GIS Data Management Market Outlook

The global GIS Data Management market size is projected to grow from USD 12.5 billion in 2023 to USD 25.6 billion by 2032, exhibiting a CAGR of 8.4% during the forecast period. This impressive growth is driven by the increasing adoption of geographic information systems (GIS) across various sectors such as urban planning, disaster management, and agriculture. The rising need for effective data management systems to handle the vast amounts of spatial data generated daily also significantly contributes to the market's expansion.

One of the primary growth factors for the GIS Data Management market is the burgeoning demand for spatial data analytics. Businesses and governments are increasingly leveraging GIS data to make informed decisions and strategize operational efficiencies. With the rapid urbanization and industrialization worldwide, there's an unprecedented need to manage and analyze geographic data to plan infrastructure, monitor environmental changes, and optimize resource allocation. Consequently, the integration of GIS with advanced technologies like artificial intelligence and machine learning is becoming more prominent, further fueling market growth.

Another significant factor propelling the market is the advancement in GIS technology itself. The development of sophisticated software and hardware solutions for GIS data management is making it easier for organizations to capture, store, analyze, and visualize geographic data. Innovations such as 3D GIS, real-time data processing, and cloud-based GIS solutions are transforming the landscape of geographic data management. These advancements are not only enhancing the capabilities of GIS systems but also making them more accessible to a broader range of users, from small enterprises to large governmental agencies.

The growing implementation of GIS in disaster management and emergency response activities is also a critical factor driving market growth. GIS systems play a crucial role in disaster preparedness, response, and recovery by providing accurate and timely geographic data. This data helps in assessing risks, coordinating response activities, and planning resource deployment. With the increasing frequency and intensity of natural disasters, the reliance on GIS data management systems is expected to grow, resulting in higher demand for GIS solutions across the globe.

Geospatial Solutions are becoming increasingly integral to the GIS Data Management landscape, offering enhanced capabilities for spatial data analysis and visualization. These solutions provide a comprehensive framework for integrating various data sources, enabling users to gain deeper insights into geographic patterns and trends. As organizations strive to optimize their operations and decision-making processes, the demand for robust geospatial solutions is on the rise. These solutions not only facilitate the efficient management of spatial data but also support advanced analytics and real-time data processing. By leveraging geospatial solutions, businesses and governments can improve their strategic planning, resource allocation, and environmental monitoring efforts, thereby driving the overall growth of the GIS Data Management market.

Regionally, North America holds a significant share of the GIS Data Management market, driven by high technology adoption rates and substantial investments in GIS technologies by government and private sectors. However, Asia Pacific is anticipated to witness the highest growth rate during the forecast period. The rapid urbanization, economic development, and increasing adoption of advanced technologies in countries like China and India are major contributors to this growth. Governments in this region are also focusing on smart city projects and infrastructure development, which further boosts the demand for GIS data management solutions.

Component Analysis

The GIS Data Management market is segmented by component into software, hardware, and services. The software segment is the largest and fastest-growing segment, driven by the continuous advancements in GIS software capabilities. GIS software applications enable users to analyze spatial data, create maps, and manage geographic information efficiently. The integration of GIS software with other enterprise systems and the development of user-friendly interfaces are key factors propelling the growth of this segment. Furthermore, the rise of mobile GIS applications, which allow field data collectio

You have been assigned a new project, which you have researched, and you have identified the data that you need.The next step is to gather, organize, and potentially create the data that you need for your project analysis.In this course, you will learn how to gather and organize data using ArcGIS Pro. You will also create a file geodatabase where you will store the data that you import and create.After completing this course, you will be able to perform the following tasks:Create a geodatabase in ArcGIS Pro.Create feature classes in ArcGIS Pro by exporting and importing data.Create a new, empty feature class in ArcGIS Pro.

This Esri File GeoDatabase (FGDB) contains digital tax parcel data for Maine Organized Towns and includes the following: Parcels (feature layer); Parcels_ADB (table); and GEOCODES (table).Within Maine, real property data is maintained by the government organization responsible for assessing and collecting property tax for a given location. Organized towns and townships maintain authoritative data for their communities and may voluntarily submit these data to the Maine GeoLibrary Parcel Project. Maine Parcels Organized Towns and Maine Parcels Organized Towns ADB are the product of these voluntary submissions. Communities provide updates to the Maine GeoLibrary on a non-regular basis, which affects the currency of Maine GeoLibrary parcels data; some data are more than ten years old. Another resource for real property transaction data is the County Registry of Deeds, although organized town data should very closely match registry information, except in the case of in-process property conveyance transactions.In Unorganized Territories (defined as those regions of the state without a local government that assesses real property and collects property tax), the Maine Revenue Service is the authoritative source for parcel data. Maine Parcels Unorganized Territory is the authoritative GIS data layer for the Unorganized Territories. However, it must always be used with auxiliary data obtained from the online resources of the Maine Revenue Service to compile up-to-date parcel ownership information.Property maps are a fundamental base for many municipal activities. Although GIS parcel data cannot replace detailed ground surveys, the data can assist municipal officials with functions such as accurate property tax assessment, planning and zoning. Towns can link maps to an assessor's database and display local information, while town officials can show taxpayers how proposed development or changes in municipal services and regulations may affect the community. In many towns, parcel data also helps to provide public notices, plan bus routes, and carry out other municipal services.This dataset contains municipality-submitted parcel data along with previously developed parcel data acquired through the Municipal Grants Project supported by the Maine Library of Geographic Information (MLGI). Grant recipient parcel data submissions were guided by standards presented to the MLGI Board on May 21, 2005,outlined in "Standards for Digital Parcel Files".GEOCODES is a table that lists standardized names and unique identifiers for Maine minor civil divisions and reservations, which represents the first official Standard Geographic Code endorsed and adopted by the Governor of Maine, on July 1, 1971.

This GIS overlay is a component of the U.S. Geological Survey, Woods Hole Science Center's, Gulf of Mexico GIS database. The Gulf of Mexico GIS database is intended to organize and display USGS held data and provide on-line (WWW) access to the data and/or metadata.

Enroll in this plan to get familiar with the user interface, apply commonly used tools, and master the basics of mapping and analyzing data using ArcGIS Pro.Goals Install ArcGIS Pro and efficiently locate tools, options, and user interface elements. Add data to a map, symbolize map features to represent type, categories, or quantities; and optimize map display at various scales. Create a file geodatabase to organize and accurately maintain GIS data over time. Complete common mapping, editing, and analysis workflows.

This GIS overlay is a component of the U.S. Geological Survey, Woods Hole Field Center's, Gulf of Mexico ArcView GIS database. The Gulf of Mexico GIS database is intended to organize and display USGS held data and provide on-line (WWW) access to the data and/or metadata. Additional GIS overlays downloaded from the WWW, such as the one described here, are also included in the Gulf of Mexico ArcView GIS database. Attempts to properly attribute such GIS overlays with the WWW address and data compilers has been made.

This GIS overlay is a component of the U. S Geological Survey, Woods Hole Field Center's, Gulf of Mexico ArcView GIS database. The Gulf of Mexico GIS database is intended to organize and display USGS held data and provide on-line (WWW) access to the data and/or metadata.

Geographic Information System (GIS) analyses are an essential part of natural resource management and research. Calculating and summarizing data within intersecting GIS layers is common practice for analysts and researchers. However, the various tools and steps required to complete this process are slow and tedious, requiring many tools iterating over hundreds, or even thousands of datasets. USGS scientists will combine a series of ArcGIS geoprocessing capabilities with custom scripts to create tools that will calculate, summarize, and organize large amounts of data that can span many temporal and spatial scales with minimal user input. The tools work with polygons, lines, points, and rasters to calculate relevant summary data and combine them into a single output table that can be easily incorporated into statistical analyses. These tools are useful for anyone interested in using an automated script to quickly compile summary information within all areas of interest in a GIS dataset

This GIS overlay is a component of the U. S. Geological Survey, Woods Hole Field Center's, Gulf of Mexico ArcView GIS database. The Gulf of Mexico GIS database is intended to organize and display USGS held data and provide on-line (WWW) access to the data and/or metadata.

Introduction

The Cloud GIS market is on a trajectory of robust growth, projected to reach a value of USD 3,303.1 Million by 2033, from USD 891 Million in 2023, with a compound annual growth rate (CAGR) of 14% during the forecast period spanning from 2024 to 2033. Cloud GIS, a technology leveraging cloud computing to manage geographic information system (GIS) data, is witnessing this expansion due to various factors, including the rising demand for real-time data access, the scalability of cloud services, and ongoing digital transformation efforts across industries.

The Cloud Geographic Information System (GIS) market is experiencing significant growth, driven by the increasing adoption of cloud technologies across various sectors. This growth can be attributed to several factors, including the scalability, flexibility, and cost-effectiveness of cloud-based solutions. These systems enable users to store, manage, and analyze geographical data without substantial investment in hardware infrastructure, making GIS tools accessible to a broader range of industries and organizations.

However, the market faces challenges, notably concerns regarding data security and privacy. As geographic data often includes sensitive information, the potential for data breaches makes some organizations hesitant to adopt cloud-based GIS solutions. Moreover, the reliance on continuous internet connectivity can pose operational challenges in regions with unstable internet services.

Despite these challenges, the Cloud GIS market presents substantial opportunities for new entrants. The ongoing digital transformation and the expanding need for location-based data across sectors like urban planning, environmental monitoring, and transportation logistics create a fertile ground for innovative solutions. New players can differentiate themselves by offering enhanced security features, customized solutions, and robust offline capabilities to address existing market gaps.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Relevant publicly available GIS data sets that cover the study area were downloaded and archived. Data sets include roads, hydrology, boundary files (political and NPS), digital elevation models (DEMs), and additional aerial photographs. A geodatabase in ArcGIS 10.2 format was created as a container to store and organize the data. The mapping area was split in two sections: park and buffer. The park was defined as the area within the prescribed boundaries of the park, while the buffer pertained to the region within 500 meters (1,640 ft) outside of the park boundary. Detailed vegetation mapping using CEGL codes was performed within the park whereas more general mapping using a revised Anderson-type II hierarchical classification scheme was used in the buffer area.

Beginning with the discovery of a "curious valley" in 1903 by Captain Scott, the McMurdo Dry Valleys (MDV) in Antarctica have been impacted by humans, although there were only three brief visits prior to 1950. Since the late 1950's, human activity in the MDV has become commonplace in summer, putting pressure on the region's fragile ecosystems through camp construction and inhabitation, cross-valley transport on foot and via vehicles, and scientific research that involves sampling and deployment of instruments. Historical photographs, put alongside information from written documentation, offer an invaluable record of the changing patterns of human activity in the MDV. Photographic images often show the physical extent of field camps and research sites, the activities that were taking place, and the environmental protection measures that were being followed. Historical photographs of the MDV, however, are scattered in different places around the world, often in private collections, and there is a real danger that many of these photos may be lost, along with the information they contain. This project will collect and digitize historical photographs of sites of human activity in the MDV from archives and private collections in the United States, New Zealand, and organize them both chronologically and spatially in a GIS database. Sites of past human activities will be re-photographed to provide comparisons with the present, and re-photography will assist in providing spatial data for historical photographs without obvious location information. The results of this analysis will support effective environmental management into the future. The digital photo archive will be openly available through the McMurdo Dry Valleys Long Term Ecological Research (MCM LTER) website (www.mcmlter.org), where it can be used by scientists, environmental managers, and others interested in the region.

The central question of this project can be reformulated as a hypothesis: Despite an overall increase in human activities in the MDV, the spatial range of these activities has become more confined over time as a result of an increased awareness of ecosystem fragility and efforts to manage the region. To address this hypothesis, the project will define the spatial distribution and temporal frequency of human activity in the MDV. Photographs and reports will be collected from archives with polar collections such as the National Archives of New Zealand in Wellington and Christchurch and the Byrd Polar Research Center in Ohio. Private photograph collections will be accessed through personal connections, social media, advertisements in periodicals such as The Polar Times, and other means. Re-photography in the field will follow established techniques and will create benchmarks for future research projects. The spatial data will be stored in an ArcGIS database for analysis and quantification of the human footprint over time in the MDV. The improved understanding of changing patterns of human activity in the MDV provided by this historical photo archive will provide three major contributions: 1) a fundamentally important historic accounting of human activity to support current environmental management of the MDV; 2) defining the location and type of human activity will be of immediate benefit in two important ways: a) places to avoid for scientists interested in sampling pristine landscapes, and, b) targets of opportunity for scientists investigating the long-term environmental legacy of human activity; and 3) this research will make an innovative contribution to knowledge of the environmental history of the MDV.

Geospatial Data Management Market Outlook

According to our latest research, the global geospatial data management market size stood at USD 103.7 billion in 2024, demonstrating robust momentum driven by rapid digital transformation across industries. The market is forecasted to reach USD 271.5 billion by 2033, expanding at a remarkable CAGR of 11.2% during the 2025–2033 period. This growth is primarily fueled by the increasing adoption of location-based services, proliferation of IoT devices, and the rising need for advanced spatial analytics to support critical decision-making across sectors such as urban planning, disaster management, and transportation.

One of the primary growth factors for the geospatial data management market is the escalating reliance on spatial data analytics to drive operational efficiency and innovation. Organizations are increasingly leveraging geospatial technologies to enhance asset management, optimize logistics, and improve disaster response strategies. The integration of geospatial data with artificial intelligence and machine learning algorithms has further amplified the value proposition, enabling predictive analytics and real-time insights. This trend is particularly evident in sectors like transportation, where route optimization and traffic management are critical, and in utilities, where asset monitoring and infrastructure planning rely heavily on accurate geospatial information.

Moreover, the rapid expansion of smart city initiatives worldwide has significantly contributed to the demand for advanced geospatial data management solutions. Governments and municipal authorities are deploying sophisticated GIS platforms to manage urban growth, streamline resource allocation, and improve public services. The convergence of geospatial data with IoT sensors and cloud computing has enabled real-time monitoring of urban environments, facilitating data-driven policy making and efficient emergency response. As cities continue to grow and urbanize, the need for scalable and interoperable geospatial management tools is expected to intensify, driving further investment and innovation in this market.

Another significant driver is the increasing frequency and severity of natural disasters, which has underscored the importance of robust geospatial data management for disaster preparedness and response. Advanced geospatial analytics enable authorities to model risk scenarios, map vulnerable regions, and coordinate relief efforts more effectively. The agriculture sector is also witnessing a surge in geospatial adoption, with precision farming and crop monitoring applications helping to maximize yields and minimize resource usage. As climate change continues to pose unprecedented challenges, the ability to harness and manage spatial data will be critical for resilience and sustainability across multiple industries.

Regionally, North America currently dominates the geospatial data management market, accounting for the largest share in 2024. The presence of leading technology providers, strong government support for spatial data infrastructure, and high adoption rates of advanced analytics have collectively contributed to this leadership. However, Asia Pacific is expected to register the fastest CAGR through 2033, propelled by rapid urbanization, expanding smart city projects, and growing investments in geospatial technologies across emerging economies such as China and India. Europe, Latin America, and the Middle East & Africa are also witnessing increased adoption, albeit at varying paces, reflecting the global nature of the market’s expansion.

Component Analysis

The geospatial data management market by component is segmented into software, hardware, and services, each playing a distinct and vital role in the ecosystem. The software segment encompasses Geographic Information Systems (GIS), remote sensing software, spatial data analytics platforms, and mapping tools. This segment is witnessing rapid innovation with the introduction of cloud-native GIS platforms, open-source spatial analytics, and AI-driven mapping solutions. The demand for user-friendly, scalable, and interoperable software is surging as organizations seek to derive actionable insights from large volumes of geospatial data. Vendors are increasingly focusing on enhancing data visualization, integration capabilities, and real-time analytics to cater to diverse industry requirements.

Eight mega-drainage basins for Massachusetts, aggregated from the Major Basins layer. The delineations were based on the level 2 (4-digit) Hydrologic Unit Codes (HUCs) from the U.S. Geological Survey. Included are delineation of the Narragansett and Thames mega-basins.The HUC is a hierarchical, numeric code that uniquely identifies hydrologic units. Hydrologic units are subdivisions of watersheds nested from largest to smallest areas and are used to organize hydrologic data. In the 4-digit HUCs, the first two digits identify the region and the first four digits identify subregions.See full metadataMap service is also available.

This GIS overlay is a component of the U.S. Geological Survey, Woods Hole Science Center's, Gulf of Mexico GIS database. The Gulf of Mexico GIS database is intended to organize and display USGS held data and provide on-line (WWW) access to the data and/or metadata.

This GIS overlay is a component of the U.S. Geological Survey, Woods Hole Science Center's, Gulf of Mexico GIS database. The Gulf of Mexico GIS database is intended to organize and display USGS held data and provide on-line (WWW) access to the data and/or metadata.

The datasets are sourced from the Ugandan Energy Sector GIS Working Group Open Data Site, developed and maintained by the Ugandan Energy Sector GIS Working Group. The Ugandan Energy Sector GIS Working Group’s mission is to develop a high quality GIS for the Energy Sector of Uganda in order to drive informed decision-making. As such, it brings datasets together in one place, organize them, keep them updated, and make public data available to all stakeholders. Link: http://data-energy-gis.opendata.arcgis.com/

This data collection uses a Geographic Information System (GIS) to organize and characterize information about benthic communities and substrates, which are ecological environments of the sea floor. The communities in this data collection include coral, seagrass, bare bottom, hard bottom, tidal flat, saltmarsh, and halophila communities. This coverage shows benthic distributions of various organisms for South Florida. The data provider included a metadata file in the FGDC description with the data files in this collection.

Geographic Information System (GIS) software is required to fully utilize the contents of this accession.

This GIS overlay is a component of the U. S Geological Survey, Woods Hole Field Center's, Gulf of Mexico ArcView GIS database. The Gulf of Mexico GIS database is intended to organize and display USGS held data and provide on-line (WWW) access to the data and/or metadata.

This GIS overlay is a component of the U.S. Geological Survey, Woods Hole Science Center's, Gulf of Mexico GIS database. The Gulf of Mexico GIS database is intended to organize and display USGS held data and provide on-line (WWW) access to the data and/or metadata.