To achieve true data interoperability is to eliminate format and data model barriers, allowing you to seamlessly access, convert, and model any data, independent of format. The ArcGIS Data Interoperability extension is based on the powerful data transformation capabilities of the Feature Manipulation Engine (FME), giving you the data you want, when and where you want it.In this course, you will learn how to leverage the ArcGIS Data Interoperability extension within ArcCatalog and ArcMap, enabling you to directly read, translate, and transform spatial data according to your independent needs. In addition to components that allow you to work openly with a multitude of formats, the extension also provides a complex data model solution with a level of control that would otherwise require custom software.After completing this course, you will be able to:Recognize when you need to use the Data Interoperability tool to view or edit your data.Choose and apply the correct method of reading data with the Data Interoperability tool in ArcCatalog and ArcMap.Choose the correct Data Interoperability tool and be able to use it to convert your data between formats.Edit a data model, or schema, using the Spatial ETL tool.Perform any desired transformations on your data's attributes and geometry using the Spatial ETL tool.Verify your data transformations before, after, and during a translation by inspecting your data.Apply best practices when creating a workflow using the Data Interoperability extension.

ArcMap'ten ArcGIS Pro'ya geçişle birlikte eski Personal Geodatabase (.mdb) verilerinizi daha yeni ve verimli olan File Geodatabase (.gdb) formatına ArcGIS Data Interoperability aracılığıyla topluca nasıl dönüştürebileceğinizi göreceksiniz.Alıştırmayı yapmak için gerekli tahmini süre: 30 DakikaYazılım gereksinimi: ArcGIS Data Interoperability

This dataset supports the research article "From GIS to HBIM and Back: Multiscale Performance and Condition Assessment for Networks of Public Heritage Buildings and Construction Components" and includes:

• Data Structure (Excel file) → A structured database containing attribute information about historic buildings, based on the Italian regulatory provisions for the assessment and reduction of seismic risk in the national cultural heritage (Ministero della Cultura, 2008).
• Python Scripts → Custom scripts developed for data processing and interoperability between GIS and HBIM environments.
• Dynamo Revit Scripts → Visual programming scripts designed for automated data transfer and integration between GIS and HBIM.

The Dynamo Revit scripts (e.g. Import DB_Module C_Floors.dyn in DYNAMO.zip) originally contained a database connection string, which has been removed for security reasons.
To use the script with a database, users should manually input their connection string in the appropriate section of the script, following this format:
Server=your_server_address; Database=your_database; Uid=your_username; Pwd=your_password.

The geospatial data fusion market is experiencing robust growth, driven by increasing demand for location-based intelligence across various sectors. The convergence of diverse data sources, including satellite imagery, LiDAR, sensor networks, and social media, is fueling innovation in applications such as precision agriculture, urban planning, environmental monitoring, and disaster response. This market's expansion is propelled by advancements in data processing capabilities, cloud computing infrastructure, and the development of sophisticated algorithms for data integration and analysis. While the market size and CAGR were not explicitly provided, a reasonable estimation, considering the industry trends and the presence of key players like Esri, indicates a significant market value, potentially exceeding $5 billion in 2025, with a Compound Annual Growth Rate (CAGR) of around 15% projected through 2033. This growth is further supported by the rising adoption of IoT devices and the increasing availability of high-resolution geospatial data. However, challenges remain. Data interoperability issues, the need for skilled professionals to manage complex datasets, and concerns regarding data security and privacy could potentially restrain market growth. Nevertheless, ongoing technological advancements and the increasing recognition of the value of geospatial insights across various industries are likely to overcome these hurdles, ensuring continued market expansion in the forecast period. The segmentation of the market is likely diverse, encompassing software, services, and hardware solutions tailored to specific industry needs, with further regional variations in adoption rates driven by factors such as infrastructure development and government regulations. The competitive landscape involves established players alongside emerging technology companies, all vying for market share through innovative solutions and strategic partnerships.

Geographic Information System (GIS) Software Market Outlook

According to our latest research, the global Geographic Information System (GIS) Software market size reached USD 11.6 billion in 2024, reflecting a robust demand for spatial data analytics and location-based services across various industries. The market is experiencing a significant growth trajectory, driven by a CAGR of 12.4% from 2025 to 2033. By the end of 2033, the GIS Software market is forecasted to attain a value of USD 33.5 billion. This remarkable expansion is primarily attributed to the integration of advanced technologies such as artificial intelligence, IoT, and cloud computing, which are enhancing the capabilities and accessibility of GIS platforms.

One of the major growth factors propelling the GIS Software market is the increasing adoption of location-based services across urban planning, transportation, and utilities management. Governments and private organizations are leveraging GIS solutions to optimize infrastructure development, streamline resource allocation, and improve emergency response times. The proliferation of smart city initiatives worldwide has further fueled the demand for GIS tools, as urban planners and municipal authorities require accurate spatial data for effective decision-making. Additionally, the evolution of 3D GIS and real-time mapping technologies is enabling more sophisticated modeling and simulation, expanding the scope of GIS applications beyond traditional mapping to include predictive analytics and scenario planning.

Another significant driver for the GIS Software market is the rapid digitization of industries such as agriculture, mining, and oil & gas. Precision agriculture, for example, relies heavily on GIS platforms to monitor crop health, manage irrigation, and enhance yield forecasting. Similarly, the mining sector uses GIS for exploration, environmental impact assessment, and asset management. The integration of remote sensing data with GIS software is providing stakeholders with actionable insights, leading to higher efficiency and reduced operational risks. Furthermore, the growing emphasis on environmental sustainability and regulatory compliance is prompting organizations to invest in advanced GIS solutions for monitoring land use, tracking deforestation, and managing natural resources.

The expanding use of cloud-based GIS solutions is also a key factor driving market growth. Cloud deployment offers scalability, cost-effectiveness, and remote accessibility, making GIS tools more accessible to small and medium enterprises as well as large organizations. The cloud model supports real-time data sharing and collaboration, which is particularly valuable for disaster management and emergency response teams. As organizations increasingly prioritize digital transformation, the demand for cloud-native GIS platforms is expected to rise, supported by advancements in data security, interoperability, and integration with other enterprise systems.

Regionally, North America remains the largest market for GIS Software, accounting for a significant share of global revenues. This leadership is underpinned by substantial investments in smart infrastructure, advanced transportation systems, and environmental monitoring programs. The Asia Pacific region, however, is witnessing the fastest growth, driven by rapid urbanization, government-led digital initiatives, and the expansion of the utility and agriculture sectors. Europe continues to demonstrate steady adoption, particularly in environmental management and urban planning, while Latin America and the Middle East & Africa are emerging as promising markets due to increasing investments in infrastructure and resource management.

Component Analysis

The GIS Software market is segmented by component into Software and Services, each playing a pivotal role in the overall value chain. The software segment includes comprehensive GIS platforms, spatial analytics tools, and specialized applications

Presentation for AWRA Geospatial Technologies Conference May 10, 2022 https://www.awra.org/Members/Events_and_Education/Events/2022_GIS_Conference/2022_GIS_Conference.aspx

HydroShare is a web-based repository and hydrologic information system operated by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) for users to share, collaborate around, and publish data, models, scripts, and applications associated with water related research. It serves as a repository for data and models to meet Findable, Accessible, Interoperable, and Reusable (FAIR) open data mandates. Beyond content storage, the HydroShare repository also links with connected computational systems providing immediate value to users through the ability to reduce the needs for software installation and configuration and to document workflows, enhancing reproducibility. These approaches have facilitated considerable sharing and publication of data associated with research in HydroShare, enabling its re-use and the integration of data from multiple users to support broader synthesis studies. Data types supported include multidimensional netCDF, time series, geographic rasters and features. For some of these, standard data services, such as OpenDAP services for netCDF or Open Geospatial Consortium web services for geographic data types are automatically established when data is made public, improving machine readability and system interoperability. This presentation will describe geospatial data in HydroShare focusing on the geospatial feature and raster aggregations used to hold geospatial data and the functionality developed to automatically harvest metadata from these data types, simplifying the process of metadata creation for users. We will also describe how geospatial data services established for public resources holding geospatial data in HydroShare enable the data to be accessed by third party web applications adding to the functionality supported by HydroShare as a content storage element within a software ecosystem of interoperating systems.

Overview The Office of the Geographer and Global Issues at the U.S. Department of State produces the Large Scale International Boundaries (LSIB) dataset. The current edition is version 11.4 (published 24 February 2025). The 11.4 release contains updated boundary lines and data refinements designed to extend the functionality of the dataset. These data and generalized derivatives are the only international boundary lines approved for U.S. Government use. The contents of this dataset reflect U.S. Government policy on international boundary alignment, political recognition, and dispute status. They do not necessarily reflect de facto limits of control. National Geospatial Data Asset This dataset is a National Geospatial Data Asset (NGDAID 194) managed by the Department of State. It is a part of the International Boundaries Theme created by the Federal Geographic Data Committee. Dataset Source Details Sources for these data include treaties, relevant maps, and data from boundary commissions, as well as national mapping agencies. Where available and applicable, the dataset incorporates information from courts, tribunals, and international arbitrations. The research and recovery process includes analysis of satellite imagery and elevation data. Due to the limitations of source materials and processing techniques, most lines are within 100 meters of their true position on the ground. Cartographic Visualization The LSIB is a geospatial dataset that, when used for cartographic purposes, requires additional styling. The LSIB download package contains example style files for commonly used software applications. The attribute table also contains embedded information to guide the cartographic representation. Additional discussion of these considerations can be found in the Use of Core Attributes in Cartographic Visualization section below. Additional cartographic information pertaining to the depiction and description of international boundaries or areas of special sovereignty can be found in Guidance Bulletins published by the Office of the Geographer and Global Issues: https://data.geodata.state.gov/guidance/index.html Contact Direct inquiries to internationalboundaries@state.gov. Direct download: https://data.geodata.state.gov/LSIB.zip Attribute Structure The dataset uses the following attributes divided into two categories: ATTRIBUTE NAME | ATTRIBUTE STATUS CC1 | Core CC1_GENC3 | Extension CC1_WPID | Extension COUNTRY1 | Core CC2 | Core CC2_GENC3 | Extension CC2_WPID | Extension COUNTRY2 | Core RANK | Core LABEL | Core STATUS | Core NOTES | Core LSIB_ID | Extension ANTECIDS | Extension PREVIDS | Extension PARENTID | Extension PARENTSEG | Extension These attributes have external data sources that update separately from the LSIB: ATTRIBUTE NAME | ATTRIBUTE STATUS CC1 | GENC CC1_GENC3 | GENC CC1_WPID | World Polygons COUNTRY1 | DoS Lists CC2 | GENC CC2_GENC3 | GENC CC2_WPID | World Polygons COUNTRY2 | DoS Lists LSIB_ID | BASE ANTECIDS | BASE PREVIDS | BASE PARENTID | BASE PARENTSEG | BASE The core attributes listed above describe the boundary lines contained within the LSIB dataset. Removal of core attributes from the dataset will change the meaning of the lines. An attribute status of “Extension” represents a field containing data interoperability information. Other attributes not listed above include “FID”, “Shape_length” and “Shape.” These are components of the shapefile format and do not form an intrinsic part of the LSIB. Core Attributes The eight core attributes listed above contain unique information which, when combined with the line geometry, comprise the LSIB dataset. These Core Attributes are further divided into Country Code and Name Fields and Descriptive Fields. County Code and Country Name Fields “CC1” and “CC2” fields are machine readable fields that contain political entity codes. These are two-character codes derived from the Geopolitical Entities, Names, and Codes Standard (GENC), Edition 3 Update 18. “CC1_GENC3” and “CC2_GENC3” fields contain the corresponding three-character GENC codes and are extension attributes discussed below. The codes “Q2” or “QX2” denote a line in the LSIB representing a boundary associated with areas not contained within the GENC standard. The “COUNTRY1” and “COUNTRY2” fields contain the names of corresponding political entities. These fields contain names approved by the U.S. Board on Geographic Names (BGN) as incorporated in the ‘"Independent States in the World" and "Dependencies and Areas of Special Sovereignty" lists maintained by the Department of State. To ensure maximum compatibility, names are presented without diacritics and certain names are rendered using common cartographic abbreviations. Names for lines associated with the code "Q2" are descriptive and not necessarily BGN-approved. Names rendered in all CAPITAL LETTERS denote independent states. Names rendered in normal text represent dependencies, areas of special sovereignty, or are otherwise presented for the convenience of the user. Descriptive Fields The following text fields are a part of the core attributes of the LSIB dataset and do not update from external sources. They provide additional information about each of the lines and are as follows: ATTRIBUTE NAME | CONTAINS NULLS RANK | No STATUS | No LABEL | Yes NOTES | Yes Neither the "RANK" nor "STATUS" fields contain null values; the "LABEL" and "NOTES" fields do. The "RANK" field is a numeric expression of the "STATUS" field. Combined with the line geometry, these fields encode the views of the United States Government on the political status of the boundary line. ATTRIBUTE NAME | | VALUE | RANK | 1 | 2 | 3 STATUS | International Boundary | Other Line of International Separation | Special Line A value of “1” in the “RANK” field corresponds to an "International Boundary" value in the “STATUS” field. Values of ”2” and “3” correspond to “Other Line of International Separation” and “Special Line,” respectively. The “LABEL” field contains required text to describe the line segment on all finished cartographic products, including but not limited to print and interactive maps. The “NOTES” field contains an explanation of special circumstances modifying the lines. This information can pertain to the origins of the boundary lines, limitations regarding the purpose of the lines, or the original source of the line. Use of Core Attributes in Cartographic Visualization Several of the Core Attributes provide information required for the proper cartographic representation of the LSIB dataset. The cartographic usage of the LSIB requires a visual differentiation between the three categories of boundary lines. Specifically, this differentiation must be between: International Boundaries (Rank 1); Other Lines of International Separation (Rank 2); and Special Lines (Rank 3). Rank 1 lines must be the most visually prominent. Rank 2 lines must be less visually prominent than Rank 1 lines. Rank 3 lines must be shown in a manner visually subordinate to Ranks 1 and 2. Where scale permits, Rank 2 and 3 lines must be labeled in accordance with the “Label” field. Data marked with a Rank 2 or 3 designation does not necessarily correspond to a disputed boundary. Please consult the style files in the download package for examples of this depiction. The requirement to incorporate the contents of the "LABEL" field on cartographic products is scale dependent. If a label is legible at the scale of a given static product, a proper use of this dataset would encourage the application of that label. Using the contents of the "COUNTRY1" and "COUNTRY2" fields in the generation of a line segment label is not required. The "STATUS" field contains the preferred description for the three LSIB line types when they are incorporated into a map legend but is otherwise not to be used for labeling. Use of the “CC1,” “CC1_GENC3,” “CC2,” “CC2_GENC3,” “RANK,” or “NOTES” fields for cartographic labeling purposes is prohibited. Extension Attributes Certain elements of the attributes within the LSIB dataset extend data functionality to make the data more interoperable or to provide clearer linkages to other datasets. The fields “CC1_GENC3” and “CC2_GENC” contain the corresponding three-character GENC code to the “CC1” and “CC2” attributes. The code “QX2” is the three-character counterpart of the code “Q2,” which denotes a line in the LSIB representing a boundary associated with a geographic area not contained within the GENC standard. To allow for linkage between individual lines in the LSIB and World Polygons dataset, the “CC1_WPID” and “CC2_WPID” fields contain a Universally Unique Identifier (UUID), version 4, which provides a stable description of each geographic entity in a boundary pair relationship. Each UUID corresponds to a geographic entity listed in the World Polygons dataset. These fields allow for linkage between individual lines in the LSIB and the overall World Polygons dataset. Five additional fields in the LSIB expand on the UUID concept and either describe features that have changed across space and time or indicate relationships between previous versions of the feature. The “LSIB_ID” attribute is a UUID value that defines a specific instance of a feature. Any change to the feature in a lineset requires a new “LSIB_ID.” The “ANTECIDS,” or antecedent ID, is a UUID that references line geometries from which a given line is descended in time. It is used when there is a feature that is entirely new, not when there is a new version of a previous feature. This is generally used to reference countries that have dissolved. The “PREVIDS,” or Previous ID, is a UUID field that contains old versions of a line. This is an additive field, that houses all Previous IDs. A new version of a feature is defined by any change to the

The global spatial information service market, valued at $3,360 million in 2025, is projected to experience robust growth, driven by increasing demand for location-based services across diverse sectors. The Compound Annual Growth Rate (CAGR) of 12.8% from 2025 to 2033 indicates significant expansion potential. Key drivers include the rising adoption of cloud-based solutions offering scalability and cost-effectiveness, the proliferation of smart city initiatives relying heavily on spatial data for efficient urban planning and management, and the increasing use of geospatial analytics for informed decision-making in areas such as precision agriculture, logistics, and disaster response. Market segmentation reveals strong growth in both city and rural applications, with cloud-based solutions gaining wider acceptance over on-premise deployments. Leading companies like Esri, Hexagon AB, and Trimble are shaping the market landscape through continuous innovation and strategic partnerships, while emerging players like Planet Labs are contributing to increased data availability and analytical capabilities. Regional analysis suggests North America and Europe will maintain a significant market share, but Asia-Pacific is poised for substantial growth fueled by rapid urbanization and technological advancements. The market’s continued expansion will be influenced by factors such as advancements in sensor technologies, improving data processing capabilities, and increasing government investments in geospatial infrastructure. The restraints on market growth are primarily related to data security and privacy concerns surrounding the use of sensitive location data. High initial investment costs for implementing complex spatial information systems, especially for smaller organizations, also present a barrier. Furthermore, the interoperability challenges between different spatial data formats and systems require addressing to ensure seamless data sharing and integration. However, these challenges are being actively addressed through the development of industry standards and robust security protocols. Ongoing advancements in artificial intelligence and machine learning are expected to further enhance the analytical capabilities of spatial information services, leading to more sophisticated applications and expanded market opportunities. The forecast period of 2025-2033 suggests a substantial market expansion, exceeding $10 billion, driven by the continuous integration of spatial data into various applications and the increasing need for precise location intelligence.

A combination of stormwater system data throughout Stark County, Ohio. The data is combined using an ETL via the data interoperability extension for ArcGIS Pro. Each weekend, the ETL is automatically ran via Python/Windows Task Scheduler to update the data with any changes from the past week from each of the source datasets. The source data is stored in ArcGIS SDE databases that Stark County GIS (SCGIS) provides for departments, cities, villages, and townships within the county. SCGIS currently maintains SDE databases for Canton, Alliance, Louisville, North Canton, Beach City, Easton Canton, Minerva, Meyers Lake, Stark County Engineer (SCE), and each of the townships. In addition to those datasets (which are updated weekly), this layer also includes data from the cities of Massillon and Canal Fulton, which are not stored in databases maintained by SCGIS. Data for those two cities is updated separately as new iterations become available.As this layer encompasses the entire county, source feature classes are consolidated into 4 layers to improve performance on ArcGIS Online. Discharge points are the point at which water exits part of the stormwater system, such as the outlet of a pipe or ditch. It includes outfalls defined under NPDES Phase II. Structures includes both inlets (catch basins, yard drains, etc.) and manholes. Pipes includes storm sewers, as well as culverts (pipes in which both ends are daylit). Finally, the ditches layer includes roadside ditches, as well as off-road ditches in some areas/instances.

The geospatial solutions market, valued at $214,710 million in 2025, is projected to experience robust growth, driven by increasing adoption across diverse sectors. A Compound Annual Growth Rate (CAGR) of 7.2% from 2025 to 2033 indicates a significant expansion of this market. Key drivers include the rising demand for precise location intelligence in urban planning, infrastructure development, and resource management. The integration of advanced technologies like AI, machine learning, and IoT further fuels market growth, enhancing data analytics and decision-making capabilities. The market is segmented by hardware, software, and services, catering to applications in utility, business, transportation, defense, infrastructure, natural resources, and other sectors. North America currently holds a significant market share due to high technological advancements and substantial investments in geospatial technologies. However, the Asia-Pacific region is expected to witness rapid growth fueled by increasing urbanization and infrastructure projects. Competition is fierce, with major players including HERE Technologies, Esri, Hexagon, and Google vying for market dominance. The ongoing development of high-resolution imagery, improved data processing capabilities, and cloud-based solutions are shaping the future of the geospatial solutions landscape. The restraints to market growth include high initial investment costs for advanced technologies, concerns about data privacy and security, and the need for skilled professionals to manage and interpret complex geospatial data. However, the growing awareness of the benefits of geospatial solutions, coupled with ongoing technological advancements, is expected to mitigate these challenges. The increasing availability of open-source geospatial data and software is also democratizing access to these technologies, driving wider adoption across various industries and geographical regions. Future growth will depend on successful integration with emerging technologies, expanding the applications of geospatial data analysis, and addressing concerns related to data accessibility, security, and interoperability. The market’s evolution is likely to be characterized by increased collaboration between technology providers, government agencies, and private sector organizations.

The ArcGIS Data Interoperability extension enables you to work with data stored in a significant number of formats that are native and non-native to ArcGIS. From a simple translation between two formats to complex transformations on data content and structure, this extension provides the solution to overcome interoperability barriers.After completing this course, you will be able to:Use existing translation parameters to control data translations.Translate multiple datasets at once.Use parameters to change the coordinate system of the data.

This reference data provides a standard list of values for all Countries, Territories and Geographic areas. This list is intended to standardize the way Countries, Territories and Geographic areas are described in datasets to enable data interoperability and improve data quality. The data dictionary explains what each column means in the list.

OBIS-USA provides aggregated, interoperable biogeographic data collected primarily from U.S. waters and oceanic regions--the Arctic, the Atlantic and Pacific oceans, the Caribbean Sea, Gulf of Mexico and the Great Lakes. It provides access to datasets from state and federal agencies as well as educational and research institutions. OBIS-USA handles both specimen-based data and survey results. Survey data come from recovered archives and current research activities. The datasets document where and when species were observed or collected, bringing together marine biogeographic data that are spatially, taxonomically, and temporally comprehensive. The public OBIS-USA site (http://www.usgs.gov/obis-usa) provides actual data contents as well as summary data about what is contained in each dataset to assist users in evaluating suitability for use. Current functionality allows the user to locate, view, and aggregate the datasets and FGDC compliant metadata as well as to view and search the taxonomic, geographic, and temporal extent. To promote data interoperability, the data are available in accordance with the marine-focused implementation of the Darwin Core data standard. In addition to basic download functions (tab-delimited), OBIS-USA offers web services for query flexibility and a wide range of output formats, such as kml, NetCDF, MATLAB, json, and graph or map output, to enable diverse types of scientific and geospatial data use and analysis platforms and products. OBIS-USA's two web services (ERDDAP and GeoServer) enable integration of OBIS-USA biogeographic data with other data types, such as seafloor geology, physical oceanography, water chemistry, and climate data. The NOAA Environmental Research Division Data Access Program(ERRDDAP) enables users to query scientific data by flexible parameters and obtain output in many formats. Access can be found at http://www1.usgs.gov/erddap/tabledap/AllMBG.html . OBIS-USA uses the tabledap component of ERDDAP to access Darwin-Core-type tabular spatial data; tabledap is a superset of the OPeNDAP DAP constraint protocol. OBIS-USA offers an ESRI REST Service with access to Darwin-Core-type point data at http://gis1.usgs.gov/arcgis/rest/services/OBISUSA/OBIS_USA_All_Marine_Biogeographic_Records/MapServer/ and an OGC compliant Web Mapping Service (wms) http://gis1.usgs.gov/arcgis/services/OBISUSA/OBIS_USA_All_Marine_Biogeographic_Records/MapServer/WMSServer?request=GetCapabilities&service=WMS. OBIS-USA and collaborators are further deploying the Darwin Core standard to capture richer information, such as absence and abundance, observations on effort, individual tracking, and more advanced biogeography capabilities. Data are accepted into OBIS-USA from the data originator or holder, minimizing the burden on the participant. OBIS-USA works with data providers to understand the best process to transfer the data, review the data prior to their release, gather comprehensive metadata, and then allow public access to this information. Becoming part of the OBIS-USA network is intended to have tangible benefits for participants, for example, freeing the participant from responding to requests for data and alleviating security concerns since users do not directly access the participant's computers.

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

The New Jersey Office of Information Technology, Office of GIS (NJOGIS), in partnership with several local GIS and public safety agencies, has built a comprehensive statewide NG9-1-1 database meeting and exceeding the requirements of the National Emergency Number Association (NENA) 2018 NG9-1-1 GIS Data Standard (NENA-STA-006.1-2018). The existing New Jersey Statewide Address Point data last published in 2016 has been transformed in the NENA data model to create this new address point data.The initial address points were processed from statewide parcel records joined with the statewide Tax Assessor's (MOD-IV) database in 2015. Address points supplied by Monmouth County, Sussex County, Morris County and Montgomery Township in Somerset County were incorporated into the statewide address points using customized Extract, Transform and Load (ETL) procedures.The previous version of the address points was loaded into New Jersey's version of the NENA NG9-1-1 data model using Extract, Transform and Load (ETL) procedures created with Esri's Data Interoperability Extension. Subsequent manual and bulk processing corrections and additions have been made, and are ongoing.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

Open-Source GIS plays a pivotal role in advancing GIS education, fostering research collaboration, and supporting global sustainability by enabling the sharing of data, models, and knowledge. However, the integration of big data, deep learning methods, and artificial intelligence deep learning in geospatial research presents significant challenges for GIS education. These include increasing software learning costs, higher computational power demand, and the management of fragmented information in the Web 2.0 context. Addressing these challenges while integrating emerging GIS innovations and restructuring GIS knowledge systems is crucial for the evolution of GIS Education 3.0. This study introduces a Visual Programming-based Geospatial Cyberinfrastructure (V-GCI) framework, integrated with the replicable and reproducible (R&R) framework, to enhance GIS function compatibility, learning scalability, and web GIS application interoperability. Through a case study on spatial accessibility using the generalized two-step floating catchment area method (G2SFCA), this paper demonstrates how V-GCI can reshape the GIS knowledge tree and its potential to enhance replicability and reproducibility within open-source GIS Education 3.0.

The GIS Controller market is growing tremendously with the surging adoption rates of smart grids, automation, and advanced electrical systems for distribution purposes. GIS controls are an inevitable part of high-voltage gis systems along with monitoring needs to ensure well-transmitted electric power and distributive needs around utilities, industrial manufacturing, as well as any other energy distribution sectors.Fueled by the requirement for enhancements in reliability, minimization of maintenance cost, and better performance in the power systems, demand in GIS controllers space is presently being fueled. Integration along with smart grids in line with the renewable sources further helps to propel the market growth-for real-time monitoring and control of electrical networks.Some of the main market trends include automation, digitalization, and energy efficiency. Significant adoption is happening in North America, Europe, and Asia-Pacific. High installation costs and the complexity in maintenance continue to cause hurdles in the growth of the market. Innovations that can be helpful in such issues are expected from the manufacturer.Concentration & Characteristics Key drivers for this market are: Increasing demand for geospatial data in decision-making. Technological advancements enhancing data processing capabilities. Government initiatives promoting GIS adoption. Growing investment in infrastructure and development projects.. Potential restraints include: Data interoperability and standardization issues. Limited technical expertise in some sectors. Security breaches and data privacy concerns.. Notable trends are: GIS controllers enable real-time data acquisition and visualization, enhancing decision-making. Cloud computing provides scalability, flexibility, and cost-effectiveness. GIS controllers integrate with IoT devices and analyze large datasets to provide actionable insights. Increased focus on protecting sensitive geospatial data due to privacy concerns..

The Government Information Construction Service market is experiencing robust growth, driven by increasing government initiatives to modernize infrastructure, enhance citizen services, and improve data management capabilities. The market's expansion is fueled by a rising need for efficient and secure data handling, particularly in the context of smart city development and the increasing adoption of cloud-based solutions. This shift towards cloud-based services offers scalability, cost-effectiveness, and improved accessibility, surpassing traditional on-premises systems. While the initial investment for cloud migration can be substantial, the long-term benefits in terms of reduced maintenance costs and enhanced agility are compelling government agencies to embrace this technology. Furthermore, the growing adoption of data analytics and artificial intelligence (AI) within government operations is further fueling market growth, enabling better decision-making and enhanced service delivery. However, challenges remain, including concerns about data security, interoperability issues across different systems, and the need for skilled professionals to manage and maintain these complex systems. Regional variations exist within the market, with North America and Europe currently holding the largest market share, due to advanced digital infrastructure and high government spending on IT initiatives. However, Asia-Pacific is emerging as a region with significant growth potential, driven by substantial investments in digital transformation across various governments within the region. The market is segmented by application (city and rural) and deployment type (cloud-based and on-premises). Cloud-based solutions are witnessing rapid adoption, while on-premises deployments remain relevant, particularly in sectors with stringent security requirements. Key players like IBM, Microsoft, SAP, Oracle, and Accenture are actively involved in providing solutions, fostering competition and innovation within the sector. The forecast period (2025-2033) anticipates sustained growth, propelled by continued digital transformation efforts and the increasing importance of data-driven governance. Let's assume a 2025 market size of $15 billion, with a CAGR of 12% for the forecast period. This implies a substantial market expansion by 2033.

Geospatial analyses of human-environment interactions are challenged by the multi-scale, multi-dimensional nature of human-environment systems. Research in such contexts must often rely on integrating multiple, independently produced data sources, which presents heterogenous data qualities and interoperability challenges. Understanding data quality and transparency becomes increasingly important in these contexts, and multi‐granularity and context specific spatial data quality indicators are needed. We develop a data pedigree system that accounts for multiple data quality aspects, geospatial ambiguities that may hinder interoperability, and the fitness-for-use of each data source for indicating causal linkages between human activities and environmental change. We demonstrate its application to a particularly challenging and data sparse case study of identifying the location and timing of transnational cocaine trafficking, or ‘narco-trafficking’, in Central America with five spatial and temporal data quality indicators: geographic clarity, geographic interpretation, provenance, temporal specificity, and narco-trafficking certainty. The proposed data pedigree system provides a systematic and coherent analytical framework for interoperability, comparison, and corroboration of fragmented and incomplete data, which are needed to support advanced geospatial analyses, such as causal inference techniques. The study demonstrates the transferability and operationalization of the data pedigree system for examining complex human-environment interactions, especially those influenced by illicit economies.