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

This data set contains small-scale base GIS data layers compiled by the National Park Service Servicewide Inventory and Monitoring Program and Water Resources Division for use in a Baseline Water Quality Data Inventory and Analysis Report that was prepared for the park. The report presents the results of surface water quality data retrievals for the park from six of the United States Environmental Protection Agency's (EPA) national databases: (1) Storage and Retrieval (STORET) water quality database management system; (2) River Reach File (RF3) Hydrography; (3) Industrial Facilities Discharges; (4) Drinking Water Supplies; (5) Water Gages; and (6) Water Impoundments. The small-scale GIS data layers were used to prepare the maps included in the report that depict the locations of water quality monitoring stations, industrial discharges, drinking intakes, water gages, and water impoundments. The data layers included in the maps (and this dataset) vary depending on availability, but generally include roads, hydrography, political boundaries, USGS 7.5' minute quadrangle outlines, hydrologic units, trails, and others as appropriate. The scales of each layer vary depending on data source but are generally 1:100,000.

Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - Lab Manual. CRC Press. https://doi.org/10.1201/9781003304357 KNIME Hub Dataset and Workflow for Computational Methods and GIS Applications in Social Science-Lab Manual Update Log If Python package not found in Package Management, use ArcGIS Pro's Python Command Prompt to install them, e.g., conda install -c conda-forge python-igraph leidenalg NetworkCommDetPro in CMGIS-V3-Tools was updated on July 10,2024 Add spatial adjacency table into Florida on June 29,2024 The dataset and tool for ABM Crime Simulation were updated on August 3, 2023, The toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv ...

The research focus in the field of remotely sensed imagery has shifted from collection and warehousing of data ' tasks for which a mature technology already exists, to auto-extraction of information and knowledge discovery from this valuable resource ' tasks for which technology is still under active development. In particular, intelligent algorithms for analysis of very large rasters, either high resolutions images or medium resolution global datasets, that are becoming more and more prevalent, are lacking. We propose to develop the Geospatial Pattern Analysis Toolbox (GeoPAT) a computationally efficient, scalable, and robust suite of algorithms that supports GIS processes such as segmentation, unsupervised/supervised classification of segments, query and retrieval, and change detection in giga-pixel and larger rasters. At the core of the technology that underpins GeoPAT is the novel concept of pattern-based image analysis. Unlike pixel-based or object-based (OBIA) image analysis, GeoPAT partitions an image into overlapping square scenes containing 1,000'100,000 pixels and performs further processing on those scenes using pattern signature and pattern similarity ' concepts first developed in the field of Content-Based Image Retrieval. This fusion of methods from two different areas of research results in orders of magnitude performance boost in application to very large images without sacrificing quality of the output.

GeoPAT v.1.0 already exists as the GRASS GIS add-on that has been developed and tested on medium resolution continental-scale datasets including the National Land Cover Dataset and the National Elevation Dataset. Proposed project will develop GeoPAT v.2.0 ' much improved and extended version of the present software. We estimate an overall entry TRL for GeoPAT v.1.0 to be 3-4 and the planned exit TRL for GeoPAT v.2.0 to be 5-6. Moreover, several new important functionalities will be added. Proposed improvements includes conversion of GeoPAT from being the GRASS add-on to stand-alone software capable of being integrated with other systems, full implementation of web-based interface, writing new modules to extent it applicability to high resolution images/rasters and medium resolution climate data, extension to spatio-temporal domain, enabling hierarchical search and segmentation, development of improved pattern signature and their similarity measures, parallelization of the code, implementation of divide and conquer strategy to speed up selected modules.

The proposed technology will contribute to a wide range of Earth Science investigations and missions through enabling extraction of information from diverse types of very large datasets. Analyzing the entire dataset without the need of sub-dividing it due to software limitations offers important advantage of uniformity and consistency. We propose to demonstrate the utilization of GeoPAT technology on two specific applications. The first application is a web-based, real time, visual search engine for local physiography utilizing query-by-example on the entire, global-extent SRTM 90 m resolution dataset. User selects region where process of interest is known to occur and the search engine identifies other areas around the world with similar physiographic character and thus potential for similar process. The second application is monitoring urban areas in their entirety at the high resolution including mapping of impervious surface and identifying settlements for improved disaggregation of census data.

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GIS Market Size 2025-2029

The GIS market size is forecast to increase by USD 24.07 billion, at a CAGR of 20.3% between 2024 and 2029.

The Global Geographic Information System (GIS) market is experiencing significant growth, driven by the increasing integration of Building Information Modeling (BIM) and GIS technologies. This convergence enables more effective spatial analysis and decision-making in various industries, particularly in soil and water management. However, the market faces challenges, including the lack of comprehensive planning and preparation leading to implementation failures of GIS solutions. Companies must address these challenges by investing in thorough project planning and collaboration between GIS and BIM teams to ensure successful implementation and maximize the potential benefits of these advanced technologies.
By focusing on strategic planning and effective implementation, organizations can capitalize on the opportunities presented by the growing adoption of GIS and BIM technologies, ultimately driving operational efficiency and innovation.

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

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The global Geographic Information Systems (GIS) market continues to evolve, driven by the increasing demand for advanced spatial data analysis and management solutions. GIS technology is finding applications across various sectors, including natural resource management, urban planning, and infrastructure management. The integration of Bing Maps, terrain analysis, vector data, Lidar data, and Geographic Information Systems enables precise spatial data analysis and modeling. Hydrological modeling, spatial statistics, spatial indexing, and route optimization are essential components of GIS, providing valuable insights for sectors such as public safety, transportation planning, and precision agriculture. Location-based services and data visualization further enhance the utility of GIS, enabling real-time mapping and spatial analysis.

The ongoing development of OGC standards, spatial data infrastructure, and mapping APIs continues to expand the capabilities of GIS, making it an indispensable tool for managing and analyzing geospatial data. The continuous unfolding of market activities and evolving patterns in the market reflect the dynamic nature of this technology and its applications.

How is this GIS Industry segmented?

The GIS industry 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.

Product

  Software
  Data
  Services


Type

  Telematics and navigation
  Mapping
  Surveying
  Location-based services


Device

  Desktop
  Mobile


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa

    UAE


  APAC

    China
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Product Insights

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

The Global Geographic Information System (GIS) market encompasses a range of applications and technologies, including raster data, urban planning, geospatial data, geocoding APIs, GIS services, routing APIs, aerial photography, satellite imagery, GIS software, geospatial analytics, public safety, field data collection, transportation planning, precision agriculture, OGC standards, location intelligence, remote sensing, asset management, network analysis, spatial analysis, infrastructure management, spatial data standards, disaster management, environmental monitoring, spatial modeling, coordinate systems, spatial overlay, real-time mapping, mapping APIs, spatial join, mapping applications, smart cities, spatial data infrastructure, map projections, spatial databases, natural resource management, Bing Maps, terrain analysis, vector data, Lidar data, and geographic information systems.

The software segment includes desktop, mobile, cloud, and server solutions. Open-source GIS software, with its industry-specific offerings, poses a challenge to the market, while the adoption of cloud-based GIS software represents an emerging trend. However, the lack of standardization and interoperability issues hinder the widespread adoption of cloud-based solutions. Applications in sectors like public safety, transportation planning, and precision agriculture are driving market growth. Additionally, advancements in technologies like remote sensing, spatial modeling, and real-time mapping are expanding the market's scope.

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The Software segment was valued at USD 5.06 billion in 2019

NOTE: This file includes data for all 5 boroughs and has a size of 4.60 GB. Individual borough files are available for download from the metadata attachments section. Citywide Geographic Information System (GIS) land cover layer that displays land cover classification, plus pervious and impervious area and percentage at the parcel level, separated into 5 geodatabases, one per borough. DEP hosted a webinar on this study on June 23, 2020. A recording of the webinar, plus a PDF of the webinar presentation, accompany this dataset and are available for download. Please direct questions and comments to DEP at imperviousmap@dep.nyc.gov. This citywide parcel-level impervious area GIS layer was developed by the City of New York to support stormwater-related planning, and is provided solely for informational purposes. The accuracy of the data should be independently verified for any other purpose. The City disclaims any liability for errors and makes no warranties express or implied, including, but not limited to, implied warranties of merchantability and fitness for a particular purpose as to the quality, content, accuracy or completeness of the information, text graphics, links and other items contained in this GIS layer.

The construction of this data model was adapted from the Telvent Miner & Miner ArcFM MultiSpeak data model to provide interface functionality with Milsoft Utility Solutions WindMil engineering analysis program. Database adaptations, GPS data collection, and all subsequent GIS processes were performed by Southern Geospatial Services for the Town of Apex Electric Utilities Division in accordance to the agreement set forth in the document "Town of Apex Electric Utilities GIS/GPS Project Proposal" dated March 10, 2008. Southern Geospatial Services disclaims all warranties with respect to data contained herein. Questions regarding data quality and accuracy should be directed to persons knowledgeable with the forementioned agreement.The data in this GIS with creation dates between March of 2008 and April of 2024 were generated by Southern Geospatial Services, PLLC (SGS). The original inventory was performed under the above detailed agreement with the Town of Apex (TOA). Following the original inventory, SGS performed maintenance projects to incorporate infrastructure expansion and modification into the GIS via annual service agreements with TOA. These maintenances continued through April of 2024.At the request of TOA, TOA initiated in house maintenance of the GIS following delivery of the final SGS maintenance project in April of 2024. GIS data created or modified after April of 2024 are not the product of SGS.With respect to SGS generated GIS data that are point features:GPS data collected after January 1, 2013 were surveyed using mapping grade or survey grade GPS equipment with real time differential correction undertaken via the NC Geodetic Surveys Real Time Network (VRS). GPS data collected prior to January 1, 2013 were surveyed using mapping grade GPS equipment without the use of VRS, with differential correction performed via post processing.With respect to SGS generated GIS data that are line features:Line data in the GIS for overhead conductors were digitized as straight lines between surveyed poles. Line data in the GIS for underground conductors were digitized between surveyed at grade electric utility equipment. The configurations and positions of the underground conductors are based on TOA provided plans. The underground conductors are diagrammatic and cannot be relied upon for the determination of the actual physical locations of underground conductors in the field.The Service Locations feature class was created by Southern Geospatial Services (SGS) from a shapefile of customer service locations generated by dataVoice International (DV) as part of their agreement with the Town of Apex (TOA) regarding the development and implemention of an Outage Management System (OMS).Point features in this feature class represent service locations (consumers of TOA electric services) by uniquely identifying the features with the same unique identifier as generated for a given service location in the TOA Customer Information System (CIS). This is also the mechanism by which the features are tied to the OMS. Features are physically located in the GIS based on CIS address in comparison to address information found in Wake County GIS property data (parcel data). Features are tied to the GIS electric connectivity model by identifying the parent feature (Upline Element) as the transformer that feeds a given service location.SGS was provided a shapefile of 17992 features from DV. Error potentially exists in this DV generated data for the service location features in terms of their assigned physical location, phase, and parent element.Regarding the physical location of the features, SGS had no part in physically locating the 17992 features as provided by DV and cannot ascertain the accuracy of the locations of the features without undertaking an analysis designed to verify or correct for error if it exists. SGS constructed the feature class and loaded the shapefile objects into the feature class and thus the features exist in the DV derived location. SGS understands that DV situated the features based on the address as found in the CIS. No features were verified as to the accuracy of their physical location when the data were originally loaded. It is the assumption of SGS that the locations of the vast majority of the service location features as provided by DV are in fact correct.SGS understands that as a general rule that DV situated residential features (individually or grouped) in the center of a parcel. SGS understands that for areas where multiple features may exist in a given parcel (such as commercial properties and mobile home parks) that DV situated features as either grouped in the center of the parcel or situated over buildings, structures, or other features identifiable in air photos. It appears that some features are also grouped in roads or other non addressed locations, likely near areas where they should physically be located, but that these features were not located in a final manner and are either grouped or strung out in a row in the general area of where DV may have expected they should exist.Regarding the parent and phase of the features, the potential for error is due to the "first order approximation" protocol employed by DV for assigning the attributes. With the features located as detailed above, SGS understands that DV identified the transformer closest to the service location (straight line distance) as its parent. Phase was assigned to the service location feature based on the phase of the parent transformer. SGS expects that this protocol correctly assigned parent (and phase) to a significant portion of the features, however this protocol will also obviously incorretly assign parent in many instances.To accurately identify parent for all 17992 service locations would require a significant GIS and field based project. SGS is willing to undertake a project of this magnitude at the discretion of TOA. In the meantime, SGS is maintaining (editing and adding to) this feature class as part of the ongoing GIS maintenance agreement that is in place between TOA and SGS. In lieu of a project designed to quality assess and correct for the data provided by DV, SGS will verify the locations of the features at the request of TOA via comparison of the unique identifier for a service location to the CIS address and Wake County parcel data address as issues arise with the OMS if SGS is directed to focus on select areas for verification by TOA. Additionally, as SGS adds features to this feature class, if error related to the phase and parent of an adjacent feature is uncovered during a maintenance, it will be corrected for as part of that maintenance.With respect to the additon of features moving forward, TOA will provide SGS with an export of CIS records for each SGS maintenance, SGS will tie new accounts to a physical location based on address, SGS will create a feature for the CIS account record in this feature class at the center of a parcel for a residential address or at the center of a parcel or over the correct (or approximately correct) location as determined via air photos or via TOA plans for commercial or other relevant areas, SGS will identify the parent of the service location as the actual transformer that feeds the service location, and SGS will identify the phase of the service address as the phase of it's parent.Service locations with an ObjectID of 1 through 17992 were originally physically located and attributed by DV.Service locations with an ObjectID of 17993 or higher were originally physically located and attributed by SGS.DV originated data are provided the Creation User attribute of DV, however if SGS has edited or verified any aspect of the feature, this attribute will be changed to SGS and a comment related to the edits will be provided in the SGS Edits Comments data field. SGS originated features will be provided the Creation User attribute of SGS. Reference the SGS Edits Comments attribute field Metadata for further information.

GIS Data Management Market Outlook

The global GIS Data Management market size is projected to grow from USD 12.5 billion in 2023 to USD 25.6 billion by 2032, exhibiting a CAGR of 8.4% during the forecast period. This impressive growth is driven by the increasing adoption of geographic information systems (GIS) across various sectors such as urban planning, disaster management, and agriculture. The rising need for effective data management systems to handle the vast amounts of spatial data generated daily also significantly contributes to the market's expansion.

One of the primary growth factors for the GIS Data Management market is the burgeoning demand for spatial data analytics. Businesses and governments are increasingly leveraging GIS data to make informed decisions and strategize operational efficiencies. With the rapid urbanization and industrialization worldwide, there's an unprecedented need to manage and analyze geographic data to plan infrastructure, monitor environmental changes, and optimize resource allocation. Consequently, the integration of GIS with advanced technologies like artificial intelligence and machine learning is becoming more prominent, further fueling market growth.

Another significant factor propelling the market is the advancement in GIS technology itself. The development of sophisticated software and hardware solutions for GIS data management is making it easier for organizations to capture, store, analyze, and visualize geographic data. Innovations such as 3D GIS, real-time data processing, and cloud-based GIS solutions are transforming the landscape of geographic data management. These advancements are not only enhancing the capabilities of GIS systems but also making them more accessible to a broader range of users, from small enterprises to large governmental agencies.

The growing implementation of GIS in disaster management and emergency response activities is also a critical factor driving market growth. GIS systems play a crucial role in disaster preparedness, response, and recovery by providing accurate and timely geographic data. This data helps in assessing risks, coordinating response activities, and planning resource deployment. With the increasing frequency and intensity of natural disasters, the reliance on GIS data management systems is expected to grow, resulting in higher demand for GIS solutions across the globe.

Geospatial Solutions are becoming increasingly integral to the GIS Data Management landscape, offering enhanced capabilities for spatial data analysis and visualization. These solutions provide a comprehensive framework for integrating various data sources, enabling users to gain deeper insights into geographic patterns and trends. As organizations strive to optimize their operations and decision-making processes, the demand for robust geospatial solutions is on the rise. These solutions not only facilitate the efficient management of spatial data but also support advanced analytics and real-time data processing. By leveraging geospatial solutions, businesses and governments can improve their strategic planning, resource allocation, and environmental monitoring efforts, thereby driving the overall growth of the GIS Data Management market.

Regionally, North America holds a significant share of the GIS Data Management market, driven by high technology adoption rates and substantial investments in GIS technologies by government and private sectors. However, Asia Pacific is anticipated to witness the highest growth rate during the forecast period. The rapid urbanization, economic development, and increasing adoption of advanced technologies in countries like China and India are major contributors to this growth. Governments in this region are also focusing on smart city projects and infrastructure development, which further boosts the demand for GIS data management solutions.

Component Analysis

The GIS Data Management market is segmented by component into software, hardware, and services. The software segment is the largest and fastest-growing segment, driven by the continuous advancements in GIS software capabilities. GIS software applications enable users to analyze spatial data, create maps, and manage geographic information efficiently. The integration of GIS software with other enterprise systems and the development of user-friendly interfaces are key factors propelling the growth of this segment. Furthermore, the rise of mobile GIS applications, which allow field data collectio

The input file contains supply data (based on data from geoportal of piedmont and OSM data) and flood map (based on data from geoportal of piedmont) for the Alessandria area in Italy, detailing both basic and disrupted flood scenarios to be analyzed in GIS software. It includes information on closed bridges during flood events. The output file presents the analysis results for both the basic and disrupted scenarios.

The U.S. Geological Survey, in collaboration with the National Oceanic and Atmospheric Administration's (NOAA) National Marine Sanctuary Program, conducted seabed mapping and related research in the Stellwagen Bank National Marine Sanctuary region from 1993 to 2004. The mapped area is approximately 3,700 square km (1,100 square nm) in size and was subdivided into 18 quadrangles. Several series of sea floor maps of the region based on multibeam sonar surveys have been published. In addition, 2,628 seabed sediment samples were collected and analyzed and approximately 10,600 still photographs of the seabed were acquired during the project. These data provide the basis for scientists, policymakers, and managers for understanding the complex ecosystem of the sanctuary region and for monitoring and managing its economic and natural resources.

This data set contains small-scale base GIS data layers compiled by the National Park Service Servicewide Inventory and Monitoring Program and Water Resources Division for use in a Baseline Water Quality Data Inventory and Analysis Report that was prepared for the park. The report presents the results of surface water quality data retrievals for the park from six of the United States Environmental Protection Agency's (EPA) national databases: (1) Storage and Retrieval (STORET) water quality database management system; (2) River Reach File (RF3) Hydrography; (3) Industrial Facilities Discharges; (4) Drinking Water Supplies; (5) Water Gages; and (6) Water Impoundments. The small-scale GIS data layers were used to prepare the maps included in the report that depict the locations of water quality monitoring stations, industrial discharges, drinking intakes, water gages, and water impoundments. The data layers included in the maps (and this dataset) vary depending on availability, but generally include roads, hydrography, political boundaries, USGS 7.5' minute quadrangle outlines, hydrologic units, trails, and others as appropriate. The scales of each layer vary depending on data source but are generally 1:100,000.

This data set contains small-scale base GIS data layers compiled by the National Park Service Servicewide Inventory and Monitoring Program and Water Resources Division for use in a Baseline Water Quality Data Inventory and Analysis Report that was prepared for the park. The report presents the results of surface water quality data retrievals for the park from six of the United States Environmental Protection Agency's (EPA) national databases: (1) Storage and Retrieval (STORET) water quality database management system; (2) River Reach File (RF3) Hydrography; (3) Industrial Facilities Discharges; (4) Drinking Water Supplies; (5) Water Gages; and (6) Water Impoundments.

The small-scale GIS data layers were used to prepare the maps included in the report that depict the locations of water quality monitoring stations, industrial discharges, drinking intakes, water gages, and water impoundments. The data layers included in the maps (and this dataset) vary depending on availability, but generally include roads, hydrography, political boundaries, USGS 7.5' minute quadrangle outlines, hydrologic units, trails, and others as appropriate. The scales of each layer vary depending on data source but are generally 1:100,000.

This dataset contains model-based ZIP Code Tabulation Area (ZCTA) level estimates in GIS-friendly format. PLACES covers the entire United States—50 states and the District of Columbia—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2022 or 2021 data, Census Bureau 2020 population counts, and American Community Survey (ACS) 2018–2022 estimates. The 2024 release uses 2022 BRFSS data for 36 measures and 2021 BRFSS data for 4 measures (high blood pressure, high cholesterol, cholesterol screening, and taking medicine for high blood pressure control among those with high blood pressure) that the survey collects data on every other year. These data can be joined with the Census 2021 ZCTA boundary file in a GIS system to produce maps for 40 measures at the ZCTA level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=3b7221d4e47740cab9235b839fa55cd7

Over the past several decades, significant improvements in processing capacity and GIS software sophistication have encouraged the development and use of computer based models of archaeological sensitivity to augment traditional research approaches and field investigations. The Vermont Archaeological Sensitivity Model (VTASM), a GIS-based framework for simulating archaeological sensitivity statewide, is a recent example of this trend.

ESRI grid showing homogeneity, heterogeneity, eddie activity and frontal activity. This grid has been produced by CSIRO for the National Oceans Office, as part of an ongoing commitment to natural resource planning and management through the 'National Marine Bioregionalisation' project.

Japanese Knotweed s.l. taxa are amongst the most aggressive vascular plant Invasive Alien Species (IAS) in the world. These taxa form dense, suppressive monocultures and are persistent, pervasive invaders throughout the more economically developed countries (MEDCs) of the world. The current paper utilises the Object-Based Image Analysis (OBIA) approach of Definiens Imaging Developer software, in combination with very high spatial resolution (VHSR) colour infra-red (CIR) and visible-band (RGB) aerial photography in order to detect Japanese Knotweed s.l. taxa in Wales (UK). An algorithm was created using Definiens in order to detect these taxa, using variables found to effectively distinguish them from landscape and vegetation features. The results of the detection algorithm were accurate, as confirmed by field validation and desk-based studies. Further, these results may be incorporated into Geographical Information Systems (GIS) research as they are readily transferable as vector polygons (shapefiles). The successful detection results developed within the Definiens software should enable greater management and control efficacy. Further to this, the basic principles of the detection process could enable detection of these taxa worldwide, given the (relatively) limited technical requirements necessary to conduct further analyses.

The global Geographic Information System (GIS) Tools market, valued at $2979.7 million in 2025, is projected to experience robust growth, driven by increasing adoption across various sectors. The Compound Annual Growth Rate (CAGR) of 5.5% from 2025 to 2033 indicates a substantial market expansion. Key drivers include the rising need for spatial data analysis in urban planning, environmental management, and infrastructure development. The increasing availability of cloud-based GIS solutions, offering enhanced scalability and accessibility, further fuels market growth. Furthermore, advancements in data visualization and analytics capabilities within GIS tools are attracting a wider range of users. Segmentation reveals a significant market share held by large enterprises, reflecting the considerable resources and data management needs of these organizations. Cloud-based solutions dominate the market, reflecting the shift towards flexible and cost-effective technology deployments. While the market faces some restraints like the high initial investment costs associated with implementing GIS systems and the requirement for skilled personnel, the overall market outlook remains positive due to the expanding applications and technological advancements in the field. North America currently holds a significant market share, driven by high technological adoption rates and the presence of major GIS solution providers. However, the Asia-Pacific region is expected to witness substantial growth in the coming years due to increasing urbanization and infrastructure development initiatives in rapidly growing economies like China and India. The market's growth trajectory is also influenced by the evolving needs of various industry verticals. The application of GIS tools in precision agriculture, disaster management, and supply chain optimization is creating new opportunities for market expansion. The integration of GIS with other technologies, such as IoT and AI, is leading to the development of more sophisticated and insightful applications. This convergence is enabling real-time data analysis and predictive modeling, offering businesses and governments valuable insights for informed decision-making. The competitive landscape is characterized by a mix of established players and emerging startups, driving innovation and fostering competition, ultimately benefiting end-users with a diverse range of solutions and pricing models. The ongoing development of open-source GIS software is also creating a more accessible and collaborative environment within the ecosystem.

This dataset is GIS data representing waypoints and routes in the Mawson area, Antarctica. It includes routes in the Framnes Mountains and routes west and east of Mawson along the Mawson Coast.

The waypoint and route data held by the Australian Antarctic Data Centre are routinely updated using feedback provided by the Australian Antarctic Division's Field Training Officers and Station Leaders with approval for changes given by the Australian Antarctic Division's Field Support Coordinator.

This dataset identifies the project boundaries for registered Emissions Reduction Fund (ERF) area based projects. Area based projects are generally savanna burning and sequestration activities. These ERF Projects are registered across a declared project area identified by project proponents at the time of registration. These project areas generally encompass the entire cadastral boundaries for the properties for which the participants intend to conduct their project activities and for which they hold the legal rights. For sequestration projects the project area does not generally represent the actual extent of a project activity which is generally a subset of the project area. These subset areas are known as Carbon Estimation Areas (CEA) which are defined by rules set out in the individual ERF methods. A project can contain one or many CEAs. The dataset includes basic attribution including: Scheme Participant; Project Name; Project ID; Method; Method Type; Project Description; Date Project Registered; Project location (State); Project location (Postcode); Permanence Period; and, Project Status (Active or Revoked) The Clean Energy Regulator publishes and maintains a project register which contains further details about projects registered under the Emissions Reduction Fund. The project register is published on the Clean Energy Regulator website at http://www.cleanenergyregulator.gov.au/DocumentAssets/Pages/Emissions-Reduction-Fund-Register.aspx and is the point of truth for information about ERF projects. The project register contains attributes not in the spatial dataset, such as, the number of Australian carbon credit units (ACCUs) issued, whether any units have been relinquished, or if that land has a carbon maintenance obligation in place. However, the Project Id attribute (PROJ_ID) can be used to link the mapping data with the project register if analysis of those attributes is required. Notes: 1. Users should be aware that the project register is updated on a weekly basis. 2. The dataset does not contain the boundaries of ten projects which have had their location suppressed or partially suppressed. 3. The dataset contains revoked projects. These are identified as being revoked in the status column

The global field data collection software market is experiencing robust growth, driven by the increasing need for efficient data management across diverse sectors. The market's expansion is fueled by several key factors: the rising adoption of mobile technologies and cloud-based solutions for improved data accessibility and real-time analysis; the increasing demand for automation in data collection processes to reduce manual errors and improve productivity; and the growing emphasis on data-driven decision-making across industries such as construction, environmental monitoring, and oil and gas. This shift towards digitalization is transforming traditional fieldwork practices, leading to enhanced accuracy, reduced operational costs, and improved overall efficiency. We estimate the market size in 2025 to be approximately $2.5 billion, with a Compound Annual Growth Rate (CAGR) of 15% projected through 2033. This growth is expected to be further fueled by advancements in AI and machine learning, which enhance data analysis capabilities and provide valuable insights from collected field data. While challenges remain, including concerns regarding data security and integration with existing systems, the overall market outlook remains positive, with significant opportunities for software vendors and service providers. The market segmentation reveals significant opportunities across various applications and deployment types. The cloud-based segment is experiencing the fastest growth, driven by its scalability, accessibility, and cost-effectiveness. The construction, environmental monitoring, and oil and gas sectors are major consumers of field data collection software, demonstrating a strong demand for solutions that streamline workflows, enhance safety protocols, and optimize resource allocation. Geographic analysis suggests North America and Europe are currently the largest markets, although the Asia-Pacific region is expected to witness substantial growth in the coming years due to increasing infrastructure development and industrialization. The competitive landscape is dynamic, with both established players and emerging startups offering specialized solutions. The success of these companies hinges on their ability to provide robust, user-friendly software with strong integration capabilities and advanced analytical features.