In this course, you will learn about some common types of data used for GIS mapping and analysis, and practice adding data to a file geodatabase to support a planned project.Goals Create a file geodatabase. Add data to a file geodatabase. Create an empty geodatabase feature class.

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.

This dataset is a compilation of address point data for the City of Tempe. The dataset contains a point location, the official address (as defined by The Building Safety Division of Community Development) for all occupiable units and any other official addresses in the City. There are several additional attributes that may be populated for an address, but they may not be populated for every address. Contact: Lynn Flaaen-Hanna, Development Services Specialist Contact E-mail Link: Map that Lets You Explore and Export Address Data Data Source: The initial dataset was created by combining several datasets and then reviewing the information to remove duplicates and identify errors. This published dataset is the system of record for Tempe addresses going forward, with the address information being created and maintained by The Building Safety Division of Community Development. Data Source Type: ESRI ArcGIS Enterprise Geodatabase Preparation Method: N/A Publish Frequency: Weekly Publish Method: Automatic Data Dictionary

The FDOT GIS Roads with Median Types feature class provides spatial information on Florida Median Types distinguishing between lawn, paved, painted, and curbed medians. It also notes where a fence, guardrail, or barrier wall divides the two sides of a divided road. A median is defined as a barrier or other physical separation between two lanes of traffic traveling in opposite directions, which can either be raised, painted, or paved. This information is required for all functionally classified roadways On or Off the SHS. This dataset is maintained by the Transportation Data & Analytics office (TDA). The source spatial data for this hosted feature layer was created on: 05/31/2025.For more details please review the FDOT RCI Handbook Download Data: Enter Guest as Username to download the source shapefile from here: https://ftp.fdot.gov/file/d/FTP/FDOT/co/planning/transtat/gis/shapefiles/median_type.zip

The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making.

   BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions.


   Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself.


   For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise.


   Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. 


   This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery.


   See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt


   See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

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 dataset represents a unique compiled environmental data set for the circumpolar Arctic ocean region 45N to 90N region. It consists of 170 layers (mostly marine, some terrestrial) in ArcGIS 10 format to be used with a Geographic Information System (GIS) and which are listed below in detail. Most layers are long-term average raster GRIDs for the summer season, often by ocean depth, and represent value-added products easy to use. The sources of the data are manifold such as the World Ocean Atlas 2009 (WOA09), International Bathimetric Chart of the Arctic Ocean (IBCAO), Canadian Earth System Model 2 (CanESM2) data (the newest generation of models available) and data sources such as plankton databases and OBIS. Ocean layers were modeled and predicted into the future and zooplankton species were modeled based on future data: Calanus hyperboreus (AphiaID104467), Metridia longa (AphiaID 104632), M. pacifica (AphiaID 196784) and Thysanoessa raschii (AphiaID 110711). Some layers are derived within ArcGIS. Layers have pixel sizes between 1215.819573 meters and 25257.72929 meters for the best pooled model, and between 224881.2644 and 672240.4095 meters for future climate data. Data was then reprojected into North Pole Stereographic projection in meters (WGS84 as the geographic datum). Also, future layers are included as a selected subset of proposed future climate layers from the Canadian CanESM2 for the next 100 years (scenario runs rcp26 and rcp85). The following layer groups are available: bathymetry (depth, derived slope and aspect); proximity layers (to,glaciers,sea ice, protected areas, wetlands, shelf edge); dissolved oxygen, apparent oxygen, percent oxygen, nitrogen, phosphate, salinity, silicate (all for August and for 9 depth classes); runoff (proximity, annual and August); sea surface temperature; waterbody temperature (12 depth classes); modeled ocean boundary layers (H1, H2, H3 and Wx).This dataset is used for a M.Sc. thesis by the author, and freely available upon request. For questions and details we suggest contacting the authors. Process_Description: Please contact Moritz Schmid for the thesis and detailed explanations. Short version: We model predicted here for the first time ocean layers in the Arctic Ocean based on a unique dataset of physical oceanography. Moreover, we developed presence/random absence models that indicate where the studied zooplankton species are most likely to be present in the Arctic Ocean. Apart from that, we develop the first spatially explicit models known to science that describe the depth in which the studied zooplankton species are most likely to be at, as well as their distribution of life stages. We do not only do this for one present day scenario. We modeled five different scenarios and for future climate data. First, we model predicted ocean layers using the most up to date data from various open access sources, referred here as best-pooled model data. We decided to model this set of stratification layers after discussions and input of expert knowledge by Professor Igor Polyakov from the International Arctic Research Center at the University of Alaska Fairbanks. We predicted those stratification layers because those are the boundaries and layers that the plankton has to cross for diel vertical migration and a change in those would most likely affect the migration. I assigned 4 variables to the stratification layers. H1, H2, H3 and Wx. H1 is the lower boundary of the mixed layer depth. Above this layer a lot of atmospheric disturbance is causing mixing of the water, giving the mixed layer its name. H2, the middle of the halocline is important because in this part of the ocean a strong gradient in salinity and temperature separates water layers. H3, the isotherm is important, because beneath it flows denser and colder Atlantic water. Wx summarizes the overall width of the described water column. Ocean layers were predicted using machine learning algorithms (TreeNet, Salford Systems). Second, ocean layers were included as predictors and used to predict the presence/random absence, most likely depth and life stage layers for the zooplankton species: Calanus hyperboreus, Metridia longa, Metridia pacifica and Thysanoessa raschii, This process was repeated for future predictions based on the CanESM2 data (see in the data section). For zooplankton species the following layers were developed and for the future. C. hyperboreus: Best-pooled model as well as future predictions (rcp26 including ocean layer(also excluding), rcp85 including oocean layers (also excluding) for 2010 and 2100.For parameters: Presence/random absence, most likely depth and life stage layers M. longa: Best-pooled model as well as future predictions (rcp26 including ocean layer(also excluding), rcp85 including oocean layers (also excluding) for 2010 and 2100. For parameters: Presence/rand... Visit https://dataone.org/datasets/f63d0f6c-7d53-46ce-b755-42a368007601 for complete metadata about this dataset.

This U.S. Geological Survey (USGS) data release presents a digital database of geospatially enabled vector layers and tabular data transcribed from the geologic map of the Lake Owen quadrangle, Albany County, Wyoming, which was originally published as U.S. Geological Survey Geologic Quadrangle Map GQ-1304 (Houston and Orback, 1976). The 7.5-minute Lake Owen quadrangle is located in southeastern Wyoming approximately 25 miles (40 kilometers) southwest of Laramie in the west-central interior of southern Albany County, and covers most of the southern extent of Sheep Mountain, the southeastern extent of Centennial Valley, and a portion of the eastern Medicine Bow Mountains. This relational geodatabase, with georeferenced data layers digitized at the publication scale of 1:24,000, organizes and describes the geologic and structural data covering the quadrangle's approximately 35,954 acres and enables the data for use in spatial analyses and computer cartography. The data types presented in this release include geospatial features (points, lines, and polygons) with matching attribute tables, nonspatial descriptive and reference tables, and ancillary resource files for correct symbolization, in formats that conform to the Geologic Map Schema (GeMS) developed and released by the U.S. Geological Survey's National Cooperative Geologic Mapping Program (GeMS, 2020). When reconstructed from the geodatabase's vector layers and tabular data that has been symbolized according to specifications encoded in the accompanying style file, and using the supplied Federal Geographic Data Committee (FGDC) GeoAge font for labeling formations and GeoSym fonts for structural line decorations and orientation measurement symbols, this data release presents the Geologic Map as shown on the published GQ-1304 map sheet. These GIS data augment but do not supersede the information presented on GQ-1304. References: Houston, R.S., and Orback, C.J., 1976, Geologic Map of the Lake Owen Quadrangle, Albany County, Wyoming: U.S. Geological Survey Geologic Quadrangle Map GQ-1304, scale 1:24,000, https://doi.org/10.3133/gq1304. U.S. Geological Survey National Cooperative Geologic Mapping Program, 2020, GeMS (Geologic Map Schema)- A standard format for the digital publication of geologic maps: U.S. Geological Survey Techniques and Methods, book 11, chap. B10, 74 p., https://doi.org//10.3133/tm11B10.

Various geospatial data sets have been packaged in an ArcGIS Pro .aprx. The user will need the ArcGIS Pro software to access and view the data. For more information on ArcGIS Pro go to https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview. Note that Metadata for various geospatial data files can be accessed by selecting View Metadata within ArcGISPro.

Important Note: This item is in mature support as of September 2023 and will be retired in December 2025. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.

The USGS Protected Areas Database of the United States (PAD-US) is the official inventory of public parks and other protected open space. The spatial data in PAD-US represents public lands held in trust by thousands of national, state and regional/local governments, as well as non-profit conservation organizations.Manager Type provides a coarse level land manager description from the PAD-US "Agency Type" Domain, "Manager Type" Field (for example, Federal, State, Local Government, Private).PAD-US is published by the U.S. Geological Survey (USGS) Science Analytics and Synthesis (SAS), Gap Analysis Project (GAP). GAP produces data and tools that help meet critical national challenges such as biodiversity conservation, recreation, public health, climate change adaptation, and infrastructure investment. See the GAP webpage for more information about GAP and other GAP data including species and land cover.Dataset SummaryPhenomenon Mapped: This layer displays protected areas symbolized by manager type.Coordinate System: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, the US Virgin Islands, the Northern Mariana Islands and other Pacific Ocean IslandsVisible Scale: 1:1,000,000 and largerSource: U.S. Geological Survey (USGS) Science Analytics and Synthesis (SAS), Gap Analysis Project (GAP) PAD-US version 3.0Publication Date: July 2022Attributes included in this layer are: CategoryOwner TypeOwner NameLocal OwnerManager TypeManager NameLocal ManagerDesignation TypeLocal DesignationUnit NameLocal NameSourcePublic AccessGAP Status - Status 1, 2, 3 or 4GAP Status DescriptionInternational Union for Conservation of Nature (IUCN) Description - I: Strict Nature Reserve, II: National Park, III: Natural Monument or Feature, IV: Habitat/Species Management Area, V: Protected Landscape/Seascape, VI: Protected area with sustainable use of natural resources, Other conservation area, UnassignedDate of EstablishmentThe source data for this layer are available here. What can you do with this Feature Layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:1,000,000 or larger but a vector tile layer created from the same data can be used at smaller scales to produce a webmap that displays across the full range of scales. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could set a filter for Gap Status Code = 3 to create a map of only the GAP Status 3 areas.Add labels and set their propertiesCustomize the pop-upArcGIS ProAdd this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. Note that many features in the PAD-US database overlap. For example wilderness area designations overlap US Forest Service and other federal lands. Any analysis should take this into consideration. An imagery layer created from the same data set can be used for geoprocessing analysis with larger extents and eliminates some of the complications arising from overlapping polygons.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis 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.

This data set consists of an ArcView shapefile set that contains locations of sampled coral reef fish species at the National Marine Sanctuary along the Florida Keys. The dataset contains information about 5 classes of coral reefs, 216 fish species, and 6 benthic habitat.

Environmental Sensitivity Index (ESI) data characterize the marine and coastal environments and wildlife based on sensitivity to spilled oil. Coastal species that are listed as threatened, endangered, or as a species of concern, by either federal or state governments, are a primary focus. A subset of the ESI data, the ESI Threatened and Endangered Species (T&E) databases focus strictly on these species. Species are mapped individually. In addition to showing spatial extent, each species polygon, point, or line has attributes describing abundance, seasonality, threatened/endangered status, and life history. Both the state and federal status is provided, along with the year the ESI data were published. This is important, as the status of a species can vary over time. As always, the ESI data are a snapshot in time. The biology layers focus on threatened/endangered status, areas of high concentration, and areas where sensitive life stages may occur. Supporting data tables provide species-/location-specific abundance, seasonality, status, life history, and source information. Human-use resources mapped include managed areas (parks, refuges, critical habitats, etc.) and resources that may be impacted by oiling and/or cleanup, such as beaches, archaeological sites, marinas, etc. ESIs are available for the majority of the US coastline, as well as the US territories. ESI data are available as PDF maps, as well as in a variety of GIS formats. For more information, go to http://response.restoration.noaa.gov/esi . To download complete ESI data sets, go to http://response.restoration.noaa.gov/esi_download .

This dataset is a series of digital map-posters accompanying the AdaptNRM Guide: Helping Biodiversity Adapt: supporting climate adaptation planning using a community-level modelling approach.

These represent supporting materials and information about the community-level biodiversity models applied to climate change. Map posters are organised by four biological groups (vascular plants, mammals, reptiles and amphibians), two climate change scenario (1990-2050 MIROC5 and CanESM2 for RCP8.5), and five measures of change in biodiversity.

The map-posters present the nationally consistent data at locally relevant resolutions in eight parts – representing broad groupings of NRM regions based on the cluster boundaries used for climate adaptation planning (http://www.environment.gov.au/climate-change/adaptation) and also Nationally.

Map-posters are provided in PNG image format at moderate resolution (300dpi) to suit A0 printing. The posters were designed to meet A0 print size and digital viewing resolution of map detail. An additional set in PDF image format has been created for ease of download for initial exploration and printing on A3 paper. Some text elements and map features may be fuzzy at this resolution.

Each map-poster contains four dataset images coloured using standard legends encompassing the potential range of the measure, even if that range is not represented in the dataset itself or across the map extent.

Most map series are provided in two parts: part 1 shows the two climate scenarios for vascular plants and mammals and part 2 shows reptiles and amphibians. Eight cluster maps for each series have a different colour theme and map extent. A national series is also provided. Annotation briefly outlines the topics presented in the Guide so that each poster stands alone for quick reference.

An additional 77 National maps presenting the probability distributions of each of 77 vegetation types – NVIS 4.1 major vegetation subgroups (NVIS subgroups) - are currently in preparation.

Example citations:

Williams KJ, Raisbeck-Brown N, Prober S, Harwood T (2015) Generalised projected distribution of vegetation types – NVIS 4.1 major vegetation subgroups (1990 and 2050), A0 map-poster 8.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/.

Williams KJ, Raisbeck-Brown N, Harwood T, Prober S (2015) Revegetation benefit (cleared natural areas) for vascular plants and mammals (1990-2050), A0 map-poster 9.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/.

This dataset has been delivered incrementally. Please check that you are accessing the latest version of the dataset. Lineage: The map posters show case the scientific data. The data layers have been developed at approximately 250m resolution (9 second) across the Australian continent to incorporate the interaction between climate and topography, and are best viewed using a geographic information system (GIS). Each data layers is 1Gb, and inaccessible to non-GIS users. The map posters provide easy access to the scientific data, enabling the outputs to be viewed at high resolution with geographical context information provided.

Maps were generated using layout and drawing tools in ArcGIS 10.2.2

A check list of map posters and datasets is provided with the collection.

Map Series: 7.(1-77) National probability distribution of vegetation type – NVIS 4.1 major vegetation subgroup pre-1750 #0x

8.1 Generalised projected distribution of vegetation types (NVIS subgroups) (1990 and 2050)

9.1 Revegetation benefit (cleared natural areas) for plants and mammals (1990-2050)

9.2 Revegetation benefit (cleared natural areas) for reptiles and amphibians (1990-2050)

10.1 Need for assisted dispersal for vascular plants and mammals (1990-2050)

10.2 Need for assisted dispersal for reptiles and amphibians (1990-2050)

11.1 Refugial potential for vascular plants and mammals (1990-2050)

11.1 Refugial potential for reptiles and amphibians (1990-2050)

12.1 Climate-driven future revegetation benefit for vascular plants and mammals (1990-2050)

12.2 Climate-driven future revegetation benefit for vascular reptiles and amphibians (1990-2050)

Table from the American Community Survey (ACS) 5-year series on household types and population related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B11003 Family Type by Presence and Age of Own Children under 18 Years, B11005 Households by Presence of People Under 18 Years by Household Type, B11007 Households by Presence of People 65 Years and Over by Household Type, B11001 Household Type (Including Living Alone), B11002 Household Type by Relatives and Nonrelatives for Population in Households, B25003 Tenure, B25008 Total Population in Occupied Housing Units by Tenure, B09019 Household Type (Including Living Alone) by Relationship. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.

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.

© 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

This feature contains vector lines representing the shoreline and coastal habitats of California. Line segments are classified according to the Environmental Sensitivity Index (ESI) classification system and are a compilation of the ESI data from the most recent ESI atlas publications. The ESI data includes information for three main components: shoreline habitats, sensitive biological resources, and human-use resources. This California dataset contains only the ESI shoreline data layer and is a merged set of individual ESI data sets to cover the entire California coast. For many parts of the California shoreline, the NOAA-ESI database lists several shoreline types present at a given location, described from landward to seaward. A simplified singular classification [Map_Class] was created to generalize the most dominant features of the multiple shore type attributes present in the raw data. More information can be found at the source citation at ESI Guidelines | response.restoration.noaa.gov Attributes: Line: Type of geographic feature (H: Hydrography, P: Pier, S: Shoreline) Most_sensitive: If multiple shoreline types appear in ESI classification, this field represents the highest value (most sensitive type); otherwise it is the same value as the ESI field. Shore_code: The ESI shoreline type. In many cases shorelines are ranked with multiple codes, such as "6B/3A" (listed landward to seaward). Source: Original year of ESI data. Esi_description: Concatenation of shore type descriptions (listed landward to seaward) Shoretype_1: Numeric classification for the first (most landward) ESI type. Shoretype_1_name: Physical description for the first ESI type. Shoretype_2: Numeric classification for the second ESI type. Shoretype_2_name: Physical description for the second ESI type Shoretype_3: Numeric classification for the third (most seaward) ESI type. Shoretype_3_name: Physical description for the third ESI type. Map_class: Generalized ESI shoreline type for simplified sym

Urban Tree Canopy Assessment. This was created using the Urban Tree Canopy Syracuse 2010 (All Layers) file HERE.The data for this map was created using LIDAR and other spatial analysis tools to identify and measure tree canopy in the landscape. This was a collaboration between the US Forest Service Northern Research Station (USFS), the University of Vermont Spatial Laboratory, and SUNY ESF. Because the full map is too large to be viewed in ArcGIS Online, this has been reduced to a vector tile layer to allow it to be viewed online. To download and view the shapefiles and all of the layers, you can download the data HERE and view this in either ArcGIS Pro or QGIS.Data DictionaryDescription source  USDA Forest ServiceList of values  Value 1 Description Tree CanopyValue 2 Description Grass/ShrubValue 3 Description Bare SoilValue 4 Description WaterValue 5 Description BuildingsValue 6 Description Roads/RailroadsValue 7 Description Other PavedField Class Alias Class Data type String Width 20Geometric objects  Feature class name landcover_2010_syracusecity Object type  complex Object count 7ArcGIS Feature Class Properties Feature class name landcover_2010_syracusecity Feature type  Simple Geometry type Polygon Has topology FALSE Feature count 7 Spatial index TRUE Linear referencing  FALSEDistributionAvailable format  Name ShapefileTransfer options  Transfer size 163.805Description Downloadable DataFieldsDetails for object landcover_2010_syracusecityType Feature Class Row count  7 Definition  UTCField FIDAlias FID Data type OID Width  4 Precision 0 Scale 0Field descriptionInternal feature number.Description source ESRIDescription of valueSequential unique whole numbers that are automatically generated.Field ShapeAlias Shape Data type Geometry Width 0 Precision 0 Scale 0Field description Feature geometry.Description source  ESRIDescription of values Coordinates defining the features.Field CodeAlias Code Data type Number Width 4Overview Description  Metadata DetailsMetadata language  English Metadata character set utf8 - 8 bit UCS Transfer FormatScope of the data described by the metadata  dataset Scope name  datasetLast update 2011-06-02ArcGIS metadata properties Metadata format ArcGIS 1.0 Metadata style North American Profile of ISO19115 2003Created in ArcGIS for the item 2011-06-02 16:48:35 Last modified in ArcGIS for the item 2011-06-02 16:44:43Automatic updates Have been performed Yes Last update 2011-06-02 16:44:43Item location history  Item copied or moved 2011-06-02 16:48:35 From T:\TestSites\NY\Syracuse\Temp\landcover_2010_syracusecity To \T7500\F$\Export\LandCover_2010_SyracuseCity\landcover_2010_syracusecity