessed on January 2017.
You may not distribute, modify, transmit, reuse, repost, copy, or use this content, whether in whole or in part, for commercial purposes or for personal gain, without express advance written permission from Map of Life. The data are and will remain the exclusive property of Map of Life, Yale University, and its licensors. These data are protected by copyright, trademark, and other laws of both the United States and foreign countries. Map of Life trademarks and trade dress may not be used in connection with any product or service without the prior written consent of Map of Life, Yale University.
By downloading this data, you agree to defend, indemnify and hold harmless Map of Life, Yale University and its licensee and licensors, and their employees, contractors, agents, officers and directors, from and against any and all claims, damages, obligations, losses, liabilities, costs or debt, and expenses (including but not limited to attorney's fees), resulting from or arising out of a) your use and access of the data, by you or any person using your account and password; b) a breach of these terms; or c) content posted on the platform.
Your use of the data is at your sole risk. The data are provided on an \"AS IS\" and \"AS AVAILABLE\" basis. The data are provided without warranties of any kind, whether express or implied, including, but not limited to, implied warranties of merchantability, fitness for a particular purpose, non-infringement or course of performance.
In no event shall Map of Life, Yale University, nor its directors, employees, partners, agents, suppliers, or affiliates, be liable for any indirect, incidental, special, consequential or punitive damages, including without limitation, loss of profits, data, use, goodwill, or other intangible losses, resulting from (i) your access to or use of or inability to access or use the data; (ii) any conduct or content of any third party on the data; and (iii) unauthorized access, use or alteration of your transmissions or content, whether based on warranty, contract, tort (including negligence) or any other legal theory, whether or not we have been informed of the possibility of such damage, and even if a remedy set forth herein is found to have failed of its essential purpose.
For more information, refer to the Map of Life Terms of Service.
Overview
This layer shows species rarity patterns for bird species of southern Africa based on data availability. Species rarity is the average range-restrictedness of all species expected to occur in each grid cell. Here rarity is expressed as a raw value from low (purple) to high (yellow). NOTE: raw rarity values are very small (<1).
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.
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.
Data source used to predict species ranges
Occurrences: GBIF (2017). Global Biodiversity Information Facility Data Portal. Accessed on 5th July 2017.
Expert maps: IUCN (2017). International Union for Conservation of Nature - Red List of Threatened Species. Accessed on January 2017.
You may not distribute, modify, transmit, reuse, repost, copy, or use this content, whether in whole or in part, for commercial purposes or for personal gain, without express advance written permission from Map of Life. The data are and will remain the exclusive property of Map of Life, Yale University, and its licensors. These data are protected by copyright, trademark, and other laws of both the United States and foreign countries. Map of Life trademarks and trade dress may not be used in connection with any product or service without the prior written consent of Map of Life, Yale University.
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Your use of the data is at your sole risk. The data are provided on an \"AS IS\" and \"AS AVAILABLE\" basis. The data are provided without warranties of any kind, whether express or implied, including, but not limited to, implied warranties of merchantability, fitness for a particular purpose, non-infringement or course of performance.
In no event shall Map of Life, Yale University, nor its directors, employees, partners, agents, suppliers, or affiliates, be liable for any indirect, incidental, special, consequential or punitive damages, including without limitation, loss of profits, data, use, goodwill, or other intangible losses, resulting from (i) your access to or use of or inability to access or use the data; (ii) any conduct or content of any third party on the data; and (iii) unauthorized access, use or alteration of your transmissions or content, whether based on warranty, contract, tort (including negligence) or any other legal theory, whether or not we have been informed of the possibility of such damage, and even if a remedy set forth herein is found to have failed of its essential purpose.
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Overview
This layer shows species rarity patterns for mammal species of southern Africa based on data availability. Species rarity is the average range-restrictedness of all species expected to occur in each grid cell. Here rarity is expressed as a raw value from low (purple) to high (yellow). NOTE: raw rarity values are very small (<1).
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.
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.
Data source used to predict species ranges
Occurrences: GBIF (2017). Global Biodiversity Information Facility Data Portal. Accessed on 5th July 2017.
Expert maps: IUCN (2017). International Union for Conservation of Nature - Red List of Threatened Species. Accessed on January 2017.
You may not distribute, modify, transmit, reuse, repost, copy, or use this content, whether in whole or in part, for commercial purposes or for personal gain, without express advance written permission from Map of Life. The data are and will remain the exclusive property of Map of Life, Yale University, and its licensors. These data are protected by copyright, trademark, and other laws of both the United States and foreign countries. Map of Life trademarks and trade dress may not be used in connection with any product or service without the prior written consent of Map of Life, Yale University.
By downloading this data, you agree to defend, indemnify and hold harmless Map of Life, Yale University and its licensee and licensors, and their employees, contractors, agents, officers and directors, from and against any and all claims, damages, obligations, losses, liabilities, costs or debt, and expenses (including but not limited to attorney's fees), resulting from or arising out of a) your use and access of the data, by you or any person using your account and password; b) a breach of these terms; or c) content posted on the platform.
Your use of the data is at your sole risk. The data are provided on an \"AS IS\" and \"AS AVAILABLE\" basis. The data are provided without warranties of any kind, whether express or implied, including, but not limited to, implied warranties of merchantability, fitness for a particular purpose, non-infringement or course of performance.
In no event shall Map of Life, Yale University, nor its directors, employees, partners, agents, suppliers, or affiliates, be liable for any indirect, incidental, special, consequential or punitive damages, including without limitation, loss of profits, data, use, goodwill, or other intangible losses, resulting from (i) your access to or use of or inability to access or use the data; (ii) any conduct or content of any third party on the data; and (iii) unauthorized access, use or alteration of your transmissions or content, whether based on warranty, contract, tort (including negligence) or any other legal theory, whether or not we have been informed of the possibility of such damage, and even if a remedy set forth herein is found to have failed of its essential purpose.
For more information, refer to the Map of Life Terms of Service.
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.
You may not distribute, modify, transmit, reuse, repost, copy, or use this content, whether in whole or in part, for commercial purposes or for personal gain, without express advance written permission from Map of Life. The data are and will remain the exclusive property of Map of Life, Yale University, and its licensors. These data are protected by copyright, trademark, and other laws of both the United States and foreign countries. Map of Life trademarks and trade dress may not be used in connection with any product or service without the prior written consent of Map of Life, Yale University.
By downloading this data, you agree to defend, indemnify and hold harmless Map of Life, Yale University and its licensee and licensors, and their employees, contractors, agents, officers and directors, from and against any and all claims, damages, obligations, losses, liabilities, costs or debt, and expenses (including but not limited to attorney's fees), resulting from or arising out of a) your use and access of the data, by you or any person using your account and password; b) a breach of these terms; or c) content posted on the platform.
Your use of the data is at your sole risk. The data are provided on an \"AS IS\" and \"AS AVAILABLE\" basis. The data are provided without warranties of any kind, whether express or implied, including, but not limited to, implied warranties of merchantability, fitness for a particular purpose, non-infringement or course of performance.
In no event shall Map of Life, Yale University, nor its directors, employees, partners, agents, suppliers, or affiliates, be liable for any indirect, incidental, special, consequential or punitive damages, including without limitation, loss of profits, data, use, goodwill, or other intangible losses, resulting from (i) your access to or use of or inability to access or use the data; (ii) any conduct or content of any third party on the data; and (iii) unauthorized access, use or alteration of your transmissions or content, whether based on warranty, contract, tort (including negligence) or any other legal theory, whether or not we have been informed of the possibility of such damage, and even if a remedy set forth herein is found to have failed of its essential purpose.
For more information, refer to the Map of Life Terms of Service.
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 rarity in this case refers to the “average range-size rarity”, also called “range size rarity.” It is the average of the inverse global range sizes of species predicted to occur in a grid cell. Here rarity is 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.
You may not distribute, modify, transmit, reuse, repost, copy, or use this content, whether in whole or in part, for commercial purposes or for personal gain, without express advance written permission from Map of Life. The data are and will remain the exclusive property of Map of Life, Yale University, and its licensors. These data are protected by copyright, trademark, and other laws of both the United States and foreign countries. Map of Life trademarks and trade dress may not be used in connection with any product or service without the prior written consent of Map of Life, Yale University.
By downloading this data, you agree to defend, indemnify and hold harmless Map of Life, Yale University and its licensee and licensors, and their employees, contractors, agents, officers and directors, from and against any and all claims, damages, obligations, losses, liabilities, costs or debt, and expenses (including but not limited to attorney's fees), resulting from or arising out of a) your use and access of the data, by you or any person using your account and password; b) a breach of these terms; or c) content posted on the platform.
Your use of the data is at your sole risk. The data are provided on an \"AS IS\" and \"AS AVAILABLE\" basis. The data are provided without warranties of any kind, whether express or implied, including, but not limited to, implied warranties of merchantability, fitness for a particular purpose, non-infringement or course of performance.
In no event shall Map of Life, Yale University, nor its directors, employees, partners, agents, suppliers, or affiliates, be liable for any indirect, incidental, special, consequential or punitive damages, including without limitation, loss of profits, data, use, goodwill, or other intangible losses, resulting from (i) your access to or use of or inability to access or use the data; (ii) any conduct or content of any third party on the data; and (iii) unauthorized access, use or alteration of your transmissions or content, whether based on warranty, contract, tort (including negligence) or any other legal theory, whether or not we have been informed of the possibility of such damage, and even if a remedy set forth herein is found to have failed of its essential purpose.
For more information, refer to the Map of Life Terms of Service.
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.
You may not distribute, modify, transmit, reuse, repost, copy, or use this content, whether in whole or in part, for commercial purposes or for personal gain, without express advance written permission from Map of Life. The data are and will remain the exclusive property of Map of Life, Yale University, and its licensors. These data are protected by copyright, trademark, and other laws of both the United States and foreign countries. Map of Life trademarks and trade dress may not be used in connection with any product or service without the prior written consent of Map of Life, Yale University.
By downloading this data, you agree to defend, indemnify and hold harmless Map of Life, Yale University and its licensee and licensors, and their employees, contractors, agents, officers and directors, from and against any and all claims, damages, obligations, losses, liabilities, costs or debt, and expenses (including but not limited to attorney's fees), resulting from or arising out of a) your use and access of the data, by you or any person using your account and password; b) a breach of these terms; or c) content posted on the platform.
Your use of the data is at your sole risk. The data are provided on an \"AS IS\" and \"AS AVAILABLE\" basis. The data are provided without warranties of any kind, whether express or implied, including, but not limited to, implied warranties of merchantability, fitness for a particular purpose, non-infringement or course of performance.
In no event shall Map of Life, Yale University, nor its directors, employees, partners, agents, suppliers, or affiliates, be liable for any indirect, incidental, special, consequential or punitive damages, including without limitation, loss of profits, data, use, goodwill, or other intangible losses, resulting from (i) your access to or use of or inability to access or use the data; (ii) any conduct or content of any third party on the data; and (iii) unauthorized access, use or alteration of your transmissions or content, whether based on warranty, contract, tort (including negligence) or any other legal theory, whether or not we have been informed of the possibility of such damage, and even if a remedy set forth herein is found to have failed of its essential purpose.
For more information, refer to the Map of Life Terms of Service.
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.
You may not distribute, modify, transmit, reuse, repost, copy, or use this content, whether in whole or in part, for commercial purposes or for personal gain, without express advance written permission from Map of Life. The data are and will remain the exclusive property of Map of Life, Yale University, and its licensors. These data are protected by copyright, trademark, and other laws of both the United States and foreign countries. Map of Life trademarks and trade dress may not be used in connection with any product or service without the prior written consent of Map of Life, Yale University.
By downloading this data, you agree to defend, indemnify and hold harmless Map of Life, Yale University and its licensee and licensors, and their employees, contractors, agents, officers and directors, from and against any and all claims, damages, obligations, losses, liabilities, costs or debt, and expenses (including but not limited to attorney's fees), resulting from or arising out of a) your use and access of the data, by you or any person using your account and password; b) a breach of these terms; or c) content posted on the platform.
Your use of the data is at your sole risk. The data are provided on an \"AS IS\" and \"AS AVAILABLE\" basis. The data are provided without warranties of any kind, whether express or implied, including, but not limited to, implied warranties of merchantability, fitness for a particular purpose, non-infringement or course of performance.
In no event shall Map of Life, Yale University, nor its directors, employees, partners, agents, suppliers, or affiliates, be liable for any indirect, incidental, special, consequential or punitive damages, including without limitation, loss of profits, data, use, goodwill, or other intangible losses, resulting from (i) your access to or use of or inability to access or use the data; (ii) any conduct or content of any third party on the data; and (iii) unauthorized access, use or alteration of your transmissions or content, whether based on warranty, contract, tort (including negligence) or any other legal theory, whether or not we have been informed of the possibility of such damage, and even if a remedy set forth herein is found to have failed of its essential purpose.
For more information, refer to the Map of Life Terms of Service.
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.
You may not distribute, modify, transmit, reuse, repost, copy, or use this content, whether in whole or in part, for commercial purposes or for personal gain, without express advance written permission from Map of Life. The data are and will remain the exclusive property of Map of Life, Yale University, and its licensors. These data are protected by copyright, trademark, and other laws of both the United States and foreign countries. Map of Life trademarks and trade dress may not be used in connection with any product or service without the prior written consent of Map of Life, Yale University.
By downloading this data, you agree to defend, indemnify and hold harmless Map of Life, Yale University and its licensee and licensors, and their employees, contractors, agents, officers and directors, from and against any and all claims, damages, obligations, losses, liabilities, costs or debt, and expenses (including but not limited to attorney's fees), resulting from or arising out of a) your use and access of the data, by you or any person using your account and password; b) a breach of these terms; or c) content posted on the platform.
Your use of the data is at your sole risk. The data are provided on an \"AS IS\" and \"AS AVAILABLE\" basis. The data are provided without warranties of any kind, whether express or implied, including, but not limited to, implied warranties of merchantability, fitness for a particular purpose, non-infringement or course of performance.
In no event shall Map of Life, Yale University, nor its directors, employees, partners, agents, suppliers, or affiliates, be liable for any indirect, incidental, special, consequential or punitive damages, including without limitation, loss of profits, data, use, goodwill, or other intangible losses, resulting from (i) your access to or use of or inability to access or use the data; (ii) any conduct or content of any third party on the data; and (iii) unauthorized access, use or alteration of your transmissions or content, whether based on warranty, contract, tort (including negligence) or any other legal theory, whether or not we have been informed of the possibility of such damage, and even if a remedy set forth herein is found to have failed of its essential purpose.
For more information, refer to the Map of Life Terms of Service.
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.
You may not distribute, modify, transmit, reuse, repost, copy, or use this content, whether in whole or in part, for commercial purposes or for personal gain, without express advance written permission from Map of Life. The data are and will remain the exclusive property of Map of Life, Yale University, and its licensors. These data are protected by copyright, trademark, and other laws of both the United States and foreign countries. Map of Life trademarks and trade dress may not be used in connection with any product or service without the prior written consent of Map of Life, Yale University.
By downloading this data, you agree to defend, indemnify and hold harmless Map of Life, Yale University and its licensee and licensors, and their employees, contractors, agents, officers and directors, from and against any and all claims, damages, obligations, losses, liabilities, costs or debt, and expenses (including but not limited to attorney's fees), resulting from or arising out of a) your use and access of the data, by you or any person using your account and password; b) a breach of these terms; or c) content posted on the platform.
Your use of the data is at your sole risk. The data are provided on an \"AS IS\" and \"AS AVAILABLE\" basis. The data are provided without warranties of any kind, whether express or implied, including, but not limited to, implied warranties of merchantability, fitness for a particular purpose, non-infringement or course of performance.
In no event shall Map of Life, Yale University, nor its directors, employees, partners, agents, suppliers, or affiliates, be liable for any indirect, incidental, special, consequential or punitive damages, including without limitation, loss of profits, data, use, goodwill, or other intangible losses, resulting from (i) your access to or use of or inability to access or use the data; (ii) any conduct or content of any third party on the data; and (iii) unauthorized access, use or alteration of your transmissions or content, whether based on warranty, contract, tort (including negligence) or any other legal theory, whether or not we have been informed of the possibility of such damage, and even if a remedy set forth herein is found to have failed of its essential purpose.
For more information, refer to the Map of Life Terms of Service.
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.
You may not distribute, modify, transmit, reuse, repost, copy, or use this content, whether in whole or in part, for commercial purposes or for personal gain, without express advance written permission from Map of Life. The data are and will remain the exclusive property of Map of Life, Yale University, and its licensors. These data are protected by copyright, trademark, and other laws of both the United States and foreign countries. Map of Life trademarks and trade dress may not be used in connection with any product or service without the prior written consent of Map of Life, Yale University.
By downloading this data, you agree to defend, indemnify and hold harmless Map of Life, Yale University and its licensee and licensors, and their employees, contractors, agents, officers and directors, from and against any and all claims, damages, obligations, losses, liabilities, costs or debt, and expenses (including but not limited to attorney's fees), resulting from or arising out of a) your use and access of the data, by you or any person using your account and password; b) a breach of these terms; or c) content posted on the platform.
Your use of the data is at your sole risk. The data are provided on an \"AS IS\" and \"AS AVAILABLE\" basis. The data are provided without warranties of any kind, whether express or implied, including, but not limited to, implied warranties of merchantability, fitness for a particular purpose, non-infringement or course of performance.
In no event shall Map of Life, Yale University, nor its directors, employees, partners, agents, suppliers, or affiliates, be liable for any indirect, incidental, special, consequential or punitive damages, including without limitation, loss of profits, data, use, goodwill, or other intangible losses, resulting from (i) your access to or use of or inability to access or use the data; (ii) any conduct or content of any third party on the data; and (iii) unauthorized access, use or alteration of your transmissions or content, whether based on warranty, contract, tort (including negligence) or any other legal theory, whether or not we have been informed of the possibility of such damage, and even if a remedy set forth herein is found to have failed of its essential purpose.
For more information, refer to the Map of Life Terms of Service.
This dataset represents above- and below-ground terrestrial carbon\nstorage (tonnes (t) of C per hectare (ha)) for circa 2010.
This layer supports analysis but, if needed, a direct download of the data can be accessed here.
The dataset was constructed by combining the most reliable publicly\navailable datasets and overlying them with the ESA CCI landcover map for the\nyear 2010 [ESA, 2017], assigning to each grid cell the corresponding\nabove-ground biomass value from the biomass map that was most appropriate for\nthe grid cell’s landcover type.
Input carbon datasets were identified through a literature review of\nexisting datasets on biomass carbon in terrestrial ecosystems published in\npeer-reviewed literature. To determine which datasets to combine to produce the\nglobal carbon density map, identified datasets were evaluated based on\nresolution, accuracy, biomass definition and reference date (see table 1 for\nfurther information on datasets selected).
| \n Dataset \n | \n \n Scope \n | \n \n Year \n | \n \n Resolution \n | \n \n Definition \n | \n
| \n Santoro et al. 2018 \n | \n \n Global \n | \n \n 2010 \n | \n \n 100 m \n | \n \n Above-ground woody biomass for trees\n that are >10 cm diameter-at-breast-height, masked to Landsat-derived\n canopy cover for 2010; biomass is expressed as oven-dry weight of the woody\n parts (stem, bark, branches and twigs) of all living trees excluding stump\n and roots. \n | \n
| \n Xia et al. 2014 \n | \n \n Global \n | \n \n 1982-2006 \n | \n \n 8 km \n | \n \n Above-ground grassland biomass. \n | \n
| \n Bouvet et al. 2018 \n | \n \n Africa \n | \n \n 2010 \n | \n \n 25 m \n | \n \n Above-ground woodland and savannah\n biomass; low woody biomass areas, which therefore exclude dense forests and\n deserts. \n | \n
| \n Spawn et al. 2017 \n | \n \n Global \n | \n \n 2010 \n | \n \n 300 m \n | \n \n Synthetic, global above- and\n below-ground biomass maps that combine recently released satellite-based data\n of standing forest biomass with novel estimates for non-forest biomass\n stocks. \n | \n
After aggregating each selected dataset to a nominal scale of 300 m\nresolution, forest categories in the CCI ESA 2010 landcover dataset were used\nto extract above-ground biomass from Santoro et al. 2018 for forest areas.\nWoodland and savanna biomass were then incorporated for Africa from Bouvet et\nal. 2018., and from Santoro et al. 2018 for areas outside of Africa and outside\nof forest. Biomass from croplands, sparse vegetation and grassland landcover\nclasses from CCI ESA, in addition to shrubland areas outside Africa missing\nfrom Santoro et al. 2018, were extracted from were extracted from Xia et al.\n2014. and Spawn et al. 2017 averaged by ecological zone for each landcover\ntype.
Below-ground biomass were added using root-to-shoot ratios from the\n2006 IPCC guidelines for National Greenhouse Gas Inventories (IPCC, 2006). No\nbelow-ground values were assigned to croplands as ratios were unavailable.\nAbove- and below-ground biomass were then summed together and multiplied by 0.5\nto convert to carbon, generating a single above-and-below-ground biomass carbon\nlayer.
This dataset has not been validated.
", "keyword": [ "biomass", "carbon", "unep-wcmc" ], "issued": "2021-05-04T15:00:16.000Z", "modified": "2021-05-10T15:23:24.000Z", "publisher": { "name": "UN Environment World Conservation Monitoring Centre" }, "contactPoint": { "@type": "vcard:Contact", "fn": "UNEP-WCMC-Publisher", "hasEmail": "mailto: info@unep-wcmc.org" }, "accessLevel": "public", "spatial": "-180.0000,-62.8496,180.0000,89.0000", "license": "https://creativecommons.org/licenses/by-sa/4.0", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://wb-sdgs.hub.arcgis.com/datasets/UNEP-WCMC::above-and-below-ground-biomass-carbon-tonnes-ha" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://data-gis.unep-wcmc.org/portal/sharing/servers/8a8d4e24683a46e6b039aea78c8af20f/rest/services/World_Biomass_Carbon/ImageServer" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=d308acff91544f90af5f1ebdde50549f", "landingPage": "https://wb-sdgs.hub.arcgis.com/apps/Harvard-CGA::the-harvard-map-collection-presents-embellishing-the-maphow-cartographers-confronted-empty-spaces", "title": "The Harvard Map Collection Presents - Embellishing the Map:How Cartographers Confronted Empty Spaces", "description": "Take a looks at the Harvard Map Collection's interactive exhibit 'Embellishing the Map,' which explores the myriad varieties and uses of embellishments found on the library's extraordinary collection of maps.
This exhibition presents maps chosen from the Harvard Map Collection that display how European cartographers, mainly from the Low Countries of the 16th and 17th centuries, embellished maps with a variety of illustrative, non-cartographic elements. With echoes of the classical world’s anxiety of the “horror vacuii” (fear of empty spaces), the uncharted and unknown spaces are populated with sea creatures and animals, from the mythic and fantastic to the zoologically accurate, and many varieties of ships plying the open seas. All in their natural habitat, which is to say located on the land and seas of the map, not as artistic embellishments in cartouches or title panels (something for another exhibition, perhaps). The sources for the cartographic fauna run the gamut from classical sources (the histories of Herodotus and Pliny the Elder), Medieval bestiaries and compendiums of the natural world (Hortus Sanitatis), to accounts from the ever peripatetic explorers.
The maps are presented in loosely geographic order, beginning (where everything begins) with the heavens, then, after a medieval view of the known world, moves from the Western Hemisphere eastward to the Pacific Ocean. Besides the few modern, more thematic maps that have been included for contrast, chronologically this exhibition effectively ends before the ascendancy of the Royally sponsored French cartographers of the 18th century. The maps of Delisle, Bellin, d’Anville and the distinguished Cassini dynasty migrate the sea creatures, animals and ships to the pages and articles of Diederot’s grand Encyclopedia. What now is presented on the map reflects the science of cartography and measurement reigning supreme, not alas (as seen in the 1541 map “Tabula noua partis Africae”), a King riding a bridled Sea Carp!
", "keyword": [ "Story Map", "Map Journal", "libraries and museums", "Historical Maps", "cartography", "embellishments", "map design", "History and Culture", "animals", "historical" ], "issued": "2017-07-05T14:29:08.000Z", "modified": "2021-05-06T20:17:57.000Z", "publisher": { "name": "Center for Geographic Analysis @Harvard University " }, "contactPoint": { "@type": "vcard:Contact", "fn": "Harvard_Map_Collection", "hasEmail": "https://gis.harvard.edu/contactus" }, "accessLevel": "public", "spatial": "", "license": "http://library.harvard.edu/map", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://wb-sdgs.hub.arcgis.com/apps/Harvard-CGA::the-harvard-map-collection-presents-embellishing-the-maphow-cartographers-confronted-empty-spaces" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://harvard-cga.maps.arcgis.com/apps/MapJournal/index.html?appid=d308acff91544f90af5f1ebdde50549f" } ], "theme": "" }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=64aff05d66ff443caf9711fd988e21dd&sublayer=0", "landingPage": "https://wb-sdgs.hub.arcgis.com/datasets/africa::africa-countries", "title": "Africa Countries", "description": "This layer shows the countries of Africa. You can click on the map to get info on each country, including its name and flag, as well as links to detailed information in The World Factbook and UN Human Development Reports. This work is licensed under the Esri Master License Agreement.According to the United Nations, 54% of the world’s population resides in urban areas in the year 2014. It is projected that by 2050 this number will increase by 12%. The direct effect of this urban drift has had profound effects on social, economic and ecological systems, causing stresses on the environment and society. The social and economic implications include impacts from human activities such as transport, industrialization, combustion, construction etc., all of which have a direct or indirect bearing on the environment. These pollution sources have led to release of pollutants such as Nitrogen dioxide (NO2), Particulate Matter (PM) and Sulphur dioxide (SO2) into the atmosphere. It is believed that air pollution is influenced by urban dynamics.
In this project, we present a method for predicting historical air quality (as measured by daily median PM25 concentration) for locations where no ground-based sensors are present, by using weather data and remote sensing data from sources like the Sentinel 5P satellite.
Air quality data is obtained for 555 cities and supplemented by satellite and weather data. This is then used to build a model to predict the air quality for a given date and location. A competition hosted by Zindi was used to crowd-source the creation of the model used, with the winning code forming the basis of our modelling approach.
We use the trained model to create a new dataset of historical air quality predictions for cities across Africa, available at https://github.com/johnowhitaker/air_quality_prediction.
For access to the original data see https://search.datacite.org/works/10.15493/sarva.301020-2.
", "keyword": [ "Air quality", "PM2.5", "pollution", "satellite", "particulate", "timeseries" ], "issued": "2020-10-30T15:05:53.000Z", "modified": "2021-01-20T13:16:28.000Z", "publisher": { "name": "Zindi" }, "contactPoint": { "@type": "vcard:Contact", "fn": "Curation", "hasEmail": "mailto:datascience@SAEON.ac.za" }, "accessLevel": "public", "spatial": "8.6715,-36.3942,41.1031,-20.8274", "license": "https://creativecommons.org/licenses/by-sa/4.0", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://wb-sdgs.hub.arcgis.com/maps/NRF-SAEON::south-african-air-quality-pm2-5-predictions-date-selector" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=c87bf2864a1d4e639e89b09fda6df247", "landingPage": "https://wb-sdgs.hub.arcgis.com/maps/tunisia::ruines-de-carthage-tunisia-by-graphtech-", "title": "Ruines de Carthage (Tunisia) by Graphtech.", "description": "According to the United Nations, 54% of the world’s population resides in urban areas in the year 2014. It is projected that by 2050 this number will increase by 12%. The direct effect of this urban drift has had profound effects on social, economic and ecological systems, causing stresses on the environment and society. The social and economic implications include impacts from human activities such as transport, industrialization, combustion, construction etc., all of which have a direct or indirect bearing on the environment. These pollution sources have led to release of pollutants such as Nitrogen dioxide (NO2), Particulate Matter (PM) and Sulphur dioxide (SO2) into the atmosphere. It is believed that air pollution is influenced by urban dynamics.
In this project, we present a method for predicting historical air quality (as measured by daily median PM25 concentration) for locations where no ground-based sensors are present, by using weather data and remote sensing data from sources like the Sentinel 5P satellite.
Air quality data is obtained for 555 cities and supplemented by satellite and weather data. This is then used to build a model to predict the air quality for a given date and location. A competition hosted by Zindi was used to crowd-source the creation of the model used, with the winning code forming the basis of our modelling approach.
We use the trained model to create a new dataset of historical air quality predictions for cities across Africa, available at https://github.com/johnowhitaker/air_quality_prediction.
For access to the original data, see https://search.datacite.org/works/10.15493/sarva.301020-2.