AndreaBozzo/ceres-open-data-index huggingface dataset:
WorldPop Population Density 2000-2020 1km

Note: We have shared a new set of layers based on the newer [WorldPop Global2 model]. These layers are available in this [ArcGIS Online Group]. Please consider joining the group and switching to use these layers in the coming months. This layer contains [WorldPop's] 1-km resolution annual estimates of population density from the year 2000 to 2020. The estimates in this layer use WorldPop's [Top-down unconstrained method] which estimates the total population for each cell with a Random Forest-based model and converted these values to population density by dividing the number of people in each pixel by the pixel surface area. This diagram visually describes this model that uses known populated locations to analyze imagery to find similarly populated locations. [top down unconstrained] The DOI for the original WorldPop.org population density data is [10.5258/SOTON/WP00674]. Recommended Citation: WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation. Accessed from https://worldpop.arcgis.com/arcgis/rest/services/WorldPop_Population_Density_1km/ImageServer, which was acquired from [https://www.worldpop.org/doi/10.5258/SOTON/WP00674] on 15 Sep, 2021.
Watershed Boundary

All HydroBASINS layers were derived from World Wildlife Fund’s HydroSHEDS data based on a grid resolution of 15 arc-seconds (approximately 500 m at the equator). Watersheds were delineated in a consistent manner at different scales, and a hierarchical sub-basin breakdown was created following the topological concept of the Pfafstetter coding system. The resulting polygon layers are termed HydroBASINS and represent a subset of the HydroSHEDS database. This shapefile contains (sub-)basin polygons for Africa at Pfafstetter level 8.
WorldPop Total Population 2000-2020 1km

Note: We have shared a new set of layers based on the newer [WorldPop Global2 model]. These layers are available in this [ArcGIS Online Group]. Please consider joining the group and switching to use these layers in the coming months. This layer contains [WorldPop's] 1-km resolution annual estimates of total population from the year 2000 to 2020. WorldPop estimated the total population for each cell in this data using a random forest-based dasymetric redistribution model. The estimates in this layer use WorldPop's [Top-down unconstrained method] which estimates the total population for each cell with a Random Forest-based model. This diagram visually describes this model that uses known populated locations to analyze imagery to find similarly populated locations. [Top down unconstrained method] The DOI for the original WorldPop.org total population data is [10.5258/SOTON/WP00647]. Recommended Citation: WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation. Accessed from https://worldpop.arcgis.com/arcgis/rest/services/WorldPop_Total_Population_1km/ImageServer, which was acquired from [https://www.worldpop.org/doi/10.5258/SOTON/WP00647] on 15 Sep, 2021.
WorldPop Total Population 2000-2020 100m

Note: We have shared a new set of layers based on the newer [WorldPop Global2 model]. These layers are available in this [ArcGIS Online Group]. Please consider joining the group and switching to use these layers in the coming months. This layer contains [WorldPop's] 100-meter resolution annual estimates of total population from the year 2000 to 2020. Usage notes: * This layer is configured to be viewed only at a scale range for large-scale maps, i.e., zoomed into small areas of the world. * Because the underlying data for this layer is relatively large and because raster pyramids cannot accurately represent aggregated population counts, there are no pyramids. Thus, this layer may at times require 10 to 15 seconds to draw. * We recommend using this layer in in conjunction with [WorldPop's 1-km resolution Total Population layer] to create web maps that allow users to pan and zoom to wider areas; this [web map] contains an example of this combination. WorldPop estimated the total population for each cell in this data using a random forest-based dasymetric redistribution model (Stevens, F. R., Gaughan, A. E., Linard, C., & Tatem, A. J. (2015). [Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data]. PloS one, 10(2), e0107042). The estimates in this layer use WorldPop's [Top-down unconstrained method] which estimates the total population for each cell with a Random Forest-based dasymetric model. This diagram visually describes this model that uses known populated locations to analyze imagery to find similarly populated locations. [Top down unconstrained method] The DOI for the original WorldPop.org total population data is [10.5258/SOTON/WP00645]. Recommended Citation: WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation. Accessed from https://worldpop.arcgis.com/arcgis/rest/services/WorldPop_Total_Population_1km/ImageServer, which was acquired from https://www.worldpop.org/doi/10.5258/SOTON/WP00645 on 15 Sep, 2021.
Biodiversity Intactness Index 1970 to 2050

This map layer of Biodiversity Intactness Index (BII) summarizes the change in ecological communities in response to climate change and human pressures. The BII is an estimated percentage change from 1970 as the species baseline, and future abundance in any given area, based on 2050 predictions of human pressures and climate change. The map uses a divergent color schema Blue-Green-Yellow. In blue, we can see areas with little to no biodiversity loss, areas in green represent areas of biodiversity loss where most species remain viable, and areas in yellow represent the most extreme areas of biodiversity loss with the potential for significant species loss. Summary of the Biodiversity Intactness Index (BII) Methodology: "Using the PREDICTS database of local biodiversity measures at thousands of sites around the world, we statistically modelled how total abundance of organisms and compositional similarity responded to land use and related pressures. We combined these models with spatio-temporal projections of explanatory variables (at 0.25 degrees spatial resolution) from the year 1970 to 2050 under five Shared Socioeconomic Pathways (SSPs) to project the Biodiversity Intactness Index (BII). Mean BII (weighted by cell area) was calculated at the country, subregion, inter-region and global level. We used cross-validation (leaving one biome out in turn) to produce decadal upper and lower uncertainty margins for 1970-2050." To learn more details about the methodology please visit: [https://adrianadepalma.github.io/BII_tutorial/]
2010 Gridded Livestock of the World (GLW3) - Goats

Gridded Livestock of the World (GLW3) is a spatial dataset that shows the global distribution of the major types of livestock (cattle, sheep, goats, pigs, chickens, horses, buffalo, ducks). Currently in its third version, the distribution patterns refer to 2010 and are available at a spatial resolution of 5 arc-minutes, approximately 10 km at the equator. In this version (DA), livestock numbers are disaggregated within census polygons according to weights established by statistical models using high resolution spatial covariates ([dasymetric weighting]). For the detailed background and Metadata visit: [https://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1236449/]
2010 Gridded Livestock of the World (GLW3) - Pigs

Gridded Livestock of the World (GLW3) is a spatial dataset that shows the global distribution of the major types of livestock (cattle, sheep, goats, pigs, chickens, horses, buffalo, ducks). Currently in its third version, the distribution patterns refer to 2010 and are available at a spatial resolution of 5 arc-minutes, approximately 10 km at the equator. In this version (DA), livestock numbers are disaggregated within census polygons according to weights established by statistical models using high resolution spatial covariates ([dasymetric weighting]). For the detailed background and Metadata visit: [https://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1236449/]
Amboseli Ecosystem Elephant corridors

The Amboseli Wildlife Corridors are essential movement arteries identified by AWF through two+ decades of empirical data, including KWS-led aerial surveys, BigLife and AWF radio-collar tracking, AWF field observations.

Major Corridors (Tier 1) represent irreplaceable primary 'highway' segments like Amboseli-Kimana, while Minor Corridors (Tier 2) provide seasonal dispersal routes. Both are integrated into the GG Design Layer to secure movement routes through 2050.
2010 Gridded Livestock of the World (GLW3) - Sheep

Gridded Livestock of the World (GLW3) is a spatial dataset that shows the global distribution of the major types of livestock (cattle, sheep, goats, pigs, chickens, horses, buffalo, ducks). Currently in its third version, the distribution patterns refer to 2010 and are available at a spatial resolution of 5 arc-minutes, approximately 10 km at the equator. In this version (DA), livestock numbers are disaggregated within census polygons according to weights established by statistical models using high resolution spatial covariates ([dasymetric weighting]). For the detailed background and Metadata visit: [https://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1236449/]
WDPA - World Database of Protected Areas

The World Database on Protected Areas (WDPA) is the most comprehensive global database of marine and terrestrial protected areas, updated on a monthly basis, and is one of the key global biodiversity data sets being widely used by scientists, businesses, governments, International secretariats and others to inform planning, policy decisions and management. The WDPA is a joint project between UN Environment and the International Union for Conservation of Nature (IUCN). The compilation and management of the WDPA is carried out by UN Environment World Conservation Monitoring Centre (UNEP-WCMC), in collaboration with governments, non-governmental organisations, academia and industry. There are monthly updates of the data which are made available online through the Protected Planet website where the data is both viewable and downloadable. Data and information on the world's protected areas compiled in the WDPA are used for reporting to the Convention on Biological Diversity on progress towards reaching the Aichi Biodiversity Targets (particularly Target 11), to the UN to track progress towards the 2030 Sustainable Development Goals, to some of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) core indicators, and other international assessments and reports including the Global Biodiversity Outlook, as well as for the publication of the United Nations List of Protected Areas. Every two years, UNEP-WCMC releases the Protected Planet Report on the status of the world's protected areas and recommendations on how to meet international goals and targets. Many platforms are incorporating the WDPA to provide integrated information to diverse users, including businesses and governments, in a range of sectors including mining, oil and gas, and finance. For example, the WDPA is included in the Integrated Biodiversity Assessment Tool, an innovative decision support tool that gives users easy access to up-to-date information that allows them to identify biodiversity risks and opportunities within a project boundary. The reach of the WDPA is further enhanced in services developed by other parties, such as the Global Forest Watch and the Digital Observatory for Protected Areas, which provide decision makers with access to monitoring and alert systems that allow whole landscapes to be managed better. Together, these applications of the WDPA demonstrate the growing value and significance of the Protected Planet initiative.
European Space Agency Climate Change Initiative - Land Cover 2018

Beta Notice: This item is currently in [beta] and is intended for early access, testing, and feedback. It is not recommended for production use, as functionality and content are subject to change without notice. This layer displays global land cover in 10 classes for the year 2018 produced by the European Space Agency Climate Change Initiative (ESA CCI). The 36 classes of the traditional ESA CCI land cover has been simplified to match the Esri 2050 land cover prediction layers. Phenomenon mapped: Land Cover Data Projection: Cylindrical Equal Area Mosaic Projection: Cylindrical Equal Area Extent: Global Cell Size: 300m Source Type: Thematic Visible Scale: All scales Source: ESA Climate Change Initiative Publication date: 2018 ESA CCI land cover data for the years 1992-2019 are available [here]. What can you do with this layer? This layer may be displayed in a map to show generalized land cover classes for the world in the year 2018. This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro. Then, an area count of land cover types may be produced for a feature dataset using the zonal statistics tool. Statistics may be compared with the statistics from other years to show a trend. More technical documentation on the source datasets is available here: [https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=doc]
European Space Agency Climate Change Initiative - Land Cover 2010

Beta Notice: This item is currently in [beta] and is intended for early access, testing, and feedback. It is not recommended for production use, as functionality and content are subject to change without notice. This layer displays global land cover in 10 classes for the year 2010 produced by the [European Space Agency Climate Change Initiative] (ESA CCI). The 36 classes of the traditional ESA CCI land cover has been simplified to match the Esri 2050 land cover prediction layers. Phenomenon mapped: Land Cover Data Projection: Cylindrical Equal Area Mosaic Projection: Cylindrical Equal Area Extent: Global Cell Size: 300m Source Type: Thematic Visible Scale: All scales Source: ESA Climate Change Initiative Publication date: 2010 ESA CCI land cover data for the years 1992-2019 are available [here]. What can you do with this layer? This layer may be displayed in a map to show generalized land cover classes for the world in the year 2010. This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro. Then, an area count of land cover types may be produced for a feature dataset using the zonal statistics tool. Statistics may be compared with the statistics from other years to show a trend. More technical documentation on the source datasets is available here: [https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=doc] Index of all classes in this layer: 1. Mostly Cropland 2. Grassland, Scrub, or Shrub 3. Mostly Deciduous Forest 4. Mostly Needleleaf/Evergreen Forest 5. Sparse Vegetation 6. Bare Area 7. Swampy or Often Flooded Vegetation 8. Artificial Surface or Urban Area 9. Surface Water 10. Permanent Snow and Ice
OpenStreetMap Waterways for Africa

This feature layer provides access to OpenStreetMap (OSM) waterways 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 line (way) data in ArcGIS Online that is updated with minutely diffs from the OSM planet file. This feature layer view includes waterway features defined as a query against the hosted feature layer (i.e. waterway is not blank). In OSM, a waterway describes rivers, streams and ditches with a flow of water from one place to another. These features are identified with a [waterway tag]. There are hundreds of different tag values for waterway used in the OSM database. In this feature layer, unique symbols are used for several of the most popular waterway types, while lesser used types are grouped in an "other" category. Zoom in to large scales (e.g. City level or 1:80k scale) to see the waterway features display. You can click on a feature to get the name of the waterway (if available). The name of the waterway will display by default at large scales (e.g. Street level of 1:5k scale). Labels can be turned off in your map if you prefer. Create New Layer If you would like to create a more focused version of this waterway layer displaying just one or two waterway types, you can do that easily! Just add the layer to a map, copy the layer in the content window, add a filter to the new layer (e.g. waterway is dam), rename the layer as appropriate, and save layer. You can also change the layer symbols or popup if you like. Esri may publish a few such layers (e.g. streams and rivers) that are ready to use, but not for every type of waterway. Important Note: if you do create a new layer, it should be provided under the same Terms of Use and include the same Credits as this layer. You can copy and paste the Terms of Use and Credits info below in the new Item page as needed.
OpenStreetMap Highways for Africa

This feature layer provides access to OpenStreetMap (OSM) highways data for Africa, which is updated every 5 minutes with the latest edits. This [hosted feature layer view] is referencing a hosted feature layer of OSM line (way) data in ArcGIS Online that is updated with minutely diffs from the OSM planet file. This feature layer view includes highway features defined as a query against the hosted feature layer (i.e. highway is not blank). In OSM, a highway describes any kind of motorway, road, street or path. These features are identified with a [highway tag]. There are hundreds of different tag values for highway used in the OSM database. In this feature layer, unique symbols are used for several of the most popular highway types, while lesser used types are grouped in an "other" category. Zoom in to large scales (e.g. Streets level or 1:20k scale) to see the highway features display. You can click on a feature to get the name of the highway (if available). The name of the highway will display by default at large scales (e.g. Street level of 1:5k scale). Labels can be turned off in your map if you prefer. Create New Layer If you would like to create a more focused version of this highway layer displaying just one or two highway types, you can do that easily! Just add the layer to a map, copy the layer in the content window, add a filter to the new layer (e.g. highway is path), rename the layer as appropriate, and save layer. You can also change the layer symbols or popup if you like. Esri may publish a few such layers (e.g. cycleway and pedestrian) that are ready to use, but not for every type of highway. Important Note: if you do create a new layer, it should be provided under the same Terms of Use and include the same Credits as this layer. You can copy and paste the Terms of Use and Credits info below in the new Item page as needed.
2010 Gridded Livestock of the World (GLW3) - Cattle

Gridded Livestock of the World (GLW3) is a spatial dataset that shows the global distribution of the major types of livestock (cattle, sheep, goats, pigs, chickens, horses, buffalo, ducks). Currently in its third version, the distribution patterns refer to 2010 and are available at a spatial resolution of 5 arc-minutes, approximately 10 km at the equator. In this version (DA), livestock numbers are disaggregated within census polygons according to weights established by statistical models using high resolution spatial covariates ([dasymetric weighting]). For the detailed background and Metadata visit: [https://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1236449/]
2010 Gridded Livestock of the World (GLW3) - Chickens

Gridded Livestock of the World (GLW3) is a spatial dataset that shows the global distribution of the major types of livestock (cattle, sheep, goats, pigs, chickens, horses, buffalo, ducks). Currently in its third version, the distribution patterns refer to 2010 and are available at a spatial resolution of 5 arc-minutes, approximately 10 km at the equator. In this version (DA), livestock numbers are disaggregated within census polygons according to weights established by statistical models using high resolution spatial covariates ([dasymetric weighting]). For the detailed background and Metadata visit: [https://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1236449/]