ArcGIS is a platform, and the platform is extending to the web. ArcGIS Online offers shared content, and has become a living atlas of the world. Ready-to-use curated content is published by Esri, Partners, and Users, and Esri is getting the ball rolling by offering authoritative data layers and tools.Specifically for Natural Resources data, Esri is offering foundational data useful for biogeographic analysis, natural resource management, land use planning and conservation. Some of the layers available are Land Cover, Wilderness Areas, Soils Range Production, Soils Frost Free Days, Watershed Delineation, Slope. The layers are available as Image Services that are analysis-ready and Geoprocessing Services that extract data for download and perform analysis.We've made large strides with online analysis. The latest release of ArcGIS Online's map viewer allows you to perform analysis on ArcGIS Online. Some of the currently available analysis tools are Find Hot Spots, Create Buffers, Summarize Within, Summarize Nearby. In addition, we've created Ready-to-use Esri hosted analysis tools that run on Esri hosted data. These are in Beta, and they include Watershed Delineation, Viewshed, Profile, and Summarize Elevation.

Geographic Information System (GIS) Software Market Outlook

The global Geographic Information System (GIS) software market size is projected to grow from USD 9.1 billion in 2023 to USD 18.5 billion by 2032, reflecting a compound annual growth rate (CAGR) of 8.5% over the forecast period. This growth is driven by the increasing application of GIS software across various sectors such as agriculture, construction, transportation, and utilities, along with the rising demand for location-based services and advanced mapping solutions.

One of the primary growth factors for the GIS software market is the widespread adoption of spatial data by various industries to enhance operational efficiency. In agriculture, for instance, GIS software plays a crucial role in precision farming by aiding in crop monitoring, soil analysis, and resource management, thereby optimizing yield and reducing costs. In the construction sector, GIS software is utilized for site selection, design and planning, and infrastructure management, making project execution more efficient and cost-effective.

Additionally, the integration of GIS with emerging technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT) is significantly enhancing the capabilities of GIS software. AI-driven data analytics and IoT-enabled sensors provide real-time data, which, when combined with spatial data, results in more accurate and actionable insights. This integration is particularly beneficial in fields like smart city planning, disaster management, and environmental monitoring, further propelling the market growth.

Another significant factor contributing to the market expansion is the increasing government initiatives and investments aimed at improving geospatial infrastructure. Governments worldwide are recognizing the importance of GIS in policy-making, urban planning, and public safety, leading to substantial investments in GIS technologies. For example, the U.S. governmentÂ’s Geospatial Data Act emphasizes the development of a cohesive national geospatial policy, which in turn is expected to create more opportunities for GIS software providers.

Geographic Information System Analytics is becoming increasingly pivotal in transforming raw geospatial data into actionable insights. By employing sophisticated analytical tools, GIS Analytics allows organizations to visualize complex spatial relationships and patterns, enhancing decision-making processes across various sectors. For instance, in urban planning, GIS Analytics can identify optimal locations for new infrastructure projects by analyzing population density, traffic patterns, and environmental constraints. Similarly, in the utility sector, it aids in asset management by predicting maintenance needs and optimizing resource allocation. The ability to integrate GIS Analytics with other data sources, such as demographic and economic data, further amplifies its utility, making it an indispensable tool for strategic planning and operational efficiency.

Regionally, North America holds the largest share of the GIS software market, driven by technological advancements and high adoption rates across various sectors. Europe follows closely, with significant growth attributed to the increasing use of GIS in environmental monitoring and urban planning. The Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, fueled by rapid urbanization, infrastructure development, and government initiatives in countries like China and India.

Component Analysis

The GIS software market is segmented into software and services, each playing a vital role in meeting the diverse needs of end-users. The software segment encompasses various types of GIS software, including desktop GIS, web GIS, and mobile GIS. Desktop GIS remains the most widely used, offering comprehensive tools for spatial analysis, data management, and visualization. Web GIS, on the other hand, is gaining traction due to its accessibility and ease of use, allowing users to access GIS capabilities through a web browser without the need for extensive software installations.

Mobile GIS is another crucial aspect of the software segment, providing field-based solutions for data collection, asset management, and real-time decision making. With the increasing use of smartphones and tablets, mobile GIS applications are becoming indispensable for sectors such as utilities, transportation, and

This workshop will guide you through using Python Notebooks in ArcGIS Online, including how to access and view data and calculate descriptive statistics. You will also learn how to perform area-level aggregation of Statistics Canada’s proximity measures for a select region, and local vs. global area calculations.In this workshop, you will use a Python notebook to analyze the proximity-measures data you worked with in the previous workshop.

Starting from the data measured through laboratory tests during 2020 and 2021, a set of parameters have been evaluated. Each day of analysis has been associated to a class, basing on thementioned values. In such a way it was possible to compute the percentage of times in which the water from a specific plant has been in the different classes . Collecting the data measured in each plant, the four classes have been defined setting limit values of these parameters. a set of rules was defined in order to identify the dominant water quality class for each WWT plant, based on the previously obtained percentages. :•If the effluent falls in water quality class A at least 90% of the time →the dominant class for that plant is A. •If the effluent falls in water quality class A less than 90% of the time AND 0% of the time in class D →the dominant class for that plant is B. •If the effluent falls in water quality class A less than 90% of the time AND below 10% in class D →the dominant class for that plant is C. •If the effluent fallsin water quality class D more than 10% of the time →the dominant class for that plant is D. This allowed a classification of the available WWTPs according to their dominant class and, thus, application of the EU regulation 2020/741. Therefore, for each WWTP, the WebGIS can show what crop types can be irrigated with their effluent, as well as the allowed irrigation method/s

The safety and regularity of airport operations are influenced by the landing use around it. In order to preserve safety of instrument landing operations at airports, the International Civil Aviation Organization (ICAO) has established an imaginary surface called Visual Segment Surface (VSS). Any object, natural or artificial which extends above the surface is considered an obstacle. Obstacles can generate a lot of restrictions, such as the cancellation of the instrument approach procedure. This study aims at developing a methodology for using Geographic Information System (GIS) to support management of obstacles in VSS. As a case study, Campo de Marte Airport, in São Paulo, was selected, in which the existence of obstacles can derail the implementation of instrument approach procedures. Two tools were used, a GIS desktop and a WEBGIS. The GIS application was effective in the analysis of obstacles, even without using a suite of tools for 3D analysis. The application was enough to integrate spatial analysis in two dimensions with a query based on analytical geometry concepts.

Learn more about the Geographic Information System Gis Tools Market Report by Market Research Intellect, which stood at USD 12.1 billion in 2024 and is forecast to expand to USD 23.5 billion by 2033, growing at a CAGR of 8.2%.Discover how new strategies, rising investments, and top players are shaping the future.

ESYS plc and the Department of Geomatic Engineering at University College London (UCL) have been funded by the British National Space Centre (BNSC) to develop a web GIS service to serve geographic data derived from remote sensing datasets. Funding was provided as part of the BNSC International Co-operation Programme 2 (ICP-2).

Particular aims of the project were to:

1. use Open Geospatial Consortium (OGC, recently renamed from the OpenGIS Consortium) technologies for map and data serving;

2. serve datasets for Europe and Africa, particularly Landsat TM and Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) data;

3. provide a website giving access to the served data;

4. provide software scripts, etc., and a document reporting the data processing and software set-up methods developed during the project.

ICEDS was inspired in particular by the Committee on Earth Observing Satellites (CEOS) CEOS Landsat and SRTM Project (CLASP) proposal. An express intention of ICEDS (aim 4 in the list above) was therefore that the solution developed by ESYS and UCL should be redistributable, for example, to other CEOS members. This was taken to mean not only software scripts but also the methods developed by the project team to prepare the data and set up the server. In order to be compatible with aim 4, it was also felt that the use of Open Source, or at least 'free-of-cost' software for the Web GIS serving was an essential component. After an initial survey of the Web GIS packages available at the time , the ICEDS team decided to use the Deegree package, a free software initiative founded by the GIS and Remote Sensing unit of the Department of Geography, University of Bonn , and lat/lon . However the Red Spider web mapping software suite was also provided by IONIC Software - this is a commercial web mapping package but was provided pro bono by IONIC for this project and has been used in parallel to investigate the possibilities and limitations opened up by using a commercial package.

http://iceds.ge.ucl.ac.uk/

HydroShare is an online, collaborative system for open sharing of hydrologic data, analytical tools, and models. It supports the sharing of and collaboration around “resources” which are defined primarily by standardized metadata, content data models for each resource type, and an overarching resource data model based on the Open Archives Initiative’s Object Reuse and Exchange (OAI-ORE) standard and a hierarchical file packaging system called “BagIt”. HydroShare expands the data sharing capability of the CUAHSI Hydrologic Information System by broadening the classes of data accommodated to include geospatial and multidimensional space-time datasets commonly used in hydrology. HydroShare also includes new capability for sharing models, model components, and analytical tools and will take advantage of emerging social media functionality to enhance information about and collaboration around hydrologic data and models. It also supports web services and server/cloud based computation operating on resources for the execution of hydrologic models and analysis and visualization of hydrologic data. HydroShare uses iRODS as a network file system for underlying storage of datasets and models. Collaboration is enabled by casting datasets and models as "social objects". Social functions include both private and public sharing, formation of collaborative groups of users, and value-added annotation of shared datasets and models. The HydroShare web interface and social media functions were developed using the Django web application framework coupled to iRODS. Data visualization and analysis is supported through the Tethys Platform web GIS software stack. Links to external systems are supported by RESTful web service interfaces to HydroShare’s content. This presentation will introduce the HydroShare functionality developed to date and describe ongoing development of functionality to support collaboration and integration of data and models.

Slides for AGU 2015 presentation H42A-04, December 17, 2015

Check Market Research Intellect's Geographic Information Systems Platform Market Report, pegged at USD 12.5 billion in 2024 and projected to reach USD 25.1 billion by 2033, advancing with a CAGR of 8.7% (2026-2033).Explore factors such as rising applications, technological shifts, and industry leaders.

The Wood Retained All Years MCF feature layer contains retained roundwood totals for states across individual years and a total for all years. Roundwood volume (referred to as receipts in the dataset) is reported in thousand cubic feet (MCF). This feature layer was created for the Wood Flow Visualization web application and is referenced in:Wood Flow Radials Web MapWood Flow Radials DashboardCurrently, the dashboard contains data for the Southern Research Station (SRS). Data from other research stations will be added in the coming months.About FIA's BIGMAPThe USDA Forest Service’s Forest Inventory and Analysis (FIA) program is the authoritative source of information about the conditions of the Agency’s forested lands. Within the FIA program, a new secure, cloud-based, and flexible computing environment has been created, named the Big Data Mapping & Analytics Platform (BIGMAP). BIGMAP is designed to store, process, analyze, and deliver Forest Service content. It does so in ways that streamline our internal workflows and make it easy to share authoritative, map-based content through web technologies. BIGMAP leverages commercial off-the-shelf solutions, reducing development and maintenance costs over the longer term. This focus capitalizes upon Agency investments in FIA and other data. The resulting, authoritative map content will populate the Agency’s WebGIS library for use by Agency managers, decision-makers, and other interested parties.

This dataset provides ward-level geographic boundary data and population statistics for Delhi, India, in KML format. It contains the outlines of municipal wards along with attributes such as:

• Ward number and name
• Assembly constituency (AC) number and name
• Total population per ward
• Scheduled Caste (SC) population
• 2022 naming conventions

The dataset is useful for geospatial analysis, urban planning, civic data visualization, and demographic research. It can be imported into GIS tools like QGIS or web-mapping libraries such as Leaflet or Google Maps.

Original Source: Open City - Delhi Wards Information
License: Open Database License (ODbL) v1.0
Format: XML-based KML
Projection: WGS 84 (EPSG:4326)

This version is suitable for direct use in Python (with fastkml, geopandas, or shapely), and in web GIS applications. The data has not been altered.

The Woodflow State Receipts MCF feature layer contains roundwood production, receipts, retained, imported, and exported by state, year, class, and product. Roundwood volume is reported in thousand cubic feet (MCF). This feature layer was created for the Wood Flow Visualization web application and is referenced in:Wood Flow Class Comparison Web MapWood Flow Class Comparison Web AppCurrently, the dashboard contains data for the Southern Research Station (SRS). Data from other research stations will be added in the coming months.About FIA's BIGMAPThe USDA Forest Service’s Forest Inventory and Analysis (FIA) program is the authoritative source of information about the conditions of the Agency’s forested lands. Within the FIA program, a new secure, cloud-based, and flexible computing environment has been created, named the Big Data Mapping & Analytics Platform (BIGMAP). BIGMAP is designed to store, process, analyze, and deliver Forest Service content. It does so in ways that streamline our internal workflows and make it easy to share authoritative, map-based content through web technologies. BIGMAP leverages commercial off-the-shelf solutions, reducing development and maintenance costs over the longer term. This focus capitalizes upon Agency investments in FIA and other data. The resulting, authoritative map content will populate the Agency’s WebGIS library for use by Agency managers, decision-makers, and other interested parties.

The project aims at developing a joint cross-border strategy for maintenance of biodiversity and conservation of the population of Caretta caretta, a sea turtle species seriously threatened by the impact of human activities. Main foreseen actions: Development of a Management Model for marine areasManagement Plans for the Marine areas involved, through elaboration of bathymetric and habitat maps, identifying stress factors Implementation of a GIS to share information resulting from maps setting a Tank of First Recovery for the protection of C. carettaA Database collecting information on survey coasts report, areas of potential nesting; biometric data, web GIS movements of the 20 specimens paths through ARGOS satellite system An Action Plan for the protection of the population of sea turtles A cross-border Association providing exchange of expertise and the identification of joint objectives for conservation. AccConID=24 AccConstrDescription=This license lets others remix, tweak, and build upon your work non-commercially, and although their new works must also acknowledge you and be non-commercial, they don’t have to license their derivative works on the same terms AccConstrDisplay=This dataset is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. AccConstrEN=Attribution-NonCommercial (CC BY-NC) AccessConstraint=Attribution-NonCommercial (CC BY-NC) AccessConstraints=This dataset is a summarized representation of the telemetry locations aggregated per species per 1-degree cell. Acronym=None added_date=2024-02-26 09:27:07.650000 BrackishFlag=0 CDate=2023-04-07 cdm_data_type=Other CheckedFlag=0 Citation=Rees A. 2021. Movements and distribution of loggerheads from Amvrakikos Gulf, Greece 2013. Data originated from Satellite Tracking and Analysis Tool (STAT; http://www.seaturtle.org/tracking/index.shtml?project_id=875). Comments=None ContactEmail=arees@seaturtle.org Conventions=COARDS, CF-1.6, ACDD-1.3 CurrencyDate=None DasID=8279 DasOrigin=None DasType=Data DasTypeID=1 DateLastModified={'date': '2025-08-12 01:34:46.196267', 'timezone_type': 1, 'timezone': '+02:00'} DescrCompFlag=0 DescrTransFlag=0 Easternmost_Easting=58.5 EmbargoDate=None EngAbstract=The project aims at developing a joint cross-border strategy for maintenance of biodiversity and conservation of the population of Caretta caretta, a sea turtle species seriously threatened by the impact of human activities. Main foreseen actions: Development of a Management Model for marine areasManagement Plans for the Marine areas involved, through elaboration of bathymetric and habitat maps, identifying stress factors Implementation of a GIS to share information resulting from maps setting a Tank of First Recovery for the protection of C. carettaA Database collecting information on survey coasts report, areas of potential nesting; biometric data, web GIS movements of the 20 specimens paths through ARGOS satellite system An Action Plan for the protection of the population of sea turtles A cross-border Association providing exchange of expertise and the identification of joint objectives for conservation. EngDescr=Original provider: ARCHELON Dataset credits: Data provider ARCHELON (2013-Present) Originating data center Satellite Tracking and Analysis Tool (STAT)Project partner Lead partner for the combined project is the Management Consortium of Torre Guaceto. Greek partners in Amvrakikos are:ETANAMThe Development Agency for South Epirus: Amvrakikos (ETANAM; S.A.L.G.O.) was founded in 1988 with a main objective the utilization of resources of the entire Amvrakikos region, an area presenting significant growth potential, as well as the rational management and protection of Amvrakikos Gulf which is one of the most important habitats in Greece and is protected by the Ramsar Treaty.Amvrakikos Wetlands Management BodyThe 'Amvrakikos Wetlands' Management Body' is a non profit private body, established by the Ministry of the Environment, Energy and Climate Change with a goal to manage the priority species and habitats by balancing and controlling the different activities that take place in the core of the Amvrakikos Wetlands National Park, W.Greece, including special terrestrial and marine ecosystems.ARCHELON, the Sea Turtle Protection Society of GreeceARCHELON, founded in 1983, is a nationwide, non-profit, non-governmental organization working for the study and protection of sea turtles and their habitats in Greece. The Society’s activities include monitoring of turtle populations, management of nesting beaches, rehabilitation of injured turtles, lobbying, environmental education, and raising public awareness. ATEPEATEPE Ecosystems management Ltd was founded in 2011 and specializes in environmental and ecosystems management issues. Project sponsor or sponsor description The project is arranged under the PRO ACT NATURA 2000 initiative. Supplemental information: Visit STAT's project page for additional information. This dataset is a summarized representation of the telemetry locations aggregated per species per 1-degree cell. FreshFlag=0 GBIF_UUID=b12abd4c-41e6-4f42-8a80-ab7c3964be77 geospatial_lat_max=46.5 geospatial_lat_min=33.5 geospatial_lat_units=degrees_north geospatial_lon_max=58.5 geospatial_lon_min=-8.5 geospatial_lon_units=degrees_east infoUrl=None InputNotes=None institution=None License=https://creativecommons.org/licenses/by-nc/4.0 Lineage=Prior to publication data undergo quality control checked which are described in https://github.com/EMODnet/EMODnetBiocheck?tab=readme-ov-file#understanding-the-output MarineFlag=1 modified_sync=2024-02-19 00:00:00 Northernmost_Northing=46.5 OrigAbstract=None OrigDescr=None OrigDescrLang=None OrigDescrLangNL=None OrigLangCode=None OrigLangCodeExtended=None OrigLangID=None OrigTitle=None OrigTitleLang=None OrigTitleLangCode=None OrigTitleLangID=None OrigTitleLangNL=None Progress=None PublicFlag=1 ReleaseDate=Apr 24 2021 12:00AM ReleaseDate0=2021-04-24 RevisionDate=None SizeReference=None sourceUrl=(local files) Southernmost_Northing=33.5 standard_name_vocabulary=CF Standard Name Table v70 StandardTitle=Movements and distribution of loggerheads from Amvrakikos Gulf, Greece 2013 (aggregated per 1-degree cell) StatusID=1 subsetVariables=ScientificName,BasisOfRecord,YearCollected,MonthCollected,DayCollected,sex,lifestage,aphia_id TerrestrialFlag=0 UDate=2025-03-26 VersionDate=Apr 24 2021 12:00AM VersionDay=None VersionMonth=None VersionName=None VersionYear=None VlizCoreFlag=1 Westernmost_Easting=-8.5

Market Research Intellect's Gis In Telecom Market Report highlights a valuation of USD 5.2 billion in 2024 and anticipates growth to USD 10.1 billion by 2033, with a CAGR of 8.5% from 2026-2033.Explore insights on demand dynamics, innovation pipelines, and competitive landscapes.

The Woodflow Centroids Boundaries feature layer contains centroids and boundaries for North American states and provinces (USA, Canada, and Mexico). Also includes two international sites (east and west) for distribution outside of North America. The dataset was created to support the generation of flow lines (inflow and outflow) for timber production movement for the FIA BIGMAP Wood Flow Visualization web application.About FIA's BIGMAPThe USDA Forest Service’s Forest Inventory and Analysis (FIA) program is the authoritative source of information about the conditions of the Agency’s forested lands. Within the FIA program, a new secure, cloud-based, and flexible computing environment has been created, named the Big Data Mapping & Analytics Platform (BIGMAP). BIGMAP is designed to store, process, analyze, and deliver Forest Service content. It does so in ways that streamline our internal workflows and make it easy to share authoritative, map-based content through web technologies. BIGMAP leverages commercial off-the-shelf solutions, reducing development and maintenance costs over the longer term. This focus capitalizes upon Agency investments in FIA and other data. The resulting, authoritative map content will populate the Agency’s WebGIS library for use by Agency managers, decision-makers, and other interested parties.

The Woodflow MCF feature layer contains annual timber production volumes moving across the United States (between states) and some international locations. The data includes state, year, wood class (hardwood and softwood) and product. Roundwood volume (referred to as receipts in the dataset) is reported in thousand cubic feet (MCF). This feature layer was created for the Wood Flow Visualization web application and is referenced in:Wood Flow Details Web MapWood Flow Details DashboardCurrently, the dashboard contains data for the Southern Research Station (SRS). Data from other research stations will be added in the coming months.About FIA's BIGMAPThe USDA Forest Service’s Forest Inventory and Analysis (FIA) program is the authoritative source of information about the conditions of the Agency’s forested lands. Within the FIA program, a new secure, cloud-based, and flexible computing environment has been created, named the Big Data Mapping & Analytics Platform (BIGMAP). BIGMAP is designed to store, process, analyze, and deliver Forest Service content. It does so in ways that streamline our internal workflows and make it easy to share authoritative, map-based content through web technologies. BIGMAP leverages commercial off-the-shelf solutions, reducing development and maintenance costs over the longer term. This focus capitalizes upon Agency investments in FIA and other data. The resulting, authoritative map content will populate the Agency’s WebGIS library for use by Agency managers, decision-makers, and other interested parties.

Using ArcGIS or other GIS tools, the foundational data in the OCIS hexagon layers can be extended and with other data integrations. Using the web GIS services or data export enabled by the OCIS Hub and to join other variables. Extend the power of hexes by subsetting and downloading your choice of data. Using the Hex to Raster Notebook, a tool to transform raw ocean data into a multidimensional raster, you can integrate these data into suitability analysis, classification, and prediction workflows.The Hex to Raster Notebook converts ocean data into a multidimensional raster within hexagon polygons, allowing users to explore attributes like temperature, salinity, pH, and dissolved oxygen over time and depth. With customizable grid levels from h3 to h6, the tool supports tailored data retrieval across years and depths.

In 2006, Wild Utah Project realeased the Rapid Stream-Riparian Assessment protocol. The Rapid Stream-Riparian Assessment involves a quantitative evaluation of between two to seven indicator variables in five different ecological categories: water quality, fluvial geomorphology, aquatic and fish habitat, vegetation composition and structure, and terrestrial wildlife habitat.

A first version of this database was created by the Darmatech Group in 2006 using MySQL as the main plataform for this database. In 2017, Wild Utah Project began an update process of this first RSRA database version to accomodate advanced data query and analysis functions by implementing a workgroup geodatabase platform. Hence, this service contains the contents of the second version of the RSRA database (version 2018.1) and wich will be use in conjuction with Web GIS capabilities.

For more information about this database, please contact Wild Utah Project GIS Lab at gislab@wildutahproject.org

The Woodflow by Class MCF feature layer contains timber production volumes by year, state, and wood class (hardwood and softwood). Volume (referred to as receipts in the dataset) is reported in thousand cubic feet (MCF). This feature layer was created for the Wood Flow Visualization web application and is referenced in:Wood Flow Trends Web MapWood Flow Trends DashboardGet Started Web MapGet Started DashboardCurrently, the dashboard contains data for the Southern Research Station (SRS). Data from other research stations will be added in the coming months.About FIA's BIGMAPThe USDA Forest Service’s Forest Inventory and Analysis (FIA) program is the authoritative source of information about the conditions of the Agency’s forested lands. Within the FIA program, a new secure, cloud-based, and flexible computing environment has been created, named the Big Data Mapping & Analytics Platform (BIGMAP). BIGMAP is designed to store, process, analyze, and deliver Forest Service content. It does so in ways that streamline our internal workflows and make it easy to share authoritative, map-based content through web technologies. BIGMAP leverages commercial off-the-shelf solutions, reducing development and maintenance costs over the longer term. This focus capitalizes upon Agency investments in FIA and other data. The resulting, authoritative map content will populate the Agency’s WebGIS library for use by Agency managers, decision-makers, and other interested parties.