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.

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.

Building information modeling (BIM) allows representation of detailed information regarding building elements while geographic information system (GIS) allows representation of spatial information about buildings and their surroundings. Overlapping these domains will combine their individual features and provide support to important activities such as building emergency response, construction site safety, construction supply chain management, and sustainable urban design. Interoperability through open data standards is one method of connecting software tools from BIM and GIS domains. However, no single open data standard available today can support all information from the two domains. As a result, many researchers have been working to overlap or connect different open data standards to enhance interoperability. An overview of these studies will help identify the different approaches used and determine the approach with the most potential to enhance interoperability. This paper adopted a strong definition of interoperability using information technology (IT) based standard documents. Based on this definition, previous approaches towards improving interoperability between BIM and GIS applications through open data standards were studied. The result shows previous approaches have implemented data conversion, data integration, and linked data approaches. Between these methods, linked data emerged as having the most potential to connect open data standards and expand interoperability between BIM and GIS applications because it allows information exchange without editing the original data. The paper also identifies the main challenges in implementing linked data technologies for interoperability and provides directions for future research.

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

High-quality GIS land use maps for the Twin Cities Metropolitan Area for 1968 that were developed from paper maps (no GIS version existed previously).The GIS shapefiles were exported using ArcGIS Quick Import Tool from the Data Interoperability Toolbox. The coverage files was imported into a file geodatabase then exported to a .shp file for long-term use without proprietary software. An example output of the final GIS file is include as a pdf, in addition, a scan of the original 1968 map (held in the UMN Borchert Map Library) is included as a pdf. Metadata was extracted as an xml file. Finally, all associated coverage files and original map scans were zipped into one file for download and reuse. Data was uploaded to ArcGIS Online 3/9/2020. Original dataset available from the Data Repository of the University of Minnesota: http://dx.doi.org/10.13020/D63W22

The BIM Software Market is booming, projected to reach [estimated 2033 value in billions] by 2033, growing at a CAGR of 13.90%. Discover key trends, drivers, and leading companies shaping this dynamic sector. Learn more about market segmentation, regional analysis, and future projections for BIM software adoption. Recent developments include: July 2024 - Esri and Autodesk have deepened their partnership to enhance data interoperability between Geographic Information Systems (GIS) and Building Information Modeling (BIM), with ArcGIS Pro now offering direct-read support for BIM and CAD elements from Autodesk's tools. This collaboration aims to integrate GIS and BIM workflows more seamlessly, potentially transforming how architects, engineers, and construction professionals work with geospatial and design data in the AEC industry., June 2024 - Hexagon, the Swedish technology giant, has acquired Voyansi, a Cordoba-based company specializing in Building Information Modelling (BIM), to enhance its portfolio of BIM solutions. This acquisition not only strengthens Hexagon's position in the global BIM market but also recognizes the talent in Argentina's tech sector, particularly in Córdoba, where Voyansi has been developing design, architecture, and engineering services for global construction markets for the past 15 years., April 2024 - Hyundai Engineering has partnered with Trimble Solution Korea to co-develop a Building Information Modeling (BIM) process management program, aiming to enhance construction site productivity through advanced 3D modeling technology. This collaboration highlights the growing importance of BIM in the construction industry, with the potential to optimize steel structure and precast concrete construction management, shorten project timelines, and reduce costs compared to traditional construction methods.. Key drivers for this market are: Governmental Mandates and International Standards Encouraging BIM Adoption, Boosting Project Performance and Productivity. Potential restraints include: Governmental Mandates and International Standards Encouraging BIM Adoption, Boosting Project Performance and Productivity. Notable trends are: Government Mandates Fueling BIM Growth.

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.

Utility Network GIS Migration Market Outlook

According to our latest research, the global Utility Network GIS Migration market size reached USD 2.04 billion in 2024, with a robust compound annual growth rate (CAGR) of 13.2% projected for the period from 2025 to 2033. By 2033, the market is anticipated to attain a value of USD 5.67 billion. The primary growth factor driving this surge is the increasing need for utilities to modernize legacy Geographic Information Systems (GIS) and integrate advanced digital mapping, asset management, and real-time data analytics to enhance operational efficiency and regulatory compliance.

One of the key growth drivers for the Utility Network GIS Migration market is the accelerating pace of digital transformation across utility sectors such as electricity, water, gas, and telecommunications. Utilities are under immense pressure to improve service reliability, reduce operational costs, and comply with evolving regulatory frameworks. The migration from traditional GIS platforms to next-generation utility network GIS solutions enables organizations to leverage spatial analytics, automate workflows, and support the integration of smart grid technologies. The proliferation of distributed energy resources, IoT devices, and the need for advanced outage management systems have further intensified the demand for robust and scalable GIS migration strategies. Utilities are increasingly prioritizing the modernization of their spatial data infrastructure to ensure seamless data flow, improve asset tracking, and enhance customer engagement, thereby fueling market expansion.

Another significant growth factor is the rising adoption of cloud-based GIS solutions, which offer utilities unparalleled flexibility, scalability, and cost-effectiveness. Cloud deployment models enable utilities to efficiently manage and analyze vast volumes of spatial and non-spatial data without the burden of maintaining on-premises infrastructure. This shift not only reduces capital expenditure but also accelerates the deployment of new functionalities and ensures rapid disaster recovery. Moreover, cloud-based GIS platforms facilitate real-time collaboration among field and office teams, enabling faster decision-making and improving response times during emergencies. The growing emphasis on sustainability, grid modernization, and the integration of renewable energy sources is prompting utilities to invest in cloud-enabled GIS migration projects to future-proof their operations and achieve long-term operational excellence.

The increasing regulatory focus on data accuracy, cybersecurity, and interoperability is also propelling the Utility Network GIS Migration market. Regulatory bodies worldwide are mandating utilities to maintain precise and up-to-date spatial data for effective asset management, outage response, and infrastructure planning. As a result, utilities are compelled to migrate from outdated GIS systems to advanced platforms that offer robust data governance, security, and integration capabilities. The need to comply with standards such as the Common Information Model (CIM) and industry-specific regulations is driving utilities to adopt sophisticated GIS migration strategies. Furthermore, the emergence of advanced technologies such as artificial intelligence, machine learning, and big data analytics is enabling utilities to extract deeper insights from spatial data, optimize maintenance schedules, and proactively address infrastructure vulnerabilities, thereby fostering market growth.

From a regional perspective, North America continues to dominate the Utility Network GIS Migration market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The rapid modernization of utility infrastructure, extensive deployment of smart grids, and the presence of leading GIS solution providers have positioned North America at the forefront of market growth. In Europe, stringent regulatory mandates and the push for sustainable energy transition are driving significant investments in GIS migration projects. Meanwhile, the Asia Pacific region is witnessing the fastest growth, fueled by large-scale infrastructure development, urbanization, and increasing government initiatives to improve utility services. The Middle East & Africa and Latin America are also emerging as promising markets, supported by ongoing digitalization efforts and investments in utility infrastructure upgrades.

Component Analysis

The OGC (Open Geospatial Consortium) standard API, which is an international standard developed for the sharing and interoperability of spatial data, enables efficient provision and use of various geographic information such as maps, features, and rasters on the web. The latest OGC API adopts a RESTful structure to enhance development convenience and expandability, and inherits existing standards such as WMS and WFS in a modern way.

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.

Esri ArcGIS Mission for Defense Market Outlook

According to our latest research, the global Esri ArcGIS Mission for Defense market size in 2024 stands at USD 2.14 billion, with a robust CAGR of 10.2% projected through the forecast period. By 2033, the market is expected to reach USD 5.1 billion as per our CAGR calculations. This growth is primarily driven by the escalating demand for advanced geospatial intelligence and real-time situational awareness solutions in defense and security operations worldwide. The increasing complexity of modern warfare, coupled with the integration of digital transformation strategies within defense sectors, is fueling significant investments in cutting-edge mission management platforms such as Esri ArcGIS Mission. As per our latest research, the market’s upward trajectory is further supported by the growing emphasis on interoperability, data-driven decision-making, and the need for seamless collaboration among defense forces and allied agencies.

A key growth factor for the Esri ArcGIS Mission for Defense market is the rapid evolution of modern warfare tactics and the proliferation of asymmetric threats. Defense agencies are increasingly prioritizing real-time geospatial intelligence and mission planning capabilities to respond effectively to dynamic and unpredictable operational environments. The integration of Esri ArcGIS Mission enables defense forces to visualize, analyze, and share mission-critical data, thereby enhancing situational awareness and operational agility. Furthermore, the adoption of artificial intelligence and machine learning within geospatial platforms is empowering defense organizations to automate threat detection, optimize resource allocation, and streamline mission execution, thereby driving the adoption of advanced GIS solutions at a global scale.

Another significant driver is the expanding role of multi-domain operations (MDO) and the need for cross-agency collaboration in defense missions. The Esri ArcGIS Mission platform is uniquely positioned to facilitate real-time collaboration among diverse defense units, including the army, navy, air force, and homeland security agencies. By providing a unified operational picture, the platform enhances inter-agency coordination and supports joint mission planning, execution, and debriefing. The increasing frequency of multinational exercises and coalition operations further underscores the importance of interoperable mission management solutions that can seamlessly integrate data from disparate sources and deliver actionable intelligence to commanders in the field.

The ongoing digital transformation initiatives within defense ministries and intelligence agencies are also propelling market expansion. Governments worldwide are investing heavily in upgrading their command, control, communications, computers, intelligence, surveillance, and reconnaissance (C4ISR) infrastructure, with a focus on leveraging geospatial analytics for strategic advantage. Esri ArcGIS Mission’s ability to ingest, process, and visualize vast volumes of geospatial data in real time is proving indispensable for defense agencies seeking to enhance operational efficiency, reduce response times, and mitigate risks. Additionally, the growing adoption of cloud-based deployment models is enabling defense organizations to scale their mission management capabilities rapidly, improve data accessibility, and ensure business continuity during critical operations.

Regionally, North America continues to dominate the Esri ArcGIS Mission for Defense market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The United States, in particular, is a major contributor to market growth, driven by substantial defense budgets, advanced technological infrastructure, and the presence of leading GIS solution providers. Europe is witnessing steady adoption of mission management platforms, supported by collaborative defense initiatives and modernization programs across NATO member states. Meanwhile, Asia Pacific is emerging as a high-growth region, fueled by rising geopolitical tensions, increased defense spending, and a growing focus on indigenous technology development in countries such as China, India, and Japan.

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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, data storytelling, geospati

The size of the GIS Controller Market market was valued at USD 30.081 billion in 2024 and is projected to reach USD 79.53 billion by 2033, with an expected CAGR of 14.90% during the forecast period. 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..

Spatial Data Infrastructure Market Outlook

According to our latest research, the global Spatial Data Infrastructure (SDI) market size reached USD 2.61 billion in 2024. The market is projected to expand at a robust CAGR of 14.6% from 2025 to 2033, reaching a forecasted value of USD 8.16 billion by 2033. This accelerated growth is primarily driven by the increasing integration of geospatial technologies in urban planning, disaster management, and environmental monitoring, as well as the rising demand for real-time spatial data across various end-user industries. The proliferation of smart city initiatives and advancements in cloud computing are further catalyzing the adoption of SDI solutions globally.

One of the most significant growth factors for the Spatial Data Infrastructure market is the surging demand for advanced geospatial analytics in urban planning and management. With rapid urbanization and the emergence of smart cities, governments and organizations are increasingly investing in technologies that facilitate efficient spatial data collection, sharing, and analysis. SDI platforms enable seamless data interoperability and integration across multiple agencies, supporting informed decision-making for land use, infrastructure development, and resource allocation. The availability of high-resolution satellite imagery and the adoption of IoT-enabled sensors are enhancing the granularity and accuracy of spatial data, further boosting the marketÂ’s growth trajectory.

Another critical driver for the SDI market is the growing necessity for robust disaster management and environmental monitoring systems. Natural disasters and climate change events are becoming more frequent and severe, necessitating real-time spatial data for effective risk assessment, emergency response, and recovery planning. SDI solutions empower authorities to map vulnerable zones, monitor environmental changes, and coordinate rescue operations efficiently. Furthermore, the increasing integration of artificial intelligence and machine learning algorithms with SDI platforms is enabling predictive analytics and automated anomaly detection, thereby strengthening disaster preparedness and mitigation strategies across regions.

The exponential rise in digital transformation initiatives across industries is also fueling the demand for spatial data infrastructure solutions. Sectors such as transportation, utilities, and commercial enterprises are leveraging SDI to optimize asset management, enhance operational efficiency, and improve customer experiences. The transition from traditional on-premises deployments to scalable cloud-based SDI solutions is making spatial data more accessible and cost-effective, especially for small and medium enterprises. Additionally, the growing emphasis on open data policies and interoperability standards by governments and international organizations is fostering a collaborative ecosystem, which is essential for the sustainable growth of the SDI market.

From a regional perspective, North America continues to dominate the Spatial Data Infrastructure market, driven by substantial investments in smart infrastructure, strong government support, and the presence of leading technology providers. Europe follows closely, with significant advancements in environmental monitoring and urban planning initiatives. Meanwhile, the Asia Pacific region is witnessing the fastest growth, propelled by rapid urbanization, large-scale infrastructure projects, and increasing adoption of digital technologies in emerging economies. Latin America and the Middle East & Africa are also experiencing steady growth, supported by ongoing digitalization efforts and international collaborations in spatial data management.

Geospatial Data Management is becoming increasingly vital in the context of Spatial Data Infrastructure (SDI) as it underpins the effective collection, storage, and dissemination of spatial information. With the proliferation of data sources such as satellite imagery, drones, and IoT devices, managing this vast amount of geospatial data efficiently is crucial for enabling real-time analytics and decision-making. Organizations are investing in advanced geospatial data management systems to ensure data accuracy, consistency, and accessibility, which are essential for applications ranging from urban planning to disaster mana

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 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.

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 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.

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.