The Unpublished Digital Geomorphological Map of the William Floyd Estate, Fire Island National Seashore and Vicinity, New York is composed of GIS data layers and GIS tables in a 10.0 file geodatabase (wife_geomorphology.gdb), a 10.0 ArcMap (.MXD) map document (wife_geomorphology.mxd), and individual 10.0 layer (.LYR) files for each GIS data layer, an ancillary map information (.PDF) document (fiis_geomorphology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.TXT) and FAQ (.HTML) formats, and a GIS readme file (fiis_gis_readme.pdf). Please read the fiis_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.0 shapefile format contact Stephanie O’Meara (stephanie.omeara@colostate.edu; see contact information below). The data is also available as a 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. Google Earth software is available for free at: http://www.google.com/earth/index.html. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Rutgers University, Institute of Marine and Coastal Sciences. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (wife_metadata_faq.html; available at http://nrdata.nps.gov/geology/gri_data/gis/fiis/wife_metadata_faq.html). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:12,000 and United States National Map Accuracy Standards features are within (horizontally) 6.1 meters or 20 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.1. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data projection is NAD83, UTM Zone 18N, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth. The data is within the area of interest of Fire Island National Seashore.

ArcGIS Map Packages and GIS Data for Gillreath-Brown, Nagaoka, and Wolverton (2019)

**When using the GIS data included in these map packages, please cite all of the following:

Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, 2019. PLoSONE 14(8):e0220457. http://doi.org/10.1371/journal.pone.0220457

Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. ArcGIS Map Packages for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al., 2019. Version 1. Zenodo. https://doi.org/10.5281/zenodo.2572018

OVERVIEW OF CONTENTS

This repository contains map packages for Gillreath-Brown, Nagaoka, and Wolverton (2019), as well as the raw digital elevation model (DEM) and soils data, of which the analyses was based on. The map packages contain all GIS data associated with the analyses described and presented in the publication. The map packages were created in ArcGIS 10.2.2; however, the packages will work in recent versions of ArcGIS. (Note: I was able to open the packages in ArcGIS 10.6.1, when tested on February 17, 2019). The primary files contained in this repository are:

• Raw DEM and Soils data
  • Digital Elevation Model Data (Map services and data available from U.S. Geological Survey, National Geospatial Program, and can be downloaded from the National Elevation Dataset)
    • DEM_Individual_Tiles: Individual DEM tiles prior to being merged (1/3 arc second) from USGS National Elevation Dataset.
    • DEMs_Merged: DEMs were combined into one layer. Individual watersheds (i.e., Goodman, Coffey, and Crow Canyon) were clipped from this combined DEM.
  • Soils Data (Map services and data available from Natural Resources Conservation Service Web Soil Survey, U.S. Department of Agriculture)
    • Animas-Dolores_Area_Soils: Small portion of the soil mapunits cover the northeastern corner of the Coffey Watershed (CW).
    • Cortez_Area_Soils: Soils for Montezuma County, encompasses all of Goodman (GW) and Crow Canyon (CCW) watersheds, and a large portion of the Coffey watershed (CW).
• ArcGIS Map Packages
  • Goodman_Watershed_Full_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the full Goodman Watershed (GW).
  • Goodman_Watershed_Mesa-Only_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the mesa-only Goodman Watershed.
  • Crow_Canyon_Watershed_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the Crow Canyon Watershed (CCW).
  • Coffey_Watershed_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the Coffey Watershed (CW).

For additional information on contents of the map packages, please see see "Map Packages Descriptions" or open a map package in ArcGIS and go to "properties" or "map document properties."

LICENSES

Code: MIT year: 2019
Copyright holders: Andrew Gillreath-Brown, Lisa Nagaoka, and Steve Wolverton

CONTACT

Andrew Gillreath-Brown, PhD Candidate, RPA
Department of Anthropology, Washington State University
andrew.brown1234@gmail.com – Email
andrewgillreathbrown.wordpress.com – Web

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.

A total of 39 classes of land cover were mapped on Fire Island and the William Floyd Estate (Table 6.). These are comprised of 24 types mapped to NVCS association, 1 complex of 2 NVCS alliances, and 14 non-NVCS classes (Figure 5a-d.). Four associations were identified on Fire Island and the William Floyd Estate but do not appear on the map due to their rarity, small relative size, and/or difficulties in identifying them with aerial photography. These types were Oligohaline Marsh, Brackish Marsh, Salt Panne, and North Atlantic Upper Ocean Beach.

Prior to upload to the AGOL cloud, the Martha's Vineyard Commission set a definition query to only include those parcels having an Assessor's Use Code of either 6*, 7*, or 8*.The parcel boundaries and info are the most recent available per MassGIS as of June 2019. For the most current assessing info, please contact the Town's Assessing Office.The parcel boundaries and attribute table conform to the State's Level 3 Parcel Data Standard. See MassGIS for full details.See the MA Property Types Classification Code for a full explanation of each code.

A current, accurate spatial representation of all historic properties listed on the National Register of Historic Places is of interest to Federal agencies, the National Park Service, State Historic and Tribal Historic Preservation Offices, local government and certified local governments, consultants, academia, and the interested public. This interest stems from the regulatory processes of managing cultural resources that are consistent with the National Historic Preservation Act as Amended (NHPA), the National Environmental Policy Act as Amended, the Archaeological Resources Protection Act, and other laws related to cultural resources. The regulations promulgating these laws require the use of spatial data in support of various decisions and actions related to cultural resource management.The information contained in the feature attribute tables for this dataset is not descriptive. Rather the tables document how the data was created, where it came from, who created the data, what map parameters were used e.g. source scale, source accuracy, source coordinate system etc. Also included is information on the name of the resource, status of the resource i.e. does it still exist, is it restricted and what if any constraints are associated with the resource. Please note that each historic property listed on the National Register has its own nominating history and therefore location information collected in the nominating process is different from one property to another. Therefore metadata has been created for each listed historic property to inform the potential user of the history or lineage of the spatial information associated with the historic property. Locations associated with restricted National Register of Historic Places properties are not included in this GeoDatabase and must be requested from the National Park Service, National Register Program.The metadata in the feature attribute table are compliant with the National Park Serviceâ s Cultural Resource Spatial Data Transfer Standards. These standards were created to facilitate the exchange of spatial data within a variety of contexts, particularly Sections 106 and 110 of NHPA as well as in the context of disaster recovery events. Often locations of National Register listed properties are needed in these situations. The National Register Geo-spatial dataset is organized as a geo-database with feature class definitions based on the National Registerâ s Resource Type designations i.e. historic buildings, historic districts, historic structures, historic objects, and historic sites. The definitions of these types can be found in National Register Bulletin 16A and in the metadata statements for each feature class.

HUD’s Multifamily Housing property portfolio consist primarily of rental housing properties with five or more dwelling units such as apartments or town houses, but can also include nursing homes, hospitals, elderly housing, mobile home parks, retirement service centers, and occasionally vacant land. HUD provides subsidies and grants to property owners and developers in an effort to promote the development and preservation of affordable rental units for low-income populations, and those with special needs such as the elderly, and disabled. The portfolio can be broken down into two basic categories: insured, and assisted. The three largest assistance programs for Multifamily Housing are Section 8 Project Based Assistance, Section 202 Supportive Housing for the Elderly, and Section 811 Supportive Housing for Persons with Disabilities. The Multifamily property locations represent the approximate location of the property. The locations of individual buildings associated with each property are not depicted here. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes: ‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) ‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) ‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) ‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) ‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) ‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) Null - Could not be geocoded (does not appear on the map) For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. In an effort to protect Personally Identifiable Information (PII), the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10. To learn more about Multifamily Housing visit: https://www.hud.gov/program_offices/housing/mfh, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov.Data Dictionary: DD_HUD Assisted Multifamily Properties Date of Coverage: 12/2023

**Suggested to use 'Download' button instead of 'Open in ArcGIS Pro'The REST service page displays all data provided in this layer package: https://arcgis.dnr.alaska.gov/arcgis/rest/services/Mapper/Mineral_Estate_Layers/MapServer

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastre Theme ( https://communities.geoplatform.gov/ngda-cadastre/ ). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means. The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all open space public and nonprofit lands and waters. Most are public lands owned in fee (the owner of the property has full and irrevocable ownership of the land); however, permanent and long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g. 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of U.S. public land and other protected areas, compiling “best available” data provided by managing agencies and organizations. The PAD-US geodatabase maps and describes areas using thirty-six attributes and five separate feature classes representing the U.S. protected areas network: Fee (ownership parcels), Designation, Easement, Marine, Proclamation and Other Planning Boundaries. An additional Combined feature class includes the full PAD-US inventory to support data management, queries, web mapping services, and analyses. The Feature Class (FeatClass) field in the Combined layer allows users to extract data types as needed. A Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) facilitates the extraction of authoritative federal data provided or recommended by managing agencies from the Combined PAD-US inventory. This PAD-US Version 3.0 dataset includes a variety of updates from the previous Version 2.1 dataset (USGS, 2020, https://doi.org/10.5066/P92QM3NT ), achieving goals to: 1) Annually update and improve spatial data representing the federal estate for PAD-US applications; 2) Update state and local lands data as state data-steward and PAD-US Team resources allow; and 3) Automate data translation efforts to increase PAD-US update efficiency. The following list summarizes the integration of "best available" spatial data to ensure public lands and other protected areas from all jurisdictions are represented in the PAD-US (other data were transferred from PAD-US 2.1). Federal updates - The USGS remains committed to updating federal fee owned lands data and major designation changes in annual PAD-US updates, where authoritative data provided directly by managing agencies are available or alternative data sources are recommended. The following is a list of updates or revisions associated with the federal estate: 1) Major update of the Federal estate (fee ownership parcels, easement interest, and management designations where available), including authoritative data from 8 agencies: Bureau of Land Management (BLM), U.S. Census Bureau (Census Bureau), Department of Defense (DOD), U.S. Fish and Wildlife Service (FWS), National Park Service (NPS), Natural Resources Conservation Service (NRCS), U.S. Forest Service (USFS), and National Oceanic and Atmospheric Administration (NOAA). The federal theme in PAD-US is developed in close collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://communities.geoplatform.gov/ngda-govunits/federal-lands-workgroup/ ). 2) Improved the representation (boundaries and attributes) of the National Park Service, U.S. Forest Service, Bureau of Land Management, and U.S. Fish and Wildlife Service lands, in collaboration with agency data-stewards, in response to feedback from the PAD-US Team and stakeholders. 3) Added a Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) to the PAD-US 3.0 geodatabase to facilitate the extraction (by Data Provider, Dataset Name, and/or Aggregator Source) of authoritative data provided directly (or recommended) by federal managing agencies from the full PAD-US inventory. A summary of the number of records (Frequency) and calculated GIS Acres (vs Documented Acres) associated with features provided by each Aggregator Source is included; however, the number of records may vary from source data as the "State Name" standard is applied to national files. The Feature Class (FeatClass) field in the table and geodatabase describe the data type to highlight overlapping features in the full inventory (e.g. Designation features often overlap Fee features) and to assist users in building queries for applications as needed. 4) Scripted the translation of the Department of Defense, Census Bureau, and Natural Resource Conservation Service source data into the PAD-US format to increase update efficiency. 5) Revised conservation measures (GAP Status Code, IUCN Category) to more accurately represent protected and conserved areas. For example, Fish and Wildlife Service (FWS) Waterfowl Production Area Wetland Easements changed from GAP Status Code 2 to 4 as spatial data currently represents the complete parcel (about 10.54 million acres primarily in North Dakota and South Dakota). Only aliquot parts of these parcels are documented under wetland easement (1.64 million acres). These acreages are provided by the U.S. Fish and Wildlife Service and are referenced in the PAD-US geodatabase Easement feature class 'Comments' field. State updates - The USGS is committed to building capacity in the state data-steward network and the PAD-US Team to increase the frequency of state land updates, as resources allow. The USGS supported efforts to significantly increase state inventory completeness with the integration of local parks data in the PAD-US 2.1, and developed a state-to-PAD-US data translation script during PAD-US 3.0 development to pilot in future updates. Additional efforts are in progress to support the technical and organizational strategies needed to increase the frequency of state updates. The PAD-US 3.0 included major updates to the following three states: 1) California - added or updated state, regional, local, and nonprofit lands data from the California Protected Areas Database (CPAD), managed by GreenInfo Network, and integrated conservation and recreation measure changes following review coordinated by the data-steward with state managing agencies. Developed a data translation Python script (see Process Step 2 Source Data Documentation) in collaboration with the data-steward to increase the accuracy and efficiency of future PAD-US updates from CPAD. 2) Virginia - added or updated state, local, and nonprofit protected areas data (and removed legacy data) from the Virginia Conservation Lands Database, provided by the Virginia Department of Conservation and Recreation's Natural Heritage Program, and integrated conservation and recreation measure changes following review by the data-steward. 3) West Virginia - added or updated state, local, and nonprofit protected areas data provided by the West Virginia University, GIS Technical Center. For more information regarding the PAD-US dataset please visit, https://www.usgs.gov/gapanalysis/PAD-US/. For more information about data aggregation please review the PAD-US Data Manual available at https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-manual . A version history of PAD-US updates is summarized below (See https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-history for more information): 1) First posted - April 2009 (Version 1.0 - available from the PAD-US: Team pad-us@usgs.gov). 2) Revised - May 2010 (Version 1.1 - available from the PAD-US: Team pad-us@usgs.gov). 3) Revised - April 2011 (Version 1.2 - available from the PAD-US: Team pad-us@usgs.gov). 4) Revised - November 2012 (Version 1.3) https://doi.org/10.5066/F79Z92XD 5) Revised - May 2016 (Version 1.4) https://doi.org/10.5066/F7G73BSZ 6) Revised - September 2018 (Version 2.0) https://doi.org/10.5066/P955KPLE 7) Revised - September 2020 (Version 2.1) https://doi.org/10.5066/P92QM3NT 8) Revised - January 2022 (Version 3.0) https://doi.org/10.5066/P9Q9LQ4B Comparing protected area trends between PAD-US versions is not recommended without consultation with USGS as many changes reflect improvements to agency and organization GIS systems, or conservation and recreation measure classification, rather than actual changes in protected area acquisition on the ground.

This data set consists of general soil association units. It was developed by the National Cooperative Soil Survey and supersedes the State Soil Geographic (STATSGO) data set published in 1994. It consists of a broad based inventory of soils and nonsoil areas that occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. The data set was created by generalizing more detailed soil survey maps. Where more detailed soil survey maps were not available, data on geology, topography, vegetation, and climate were assembled, together with Land Remote Sensing Satellite (LANDSAT) images. Soils of like areas were studied, and the probable classification and extent of the soils were determined.

Map unit composition was determined by transecting or sampling areas on the more detailed maps and expanding the data statistically to characterize the whole map unit.

This data set consists of georeferenced vector digital data and tabular digital data. The map data were collected in 1-by 2-degree topographic quadrangle units and merged into a seamless national data set. It is distributed in state/territory and national extents. The soil map units are linked to attributes in the National Soil Information System data base which gives the proportionate extent of the component soils and their properties.

MnGeo adapted the NRCS metadata record to create this record.

This layer package contains GIS data in Esri file geodatabase format. This data is also available for download as a zip archive in shapefile format.Digital parcel files for the Town of Amherst, MA as of December 31, 2013. The Town converted its existing analog tax maps to digital format in 1998. At the time of conversion, tax maps consisted of 108 27"x39" mylar sheets at 1"=100', originally created in 1957 from controlled and rectified photography taken by Air Survey Inc. (VA) in 1956. The tax maps were scanned, and digital line files were created with text annotation. The new line files were then overlayed onto digital color orthophotos produced in 1999, and updated, by first matching road right-of-ways, then adjusting all parcel boundaries. This data set is a spatial view that is created through a one-to-many join between TOA_Parcels_Poly and TOA_CAMA_TABLE. The join is through Map & Lot, which creates stacked parcel polygons in cases where there are multiple block numbers (accounts) for one parcel; this occurs primarily with condominium complexes, as well as with properties with agricultural preservation restrictions. This data set is refreshed on a nightly basis & reflects current information from the Town of Amherst Assessor's Vision Appraisal Database.

The interactive map creation tools market is experiencing robust growth, driven by increasing demand for visually engaging data representation across diverse sectors. The market, estimated at $2.5 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $7.8 billion by 2033. This expansion is fueled by several key factors. The rising adoption of location-based services (LBS) and geographic information systems (GIS) across industries like real estate, tourism, logistics, and urban planning is a major catalyst. Businesses are increasingly leveraging interactive maps to enhance customer engagement, improve operational efficiency, and gain valuable insights from geospatial data. Furthermore, advancements in mapping technologies, including the integration of AI and machine learning for improved data analysis and visualization, are contributing to market growth. The accessibility of user-friendly tools, coupled with the decreasing cost of cloud-based solutions, is also making interactive map creation more accessible to a wider range of users, from individuals to large corporations. However, the market also faces certain challenges. Data security and privacy concerns surrounding the use of location data are paramount. The need for specialized skills and expertise to effectively utilize advanced mapping technologies may also hinder broader adoption, particularly among smaller businesses. Competition among established players like Mapbox, ArcGIS StoryMaps, and Google, alongside emerging innovative solutions, necessitates constant innovation and differentiation. Nevertheless, the overall market outlook remains positive, with continued technological advancements and rising demand for data visualization expected to propel growth in the coming years. Specific market segmentation data, while unavailable, can be reasonably inferred from existing market trends, suggesting a strong dominance of enterprise-grade solutions, but with substantial growth expected from simpler, more user-friendly tools designed for individuals and small businesses.

The gSSURGO dataset provides detailed soil survey mapping in raster format with ready-to-map attributes organized in statewide tiles for desktop GIS. gSSURGO is derived from the official Soil Survey Geographic (SSURGO) Database. SSURGO generally has the most detailed level of soil geographic data developed by the National Cooperative Soil Survey (NCSS) in accordance with NCSS mapping standards. The tabular data represent the soil attributes and are derived from properties and characteristics stored in the National Soil Information System (NASIS).

The gSSURGO data were prepared by merging the traditional vector-based SSURGO digital map data and tabular data into statewide extents, adding a statewide gridded map layer derived from the vector layer, and adding a new value-added look up table (valu) containing ready-to-map attributes. The gridded map layer is in an ArcGIS file geodatabase in raster format, thus it has the capacity to store significantly more data and greater spatial extents than the traditional SSURGO product. The raster map data have a 10-meter cell size that approximates the vector polygons in an Albers Equal Area projection. Each cell (and polygon) is linked to a map unit identifier called the map unit key. A unique map unit key is used to link the raster cells and polygons to attribute tables.

For more information, see the gSSURGO webpage: https://www.nrcs.usda.gov/resources/data-and-reports/description-of-gridded-soil-survey-geographic-gssurgo-database

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Geographic Information System Analytics Market Size 2024-2028

The geographic information system analytics market size is forecast to increase by USD 12 billion at a CAGR of 12.41% between 2023 and 2028.

The GIS Analytics Market analysis is experiencing significant growth, driven by the increasing need for efficient land management and emerging methods in data collection and generation. The defense industry's reliance on geospatial technology for situational awareness and real-time location monitoring is a major factor fueling market expansion. Additionally, the oil and gas industry's adoption of GIS for resource exploration and management is a key trend. Building Information Modeling (BIM) and smart city initiatives are also contributing to market growth, as they require multiple layered maps for effective planning and implementation. The Internet of Things (IoT) and Software as a Service (SaaS) are transforming GIS analytics by enabling real-time data processing and analysis.
Augmented reality is another emerging trend, as it enhances the user experience and provides valuable insights through visual overlays. Overall, heavy investments are required for setting up GIS stations and accessing data sources, making this a promising market for technology innovators and investors alike.

What will be the Size of the GIS Analytics Market during the forecast period?

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The geographic information system analytics market encompasses various industries, including government sectors, agriculture, and infrastructure development. Smart city projects, building information modeling, and infrastructure development are key areas driving market growth. Spatial data plays a crucial role in sectors such as transportation, mining, and oil and gas. Cloud technology is transforming GIS analytics by enabling real-time data access and analysis. Startups are disrupting traditional GIS markets with innovative location-based services and smart city planning solutions. Infrastructure development in sectors like construction and green buildings relies on modern GIS solutions for efficient planning and management. Smart utilities and telematics navigation are also leveraging GIS analytics for improved operational efficiency.
GIS technology is essential for zoning and land use management, enabling data-driven decision-making. Smart public works and urban planning projects utilize mapping and geospatial technology for effective implementation. Surveying is another sector that benefits from advanced GIS solutions. Overall, the GIS analytics market is evolving, with a focus on providing actionable insights to businesses and organizations.

How is this Geographic Information System Analytics Industry segmented?

The geographic information system analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Retail and Real Estate
  Government
  Utilities
  Telecom
  Manufacturing and Automotive
  Agriculture
  Construction
  Mining
  Transportation
  Healthcare
  Defense and Intelligence
  Energy
  Education and Research
  BFSI


Components

  Software
  Services


Deployment Modes

  On-Premises
  Cloud-Based


Applications

  Urban and Regional Planning
  Disaster Management
  Environmental Monitoring Asset Management
  Surveying and Mapping
  Location-Based Services
  Geospatial Business Intelligence
  Natural Resource Management


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    South Korea


  Middle East and Africa

    UAE


  South America

    Brazil


  Rest of World

By End-user Insights

The retail and real estate segment is estimated to witness significant growth during the forecast period.

The GIS analytics market analysis is witnessing significant growth due to the increasing demand for advanced technologies in various industries. In the retail sector, for instance, retailers are utilizing GIS analytics to gain a competitive edge by analyzing customer demographics and buying patterns through real-time location monitoring and multiple layered maps. The retail industry's success relies heavily on these insights for effective marketing strategies. Moreover, the defense industries are integrating GIS analytics into their operations for infrastructure development, permitting, and public safety. Building Information Modeling (BIM) and 4D GIS software are increasingly being adopted for construction project workflows, while urban planning and designing require geospatial data for smart city planning and site selection.

The oil and gas industry is leveraging satellite imaging and IoT devices for land acquisition and mining operations. In the public sector,

Soil Data Viewer is a tool built as an extension to ArcMap that allows a user to create soil-based thematic maps. The application can also be run independently of ArcMap, but output is then limited to a tabular report. The soil survey attribute database associated with the spatial soil map is a complicated database with more than 50 tables. Soil Data Viewer provides users access to soil interpretations and soil properties while shielding them from the complexity of the soil database. Each soil map unit, typically a set of polygons, may contain multiple soil components that have different use and management. Soil Data Viewer makes it easy to compute a single value for a map unit and display results, relieving the user from the burden of querying the database, processing the data and linking to the spatial map. Soil Data Viewer contains processing rules to enforce appropriate use of the data. This provides the user with a tool for quick geospatial analysis of soil data for use in resource assessment and management. Resources in this dataset:Resource Title: Soil Data Viewer. File Name: Web Page, url: https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/home/?cid=nrcs142p2_053620 Soil Data Viewer is a tool built as an extension to ArcMap that allows a user to create soil-based thematic maps. The application can also be run independent of ArcMap, but output is then limited to a tabular report. Soil Data Viewer contains processing rules to enforce appropriate use of the data. This provides the user with a tool for quick geospatial analysis of soil data for use in resource assessment and management. Links to download and install Download Soil Data Viewer 6.2 for use with ArcGIS 10.x and Windows XP, Windows 7, Windows 8.x, or Windows 10. Earlier versions are also available.

This repository of global hydrogeologic datasets contains aquifer properties on 0.5° scale, including depth to groundwater (Fan et al., 2013), aquifer thickness (de Graaf et al., 2015), WHYMap aquifer classes (Richts et al., 2011), porosity and permeability (Gleeson et al., 2014), digitized and geo-processed from their respective sources. Globally gridded aquifer properties could be used independently to estimate global groundwater availability or used as critical inputs to the superwell model to simulate groundwater extraction and provide estimates of pumped volumes and unit costs under user-specific scenarios. Key resources related to this data are: Niazi, H., Ferencz, S., Graham, N., Yoon, J., Wild, T., Hejazi, M., Watson, D., & Vernon, C. (2024; In-prep). Long-term Hydro-economic Assessment Tool for Evaluating Global Groundwater Cost and Supply: Superwell v1. Geoscientific Model Development. superwell model repository which uses this data to simulate groundwater extraction and provides estimates of the global extractable volumes and unit-costs ($/km3) of accessible groundwater production under user-specified extraction scenarios. Repository Overview Main output: aquifer_properties.csv contains all processed outputs, including aquifer properties like porosity, permeability, aquifer thickness, and depth to groundwater. shapefiles.zip: contains all digitized GIS databases and shapefile for all aquifer properties prep_inputs.R: R script that processes the shapefiles to produce the aquifer_properties file plot_inputs.R: R script for plotting the maps and conducting preliminary analysis on the available groundwater volume basin_to_country_mapping.csv, basin_country_region_mapping.csv and continent_county_mapping.csv provide the mapping between continents, 32 energy-economic macro regions, countries, and water basins for post-processing aquifer_properties.csv Maps: Each map visualizes the spatial distribution of one of the aquifer properties across the globe map_in_Porosity.png map_in_Permeability.png map_in_Aquifer_thickness.png map_in_Depth_to_water.png map_in_Grid_area_km.png map_in_WHYClass.png Sample inputs sample_inputs.py: this script samples inputs from the aquifer_properties dataset, ensuring the sampled and original inputs maintain the same distributions sampled_data_100.csv contains 100 sampled data points and sampled_data_100.png compares their distributions Dataset Overview The main outputs are consolidated in a comprehensive aquifer_properties.csv file and include the following fields: GridCellID: Unique identifier for each (roughly 0.5°) grid cell Continent: Continent name Country: Country name GCAM_basin_ID: Identifier for GCAM hydrologic basin Basin_long_name: Full name of the basin WHYClass: Hydrogeologic classification based on WHYMap aquifer classes (Richts et al., 2011) Porosity: Soil porosity (%) (Gleeson et al., 2014) Permeability: Soil permeability (in square meters; Gleeson et al., 2014) Aquifer_thickness: Thickness of the aquifer (in meters; de Graaf et al., 2015) Depth_to_water: Depth to groundwater (in meters; Fan et al., 2013) Grid_area: Area of the grid cell (in square meters) Key References The datasets are digitized versions of global hydrogeologic properties from the following key literature sources: Depth to Groundwater: Fan, Y., Li, H., & Miguez-Macho, G. (2013). Global Patterns of Groundwater Table Depth. Science, 339(6122), 940-943. https://doi.org/10.1126/science.1229881 Aquifer Thickness: de Graaf, I. E. M., Sutanudjaja, E. H., van Beek, L. P. H., & Bierkens, M. F. P. (2015). A high-resolution global-scale groundwater model. Hydrol. Earth Syst. Sci., 19(2), 823-837. https://doi.org/10.5194/hess-19-823-2015 Porosity and Permeability: Gleeson, T., Moosdorf, N., Hartmann, J., & van Beek, L. P. H. (2014). A glimpse beneath earth's surface: GLobal HYdrogeology MaPS (GLHYMPS) of permeability and porosity. Geophysical Research Letters, 41(11), 3891-3898. https://doi.org/10.1002/2014GL059856 Aquifer classes: Richts, A., Struckmeier, W. F., & Zaepke, M. (2011). WHYMAP and the Groundwater Resources Map of the World 1:25,000,000. In J. A. A. Jones (Ed.), Sustaining Groundwater Resources: A Critical Element in the Global Water Crisis (pp. 159-173). Springer Netherlands. https://doi.org/10.1007/978-90-481-3426-7_10 Cite as Niazi, H., Watson, D., Hejazi, M., Yonkofski, C., Ferencz, S., Vernon, C., Graham, N., Wild, T., & Yoon, J. (2024). Global Geo-processed Data of Aquifer Properties by 0.5° Grid, Country and Water Basins. MSD-LIVE Data Repository. https://doi.org/10.57931/2307831 Contact Reach out to Hassan Niazi or open an issue in superwell repository for questions or suggestions.

This web map created by the Colorado Governor's Office of Information Technology GIS team, serves as a basemap specific to the state of Colorado. The basemap includes general layers such as counties, municipalities, roads, waterbodies, state parks, national forests, national wilderness areas, and trails.Layers:Layer descriptions and sources can be found below. Layers have been modified to only represent features within Colorado and are not up to date. Layers last updated February 23, 2023. Colorado State Extent: Description: “This layer provides generalized boundaries for the 50 States and the District of Columbia.” Notes: This layer was filtered to only include the State of ColoradoSource: Esri Living Atlas USA States Generalized Boundaries Feature LayerState Wildlife Areas:Description: “This data was created by the CPW GIS Unit. Property boundaries are created by dissolving CDOWParcels by the property name, and property type and appending State Park boundaries designated as having public access. All parcel data correspond to legal transactions made by the CPW Real Estate Unit. The boundaries of the CDOW Parcels were digitized using metes and bounds, BLM's GCDB dataset, the PLSS dataset (where the GCDB dataset was unavailable) and using existing digital data on the boundaries.” Notes: The state wildlife areas layer in this basemap is filtered from the CPW Managed Properties (public access only) layer from this feature layer hosted in ArcGIS Online Source: Colorado Parks and Wildlife CPW Admin Data Feature LayerMunicipal Boundaries:Description: "Boundaries data from the State Demography Office of Colorado Municipalities provided by the Department of Local Affairs (DOLA)"Source: Colorado Information Marketplace Municipal Boundaries in ColoradoCounties:Description: “This layer presents the USA 2020 Census County (or County Equivalent) boundaries of the United States in the 50 states and the District of Columbia. It is updated annually as County (or County Equivalent) boundaries change. The geography is sources from US Census Bureau 2020 TIGER FGDB (National Sub-State) and edited using TIGER Hydrology to add a detailed coastline for cartographic purposes. Geography last updated May 2022.” Notes: This layer was filtered to only include counties in the State of ColoradoSource: Esri USA Census Counties Feature LayerInterstates:Description: Authoritative data from the Colorado Department of Transportation representing Highways Notes: Interstates are filtered by route sign from this CDOT Highways layer Source: Colorado Department of Transportation Highways REST EndpointU.S. Highways:Description: Authoritative data from the Colorado Department of Transportation representing Highways Notes: U.S. Highways are filtered by route sign from this CDOT Highways layer Source: Colorado Department of Transportation Highways REST EndpointState Highways:Description: Authoritative data from the Colorado Department of Transportation representing Highways Notes: State Highways are filtered by route sign from this CDOT Highways layer Source: Colorado Department of Transportation Highways REST EndpointMajor Roads:Description: Authoritative data from the Colorado Department of Transportation representing major roads Source: Colorado Department of Transportation Major Roads REST EndpointLocal Roads:Description: Authoritative data from the Colorado Department of Transportation representing local roads Source: Colorado Department of Transportation Local Roads REST EndpointRail Lines:Description: Authoritative data from the Colorado Department of Transportation representing rail lines Source: Colorado Department of Transportation Rail Lines REST EndpointCOTREX Trails:Description: “The Colorado Trail System, now titled the Colorado Trail Explorer (COTREX), endeavors to map every trail in the state of Colorado. Currently their are nearly 40,000 miles of trails mapped. Trails come from a variety of sources (USFS, BLM, local parks & recreation departments, local governments). Responsibility for accuracy of the data rests with the source.These data were last updated on 2/5/2019” Source: Colorado Parks and Wildlife CPW Admin Data Feature LayerNHD Waterbodies:Description: “The National Hydrography Dataset Plus (NHDplus) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US EPA Office of Water and the US Geological Survey, the NHDPlus provides mean annual and monthly flow estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses.”Notes: This layer was filtered to only include waterbodies in the State of ColoradoSource: National Hydrography Dataset Plus Version 2.1 Feature LayerNHD Flowlines:Description: “The National Hydrography Dataset Plus (NHDplus) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US EPA Office of Water and the US Geological Survey, the NHDPlus provides mean annual and monthly flow estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses.”Notes: This layer was filtered to only include flowline features in the State of ColoradoSource: National Hydrography Dataset Plus Version 2.1 Feature LayerState Parks:Description: “This data was created by the CPW GIS Unit. Property boundaries are created by dissolving CDOWParcels by the property name, and property type and appending State Park boundaries designated as having public access. All parcel data correspond to legal transactions made by the CPW Real Estate Unit. The boundaries of the CDOW Parcels were digitized using metes and bounds, BLM's GCDB dataset, the PLSS dataset (where the GCDB dataset was unavailable) and using existing digital data on the boundaries.” Notes: The state parks layer in this basemap is filtered from the CPW Managed Properties (public access only) layer from this feature layer Source: Colorado Parks and Wildlife CPW Admin Data Feature LayerDenver Parks:Description: "This dataset should be used as a reference to locate parks, golf courses, and recreation centers managed by the Department of Parks and Recreation in the City and County of Denver. Data is based on parcel ownership and does not include other areas maintained by the department such as medians and parkways. The data should be used for planning and design purposes and cartographic purposes only."Source: City and County of Denver Parks REST EndpointNational Wilderness Areas:Description: “A parcel of Forest Service land congressionally designated as wilderness such as National Wilderness Area.”Notes: This layer was filtered to only include National Wilderness Areas in the State of ColoradoSource: United States Department of Agriculture National Wilderness Areas REST EndpointNational Forests: Description: “A depiction of the boundaries encompassing the National Forest System (NFS) lands within the original proclaimed National Forests, along with subsequent Executive Orders, Proclamations, Public Laws, Public Land Orders, Secretary of Agriculture Orders, and Secretary of Interior Orders creating modifications thereto, along with lands added to the NFS which have taken on the status of 'reserved from the public domain' under the General Exchange Act. The following area types are included: National Forest, Experimental Area, Experimental Forest, Experimental Range, Land Utilization Project, National Grassland, Purchase Unit, and Special Management Area.”Notes: This layer was filtered to only include National Forests in the State of ColoradoSource: United States Department of Agriculture Original Proclaimed National Forests REST Endpoint

Use this web map to link to other geospatial datasets available through county and city sites (Not comprehensive). May need to zoom in to see the participating cities. The county boundaries and city points were published by Washington State agencies and downloaded from geo.wa.gov. Locations are approximate, and no warranties are made regarding this data. The canvas basemap has been compiled by Esri and the ArcGIS user community from a variety of best available sources. Want to have your data site listed? Contact the Geospatial Program Office.

The 2020 TIGER/Line Shapefiles contain current geographic extent and boundaries of both legal and statistical entities (which have no governmental standing) for the United States, the District of Columbia, Puerto Rico, and the Island areas. This vintage includes boundaries of governmental units that match the data from the surveys that use 2020 geography (e.g., 2020 Population Estimates and the 2020 American Community Survey). In addition to geographic boundaries, the 2020 TIGER/Line Shapefiles also include geographic feature shapefiles and relationship files. Feature shapefiles represent the point, line and polygon features in the MTDB (e.g., roads and rivers). Relationship files contain additional attribute information users can join to the shapefiles. Both the feature shapefiles and relationship files reflect updates made in the database through September 2020. To see how the geographic entities, relate to one another, please see our geographic hierarchy diagrams here.Census Urbanized Areashttps://www2.census.gov/geo/tiger/TIGER2020/UACCensus Urban/Rural Census Block Shapefileshttps://www.census.gov/cgi-bin/geo/shapefiles/index.php2020 TIGER/Line and Redistricting shapefiles:https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.2020.htmlTechnical documentation:https://www2.census.gov/geo/pdfs/maps-data/data/tiger/tgrshp2020/TGRSHP2020_TechDoc.pdfTIGERweb REST Services:https://tigerweb.geo.census.gov/tigerwebmain/TIGERweb_restmapservice.htmlTIGERweb WMS Services:https://tigerweb.geo.census.gov/tigerwebmain/TIGERweb_wms.htmlThe legal entities included in these shapefiles are:American Indian Off-Reservation Trust LandsAmerican Indian Reservations – FederalAmerican Indian Reservations – StateAmerican Indian Tribal Subdivisions (within legal American Indian areas)Alaska Native Regional CorporationsCongressional Districts – 116th CongressConsolidated CitiesCounties and Equivalent Entities (except census areas in Alaska)Estates (US Virgin Islands only)Hawaiian Home LandsIncorporated PlacesMinor Civil DivisionsSchool Districts – ElementarySchool Districts – SecondarySchool Districts – UnifiedStates and Equivalent EntitiesState Legislative Districts – UpperState Legislative Districts – LowerSubminor Civil Divisions (Subbarrios in Puerto Rico)The statistical entities included in these shapefiles are:Alaska Native Village Statistical AreasAmerican Indian/Alaska Native Statistical AreasAmerican Indian Tribal Subdivisions (within Oklahoma Tribal Statistical Areas)Block Groups3-5Census AreasCensus BlocksCensus County Divisions (Census Subareas in Alaska)Unorganized Territories (statistical county subdivisions)Census Designated Places (CDPs)Census TractsCombined New England City and Town AreasCombined Statistical AreasMetropolitan and Micropolitan Statistical Areas and related statistical areasMetropolitan DivisionsNew England City and Town AreasNew England City and Town Area DivisionsOklahoma Tribal Statistical AreasPublic Use Microdata Areas (PUMAs)State Designated Tribal Statistical AreasTribal Designated Statistical AreasUrban AreasZIP Code Tabulation Areas (ZCTAs)Shapefiles - Features:Address Range-FeatureAll Lines (called Edges)All RoadsArea HydrographyArea LandmarkCoastlineLinear HydrographyMilitary InstallationPoint LandmarkPrimary RoadsPrimary and Secondary RoadsTopological Faces (polygons with all geocodes)Relationship Files:Address Range-Feature NameAddress RangesFeature NamesTopological Faces – Area LandmarkTopological Faces – Area HydrographyTopological Faces – Military Installations

**Suggested to use 'Download' button instead of 'Open in ArcGIS Pro'The REST service page displays all data provided in this layer package: https://arcgis.dnr.alaska.gov/arcgis/rest/services/Mapper/Water_Estate_Layers/MapServer

This is a connection to the Crawford County Government public platform for exploring and downloading open data, discovering and building apps, and engaging to solve important local issues. You can analyze and combine Open Datasets using maps, as well as develop new web and mobile applications. Let's make our great community even better, together!

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