Abstract Although universities conduct research in the SDI field, they have repeatedly erred when storing, preserving, and sharing their geospatial data. The general objective of this research is to develop a proposal for a Spatial Data Infrastructure (SDI) for the graduate programs of the Department of Earth Sciences at Federal University of Paraná (UFPR). In order to initiate the process of implementing UFPR’s academic SDI, a sample of the theses and dissertations of the aforementioned programs was collected. Subsequently, a survey of the metadata of the spatial study areas and publications was conducted, and finally, these metadata were made available as a catalog through GeoNetwork. The metadata, ISO TC-221, OGC and WC3 standards recommended by INDE-BR were adopted, and free and open source software was used. In this paper, the results of the metadata survey and their availability are presented in the form of a catalog, as well as a synthesis of the reflections made during the execution of the research, in order to help define the characteristics of academic SDIs in the country. The cataloging of historical metadata was found to be viable and to facilitate the dissemination of geospatial data to the scientific community.

This feature class is part of the Cadastral National Spatial Data Infrastructure (NSDI) CADNSDI publication data set for rectangular and non-rectangular Public Land Survey System (PLSS) data set. The metadata description in the Cadastral Reference System Feature Data Set more fully describes the entire data set. This feature class is the second division of the PLSS is quarter, quarter-quarter, sixteenth or government lot divisions of the PLSS. The second and third divisions are combined into this feature class as an intentional de-normalization of the PLSS hierarchical data. The polygons in this feature class represent the smallest division to the sixteenth that has been defined for the first division. For example In some cases sections have only been divided to the quarter. Divisions below the sixteenth are in the Special Survey or Parcel Feature Class.

The vector grid system provides a spatial and statistical infrastructure that allows the integration of environmental and socio-economic data. Its exploitation allows the crossing of different spatial data within the same grid units. Project results obtained using this grid system can be more easily linked. This grid system forms the geographic and statistical infrastructure of the Southern Quebec Land Accounts of the Institute of Statistics of Quebec (ISQ). It forms the geospatial and statistical context for the development of ecosystem accounting in Quebec. **In order to improve the vector grid system and the Land Accounts of Southern Quebec and to better anticipate the future needs of users, we would like to be informed of their use (field of application, objectives of use, territory, association with other products, etc.). You can write to us at maxime.keith@stat.gouv.qc.ca **. This grid system allows the spatial integration of various data relating, for example, to human populations, the economy or the characteristics of land. The ISQ wishes to encourage the use of this system in projects that require the integration of several data sources, the analysis of this data at different spatial scales and the monitoring of this data over time. The fixed geographic references of the grids simplify the compilation of statistics according to different territorial divisions and facilitate the monitoring of changes over time. In particular, the grid system promotes the consistency of data at the provincial level. The spatial intersection of the grid and the spatial data layer to be integrated makes it possible to transfer the information underlying the layer within each cell of the grid. In the case of the Southern Quebec Land Accounts, the spatial intersection of the grid and each of the three land cover layers (1990s, 2000s and 2010s) made it possible to report the dominant coverage within each grid cell. The set of matrix files of Southern Quebec Land Accounts is the result of this intersection. **Characteristics: ** The product includes two vector grids: one formed of cells of 1 km² (or 1,000 m on a side), which covers all of Quebec, and another of 2,500 m² cells (or 50 m on a side, or a quarter of a hectare), which fits perfectly into the first and covers Quebec territory located south of the 52nd parallel. Note that the nomenclature of this system, designed according to a Cartesian plan, was developed so that it was possible to integrate cells with finer resolutions (up to 5 meters on a side). In its 2024 update, the 50 m grid system is divided into 331 parts with a side of 50 km in order to limit the number of cells per part of the grid to millions and thus facilitate geospatial processing. This grid includes a total of approximately 350 million cells or 875,000 km2. It is backwards compatible with the 50m grid broadcast by the ISQ in 2018 (spatial structure and unique identifiers are identical, only the fragmentation is different). **Attribute information for 50 m cells: ** * ID_m50: unique code of the cell; * CO_MUN_2022: geographic code of the municipality of January 2022; * CERQ_NV2: code of the natural region of the ecological reference framework of Quebec; * CL_COUV_T50: unique code of the cell; * CL_COUV_T00, CL_COUV_T01: codes for coverage classes Terrestrial maps from the years 1990, 2000 and 2010. Note: the 2000s are covered by two land cover maps: CL_COUV_T01A and CL_COUV_T01b. The first inventories land cover prior to reassessment using the 2010s map, while the second shows land cover after this reassessment process. **Complementary entity classes: ** * Index_grille50m: index of the parts of the grid; * Decoupage_mun_01_2022: division of municipalities; * Decoupage_MRC_01_2022: division of geographical MRCs; * Decoupage_RA_01_2022: division of administrative regions. Source: System on administrative divisions [SDA] of the Ministry of Natural Resources and Forests [MRNF], January 2022, allows statistical compilations to be carried out according to administrative divisions hierarchically superior to municipalities. * Decoupage_CERQ_NV2_2018: division of level 2 of the CERQ, natural regions. Source: Ministry of the Environment, the Fight against Climate Change, Wildlife and Parks [MELCCFP]. Geospatial processes delivered with the grid (only with the FGDB data set) : * ArcGIS ModelBuilder allowing the spatial intersection and the selection of the dominant value of the geographic layer to populate the grid; * ModelBuilder allowing the statistical compilation of results according to various divisions. Additional information on the grid in the report Southern Quebec Land Accounts published in October 2018 (p. 46). View the results of the Southern Quebec Land Accounts on the interactive map of the Institut de la Statistique du Québec.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

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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,

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Geospatial Analytics Market Size 2025-2029

The geospatial analytics market size is forecast to increase by USD 178.6 billion, at a CAGR of 21.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing adoption of geospatial analytics in sectors such as healthcare and insurance. This trend is fueled by the ability of geospatial analytics to provide valuable insights from location-based data, leading to improved operational efficiency and decision-making. Additionally, emerging methods in data collection and generation, including the use of drones and satellite imagery, are expanding the scope and potential of geospatial analytics. However, the market faces challenges, including data privacy and security concerns. With the vast amounts of sensitive location data being collected and analyzed, ensuring its protection is crucial for companies to maintain trust with their customers and avoid regulatory penalties. Navigating these challenges and capitalizing on the opportunities presented by the growing adoption of geospatial analytics requires a strategic approach from industry players. Companies must prioritize data security, invest in advanced analytics technologies, and collaborate with stakeholders to build trust and transparency. By addressing these challenges and leveraging the power of geospatial analytics, businesses can gain a competitive edge and unlock new opportunities in various industries.

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

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, driven by the increasing demand for location-specific insights across various sectors. Urban planning relies on geospatial optimization and data enrichment to enhance city designs and improve infrastructure. Cloud-based geospatial solutions facilitate real-time data access, enabling location intelligence for public safety and resource management. Spatial data standards ensure interoperability among different systems, while geospatial software and data visualization tools provide valuable insights from satellite imagery and aerial photography. Geospatial services offer data integration, spatial data accuracy, and advanced analytics capabilities, including 3D visualization, route optimization, and data cleansing. Precision agriculture and environmental monitoring leverage geospatial data to optimize resource usage and monitor ecosystem health. Infrastructure management and real estate industries rely on geospatial data for asset tracking and market analysis. Spatial statistics and disaster management applications help mitigate risks and respond effectively to crises. Geospatial data management and quality remain critical as the volume and complexity of data grow. Geospatial modeling and interoperability enable seamless data sharing and collaboration. Sensor networks and geospatial data acquisition technologies expand the reach of geospatial analytics, while AI-powered geospatial analytics offer new opportunities for predictive analysis and automation. The ongoing development of geospatial technologies and applications underscores the market's continuous dynamism, providing valuable insights and solutions for businesses and organizations worldwide.

How is this Geospatial Analytics Industry segmented?

The geospatial analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. TechnologyGPSGISRemote sensingOthersEnd-userDefence and securityGovernmentEnvironmental monitoringMining and manufacturingOthersApplicationSurveyingMedicine and public safetyMilitary intelligenceDisaster risk reduction and managementOthersTypeSurface and field analyticsGeovisualizationNetwork and location analyticsOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKAPACChinaIndiaJapanSouth AmericaBrazilRest of World (ROW)

By Technology Insights

The gps segment is estimated to witness significant growth during the forecast period.The market encompasses various applications and technologies, including geospatial optimization, data enrichment, location-based services (LBS), spatial data standards, public safety, geospatial software, resource management, location intelligence, geospatial data visualization, geospatial services, data integration, 3D visualization, satellite imagery, remote sensing, GIS platforms, spatial data infrastructure, aerial photography, route optimization, data cleansing, precision agriculture, spatial interpolation, geospatial databases, transportation planning, spatial data accuracy, spatial analysis, map projections, interactive maps, marketing analytics, d

Geospatial Analytics Market Outlook

As per our latest research, the global geospatial analytics market size stood at USD 98.2 billion in 2024, exhibiting robust momentum driven by the accelerating adoption of spatial data solutions across industries. The market is projected to expand at a CAGR of 13.5% during the forecast period, reaching a remarkable USD 286.5 billion by 2033. This impressive growth is fueled by increasing demand for location-based services, smart city initiatives, and the integration of artificial intelligence with geospatial technologies, which are transforming how organizations derive actionable insights from spatial data.

One of the primary growth factors propelling the geospatial analytics market is the rapid proliferation of advanced sensor technologies and the exponential increase in spatial data generation. The widespread deployment of Internet of Things (IoT) devices, satellites, drones, and mobile sensors is generating vast volumes of geospatial data, which organizations are leveraging to enhance decision-making processes. Additionally, the integration of real-time data streams with sophisticated analytics platforms is enabling businesses and governments to monitor, predict, and respond to dynamic environmental and operational changes with unprecedented accuracy and speed. This trend is particularly evident in sectors such as urban planning, disaster management, and logistics, where location intelligence is critical for optimizing resources and improving outcomes.

Another significant driver of the geospatial analytics market is the growing emphasis on smart city development and infrastructure modernization worldwide. Governments and municipal authorities are increasingly investing in geospatial technologies to support urban planning, infrastructure management, and public safety initiatives. The ability to visualize, analyze, and simulate spatial data is enabling more effective land use planning, traffic management, and utility monitoring, thereby enhancing the quality of urban life. Furthermore, the integration of geospatial analytics with other emerging technologies, such as artificial intelligence and machine learning, is unlocking new possibilities for predictive modeling and scenario analysis, further boosting market growth.

The increasing adoption of cloud-based geospatial analytics platforms is also a crucial factor contributing to market expansion. Cloud deployment offers significant advantages in terms of scalability, cost-efficiency, and accessibility, allowing organizations of all sizes to leverage advanced spatial analytics without the need for substantial upfront investments in hardware and infrastructure. This democratization of geospatial analytics is particularly beneficial for small and medium enterprises (SMEs), which can now access powerful tools for location intelligence, supply chain optimization, and risk management. Moreover, the cloud model facilitates seamless integration with other enterprise applications and data sources, driving greater operational agility and innovation across industries.

From a regional perspective, North America continues to dominate the geospatial analytics market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The United States remains at the forefront of technological innovation and adoption, supported by a robust ecosystem of geospatial solution providers, research institutions, and government agencies. Meanwhile, Asia Pacific is witnessing the fastest growth, driven by rapid urbanization, infrastructure development, and increasing investments in smart city projects across countries such as China, India, and Japan. These regional dynamics underscore the global nature of geospatial analytics adoption and the diverse opportunities for market participants worldwide.

Component Analysis

The geospatial analytics market by component is segmented into software, hardware, and services, each playing a distinct yet interconnected role in the value chain. The software segment r

Today, organizations must work beyond borders, jurisdictions, and sectors to address shared challenges. Collaboration is key whether you call your initiative a Spatial Data Infrastructure (SDI), Open Data, Digital Twin, Knowledge Infrastructure, Digital Ecosystem, or otherwise.The OneMap SDI template can be used to connect multiple organizations to collaborate and share open and secure data with internal and external stakeholders.View example hubsDiscover good practice guides and implementation patternsLearn more about integrated geospatial infrastructureThe 'OneMap' Hub concept is multi-organizational. The website is designed to help communities of practice jumpstart your initiatives. Use it to share and collaborate, provide focus on thematic topics, and more.This item is available to ArcGIS Hub Basic and Premium licensed organizations.v3 template: 07/15/2024

The OneMap template can be used to connect multiple organizations to collaborate and share with internal and external stakeholders.Today, organizations must work beyond borders, jurisdictions, and sectors to address shared challenges. Collaboration is key whether you call your initiative a Spatial Data Infrastructure (SDI), Open Data, Digital Twin, Knowledge Infrastructure, Digital Ecosystem, or otherwise. The term ‘OneMap’ is a placeholder for your community GIS branding.View example hubsDiscover good practice guides and implementation patternsLearn more about integrated geospatial infrastructureThe 'OneMap' Hub concept is multi-organizational. The website is designed to help communities of practice jumpstart your initiatives. Use it to share and collaborate, provide focus on thematic topics, and more.This item is available to ArcGIS Hub Basic and Premium licensed organizations.

This geodatabase reflects the U.S. Geological Survey’s (USGS) ongoing commitment to its mission of understanding the nature and distribution of global mineral commodity supply chains by updating and publishing the georeferenced locations of mineral commodity production and processing facilities, mineral exploration and development sites, and mineral commodity exporting ports in Africa. The geodatabase and geospatial data layers serve to create a new geographic information product in the form of a geospatial portable document format (PDF) map. The geodatabase contains data layers from USGS, foreign governmental, and open-source sources as follows: (1) mineral production and processing facilities, (2) mineral exploration and development sites, (3) mineral occurrence sites and deposits, (4) undiscovered mineral resource tracts for Gabon and Mauritania, (5) undiscovered mineral resource tracts for potash, platinum-group elements, and copper, (6) coal occurrence areas, (7) electric power generating facilities, (8) electric power transmission lines, (9) liquefied natural gas terminals, (10) oil and gas pipelines, (11) undiscovered, technically recoverable conventional and continuous hydrocarbon resources (by USGS geologic/petroleum province), (12) cumulative production, and recoverable conventional resources (by oil- and gas-producing nation), (13) major mineral exporting maritime ports, (14) railroads, (15) major roads, (16) major cities, (17) major lakes, (18) major river systems, (19) first-level administrative division (ADM1) boundaries for all countries in Africa, and (20) international boundaries for all countries in Africa.

This feature class is part of the Cadastral National Spatial Data Infrastructure (NSDI) CADNSDI publication data set for rectangular and non-rectangular Public Land Survey System (PLSS) data set. The metadata description in the Cadastral Reference System Feature Data Set more fully describes the entire data set. This feature class is the second division of the PLSS is quarter, quarter-quarter, sixteenth or government lot divisions of the PLSS. The second and third divisions are combined into this feature class as an intentional de-normalization of the PLSS hierarchical data. The polygons in this feature class represent the smallest division to the sixteenth that has been defined for the first division. For example In some cases sections have only been divided to the quarter. Divisions below the sixteenth are in the Special Survey or Parcel Feature Class.

The Coastal Monitoring group maintain a network of remote oceanographic monitoring equipment in the sea loughs and coastal seas around the north of Ireland. This is combined with survey and cruise programmes to allow access to both fixed point sentinel monitoring data in real-time as well as broader water body characterisations. These survey stations support the sentinel monitoring data as real data is sampled to allow modelling and referencing.

Users outside of the Spatial NI Portal please use Resource Locator 2.

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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 effective spatial analysis and decision-making in various industries, particularly in soil and water management. However, the market faces challenges, including the lack of comprehensive planning and preparation leading to implementation failures of GIS solutions. Companies must address these challenges by investing in thorough project planning and collaboration between GIS and BIM teams to ensure successful implementation and maximize the potential benefits of these advanced technologies.
By focusing on strategic planning and effective implementation, organizations can capitalize on the opportunities presented by the growing adoption of GIS and BIM technologies, ultimately driving operational efficiency and innovation.

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

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The global Geographic Information Systems (GIS) market continues to evolve, driven by the increasing demand for advanced spatial data analysis and management solutions. GIS technology is finding applications across various sectors, including natural resource management, urban planning, and infrastructure management. The integration of Bing Maps, terrain analysis, vector data, Lidar data, and Geographic Information Systems enables precise spatial data analysis and modeling. Hydrological modeling, spatial statistics, spatial indexing, and route optimization are essential components of GIS, providing valuable insights for sectors such as public safety, transportation planning, and precision agriculture. Location-based services and data visualization further enhance the utility of GIS, enabling real-time mapping and spatial analysis.

The ongoing development of OGC standards, spatial data infrastructure, and mapping APIs continues to expand the capabilities of GIS, making it an indispensable tool for managing and analyzing geospatial data. The continuous unfolding of market activities and evolving patterns in the market reflect the dynamic nature of this technology and its applications.

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.

Product

  Software
  Data
  Services


Type

  Telematics and navigation
  Mapping
  Surveying
  Location-based services


Device

  Desktop
  Mobile


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa

    UAE


  APAC

    China
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.

The Global Geographic Information System (GIS) market encompasses a range of applications and technologies, including raster data, urban planning, geospatial data, geocoding APIs, GIS services, routing APIs, aerial photography, satellite imagery, GIS software, geospatial analytics, public safety, field data collection, transportation planning, precision agriculture, OGC standards, location intelligence, remote sensing, asset management, network analysis, spatial analysis, infrastructure management, spatial data standards, disaster management, environmental monitoring, spatial modeling, coordinate systems, spatial overlay, real-time mapping, mapping APIs, spatial join, mapping applications, smart cities, spatial data infrastructure, map projections, spatial databases, natural resource management, Bing Maps, terrain analysis, vector data, Lidar data, and geographic information systems.

The software segment includes desktop, mobile, cloud, and server solutions. Open-source GIS software, with its industry-specific offerings, poses a challenge to the market, while the adoption of cloud-based GIS software represents an emerging trend. However, the lack of standardization and interoperability issues hinder the widespread adoption of cloud-based solutions. Applications in sectors like public safety, transportation planning, and precision agriculture are driving market growth. Additionally, advancements in technologies like remote sensing, spatial modeling, and real-time mapping are expanding the market's scope.

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The Software segment was valued at USD 5.06 billion in 2019

This feature class is part of the Cadastral National Spatial Data Infrastructure (NSDI) CADNSDI publication data set for rectangular and non-rectangular Public Land Survey System (PLSS) data set. The metadata description in the Cadastral Reference System Feature Data Set more fully describes the entire data set. This feature class is the second division of the PLSS is quarter, quarter-quarter, sixteenth or government lot divisions of the PLSS. The second and third divisions are combined into this feature class as an intentional de-normalization of the PLSS hierarchical data. The polygons in this feature class represent the smallest division to the sixteenth that has been defined for the first division. For example In some cases sections have only been divided to the quarter. Divisions below the sixteenth are in the Special Survey or Parcel Feature Class.

This feature class is part of the Cadastral National Spatial Data Infrastructure (NSDI) CADNSDI publication data set for rectangular and non-rectangular Public Land Survey System (PLSS) data set. The metadata description in the Cadastral Reference System Feature Data Set more fully describes the entire data set. This feature class is the second division of the PLSS is quarter, quarter-quarter, sixteenth or government lot divisions of the PLSS. The second and third divisions are combined into this feature class as an intentional de-normalization of the PLSS hierarchical data. The polygons in this feature class represent the smallest division to the sixteenth that has been defined for the first division. For example In some cases sections have only been divided to the quarter. Divisions below the sixteenth are in the Special Survey or Parcel Feature Class.

This feature class is part of the Cadastral National Spatial Data Infrastructure (NSDI) CADNSDI publication data set for rectangular and non-rectangular Public Land Survey System (PLSS) data set. The metadata description in the Cadastral Reference System Feature Data Set more fully describes the entire data set. This feature class is the second division of the PLSS is quarter, quarter-quarter, sixteenth or government lot divisions of the PLSS. The second and third divisions are combined into this feature class as an intentional de-normalization of the PLSS hierarchical data. The polygons in this feature class represent the smallest division to the sixteenth that has been defined for the first division. For example In some cases sections have only been divided to the quarter. Divisions below the sixteenth are in the Special Survey or Parcel Feature Class.

This feature class is part of the Cadastral National Spatial Data Infrastructure (NSDI) CADNSDI publication data set for rectangular and non-rectangular Public Land Survey System (PLSS) data set. The metadata description in the Cadastral Reference System Feature Data Set more fully describes the entire data set. This feature class is the second division of the PLSS is quarter, quarter-quarter, sixteenth or government lot divisions of the PLSS. The second and third divisions are combined into this feature class as an intentional de-normalization of the PLSS hierarchical data. The polygons in this feature class represent the smallest division to the sixteenth that has been defined for the first division. For example In some cases sections have only been divided to the quarter. Divisions below the sixteenth are in the Special Survey or Parcel Feature Class.

This is a MD iMAP hosted service layer. Find more information at http://imap.maryland.gov. These data map hub and corridor elements within the green infrastructure. The Green Infrastructure Assessment was developed to provide decision support for Maryland's Department of Natural Resources land conservation programs. Methods used to identify and rank green infrastructure lands are intended soley for this use. Other applications are at the discretion of the user. The Maryland Department of Natural Resources is not responsible for any inaccuracies in the data and does not necessarily endorse any uses or products derived from the data other than those for which the data were originally intended. Maryland's green infrastructure is a network of undeveloped lands that provide the bulk of the state's natural support system. Ecosystem services - such as cleaning the air - filtering water - storing and cycling nutrients - conserving soils - regulating climate - and maintaining hydrologic function - are all provided by the existing expanses of forests - wetlands - and other natural lands. These ecologically valuable lands also provide marketable goods and services - like forest products - fish and wildlife - and recreation. The Green Infrastructure serves as vital habitat for wild species and contributes in many ways to the health and quality of life for Maryland residents. To identify and prioritize Maryland's green infrastructure - we developed a tool called the Green Infrastructure Assessment (GIA). The GIA was based on principles of landscape ecology and conservation biology - and provides a consistent approach to evaluating land conservation and restoration efforts in Maryland. It specifically attempts to recognize: a variety of natural resource values (as opposed to a single species of wildlife - for example) - how a given place fits into a larger system - the ecological importance of natural open space in rural and developed areas - the importance of coordinating local - state and even interstate planning - and the need for a regional or landscape-level view for wildlife conservation. The GIA identified two types of important resource lands - hubs"" and ""corridors."" Hubs typically large contiguous areas - separated by major roads and/or human land uses - that contain one or more of the following: Large blocks of contiguous interior forest (containing at least 250 acres - plus a transition zone of 300 feet) Large wetland complexes - with at least 250 acres of unmodified wetlands; Important animal and plant habitats of at least 100 acres - including rare - threatened - and endangered species locations - unique ecological communities - and migratory bird habitats; relatively pristine stream and river segments (which - when considered with adjacent forests and wetlands - are at least 100 acres) that support trout - mussels - and other sensitive aquatic organisms; and existing protected natural resource lands which contain one or more of the above (for example - state parks and forests - National Wildlife Refuges - locally owned reservoir properties - major stream valley parks - and Nature Conservancy preserves). In the GIA model - the above features were identified from Geographic Information Systems (GIS) spatial data that covered the entire state. Developed areas and major roads were excluded - areas less than 100 contiguous acres were dropped - adjacent forest and wetland were added to the remaining hubs - and the edges were smoothed. The average size of all hubs in the state is approximately 2200 acres. Corridors are linear features connecting hubs together to help animals and plant propagules to move between hubs. Corridors were identified using many sets of data - including land cover - roads - streams - slope - flood plains - aquatic resource data - and fish blockages. Generally speaking - corridors connect hubs of similar type (hubs containing forests are connected to one ano

This feature class is part of the Cadastral National Spatial Data Infrastructure (NSDI) CADNSDI publication data set for rectangular and non-rectangular Public Land Survey System (PLSS) data set. The metadata description in the Cadastral Reference System Feature Data Set more fully describes the entire data set. This feature class is the second division of the PLSS is quarter, quarter-quarter, sixteenth or government lot divisions of the PLSS. The second and third divisions are combined into this feature class as an intentional de-normalization of the PLSS hierarchical data. The polygons in this feature class represent the smallest division to the sixteenth that has been defined for the first division. For example In some cases sections have only been divided to the quarter. Divisions below the sixteenth are in the Special Survey or Parcel Feature Class.

Geographic Information System Market Outlook

As per our latest research, the global Geographic Information System (GIS) market size reached USD 12.3 billion in 2024. The industry is experiencing robust expansion, driven by a surging demand for spatial data analytics across diverse sectors. The market is projected to grow at a CAGR of 11.2% from 2025 to 2033, reaching an estimated USD 31.9 billion by 2033. This accelerated growth is primarily attributed to the integration of advanced technologies such as artificial intelligence, IoT, and cloud computing with GIS solutions, as well as the increasing adoption of location-based services and smart city initiatives worldwide.

One of the primary growth factors fueling the GIS market is the rapid adoption of geospatial analytics in urban planning and infrastructure development. Governments and private enterprises are leveraging GIS to optimize land use, manage resources efficiently, and enhance public services. Urban planners utilize GIS to analyze demographic trends, plan transportation networks, and ensure sustainable development. The integration of GIS with Building Information Modeling (BIM) and real-time data feeds has further amplified its utility in smart city projects, driving demand for sophisticated GIS platforms. The proliferation of IoT devices and sensors has also enabled the collection of high-resolution geospatial data, which is instrumental in developing predictive models for urban growth and disaster management.

Another significant driver of the GIS market is the increasing need for disaster management and risk mitigation. GIS technology plays a pivotal role in monitoring natural disasters such as floods, earthquakes, and wildfires. By providing real-time spatial data, GIS enables authorities to make informed decisions, coordinate response efforts, and allocate resources effectively. The growing frequency and intensity of natural disasters, coupled with heightened awareness about climate change, have compelled governments and humanitarian organizations to invest heavily in advanced GIS solutions. These investments are not only aimed at disaster response but also at long-term resilience planning, thereby expanding the scope and scale of GIS applications.

The expanding application of GIS in the agriculture and utilities sectors is another crucial growth factor. Precision agriculture relies on GIS to analyze soil conditions, monitor crop health, and optimize irrigation practices, ultimately boosting productivity and sustainability. In the utilities sector, GIS is indispensable for asset management, network optimization, and outage response. The integration of GIS with remote sensing technologies and drones has revolutionized data collection and analysis, enabling more accurate and timely decision-making. Moreover, the emergence of cloud-based GIS platforms has democratized access to geospatial data and analytics, empowering small and medium enterprises to harness the power of GIS for operational efficiency and strategic planning.

From a regional perspective, North America continues to dominate the GIS market, supported by substantial investments in smart infrastructure, advanced research capabilities, and a strong presence of leading technology providers. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid urbanization, government initiatives for digital transformation, and increasing adoption of GIS in agriculture and disaster management. Europe is also witnessing significant growth, particularly in transportation, environmental monitoring, and public safety applications. The Middle East & Africa and Latin America are gradually catching up, with growing investments in infrastructure development and resource management. This regional diversification is expected to drive innovation and competition in the global GIS market over the forecast period.

Component Analysis

The Geographic Information System market is segmented by component into hardware, software, and services, each playing a unique role

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