Assistance requests for services, care, or resources supported via phone, in-person, postal mail or electronic mail. Assistance and support involve connecting City veterans and their families to a coordinated network of public, private and non-profit organizations.

These files contain U.S. national map packages (ESRI ArcMap 10.5) for 2000 and 2020, including:

Heating season length and intensity (monthly heating degree-days evaluated from a base temperature of 18.3 ºC);

Residential heating energy needs (monthly kWh per household);

Solar heating resources on 10m2 surfaces of optimal tilt (monthly Wh);

Optimal south-facing tilt values for solar heat collection (angular degrees above horizontal);

Net solar heating resources on 10m2 optimally-tilted collector surfaces (NSHR10) (monthly MWh per household and sums over 10km x 10km sectors);

Proportions of the NSHR10 provided by diffuse radiation (monthly percentage by location);

Collector areas needed to intercept solar radiation equal to household heating needs (monthly m2);

Median absolute deviations in the NSHR10 obtained with twelve consecutive years of solar radiation data (annual MWh and percentage).

Please see associated publication (Rempel et al. 2020) for source data and methodological details.

This data release, RI_WRpts.gdb, consists of information from Rhode Island Ground-water maps published by the Rhode Island Water Resources Coordinating Board, the Rhode Island Port and Industrial Development Commission, Rhode Island Industrial Commission, and the Rhode Island Development Council; in cooperation with the U.S. Geological Survey. The point data on these maps have been digitized into a standard ArcGIS geodatabase format. Data about wells and test borings consists of geographic location, identification number, geologic material (bedrock or unconsolidated), altitude in feet of the bedrock surface or altitude of the bottom of well, and data source. Seismic survey locations and bedrock outcrops where they are shown as points on the source maps are also included. The Ground-water maps, published between 1948 and 1964, also show geologic information which is being used to create a revised surficial materials database for future publication.

A collection of geospatial files, map images, publication documentation, and informational resources in support of the Geologic Map of North America.

Data was compiled from published sources by US Geological Survey geoscientists Mark J. Mihalasky, Susan M. Hall and Robert A. Zielinski. This dataset was provided to the U.S. Energy Information Administration in February of 2019 to facilitate updating of national uranium resource distribution maps. The location of uranium provinces, districts and select important deposits located outside of these broader regions was taken from a variety of sources listed alphabetically below.Adams S.S.; Smith R.B., 1981, Geology and recognition criteria for sandstone uranium deposits in mixed fluvial-shallow marine sedimentary sequences, South Texas; U.S. Department of Energy Report GJBX-4(81), 145 p.Colorado Geological Survey, 2018, Uranium Districts – Colorado; published on the Colorado Geological Survey website at http://coloradogeologicalsurvey.org/energy-resources/uranium2/map/.Chenoweth, W.L., 1980, Uranium in Colorado; Rocky Mountain Association of Geologists, 1980 Symposium, p. 217-224Gloyn, R.W.; Bon, R.L.; Wakefield, S.; Krahulec, K., 2005, Uranium and vanadium map of Utah; Map 215, Utah Department of Natural Resources, Utah Geological Survey, 1:750,000 scale, 1 sheet. Metadata download at: https://gis.utah.gov/data/energy/uranium/Gregory R.W., 2016, Uranium: Geology and Applications; Wyoming State Geological Survey Public Information Circular No 46, 36 p.Keith, S.B.; Gest, D.E.; DeWitt, E; 1983, Metallic mineral districts of Arizona; Arizona Bureau of Geology and Mineral Technology, Geological Survey Branch, Tucson, AZ, 1:1,000,000 scale, 1 sheetKyle L, Beahm D, 2013, NI 43-101 preliminary economic assessment update (revised), Coles Hill uranium property, Pittsylvania County, VA USA; prepared by Lyntek Incorporated, Lakewood, CO; 2013, 126 p. Figure 1.1.McLemore, V.T. and Chenoweth, W.L., 1989, Uranium resources in New Mexico; New Mexico Bureau of Mines and Minerals Resources, Resource Map 18, 36 p. Available at: https://geoinfo.nmt.edu/faq/mining/home.html

National-scale geologic, geophysical, and mineral resource raster and vector data covering the United States, Canada, and Australia are provided in this data release. The data were compiled as part of the tri-national Critical Minerals Mapping Initiative (CMMI). The CMMI, established in 2019, is an international science collaboration between the U.S. Geological Survey (USGS), Geoscience Australia (GA), and the Geological Survey of Canada (GSC). One aspect of the CMMI is to use national- to global-scale earth science data to map where critical mineral prospectivity may exist using advanced machine learning approaches (Kelley, 2020). The geoscience information presented in this report include the training and evidential layers that cover all three countries and underpin the resultant prospectivity models for basin-hosted Pb-Zn mineralization described in Lawley and others (2021). It is expected that these data layers will be useful to many regional- to continental-scale studies related to a wide range of earth science research. Therefore, the data layers are organized using widely accepted GIS formats in the same map projection to increase efficiency and effectiveness of future studies. All datasets have a common geographic projection in decimal degrees using a WGS84 datum. Data for the various training and evidential layers were either derived for this study or were extracted from previous national to global-scale compilations. Data from outside work are provided here as a courtesy for completeness of the model and should be cited as the original source. Original references are provided on each child page. Where possible, data for the United States were merged to data for Canada to provide composite data that allow for continuity and seamless analyses of the earth science data across the two countries. Earth science data provided in this report include training data for the models. Training data include a mineral resource database of Pb-Zn deposits and occurrences related to either carbonate-hosted (Mississippi Valley type-MVT) or clastic-dominated (aka sedex) Pb-Zn mineralization. Evidential layers that were used as input to the models include GeoTIFF grid files consisting of ground, airborne, and satellite geophysical data (magnetic, gravity, tomography, seismic) and several related derivative products. Geologic layers incorporated into the models include shapefiles of modified lithology and faults for the United States, Canada and Australia. A global database of ancient and modern passive margins is provided here as well as a link to a database mapping the global distribution of black shale units from a previous USGS study. GeoTIFF grids of the final prospectivity models for MVT and for clastic-dominated Pb-Zn mineralization across the US, Canada, and Australia from Lawley and others (2021) are also included. Each child page describes the particular data layer and related derivative products if applicable. Kelley, K.D., 2020, International geoscience collaboration to support critical mineral discovery: U.S. Geological Survey Fact Sheet 2020–3035, 2 p., https://doi.org/10.3133/fs20203035. Lawley, C.J.M., McCafferty, A.E., Graham, G.E., Huston, D.L., Kelley, K.D., Czarnota, K., Paradis, S., Peter, J.M., Hayward, N., Barlow, M., Emsbo, P., Coyan, J., San Juan, C.A., and Gadd, M.G., 2022, Data-driven prospectivity modelling of sediment-hosted Zn-Pb mineral systems and their critical raw materials: Ore Geology Reviews, v. 141, no. 104635, https://doi.org/10.1016/j.oregeorev.2021.104635.

FEMA, as the administrator of the National Flood Insurance Program (NFIP), has created Advisory Base Flood Elevations (ABFEs) and storm erosion areas for the United States Virgin Islands (USVI). The ABFE information, storm erosion data, and related layers depicted on this web service for the USVI can serve as a guide to understanding current flood and erosion hazard conditions that communities should build to in order to reduce impacts of similar events in the future. All elevations included on the map are referenced to the Virgin Island Vertical Datum of 2009 (VIVD 09).Data DownloadGIS data and PDF maps that support this web map can be downloaded at the locations indicated below:GIS Data in shapefile format can be downloaded by clicking hereGIS Data in ESRI's File GeoDatabase format can be downloaded by clicking herePDF Maps:Map panels for the entire territory, in Portable Document Format (PDF) can be downloaded by clicking here. The downloaded zip file contains map panels for the entire study area. A grid of all map panels (panel index) in PDF format for St.Thomas and St.John can be accessed here.A grid of all map panels (panel index) in PDF format for St.Croix can be accessed here.Individual map panels can be accessed directly from the map viewer, by locating the panel of interest and by clicking on the panel to activate a pop-up that contains the link to the panel.

The Digital Geologic-GIS Map of the Hatch Trading Post Quadrangle, Utah is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (hatp_geology.gdb), and a 2.) Open Geospatial Consortium (OGC) geopackage. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (hatp_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (hatp_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (hove_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (hove_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (hatp_geology_metadata_faq.pdf). Please read the hove_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. QGIS software is available for free at: https://www.qgis.org/en/site/. 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). For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri.htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: National Park Service Geologic Resources Inventory 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 (hatp_geology_metadata.txt or hatp_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 1:250,000 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 ArcGIS, QGIS 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).

Datasets associated with the data publication "A map of pollinator floral resource habitats in the agricultural landscape of Central New York". Floral resource maps were produced for 12 counties in New York State (USA): Cayuga, Chemung, Cortland, Monroe, Onondaga, Ontario, Schuyler, Seneca, Tioga, Tompkins, Wayne, and Yates. The dataset covers 8 years from 2012 to 2019.

Each year has two alternative versions that represent urban areas in Monroe, Seneca, and Wayne counties differently. Datasets with the prefix "final_cat_" use categorical variables and datasets with the prefix "final_cont_" use a continuous value. See the associated publication for more values about the meaning and interpretation of these values.

The results of a new EGS geothermal resource assessment of the eastern US, focused on the Northeastern US and based on use of Bottom Hole Temperatures (BHT), are summarized. A total of 5,800 heat flow points are now available for the area as opposed to the 323 used to produce the 2004 Geothermal Map of North America. The challenge is determining heat flow and subsurface temperature in areas where no data or limited conventional heat flow data exist in the previous assessments (most of the eastern 2/3 of the US). The techniques used to allow large scale use of BHT data for heat flow calculations are described. The process for the temperature-at-depth calculation is updated to better accommodate the use of BHT data. The geophysical data are also utilized as an ancillary predictor to the heat flow determination process in areas with limited or no thermal data. This study uses the same process to calculate heat storage when the thermal properties and temperature at depth are known described in the Future of Geothermal Energy report. Heat-in-place values have been updated for the northeastern US. Because of the higher data density the new temperature at depth maps show more localized temperature anomalies then the older maps and are a first step in the identification of site specific geothermal anomalies for further research and development. An important result is the identification and delineation of a significant thermal anomaly in eastern West Virginia.

The Digital Geologic-GIS Map of Everglades National Park and Vicinity, Florida is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (ever_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 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. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (ever_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (ever_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (ever_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (ever_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (ever_geology_metadata_faq.pdf). Please read the ever_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. 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). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Florida 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 (ever_geology_metadata.txt or ever_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 1:675,000 and United States National Map Accuracy Standards features are within (horizontally) 342.9 meters or 1125 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, QGIS 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).

This Coastal Barrier Resources System (CBRS) data set, produced by the U.S. Fish and Wildlife Service (Service), contains areas designated as undeveloped coastal barriers in accordance with the Coastal Barrier Resources Act (CBRA), 16 U.S.C. 3501 et seq., as amended. The boundaries used to create the polygons herein were compiled from the official John H. Chafee Coastal Barrier Resources System CBRS maps, which are accessible at the Service’s Headquarters office or https://www.fws.gov/program/coastal-barrier-resources-act/maps-and-data. These digital polygons are only representations of the CBRS boundaries shown on the official CBRS maps and are not to be considered authoritative. The Service is not responsible for any misuse or misinterpretation of this digital data set, including use of the data to determine eligibility for federal financial assistance such as federal flood insurance. As maps are revised, this data set will be updated with the new boundaries. CBRS boundaries viewed using the CBRS Mapper or the shapefile are subject to misrepresentations beyond the Service’s control, including misalignments of the boundaries with third party base layers and mis-projections of spatial data. The official CBRS map is the controlling document and should be consulted for all official determinations. Official determinations are recommended for all properties that are in close proximity (within 20 feet) of a CBRS boundary. For an official determination of whether or not an area or specific property is located within the CBRS, please follow the procedures found at https://www.fws.gov/service/coastal-barrier-resources-system-property-documentation. For any questions regarding the CBRS, please contact your local Service field office or email CBRA@fws.gov. Contact information for Service field offices can be found at https://www.fws.gov/our-facilities.Data Set Contact: U.S. Fish and Wildlife Service Natural Resource Program Center, GIS Team Lead, richard_easterbrook@fws.gov

This cartographic quality series of 1:50 000 scale monochrome maps cover the provincial extent of Alberta comprised of 764 maps that are individually named using the National Topographic System (NTS) map sheet identifier. These maps display the Alberta Township System (ATS), hydrographic features, municipalities, roads, cutlines, facilities, pipelines, powerlines, railways, select geo-administrative features (parks, reserves, etc.). At this scale 1.0 cm on the map represents 0.5 km on the ground. Each map covers an area of 0.50 degree longitude by 0.25 degree latitude. This product can be viewed on a computer, printed or be plotted in part or in whole. All maps contained within a 1:250 000 block (generally up to 16 map sheets) will be included in the NTS Block download. This series is not updated on a regular basis and may contain a range of publication dates.

As a result of a Latin American Coal Assessment, the USGS published the first Coal Map of South America (Weaver and Wood, 1994) and developed a cooperative inter-American exchange of geologic information which lead to a better understanding of the potential for coal resource utilization in the western hemisphere. This coal study was started by the late Gordon H. Wood, Jr. The original compilation, completed before his death, was a result of library research and it did not include updated information from scientists and others in the coal-bearing countries of South America. During the Fall of 1991, Jean N. Weaver visited Uruguay, Argentina, Chile, Peru, Ecuador, Colombia, Venezuela, Brazil, and Bolivia. The purpose of the nine-country visit was twofold: (1) to discuss with geologists and other authorities in each country the quantity, quality, and distribution of known coal resources and the status of coal recovery and utilization and (2) to inform them of the current role of coal research in the U.S. Geological Survey. Paraguay was not visited because of time constraints. Guyana and Suriname were visited in the spring of 1993.

The Digital Geologic-GIS Map of the Rhoda Quadrangle, Kentucky is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (rhod_geology.gdb), and a 2.) Open Geospatial Consortium (OGC) geopackage. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (rhod_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (rhod_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (maca_abli_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (maca_abli_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (rhod_geology_metadata_faq.pdf). Please read the maca_abli_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. QGIS software is available for free at: https://www.qgis.org/en/site/. 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). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). 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 (rhod_geology_metadata.txt or rhod_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 ArcGIS, QGIS 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).

Mineral resource occurrence data covering the world, most thoroughly within the U.S. This database contains the records previously provided in the Mineral Resource Data System (MRDS) of USGS and the Mineral Availability System/Mineral Industry Locator System (MAS/MILS) originated in the U.S. Bureau of Mines, which is now part of USGS. The MRDS is a large and complex relational database developed over several decades by hundreds of researchers and reporters. While database records describe mineral resources worldwide, the compilation of information was intended to cover the United States completely, and its coverage of resources in other countries is incomplete. The content of MRDS records was drawn from reports previously published or made available to USGS researchers. Some of those original source materials are no longer available. The information contained in MRDS was intended to reflect the reports used as sources and is current only as of the date of those source reports. Consequently MRDS does not reflect up-to-date changes to the operating status of mines, ownership, land status, production figures and estimates of reserves and resources, or the nature, size, and extent of workings. Information on the geological characteristics of the mineral resource are likely to remain correct, but aspects involving human activity are likely to be out of date.

Pairwise correlation values between variables used in global RSF models and best fit term associated with oil and gas development (producing wells within 5km).

m—mean;sd-standard deviation;2-quadratic term;cur—current year; lag– 1 year lagged* Correlated variable removed for inclusion in multivariate modelAsterisks denote correlated variables removed from multivariate RSF models.

Soil map units are the basic geographic unit of the Soil Survey Geographic Database (SSURGO). The SSURGO dataset is a compilation of soils information collected over the last century by the Natural Resources Conservation Service (NRCS). Map units delineate the extent of different soils. Data for each map unit contains descriptions of the soil’s components, productivity, unique properties, and suitability interpretations.Each soil type has a unique combination of physical, chemical, nutrient and moisture properties. Soil type has ramifications for engineering and construction activities, natural hazards such as landslides, agricultural productivity, the distribution of native plant and animal life and hydrologic and other physical processes. Soil types in the context of climate and terrain can be used as a general indicator of engineering constraints, agriculture suitability, biological productivity and the natural distribution of plants and animals.Dataset SummaryPhenomenon Mapped: Soils of the United States and associated territoriesCoordinate System: Web Mercator Auxiliary SphereExtent: The 50 United States, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaVisible Scale: 1:144,000 to 1:1,000Resolution/Tolerance: 1 meter/2 metersNumber of Features: 36,543,233Feature Request Limit: 10,000Source: USDA Natural Resources Conservation ServicePublication Date: October 1, 2019ArcGIS Server URL: https://landscape11.arcgis.com/arcgis/rest/servicesData from the gNATSGO database was used to create the layer for the contiguous United States, Alaska, Puerto Rico, and the U.S. Virgin Islands. The remaining areas were created with the gSSURGO database (Hawaii, Guam, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American Samoa).AttributesKey fields from nine commonly used SSURGO tables were compiled to create the 173 attribute fields in this layer. Some fields were joined directly to the SSURGO Map Unit polygon feature class while others required summarization and other processing to create a 1:1 relationship between the attributes and polygons prior to joining the tables. Attributes of this layer are listed below in their order of occurrence in the attribute table and are organized by the SSURGO table they originated from and the processing methods used on them.Map Unit Polygon Feature Class Attribute TableThe fields in this table are from the attribute table of the Map Unit polygon feature class which provides the geographic extent of the map units.Area SymbolSpatial VersionMap Unit SymbolMap Unit TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the table using the Map Unit Key field.Map Unit NameMap Unit KindFarmland ClassInterpretive FocusIntensity of MappingIowa Corn Suitability RatingLegend TableThis table has 1:1 relationship with the Map Unit table and was joined using the Legend Key field.Project ScaleSurvey Area Catalog TableThe fields in this table have a 1:1 relationship with the polygons and were joined to the Map Unit table using the Survey Area Catalog Key and Legend Key fields.Survey Area VersionTabular VersionMap Unit Aggregated Attribute TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the Map Unit attribute table using the Map Unit Key field.Slope Gradient - Dominant ComponentSlope Gradient - Weighted AverageBedrock Depth - MinimumWater Table Depth - Annual MinimumWater Table Depth - April to June MinimumFlooding Frequency - Dominant ConditionFlooding Frequency - MaximumPonding Frequency - PresenceAvailable Water Storage 0-25 cm - Weighted AverageAvailable Water Storage 0-50 cm - Weighted AverageAvailable Water Storage 0-100 cm - Weighted AverageAvailable Water Storage 0-150 cm - Weighted AverageDrainage Class - Dominant ConditionDrainage Class - WettestHydrologic Group - Dominant ConditionIrrigated Capability Class - Dominant ConditionIrrigated Capability Class - Proportion of Mapunit with Dominant ConditionNon-Irrigated Capability Class - Dominant ConditionNon-Irrigated Capability Class - Proportion of Mapunit with Dominant ConditionRating for Buildings without Basements - Dominant ConditionRating for Buildings with Basements - Dominant ConditionRating for Buildings with Basements - Least LimitingRating for Buildings with Basements - Most LimitingRating for Septic Tank Absorption Fields - Dominant ConditionRating for Septic Tank Absorption Fields - Least LimitingRating for Septic Tank Absorption Fields - Most LimitingRating for Sewage Lagoons - Dominant ConditionRating for Sewage Lagoons - Dominant ComponentRating for Roads and Streets - Dominant ConditionRating for Sand Source - Dominant ConditionRating for Sand Source - Most ProbableRating for Paths and Trails - Dominant ConditionRating for Paths and Trails - Weighted AverageErosion Hazard of Forest Roads and Trails - Dominant ComponentHydric Classification - PresenceRating for Manure and Food Processing Waste - Weighted AverageComponent Table – Dominant ComponentMap units have one or more components. To create a 1:1 join component data must be summarized by map unit. For these fields a custom script was used to select the component with the highest value for the Component Percentage Representative Value field (comppct_r). Ties were broken with the Slope Representative Value field (slope_r). Components with lower average slope were selected as dominant. If both soil order and slope were tied, the first value in the table was selected.Component Percentage - Low ValueComponent Percentage - Representative ValueComponent Percentage - High ValueComponent NameComponent KindOther Criteria Used to Identify ComponentsCriteria Used to Identify Components at the Local LevelRunoff ClassSoil loss tolerance factorWind Erodibility IndexWind Erodibility GroupErosion ClassEarth Cover 1Earth Cover 2Hydric ConditionHydric RatingAspect Range - Counter Clockwise LimitAspect - Representative ValueAspect Range - Clockwise LimitGeomorphic DescriptionNon-Irrigated Capability SubclassNon-Irrigated Unit Capability ClassIrrigated Capability SubclassIrrigated Unit Capability ClassConservation Tree Shrub GroupGrain Wildlife HabitatGrass Wildlife HabitatHerbaceous Wildlife HabitatShrub Wildlife HabitatConifer Wildlife HabitatHardwood Wildlife HabitatWetland Wildlife HabitatShallow Water Wildlife HabitatRangeland Wildlife HabitatOpenland Wildlife HabitatWoodland Wildlife HabitatWetland Wildlife HabitatSoil Slip PotentialSusceptibility to Frost HeavingConcrete CorrosionSteel CorrosionTaxonomic ClassTaxonomic OrderTaxonomic SuborderGreat GroupSubgroupParticle SizeParticle Size ModCation Exchange Activity ClassCarbonate ReactionTemperature ClassMoist SubclassSoil Temperature RegimeEdition of Keys to Soil Taxonomy Used to Classify SoilCalifornia Storie IndexComponent KeyComponent Table – Weighted AverageMap units may have one or more soil components. To create a 1:1 join, data from the Component table must be summarized by map unit. For these fields a custom script was used to calculate an average value for each map unit weighted by the Component Percentage Representative Value field (comppct_r).Slope Gradient - Low ValueSlope Gradient - Representative ValueSlope Gradient - High ValueSlope Length USLE - Low ValueSlope Length USLE - Representative ValueSlope Length USLE - High ValueElevation - Low ValueElevation - Representative ValueElevation - High ValueAlbedo - Low ValueAlbedo - Representative ValueAlbedo - High ValueMean Annual Air Temperature - Low ValueMean Annual Air Temperature - Representative ValueMean Annual Air Temperature - High ValueMean Annual Precipitation - Low ValueMean Annual Precipitation - Representative ValueMean Annual Precipitation - High ValueRelative Effective Annual Precipitation - Low ValueRelative Effective Annual Precipitation - Representative ValueRelative Effective Annual Precipitation - High ValueDays between Last and First Frost - Low ValueDays between Last and First Frost - Representative ValueDays between Last and First Frost - High ValueRange Forage Annual Potential Production - Low ValueRange Forage Annual Potential Production - Representative ValueRange Forage Annual Potential Production - High ValueInitial Subsidence - Low ValueInitial Subsidence - Representative ValueInitial Subsidence - High ValueTotal Subsidence - Low ValueTotal Subsidence - Representative ValueTotal Subsidence - High ValueCrop Productivity IndexEsri SymbologyThis field was created to provide symbology based on the Taxonomic Order field (taxorder). Because some mapunits have a null value for soil order, a custom script was used to populate this field using the Component Name (compname) and Mapunit Name (muname) fields. This field was created using the dominant soil order of each mapunit.Esri SymbologyHorizon TableEach map unit polygon has one or more components and each component has one or more layers known as horizons. To incorporate this field from the Horizon table into the attributes for this layer, a custom script was used to first calculate the mean value weighted by thickness of the horizon for each component and then a mean value of components weighted by the Component Percentage Representative Value field for each map unit. K-Factor Rock FreeEsri Soil OrderThese fields were calculated from the Component table using a model that included the Pivot Table Tool, the Summarize Tool and a custom script. The first 11 fields provide the sum of Component Percentage Representative Value for each soil order for each map unit. The Soil Order Dominant Condition field was calculated by selecting the highest value in the preceding 11 soil order fields. In the case of tied values the component with the lowest average slope value (slope_r) was selected. If both soil order and slope

(See USGS Digital Data Series DDS-69-E) A geographic information system focusing on the Jurassic-Cretaceous Cotton Valley Group was developed for the U.S. Geological Survey's (USGS) 2002 assessment of undiscovered, technically recoverable oil and natural gas resources of the Gulf Coast Region. The USGS Energy Resources Science Center has developed map and metadata services to deliver the 2002 assessment results GIS data and services online. The Gulf Coast assessment is based on geologic elements of a total petroleum system (TPS) as described in Dyman and Condon (2005). The estimates of undiscovered oil and gas resources are within assessment units (AUs). The hydrocarbon assessment units include the assessment results as attributes within the AU polygon feature class (in geodatabase and shapefile format). Quarter-mile cells of the land surface that include single or multiple wells were created by the USGS to illustrate the degree of exploration and the type and distribution of production for each assessment unit. Other data that are available in the map documents and services include the TPS and USGS province boundaries. To easily distribute the Gulf Coast maps and GIS data, a web mapping application has been developed by the USGS, and customized ArcMap (by ESRI) projects are available for download at the Energy Resources Science Center Gulf Coast website. ArcGIS Publisher (by ESRI) was used to create a published map file (pmf) from each ArcMap document (.mxd). The basemap services being used in the GC map applications are from ArcGIS Online Services (by ESRI), and include the following layers: -- Satellite imagery -- Shaded relief -- Transportation -- States -- Counties -- Cities -- National Forests With the ESRI_StreetMap_World_2D service, detailed data, such as railroads and airports, appear as the user zooms in at larger scales. This map service shows the structural configuration on the top of the Cotton Valley Group in feet below sea level. The map was produced by calculating the difference between a datum at the land surface (either the kelly bushing elevation or the ground surface elevation) and the reported depth of the Cotton Valley Group. This map service also shows the thickness of the interval from the top of the Cotton Valley Group to the top of the Smackover Formation.