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.

Overview

Empower your location data visualizations with our edge-matched polygons, even in difficult geographies.

Our self-hosted GIS data cover administrative and postal divisions with up to 6 precision levels: a zip code layer and up to 5 administrative levels. All levels follow a seamless hierarchical structure with no gaps or overlaps.

The geospatial data shapes are offered in high-precision and visualization resolution and are easily customized on-premise.

Use cases for the Global Boundaries Database (GIS data, Geospatial data)

• In-depth spatial analysis

• Clustering

• Geofencing

• Reverse Geocoding

• Reporting and Business Intelligence (BI)

Product Features

• Coherence and precision at every level

• Edge-matched polygons

• High-precision shapes for spatial analysis

• Fast-loading polygons for reporting and BI

• Multi-language support

For additional insights, you can combine the GIS data with:

• Population data: Historical and future trends

• UNLOCODE and IATA codes

• Time zones and Daylight Saving Time (DST)

Data export methodology

Our geospatial data packages are offered in variable formats, including - .shp - .gpkg - .kml - .shp - .gpkg - .kml - .geojson

All GIS data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Why companies choose our map data

• Precision at every level

• Coverage of difficult geographies

• No gaps, nor overlaps

Note: Custom geospatial data packages are available. Please submit a request via the above contact button for more details.

Snapshot img

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,

Snapshot img

North America Geographic Information System Market Size 2025-2029

The geographic information system market size in North America is forecast to increase by USD 11.4 billion at a CAGR of 23.7% between 2024 and 2029.

The market is experiencing significant growth due to the increasing adoption of advanced technologies such as artificial intelligence, satellite imagery, and sensors in various industries. In fleet management, GIS software is being used to optimize routes and improve operational efficiency. In the context of smart cities, GIS solutions are being utilized for content delivery, public safety, and building information modeling. The demand for miniaturization of technologies is also driving the market, allowing for the integration of GIS into smaller devices and applications. However, data security concerns remain a challenge, as the collection and storage of sensitive information requires robust security measures. The insurance industry is also leveraging GIS for telematics and risk assessment, while the construction sector uses GIS for server-based project management and planning. Overall, the GIS market is poised for continued growth as these trends and applications continue to evolve.

What will be the Size of the market During the Forecast Period?

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The Geographic Information System (GIS) market encompasses a range of technologies and applications that enable the collection, management, analysis, and visualization of spatial data. Key industries driving market growth include transportation, infrastructure planning, urban planning, and environmental monitoring. Remote sensing technologies, such as satellite imaging and aerial photography, play a significant role in data collection. Artificial intelligence and the Internet of Things (IoT) are increasingly integrated into GIS solutions for real-time location data processing and operational efficiency.
Applications span various sectors, including agriculture, natural resources, construction, and smart cities. GIS is essential for infrastructure analysis, disaster management, and land management. Geospatial technology enables spatial data integration, providing valuable insights for decision-making and optimization. Market size is substantial and growing, fueled by increasing demand for efficient urban planning, improved infrastructure, and environmental sustainability. Geospatial startups continue to emerge, innovating in areas such as telematics, natural disasters, and smart city development.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Software
  Data
  Services


Deployment

  On-premise
  Cloud


Geography

  North America

    Canada
    Mexico
    US

By Component Insights

The software segment is estimated to witness significant growth during the forecast period.

The Geographic Information System (GIS) market encompasses desktop, mobile, cloud, and server software for managing and analyzing spatial data. In North America, industry-specific GIS software dominates, with some commercial entities providing open-source alternatives for limited functions like routing and geocoding. Despite this, counterfeit products pose a threat, making open-source software a viable option for smaller applications. Market trends indicate a shift towards cloud-based GIS solutions for enhanced operational efficiency and real-time location data. Spatial data applications span various sectors, including transportation infrastructure planning, urban planning, natural resources management, environmental monitoring, agriculture, and disaster management. Technological innovations, such as artificial intelligence, the Internet of Things (IoT), and satellite imagery, are revolutionizing GIS solutions.

Cloud-based GIS solutions, IoT integration, and augmented reality are emerging trends. Geospatial technology is essential for smart city projects, climate monitoring, intelligent transportation systems, and land management. Industry statistics indicate steady growth, with key players focusing on product innovation, infrastructure optimization, and geospatial utility solutions.

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Market Dynamics

Our North America Geographic Information System Market researchers analyzed the data with 2024 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

What are the key market drivers leading to the rise in the adoption of the North America Geographic Information System Market?

Rising applications of geographi

GapMaps Live is an easy-to-use location intelligence platform available across 25 countries globally that allows you to visualise your own store data, combined with the latest demographic, economic and population movement intel right down to the micro level so you can make faster, smarter and surer decisions when planning your network growth strategy.

With one single login, you can access the latest estimates on resident and worker populations, census metrics (eg. age, income, ethnicity), consuming class, retail spend insights and point-of-interest data across a range of categories including fast food, cafe, fitness, supermarket/grocery and more.

Some of the world's biggest brands including McDonalds, Subway, Burger King, Anytime Fitness and Dominos use GapMaps Live as a vital strategic tool where business success relies on up-to-date, easy to understand, location intel that can power business case validation and drive rapid decision making.

Primary Use Cases for GapMaps Live includes:

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5. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)
6. Customer Profiling
7. Target Marketing
8. Market Share Analysis

Some of features our clients love about GapMaps Live include: - View business locations, competitor locations, demographic, economic and social data around your business or selected location - Understand consumer visitation patterns (“where from” and “where to”), frequency of visits, dwell time of visits, profiles of consumers and much more. - Save searched locations and drop pins - Turn on/off all location listings by category - View and filter data by metadata tags, for example hours of operation, contact details, services provided - Combine public data in GapMaps with views of private data Layers - View data in layers to understand impact of different data Sources - Share maps with teams - Generate demographic reports and comparative analyses on different locations based on drive time, walk time or radius. - Access multiple countries and brands with a single logon - Access multiple brands under a parent login - Capture field data such as photos, notes and documents using GapMaps Connect and integrate with GapMaps Live to get detailed insights on existing and proposed store locations.

Example extract, transform, and load (ETL) framework with comments and print statements to develop a script using the "run tools in Pro and copy script to a file" method to assist in NG911 transition by transforming and loading local addresses and road centerlines into the NENA Next Generation 9-1-1 GIS Data Model Schema. Created on 20220915 as a supplement to a "Supporting Extract, Transform, and Load Development for Next Generation 9-1-1" presentation delivered at GIS Pro 2022. Originally developed by Matt Gerike, Virginia Geographic Information Network, September 2022.Parity logic contributed by Charles Grant, City of Salem, Virginia, March 2021.See here for resources and context about using the NG9-1-1 GIS data model templates.Additional resources and recommendations on GIS related topics are available on the VGIN 9-1-1 & GIS page.

In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California’s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of “lands” from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay’s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service–Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Offshore of Point Conception map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and photographic imagery; these “ground-truth” surveying data are available from the CSMP Video and Photograph Portal at https://doi.org/10.5066/F7J1015K. The “seafloor character” data layer shows classifications of the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. The “potential habitats” polygons are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Representative seismic-reflection profile data from the map area is also include and provides information on the subsurface stratigraphy and structure of the map area. The distribution and thickness of young sediment (deposited over the past about 21,000 years, during the most recent sea-level rise) is interpreted on the basis of the seismic-reflection data. The geologic polygons merge onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery seafloor-sediment and rock samplesdigital camera and video imagery, and high-resolution seismic-reflection profiles. The information provided by the map sheets, pamphlet, and data catalog has a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues. Web services were created using an ArcGIS service definition file. The ArcGIS REST service and OGC WMS service include all Offshore of Point Conception map area data layers. Data layers are symbolized as shown on the associated map sheets.

Geographic Information System Market Outlook

As per our latest research, the global Geographic Information System (GIS) market size reached USD 12.3 billion in 2024. The industry is experiencing robust expansion, driven by a surging demand for spatial data analytics across diverse sectors. The market is projected to grow at a CAGR of 11.2% from 2025 to 2033, reaching an estimated USD 31.9 billion by 2033. This accelerated growth is primarily attributed to the integration of advanced technologies such as artificial intelligence, IoT, and cloud computing with GIS solutions, as well as the increasing adoption of location-based services and smart city initiatives worldwide.

One of the primary growth factors fueling the GIS market is the rapid adoption of geospatial analytics in urban planning and infrastructure development. Governments and private enterprises are leveraging GIS to optimize land use, manage resources efficiently, and enhance public services. Urban planners utilize GIS to analyze demographic trends, plan transportation networks, and ensure sustainable development. The integration of GIS with Building Information Modeling (BIM) and real-time data feeds has further amplified its utility in smart city projects, driving demand for sophisticated GIS platforms. The proliferation of IoT devices and sensors has also enabled the collection of high-resolution geospatial data, which is instrumental in developing predictive models for urban growth and disaster management.

Another significant driver of the GIS market is the increasing need for disaster management and risk mitigation. GIS technology plays a pivotal role in monitoring natural disasters such as floods, earthquakes, and wildfires. By providing real-time spatial data, GIS enables authorities to make informed decisions, coordinate response efforts, and allocate resources effectively. The growing frequency and intensity of natural disasters, coupled with heightened awareness about climate change, have compelled governments and humanitarian organizations to invest heavily in advanced GIS solutions. These investments are not only aimed at disaster response but also at long-term resilience planning, thereby expanding the scope and scale of GIS applications.

The expanding application of GIS in the agriculture and utilities sectors is another crucial growth factor. Precision agriculture relies on GIS to analyze soil conditions, monitor crop health, and optimize irrigation practices, ultimately boosting productivity and sustainability. In the utilities sector, GIS is indispensable for asset management, network optimization, and outage response. The integration of GIS with remote sensing technologies and drones has revolutionized data collection and analysis, enabling more accurate and timely decision-making. Moreover, the emergence of cloud-based GIS platforms has democratized access to geospatial data and analytics, empowering small and medium enterprises to harness the power of GIS for operational efficiency and strategic planning.

From a regional perspective, North America continues to dominate the GIS market, supported by substantial investments in smart infrastructure, advanced research capabilities, and a strong presence of leading technology providers. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid urbanization, government initiatives for digital transformation, and increasing adoption of GIS in agriculture and disaster management. Europe is also witnessing significant growth, particularly in transportation, environmental monitoring, and public safety applications. The Middle East & Africa and Latin America are gradually catching up, with growing investments in infrastructure development and resource management. This regional diversification is expected to drive innovation and competition in the global GIS market over the forecast period.

Component Analysis

The Geographic Information System market is segmented by component into hardware, software, and services, each playing a unique role

Snapshot img

The cloud gis market size will grow up to $ 690.39 mn at a CAGR of 14% during 2021-2025.

This cloud gis market analysis report entails exhaustive statistical qualitative and quantitative data on End-user (Government, Public safety, Transportation, Business, and Others) and Geography (North America, APAC, Europe, MEA, and South America) and their contribution to the target market. View our sample report to gather market insights on the segmentations. Furthermore, with the latest key findings on the post COVID-19 impact on the market, available in this report, you can create successful business strategies to generate new sales opportunities.

What will the Cloud GIS Market Size be in 2021?

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Cloud GIS Market: Key Drivers and Trends

According to our research output, there has been a neutral impact on the market growth post COVID-19 era. Key drivers such as the rising popularity of cloud gis due to ease in data accessibility are notably supporting the cloud gis market growth. On the other hand, factors such as threat of security have been identified as market challenges that limit the growth of market vendors. This report offers detailed insights on the challenges to stay prepared for the obstacles in the future, which will help companies analyze and develop growth strategies.

This post-pandemic cloud gis market report has assessed the shift in consumer behavior and identified trends and drivers that will help market players outmaneuver challenges. Technology innovations, implementation, and improvisation scope identified in the cloud gis market trends is essential for building new business opportunities across segmentations and geographies.

Who are the Major Cloud GIS Market Vendors?

The cloud gis market forecast report provides insights on complete key vendor profiles and their business strategies to reimage themselves. The profiles covered in the report are as follows:

AmigoCloud Inc. Blue Marble Geographics Caliper Corp. Computer Aided Development Corp. Ltd. Environmental Systems Research Institute Inc. GIS Cloud Ltd. HERE Global BV Hexagon AB Mapbox Inc. Pitney Bowes Inc.

The cloud gis market is fragmented and the vendors are deploying various organic and inorganic growth strategies to compete in the market. Click here to uncover other successful business strategies deployed by the vendors.

Furthermore, our research experts have outlined the magnitude of the economic impact on each segment and recovery expectations post pandemic. To recover from post COVID-19 impact, market vendors should create strategies to grab business opportunities from the fast-growing segments, while refining their scope of growth in the slow-growing ones.

For insights on complete key vendor profiles, download a free sample of the cloud gis market forecast report. The profiles include information on the production, sustainability, and prospects of the leading companies. The report's vendor landscape section also provides industry risk assessment in terms of labor cost, raw material price fluctuation, and other parameters, which is crucial for effective business planning.

Which are the Key Regions for Cloud GIS Market?

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The cloud gis market size, share, & trends analysis report offers an up-to-date study of the geographical composition of the market. 40% of the market’s growth will originate from North America during the forecast period. US, China, Japan, Germany, and Canada are the key markets for cloud gis market in North America.

North America has been recording significant growth rate and is expected to offer several growth opportunities to market vendors during the forecast period. Easy distribution of data has been identified as one of the chief factors that will drive the cloud gis market growth in North America over the forecast period. To garner further competitive intelligence and regional opportunities in store for vendors, view our sample report.

What are the Revenue-generating End-user Segments in the Cloud GIS Market?

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The cloud gis market share growth by the _ segment has been significant. The cloud gis market report provides comprehensive understanding of the subsegments of the target market to identify niche customer groups and demographic requirements. Furthermore, the report provides insights on the impact of COVID-19 on market segments, which can be used to deduce transformation patterns in consumer behavior in the coming years and improvise business plans.

This report provides an accurate prediction of the contribution of all the segments to the growth of the cloud gis market

Geographic Information System (GIS) Software Market Outlook

The global Geographic Information System (GIS) software market size is projected to grow from USD 9.1 billion in 2023 to USD 18.5 billion by 2032, reflecting a compound annual growth rate (CAGR) of 8.5% over the forecast period. This growth is driven by the increasing application of GIS software across various sectors such as agriculture, construction, transportation, and utilities, along with the rising demand for location-based services and advanced mapping solutions.

One of the primary growth factors for the GIS software market is the widespread adoption of spatial data by various industries to enhance operational efficiency. In agriculture, for instance, GIS software plays a crucial role in precision farming by aiding in crop monitoring, soil analysis, and resource management, thereby optimizing yield and reducing costs. In the construction sector, GIS software is utilized for site selection, design and planning, and infrastructure management, making project execution more efficient and cost-effective.

Additionally, the integration of GIS with emerging technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT) is significantly enhancing the capabilities of GIS software. AI-driven data analytics and IoT-enabled sensors provide real-time data, which, when combined with spatial data, results in more accurate and actionable insights. This integration is particularly beneficial in fields like smart city planning, disaster management, and environmental monitoring, further propelling the market growth.

Another significant factor contributing to the market expansion is the increasing government initiatives and investments aimed at improving geospatial infrastructure. Governments worldwide are recognizing the importance of GIS in policy-making, urban planning, and public safety, leading to substantial investments in GIS technologies. For example, the U.S. governmentÂ’s Geospatial Data Act emphasizes the development of a cohesive national geospatial policy, which in turn is expected to create more opportunities for GIS software providers.

Geographic Information System Analytics is becoming increasingly pivotal in transforming raw geospatial data into actionable insights. By employing sophisticated analytical tools, GIS Analytics allows organizations to visualize complex spatial relationships and patterns, enhancing decision-making processes across various sectors. For instance, in urban planning, GIS Analytics can identify optimal locations for new infrastructure projects by analyzing population density, traffic patterns, and environmental constraints. Similarly, in the utility sector, it aids in asset management by predicting maintenance needs and optimizing resource allocation. The ability to integrate GIS Analytics with other data sources, such as demographic and economic data, further amplifies its utility, making it an indispensable tool for strategic planning and operational efficiency.

Regionally, North America holds the largest share of the GIS software market, driven by technological advancements and high adoption rates across various sectors. Europe follows closely, with significant growth attributed to the increasing use of GIS in environmental monitoring and urban planning. The Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, fueled by rapid urbanization, infrastructure development, and government initiatives in countries like China and India.

Component Analysis

The GIS software market is segmented into software and services, each playing a vital role in meeting the diverse needs of end-users. The software segment encompasses various types of GIS software, including desktop GIS, web GIS, and mobile GIS. Desktop GIS remains the most widely used, offering comprehensive tools for spatial analysis, data management, and visualization. Web GIS, on the other hand, is gaining traction due to its accessibility and ease of use, allowing users to access GIS capabilities through a web browser without the need for extensive software installations.

Mobile GIS is another crucial aspect of the software segment, providing field-based solutions for data collection, asset management, and real-time decision making. With the increasing use of smartphones and tablets, mobile GIS applications are becoming indispensable for sectors such as utilities, transportation, and

Interesting, largely unexplored data analysis and information retrieval opportunities exist for GIS data. In their current form, traditional data usage patterns for data persisted in shapefiles or spatially-enabled relational databases are limited. Opportunities exist to achieve ESIP’s Winter 2019 theme of ‘increasing the use and value of Earth science data and information’ by transforming geospatial data from their original formats into their Resource Description Framework (RDF) manifestation. This work establishes an innovative workflow enabling the publication for Geospatial data persisted in geospatially enabled databases (PostGIS and MonetDB), ESRI shapefiles and XML, GML, KML, JSON, GeoJSON and CSV documents as graphs of linked open geospatial data. This affords the capability to identify implicit connections between related data that wasn't previously linked e.g. automating the detection of features present within large hydrography datasets as well as smaller regional examples and resolving features in a consistent fashion. This previously unavailable capability is achieved through the use of a semantic technology stack which leverages well matured standards within the Semantic Web space such as RDF as the data model, GeoSPARQL as the data access language and International Resource Identifier’s (IRI) for uniquely identifying and referencing entities such as rivers, streams and other water bodies. In anticipation of NASA’s forthcoming Surface Water Ocean Topography (SWOT – https://swot.jpl.nasa.gov) mission, which once launched in 2021 will make NASA’s first-ever global survey of Earth’s surface water, this work uses Hydrography data products (USGS’s National Hydrography Dataset and other topically relevant examples) as the topic matter. The compelling result is a new, innovative data analysis and information retrieval capability which will increases the use and value of Earth science data (GIS) and information. This presentation was given at the Earth Science Information Partners (ESIP) Winter Meeting in January 2019.

CrimeMapTutorial is a step-by-step tutorial for learning crime mapping using ArcView GIS or MapInfo Professional GIS. It was designed to give users a thorough introduction to most of the knowledge and skills needed to produce daily maps and spatial data queries that uniformed officers and detectives find valuable for crime prevention and enforcement. The tutorials can be used either for self-learning or in a laboratory setting. The geographic information system (GIS) and police data were supplied by the Rochester, New York, Police Department. For each mapping software package, there are three PDF tutorial workbooks and one WinZip archive containing sample data and maps. Workbook 1 was designed for GIS users who want to learn how to use a crime-mapping GIS and how to generate maps and data queries. Workbook 2 was created to assist data preparers in processing police data for use in a GIS. This includes address-matching of police incidents to place them on pin maps and aggregating crime counts by areas (like car beats) to produce area or choropleth maps. Workbook 3 was designed for map makers who want to learn how to construct useful crime maps, given police data that have already been address-matched and preprocessed by data preparers. It is estimated that the three tutorials take approximately six hours to complete in total, including exercises.

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.

The Hillslope Study sites represent a gradient of landscapes, including forested, valley agriculture, and mountain housing developments. These locations and plots were used to collect samples of various matrices for numerous analyses at differing intervals. The data set consists of Open Office spreadsheet and other files that document all the Hillslope Study locations.

Water underneath the ground is important and EGLE staff use GIS to help protect it. Use the "Swipe" tool here to compare and contrast two maps. On the left is regular aerial imagery. On the right you can see even more about the landscape using geospatial analyses! This additional information is important for staff to know about when protecting Michigan's groundwater.This example is embedded into the MI EnviroLearning Hub. If you have any questions about the content within this story map, please contact EGLE-Maps@Michigan.gov.

This submission contains a number of maps and shapefiles related to the Utah FORGE site. Examples include geologic maps (several variations) and GIS data for the Utah FORGE site outline. All data are georeferenced to UTM, zone 12N, NAD 83, NAVD 88.

Interagency Wildland Fire Perimeter History (IFPH) Overview This national fire history perimeter data layer of conglomerated agency perimeters was developed in support of the WFDSS application and wildfire decision support. The layer encompasses the fire perimeter datasets of the USDA Forest Service, US Department of Interior Bureau of Land Management, Bureau of Indian Affairs, Fish and Wildlife Service, and National Park Service, the Alaska Interagency Fire Center, CalFire, and WFIGS History. Perimeters are included thru the 2024 fire season. Requirements for fire perimeter inclusion, such as minimum acreage requirements, are set by the contributing agencies. WFIGS, NPS and CALFIRE data now include Prescribed Burns. Data InputSeveral data sources were used in the development of this layer, links are provided where possible below. In addition, many agencies are now using WFIGS as their authoritative source, beginning in mid-2020.Alaska fire history (WFIGS pull for updates began 2022)USDA FS Regional Fire History Data (WFIGS pull for updates began 2024)BLM Fire Planning and Fuels (WFIGS pull for updates began 2020)National Park Service - Includes Prescribed Burns (WFIGS pull for updates began 2020)Fish and Wildlife Service (WFIGS pull for updates began 2024)Bureau of Indian Affairs (Incomplete, 2017-2018 from BIA, WFIGS pull for updates began 2020)CalFire FRAS - Includes Prescribed Burns (CALFIRE only source, non-fed fires)WFIGS - updates included since mid-2020, unless otherwise noted Data LimitationsFire perimeter data are often collected at the local level, and fire management agencies have differing guidelines for submitting fire perimeter data. Often data are collected by agencies only once annually. If you do not see your fire perimeters in this layer, they were not present in the sources used to create the layer at the time the data were submitted. A companion service for perimeters entered into the WFDSS application is also available, if a perimeter is found in the WFDSS service that is missing in this Agency Authoritative service or a perimeter is missing in both services, please contact the appropriate agency Fire GIS Contact listed in the table below.Attributes This dataset implements the NWCG Wildland Fire Perimeters (polygon) data standard.https://www.nwcg.gov/sites/default/files/stds/WildlandFirePerimeters_definition.pdfIRWINID - Primary key for linking to the IRWIN Incident dataset. The origin of this GUID is the wildland fire locations point data layer maintained by IrWIN. (This unique identifier may NOT replace the GeometryID core attribute) FORID - Unique identifier assigned to each incident record in the Fire Occurence Data Records system. (This unique identifier may NOT replace the GeometryID core attribute) INCIDENT - The name assigned to an incident; assigned by responsible land management unit. (IRWIN required). Officially recorded name. FIRE_YEAR (Alias) - Calendar year in which the fire started. Example: 2013. Value is of type integer (FIRE_YEAR_INT). AGENCY - Agency assigned for this fire - should be based on jurisdiction at origin. SOURCE - System/agency source of record from which the perimeter came. DATE_CUR - The last edit, update, or other valid date of this GIS Record. Example: mm/dd/yyyy. MAP_METHOD - Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality.GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; Digitized-Topo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; Other GIS_ACRES - GIS calculated acres within the fire perimeter. Not adjusted for unburned areas within the fire perimeter. Total should include 1 decimal place. (ArcGIS: Precision=10; Scale=1). Example: 23.9 UNQE_FIRE_ - Unique fire identifier is the Year-Unit Identifier-Local Incident Identifier (yyyy-SSXXX-xxxxxx). SS = State Code or International Code, XXX or XXXX = A code assigned to an organizational unit, xxxxx = Alphanumeric with hyphens or periods. The unit identifier portion corresponds to the POINT OF ORIGIN RESPONSIBLE AGENCY UNIT IDENTIFIER (POOResonsibleUnit) from the responsible unit’s corresponding fire report. Example: 2013-CORMP-000001 LOCAL_NUM - Local incident identifier (dispatch number). A number or code that uniquely identifies an incident for a particular local fire management organization within a particular calendar year. Field is string to allow for leading zeros when the local incident identifier is less than 6 characters. (IRWIN required). Example: 123456. UNIT_ID - NWCG Unit Identifier of landowner/jurisdictional agency unit at the point of origin of a fire. (NFIRS ID should be used only when no NWCG Unit Identifier exists). Example: CORMP COMMENTS - Additional information describing the feature. Free Text.FEATURE_CA - Type of wildland fire polygon: Wildfire (represents final fire perimeter or last daily fire perimeter available) or Prescribed Fire or Unknown GEO_ID - Primary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature. Globally Unique Identifier (GUID). Cross-Walk from sources (GeoID) and other processing notesAK: GEOID = OBJECT ID of provided file geodatabase (4,781 Records thru 2021), other federal sources for AK data removed. No RX data included.CA: GEOID = OBJECT ID of downloaded file geodatabase (8,480 Records, federal fires removed, includes RX. Significant cleanup occurred between 2023 and 2024 data pulls resulting in fewer perimeters).FWS: GEOID = OBJECTID of service download combined history 2005-2021 (2,959 Records), includes RX.BIA: GEOID = "FireID" 2017/2018 data (382 records). No RX data included.NPS: GEOID = EVENT ID 15,237 records, includes RX. In 2024/2023 dataset was reduced by combining singlepart to multpart based on valid Irwin, FORID or Unique Fire IDs. RX data included.BLM: GEOID = GUID from BLM FPER (23,730 features). No RX data included.USFS: GEOID=GLOBALID from EDW records (48,569 features), includes RXWFIGS: GEOID=polySourceGlobalID (9724 records added or replaced agency record since mid-2020)Attempts to repair Unique Fire ID not made. Attempts to repair dates not made. Verified all IrWIN IDs and FODRIDs present via joins and cross checks to the respective dataset. Stripped leading and trailing spaces, fixed empty values to

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)

These data show the locations for sweep samples of grasshoppers across Konza Prairie (GIS400). These data may be used in conjunction with the sweep sample datasets (CGR0X).

The GIS component of Virginia's NG9-1-1 deployments is moving in waves, with new groups of localities starting the onboarding process every three months. Well into our third wave, new resources and recommendations on GIS related topics are now available on the VGIN 9-1-1 & GIS page. This is available as a large combined document, Next Generation 9-1-1 GIS Recommendations. However since some information is more useful for localities earlier in their project and other information more useful later, we are also posting each section as its own document. The parts include:1) Boundaries in Next Generation 9-1-12) Preparing Your Data and Provisioning into EGDMS3) Outsourced GIS Data Maintenance and NG9-1-14) Emergency Service Boundary Layers5) Attribution6) What's NextSome of the parts are technical that reflect choices and options to make with boundary lines, or specific recommendations on how to create globally unique IDs or format display name fields. In these areas, we hope to share recommendations from Intrado and point users to specific portions of the NENA GIS Data Model Standard for examples. The current version is 1.1, published February 2021.