The Unpublished Digital Geomorphic Map of Timucuan Ecological and Historic Preserve, and Fort Caroline National Memorial, Florida is composed of GIS data layers and GIS tables in a 10.0 file geodatabase (tifg_geology.gdb), a 10.0 ArcMap (.MXD) map document (tifg_geology.mxd), and individual 10.0 layer (.LYR) files for each GIS data layer, an ancillary map information (.PDF) document (tifo_geology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.TXT) and FAQ (.HTML) formats, and a GIS readme file (timu_foca_gis_readme.pdf). Please read the timu_foca_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.0 shapefile format contact Stephanie O’Meara (stephanie.o’meara@colostate.edu; see contact information below). The data is also available as a 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. Google Earth software is available for free at: http://www.google.com/earth/index.html. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Florida Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (tifg_metadata_faq.html; available at http://nrdata.nps.gov/geology/gri_data/gis/timu/tifg_metadata_faq.html). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual _location as presented by this dataset. Users of this data should thus not assume the _location of features is exactly where they are portrayed in Google Earth, ArcGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.1. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data projection is NAD83, UTM Zone 17N, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth. The data is within the area of interest of Timucuan Ecological and Historic Preserve and Fort Caroline National Memorial.

The Unpublished Digital Geologic-GIS Map of Lake Clark National Park and Preserve and Vicinity, Alaska is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (lacl_geology.gdb), a 10.1 ArcMap (.mxd) map document (lacl_geology.mxd), individual 10.1 layer (.lyr) files for each GIS data layer, an ancillary map information document (lacl_geology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.txt) and FAQ (.pdf) formats, and a GIS readme file (lacl_geology_gis_readme.pdf). Please read the lacl_geology_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.1 shapefile format contact Stephanie O'Meara (stephanie.omeara@colostate.edu; see contact information below). The data is also available as a 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. Google Earth software is available for free at: http://www.google.com/earth/index.html. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (lacl_geology_metadata.txt or lacl_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:1584,000 and United States National Map Accuracy Standards features are within (horizontally) 804.7 meters or 2640 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm). The GIS data projection is NAD83, Alaska Albers, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth. The data is within the area of interest of Lake Clark National Park and Preserve.

The Unpublished Digital Bedrock Geologic Map of Aniakchak National Monument and Preserve and Vicinity, Alaska is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (ania_geology.gdb), a 10.1 ArcMap (.MXD) map document (ania_geology.mxd), individual 10.1 layer (.LYR) files for each GIS data layer, an ancillary map information (.PDF) document (ania_geology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.TXT) and FAQ (.HTML) formats, and a GIS readme file (ania_gis_readme.pdf). Please read the ania_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.1 shapefile format contact Stephanie O’Meara (stephanie.o’meara@colostate.edu; see contact information below). The data is also available as a 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. Google Earth software is available for free at: http://www.google.com/earth/index.html. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (ania_metadata_faq.html; available at http://nrdata.nps.gov/geology/gri_data/gis/ania/ania_metadata_faq.html). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:250,000 and United States National Map Accuracy Standards features are within (horizontally) 254 meters or 833.3 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.2. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data projection is NAD83, UTM Zone AD_1983_Alaska_AlbersN, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth. The data is within the area of interest of Aniakchak National Monument and Preserve.

description: The Unpublished Digital Geologic Map of Katmai National Park and Preserve, and Alagnak Wild River and Vicinity, Alaska is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (katm_geology.gdb), a 10.1 ArcMap (.MXD) map document (katm_geology.mxd), individual 10.1 layer (.LYR) files for each GIS data layer, an ancillary map information (.PDF) document (katm_geology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.TXT) and FAQ (.HTML) formats, and a GIS readme file (katm_gis_readme.pdf). Please read the katm_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.1 shapefile format contact Stephanie OMeara (stephanie.omeara@colostate.edu; see contact information below). The data is also available as a 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. Google Earth software is available for free at: http://www.google.com/earth/index.html. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey, National Park Service Geologic Resources Inventory, Smithsonian Institution and Alaska Division of Geological and Geophysical Surveys. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (katm_metadata_faq.html; available at http://nrdata.nps.gov/geology/gri_data/gis/katm/katm_metadata_faq.html). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:250,000 and United States National Map Accuracy Standards features are within (horizontally) 127 meters or 416.7 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data projection is NAD83, UTM Zone AD_1983_Alaska_AlbersN, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth. The data is within the area of interest of Katmai National Park and Preserve, and Alagnak Wild River.; abstract: The Unpublished Digital Geologic Map of Katmai National Park and Preserve, and Alagnak Wild River and Vicinity, Alaska is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (katm_geology.gdb), a 10.1 ArcMap (.MXD) map document (katm_geology.mxd), individual 10.1 layer (.LYR) files for each GIS data layer, an ancillary map information (.PDF) document (katm_geology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.TXT) and FAQ (.HTML) formats, and a GIS readme file (katm_gis_readme.pdf). Please read the katm_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.1 shapefile format contact Stephanie OMeara (stephanie.omeara@colostate.edu; see contact information below). The data is also available as a 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. Google Earth software is available for free at: http://www.google.com/earth/index.html. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey, National Park Service Geologic Resources Inventory, Smithsonian Institution and Alaska Division of Geological and Geophysical Surveys. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (katm_metadata_faq.html; available at http://nrdata.nps.gov/geology/gri_data/gis/katm/katm_metadata_faq.html). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:250,000 and United States National Map Accuracy Standards features are within (horizontally) 127 meters or 416.7 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data projection is NAD83, UTM Zone AD_1983_Alaska_AlbersN, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth. The data is within the area of interest of Katmai National Park and Preserve, and Alagnak Wild River.

This dataset is a compilation of available oil and gas pipeline data and is maintained by BSEE. Pipelines are used to transport and monitor oil and/or gas from wells within the outer continental shelf (OCS) to resource collection locations. Currently, pipelines managed by BSEE are found in Gulf of Mexico and southern California waters.

© MarineCadastre.gov This layer is a component of BOEMRE Layers.

This Map Service contains many of the primary data types created by both the Bureau of Ocean Energy Management (BOEM) and the Bureau of Safety and Environmental Enforcement (BSEE) within the Department of Interior (DOI) for the purpose of managing offshore federal real estate leases for oil, gas, minerals, renewable energy, sand and gravel. These data layers are being made available as REST mapping services for the purpose of web viewing and map overlay viewing in GIS systems. Due to re-projection issues which occur when converting multiple UTM zone data to a single national or regional projected space, and line type changes that occur when converting from UTM to geographic projections, these data layers should not be used for official or legal purposes. Only the original data found within BOEM/BSEE’s official internal database, federal register notices or official paper or pdf map products may be considered as the official information or mapping products used by BOEM or BSEE. A variety of data layers are represented within this REST service are described further below. These and other cadastre information the BOEM and BSEE produces are generated in accordance with 30 Code of Federal Regulations (CFR) 256.8 to support Federal land ownership and mineral resource management.

For more information – Contact: Branch Chief, Mapping and Boundary Branch, BOEM, 381 Elden Street, Herndon, VA 20170. Telephone (703) 787-1312; Email: mapping.boundary.branch@boem.gov

The REST services for National Level Data can be found here: http://gis.boemre.gov/arcgis/rest/services/BOEM_BSEE/MMC_Layers/MapServer

REST services for regional level data can be found by clicking on the region of interest from the following URL: http://gis.boemre.gov/arcgis/rest/services/BOEM_BSEE

Individual Regional Data or in depth metadata for download can be obtained in ESRI Shape file format by clicking on the region of interest from the following URL: http://www.boem.gov/Oil-and-Gas-Energy-Program/Mapping-and-Data/Index.aspx

Currently the following layers are available from this REST location:

OCS Drilling Platforms -Locations of structures at and beneath the water surface used for the purpose of exploration and resource extraction. Only platforms in federal Outer Continental Shelf (OCS) waters are included. A database of platforms and rigs is maintained by BSEE.

OCS Oil and Natural Gas Wells -Existing wells drilled for exploration or extraction of oil and/or gas products. Additional information includes the lease number, well name, spud date, the well class, surface area/block number, and statistics on well status summary. Only wells found in federal Outer Continental Shelf (OCS) waters are included. Wells information is updated daily. Additional files are available on well completions and well tests. A database of wells is maintained by BSEE.

OCS Oil & Gas Pipelines -This dataset is a compilation of available oil and gas pipeline data and is maintained by BSEE. Pipelines are used to transport and monitor oil and/or gas from wells within the outer continental shelf (OCS) to resource collection locations. Currently, pipelines managed by BSEE are found in Gulf of Mexico and southern California waters.

Unofficial State Lateral Boundaries - The approximate location of the boundary between two states seaward of the coastline and terminating at the Submerged Lands Act Boundary. Because most State boundary locations have not been officially described beyond the coast, are disputed between states or in some cases the coastal land boundary description is not available, these lines serve as an approximation that was used to determine a starting point for creation of BOEM’s OCS Administrative Boundaries. GIS files are not available for this layer due to its unofficial status.

BOEM OCS Administrative Boundaries - Outer Continental Shelf (OCS) Administrative Boundaries Extending from the Submerged Lands Act Boundary seaward to the Limit of the United States OCS (The U.S. 200 nautical mile Limit, or other marine boundary)For additional details please see the January 3, 2006 Federal Register Notice.

BOEM Limit of OCSLA ‘8(g)’ zone - The Outer Continental Shelf Lands Act '8(g) Zone' lies between the Submerged Lands Act (SLA) boundary line and a line projected 3 nautical miles seaward of the SLA boundary line. Within this zone, oil and gas revenues are shared with the coastal state(s). The official version of the ‘8(g)’ Boundaries can only be found on the BOEM Official Protraction Diagrams (OPDs) or Supplemental Official Protraction described below.

Submerged Lands Act Boundary - The SLA boundary defines the seaward limit of a state's submerged lands and the landward boundary of federally managed OCS lands. The official version of the SLA Boundaries can only be found on the BOEM Official Protraction Diagrams (OPDs) or Supplemental Official Protraction Diagrams described below.

Atlantic Wildlife Survey Tracklines(2005-2012) - These data depict tracklines of wildlife surveys conducted in the Mid-Atlantic region since 2005. The tracklines are comprised of aerial and shipboard surveys. These data are intended to be used as a working compendium to inform the diverse number of groups that conduct surveys in the Mid-Atlantic region.The tracklines as depicted in this dataset have been derived from source tracklines and transects. The tracklines have been simplified (modified from their original form) due to the large size of the Mid-Atlantic region and the limited ability to map all areas simultaneously.The tracklines are to be used as a general reference and should not be considered definitive or authoritative. This data can be downloaded from http://www.boem.gov/uploadedFiles/BOEM/Renewable_Energy_Program/Mapping_and_Data/ATL_WILDLIFE_SURVEYS.zip

BOEM OCS Protraction Diagrams & Leasing Maps - This data set contains a national scale spatial footprint of the outer boundaries of the Bureau of Ocean Energy Management’s (BOEM’s) Official Protraction Diagrams (OPDs) and Leasing Maps (LMs). It is updated as needed. OPDs and LMs are mapping products produced and used by the BOEM to delimit areas available for potential offshore mineral leases, determine the State/Federal offshore boundaries, and determine the limits of revenue sharing and other boundaries to be considered for leasing offshore waters. This dataset shows only the outline of the maps that are available from BOEM.Only the most recently published paper or pdf versions of the OPDs or LMs should be used for official or legal purposes. The pdf maps can be found by going to the following link and selecting the appropriate region of interest. http://www.boem.gov/Oil-and-Gas-Energy-Program/Mapping-and-Data/Index.aspx Both OPDs and LMs are further subdivided into individual Outer Continental Shelf(OCS) blocks which are available as a separate layer. Some OCS blocks that also contain other boundary information are known as Supplemental Official Block Diagrams (SOBDs.) Further information on the historic development of OPD's can be found in OCS Report MMS 99-0006: Boundary Development on the Outer Continental Shelf: http://www.boemre.gov/itd/pubs/1999/99-0006.PDF Also see the metadata for each of the individual GIS data layers available for download. The Official Protraction Diagrams (OPDs) and Supplemental Official Block Diagrams (SOBDs), serve as the legal definition for BOEM offshore boundary coordinates and area descriptions.

BOEM OCS Lease Blocks - Outer Continental Shelf (OCS) lease blocks serve as the legal definition for BOEM offshore boundary coordinates used to define small geographic areas within an Official Protraction Diagram (OPD) for leasing and administrative purposes. OCS blocks relate back to individual Official Protraction Diagrams and are not uniquely numbered. Only the most recently published paper or pdf

This layer is a time series of the annual ESA CCI (Climate Change Initiative) land cover maps of the world. ESA has produced land cover maps for the years 1992-2020. These are available at the European Space Agency Climate Change Initiative website.Time Extent: 1992-2020Cell Size: 300 meter Source Type: ThematicPixel Type: 8 Bit UnsignedData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary Sphere Extent: GlobalSource: ESA Climate Change InitiativeUpdate Cycle: Annual until 2020, no updates thereafterWhat can you do with this layer? This layer may be added to ArcGIS Online maps and applications and shown in a time series to watch a "time lapse" view of land cover change since 1992 for any part of the world. The same behavior exists when the layer is added to ArcGIS Pro. In addition to displaying all layers in a series, this layer may be queried so that only one year is displayed in a map. This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro with a query set to display just one year. 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. To sum up area by land cover using this service, or any other analysis, be sure to use an equal area projection, such as Albers or Equal Earth. Different Classifications Available to Map Five processing templates are included in this layer. The processing templates may be used to display a smaller set of land cover classes.Cartographic Renderer (Default Template)Displays all ESA CCI land cover classes.*Forested lands TemplateThe forested lands template shows only forested lands (classes 50-90).Urban Lands TemplateThe urban lands template shows only urban areas (class 190).Converted Lands TemplateThe converted lands template shows only urban lands and lands converted to agriculture (classes 10-40 and 190).Simplified RendererDisplays the map in ten simple classes which match the ten simplified classes used in 2050 Land Cover projections from Clark University.Any of these variables can be displayed or analyzed by selecting their processing template. In ArcGIS Online, select the Image Display Options on the layer. Then pull down the list of variables from the Renderer options. Click Apply and Close. In ArcGIS Pro, go into the Layer Properties. Select Processing Templates from the left hand menu. From the Processing Template pull down menu, select the variable to display. Using Time By default, the map will display as a time series animation, one year per frame. A time slider will appear when you add this layer to your map. To see the most current data, move the time slider until you see the most current year. In addition to displaying the past quarter century of land cover maps as an animation, this time series can also display just one year of data by use of a definition query. For a step by step example using ArcGIS Pro on how to display just one year of this layer, as well as to compare one year to another, see the blog called Calculating Impervious Surface Change. Hierarchical ClassificationLand cover types are defined using the land cover classification (LCCS) developed by the United Nations, FAO. It is designed to be as compatible as possible with other products, namely GLCC2000, GlobCover 2005 and 2009. This is a heirarchical classification system. For example, class 60 means "closed to open" canopy broadleaved deciduous tree cover. But in some places a more specific type of broadleaved deciduous tree cover may be available. In that case, a more specific code 61 or 62 may be used which specifies "open" (61) or "closed" (62) cover. Land Cover Processing To provide consistency over time, these maps are produced from baseline land cover maps, and are revised for changes each year depending on the best available satellite data from each period in time. These revisions were made from AVHRR 1km time series from 1992 to 1999, SPOT-VGT time series between 1999 and 2013, and PROBA-V data for years 2013, 2014 and 2015. When MERIS FR or PROBA-V time series are available, changes detected at 1 km are re-mapped at 300 m. The last step consists in back- and up-dating the 10-year baseline LC map to produce the 24 annual LC maps from 1992 to 2015. Source data The datasets behind this layer were extracted from NetCDF files and TIFF files produced by ESA. Years 1992-2015 were acquired from ESA CCI LC version 2.0.7 in TIFF format, and years 2016-2018 were acquired from version 2.1.1 in NetCDF format. These are downloadable from ESA with an account, after agreeing to their terms of use. https://maps.elie.ucl.ac.be/CCI/viewer/download.php CitationESA. Land Cover CCI Product User Guide Version 2. Tech. Rep. (2017). Available at: maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdfMore 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:10 Cropland, rainfed11 Herbaceous cover12 Tree or shrub cover20 Cropland, irrigated or post-flooding30 Mosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)40 Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%) 50 Tree cover, broadleaved, evergreen, closed to open (>15%)60 Tree cover, broadleaved, deciduous, closed to open (>15%)61 Tree cover, broadleaved, deciduous, closed (>40%)62 Tree cover, broadleaved, deciduous, open (15-40%)70 Tree cover, needleleaved, evergreen, closed to open (>15%)71 Tree cover, needleleaved, evergreen, closed (>40%)72 Tree cover, needleleaved, evergreen, open (15-40%)80 Tree cover, needleleaved, deciduous, closed to open (>15%)81 Tree cover, needleleaved, deciduous, closed (>40%)82 Tree cover, needleleaved, deciduous, open (15-40%)90 Tree cover, mixed leaf type (broadleaved and needleleaved)100 Mosaic tree and shrub (>50%) / herbaceous cover (<50%)110 Mosaic herbaceous cover (>50%) / tree and shrub (<50%)120 Shrubland121 Shrubland evergreen122 Shrubland deciduous130 Grassland140 Lichens and mosses150 Sparse vegetation (tree, shrub, herbaceous cover) (<15%)151 Sparse tree (<15%)152 Sparse shrub (<15%)153 Sparse herbaceous cover (<15%)160 Tree cover, flooded, fresh or brakish water170 Tree cover, flooded, saline water180 Shrub or herbaceous cover, flooded, fresh/saline/brakish water190 Urban areas200 Bare areas201 Consolidated bare areas202 Unconsolidated bare areas210 Water bodies

The Unpublished Digital Geologic-GIS Map of Parts of Great Sand Dunes National Park and Preserve (Sangre de Cristo Mountains and part of the Dunes), Colorado is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (gsam_geology.gdb), a 10.1 ArcMap (.mxd) map document (gsam_geology.mxd), individual 10.1 layer (.lyr) files for each GIS data layer, an ancillary map information document (grsa_geology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.txt) and FAQ (.pdf) formats, and a GIS readme file (grsa_geology_gis_readme.pdf). Please read the grsa_geology_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.1 shapefile format contact Stephanie O'Meara (stephanie.omeara@colostate.edu; see contact information below). The data is also available as a 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. Google Earth software is available for free at: http://www.google.com/earth/index.html. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (gsam_geology_metadata.txt or gsam_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual _location as presented by this dataset. Users of this data should thus not assume the _location of features is exactly where they are portrayed in Google Earth, ArcGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data projection is NAD83, UTM Zone 13N, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth. The data is within the area of interest of Great Sand Dunes National Park and Preserve.

Township boundaries were generated from radian measurements of township corner coordinates, represented to the nearest 0.001 second, recorded on official protraction diagrams of the state from BLM and ADNR. ADNR used 1994 AEH coordinate files from BLM as the basis of its work. BLM provided information for 18,654 land-based townships, and ADNR added another 774 (prior to 1996) townships that cover marine areas. Based on ADNR research, corner coordinates were modified for approximately 600 townships to correct the east-west and/or north-south alignment of neighboring townships. ADNR research also ensured that townships match across meridian lines.

Out of a total 19,425 townships currently defined for the state, 52 were identified by BLM as being irregular, that is, they cannot be describe by four corner points. During ADNR processing, many other minor adjustments were made to resolve spatial anomalies. Irregular townships are outlined using as many corner points as necessary, which was typically six to represent L-shaped townships. Many complex townships, including those along the US/Canadian border and those where meridians join, are described by more than six corner points; a few by only 3 points.

Using a geographic projection, ADNR created a double-precision coverage for the entire state from a compilation of the regular and irregular townships. Several iterations using ARC/INFO were required to find and resolve discrepancies in the tabular database. Arcs were densified while the township outlines were still in a geographic projection, to maintain the proper curvature of boundary lines during subsequent projection to other coordinate systems. The final result is a set of statewide coverages, both single-precision and double-precision, in both the Albers projection and geographic coordinates. The final coverages maintain as closely as possible the original protracted coordinate values.

The Unpublished Digital Geologic Map of portions of Craters of the Moon National Monument and Preserve, Idaho is composed of GIS data layers and GIS tables in a 10.0 file geodatabase (cotm_geology.gdb), a 10.0 ArcMap (.MXD) map document (cotm_geology.mxd), and individual 10.0 layer (.LYR) files for each GIS data layer, an ancillary map information (.PDF) document (cotm_geology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.TXT) and FAQ (.HTML) formats, and a GIS readme file (cotm_gis_readme.pdf). Please read the cotm_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.0 shapefile format contact Stephanie O’Meara (stephanie_o’meara@colostate.edu; see contact information below). The data is also available as a 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. Google Earth software is available for free at: http://www.google.com/earth/index.html. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (cotm_metadata_faq.html; available at http://nrdata.nps.gov/geology/gri_data/gis/crmo/cotm_metadata_faq.html). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.1. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data projection is NAD83, UTM Zone 12N, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth. The data is within the area of interest of Craters of the Moon National Monument and Preserve

The Unpublished Digital Geologic Map of Bering Land Bridge National Preserve and Vicinity, Alaska is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (bela_geology.gdb), a 10.1 ArcMap (.MXD) map document (bela_geology.mxd), individual 10.1 layer (.LYR) files for each GIS data layer, an ancillary map information (.PDF) document (bela_geology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.TXT) and FAQ (.HTML) formats, and a GIS readme file (bela_gis_readme.pdf). Please read the bela_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.1 shapefile format contact Stephanie O’Meara (stephanie.omeara@colostate.edu; see contact information below). The data is also available as a 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. Google Earth software is available for free at: http://www.google.com/earth/index.html. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (bela_metadata_faq.html; available at http://nrdata.nps.gov/geology/gri_data/gis/bela/bela_metadata_faq.html). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:500,000 and United States National Map Accuracy Standards features are within (horizontally) 254 meters or 833.3 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.2. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data projection is NAD83, UTM Zone AD_1983_Alaska_AlbersN, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth. The data is within the area of interest of Bering Land Bridge National Preserve.

This dynamic World Elevation Terrain layer returns float values representing ground heights in meters and compiles multi-resolution data from many authoritative data providers from across the globe. Heights are orthometric (sea level = 0), and water bodies that are above sea level have approximated nominal water heights.Height units: MetersUpdate Frequency: QuarterlyCoverage: World/GlobalData Sources: This layer is compiled from a variety of best available sources from several data providers. To see the coverage and extents of various datasets comprising this service in an interactive map, see World Elevation Coverage Map.What can you do with this layer?Use for Visualization: This layer is generally not optimal for direct visualization. By default, 32 bit floating point values are returned, resulting in higher bandwidth requirements. Therefore, usage should be limited to applications requiring elevation data values. Alternatively, client applications can select from numerous additional functions, applied on the server, that return rendered data. For visualizations such as multi-directional hillshade, hillshade, elevation tinted hillshade, and slope, consider using the appropriate server-side function defined on this service.Use for Analysis: Yes. This layer provides data as floating point elevation values suitable for use in analysis. There is a limit of 5000 rows x 5000 columns.Note: This layer combine data from different sources and resamples the data dynamically to the requested projection, extent and pixel size. For analyses using ArcGIS Desktop, it is recommended to filter a dataset, specify the projection, extent and cell size using the Make Image Server Layer geoprocessing tool. The extent is factor of cell size and rows/columns limit. e.g. if cell size is 10 m, the extent for analysis would be less than 50,000 m x 50,000 m.Server Functions: This layer has server functions defined for the following elevation derivatives. In ArcGIS Pro, server function can be invoked from Layer Properties - Processing Templates.

Slope Degrees Slope Percent Aspect Ellipsoidal height Hillshade Multi-Directional Hillshade Dark Multi-Directional Hillshade Elevation Tinted Hillshade Slope Map Aspect Map Mosaic Method: This image service uses a default mosaic method of "By Attribute”, using Field 'Best' and target of 0. Each of the rasters has been attributed with ‘Best’ field value that is generally a function of the pixel size such that higher resolution datasets are displayed at higher priority. Other mosaic methods can be set, but care should be taken as the order of the rasters may change. Where required, queries can also be set to display only specific datasets such as only NED or the lock raster mosaic rule used to lock to a specific dataset.Accuracy: Accuracy will vary as a function of location and data source. Please refer to the metadata available in the layer, and follow the links to the original sources for further details. An estimate of CE90 and LE90 are included as attributes, where available.This layer allows query, identify, and export image requests. The layer is restricted to a 5,000 x 5,000 pixel limit in a single request.This layer is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.

Township boundaries were generated from radian measurements of township corner coordinates, represented to the nearest 0.001 second, recorded on official protraction diagrams of the state from BLM and ADNR. ADNR used 1994 AEH coordinate files from BLM as the basis of its work. BLM provided information for 18,654 land-based townships, and ADNR added another 774 (prior to 1996) townships that cover marine areas. Based on ADNR research, corner coordinates were modified for approximately 600 townships to correct the east-west and/or north-south alignment of neighboring townships. ADNR research also ensured that townships match across meridian lines. Out of a total 19,425 townships currently defined for the state, 52 were identified by BLM as being irregular, that is, they cannot be describe by four corner points. During ADNR processing, many other minor adjustments were made to resolve spatial anomalies. Irregular townships are outlined using as many corner points as necessary, which was typically six to represent L-shaped townships. Many complex townships, including those along the US/Canadian border and those where meridians join, are described by more than six corner points; a few by only 3 points. Using a geographic projection, ADNR created a double-precision coverage for the entire state from a compilation of the regular and irregular townships. Several iterations using ARC/INFO were required to find and resolve discrepancies in the tabular database. Arcs were densified while the township outlines were still in a geographic projection, to maintain the proper curvature of boundary lines during subsequent projection to other coordinate systems. The final result is a set of statewide coverages, both single-precision and double-precision, in both the Albers projection and geographic coordinates. The final coverages maintain as closely as possible the original protracted coordinate values.

Township boundaries were generated from radian measurements of township corner coordinates, represented to the nearest 0.001 second, recorded on official protraction diagrams of the state from BLM and ADNR. ADNR used 1994 AEH coordinate files from BLM as the basis of its work. BLM provided information for 18,654 land-based townships, and ADNR added another 774 (prior to 1996) townships that cover marine areas. Based on ADNR research, corner coordinates were modified for approximately 600 townships to correct the east-west and/or north-south alignment of neighboring townships. ADNR research also ensured that townships match across meridian lines.

Out of a total 19,425 townships currently defined for the state, 52 were identified by BLM as being irregular, that is, they cannot be describe by four corner points. During ADNR processing, many other minor adjustments were made to resolve spatial anomalies. Irregular townships are outlined using as many corner points as necessary, which was typically six to represent L-shaped townships. Many complex townships, including those along the US/Canadian border and those where meridians join, are described by more than six corner points; a few by only 3 points.

Using a geographic projection, ADNR created a double-precision coverage for the entire state from a compilation of the regular and irregular townships. Several iterations using ARC/INFO were required to find and resolve discrepancies in the tabular database. Arcs were densified while the township outlines were still in a geographic projection, to maintain the proper curvature of boundary lines during subsequent projection to other coordinate systems. The final result is a set of statewide coverages, both single-precision and double-precision, in both the Albers projection and geographic coordinates. The final coverages maintain as closely as possible the original protracted coordinate values.

Soil is the foundation of life on earth. More living things by weight live in the soil than upon it. It determines what crops we can grow, what structures we can build, what forests can take root.This layer contains the physical soil variable percent clay (clay).Within the subset of soil that is smaller than 2mm in size, also known as the fine earth portion, clay is defined as particles that are smaller than 0.002mm, making them only visible in an electron microscope. Clay soils contain low amounts of air, and water drains through them very slowly.This layer is a general, medium scale global predictive soil layer suitable for global mapping and decision support. In many places samples of soils do not exist so this map represents a prediction of what is most likely in that location. The predictions are made in six depth ranges by soilgrids.org, funded by ISRIC based in Wageningen, Netherlands.Each 250m pixel contains a value predicted for that area by soilgrids.org from best available data worldwide. Data for percent clay are provided at six depth ranges from the surface to 2 meters below the surface. Each variable and depth range may be accessed in the layer's multidimensional properties.Dataset SummaryPhenomenon Mapped: Proportion of clay particles (< 0.002 mm) in the fine earth fraction in g/100g (%)Cell Size: 250 metersPixel Type: 32 bit float, converted from online data that is 16 Bit Unsigned IntegerCoordinate System: Web Mercator Auxiliary Sphere, projected via nearest neighbor from goode's homolosine land (250m)Extent: World land area except AntarcticaVisible Scale: All scales are visibleNumber of Columns and Rows: 160300, 100498Source: Soilgrids.orgPublication Date: May 2020Data from the soilgrids.org mean predictions for clay were used to create this layer. You may access the percent clay in one of six depth ranges. To select one choose the depth variable in the multidimensional selector in your map client.Mean depth (cm)Actual depth range of data-2.50-5cm depth range-105-15cm depth range-22.515-30cm depth range-4530-60cm depth range-8060-100cm depth range-150100-200cm depth rangeWhat can you do with this Layer?This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map: In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "world soils soilgrids" in the search box and browse to the layer. Select the layer then click Add to Map. In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "world soils soilgrids" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.This layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.More information about soilgrids layersAnswers to many questions may be found at soilgrids.org (ISRIC) frequently asked questions (faq) page about the data.To make this layer, Esri reprojected the expected value of ISRIC soil grids from soilgrids' source projection (goode's land WKID 54052) to web mercator projection, nearest neighbor, to facilitate online mapping. The resolution in web mercator projection is the same as the original projection, 250m. But keep in mind that the original dataset has been reprojected to make this web mercator version.This multidimensional soil collection serves the mean or expected value for each soil variable as calculated by soilgrids.org. For all other distributions of the soil variable, be sure to download the data directly from soilgrids.org. The data are available in VRT format and may be converted to other image formats within ArcGIS Pro.Accessing this layer's companion uncertainty layerBecause data quality varies worldwide, the uncertainty of the predicted value varies worldwide. A companion uncertainty layer exists for this layer which you can use to qualify the values you see in this map for analysis. Choose a variable and depth in the multidimensional settings of your map client to access the companion uncertainty layer.

The Unpublished Digital Geologic-GIS Map of the Brooks Range and Vicinity, Alaska is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (arcn_geology.gdb), a 10.1 ArcMap (.MXD) map document (arcn_geology.mxd), individual 10.1 layer (.LYR) files for each GIS data layer, an ancillary map information (.PDF) document (arcn_geology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.TXT) and FAQ (.HTML) formats, and a GIS readme file (gaar_gis_readme.pdf). Please read the gaar_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.1 shapefile format contact Stephanie O’Meara (stephanie.omeara@colostate.edu; see contact information below). The data is also available as a 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. Google Earth software is available for free at: http://www.google.com/earth/index.html. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey and U.S. Geological Survey . Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (arcn_metadata_faq.html; available at http://nrdata.nps.gov/geology/gri_data/gis/gaar/arcn_metadata_faq.html). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:25,000 and United States National Map Accuracy Standards features are within (horizontally) 127 meters or 416.7 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data projection is NAD83, UTM Zone AD_1983_Alaska_AlbersN, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth. The data is within the area of interest of Gates of the Arctic National Park and Preserve, Cape Krusenstern National Monument, Noatak National Preserve and Kobuk Valley National Park.

Important: Our technical support team is available to assist you during business hours only. Please keep in mind that we can only address technical difficulties during these hours. When using the product to make decisions, please take this into consideration.

Abstract This spatial product shows consistent ‘near real-time’ bushfire and prescribed burn boundaries for all jurisdictions who have the technical ability or appropriate licence conditions to provide this information. Currency Maintenance of the underlying data is the responsibility of the custodian. Geoscience Australia has automated methods of regularly checking for changes in source data. Once detected the dataset and feeds will be updated as soon as possible. NOTE: The update frequency of the underlying data from the jurisdictions varies and, in most cases, does not line up to this product’s update cycle. Date created: November 2023 Modification frequency: Every 15 Minutes Spatial Extent

West Bounding Longitude: 113° South Bounding Latitude: -44° East Bounding Longitude: 154° North Bounding Latitude: -10°

Source Information The project team initially identified a list of potential source data through jurisdictional websites and the Emergency Management LINK catalogue. These were then confirmed by each jurisdiction through the EMSINA National and EMSINA Developers networks. This Webservice contains authoritative data sourced from:

Australian Capital Territory - Emergency Service Agency (ESA) New South Wales - Rural Fire Service (RFS) Queensland - Queensland Fire and Emergency Service (QFES) South Australia - Country Fire Service (CFS)
Tasmania - Tasmania Fire Service (TFS)
Victoria – Department of Environment, Land, Water and Planning (DELWP)
Western Australia – Department of Fire and Emergency Services (DFES)

The completeness of the data within this webservice is reliant on each jurisdictional source and the information they elect to publish into their Operational Bushfire Boundary webservices. Known Limitations:

This dataset does not contain information from the Northern Territory government. This dataset contains a subset of the Queensland bushfire boundary data. The Queensland ‘Operational’ feed that is consumed within this National Database displays a the last six (6) months of incident boundaries. In order to make this dataset best represent a ‘near-real-time’ or current view of operational bushfire boundaries Geoscience Australia has filtered the Queensland data to only incorporate the last two (2) weeks data. Geoscience Australia is aware of duplicate data (features) may appear within this dataset. This duplicate data is commonly represented in the regions around state borders where it is operationally necessary for one jurisdiction to understand cross border situations. Care must be taken when summing the values to obtain a total area burnt. The data within this aggregated National product is a spatial representation of the input data received from the custodian agencies. Therefore, data quality and data completion will vary. If you wish to assess more information about specific jurisdictional data and/or data feature(s) it is strongly recommended that you contact the appropriate custodian.

The accuracy of the data attributes within this webservice is reliant on each jurisdictional source and the information they elect to publish into their Operational Bushfire Boundary webservices. Note: Geoscience Australia has, where possible, attempted to align the data to the (as of October 2023) draft National Current Incident Extent Feeds Data Dictionary. However, this has not been possible in all cases. Work to progress this alignment will be undertaken after the publication of this dataset, once this project enters a maintenance period. Catalog entry: Bushfire Boundaries – Near Real-Time Lineage Statement Version 1 and 2 (2019/20): This dataset was first built by EMSINA, Geoscience Australia, and Esri Australia staff in early January 2020 in response to the Black Summer Bushfires. The product was aimed at providing a nationally consistent dataset of bushfire boundaries. Version 1 was released publicly on 8 January 2020 through Esri AGOL software.
Version 2 of the product was released in mid-February as EMSINA and Geoscience Australia began automating the product. The release of version 2 exhibited a reformatted attributed table to accommodate these new automation scripts. The product was continuously developed by the three entities above until early May 2020 when both the scripts and data were handed over to the National Bushfire Recovery Agency. The EMSINA Group formally ended their technical involvement with this project on June 30, 2020. Version 3 (2020/21): A 2020/21 version of the National Operational Bushfire Boundaries dataset was agreed to by the Australian Government. It continued to extend upon EMSINA’s 2019/20 Version 2 product. This product was owned and managed by the Australian Government Department of Home Affairs, with Geoscience Australia identified as the technical partners responsible for development and delivery. Work on Version 3 began in August 2020 with delivery of this product occurring on 14 September 2020. Version 4 (2021/22): A 2021/22 version of the National Operational Bushfire Boundaries dataset was produced by Geoscience Australia. This product was owned and managed by Geoscience Australia, who provided both development and delivery. Work on Version 4 began in August 2021 with delivery of this product occurring on 1 September 2021. The dataset was discontinued in May 2022 because of insufficient Government funding. Version 5 (2023/25): A 2023/25 version of the National Near-Real-Time Bushfire Boundaries dataset is produced by Geoscience Australia under funding from the National Bushfire Intelligence Capability (NBIC) - CSIRO. NBIC and Geoscience Australia have also partnered with the EMSINA Group to assist with accessing and delivering this dataset. This dataset is the first time where the jurisdictional attributes are aligned to AFAC’s National Bushfire Schema.
Work on Version 5 began in August 2023 and was released in late 2023 under formal access arrangements with the States and Territories. Data Dictionary Geoscience Australia has not included attributes added automatically by spatial software processes in the table below.

Attribute Name Description

fire_id ID attached to fire (e.g. incident ID, Event ID, Burn ID).

fire_name Incident name. If available.

fire_type Binary variable to describe whether a fire was a bushfire or prescribed burn.

ignition_date The date of the ignition of a fire event. Date and time are local time zone from the State where the fire is located and stored as a string.

capt_date The date of the incident boundary was captured or updated. Date and time are local time zone from the Jurisdiction where the fire is located and stored as a string.

capt_method Categorical variable to describe the source of data used for defining the spatial extent of the fire.

area_ha Burnt area in Hectares. Currently calculated field so that all areas calculations are done in the same map projection. Jurisdiction supply area in appropriate projection to match state incident reporting system.

perim_km ) Burnt perimeter in Kilometres. Calculated field so that all areas calculations are done in the same map projection. Jurisdiction preference is that supplied perimeter calculations are used for consistency with jurisdictional reporting.

state State custodian of the data. NOTE: Currently some states use and have in their feeds cross border data

agency Agency that is responsible for the incident

date_retrieved The date and time that Geoscience Australia retrieved this data from the jurisdictions, stored as UTC. Please note when viewed in ArcGIS Online, the date is converted from UTC to your local time.

Contact Geoscience Australia, clientservices@ga.gov.au

a. Data content (data file/table name, including observation indicators) This data includes a 10m resolution woody swamp wetland spatial distribution dataset in the Greater Khingan Range, reflecting the spatial distribution of woody swamps in the region and providing data support for wetland protection in the Greater Khingan Range area. b. Construction purpose Wetlands, as one of the three major ecosystems on Earth, are not only important storage areas for freshwater resources, but also can regulate climate and protect ecological diversity. Forest wetlands, as an important component of wetlands, have strong carbon sequestration capabilities, abundant carbon storage, and significant importance for global carbon and oxygen balance. The woody swamp resources in the Greater Khingan Range region of China are abundant, but in recent years, with the degradation of permafrost, the conversion between swamps and woody vegetation has intensified, which has also had a huge impact on the regional landscape. Therefore, precise classification of forests and wetlands is of great significance for understanding global change. c. Service target It can widely serve scientific researchers in related disciplines such as black soil protection and ecological monitoring. d. Time range of data 2020 e. The spatial scope of data Daxing'anling area f. Projection method of data Projected Coordinate System：Krasovsky_1940_Albers Geographic Coordinate System：GCS_Krasovsky_1940 g. The disciplinary scope of data Biology>Ecology>Regional Ecology h. Quantity of data The total data volume is approximately 4.16MB

These population projections were prepared by the Australian Bureau of Statistics (ABS) for Geoscience Australia. The projections are not official ABS data and are owned by Geoscience Australia. These projections are for Statistical Areas Level 2 (SA2s) and Local Government Areas (LGAs), and are projected out from a base population as at 30 June 2022, by age and sex. Projections are for 30 June 2023 to 2032, with results disaggregated by age and sex.

Method The cohort-component method was used for these projections. In this method, the base population is projected forward annually by calculating the effect of births, deaths and migration (the components) within each age-sex cohort according to the specified fertility, mortality and overseas and internal migration assumptions. The projected usual resident population by single year of age and sex was produced in four successive stages – national, state/territory, capital city/rest of state, and finally SA2s. Assumptions were made for each level and the resulting projected components and population are constrained to the geographic level above for each year.
These projections were derived from a combination of assumptions published in Population Projections, Australia, 2022 (base) to 2071 on 23 November 2023, and historical patterns observed within each state/territory.

Projections – capital city/rest of state regions The base population is 30 June 2022 Estimated Resident Population (ERP) as published in National, state and territory population, June 2022. For fertility, the total fertility rate (at the national level) is based on the medium assumption used in Population Projections, Australia, 2022 (base) to 2071, of 1.6 babies per woman being phased in from 2022 levels over five years to 2027, before remaining steady for the remainder of the projection span. Observed state/territory, and greater capital city level fertility differentials were applied to the national data so that established trends in the state and capital city/rest of state relativities were preserved. Mortality rates are based on the medium assumption used in Population Projections, Australia, 2022 (base) to 2071, and assume that mortality rates will continue to decline across Australia with state/territory differentials persisting. State/territory and capital city/rest of state differentials were used to ensure projected deaths are consistent with the historical trend. Annual net overseas migration (NOM) is based on the medium assumption used in Population Projections, Australia, 2022 (base) to 2071, with an assumed gain (at the national level) of 400,000 in 2022-23, increasing to 315,000 in 2023-24, then declining to 225,000 in 2026-27, after which NOM is assumed to remain constant. State and capital city/rest of state shares are based on a weighted average of NOM data from 2010 to 2019 at the state and territory level to account for the impact of COVID-19. For internal migration, net gains and losses from states and territories and capital city/rest of state regions are based on the medium assumption used in Population Projections, Australia, 2022 (base) to 2071, and assume that net interstate migration will trend towards long-term historic average flows.

Projections – Statistical Areas Level 2 The base population for each SA2 is the estimated resident population in each area by single year of age and sex, at 30 June 2022, as published in Regional population by age and sex, 2022 on 28 September 2023. The SA2-level fertility and mortality assumptions were derived by combining the medium scenario state/territory assumptions from Population Projections, Australia, 2022 (base) to 2071, with recent fertility and mortality trends in each SA2 based on annual births (by sex) and deaths (by age and sex) published in Regional Population, 2021-22 and Regional Population by Age and Sex, 2022. Assumed overseas and internal migration for each SA2 is based on SA2-specific annual overseas and internal arrivals and departures estimates published in Regional Population, 2021-22 and Regional Population by Age and Sex, 2022. The internal migration data was strengthened with SA2-specific data from the 2021 Census, based on the usual residence one year before Census night question. Assumptions were applied by SA2, age and sex. Assumptions were adjusted for some SA2s, to provide more plausible future population levels, and age and sex distribution changes, including areas where populations may not age over time, for example due to significant resident student and defence force populations. Most assumption adjustments were made via the internal migration component. For some SA2s with zero or a very small population base, but where significant population growth is expected, replacement migration age/sex profiles were applied. All SA2-level components and projected projections are constrained to the medium series of capital city/rest of state data in Population Projections, Australia, 2022 (base) to 2071.

Projections – Local Government Areas The base population for each LGA is the estimated resident population in each area by single year of age and sex, at 30 June 2022, as published in Regional population by age and sex, 2022 on 28 September 2023. Projections for 30 June 2023 to 2032 were created by converting from the SA2-level population projections to LGAs by age and sex. This was done using an age-specific population correspondence, where the data for each year of the projection span were converted based on 2021 population shares across SA2s. The LGA and SA2 projections are congruous in aggregation as well as in isolation. Unlike the projections prepared at SA2 level, no LGA-specific projection assumptions were used.

Nature of projections and considerations for usage The nature of the projection method and inherent fluctuations in population dynamics mean that care should be taken when using and interpreting the projection results. The projections are not forecasts, but rather illustrate future changes which would occur if the stated assumptions were to apply over the projection period. These projections do not attempt to allow for non-demographic factors such as major government policy decisions, economic factors, catastrophes, wars and pandemics, which may affect future demographic behaviour. To illustrate a range of possible outcomes, alternative projection series for national, state/territory and capital city/rest of state areas, using different combinations of fertility, mortality, overseas and internal migration assumptions, are prepared. Alternative series are published in Population Projections, Australia, 2022 (base) to 2071. Only one series of SA2-level projections was prepared for this product. Population projections can take account of planning and other decisions by governments known at the time the projections were derived, including sub-state projections published by each state and territory government. The ABS generally does not have access to the policies or decisions of commonwealth, state and local governments and businesses that assist in accurately forecasting small area populations. Migration, especially internal migration, accounts for the majority of projected population change for most SA2s. Volatile and unpredictable small area migration trends, especially in the short-term, can have a significant effect on longer-term projection results. Care therefore should be taken with SA2s with small total populations and very small age-sex cells, especially at older ages. While these projections are calculated at the single year of age level, small numbers, and fluctuations across individual ages in the base population and projection assumptions limit the reliability of SA2-level projections at single year of age level. These fluctuations reduce and reliability improves when the projection results are aggregated to broader age groups such as the five-year age bands in this product. For areas with small elderly populations, results aggregated to 65 and over are more reliable than for the individual age groups above 65. With the exception of areas with high planned population growth, SA2s with a base total population of less than 500 have generally been held constant for the projection period in this product as their populations are too small to be reliably projected at all, however their (small) age/sex distributions may change slightly. These SA2s are listed in the appendix. The base (2022) SA2 population estimates and post-2022 projections by age and sex include small artificial cells, including 1s and 2s. These are the result of a confidentialisation process and forced additivity, to control SA2 and capital city/rest of state age/sex totals, being applied to their original values. SA2s and LGAs in this product are based on the Australian Statistical Geography Standard (ASGS) boundaries as at the 2021 Census (ASGS Edition 3). For further information, see Australian Statistical Geography Standard (ASGS) Edition 3.

Made possible by the Digital Atlas of Australia The Digital Atlas of Australia is a key Australian Government initiative being led by Geoscience Australia, highlighted in the Data and Digital Government Strategy. It brings together trusted datasets from across government in an interactive, secure, and easy-to-use geospatial platform. The Australian Bureau of Statistics (ABS) is working in partnership with Geoscience Australia to establish a set of web services to make ABS data available in the Digital Atlas of Australia.

Contact the Australian Bureau of Statistics If you have questions or feedback about this web service, please email geography@abs.gov.au. To subscribe to updates about ABS web services and geospatial products, please complete this form. For information about how the ABS manages any personal information you provide view the ABS privacy policy.

Data and geography references Source data publication: Population Projections, Australia, 2022 (base)

Summary

Rail Crossings is a spatial file maintained by the Federal Railroad Administration (FRA) for use by States and railroads.

Description

FRA Grade Crossings is a spatial file that originates from the National Highway-Rail Crossing, Inventory Program. The program is to provide information to Federal, State, and local governments, as well as the railroad industry for the improvements of safety at highway-rail crossing.

Credits

Federal Railroad Administration (FRA)

Use limitations

There are no access and use limitations for this item.

Extent

West -79.491008 East -75.178954 North 39.733500 South 38.051719

Scale Range Maximum (zoomed in) 1:5,000 Minimum (zoomed out) 1:150,000,000

ArcGIS Metadata ▼►Topics and Keywords ▼►Themes or categories of the resource  transportation

* Content type  Downloadable Data Export to FGDC CSDGM XML format as Resource Description No

Temporal keywords  2013

Theme keywords  Rail

Theme keywords  Grade Crossing

Theme keywords  Rail Crossings

Citation ▼►Title rr_crossings Creation date 2013-03-15 00:00:00

Presentation formats  * digital map

Citation Contacts ▼►Responsible party  Individual's name Raquel Hunt Organization's name Federal Railroad Administration (FRA) Contact's position GIS Program Manager Contact's role  custodian

Responsible party  Organization's name Research and Innovative Technology Administration/Bureau of Transportation Statistics Individual's name National Transportation Atlas Database (NTAD) 2013 Contact's position Geospatial Information Systems Contact's role  distributor

Contact information  ▼►Phone  Voice 202-366-DATA

Address  Type  Delivery point 1200 New Jersey Ave. SE City Washington Administrative area DC Postal code 20590 e-mail address answers@BTS.gov

Resource Details ▼►Dataset languages  * English (UNITED STATES) Dataset character set  utf8 - 8 bit UCS Transfer Format

Spatial representation type  * vector

* Processing environment Microsoft Windows 7 Version 6.1 (Build 7600) ; Esri ArcGIS 10.2.0.3348

Credits Federal Railroad Administration (FRA)

ArcGIS item properties  * Name USDOT_RRCROSSINGS_MD * Size 0.047 Location withheld * Access protocol Local Area Network

Extents ▼►Extent  Geographic extent  Bounding rectangle  Extent type  Extent used for searching * West longitude -79.491008 * East longitude -75.178954 * North latitude 39.733500 * South latitude 38.051719 * Extent contains the resource Yes

Extent in the item's coordinate system  * West longitude 611522.170675 * East longitude 1824600.445629 * South latitude 149575.449134 * North latitude 752756.624659 * Extent contains the resource Yes

Resource Points of Contact ▼►Point of contact  Individual's name Raquel Hunt Organization's name Federal Railroad Administration (FRA) Contact's position GIS Program Manager Contact's role  custodian

Resource Maintenance ▼►Resource maintenance  Update frequency  annually

Resource Constraints ▼►Constraints  Limitations of use There are no access and use limitations for this item.

Spatial Reference ▼►ArcGIS coordinate system  * Type Projected * Geographic coordinate reference GCS_North_American_1983_HARN * Projection NAD_1983_HARN_StatePlane_Maryland_FIPS_1900_Feet * Coordinate reference details  Projected coordinate system  Well-known identifier 2893 X origin -120561100 Y origin -95444400 XY scale 36953082.294548117 Z origin -100000 Z scale 10000 M origin -100000 M scale 10000 XY tolerance 0.0032808333333333331 Z tolerance 0.001 M tolerance 0.001 High precision true Latest well-known identifier 2893 Well-known text PROJCS["NAD_1983_HARN_StatePlane_Maryland_FIPS_1900_Feet",GEOGCS["GCS_North_American_1983_HARN",DATUM["D_North_American_1983_HARN",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["False_Easting",1312333.333333333],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-77.0],PARAMETER["Standard_Parallel_1",38.3],PARAMETER["Standard_Parallel_2",39.45],PARAMETER["Latitude_Of_Origin",37.66666666666666],UNIT["Foot_US",0.3048006096012192],AUTHORITY["EPSG",2893]]

Reference system identifier  * Value 2893 * Codespace EPSG * Version 8.1.1

Spatial Data Properties ▼►Vector  ▼►* Level of topology for this dataset  geometry only

Geometric objects  Feature class name USDOT_RRCROSSINGS_MD * Object type  point * Object count 1749

ArcGIS Feature Class Properties  ▼►Feature class name USDOT_RRCROSSINGS_MD * Feature type Simple * Geometry type Point * Has topology FALSE * Feature count 1749 * Spatial index TRUE * Linear referencing FALSE

Data Quality ▼►Scope of quality information  ▼►Resource level  attribute Scope description  Attributes The States and railroads maintain their own file and get updated to the FRA. The information is reported to the FRA on the U.S. DOT-ARR Crossing inventory form.

Attributes The quality of the inventory can vary because a record of grade crossing location is being maintained by each state and railroad that is responsible for maintaining its respective information.

Lineage ▼►Lineage statement The data was downloaded from the HWY-Rail Crossing Inventory Files. All crossings that were closed or abandon were queried out of the data. All of the crossings with a zero within the latitude or longitude were queried out. Any crossing outside a bounding box of box ((Latitude >= 18 & Latitude <= 72) AND (Longitude >= -171 & Longitude <= -63)) were queried out.

Geoprocessing history ▼►Process  Date 2013-08-14 10:41:15 Tool location c:\program files (x86)\arcgis\desktop10.0\ArcToolbox\Toolboxes\Data Management Tools.tbx\Project Command issued Project RR_CROSSINGS_MD_USDOT \shagbfs\gis_projects\Railroad_Crossings_MD\Railroad_Crossings_MD.gdb\RR_CROSSINGS_MD_USDOT_83FTHARN PROJCS['NAD_1983_HARN_StatePlane_Maryland_FIPS_1900_Feet',GEOGCS['GCS_North_American_1983_HARN',DATUM['D_North_American_1983_HARN',SPHEROID['GRS_1980',6378137.0,298.257222101]],PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION['Lambert_Conformal_Conic'],PARAMETER['False_Easting',1312333.333333333],PARAMETER['False_Northing',0.0],PARAMETER['Central_Meridian',-77.0],PARAMETER['Standard_Parallel_1',38.3],PARAMETER['Standard_Parallel_2',39.45],PARAMETER['Latitude_Of_Origin',37.66666666666666],UNIT['Foot_US',0.3048006096012192]] WGS_1984_(ITRF00)_To_NAD_1983_HARN GEOGCS['GCS_WGS_1984',DATUM['D_WGS_1984',SPHEROID['WGS_1984',6378137.0,298.25722356]],PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]] Include in lineage when exporting metadata No

Distribution ▼►Distributor  ▼►Contact information  Individual's name Office of Geospatial Information Systems Organization's name Research and Innovative Technology Administration's Bureau of Transportation Statistics (RITA/BTS) Contact's role  distributor

Contact information  ▼►Phone  Voice 202-366-DATA

Address  Type  Delivery point 1200 New Jersey Ave. SE City Washington Administrative area DC Postal code 20590 Country US e-mail address answers@bts.gov

Available format  Name Shapefile Version 2013 File decompression technique no compression applied

Ordering process  Instructions Call (202-366-DATA), or E-mail (answers@bts.gov) RITA/BTS to request the National Transportation Atlas Databases (NTAD) 2013 DVD. The NTAD DVD can be ordered from the online bookstore at www.bts.gov. Individual datasets from the NTAD can also be downloaded from the Office of Geospatial Information Systems website at http://www.bts.gov/programs/geographic_information_services/

Transfer options  Transfer size 6.645

Medium of distribution  Medium name  DVD

How data is written  iso9660 (CD-ROM) Recording density 650 Density units of measure Megabytes

Transfer options  Online source  Description  National Transportation Atlas Databases (NTAD) 2013

Distribution format  * Name Shapefile Version 2013

Transfer options  * Transfer size 0.047

Online source  Location http://www.bts.gov/programs/geographic_information_services/

Fields ▼►Details for object USDOT_RRCROSSINGS_MD ▼►* Type Feature Class * Row count 1749

Field FID ▼►* Alias FID * Data type OID * Width 4 * Precision 0 * Scale 0 * Field description Internal feature number.

* Description source ESRI

* Description of values Sequential unique whole numbers that are automatically generated.

Field Shape ▼►* Alias Shape * Data type Geometry * Width 0 * Precision 0 * Scale 0 * Field description Feature geometry.

* Description source ESRI

* Description of values Coordinates defining the features.

Field OBJECTID ▼►* Alias OBJECTID * Data type Integer * Width 9 * Precision 9 * Scale 0

Field CROSSING ▼►* Alias CROSSING * Data type String * Width 7 * Precision 0 * Scale 0 Field description US DOT Valid Crossing ID Number

Description source FRA

Field RAILROAD ▼►* Alias RAILROAD * Data type String * Width 4 * Precision 0 * Scale 0 Field description The

This layer shows particulate matter in the air sized 2.5 micrometers of smaller (PM 2.5). The data is aggregated from NASA Socioeconomic Data and Applications Center (SEDAC) gridded data into country boundaries, administrative 1 boundaries, and 50 km hex bins. The unit of measurement is micrograms per cubic meter.The layer shows the annual average PM 2.5 from 1998 to 2016, highlighting if the overall mean for an area meets the World Health Organization guideline of 10 micrograms per cubic meter annually. Areas that don't meet the guideline and are above the threshold are shown in red, and areas that are lower than the guideline are in grey.The data is averaged for each year and over the the 19 years to provide an overall picture of air quality globally. Some of the things we can learn from this layer:What is the average annual PM 2.5 value over 19 years? (1998-2016)What is the annual average PM 2.5 value for each year from 1998 to 2016?What is the statistical trend for PM 2.5 over the 19 years? (downward or upward)Are there hot spots (or cold spots) of PM 2.5 over the 19 years?How many people are impacted by the air quality in an area?What is the death rate caused by the joint effects of air pollution?Choose a different attribute to symbolize in order to reveal any of the patterns above.A space time cube was performed on a multidimensional mosaic version of the data in order to derive an emerging hot spot analysis, trends, and a 19-year average. The country and administrative 1 layers provide a population-weighted PM 2.5 value to emphasize which areas have a higher human impact. Citations:van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2018. Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4ZK5DQS. Accessed 1 April 2020van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2016. Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites. Environmental Science & Technology 50 (7): 3762-3772. https://doi.org/10.1021/acs.est.5b05833.Boundaries and population figures:Antarctica is excluded from all maps because it was not included in the original NASA grids.50km hex bins generated using the Generate Tessellation tool - projected to Behrmann Equal Area projection for analysesPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Administrative boundaries from World Administrative Divisions layer from ArcGIS Living Atlas - projected to Behrmann Equal Area projection for analyses and hosted in Web MercatorSources: Garmin, CIA World FactbookPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Country boundaries from Esri 2019 10.8 Data and Maps - projected to Behrmann Equal Area projection for analyses and hosted in Web Mercator. Sources: Garmin, Factbook, CIAPopulation figures attached to the country boundaries come from the World Population Estimate 2016 Sources Living Atlas layer Data processing notes:NASA's GeoTIFF files for 19 years (1998-2016) were first brought into ArcGIS Pro 2.5.0 and put into a multidimensional mosaic dataset.For each geography level, the following was performed: Zonal Statistics were run against the mosaic as a multidimensional layer.A Space Time Cube was created to compare the 19 years of PM 2.5 values and detect hot/cold spot patterns. To learn more about Space Time Cubes, visit this page.The Space Time Cube is processed for Emerging Hot Spots where we gain the trends and hot spot results.The layers are hosted in Web Mercator Auxillary Sphere projection, but were processed using an equal area projection: Behrmann. If using this layer for analysis, it is recommended to start by projecting the data back to Behrmann.The country and administrative layer were dissolved and joined with population figures in order to visualize human impact.The dissolve tool ensures that each geographic area is only symbolized once within the map.Country boundaries were generalized post-analysis for visualization purposes. The tolerance used was 700m. If performing analysis with this layer, find detailed country boundaries in ArcGIS Living Atlas. To create the population-weighted attributes on the country and Admin 1 layers, the hex value population values were used to create the weighting. Within each hex bin, the total population figure and average PM 2.5 were multiplied.The hex bins were converted into centroids and the PM2.5 and population figures were summarized within the country and Admin 1 boundaries.The summation of the PM 2.5 values were then divided by the total population of each geography. This population value was determined by summarizing the population values from the hex bins within each geography.Some artifacts in the hex bin layer as a result of the input NASA rasters. Because the gridded surface is created from multiple satellites, there are strips within some areas that are a result of satellite paths. Some areas also have more of a continuous pattern between hex bins as a result of the input rasters.Within the country layer, an air pollution attributable death rate is included. 2016 figures are offered by the World Health Organization (WHO). Values are offered as a mean, upper value, lower value, and also offered as age standardized. Values are for deaths caused by all possible air pollution related diseases, for both sexes, and all age groups. For more information visit this page, and here for methodology. According to WHO, the world average was 95 deaths per 100,000 people.To learn the techniques used in this analysis, visit the Learn ArcGIS lesson Investigate Pollution Patterns with Space-Time Analysis by Esri's Kevin Bulter and Lynne Buie.