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).

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. We converted the photointerpreted data into a format usable in a geographic information system (GIS) by employing three fundamental processes: (1) orthorectify, (2) digitize, and (3) develop the geodatabase. All digital map automation was projected in Universal Transverse Mercator (UTM), Zone 16, using the North American Datum of 1983 (NAD83). Orthorectify: We orthorectified the interpreted overlays by using OrthoMapper, a softcopy photogrammetric software for GIS. One function of OrthoMapper is to create orthorectified imagery from scanned and unrectified imagery (Image Processing Software, Inc., 2002). The software features a method of visual orientation involving a point-and-click operation that uses existing orthorectified horizontal and vertical base maps. Of primary importance to us, OrthoMapper also has the capability to orthorectify the photointerpreted overlays of each photograph based on the reference information provided. Digitize: To produce a polygon vector layer for use in ArcGIS (Environmental Systems Research Institute [ESRI], Redlands, California), we converted each raster-based image mosaic of orthorectified overlays containing the photointerpreted data into a grid format by using ArcGIS. In ArcGIS, we used the ArcScan extension to trace the raster data and produce ESRI shapefiles. We digitally assigned map-attribute codes (both map-class codes and physiognomic modifier codes) to the polygons and checked the digital data against the photointerpreted overlays for line and attribute consistency. Ultimately, we merged the individual layers into a seamless layer. Geodatabase: At this stage, the map layer has only map-attribute codes assigned to each polygon. To assign meaningful information to each polygon (e.g., map-class names, physiognomic definitions, links to NVCS types), we produced a feature-class table, along with other supportive tables and subsequently related them together via an ArcGIS Geodatabase. This geodatabase also links the map to other feature-class layers produced from this project, including vegetation sample plots, accuracy assessment (AA) sites, aerial photo locations, and project boundary extent. A geodatabase provides access to a variety of interlocking data sets, is expandable, and equips resource managers and researchers with a powerful GIS tool.

This open data site is for exploring, accessing and downloading Kentucky-specific GIS data and discovering mapping apps. It provides simple access to information and tools that allow users to understand geospatial data. You can analyze and combine datasets using maps, as well as develop new web and mobile applications. Explore data by category, interact with web mapping applications, use Story Maps, or access our services directly. All data on the site is fed from a variety of authoritative sources.DO NOT DELETE OR MODIFY THIS ITEM. This item is managed by the ArcGIS Hub application. To make changes to this site, please visit https://hub.arcgis.com/admin/

HEPGIS is a web-based interactive geographic map server that allows users to navigate and view geo-spatial data, print maps, and obtain data on specific features using only a web browser. It includes geo-spatial data used for transportation planning. HEPGIS previously received ARRA funding for development of Economically distressed Area maps. It is also being used to demonstrate emerging trends to address MPO and statewide planning regulations/requirements , enhanced National Highway System, Primary Freight Networks, commodity flows and safety data . HEPGIS has been used to help implement MAP-21 regulations and will help implement the Grow America Act, particularly related to Ladder of Opportunities and MPO reforms.

Unlock precise, high-quality GIS data covering 4.5M+ verified locations across Italy. With 50+ enriched attributes including coordinates, building structures, and spatial geometry our dataset provides the granularity and accuracy needed for in-depth spatial analysis. Powered by AI-driven enrichment and deduplication, and backed by 30+ years of expertise, our GIS solutions support industries ranging from mapping and navigation to urban planning and market analysis, helping businesses and organizations make smarter, data-driven decisions.

Key use cases of GIS Data helping our customers :

1. Optimize Mapping & Spatial Analysis : Use GIS data to analyse landscapes, urban infrastructure, and competitor locations, ensuring data-driven planning and decision-making.
2. Enhance Navigation & Location-Based Services : Improve real-time route planning, asset tracking, and EV charging station discovery for seamless location-based experiences.
3. Identify Strategic Sites for Business Expansion : Leverage GIS intelligence to select optimal retail sites, franchise locations, and warehouses with precision.
4. Improve Logistics & Address Accuracy : Streamline delivery networks, validate addresses, and optimize courier routes to boost efficiency and customer satisfaction.
5. Support Environmental & Urban Development Initiatives : Utilize GIS insights for disaster preparedness, sustainable city planning, and land-use management.

HEPGIS is a web-based interactive geographic map server that allows users to navigate and view geo-spatial data, print maps, and obtain data on specific features using only a web browser. It includes geo-spatial data used for transportation planning. HEPGIS previously received ARRA funding for development of Economically distressed Area maps. It is also being used to demonstrate emerging trends to address MPO and statewide planning regulations/requirements , enhanced National Highway System, Primary Freight Networks, commodity flows and safety data . HEPGIS has been used to help implement MAP-21 regulations and will help implement the Grow America Act, particularly related to Ladder of Opportunities and MPO reforms.

The digital map market, currently valued at $25.55 billion in 2025, is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 13.39% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing adoption of location-based services (LBS) across diverse sectors like automotive, logistics, and smart city initiatives is a primary catalyst. Furthermore, advancements in technologies such as AI, machine learning, and high-resolution satellite imagery are enabling the creation of more accurate, detailed, and feature-rich digital maps. The shift towards cloud-based deployment models offers scalability and cost-effectiveness, further accelerating market growth. While data privacy concerns and the high initial investment costs for sophisticated mapping technologies present some challenges, the overall market outlook remains overwhelmingly positive. The competitive landscape is dynamic, with established players like Google, TomTom, and ESRI vying for market share alongside innovative startups offering specialized solutions. The segmentation of the market by solution (software and services), deployment (on-premise and cloud), and industry reveals significant opportunities for growth in sectors like automotive navigation, autonomous vehicle development, and precision agriculture, where real-time, accurate mapping data is crucial. The Asia-Pacific region, driven by rapid urbanization and technological advancements in countries like China and India, is expected to witness particularly strong growth. The market's future hinges on continuous innovation. We anticipate a rise in the demand for 3D maps, real-time updates, and integration with other technologies like the Internet of Things (IoT) and augmented reality (AR). Companies are focusing on enhancing the accuracy and detail of their maps, incorporating real-time traffic data, and developing tailored solutions for specific industry needs. The increasing adoption of 5G technology promises to further boost the market by enabling faster data transmission and real-time updates crucial for applications like autonomous driving and drone delivery. The development of high-precision mapping solutions catering to specialized sectors like infrastructure management and disaster response will also fuel future growth. Ultimately, the digital map market is poised for continued expansion, driven by technological advancements and increased reliance on location-based services across a wide spectrum of industries. Recent developments include: December 2022 - The Linux Foundation has partnered with some of the biggest technology companies in the world to build interoperable and open map data in what is an apparent move t. The Overture Maps Foundation, as the new effort is called, is officially hosted by the Linux Foundation. The ultimate aim of the Overture Maps Foundation is to power new map products through openly available datasets that can be used and reused across applications and businesses, with each member throwing their data and resources into the mix., July 27, 2022 - Google declared the launch of its Street View experience in India in collaboration with Genesys International, an advanced mapping solutions company, and Tech Mahindra, a provider of digital transformation, consulting, and business re-engineering solutions and services. Google, Tech Mahindra, and Genesys International also plan to extend this to more than around 50 cities by the end of the year 2022.. Key drivers for this market are: Growth in Application for Advanced Navigation System in Automotive Industry, Surge in Demand for Geographic Information System (GIS); Increased Adoption of Connected Devices and Internet. Potential restraints include: Complexity in Integration of Traditional Maps with Modern GIS System. Notable trends are: Surge in Demand for GIS and GNSS to Influence the Adoption of Digital Map Technology.

HEPGIS is a web-based interactive geographic map server that allows users to navigate and view geo-spatial data, print maps, and obtain data on specific features using only a web browser. It includes geo-spatial data used for transportation planning. HEPGIS previously received ARRA funding for development of Economically distressed Area maps. It is also being used to demonstrate emerging trends to address MPO and statewide planning regulations/requirements , enhanced National Highway System, Primary Freight Networks, commodity flows and safety data . HEPGIS has been used to help implement MAP-21 regulations and will help implement the Grow America Act, particularly related to Ladder of Opportunities and MPO reforms.

One stop shop for viewing all Porter County GIS data.

Unlock precise, high-quality GIS data covering 164M+ verified locations across 220+ countries. With 50+ enriched attributes including coordinates, building structures, and spatial geometry our dataset provides the granularity and accuracy needed for in-depth spatial analysis. Powered by AI-driven enrichment and deduplication, and backed by 30+ years of expertise, our GIS solutions support industries ranging from mapping and navigation to urban planning and market analysis, helping businesses and organizations make smarter, data-driven decisions.

Key use cases of GIS Data helping our customers :

1. Optimize Mapping & Spatial Analysis : Use GIS data to analyse landscapes, urban infrastructure, and competitor locations, ensuring data-driven planning and decision-making.
2. Enhance Navigation & Location-Based Services : Improve real-time route planning, asset tracking, and EV charging station discovery for seamless location-based experiences.
3. Identify Strategic Sites for Business Expansion : Leverage GIS intelligence to select optimal retail sites, franchise locations, and warehouses with precision.
4. Improve Logistics & Address Accuracy : Streamline delivery networks, validate addresses, and optimize courier routes to boost efficiency and customer satisfaction.
5. Support Environmental & Urban Development Initiatives : Utilize GIS insights for disaster preparedness, sustainable city planning, and land-use management.

Web map used in Delaware County GIS Data Extract application that allows users to extract Delaware County, Ohio GIS data in various formats.

Unlock precise, high-quality GIS data covering 3M+ verified locations across 7 countries. With 50+ enriched attributes including coordinates, building structures, and spatial geometry our dataset provides the granularity and accuracy needed for in-depth spatial analysis. Powered by AI-driven enrichment and deduplication, and backed by 30+ years of expertise, our GIS solutions support industries ranging from mapping and navigation to urban planning and market analysis, helping businesses and organizations make smarter, data-driven decisions.

Key use cases of GIS Data helping our customers :

1. Optimize Mapping & Spatial Analysis : Use GIS data to analyse landscapes, urban infrastructure, and competitor locations, ensuring data-driven planning and decision-making.
2. Enhance Navigation & Location-Based Services : Improve real-time route planning, asset tracking, and EV charging station discovery for seamless location-based experiences.
3. Identify Strategic Sites for Business Expansion : Leverage GIS intelligence to select optimal retail sites, franchise locations, and warehouses with precision.
4. Improve Logistics & Address Accuracy : Streamline delivery networks, validate addresses, and optimize courier routes to boost efficiency and customer satisfaction.
5. Support Environmental & Urban Development Initiatives : Utilize GIS insights for disaster preparedness, sustainable city planning, and land-use management.

Crowther_Nature_Files.zip This description pertains to the original download. Details on revised (newer) versions of the datasets are listed below. When more than one version of a file exists in Figshare, the original DOI will take users to the latest version, though each version technically has its own DOI. -- Two global maps (raster files) of tree density. These maps highlight how the number of trees varies across the world. One map was generated using biome-level models of tree density, and applied at the biome scale. The other map was generated using ecoregion-level models of tree density, and applied at the ecoregion scale. For this reason, transitions between biomes or between ecoregions may be unrealistically harsh, but large-scale estimates are robust (see Crowther et al 2015 and Glick et al 2016). At the outset, this study was intended to generate reliable estimates at broad spatial scales, which inherently comes at the cost of fine-scale precision. For this reason, country-scale (or larger) estimates are generally more robust than individual pixel-level estimates. Additionally, due to data limitations, estimates for Mangroves and Tropical coniferous forest (as identified by WWF and TNC) were generated using models constructed from Topical moist broadleaf forest data and Temperate coniferous forest data, respectively. Because we used ecological analogy, the estimates for these two biomes should be considered less reliable than those of other biomes . These two maps initially appeared in Crowther et al (2015), with the biome map being featured more prominently. Explicit publication of the data is associated with Glick et al (2016). As they are produced, updated versions of these datasets, as well as alternative formats, will be made available under Additional Versions (see below).

Methods: We collected over 420,000 ground-sources estimates of tree density from around the world. We then constructed linear regression models using vegetative, climatic, topographic, and anthropogenic variables to produce forest tree density estimates for all locations globally. All modeling was done in R. Mapping was done using R and ArcGIS 10.1.

Viewing Instructions: Load the files into an appropriate geographic information system (GIS). For the original download (ArcGIS geodatabase files), load the files into ArcGIS to view or export the data to other formats. Because these datasets are large and have a unique coordinate system that is not read by many GIS, we suggest loading them into an ArcGIS dataframe whose coordinate system matches that of the data (see File Format). For GeoTiff files (see Additional Versions), load them into any compatible GIS or image management program.

Comments: The original download provides a zipped folder that contains (1) an ArcGIS File Geodatabase (.gdb) containing one raster file for each of the two global models of tree density – one based on biomes and one based on ecoregions; (2) a layer file (.lyr) for each of the global models with the symbology used for each respective model in Crowther et al (2015); and an ArcGIS Map Document (.mxd) that contains the layers and symbology for each map in the paper. The data is delivered in the Goode homolosine interrupted projected coordinate system that was used to compute biome, ecoregion, and global estimates of the number and density of trees presented in Crowther et al (2015). To obtain maps like those presented in the official publication, raster files will need to be reprojected to the Eckert III projected coordinate system. Details on subsequent revisions and alternative file formats are list below under Additional Versions.----------

Additional Versions: Crowther_Nature_Files_Revision_01.zip contains tree density predictions for small islands that are not included in the data available in the original dataset. These predictions were not taken into consideration in production of maps and figures presented in Crowther et al (2015), with the exception of the values presented in Supplemental Table 2. The file structure follows that of the original data and includes both biome- and ecoregion-level models.

Crowther_Nature_Files_Revision_01_WGS84_GeoTiff.zip contains Revision_01 of the biome-level model, but stored in WGS84 and GeoTiff format. This file was produced by reprojecting the original Goode homolosine files to WGS84 using nearest neighbor resampling in ArcMap. All areal computations presented in the manuscript were computed using the Goode homolosine projection. This means that comparable computations made with projected versions of this WGS84 data are likely to differ (substantially at greater latitudes) as a product of the resampling. Included in this .zip file are the primary .tif and its visualization support files.

References:

Crowther, T. W., Glick, H. B., Covey, K. R., Bettigole, C., Maynard, D. S., Thomas, S. M., Smith, J. R., Hintler, G., Duguid, M. C., Amatulli, G., Tuanmu, M. N., Jetz, W., Salas, C., Stam, C., Piotto, D., Tavani, R., Green, S., Bruce, G., Williams, S. J., Wiser, S. K., Huber, M. O., Hengeveld, G. M., Nabuurs, G. J., Tikhonova, E., Borchardt, P., Li, C. F., Powrie, L. W., Fischer, M., Hemp, A., Homeier, J., Cho, P., Vibrans, A. C., Umunay, P. M., Piao, S. L., Rowe, C. W., Ashton, M. S., Crane, P. R., and Bradford, M. A. 2015. Mapping tree density at a global scale. Nature, 525(7568): 201-205. DOI: http://doi.org/10.1038/nature14967Glick, H. B., Bettigole, C. B., Maynard, D. S., Covey, K. R., Smith, J. R., and Crowther, T. W. 2016. Spatially explicit models of global tree density. Scientific Data, 3(160069), doi:10.1038/sdata.2016.69.

GIS data is available on the Forest’s FTP site in the form of “shape files” or layers and is available free for downloading. To utilize these data layers you will need a program that uses the Geographic Information System (GIS) such as ESRI’s ArcMap, ArcView or the free map reading program ArcGIS Explorer. ArcGIS Explorer has tools that let you zoom in/out, print the map, and query data. It also has map tips to identify features, and a help menu. ArcGIS Explorer is available as a free download from the ESRI website. Included is a list of GIS data files available for the Shawnee National Forest. These GIS data files are updated on a continuing basis. It should be noted that this data may have been developed from sources of differing accuracy, accurate only at certain scales, based on modeling or interpretation, or incomplete while being created or revised. Overall accuracy, completeness and timeliness may vary. The following geospatial information/data was prepared by the Shawnee National Forests (US Forest Service). The Forest Service reserves the right to correct, update, modify or replace GIS data without notification. Resources in this dataset:Resource Title: Geospatial Data. File Name: Web Page, url: https://www.fs.usda.gov/main/shawnee/landmanagement/gis Information about the geospatial data and a ftp link to download Forest GIS Data Shapefiles.

GIS data: This data set consists of 6 classes of zoning features: zoning districts, special purpose districts, special purpose district subdistricts, limited height districts, commercial overlay districts, and zoning map amendments.

All previously released versions of this data are available at BYTES of the BIG APPLE- Archive

Updates for this dataset, along with other multilayered maps on NYC Open Data, are temporarily paused while they are moved to a new mapping format. Please visit https://www.nyc.gov/site/planning/data-maps/open-data/dwn-gis-zoning.page to utilize this data in the meantime

This U.S. Geological Survey (USGS) data release provides an updated digital geospatial database for the geologic map of the Salmon National Forest and vicinity, east-central Idaho (Evans and Green, 2003). Attribute tables and geospatial features (points, lines and polygons) conform to the Geologic Map Schema (USGS NCGMP, 2020) and represent the geologic map as published in USGS Investigations Series Map I-2765. Minor errors, such as mistakes in line decoration or differences between the digital data and the map image, are corrected in this version.

The database represents the geology for the 11,265 square kilometer, geologically complex Salmon National Forest in two plates, at a publication scale of 1:100,000. The map covers primarily Lemhi County, but also includes minor parts of Beaverhead, Custer, Idaho, Ravalli and Valley Counties. New geologic mapping was undertaken between 1990 and 2002 and synthesized with older published maps, providing significant stratigraphic and struc ...

The data contained in this map reflects project location data supplied by SNC grantees for grants awarded by the Sierra Nevada Conservancy. Some implementation projects predate SNC’s current policy, beginning in 2017, to acquire polygons for all new projects. As a result project location data on this map consists of a mixture of both point and polygon grant locations. Additionally, some planning, education, and other grants were region wide. These are only represented by location points provided by the grantee as well. The layers in this map may update as our Governing Board awards new grants.

The data release for the geologic and structure maps of the Wallace 1 x 2 degrees quadrangle, Montana and Idaho, is a Geologic Map Schema (GeMS)-compliant version that updates the GIS files for the geologic map published in U.S. Geological Survey (USGS) Miscellaneous Investigations Series Map I-1509-A (Harrison and others, 2000). The updated digital data present the attribute tables and geospatial features (points, lines and polygons) in the format that meets GeMS requirements. This data release presents the geologic map as shown on the plates and captured in geospatial data for the published map. Minor errors, such as mistakes in line decoration or differences between the digital data and the map image, are corrected in this version. The database represents the geology for the 16,754 square kilometer, geologically complex Wallace quadrangle in northern Idaho and western Montana, at a publication scale of 1:250,000. The map covers primarily Lake, Mineral, Sanders and Shoshone Counties, but also includes minor parts of Flathead, Lincoln, and Missoula Counties. These GIS data supersede those in the interpretive report: Harrison, J.E., Griggs, A.B., Wells, J.D., Kelley, W.N., Derkey, P.D., and EROS Data Center, 2000, Geologic and structure maps of the Wallace 1- x 2- degree quadrangle, Montana and Idaho: a digital database: U.S. Geological Survey Miscellaneous Investigations Series Map I-1509-A, https://pubs.usgs.gov/imap/i1509a/.

This mapping tool provides a representation of the general watershed boundaries for stream systems declared fully appropriated by the State Water Board. The boundaries were created by Division of Water Rights staff by delineating FASS critical reaches and consolidating HUC 12 sub-watersheds to form FASS Watershed boundaries. As such, the boundaries are in most cases conservative with respect to the associated stream system. However, users should check neighboring FASS Watersheds to ensure the stream system of interest is not restricted by other FASS listings. For more information regarding the Declaration of Fully Appropriated Stream Systems, visit the Division of Water Rights’ Fully Appropriated Streams webpage. How to Use the Interactive Mapping Tool: If it is your first time viewing the map, you will need to click the “OK” box on the splash screen and agree to the disclaimer before continuing. Navigate to your point of interest by either using the search bar or by zooming in on the map. You may enter a stream name, street address, or watershed ID in the search bar. Click on the map to identify the location of interest and one or more pop-up boxes may appear with information about the fully appropriated stream systems within the general watershed boundaries of the identified location. The information provided in the pop-up box may include: (a) stream name, (b) tributary, (c) season declared fully appropriated, (d) Board Decisions/Water Right Orders, and/or (e) court references/adjudications. You may toggle the FAS Streams reference layer on and off to find representative critical reaches associated with the FASS Watershed layer. Please note that this layer is for general reference purposes only and ultimately the critical reach listed in Appendix A of Water Rights Order 98-08 and Appendix A together with any associated footnotes controls. Note: A separate FAS Watershed boundary layer was created for the Bay-Delta Watershed. The Bay-Delta Watershed layer should be toggled on to check if the area of interest is fully appropriated under State Water Board Decision 1594.

GIS Maps, Transportation Data, and Reports for all modes of travel throughout Massachusetts.