Geographic Information System (GIS) Analytics Market Outlook

The global Geographic Information System (GIS) Analytics market size is projected to grow remarkably from $9.1 billion in 2023 to $21.7 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 10.2% during the forecast period. This substantial growth can be attributed to several factors such as technological advancements in GIS, increasing adoption in various industry verticals, and the rising importance of spatial data for decision-making processes.

The primary growth driver for the GIS Analytics market is the increasing need for accurate and efficient spatial data analysis to support critical decision-making processes across various industries. Governments and private sectors are investing heavily in GIS technology to enhance urban planning, disaster management, and resource allocation. With the world becoming more data-driven, the reliance on GIS for geospatial data has surged, further propelling its market growth. Additionally, the integration of artificial intelligence (AI) and machine learning (ML) with GIS is revolutionizing the analytics capabilities, offering deeper insights and predictive analytics.

Another significant growth factor is the expanding application of GIS analytics in disaster management and emergency response. Natural disasters such as hurricanes, earthquakes, and wildfires have highlighted the importance of GIS in disaster preparedness, response, and recovery. The ability to analyze spatial data in real-time allows for quicker and more efficient allocation of resources, thus minimizing the impact of disasters. Moreover, GIS analytics plays a pivotal role in climate change studies, helping scientists and policymakers understand and mitigate the adverse effects of climate change.

The transportation sector is also a major contributor to the growth of the GIS Analytics market. With the rapid urbanization and increasing traffic congestion in cities, there is a growing demand for effective transport management solutions. GIS analytics helps in route optimization, traffic management, and infrastructure development, thereby enhancing the overall efficiency of transportation systems. The integration of GIS with Internet of Things (IoT) devices and sensors is further enhancing the capabilities of traffic management systems, contributing to the market growth.

Regionally, North America is the largest market for GIS analytics, driven by the high adoption rate of advanced technologies and significant investment in geospatial infrastructure by both public and private sectors. The Asia Pacific region is expected to witness the highest growth rate during the forecast period due to the rapid urbanization, infrastructural developments, and increasing government initiatives for smart city projects. Europe and Latin America are also contributing significantly to the market growth owing to the increasing use of GIS in urban planning and environmental monitoring.

Component Analysis

The GIS Analytics market can be segmented by component into software, hardware, and services. The software segment holds the largest market share due to the continuous advancements in GIS software solutions that offer enhanced functionalities such as data visualization, spatial analysis, and predictive modeling. The increasing adoption of cloud-based GIS software solutions, which offer scalable and cost-effective options, is further driving the growth of this segment. Additionally, open-source GIS software is gaining popularity, providing more accessible and customizable options for users.

The hardware segment includes GIS data collection devices such as GPS units, remote sensing instruments, and other data acquisition tools. This segment is witnessing steady growth due to the increasing demand for high-precision GIS data collection equipment. Technological advancements in hardware, such as the development of LiDAR and drones for spatial data collection, are significantly enhancing the capabilities of GIS analytics. Additionally, the integration of mobile GIS devices is facilitating real-time data collection, contributing to the growth of the hardware segment.

The services segment encompasses consulting, implementation, training, and maintenance services. This segment is expected to grow at a significant pace due to the increasing demand for professional services to manage and optimize GIS systems. Organizations are seeking expert consultants to help them leverage GIS analytics for strategic decision-making and operational efficiency. Additionally, the growing complexity o

The global GIS Consulting Service market is expected to reach 1637 million by 2023, growing at a CAGR of 15% during the forecast period. Geospatial data analytics, predictive modeling, and situational awareness are key drivers of the market growth. The rising adoption of GIS in various industries, such as transportation, agriculture, energy, and government, is contributing to the market's expansion. The market is segmented based on type, application, and region. By type, the market is divided into custom mapping services, GIS mapping software development, and others. The custom mapping services segment is expected to hold the largest share of the market due to the increasing demand for customized maps for specific purposes. By application, the market is segmented into transportation, agriculture, energy, and others. The transportation segment is expected to witness the highest growth rate due to the growing use of GIS in traffic management, route optimization, and logistics. By region, the market is divided into North America, South America, Europe, Middle East & Africa, and Asia Pacific. North America is expected to hold the largest share of the market due to the presence of key players and the early adoption of GIS technology. Asia Pacific is expected to experience the highest growth rate due to the increasing infrastructure development and urbanization in the region.

Spatial Data Modeller, SDM, is a collection of tools for use with GIS software for adding categorical maps with interval, ordinal, or ratio scale maps to produce a predictive map of where something of interest is likely to occur. The tools include the data-driven methods of Weights of Evidence, Logistic Regression, and two supervised and one unsupervised neural network methods, and categorical tools for a knowledge-driven method Fuzzy Logic. All of the tools have help files that include references to publications discussing the applications of the methods implemented in the tool. Several of the tools create output rasters, tables, or files that require the user to enter a name. Default values are provided in most cases to serve as suggestions of the style of naming that has been found useful. These names, following ArcGIS conventions, can be changed to meet the user’s needs. To make all of the features of SDM work properly it is required that several Environment parameters are set. See the discussion of Environment Settings below for the details. The Weights of Evidence, WofE, and Logistic Regression, LR, tools addresses area as the count of unit cells. It is assumed in the WofE and LR tools that the data has spatial units of meters. If your data has other spatial units, these WofE and LR tools may not work properly.

Website:

http://www.ige.unicamp.br/sdm/

Analysis of ‘SPFC Planning Area’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/add9bafd-a0be-4301-9b2d-61272f54135a on 26 January 2022.

--- Dataset description provided by original source is as follows ---

--- Original source retains full ownership of the source dataset ---

Analysis of ‘500 Cities: City-level Data (GIS Friendly Format), 2016 release’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/2d5749f7-4fca-46d0-8550-b5eddc851aad on 26 January 2022.

--- Dataset description provided by original source is as follows ---

2014, 2013. Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. 500 cities project census city-level data in GIS-friendly format can be joined with city spatial data (https://chronicdata.cdc.gov/500-Cities/500-Cities-City-Boundaries/n44h-hy2j) in a geographic information system (GIS) to produce maps of 27 measures at the city-level.

--- Original source retains full ownership of the source dataset ---

GIS Mapping Software Market Outlook

The global GIS Mapping Software market size was valued at approximately USD 8.5 billion in 2023 and is projected to reach around USD 17.5 billion by 2032, growing at a CAGR of 8.3% from 2024 to 2032. This robust growth is driven by the increasing adoption of geospatial technologies across various sectors, including urban planning, disaster management, and agriculture.

One of the primary growth factors for the GIS Mapping Software market is the rising need for spatial data analytics. Organizations are increasingly recognizing the value of geographical data in making informed decisions, driving the demand for sophisticated mapping solutions. Furthermore, advancements in satellite imaging technology and the increasing availability of high-resolution imagery are enhancing the capabilities of GIS software, making it a crucial tool for various applications.

Another significant driver is the integration of GIS with emerging technologies such as artificial intelligence (AI) and the Internet of Things (IoT). These integrations are facilitating real-time data processing and analysis, thereby improving the efficiency and accuracy of GIS applications. For instance, in urban planning and disaster management, real-time data can significantly enhance predictive modeling and response strategies. This synergy between GIS and cutting-edge technologies is expected to fuel market growth further.

The growing emphasis on sustainable development and smart city initiatives globally is also contributing to the market's expansion. Governments and private entities are investing heavily in GIS technologies to optimize resource management, enhance public services, and improve urban infrastructure. These investments are particularly evident in developing regions where urbanization rates are high, and there is a pressing need for efficient spatial planning and management.

In terms of regional outlook, North America holds a significant share of the GIS Mapping Software market, driven by robust technological infrastructure and high adoption rates across various industries. However, Asia Pacific is expected to witness the highest growth rate during the forecast period. This growth is attributed to rapid urbanization, increasing government initiatives for smart cities, and rising investments in infrastructure development.

The Geographic Information Systems Platform has become an integral part of modern spatial data management, offering a comprehensive framework for collecting, analyzing, and visualizing geographic data. This platform facilitates the integration of diverse data sources, enabling users to create detailed maps and spatial models that support decision-making across various sectors. With the increasing complexity of urban environments and the need for efficient resource management, the Geographic Information Systems Platform provides the tools necessary for real-time data processing and analysis. Its versatility and scalability make it an essential component for organizations looking to leverage geospatial data for strategic planning and operational efficiency.

Component Analysis

The GIS Mapping Software market is segmented by component into software and services. The software segment dominates the market, primarily due to the continuous advancements in GIS software capabilities. Modern GIS software offers a range of functionalities, from basic mapping to complex spatial analysis, making it indispensable for various sectors. These software solutions are increasingly user-friendly, allowing even non-experts to leverage geospatial data effectively.

Moreover, the software segment is witnessing significant innovation with the integration of AI and machine learning algorithms. These advancements are enabling more sophisticated data analysis and predictive modeling, which are crucial for applications such as disaster management and urban planning. The adoption of cloud-based GIS software is also on the rise, offering scalability and real-time data processing capabilities, which are essential for dynamic applications like transport management.

The services segment, although smaller than the software segment, is also experiencing growth. This includes consulting, implementation, and maintenance services that are critical for the successful deployment and operation of GIS systems. The increasing complexity of GIS applications nec

The global geological consulting services market, valued at $730 million in 2025, is projected to experience robust growth, driven by a compound annual growth rate (CAGR) of 5.5% from 2025 to 2033. This expansion is fueled by several key factors. The increasing demand for mineral resources, coupled with the growing complexity of exploration and extraction projects, necessitates specialized geological expertise. Furthermore, stringent environmental regulations and the rising focus on sustainable mining practices are driving demand for comprehensive geological assessments and risk mitigation strategies. Technological advancements, such as the adoption of advanced geospatial technologies and data analytics, are further enhancing the efficiency and accuracy of geological consulting services, contributing to market growth. Key players like Alpha Geoscience, AMC Consultants, and SRK Consulting are leveraging these trends to expand their service offerings and geographic reach. The market's segmentation likely reflects variations in service types (e.g., exploration, mine geology, environmental remediation) and geographic focus, with North America and Europe potentially dominating the market share due to established mining industries and regulatory frameworks. The market faces some challenges, however. Fluctuations in commodity prices can impact exploration budgets, potentially affecting demand for geological consulting services. Additionally, the availability of skilled geological professionals and competition from emerging market players could create pressure on pricing and profit margins. Nevertheless, the long-term outlook remains positive, driven by the ongoing need for responsible resource management, technological innovation, and the increasing complexity of geological projects worldwide. The projected growth suggests significant opportunities for existing players to expand their services and for new entrants to establish a presence in this dynamic market. Successful companies will be those that effectively leverage technological advancements, develop specialized expertise, and effectively navigate the complexities of environmental regulations and global market dynamics.

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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

Analysis of ‘State Water Project Monuments’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/812a676f-cf16-4ef6-a920-6a6d6dd1a82d on 12 February 2022.

--- Dataset description provided by original source is as follows ---

This data set was collected to create a comprehensive platform to visualize the boundary and control monuments of the California State Water Project. The majority of the boundary monuments were set during the construction of the state water project, subsequent acquisition and surveys have resulted in more monuments be placed. Monuments that were set prior to the use of this GIS feature class were extracted from Department CAD maps know as "Property Management Maps". The associated data is considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standard version 3.1, dated September 11, 2019.DWR makes no warranties or guarantees —either expressed or implied — as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements or suggestions should be forwardedgis@water.ca.gov.

--- Original source retains full ownership of the source dataset ---

Analysis of ‘River Management Engineer Districts’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/39404a52-f8cc-4216-ac71-49cefdb7bc57 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

--- Original source retains full ownership of the source dataset ---

Modern American political campaigns are typically conceptualized as "candidate-centered" and treated as conditionally independent in quantitative analyses. In reality, however, these campaigns are linked by professional consulting firms, who are important agents of campaign strategy diffusion within the extended party networks of the contemporary era. To test our hypothesis that consultants disseminate campaign strategies among their clients, we analyze new data on U.S. House elections derived from Federal Election Commission records. Using spatial autoregressive models, we find that candidates who share consultants are more likely to use similar campaign strategies than we would otherwise expect conditional on numerous explanatory variables. These results, which largely withstand an extensive series of robustness and falsification tests, suggest that consultants play a key role in diffusing strategies among Congressional campaigns.

In 2022, on behalf of the North Carolina Center for Geographic Information and Analysis four contractors captured 6-inch pixel 4-band (RGB-IR) orthoimagery for the 26 Northern Piedmont and mountain counties in North Carolina. These contractors are Spatial Data Consultants, Inc., Sanborn Map Company, Atlas Geographic Data, Surdex Corporation, and Survey and Mapping (SAM). This file documents the mosaic of the imagery exposures and associated seamlines used to generate the collection of imagery by the contractors for the entire study area of North Carolina for the following counties , Alamance, Alexander, Alleghany, Ashe, Avery, Burke, Caldwell, Caswell, Catawba, Davidson, Davie, Forsyth, Guilford, Iredell, Madison, Mcdowell, Mitchell, Randolph, Rockingham, Rowan, Stokes, Surry, Watauga, Wilkes, Yadkin, Yancey; and the date each exposure was flown. Seamlines were converted to polygon shapefile format, checked for gaps and overlaps, attributed, and trimmed to the study area boundary. This dataset contains a polygon seamline index attributed with, Exposure date the season. These attributes were based on information collected from individual frames and from flight line datasets. This seamline dataset was created to identify the individual exposure frames and the seamlines that join these exposures together to generate the orthoimagery product for the Northern Piedmont and Mountains 2022 Project (NPM22). The purpose of this dataset derives information from the exploitation flight lines to provide the specific dates of imagery acquisition for each image frame.

Analysis of ‘Applikazzjonijiet tal-Ippjanar ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/9187ffff-6a0d-473d-8cfb-7f83651edb7f on 16 January 2022.

--- Dataset description provided by original source is as follows ---

Planning Applications details and spatial data from 2003 to date The Planning applications data is extracted from the APAS Planning system and holds details on the progress of a planning application in three separate spreadsheets (BASE, FURINFO, APPEAL) and spatially available DCC_PlanApps Shape file and the DCC_PlanApps.csv. ''Base datasheet contains Application reference, dates, description, location, decision etc. 'REG_REF Registration Reference no.'APNDAT Date application made'PROPOSAL Short Description of proposed development'RGNDAT Date application is registered by Planning Authority 'LOCATION Application address'APPTYPE DECISION Grant, Refuse, Split Decision, Further Information, Clarification of Further Information etc'LONG_PROPOSAL Long Description of proposed development'FINAL_GRNT_DATE Date of Final grant of planning permission'DECDAT Date of planning authority decision'TIME_EXP Extension of Time to?.'STAGE Unregistered application, Application registered, Application Decided, Managers Report, Decision issued, Appeal lodge, Appeal decided etc ''To chart the progress of a planning application read the ?Base? sheet in conjunction with Conditions and Reasons ?CRD? sheet.: 'APNID, Application ID'TYPE, Granted, Refused, Split, Withdrawn etc C (Condition), R (Reason) D (Directive) I(Informative) N (Note)'CODE, Codes for Standard Conditions attached'DESCRIPTION Descriptive text for condition, reason, directive etc.''Details of applications that were requested further information are included in a separate file ?FURINFO?. Appeal information is also held on a separate sheet ?APPEAL?. ''Planning Applications Spatial data ?PlanAppsSpatial? planning contains file reference number and location co-ordinates; the spatial data is in ShapeFile format, ITM projection.''The planning application data is updated every night

--- Original source retains full ownership of the source dataset ---

Download .zipThis file contains the data used by the Division of Wildlife for the construction of lake maps. Data was collected in the Ohio State Plane Coordinate System for both the northern and southern state planes in the Lambert Projection Zone. Except for the lakes in extreme western Ohio which is in UTM zone 16N the majority of lakes are in UTM zone 17N and datum NAD83. Data were collected by the Ohio Division of Wildlife using a Trimble GPS Pathfinder Pro XRS receiver and Recon datalogger. Geocoding of depths typically occurred during water levels that were ± 60 cm of full recreational pool while transversing the reservoir at 100m intervals driving at a vessel speed of 2.0-2.5 m/s. Depth contour lines were derived by creating a raster file from the point bathymetry and boundary lake data. ArcGIS Spatial Analyst Interpolation tool outputs point data that is then changed into polyline contours using the Spatial Analyst Surface tool. Additional details on the digitizing process are available upon request.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesDivision of Wildlife2105 Morse Rd, Bldg G-2Columbus, OH, 43289Telephone: 614-265-6522Email: gis.support@dnr.ohio.gov

Download .zipThis file contains the data used by the Division of Wildlife for the construction of lake maps. Data was collected in the Ohio State Plane Coordinate System for both the northern and southern state planes in the Lambert Projection Zone. Except for the lakes in extreme western Ohio which is in UTM zone 16N the majority of lakes are in UTM zone 17N and datum NAD83. Data were collected by the Ohio Division of Wildlife using a Trimble GPS Pathfinder Pro XRS receiver and Recon datalogger. Geocoding of depths typically occurred during water levels that were ± 60 cm of full recreational pool while transversing the reservoir at 100m intervals driving at a vessel speed of 2.0-2.5 m/s. Depth contour lines were derived by creating a raster file from the point bathymetry and boundary lake data. ArcGIS Spatial Analyst Interpolation tool outputs point data that is then changed into polyline contours using the Spatial Analyst Surface tool. Additional details on the digitizing process are available upon request.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesDivision of Wildlife2094 Morse Rd, Bldg G-2Columbus, OH, 43278Telephone: 614-265-6511Email: gis.support@dnr.ohio.gov

Download .zipThis file contains point data used for the construction of lake maps for State of Ohio. Data was collected in the Ohio State Plane Coordinate System for both the northern and southern state planes in the Lambert Projection Zone. Except for the lakes in extreme western Ohio which is in UTM zone 16N the majority of lakes are in UTM zone 17N and datum NAD83. The data was collected by fisheries biologists with the Ohio Division of Wildlife using a Trimble GPS Pathfinder Pro XRS receiver and Recon datalogger. Geocoding of depths occurred during water levels that were ± 60 cm of full recreational pool while transversing the reservoir at 100m intervals driving at a vessel speed of 2.0-2.5 m/s. Depth contour lines were derived from this data by creating a raster file from the point bathymetry and boundary lake data. The Spatial Analyst Interpolation tool outputs point data that is then changed into polyline contours using the Spatial Analyst Surface tool. Additional details on the digitizing process are available upon request.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesDivision of Wildlife2091 Morse Rd, Bldg G-2Columbus, OH, 43275Telephone: 614-265-6508Email: gis.support@dnr.ohio.gov

Download .zipThis file contains the data used by the Division of Wildlife for the construction of lake maps. Data was collected in the Ohio State Plane Coordinate System for both the northern and southern state planes in the Lambert Projection Zone. Except for the lakes in extreme western Ohio which is in UTM zone 16N the majority of lakes are in UTM zone 17N and datum NAD83. Data were collected by the Ohio Division of Wildlife using a Trimble GPS Pathfinder Pro XRS receiver and Recon datalogger. Geocoding of depths typically occurred during water levels that were ± 60 cm of full recreational pool while transversing the reservoir at 100m intervals driving at a vessel speed of 2.0-2.5 m/s. Depth contour lines were derived by creating a raster file from the point bathymetry and boundary lake data. ArcGIS Spatial Analyst Interpolation tool outputs point data that is then changed into polyline contours using the Spatial Analyst Surface tool. Additional details on the digitizing process are available upon request.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesDivision of Wildlife2045 Morse Rd, Bldg G-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov

Download .zipThis file contains the data used by the Division of Wildlife for the construction of lake maps. Data was collected in the Ohio State Plane Coordinate System for both the northern and southern state planes in the Lambert Projection Zone. Except for the lakes in extreme western Ohio which is in UTM zone 16N the majority of lakes are in UTM zone 17N and datum NAD83. Data were collected by the Ohio Division of Wildlife using a Trimble GPS Pathfinder Pro XRS receiver and Recon datalogger. Geocoding of depths typically occurred during water levels that were ± 60 cm of full recreational pool while transversing the reservoir at 100m intervals driving at a vessel speed of 2.0-2.5 m/s. Depth contour lines were derived by creating a raster file from the point bathymetry and boundary lake data. ArcGIS Spatial Analyst Interpolation tool outputs point data that is then changed into polyline contours using the Spatial Analyst Surface tool. Additional details on the digitizing process are available upon request.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesDivision of Wildlife2045 Morse Rd, Bldg G-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov

Download .zipThis file contains point data used for the construction of lake maps for State of Ohio. Data was collected in the Ohio State Plane Coordinate System for both the northern and southern state planes in the Lambert Projection Zone. Except for the lakes in extreme western Ohio which is in UTM zone 16N the majority of lakes are in UTM zone 17N and datum NAD83. The data was collected by fisheries biologists with the Ohio Division of Wildlife using a Trimble GPS Pathfinder Pro XRS receiver and Recon datalogger. Geocoding of depths occurred during water levels that were ± 60 cm of full recreational pool while transversing the reservoir at 100m intervals driving at a vessel speed of 2.0-2.5 m/s. Depth contour lines were derived from this data by creating a raster file from the point bathymetry and boundary lake data. The Spatial Analyst Interpolation tool outputs point data that is then changed into polyline contours using the Spatial Analyst Surface tool. Additional details on the digitizing process are available upon request.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesDivision of Wildlife2045 Morse Rd, Bldg G-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov