This was developed for a forthcoming paper. A reference will be posted here when it is published.

This database supports the work of the Digital Elevation Model Intercomparison eXperiment (DEMIX) working group (Strobl and others, 2021; Guth and others, 2021; Bielski and others, 2024). The four files have the database tables in CSV format.

• Difference distributions for elevation, slope, and surface roughness. The provides continuity with \cite{BielskiOthers2024, GuthOthers2024}; for readers who want, it has the statistics like RMSE and LE90 for elevation and two LSPs, as well as the signed mean and median differences.
• FUV for a mixed suite of LSPs chosen to sample the full range of LSPs calculated from DEMs. These provide a better rankings of the test DEMs, and provides an estimate of the robustness of LSPs and suggest that some should be used with caution.
• FUV for the partial derivatives used for slope, aspect, and curvature.
• FUV for the suite of integrated curvature measures (Minar and others, 2020.

This version adds to CopDEM, ALOS AW3D30, and FABDEM:

• FathomDEM (Uhe and others, 2025)
• EDTM 1.1 (https://s3.eu-central-1.wasabisys.com/openlandmap/dtm/dtm.bareearth_ensemble_p10_30m_s_2018_go_epsg4326_v20230221.tif, https://doi.org/10.5281/zenodo.7676373)
• GEDTM 1.2—this is the best version of 1.2 (there are at least two others,which have no scaling for the elevations) (https://s3.opengeohub.org/global/dtm/v1.2/gedtm_rf_m_30m_s_20060101_20151231_go_epsg.4326.3855_v1.2.tif
• GEDTM 1.1—this has 0.9” spacing https://s3.opengeohub.org/global/edtm/gedtm_rf_m_30m_s_20060101_20151231_go_epsg.4326.3855_v20250611.tif, https://doi.org/10.5281/zenodo.15689805
• GEDTM v 0—this has 0.9” spacing https://s3.opengeohub.org/global/edtm/legendtm_rf_30m_m_s_20000101_20231231_go_epsg.4326_v20250130.tif, https://doi.org/10.5281/zenodo.14900181

The database contains 1381 tiles, about 10x10 km, in 140 areas. The tiles are based on the local projected grid, a change from earlier versions of the DEMIX database which used geographic outlines.

It does not consider the low altitude coastal DEMs; for those use version 3 (https://zenodo.org/records/13331458 ).

References:

Bielski, C.; López-Vázquez, C.; Grohmann, C.H.; Guth. P.L.; Hawker, L.; Gesch, D.; Trevisani, S.; Herrera-Cruz, V.; Riazanoff, S.; Corseaux, A.; Reuter, H.; Strobl, P., 2024. Novel approach for ranking DEMs: Copernicus DEM improves one arc second open global topography. IEEE Transactions on Geoscience & Remote Sensing. vol. 62, pp. 1-22, 2024, Art no. 4503922, https://doi.org/10.1109/TGRS.2024.3368015

Guth, P.L.; Trevisani, S.; Grohmann, C.H.; Lindsay, J.; Gesch, D.; Hawker, L.; Bielski, C. Ranking of 10 Global One-Arc-Second DEMs Reveals Limitations in Terrain Morphology Representation. Remote Sens. 2024, 16, 3273. https://doi.org/10.3390/rs16173273

Guth, P.L.; Van Niekerk, A.; Grohmann, C.H.; Muller, J.-P.; Hawker, L.; Florinsky, I.V.; Gesch, D.; Reuter, H.I.; Herrera-Cruz, V.; Riazanoff, S.; López-Vázquez, C.; Carabajal, C.C.; Albinet, C.; Strobl, P. Digital Elevation Models: Terminology and Definitions. Remote Sens. 2021, 13, 3581. https://doi.org/10.3390/rs13183581

Minár, J., Ian S. Evans, Marián Jenčo, 2020, A comprehensive system of definitions of land surface (topographic) curvatures, with implications for their application in geoscience modelling and prediction, Earth-Science Reviews, Volume 211, 103414, ISSN 0012-8252, https://doi.org/10.1016/j.earscirev.2020.103414

Strobl, P.A.; Bielski, C.; Guth, P.L.; Grohmann, C.H.; Muller, J.P.; López-Vázquez, C.; Gesch, D.B.; Amatulli, G.; Riazanoff, S.; Carabajal, C. The Digital Elevation Model Intercomparison eXperiment DEMIX, a community based approach at global DEM benchmarking. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2021, XLIII-B4-2021, 395–400. https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-395-2021

Uhe, P., Lucas, C., Hawker, L., Brine, M., Wilkinson, H., Cooper, A., & Sampson, C. (2025). FathomDEM: an improved global terrain map using a hybrid vision transformer model. Environmental Research Letters, 20(3), 034002. https://doi.org/10.1088/1748-9326/ada972

Plan submitted by: redistrictadmin on 10/15/2021 USER DESCRIPTION: In this version based off CD Test Map Version 3.4, CD Test Map Version 3.5 looks to match the eastern border of District 7 to that of District 7’s in CD Test Map Version 3.0 in order to achieve population balancing. The eastern border of District 6 is pushed east to take Tucson south of the Rillito River. District 6 is also pushed north into Pinal County, west of I-79. Additions to District 6 include Red Rock, Eloy, Arizona City, Picacho, Maricopa, and some of Casa Grande. For more information on the methodology used to create these boundaries, please visit: https://redistricting-irc-az.hub.arcgis.com/pages/draft-maps USER PLAN OBJECTIVE: N/A

This series of products from MODIS represents the only daily global composites available and is suitable for use at global and regional levels. This True Color band composition (Bands 1 4 3 | Red, Green, Blue) most accurately shows how we see the earth’s surface with our own eyes. It is a natural looking image that is useful for land surface, oceanic and atmospheric analysis. There are four True Color products in total. For each satellite (Aqua and Terra) there is a 250 meter corrected reflectance product and a 500 meter surface reflectance product. Although the resolution is coarser than other satellites, this allows for a global collection of imagery on a daily basis, which is made available in near real-time. In contrast, Landsat needs 16 days to collect a global composite. Besides the maximum resolution difference, the surface and corrected reflectance products also differ in the algorithm used for atmospheric correction.NASA Global Imagery Browse Services (GIBS)This image layer provides access to a subset of the NASA Global Imagery Browse Services (GIBS), which are a set of standard services to deliver global, full-resolution satellite imagery. The GIBS goal is to enable interactive exploration of NASA's Earth imagery for a broad range of users. The purpose of this image layer, and the other GIBS image services hosted by Esri, is to enable convenient access to this beautiful and useful satellite imagery for users of ArcGIS. The source data used by this image layer is a finished image; it is not recommended for quantitative analysis.Several full resolution, global imagery products are built and served by GIBS in near real-time (usually within 3.5 hours of observation). These products are built from NASA Earth Observing System satellites data courtesy of LANCE data providers and other sources. The MODIS instrument aboard Terra and Aqua satellites, the AIRS instrument aboard Aqua, and the OMI instrument aboard Aura are used as sources. Several of the MODIS global products are made available on this Esri hosted service.This image layer hosted by Esri provides direct access to one of the GIBS image products. The Esri servers do not store any of this data itself. Instead, for each received data request, multiple image tiles are retrieved from GIBS, which are then processed and assembled into the proper image for the response. This processing takes place on-the-fly, for each and every request. This ensures that any update to the GIBS data is immediately available in the Esri mosaic service.Note on Time: The image service supporting this map is time enabled, but time has been disabled on this image layer so that the most recent imagery displays by default. If you would like to view imagery over time, you can update the layer properties to enable time animation and configure time settings. The results can be saved in a web map to use later or share with others.

This map displays average July temperatures for the contiguous United States along with demographic and information on disadvantaged populations.This content was created to enhance the environmental education curriculum with additional tools, lesson improvements, and local Michigan data using geographic information systems (GIS) technology. Visit the geospatial learning portal at learn-egle.hub.arcgis.com. For questions and comments, reach out to EGLE-Classroom@Michigan.gov. For more information about the environmental education curriculum, see below.MEECS (Michigan Environmental Education Curriculum Support) is a state-specific environmental education curriculum funded and managed by EGLE to help students learn about Michigan's economy and environment through inquiry oriented, data-based lessons in Science and Social Studies.MEECS units apply to grades 3-12 and can be used individually, adopted into a school's multi-year science curricula, or combined to form the basis for an integrated science course. Since their development, MEECS lessons have been field tested by over 200 Michigan classrooms and have reached roughly 8,000 educators and 400,000 Michigan students.The MEECS Climate Change unit is a multi-faceted unit comprised of two separate functional units: Science and Impacts. Climate Change: Science focuses on the physical nature of climate, and focuses on causes, analysis, modeling, and an overall exploration into mechanisms of the Energy Cycle. Climate Change: Impacts focuses on the repercussions of climate change to engage more students to be future-oriented. The effects of climate change are examined at the global and local scales; special emphasis is placed on climate change as it pertains to Michigan's Great Lakes. (Last revised 2013; New unit to be released July 2023).

Retirement Notice: This beta item 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 layer represents CMIP6 future projections of temperature variation over an entire year. This layer can be used to compare with recent climate histories to better understand the potential impacts of future climate change.WorldClim produced this projection as part of a series of 19 bioclimate variables identified by the USGS and provides this description:"Bioclimatic variables are derived from the monthly temperature and rainfall values in order to generate more biologically meaningful variables. These are often used in species distribution modeling and related ecological modeling techniques. The bioclimatic variables represent annual trends (e.g., mean annual temperature, annual precipitation) seasonality (e.g., annual range in temperature and precipitation) and extreme or limiting environmental factors (e.g., temperature of the coldest and warmest month, and precipitation of the wet and dry quarters). A quarter is a period of three months (1/4 of the year)."Time Extent: averages from 2021-2040, 2041-2060, 2061-2080, 2081-2100Units: deg CCell Size: 2.5 minutes (~5 km)Source Type: StretchedPixel Type: 32 Bit FloatData Projection: GCS WGS84Mosaic Projection: GCS WGS84Extent: GlobalSource: WorldClim CMIP6 Bioclimate Climate ScenariosThe CMIP6 climate experiments use Shared Socioeconomic Pathways (SSPs) to model future climate scenarios. Each SSP pairs a human/community behavior component with the traditional RCP greenhouse gas forcing from the previous CMIP5. Three SSPs were chosen by Esri to be included in the service based on user requests: SSP2 4.5, SSP3 7.0 and SSP5 8.5.SSPScenarioEstimated warming(2041–2060)Estimated warming(2081–2100)Very likely range in °C(2081–2100)SSP2-4.5intermediate GHG emissions:CO2 emissions around current levels until 2050, then falling but not reaching net zero by 21002.0 °C2.7 °C2.1 – 3.5SSP3-7.0high GHG emissions:CO2 emissions double by 21002.1 °C3.6 °C2.8 – 4.6SSP5-8.5very high GHG emissions:CO2 emissions triple by 20752.4 °C4.4 °C3.3 – 5.7While the 8.5 scenario is no longer generally considered likely, SSP3 7.0 has been included and is considered the high end of possibilities. SSP5 8.5 has been retained since many organizations report to this threshold. The warming associated with SSP2 4.5 is equivalent to the global targets set at the 2021 United Nations COP26 meetings in Glasgow. Processing the Climate DataWorldClim provides 20-year averaged outputs for the various SSPs from 24 global climate models. A selection of 13 models were averaged for each variable and time based on Mahony et al 2022. These models included ACCESS-ESM1-5, BCC-CSM2-MR, CanESM5, CNRM-ESM2-1, EC-Earth3-Veg, GFDL-ESM4, GISS-E2-1-G, INM-CM5-0, IPSL-CM6A-LR, MIROC6, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL. GFDL-ESM4 was not available for SSP2 4.5 or SSP5 8.5. Accessing the Multidimensional InformationThe time and SSP scenario are built into the layer using a multidimensional raster. Enable the time slider to move across the 20-year average periods. In ArcGIS Online and Pro, use the Multidimensional Filter to select the SSP (SSP2 4.5 is the default). What can you do with this layer?These multidimensional imagery tiles support analysis using ArcGIS Online or Pro. Use the Bioclimate Baseline layer to see the difference in pixels and calculate change from the historic period into the future. Use the Multidimensional tab in ArcGIS Pro to access a variety of useful tools. Each layer or variable can be styled using the Image Display options. Known Quality IssuesEach model is downscaled from ~100km resolution to ~5km resolution by WorldClim. Some artifacts are inevitable, especially at a global scale. Some variables have distinct transitions, especially in Greenland. Also, SSP2 4.5 has missing data for several variables in Antarctica.Related LayersBioclimate 1 Annual Mean TemperatureBioclimate 2 Mean Diurnal RangeBioclimate 3 IsothermalityBioclimate 4 Temperature SeasonalityBioclimate 5 Max Temperature of Warmest MonthBioclimate 6 Min Temperature Of Coldest MonthBioclimate 7 Temperature Annual RangeBioclimate 8 Mean Temperature Of Wettest QuarterBioclimate 9 Mean Temperature Of Driest QuarterBioclimate 10 Mean Temperature Of Warmest QuarterBioclimate 11 Mean Temperature Of Coldest QuarterBioclimate 12 Annual PrecipitationBioclimate 13 Precipitation Of Wettest MonthBioclimate 14 Precipitation Of Driest MonthBioclimate 15 Precipitation SeasonalityBioclimate 16 Precipitation Of Wettest QuarterBioclimate 17 Precipitation Of Driest QuarterBioclimate 18 Precipitation Of Warmest QuarterBioclimate 19 Precipitation Of Coldest QuarterBioclimate Baseline 1970-2000

The Global Imagery Browse Services (GIBS) system is a core EOSDIS component which provides a scalable, responsive, highly available, and community standards based set of imagery services. These services are designed with the goal of advancing user interactions with EOSDIS’ inter-disciplinary data through enhanced visual representation and discovery.The GIBS imagery archive includes approximately 1000 imagery products representing visualized science data from the NASA Earth Observing System Data and Information System (EOSDIS). Each imagery product is generated at the native resolution of the source data to provide "full resolution" visualizations of a science parameter. GIBS works closely with the science teams to identify the appropriate data range and color mappings, where appropriate, to provide the best quality imagery to the Earth science community. Many GIBS imagery products are generated by the EOSDIS LANCE near real-time processing system resulting in imagery available in GIBS within 3.5 hours of observation. These products and others may also extend from present to the beginning of the satellite mission. In addition, GIBS makes available supporting imagery layers such as data/no-data, water masks, orbit tracks, and graticules to improve imagery usage.The GIBS team is actively engaging the NASA EOSDIS Distributed Active Archive Centers (DAACs) to add more imagery products and to extend their coverage throughout the life of the mission. The remainder of this page provides a structured view of the layers currently available within GIBS grouped by science discipline and science observation. For information regarding how to access these products, see the GIBS API section of this wiki. For information regarding how to access these products through an existing client, refer to the Map Library and GIS Client sections of this wiki. If you are aware of a science parameter that you would like to see visualized, please contact us at support@earthdata.nasa.gov.

The Global Imagery Browse Services (GIBS) system is a core EOSDIS component which provides a scalable, responsive, highly available, and community standards based set of imagery services. These services are designed with the goal of advancing user interactions with EOSDIS’ inter-disciplinary data through enhanced visual representation and discovery.GIBS Available Imagery ProductsThe GIBS imagery archive includes approximately 1000 imagery products representing visualized science data from the NASA Earth Observing System Data and Information System (EOSDIS). Each imagery product is generated at the native resolution of the source data to provide "full resolution" visualizations of a science parameter. GIBS works closely with the science teams to identify the appropriate data range and color mappings, where appropriate, to provide the best quality imagery to the Earth science community. Many GIBS imagery products are generated by the EOSDIS LANCE near real-time processing system resulting in imagery available in GIBS within 3.5 hours of observation. These products and others may also extend from present to the beginning of the satellite mission. In addition, GIBS makes available supporting imagery layers such as data/no-data, water masks, orbit tracks, and graticules to improve imagery usage.The GIBS team is actively engaging the NASA EOSDIS Distributed Active Archive Centers (DAACs) to add more imagery products and to extend their coverage throughout the life of the mission. The remainder of this page provides a structured view of the layers currently available within GIBS grouped by science discipline and science observation. For information regarding how to access these products, see the GIBS API section of this wiki. For information regarding how to access these products through an existing client, refer to the Map Library and GIS Client sections of this wiki. If you are aware of a science parameter that you would like to see visualized, please contact us at support@earthdata.nasa.gov. https://wiki.earthdata.nasa.gov/display/GIBS/GIBS+Available+Imagery+Products#expand-AerosolOpticalDepth29ProductsNASA GIS API for Developers https://wiki.earthdata.nasa.gov/display/GIBS/GIBS+API+for+Developers

GEBCO 2022 TopoBathy (ice surface) elevation service with 3.5x vertical exaggeration, for use in oceanographic or terrestrial 3D web visualizations. Image service cached from level 0 to level 11.GEBCO 2022 Vertical Exaggeration Products:3.5x vertical exaggeration 5x vertical exaggeration 10x vertical exaggerationSourceGEBCO is a global terrain model for ocean and land providing elevation data in meters on a 15 arc-second interval grid. It is accompanied by a Type Identifier (TID) Grid that gives information on the types of source data that the GEBCO_2022 Grid is based. More Info.What can you do with this layer?Use for visualization in a 3D Web Scene.Layers associated with the GEBCO 2022 product:GEBCO Type Identifier 2022GEBCO Depth Zones 2022GEBCO 500m Contours 2022GEBCO Shaded Relief 2022GEBCO Bathymetry 2022For more GEBCO related layers and maps please visit the GEBCO ArcGIS Online Group.

Massive 3.5M+ POI database providing multi-industry places data across the US and Canada. Includes automotive, retail, dining, healthcare, education, and more. Built for GIS developers, mapping platforms, and analysts conducting market research, spatial analysis, and location intelligence.

The Global Imagery Browse Services (GIBS) system is a core EOSDIS component which provides a scalable, responsive, highly available, and community standards based set of imagery services. These services are designed with the goal of advancing user interactions with EOSDIS’ inter-disciplinary data through enhanced visual representation and discovery.GIBS Available Imagery ProductsThe GIBS imagery archive includes approximately 1000 imagery products representing visualized science data from the NASA Earth Observing System Data and Information System (EOSDIS). Each imagery product is generated at the native resolution of the source data to provide "full resolution" visualizations of a science parameter. GIBS works closely with the science teams to identify the appropriate data range and color mappings, where appropriate, to provide the best quality imagery to the Earth science community. Many GIBS imagery products are generated by the EOSDIS LANCE near real-time processing system resulting in imagery available in GIBS within 3.5 hours of observation. These products and others may also extend from present to the beginning of the satellite mission. In addition, GIBS makes available supporting imagery layers such as data/no-data, water masks, orbit tracks, and graticules to improve imagery usage.The GIBS team is actively engaging the NASA EOSDIS Distributed Active Archive Centers (DAACs) to add more imagery products and to extend their coverage throughout the life of the mission. The remainder of this page provides a structured view of the layers currently available within GIBS grouped by science discipline and science observation. For information regarding how to access these products, see the GIBS API section of this wiki. For information regarding how to access these products through an existing client, refer to the Map Library and GIS Client sections of this wiki. If you are aware of a science parameter that you would like to see visualized, please contact us at support@earthdata.nasa.gov. https://wiki.earthdata.nasa.gov/display/GIBS/GIBS+Available+Imagery+Products#expand-AerosolOpticalDepth29ProductsNASA GIS API for Developers https://wiki.earthdata.nasa.gov/display/GIBS/GIBS+API+for+Developers

This space-time cube contains basic population and housing variables for Public Use Microdata Areas (PUMAs), annually from 2010 to 2023. The variables are from the American Community Survey (ACS) 1-year estimates.A space-time cube is a powerful data structure used to visualize and analyze spatio-temporal data in ArcGIS Pro. Some examples of what you can do with this space-time cube: Create a compelling three-dimensional visualization of homeownership rate through timeFind emerging hot spots of specific race or Hispanic origin groupsIdentify change points of vacant housing unitsForecast future population valuesTo access this space-time cube, click Download, then unzip the downloaded folder. The folder contains a space-time cube (.nc), a file geodatabase (.gdb) containing the PUMA boundaries, and a csv file (.csv) describing the ACS variables in the space-time cube.To view a short tutorial on getting started with this space-time cube, read this blog article. To learn more about how to create and work with space-time cubes in ArcGIS Pro, view the learning path.placeholderSpace Time Cube ContentsSpatial unit and extent: 2020 vintage Public Use Microdata Areas (PUMA) boundaries for the entire United States, Puerto Rico, and Guam. Downloaded from US Census TIGER geodatabases National Sub-State Geography Database, with water and coastlines erased using 2023 500k TIGER Cartographic Boundary Shapefiles. Temporal interval and extent: one year interval, between 2010 and 2023 .Data source: ACS 1-year estimates downloaded from data.census.gov for each year between 2010 and 2023 (except 2020). Table(s) B01001, B03002, B05003, B05011, B19049, B25002, B25003, B25058, B25077.Variables: includes 32 variables on the following themes: population, race and Hispanic origin, foreign-born, housing occupancy, and housing tenure. To view a full listing of the variables, consult the .csv file contained within the downloaded folder.Processing Notes and Usage Tips The space-time cube contains variables that are directly sources from ACS, plus variables that have been calculated using ACS variables. The calculated variables can be identified by the “_calc_” stub in the field name. The spreadsheet contained within the downloaded folder provides more information on each variable source and calculation. It also contains field aliases, which can optionally be used to add aliases to the space-time cube layer or any other feature classes which are derived from the space-time cube (see blog article for information on how to do this). The field aliases were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. The ACS did not publish 1 year estimates for 2020. The variable values for this year were imputed using the temporal trend method of the Create Space Time Cube from Defined Locations tool, which uses the Interpolated Univariate Spline method from the SciPy Interpolation package. This can introduce some unexpected artifacts in the values for this year, for example: count statistics may include decimal places or may become negative, and variables that should sum together to reach the total of another variable may not. Therefore it is advised to take caution when making any conclusions from analysis which are focused around this year. The PUMA boundaries change after each decennial census. For the time series of this space-time cube, there was a boundary change between 2011 and 2012 (from the 2000 census to 2010), and another between 2021 and 2022 (from the 2010 census to 2020). Therefore, apportionment was required for all years between 2010 and 2021 to be able to accurately create a time series based on the 2020 PUMA geographies. A weighted apportionment approach was used, applying either population or housing weights depending on the variable. Apportionment enables us to create longer time-series or time-series which are more current, however it also adds an additional source of error to the ACS estimates. A version of this space-time cube without apportionment, for 2012 to 2021, is provided at LINK TO OTHER CUBE. ACS update the population controls after every decennial census, which can sometimes cause slight shifts in values. For this space-time cube, these happened between from 2011 and 2012, and 2021 and 2022. Therefore it is advised to take caution when making any conclusions from analysis which are focused around these years. A version of this space-time cube without these effects, for 2012 to 2021, is provided at LINK TO OTHER CUBE. In order to have access to the latest functionality, it is recommended to use the most recent version of ArcGIS Pro to work with the space-time cube. In particular, in ArcGIS Pro 3.5, significant enhancements were made to space-time cube visualization workflows. Native space-time cube analysis and visualization is not currently supported in ArcGIS Online. However once visualization or analysis has taken place in ArcGIS Pro, the resulting space-time cube layer can be published as a Web Scene, which can be visualized in Scene Viewer.ACS InformationInformation about the United States Census Bureau's American Community Survey (ACS): About the Survey Geography & ACS Technical Documentation News & UpdatesPlease cite the Census and ACS when using this data.Data Note from the Census: Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.

The Global Imagery Browse Services (GIBS) system is a core EOSDIS component which provides a scalable, responsive, highly available, and community standards based set of imagery services. These services are designed with the goal of advancing user interactions with EOSDIS’ inter-disciplinary data through enhanced visual representation and discovery.GIBS Available Imagery ProductsThe GIBS imagery archive includes approximately 1000 imagery products representing visualized science data from the NASA Earth Observing System Data and Information System (EOSDIS). Each imagery product is generated at the native resolution of the source data to provide "full resolution" visualizations of a science parameter. GIBS works closely with the science teams to identify the appropriate data range and color mappings, where appropriate, to provide the best quality imagery to the Earth science community. Many GIBS imagery products are generated by the EOSDIS LANCE near real-time processing system resulting in imagery available in GIBS within 3.5 hours of observation. These products and others may also extend from present to the beginning of the satellite mission. In addition, GIBS makes available supporting imagery layers such as data/no-data, water masks, orbit tracks, and graticules to improve imagery usage.The GIBS team is actively engaging the NASA EOSDIS Distributed Active Archive Centers (DAACs) to add more imagery products and to extend their coverage throughout the life of the mission. The remainder of this page provides a structured view of the layers currently available within GIBS grouped by science discipline and science observation. For information regarding how to access these products, see the GIBS API section of this wiki. For information regarding how to access these products through an existing client, refer to the Map Library and GIS Client sections of this wiki. If you are aware of a science parameter that you would like to see visualized, please contact us at support@earthdata.nasa.gov. https://wiki.earthdata.nasa.gov/display/GIBS/GIBS+Available+Imagery+Products#expand-AerosolOpticalDepth29ProductsNASA GIS API for Developers https://wiki.earthdata.nasa.gov/display/GIBS/GIBS+API+for+Developers

This feature layer represents Sustainable Development Goal indicator 3.5.2 'Prevalence of Drinking Alcohol as Percentage of the Population' for Ireland. This layer was created using Irish Health Survey 2015 data produced by Central Statistics Office (CSO) and NUTS 3 boundary data produced by Tailte Éireann. Note that the NUTS 3 boundary refers to the former Regional Authorities established under the NUTS Regulation (Regulation (EU) 1059/2003). These boundaries were subsequently revised in 2016 through Commission Regulation (EU) 2016/2066 amending annexes to Regulation 1059/2003 (more info).

In 2015 UN countries adopted a set of 17 goals to end poverty, protect the planet and ensure prosperity for all as part of a new sustainable development agenda. Each goal has specific targets to help achieve the goals set out in the agenda by 2030. Governments are committed to establishing national frameworks for the achievement of the 17 Goals and to review progress using accessible quality data. With these goals in mind the CSO and Tailte Éireann are working together to link geography and statistics to produce indicators that help communicate and monitor Ireland’s performance in relation to achieving the 17 sustainable development goals.The indicator displayed supports the efforts to achieve goal number 3 which aims to ensure healthy lives and promote well-being for all at all ages.

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 layer represents CMIP6 future projections of total precipitation during the three wettest months of the year. This layer can be used to compare with recent climate histories to better understand the potential impacts of future climate change.WorldClim produced this projection as part of a series of 19 bioclimate variables identified by the USGS and provides this description:"Bioclimatic variables are derived from the monthly temperature and rainfall values in order to generate more biologically meaningful variables. These are often used in species distribution modeling and related ecological modeling techniques. The bioclimatic variables represent annual trends (e.g., mean annual temperature, annual precipitation) seasonality (e.g., annual range in temperature and precipitation) and extreme or limiting environmental factors (e.g., temperature of the coldest and warmest month, and precipitation of the wet and dry quarters). A quarter is a period of three months (1/4 of the year)."Time Extent: averages from 2021-2040, 2041-2060, 2061-2080, 2081-2100Units: mmCell Size: 2.5 minutes (~5 km)Source Type: StretchedPixel Type: 32 Bit FloatData Projection: GCS WGS84Mosaic Projection: GCS WGS84Extent: GlobalSource: WorldClim CMIP6 Bioclimate Climate ScenariosThe CMIP6 climate experiments use Shared Socioeconomic Pathways (SSPs) to model future climate scenarios. Each SSP pairs a human/community behavior component with the traditional RCP greenhouse gas forcing from the previous CMIP5. Three SSPs were chosen by Esri to be included in the service based on user requests: SSP2 4.5, SSP3 7.0 and SSP5 8.5.SSPScenarioEstimated warming(2041–2060)Estimated warming(2081–2100)Very likely range in °C(2081–2100)SSP2-4.5intermediate GHG emissions:CO2 emissions around current levels until 2050, then falling but not reaching net zero by 21002.0 °C2.7 °C2.1 – 3.5SSP3-7.0high GHG emissions:CO2 emissions double by 21002.1 °C3.6 °C2.8 – 4.6SSP5-8.5very high GHG emissions:CO2 emissions triple by 20752.4 °C4.4 °C3.3 – 5.7While the 8.5 scenario is no longer generally considered likely, SSP3 7.0 has been included and is considered the high end of possibilities. SSP5 8.5 has been retained since many organizations report to this threshold. The warming associated with SSP2 4.5 is equivalent to the global targets set at the 2021 United Nations COP26 meetings in Glasgow. Processing the Climate DataWorldClim provides 20-year averaged outputs for the various SSPs from 24 global climate models. A selection of 13 models were averaged for each variable and time based on Mahony et al 2022. These models included ACCESS-ESM1-5, BCC-CSM2-MR, CanESM5, CNRM-ESM2-1, EC-Earth3-Veg, GFDL-ESM4, GISS-E2-1-G, INM-CM5-0, IPSL-CM6A-LR, MIROC6, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL. GFDL-ESM4 was not available for SSP2 4.5 or SSP5 8.5. Accessing the Multidimensional InformationThe time and SSP scenario are built into the layer using a multidimensional raster. Enable the time slider to move across the 20-year average periods. In ArcGIS Online and Pro, use the Multidimensional Filter to select the SSP (SSP2 4.5 is the default). What can you do with this layer?These multidimensional imagery tiles support analysis using ArcGIS Online or Pro. Use the Bioclimate Baseline layer to see the difference in pixels and calculate change from the historic period into the future. Use the Multidimensional tab in ArcGIS Pro to access a variety of useful tools. Each layer or variable can be styled using the Image Display options. Known Quality IssuesEach model is downscaled from ~100km resolution to ~5km resolution by WorldClim. Some artifacts are inevitable, especially at a global scale. Some variables have distinct transitions, especially in Greenland. Also, SSP2 4.5 has missing data for several variables in Antarctica. Related LayersBioclimate 1 Annual Mean TemperatureBioclimate 2 Mean Diurnal RangeBioclimate 3 IsothermalityBioclimate 4 Temperature SeasonalityBioclimate 5 Max Temperature of Warmest MonthBioclimate 6 Min Temperature Of Coldest MonthBioclimate 7 Temperature Annual RangeBioclimate 8 Mean Temperature Of Wettest QuarterBioclimate 9 Mean Temperature Of Driest QuarterBioclimate 10 Mean Temperature Of Warmest QuarterBioclimate 11 Mean Temperature Of Coldest QuarterBioclimate 12 Annual PrecipitationBioclimate 13 Precipitation Of Wettest MonthBioclimate 14 Precipitation Of Driest MonthBioclimate 15 Precipitation SeasonalityBioclimate 16 Precipitation Of Wettest QuarterBioclimate 17 Precipitation Of Driest QuarterBioclimate 18 Precipitation Of Warmest QuarterBioclimate 19 Precipitation Of Coldest QuarterBioclimate Baseline 1970-2000

Retirement Notice: This beta item 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 layer represents CMIP6 future projections of total precipitation during the three coldest months of the year. This layer can be used to compare with recent climate histories to better understand the potential impacts of future climate change.WorldClim produced this projection as part of a series of 19 bioclimate variables identified by the USGS and provides this description:"Bioclimatic variables are derived from the monthly temperature and rainfall values in order to generate more biologically meaningful variables. These are often used in species distribution modeling and related ecological modeling techniques. The bioclimatic variables represent annual trends (e.g., mean annual temperature, annual precipitation) seasonality (e.g., annual range in temperature and precipitation) and extreme or limiting environmental factors (e.g., temperature of the coldest and warmest month, and precipitation of the wet and dry quarters). A quarter is a period of three months (1/4 of the year)."Time Extent: averages from 2021-2040, 2041-2060, 2061-2080, 2081-2100Units: mmCell Size: 2.5 minutes (~5 km)Source Type: StretchedPixel Type: 32 Bit FloatData Projection: GCS WGS84Mosaic Projection: GCS WGS84Extent: GlobalSource: WorldClim CMIP6 Bioclimate Climate ScenariosThe CMIP6 climate experiments use Shared Socioeconomic Pathways (SSPs) to model future climate scenarios. Each SSP pairs a human/community behavior component with the traditional RCP greenhouse gas forcing from the previous CMIP5. Three SSPs were chosen by Esri to be included in the service based on user requests: SSP2 4.5, SSP3 7.0 and SSP5 8.5.SSPScenarioEstimated warming(2041–2060)Estimated warming(2081–2100)Very likely range in °C(2081–2100)SSP2-4.5intermediate GHG emissions:CO2 emissions around current levels until 2050, then falling but not reaching net zero by 21002.0 °C2.7 °C2.1 – 3.5SSP3-7.0high GHG emissions:CO2 emissions double by 21002.1 °C3.6 °C2.8 – 4.6SSP5-8.5very high GHG emissions:CO2 emissions triple by 20752.4 °C4.4 °C3.3 – 5.7While the 8.5 scenario is no longer generally considered likely, SSP3 7.0 has been included and is considered the high end of possibilities. SSP5 8.5 has been retained since many organizations report to this threshold. The warming associated with SSP2 4.5 is equivalent to the global targets set at the 2021 United Nations COP26 meetings in Glasgow. Processing the Climate DataWorldClim provides 20-year averaged outputs for the various SSPs from 24 global climate models. A selection of 13 models were averaged for each variable and time based on Mahony et al 2022. These models included ACCESS-ESM1-5, BCC-CSM2-MR, CanESM5, CNRM-ESM2-1, EC-Earth3-Veg, GFDL-ESM4, GISS-E2-1-G, INM-CM5-0, IPSL-CM6A-LR, MIROC6, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL. GFDL-ESM4 was not available for SSP2 4.5 or SSP5 8.5. Accessing the Multidimensional InformationThe time and SSP scenario are built into the layer using a multidimensional raster. Enable the time slider to move across the 20-year average periods. In ArcGIS Online and Pro, use the Multidimensional Filter to select the SSP (SSP2 4.5 is the default). What can you do with this layer?These multidimensional imagery tiles support analysis using ArcGIS Online or Pro. Use the Bioclimate Baseline layer to see the difference in pixels and calculate change from the historic period into the future. Use the Multidimensional tab in ArcGIS Pro to access a variety of useful tools. Each layer or variable can be styled using the Image Display options. Known Quality IssuesEach model is downscaled from ~100km resolution to ~5km resolution by WorldClim. Some artifacts are inevitable, especially at a global scale. Some variables have distinct transitions, especially in Greenland. Also, SSP2 4.5 has missing data for several variables in Antarctica.Related LayersBioclimate 1 Annual Mean TemperatureBioclimate 2 Mean Diurnal Range Bioclimate 3 Isothermality Bioclimate 4 Temperature Seasonality Bioclimate 5 Max Temperature of Warmest MonthBioclimate 6 Min Temperature Of Coldest MonthBioclimate 7 Temperature Annual Range Bioclimate 8 Mean Temperature Of Wettest QuarterBioclimate 9 Mean Temperature Of Driest QuarterBioclimate 10 Mean Temperature Of Warmest QuarterBioclimate 11 Mean Temperature Of Coldest QuarterBioclimate 12 Annual PrecipitationBioclimate 13 Precipitation Of Wettest MonthBioclimate 14 Precipitation Of Driest MonthBioclimate 15 Precipitation Seasonality Bioclimate 16 Precipitation Of Wettest QuarterBioclimate 17 Precipitation Of Driest QuarterBioclimate 18 Precipitation Of Warmest QuarterBioclimate 19 Precipitation Of Coldest QuarterBioclimate Baseline 1970-2000

The Global Imagery Browse Services (GIBS) system is a core EOSDIS component which provides a scalable, responsive, highly available, and community standards based set of imagery services. These services are designed with the goal of advancing user interactions with EOSDIS’ inter-disciplinary data through enhanced visual representation and discovery.The GIBS imagery archive includes approximately 1000 imagery products representing visualized science data from the NASA Earth Observing System Data and Information System (EOSDIS). Each imagery product is generated at the native resolution of the source data to provide "full resolution" visualizations of a science parameter. GIBS works closely with the science teams to identify the appropriate data range and color mappings, where appropriate, to provide the best quality imagery to the Earth science community. Many GIBS imagery products are generated by the EOSDIS LANCE near real-time processing system resulting in imagery available in GIBS within 3.5 hours of observation. These products and others may also extend from present to the beginning of the satellite mission. In addition, GIBS makes available supporting imagery layers such as data/no-data, water masks, orbit tracks, and graticules to improve imagery usage.The GIBS team is actively engaging the NASA EOSDIS Distributed Active Archive Centers (DAACs) to add more imagery products and to extend their coverage throughout the life of the mission. The remainder of this page provides a structured view of the layers currently available within GIBS grouped by science discipline and science observation. For information regarding how to access these products, see the GIBS API section of this wiki. For information regarding how to access these products through an existing client, refer to the Map Library and GIS Client sections of this wiki. If you are aware of a science parameter that you would like to see visualized, please contact us at support@earthdata.nasa.gov.

This is a continuously updated map of earthquake data for the last 90 days with a magnitude 3.5 or greater. Zoom in to view the shake intensity around significant earthquakes.About the Data: Recent Earthquakes: This service presents recent earthquake information from the USGS Prompt Assessment of Global Earthquakes for Response (PAGER) program. In addition to displaying earthquakes by magnitude, this service also presents earthquakes by impact. Impact is measured by population as well as models for economic and fatality loss. For more details, see: https://earthquake.usgs.gov/earthquakes/pager.

The Global Imagery Browse Services (GIBS) system is a core EOSDIS component which provides a scalable, responsive, highly available, and community standards based set of imagery services. These services are designed with the goal of advancing user interactions with EOSDIS’ inter-disciplinary data through enhanced visual representation and discovery.The GIBS imagery archive includes approximately 1000 imagery products representing visualized science data from the NASA Earth Observing System Data and Information System (EOSDIS). Each imagery product is generated at the native resolution of the source data to provide "full resolution" visualizations of a science parameter. GIBS works closely with the science teams to identify the appropriate data range and color mappings, where appropriate, to provide the best quality imagery to the Earth science community. Many GIBS imagery products are generated by the EOSDIS LANCE near real-time processing system resulting in imagery available in GIBS within 3.5 hours of observation. These products and others may also extend from present to the beginning of the satellite mission. In addition, GIBS makes available supporting imagery layers such as data/no-data, water masks, orbit tracks, and graticules to improve imagery usage.The GIBS team is actively engaging the NASA EOSDIS Distributed Active Archive Centers (DAACs) to add more imagery products and to extend their coverage throughout the life of the mission. The remainder of this page provides a structured view of the layers currently available within GIBS grouped by science discipline and science observation. For information regarding how to access these products, see the GIBS API section of this wiki. For information regarding how to access these products through an existing client, refer to the Map Library and GIS Client sections of this wiki. If you are aware of a science parameter that you would like to see visualized, please contact us at support@earthdata.nasa.gov.

Series Name: Alcohol consumption per capita (aged 15 years and older) within a calendar year (litres of pure alcohol)Series Code: SH_ALC_CONSPTRelease Version: 2021.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 3.5.2: Alcohol per capita consumption (aged 15 years and older) within a calendar year in litres of pure alcoholTarget 3.5: Strengthen the prevention and treatment of substance abuse, including narcotic drug abuse and harmful use of alcoholGoal 3: Ensure healthy lives and promote well-being for all at all agesFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

The Global Imagery Browse Services (GIBS) system is a core EOSDIS component which provides a scalable, responsive, highly available, and community standards based set of imagery services. These services are designed with the goal of advancing user interactions with EOSDIS’ inter-disciplinary data through enhanced visual representation and discovery.GIBS Available Imagery ProductsThe GIBS imagery archive includes approximately 1000 imagery products representing visualized science data from the NASA Earth Observing System Data and Information System (EOSDIS). Each imagery product is generated at the native resolution of the source data to provide "full resolution" visualizations of a science parameter. GIBS works closely with the science teams to identify the appropriate data range and color mappings, where appropriate, to provide the best quality imagery to the Earth science community. Many GIBS imagery products are generated by the EOSDIS LANCE near real-time processing system resulting in imagery available in GIBS within 3.5 hours of observation. These products and others may also extend from present to the beginning of the satellite mission. In addition, GIBS makes available supporting imagery layers such as data/no-data, water masks, orbit tracks, and graticules to improve imagery usage.The GIBS team is actively engaging the NASA EOSDIS Distributed Active Archive Centers (DAACs) to add more imagery products and to extend their coverage throughout the life of the mission. The remainder of this page provides a structured view of the layers currently available within GIBS grouped by science discipline and science observation. For information regarding how to access these products, see the GIBS API section of this wiki. For information regarding how to access these products through an existing client, refer to the Map Library and GIS Client sections of this wiki. If you are aware of a science parameter that you would like to see visualized, please contact us at support@earthdata.nasa.gov. https://wiki.earthdata.nasa.gov/display/GIBS/GIBS+Available+Imagery+Products#expand-AerosolOpticalDepth29ProductsNASA GIS API for Developers https://wiki.earthdata.nasa.gov/display/GIBS/GIBS+API+for+Developers