Geospatial Solutions Market size was valued at USD 282.75 Billion in 2024 and is projected to reach USD 650.14 Billion by 2031, growing at a CAGR of 12.10% during the forecast period 2024-2031.

Geospatial Solutions Market: Definition/ Overview

Geospatial solutions are applications and technologies that use spatial data to address geography, location, and Earth’s surface problems. They use tools like GIS, remote sensing, GPS, satellite imagery analysis, and spatial modelling. These solutions enable informed decision-making, resource allocation optimization, asset management, environmental monitoring, infrastructure planning, and addressing challenges in sectors like urban planning, agriculture, transportation, disaster management, and natural resource management. They empower users to harness spatial information for better understanding and decision-making in various contexts.

Geospatial solutions are technologies and methodologies used to analyze and visualize spatial data, ranging from urban planning to agriculture. They use GIS, remote sensing, and GNSS to gather, process, and interpret data. These solutions help users make informed decisions, solve complex problems, optimize resource allocation, and enhance situational awareness. They are crucial in addressing challenges and unlocking opportunities in today’s interconnected world, such as mapping land use patterns, monitoring ecosystem changes, and real-time asset tracking.

This geodatabase reflects the U.S. Geological Survey’s (USGS) ongoing commitment to its mission of understanding the nature and distribution of global mineral commodity supply chains by updating and publishing the georeferenced locations of mineral commodity production and processing facilities, mineral exploration and development sites, and mineral commodity exporting ports in Africa. The geodatabase and geospatial data layers serve to create a new geographic information product in the form of a geospatial portable document format (PDF) map. The geodatabase contains data layers from USGS, foreign governmental, and open-source sources as follows: (1) mineral production and processing facilities, (2) mineral exploration and development sites, (3) mineral occurrence sites and deposits, (4) undiscovered mineral resource tracts for Gabon and Mauritania, (5) undiscovered mineral resource tracts for potash, platinum-group elements, and copper, (6) coal occurrence areas, (7) electric power generating facilities, (8) electric power transmission lines, (9) liquefied natural gas terminals, (10) oil and gas pipelines, (11) undiscovered, technically recoverable conventional and continuous hydrocarbon resources (by USGS geologic/petroleum province), (12) cumulative production, and recoverable conventional resources (by oil- and gas-producing nation), (13) major mineral exporting maritime ports, (14) railroads, (15) major roads, (16) major cities, (17) major lakes, (18) major river systems, (19) first-level administrative division (ADM1) boundaries for all countries in Africa, and (20) international boundaries for all countries in Africa.

Overview

Empower your location data visualizations with our edge-matched polygons, even in difficult geographies.

Our self-hosted GIS data cover administrative and postal divisions with up to 6 precision levels: a zip code layer and up to 5 administrative levels. All levels follow a seamless hierarchical structure with no gaps or overlaps.

The geospatial data shapes are offered in high-precision and visualization resolution and are easily customized on-premise.

Use cases for the Global Boundaries Database (GIS data, Geospatial data)

• In-depth spatial analysis

• Clustering

• Geofencing

• Reverse Geocoding

• Reporting and Business Intelligence (BI)

Product Features

• Coherence and precision at every level

• Edge-matched polygons

• High-precision shapes for spatial analysis

• Fast-loading polygons for reporting and BI

• Multi-language support

For additional insights, you can combine the GIS data with:

• Population data: Historical and future trends

• UNLOCODE and IATA codes

• Time zones and Daylight Saving Time (DST)

Data export methodology

Our geospatial data packages are offered in variable formats, including - .shp - .gpkg - .kml - .shp - .gpkg - .kml - .geojson

All GIS data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Why companies choose our map data

• Precision at every level

• Coverage of difficult geographies

• No gaps, nor overlaps

Note: Custom geospatial data packages are available. Please submit a request via the above contact button for more details.

ArcGIS Map Packages and GIS Data for Gillreath-Brown, Nagaoka, and Wolverton (2019)

**When using the GIS data included in these map packages, please cite all of the following:

Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, 2019. PLoSONE 14(8):e0220457. http://doi.org/10.1371/journal.pone.0220457

Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. ArcGIS Map Packages for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al., 2019. Version 1. Zenodo. https://doi.org/10.5281/zenodo.2572018

OVERVIEW OF CONTENTS

This repository contains map packages for Gillreath-Brown, Nagaoka, and Wolverton (2019), as well as the raw digital elevation model (DEM) and soils data, of which the analyses was based on. The map packages contain all GIS data associated with the analyses described and presented in the publication. The map packages were created in ArcGIS 10.2.2; however, the packages will work in recent versions of ArcGIS. (Note: I was able to open the packages in ArcGIS 10.6.1, when tested on February 17, 2019). The primary files contained in this repository are:

• Raw DEM and Soils data
  • Digital Elevation Model Data (Map services and data available from U.S. Geological Survey, National Geospatial Program, and can be downloaded from the National Elevation Dataset)
    • DEM_Individual_Tiles: Individual DEM tiles prior to being merged (1/3 arc second) from USGS National Elevation Dataset.
    • DEMs_Merged: DEMs were combined into one layer. Individual watersheds (i.e., Goodman, Coffey, and Crow Canyon) were clipped from this combined DEM.
  • Soils Data (Map services and data available from Natural Resources Conservation Service Web Soil Survey, U.S. Department of Agriculture)
    • Animas-Dolores_Area_Soils: Small portion of the soil mapunits cover the northeastern corner of the Coffey Watershed (CW).
    • Cortez_Area_Soils: Soils for Montezuma County, encompasses all of Goodman (GW) and Crow Canyon (CCW) watersheds, and a large portion of the Coffey watershed (CW).
• ArcGIS Map Packages
  • Goodman_Watershed_Full_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the full Goodman Watershed (GW).
  • Goodman_Watershed_Mesa-Only_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the mesa-only Goodman Watershed.
  • Crow_Canyon_Watershed_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the Crow Canyon Watershed (CCW).
  • Coffey_Watershed_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the Coffey Watershed (CW).

For additional information on contents of the map packages, please see see "Map Packages Descriptions" or open a map package in ArcGIS and go to "properties" or "map document properties."

LICENSES

Code: MIT year: 2019
Copyright holders: Andrew Gillreath-Brown, Lisa Nagaoka, and Steve Wolverton

CONTACT

Andrew Gillreath-Brown, PhD Candidate, RPA
Department of Anthropology, Washington State University
andrew.brown1234@gmail.com – Email
andrewgillreathbrown.wordpress.com – Web

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

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

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

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles.

Our final map product is a geographic information system (GIS) database of vegetation structure and composition across the Crater Lake National Park terrestrial landscape, including wetlands. The database includes photos we took at all relevé, validation, and accuracy assessment plots, as well as the plots that were done in the previous wetlands inventory. We conducted an accuracy assessment of the map by evaluating 698 stratified random accuracy assessment plots throughout the project area. We intersected these field data with the vegetation map, resulting in an overall thematic accuracy of 86.2 %. The accuracy of the Cliff, Scree & Rock Vegetation map unit was difficult to assess, as only 9% of this vegetation type was available for sampling due to lack of access. In addition, fires that occurred during the 2017 accuracy assessment field season affected our sample design and may have had a small influence on the accuracy. Our geodatabase contains the locations where particular associations are found at 600 relevé plots, 698 accuracy assessment plots, and 803 validation plots.

Abstract : The search for the most appropriate GIS data model to integrate, manipulate and analyse spatio-temporal data raises several research questions about the conceptualisation of geographic spaces. Although there is now a general consensus that many environmental phenomena require field and object conceptualisations to provide a comprehensive GIS representation, there is still a need for better integration of these dual representations of space within a formal spatio-temporal database. The research presented in this paper introduces a hybrid and formal dual data model for the representation of spatio-temporal data. The whole approach has been fully implemented in PostgreSQL and its spatial extension PostGIS, where the SQL language is extended by a series of data type constructions and manipulation functions to support hybrid queries. The potential of the approach is illustrated by an application to underwater geomorphological dynamics oriented towards the monitoring of the evolution of seabed changes. A series of performance and scalability experiments are also reported to demonstrate the computational performance of the model.Data Description : The data set used in our research is a set of bathymetric surveys recorded over three years from 2009 to 2011 as Digital Terrain Models (DTM) with 2m grid spacing. The first survey was carried out in February 2009 by the French hydrographic office, the second one was recorded on August-September 2010 and the third in July 2011, both by the “Institut Universitaire Européen de la Mer”.

PA Geospatial Focus Newsletter Vol. 1 Issue 1 offers insights into upcoming events, exciting developments, and some of the incredible work being done to leverage Geographic Information Systems (GIS) within Pennsylvania.Geospatial Technology enables us to combine Geographic Information Systems (GIS), data science, and geography to analyze, visualize, interpret, and even predict outcomes to some of the most complex questions facing us. We hope you enjoy this quarterly newsletter showing the robust use of Geospatial Technology across the Commonwealth of Pennsylvania and the community who makes it all possible.

Bulk exports, in file-geodatabase format, of data that is shared via the VT EGC (Enterprise GIS Consortium) Geospatial Data Exchange Protocol.

The geospatial services market is experiencing robust growth, driven by increasing demand for location intelligence across diverse sectors. Our analysis projects a market size of $150 billion in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This expansion is fueled by several key factors. The agricultural sector leverages geospatial data for precision farming, optimizing resource allocation and maximizing yields. Similarly, research institutions and government bodies increasingly utilize geospatial analytics for environmental monitoring, urban planning, and disaster response. The integration of advanced technologies like AI and machine learning further enhances the capabilities of geospatial services, leading to more accurate and insightful analyses. Furthermore, the rising adoption of cloud-based platforms is simplifying data access and processing, making geospatial technologies more accessible to a wider range of users. Market segmentation reveals significant opportunities within specific application areas. Data collection services, encompassing remote sensing and GPS technologies, constitute a substantial segment, while data analysis services, leveraging sophisticated algorithms and modelling techniques, are experiencing rapid growth. Geographically, North America and Europe currently hold the largest market shares, although the Asia-Pacific region is projected to witness the fastest growth due to increasing infrastructure development and technological advancements. However, challenges remain, including data security concerns, the need for skilled professionals, and the high initial investment costs associated with implementing sophisticated geospatial systems. Despite these constraints, the overall market trajectory indicates a promising future for geospatial services, with continued growth driven by technological innovation and the ever-increasing reliance on location-based information across various industries.

Per Engineering Regulation 1130-2-520, USACE’s NDC and WCSC are responsible for collecting, compiling, printing, and distributing all domestic waterborne commerce statistics for which the USACE has responsibility. Per a 1998 Office of Management and Budget (OMB) memorandum, the WCSC inherited the requirement to include foreign waterborne commerce formally executed by the U.S. Census Bureau. Performance of this work is in accordance with the Rivers and Harbors Appropriation Act of 1922 (33 USC 555).

Engineering Regulation 1130-2-520 defines a port as:

(1) Port limits defined by legislative enactments of state, county, or city governments.

(2) The corporate limits of a municipality.

At minimum, the feature class includes the following attribution:

Attribute Name

Definition

Data Type

Length

featureDescription

The narrative describing the feature. This attribute column will describe how the statistical port boundary was generated using GIS. It can include the legislative description, a note that the U.S. Census Bureau municipal limit was used, or other details.

String

Max

featureName

The common name of the feature. This will be the port name as defined by the legislative enactment or the municipality. Each name should include which State(s) the port is located (ex. Louisville-Jefferson County Riverport Authority, KY).

String

80

installationId

The codes assigned by the DoD Component used to identify the site or group of sites that make up an installation. This field will remain empty, as the project focus is not on military installations.

String

11

mediaId

Used to link the record to associated multimedia records the reference data. The number used in this column will reference a related “mediaId” table that will store the source document for appropriate legislation or municipality limit reference.

String

40

metadataId

Used to represent or link to feature level metadata. For this project, a common code for the port area geometry source will be employed.

L


Legislative
Enactment




M


Municipal
Limits




O


Other

String

80

portIdpk

Primary Key. A unique, user defined identifier for each record or instance of an entity. This will be the existing four-digit port code maintained by WCSC in TOWS. A crosswalk table will also be created by the PM that correlates legacy TOWS port codes to new UN LOCODE information.

String

40

sdsId

The unique identifier for all entities in the SDSFIE. This field will remain empty and will be populated by HQUSACE if sdsID identifiers become necessary to report.

GUID

The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making. BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions. Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself. For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise. Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery. See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

The Geopspatial Fabric provides a consistent, documented, and topologically connected set of spatial features that create an abstracted stream/basin network of features useful for hydrologic modeling.The GIS vector features contained in this Geospatial Fabric (GF) data set cover the lower 48 U.S. states, Hawaii, and Puerto Rico. Four GIS feature classes are provided for each Region: 1) the Region outline ("one"), 2) Points of Interest ("POIs"), 3) a routing network ("nsegment"), and 4) Hydrologic Response Units ("nhru"). A graphic showing the boundaries for all Regions is provided at http://dx.doi.org/doi:10.5066/F7542KMD. These Regions are identical to those used to organize the NHDPlus v.1 dataset (US EPA and US Geological Survey, 2005). Although the GF Feature data set has been derived from NHDPlus v.1, it is an entirely new data set that has been designed to generically support regional and national scale applications of hydrologic models. Definition of each type of feature c ...

Market Overview:

The Vietnam geospatial analytics market size is projected to exhibit a growth rate (CAGR) of 8.90% during 2024-2032. The increasing product utilization by government authorities in various sectors, various technological advancements in satellite technology, remote sensing, and data collection methods, and the rising development of smart cities represent some of the key factors driving the market.

Geospatial analytics is a field of data analysis that focuses on the interpretation and analysis of geographic and spatial data to gain valuable insights and make informed decisions. It combines geographical information systems (GIS), advanced data analysis techniques, and visualization tools to analyze and interpret data with a spatial or geographic component. It also enables the collection, storage, analysis, and visualization of geospatial data. It provides tools and software for managing and manipulating spatial data, allowing users to create maps, perform spatial queries, and conduct spatial analysis. In addition, geospatial analytics often involves integrating geospatial data with other types of data, such as demographic data, environmental data, or economic data. This integration helps in gaining a more comprehensive understanding of complex phenomena. Moreover, geospatial analytics has a wide range of applications. For example, it can be used in urban planning to optimize transportation routes, in agriculture to manage crop yield and soil quality, in disaster management to assess and respond to natural disasters, in wildlife conservation to track animal migrations, and in business for location-based marketing and site selection.

Vietnam Geospatial Analytics Market Trends:

The Vietnamese government has recognized the importance of geospatial analytics in various sectors, including urban planning, agriculture, disaster management, and environmental monitoring. Initiatives to develop and utilize geospatial data for public projects and policy-making have spurred demand for geospatial analytics solutions. In addition, Vietnam is experiencing rapid urbanization and infrastructure development. Geospatial analytics is critical for effective urban planning, transportation management, and infrastructure optimization. This trend is driving the adoption of geospatial solutions in cities and regions across the country. Besides, Vietnam's agriculture sector is a significant driver of its economy. Geospatial analytics helps farmers and agricultural businesses optimize crop management, soil health, and resource allocation. Consequently, precision farming techniques, enabled by geospatial data, are becoming increasingly popular, which is also propelling the market. Moreover, the development of smart cities in Vietnam relies on geospatial analytics for various applications, such as traffic management, public safety, and energy efficiency. Geospatial data is central to building the infrastructure needed for smart city initiatives. Furthermore, advances in satellite technology, remote sensing, and data collection methods have made geospatial data more accessible and affordable. This has lowered barriers to entry and encouraged the use of geospatial analytics in various sectors. Additionally, the telecommunications sector in Vietnam is expanding, and location-based services, such as navigation and advertising, rely on geospatial analytics. This creates opportunities for geospatial data providers and analytics solutions in the telecommunications industry.

Vietnam Geospatial Analytics Market Segmentation:

IMARC Group provides an analysis of the key trends in each segment of the market, along with forecasts at the country level for 2024-2032. Our report has categorized the market based on component, type, technology, enterprise size, deployment mode, and vertical.

Component Insights:

https://www.imarcgroup.com/CKEditor/2e6fe72c-0238-4598-8c62-c08c0e72a138other-regions1.webp" style="height:450px; width:800px" />

• Solution
• Services
  

The report has provided a detailed breakup and analysis of the market based on the component. This includes solution and services.

Type Insights:

• Surface and Field Analytics
• Network and Location Analytics
• Geovisualization
• Others
  

A detailed breakup and analysis of the market based on the type have also been provided in the report. This includes surface and field analytics, network and location analytics, geovisualization, and others.

Technology Insights:

• Remote Sensing
• GIS
• GPS
• Others
  

The report has provided a detailed breakup and analysis of the market based on the technology. This includes remote sensing, GIS, GPS, and others.

Enterprise Size Insights:

• Large Enterprises
• Small and Medium-sized Enterprises
  

A detailed breakup and analysis of the market based on the enterprise size have also been provided in the report. This includes large enterprises and small and medium-sized enterprises.

Deployment Mode Insights:

• On-premises
• Cloud-based
  

The report has provided a detailed breakup and analysis of the market based on the deployment mode. This includes on-premises and cloud-based.

Vertical Insights:

• Automotive
• Energy and Utilities
• Government
• Defense and Intelligence
• Smart Cities
• Insurance
• Natural Resources
• Others
  

A detailed breakup and analysis of the market based on the vertical have also been provided in the report. This includes automotive, energy and utilities, government, defense and intelligence, smart cities, insurance, natural resources, and others.

Regional Insights:

https://www.imarcgroup.com/CKEditor/bbfb54c8-5798-401f-ae74-02c90e137388other-regions6.webp" style="height:450px; width:800px" />

• Northern Vietnam
• Central Vietnam
• Southern Vietnam
  

The report has also provided a comprehensive analysis of all the major regional markets, which include Northern Vietnam, Central Vietnam, and Southern Vietnam.

Competitive Landscape:

The market research report has also provided a comprehensive analysis of the competitive landscape in the market. Competitive analysis such as market structure, key player positioning, top winning strategies, competitive dashboard, and company evaluation quadrant has been covered in the report. Also, detailed profiles of all major companies have been provided.

Vietnam Geospatial Analytics Market Report Coverage:

According to Cognitive Market Research, the global Geospatial Solutions market size is USD 508421.2million in 2024 and will expand at a compound annual growth rate (CAGR) of 16.50% from 2024 to 2031.

North America held the major market of more than 40% of the global revenue with a market size of USD 203368.48 million in 2024 and will grow at a compound annual growth rate (CAGR) of 14.7% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD 152526.36 million.
Asia Pacific held the market of around 23% of the global revenue with a market size of USD 116936.88 million in 2024 and will grow at a compound annual growth rate (CAGR) of 18.5% from 2024 to 2031.
Latin America market of more than 5% of the global revenue with a market size of USD 25421.06 million in 2024 and will grow at a compound annual growth rate (CAGR) of 15.9% from 2024 to 2031.
Middle East and Africa held the major market of around 2% of the global revenue with a market size of USD 10168.42 million in 2024 and will grow at a compound annual growth rate (CAGR) of 16.2% from 2024 to 2031.
The hospitals held the highest Geospatial Solutions market revenue share in 2024.

Key Driver of the Geospatial Solutions Market

Growing Demand for Location-based Data and Insights to Increase the Demand Globally

Businesses and organizations prioritize making well-informed decisions, driving demand for location-based data and insights. Having accurate and comprehensive information about people, places, and things is becoming increasingly important. Geospatial solutions play a crucial role in gathering, evaluating, and presenting this data, which drives market growth. These technologies help with resource allocation, market targeting, and strategy planning by providing advanced tools for interpreting spatial data. Businesses use geospatial data to improve customer experiences, optimize operations, and gain competitive advantages due to the development of GPS, remote sensing, and GIS. Because of this, the geospatial industry is expanding rapidly and satisfying the changing demands of various industries looking for useful location-based insights.

Advancements in Technology to Propel Market Growth

The geospatial industry is expanding significantly due to technological advancements, including aerial images, remote sensing, GNSS (Global Navigation Satellite Systems), and LiDAR (Light Detection and Ranging). These developments provide ever-more accurate, affordable, and easily accessible ways to collect geospatial data. While GNSS offers precise global location data, remote sensing technologies allow data collection from inaccessible or remote areas. LiDAR and aerial images improve data resolution and detail, allowing for more complex analysis and visualization. The geospatial market is growing due to the ongoing development of these technologies, which enables businesses and organizations in various industries to make wise decisions, maximize operations, and seize new possibilities.

Restraints factor of the Geospatial Solutions Market

Data Privacy and Security Concerns to Limit the Sales

The widespread use of geographical data gives rise to serious privacy and security problems. The increasing accessibility and utilization of location-based data across many businesses underscores the need for strong data governance frameworks to preserve individuals' privacy and prevent potential compromises of sensitive data. Furthermore, upholding moral principles and legal compliance depends on gaining users' trust via open data policies and permission procedures. Companies may promote the responsible and ethical use of location-based information by addressing these concerns and fostering better stakeholder confidence. Additionally, companies should limit risks connected with gathering, sharing, and utilizing geospatial data.

Impact of COVID-19 on the Geospatial Solutions Market

The geospatial solutions market has experienced varying effects from the COVID-19 epidemic. Due to supply chain disruptions and economic uncertainty, several industries faced a brief pause. Still, others saw faster development as a result of the pressing need for location-based data to solve pandemic-related issues. Geospatial technologies are increasingly used in industries, including healthcare, logistics, and urban planning, to track the virus's spread, allocate resourc...

For decades, TNC’s conservation science and planning has been informed by geospatial technology. This dynamic field combines the disciplines of Geographic Information Systems (GIS), remote sensing and machine learning. At least one in every three TNC staff generates and uses maps to complete tasks such as monitoring preserves in the field and, increasingly through remote technologies, negotiate land and water transactions or illustrate the benefits of ecosystems and the costs of losing them. Together, the TNC Geospatial Systems team and the Geospatial Leadership Council have joined forces to bring you this annual report & map book that features:25 use cases or applications illustrating how geospatial technology is supporting and advancing our conservation work around the worldA global map series from our Global Science and Protect Oceans, Lands and Water teams showing crisis and last chance ecosystems under high development pressure14 feature maps depicting specific conservation projectsResults from our annual survey that reached over 1,500 staff“As we seek to tackle the biggest challenges facing our planet, it is crucial to ensure that our teams and partners are able to leverage the most accurate and rigorous mapping data—and that geospatial professionals conducting this science reflect the diversity of the places we work,” says Jennifer Morris, CEO of The Nature ConservancyFor the first time we have categorized three mapping types that convey all our conservation science and planning work into predictive modeling, prioritization with two aspects (asset & threat mapping and spatial action mapping), and monitoring & evaluation. This edition emphasizes spatial action mapping, or the mapping of strategies in priority places that inform decisions on when, where and what may be the best conservation actions to take.Fundamental to TNC’s mission is a focus on place. Maps are core to our mission in helping us understand the places we work and in engaging audiences through the stories they reveal. In this edition we have initiated the process of creating cartographic guidelines that encourage a cohesive look and feel within TNC while promoting our “conservation mapping brand.”

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

United States geospatial analytics market size reached USD 25.2 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 60.1 Billion by 2033, exhibiting a growth rate (CAGR) of 10% during 2025-2033. The growing need for facilitating data-driven decisions, along with the rising focus of government bodies on improving situational awareness and monitoring of troops and enemy movements, is primarily propelling the market growth across the country.

IMARC Group provides an analysis of the key trends in each segment of the market, along with forecasts at the country level for 2025-2033. Our report has categorized the market based on component, type, technology, enterprise size, deployment mode, and vertical.