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North America Geographic Information System Market Size 2025-2029

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

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

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

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

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

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

Component

  Software
  Data
  Services


Deployment

  On-premise
  Cloud


Geography

  North America

    Canada
    Mexico
    US

By Component Insights

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

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

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

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Market Dynamics

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

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

Rising applications of geographic

To improve public health and the environment, the United States Environmental Protection Agency (EPA) collects information about facilities, sites, or places subject to environmental regulation or of environmental interest. Through the Geospatial Data Download Service, the public is now able to download the EPA Geodata Shapefile, Feature Class or extensible markup language (XML) file containing facility and site information from EPA's national program systems. The files are Internet accessible from the Envirofacts Web site (https://www3.epa.gov/enviro/). The data may be used with geospatial mapping applications. (Note: The files omit facilities without latitude/longitude coordinates.) The EPA Geospatial Data contains the name, location (latitude/longitude), and EPA program information about specific facilities and sites. In addition, the files contain a Uniform Resource Locator (URL), which allows mapping applications to present an option to users to access additional EPA data resources on a specific facility or site.

This geospatial dataset delivers high-accuracy GPS event streams from millions of connected devices across Asia, enabling advanced mobility, mapping, and location intelligence applications. Sourced from tier-1 app developers and trusted suppliers, it provides granular insights for commercial, government, and research use.

Each record includes: Latitude & Longitude coordinates Event timestamp (epoch & date) Mobile Advertising ID (IDFA/GAID) Horizontal accuracy (~85% fill rate) Country code (ISO3) Optional metadata: IP address, carrier, device model

Access & Delivery API with polygon queries (up to 10,000 tiles) Formats: JSON, CSV, Parquet Delivery via API, AWS S3, or Google Cloud Storage Hourly or daily refresh options Historical backfill from September 2024 Credit-based pricing for scalability

Compliance Fully compliant with GDPR and CCPA, with clear opt-in/out mechanisms and transparent privacy policies.

Use Cases Advanced mapping and GIS solutions Urban mobility and infrastructure planning Commercial site selection and market expansion Geofencing and targeted advertising Disaster response planning and risk assessment Transportation and logistics optimization

The Cropland Data Layer (CDL), hosted on CropScape, provides a raster, geo-referenced, crop-specific land cover map for the continental United States. The CDL also includes a crop mask layer and planting frequency layers, as well as boundary, water and road layers. The Boundary Layer options provided are County, Agricultural Statistics Districts (ASD), State, and Region. The data is created annually using moderate resolution satellite imagery and extensive agricultural ground truth. Users can select a geographic area of interest or import one, then access acreage statistics for a specific year or view the change from one year to another. The data can be exported or added to the CDL. The information is useful for issues related to agricultural sustainability, biodiversity, and land cover monitoring, especially due to extreme weather events. Resources in this dataset:Resource Title: CropScape and Cropland Data Layer - National Download. File Name: Web Page, url: https://www.nass.usda.gov/Research_and_Science/Cropland/Release/index.php Downloads available as zipped files at https://www.nass.usda.gov/Research_and_Science/Cropland/Release/index.php --

National CDL's -- by year, 2008-2020. Cropland Data Layer provides a raster, geo-referenced, crop-specific land cover map for the continental United States. The CDL also includes a crop mask layer and planting frequency layers, as well as boundary, water and road layers. The Boundary Layer options provided are County, Agricultural Statistics Districts (ASD), State, and Region. National Cultivated Layer -- based on the most recent five years (2013-2020). National Frequency Layer -- the 2017 Crop Frequency Layer identifies crop specific planting frequency and are based on land cover information derived from the 2008 through 2020CDL's. There are currently four individual crop frequency data layers that represent four major crops: corn, cotton, soybeans, and wheat. National Confidence Layer -- the Confidence Layer spatially represents the predicted confidence that is associated with that output pixel, based upon the rule(s) that were used to classify it. Western/Eastern/Central U.S.

Visit https://nassgeodata.gmu.edu/CropScape/ for the interactive map including tutorials and basic instructions. These options include a "Demo Video", "Help", "Developer Guide", and "FAQ".

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

Links to recordings of the Integrated Services Program and 9-1-1 & Geospatial Services Bureau webinar series, including NG9-1-1 GIS topics such as: data preparation; data provisioning and maintenance; boundary best practices; and extract, transform, and load (ETL). Offerings include:Topic: Virginia Next Generation 9-1-1 Dashboard and Resources Update Description: Virginia recently updated the NG9-1-1 Dashboard with some new tabs and information sources and continues to develop new resources to assist the GIS data work. This webinar provides an overview of changes, a demonstration of new functionality, and a guide to finding and using new resources that will benefit Virginia public safety and GIS personnel with roles in their NG9-1-1 projects. Wednesday 16 June 2021. Recording available at: https://vimeo.com/566133775Topic: Emergency Service Boundary GIS Data Layers and Functions in your NG9-1-1 PSAP Description: Law, Fire, and Emergency Medical Service (EMS) Emergency Service Boundary (ESB) polygons are required elements of the NENA NG9-1-1 GIS data model stack that indicate which agency is responsible for primary response. While this requirement must be met in your Virginia NG9-1-1 deployment with AT&T and Intrado, there are quite a few ways you could choose to implement these polygons. PSAPs and their GIS support must work together to understand how this information will come into a NG9-1-1 i3 PSAP and how it will replace traditional ESN information in order to make good choices while implementing these layers. This webinar discusses:the function of ESNs in your legacy 9-1-1 environment, the role of ESBs in NG9-1-1, and how ESB information appears in your NG9-1-1 PSAP. Wednesday, 22 July 2020. Recording available at: https://vimeo.com/441073056#t=360sTopic: "The GIS Folks Handle That": What PSAP Professionals Need to Know about the GIS Project Phase of Next Generation 9-1-1 DeploymentDescription: Next Generation 9-1-1 (NG9-1-1) brings together the worlds of emergency communication and spatial data and mapping. While it may be tempting for PSAPs to outsource cares and concerns about road centerlines and GIS data provisioning to 'the GIS folks', GIS staff are crucial to the future of emergency call routing and location validation. Data required by NG9-1-1 usually builds on data that GIS staff already know and use for other purposes, so the transition requires them to learn more about PSAP operations and uses of core data. The goal of this webinar is to help the PSAP and GIS worlds come together by explaining the role of the GIS Project in the Virginia NG9-1-1 Deployment Steps, exploring how GIS professionals view NG9-1-1 deployment as a project, and fostering a mutual understanding of how GIS will drive NG9-1-1. 29 January 2020. Recording available at: https://vimeo.com/showcase/9791882/video/761225474Topic: Getting Your GIS Data from Here to There: Processes and Best Practices for Extract, Transform and Load (ETL) Description: During the fall of 2019, VITA-ISP staff delivered workshops on "Tools and Techniques for Managing the Growing Role of GIS in Enterprise Software." This session presents information from the workshops related to the process of extracting, transforming, and loading data (ETL), best practices for ETL, and methods for data schema comparison and field mapping as a webinar. These techniques and skills assist GIS staff with their growing role in Next Generation 9-1-1 but also apply to many other projects involving the integration and maintenance of GIS data. 19 February 2020. Recording available at: https://vimeo.com/showcase/9791882/video/761225007Topic: NG9-1-1 GIS Data Provisioning and MaintenanceDescription: VITA ISP pleased to announce an upcoming webinar about the NG9-1-1 GIS Data Provisioning and Maintenance document provided by Judy Doldorf, GISP with the Fairfax County Department of Information Technology and RAC member. This document was developed by members of the NG9-1-1 GIS workgroup within the VITA Regional Advisory Council (RAC) and is intended to provide guidance to local GIS and PSAP authorities on the GIS datasets and associated GIS to MSAG/ALI validation and synchronization required for NG9-1-1 services. The document also provides guidance on geospatial call routing readiness and the short- and long-term GIS data maintenance workflow procedures. In addition, some perspective and insight from the Fairfax County experience in GIS data preparation for the AT&T and West solution will be discussed in this webinar. 31 July 2019. Recording available at: https://vimeo.com/showcase/9791882/video/761224774Topic: NG9-1-1 Deployment DashboardDescription: I invite you to join us for a webinar that will provide an overview of our NG9-1-1 Deployment Dashboard and information about other online ISP resources. The ISP website has been long criticized for being difficult to use and find information. The addition of the Dashboard and other changes to the website are our attempt to address some of these concerns and provide an easier way to find information especially as we undertake NG9-1-1 deployment. The Dashboard includes a status map of all Virginia PSAPs as it relates to the deployment of NG9-1-1, including the total amount of funding requested by the localities and awards approved by the 9-1-1 Services Board. During this webinar, Lyle Hornbaker, Regional Coordinator for Region 5, will navigate through the dashboard and provide tips on how to more effectively utilize the ISP website. 12 June 2019. Recording not currently available. Please see the Virginia Next Generation 9-1-1 Dashboard and Resources Update webinar recording from 16 June 2021. Topic: PSAP Boundary Development Tools and Process RecommendationDescription: This webinar will be presented by Geospatial Program Manager Matt Gerike and VGIN Coordinator Joe Sewash. With the release of the PSAP boundary development tools and PSAP boundary segment compilation guidelines on the VGIN Clearinghouse in March, this webinar demonstrates the development tools, explains the process model, and discusses methods, tools, and resources available for you as you work to complete PSAP boundary segments with your neighbors. 15 May 2019. Recording available at: https://www.youtube.com/watch?v=kI-1DkUQF9Q&feature=youtu.beTopic: NG9-1-1 Data Preparation - Utilizing VITA's GIS Data Report Card ToolDescription: This webinar, presented by VGIN Coordinator Joe Sewash, Geospatial Program Manager Matt Gerike, and Geospatial Analyst Kenny Brevard will provide an overview of the first version of the tools that were released on March 25, 2019. These tools will allow localities to validate their GIS data against the report card rules, the MSAG and ALI checks used in previous report cards, and the analysis listed in the NG9-1-1 migration proposal document. We will also discuss the purpose of the tools, input requirements, initial configuration, how to run them, and how to make sense of your results. 10 April 2019. Recording available at: https://vimeo.com/showcase/9791882/video/761224495Topic: NG9-1-1 PSAP Boundary Best Practice WebinarDescription: During the months of November and December, VITA ISP staff hosted regional training sessions about best practices for PSAP boundaries as they relate to NG9-1-1. These sessions were well attended and very interactive, therefore we feel the need to do a recap and allow those that may have missed the training to attend a makeup session. 30 January 2019. Recording not currently available. Please see the PSAP Boundary Development Tools and Process Recommendation webinar recording from 15 May 2019.Topic: NG9-1-1 GIS Overview for ContractorsDescription: The Commonwealth of Virginia has started its migration to next generation 9-1-1 (NG9-1-1). This migration means that there will be a much greater reliance on geographic information (GIS) to locate and route 9-1-1 calls. VITA ISP has conducted an assessment of current local GIS data and provided each locality with a report. Some of the data from this report has also been included in the localities migration proposal, which identifies what data issues need to be resolved before the locality can migrate to NG9-1-1. Several localities in Virginia utilize a contractor to maintain their GIS data. This webinar is intended for those contractors to review the data in the report, what is included in the migration proposal and how they may be called on to assist the localities they serve. It will still ultimately be up to each locality to determine whether they engage a contractor for assistance, but it is important for the contractor community to understand what is happening and have an opportunity to ask questions about the intent and goals. This webinar will provide such an opportunity. 22 August 2018. Recording not currently available. Please contact us at NG911GIS@vdem.virginia.gov if you are interested in this content.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. The 2006 revised preliminary map and the final 2007 post-AA maps were created following the USGS/NPS Vegetation Mapping Program protocols. Using ArcMap 9.2 (ESRI 1999–2006), polygon boundaries were revised on screen based on the plot data, field observations, classification analyses, aerial photography signatures, and topographic maps. Each polygon was assigned the USNVC Community Element Global (CEGL) code of a preliminary vegetation association based on the information sources listed above. Second, third, and fourth CEGL code choices were entered in cases of uncertainty, or for polygons representing mosaics of two or more types.

Geospatial Data Provider Liability Insurance Market Outlook

According to our latest research, the global geospatial data provider liability insurance market size reached USD 1.42 billion in 2024, with a robust compound annual growth rate (CAGR) of 12.3% projected through the forecast period. This strong growth trajectory is expected to drive the market to a value of USD 4.01 billion by 2033. The increasing integration of geospatial data across diverse industries, coupled with heightened regulatory scrutiny and the rising frequency of data-related litigation, are among the primary factors fueling the expansion of this market.

The growth of the geospatial data provider liability insurance market is propelled by the surging adoption of geospatial technologies in critical sectors such as urban planning, transportation, agriculture, and environmental monitoring. As organizations increasingly rely on accurate geospatial data for decision-making, the risks associated with errors, omissions, or cyber threats have become more pronounced. This heightened risk profile has driven demand for specialized insurance products that can mitigate financial losses and legal liabilities arising from data inaccuracies or breaches. The proliferation of location-based services and remote sensing applications further intensifies the need for comprehensive liability coverage, as even minor inaccuracies can result in significant operational disruptions or legal consequences.

Another significant growth driver is the evolving regulatory landscape governing data privacy, usage, and sharing. Governments and regulatory bodies across regions are implementing stricter compliance requirements for geospatial data usage, which in turn increases the potential for legal claims against data providers. The introduction of new data protection laws, such as the General Data Protection Regulation (GDPR) in Europe and similar frameworks in other regions, has made it imperative for geospatial data providers to secure liability insurance. This regulatory pressure not only elevates the baseline risk for providers but also creates opportunities for insurers to develop tailored products addressing the unique exposures faced by this sector. The ongoing digital transformation and the expansion of smart city initiatives globally further amplify these risks and opportunities.

A third critical factor contributing to market growth is the increasing complexity and sophistication of cyber threats targeting geospatial data providers. With the rapid digitization of geospatial information and the expansion of cloud-based platforms, the industry faces heightened exposure to cyberattacks, data breaches, and ransomware incidents. These threats can lead to significant financial losses, reputational damage, and legal liabilities for data providers. As a result, organizations are seeking specialized cyber liability insurance policies that offer protection against a wide range of cyber risks. The insurance market is responding by innovating new products and coverage options that address the emerging risks associated with the digitalization of geospatial data, thereby driving overall market growth.

From a regional perspective, North America currently dominates the geospatial data provider liability insurance market, owing to its advanced technological infrastructure, high adoption of geospatial services, and stringent regulatory environment. Europe follows closely, benefiting from robust data protection regulations and a mature insurance sector. The Asia Pacific region is expected to witness the fastest growth over the forecast period, driven by rapid urbanization, increasing investments in smart city projects, and growing awareness of data-related risks. Latin America and the Middle East & Africa, while representing smaller shares of the global market, are poised for steady growth as digital transformation initiatives gain momentum in these regions. The interplay of regional regulatory frameworks, technological adoption rates, and insurance market maturity will continue to shape the competitive landscape and growth prospects of the global geospatial data provider liability insurance market.

Coverage Type Analysis

The coverage type segment of the geospatial data provider liability insurance market encompasses various insurance solutions designed to address the multifaceted risks faced by data providers. Errors & Omissions (E&O) insurance remains the most sought-a

Wildfire geospatial data (public) group provides public access to a variety of wildfire related data generated by the Government of Alberta. Available services include Map Image Layer, Feature Layer, Web Map Service (WMS), Web Feature Service (WFS), KML/KMZ, Imagery Layer and Web Content Service (WCS). Not all data is available in all types of services. Alberta Fire Status includes two data services related to fire activity in Alberta. Alberta Fire Status - Fire Locations and Perimeters service consists of two layers. There is a point layer for Alberta fire locations and one polygon layer for Alberta fire perimeters. This service the best option for collecting statistics on the current fire situation. Alberta Fire Status by Category Group - Fire Locations and Perimeters service consists of eight layers. Four point layers provide Alberta fire locations and four polygon layers provide Alberta fire perimeters. This service is the best option for mapping purposes Both Alberta Fire Status services are available as a Map Image Layer, Feature Layer, WMS, WFS and KML/KMZ. Other services include data such as weather, fire behaviour, fire control orders, NOTAM, Forest Areas, and other fire related boundary data. FireWeb External Application can be used to spatially view a variety of wildfire related data. Access it through the "FireWeb External Application" group. Current Wildfire GIS Data is also available in a File Geodatabase. The file includes the active fire point data and the fire perimeter data. The file is zipped up and placed on a secure FTP site twice per day at 10:03 and 15:03.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Following the vegetation plot data analysis, the preliminary vegetation map was edited and refined to produce a revised preliminary vegetation map prior to thematic accuracy assessment. Using ArcMap 9.2 (ESRI 1999-2006), polygon boundaries were revised on screen based on the plot data, field observations, classification analyses, aerial photography signatures, and topographic maps. Each polygon was assigned the NVC Community Element Global (CEGL) code of a preliminary vegetation association based on the information sources listed above. Second, third, and fourth CEGL code choices were entered in cases of uncertainty, or for polygons representing mosaics of two or more types.

This is a link to GeoData@Wisconsin. GeoData@Wisconsin is an online geoportal that provides discovery and access to Wisconsin geospatial data, imagery, and scanned maps. It is developed and maintained by the UW-Madison Geography Department's Robinson Map Library and State Cartographer's Office. The geoportal combines a map-based spatial search with traditional keyword searching and faceted browsing options to locate and download geospatial data. See the Help section for tips on keyword searching, descriptions of the different facets and collections, and how to interact with the map search function.

This geospatial layer is a spatial index for the CED (Conservation Efforts Database https://conservationefforts.org/), serving as a spatial framework for summary reports by area (a.k.a. polygon). In addition, this SRU (Sagebrush Reporting Unit) data is an option for data providers to provide spatial ambiguity to alleviate concerns of too much spatial detail representing private landowners’ efforts efforts and to protect Personally Identifiable Information. This option allows CED data providers to pick a predetermined SRU instead of submitting the explicit effort boundary. These SRUs are large enough to provide spatial ambiguity and obscure private landowner locations. This SRU data is in the format of a GIS polygon layer and is an aggregate of USGS partner’s lek cluster layer, BLM HAF data modified by Oregon, Idaho layers, and CED development team modification for CED purposes.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. A vegetation map of Fredericksburg and Spotsylvania National Military Park was created following the USGS-NPS Vegetation Mapping Program protocols. Vegetation map classes were crosswalked to the Natural Communities of Virginia and to the USNVC in order to provide a regional and global context for the park’s vegetation. Ten map classes represent later successional forests and cover approximately 48% (1,510 ha [3,731 ac]) of the park. These map classes can be broadly characterized based on different environmental settings, such as upland forests, alluvial floodplain forests, and non-alluvial wetlands. Early successional or transitional vegetation covers 31% (966 ha [2,387 ac]) of the land in the park. Cultural map classes cover 21% (680 ha [1,680 ac]) of the park, and include the Anderson land-use categories and other man-made or maintained areas in the park.

In order to improve the capacity of storage, exploration and processing of sensor data, a spatial DBMS was used and the Aquopts system was implemented.

In field surveys using different sensors on the aquatic environment, the existence of spatial attributes in the dataset is common, motivating the adoption of PostgreSQL and its spatial extension PostGIS. To enable the insertion of new data sets as well as new devices and sensing equipment, the database was modeled to support updates and provide structures for storing all the data collected in the field campaigns in conjunction with other possible future data sources. The database model provides resources to manage spatial and temporal data and allows flexibility to select and filter the dataset.

The data model ensures the storage integrity of the information related to the samplings performed during the field survey in an architecture that benefits the organization and management of the data. However, in addition to the storage specified on the data model, there are several procedures that need to be applied to the data to prepare it for analysis. Some validations are important to identify spurious data that may represent important sources of information about data quality. Other corrections are essential to tweak the data and eliminate undesirable effects. Some equations can be used to produce other factors that can be obtained from the combination of attributes. In general, the processing steps comprise a cycle of important operations that are directly related to the characteristics of the data set. Considering the data of the sensors stored in the database, an interactive prototype system, named Aquopts, was developed to perform the necessary standardization and basic corrections and produce useful data for analysis, according to the correction methods known in the literature.

The system provides resources for the analyst to automate the process of reading, inserting, integrating, interpolating, correcting, and other calculations that are always repeated after exporting field campaign data and producing new data sets. All operations and processing required for data integration and correction have been implemented from the PHP and Python language and are available from a Web interface, which can be accessed from any computer connected to the internet. The data access cab be access online (http://sertie.fct.unesp.br/aquopts), but the resources are restricted by registration and permissions for each user. After their identification, the system evaluates the access permissions and makes available the options of insertion of new datasets.

The source-code of the entire Aquopts system are available at: https://github.com/carmoafc/aquopts

The system and additional results were described on the official paper (under review)

Geospatial Analytics as a Service Market Outlook

According to our latest research, the global Geospatial Analytics as a Service market size reached USD 8.7 billion in 2024, driven by rapid digital transformation across industries and the increasing adoption of cloud-based geospatial solutions. The market is poised to expand at a robust CAGR of 18.4% during the forecast period, with the forecasted market size expected to reach USD 43.1 billion by 2033. This remarkable growth is attributed to the escalating demand for real-time location-based insights and the proliferation of IoT and connected devices, which are transforming how organizations leverage geospatial data for strategic decision-making.

One of the primary growth factors propelling the Geospatial Analytics as a Service market is the surge in smart city initiatives globally. Governments and municipalities are increasingly leveraging geospatial analytics to optimize urban planning, manage infrastructure, and enhance public services. The integration of advanced technologies such as artificial intelligence, machine learning, and big data analytics with geospatial platforms is enabling more precise mapping, predictive modeling, and resource allocation. As urban populations swell and cities face mounting challenges related to traffic congestion, pollution, and resource management, the need for sophisticated geospatial analytics becomes ever more critical. This trend is anticipated to drive sustained investment and innovation in the sector.

Another significant driver is the growing need for disaster management and environmental monitoring solutions. With the frequency and severity of natural disasters on the rise due to climate change, governments and organizations are increasingly turning to geospatial analytics to enhance their preparedness and response capabilities. These solutions enable real-time monitoring of weather patterns, flood risks, wildfire outbreaks, and other environmental hazards, facilitating timely interventions and minimizing damage. The ability to integrate data from satellites, drones, and ground sensors into a unified geospatial platform allows for comprehensive situational awareness and more effective resource deployment during emergencies.

The proliferation of IoT devices and advancements in cloud computing have also played a pivotal role in accelerating the adoption of Geospatial Analytics as a Service. The cloud deployment model offers scalability, cost-effectiveness, and seamless integration with other enterprise systems, making it an attractive option for organizations of all sizes. Furthermore, the increasing availability of high-resolution satellite imagery and real-time geospatial data streams is enabling businesses across sectors such as agriculture, transportation, and utilities to optimize operations, improve supply chain visibility, and enhance customer experiences. These technological advancements are expected to open up new avenues for market growth in the coming years.

Regionally, North America continues to dominate the Geospatial Analytics as a Service market, accounting for the largest share in 2024, driven by the presence of major technology providers, high adoption rates among government and private sector organizations, and robust investments in R&D. However, Asia Pacific is emerging as a high-growth region, fueled by rapid urbanization, infrastructure development, and increasing government focus on smart city projects. Europe remains a key market, particularly in sectors such as transportation, environmental monitoring, and defense, while Latin America and the Middle East & Africa are witnessing growing interest in geospatial solutions for resource management and disaster response.

Component Analysis

The Component segment of the Geospatial Analytics as a Service market is categorized into Software, Services, and Platform. Each of these components plays a vital role in delivering comprehensive geospatial analytics solutions to end-users across various industries. The Software segment encompasses a broad range of geospatial applications and tools, including GIS software, remote sensing software, and spatial data management tools. These solutions enable organizations to process, analyze, and visualize geospatial data with high accuracy and efficiency. The growing demand for advanced mapping, predictive analytics, and real-time location intelligence is driving continuous innovation in geo

In the Great Lakes basin, there are numerous organizations undertaking scientific monitoring and research efforts with the goal of identifying threats and evaluating management strategies that will protect and restore the Great Lakes ecosystem. Coordination among all these stakeholders is a challenge, and having a centralized location where researchers and managers can identify relevant scientific activities and access fundamental information about these activities is crucial for efficient management. The Science in the Great Lakes (SiGL) Mapper was a map-based discovery tool that spatially displayed basin-wide multidisciplinary monitoring and research activities conducted by both USGS and partners from all five Great Lakes. It was designed to help Great Lakes researchers and managers strategically plan, implement, and analyze monitoring and restoration activities by providing easy access to historical and on-going project metadata while allowing them to identify gaps (spatially and topically) that have been underrepresented in previous efforts or need further study. SiGL provided a user-friendly and efficient way to explore Great Lakes projects and data through robust search options while also providing a critical spatial perspective through its interactive mapping interface.

Background

Preventive and health-promoting policies can guide (place and space-specific) factors influencing human health, such as the physical and social environment. Required is data that can lead to a more nuanced decision-making process and identify both, existing and future challenges. Along with the rise of new technologies, and thus the multiple opportunities to use and process data, new options have emerged to measure and monitor factors that affect health. Thus, in recent years, several gateways for open data (including governmental and geospatial data) became available. At present, an increasing number of research institutions as well as (state and private) companies and citizens' initiatives provide data. However, there is a lack of overviews covering the range of such offerings regarding health. In particular, for geographically differentiated analyses, there are challenges related to data availability at different spatial levels and the growing number of data providers. ...

Our dataset provides detailed and precise insights into the business, commercial, and industrial aspects of any given area in the USA (Including Point of Interest (POI) Data and Foot Traffic. The dataset is divided into 150x150 sqm areas (geohash 7) and has over 50 variables. - Use it for different applications: Our combined dataset, which includes POI and foot traffic data, can be employed for various purposes. Different data teams use it to guide retailers and FMCG brands in site selection, fuel marketing intelligence, analyze trade areas, and assess company risk. Our dataset has also proven to be useful for real estate investment.- Get reliable data: Our datasets have been processed, enriched, and tested so your data team can use them more quickly and accurately.- Ideal for trainning ML models. The high quality of our geographic information layers results from more than seven years of work dedicated to the deep understanding and modeling of geospatial Big Data. Among the features that distinguished this dataset is the use of anonymized and user-compliant mobile device GPS location, enriched with other alternative and public data.- Easy to use: Our dataset is user-friendly and can be easily integrated to your current models. Also, we can deliver your data in different formats, like .csv, according to your analysis requirements. - Get personalized guidance: In addition to providing reliable datasets, we advise your analysts on their correct implementation.Our data scientists can guide your internal team on the optimal algorithms and models to get the most out of the information we provide (without compromising the security of your internal data).Answer questions like: - What places does my target user visit in a particular area? Which are the best areas to place a new POS?- What is the average yearly income of users in a particular area?- What is the influx of visits that my competition receives?- What is the volume of traffic surrounding my current POS?This dataset is useful for getting insights from industries like:- Retail & FMCG- Banking, Finance, and Investment- Car Dealerships- Real Estate- Convenience Stores- Pharma and medical laboratories- Restaurant chains and franchises- Clothing chains and franchisesOur dataset includes more than 50 variables, such as:- Number of pedestrians seen in the area.- Number of vehicles seen in the area.- Average speed of movement of the vehicles seen in the area.- Point of Interest (POIs) (in number and type) seen in the area (supermarkets, pharmacies, recreational locations, restaurants, offices, hotels, parking lots, wholesalers, financial services, pet services, shopping malls, among others). - Average yearly income range (anonymized and aggregated) of the devices seen in the area.Notes to better understand this dataset:- POI confidence means the average confidence of POIs in the area. In this case, POIs are any kind of location, such as a restaurant, a hotel, or a library. - Category confidences, for example"food_drinks_tobacco_retail_confidence" indicates how confident we are in the existence of food/drink/tobacco retail locations in the area. - We added predictions for The Home Depot and Lowe's Home Improvement stores in the dataset sample. These predictions were the result of a machine-learning model that was trained with the data. Knowing where the current stores are, we can find the most similar areas for new stores to open.How efficient is a Geohash?Geohash is a faster, cost-effective geofencing option that reduces input data load and provides actionable information. Its benefits include faster querying, reduced cost, minimal configuration, and ease of use.Geohash ranges from 1 to 12 characters. The dataset can be split into variable-size geohashes, with the default being geohash7 (150m x 150m).

Open-Source Geospatial Intelligence for Security Market Outlook

According to our latest research, the global open-source geospatial intelligence for security market size reached USD 2.41 billion in 2024, with a robust year-on-year expansion driven by growing security demands and technological advancements. The market is projected to continue its upward trajectory, reaching USD 6.12 billion by 2033, reflecting a compelling CAGR of 10.8% over the forecast period. This remarkable growth is underpinned by the increasing integration of open-source geospatial data and analytics into security operations, as organizations worldwide seek to enhance situational awareness, streamline threat detection, and improve response mechanisms in a rapidly evolving threat landscape.

One of the primary growth factors propelling the open-source geospatial intelligence for security market is the proliferation of data sources and the democratization of geospatial analytics. The advent of high-resolution satellite imagery, drone surveillance, and crowd-sourced geospatial data has significantly expanded the pool of information available for security applications. Organizations are increasingly turning to open-source platforms to access, analyze, and synthesize this data in real time, enabling more agile and informed decision-making. Moreover, the cost-effectiveness of open-source solutions compared to proprietary alternatives is attracting both public and private sector entities, particularly in resource-constrained environments where maximizing operational efficiency is paramount. As the volume and variety of geospatial data continue to grow, the need for interoperable, scalable, and customizable open-source intelligence platforms will only intensify, further fueling market expansion.

Another significant driver is the evolving nature of security threats, which demand more sophisticated and adaptable intelligence tools. From border security and disaster management to critical infrastructure protection and military operations, the landscape of security challenges is becoming increasingly complex and multidimensional. Open-source geospatial intelligence platforms empower organizations to integrate multiple data streams, conduct advanced spatial analyses, and generate actionable insights tailored to specific mission requirements. The flexibility to customize analytical workflows, coupled with the rapid dissemination of intelligence products, positions open-source solutions as indispensable assets in the fight against both conventional and asymmetric threats. Additionally, the collaborative ethos inherent in open-source communities fosters innovation, accelerates the development of new features, and ensures that solutions remain at the cutting edge of technological progress.

The rapid adoption of cloud computing and advancements in artificial intelligence (AI) and machine learning (ML) are further amplifying the capabilities of open-source geospatial intelligence for security. Cloud-based deployment models facilitate seamless data integration, real-time processing, and global accessibility, making it easier for security agencies to collaborate across jurisdictions and respond to incidents as they unfold. Meanwhile, AI and ML algorithms are enhancing the automation of geospatial analysis, enabling the rapid identification of patterns, anomalies, and emerging threats within vast datasets. These technological enablers are not only improving the speed and accuracy of intelligence generation but also reducing the manpower required for routine analytical tasks. As governments and organizations increasingly prioritize digital transformation, the synergy between open-source geospatial intelligence, cloud infrastructure, and AI-driven analytics will remain a key catalyst for market growth.

Regionally, North America continues to dominate the open-source geospatial intelligence for security market, accounting for the largest share in 2024. This leadership is attributed to the region's advanced security infrastructure, significant investments in geospatial technologies, and the presence of major industry players. Europe follows closely, driven by cross-border security initiatives and robust regulatory frameworks supporting data sharing and interoperability. The Asia Pacific region is emerging as a high-growth market, propelled by increasing security concerns, rapid urbanization, and government-led digitalization programs. Meanwhile, Latin America and the Middle East & Africa are witnessing steady growth as nation

A Minimum Bounding Box (MBB) is a rectangle which bounds a geographic feature or dataset. It is commonly used in spatial information systems as a simplified way of describing the spatial extent of a resource. MBBs are typically indexed for searching and discovering resources relevant to a given geographic area of interest. However, this simplification leads to a loss of precision in the description of the extent and can affect the overall precision of the search results.We propose an alternative technique for describing the spatial extent based on the use of DGGS tiles. To measure the precision improvements offered by our method, we designed and implemented an empirical method for evaluating the average precision, and applied it to three different systems: one based on MBB, another on Convex Hull, and ours based on DGGS.The three methods were evaluated with the same test collection obtained from some of the main European geospatial data catalogues from the INSPIRE directive.The results showed that our method outperformed the other two.Where the catalogue average precision of the MBB search scenarios is between 73% and 97%, the DGGS is between 96% and 99%. Additionally, we propose a realistic method of transitioning from the current technologies to the technology we are proposing, considering the current state of the spatial data infrastructures.