Tool and data set of road networks for 80 of the most populated urban areas in the world. The data consist of a graph edge list for each city and two corresponding GIS shapefiles (i.e., links and nodes).Make your own data with our ArcGIS, QGIS, and python tools available at: http://csun.uic.edu/codes/GISF2E.htmlPlease cite: Karduni,A., Kermanshah, A., and Derrible, S., 2016, "A protocol to convert spatial polyline data to network formats and applications to world urban road networks", Scientific Data, 3:160046, Available at http://www.nature.com/articles/sdata201646

The Intelligent Road Network dataset provided by the Transport Department includes traffic directions, turning restrictions at road junctions, stopping restrictions, on-street parking spaces and other road traffic data for supporting the development of intelligent transport system, fleet management system and car navigation etc. by the public.

Esri China (HK) has prepared this File Geodatabase containing a Network Dataset for the Intelligent Road Network to support Esri GIS users to use the dataset in ArcGIS Pro without going through long configuration steps. Please refer to this guideline to use the Road Network Dataset in ArcGIS Pro for routing analysis. This network dataset has been configured and deployed the following restrictions:

Speed LimitTurnIntersectionTraffic FeaturesPedestrian ZoneTraffic Sign of ProhibitionVehicle RestrictionThe coordinate system of this dataset is Hong Kong 1980 Grid.The objectives of uploading the network dataset to ArcGIS Online platform are to facilitate our Hong Kong ArcGIS users to utilize the data in a spatial ready format and save their data conversion effort.For details about the schema and information about the content and relationship of the data, please refer to the data dictionary provided by Transport Department at https://data.gov.hk/en-data/dataset/hk-td-tis_15-road-network-v2.For details about the data, source format and terms of conditions of usage, please refer to the website of DATA.GOV.HK at https://data.gov.hk.Dataset last updated on: 2021 July

Snapshot img

GIS In Telecom Sector Market Size 2025-2029

The GIS in telecom sector market size is forecast to increase by USD 2.35 billion at a CAGR of 15.7% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing adoption of Geographic Information Systems (GIS) for capacity planning in the telecommunications industry. GIS technology enables telecom companies to optimize network infrastructure, manage resources efficiently, and improve service delivery. Telecommunication assets and network management systems require GIS integration for efficient asset management and network slicing. However, challenges persist in this market. A communication gap between developers and end-users poses a significant obstacle.
Companies seeking to capitalize on opportunities in the market must focus on addressing these challenges, while also staying abreast of technological advancements and market trends. Effective collaboration between developers and end-users, coupled with strategic investments, will be essential for success in this dynamic market. Telecom companies must bridge this divide to ensure the development of user-friendly and effective GIS solutions. Network densification and virtualization platforms are key trends, allowing for efficient spectrum management and data monetization. Additionally, the implementation of GIS in the telecom sector requires substantial investment in technology and infrastructure, which may deter smaller players from entering the market.

What will be the Size of the GIS In Telecom Sector Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free Sample

In the dynamic telecom sector, GIS technology plays a pivotal role in customer analysis, network planning, and infrastructure development. Customer experiences are enhanced through location-based services and real-time data analysis, enabling telecom companies to tailor offerings and improve service quality. Network simulation and capacity planning are crucial for network evolution, with machine learning and AI integration facilitating network optimization and compliance with industry standards.
IOT connectivity and network analytics platforms offer valuable insights for smart city infrastructure development, with 3D data analysis and network outage analysis ensuring network resilience. Telecom industry partnerships foster innovation and collaboration, driving the continuous evolution of the sector. Consulting firms offer expertise in network compliance and network management, ensuring regulatory adherence and optimal network performance.

How is this GIS In Telecom Sector Industry segmented?

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

Product

  Software
  Data
  Services


Deployment

  On-premises
  Cloud


Application

  Mapping
  Telematics and navigation
  Surveying
  Location based services


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Product Insights

The software segment is estimated to witness significant growth during the forecast period. In the telecom sector, the deployment of 5G networks is driving the need for advanced Geographic Information Systems (GIS) to optimize network performance and efficiency. GIS technology enables spatial analysis, network automation, capacity analysis, and bandwidth management, all crucial elements in the rollout of 5G networks. Large enterprises and telecom consulting firms are integrating GIS data into their operations for network planning, optimization, and troubleshooting. Machine learning and artificial intelligence are transforming GIS applications, offering predictive analytics and real-time network performance monitoring. Network virtualization and software-defined networking are also gaining traction, enhancing network capacity and improving network reliability and maintenance.

GIS software companies provide solutions for desktops, mobiles, cloud, and servers, catering to various industry needs. Smart city initiatives and location-based services are expanding the use cases for GIS in telecom, offering new opportunities for growth. Infrastructure deployment and population density analysis are critical factors in network rollout and capacity enhancement. Network security and performance monitoring are essential components of GIS applications, ensuring network resilience and customer experience management. Edge computing and network latency reduction are also significant trends,

Features of different accessibility analysis methods.

The risk of natural disasters, many of which are amplified by climate change, requires the protection of emergency evacuation routes to permit evacuees safe passage. California has recognized the need through the AB 747 Planning and Zoning Law, which requires each county and city in California to update their - general plans to include safety elements from unreasonable risks associated with various hazards, specifically evacuation routes and their capacity, safety, and viability under a range of emergency scenarios. These routes must be identified in advance and maintained so they can support evacuations. Today, there is a lack of a centralized database of the identified routes or their general assessment. Consequently, this proposal responds to Caltrans’ research priority for “GIS Mapping of Emergency Evacuation Routes.†Specifically, the project objectives are: 1) create a centralized GIS database, by collecting and compiling available evacuation route GIS layers, and the safety eleme..., The project used the following public datasets: • Open Street Map. The team collected the road network arcs and nodes of the selected localities and the team will make public the graph used for each locality. • National Risk Index (NRI): The team used the NRI obtained publicly from FEMA at the census tract level. • American Community Survey (ACS): The team used ACS data to estimate the Social Vulnerability Index at the census block level. Then the author developed a measurement to estimate the road network performance risk at the node level, by estimating the Hansen accessibility index, betweenness centrality and the NRI. Create a set of CSV files with the risk for more than 450 localities in California, on around 18 natural hazards. I also have graphs of the RNP risk at the regional level showing the directionality of the risk., , # Data from: Improving public safety through spatial synthesis, mapping, modeling, and performance analysis of emergency evacuation routes in California localities

https://doi.org/10.5061/dryad.w9ghx3g0j

Description of the data and file structure

For this project’s analysis, the team obtained data from FEMA's National Risk Index, including the Social Vulnerability Index (SOVI).

To estimate SOVI, the team used data from the American Community Survey (ACS) to calculate SOVI at the census block level.

Using the graphs obtained from OpenStreetMap (OSM), the authors estimated the Hansen Accessibility Index (Ai) and the normalized betweenness centrality (BC) for each node in the graph.

The authors estimated the Road Network Performance (RNP) risk at the node level by combining NRI, Ai, and BC. They then grouped the RNP to determine the RNP risk at the regional level and generated the radial histogram. Finally, the authors calculated each ana...

This table contains a list of the participants, or named organizations, of the Social Network Analysis done as part of the Pacific Northwest Coastal Conservation Blueprint which is a component of the Pacific Northwest Coast Landscape Conservation Design. A social network analysis maps out the who, what, and where of conservation collaboration, helping us to think more strategically about conservation at the landscape scale by identifying who entities collaborate with, and the conservation priorities, strategies, capacity needs, strengths, and geographic areas of interest.For more information on the larger Pacific Northwest Coast Landscape Conservation Design project that the Social Network Analysis is a part of please see the project website: http://columbiacoastblueprint.org/

Shortest Route Analysis Of Dhaka City Roads Using Various Gis Techniques (Dataset And Sample Outputs)
This dataset falls under the category Public Transport Transport Network Geometries (Geodata).
It contains the following data: This repository is the dataset of the related paper "Shortest Route Analysis of Dhaka City Roads Using Various GIS Techniques".The data presented here are collected and gathered together from several separate locations. All the probable original sources of the dataset are open-source or free to distribute licensed. The dataset has the following items: 1. Road network of Dhaka city. 2. Bus Route network of Dhaka city. 3. Future metro Route network of Dhaka city. 4. All the bus stands in Bangladesh. 5. All planned metro station in Dhaka city. 6. The output of some sample random two points shortest or cheapest path from the related paper.
This dataset was scouted on 2022-02-23 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://data.mendeley.com/datasets/j5b93k2xhk/1\

InputData DirectoryThis network dataset is an example of a network to which paths from other networks (i.e. Networks A and B) can be compared.Contains two directories:a) NetworkCb) NetworkPaths'NetworkC' Directory- This network is based upon a subset of the Missouri Department of Transportation (MoDOT) July 2016 road dataset listed in the references.- NetworkC contains an ESRI .gdb (NetworkCdata.gdb) in which the arcs and nodes for Network C can be found as well as an ArcGIS ND Network Analyst configuration file. - Featuredataset: NetworkCsub - Network arcs: NetworkCsub - Network file: NetworkCsub_ND - Network junctions: NetworkCsub_ND_Junctions'NetworkPaths' contains ESRI .gdbs representing:a) A collection of routes between OD pairs in each network (InputPaths.gdb) - The densified routes used in the application (densified at 10m): (Net_A_routelines; Net_B_routelines; Net_C_routelines) - The original routes with original set of vertices (non densified): (Net_A_routes; Net_B_routes; Net_C_routes)b) The origin and destination points for the paths (ODNodes.gdb) - These were used to generate the shortest paths for each network, serving as the paths to be compared - origins: originLocations - destinations: destinationLocations_'OutputData' DirectoryContains the comparisons of paths to networks:NetAToB: comparison of paths from network A to network BNetAToC: comparison of paths from network A to network CNetBToA: comparison of paths from network B to network ANetBToC: comparison of paths from network B to network CNetCToA: comparison of paths from network C to network ANetCToB: comparison of paths from network C to network BInside each directory is a collection of ESRI .gdb which contains the individual paths used in the analysis as inputa) NetworkAPaths.gdbb) NetworkBPaths.gdbc) NetworkCPaths.gdbInside each directory is a collection of ESRI .gdb which contains the vertices of the individual paths used in the analysis as inputa) NetworkAPathPoints.gdbb) NetworkBPathPoints.gdbc) NetworkCPathPoints.gdbAlso included is a collection of ESRI .gdb that represent the original path nodes that could be assigned to the comparison network. In this case, only nodes that were within 20m of the comparison network could be assigned. Each path node is attributed with the distance to its counterpart node in the comparison. a) Nodes in Network A paths assigned to Network B (PathANodesAssignedtoNetB.gdb)b) Nodes in Network A paths assigned to Network C (PathANodesAssignedtoNetC.gdb)c) Nodes in Network B paths assigned to Network A (PathBNodesAssignedtoNetA.gdb)d) Nodes in Network B paths assigned to Network C (PathBNodesAssignedtoNetC.gdb)e) Nodes in Network C paths assigned to Network A (PathCNodesAssignedtoNetA.gdb)f) Nodes in Network C paths assigned to Network B (PathCNodesAssignedtoNetB.gdb)Inside each directory is a collection of ESRI .gdb which contain solutions to the SCPPOD with the following naming convention:a) comparing paths in Network A to Network B SCCPODarcsPathAtoNetB.gdb for arc elements and SCCPODnodesPathAtoNetB.gdb for node elements) - The naming convention for the node solutions for path id X is ('SN_routeX_X') - The naming convention for the arc solutions for path id X is ('routX_Rt' for single polyline counterpart path; and 'routeX_Rtsplit' for a polyline representation of the counterpart path based upon the SCPPOD node output).b) comparing paths in Network A to Network C SCCPODarcsPathAtoNetC.gdb for arc elements and SCCPODnodesPathAtoNetC.gdb for node elements)c) comparing paths in Network B to Network A SCCPODarcsPathBtoNetA.gdb for arc elements and SCCPODnodesPathBtoNetA.gdb for node elements)d) comparing paths in Network B to Network C SCCPODarcsPathBtoNetC.gdb for arc elements and SCCPODnodesPathBtoNetC.gdb for node elements)e) comparing paths in Network C to Network A SCCPODarcsPathCtoNetA.gdb for arc elements and SCCPODnodesPathCtoNetA.gdb for node elements)f) comparing paths in Network C to Network B SCCPODarcsPathCtoNetB.gdb for arc elements and SCCPODnodesPathCtoNetB.gdb for node elements)The counterpart paths that were identified were then linked to the full network C to summarize the frequency with with arcs were associated with paths - Can be found in: 1. PathARepresentationinNetC.gdb 2. PathARepresentationinNetC.gdb - important attributes: a) vcntarc: number of paths utilizing arc b) ptCnt: number of path vertices associated with each arc c) AvgDist: average distance of path vertices from network arcs d) MinDist: minimum distance of path vertices from network arcs e) MaxDist: minimum distance of path vertices from network arcs

Tagged image tiles as well as the Faster-RCNN framework for automatic extraction of road intersection points from USGS historical maps of the United States of America. The data and code have been prepared for the paper entitled "Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks" submitted to "International Journal of Geographic Information Science". The image tiles have been tagged manually. The Faster RCNN framework (see https://arxiv.org/abs/1611.10012) was captured from:https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md

INFP, CRMD and UCL have developed a framework capable of analyzing the implications of natural hazards on transportation networks, also in a time-dependent manner. This is currently embedded into an ArcGIS toolbox entitled Network-risk, which has been successfully tested for Bucharest, contributing to an insightful evaluation of emergency intervention times for ambulances and firefighters, in the case of an earthquake. The files and the user manual allow a replication of our recent analysis in Toma-Danila et al. (2022) and a download of results (such as affected roads and unaccesible areas in Bucharest), in various formats. Some of the results are also presented in an ArcGIS Online app, called "Riscul seismic al Bucurestiului" (The seismic risk of Bucharest), available at https://tinyurl.com/yt32aeyx. In the files you can find: - the Bucharest road network used in the article; - facilities for Bucharest and Ilfov, such as hospitals, firestations, buildings with seismic risk or tramway lines accesible by emergency vehicles - results of the analysis: unaccesible roads and areas, service areas around facilities, closest facilities for representative points - Excel calculator for Z elevation from OpenStreetMap data - the user manual and a ArcGIS toolbox.

Main citation: - Toma-Danila D., Tiganescu A., D'Ayala D., Armas I., Sun L. (2022) Time-Dependent Framework for Analyzing Emergency Intervention Travel Times and Risk Implications due to Earthquakes. Bucharest Case Study. Frontiers in Earth Science, https://doi.org/10.3389/feart.2022.834052

Previous references: - Toma-Danila D., Armas I., Tiganescu A. (2020) Network-risk: an open GIS toolbox for estimating the implications of transportation network damage due to natural hazards, tested for Bucharest, Romania. Natural Hazards and Earth System Sciences, 20(5): 1421-1439, https://doi.org/10.5194/nhess-20-1421-2020 - Toma-Danila D. (2018) A GIS framework for evaluating the implications of urban road network failure due to earthquakes: Bucharest (Romania) case study. Natural Hazards, 93, 97-111, https://link.springer.com/article/10.1007/s11069-017-3069-y

ArcGIS tool and tutorial to convert the shapefiles into network format. The latest version of the tool is available at http://csun.uic.edu/codes/GISF2E.htmlUpdate: we now have added QGIS and python tools. To download them and learn more, visit http://csun.uic.edu/codes/GISF2E.htmlPlease cite: Karduni,A., Kermanshah, A., and Derrible, S., 2016, "A protocol to convert spatial polyline data to network formats and applications to world urban road networks", Scientific Data, 3:160046, Available at http://www.nature.com/articles/sdata201646

This is a gdb file used in open data - The gdb has the Digital River Network (0.2 ha) A GIS-based high-resolution digital river network (DRN) representing stream network within each of the 13 Freshwater Management Units (FMU) across the Northland region. The DRN has been produced by conducting catchment and drainage line analysis on a “hydrologically-enforced” digital elevation model (HE-DEM) at 2ha and 0.2 ha flow accumulation thresholds. The HE-DEM was produced by ‘burning’ of hydraulic blockages on DEM produced from 1m regional LiDAR, which ensured the waterways follow their correct natural path as often as possible. A suite of attributes has been added into the DRN GIS layers to make them useful for a range of analysis and modelling purposes in Northland.DescriptionThis GIS layer is a vector dataset (polylines) representing stream network at 0.2ha flow accumulation threshold (i.e., 0.2ha is the minimum size of a headwater catchment), which comprises individual stream segments with unique IDs. It was produced by splitting a stream definition raster (developed from a 1m flow accumulation raster) at each stream intersection. Each stream segment represents the catchment delineated at 0.2ha flow accumulation threshold, which drains that area.Detailed Methodology report describing the datasethttps://www.nrc.govt.nz/resource-library-summary/research-and-reports/rivers-and-streams/high-resolution-digital-river-network-for-northland/Other DetailsData Provided By: Manas Chakraborty (Resource Scientist- Freshwater, NRC)

ArcGIS has many analysis and geoprocessing tools that can help you solve real-world problems with your data. In some cases, you are able to run individual tools to complete an analysis. But sometimes you may require a more comprehensive way to create, share, and document your analysis workflow.In these situations, you can use a built-in application called ModelBuilder to create a workflow that you can reuse, modify, save, and share with others.In this course, you will learn the basics of working with ModelBuilder and creating models. Models contain many different elements, many of which you will learn about. You will also learn how to work with models that others create and share with you. Sharing models is one of the major advantages of working with ModelBuilder and models in general. You will learn how to prepare a model for sharing by setting various model parameters.After completing this course, you will be able to:Identify model elements and states.Describe a prebuilt model's processes and outputs.Create and document models for site selection and network analysis.Define model parameters and prepare a model for sharing.

This submission contains an ESRI map package (.mpk) with an embedded geodatabase for GIS resources used or derived in the Nevada Machine Learning project, meant to accompany the final report. The package includes layer descriptions, layer grouping, and symbology. Layer groups include: new/revised datasets (paleo-geothermal features, geochemistry, geophysics, heat flow, slip and dilation, potential structures, geothermal power plants, positive and negative test sites), machine learning model input grids, machine learning models (Artificial Neural Network (ANN), Extreme Learning Machine (ELM), Bayesian Neural Network (BNN), Principal Component Analysis (PCA/PCAk), Non-negative Matrix Factorization (NMF/NMFk) - supervised and unsupervised), original NV Play Fairway data and models, and NV cultural/reference data. See layer descriptions for additional metadata. Smaller GIS resource packages (by category) can be found in the related datasets section of this submission. A submission linking the full codebase for generating machine learning output models is available through the "Related Datasets" link on this page, and contains results beyond the top picks present in this compilation.

The global Geographic Information System (GIS) in Telecom market is expected to reach $1092.7 million by 2033, expanding at a CAGR of 3.0% from 2025 to 2033. Drivers of the market include the increasing adoption of GIS in network planning and optimization, asset management, and customer relationship management. Cloud-based GIS solutions are gaining traction due to their cost-effectiveness and scalability. Large enterprises are expected to dominate the market segment due to their complex infrastructure and data management requirements. Key players in the GIS market for Telecom include Esri, Hexagon, Trimble, and Pitney Bowes. North America is expected to hold the largest market share due to the presence of major telecom companies and the early adoption of GIS technologies. The Asia Pacific region is projected to exhibit the fastest growth rate due to the rapid expansion of the telecom industry in countries such as China and India. Telecommunication companies utilize GIS to optimize network planning and automate asset management, resulting in improved efficiency and cost savings. The emergence of 5G and IoT is creating new opportunities for GIS in telecom, driving market growth in the coming years. The global Geographic Information System (GIS) in Telecom market is projected to reach $20 billion by 2026, growing at a CAGR of 9.2% from 2021 to 2026. The market is driven by the increasing demand for location-based services, the need for improved network planning and optimization, and the rise of smart cities.

The global GIS in Telecom market is projected to grow from USD 1092.7 million in 2025 to USD 1528.4 million by 2033, at a CAGR of 3.0% during the forecast period. The market is driven by the increasing adoption of GIS technology in the telecom industry to improve network planning, optimization, and management. The growing need for accurate and up-to-date geospatial data for telecom network management is also fueling the market growth. The software segment holds the largest market share and is expected to continue to dominate during the forecast period. The increasing adoption of GIS software for network planning, design, and optimization is driving the growth of this segment. The data segment is also expected to witness significant growth due to the increasing demand for geospatial data for various telecom applications. The services segment is projected to grow at a steady pace due to the rising need for consulting and support services from GIS vendors. The large enterprise segment holds the largest market share due to the high adoption of GIS technology by large telecom operators to manage their complex networks. GIS (Geographic Information Systems) plays a crucial role in the telecommunications industry, enabling network optimization, asset management, and customer service enhancements.

Download In State Plane Projection Here. ** The Street Centerline feature class now follows the NG911/State of Illinois data specifications including a StreetNameAlias table. The download hyperlink above also contains a full network topology for use with the Esri Network Analyst extension ** These street centerlines were developed for a myriad of uses including E-911, as a cartographic base, and for use in spatial analysis. This coverage should include all public and selected private roads within Lake County, Illinois. Roads are initially entered using recorded documents and then later adjusted using current aerial photography. This dataset should satisfy National Map Accuracy Standards for a 1:1200 product. These centerlines have been provided to the United States Census Bureau and were used to conflate the TIGER road features for Lake County. The Census Bureau evaluated these centerlines and, based on field survey of 109 intersections, determined that there is a 95% confidence level that the coordinate positions in the centerline dataset fall within 1.9 meters of their true ground position. The fields PRE_DIR, ST_NAME, ST_TYPE and SUF_DIR are formatted according to United States Postal Service standards. Update Frequency: This dataset is updated on a weekly basis.

The Freight Analysis Framework (FAF5) - Network Links dataset was created from 2017 base year data and was published on April 11, 2022 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The FAF (Version 5) Network contains 487,384 link features. All link features are topologically connected to permit network pathbuilding and vehicle assignment using a variety of assignment algorithms. The FAF Link and the FAF Node datasets can be used together to create a network. The link features include all roads represented in prior FAF networks, and all roads in the National Highway System (NHS) and the National Highway Freight Network (NHFN) that are currently open to traffic. Other included links provide connections between intersecting routes, and to select intermodal facilities and all U.S. counties. The network consists of over 588,000 miles of equivalent road mileage. The dataset covers the 48 contiguous States plus the District of Columbia, Alaska, and Hawaii. A data dictionary, or other source of attribute information, is accessible at https://res1doid-o-torg.vcapture.xyz/10.21949/1529027

GIS in Telecom Sector Market Outlook

The global GIS in telecom sector market size was valued at approximately USD 1.7 billion in 2023 and is projected to reach USD 4.5 billion by 2032, growing at a CAGR of 11.5% during the forecast period. This substantial growth is driven by the increasing demand for advanced mapping and analysis tools in the telecom industry, which plays a crucial role in enhancing network performance, managing assets, and optimizing location-based services. The rapid technological advancements in geospatial data processing and the increasing integration of GIS with IoT, 5G, and AI technologies further contribute to the market’s expansion.

The growth factors for the GIS in telecom sector market are multifaceted and robust. The primary driver is the rising demand for enhanced customer experience and network efficiency, which GIS technology offers through precise mapping and real-time data analytics. Telecom operators are increasingly adopting GIS to optimize their network management processes, reduce operational costs, and improve service delivery. Additionally, the burgeoning demand for location-based services and the growing utilization of GIS in planning and deploying 5G networks are significant contributors to market growth. These applications are essential for telecom companies seeking to expand their networks and enhance connectivity, especially in rural and underserved areas.

The integration of GIS with emerging technologies such as IoT and AI is also a critical growth driver in this market. As telecom companies strive to offer more personalized and efficient services, the role of GIS in analyzing large volumes of geospatial data becomes vital. This integration facilitates better decision-making processes, enabling telecom operators to tailor their services according to specific geographic and demographic needs. Furthermore, GIS technology provides significant cost benefits by optimizing asset management and ensuring more efficient use of resources, which is increasingly appealing in a competitive market landscape.

Another growth factor is the increasing regulatory mandates and policies aimed at improving telecom infrastructure. Governments across the globe are investing heavily in modernizing telecom networks, and GIS plays a crucial role in these initiatives. By providing comprehensive spatial data and analytics, GIS technology assists in the strategic planning and deployment of telecom infrastructure, ensuring compliance with regulatory standards. Moreover, the rise in smart city projects, which rely heavily on advanced telecom networks, further propels the demand for GIS solutions in the telecom sector.

Regionally, North America dominates the GIS in telecom sector market due to its early adoption of advanced technologies and significant investments in telecom infrastructure. The presence of major telecom companies and technology providers also contributes to the region's leading position. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by the rapid expansion of telecom networks and the increasing focus on digital transformation initiatives. Emerging economies in this region are investing heavily in 5G deployment and smart city projects, which boost the demand for GIS solutions. Europe and Latin America also present significant opportunities for market growth, with ongoing investments in network modernization and digital infrastructure development.

Component Analysis

The GIS in telecom sector market is segmented into software, hardware, and services, each playing a pivotal role in the industry’s development. The software segment, which includes GIS mapping and analytics tools, is expected to hold the largest market share. This is attributed to the increasing demand for advanced software solutions that enable telecom operators to analyze geospatial data for network optimization and strategic planning. The continuous evolution of software capabilities, such as real-time analytics and cloud-based services, further propels the demand for GIS software in the telecom sector.

Hardware components, which include GPS devices, GNSS receivers, and other geospatial data collection tools, are crucial for data acquisition in GIS applications. Although this segment may not be as large as the software segment, its importance cannot be overstated. Advances in hardware technology have significantly improved data accuracy and processing speeds, enabling telecom companies to efficiently collect and analyze large volumes of geospatial data. The increasing integration of these hardwar

Snapshot img

GIS Market Size 2025-2029

The GIS market size is forecast to increase by USD 24.07 billion, at a CAGR of 20.3% between 2024 and 2029.

The Global Geographic Information System (GIS) market is experiencing significant growth, driven by the increasing integration of Building Information Modeling (BIM) and GIS technologies. This convergence enables more effective spatial analysis and decision-making in various industries, particularly in soil and water management. However, the market faces challenges, including the lack of comprehensive planning and preparation leading to implementation failures of GIS solutions. Companies must address these challenges by investing in thorough project planning and collaboration between GIS and BIM teams to ensure successful implementation and maximize the potential benefits of these advanced technologies.
By focusing on strategic planning and effective implementation, organizations can capitalize on the opportunities presented by the growing adoption of GIS and BIM technologies, ultimately driving operational efficiency and innovation.

What will be the Size of the GIS Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free Sample

The global Geographic Information Systems (GIS) market continues to evolve, driven by the increasing demand for advanced spatial data analysis and management solutions. GIS technology is finding applications across various sectors, including natural resource management, urban planning, and infrastructure management. The integration of Bing Maps, terrain analysis, vector data, Lidar data, and Geographic Information Systems enables precise spatial data analysis and modeling. Hydrological modeling, spatial statistics, spatial indexing, and route optimization are essential components of GIS, providing valuable insights for sectors such as public safety, transportation planning, and precision agriculture. Location-based services and data visualization further enhance the utility of GIS, enabling real-time mapping and spatial analysis.

The ongoing development of OGC standards, spatial data infrastructure, and mapping APIs continues to expand the capabilities of GIS, making it an indispensable tool for managing and analyzing geospatial data. The continuous unfolding of market activities and evolving patterns in the market reflect the dynamic nature of this technology and its applications.

How is this GIS Industry segmented?

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

Product

  Software
  Data
  Services


Type

  Telematics and navigation
  Mapping
  Surveying
  Location-based services


Device

  Desktop
  Mobile


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa

    UAE


  APAC

    China
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Product Insights

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

The Global Geographic Information System (GIS) market encompasses a range of applications and technologies, including raster data, urban planning, geospatial data, geocoding APIs, GIS services, routing APIs, aerial photography, satellite imagery, GIS software, geospatial analytics, public safety, field data collection, transportation planning, precision agriculture, OGC standards, location intelligence, remote sensing, asset management, network analysis, spatial analysis, infrastructure management, spatial data standards, disaster management, environmental monitoring, spatial modeling, coordinate systems, spatial overlay, real-time mapping, mapping APIs, spatial join, mapping applications, smart cities, spatial data infrastructure, map projections, spatial databases, natural resource management, Bing Maps, terrain analysis, vector data, Lidar data, and geographic information systems.

The software segment includes desktop, mobile, cloud, and server solutions. Open-source GIS software, with its industry-specific offerings, poses a challenge to the market, while the adoption of cloud-based GIS software represents an emerging trend. However, the lack of standardization and interoperability issues hinder the widespread adoption of cloud-based solutions. Applications in sectors like public safety, transportation planning, and precision agriculture are driving market growth. Additionally, advancements in technologies like remote sensing, spatial modeling, and real-time mapping are expanding the market's scope.

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The Software segment was valued at USD 5.06 billion in 2019 and sho