The Geospatial Information Systems (GIS) market for the energy and utilities sector is experiencing robust growth, driven by the increasing need for efficient asset management, improved network planning, and enhanced operational efficiency. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033, reaching approximately $28 billion by 2033. This growth is fueled by several key factors. Firstly, the expanding adoption of smart grids and renewable energy sources necessitates sophisticated GIS solutions for monitoring, managing, and optimizing energy distribution. Secondly, the rising demand for improved infrastructure planning and maintenance, particularly in aging grids, is driving investment in GIS technologies for predictive maintenance and risk assessment. Thirdly, the increasing availability of high-resolution satellite imagery and advanced analytics capabilities is enhancing the accuracy and insights derived from GIS applications. The market is segmented by application (SMEs and Large Enterprises) and type (Cloud-based and On-premises). Large enterprises currently dominate the market due to higher budgets and complex infrastructure needs, but the SME segment shows significant growth potential as cloud-based GIS solutions become more accessible and affordable. Geographical distribution reveals strong market presence in North America and Europe, fueled by established infrastructure and early adoption of GIS technologies. However, significant growth opportunities exist in Asia-Pacific, particularly in developing economies like India and China, where rapid urbanization and infrastructure development are driving demand for GIS solutions. While the market faces restraints such as high initial investment costs and the need for skilled professionals, the overall growth trajectory remains positive. The increasing integration of GIS with other technologies, such as IoT and AI, is expected to further enhance its capabilities and drive market expansion. Key players in this space include Precisely, Esri, Autodesk, and others, constantly innovating to provide advanced GIS solutions tailored to the specific needs of the energy and utilities sector. The competitive landscape is characterized by both established players and emerging technology providers.

The Grid Garage Toolbox is designed to help you undertake the Geographic Information System (GIS) tasks required to process GIS data (geodata) into a standard, spatially aligned format. This format …Show full descriptionThe Grid Garage Toolbox is designed to help you undertake the Geographic Information System (GIS) tasks required to process GIS data (geodata) into a standard, spatially aligned format. This format is required by most, grid or raster, spatial modelling tools such as the Multi-criteria Analysis Shell for Spatial Decision Support (MCAS-S). Grid Garage contains 36 tools designed to save you time by batch processing repetitive GIS tasks as well diagnosing problems with data and capturing a record of processing step and any errors encountered. Grid Garage provides tools that function using a list based approach to batch processing where both inputs and outputs are specified in tables to enable selective batch processing and detailed result reporting. In many cases the tools simply extend the functionality of standard ArcGIS tools, providing some or all of the inputs required by these tools via the input table to enable batch processing on a 'per item' basis. This approach differs slightly from normal batch processing in ArcGIS, instead of manually selecting single items or a folder on which to apply a tool or model you provide a table listing target datasets. In summary the Grid Garage allows you to: List, describe and manage very large volumes of geodata. Batch process repetitive GIS tasks such as managing (renaming, describing etc.) or processing (clipping, resampling, reprojecting etc.) many geodata inputs such as time-series geodata derived from satellite imagery or climate models. Record any errors when batch processing and diagnose errors by interrogating the input geodata that failed. Develop your own models in ArcGIS ModelBuilder that allow you to automate any GIS workflow utilising one or more of the Grid Garage tools that can process an unlimited number of inputs. Automate the process of generating MCAS-S TIP metadata files for any number of input raster datasets. The Grid Garage is intended for use by anyone with an understanding of GIS principles and an intermediate to advanced level of GIS skills. Using the Grid Garage tools in ArcGIS ModelBuilder requires skills in the use of the ArcGIS ModelBuilder tool. Download Instructions: Create a new folder on your computer or network and then download and unzip the zip file from the GitHub Release page for each of the following items in the 'Data and Resources' section below. There is a folder in each zip file that contains all the files. See the Grid Garage User Guide for instructions on how to install and use the Grid Garage Toolbox with the sample data provided.

Snapshot img

GIS In Utility Industry Market Size 2025-2029

The gis in utility industry market size is forecast to increase by USD 3.55 billion, at a CAGR of 19.8% between 2024 and 2029.

The utility industry's growing adoption of Geographic Information Systems (GIS) is driven by the increasing need for efficient and effective infrastructure management. GIS solutions enable utility companies to visualize, analyze, and manage their assets and networks more effectively, leading to improved operational efficiency and customer service. A notable trend in this market is the expanding application of GIS for water management, as utilities seek to optimize water distribution and reduce non-revenue water losses. However, the utility GIS market faces challenges from open-source GIS software, which can offer cost-effective alternatives to proprietary solutions. These open-source options may limit the functionality and support available to users, necessitating careful consideration when choosing a GIS solution. To capitalize on market opportunities and navigate these challenges, utility companies must assess their specific needs and evaluate the trade-offs between cost, functionality, and support when selecting a GIS provider. Effective strategic planning and operational execution will be crucial for success in this dynamic market.

What will be the Size of the GIS In Utility Industry 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 SampleThe Global Utilities Industry Market for Geographic Information Systems (GIS) continues to evolve, driven by the increasing demand for advanced data management and analysis solutions. GIS services play a crucial role in utility infrastructure management, enabling asset management, data integration, project management, demand forecasting, data modeling, data analytics, grid modernization, data security, field data capture, outage management, and spatial analysis. These applications are not static but rather continuously unfolding, with new patterns emerging in areas such as energy efficiency, smart grid technologies, renewable energy integration, network optimization, and transmission lines. Spatial statistics, data privacy, geospatial databases, and remote sensing are integral components of this evolving landscape, ensuring the effective management of utility infrastructure. Moreover, the adoption of mobile GIS, infrastructure planning, customer service, asset lifecycle management, metering systems, regulatory compliance, GIS data management, route planning, environmental impact assessment, mapping software, GIS consulting, GIS training, smart metering, workforce management, location intelligence, aerial imagery, construction management, data visualization, operations and maintenance, GIS implementation, and IoT sensors is transforming the industry. The integration of these technologies and services facilitates efficient utility infrastructure management, enhancing network performance, improving customer service, and ensuring regulatory compliance. The ongoing evolution of the utilities industry market for GIS reflects the dynamic nature of the sector, with continuous innovation and adaptation to meet the changing needs of utility providers and consumers.

How is this GIS In Utility Industry Industry segmented?

The gis in utility industry 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. ProductSoftwareDataServicesDeploymentOn-premisesCloudGeographyNorth AmericaUSCanadaEuropeFranceGermanyRussiaMiddle East and AfricaUAEAPACChinaIndiaJapanSouth AmericaBrazilRest of World (ROW).

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.In the utility industry, Geographic Information Systems (GIS) play a pivotal role in optimizing operations and managing infrastructure. Utilities, including electricity, gas, water, and telecommunications providers, utilize GIS software for asset management, infrastructure planning, network performance monitoring, and informed decision-making. The GIS software segment in the utility industry encompasses various solutions, starting with fundamental GIS software that manages and analyzes geographical data. Additionally, utility companies leverage specialized software for field data collection, energy efficiency, smart grid technologies, distribution grid design, renewable energy integration, network optimization, transmission lines, spatial statistics, data privacy, geospatial databases, GIS services, project management, demand forecasting, data modeling, data analytics, grid modernization, data security, field data capture, outage ma

Line segment labels delineate north/south or east/west running streets with recommended address ranges. Data is used by Planning and GIS departments for new addresses and to follow a logical scheme throughout the city.

Pulling from historic resources the Department of Planning and Urban Design created a map showing the Dallas City Boundary in 1855 with transportation infrastructure present that year - the Dallas street grid plan.

The GIS database has been developed by under the Small Hydropower Mapping and Improved Geospatial Electrification Planning in Indonesia Project [Project ID: P145273].

The scope of the project was to facilitate and improve the planning and investment process for small hydro development both grid and isolated systems through:

• building up a central database on smal hydro at national scale and validating the mapping of small hydro in NTT, Maluku, Maluku Utara and Sulawesi

• improved electrification planning by integrating small hydro potential for the provinces of NTT, Maluku, Maluku Utara and Sulawesi into the planning process.

Please refer to the country project page for additional outputs and reports: http://esmap.org/re_mapping_indonesia

The GIS database contains the following datasets:

• SHP(promising sites)
• Admin Divisions
• Topomas_grid
• Rivers, Geology
• Forest_areas
• Roads
• RainfallGauges
• RunoffGauges
• ElectricSystem,
  each accompanied by a metadata file.

Please cite as: [Data/information/map obtained from the] “World Bank via ENERGYDATA.info, under a project funded by the Energy Sector Management Assistance Program (ESMAP). For more information: Indonesia - Small Hydro GIS Atlas, 2017, https://energydata.info/dataset/indonesia-small-hydro-gis-database-2017"

This layer represents a 10,000 by 10,000 meter United States National Grid (USNG) for Florida. The USNG is an alpha-numeric reference system based on the UTM coordinate system and is similar to the Military Grid Reference System. Use of the USNG ensures a uniform grid mapping and positional reporting system for search and rescue, emergency planning, response, and recovery. Please contact GIS.Librarian@floridadep.gov for more information.

The GIS database has been developed by under the Small Hydropower Mapping and Improved Geospatial Electrification Planning in Indonesia Project [Project ID: P145273]. The scope of the project was to facilitate and improve the planning and investment process for small hydro development both grid and isolated systems through: building up a central database on smal hydro at national scale and validating the mapping of small hydro in NTT, Maluku, Maluku Utara and Sulawesi improved electrification planning by integrating small hydro potential for the provinces of NTT, Maluku, Maluku Utara and Sulawesi into the planning process. Please refer to the country project page for additional outputs and reports: https://www.esmap.org/re-mapping/indonesia The GIS database contains the following datasets: SHP(promising sites) Admin Divisions Topomas_grid Rivers, Geology Forest_areas Roads RainfallGauges RunoffGauges ElectricSystem, each accompanied by a metadata file. Please cite as: [Data/information/map obtained from the] “World Bank via ENERGYDATA.info, under a project funded by the Energy Sector Management Assistance Program (ESMAP). For more information: Indonesia Small Hydro GIS Atlas, 2017, https://energydata.info/dataset/indonesia-small-hydro-gis-database-2017"

This layer contains the boundaries and IDs of the Maryland tax maps produced by Maryland Department of Planning. Tax maps, also known as assessment maps, property maps or parcel maps, are a graphic representation of real property showing and defining individual property boundaries in relationship to contiguous real property.This is a MD iMAP hosted service layer. Find more information at https://imap.maryland.gov.Feature Service Layer Link:https://mdgeodata.md.gov/imap/rest/services/PlanningCadastre/MD_PropertyData/MapServer/2

Supplementary information files for article: 'The future scope of large-scale solar in the UK: site suitability and target analysis'.Abstract:This paper uses site suitability analysis to identify locations for solar farms in the UK to help meet climate change targets. A set of maps, each representing a given suitability criterion, is created with geographical information systems (GIS) software. These are combined to give a Boolean map of areas which are appropriate for large-scale solar farm installation. Several scenarios are investigated by varying the criteria, which include geographical (land use) factors, solar energy resource and electrical distribution network constraints. Some are dictated by the physical and technical requirements of large-scale solar construction, and some by government or distribution network operator (DNO) policy. It is found that any suitability map which does not heed planning permission and grid constraints will overstate potential solar farm area by up to 97%. This research finds sufficient suitable land to meet Future Energy Scenarios (UK National Grid outlines for the coming energy landscape).

The Land Utilization in Hong Kong (LUHK) is a broad-brush presentation of distribution of land uses in Hong Kong, compiled using the updated satellite images, in-house survey information of the Planning Department, and other relevant information from various government departments (the dates on which the satellite images were taken and on which the information was collected are shown in the associated data description files). The LUHK raster grids have a spatial resolution of 10m. The area of land uses are approximated to the nearest square km. The data is not designed or intended for detailed analysis or calculation purposes.As definitions of some land use classes and methodology are updated from time to time, the figures this year may not be comparable directly to those provided in previous years. The datasets are available for download in API.

A polygon feature class of the US National Grid 1K boundaries within Miami-Dade County. This layer represents a 1,000 by 1,000 meter United States National Grid (USNG) for Miami-Dade County. The USNG is an alpha-numeric reference system based on the UTM coordinate system and is similar to the Military Grid Reference System. Use of the USNG ensures a uniform grid mapping and positional reporting system for search and rescue, emergency planning, response, and recovery.Updated: Not Planned The data was created using: Projected Coordinate System: WGS_1984_Web_Mercator_Auxiliary_SphereProjection: Mercator_Auxiliary_Sphere

4,000 foot by 6,000 foot Washington Suburban Sanitary Commission (WSSC) gridsFor more information, contact: GIS Manager Information Technology & Innovation (ITI) Montgomery County Planning Department, MNCPPC T: 301-650-5620

This layer is sourced from gis.arlingtontx.gov.

Arlington Texas Zoning Information

Made available for NPDC GeoHUB (GIS Hub Site and Open Data Portal) :A full description is available in the Metadata. SeeTerms of Use.Notes:The "Updated" date, noted here in the item, does not accurately reflect the currency of the data within the Feature Layer.The data available for download on NPDC GeoHUB is updated daily, this results in differences between what is available online and NPDC's databases.

ESRI grid and shape files showing Wind Stress on Australia's oceans from data supplied by the National Centers for Environmental Prediction (NCEP) Re-analysis Project, 1976 to 1998 inclusive. These grid and shape files have been produced by CSIRO for the National Oceans Office, as part of an ongoing commitment to natural resource planning and management through the 'National Marine Bioregionalisation' project. Variations in onscreen colour representation or printed reproduction may affect percep...Show full descriptionESRI grid and shape files showing Wind Stress on Australia's oceans from data supplied by the National Centers for Environmental Prediction (NCEP) Re-analysis Project, 1976 to 1998 inclusive. These grid and shape files have been produced by CSIRO for the National Oceans Office, as part of an ongoing commitment to natural resource planning and management through the 'National Marine Bioregionalisation' project. Variations in onscreen colour representation or printed reproduction may affect perception of the contained data.

This data set contains BOEM Planning Area outlines in ESRI shapefile format for the BOEM Gulf of Mexico Region. The old Gulf of Mexico Planning Area outlines were changed as of 2008. The divisions between these new Planning Areas for the Eastern, Central, and Western Gulf of Mexico are based on the OCS Administrative Boundaries between TX and LA and FL and AL. Except for a portion of the line between the Eastern and Central Planning Area the boundaries follow the block grid, depending on whether the block is mostly on one side of the Administrative Boundary, or the other. Legal definitions for all the OCS BOEM Planning Areas may be found at the following site: https://www.boem.gov/Oil-and-Gas-Energy-Program/Mapping-and-Data/Index.aspxThe Straits of Florida Planning Area (Atlantic Region) is essentially unchanged. The Submerged Lands Act (Fed/State) boundary, along with the Continental Shelf Boundary (CSB), the Limit of Protraction, and the boundary between the Atlantic and Gulf of Mexico Regions were used to complete the polygons for the Planning Areas. Because GIS projection and topology functions can change or generalize coordinates, and because shapefiles cannot represent true arcs, these GIS files are considered to be approximate and are NOT an OFFICIAL record for the exact block coordinates or areas. The Official Protraction Diagrams (OPDs) and Supplemental Official Block Diagrams (SOBDs) serve as the legal definition for BOEM offshore boundary coordinates and area descriptions. If any discrepancies are found between these feature classes and the OPDs and SOBDs, it is the OPD and SOBD diagrams which take precedence. Also note that the BOEM cadastre is developed on a UTM projection, and most Planning Areas span multiple UTM ZONES. This means that area values computed from these shapefiles will not match the official BOEM areas.

ESRI grid of mean sea surface currents derived from annual and semi-annual temperature and salinity cycles stored in CARS2000. CARS is a set of seasonal maps of temperature, salinity, dissolved oxygen, nitrate, phosphate and silicate, generated using Loess mapping from all available oceanographic data in the region. It covers the region 100-200E, 50-0S, on a 0.5 degree grid, and on 56 standard depth levels. Higher resolution versions are also available for the Australian continental shelf. The data was obtained from the World Ocean Atlas 98 and CSIRO Marine and NIWA archives. It was designed to improve on the Levitus WOA98 Atlas, in the Australian region. These grids have been produced by CSIRO for the National Oceans Office, as part of an ongoing commitment to natural resource planning and management through the 'National Marine Bioregionalisation' project. Variations in onscreen colour representation or printed reproduction may affect perception of the contained data.

This is a publicly available map image service with limited GIS attributes. A downloadable version of this data is now available through the MDOT GIS Open Data Portal: Download MDOT SHA Right-of-Way Polygons (Open Data Portal) The following related versions of this data are available here:MDOT SHA Right-of-Way (Secured)Line dataFull attribute tableAccessible to only MDOT employees and contractors upon requestMDOT SHA Right-of-Way (Map Image Service)Read-only map serviceLine dataLimited attributes (quality level)Accessible to publicMDOT SHA Right-of-Way data is a composite layer of PSD field-collected survey sources, PSD in-house computations, traced PSD hardcopy materials, and historical Maryland Department of Planning (MDP) parcel boundaries.This data product was intended to replace MDOT SHA Planning Level Right-of-Way (Tax Map Legacy), which is an increasingly obsolete legacy product for MDOT SHA Right-of-Way information that in some areas remains the most comprehensive. For continuity, many MDP parcel boundaries found in MDOT SHA Planning Level Right-of-Way (Tax Map Legacy) have been incorporated into MDOT SHA Right-of-Way data with an "Estimated" quality level. Please see below for a description of the primary attribute.-----------------------------------------------------The polygons in this layer are divided into 318 arbitrary grid zones across the State of Maryland. Updates to the parent ROW boundary line data set [MDOT SHA Right-of-Way (Secured)] are made by grid and reflected in this polygon layer.For more information or to report errors in this data, please contact MDOT SHA OIT Enterprise Information Services:Email: GIS@mdot.maryland.gov

The layers presents the predicted water surface elevation during a 1-percent-annual-chance flood (also referred to as a 100-year flood) in Baltimore County. This grid is derived from FEMA's Flood Risk Database.