Learn how defense and intelligence users can leverage ArcGIS GeoEvent Server and ArcGIS GeoAnalytics Server to connect to real-time data feeds and run analytics on the stored data. From tracking units in the field to analyzing intelligence feeds and weather, ArcGIS GeoEvent Server enables users to stay current on what is happening. When you want to analyze massive amounts of stored track and report data, ArcGIS GeoAnalytics Server uses distributed computing to return spatiotemporal insight helping you make better planning decisions.

Geo-Analytics SaaS Platform Market Outlook

According to our latest research, the global Geo-Analytics SaaS Platform market size reached USD 5.6 billion in 2024, demonstrating robust growth driven by the increasing demand for spatial data analytics across industries. The market is projected to expand at a CAGR of 18.1% from 2025 to 2033, reaching a forecasted value of USD 28.4 billion by 2033. This remarkable growth trajectory is primarily fueled by the rapid adoption of cloud-based geospatial analytics solutions, the proliferation of IoT devices, and the growing emphasis on real-time location intelligence for business decision-making.

One of the primary growth factors propelling the Geo-Analytics SaaS Platform market is the surging need for advanced location intelligence across various sectors. Organizations are increasingly leveraging geo-analytics platforms to gain actionable insights from spatial data, enabling them to optimize operations, enhance customer experiences, and drive competitive advantage. The integration of AI and machine learning algorithms within these platforms has further amplified their analytical capabilities, allowing for predictive analytics and more accurate forecasting. As businesses continue to embrace digital transformation, the deployment of geo-analytics solutions has become essential for informed decision-making and strategic planning.

Another significant driver is the rising adoption of cloud-based deployment models. Cloud-based geo-analytics SaaS platforms offer unparalleled scalability, flexibility, and cost-efficiency, making them highly attractive to organizations of all sizes. These platforms eliminate the need for extensive on-premises infrastructure, reduce maintenance costs, and provide seamless access to real-time geospatial data from anywhere in the world. The shift towards cloud computing has also facilitated easier integration with other enterprise applications, such as ERP and CRM systems, further expanding the utility and adoption of geo-analytics solutions across industries.

The expanding application landscape of geo-analytics is also contributing to market growth. Industries such as transportation, logistics, retail, BFSI, healthcare, and government are increasingly utilizing geo-analytics for a wide range of use cases, including asset tracking, risk management, supply chain optimization, and urban planning. The ability to visualize and analyze spatial data in real-time has become a critical enabler for operational efficiency and strategic decision-making. Moreover, the ongoing advancements in geospatial data collection technologies, such as drones, satellites, and IoT sensors, are providing richer data sets for analysis, further enhancing the value proposition of geo-analytics SaaS platforms.

Regionally, North America continues to lead the global Geo-Analytics SaaS Platform market, accounting for the largest revenue share in 2024. The region's dominance is attributed to the high concentration of technology-driven enterprises, advanced IT infrastructure, and significant investments in smart city initiatives. Europe and Asia Pacific are also witnessing rapid growth, driven by increasing digitalization efforts, government initiatives for urban development, and the rising adoption of location-based services. Emerging markets in Latin America and the Middle East & Africa are gradually catching up, supported by growing awareness and investments in geospatial technologies.

Component Analysis

The Component segment of the Geo-Analytics SaaS Platform market is broadly categorized into Software and Services. Software solutions form the backbone of geo-analytics platforms, offering a comprehensive suite of tools for data visualization, spatial analysis, and predictive modeling. These software solutions are continuously evolving, with vendors integrating advanced features such as artificial intelligence, machine learning, and real-time analytics to enhance functionality. The growin

Geo-Analytics SaaS Platform Market Outlook

According to our latest research, the global Geo-Analytics SaaS Platform market size reached USD 4.8 billion in 2024, reflecting robust adoption across industries. The market is projected to expand at a CAGR of 17.2% from 2025 to 2033, reaching an estimated USD 19.1 billion by 2033. This growth is primarily driven by the increasing demand for location-based intelligence, the proliferation of IoT devices, and the rising need for advanced analytics to optimize business operations in real time.

The Geo-Analytics SaaS Platform market is experiencing significant momentum due to the exponential growth in spatial data generated by connected devices and sensors. Organizations are increasingly recognizing the value of integrating geospatial analytics into their decision-making processes to gain actionable insights and enhance operational efficiency. The integration of artificial intelligence and machine learning within geo-analytics platforms is further augmenting their analytical capabilities, enabling predictive modeling and real-time visualization. This, in turn, is driving adoption across sectors such as transportation, logistics, retail, and urban planning, where location intelligence is becoming indispensable for strategic planning and resource allocation.

Another compelling growth factor for the Geo-Analytics SaaS Platform market is the shift toward cloud-based solutions. Cloud deployment offers unparalleled scalability, flexibility, and cost-effectiveness, making geo-analytics accessible to organizations of all sizes. The ability to aggregate and analyze vast datasets from multiple sources in real time is a game-changer for industries like BFSI, healthcare, and energy, which rely on timely and accurate data for risk management and compliance. Furthermore, the rise of smart cities and the increasing focus on sustainability initiatives are fueling demand for geo-analytics platforms that can support urban infrastructure management, environmental monitoring, and disaster response.

The emergence of digital transformation initiatives and the growing emphasis on customer-centric business models are also propelling the Geo-Analytics SaaS Platform market forward. Retailers, for instance, are leveraging geo-analytics to optimize store locations, personalize marketing campaigns, and enhance supply chain efficiency. Similarly, the transportation and logistics sector is harnessing these platforms to improve route planning, asset tracking, and fleet management. As organizations strive to stay competitive in a rapidly evolving landscape, the ability to derive actionable insights from spatial data is becoming a key differentiator, further accelerating market growth.

From a regional perspective, North America currently dominates the Geo-Analytics SaaS Platform market, accounting for the largest revenue share in 2024. This is attributed to the presence of leading technology companies, high adoption rates of advanced analytics, and significant investments in smart infrastructure. However, the Asia Pacific region is expected to witness the fastest growth over the forecast period, driven by rapid urbanization, expanding digital ecosystems, and increasing government initiatives to promote smart city projects. Europe, Latin America, and the Middle East & Africa are also poised for steady growth, supported by rising awareness of the benefits of geo-analytics and the expansion of cloud computing infrastructure.

Component Analysis

The Geo-Analytics SaaS Platform market by component is segmented into software and services, each playing a pivotal role in the ecosystem. The software segment holds the largest market share, driven by the continuous evolution of geo-analytics platforms that offer advanced features such as real-time data visualization, spatial modeling, and predictive analytics. These platforms are designed to integrate seamlessly with existing enterprise systems, enabling organizations to harness the power of location intelligence without significant infrastructure investments. The growing adoption of AI-powered analytics and the emphasis on user-friendly interfaces are further enhancing the appeal of geo-analytics software solutions, making them indispensable tools for data-driven decision-making.

The services segment, encompassing consulting, implementation, and support services, is witnessing robust growth as organizations see

The Geoinformation and Big Data Research Laboratory (GIBD) at the University of South Carolina developed the OD Flow Explorer (http://gis.cas.sc.edu/GeoAnalytics/od.html), which provides free human mobility data derived by global wide Geotagged Tweets and the US Safegraph data. To make the data analysis reproducible, replicable, and expandable, we created three case studies based on workflow to visualize data in dynamic maps, and analyze human mobilities' impact on COVID-19 disease transmission in the US and Europe.

The National Population Database (NPD) is a point-based Geographical Information System (GIS) dataset that combines locational information from providers like the Ordnance Survey with population information about those locations, mainly sourced from Government statistics. The points (and sometimes polygons) represent individual buildings, so the NPD allows detailed local analysis for anywhere in Great Britain.

The Health & Safety Laboratory (HSL) working with Staffordshire University originally created the NPD in 2004 to help its parent organisation, the Health and Safety Executive (HSE), assess the risks to society of major hazard sites e.g. oil refineries, chemical works and gas holders. Of particular interest to HSE were 'sensitive' populations e.g. schools and hospitals where the people at those locations may be more vulnerable to harm and potentially harder to evacuate in an emergency. The data is split into 5 themes: residential, sensitive populations, transport, workplaces and leisure.

More information about the NPD can be found here:

https://www.hsl.gov.uk/what-we-do/better-decisions/geoanalytics/national-population-database

The NPD was created using various datasets available within Government as part of the Public Sector Mapping Agreement (PSMA) and contains other intellectual property so is only available under license and for a fee. Please contact the HSL GIS Team if you would like to discuss gaining access to the sample or full dataset.

Geospatial Analytics Market Outlook

As per our latest research, the global geospatial analytics market size stood at USD 98.2 billion in 2024, exhibiting robust momentum driven by the accelerating adoption of spatial data solutions across industries. The market is projected to expand at a CAGR of 13.5% during the forecast period, reaching a remarkable USD 286.5 billion by 2033. This impressive growth is fueled by increasing demand for location-based services, smart city initiatives, and the integration of artificial intelligence with geospatial technologies, which are transforming how organizations derive actionable insights from spatial data.

One of the primary growth factors propelling the geospatial analytics market is the rapid proliferation of advanced sensor technologies and the exponential increase in spatial data generation. The widespread deployment of Internet of Things (IoT) devices, satellites, drones, and mobile sensors is generating vast volumes of geospatial data, which organizations are leveraging to enhance decision-making processes. Additionally, the integration of real-time data streams with sophisticated analytics platforms is enabling businesses and governments to monitor, predict, and respond to dynamic environmental and operational changes with unprecedented accuracy and speed. This trend is particularly evident in sectors such as urban planning, disaster management, and logistics, where location intelligence is critical for optimizing resources and improving outcomes.

Another significant driver of the geospatial analytics market is the growing emphasis on smart city development and infrastructure modernization worldwide. Governments and municipal authorities are increasingly investing in geospatial technologies to support urban planning, infrastructure management, and public safety initiatives. The ability to visualize, analyze, and simulate spatial data is enabling more effective land use planning, traffic management, and utility monitoring, thereby enhancing the quality of urban life. Furthermore, the integration of geospatial analytics with other emerging technologies, such as artificial intelligence and machine learning, is unlocking new possibilities for predictive modeling and scenario analysis, further boosting market growth.

The increasing adoption of cloud-based geospatial analytics platforms is also a crucial factor contributing to market expansion. Cloud deployment offers significant advantages in terms of scalability, cost-efficiency, and accessibility, allowing organizations of all sizes to leverage advanced spatial analytics without the need for substantial upfront investments in hardware and infrastructure. This democratization of geospatial analytics is particularly beneficial for small and medium enterprises (SMEs), which can now access powerful tools for location intelligence, supply chain optimization, and risk management. Moreover, the cloud model facilitates seamless integration with other enterprise applications and data sources, driving greater operational agility and innovation across industries.

The emergence of Geo-Analytics SaaS Platform solutions is revolutionizing how businesses and governments harness geospatial data. These platforms offer a comprehensive suite of tools that allow users to analyze and visualize spatial information in a cloud-based environment, providing unparalleled flexibility and scalability. By leveraging these platforms, organizations can streamline their geospatial workflows, reduce operational costs, and enhance decision-making processes. The integration of advanced analytics capabilities, such as predictive modeling and machine learning, within these platforms is enabling users to uncover hidden patterns and insights from complex datasets. As the demand for real-time spatial analysis continues to grow, the adoption of Geo-Analytics SaaS Platform solutions is expected to accelerate, driving further innovation and market expansion.

From a regional perspective, North America continues to dominate the geospatial analytics market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The United States remains at the forefront of technological innovation and adoption, supported by a robust ecosystem of geospatial solution providers, research institutions, and government agencies. Meanwhile, Asia Pacific is witnes

CyberGIS-Jupyter for Water Quarterly Release Announcement (2020 Q2)

Dear HydroShare Users,

We are pleased to announce a new quarterly release of CyberGIS-Jupyter for Water (CJW) platform at https://go.illinois.edu/cybergis-jupyter-water. This release includes new capabilities to support the geoanalytics suite of GRASS for model pre/post-processing, PostGIS database, and Landlab Earth surface modelling toolkit along with several enhancements to job submission middleware, system security as well as service infrastructure. Please refer to the following list for details and examples.

Please let us know if you have any questions or run into any problems (help@cybergis.org). Any feedback would be greatly appreciated.

Best regards, CyberGIS-Hydro team

GRASS GIS for model pre/post-processing: Learn how to consolidate the features of the GRASS geoanalytics suite to support pre/post-processing for SUMMA and RHESSYs models in CJW. Example notebooks: https://www.hydroshare.org/resource/4cbcfdd6e7f943e2969dd52e780bc52d/

Manage geospatial data with PostGIS: PostGIS is an extension to the PostgreSQL object-relational database system which allows geospatial data to be efficiently stored while providing various advanced functions for in-situ data analysis and processing. Example notebooks: https://www.hydroshare.org/resource/bb779d4cce564dd6afcf463c8910786f/

Security and service infrastructure enhancements Trusted group: Starting from this release, all users are required to join the “CyberGIS-Jupyter for Water” trusted group at https://www.hydroshare.org/group/157 in order to access the CJW platform, which is a preventive measure to protect the shared computing resources from being abused by malicious users. A complete user profile page is highly recommended to expedite the approval process. User metric submission to XSEDE: CJW, as a science gateway, is now sending unique user usage metrics to XSEDE to comply with its requirements.

Landlab for enabling collaborative numerical modeling in Earth sciences using knowledge infrastructure Example notebooks: https://www.hydroshare.org/resource/370c288b61b84794b847ef85c4dd4ffb/ https://www.hydroshare.org/resource/6add6bee06bb4050bfe23e1081627614/

Job submission enhancements Refactored the structure of the cyberGIS job submission system Data-driven implementation for avoiding excessive data transmission between HydroShare and CJW Add the specification of input parameters into a JSON file to improve the flexibility and generality of model management Enable HPC-SUMMA object that can directly call SUMMA Example notebooks: https://www.hydroshare.org/resource/4a4a22a69f92497ead81cc48700ba8f8/

This point layer consists of GLOBE Observer Mosquito Habitat Mapper (MHM) and GLOBE Observer Land Cover (LC) observation data resulting from the following processing steps:MHM GEOJSON Data was pulled from this GLOBE API URL: https://api.globe.gov/search/v1/measurement/protocol/measureddate/?protocols=mosquito_habitat_mapper&startdate=2017-05-01&enddate=2022-12-31&geojson=TRUE&sample=FALSE Only device-reported measurements are kept- "DataSource" = "GLOBE Observer App" As we are only interested in device measurements, latitude and longitude are determined from "MeasurementLatitude" and "MeasurementLongitude". All instances of duplicate photos have been removed from the dataset.LC GEOJSON Data was pulled from this GLOBE API URL:https://api.globe.gov/search/v1/measurement/protocol/measureddate/?protocols=land_covers&startdate=2018-09-01&enddate=2022-12-31&geojson=TRUE&sample=FALSE Only device-reported measurements are kept- "DataSource" = "GLOBE Observer App" As we are only interested in device measurements, latitude and longitude are determined from "MeasurementLatitude" and "MeasurementLongitude".ConcurrenceThese two layers were then combined using a spatiotemporal join with the following conditions: Tool: Geoanalytics Desktop Tools -> Join Features Target Layer: LC Join Type: one to many Join Layer: MHM Coordinate fields used: MeasurementLatitude, MeasurementLongitude Time fields used: MeasuredAt (UTC time) Spatial Proximity: 100 meters (NEAR_GEODESIC) Temporal Proximity: 60 minutes (NEAR) Attribute match: UserIDThe result is a dataset consisting of all paired instances where the same observer (Userid) collected a Mosquito Habitat Mapper observation within 100 meters and 1 hour of collecting a Land Cover observation.Additional fields include:lc_mhm_obsID_pair': A string representing the two paired observations- {lc_LandCoverId}_{mhm_MosquitoHabitatMapperId}'lc_latlon': A string representing the coordinates of the LC observation - "({lc_MeasurementLatitude}, {lc_MeasurementLongitude})"'mhm_latlon': A string representing the coordinates of the MHM observation - "({mhm_MeasurementLatitude}, {mhm_MeasurementLongitude})"'spatialDistanceMeters': Numeric value representing the distance between the two paired observations in meters'temporalDistanceMinutes': Numeric value representing the time delta between the two paired observations in minutes'squareBuffer': A polygon string representing a 100m square centered on the LC observation coordinates. This may be used in conjunction with additional map layers to evaluate the land cover types near the observation coordinates. (n.b. This is not the buffer used in calculating spatiotemporal concurrence)For the purposes of this visualization, point geometries are defined by the land cover measurement coordinates.

The GeoAnalytics software development team has created a new tool to trace proximity events– a tool we’re calling Proximity Tracing. This tool searches for when and where individual entities (for example, animals, people, vehicles, devices) are within a given proximity to other individuals in space and time – what we’re calling proximity events. Tracing potential proximity events can be applied to contact tracing – to help find potential contact events._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...