Historical ownership data of GIS by Morningstar Investment Services LLC

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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 st

This data set is intended to serve as a resource for analysis rather than regulatory delineations. Digitizing of this data was initially done by Aerial Information Systems, Inc., Redlands, CA, under direction of the New Jersey Department of Environmental Protection (NJDEP), Bureau of Geographic Information System (BGIS). Conflation of USGS 1:24,000 High resolution NHD attributes was done by Data Enhancement Services, LLC (DES) partnered with Civil Solutions. This statewide conflation of NHD High resolution information was completed in 2010. All QA/QC was done by NJDEP, Bureau of GIS and USGS. Somerset County data extracted & processed from the latest NJDEP dataset by the Somerset County Office of GIS Services (SCOGIS) on April 05, 2024

Historical holdings data showing quarterly positions, market values, shares held, and portfolio percentages for GIS held by Morningstar Investment Services LLC from Q3 2013 to Q4 2024

The global Geographic Information System (GIS) market was valued at USD 10.76 billion in 2025 and is projected to grow at a CAGR of 8.7% from 2025 to 2033. The increasing adoption of GIS in various industries, such as utilities, construction, and transportation, is driving the market growth. Additionally, the rising demand for accurate and timely geospatial information for decision-making is further fueling the market expansion. Key market trends include the increasing popularity of cloud-based GIS solutions, the integration of GIS with other technologies such as IoT and AI, and the growing adoption of GIS in developing countries. The hardware segment is expected to hold the largest market share, followed by the software and services segments. North America is the largest regional market for GIS, followed by Europe and Asia Pacific. The increasing adoption of GIS in smart city projects and the need for improved infrastructure management are expected to drive growth in the GIS market in these regions. Major players in the market include Autodesk Inc., Bentley Systems, CARTO, Environmental Systems Research Institute, Inc., Hexagon AB, Pitney Bowes Inc., SuperMap Software Co., Ltd., TOPCON CORPORATION, Trimble Inc., and L3Harris Technologies, Inc. The global Geographic Information System (GIS) market is growing rapidly, driven by the increasing adoption of GIS technology across various industries. The market is expected to reach USD 400 billion by 2027, growing at a CAGR of 15%. Recent developments include: In July 2024, Ceinsys Tech Ltd. announced the expansion of its GIS services portfolio in the U.S. market with the asset purchase of Virtual Tours, LLC. , In May 2024, NV5 Global, Inc. announced the acquisition of GIS Solutions, Inc., which provides enterprise GIS technologies and services such as GIS application development and cloud-based database design. , In April 2023, Trimble Inc. launched Trimble Unity AMS solution, which is the GIS-centric electric-based platform developed to manage the lifecycle of asset infrastructure. .

In the spring of 2008, the City of Baltimore expressed an interest to upgrade the City GIS Database with mapping quality airborne LiDAR data. The City of Baltimore currently had in place a contract for mapping GIS/services with the KCI/Sanborn Joint Venture Partnership, L.L.C. under Project 1051. The City of Baltimore issued Change Order #1 on on Project 1051 for the LiDAR acquisition and processing. KCI/Sanborn acquired the LiDAR data over the City of Baltimore (approximately 90 square miles) during one long mission on 15 April 2008. A Leica Airborne Airborne Laser Scanner Model ALS 50 was used in a Sanborn Aero Commander 500B (Registration N6172X) to acquire the data. The airborne mission was flown 15 April 2008. The LiDAR system acquired calibration data the same day by conducting flight passes over a known ground surface before and the LiDAR mission. During final data processing, the calibration parameters were used in the final post-processing software. The acquired LiDAR data were processed to obtain the following deliverables: -DEMs of first and last returns and bare earth (all point data) -LAS format data in a tile grid provided by the City/KCI -Gridded DEM in ESRI format (Arc Binary Grid -Arc/INFO Lattice ) produced from Bare Earth Mass Points in 1-meter resolution (Grid size) -Associated FGDC compliant metadata in XML format The project specifications called for the LiDAR survey to comply with industry standard FEMA guidelines for accuracy. The FEMA requirement for this type of mapping calls for vertical errors not to exceed 0.185 meter (0.61 feet) RMSE when compared with ground check points over open flat terrain and 0.370 meter (1.22 feet) for other types of terrain. This accuracy requirement was comfortably met with comparing LiDAR derived elevations against 50 ground control check points provided by KCI Technologies. These control points were photo control points used as part of the photogrammetric mapping project and independent LiDAR checkpoints surveyed by KCI and Mercado after the data was delivered. The RMSE error observed was 0.058 meter (0.189 feet) over all 50 check points.

Product: These lidar data are processed Classified LAS 1.4 files, formatted to 23,381 individual 2,500 ft x 2,500 ft tiles; used to create intensity images, 3D breaklines and hydro-flattened DEMs as necessary. Geographic Extent: CT Statewide covering approximately 5,241 square miles. Dataset Description: CT Statewide GIS Services Lidar project called for the Planning, Acquisition, proces...

A map used in the Lead Service Line Viewer application to view the utility and customer side of lead service lines.

The India Geospatial Analytics Market is experiencing robust growth, projected to reach $1.38 billion in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 14.82% from 2025 to 2033. This expansion is fueled by several key drivers. Firstly, increasing government initiatives promoting digitalization and infrastructure development create significant demand for geospatial data and analytics across sectors like agriculture, utilities, and defense. Secondly, the rising adoption of advanced technologies such as AI, Machine Learning, and IoT enhances the capabilities of geospatial analytics, leading to more accurate insights and improved decision-making. Furthermore, the growing need for efficient resource management, precise urban planning, and enhanced disaster response mechanisms further propel market growth. Segmentation reveals strong contributions from surface analysis and network analysis within the 'By Type' category, while the 'By End-user Vertical' segment is dominated by Agriculture, Utility & Communication, and Defense & Intelligence sectors, reflecting their significant reliance on location-based intelligence. However, challenges exist. Data security and privacy concerns, particularly with sensitive location data, pose a restraint. The high cost of implementation and the requirement for specialized expertise also hinder wider adoption. Despite these challenges, the market's positive trajectory is anticipated to continue, driven by increasing data availability, improved technological capabilities, and growing awareness of the value of geospatial insights across various industries. The competitive landscape includes both global giants like Google and Esri, as well as domestic players like Esri India and Matrix Geo Solutions, indicating a dynamic market with opportunities for both established companies and emerging businesses. The forecast period of 2025-2033 promises further significant expansion, making the India Geospatial Analytics Market an attractive investment opportunity. Recent developments include: January 2023: Eris India, a company providing Geographic Information System (GIS) software and solutions, announced that the company is developing a policy map to offer data to help states and policymakers in decision-making. The Policy Maps have been designed to provide meaningful insights into various government functions., July 2022: Google announced a new partnership in India with local authorities and organizations in order to provide customized features for the diverse needs of the people in the country. Also, Google is building helpful maps that provide more visual and accurate navigation.. Key drivers for this market are: Increasing Demand of Location Based Service, Growing Availability of Spatial Data. Potential restraints include: Increasing Demand of Location Based Service, Growing Availability of Spatial Data. Notable trends are: Increasing Demand of Location Based Service.

NOTE: This metadata file contains Draft information for Flowlines delineated for NJ from 2002 color infrared (CIR) imagery with attributes extracted from the National Hydrography Dataset (NHD). Digitizing of this data was initially done by Aerial Information Systems, Inc., Redlands, CA, under direction of the New Jersey Department of Environmental Protection (NJDEP), Bureau of Geographic Information System (BGIS). Conflation of USGS 1:24,000 High resolution NHD attributes was done by Data Enhancement Services, LLC (DES) partnered with Civil Solutions. This statewide conflation of NHD High resolution information was completed in 2010. All QA/QC was done by NJDEP, Bureau of GIS and USGS. This represents a subset of the statewide extract for NJ dated August 25, 2015.

The data included in this publication depict the 2024 version of components of wildfire risk for all lands in the United States that: 1) are landscape-wide (i.e., measurable at every pixel across the landscape); and 2) represent in situ risk - risk at the location where the adverse effects take place on the landscape.National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. Additional methodology documentation is provided in a methods document (\Supplements\WRC_V2_Methods_Landscape-wideRisk.pdf) packaged in the data download.The specific raster datasets in this publication include:Risk to Potential Structures (RPS): A measure that integrates wildfire likelihood and intensity with generalized consequences to a home on every pixel. For every place on the landscape, it poses the hypothetical question, "What would be the relative risk to a house if one existed here?" This allows comparison of wildfire risk in places where homes already exist to places where new construction may be proposed. This dataset is referred to as Risk to Homes in the Wildfire Risk to Communities web application.Conditional Risk to Potential Structures (cRPS): The potential consequences of fire to a home at a given location, if a fire occurs there and if a home were located there. Referred to as Wildfire Consequence in the Wildfire Risk to Communities web application.Exposure Type: Exposure is the spatial coincidence of wildfire likelihood and intensity with communities. This layer delineates where homes are directly exposed to wildfire from adjacent wildland vegetation, indirectly exposed to wildfire from indirect sources such as embers and home-to-home ignition, or not exposed to wildfire due to distance from direct and indirect ignition sources.Burn Probability (BP): The annual probability of wildfire burning in a specific location. Referred to as Wildfire Likelihood in the Wildfire Risk to Communities web application.Conditional Flame Length (CFL): The mean flame length for a fire burning in the direction of maximum spread (headfire) at a given location if a fire were to occur; an average measure of wildfire intensity.Flame Length Exceedance Probability - 4 ft (FLEP4): The conditional probability that flame length at a pixel will exceed 4 feet if a fire occurs; indicates the potential for moderate to high wildfire intensity.Flame Length Exceedance Probability - 8 ft (FLEP8): the conditional probability that flame length at a pixel will exceed 8 feet if a fire occurs; indicates the potential for high wildfire intensity.Wildfire Hazard Potential (WHP): An index that quantifies the relative potential for wildfire that may be difficult to manage, used as a measure to help prioritize where fuel treatments may be needed.Additional methodology documentation is provided with the data publication download. Metadata and Downloads.Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.

The data included in this publication depict components of wildfire risk specifically for populated areas in the United States. These datasets represent where people live in the United States and the in situ risk from wildfire, i.e., the risk at the location where the adverse effects take place.National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. The data products in this publication that represent where people live, reflect 2021 estimates of housing unit and population counts from the U.S. Census Bureau, combined with building footprint data from Onegeo and USA Structures, both reflecting 2022 conditions.The specific raster datasets included in this publication include:Building Count: Building Count is a 30-m raster representing the count of buildings in the building footprint dataset located within each 30-m pixel.Building Density: Building Density is a 30-m raster representing the density of buildings in the building footprint dataset (buildings per square kilometer [km²]).Building Coverage: Building Coverage is a 30-m raster depicting the percentage of habitable land area covered by building footprints.Population Count (PopCount): PopCount is a 30-m raster with pixel values representing residential population count (persons) in each pixel.Population Density (PopDen): PopDen is a 30-m raster of residential population density (people/km²).Housing Unit Count (HUCount): HUCount is a 30-m raster representing the number of housing units in each pixel.Housing Unit Density (HUDen): HUDen is a 30-m raster of housing-unit density (housing units/km²).Housing Unit Exposure (HUExposure): HUExposure is a 30-m raster that represents the expected number of housing units within a pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year.Housing Unit Impact (HUImpact): HUImpact is a 30-m raster that represents the relative potential impact of fire to housing units at any pixel, if a fire were to occur. It is an index that incorporates the general consequences of fire on a home as a function of fire intensity and uses flame length probabilities from wildfire modeling to capture likely intensity of fire.Housing Unit Risk (HURisk): HURisk is a 30-m raster that integrates all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on pixels where housing unit density is greater than zero.Additional methodology documentation is provided with the data publication download. Metadata and Downloads.Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.

A public view of non-emergency requests submitted by general public and internal staff.

description: The National Oceanic and Atmospheric Administration's (NOAA) National Ocean Service (NOS) is tasked with the coral mapping element of the U.S. Coral Reef Task Force (CRTF) under the authority of Executive Order 13089. NOS is responsible for coral reef mapping in Puerto Rico, the U.S. Virgin Islands, the Northwest Hawaiian Islands, the Main Eight Hawaiian Islands, and the U.S. Territories and Freely Associated States of the Pacific. Space Imaging, Inc (SI) and Analytical Laboratories of Hawaii, LLC (ALH) has produced GIS-compatible benthic habitat digital maps of the Republic of Palau using the classification scheme defined by NOAA. The map products produced through this project include baseline data of U.S. coral reefs, location of coral reef boundaries and overall coral reef cover, and the geomorphologic structure in and around coral reef systems. Maps include the 9 major and 23 detailed biological cover types, 4 major and 14 detailed geomorphological structure types, and 15 mutually exclusive zones specified in NOAA's hierarchical classification manual for coastal waters of the Republic of Palau. Benthic habitats are delineated from the coastline to water depths of 30 meters in GIS using manual interpretation techniques. NOAA utilizes IKONOS Multispectral (MSI) Satellite Imagery from Space Imaging, Inc., consisting of both newly acquired and archived imagery. The imagery consists of 1m panchromatic and 4m MSI with a horizontal accuracy of at least 5m CE95 at 1:4,800 National Map Accuracy Standard (NMAS). Once the imagery is processed, NOAA will use ALH's hand-digitize GIS approach to produce the benthic habitat maps at a minimum mapping unit of one acre, as well as NOAA's preferred random stratified accuracy assessment method.; abstract: The National Oceanic and Atmospheric Administration's (NOAA) National Ocean Service (NOS) is tasked with the coral mapping element of the U.S. Coral Reef Task Force (CRTF) under the authority of Executive Order 13089. NOS is responsible for coral reef mapping in Puerto Rico, the U.S. Virgin Islands, the Northwest Hawaiian Islands, the Main Eight Hawaiian Islands, and the U.S. Territories and Freely Associated States of the Pacific. Space Imaging, Inc (SI) and Analytical Laboratories of Hawaii, LLC (ALH) has produced GIS-compatible benthic habitat digital maps of the Republic of Palau using the classification scheme defined by NOAA. The map products produced through this project include baseline data of U.S. coral reefs, location of coral reef boundaries and overall coral reef cover, and the geomorphologic structure in and around coral reef systems. Maps include the 9 major and 23 detailed biological cover types, 4 major and 14 detailed geomorphological structure types, and 15 mutually exclusive zones specified in NOAA's hierarchical classification manual for coastal waters of the Republic of Palau. Benthic habitats are delineated from the coastline to water depths of 30 meters in GIS using manual interpretation techniques. NOAA utilizes IKONOS Multispectral (MSI) Satellite Imagery from Space Imaging, Inc., consisting of both newly acquired and archived imagery. The imagery consists of 1m panchromatic and 4m MSI with a horizontal accuracy of at least 5m CE95 at 1:4,800 National Map Accuracy Standard (NMAS). Once the imagery is processed, NOAA will use ALH's hand-digitize GIS approach to produce the benthic habitat maps at a minimum mapping unit of one acre, as well as NOAA's preferred random stratified accuracy assessment method.

This project is a cooperative effort between the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment, BAE Systems Spectral Solutions LLC, and Analytical Laboratories of Hawaii, LLC. IKONOS and Quickbird imagery was purchased to support the Pacific Islands Geographic Information System (GIS) project and the National Ocean Service's (NOS) coral mapping activities. One-meter panchromatic and four-meter multi-spectral data were purchased for each study area. The enhanced spectral resolution of multispectral imagery and control of bandwidths of multispectral data yield an advantage over color aerial photography particularly when coral health and time series analysis of coral reef community structure are of interest. The IKONOS and Quickbird imagery was processed to minimize atmospheric and water column effects. Photointerpreters can accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor using a software interface such as the Habitat Digitizer.

The County of Queen Anne Maryland requested delivery of three dimensional classified point cloud and hydro-flattened terrain data derived from LiDAR (Light Detection and Ranging) technology for the entirety of Queen Anne's County, MD. Remotely sensed, geographically referenced elevation measurements were collected by Axis Geospatial, LLC using airborne LiDAR sensors.This is a MD iMAP hosted service. Find more information at https://imap.maryland.gov.Image Service Link: https://mdgeodata.md.gov/lidar/rest/services/QueenAnnes/MD_queenannes_dem_ft/ImageServer

The US geospatial imagery analytics market is experiencing robust growth, fueled by increasing adoption across diverse sectors. The global market's substantial size of $5.38 billion in 2025 and a Compound Annual Growth Rate (CAGR) of 24.14% project significant expansion through 2033. While precise figures for the US market segment are unavailable, a reasonable estimation, considering the US's significant technological advancement and market dominance in related fields, would place its 2025 market size at approximately $2.0 billion. This substantial value is driven by several key factors. The rising demand for precise location intelligence across various sectors such as insurance (risk assessment and fraud detection), agriculture (precision farming and yield optimization), defense and security (surveillance and intelligence gathering), and environmental monitoring (disaster management and climate change analysis) are primary growth catalysts. Technological advancements like improved sensor technologies, enhanced image processing algorithms, and the proliferation of cloud-based solutions further accelerate market expansion. The increasing availability of high-resolution satellite imagery and the development of sophisticated analytics platforms are also contributing to the market's growth trajectory. However, the market faces certain restraints. High initial investment costs for implementing geospatial imagery analytics solutions, especially for SMEs, can pose a barrier to entry. Moreover, concerns regarding data privacy and security, along with the complexity of data analysis and interpretation, can hinder wider adoption. Despite these challenges, the long-term outlook remains positive. The continuous development of user-friendly software, the decreasing cost of data storage and processing, and growing government initiatives promoting the use of geospatial technologies are expected to mitigate these limitations and propel the market toward sustained growth. The market segmentation by deployment (on-premise and cloud), organization size (SMEs and large enterprises), and vertical industries presents diverse opportunities for growth and specialization within the US market. The competitive landscape is characterized by a mix of established technology giants and specialized geospatial analytics providers, each vying for a share of this rapidly expanding market. Recent developments include: May 2023: CAPE Analytics, a player in AI-powered geospatial property intelligence, has extended its partnership with The Hanover Insurance Group, which provides independent agents with the best insurance coverage and prices. Integrating geospatial analytics and inspection and rating models into Hanover's underwriting procedure is the central component of the partnership expansion. The company's rating plans will benefit from this strategic move, improving workflows, new and renewal underwriting outcomes, and pricing segmentation., March 2023 : Carahsoft Technology Corp., The Trusted Government IT Solutions Provider, and Orbital Insight, a player in geospatial intelligence, announced a partnership. By the terms of the agreement, Carahsoft will act as Orbital Insight's Master Government Aggregator, making the leading AI-powered geospatial data analytics available to the public sector through Carahsoft's reseller partners and contracts for Information Technology Enterprise Solutions - Software 2 (ITES-SW2), NASA Solutions for Enterprise-Wide Procurement (SEWP) V, National Association of State Procurement Officials (NASPO) ValuePoint, National Cooperative Purchasing.. Key drivers for this market are: Increasing demand for Location based services, Technological innovations in geospatial imagery services. Potential restraints include: Increasing demand for Location based services, Technological innovations in geospatial imagery services. Notable trends are: Small Satellities will Boost Market Growth.

An ArcGIS Web AppBuilder application used to view lead service lines.

This Phase I Summary Completion Report was created for the City of Dallas, TX s regarding the cleanup of the Blue Star recycling facility."Modern Geosciences, LLC (Modern) is pleased to provide the attached Project Summary Report (PSR) to the City of Dallas (City) for the above-referenced project (Site). The attached document provides a summary of the project and our monitoring results.This Project Summary Report (PSR) was prepared by Modern Geosciences, LLC (Modern) for the City of Dallas (City) to provide a summary of air monitoring data collected during the full duration of the removal of staged asphaltic shingles from the Former Blue Star Recycling facility located at 9505 and 9527 South Central Expressway, Dallas, Dallas County, Texas (Site.)"

NOTE: This dataset contains information for Streams (NHDFlowlines) delineated for NJ from 2020 color infrared (CIR) imagery with attributes extracted from the National Hydrography Dataset (NHD). Updates are made with relation to the waterbodies 2020 extracted from the Landuse 2020 Update. This represents a schema update to a subset of the statewide extract for NJ dated December 23, 2023. Digitizing of this data was initially done by Aerial Information Systems, Inc., Redlands, CA, under direction of the New Jersey Department of Environmental Protection (NJDEP), Bureau of Geographic Information System (BGIS). Conflation of USGS 1:24,000 High resolution NHD attributes was done by Data Enhancement Services, LLC (DES) partnered with Civil Solutions. This statewide conflation of NHD High resolution information was completed in 2010. All QA/QC was done by NJDEP, Bureau of GIS and USGS.