This paper provides an abstract analysis of parallel processing strategies for spatial and spatio-temporal data. It isolates aspects such as data locality and computational locality as well as redundancy and locally sequential access as central elements of parallel algorithm design for spatial data. Furthermore, the paper gives some examples from simple and advanced GIS and spatial data analysis highlighting both that big data systems have been around long before the current hype of big data and that they follow some design principles which are inevitable for spatial data including distributed data structures and messaging, which are, however, incompatible with the popular MapReduce paradigm. Throughout this discussion, the need for a replacement or extension of the MapReduce paradigm for spatial data is derived. This paradigm should be able to deal with the imperfect data locality inherent to spatial data hindering full independence of non-trivial computational tasks. We conclude that more research is needed and that spatial big data systems should pick up more concepts like graphs, shortest paths, raster data, events, and streams at the same time instead of solving exactly the set of spatially separable problems such as line simplifications or range queries in manydifferent ways.

Geospatial Big Data Platform for Defense Market Outlook

According to our latest research, the global Geospatial Big Data Platform for Defense market size in 2024 reached USD 4.3 billion, driven by the increasing adoption of advanced data analytics and real-time situational awareness solutions across defense sectors worldwide. The market is expected to grow at a robust CAGR of 12.7% from 2025 to 2033, with a projected value of USD 12.7 billion by the end of 2033. This notable expansion is primarily attributed to the rising need for intelligent decision-making, enhanced surveillance capabilities, and the integration of AI-powered geospatial analytics in defense operations.

One of the primary growth factors fueling the Geospatial Big Data Platform for Defense market is the exponential increase in data generated from modern defense systems, including satellites, unmanned aerial vehicles (UAVs), and ground-based sensors. The ability to process, analyze, and visualize massive volumes of geospatial data in near real-time is critical for mission success, especially in intelligence, surveillance, and reconnaissance (ISR) activities. Defense organizations are increasingly investing in advanced platforms that can handle structured and unstructured data, enabling commanders to gain actionable insights and maintain a tactical advantage on the battlefield. The integration of AI and machine learning algorithms into geospatial platforms further enhances data processing speed and accuracy, making these solutions indispensable for modern defense strategies.

Another significant driver is the growing emphasis on interoperability and collaboration among allied forces. Modern military operations often involve joint missions that require seamless data sharing and situational awareness across different branches and nations. Geospatial big data platforms are designed to support standardized data formats, secure communication protocols, and multi-domain operations, facilitating effective coordination in complex scenarios. As defense budgets continue to prioritize digital transformation and network-centric warfare capabilities, the demand for interoperable geospatial solutions is expected to surge, creating substantial growth opportunities for platform providers and technology vendors.

Furthermore, the increasing prevalence of asymmetric warfare and evolving security threats necessitates rapid and informed decision-making. Geospatial big data platforms enable defense agencies to monitor potential threats, track troop movements, and assess environmental variables in real time. The adoption of cloud-based platforms further enhances accessibility and scalability, allowing defense personnel to access critical data from remote or contested environments. As governments worldwide recognize the strategic importance of geospatial intelligence, investments in next-generation big data platforms are projected to accelerate, reinforcing the market’s upward trajectory.

Regionally, North America maintains a dominant position in the Geospatial Big Data Platform for Defense market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The United States, with its substantial defense budget and focus on technological innovation, leads the adoption of advanced geospatial solutions. Europe’s market growth is driven by modernization initiatives and collaborative defense projects among EU member states, while Asia Pacific is witnessing rapid expansion due to rising defense expenditures in China, India, and Southeast Asian countries. Latin America and the Middle East & Africa are gradually increasing their investments in geospatial intelligence, though their market shares remain comparatively smaller. As geopolitical tensions and security challenges persist, regional markets are expected to experience varying growth rates, shaped by local priorities and technological advancements.

Component Analysis

The Component segment of the Geospati

This dataset includes three examples of code for estimating kriging models for big data using bootstrapping. The relevant data examples include: (1) 4,037 oil and gas well in West Virginia. (2) 304,115 campaign donors in California. (3) The biomass of 437 trees from the the Bartlett Experimental Forest.

Geospatial Data Platform Market Outlook

According to our latest research, the global geospatial data platform market size reached USD 108.5 billion in 2024, demonstrating robust expansion driven by digital transformation and increasing demand for location-based analytics. The market is projected to grow at a CAGR of 13.7% from 2025 to 2033, reaching a forecasted value of USD 341.2 billion by 2033. This remarkable growth is attributed to the rising integration of geospatial technologies across sectors such as urban planning, disaster management, transportation, and agriculture, alongside ongoing advancements in cloud computing and artificial intelligence that are reshaping how spatial data is collected, processed, and utilized.

One of the primary growth factors fueling the geospatial data platform market is the escalating adoption of smart city initiatives globally. Urbanization has compelled governments and municipalities to seek innovative solutions for infrastructure management, resource allocation, and public safety, all of which heavily rely on real-time geospatial data. The proliferation of Internet of Things (IoT) devices and sensors has further enriched the data ecosystem, enabling more granular and actionable insights. As cities become more connected and data-driven, the need for robust geospatial platforms that can aggregate, analyze, and visualize complex datasets is becoming indispensable, driving both public and private sector investments in this technology.

Another significant driver is the increasing frequency and intensity of natural disasters, which has heightened the reliance on geospatial data platforms for disaster management and mitigation. Accurate geospatial intelligence is critical for early warning systems, emergency response planning, and post-disaster recovery. Governments, humanitarian agencies, and insurance companies are leveraging these platforms to enhance situational awareness, optimize resource deployment, and minimize losses. The integration of satellite imagery, drone data, and advanced analytics within geospatial platforms enables rapid assessment of affected areas, improving the efficacy of relief operations and long-term resilience planning.

The expansion of the geospatial data platform market is also being propelled by the transformation of industries such as agriculture, utilities, and transportation. Precision agriculture, for example, utilizes spatial data to optimize crop yields, monitor soil health, and manage water resources efficiently. Utilities are adopting geospatial solutions for asset management, outage tracking, and network optimization, while the transportation and logistics sector is leveraging these platforms for route planning, fleet management, and supply chain visibility. The convergence of artificial intelligence, machine learning, and big data analytics with geospatial data platforms is unlocking new levels of operational efficiency and strategic decision-making across these industries.

From a regional perspective, North America continues to dominate the geospatial data platform market due to its advanced technological infrastructure, strong presence of leading market players, and substantial government investments in geospatial intelligence. However, the Asia Pacific region is witnessing the fastest growth, driven by rapid urbanization, expanding infrastructure projects, and increasing adoption of geospatial technologies in emerging economies such as China and India. Europe remains a significant market, supported by regulatory mandates for spatial data sharing and the emphasis on sustainability and environmental monitoring. Latin America and the Middle East & Africa are also experiencing steady growth, albeit from a smaller base, as digital transformation initiatives gain momentum across diverse sectors.

The emergence of the Spatial Computing Platform is revolutionizing how geospatial data is processed and utilized. This platform integrates spatial computing with geospatial technologies, enabling more immersive and interactive data visualization. By leveraging augmented reality (AR) and virtual reality (VR), spatial computing platforms allow users to experience geospatial data in three dimensions, providing a deeper understanding of spatial relationships and patterns. This innovation is particularly beneficial in fields such as urban plannin

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).

Geospatial Intelligence Market Outlook

According to our latest research, the global geospatial intelligence market size reached USD 18.7 billion in 2024, demonstrating robust expansion driven by the integration of advanced analytics and real-time data processing capabilities. The market is expected to grow at a CAGR of 12.4% from 2025 to 2033, propelling the industry to a forecasted value of USD 53.1 billion by 2033. This growth is primarily attributed to the increasing adoption of geospatial solutions across defense, government, and commercial sectors, coupled with continuous technological advancements in remote sensing, AI, and big data analytics.

One of the most significant growth factors for the geospatial intelligence market is the surging demand for real-time location-based services, which are increasingly critical for national security, urban planning, and disaster management. Governments and defense agencies are leveraging geospatial intelligence to enhance surveillance, reconnaissance, and operational planning, thereby improving situational awareness and decision-making processes. The proliferation of high-resolution satellite imagery, UAVs (unmanned aerial vehicles), and IoT-based sensors has further amplified the need for sophisticated geospatial analytics. These technologies enable stakeholders to monitor and respond to dynamic events with unprecedented speed and accuracy, catalyzing the widespread adoption of geospatial intelligence solutions across multiple domains.

Another pivotal driver is the rapid digital transformation witnessed across industries such as transportation, utilities, agriculture, and environmental monitoring. Organizations are increasingly utilizing geospatial intelligence to optimize logistics, manage resources, and ensure regulatory compliance. The integration of AI and machine learning algorithms with geospatial data enables predictive analytics, anomaly detection, and automated mapping, significantly enhancing operational efficiency. Moreover, the growing emphasis on sustainability and climate resilience has spurred investments in geospatial technologies for environmental monitoring, land use planning, and disaster risk reduction. As global challenges like urbanization and climate change intensify, the reliance on geospatial intelligence for informed decision-making is expected to deepen further.

The evolution of cloud computing and big data platforms has also transformed the geospatial intelligence landscape by making advanced analytics more accessible and scalable. Cloud-based deployment models allow organizations to process and analyze vast volumes of geospatial data in real time, facilitating seamless collaboration and data sharing across distributed teams. This shift has democratized access to geospatial intelligence, enabling small and medium enterprises to benefit from sophisticated geospatial analytics without significant upfront investments in hardware or infrastructure. Additionally, the rise of open data initiatives and the availability of public geospatial datasets have fostered innovation and the development of new applications, further propelling market growth.

From a regional perspective, North America continues to dominate the geospatial intelligence market, driven by substantial investments in defense modernization, smart city initiatives, and advanced research. However, the Asia Pacific region is emerging as the fastest-growing market, fueled by rapid urbanization, infrastructure development, and increasing adoption of geospatial technologies in countries like China, India, and Japan. Europe also maintains a strong presence, particularly in environmental monitoring and government-led geospatial initiatives. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, spurred by growing awareness and the need for improved resource management and security solutions.

Component Analysis

The geospatial intelligence market is segmented by component into software, hardware, and services, eac

The Middle East Geospatial Analytics market is booming, projected to reach $2B+ by 2033 with an 8.15% CAGR. Driven by infrastructure development and smart city initiatives, this report analyzes market trends, key players (Esri, Autodesk), and segment growth across sectors like agriculture, defense, and utilities in Saudi Arabia, UAE, and other Middle Eastern nations. Recent developments include: June 2023: Autodesk and Esri's partnership accelerated innovations in AEC. Autodesk's InfoWater Pro and Esri's ArcGIS Pro were integrated to make this possible, and there are many more examples of how their partnership with Esri enables BIM and GIS data to flow between respective solutions seamlessly. The result is that project stakeholders can now visualize, understand, and analyze infrastructure within its real-world context., February 2023: Mercedes-Benz and Google announced a long-term strategic partnership to accelerate auto innovation and create the industry's next-generation digital luxury car experience. With this partnership, Mercedes-Benz will be the first automaker to build its branded navigation experience based on new in-car data and navigation capabilities from the Google Maps Platform. This will give the luxury automaker access to Google's leading geospatial offering, including detailed information about places, real-time and predictive traffic information, automatic rerouting, and more.. Key drivers for this market are: Increasing in Demand for Location Intelligence, Advancements of Big Data Analytics. Potential restraints include: Increasing in Demand for Location Intelligence, Advancements of Big Data Analytics. Notable trends are: Surface Analysis is Expected to Hold Significant Share of the Market.

Example data associated with geodl package (https://github.com/maxwell-geospatial/geodl). This version does not include the image chips used to train models, so is smaller.

Managing 245 million acres of land and 700 million acres of mineral estate is a big task. The BLM recognizes that geospatial information is a critical tool for managing public lands. We’ve already made great strides in creating national datasets, supporting almost every program in the Bureau. The BLM has adopted a ground-up approach to managing public lands, and the geospatial program is providing the structure and tools to accomplish this strategy. We manage spatial data to support multiple activities at varying scales.

The BLM's geospatial strategy focuses on collection, organization, and use of baseline resource management data, like fenceline and transportation data and enhancing predictions based on geospatial data. Examples of activities that require geospatial data include planning and resource management, special status species monitoring, regional mitigation, and renewable energy projects, just to name a few.

An important factor in implementing our strategy is using a geographic information system (GIS) that is consistent and integrated within the Bureau and the Department of the Interior. This internal cohesion enhances the BLM's ability to partner with other Federal agencies, collaborate with State and Tribal governments, and communicate with the public.

Xtract.io’s massive 3.5M+ POI database represents a transformative resource for advanced location intelligence across the United States and Canada. Data scientists, GIS professionals, big data analysts, market researchers, and strategic planners can leverage these comprehensive places data insights to develop sophisticated market strategies, conduct advanced spatial analyses, and gain a deep understanding of regional geographical landscapes.

Point of Interest (POI) data, also known as places data, provides the exact location of buildings, stores, or specific places. It has become essential for businesses to make smarter, geography-driven decisions in today's competitive landscape with comprehensive POI coverage.

LocationsXYZ, the POI data product from Xtract.io, offers a comprehensive POI database of 6 million locations across the US, UK, and Canada, spanning 11 diverse industries, including: -Retail -Restaurants -Healthcare -Automotive -Public utilities (e.g., ATMs, park-and-ride locations) -Shopping malls, and more

Why Choose LocationsXYZ for Comprehensive Location Data? At LocationsXYZ, we: -Deliver 3.5M+ POI data with 95% accuracy -Refresh places data every 30, 60, or 90 days to ensure the most recent information -Create on-demand comprehensive POI datasets tailored to your specific needs -Handcraft boundaries (geofences) for locations to enhance accuracy -Provide multi-industry POI data and polygon data in multiple file formats

Unlock the Power of Places Data With our comprehensive location intelligence, you can: -Perform thorough market analyses across multiple industries -Identify the best locations for new stores using POI database insights -Gain insights into consumer behavior with places data -Achieve an edge with competitive intelligence using comprehensive coverage

LocationsXYZ has empowered businesses with geospatial insights and comprehensive location data, helping them scale and make informed decisions. Join our growing list of satisfied customers and unlock your business's potential with our cutting-edge 3.5M+ POI database.

The size of the Israel Geospatial Analytics Market market was valued at USD 1.69 Million in 2023 and is projected to reach USD 2.53 Million by 2032, with an expected CAGR of 5.93% during the forecast period. Recent developments include: June 2023: Autodesk and Esri's partnership accelerates innovations in AEC. Autodesk's InfoWater Pro and Esri's ArcGIS Pro were integrated to make this possible, and there are many more examples of how their partnership with Esri enables BIM and GIS data to flow between respective solutions seamlessly. The result is that project stakeholders can now visualize, understand, and analyze infrastructure within its real-world context., February 2023: Mercedes-Benz and Google announced a long-term strategic partnership to accelerate auto innovation and create the industry's next-generation digital luxury car experience. With this partnership, Mercedes-Benz will be the first automaker to build its branded navigation experience based on new in-car data and navigation capabilities from the Google Maps Platform. This will give the luxury automaker access to Google's leading geospatial offering, including detailed information about places, real-time and predictive traffic information, automatic rerouting, and more.. Key drivers for this market are: Increasing in Demand for Location Intelligence, Advancements of Big Data Analytics. Potential restraints include: High Costs and Operational Concerns, Concerns related to Geoprivacy and Confidential Data. Notable trends are: Surface Analysis is Expected to Hold Significant Share of the Market.

Data files for the examples in the book Geographic Data Science in R: Visualizing and Analyzing Environmental Change by Michael C. Wimberly.

This resource contains the pre-computed land indication datasets (also known as the "Prior" datasets) developed for the preprint "Methodological Framework for Determining the Land Eligibility of Renewable Energy Sources" [1] and evaluated in the publication "Evaluating Land Eligibility Constraints of Renewable Energy Sources in Europe" [2]. Please cite these sources if this data is used in published works. Note that the original sources from which these datasets were processed are also indicated in the README.txt file, which should also be credited.

The main aspects of the Prior datasets are: 1. Each Prior dataset is a raster file covering the European domain (see note N1) * Spatial reference system is set as "EPSG:3035" * Spatial resolution is 100 meters * Datatype is "UInt8" 2. Each dataset represents exactly one geospatial criteria * Example: "road_proximity" = The distance of each pixel from the nearest roadway 3. Pixel values represent edge indexes, rather than the explicit values themselves * This was chosen to conserve storage space * Example: For "road_proximity"... "Index Value" "Edge Value" 0 = "within 0 meters" 1 = "within 50 meters" 2 = "within 100 meters" 3 = "within 200 meters" 4. Each prior dataset represents a processed view of the fundamental data source, therefore if any of this data is used in a published work the fundamental source should also be cited

View README.txt for more information

The following datasets are available with this resource: 1. agriculture_arable_proximity 2. agriculture_heterogeneous_proximity 3. agriculture_pasture_proximity 4. agriculture_permanent_crop_proximity 5. agriculture_proximity 6. airfield_proximity 7. airport_proximity 8. camping_proximity 9. dni_threshold 10. elevation_threshold 11. ghi_threshold 12. industrial_proximity 13. lake_proximity 14. leisure_proximity 15. mining_proximity 16. ocean_proximity 17. power_line_proximity 18. connection_distance 19. protected_biosphere_proximity 20. protected_bird_proximity 21. protected_habitat_proximity 22. protected_landscape_proximity 23. protected_natural_monument_proximity 24. protected_park_proximity 25. protected_reserve_proximity 26. protected_wilderness_proximity 27. railway_proximity 28. river_proximity 29. roads_main_proximity 30. roads_proximity 31. access_distance 32. roads_secondary_proximity 33. sand_proximity 34. settlement_proximity 35. settlement_urban_proximity 36. slope_north_facing_threshold 37. slope_threshold 38. touristic_proximity 39. waterbody_proximity 40. wetland_proximity 41. windspeed_100m_threshold 42. windspeed_50m_threshold 43. woodland_coniferous_proximity 44. woodland_deciduous_proximity 45. woodland_mixed_proximity 46. woodland_proximity

AI for 3D GIS Analytics Market Outlook

According to our latest research, the AI for 3D GIS Analytics market size reached USD 2.38 billion in 2024, reflecting robust adoption across various industries. The market is expected to grow at a CAGR of 18.7% from 2025 to 2033, forecasting a value of USD 12.3 billion by 2033. The primary growth driver for this market is the increasing demand for advanced spatial analytics solutions that leverage artificial intelligence to enable more precise, real-time, and actionable insights from complex geospatial data sets.

The rapid urbanization across the globe is significantly fueling the adoption of AI for 3D GIS Analytics. City planners and government agencies are increasingly relying on these advanced systems to model urban growth, optimize land use, and manage infrastructure development efficiently. The integration of AI with 3D GIS enables the processing of large-scale geospatial data, automating the analysis of urban expansion, transportation networks, and public utilities. This not only improves the accuracy of planning but also reduces the time and resources required for manual data interpretation. As urban populations swell, the need for smarter, data-driven city management solutions is propelling the growth of this market.

Another major growth factor is the rising emphasis on environmental monitoring and disaster management. Governments and organizations are leveraging AI for 3D GIS Analytics to assess environmental changes, predict natural disasters, and respond more effectively to emergencies. AI-powered 3D GIS platforms can analyze satellite imagery, sensor data, and historical records to identify patterns and predict potential risks such as floods, landslides, or wildfires. This proactive approach not only saves lives but also minimizes economic losses, making these solutions indispensable for both public and private sector stakeholders. The growing frequency of extreme weather events and environmental hazards is thus accelerating the adoption of AI-driven 3D GIS analytics worldwide.

Technological advancements in cloud computing and the proliferation of IoT devices have also played a crucial role in the expansion of the AI for 3D GIS Analytics market. The cloud-based deployment of 3D GIS solutions enables organizations to access and process vast geospatial datasets without the need for significant on-premises infrastructure investment. Meanwhile, IoT sensors continuously feed real-time data into these systems, enhancing the granularity and accuracy of spatial analysis. The convergence of AI, cloud, and IoT technologies is fostering a new era of intelligent geospatial analytics, enabling industries such as utilities, transportation, and real estate to optimize operations, reduce costs, and enhance service delivery.

Regionally, North America holds the largest share in the AI for 3D GIS Analytics market due to the early adoption of advanced technologies and substantial investments in smart city projects. Europe follows closely, driven by stringent regulations on environmental monitoring and urban planning. The Asia Pacific region is expected to witness the fastest growth, propelled by rapid urbanization, infrastructure development, and increasing government initiatives to harness AI for spatial analytics. Latin America and the Middle East & Africa are also emerging as promising markets, supported by growing awareness and investments in geospatial intelligence solutions.

Component Analysis

The AI for 3D GIS Analytics market by component is segmented into software, hardware, and services, each playing a pivotal role in the ecosystem. The software segment dominates the market, accounting for the largest revenue share in 2024. This dominance is attributed to the continuous innovations in AI algorithms and 3D visualization tools that enhance the capability of GIS platforms to process, analyze, and visualize complex spatial data. Leading software providers are integrating machine learning, deep learning, and computer vision technologies to automate feature extraction, anomaly detection,

Enterprise GIS Market Outlook

According to our latest research, the global Enterprise GIS market size reached USD 8.4 billion in 2024, reflecting the rapid adoption of geospatial technologies across various sectors. The market is expected to grow at a robust CAGR of 11.2% during the forecast period, reaching a projected value of USD 24.1 billion by 2033. This remarkable growth is primarily driven by the increasing need for real-time geographic data, advancements in cloud-based GIS solutions, and the rising integration of GIS with emerging technologies such as IoT, AI, and big data analytics.

One of the most significant growth factors for the Enterprise GIS market is the expanding requirement for spatial data analytics in decision-making processes across industries. As organizations strive to enhance operational efficiency and resource allocation, the demand for advanced mapping and spatial analysis tools has surged. Enterprises in sectors like utilities, government, transportation, and oil & gas are leveraging GIS platforms for asset management, infrastructure planning, and disaster management. The ability of Enterprise GIS to provide actionable insights through real-time data visualization and predictive analytics is proving invaluable for both public and private sector entities, thereby fueling market expansion.

Another key driver is the technological evolution of GIS platforms, particularly the shift towards cloud-based deployment models. Cloud-based Enterprise GIS solutions offer scalable, flexible, and cost-effective alternatives to traditional on-premises systems. This transition enables organizations to manage vast geospatial datasets, collaborate across distributed teams, and integrate GIS capabilities with other enterprise applications. The proliferation of mobile devices and IoT sensors is further augmenting the adoption of cloud GIS, as it facilitates seamless data collection, sharing, and analysis from remote locations. The result is a significant boost in the operational agility and responsiveness of enterprises, which is accelerating the adoption of Enterprise GIS solutions globally.

The increasing regulatory emphasis on sustainable development, urban planning, and environmental monitoring is also contributing to the growth of the Enterprise GIS market. Governments and regulatory bodies worldwide are mandating the use of spatial data for land management, infrastructure development, and resource conservation. This regulatory push is compelling organizations to invest in robust GIS platforms that can support compliance, reporting, and long-term planning. Furthermore, the integration of artificial intelligence and machine learning with GIS is enabling predictive modeling and automation, which are critical for proactive decision-making in dynamic environments. These factors collectively underscore the strategic importance of Enterprise GIS in driving digital transformation and resilience across industries.

From a regional perspective, North America continues to dominate the Enterprise GIS market, accounting for the largest revenue share in 2024. The region’s leadership is attributed to the presence of major GIS vendors, advanced IT infrastructure, and high adoption rates across government and utility sectors. Meanwhile, Asia Pacific is emerging as the fastest-growing region, propelled by rapid urbanization, infrastructure investments, and government initiatives promoting smart cities and digital governance. Europe also holds a significant share, driven by stringent environmental regulations and the increasing adoption of geospatial technologies in sectors such as transportation and telecommunications. Latin America and the Middle East & Africa are witnessing steady growth, supported by investments in infrastructure modernization and resource management.

Component Analysis

The Enterprise GIS market by component is segmented into software, hardware, and services, each playing a pivotal role in the overall ecosystem. The software segment current

This resource contains the CAMELS-SPAT data set. CAMELS-SPAT provides data that can support hydrologic modeling and analysis for 1426 streamflow measurement stations located across the United States and Canada.

The area upstream of each station has been divided into various subbasins. The provided data include: (1) shapefiles outlining the location of each basin and its subbasins, (2) streamflow observations at daily and hourly resolution at the outlet of each basin, (3) meteorological data from 4 different data sets (RDRS, EM-Earth, ERA5, Daymet), at their native gridded resolution as well as averaged to the basin and subbasin level, (4) geospatial data from 11 different data at their native gridded resolution, and (5) statistical summaries (i.e. catchment attributes) calculated from the streamflow, meteorological and geospatial data at the basin and subbasin level.

Data set structure is described in the README found in this repository. Data set development is described in Knoben et al (under review; https://doi.org/10.5194/egusphere-2025-893). When using the CAMELS-SPAT data, please follow the attribution guidelines provided in Section 6 in this paper (briefly, individual attribution of any data set included in CAMELS-SPAT is requested if this data is used). BibTeX entries for the individual data sources aggregated in CAMELS-SPAT are provided in the citation.bib file found in this repository.

Temporary reference: Knoben, W. J. M., Keshavarz, K., Torres-Rojas, L., Thébault, C., Chaney, N. W., Pietroniro, A., and Clark, M. P.: Catchment Attributes and MEteorology for Large-Sample SPATially distributed analysis (CAMELS-SPAT): Streamflow observations, forcing data and geospatial data for hydrologic studies across North America, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-893, 2025

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The GIS market share in EMEA is expected to increase to USD 2.01 billion from 2021 to 2026, and the market’s growth momentum will accelerate at a CAGR of 8.23%.

This EMEA GIS market research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches. Furthermore, this report extensively covers GIS market in EMEA segmentation by:

Component - Software, data, and services
End-user - Government, utilities, military, telecommunication, and others

What will the GIS Market Size in EMEA be During the Forecast Period?

Download the Free Report Sample to Unlock the GIS Market Size in EMEA for the Forecast Period and Other Important Statistics

The EMEA GIS market report also offers information on several market vendors, including arxiT SA, Autodesk Inc., Bentley Systems Inc., Cimtex International, CNIM SA, Computer Aided Development Corp. Ltd., Environmental Systems Research Institute Inc., Fugro NV, General Electric Co., HERE Global BV, Hexagon AB, Hi-Target, Mapbox Inc., Maxar Technologies Inc., Pitney Bowes Inc., PSI Services LLC, Rolta India Ltd., SNC Lavalin Group Inc., SuperMap Software Co. Ltd., Takor Group Ltd., and Trimble Inc. among others.

GIS Market in EMEA: Key Drivers, Trends, and Challenges

The integration of BIM and GIS is notably driving the GIS market growth in EMEA, although factors such as data viability and risk of intrusion may impede market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the GIS industry in EMEA. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

Key GIS Market Driver in EMEA

One of the key factors driving the geographic information system (GIS) market growth in EMEA is the integration of BIM and GIS. A GIS adds value to BIM by visualizing and analyzing the data with regard to the buildings and surrounding features, such as environmental and demographic information. BIM data and workflows include information regarding sensors and the placement of devices in IoT-connected networks. For instance, Dubai's Civil Defense Department has integrated GIS data with its automatic fire surveillance system. This information is provided in a matter of seconds on the building monitoring systems of the Civil Defense Department. Furthermore, location-based services offered by GIS providers help generate huge volumes of data from stationary and moving devices and enable users to perform real-time spatial analytics and derive useful geographic insights from it. Owing to the advantages associated with the integration of BIM with GIS solutions, the demand for GIS solutions is expected to increase during the forecast period.

Key GIS Market Challenge in EMEA

One of the key challenges to the is the GIS market growth in EMEA is the data viability and risk of intrusion. Hackers can hack into these systems with malicious intentions and manipulate the data, which could have destructive or negative repercussions. Such hacking of data could cause nationwide chaos. For instance, if a hacker manipulated the traffic management database, massive traffic jams and accidents could result. If a hacker obtained access to the database of a national disaster management organization and manipulated the data to create a false disaster situation, it could lead to a panic situation. Therefore, the security infrastructure accompanying the implementation of GIS software solutions must be robust. Such security threats may impede market growth in the coming years.

Key GIS Market Trend in EMEA

Integration of augmented reality (AR) and GIS is one of the key geographic information system market trends in EMEA that is expected to impact the industry positively in the forecast period. AR apps could provide GIS content to professional end-users and aid them in making decisions on-site, using advanced and reliable information available on their mobile devices and smartphones. For instance, when the user simply points the camera of the phone at the ground, the application will be able to show the user the location and orientation of water pipes and electric cables that are concealed underground. Organizations such as the Open Geospatial Consortium (OGC) and the World Wide Web Consortium (W3C) are seeking investments and are open to sponsors for an upcoming AR pilot project, which seeks to advance the standards of AR technology at both respective organizations. Such factors will further support the market growth in the coming years.

This GIS market in EMEA analysis report also provides detailed information on other upcoming trends and challenges that will have a far-reaching effect on the market growth. The actionable insights on the trends and challenges will help companies evaluate and develop growth strategies for 2022-202

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

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

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

Overview

The Office of the Geographer and Global Issues at the U.S. Department of State produces the Large Scale International Boundaries (LSIB) dataset. The current edition is version 11.4 (published 24 February 2025). The 11.4 release contains updated boundary lines and data refinements designed to extend the functionality of the dataset. These data and generalized derivatives are the only international boundary lines approved for U.S. Government use. The contents of this dataset reflect U.S. Government policy on international boundary alignment, political recognition, and dispute status. They do not necessarily reflect de facto limits of control.

National Geospatial Data Asset

This dataset is a National Geospatial Data Asset (NGDAID 194) managed by the Department of State. It is a part of the International Boundaries Theme created by the Federal Geographic Data Committee.

Dataset Source Details

Sources for these data include treaties, relevant maps, and data from boundary commissions, as well as national mapping agencies. Where available and applicable, the dataset incorporates information from courts, tribunals, and international arbitrations. The research and recovery process includes analysis of satellite imagery and elevation data. Due to the limitations of source materials and processing techniques, most lines are within 100 meters of their true position on the ground.

Cartographic Visualization

The LSIB is a geospatial dataset that, when used for cartographic purposes, requires additional styling. The LSIB download package contains example style files for commonly used software applications. The attribute table also contains embedded information to guide the cartographic representation. Additional discussion of these considerations can be found in the Use of Core Attributes in Cartographic Visualization section below.

Additional cartographic information pertaining to the depiction and description of international boundaries or areas of special sovereignty can be found in Guidance Bulletins published by the Office of the Geographer and Global Issues: https://data.geodata.state.gov/guidance/index.html

Contact

Direct inquiries to internationalboundaries@state.gov. Direct download: https://data.geodata.state.gov/LSIB.zip

Attribute Structure

The dataset uses the following attributes divided into two categories: ATTRIBUTE NAME | ATTRIBUTE STATUS CC1 | Core CC1_GENC3 | Extension CC1_WPID | Extension COUNTRY1 | Core CC2 | Core CC2_GENC3 | Extension CC2_WPID | Extension COUNTRY2 | Core RANK | Core LABEL | Core STATUS | Core NOTES | Core LSIB_ID | Extension ANTECIDS | Extension PREVIDS | Extension PARENTID | Extension PARENTSEG | Extension

These attributes have external data sources that update separately from the LSIB: ATTRIBUTE NAME | ATTRIBUTE STATUS CC1 | GENC CC1_GENC3 | GENC CC1_WPID | World Polygons COUNTRY1 | DoS Lists CC2 | GENC CC2_GENC3 | GENC CC2_WPID | World Polygons COUNTRY2 | DoS Lists LSIB_ID | BASE ANTECIDS | BASE PREVIDS | BASE PARENTID | BASE PARENTSEG | BASE

The core attributes listed above describe the boundary lines contained within the LSIB dataset. Removal of core attributes from the dataset will change the meaning of the lines. An attribute status of “Extension” represents a field containing data interoperability information. Other attributes not listed above include “FID”, “Shape_length” and “Shape.” These are components of the shapefile format and do not form an intrinsic part of the LSIB.

Core Attributes

The eight core attributes listed above contain unique information which, when combined with the line geometry, comprise the LSIB dataset. These Core Attributes are further divided into Country Code and Name Fields and Descriptive Fields.

County Code and Country Name Fields

“CC1” and “CC2” fields are machine readable fields that contain political entity codes. These are two-character codes derived from the Geopolitical Entities, Names, and Codes Standard (GENC), Edition 3 Update 18. “CC1_GENC3” and “CC2_GENC3” fields contain the corresponding three-character GENC codes and are extension attributes discussed below. The codes “Q2” or “QX2” denote a line in the LSIB representing a boundary associated with areas not contained within the GENC standard.

The “COUNTRY1” and “COUNTRY2” fields contain the names of corresponding political entities. These fields contain names approved by the U.S. Board on Geographic Names (BGN) as incorporated in the ‘"Independent States in the World" and "Dependencies and Areas of Special Sovereignty" lists maintained by the Department of State. To ensure maximum compatibility, names are presented without diacritics and certain names are rendered using common cartographic abbreviations. Names for lines associated with the code "Q2" are descriptive and not necessarily BGN-approved. Names rendered in all CAPITAL LETTERS denote independent states. Names rendered in normal text represent dependencies, areas of special sovereignty, or are otherwise presented for the convenience of the user.

Descriptive Fields

The following text fields are a part of the core attributes of the LSIB dataset and do not update from external sources. They provide additional information about each of the lines and are as follows: ATTRIBUTE NAME | CONTAINS NULLS RANK | No STATUS | No LABEL | Yes NOTES | Yes

Neither the "RANK" nor "STATUS" fields contain null values; the "LABEL" and "NOTES" fields do. The "RANK" field is a numeric expression of the "STATUS" field. Combined with the line geometry, these fields encode the views of the United States Government on the political status of the boundary line.

ATTRIBUTE NAME | | VALUE | RANK | 1 | 2 | 3 STATUS | International Boundary | Other Line of International Separation | Special Line

A value of “1” in the “RANK” field corresponds to an "International Boundary" value in the “STATUS” field. Values of ”2” and “3” correspond to “Other Line of International Separation” and “Special Line,” respectively.

The “LABEL” field contains required text to describe the line segment on all finished cartographic products, including but not limited to print and interactive maps.

The “NOTES” field contains an explanation of special circumstances modifying the lines. This information can pertain to the origins of the boundary lines, limitations regarding the purpose of the lines, or the original source of the line.

Use of Core Attributes in Cartographic Visualization

Several of the Core Attributes provide information required for the proper cartographic representation of the LSIB dataset. The cartographic usage of the LSIB requires a visual differentiation between the three categories of boundary lines. Specifically, this differentiation must be between:

• International Boundaries (Rank 1);
• Other Lines of International Separation (Rank 2); and
• Special Lines (Rank 3).

Rank 1 lines must be the most visually prominent. Rank 2 lines must be less visually prominent than Rank 1 lines. Rank 3 lines must be shown in a manner visually subordinate to Ranks 1 and 2. Where scale permits, Rank 2 and 3 lines must be labeled in accordance with the “Label” field. Data marked with a Rank 2 or 3 designation does not necessarily correspond to a disputed boundary. Please consult the style files in the download package for examples of this depiction.

The requirement to incorporate the contents of the "LABEL" field on cartographic products is scale dependent. If a label is legible at the scale of a given static product, a proper use of this dataset would encourage the application of that label. Using the contents of the "COUNTRY1" and "COUNTRY2" fields in the generation of a line segment label is not required. The "STATUS" field contains the preferred description for the three LSIB line types when they are incorporated into a map legend but is otherwise not to be used for labeling.

Use of

GEOINT Data Fabric Market Outlook

According to our latest research, the global GEOINT Data Fabric market size in 2024 stands at USD 4.28 billion, with a robust compound annual growth rate (CAGR) of 14.7% expected through the forecast period. By 2033, the GEOINT Data Fabric market is projected to reach an impressive USD 13.51 billion. This growth is primarily driven by the escalating demand for real-time geospatial intelligence, increasing adoption of advanced analytics, and the proliferation of connected devices and sensors across multiple sectors. The integration of artificial intelligence and machine learning with geospatial data is transforming how organizations derive actionable insights, fueling the rapid expansion of the GEOINT Data Fabric market globally.

One of the primary growth factors for the GEOINT Data Fabric market is the surging need for sophisticated situational awareness solutions in defense and intelligence applications. As global security threats become more complex and multidimensional, defense agencies are investing heavily in advanced geospatial intelligence platforms that can seamlessly integrate data from diverse sources, including satellite imagery, signals intelligence, and open-source data. The ability to process and analyze massive volumes of geospatial data in real time is critical for mission planning, threat assessment, and operational effectiveness. Furthermore, the ongoing modernization of military infrastructure and the increasing reliance on unmanned systems and autonomous vehicles are further intensifying the demand for robust GEOINT Data Fabric solutions.

Another significant driver is the expanding role of GEOINT Data Fabric in disaster management and urban planning. Governments and organizations worldwide are leveraging geospatial intelligence to enhance disaster preparedness, response, and recovery operations. The integration of real-time imagery, environmental monitoring data, and predictive analytics enables authorities to make informed decisions during natural disasters such as floods, wildfires, and earthquakes. Additionally, urban planners are utilizing GEOINT Data Fabric to optimize infrastructure development, manage resources efficiently, and ensure sustainable urban growth. The rising frequency of climate-related events and the need for resilient urban ecosystems are accelerating the adoption of GEOINT Data Fabric technologies across various regions.

Technological advancements and the growing adoption of cloud-based deployment models are also propelling the GEOINT Data Fabric market forward. Cloud infrastructure offers unparalleled scalability, flexibility, and cost-effectiveness, enabling organizations to store, process, and analyze vast amounts of geospatial data without the limitations of traditional on-premises systems. The convergence of big data analytics, IoT, and artificial intelligence within cloud environments is unlocking new possibilities for real-time geospatial intelligence, predictive modeling, and automated decision-making. As organizations seek to harness the full potential of their geospatial data assets, the demand for integrated, secure, and scalable GEOINT Data Fabric solutions is expected to surge.

From a regional perspective, North America continues to dominate the GEOINT Data Fabric market, driven by substantial investments in defense modernization, technological innovation, and a strong presence of leading market players. However, the Asia Pacific region is witnessing the fastest growth, fueled by increasing government initiatives, rapid urbanization, and expanding commercial applications of geospatial intelligence. Europe is also emerging as a significant market, supported by cross-border collaborations, regulatory mandates, and a growing focus on environmental monitoring and smart city projects. The Middle East & Africa and Latin America are gradually embracing GEOINT Data Fabric solutions, particularly in sectors such as oil and gas, agriculture, and infrastructure development.

Component Analysis