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Geographic Information System Analytics Market Size 2024-2028

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

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

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

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

How is this Geographic Information System Analytics Industry segmented?

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

End-user

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


Components

  Software
  Services


Deployment Modes

  On-Premises
  Cloud-Based


Applications

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


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    South Korea


  Middle East and Africa

    UAE


  South America

    Brazil


  Rest of World

By End-user Insights

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

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

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

Geospatial Analytics Software Market Outlook

The global geospatial analytics software market size is projected to grow from USD 50.1 billion in 2023 to USD 114.5 billion by 2032, reflecting a robust compound annual growth rate (CAGR) of 9.5%. This remarkable growth is largely driven by the increasing adoption of geospatial technologies across various sectors, including urban planning, agriculture, transportation, and disaster management. The surge in the utilization of geospatial data for strategic decision-making, coupled with advancements in technology such as artificial intelligence (AI) and big data analytics, plays a pivotal role in propelling market growth.

One of the key growth factors of the geospatial analytics software market is the rapid digital transformation occurring globally. Governments and enterprises are increasingly recognizing the value of geospatial data in enhancing operational efficiency and strategic planning. The rise in smart city initiatives across the world has bolstered the demand for geospatial analytics, as cities leverage these technologies to optimize infrastructure, manage resources, and improve public services. Additionally, the integration of AI and machine learning with geospatial analytics has enhanced the accuracy and predictive capabilities of these systems, further driving their adoption.

Another significant driver is the growing need for disaster management and climate change adaptation. As the frequency and intensity of natural disasters increase due to climate change, there is a heightened demand for geospatial analytics to predict, monitor, and mitigate the impact of such events. Geospatial software aids in mapping hazard zones, planning evacuation routes, and assessing damage post-disaster. This capability is crucial for governments and organizations involved in disaster management and mitigation, thereby boosting the market growth.

The transportation and logistics sector is also a major contributor to the growth of the geospatial analytics software market. The advent of autonomous vehicles and the continuous evolution of logistics and supply chain management have heightened the need for precise geospatial data. Geospatial analytics enables real-time tracking, route optimization, and efficient fleet management, which are critical for the smooth operation of transportation systems. This trend is expected to continue, driving the demand for geospatial analytics solutions in transportation and logistics.

On a regional level, North America is anticipated to dominate the geospatial analytics software market, driven by technological advancements and substantial investments in geospatial technologies. The presence of major market players and the high adoption rate of advanced technologies in sectors such as defense, agriculture, and urban planning contribute to this dominance. However, the Asia Pacific region is expected to witness the highest growth rate, fueled by rapid urbanization, government initiatives for smart cities, and increasing investments in infrastructure development.

GIS Software plays a crucial role in the geospatial analytics software market, offering powerful tools for data visualization, spatial analysis, and geographic mapping. As organizations across various sectors increasingly rely on geospatial data for strategic decision-making, GIS Software provides the necessary infrastructure to manage, analyze, and interpret this data effectively. Its integration with other technologies such as AI and machine learning enhances its capabilities, enabling more accurate predictions and insights. This makes GIS Software an indispensable component for industries like urban planning, agriculture, and transportation, where spatial data is pivotal for optimizing operations and improving outcomes. The growing demand for GIS Software is a testament to its importance in driving the geospatial analytics market forward.

Component Analysis

The geospatial analytics software market is segmented into software and services when considering components. The software segment includes comprehensive solutions that integrate various geospatial data types and provide analytical tools for mapping, visualization, and data processing. This segment is expected to hold the largest market share due to the increasing adoption of these solutions in various industries for efficient data management and decision-making. The continuous advancements in software capabilities, such as the inclusion of AI and machine learning algorithms

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 ...

Database created for replication of GeoStoryTelling. Our life stories evolve in specific and contextualized places. Although our homes may be our primarily shaping environment, our homes are themselves situated in neighborhoods that expose us to the immediate “real world” outside home. Indeed, the places where we are currently experiencing, and have experienced life, play a fundamental role in gaining a deeper and more nuanced understanding of our beliefs, fears, perceptions of the world, and even our prospects of social mobility. Despite the immediate impact of the places where we experience life in reaching a better understanding of our life stories, to date most qualitative and mixed methods researchers forego the analytic and elucidating power that geo-contextualizing our narratives bring to social and health research. From this view then, most research findings and conclusions may have been ignoring the spatial contexts that most likely have shaped the experiences of research participants. The main reason for the underuse of these geo-contextualized stories is the requirement of specialized training in geographical information systems and/or computer and statistical programming along with the absence of cost-free and user-friendly geo-visualization tools that may allow non-GIS experts to benefit from geo-contextualized outputs. To address this gap, we present GeoStoryTelling, an analytic framework and user-friendly, cost-free, multi-platform software that enables researchers to visualize their geo-contextualized data narratives. The use of this software (available in Mac and Windows operative systems) does not require users to learn GIS nor computer programming to obtain state-of-the-art, and visually appealing maps. In addition to providing a toy database to fully replicate the outputs presented, we detail the process that researchers need to follow to build their own databases without the need of specialized external software nor hardware. We show how the resulting HTML outputs are capable of integrating a variety of multi-media inputs (i.e., text, image, videos, sound recordings/music, and hyperlinks to other websites) to provide further context to the geo-located stories we are sharing (example https://cutt.ly/k7X9tfN). Accordingly, the goals of this paper are to describe the components of the methodology, the steps to construct the database, and to provide unrestricted access to the software tool, along with a toy dataset so that researchers may interact first-hand with GeoStoryTelling and fully replicate the outputs discussed herein. Since GeoStoryTelling relied on OpenStreetMap its applications may be used worldwide, thus strengthening its potential reach to the mixed methods and qualitative scientific communities, regardless of location around the world. Keywords: Geographical Information Systems; Interactive Visualizations; Data StoryTelling; Mixed Methods & Qualitative Research Methodologies; Spatial Data Science; Geo-Computation.

In this course, you will explore a variety of open-source technologies for working with geosptial data, performing spatial analysis, and undertaking general data science. The first component of the class focuses on the use of QGIS and associated technologies (GDAL, PROJ, GRASS, SAGA, and Orfeo Toolbox). The second component of the class introduces Python and associated open-source libraries and modules (NumPy, Pandas, Matplotlib, Seaborn, GeoPandas, Rasterio, WhiteboxTools, and Scikit-Learn) used by geospatial scientists and data scientists. We also provide an introduction to Structured Query Language (SQL) for performing table and spatial queries. This course is designed for individuals that have a background in GIS, such as working in the ArcGIS environment, but no prior experience using open-source software and/or coding. You will be asked to work through a series of lecture modules and videos broken into several topic areas, as outlined below. Fourteen assignments and the required data have been provided as hands-on opportunites to work with data and the discussed technologies and methods. If you have any questions or suggestions, feel free to contact us. We hope to continue to update and improve this course. This course was produced by West Virginia View (http://www.wvview.org/) with support from AmericaView (https://americaview.org/). This material is based upon work supported by the U.S. Geological Survey under Grant/Cooperative Agreement No. G18AP00077. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey. After completing this course you will be able to: apply QGIS to visualize, query, and analyze vector and raster spatial data. use available resources to further expand your knowledge of open-source technologies. describe and use a variety of open data formats. code in Python at an intermediate-level. read, summarize, visualize, and analyze data using open Python libraries. create spatial predictive models using Python and associated libraries. use SQL to perform table and spatial queries at an intermediate-level.

Data for maps and figures in "Global Potential for Harvesting Drinking Water from Air using Solar Energy" in Nature.

The Geographic Information System (GIS) Analytics market is experiencing robust growth, projected to reach $15.10 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 12.41% from 2025 to 2033. This expansion is fueled by several key drivers. Increasing adoption of cloud-based GIS solutions enhances accessibility and scalability for diverse industries. The growing need for data-driven decision-making across sectors like retail, real estate, government, and telecommunications is a significant catalyst. Furthermore, advancements in artificial intelligence (AI) and machine learning (ML) integrated with GIS analytics are revolutionizing spatial data analysis, enabling more sophisticated predictive modeling and insightful interpretations. The market's segmentation reflects this broad adoption, with retail and real estate, government and utilities, and telecommunications representing key end-user segments, each leveraging GIS analytics for distinct applications such as location optimization, infrastructure management, and network planning. Competitive pressures are shaping the market landscape, with established players like Esri, Trimble, and Autodesk innovating alongside emerging tech companies focusing on AI and specialized solutions. The North American market currently holds a significant share, driven by early adoption and technological advancements. However, Asia-Pacific is expected to witness substantial growth due to rapid urbanization and increasing investment in infrastructure projects. Market restraints primarily involve the high cost of implementation and maintenance of advanced GIS analytics solutions and the need for skilled professionals to effectively utilize these technologies. However, the overall outlook remains extremely positive, driven by continuous technological innovation and escalating demand across multiple sectors. The future trajectory of the GIS analytics market hinges on several factors. Continued investment in research and development, especially in AI and ML integration, will be crucial for unlocking new possibilities. Furthermore, the simplification of GIS analytics software and the development of user-friendly interfaces will broaden accessibility beyond specialized technical experts. Growing data volumes from various sources (IoT, remote sensing) present both opportunities and challenges; efficient data management and analytics techniques will be paramount. The market's success also depends on addressing cybersecurity concerns related to sensitive geospatial data. Strong partnerships between technology providers and end-users will be vital in optimizing solution implementation and maximizing return on investment. Government initiatives promoting the use of GIS technology for smart city development and infrastructure planning will also play a significant role in market expansion. Overall, the GIS analytics market is poised for sustained growth, driven by technological advancements, increasing data availability, and heightened demand for location-based intelligence across a wide range of industries.

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

Various Geospatial Data and Geographic Information Systems (GIS) presentations, workshops and tutorials. For the live versions of these files and material, please see uoft.me/GIS

The Geographic Information System (GIS) market is experiencing robust growth, projected to reach $5.15 billion in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 20.55% from 2025 to 2033. This expansion is fueled by several key drivers. Increasing urbanization and the need for efficient urban planning are creating significant demand for GIS solutions. Furthermore, advancements in technology, particularly in cloud computing and artificial intelligence (AI), are enhancing GIS capabilities, leading to wider adoption across various sectors. The integration of GIS with other technologies like IoT (Internet of Things) and big data analytics is enabling more sophisticated spatial analysis and decision-making. Industries like transportation, utilities, and agriculture are leveraging GIS for improved asset management, infrastructure planning, and precision farming. The market is segmented by component (software, data, services) and deployment (on-premise, cloud), with the cloud-based deployment model experiencing faster growth due to its scalability and cost-effectiveness. The competitive landscape is characterized by a mix of established players like Esri, Autodesk, and Trimble, and emerging technology providers, creating a dynamic market with significant innovation. However, factors like high initial investment costs and the need for skilled professionals to implement and manage GIS systems pose challenges to market growth. Despite these restraints, the long-term outlook for the GIS market remains positive. The increasing availability of geospatial data, coupled with declining hardware costs and improvements in user interfaces, is making GIS technology more accessible to a wider range of users. The integration of GIS into mobile applications and the rise of location-based services further broaden the market's potential. Government initiatives promoting smart cities and digital infrastructure development are also contributing to market growth. The North American region, particularly the United States, currently holds a significant market share due to early adoption and a robust technology ecosystem. However, other regions, especially in Asia-Pacific and Europe, are experiencing rapid growth, driven by increasing infrastructure investments and the adoption of advanced technologies. Future growth will be influenced by continued technological innovation, the availability of skilled workforce, and government regulations related to geospatial data management.

The geospatial analytics market size is predicted to rise from $93.49 billion in 2024 to $362.45 billion by 2035, growing at a CAGR of 13.1% from 2024 to 2035.

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

Geographic Information System (GIS) Tools Market Outlook

The global Geographic Information System (GIS) tools market size was valued at approximately USD 10.8 billion in 2023, and it is projected to reach USD 21.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.9% from 2024 to 2032. The increasing demand for spatial data analytics and the rising adoption of GIS tools across various industries are significant growth factors propelling the market forward.

One of the primary growth factors for the GIS tools market is the surging demand for spatial data analytics. Spatial data plays a critical role in numerous sectors, including urban planning, environmental monitoring, disaster management, and natural resource exploration. The ability to visualize and analyze spatial data provides organizations with valuable insights, enabling them to make informed decisions. Advances in technology, such as the integration of artificial intelligence (AI) and machine learning (ML) with GIS, are enhancing the capabilities of these tools, further driving market growth.

Moreover, the increasing adoption of GIS tools in the construction and agriculture sectors is fueling market expansion. In construction, GIS tools are used for site selection, route planning, and resource management, enhancing operational efficiency and reducing costs. Similarly, in agriculture, GIS tools aid in precision farming, crop monitoring, and soil analysis, leading to improved crop yields and sustainable farming practices. The ability of GIS tools to provide real-time data and analytics is particularly beneficial in these industries, contributing to their widespread adoption.

The growing importance of location-based services (LBS) in various applications is another key driver for the GIS tools market. LBS are extensively used in navigation, logistics, and transportation, providing real-time location information and route optimization. The proliferation of smartphones and the development of advanced GPS technologies have significantly increased the demand for LBS, thereby boosting the GIS tools market. Additionally, the integration of GIS with other technologies, such as the Internet of Things (IoT) and Big Data, is creating new opportunities for market growth.

Regionally, North America holds a significant share of the GIS tools market, driven by the high adoption of advanced technologies and the presence of major market players. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, owing to increasing investments in infrastructure development, smart city projects, and the growing use of GIS tools in emerging economies such as China and India. Europe, Latin America, and the Middle East & Africa are also expected to contribute to market growth, driven by various government initiatives and increasing awareness of the benefits of GIS tools.

Component Analysis

The GIS tools market can be segmented by component into software, hardware, and services. The software segment is anticipated to dominate the market due to the increasing demand for advanced GIS software solutions that offer enhanced data visualization, spatial analysis, and decision-making capabilities. GIS software encompasses a wide range of applications, including mapping, spatial data analysis, and geospatial data management, making it indispensable for various industries. The continuous development of user-friendly and feature-rich software solutions is expected to drive the growth of this segment.

Hardware components in the GIS tools market include devices such as GPS units, remote sensing devices, and plotting and digitizing tools. The hardware segment is also expected to witness substantial growth, driven by the increasing use of advanced hardware devices that provide accurate and real-time spatial data. The advancements in GPS technology and the development of sophisticated remote sensing devices are key factors contributing to the growth of the hardware segment. Additionally, the integration of hardware with IoT and AI technologies is enhancing the capabilities of GIS tools, further propelling market expansion.

The services segment includes consulting, integration, maintenance, and support services related to GIS tools. This segment is expected to grow significantly, driven by the increasing demand for specialized services that help organizations effectively implement and manage GIS solutions. Consulting services assist organizations in selecting the right GIS tools and optimizing their use, while integration services ensure seamless integr

**THIS NEWER 2016 DIGITAL MAP REPLACES THE OLDER 2014 VERSION OF THE GRI GATE Geomorphological-GIS data. The Unpublished Digital Pre-Hurricane Sandy Geomorphological-GIS Map of the Staten Island Unit, Gateway National Recreation Area, New York is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (stis_geomorphology.gdb), a 10.1 ArcMap (.MXD) map document (stis_geomorphology.mxd), individual 10.1 layer (.LYR) files for each GIS data layer, an ancillary map information (.PDF) document (gate_geology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.TXT) and FAQ (.HTML) formats, and a GIS readme file (gate_gis_readme.pdf). Please read the gate_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.1 shapefile format contact Stephanie O’Meara (stephanie.omeara@colostate.edu; see contact information below). The data is also available as a 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. Google Earth software is available for free at: http://www.google.com/earth/index.html. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Rutgers University Institute of Marine and Coastal Sciences. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (stis_pre-sandy_metadata_faq.html; available at http://nrdata.nps.gov/geology/gri_data/gis/gate/stis_pre-sandy_metadata_faq.html). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:6,000 and United States National Map Accuracy Standards features are within (horizontally) 5.08 meters or 16.67 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data projection is NAD83, UTM Zone 18N, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth. The data is within the area of interest of Gateway National Recreation Area.

**THIS NEWER 2016 DIGITAL MAP REPLACES THE OLDER 2014 VERSION OF THE GRI GATE Geomorphological-GIS data. The Unpublished Digital Pre-Hurricane Sandy Geomorphological-GIS Map of the Jamaica Bay Unit, Gateway National Recreation Area, New York is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (jaba_geomorphology.gdb), a 10.1 ArcMap (.MXD) map document (jaba_geomorphology.mxd), individual 10.1 layer (.LYR) files for each GIS data layer, an ancillary map information (.PDF) document (gate_geomorphology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.TXT) and FAQ (.HTML) formats, and a GIS readme file (gate_gis_readme.pdf). Please read the gate_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.1 shapefile format contact Stephanie O’Meara (stephanie.omeara@colostate.edu; see contact information below). The data is also available as a 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. Google Earth software is available for free at: http://www.google.com/earth/index.html. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Rutgers University Institute of Marine and Coastal Sciences. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (jaba_pre-sandy_metadata_faq.html; available at http://nrdata.nps.gov/geology/gri_data/gis/gate/jaba_pre-sandy_metadata_faq.html). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:6,000 and United States National Map Accuracy Standards features are within (horizontally) 5.08 meters or 16.67 feet of their actual _location as presented by this dataset. Users of this data should thus not assume the _location of features is exactly where they are portrayed in Google Earth, ArcGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data projection is NAD83, UTM Zone 18N, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth. The data is within the area of interest of Gateway National Recreation Area

Geographic Information System (GIS) Analytics Market Outlook

The global Geographic Information System (GIS) Analytics market size is projected to grow remarkably from $9.1 billion in 2023 to $21.7 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 10.2% during the forecast period. This substantial growth can be attributed to several factors such as technological advancements in GIS, increasing adoption in various industry verticals, and the rising importance of spatial data for decision-making processes.

The primary growth driver for the GIS Analytics market is the increasing need for accurate and efficient spatial data analysis to support critical decision-making processes across various industries. Governments and private sectors are investing heavily in GIS technology to enhance urban planning, disaster management, and resource allocation. With the world becoming more data-driven, the reliance on GIS for geospatial data has surged, further propelling its market growth. Additionally, the integration of artificial intelligence (AI) and machine learning (ML) with GIS is revolutionizing the analytics capabilities, offering deeper insights and predictive analytics.

Another significant growth factor is the expanding application of GIS analytics in disaster management and emergency response. Natural disasters such as hurricanes, earthquakes, and wildfires have highlighted the importance of GIS in disaster preparedness, response, and recovery. The ability to analyze spatial data in real-time allows for quicker and more efficient allocation of resources, thus minimizing the impact of disasters. Moreover, GIS analytics plays a pivotal role in climate change studies, helping scientists and policymakers understand and mitigate the adverse effects of climate change.

The transportation sector is also a major contributor to the growth of the GIS Analytics market. With the rapid urbanization and increasing traffic congestion in cities, there is a growing demand for effective transport management solutions. GIS analytics helps in route optimization, traffic management, and infrastructure development, thereby enhancing the overall efficiency of transportation systems. The integration of GIS with Internet of Things (IoT) devices and sensors is further enhancing the capabilities of traffic management systems, contributing to the market growth.

Regionally, North America is the largest market for GIS analytics, driven by the high adoption rate of advanced technologies and significant investment in geospatial infrastructure by both public and private sectors. The Asia Pacific region is expected to witness the highest growth rate during the forecast period due to the rapid urbanization, infrastructural developments, and increasing government initiatives for smart city projects. Europe and Latin America are also contributing significantly to the market growth owing to the increasing use of GIS in urban planning and environmental monitoring.

Component Analysis

The GIS Analytics market can be segmented by component into software, hardware, and services. The software segment holds the largest market share due to the continuous advancements in GIS software solutions that offer enhanced functionalities such as data visualization, spatial analysis, and predictive modeling. The increasing adoption of cloud-based GIS software solutions, which offer scalable and cost-effective options, is further driving the growth of this segment. Additionally, open-source GIS software is gaining popularity, providing more accessible and customizable options for users.

The hardware segment includes GIS data collection devices such as GPS units, remote sensing instruments, and other data acquisition tools. This segment is witnessing steady growth due to the increasing demand for high-precision GIS data collection equipment. Technological advancements in hardware, such as the development of LiDAR and drones for spatial data collection, are significantly enhancing the capabilities of GIS analytics. Additionally, the integration of mobile GIS devices is facilitating real-time data collection, contributing to the growth of the hardware segment.

The services segment encompasses consulting, implementation, training, and maintenance services. This segment is expected to grow at a significant pace due to the increasing demand for professional services to manage and optimize GIS systems. Organizations are seeking expert consultants to help them leverage GIS analytics for strategic decision-making and operational efficiency. Additionally, the growing complexity o

The U.S. Geological Survey (USGS) has compiled a geodatabase containing mineral-related geospatial data for 10 countries of interest in Southwest Asia (area of study): Afghanistan, Cambodia, Laos, India, Indonesia, Iran, Nepal, North Korea, Pakistan, and Thailand. The data can be used in analyses of the extractive fuel and nonfuel mineral industries and related economic and physical infrastructure integral for the successful operation of the mineral industries within the area of study as well as the movement of mineral products across domestic and global markets. This geodatabase reflects the USGS ongoing commitment to its mission of understanding the nature and distribution of global mineral commodity supply chains by updating and publishing the georeferenced locations of mineral commodity production and processing facilities, mineral exploration and development sites, and mineral commodity exporting ports for the countries in the area of study. The geodatabase contains data feat ...

The size of the Africa Geospatial Analytics market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 6.99% during the forecast period.Geospatial analytics is a tool where the potential of location-based data, which is fast catching up in the African continent, is pooled to utilize the integration of the geographic information systems (GIS), global positioning systems (GPS), and remote sensing technologies. These empower organizations to interpret and make value out of spatial data for analytical purposes. Geospatial analytics analyzes patterns and trends as well as their relationships within geographic contexts that will give a more holistic understanding of complex phenomena.Geospatial analytics alters most aspects of life in these areas in Africa. It is helpful in optimizing crops and resources in precision farming. Farmers learn when to make agriculture decisions through analysis of data on soil quality, weather, and crop health for maximization of its produce. In urban planning, it helps in urban development, infrastructure planning, and disaster management. Mapping the pattern of growth for cities, identification of vulnerable areas, and even the optimization of resource allocation makes cities sustainable and resilient. Geospatial analytics is also important in natural resource management, environmental conservation, and climate change adaptation. Monitoring deforestation, tracking populations of wildlife, and assessing the impact of climate will help policymakers further strategize towards a more effective implementation of conservation. Africa is continuing to step into the technological fray, and the market for geospatial analytics is supposed to grow its presence multifold.From seemingly endless savannahs, countless forests, substantive natural riches, and burgeoning cities, Africa has much to harness from the insights offered by geospatial analytics: Address real challenges, unlock fresh opportunities, and drive sustainable development across the continent. Recent developments include: November 2022: A Memorandum of Understanding (MOU) was signed by SaskTel and Axiom Exploration Group to jointly explore opportunities to assist organizations throughout Saskatchewan in enhancing and modernizing their operations through the gathering and analysis of geospatial and other geophysical data., September 2022: A two-day conference on Data Analytics and visualization was held by Women in GIS Kenya in association with Pathways International, Esri Eastern Africa, Nakala Analytics, and the University of Nairobi, Department of Geospatial and Space Technology.. Key drivers for this market are: Commercialization of spatial data, Increased smart city & infrastructure projects. Potential restraints include: High costs associated with geospatial technologies. Notable trends are: Commercialization of Spatial Data.

The Geospatial Analytics Market size was valued at USD 79.06 USD billion in 2023 and is projected to reach USD 202.74 USD billion by 2032, exhibiting a CAGR of 14.4 % during the forecast period. The growing adoption of location-based technologies and the increasing need for data-driven decision-making in various industries are key factors driving market growth. Geospatial analytics captures, produces and displays GIS (geographic information system)-maps and pictures that may be weather maps, GPS or satellite photos. The geospatial analysis as a tool works with state of art technology in every formats namely; the GPS, sensors that locates, social media, mobile devices, multi of the satellite imagery to produce data visualizations that are facilitating trend-finding in complex relations between people and places as well are the situations' understanding. Visualizations are depicted through the use of maps, graphs, figures, and cartograms that illustrate the entire historical picture as well as a current changing trend. This is why the forecast becomes more confident and the situation is anticipated better. Recent developments include: February 2024: Placer.ai and Esri, a Geographic Information System (GIS) technology provider, partnered to empower customers with enhanced analytics capabilities, integrating consumer behavior analysis. Additionally, the agreement will foster collaborations to unlock further features by synergizing our respective product offerings., December 2023: CKS and Esri India Technologies Pvt Ltd teamed up to introduce the 'MMGEIS' program, focusing on students from 8th grade to undergraduates, to position India as a global leader in geospatial technology through skill development and innovation., December 2023: In collaboration with Bayanat, the UAE Space Agency revealed the initiation of the operational phase of the Geospatial Analytics Platform during its participation in organizing the Space at COP28 initiatives., November 2023: USAID unveiled its inaugural Geospatial Strategy, designed to harness geospatial data and technology for more targeted international program delivery. The strategy foresees a future where geographic methods enhance the effectiveness of USAID's efforts by pinpointing development needs, monitoring program implementation, and evaluating outcomes based on location., May 2023: TomTom International BV, a geolocation technology specialist, expanded its partnership with Alteryx, Inc. Through this partnership, Alteryx will use TomTom’s Maps APIs and location data to integrate spatial data into Alteryx’s products and location insights packages, such as Alteryx Designer., May 2023: Oracle Corporation announced the launch of Oracle Spatial Studio 23.1, available in the Oracle Cloud Infrastructure (OCI) marketplace and for on-premises deployment. Users can browse, explore, and analyze geographic data stored in and managed by Oracle using a no-code mapping tool., May 2023: CAPE Analytics, a property intelligence company, announced an enhanced insurance offering by leveraging Google geospatial data. Google’s geospatial data can help CAPE create appropriate solutions for insurance carriers., February 2023: HERE Global B.V. announced a collaboration with Cognizant, an information technology, services, and consulting company, to offer digital customer experience using location data. In this partnership, Cognizant will utilize the HERE location platform’s real-time traffic data, weather, and road attribute data to develop spatial intelligent solutions for its customers., July 2022: Athenium Analytics, a climate risk analytics company, launched a comprehensive tornado data set on the Esri ArcGIS Marketplace. This offering, which included the last 25 years of tornado insights from Athenium Analytics, would extend its Bronze partner relationship with Esri. . Key drivers for this market are: Advancements in Technologies to Fuel Market Growth. Potential restraints include: Lack of Standardization Coupled with Shortage of Skilled Workforce to Limit Market Growth. Notable trends are: Rise of Web-based GIS Platforms Will Transform Market.

Several datasets, maps, and shapefiles are made available by the Alaska Department of Oil and Gas pertaining to resource development on the North Slope and the Beaufort Sea, including: Oil, gas, natural gas, geothermal, and exploration sites lease boundaries. Information also included for units and participating areas (PAs).