The Geographic Management Information System (GeoMIS) is a FISMA Moderate minor application built using ArcGIS Server and portal, Microsoft SQL, and a web-facing front-end. The system can be accessed over the internet via https://www.usaidgiswbg.com using a web browser. GeoMIS is based on a commercial off-the-shelf product developed by Esri. Esri is creates geographic information system (GIS) software, web GIS and geodatabase management applications and is based in California. GeoMISIt is maintained by an Israeli company, Systematics (see Attachment 3) which is EsriI's agent in Israel. The mission has an annual maintenance contract with Systematics for GeoMIS. GeoMIS has 100 users from USAID staff (USA Direct Hire and Foreign Service Nationals) and 200 users from USAID contractors and grantees. The system is installed at USAID WBG office in Tel Aviv/Israel inside the computer room in the DMZ. It has no interconnections with any other system.

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Geographic Information System Analytics Market Size 2024-2028

The geographic information system analytics market size is forecast to increase by USD 12 billion at a CAGR of 12.41% between 2023 and 2028.

The GIS Analytics Market analysis is experiencing significant growth, driven by the increasing need for efficient land management and emerging methods in data collection and generation. The defense industry's reliance on geospatial technology for situational awareness and real-time location monitoring is a major factor fueling market expansion. Additionally, the oil and gas industry's adoption of GIS for resource exploration and management is a key trend. Building Information Modeling (BIM) and smart city initiatives are also contributing to market growth, as they require multiple layered maps for effective planning and implementation. The Internet of Things (IoT) and Software as a Service (SaaS) are transforming GIS analytics by enabling real-time data processing and analysis.
Augmented reality is another emerging trend, as it enhances the user experience and provides valuable insights through visual overlays. Overall, heavy investments are required for setting up GIS stations and accessing data sources, making this a promising market for technology innovators and investors alike.

What will be the Size of the GIS Analytics Market during the forecast period?

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The geographic information system analytics market encompasses various industries, including government sectors, agriculture, and infrastructure development. Smart city projects, building information modeling, and infrastructure development are key areas driving market growth. Spatial data plays a crucial role in sectors such as transportation, mining, and oil and gas. Cloud technology is transforming GIS analytics by enabling real-time data access and analysis. Startups are disrupting traditional GIS markets with innovative location-based services and smart city planning solutions. Infrastructure development in sectors like construction and green buildings relies on modern GIS solutions for efficient planning and management. Smart utilities and telematics navigation are also leveraging GIS analytics for improved operational efficiency.
GIS technology is essential for zoning and land use management, enabling data-driven decision-making. Smart public works and urban planning projects utilize mapping and geospatial technology for effective implementation. Surveying is another sector that benefits from advanced GIS solutions. Overall, the GIS analytics market is evolving, with a focus on providing actionable insights to businesses and organizations.

How is this Geographic Information System Analytics Industry segmented?

The geographic information system analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Retail and Real Estate
  Government
  Utilities
  Telecom
  Manufacturing and Automotive
  Agriculture
  Construction
  Mining
  Transportation
  Healthcare
  Defense and Intelligence
  Energy
  Education and Research
  BFSI


Components

  Software
  Services


Deployment Modes

  On-Premises
  Cloud-Based


Applications

  Urban and Regional Planning
  Disaster Management
  Environmental Monitoring Asset Management
  Surveying and Mapping
  Location-Based Services
  Geospatial Business Intelligence
  Natural Resource Management


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    South Korea


  Middle East and Africa

    UAE


  South America

    Brazil


  Rest of World

By End-user Insights

The retail and real estate segment is estimated to witness significant growth during the forecast period.

The GIS analytics market analysis is witnessing significant growth due to the increasing demand for advanced technologies in various industries. In the retail sector, for instance, retailers are utilizing GIS analytics to gain a competitive edge by analyzing customer demographics and buying patterns through real-time location monitoring and multiple layered maps. The retail industry's success relies heavily on these insights for effective marketing strategies. Moreover, the defense industries are integrating GIS analytics into their operations for infrastructure development, permitting, and public safety. Building Information Modeling (BIM) and 4D GIS software are increasingly being adopted for construction project workflows, while urban planning and designing require geospatial data for smart city planning and site selection.

The oil and gas industry is leveraging satellite imaging and IoT devices for land acquisition and mining operations. In the public sector,

The Center for Spatial Information Science and Systems (CSISS) is an interdisciplinary research center chartered by the provost and affiliated with the College of Science at George Mason University, Fairfax VA, 22030, U.S.A.CSISS currently operates Laboratory for Advanced Information Technology and Standards (LAITS)CSISS is a member of the National Committee on Information Technology Standards Technical Committee L1 and a member of Open GIS Consortium (OGC).CSISS Misson:* To conduct world-class research in spatial information science and system.* To provide state-of-art research training to post-doctoral fellows, Ph.D. and Master students in the field.CSISS Research Focus:* Theory and methodology of spatial information science;* Standards and Interoperability of spatial data, information, knowledge, and systems;* Architecture and prototype of widely distributed large spatial information systems, such as NSDI, GSDI, and GEOSS, as well as service-based spatial knowledge and decision-making systems;* Exploration of new information technologies that have potential applications in Spatial Information Science (SIS);* The applications of SIS in the social sectors having either national interests or major commercial values, such as renewable energy, location-based mobile services, intelligent transportation, and homeland security.

USGS developed The National Map Gazetteer as the Federal and national standard (ANSI INCITS 446-2008) for geographic nomenclature based on the Geographic Names Information System (GNIS). The National Map Gazetteer contains information about physical and cultural geographic features, geographic areas, and locational entities that are generally recognizable and locatable by name (have achieved some landmark status) and are of interest to any level of government or to the public for any purpose that would lead to the representation of the feature in printed or electronic maps and/or geographic information systems. The dataset includes features of all types in the United States, its associated areas, and Antarctica, current and historical, but not including roads and highways. The dataset holds the federally recognized name of each feature and defines the feature location by state, county, USGS topographic map, and geographic coordinates. Other attributes include names or spellings other than the official name, feature classification, and historical and descriptive information. The dataset assigns a unique, permanent feature identifier, the Feature ID, as a standard Federal key for accessing, integrating, or reconciling feature data from multiple data sets. This dataset is a flat model, establishing no relationships between features, such as hierarchical, spatial, jurisdictional, organizational, administrative, or in any other manner. As an integral part of The National Map, the Gazetteer collects data from a broad program of partnerships with federal, state, and local government agencies and other authorized contributors. The Gazetteer provides data to all levels of government and to the public, as well as to numerous applications through a web query site, web map, feature and XML services, file download services, and customized files upon request. The National Map download client allows free downloads of public domain geographic names data by state in a pipe-delimited text format. For additional information on the GNIS, go to https://www.usgs.gov/tools/geographic-names-information-system-gnis. See https://apps.nationalmap.gov/help/ for assistance with The National Map viewer, download client, services, or metadata. Data Refreshed March, 2025

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for geographic information systems concentration in computer information systems in the U.S.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for geographic information systems and science in the U.S.

This study seeks to aid understanding of the difficulties that organizations encounter when their units are geographically distributed and they seek to effectively distribute resources in accordance with geographical and demographic conditions. This study specifically looks at those segments of the population that require health services. The case study in question is representative of the decisions regarding municipal healthcare policies concerning the distribution of resources in terms of staff, material and equipment. It serves as a reference for the inherent difficulties of these decisions. The base of Geographic Information System (GIS) enables spatial and demographic analyses and their relationship with the data regarding the management of the required resources and skills. Analysis using an adapted Resource-Based View (RBV) allows evaluation of the internal decisions within the system in question. The results show the limits of the shared or isolated evaluation of the spatial distribution of resources, which are compromised by the decisions involved in these two approaches. In this sense, the concomitant evaluation of the distributed resources linked to the GIS results in an important analysis element, as it enables the identification of strategic resources that adequately satisfy the purposes of Curitiba's Municipal Health Units, in the state of Paraná.

This data set features a hyperlink to the New York State Department of Transportation’s (NYSDOT) Roadway Inventory System (RIS) Viewer web page, which includes a link to an interactive map. The RIS Data Viewer is a geospatially based Geographic Information System (GIS) application for displaying data contained in the roadway inventory database. The interactive map has two viewable data categories or ‘layers’. The two layers include: Federal Aid Eligible Public Roadway System and Non-Federal Aid Eligible State Routes throughout New York State.

Link Function: information

Observations and subtle shifts of vegetation communities in Lake Erie have USGS researchers concerned about the potential for Grass Carp to alter these vegetation communities. Broad-scale surveys of vegetation using remote sensing and GIS mapping, coupled with on-the-ground samples in key locations will permit assessment of the effect Grass Carp may have already had on aquatic vegetation communities and establish baseline conditions for assessing future effects. Existing aerial imagery was used with object-based image analysis to detect and map aquatic vegetation in the eastern basin of Lake Erie.

Based on open access data, 79 Mediterranean passenger ports are analyzed to compare their infrastructure, hinterland accessibility and offered multi-modality categories. Comparative Geo-spatial analysis is also carried out by using the data normalization method in order to visualize the ports' performance on maps. These data driven comprehensive analytical results can bring added value to sustainable development policy and planning initiatives in the Mediterranean Region. The analyzed elements can be also contributed to the development of passenger port performance indicators. The empirical research methods used for the Mediterranean passenger ports can be replicated for transport nodes of any region around the world to determine their relative performance on selected criteria for improvement and planning.

The Mediterranean passenger ports were initially categorized into cruise and ferry ports. The cruise ports were identified from the member list of the Association for the Mediterranean Cruise Ports (MedCruise), representing more than 80% of the cruise tourism activities per country. The identified cruise ports were mapped by selecting the corresponding geo-referenced ports from the map layer developed by the European Marine Observation and Data Network (EMODnet). The United Nations (UN) Code for Trade and Transport Locations (LOCODE) was identified for each of the cruise ports as the common criteria to carry out the selection. The identified cruise ports not listed by the EMODnet were added to the geo-database by using under license the editing function of the ArcMap (version 10.1) geographic information system software. The ferry ports were identified from the open access industry initiative data provided by the Ferrylines, and were mapped in a similar way as the cruise ports (Figure 1).

Based on the available data from the identified cruise ports, a database (see Table A1–A3) was created for a Mediterranean scale analysis. The ferry ports were excluded due to the unavailability of relevant information on selected criteria (Table 2). However, the cruise ports serving as ferry passenger ports were identified in order to maximize the scope of the analysis. Port infrastructure and hinterland accessibility data were collected from the statistical reports published by the MedCruise, which are a compilation of data provided by its individual member port authorities and the cruise terminal operators. Other supplementary sources were the European Sea Ports Organization (ESPO) and the Global Ports Holding, a cruise terminal operator with an established presence in the Mediterranean. Additionally, open access data sources (e.g. the Google Maps and Trip Advisor) were consulted in order to identify the multi-modal transports and bridge the data gaps on hinterland accessibility by measuring the approximate distances.

The Ontario government, generates and maintains thousands of datasets. Since 2012, we have shared data with Ontarians via a data catalogue. Open data is data that is shared with the public. Click here to learn more about open data and why Ontario releases it. Ontario’s Open Data Directive states that all data must be open, unless there is good reason for it to remain confidential. Ontario’s Chief Digital and Data Officer also has the authority to make certain datasets available publicly. Datasets listed in the catalogue that are not open will have one of the following labels: If you want to use data you find in the catalogue, that data must have a licence – a set of rules that describes how you can use it. A licence: Most of the data available in the catalogue is released under Ontario’s Open Government Licence. However, each dataset may be shared with the public under other kinds of licences or no licence at all. If a dataset doesn’t have a licence, you don’t have the right to use the data. If you have questions about how you can use a specific dataset, please contact us. The Ontario Data Catalogue endeavors to publish open data in a machine readable format. For machine readable datasets, you can simply retrieve the file you need using the file URL. The Ontario Data Catalogue is built on CKAN, which means the catalogue has the following features you can use when building applications. APIs (Application programming interfaces) let software applications communicate directly with each other. If you are using the catalogue in a software application, you might want to extract data from the catalogue through the catalogue API. Note: All Datastore API requests to the Ontario Data Catalogue must be made server-side. The catalogue's collection of dataset metadata (and dataset files) is searchable through the CKAN API. The Ontario Data Catalogue has more than just CKAN's documented search fields. You can also search these custom fields. You can also use the CKAN API to retrieve metadata about a particular dataset and check for updated files. Read the complete documentation for CKAN's API. Some of the open data in the Ontario Data Catalogue is available through the Datastore API. You can also search and access the machine-readable open data that is available in the catalogue. How to use the API feature: Read the complete documentation for CKAN's Datastore API. The Ontario Data Catalogue contains a record for each dataset that the Government of Ontario possesses. Some of these datasets will be available to you as open data. Others will not be available to you. This is because the Government of Ontario is unable to share data that would break the law or put someone's safety at risk. You can search for a dataset with a word that might describe a dataset or topic. Use words like “taxes” or “hospital locations” to discover what datasets the catalogue contains. You can search for a dataset from 3 spots on the catalogue: the homepage, the dataset search page, or the menu bar available across the catalogue. On the dataset search page, you can also filter your search results. You can select filters on the left hand side of the page to limit your search for datasets with your favourite file format, datasets that are updated weekly, datasets released by a particular organization, or datasets that are released under a specific licence. Go to the dataset search page to see the filters that are available to make your search easier. You can also do a quick search by selecting one of the catalogue’s categories on the homepage. These categories can help you see the types of data we have on key topic areas. When you find the dataset you are looking for, click on it to go to the dataset record. Each dataset record will tell you whether the data is available, and, if so, tell you about the data available. An open dataset might contain several data files. These files might represent different periods of time, different sub-sets of the dataset, different regions, language translations, or other breakdowns. You can select a file and either download it or preview it. Make sure to read the licence agreement to make sure you have permission to use it the way you want. Read more about previewing data. A non-open dataset may be not available for many reasons. Read more about non-open data. Read more about restricted data. Data that is non-open may still be subject to freedom of information requests. The catalogue has tools that enable all users to visualize the data in the catalogue without leaving the catalogue – no additional software needed. Have a look at our walk-through of how to make a chart in the catalogue. Get automatic notifications when datasets are updated. You can choose to get notifications for individual datasets, an organization’s datasets or the full catalogue. You don’t have to provide and personal information – just subscribe to our feeds using any feed reader you like using the corresponding notification web addresses. Copy those addresses and paste them into your reader. Your feed reader will let you know when the catalogue has been updated. The catalogue provides open data in several file formats (e.g., spreadsheets, geospatial data, etc). Learn about each format and how you can access and use the data each file contains. A file that has a list of items and values separated by commas without formatting (e.g. colours, italics, etc.) or extra visual features. This format provides just the data that you would display in a table. XLSX (Excel) files may be converted to CSV so they can be opened in a text editor. How to access the data: Open with any spreadsheet software application (e.g., Open Office Calc, Microsoft Excel) or text editor. Note: This format is considered machine-readable, it can be easily processed and used by a computer. Files that have visual formatting (e.g. bolded headers and colour-coded rows) can be hard for machines to understand, these elements make a file more human-readable and less machine-readable. A file that provides information without formatted text or extra visual features that may not follow a pattern of separated values like a CSV. How to access the data: Open with any word processor or text editor available on your device (e.g., Microsoft Word, Notepad). A spreadsheet file that may also include charts, graphs, and formatting. How to access the data: Open with a spreadsheet software application that supports this format (e.g., Open Office Calc, Microsoft Excel). Data can be converted to a CSV for a non-proprietary format of the same data without formatted text or extra visual features. A shapefile provides geographic information that can be used to create a map or perform geospatial analysis based on location, points/lines and other data about the shape and features of the area. It includes required files (.shp, .shx, .dbt) and might include corresponding files (e.g., .prj). How to access the data: Open with a geographic information system (GIS) software program (e.g., QGIS). A package of files and folders. The package can contain any number of different file types. How to access the data: Open with an unzipping software application (e.g., WinZIP, 7Zip). Note: If a ZIP file contains .shp, .shx, and .dbt file types, it is an ArcGIS ZIP: a package of shapefiles which provide information to create maps or perform geospatial analysis that can be opened with ArcGIS (a geographic information system software program). A file that provides information related to a geographic area (e.g., phone number, address, average rainfall, number of owl sightings in 2011 etc.) and its geospatial location (i.e., points/lines). How to access the data: Open using a GIS software application to create a map or do geospatial analysis. It can also be opened with a text editor to view raw information. Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A text-based format for sharing data in a machine-readable way that can store data with more unconventional structures such as complex lists. How to access the data: Open with any text editor (e.g., Notepad) or access through a browser. Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A text-based format to store and organize data in a machine-readable way that can store data with more unconventional structures (not just data organized in tables). How to access the data: Open with any text editor (e.g., Notepad). Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A file that provides information related to an area (e.g., phone number, address, average rainfall, number of owl sightings in 2011 etc.) and its geospatial location (i.e., points/lines). How to access the data: Open with a geospatial software application that supports the KML format (e.g., Google Earth). Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. This format contains files with data from tables used for statistical analysis and data visualization of Statistics Canada census data. How to access the data: Open with the Beyond 20/20 application. A database which links and combines data from different files or applications (including HTML, XML, Excel, etc.). The database file can be converted to a CSV/TXT to make the data machine-readable, but human-readable formatting will be lost. How to access the data: Open with Microsoft Office Access (a database management system used to develop application software). A file that keeps the original layout and

The Geographic Information System (GIS) software market is projected to expand significantly, with a market size of XXX million in 2025 and a CAGR of XX% during the forecast period of 2025-2033. The growing adoption of GIS technology across various industries, including urban planning, environmental management, and transportation, is driving market growth. Additionally, the increasing availability of spatial data and the advancements in cloud computing and mobile GIS are further fueling market expansion. Key trends in the GIS software market include the rise of web-based GIS platforms, the integration of artificial intelligence (AI) and machine learning (ML) capabilities, and the growing popularity of open-source GIS solutions. North America and Europe are the major markets for GIS software, while the Asia Pacific region is expected to witness significant growth in the coming years. Major players in the GIS software market include Esri, Hexagon, Pitney Bowes, SuperMap, Bentley Systems, GE, GeoStar, and Zondy Cyber Group. These companies offer a wide range of GIS software products and services to meet the varying needs of different industries and organizations.

This dataset includes long-term (about 22 months from Apr. 2012 to Jan. 2014) global-scale check-in data collected from Foursquare, and also two snapshots of user social networks before and after the check-in data collection period (see more details in our paper). The check-in dataset contains 22,809,624 checkins by 114,324 users on 3,820,891 venues. The social network data contains 363,704 (old) and 607,333 (new) friendships. Due to frequent requests, we also include the raw check-in dataset containing 90,048,627 checkins by 2,733,324 users on 11,180,160 venues.Please cite our paper if you publish material based on this dataset:+ Dingqi Yang, Bingqing Qu, Jie Yang, Philippe Cudre-Mauroux, Revisiting User Mobility and Social Relationships in LBSNs: A Hypergraph Embedding Approach, In Proc. of The Web Conference (WWW'19). May. 2019, San Francisco, USA. + Dingqi Yang, Bingqing Qu, Jie Yang, Philippe Cudre-Mauroux, LBSN2Vec++: Heterogeneous Hypergraph Embedding for Location-Based Social Networks, IEEE Transactions on Knowledge and Data Engineering (TKDE), 2020.

The Indigenous agreements dataset contains geographic boundaries as well as basic attribute data representing arrangements between the Government of Canada, provinces and territories, and Indigenous organizations and communities. These arrangements address Indigenous and northern affairs, such as education, economic development, child and family services, health, and housing, that have not been addressed by treaties or through other means. However, this dataset only contains the Indigenous agreements that have a geographic boundary. The Indigenous agreements dataset includes: 1) Self-government agreements which represents the Indigenous groups that govern their internal affairs and assume greater responsibility and control over the decision making that affects their communities. Self-government agreements address the structure and accountability of Indigenous governments, their law-making powers, financial arrangements and their responsibilities for providing programs and services to their members. Self-government enables Indigenous governments to work in partnership with other governments and the private sector to promote economic development and improve social conditions. These boundaries usually represent the surveyed boundaries of the Indigenous group’s Indian reserve. 2) Consultation agreements (Consultation protocol) which represents an agreement signed between the Indigenous group and one or more parties that establish a consultation process. It sets out an orderly process through which the federal and/or provincial governments can consult with an Indigenous group regarding a contemplated project or activity that may have adverse impacts on established or asserted Aboriginal or Treaty rights. These agreements include Federal Bilateral agreement, Federal Tripartite agreement and other agreements. These boundaries are usually not surveyed but help to delineate the geographic extent of the agreement. 3) Other Agreements is the catch-all category for any remaining geographies of signed agreements between the Indigenous group and other parties, that do not fit within the aforementioned categories. These boundaries are usually not surveyed but help to delineate the geographic extent of the agreement. The Indigenous agreements dataset is one of multiple datasets representing treaties and agreements between the Crown and Indigenous peoples. The Crown-Indigenous treaties and agreements geospatial datasets represent the geographic boundaries of the solemn agreements between the Crown and Indigenous peoples that set out promises, obligations and benefits for parties. The following datasets are also available: 1) The Historic treaties (formerly known as Pre-1975 treaties) dataset, which represents most signed treaties that were negotiated between Indigenous peoples and the Crown between 1725 and 1929. 2) The Modern treaties (formerly known as the Post-1975 treaties) dataset, which represents the areas of Canada where Indigenous land rights and title have not been addressed by preceding treaties or through other legal means. The Indigenous agreements dataset is Crown-Indigenous Relations and Northern Affairs Canada (CIRNAC) and Indigenous Services Canada (ISC)’s primary source for Indigenous agreements geographic boundaries on maps. This dataset can also be viewed in the Aboriginal and Treaty Rights Information System (ATRIS). This web-based system provides access to information to inform governments, industry and other interested parties in determining their consultation obligations and in carrying out their consultation research. For more information, visit https://www.rcaanc-cirnac.gc.ca/eng/1100100014686/1609421785838.

This dataset represents a unique compiled environmental data set for the circumpolar Arctic ocean region 45N to 90N region. It consists of 170 layers (mostly marine, some terrestrial) in ArcGIS 10 format to be used with a Geographic Information System (GIS) and which are listed below in detail. Most layers are long-term average raster GRIDs for the summer season, often by ocean depth, and represent value-added products easy to use. The sources of the data are manifold such as the World Ocean Atlas 2009 (WOA09), International Bathimetric Chart of the Arctic Ocean (IBCAO), Canadian Earth System Model 2 (CanESM2) data (the newest generation of models available) and data sources such as plankton databases and OBIS. Ocean layers were modeled and predicted into the future and zooplankton species were modeled based on future data: Calanus hyperboreus (AphiaID104467), Metridia longa (AphiaID 104632), M. pacifica (AphiaID 196784) and Thysanoessa raschii (AphiaID 110711). Some layers are derived within ArcGIS. Layers have pixel sizes between 1215.819573 meters and 25257.72929 meters for the best pooled model, and between 224881.2644 and 672240.4095 meters for future climate data. Data was then reprojected into North Pole Stereographic projection in meters (WGS84 as the geographic datum). Also, future layers are included as a selected subset of proposed future climate layers from the Canadian CanESM2 for the next 100 years (scenario runs rcp26 and rcp85). The following layer groups are available: bathymetry (depth, derived slope and aspect); proximity layers (to,glaciers,sea ice, protected areas, wetlands, shelf edge); dissolved oxygen, apparent oxygen, percent oxygen, nitrogen, phosphate, salinity, silicate (all for August and for 9 depth classes); runoff (proximity, annual and August); sea surface temperature; waterbody temperature (12 depth classes); modeled ocean boundary layers (H1, H2, H3 and Wx).This dataset is used for a M.Sc. thesis by the author, and freely available upon request. For questions and details we suggest contacting the authors.

Process_Description:

Please contact Moritz Schmid for the thesis and detailed explanations. Short version: We model predicted here for the first time ocean layers in the Arctic Ocean based on a unique dataset of physical oceanography. Moreover, we developed presence/random absence models that indicate where the studied zooplankton species are most likely to be present in the Arctic Ocean. Apart from that, we develop the first spatially explicit models known to science that describe the depth in which the studied zooplankton species are most likely to be at, as well as their distribution of life stages. We do not only do this for one present day scenario. We modeled five different scenarios and for future climate data.

First, we model predicted ocean layers using the most up to date data from various open access sources, referred here as best-pooled model data. We decided to model this set of stratification layers after discussions and input of expert knowledge by Professor Igor Polyakov from the International Arctic Research Center at the University of Alaska Fairbanks. We predicted those stratification layers because those are the boundaries and layers that the plankton has to cross for diel vertical migration and a change in those would most likely affect the migration. I assigned 4 variables to the stratification layers. H1, H2, H3 and Wx. H1 is the lower boundary of the mixed layer depth. Above this layer a lot of atmospheric disturbance is causing mixing of the water, giving the mixed layer its name. H2, the middle of the halocline is important because in this part of the ocean a strong gradient in salinity and temperature separates water layers. H3, the isotherm is important, because beneath it flows denser and colder Atlantic water. Wx summarizes the overall width of the described water column.

Ocean layers were predicted using machine learning algorithms (TreeNet, Salford Systems). Second, ocean layers were included as predictors and used to predict the presence/random absence, most likely depth and life stage layers for the zooplankton species: Calanus hyperboreus, Metridia longa, Metridia pacifica and Thysanoessa raschii,

This process was repeated for future predictions based on the CanESM2 data (see in the data section).

For zooplankton species the following layers were developed and for the future.

C. hyperboreus: Best-pooled model as well as future predictions (rcp26 including ocean layer(also excluding), rcp85 including oocean layers (also excluding) for 2010 and 2100.For parameters: Presence/random absence, most likely depth and life stage layers

M. longa: Best-pooled model as well as future predictions (rcp26 including ocean layer(also excluding), rcp85 including oocean layers (also excluding) for 2010 and 2100. For parameters:... Visit https://dataone.org/datasets/dcx_f63d0f6c-7d53-46ce-b755-42a368007601_2 for complete metadata about this dataset.

Web Mapping Market Outlook

The global web mapping market size was valued at approximately USD 3.5 billion in 2023 and is projected to reach USD 8.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 9.8% during the forecast period. The robust growth of this market can be attributed to the increasing demand for geographic information system (GIS) technologies and the expanding applications of web mapping across various industries.

One of the primary growth factors driving the web mapping market is the proliferation of location-based services. With the rise of smartphones and IoT devices, the demand for real-time location data has skyrocketed, fueling the need for advanced web mapping solutions. Businesses are leveraging location-based services to enhance customer engagement, optimize logistics, and improve decision-making processes. Moreover, the integration of web mapping with emerging technologies such as AI and machine learning is further bolstering market growth, allowing for more sophisticated and predictive mapping capabilities.

Another critical factor contributing to the market's expansion is the growing adoption of web mapping solutions in government and public sector initiatives. Governments across the globe are increasingly utilizing web mapping technologies for urban planning, disaster management, and community services. These technologies provide invaluable insights and real-time data that aid in making informed decisions and improving public services. The push for smart city developments and the need for efficient infrastructure management are also significant drivers for the adoption of web mapping solutions in the public sector.

Furthermore, the transportation and logistics industry is witnessing a substantial uptake of web mapping technologies. With the rise of e-commerce and the need for efficient supply chain management, companies are relying on web mapping to optimize routes, monitor shipments, and ensure timely deliveries. The integration of GPS technology and real-time tracking systems with web mapping solutions is enhancing operational efficiencies and reducing costs. This trend is likely to continue as the demand for seamless logistics and transportation services grows.

The concept of an Electronic Map has become increasingly significant in the web mapping market. Electronic maps are digital representations of geographic areas and are pivotal in providing real-time data and location-based insights. They are extensively used in various applications, from navigation systems to urban planning and environmental monitoring. The integration of electronic maps with web mapping technologies allows for enhanced visualization and analysis of spatial data, offering users detailed and interactive geographic information. As the demand for digital mapping solutions continues to grow, electronic maps are playing a crucial role in transforming how geographic information is accessed and utilized across different sectors.

On the regional front, North America remains a dominant player in the web mapping market, primarily due to the early adoption of advanced technologies and the presence of major market players in the region. The Asia Pacific region is expected to exhibit the highest growth rate during the forecast period, driven by rapid urbanization, technological advancements, and increasing investments in smart city projects. Europe and Latin America are also anticipated to witness significant growth, supported by favorable government initiatives and the expanding use of web mapping across various industries.

Component Analysis

The web mapping market can be segmented by component into software and services. The software segment encompasses a wide range of GIS and mapping software that enable users to create, visualize, and analyze geographic data. This segment is witnessing significant growth due to the increasing need for sophisticated mapping tools that offer real-time data and advanced analytical capabilities. Companies are continuously enhancing their software offerings with features like AI integration, cloud compatibility, and user-friendly interfaces, driving the adoption of web mapping software across various industries.

On the other hand, the services segment includes a variety of professional services such as consulting, implementation, and maintenance. As organizations seek to leverage web mapping technologies, they often require expert guidance and support to ensu

The United States geospatial analytics market is experiencing robust growth, driven by increasing adoption across diverse sectors. The market, valued at approximately $X billion in 2025 (assuming a proportional share of the global market size based on US economic weight), is projected to exhibit a Compound Annual Growth Rate (CAGR) of 10.04% from 2025 to 2033. This expansion is fueled by several key factors. Firstly, the rising availability of high-resolution satellite imagery, drone data, and other geospatial data sources provides rich information for analysis. Secondly, advancements in artificial intelligence (AI) and machine learning (ML) are enhancing the capabilities of geospatial analytics platforms, enabling more sophisticated insights and predictions. Thirdly, the increasing need for precise location-based services across various industries, such as precision agriculture, smart city initiatives, and autonomous vehicle development, is driving demand for sophisticated geospatial analytics solutions. Finally, government initiatives promoting data sharing and open data policies further contribute to market growth. The market is segmented by type (surface analysis, network analysis, geovisualization) and end-user vertical (agriculture, utility & communication, defense & intelligence, government, mining & natural resources, automotive & transportation, healthcare, real estate & construction). North America, particularly the US, holds a significant market share due to advanced technological infrastructure and high adoption rates across various sectors. Within the US market, significant growth is expected in sectors like precision agriculture, where geospatial analytics is used for optimized crop management and resource allocation, and in the transportation sector, supporting logistics optimization, traffic management, and autonomous vehicle navigation. The defense and intelligence sectors remain major consumers of geospatial analytics, relying on these technologies for surveillance, intelligence gathering, and military planning. The increasing adoption of cloud-based geospatial analytics platforms is also a significant trend, offering scalability, accessibility, and cost-effectiveness. However, challenges such as data security concerns, high implementation costs, and the need for skilled professionals could potentially hinder market growth. Despite these challenges, the overall market outlook for geospatial analytics in the US remains exceptionally positive, projecting substantial growth over the forecast period. Recent developments include: May 2023 : CAPE Analytics, a player in AI-powered geospatial property intelligence, has extended its partnership with The Hanover Insurance Group, which provides independent agents with the best insurance coverage and prices. Integrating geospatial analytics and inspection and rating models into Hanover's underwriting procedure is the central component of the partnership expansion. The company's rating plans will benefit from this strategic move, which will improve workflows, new and renewal underwriting outcomes, and pricing segmentation., March 2023 : Carahsoft Technology Corp., The Trusted Government IT Solutions Provider, and Orbital Insight, a player in geospatial intelligence, announced a partnership. By the terms of the agreement, Carahsoft will act as Orbital Insight's Master Government Aggregator, making the leading AI-powered geospatial data analytics available to the public sector through Carahsoft's reseller partners and contracts for Information Technology Enterprise Solutions - Software 2 (ITES-SW2), NASA Solutions for Enterprise-Wide Procurement (SEWP) V, National Association of State Procurement Officials (NASPO) ValuePoint, National Cooperative Purchasing.. Key drivers for this market are: Increasing in Demand for Location Intelligence, Advancements of Big Data Analytics. Potential restraints include: High Costs and Operational Concerns, Concerns related to Geoprivacy and Confidential Data. Notable trends are: Network Analysis is Expected to Hold Significant Share of the Market.

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GIS Market Size 2025-2029

The gis market size is forecast to increase by USD 24.07 billion at a CAGR of 20.3% between 2024 and 2029.

The Global Geographic Information System (GIS) market is experiencing significant growth, driven by the increasing integration of Building Information Modeling (BIM) and GIS technologies. This convergence enables more accurate and efficient spatial analysis for infrastructure planning and management. A key trend in the market is the expanding application of GIS solutions in soil and water management, as organizations seek to mitigate environmental risks and optimize resource utilization. However, the lack of comprehensive planning and insufficient resources for implementation can hinder the successful adoption of GIS technologies. Companies looking to capitalize on this market opportunity should focus on providing implementation support and developing user-friendly solutions to address these challenges. Effective collaboration between GIS and BIM companies, as well as strategic partnerships with industry players, can further enhance market penetration and drive innovation.

What will be the Size of the GIS Market during the forecast period?

Request Free SampleThe Geographic Information System (GIS) market in the United States is experiencing significant growth, driven by the increasing demand for advanced mapping tools and geospatial technology in various sectors. The market's size is substantial, with applications ranging from soil management and precision farming to infrastructure design, urban planning, and disaster management. Key growth factors include the integration of real-time analytics, video games, and mobile devices into GIS solutions, as well as the adoption of cloud technology and 4D GIS software. The market is also influenced by the increasing importance of location intelligence in industries such as oil and gas, transportation, and smart city planning. Moreover, the use of GIS technology in environmental monitoring, green buildings, and water resources management is gaining traction due to the growing awareness of sustainability and the need for effective resource management. Additionally, the integration of GIS with Building Information Modeling (BIM) and Augmented Reality platforms is expected to further expand the market's reach and potential applications. Overall, the market is poised for continued growth and innovation, driven by the increasing demand for accurate, timely, and actionable geospatial data.

How is this GIS Industry segmented?

The gis 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. ProductSoftwareDataServicesTypeTelematics and navigationMappingSurveyingLocation-based servicesDeviceDesktopMobileGeographyNorth AmericaUSCanadaEuropeFranceGermanyUKAPACChinaJapanSouth KoreaSouth AmericaBrazilMiddle East and AfricaUAE

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.The Global Geographic Information System (GIS) market encompasses software for desktops, mobile devices, cloud solutions, and servers. Industry-specific software dominates, with commercial companies providing open-source alternatives for smaller applications, mitigating counterfeit threats. However, the widespread adoption of open-source GIS software poses a challenge. Concurrently, cloud-based GIS software adoption is an emerging trend. Yet, the absence of standardization and interoperability issues hinder its widespread use. Key applications include soil mapping, virtual meters for business process management, precision farming, GIS collectors, green buildings, urban planning, and aerial photography. Additionally, the retail sector, location-based services, water quality management, project management, urban growth assessment, video games, and smart cities utilize GIS. Furthermore, satellite imaging, remote sensors, mobile devices, accident analysis, augmented reality platforms, land acquisition, site selection, and government sector applications are prevalent. The market dynamics include the increasing use of GNSS/GPS antennas, enterprise resource planning, smartphone adoption, grain production, transportation sector, environmental impact assessment, homeland security, and sustainable urban development. Cloud technology, community planning, traffic modeling, architecture and construction, smart city planning, land zone classification, 4D GIS software, and oil and gas industries also leverage geospatial technology. In , the market is diverse, with various applications in different sectors. While open-source software adoption challenges the market, cloud-based GIS software adoption is an emerging trend. Standardization and interoperability issues remain major barriers. Th

The dataset presents the historical railway network of Galicia and Austrian Silesia – two regions of the Habsburg Empire, covering more than 80 000 km2, currently divided among Czechia, Poland and Ukraine. The network covers the times of railway appearance and the most dynamic development of the 19th and beginning of the 20th century, up to 1914 – the outbreak of the First World War. The data can be characterized by unprecedented positional accuracy, as they were reconstructed based on the current railway network, which resulted in almost no shifts in space. Most of the lines were reconstructed based on OpenStreetMap data, and the lines, which were closed-down between 1914 and 2019, and are no longer available in spatial datasets, were reconstructed based on high-resolution satellite imageries and historical maps. Altogether, the network covers more than 5000 km on 127 lines. The data are accompanied by a set of attributes, i.e. year of construction, length, starting and final point, type (normal, narrow-gauge, etc.). It can be used in many different applications including historical accessibility mapping, migrations, economic development, the impact of past human activities on current environmental and socio-economic processes, like land use change drivers, landscape fragmentation, invasion of new species and many more. Data are available for download in the shp format.

Please note: Our work was focused on publicly accessible railway lines open for regular passenger traffic and hence did not contain the sidings constructed locally, e.g. to serve industrial sites or narrow gauge forest lines.

Acknowledgments This research was funded by the Ministry of Science and Higher Education, Republic of Poland under the frame of “National Programme for the Development of Humanities” 2015–2020, as a part of the GASID project (Galicia and Austrian Silesia Interactive Database 1857–1910, 1aH 15 0324 83).