The Digital Geologic-GIS Map of the Mammoth Cave 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 (macv_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 (macv_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 (macv_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 (macv_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 (macv_geology_metadata.txt or macv_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).

The Census of Agriculture, produced by the USDA National Agricultural Statistics Service (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2017, and provides an in-depth look at the agricultural industry.This layer summarizes corn production from the 2017 Census of Agriculture at the county level.This layer was produced from data downloaded using the USDA's QuickStats Application. The data was transformed using the Pivot Table tool in ArcGIS Pro and joined to the county boundary file provided by the USDA. The layer was published as feature layer in ArcGIS Online. Dataset SummaryPhenomenon Mapped: 2017 Corn ProductionCoordinate System: Web Mercator Auxiliary SphereExtent: 48 Contiguous United States and HawaiiVisible Scale: All ScalesSource: USDA National Agricultural Statistics Service QuickStats ApplicationPublication Date: 2017AttributesThis layer provides values for the following attributes. Note that some values are not disclosed (coded as -1 in the layer) to protect the privacy of producers in areas with limited production.Operations with SalesSales in US DollarsGrain - Area Harvested in AcresGrain - Operations with Area HarvestedGrain - Production in BushelsGrain - Irrigated Area Harvested in AcresGrain - Operations with Irrigated Area HarvestedSilage - Area Harvested in AcresSilage - Operations with Area HarvestedSilage - Production in TonsSilage - Irrigated Area Harvested in AcresSilage - Operations with Area HarvestedTraditional or Indian - Area Harvested in AcresTraditional or Indian - Operations with Area HarvestedTraditional or Indian - Production in PoundsTraditional or Indian - Irrigated Area Harvested in AcresTraditional or Indian - Operations with Area HarvestedAdditionally attributes of State Name, State Code, County Name and County Code are included to facilitate cartography and use with other layers.What can you do with this layer?This layer can be used throughout the ArcGIS system. Feature layers can be used just like any other vector layer. You can use feature layers as an input to geoprocessing tools in ArcGIS Pro or in Analysis in ArcGIS Online. Combine the layer with others in a map and set custom symbology or create a pop-up tailored for your users. For the details of working with feature layers the help documentation for ArcGIS Pro or the help documentation for ArcGIS Online are great places to start. The ArcGIS Blog is a great source of ideas for things you can do with feature layers. This layer is part of ArcGIS Living Atlas of the World that provides an easy way to find and explore many other beautiful and authoritative layers, maps, and applications on hundreds of topics.

The Digital Surficial Geologic-GIS Map of the Stroudsburg Quadrangle, New Jersey and Pennsylvania 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 (stro_surficial_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 (stro_surficial_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 (stro_surficial_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 GIS readme file (dewa_surficial_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (dewa_surficial_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 (stro_surficial_geology_metadata_faq.pdf). Please read the dewa_surficial_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: Pennsylvania 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 (stro_surficial_geology_metadata.txt or stro_surficial_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).

This dataset contains model-based place (incorporated and census designated places) estimates in GIS-friendly format. PLACES covers the entire United States—50 states and the District of Columbia —at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2021 or 2020 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2015–2019 estimates. The 2023 release uses 2021 BRFSS data for 29 measures and 2020 BRFSS data for 7 measures (all teeth lost, dental visits, mammograms, cervical cancer screening, colorectal cancer screening, core preventive services among older adults, and sleeping less than 7 hours) that the survey collects data on every other year. These data can be joined with the 2019 Census TIGER/Line place boundary file in a GIS system to produce maps for 36 measures at the place level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=2c3deb0c05a748b391ea8c9cf9903588

| https://data-seattlecitygis.opendata.arcgis.com/datasets?q=spu | Lifecycle status: Production | Purpose: to enable open access to SPU GIS data. This website includes many utility datasets from categories such as DSO, Drainage and Wastewater infrastructure, and Storm Infrastructure. Many of this datasets are linked from this website.

The City of Seattle Transportation GIS Datasets | https://data-seattlecitygis.opendata.arcgis.com/datasets?t=transportation | Lifecycle status: Production | Purpose: to enable open access to SDOT GIS data. This website includes over 60 transportation-related GIS datasets from categories such as parking, transit, pedestrian, bicycle, and roadway assets. | PDDL: https://opendatacommons.org/licenses/pddl/ | The City of Seattle makes no representation or warranty as to its accuracy. The City of Seattle has created this service for our GIS Open Data website. We do reserve the right to alter, suspend, re-host, or retire this service at any time and without notice. | Datasets: 2007 Traffic Flow Counts, 2008 Traffic Flow Counts, 2009 Traffic Flow Counts, 2010 Traffic Flow Counts, 2011 Traffic Flow Counts, 2012 Traffic Flow Counts, 2013 Traffic Flow Counts, 2014 Traffic Flow Counts, 2015 Traffic Flow Counts, 2016 Traffic Flow Counts, 2017 Traffic Flow Counts, 2018 Traffic Flow Counts, Areaways, Bike Racks, Blockface, Bridges, Channelization File Geodatabase, Collisions, Crash Cushions, Curb Ramps, dotMaps Active Projects, Dynamic Message Signs, Existing Bike Facilities, Freight Network, Greater Downtown Alleys, Guardrails, High Impact Areas, Intersections, Marked Crosswalks, One-Way Streets, Paid Area Curbspaces, Pavement Moratoriums, Pay Stations, Peak Hour Parking Restrictions, Planned Bike Facilities, Public Garages or Parking Lots, Radar Speed Signs, Restricted Parking Zone (RPZ) Program, Retaining Walls, SDOT Capital Projects Input, Seattle On Street Paid Parking-Daytime Rates, Seattle On Street Paid Parking-Evening Rates, Seattle On Street Paid Parking-Morning Rates, Seattle Streets, SidewalkObservations, Sidewalks, Snow Ice Routes, Stairways, Street Design Concept Plans, Street Ends (Shoreline), Street Furnishings, Street Signs, Street Use Permits Use Addresses, Streetcar Lines, Streetcar Stations, Traffic Beacons, Traffic Cameras, Traffic Circles, Traffic Detectors, Traffic Lanes, Traffic Signals, Transit Classification, Trees.

Commingled production is the flow of fluids, originating from two or more pools, in an unsegregated manner to a well measurement meter. Production may be commingled down-hole in the wellbore or at the surface prior to metering. Commingling may occur at any point in the life of a well, from initial design to a recompletion late in the life of a well. Commingling is a method to maximize the total recoverable hydrocarbons from a well. Commingling provides an opportunity to produce zones that may be individually uneconomic to produce, either initially or after having declined to marginal rates. Commingling may also help to lift liquids to the surface that would otherwise hinder production. Data is updated nightly.

This dataset represents the entire Industrial PinPointer database of manufacturing companies. Only those locations primarily engaged in manufacturing (SIC Codes 2000-3999) or those that are headquarters of manufacturing companies are included. This dataset covers manufacturing locations in the State of Alabama. Homeland SecurityThis dataset includes the entire Industrial PinPointer database of manufacturing companies, which includes the 2009 D2 of 2 update. Only those locations primarily engaged in manufacturing (SIC Codes 2000-3999) or those that are headquarters of manufacturing companies are included. SIC codes are not provided for 125 companies in the US territories. Where an employee count is available, only locations employing fifteen (15) or more people are included. All text fields were set to upper case, leading and trailing spaces were trimmed from all text fields, and non-printable and diacritic characters were removed from all text fields per NGA's request.Metadata

This is a dataset download, not a document. The Open button will start the download.This data layer is an element of the Oregon GIS Framework and has been clipped to the Oregon boundary and reprojected to Oregon Lambert (2992). The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released four National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, and 2011. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2016. The NLCD 2016 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2016 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2016: a streamlined process for assembling and preprocessing Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2016 production. The performance of the developed strategies and methods were tested in twenty World Reference System-2 path/row throughout the conterminous U.S. An overall agreement ranging from 71% to 97% between land cover classification and reference data was achieved for all tested area and all years. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2016 operational mapping. Questions about the NLCD 2016 land cover product can be directed to the NLCD 2016 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.

Abstract

The dataset was derived by the Bioregional Assessment Programme from Hydstra Groundwater Measurement Update - NSW Office of Water, Nov2013. The source dataset ia identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

Displays the original Hydstra measurement (HYDMEAS) tabular data records (as stored in the Hydstra software platform) in a GIS format for interpretation and analysis.

Analysis completed on this dataset includes extracts to determine location and status of current monitoring bores:

HYDMEAS - original tabular database file (dbf) showing groundwater levels

HYDMEAS_XY_all - displays all original tabular data in GIS shapefile format

HYDMEAS_unique_bores - shows one record for each unique bore station ID

HYDMEAS_2008 - All HYDMEAS data from 2008 or later

HYDMEAS_2008to2013_mulitple_reading - All HYDMEAS data from 2008 or later which has been monitored twice or more (in that period), produced to estimate groundwater level monitoring bores

National Groundwater Information System (NGIS) data supplied as a comparison of HYDMEAS monitoring estimates.

Hydstra is a water resources time series data management system developed by KISTERS Pty Ltd.

Purpose

Provide spatial distribution of groundwater level monitoring status and reading for New South Wales.

Dataset History

HYDMEAS - original tabular data

HYDMEAS_XY_all - displays all original tabular data in GIS format - Displayed as XY in ArcGIS based on Lat and Long attributes and exported as a point shapefile

HYDMEAS_unique_bores - shows one record for each unique bore ID - Dissolved HYDMEAS_XY_all based on STATION field

HYDMEAS_2008 - All HYDMEAS data from 2008 or later - Selected based on DATE field

HYDMEAS_2008to2013_mulitple_reading - All HYDMEAS data from 2008 or later which has been monitored twice or more (in that period), produced to estimate groundwater level monitoring bores - HYDMEAS_2008 dataset dissolved based on STATION and a count field added. Only bores with count of 2 or more were retained

Dataset Citation

Bioregional Assessment Programme (2014) GIS analysis of HYDMEAS - Hydstra Groundwater Measurement Update: NSW Office of Water - Nov2013. Bioregional Assessment Derived Dataset. Viewed 12 March 2019, http://data.bioregionalassessments.gov.au/dataset/d414c703-aabd-43af-81e0-30aab4d9dfb1.

Dataset Ancestors

• Derived From Hydstra Groundwater Measurement Update - NSW Office of Water, Nov2013

Version: GOGI_V10_2This data was downloaded as a File Geodatabse from EDX at https://edx.netl.doe.gov/dataset/global-oil-gas-features-database. This data was developed using a combination of big data computing, custom search and data integration algorithms, and expert driven search to collect open oil and gas data resources worldwide. This approach identified over 380 data sets and integrated more than 4.8 million features into the GOGI database.Access the technical report describing how this database was produced using the following link: https://edx.netl.doe.gov/dataset/development-of-an-open-global-oil-and-gas-infrastructure-inventory-and-geodatabase” Acknowledgements: This work was funded under the Climate and Clean Air Coalition (CCAC) Oil and Gas Methane Science Studies. The studies are managed by United Nations Environment in collaboration with the Office of the Chief Scientist, Steven Hamburg of the Environmental Defense Fund. Funding was provided by the Environmental Defense Fund, OGCI Companies (Shell, BP, ENI, Petrobras, Repsol, Total, Equinor, CNPC, Saudi Aramco, Exxon, Oxy, Chevron, Pemex) and CCAC.Link to SourcePoint of Contact: Jennifer Bauer email:jennifer.bauer@netl.doe.govMichael D Sabbatino email:michael.sabbatino@netl.doe.gov

Croplands cover ~15 million km2 of the planet and provide the bulk of the food and fiber essential to human well-being. Most global land cover data sets from satellites group croplands into just a few categories, thereby excluding information that is critical for answering key questions ranging from biodiversity conservation to food security to biogeochemical cycling. Information about agricultural land use practices like crop selection, yield, and fertilizer use is even more limited. Here we present land use data sets created by combining national, state, and county level census statistics with a global data set of croplands on a 5 minute by 5 minute (~10 km by 10 km) latitude/longitude grid.This dataset is maintained by EarthStat.org. Learn more about the dataset here.This layer was developed for chapter two, "The Living Land", of an ongoing series of Story Maps on the Anthropocene. It was designed to be used with any dark themed basemap.

The Raster Based GIS Coverage of Mexican Population is a gridded coverage (1 x 1 km) of Mexican population. The data were converted from vector into raster. The population figures were derived based on available point data (the population of known localities - 30,000 in all). Cell values were derived using a weighted moving average function (Burrough, 1986), and then calculated based on known population by state. The result from this conversion is a coverage whose population data is based on square grid cells rather than a series of vectors. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Instituto Nacional de Estadistica Geografia e Informatica (INEGI).

Waterfowl Production AreasThis National Geospatial Data Asset (NGDA) dataset, shared as a U.S. Fish and Wildlife Service (FWS) feature layer, displays Waterfowl Production Areas (WPA). Per FWS, "This data depicts lands and waters administered by the FWS in North America, U.S. Trust Territories and Possessions. It may also include inholdings that are not administered by FWS. The primary source for this information is the FWS Realty program."Iroquois National Wildlife RefugeData currency: current Federal service (FWSInterest)NGDAID: 14 (U.S. Fish and Wildlife Service National Realty Tracts)For more information: Geospatial Dataset; Waterfowl Production AreasSupport documentation: MetadataFor feedback, please contact: ArcGIScomNationalMaps@esri.comNGDA Theme CommunityThis data set is part of the NGDA Cadastre Theme Community. Per the Federal Geospatial Data Committee (FGDC), Cadastre is defined as the "past, current, and future rights and interests in real property including the spatial information necessary to describe geographic extents. Rights and interests are benefits or enjoyment in real property that can be conveyed, transferred, or otherwise allocated to another for economic remuneration. Rights and interests are recorded in land record documents. The spatial information necessary to describe geographic extents includes surveys and legal description frameworks such as the Public Land Survey System, as well as parcel-by-parcel surveys and descriptions. Does not include federal government or military facilities."For other NGDA Content: Esri Federal Datasets

The present dataset provides necessary indicators of the climate change vulnerability of Bangladesh in raster form. Geospatial databases have been created in Geographic Information System (GIS) environment mainly from two types of raw data; socioeconomic data from the Bangladesh Bureau of Statistics (BBS) and biophysical maps from various government and non-government agencies. Socioeconomic data have been transformed into a raster database through the Inverse Distance Weighted (IDW) interpolation method in GIS. On the other hand, biophysical maps have been directly recreated as GIS feature classes and eventually, the biophysical raster database has been produced. 30 socioeconomic indicators have been considered, which has been obtained from the Bangladesh Bureau of Statistics. All socioeconomic data were incorporated into the GIS database to generate maps. However, the units of some variables have been adopted directly from BBS, some have been normalized based on population, and some have been adopted as percentages. 12 biophysical system indicators have also been classified based on the collected information from different sources and literature. Biophysical maps are mainly classified in relative scales according to the intensity. These geospatial datasets have been analyzed to assess the spatial vulnerability of Bangladesh to climate change and extremes. The analysis has resulted in a climate change vulnerability map of Bangladesh with recognized hotspots, significant vulnerability factors, and adaptation measures to reduce the level of vulnerability.

The Urban Place GIS Coverage of Mexico is a vector based point Geographic Information System (GIS) coverage of 696 urban places in Mexico. Each Urban Place is geographically referenced down to one tenth of a minute. The attribute data include time-series population and selected census/geographic data items for Mexican urban places from from 1921 to 1990. The cartographic data include urban place point locations on a state boundary file of Mexico. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Instituto Nacional de Estadistica Geografia e Informatica (INEGI) and the Environmental Research Institute (ERI) of Michigan.

Under the powers of the Energy Act 2016, detailed daily production data from individual wellbores must be reported to the NSTA, for the whole life of the field, as set out in the NSTA's Reporting and Disclosure of Information and Samples Guidance. The data is reportable when permanent cessation of production occurs. This requirement has been applied to all UKCS fields that have ceased production since January 2018. The apps below provide access and insights to this reported data. The data reflects the available production history of each field and provides an insight into daily values for gas, oil and H2O; as well as the pressures and temperatures at well heads and bottom holes, where available. The datasets can be downloaded by wellbore, hydrocarbon field or production hub.

Groundnut (Arachis hypogaea), also known as peanut, is grown around the world in a broad region between 40 degrees north and south latitude. Originally from South America, major producers of groundnut include China, India and the United States. Producing 30% of Africa's total, Nigeria leads the continent's production followed by Senegal, Sudan, Ghana, and Chad. Groundnut is a valuable source of protein and oil. It has the additional benefit of enriching depleted soils by converting nitrogen from the air into a form that is required by most plants.Dataset SummaryThis layer provides access to a 5 arc-minute (approximately 10 km at the equator) cell-sized raster of the 1999-2001 annual average area of groundnut harvested in Africa. The data are in units of hectares/grid cell.The SPAM 2000 v3.0.6 data used to create this layer were produced by the International Food Policy Research Institute in 2012. This dataset was created by spatially disaggregating national and sub-national harvest data using the Spatial Production Allocation Model. Link to source metadataFor more information about this dataset and the importance of casava as a staple food see the Harvest Choice webpage.For data on other agricultural species in Africa see these layers:Groundnut (Peanut)Maize (Corn)MilletPotatoRiceSorghumSweet Potato and YamWheatData for important agricultural crops in South America are available here.What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop.This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 24,000 x 24,000 pixels which allows access to the full dataset.The source data for this layer are available here.This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started follow these links:Landscape Layers - a reintroductionLiving Atlas Discussion Group

The Digital Geomorphic-GIS Map of Gulf Islands National Seashore (5-meter accuracy and 1-foot resolution 2006-2007 mapping), Mississippi and 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 (guis_geomorphology.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 (guis_geomorphology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (guis_geomorphology.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 (guis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (guis_geomorphology.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 (guis_geomorphology_metadata_faq.pdf). Please read the guis_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: 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 (guis_geomorphology_metadata.txt or guis_geomorphology_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 1:26,000 and United States National Map Accuracy Standards features are within (horizontally) 13.2 meters or 43.3 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, 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 service contains various Aquaculture data. This includes Shellfish Production, Optimum Sites of Aquaculture potential (AQ1), Bivalve Classification area and Areas of Future Potential for Aquaculture. ------------------------------------------------------------------------------------------------------------The Shellfish Production dataset shows shellfish farm species production data grouped by water body. Water bodies were taken from the water framework directive (WFD) coastal and transitional water bodies database, and joined with the data from CEFAS. Data contains information on species present and production values. This dataset was created by ABPmer under contract to DEFRA (Contract reference MB106). An Excel spreadsheet was supplied to ABPmer by CEFAS which contained a list of waterbodies with the species cultivated per waterbody, production per waterbody and the number of businesses operating for 2007. The production data was joined to a shapefile containing waterbodies based on name of waterbody, and all sites where no shellfish cultivation occurred were removed. The same procedure was repeated with the data of species present. A shapefile containing both number of species grown and tonnes produced per waterbody was created by merging the two datasets based on waterbody name. ------------------------------------------------------------------------------------------------------------The Optimum Sites of Aquaculture Potential (AQ1) dataset shows areas identified through GIS modelling of suitable environmental conditions in East Coast Inshore and Offshore Marine Plan Areas favourable for macroalgae culture, Bivalve Bottom Culture, Finfish Cage, Lobster Restocking, Rope Cultured Bivalve Shellfish or Trestle/Bag Culture of Bivalves. This dataset has been derived from of a wider study assessing aquaculture potential in the South and East Marine Plan Areas for the Marine Management Organisation, project MMO1040. It was created using the Natural Resource model which forms part of the MMO project 1040 Spatial Trends in Aquaculture Potential in the South and East Coast Inshore and Offshore Marine Plan Areas. The Natural Resource model is made up of three existing environmental datasets: bathymetry derived from the Department of Food and Rural Affairs (Defra) Digital Elevation Model (DEM), predicted seabed sediments and combined seabed energy, both from UKSeaMap 2010 (McBreen, et al., 2010). Suitable environmental conditions applied include - low-moderate seabed energy, any sediment type and 10-25 m water depth for current potential. The depth limitations in this instance are based on the industry current reliance on scuba-divers for maintenance and husbandry. It is anticipated that as the industry develops it will become less reliant on divers and be able to move into deeper waters. Note that although the Natural Resource model used the best environmental data available for use in the study but there are significant limitations and gaps. These are outlined below and are discussed in more detail in the final project report: The model does not contain any measure of water quality (e.g. dissolved oxygen, sediment loading or contaminants) and therefore is likely to overestimate the area deemed suitable for aquaculture developments, particularly fin fish cage culture, rope grown bivalve culture and macroalgae culture. The UKSeaMap 2010 predicted seabed sediment map (McBreen, et al., 2010) is modelled at a coarse scale which has led to inaccuracies in the identification of areas which have potential for aquaculture development. UKSeaMap 2010 is known to under-estimate rock habitats because of the type of sampling data (sediment grabs) used to underpin the model. The MMO is working with JNCC to develop these data to lead to improvements in future models. The UKSeaMap 2010 combined seabed energy map included in the model (McBreen, et al., 2010) provides an approximation of the environmental conditions that are likely to limit aquaculture development (e.g. strong currents and large waves) but more accurate results could be obtained by using more precise component datasets such as the maximum wave height and tidal current range, where these datasets are available and the precise limitations of the aquaculture activities of interest are known. The dataset shows potential based on current technologies as defined in Table 10 of the MMO1040 Aquaculture Potential Final Report which is published on the MMO website's evidence pages. ------------------------------------------------------------------------------------------------------------The Bivalve Classification dataset classifies where the production of shellfish can be commercially harvested. All areas listed are designated for species that may be harvested as well as the classification of the shellfish waters. Classification of harvesting areas is required and implemented directly in England and Wales under European Regulation 854/2004. The co-ordination of the shellfish harvesting area classification monitoring programme in England and Wales is carried out by the Centre for Environment, Fisheries and Aquaculture Science, Weymouth (Cefas) on behalf of the Food Standards Agency (FSA). Cefas will make recommendations on classification according to an agreed protocol with the FSA making all final classification decisions and setting out the overall policy. Shellfish production areas are classified according to the extent to which shellfish sampled from the area are contaminated with E. coli. The Classification Zones/Production areas delineate areas where shellfish may be commercially harvested. Coordinates for the zone boundaries are calculated during a sanitary (ground) survey of the production area and where appropriate they are based on the OS Mastermap Mean High Water Line (coordinate accuracy <10m). The maps/zones are correct at time of publication but are updated when necessary depending on hygiene testing results. The current maps (jpgs) are available from the Cefas website ( https://www.cefas.co.uk/publications-data/food-safety/classification-and-microbiological-monitoring/england-and-wales-classification-and-monitoring/classification-zone-maps ) or a listing is available from the FSA website ( http://www.food.gov.uk/enforcement/monitoring/shellfish/shellharvestareas ) ------------------------------------------------------------------------------------------------------------The Current Aquaculture Potential layer highlights areas identified through GIS modelling of suitable environmental conditions in the South and East Marine Plan Areas favourable for macroalgae culture, Bivalve Bottom Culture, Finfish Cage, Lobster Restocking, Rope Cultured Bivalve Shellfish or Trestle/Bag Culture of Bivalves in the South and East Coast Marine Plan Areas. This dataset forms part of a wider study assessing different aquaculture potential in the South and East Marine Plan Areas for the Marine Management Organisation, project MMO1040. This dataset was created using the Natural Resource model which forms part of the MMO project 1040 Spatial Trends in Aquaculture Potential in the South and East Coast Inshore and Offshore Marine Plan Areas. The Natural Resource model is made up of three existing environmental datasets: bathymetry derived from the Department of Food and Rural Affairs (Defra) Digital Elevation Model (DEM), predicted seabed sediments and combined seabed energy, both from UKSeaMap 2010 (McBreen, et al., 2010). Suitable environmental conditions applied include - low-moderate seabed energy, any sediment type, 10-25 m water depth for current potential and 25-50 m water depth for near future potential). The depth limitations in this instance are based on the industry current reliance on scuba-divers for maintenance and husbandry. It is anticipated that as the industry develops it will become less reliant on divers and be able to move into deeper waters. Note that although the Natural Resource model used the best environmental data available for use in the study, there are significant limitations and gaps. These are outlined below and are discussed in more detail in the final project report: The Natural Resource model does not contain any measure of water quality (e.g. dissolved oxygen, sediment loading or contaminants) and therefore is likely to overestimate the area deemed suitable for aquaculture developments, particularly fin fish cage culture, rope grown bivalve culture and macroalgae culture. The UKSeaMap 2010 predicted seabed sediment map (McBreen, et al., 2010) is modelled at a coarse scale which has led to inaccuracies in the identification of areas which have potential for aquaculture development. UKSeaMap 2010 is known to under-estimate rock habitats because of the type of sampling data (sediment grabs) used to underpin the model. It is recommended that this component of the model is supplemented or replaced by higher resolution sediment maps where they are available for the region of interest. The UKSeaMap 2010 combined seabed energy map included in the model (McBreen, et al., 2010) provides an approximation of the environmental conditions that are likely to limit aquaculture development (e.g. strong currents and large waves) but more accurate results could be obtained by using more precise component datasets such as the maximum wave height and tidal current range, where these datasets are available and the precise limitations of the aquaculture activities of interest are known. The potential for development for the feature is "Current" (0-5 years), "Near Future" (5-10 years) or "Future" (10-20 years), the definitions of which are presented in Table 13 within the main report.