2023-26 Strategic Plan for Geospatial Data Management; Approved by the Oregon Geographic Information Council January 2023.

SDI | Data | Data Governance | News |NGA releases new data strategy to navigate digital, GEOINT revolution SPRINGFIELD, Virginia — The National Geospatial-Intelligence Agency published the agency’s data strategy Oct. 6, outlining its plans to transform and improve the way data is created, managed and shared in order to maintain dominance in the delivery of geospatial intelligence. “It is essential that we take all actions necessary to sustain our advantage in GEOINT — and that includes managing our data as a key strategic asset,’’ stated NGA Director Vice Adm. Robert Sharp in the data strategy. “With the holistic enterprise approach mapped out within this new data strategy, NGA sets forth a path for leading the way and staying ahead of our competitors.’’ The NGA Data Strategy 2021, a 28-page public document, includes both strategic goals and courses of action for the agency as it continues to chart a secure and innovative path forward while facing increasing amounts of data, risk and competition. Aligned to the agency’s Moonshot effort to “deliver trusted GEOINT with the speed, accuracy and precision required,’’ the strategy calls for the accelerated, shared and trusted use of data to help NGA better deliver on its mandates and show the way. The plan, created as a companion document to the NGA Technology Strategy published in 2020, already has played an integral role in the agency’s recent adoption of a new data governance structure to provide a coordinated framework for data policies and stewardship. The data strategy, combined with the established collaborative data governance program, guides the agency’s push to close the gap between current and future capabilities by accelerating developments in four significant focus areas: making data easily accessible, improving data reusability, improving cross-domain efficiencies and enabling next-generation GEOINT. The strategy describes four key goals being pursued by NGA to meet its mission and business needs. To achieve its desired results, the agency seeks to: — Manage data as a strategic asset: Deploy a federated enterprise data governance framework that ensures data is proactively, strategically and consistently managed while enabling agility, flexibility and innovation. Relationship to SDI'sThis reference resource provides a reference resource for SDI related activities in the intelligence community.The National Geospatial Intelligence Agency is a Federal participating organization in the Federal Geographic Data Committee. A Senior NGA Representative is a member of the FGDC Executive Committee A Senior NGA Representative is appointed by the Secretary of Interior to the National Geospatial Advisory Committee established in the Geospatial Data Act of 2018 "The head of each covered agency and the Director of the National Geospatial-Intelligence Agency shall each designate a representative of their respective agency to serve as a member of the Committee."The Geospatial Data Act of 2018 U.S.C 2804 Geospatial Standards, requires FGDC to "shall include universal data standards that shall be acceptable for the purposes of declassified intelligence community data"Additional ResourcesFederal Geographic Data CommitteeNational Geospatial Advisory CommitteeNational Geospatial Intelligence Agency National Geospatial Intelligence Agency Products and ServicesFGDC Standards

Managing 245 million acres of land and 700 million acres of mineral estate is a big task. The BLM recognizes that geospatial information is a critical tool for managing public lands. We’ve already made great strides in creating national datasets, supporting almost every program in the Bureau. The BLM has adopted a ground-up approach to managing public lands, and the geospatial program is providing the structure and tools to accomplish this strategy. We manage spatial data to support multiple activities at varying scales.

The BLM's geospatial strategy focuses on collection, organization, and use of baseline resource management data, like fenceline and transportation data and enhancing predictions based on geospatial data. Examples of activities that require geospatial data include planning and resource management, special status species monitoring, regional mitigation, and renewable energy projects, just to name a few.

An important factor in implementing our strategy is using a geographic information system (GIS) that is consistent and integrated within the Bureau and the Department of the Interior. This internal cohesion enhances the BLM's ability to partner with other Federal agencies, collaborate with State and Tribal governments, and communicate with the public.

Students in geographic information systems and science (GIS) require significant experience outside of spatial analysis, cartography, and other traditional geographic topics. Computer science knowledge, skills, and practices exist as essential components of GIS practice, but coursework in this area is not universally offered in geography or GIS degrees. To support those interested in developing such courses, this paper describes the design and implementation of a server-focused course in WebGIS at University Texas A&M University. We provide an in-depth discussion of the equipment and resources required to build and operate an on-premise CyberGIS server infrastructure suitable for supporting such classes, providing comparisons with an equivalent solution built on Amazon Web Services (AWS). We consider the comparative costs of these systems, including benefits and drawbacks of each. In comparing these deployment options, we outline the technical expertise, monetary investments, operational expenses, and organizational strategies necessary to run server-based CyberGIS courses. Finally, we reflect on assignments and feedback from students and consider their experiences in a course of this nature. This article provides a resource for GIS instructors, academic departments, or other academic units to consider during infrastructure investment, curriculum redesign, the addition of courses in degree plans, or for the development of CyberGIS components.

Global Government Open Data Management Platform Market size was valued at USD 1.75 Billion in 2024 and is projected to reach USD 3.38 Billion by 2031, growing at a CAGR of 8.54% from 2024 to 2031.

Global Government Open Data Management Platform Market Drivers

Increasing Demand for Transparency and Accountability: There is a growing public demand for transparency in government operations, which drives the adoption of open data initiatives. According to a survey by the World Bank, 85% of respondents in various countries indicated that transparency in government decisions is crucial for reducing corruption, prompting governments to implement open data platforms.

Technological Advancements: Rapid advancements in information and communication technology (ICT) facilitate the development and deployment of open data management platforms. The International Telecommunication Union (ITU) reported that global Internet penetration reached approximately 64% in 2023, enabling more citizens to access open data and engage with government services online.

Government Initiatives and Policies: Many governments are actively promoting open data through policies and initiatives. For instance, the U.S. government’s Open Data Initiative, launched in 2013, has led to the publication of over 300,000 datasets on Data.gov. Additionally, the European Union’s Open Data Directive, which aims to make public sector data available, is further encouraging governments to embrace open data practices.

description: The primary objective of the project is to develop an integrated ecological and socioeconomic land use evaluation model (the Ecosystem Portfolio Model, EPM) for Department of the Interior (DOI) resource managers to use to reconcile the need to maintain the ecological health of South Florida parks and refuges with increasing pressures for higher density development in the agricultural lands outside of the Urban Development Boundary in Miami-Dade County. The EPM has three major components: (1) an ecological value model based on ecological criteria relevant to National Park Service and US Fish & Wildlife Service resource management and species protection mandates; (2) a real estate market-based land value model sensitive to relevant land use/cover attributes indicative of conservation and development decisions; and (3) a set of socioeconomic indicators sensitive to land use/cover changes relevant to regional environmental and ecological planning. The current version is implemented for Miami-Dade County, with the protection of ecological values in the lands between the Everglades and Biscayne National Parks as the focus. The first two components have been implemented in the GIS web-enabled prototype interface and the third component is being developed in draft form in FY08 in consultation with the Florida Atlantic University Dept of Urban and Regional Planning.; abstract: The primary objective of the project is to develop an integrated ecological and socioeconomic land use evaluation model (the Ecosystem Portfolio Model, EPM) for Department of the Interior (DOI) resource managers to use to reconcile the need to maintain the ecological health of South Florida parks and refuges with increasing pressures for higher density development in the agricultural lands outside of the Urban Development Boundary in Miami-Dade County. The EPM has three major components: (1) an ecological value model based on ecological criteria relevant to National Park Service and US Fish & Wildlife Service resource management and species protection mandates; (2) a real estate market-based land value model sensitive to relevant land use/cover attributes indicative of conservation and development decisions; and (3) a set of socioeconomic indicators sensitive to land use/cover changes relevant to regional environmental and ecological planning. The current version is implemented for Miami-Dade County, with the protection of ecological values in the lands between the Everglades and Biscayne National Parks as the focus. The first two components have been implemented in the GIS web-enabled prototype interface and the third component is being developed in draft form in FY08 in consultation with the Florida Atlantic University Dept of Urban and Regional Planning.

Made available for NPDC GeoHUB (GIS Hub Site and Open Data Portal) : A full description is available in the Metadata. See Terms of Use. Notes:The "Updated" date, noted here in the item, does not accurately reflect the currency of the data within the Feature Layer. The data available for download on NPDC GeoHUB is updated daily, this results in differences between what is available online and NPDC's databases.

This data viewer provides access to a variety of geospatial data layers that can be used to help implement the Department of Interior's Integrated Rangeland Fire Management Strategy (IRFMS) which "sets forth enhanced policies and strategies for preventing suppressing rangeland fire and for restoring sagebrush landscapes impact by fire across the West".Multiple government agencies, including the Bureau of Land Management (BLM), US Geological Survey (USGS) and US Fish and Wildlife Service (USFWS), and partners are collectively contributing data layers to this viewer. These data are related to sagebrush ecosystems, fire and invasive species, and the Greater Sage-Grouse (GRSG). Point of Contact: Bureau of Land Management, National Operations Center, Fire Resource Data Liaison (BLM_OC_Fire_Geospatial@blm.gov)

An RCIS is a voluntary, non-regulatory, and non-binding conservation assessment that includes information and analyses relating to the conservation of focal species, habitats, and other conservation elements in the RCIS area. Any public agency may develop an RCIS. An RCIS establishes biological goals and objectives at the species level and describes conservation actions and habitat enhancement actions that, if implemented, will contribute to those goals and objectives. Those actions will benefit the conservation of focal species, habitats, and other natural resources, and they may be used as a basis to provide advance mitigation through the development of credits or to inform other conservation investments.

Development of a data policy and ensuring its uptake is not a trivial task within any organisation. There are many surrounding factors that may help or hinder the acceptance and imbedding of policies. Preparation and development of Geoscience Australia’s (GA) Data Strategy and Data Stewardship Policy required a combined understanding and knowledge of political, stakeholder, geoinformatics and technological landscapes external to the organisation, and an internal understanding of a vast amount of multi-disciplinary data assets and their champions within GA. Externally, from an international perspective, any data policy needs to take into account: - Regulations and compliance requirements (FAIR Principles and Trusted repositories), - Supporting data interoperability geoinformatics developments (common ontological information models, vocabularies and content standards (ISO, OGC, W3C)); - Technology trends (semantic web, machine learning, block chain); and - How these may interrelate to each other. From an Australian perspective, any GA data policy must: - Maintain a high level awareness of changes in Government priorities and policies (Australian Government Data Policy, Digital Continuity 2020); - Similar developments within other Government organisations; - Understand GA stakeholders and their roles in supporting delivery of GA goals and outcomes: the influencers, partners and consumers and how GA can communicate its Data Policy to them. Internally, to ensure the Strategy implementation, GA needs to: - Build a strong support base from executives, managers and data champions to ensure adoption of the strategy and funding; - Develop an architecture to sustain the implementation; - Ensure technological support through expert geoinformatics and Multi-Disciplinary-Teams; - Educate staff to ensure they have adequate competencies to comply with the policy. The GA Data Strategy is accompanied by a three year roadmap, which includes developing methodologies and frameworks to: - Streamline data processes, systems and tools; - Embed best practice data management; - Encourage and reward data management; - Develop data capabilities; - Strengthen and embed Data Governance. Realisation of this work is essential for GA to achieve its main goal of maximising geoscientific data potential to serve Australia.

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastre Theme ( https://communities.geoplatform.gov/ngda-cadastre/ ). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means. The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all open space public and nonprofit lands and waters. Most are public lands owned in fee (the owner of the property has full and irrevocable ownership of the land); however, permanent and long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g. 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of U.S. public land and other protected areas, compiling “best available” data provided by managing agencies and organizations. The PAD-US geodatabase maps and describes areas using thirty-six attributes and five separate feature classes representing the U.S. protected areas network: Fee (ownership parcels), Designation, Easement, Marine, Proclamation and Other Planning Boundaries. An additional Combined feature class includes the full PAD-US inventory to support data management, queries, web mapping services, and analyses. The Feature Class (FeatClass) field in the Combined layer allows users to extract data types as needed. A Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) facilitates the extraction of authoritative federal data provided or recommended by managing agencies from the Combined PAD-US inventory. This PAD-US Version 3.0 dataset includes a variety of updates from the previous Version 2.1 dataset (USGS, 2020, https://doi.org/10.5066/P92QM3NT ), achieving goals to: 1) Annually update and improve spatial data representing the federal estate for PAD-US applications; 2) Update state and local lands data as state data-steward and PAD-US Team resources allow; and 3) Automate data translation efforts to increase PAD-US update efficiency. The following list summarizes the integration of "best available" spatial data to ensure public lands and other protected areas from all jurisdictions are represented in the PAD-US (other data were transferred from PAD-US 2.1). Federal updates - The USGS remains committed to updating federal fee owned lands data and major designation changes in annual PAD-US updates, where authoritative data provided directly by managing agencies are available or alternative data sources are recommended. The following is a list of updates or revisions associated with the federal estate: 1) Major update of the Federal estate (fee ownership parcels, easement interest, and management designations where available), including authoritative data from 8 agencies: Bureau of Land Management (BLM), U.S. Census Bureau (Census Bureau), Department of Defense (DOD), U.S. Fish and Wildlife Service (FWS), National Park Service (NPS), Natural Resources Conservation Service (NRCS), U.S. Forest Service (USFS), and National Oceanic and Atmospheric Administration (NOAA). The federal theme in PAD-US is developed in close collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://communities.geoplatform.gov/ngda-govunits/federal-lands-workgroup/ ). 2) Improved the representation (boundaries and attributes) of the National Park Service, U.S. Forest Service, Bureau of Land Management, and U.S. Fish and Wildlife Service lands, in collaboration with agency data-stewards, in response to feedback from the PAD-US Team and stakeholders. 3) Added a Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) to the PAD-US 3.0 geodatabase to facilitate the extraction (by Data Provider, Dataset Name, and/or Aggregator Source) of authoritative data provided directly (or recommended) by federal managing agencies from the full PAD-US inventory. A summary of the number of records (Frequency) and calculated GIS Acres (vs Documented Acres) associated with features provided by each Aggregator Source is included; however, the number of records may vary from source data as the "State Name" standard is applied to national files. The Feature Class (FeatClass) field in the table and geodatabase describe the data type to highlight overlapping features in the full inventory (e.g. Designation features often overlap Fee features) and to assist users in building queries for applications as needed. 4) Scripted the translation of the Department of Defense, Census Bureau, and Natural Resource Conservation Service source data into the PAD-US format to increase update efficiency. 5) Revised conservation measures (GAP Status Code, IUCN Category) to more accurately represent protected and conserved areas. For example, Fish and Wildlife Service (FWS) Waterfowl Production Area Wetland Easements changed from GAP Status Code 2 to 4 as spatial data currently represents the complete parcel (about 10.54 million acres primarily in North Dakota and South Dakota). Only aliquot parts of these parcels are documented under wetland easement (1.64 million acres). These acreages are provided by the U.S. Fish and Wildlife Service and are referenced in the PAD-US geodatabase Easement feature class 'Comments' field. State updates - The USGS is committed to building capacity in the state data-steward network and the PAD-US Team to increase the frequency of state land updates, as resources allow. The USGS supported efforts to significantly increase state inventory completeness with the integration of local parks data in the PAD-US 2.1, and developed a state-to-PAD-US data translation script during PAD-US 3.0 development to pilot in future updates. Additional efforts are in progress to support the technical and organizational strategies needed to increase the frequency of state updates. The PAD-US 3.0 included major updates to the following three states: 1) California - added or updated state, regional, local, and nonprofit lands data from the California Protected Areas Database (CPAD), managed by GreenInfo Network, and integrated conservation and recreation measure changes following review coordinated by the data-steward with state managing agencies. Developed a data translation Python script (see Process Step 2 Source Data Documentation) in collaboration with the data-steward to increase the accuracy and efficiency of future PAD-US updates from CPAD. 2) Virginia - added or updated state, local, and nonprofit protected areas data (and removed legacy data) from the Virginia Conservation Lands Database, provided by the Virginia Department of Conservation and Recreation's Natural Heritage Program, and integrated conservation and recreation measure changes following review by the data-steward. 3) West Virginia - added or updated state, local, and nonprofit protected areas data provided by the West Virginia University, GIS Technical Center. For more information regarding the PAD-US dataset please visit, https://www.usgs.gov/gapanalysis/PAD-US/. For more information about data aggregation please review the PAD-US Data Manual available at https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-manual . A version history of PAD-US updates is summarized below (See https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-history for more information): 1) First posted - April 2009 (Version 1.0 - available from the PAD-US: Team pad-us@usgs.gov). 2) Revised - May 2010 (Version 1.1 - available from the PAD-US: Team pad-us@usgs.gov). 3) Revised - April 2011 (Version 1.2 - available from the PAD-US: Team pad-us@usgs.gov). 4) Revised - November 2012 (Version 1.3) https://doi.org/10.5066/F79Z92XD 5) Revised - May 2016 (Version 1.4) https://doi.org/10.5066/F7G73BSZ 6) Revised - September 2018 (Version 2.0) https://doi.org/10.5066/P955KPLE 7) Revised - September 2020 (Version 2.1) https://doi.org/10.5066/P92QM3NT 8) Revised - January 2022 (Version 3.0) https://doi.org/10.5066/P9Q9LQ4B Comparing protected area trends between PAD-US versions is not recommended without consultation with USGS as many changes reflect improvements to agency and organization GIS systems, or conservation and recreation measure classification, rather than actual changes in protected area acquisition on the ground.

Strategy and Innovation Road mapping Tools Market size was valued at USD 432.85 Billion in 2024 and is projected to reach USD 1203.37 Billion by 2031, growing at a CAGR of 15.04% during the forecast period 2024-2031.

Global Strategy and Innovation Road mapping Tools Market Drivers

Need for Strategic Planning: Organizations across various industries increasingly recognize the importance of strategic planning to navigate market uncertainties and competitive pressures. Roadmapping tools help businesses align their long-term vision with actionable steps, ensuring coherent strategy execution.

Accelerating Pace of Technological Change: Rapid advancements in technology necessitate continuous innovation. Roadmapping tools assist companies in tracking emerging technologies, assessing their impact, and integrating them into their strategic plans, thereby maintaining competitiveness.

Increased Focus on Innovation Management: To stay ahead in the market, companies are prioritizing innovation management. Roadmapping tools provide a structured approach to manage innovation processes, from ideation to implementation, ensuring that innovative ideas are systematically developed and commercialized.

Complexity of Product Development: Modern product development involves multiple stages and stakeholders. Roadmapping tools streamline this complexity by providing visual representations of timelines, milestones, and dependencies, facilitating better coordination and collaboration across teams.

Demand for Agile and Flexible Planning: The volatile business environment demands agility. Roadmapping tools enable organizations to adapt their strategies dynamically in response to market changes, regulatory shifts, and new opportunities, supporting an agile approach to planning and execution.

Integration with Digital Transformation Initiatives: As businesses embark on digital transformation journeys, roadmapping tools play a critical role in aligning digital initiatives with overall business strategy. These tools help map out digital projects, ensuring they contribute to the broader organizational goals.

Enhanced Collaboration and Communication: Effective collaboration and communication are essential for successful strategy execution. Roadmapping tools provide a centralized platform where teams can collaborate, share insights, and keep track of progress, fostering a collaborative environment.

Regulatory and Compliance Requirements: In industries with stringent regulatory requirements, roadmapping tools help organizations ensure compliance by integrating regulatory milestones and timelines into their strategic plans, reducing the risk of non-compliance.

Competitive Pressure and Market Dynamics: The need to stay competitive in a rapidly changing market drives the adoption of roadmapping tools. These tools help organizations anticipate market trends, identify competitive threats, and develop strategies to capitalize on opportunities.

Adoption of Data-Driven Decision Making: The increasing reliance on data-driven decision-making necessitates tools that can integrate various data sources and provide actionable insights. Roadmapping tools leverage data analytics to inform strategic decisions, enhancing the accuracy and effectiveness of strategic planning.

The Strategic Highway Network (STRAHNET) dataset and its geometries was updated on May 01, 2024 from the Federal Highway Administration (FHWA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The layer represents the Strategic Highway Network (STRAHNET). The STRAHNET is a subset of the National Highway System (NHS) and includes information such as road segments, direction, build date, state codes, functional classification, ownership, and other attributes related to highways in the United States. The provided information outlines the preparation of a layer that represents the National Highway System (NHS) in the context of the HPMS 2020 Geometric network and attribute definition source HPMS 2020. The NHS layer incorporates attributes from HPMS, and the conflation process involves the transfer of State_Code, Year_Record, Route_ID, Begin_Point, End_Point, and F_SYSTEM fields. Calibration of Begin_Point and End_Point is performed based on shape length and sequence for split links. The minimum positional accuracy is specified at a spatial resolution of 1:100,000 or better, although most state-submitted data ranges between 1:5,000 to 1:10,000. The spatial data organization method is described as a vector using the SDTS (Spatial Data Transfer Standard) Point and Vector Object Type with a count of 626,366 objects. The coordinate system is defined in geographic terms with a horizontal datum of D WGS 1984, employing decimal degrees for latitude and longitude. The entity and attribute information is comprehensive, covering various aspects of the NHS and HPMS data.

Geospatial Analytics System Market Outlook

The global geospatial analytics system market size was valued at approximately USD 67 billion in 2023 and is projected to reach around USD 158 billion by 2032, growing at a compound annual growth rate (CAGR) of 10.1% during the forecast period. The growth of this market is being driven by significant advancements in geospatial technologies coupled with an increasing demand for spatial data across various industries.

One of the primary growth factors for the geospatial analytics system market is the rapid adoption of Internet of Things (IoT) devices, which generate vast amounts of location-based data. This data is crucial for analytics systems that rely on geospatial information to provide insights. The proliferation of smartphones and connected devices has further accelerated the demand for geospatial analytics, as these devices generate continuous streams of geolocation data that can be analyzed for various applications such as urban planning, transportation, and disaster management.

Another key driver for the market is the increasing government initiatives aimed at improving national infrastructure and public safety. Governments worldwide are investing heavily in geospatial analytics to enhance urban planning, monitor environmental changes, and manage natural disasters effectively. The integration of geospatial analytics with other emerging technologies such as Artificial Intelligence (AI) and Machine Learning (ML) is also contributing to market growth by enabling more accurate predictions and real-time decision-making capabilities.

The growing awareness about climate change and its adverse effects is also playing a crucial role in the expansion of the geospatial analytics market. As climate change continues to pose significant risks, there is an increasing need for advanced systems that can monitor environmental changes and help in climate change adaptation strategies. Geospatial analytics systems provide crucial insights that aid in understanding and mitigating the impacts of climate change, thus driving their adoption across sectors like agriculture, forestry, and coastal management.

Regionally, North America holds a significant share of the geospatial analytics system market due to the high adoption rate of advanced technologies and substantial investments in infrastructure development. The presence of major market players and extensive research and development activities also contribute to the region's market dominance. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by rapid urbanization, increased government spending on smart city projects, and rising awareness about the benefits of geospatial analytics.

The role of Geographic Information System (GIS) Tools in the geospatial analytics market cannot be overstated. These tools are essential for capturing, storing, analyzing, and managing spatial and geographic data. They enable organizations to visualize complex data sets in a manner that is both accessible and actionable. By integrating GIS tools into their operations, businesses and governments can enhance decision-making processes, optimize resource allocation, and improve overall efficiency. The increasing sophistication of GIS tools, coupled with their ability to integrate with other technologies such as AI and IoT, is driving their adoption across various sectors. This integration facilitates the development of comprehensive solutions that address specific industry needs, from urban planning to environmental monitoring.

Component Analysis

The geospatial analytics system market can be segmented by component into software, hardware, and services. The software segment holds the largest market share, driven by the increasing need for advanced analytical tools and platforms that can process and visualize geospatial data effectively. The software solutions include Geographic Information Systems (GIS), remote sensing software, and spatial analytics tools that enable users to analyze and interpret spatial data for various applications.

The hardware segment, which includes GPS devices, sensors, and other geospatial data collection tools, is also experiencing significant growth. The demand for advanced hardware components is increasing as more sophisticated and accurate data collection methods are required for various applications such as disaster management, urban plann

Integrated geospatial infrastructure is the modern pattern for connecting organizations across borders, jurisdictions, and sectors to address shared challenges. Implementation starts with a strategy, followed by the pillars of collaborative governance, data and technology, capacity building, and engagement. It is inherently multi-organizational.Whether you call your initiative Open Data, Regional GIS, Spatial Data Infrastructure (SDI), Digital Twin, Knowledge Infrastructure, Digital Ecosystem, or otherwise, collaboration is key.This guide shares good practices for new and existing ArcGIS Administrators to get the most out of your 'OneMap' Hub. See also the complimentary Configure ArcGIS Online: 'OneMap' Good Practices and 'OneMap' Hub Template How-To Guide.

Alaska Regional Development Organizations (ARDORs), their contact information, and their Comprehensive Economic Development Strategies (CEDS).The mission of the ARDORs Program is to encourage the formation of regional development organizations to prepare and implement regional development strategies (Alaska Statute 44.33.896). Through regional development strategies, local knowledge, and coordinated implementation, ARDORs champion economic development planning for Alaska’s regions and communities by leveraging baseline support provided by the State of Alaska.ARDORs develop customized work plans that contain goals, objectives, and strategies for addressing regional economic development needs including: Facilitating development of a healthy regional economy that results in sustainable business growth, new business investment, and economic diversification. Identifying and working to eliminate regional economic development barriers. Developing and implementing a comprehensive economic development strategy. Coordinating regional planning efforts that result in new employment and business opportunities. Working to enable multiple communities to collaborate and pool limited resources. Strengthening partnerships with public, private, and non-government organizations. Providing technical assistance to encourage business startup, retention, and expansion.Source: Alaska Department of Commerce, Community & Economic Development

Made available for NPDC GeoHUB (GIS Hub Site and Open Data Portal) :A full description is available in the Metadata. SeeTerms of Use.Notes:The "Updated" date, noted here in the item, does not accurately reflect the currency of the data within the Feature Layer.The data available for download on NPDC GeoHUB is updated daily, this results in differences between what is available online and NPDC's databases.

These water quality data are one of many studies being done to assess and monitor coral reef ecosystems.The intent of this work is three fold: (1) to spatially characterize and monitor the distribution, abundance, and size of both reef fishes and macro-invertebrates (conch, lobster, Diadema); (2) to relate this information to in-situ data collected on water quality and associated habitat parameters; (3) to use this information to establish the knowledge base necessary for enacting management decisions in a spatial setting and to establish the efficacy of those management decisions. Toward this end, the Center for Coastal Monitoring and Assessment's Biogeography Branch (BB) has completed its fourth year and is beginning its fifth year of work in the US Virgin Islands and Puerto Rico. It is critical, with recent changes in management at both locations (e.g. implementation of MPAs) as well as proposed changes (e.g. zoning to manage multiple human uses) that action is taken now to accurately describe and characterize the fish/macro-invertebrate populations in these areas. It is also important that BB work closely with the individuals responsible for recommending and implementing these management strategies. Recognizing this, BB has been collaborating with partners at the University of Puerto Rico, National Park Service, US Geological Survey and the Virgin Islands Department of Planning and Natural Resources.To quantify patterns of spatial distribution and make meaningful interpretations, we must first have knowledge of the underlying variables determining species distribution. The basis for this work therefore, is the nearshore benthic habitats maps (less than 100 ft depth) created by NOAA's Biogeography Program in 2001 and NOS' bathymetry models. Using ArcView GIS software, the digitized habitat maps are stratified to select sampling stations. Sites are randomly selected within these strata to ensure coverage of the entire study region and not just a particular reef or seagrass area. At each site, fish, macro-invertebrates, and associated water quality and habitat information is then quantified following standardized protocols. By relating the data collected in the field back to the habitat maps and bathymetric models, BB is able to model and map species level and community level information. These protocols are standardized throughout the US Caribbean to enable quantification and comparison of reef fish abundance and distribution trends between locations. Armed with the knowledge of where "hot spots" of species richness and diversity are likely to occur in the seascape, the BB is in a unique position to answer questions about the efficacy of marine zoning strategies (e.g. placement of no fishing, anchoring, or snorkeling locations), and what locations are most suitable for establishing MPAs. Knowledge of the current status of fish/macro-invertebrate communities coupled with longer term monitoring will enable evaluation of management efficacy, thus it is essential to future management actions.

Made available for NPDC GeoHUB (GIS Hub Site and Open Data Portal) :A full description is available in the Metadata. SeeTerms of Use.Notes:The "Updated" date, noted here in the item, does not accurately reflect the currency of the data within the Feature Layer.The data available for download on NPDC GeoHUB is updated daily, this results in differences between what is available online and NPDC's databases.

The Geostandard Flood Directive describes the basis of geographic data produced on 120 High Flood Risk (TRI) territories and mapped for reporting purposes for the European Flood Directive. European Directive 2007/60/EC of 23 October 2007 on the assessment and management of flood risks (OJ L 288, 06-11-2007, p. 27) influences the flood prevention strategy in Europe by requiring the production of flood risk management plans for each river basin district. Article 1 of the Flood Directive specifies its objective of establishing a framework for the assessment and management of flood risks, which aims to reduce the negative consequences of flooding on human health, the environment, cultural heritage and economic activity. The objectives and implementation requirements are set out in the Law of 12 July 2010 on the National Commitment for the Environment (LENE) and the Decree of 2 March 2011. In this context, the primary objective of flood and flood risk mapping for TRIs is to contribute, by homogenising and objectivating knowledge of flood exposure, to the drafting of flood risk management plans (WRMs), to the definition of the objectives of the plan and to the development of local strategies by TRI. Thus, the purpose of this geostandard is to: 1. homogenise the production of data used for flood and flood risk maps, 2. facilitate the implementation of a GIS on each IRR. This Flood Directive GIS should become a living reference for knowledge of hazards and flood risks on these IRRs and will be used to establish flood risk management plans. IRR GIS will be integrated into a common national GIS.