The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making. BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions. Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself. For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise. Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery. See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

The SCAR Spatial Data Model has been developed for Geoscience Standing Scientific Group (GSSG). It was presented to XXVII SCAR, 15-26 July 2002, in Shanghai, China.

The Spatial Data Model is one of nine projects of the Geographic Information Program 2000-2002. The goal of this project is 'To provide a SCAR standard spatial data model for use in SCAR and national GIS databases.'

Activities within this project include:

1. Continue developing the SCAR Feature Catalogue and the SCAR Spatial Data Model
2. Provide SCAR Feature Catalogue online
3. Creation and incorporation of symbology
4. Investigate metadata / data quality requirements
5. Ensure compliance to ISO TC211 and OGC standards

Source: http://www.geoscience.scar.org/geog/geog.htm#stds

Spatial data are increasingly being available in digital form, managed in a GIS and distributed on the web. More data are being exchanged between nations/institutions and used by a variety of disciplines. Exchange of data and its multiple use makes it necessary to provide a standard framework. The Feature Catalogue is one component of the Spatial Data Model, that will provide the platform for creating understandable and accessible data to users. Care has been taken to monitor the utility of relevant emerging ISO TC211 standards.

The Feature Catalogue provides a detailed description of the nature and the structure of GIS and map information. It follows ISO/DIS 19110, Geographic Information - Methodology for feature cataloguing. The Feature Catalogue can be used in its entirety, or in part. The Feature Catalogue is a dynamic document, that will evolve with use over time. Considerable effort has gone into ensuring that the Feature Catalogue is a unified and efficient tool that can be used with any GIS software and at any scale of geographic information.

The structure includes data quality information, terminology, database types and attribute options that will apply to any GIS. The Feature Catalogue is stored in a database to enable any component of the information to be easily viewed, printed, downloaded and updated via the Web.

Market Overview:

The Vietnam geospatial analytics market size is projected to exhibit a growth rate (CAGR) of 8.90% during 2024-2032. The increasing product utilization by government authorities in various sectors, various technological advancements in satellite technology, remote sensing, and data collection methods, and the rising development of smart cities represent some of the key factors driving the market.

Geospatial analytics is a field of data analysis that focuses on the interpretation and analysis of geographic and spatial data to gain valuable insights and make informed decisions. It combines geographical information systems (GIS), advanced data analysis techniques, and visualization tools to analyze and interpret data with a spatial or geographic component. It also enables the collection, storage, analysis, and visualization of geospatial data. It provides tools and software for managing and manipulating spatial data, allowing users to create maps, perform spatial queries, and conduct spatial analysis. In addition, geospatial analytics often involves integrating geospatial data with other types of data, such as demographic data, environmental data, or economic data. This integration helps in gaining a more comprehensive understanding of complex phenomena. Moreover, geospatial analytics has a wide range of applications. For example, it can be used in urban planning to optimize transportation routes, in agriculture to manage crop yield and soil quality, in disaster management to assess and respond to natural disasters, in wildlife conservation to track animal migrations, and in business for location-based marketing and site selection.

Vietnam Geospatial Analytics Market Trends:

The Vietnamese government has recognized the importance of geospatial analytics in various sectors, including urban planning, agriculture, disaster management, and environmental monitoring. Initiatives to develop and utilize geospatial data for public projects and policy-making have spurred demand for geospatial analytics solutions. In addition, Vietnam is experiencing rapid urbanization and infrastructure development. Geospatial analytics is critical for effective urban planning, transportation management, and infrastructure optimization. This trend is driving the adoption of geospatial solutions in cities and regions across the country. Besides, Vietnam's agriculture sector is a significant driver of its economy. Geospatial analytics helps farmers and agricultural businesses optimize crop management, soil health, and resource allocation. Consequently, precision farming techniques, enabled by geospatial data, are becoming increasingly popular, which is also propelling the market. Moreover, the development of smart cities in Vietnam relies on geospatial analytics for various applications, such as traffic management, public safety, and energy efficiency. Geospatial data is central to building the infrastructure needed for smart city initiatives. Furthermore, advances in satellite technology, remote sensing, and data collection methods have made geospatial data more accessible and affordable. This has lowered barriers to entry and encouraged the use of geospatial analytics in various sectors. Additionally, the telecommunications sector in Vietnam is expanding, and location-based services, such as navigation and advertising, rely on geospatial analytics. This creates opportunities for geospatial data providers and analytics solutions in the telecommunications industry.

Vietnam Geospatial Analytics Market Segmentation:

IMARC Group provides an analysis of the key trends in each segment of the market, along with forecasts at the country level for 2024-2032. Our report has categorized the market based on component, type, technology, enterprise size, deployment mode, and vertical.

Component Insights:

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• Solution
• Services
  

The report has provided a detailed breakup and analysis of the market based on the component. This includes solution and services.

Type Insights:

• Surface and Field Analytics
• Network and Location Analytics
• Geovisualization
• Others
  

A detailed breakup and analysis of the market based on the type have also been provided in the report. This includes surface and field analytics, network and location analytics, geovisualization, and others.

Technology Insights:

• Remote Sensing
• GIS
• GPS
• Others
  

The report has provided a detailed breakup and analysis of the market based on the technology. This includes remote sensing, GIS, GPS, and others.

Enterprise Size Insights:

• Large Enterprises
• Small and Medium-sized Enterprises
  

A detailed breakup and analysis of the market based on the enterprise size have also been provided in the report. This includes large enterprises and small and medium-sized enterprises.

Deployment Mode Insights:

• On-premises
• Cloud-based
  

The report has provided a detailed breakup and analysis of the market based on the deployment mode. This includes on-premises and cloud-based.

Vertical Insights:

• Automotive
• Energy and Utilities
• Government
• Defense and Intelligence
• Smart Cities
• Insurance
• Natural Resources
• Others
  

A detailed breakup and analysis of the market based on the vertical have also been provided in the report. This includes automotive, energy and utilities, government, defense and intelligence, smart cities, insurance, natural resources, and others.

Regional Insights:

https://www.imarcgroup.com/CKEditor/bbfb54c8-5798-401f-ae74-02c90e137388other-regions6.webp" style="height:450px; width:800px" />

• Northern Vietnam
• Central Vietnam
• Southern Vietnam
  

The report has also provided a comprehensive analysis of all the major regional markets, which include Northern Vietnam, Central Vietnam, and Southern Vietnam.

Competitive Landscape:

The market research report has also provided a comprehensive analysis of the competitive landscape in the market. Competitive analysis such as market structure, key player positioning, top winning strategies, competitive dashboard, and company evaluation quadrant has been covered in the report. Also, detailed profiles of all major companies have been provided.

Vietnam Geospatial Analytics Market Report Coverage:

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.

The Spatial Data Dictionary is a specification for the capture of geoscientific spatial data. It describes fields for each feature type in a database, containing the themes currently created from Geoscience Australia's databases. It forms a foundation for the production of geoscientific spatial data by specifying rules regarding the structure of such data. The dictionary covers such matters as allowable coverage names, feature types, and attribute values. A theme is a set of spatial objects. Some of the themes in this data dictionary have associated look-up tables. Look-up tables store an additional array of attributes that may be linked to the primary attribute table of a theme. Object type, feature definition, field type, attribute case, compulsion for data entry, a list of valid values and any rules or comments regarding the feature are also given in this data dictionary. The Data Dictionary consists of four modules: • Module 1: Definitions, Rules and Terminology • Module 2: Geology, Geophysical, Geochemistry and Geochronology Themes • Module 3: Mineral Deposits and Mineral Potential Assessment Themes, Surveys and Field Observations Themes • Module 4: Urban Infrastructure Themes, Terrain Physiography Themes, Cartographic Themes

The spatial learning database for 2018 contains 5620 plots. We use it to calculate a land use map from satellite images. It is organized according to a nested 3-level nomenclature. The sources and techniques used to build the database by land use groups are described below: For agricultural areas, we use a land use database based on farmers' declarations (for EU subsidies). This is the "Registre Parcellaire Graphique" (RPG) published in France by the French Institute for Geographical and Forestry Informations (IGN). The description of this data is available here: http://professionnels.ign.fr/doc/DC_DL_RPG-2-0.pdf. These vector data localize the crops. The release times imply that we use the RPG for last year (2017). It is therefore necessary to verify the good coherence of the data with the image at very high spatial resolution (VHSR) Pleiades. The RPG provides little information on arboriculture. For these classes we called on colleagues specialized in mango, lychee and citrus crops who are familiar with their area and can locate plots in the VHSR image. The plots of the "greenhouse or shade cultivation" class are derived from the "industrial building" layer of the IGN's "BD Topo" product. A random selection of 20% of the polygons in the layer height field allows to keep a diversity of greenhouse types. Each polygon was verified by photo-interpretation of the Pleiades image. If the greenhouse or shade was not visible in the image, the polygon was removed. The distinction between mowed and grazed grasslands was completed through collaboration with colleagues from the SELMET joint research unit (Emmanuel Tillard, Expédit Rivière, Colas Gabriel Tovmassian and Jeanne Averna). For natural areas For natural areas , there is no regularly updated mapping, but the main classes can be recognized from the GIS layers of government departments that manage these areas (ONF and DEAL). Two specific classes have been added (identified by photo-interpretation): a class of shadows due to the island's steep relief (areas not visible because of the cast shade) and a class of vegetation located on steep slopes facing the morning sun called "rampart moor". The polygons for the distinction of savannahs have been improved thanks to the knowledge of Xavier Amelot (CNRS), Béatrice Moppert and Quentin Rivière (University of La Réunion). For wet land areas , the "marsh" and "water" classes were obtained by photo-interpretation of the 2018 Pleiades image. These classes are easily recognizable on this type of image. For urban areas we randomly selected polygons from the IGN BD Topo product. For the housing type building, 4 building height classes have previously been created (depending on the height of the layer field) in order to preserve a good diversity of the types of buildings present on the island. A random selection of polygons from each class was then made. The "built" layer was completed by a random selection of industrial buildings from the "industrial built" layer of the IGN's BD TOPO product. This selection was made in the "nature" field of the layer (i‧e. the following types: silo, industrial and livestock). The "photovoltaic panel" class was obtained by photo-interpretation of the polygons on 2018 Pleiades image. La base de données spatiale d'apprentissage pour 2018, est constituée de 5620 parcelles. Nous l'utilisons pour calculer une carte d'occupation du sol à partir d'image satellites. Elle est organisée selon une nomenclature emboitée à 3 niveaux. Voici une brève description des sources et techniques utilisées pour la constituer en fonction des groupes d’occupation du sol : Pour les espaces agricoles , nous disposons d’une base de données d’occupation du sol basée sur les déclarations que font des agriculteurs pour demander les subventions de l’Union Européenne. Il s’agit du Registre Parcellaire Graphique (RPG) diffusé en France par l’Institut français de l’information géographique et forestière (IGN). La description de cette donnée est disponible ici : http://professionnels.ign.fr/doc/DC_DL_RPG-2-0.pdf. Ces données vecteur sont précises et peuvent servir de modèle pour localiser les cultures. Les délais de diffusion impliquent que nous utilisons le RPG de l’année N -1. Il est donc nécessaire de vérifier la bonne cohérence des données par photo-interprétation de l’image THRS. Le RPG fournit peu d’information sur l’arboriculture. Pour ces classes nous avons fait appel aux collègues techniciens spécialisés dans les cultures de mangues, litchis et agrumes qui connaissent bien leur secteur et peuvent localiser des parcelles sur l’image THRS. Les parcelles de la classe « culture sous serre ou ombrage » sont issues de la couche « bâti industriel » de la BD Topo de l’IGN. Une sélection aléatoire de 20% des polygones dans le champ hauteur de la couche de l’IGN permet de conserver une diversité des types de serre. Chacun des polygones a été vérifié par photo-interprétation de l’image Pléiades. Si la serre ou...

Spatial information about the seafloor is critical for decision-making by marine resource science, management and tribal organizations. Coordinating data needs can help organizations leverage collective resources to meet shared goals. To help enable this coordination, the National Oceanic and Atmospheric Administration (NOAA) National Centers for Coastal Ocean Science (NCCOS) developed a spatial framework, process and online application to identify common data collection priorities for seafloor mapping, sampling and visual surveys offshore of the West Continental United States Coast (WCC). Twenty-six participants from NOAA’s West Coast Deep Sea Coral Initiative (WCDSCI) and Expanding Pacific Research and Exploration of Submerged Systems (EXPRESS) entered their priorities in an online application, using virtual coins to denote their priorities in 10x10 minute grid cells. Grid cells with more coins were higher priorities than cells with fewer coins. Participants also reported why these locations were important and what data types were needed. Results were analyzed and mapped using statistical techniques to identify significant relationships between priorities, reasons for those priorities and data needs. Ten high priority locations were broadly identified for future mapping, sampling and visual surveys. These locations were distributed throughout the WCC, primarily in depths less than 1,000 m. Participants consistently selected (1) Exploration, (2) Biota/Important Natural Area and (3) Research as their top reasons (i.e., justifications) for prioritizing locations, and (1) Benthic Habitat Map and (2) Bathymetry and Backscatter as their top data or product needs. This ESRI shapefile summarizes the results from this spatial prioritization effort. This information will enable NOAA WCDSCI, EXPRESS and other WCC organization to more efficiently leverage resources and coordinate their mapping of high priority locations along California, Oregon and Washington.

This effort was funded by NOAA’s Deep Sea Coral Research and Technology Program (DSCRTP) through its WCDSCI. The overall goal of the project was to systematically gather and quantify suggestions for seafloor mapping, sampling and visual surveys for the WCDSCI and EXPRESS. The results are expected to help WCDSCI, EXPRESS and other organizations on the WCC to identify locations where their interests overlap with other organizations, to coordinate their data needs and to leverage collective resources to meet shared goals.

There were four main steps in the WCC spatial prioritization process. The first step was to identify the technical advisory team, which included the 11 members of the DSCRTP WCDSCI Steering Committee and all of the participants involved in the EXPRESS campaign. This advisory team invited 37 participants for the prioritization. Step two was to develop the spatial framework and an online application. To do this, the WCC was divided into five subregions and 3,265 square grid cells approximately 10x10 minutes in size. Existing relevant spatial datasets (e.g., bathymetry, protected area boundaries, etc.) were compiled to help participants understand information and data gaps and to identify areas they wanted to prioritize for future data collections. These spatial datasets were housed in the online application, which was developed using Esri’s Web AppBuilder. In step three, this online application was used by 26 participants to enter their priorities in each subregion of interest. Participants allocated virtual coins in the 10x10 minute grid cells to denote their priorities. Grid cells with more coins were higher priorities than cells with fewer coins. Participants also reported why these locations were important and what data types were needed. Coin values were standardized across the subregions and used to identify spatial patterns across the WCC region as a whole. The number of coins were standardized because each subregion had a different number of grid cells and participants. Standardized coin values were analyzed and mapped using statistical techniques, including hierarchical cluster analysis, to identify significant relationships between priorities, reasons for those priorities and data needs. This ESRI shapefile contains the 10x10 minute grid cells used in this prioritization effort and associated the standardized coin values overall, as well as by organization, justification and product. For a complete description of the process and analyses please see: Costa et al. 2019.

The reference spatial database for 2019 contains 5142 plots. We use it to calculate a land use map from satellite images. It is organized according to a nested 3-level nomenclature. This is an update of the 2018 database. The sources and techniques used to build the database by land use groups are described below: For agricultural areas, we use a land use database based on farmers' declarations (for EU subsidies). This is the "Registre Parcellaire Graphique" (RPG) published in France by the French Institute for Geographical and Forestry Informations (IGN). The description of this data is available here: http://professionnels.ign.fr/doc/DC_DL_RPG-2-0.pdf. These vector data localize the crops. The release times imply that we use the RPG for last year (2018). It is therefore necessary to verify the good coherence of the data with the image at very high spatial resolution (VHSR) Pleiades. The RPG provides little information on arboriculture. For these classes we called on colleagues specialized in mango, lychee and citrus crops who are familiar with their area and can locate plots in the VHSR image. The plots of the "greenhouse or shade cultivation" class are derived from the "industrial building" layer of the IGN's "BD Topo" product. A random selection of 20% of the polygons in the layer height field allows to keep a diversity of greenhouse types. Each polygon was verified by photo-interpretation of the Pleiades image. If the greenhouse or shade was not visible in the image, the polygon was removed. The distinction between mowed and grazed grasslands was completed through collaboration with colleagues from the SELMET joint research unit (Emmanuel Tillard, Expédit Rivière, Colas Gabriel Tovmassian and Jeanne Averna). For natural areas , there is no regularly updated mapping, but the main classes can be recognized from the GIS layers of government departments that manage these areas (ONF and DEAL). Two specific classes have been added (identified by photo-interpretation): a class of shadows due to the island's steep relief (areas not visible because of the cast shade) and a class of vegetation located on steep slopes facing the morning sun called "rampart moor". The polygons for the distinction of savannahs have been improved thanks to the knowledge of Xavier Amelot (CNRS), Béatrice Moppert and Quentin Rivière (University of La Réunion). For wet land areas , the "marsh" and "water" classes were obtained by photo-interpretation of the 2019 Pleiades image. These classes are easily recognizable on this type of image. For urban areas we randomly selected polygons from the IGN BD Topo product. For the housing type building, 4 building height classes have previously been created (depending on the height of the layer field) in order to preserve a good diversity of the types of buildings present on the island. A random selection of polygons from each class was then made. The "built" layer was completed by a random selection of industrial buildings from the "industrial built" layer of the IGN's BD TOPO product. This selection was made in the "nature" field of the layer (i‧e. the following types: silo, industrial and livestock). The "photovoltaic panel" class was obtained by photo-interpretation of the polygons on 2019 Pleiades image.

Cloud GIS Market size was valued at USD 890.81 Million in 2023 and is projected to reach USD 2298.38 Million by 2031, growing at a CAGR of 14.5% from 2024 to 2031.

Key Market Drivers
• Increased Adoption of Cloud Computing: Cloud computing provides scalable resources that can be adjusted based on demand, making it easier for organizations to manage and process large GIS datasets. The pay-as-you-go pricing models of cloud services reduce the need for significant upfront investments in hardware and software, making GIS more accessible to small and medium-sized enterprises.
• Growing Need for Spatial Data Integration: The ability to integrate and analyze large volumes of spatial and non-spatial data helps organizations make more informed decisions. The proliferation of Internet of Things (IoT) devices generates massive amounts of spatial data that can be processed and analyzed using Cloud GIS.
• Advancements in GIS Technology: User-friendly interfaces and visualization tools make it easier for non-experts to use GIS applications. Advanced analytical tools and machine learning algorithms available in cloud platforms enhance the capabilities of traditional GIS.
• Increased Demand for Real-Time Data: Industries like disaster management, transportation, and logistics require real-time data processing and analysis, which is facilitated by Cloud GIS. The need for up-to-date maps and spatial data drives the adoption of cloud-based GIS solutions.
• Collaboration and Sharing Needs: The ability to access GIS data and collaborate from anywhere enhances productivity and supports remote work environments. Cloud GIS supports simultaneous access by multiple users, facilitating better teamwork and data sharing.
• Urbanization and Smart Cities Initiatives: Cloud GIS is crucial for smart city initiatives, urban planning, and infrastructure development, providing the tools needed for efficient resource management. Supports planning and monitoring of sustainable development projects by providing comprehensive spatial analysis capabilities.
• Government and Policy Support: Increased government investment in geospatial technologies and smart infrastructure projects drives the adoption of Cloud GIS. Compliance with regulatory requirements for environmental monitoring and land use planning necessitates the use of advanced GIS tools.
• Industry-Specific Applications: Precision farming and land management benefit from the advanced analytics and data integration capabilities of Cloud GIS. Epidemiology and public health monitoring rely on spatial data analysis for tracking disease outbreaks and resource allocation.

The Digital Geomorphic-GIS Map of Gulf Islands National Seashore (5-meter accuracy and 1-foot resolution 2006-2007 mapping), Mississippi and Florida is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (guis_geomorphology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (guis_geomorphology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (guis_geomorphology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (guis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (guis_geomorphology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (guis_geomorphology_metadata_faq.pdf). Please read the guis_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (guis_geomorphology_metadata.txt or guis_geomorphology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:26,000 and United States National Map Accuracy Standards features are within (horizontally) 13.2 meters or 43.3 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

The Referrals Spatial Database - Public records locations of referrals submitted to the Department under the Environment Protection and Biodiversity Conservation (EPBC Act) 1999. A proponent (those who are proposing a development) must supply the maximum extent (location) of any proposed activities that need to be assessed under the EPBC Act through an application process.Referral boundaries should not be misinterpreted as development footprints but where referrals have been received by the Department. It should be noted that not all referrals captured within the Referrals Spatial Database, are assessed and approved by the Minister for the Environment, as some are withdrawn before assessment can take place. For more detailed information on a referral a URL is provided to the EPBC Act Public notices pages. Status and detailed planning documentation is available on the EPBC Act Public notices (http://epbcnotices.environment.gov.au/referralslist/).Post September 2019, this dataset is updated using a spatial data capture tool embedded within the Referral form on the department’s website. Users are able to supply spatial data in multiple formats, review spatial data online and submitted with the completed referral form automatically. Nightly processes update this dataset that are then available for internal staff to use (usually within 24 hours). Prior to September 2019, a manual process was employed to update this dataset. In the first instance where a proponent provides GIS data, this is loaded as the polygons for a referral. Where this doesn't exist other means to digitize boundaries are employed to provide a relatively accurate reflection of the maximum extent for which the referral may impact (it is not a development footprint). This sometimes takes the form of heads up digitizing planning documents, sourcing from other state databases (such as PSMA Australia) features and coordinates supplied through the application forms.Any variations to boundaries after the initial referral (i.e. during the assessment, approval or post-approval stages) are processed on an ad hoc basis through a manual update to the dataset. The REFERRALS_PUBLIC_MV layer is a materialized view that joins the spatial polygon data with the business data (e.g. name, case id, type etc.) about a referral. This layer is available for use by the public and is available via a web service and spatial data download. The data for the web service is updated weekly, while the data download is updated quarterly.

Abstract : The search for the most appropriate GIS data model to integrate, manipulate and analyse spatio-temporal data raises several research questions about the conceptualisation of geographic spaces. Although there is now a general consensus that many environmental phenomena require field and object conceptualisations to provide a comprehensive GIS representation, there is still a need for better integration of these dual representations of space within a formal spatio-temporal database. The research presented in this paper introduces a hybrid and formal dual data model for the representation of spatio-temporal data. The whole approach has been fully implemented in PostgreSQL and its spatial extension PostGIS, where the SQL language is extended by a series of data type constructions and manipulation functions to support hybrid queries. The potential of the approach is illustrated by an application to underwater geomorphological dynamics oriented towards the monitoring of the evolution of seabed changes. A series of performance and scalability experiments are also reported to demonstrate the computational performance of the model.Data Description : The data set used in our research is a set of bathymetric surveys recorded over three years from 2009 to 2011 as Digital Terrain Models (DTM) with 2m grid spacing. The first survey was carried out in February 2009 by the French hydrographic office, the second one was recorded on August-September 2010 and the third in July 2011, both by the “Institut Universitaire Européen de la Mer”.

An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protection's CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990+. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system. This service depicts the WHR13 Type from the fveg dataset (with Wildlife Habitat Relationship classes grouped into 13 major land cover types).The full dataset can be downloaded in raster format here: GIS Mapping and Data Analytics | CAL FIREThe service represents the latest release of the data, and is updated when a new version is released. Currently it represents fveg15_1.

The Polar-orbiting Operational Environmental Satellite (POES) series offers the advantage of daily global coverage, by making nearly polar orbits 14 times per day approximately 520 miles above the surface of the Earth. The Earth's rotation allows the satellite to see a different view with each orbit, and each satellite provides two complete views of a location around the world each day. The POES constellation of weather satellites is a joint effort between the National Oceanic and Atmospheric Administration (NOAA) and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). The Advanced Very High Resolution Radiometer (AVHRR) is a cross-track scanning system with five spectral bands having a resolution of 1.1 km and a frequency of earth scans twice per day (usually 0230 and 1430 local solar time) on NOAA and EUMETSAT satellites. There are three data types produced from the NOAA POES AVHRR. The Global Area Coverage (GAC) data set is reduced resolution image data that is processed onboard the satellite taking only one line out of every three and averaging every four of five adjacent samples along the scan line; the Local Area Coverage (LAC) data set is recorded onboard at original resolution (1.1 km) for part of an orbit and later transmitted to earth; and the High Resolution Picture Transmission (HRPT) is real-time downlink data. The EUMETSAT MetOp satellite series, initially launched on October 19, 2006, produces the same three data types as well as a fourth data type, Global Full Resolution Area Coverage (FRAC 1.1 km). The MetOp polar orbiting operational meteorological satellite system is the European contribution to the Initial Joint Polar-Orbiting Operational Satellite System (IJPS). AVHRR data provide opportunities for studying and monitoring vegetation conditions in ecosystems including forests, tundra and grasslands. Applications include agricultural assessment, land cover mapping, producing image maps of large areas such as countries or continents, and tracking regional and continental snow cover. AVHRR data are also used to retrieve various geophysical parameters such as sea surface temperatures and energy budget data.

This digital dataset was created as part of a U.S. Geological Survey study, done in cooperation with the Monterey County Water Resource Agency, to conduct a hydrologic resource assessment and develop an integrated numerical hydrologic model of the hydrologic system of Salinas Valley, CA. As part of this larger study, the USGS developed this digital dataset of geologic data and three-dimensional hydrogeologic framework models, referred to here as the Salinas Valley Geological Framework (SVGF), that define the elevation, thickness, extent, and lithology-based texture variations of nine hydrogeologic units in Salinas Valley, CA. The digital dataset includes a geospatial database that contains two main elements as GIS feature datasets: (1) input data to the 3D framework and textural models, within a feature dataset called “ModelInput”; and (2) interpolated elevation, thicknesses, and textural variability of the hydrogeologic units stored as arrays of polygonal cells, within a feature dataset called “ModelGrids”. The model input data in this data release include stratigraphic and lithologic information from water, monitoring, and oil and gas wells, as well as data from selected published cross sections, point data derived from geologic maps and geophysical data, and data sampled from parts of previous framework models. Input surface and subsurface data have been reduced to points that define the elevation of the top of each hydrogeologic units at x,y locations; these point data, stored in a GIS feature class named “ModelInputData”, serve as digital input to the framework models. The location of wells used a sources of subsurface stratigraphic and lithologic information are stored within the GIS feature class “ModelInputData”, but are also provided as separate point feature classes in the geospatial database. Faults that offset hydrogeologic units are provided as a separate line feature class. Borehole data are also released as a set of tables, each of which may be joined or related to well location through a unique well identifier present in each table. Tables are in Excel and ascii comma-separated value (CSV) format and include separate but related tables for well location, stratigraphic information of the depths to top and base of hydrogeologic units intercepted downhole, downhole lithologic information reported at 10-foot intervals, and information on how lithologic descriptors were classed as sediment texture. Two types of geologic frameworks were constructed and released within a GIS feature dataset called “ModelGrids”: a hydrostratigraphic framework where the elevation, thickness, and spatial extent of the nine hydrogeologic units were defined based on interpolation of the input data, and (2) a textural model for each hydrogeologic unit based on interpolation of classed downhole lithologic data. Each framework is stored as an array of polygonal cells: essentially a “flattened”, two-dimensional representation of a digital 3D geologic framework. The elevation and thickness of the hydrogeologic units are contained within a single polygon feature class SVGF_3DHFM, which contains a mesh of polygons that represent model cells that have multiple attributes including XY location, elevation and thickness of each hydrogeologic unit. Textural information for each hydrogeologic unit are stored in a second array of polygonal cells called SVGF_TextureModel. The spatial data are accompanied by non-spatial tables that describe the sources of geologic information, a glossary of terms, a description of model units that describes the nine hydrogeologic units modeled in this study. A data dictionary defines the structure of the dataset, defines all fields in all spatial data attributer tables and all columns in all nonspatial tables, and duplicates the Entity and Attribute information contained in the metadata file. Spatial data are also presented as shapefiles. Downhole data from boreholes are released as a set of tables related by a unique well identifier, tables are in Excel and ascii comma-separated value (CSV) format.

Important Note: This item is in mature support as of September 2023 and will be retired in December 2025. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.

The USGS Protected Areas Database of the United States (PAD-US) is the official inventory of public parks and other protected open space. The spatial data in PAD-US represents public lands held in trust by thousands of national, state and regional/local governments, as well as non-profit conservation organizations.Manager Type provides a coarse level land manager description from the PAD-US "Agency Type" Domain, "Manager Type" Field (for example, Federal, State, Local Government, Private).PAD-US is published by the U.S. Geological Survey (USGS) Science Analytics and Synthesis (SAS), Gap Analysis Project (GAP). GAP produces data and tools that help meet critical national challenges such as biodiversity conservation, recreation, public health, climate change adaptation, and infrastructure investment. See the GAP webpage for more information about GAP and other GAP data including species and land cover.Dataset SummaryPhenomenon Mapped: This layer displays protected areas symbolized by manager type.Coordinate System: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, the US Virgin Islands, the Northern Mariana Islands and other Pacific Ocean IslandsVisible Scale: 1:1,000,000 and largerSource: U.S. Geological Survey (USGS) Science Analytics and Synthesis (SAS), Gap Analysis Project (GAP) PAD-US version 3.0Publication Date: July 2022Attributes included in this layer are: CategoryOwner TypeOwner NameLocal OwnerManager TypeManager NameLocal ManagerDesignation TypeLocal DesignationUnit NameLocal NameSourcePublic AccessGAP Status - Status 1, 2, 3 or 4GAP Status DescriptionInternational Union for Conservation of Nature (IUCN) Description - I: Strict Nature Reserve, II: National Park, III: Natural Monument or Feature, IV: Habitat/Species Management Area, V: Protected Landscape/Seascape, VI: Protected area with sustainable use of natural resources, Other conservation area, UnassignedDate of EstablishmentThe source data for this layer are available here. What can you do with this Feature Layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:1,000,000 or larger but a vector tile layer created from the same data can be used at smaller scales to produce a webmap that displays across the full range of scales. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could set a filter for Gap Status Code = 3 to create a map of only the GAP Status 3 areas.Add labels and set their propertiesCustomize the pop-upArcGIS ProAdd this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. Note that many features in the PAD-US database overlap. For example wilderness area designations overlap US Forest Service and other federal lands. Any analysis should take this into consideration. An imagery layer created from the same data set can be used for geoprocessing analysis with larger extents and eliminates some of the complications arising from overlapping polygons.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.

City of Cambridge, MA, GIS basemap development project encompasses the land area of City of Cambridge with a 200-foot fringe surrounding the area and Charles River shoreline towards Boston. The basemap data was developed at 1" = 40' mapping scale using digital photogrammetric techniques. Planimetric features; both man-made and natural features like vegetation, rivers have been depicted. These features are important to all GIS/mapping applications and publication. A set of data layers such as Buildings, Roads, Rivers, Utility structures, 1 ft interval contours are developed and represented in the geodatabase. The features are labeled and coded in order to represent specific feature class for thematic representation and topology between the features is maintained for an accurate representation at the 1:40 mapping scale for both publication and analysis. The basemap data has been developed using procedures designed to produce data to the National Standard for Spatial Data Accuracy (NSSDA) and is intended for use at 1" = 40 ' mapping scale. Where applicable, the vertical datum is NAVD1988.Explore all our data on the Cambridge GIS Data Dictionary.Attributes NameType DetailsDescription TYPE type: Stringwidth: 50precision: 0 Type of water body (pond, stream, wetland)

NAME type: Stringwidth: 50precision: 0 Name of water body (unpopulated)

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Aerial digital ortho-photography was the foundation imagery for map development. For Abó, the photography was acquired on April May 15, 2002 at a scale of approximately 1:3,000; for Quarai and Gran Quivira it was flown on April 2, 2003 at scales of 1:3,600 and 1:3000, respectively. The 2002-03 digital imagery has a base pixel resolution of 1.0 m. We also made use of statewide 1-meter resolution, true-color imagery from 2005 that became available in 2006 through the New Mexico Resource Geographic Information System. A 10 m spatial resolution USGS Digital Elevation Model (DEM) was used, in conjunction with ground data, to help discriminate between vegetation types based on elevation gradients and terrain. All imagery and other spatial data layers were compiled into a geodatabase and GIS using ArcGIS 9.3 (ESRI 2008).

Layers are organized by ESA listed entities. A listed entity can be a species, subspecies, distinct population segment (DPS), or evolutionarily significant unit (ESU). NMFS and the U.S. Fish and Wildlife Service share jurisdiction of some listed entities; this service only contains spatial data for NMFS critical habitat. Critical habitat has not been designated for all listed entities.Generally, each listed entity has one layer. However, a listed entity may have critical habitat locations represented by both lines and polygons. In these instances, "_poly" and "_line" are appended to the layer names to differentiate between the spatial data types. Lines represent rivers, streams, or beaches and polygons represent waterbodies, marine areas, estuaries, marshes, or watersheds. The 8 digit date (YYYYMMDD) in each layer name is the publication date of the proposed or final rule in the Federal Register.Both proposed and designated critical habitat are included in this service. To differentiate between these categories, all proposed critical habitat layers begin with "Proposed_". Proposed critical habitat will be replaced by final designations soon after a final rule is published in the Federal Register. This service version may not include spatial data for recently proposed, modified, or designated critical habitat. Additionally, spatial data are not available for the designated critical habitat of the Southern Oregon/Northern California Coast coho salmon ESU and the Snake River spring/summer-run Chinook salmon ESU. NMFS will add these spatial data when they become available. In the meantime, please consult the final rules or CFR. NMFS may periodically update existing lines or polygons if better information becomes available, such as higher resolution bathymetric surveys.The "All_critical_habitat" layer group includes merged line and polygon feature classes that contain all available spatial data for critical habitat proposed or designated by NMFS; therefore, these layers contain overlapping features. The "All_critical_habitat_line_YYYYMMDD" and "All_critical_habitat_poly_YYYYMMDD" layers should be used together to represent all available spatial data. The date appended to the layer names is the date the geoprocessing (merge) occured.Features in this service were compiled from previously developed spatial data. The methods and sources used to create these spatial data are NOT standardized. Coastlines, bathymetric contours, and river lines, for example, were all derived from a variety of sources, using many different geoprocessing techniques, over the span of decades. If information was available on source data and/or processing steps, it was documented in the metadata lineage. Metadata descriptions and the "Notes" field describe line and boundary definitions. Line and boundary definitions are specific to each proposed or designated critical habitat dataset. For example, depending on the listed entity, a coastline could represent the Mean Higher High Water (MHHW) line in one designation and the Mean Lower Low Water (MLLW) line in another designation.Metadata for each layer is a combination of standardized and unique content and can be viewed at https://www.fisheries.noaa.gov/inport/item/65207. Standardized content includes the field and value definitions, spatial reference, and metadata style (ISO 19139). All other metadata content is unique to each layer.These data have been made publicly available from an authoritative source other than this Atlas and data should be obtained directly from that source for any re-use. See the original metadata from the authoritative source for more information about these data and use limitations. The authoritative source of these data can be found at the following location: NMFS Critical Habitat

City of Cambridge, MA, GIS basemap development project encompasses the land area of City of Cambridge with a 200-foot fringe surrounding the area and Charles River shoreline towards Boston. The basemap data was developed at 1" = 40' mapping scale using digital photogrammetric techniques. Planimetric features; both man-made and natural features like vegetation, rivers have been depicted. These features are important to all GIS/mapping applications and publication. A set of data layers such as Buildings, Roads, Rivers, Utility structures, 1 ft interval contours are developed and represented in the geodatabase. The features are labeled and coded in order to represent specific feature class for thematic representation and topology between the features is maintained for an accurate representation at the 1:40 mapping scale for both publication and analysis. The basemap data has been developed using procedures designed to produce data to the National Standard for Spatial Data Accuracy (NSSDA) and is intended for use at 1" = 40 ' mapping scale. Where applicable, the vertical datum is NAVD1988.Explore all our data on the Cambridge GIS Data Dictionary.Attributes NameType DetailsDescription TYPE type: Stringwidth: 50precision: 0 Type of pool (above ground or in-ground)

TOP_GL type: Doublewidth: 8precision: 38 Elevation of highest point above ground level (NAVD88)

TOP_SL type: Doublewidth: 8precision: 38 Elevation of highest point above sea level (NAVD88)

BASE_ELEV type: Doublewidth: 8precision: 38 Base elevation of structure (NAVD88)

ELEV_GL type: Doublewidth: 8precision: 38 Elevation of pool above ground level (NAVD88)

ELEV_SL type: Doublewidth: 8precision: 38 Elevation of pool above sea level (NAVD88)