The global Geographic Information System (GIS) software market is experiencing robust growth, driven by increasing adoption across various sectors like government, utilities, and transportation. The market, currently valued at approximately $15 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This significant expansion is fueled by several key trends, including the rising demand for location-based services, the proliferation of geospatial data, and the increasing use of cloud-based GIS solutions. The cloud-based segment is rapidly gaining traction due to its scalability, cost-effectiveness, and accessibility. Furthermore, the enterprise application segment dominates the market share, reflecting the widespread adoption of GIS for complex spatial analysis and decision-making in large organizations. While the market faces some restraints, such as the high initial investment costs for some advanced systems and the need for specialized expertise, the overall growth trajectory remains positive. The increasing integration of GIS with other technologies like AI and IoT further enhances its capabilities, opening new avenues for market expansion. Major players like Esri, Google, and Pitney Bowes are leading the market, constantly innovating and expanding their product offerings to meet evolving customer needs. The regional distribution of the market shows strong performance in North America and Europe, driven by advanced technological infrastructure and high adoption rates. However, the Asia-Pacific region is emerging as a significant growth area, propelled by rapid urbanization and infrastructure development. The competitive landscape is marked by both established players and emerging startups, fostering innovation and competition. The ongoing advancements in GIS technology, including improvements in data visualization, analytics, and mobile accessibility, are expected to further propel market growth in the coming years. The integration of GIS with other technologies will lead to new applications and expanded opportunities, ultimately driving the market towards sustained expansion throughout the forecast period.

Learn state-of-the-art skills to build compelling, useful, and fun Web GIS apps easily, with no programming experience required.Building on the foundation of the previous three editions, Getting to Know Web GIS, fourth edition,features the latest advances in Esri’s entire Web GIS platform, from the cloud server side to the client side.Discover and apply what’s new in ArcGIS Online, ArcGIS Enterprise, Map Viewer, Esri StoryMaps, Web AppBuilder, ArcGIS Survey123, and more.Learn about recent Web GIS products such as ArcGIS Experience Builder, ArcGIS Indoors, and ArcGIS QuickCapture. Understand updates in mobile GIS such as ArcGIS Collector and AuGeo, and then build your own web apps.Further your knowledge and skills with detailed sections and chapters on ArcGIS Dashboards, ArcGIS Analytics for the Internet of Things, online spatial analysis, image services, 3D web scenes, ArcGIS API for JavaScript, and best practices in Web GIS.Each chapter is written for immediate productivity with a good balance of principles and hands-on exercises and includes:A conceptual discussion section to give you the big picture and principles,A detailed tutorial section with step-by-step instructions,A Q/A section to answer common questions,An assignment section to reinforce your comprehension, andA list of resources with more information.Ideal for classroom lab work and on-the-job training for GIS students, instructors, GIS analysts, managers, web developers, and other professionals, Getting to Know Web GIS, fourth edition, uses a holistic approach to systematically teach the breadth of the Esri Geospatial Cloud.AUDIENCEProfessional and scholarly. College/higher education. General/trade.AUTHOR BIOPinde Fu leads the ArcGIS Platform Engineering team at Esri Professional Services and teaches at universities including Harvard University Extension School. His specialties include web and mobile GIS technologies and applications in various industries. Several of his projects have won specialachievement awards. Fu is the lead author of Web GIS: Principles and Applications (Esri Press, 2010).Pub Date: Print: 7/21/2020 Digital: 6/16/2020 Format: Trade paperISBN: Print: 9781589485921 Digital: 9781589485938 Trim: 7.5 x 9 in.Price: Print: $94.99 USD Digital: $94.99 USD Pages: 490TABLE OF CONTENTSPrefaceForeword1 Get started with Web GIS2 Hosted feature layers and storytelling with GIS3 Web AppBuilder for ArcGIS and ArcGIS Experience Builder4 Mobile GIS5 Tile layers and on-premises Web GIS6 Spatial temporal data and real-time GIS7 3D web scenes8 Spatial analysis and geoprocessing9 Image service and online raster analysis10 Web GIS programming with ArcGIS API for JavaScriptPinde Fu | Interview with Esri Press | 2020-07-10 | 15:56 | Link.

ArcGIS Enterprise puts collaboration and flexibility at the center of your organization's GIS. It pairs industry-leading mapping and analytics capabilities with a dedicated Web GIS infrastructure to organize and share your work on any device, anywhere, at any time.

This dataset is a compilation of address point data for the City of Tempe. The dataset contains a point location, the official address (as defined by The Building Safety Division of Community Development) for all occupiable units and any other official addresses in the City. There are several additional attributes that may be populated for an address, but they may not be populated for every address. Contact: Lynn Flaaen-Hanna, Development Services Specialist Contact E-mail Link: Map that Lets You Explore and Export Address Data Data Source: The initial dataset was created by combining several datasets and then reviewing the information to remove duplicates and identify errors. This published dataset is the system of record for Tempe addresses going forward, with the address information being created and maintained by The Building Safety Division of Community Development.Data Source Type: ESRI ArcGIS Enterprise GeodatabasePreparation Method: N/APublish Frequency: WeeklyPublish Method: AutomaticData Dictionary

This page contains the help documentation for the GIS Open Data Portal. Refer to https://gisdata-csj.opendata.arcgis.com/pages/help.

Detroit Street View (DSV) is an urban remote sensing program run by the Enterprise Geographic Information Systems (EGIS) Team within the Department of Innovation and Technology at the City of Detroit. The mission of Detroit Street View is ‘To continuously observe and document Detroit’s changing physical environment through remote sensing, resulting in freely available foundational data that empowers effective city operations, informed decision making, awareness, and innovation.’ LiDAR (as well as panoramic imagery) is collected using a vehicle-mounted mobile mapping system.

Due to variations in processing, index lines are not currently available for all existing LiDAR datasets, including all data collected before September 2020. Index lines represent the approximate path of the vehicle within the time extent of the given LiDAR file. The actual geographic extent of the LiDAR point cloud varies dependent on line-of-sight.

Compressed (LAZ format) point cloud files may be requested by emailing gis@detroitmi.gov with a description of the desired geographic area, any specific dates/file names, and an explanation of interest and/or intended use. Requests will be filled at the discretion and availability of the Enterprise GIS Team. Deliverable file size limitations may apply and requestors may be asked to provide their own online location or physical media for transfer.

LiDAR was collected using an uncalibrated Trimble MX2 mobile mapping system. The data is not quality controlled, and no accuracy assessment is provided or implied. Results are known to vary significantly. Users should exercise caution and conduct their own comprehensive suitability assessments before requesting and applying this data.

Sample Dataset: https://detroitmi.maps.arcgis.com/home/item.html?id=69853441d944442f9e79199b57f26fe3

Create your own initiative by combining existing applications with a custom site. Use this initiative to form teams around a problem and invite your community to participate.

This resource was created by Esri Canada Education and Research. To browse our full collection of higher-education learning resources, please visit https://hed.esri.ca/resourcefinder/.This tutorial introduces you to using Python code in a Jupyter Notebook, an open source web application that enables you to create and share documents that contain rich text, equations and multimedia, alongside executable code and visualization of analysis outputs. The tutorial begins by stepping through the basics of setting up and being productive with Python notebooks. You will be introduced to ArcGIS Notebooks, which are Python Notebooks that are well-integrated within the ArcGIS platform. Finally, you will be guided through a series of ArcGIS Notebooks that illustrate how to create compelling notebooks for data science that integrate your own Python scripts using the ArcGIS API for Python and ArcPy in combination with thousands of open source Python libraries to enhance your analysis and visualization.To download the dataset Labs, click the Open button to the top right. This will automatically download a ZIP file containing all files and data required.You can also clone the tutorial documents and datasets for this GitHub repo: https://github.com/highered-esricanada/arcgis-notebooks-tutorial.git.Software & Solutions Used: Required: This tutorial was last tested on August 27th, 2024, using ArcGIS Pro 3.3. If you're using a different version of ArcGIS Pro, you may encounter different functionality and results.Recommended: ArcGIS Online subscription account with permissions to use advanced Notebooks and GeoEnrichmentOptional: Notebook Server for ArcGIS Enterprise 11.3+Time to Complete: 2 h (excludes processing time)File Size: 196 MBDate Created: January 2022Last Updated: August 27, 2024

The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making.

   BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions.


   Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself.


   For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise.


   Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. 


   This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery.


   See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt


   See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

The global Geographic Information System (GIS) in the Cloud market is experiencing robust growth, projected to reach $1312.6 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 16.5% from 2025 to 2033. This expansion is fueled by several key drivers. Increasing adoption of cloud-based solutions across various sectors, including government and enterprise, offers scalability, cost-effectiveness, and enhanced accessibility to powerful geospatial analytics. The rising demand for location-based services (LBS) across industries like transportation, logistics, and utilities further boosts market growth. Furthermore, advancements in cloud computing technologies, such as improved data storage and processing capabilities, and the emergence of innovative GIS applications are contributing significantly to this upward trajectory. The market segmentation reveals strong growth across SaaS, PaaS, and IaaS models, with significant opportunities within the government and enterprise application segments. While data security and privacy concerns remain a restraint, the ongoing development of robust security protocols and increasing awareness of the benefits of cloud GIS are mitigating these challenges. Competition is fierce, with established players like ESRI, Google, and Microsoft alongside emerging innovative companies constantly vying for market share, driving innovation and competitive pricing. The geographical distribution of this market shows a significant presence across North America and Europe, with Asia-Pacific emerging as a region with substantial growth potential due to increasing digitalization and infrastructure development. The competitive landscape within the GIS in the Cloud market is dynamic, marked by both established technology giants and agile specialized companies. Major players are focusing on expanding their service offerings and enhancing their platforms to cater to the evolving needs of users. This includes integrating advanced analytics capabilities, supporting diverse data formats, and enhancing interoperability with other systems. Strategic partnerships and mergers and acquisitions are frequently employed to broaden market reach and strengthen technology portfolios. Furthermore, the market is witnessing a surge in the adoption of open-source GIS solutions, offering an alternative to proprietary platforms and fostering innovation. The future of the GIS in the Cloud market points towards increased integration with other technologies such as Artificial Intelligence (AI) and Machine Learning (ML) for advanced geospatial analysis and predictive modeling, further enhancing market growth and driving new applications. Overall, the market presents a compelling investment opportunity driven by technological advancements, increasing demand, and diverse applications.

The Cloud GIS market is experiencing robust growth, projected to reach $1513.8 million in 2025 and expanding at a Compound Annual Growth Rate (CAGR) of 17.2% from 2025 to 2033. This significant expansion is driven by several key factors. The increasing adoption of cloud computing across various industries, coupled with the need for enhanced data accessibility and collaboration, is fueling demand for cloud-based Geographic Information Systems (GIS). Businesses are leveraging cloud GIS for improved operational efficiency, cost savings through reduced infrastructure needs, and streamlined data management. Furthermore, advancements in cloud-based GIS technologies, including enhanced analytical capabilities and integration with other enterprise systems, are contributing to market expansion. The accessibility and scalability offered by cloud platforms are proving particularly attractive to smaller businesses and organizations that previously lacked the resources to implement sophisticated GIS solutions. Competitive players like ESRI, Google Maps, Bing Maps, and others are continually innovating, introducing user-friendly interfaces and powerful analytics tools that further accelerate market adoption. The market segmentation reveals a dynamic landscape, with various industries utilizing cloud GIS for specific applications. While precise segment data is unavailable, we can infer strong growth in sectors like urban planning, environmental monitoring, and resource management, driven by the need for real-time data analysis and collaborative decision-making. Geographic variations in adoption rates are expected, with North America and Europe likely maintaining leading positions due to advanced technological infrastructure and early adoption. However, emerging economies in Asia and Latin America are expected to witness significant growth in the coming years as cloud infrastructure develops and awareness of cloud GIS benefits increases. While potential restraints such as data security concerns and internet connectivity challenges exist, the overall market outlook remains strongly positive, supported by continuous technological advancements and increasing industry adoption.

Location of man holes in the City of Tucson. Data in this layer is pulled from plans and drawings submitted to the Tucson Department of Transportation.PurposeLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Dataset ClassificationLevel 0 - OpenKnown UsesThis layer is intended to be used in the Open Data portal and not for regular use in ArcGIS Online and ArcGIS Enterprise.Known ErrorsLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Data ContactLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Update FrequencyLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.

Analysis of ‘Addresses’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/ed1c4987-f6ba-4ed3-8560-6e7314426948 on 11 February 2022.

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

This dataset is a compilation of address point data for the City of Tempe. The dataset contains a point location, the official address (as defined by The Building Safety Division of Community Development) for all occupiable units and any other official addresses in the City. There are several additional attributes that may be populated for an address, but they may not be populated for every address.

<p>Data Source Type: ESRI ArcGIS Enterprise Geodatabase</p>
<br />
<p>Preparation Method: N/A</p>
<br />
<p>Publish Frequency: Weekly</p>
<br />
<p>Publish Method: Automatic</p>
<br />
<p><a href='https://gis.tempe.gov/address-dictionary/' target='_blank'>Data Dictionary</a>
  <br />
</p>

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

Geoportal Server is a standards-based, open source product that enables discovery and use of geospatial resources including data and services.

With the Geoportal Server you can:

• Improve the efficiency and effectiveness of geospatial activities within your enterprise and across organizations.
• Support collaboration and cooperation among departments and organizations by facilitating the sharing of geospatial resources regardless of the GIS platform.
• Gain an enterprise-level awareness of disparate geospatial data, Web services, and activities.
• Leverage existing geospatial resources so your organization doesn't duplicate those resources or the effort to create them.
• Ensure the use of approved, high-quality datasets.
• Reduce the time users spend trying to find relevant, usable geospatial resources.

Analysis of ‘Boundaries - Enterprise Zones’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/ffef84e5-b797-458e-b254-9db146e3e45c on 26 January 2022.

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

The Illinois Enterprise Zone Program is designed to stimulate economic growth and neighborhood revitalization in economically depressed areas of the state. For more information about this program, go to http://www.commerce.state.il.us/dceo/Bureaus/Business_Development/Tax+Assistance/Enterprise-Zone.htm. The data can be viewed on the Chicago Data Portal with a web browser. However, to view or use the files outside of a web browser, you will need to use compression software and special GIS software, such as ESRI ArcGIS (shapefile) or Google Earth (KML or KMZ).

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

Designates boundaries to establish extent of livestock distribution and management within pastures. This is a published layer created by combining GIS data managed by each National Forest and attribute data stored in the Forest Service Infra database application. This dataset is designed for reporting and analysis and is not used to enter or edit data.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService OGC WMS CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

Esri ArcGIS Online (AGOL) Feature Layer which provides access to the MDOT SHA Mile Points (1000th) data product.MDOT SHA Mile Points data consists of point geometric features which represent the measures that have been calibrated along each roadway throughout the State of Maryland. This layer specifically includes roadway segments that have been calibrated with measures that increase for every 1000th (0.001) of a mile along the roadway.MDOT SHA Mile Points data is owned and maintained by the MDOT SHA Office of Planning & Preliminary Engineering (OPPE), under the MDOT SHA Data Services Division (DSD). MDOT SHA Mile Points data is updated and published on an annual basis including data for the prior year.For more information, contact MDOT SHA OIT Enterprise Information Services:Email: GIS@mdot.maryland.gov

Bridges and culverts in the City of Tucson. This data is pulled from plans and drawings submitted to the Maps and Records section of the Department of Transportation.Usage: This layer is intended to be used in the Open Data portal and not for regular use in ArcGIS Online and ArcGIS Enterprise.Link to Open Data item: https://gisdata.tucsonaz.gov/datasets/bridges-and-culverts-open-data

Esri ArcGIS Online (AGOL) Hosted Feature Layer for accessing the MDOT SHA Roadway Centerline data product.MDOT SHA Roadway Centerline data consists of linear geometric features which represent the street centerline for roadways throughout the State of Maryland. The centerline represents the geographic location on the roadway between both shoulders (physical center), which often but not always coincides with the center painted line dividing bi-directional travel lanes. Roadway Centerline data plays an important role in transportation management and planning, while also being the basis for all other roadway-related data products.MDOT SHA Roadway Centerline data is the end product of a statewide data sharing process between the Federal Highway Administration (FHWA), Maryland Department of Transportation (MDOT), Maryland Department of Transportation State Highway Administration (MDOT SHA), county governments and local municipal governments. Using a common centerline allows for better exchange of information related to the roadway system and provides opportunities for more efficient collection of information about roadway assets. Some centerlines were created in-house using imagery, GPS data, and MDOT SHA's Highway Performance Monitoring System (HPMS) database and others were received from county governments and updated in house using imagery, GPS data and MDOT SHA's HPMS database. The Centerline data includes annual HPMS updates / improvements submitted to the Federal Highway Administration (FHWA).MDOT SHA Roadway Centerline data is needed for emergency response and management, routing buses and other vehicles, planning for land use and transportation needs, continuity of roadway data and display at county boundaries leading to the same "look and feel" across jurisdictions, tracking assets on and along the roadway network, producing maps at various scales, and numerous other applications. There are opportunities to make these processes more efficient, and this program addresses a shared foundation to solve some of these issues. This data is also used by various business units throughout MDOT, as well as many other Federal, State and local government agencies.MDOT SHA Roadway Centerline data is updated & published on an annual basis for the prior year. This data is for the year 2023.For more information about each attribute field, please review the MDOT SHA Roadway Centerline - Data DictionaryFor more information related to the data, contact MDOT SHA OPPE Data Services Division (DSD):Email: DSD@mdot.maryland.govFor more information, contact MDOT SHA OIT Enterprise Information Services:Email: GIS@mdot.maryland.gov

This dataset is a compilation of ownership rights represented as parcels owned by the State of California, Department of Water Resources. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standard version 3.6, dated September 27, 2023.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 forwarded to gis@water.ca.gov. This version is considered current as of 5/29/2025.