The geographic data are built from the Technical Information Management System (TIMS). TIMS consists of two separate databases: an attribute database and a spatial database. The attribute information for offshore activities is stored in the TIMS database. The spatial database is a combination of the ARC/INFO and FINDER databases and contains all the coordinates and topology information for geographic features. The attribute and spatial databases are interconnected through the use of common data elements in both databases, thereby creating the spatial datasets. The data in the mapping files are made up of straight-line segments. If an arc existed in the original data, it has been replaced with a series of straight lines that approximate the arc. The Gulf of America OCS Region stores all its mapping data in longitude and latitude format. All coordinates are in NAD 27. Data can be obtained in three types of digital formats: INTERACTIVE MAP: The ArcGIS web maps are an interactive display of geographic information, containing a basemap, a set of data layers (many of which include interactive pop-up windows with information about the data), an extent, navigation tools to pan and zoom, and additional tools for geospatial analysis. SHP: A Shapefile is a digital vector (non-topological) storage format for storing geometric location and associated attribute information. Shapefiles can support point, line, and area features with attributes held in a dBASE format file. GEODATABASE: An ArcGIS geodatabase is a collection of geographic datasets of various types held in a common file system folder, a Microsoft Access database, or a multiuser relational DBMS (such as Oracle, Microsoft SQL Server, PostgreSQL, Informix, or IBM DB2). The geodatabase is the native data structure for ArcGIS and is the primary data format used for editing and data management.

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Geographic Information System Analytics Market Size 2024-2028

The geographic information system analytics market size is forecast to increase by USD 12 billion at a CAGR of 12.41% between 2023 and 2028.

The GIS Analytics Market analysis is experiencing significant growth, driven by the increasing need for efficient land management and emerging methods in data collection and generation. The defense industry's reliance on geospatial technology for situational awareness and real-time location monitoring is a major factor fueling market expansion. Additionally, the oil and gas industry's adoption of GIS for resource exploration and management is a key trend. Building Information Modeling (BIM) and smart city initiatives are also contributing to market growth, as they require multiple layered maps for effective planning and implementation. The Internet of Things (IoT) and Software as a Service (SaaS) are transforming GIS analytics by enabling real-time data processing and analysis.
Augmented reality is another emerging trend, as it enhances the user experience and provides valuable insights through visual overlays. Overall, heavy investments are required for setting up GIS stations and accessing data sources, making this a promising market for technology innovators and investors alike.

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

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The geographic information system analytics market encompasses various industries, including government sectors, agriculture, and infrastructure development. Smart city projects, building information modeling, and infrastructure development are key areas driving market growth. Spatial data plays a crucial role in sectors such as transportation, mining, and oil and gas. Cloud technology is transforming GIS analytics by enabling real-time data access and analysis. Startups are disrupting traditional GIS markets with innovative location-based services and smart city planning solutions. Infrastructure development in sectors like construction and green buildings relies on modern GIS solutions for efficient planning and management. Smart utilities and telematics navigation are also leveraging GIS analytics for improved operational efficiency.
GIS technology is essential for zoning and land use management, enabling data-driven decision-making. Smart public works and urban planning projects utilize mapping and geospatial technology for effective implementation. Surveying is another sector that benefits from advanced GIS solutions. Overall, the GIS analytics market is evolving, with a focus on providing actionable insights to businesses and organizations.

How is this Geographic Information System Analytics Industry segmented?

The geographic information system analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Retail and Real Estate
  Government
  Utilities
  Telecom
  Manufacturing and Automotive
  Agriculture
  Construction
  Mining
  Transportation
  Healthcare
  Defense and Intelligence
  Energy
  Education and Research
  BFSI


Components

  Software
  Services


Deployment Modes

  On-Premises
  Cloud-Based


Applications

  Urban and Regional Planning
  Disaster Management
  Environmental Monitoring Asset Management
  Surveying and Mapping
  Location-Based Services
  Geospatial Business Intelligence
  Natural Resource Management


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    South Korea


  Middle East and Africa

    UAE


  South America

    Brazil


  Rest of World

By End-user Insights

The retail and real estate segment is estimated to witness significant growth during the forecast period.

The GIS analytics market analysis is witnessing significant growth due to the increasing demand for advanced technologies in various industries. In the retail sector, for instance, retailers are utilizing GIS analytics to gain a competitive edge by analyzing customer demographics and buying patterns through real-time location monitoring and multiple layered maps. The retail industry's success relies heavily on these insights for effective marketing strategies. Moreover, the defense industries are integrating GIS analytics into their operations for infrastructure development, permitting, and public safety. Building Information Modeling (BIM) and 4D GIS software are increasingly being adopted for construction project workflows, while urban planning and designing require geospatial data for smart city planning and site selection.

The oil and gas industry is leveraging satellite imaging and IoT devices for land acquisition and mining operations. In the public sector, gover

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

Geoform is a configurable app template for form based data editing of a Feature Service. This application allows users to enter data through a form instead of a map's pop-up while leveraging the power of the Web Map and editable Feature Services. This app geo-enables data and workflows by lowering the barrier of entry for completing simple tasks. Use CasesProvides a form-based experience for entering data through a form instead of a map pop-up. This is a good choice for users who find forms a more intuitive format than pop-ups for entering data.Useful to collect new point data from a large audience of non technical staff or members of the community.Configurable OptionsGeoform has an interactive builder used to configure the app in a step-by-step process. Use Geoform to collect new point data and configure it using the following options:Choose a web map and the editable layer(s) to be used for collection.Provide a title, logo image, and form instructions/details.Control and choose what attribute fields will be present in the form. Customize how they appear in the form, the order they appear in, and add hint text.Select from over 15 different layout themes.Choose the display field that will be used for sorting when viewing submitted entries.Enable offline support, social media sharing, default map extent, locate on load, and a basemap toggle button.Choose which locate methods are available in the form, including: current location, search, latitude and longitude, USNG coordinates, MGRS coordinates, and UTM coordinates.Supported DevicesThis application is responsively designed to support use in browsers on desktops, mobile phones, and tablets.Data RequirementsThis web app includes the capability to edit a hosted feature service or an ArcGIS Server feature service. Creating hosted feature services requires an ArcGIS Online organizational subscription or an ArcGIS Developer account. Get Started This application can be created in the following ways:Click the Create a Web App button on this pageShare a map and choose to Create a Web AppOn the Content page, click Create - App - From Template Click the Download button to access the source code. Do this if you want to host the app on your own server and optionally customize it to add features or change styling.

The Human Geography Map (World Edition) web map provides a detailed vector basemap with a monochromatic style and content adjusted to support Human Geography information. Where possible, the map content has been adjusted so that it observes WCAG contrast criteria.This basemap, included in the ArcGIS Living Atlas of the World, uses 3 vector tile layers:Human Geography Label, a label reference layer including cities and communities, countries, administrative units, and at larger scales street names.Human Geography Detail, a detail reference layer including administrative boundaries, roads and highways, and larger bodies of water. This layer is designed to be used with a high degree of transparency so that the detail does not compete with your information. It is set at approximately 50% in this web map, but can be adjusted.Human Geography Base, a simple basemap consisting of land areas in a very light gray only.The vector tile layers in this web map are built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.Learn more about this basemap from the cartographic designer in Introducing a Human Geography Basemap.Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layer item referenced in this map.

This dataset was generated by the Remote Sensing Group of the TU Wien Department of Geodesy and Geoinformation (https://mrs.geo.tuwien.ac.at/), within a dedicated project by the European Space Agency (ESA). Rights are reserved with ESA. Open use is granted under the CC BY 4.0 license.With this dataset publication, we open up a new perspective on Earth's land surface, providing a normalised microwave backscatter map from spaceborne Synthetic Aperture Radar (SAR) observations. The Sentinel-1 Global Backscatter Model (S1GBM) describes Earth for the period 2016-17 by the mean C-band radar cross section in VV- and VH-polarization at a 10 m sampling, giving a high-quality impression on surface- structures and -patterns.At TU Wien, we processed 0.5 million Sentinel-1 scenes totaling 1.1 PB and performed semi-automatic quality curation and backscatter harmonisation related to orbit geometry effects. The overall mosaic quality excels (the few) existing datasets, with minimised imprinting from orbit discontinuities and successful angle normalisation in large parts of the world. Supporting the designand verification of upcoming radar sensors, the obtained S1GBM data potentially also serve land cover classification and determination of vegetation and soil states, as well as water body mapping.We invite developers from the broader user community to exploit this novel data resource and to integrate S1GBM parameters in models for various variables of land cover, soil composition, or vegetation structure.Please be referred to our peer-reviewed article at TODO: LINK TO BE PROVIDED for details, generation methods, and an in-depth dataset analysis. In this publication, we demonstrate – as an example of the S1GBM's potential use – the mapping of permanent water bodies and evaluate the results against the Global Surface Water (GSW) benchmark.Dataset RecordThe VV and VH mosaics are sampled at 10 m pixel spacing, georeferenced to the Equi7Grid and divided into six continental zones (Africa, Asia, Europe, North America, Oceania, South America), which are further divided into square tiles of 100 km extent ("T1"-tiles). With this setup, the S1GBM consists of 16071 tiles over six continents, for VV and VH each, totaling to a compressed data volume of 2.67 TB.The tiles' file-format is a LZW-compressed GeoTIFF holding 16-bit integer values, with tagged metadata on encoding and georeference. Compatibility with common geographic information systems as QGIS or ArcGIS, and geodata libraries as GDAL is given.In this repository, we provide each mosaic as tiles that are organised in a folder structure per continent. With this, twelve zipped dataset-collections per continent are available for download.Web-Based Data ViewerIn addition to this data provision here, there is a web-based data viewer set up at the facilities of the Earth Observation Data Centre (EODC) under http://s1map.eodc.eu/. It offers an intuitive pan-and-zoom exploration of the full S1GBM VV and VH mosaics. It has been designed to quickly browse the S1GBM, providing an easy and direct visual impression of the mosaics.Code AvailabilityWe encourage users to use the open-source Python package yeoda, a datacube storage access layer that offers functions to read, write, search, filter, split and load data from the S1GBM datacube. The yeoda package is openly accessible on GitHub at https://github.com/TUW-GEO/yeoda.Furthermore, for the usage of the Equi7Grid we provide data and tools via the python package available on GitHub at https://github.com/TUW-GEO/Equi7Grid. More details on the grid reference can be found in https://www.sciencedirect.com/science/article/pii/S0098300414001629.AcknowledgementsThis study was partly funded by the project "Development of a Global Sentinel-1 Land Surface Backscatter Model", ESA Contract No. 4000122681/17/NL/MP for the European Union Copernicus Programme. The computational results presented have been achieved using the Vienna Scientific Cluster (VSC). We further would like to thank our colleagues at TU Wien and EODC for supporting us on technical tasks to cope with such a large and complex data set. Last but not least, we appreciate the kind assistance and swift support of the colleagues from the TU Wien Center for Research Data Management.

IP Geo-Location Service Market Outlook

The global IP Geo-Location Service market size was valued at approximately $2.3 billion in 2023 and is projected to reach around $5.8 billion by 2032, growing at a remarkable CAGR of 11.2% during the forecast period. The primary growth factor driving this market is the increasing demand for real-time data analytics and enhanced security measures across various industries.

The burgeoning need for advanced cybersecurity solutions is a significant growth factor for the IP Geo-Location Service market. As cyber threats become more sophisticated, organizations are compelled to adopt geo-location services to pinpoint the physical location of IP addresses for fraud detection and prevention. This is particularly relevant in sectors such as BFSI and healthcare, where data breaches can have severe implications. The ability to accurately locate users can help in identifying and neutralizing potential threats, thereby ensuring data integrity and security.

Additionally, the proliferation of digital marketing and personalized content delivery is another catalyst for market growth. Businesses are increasingly leveraging geo-location data to enhance customer engagement through targeted advertising and personalized content. This is particularly significant in retail and media sectors, where understanding consumer behavior and preferences can lead to more effective marketing strategies. The ability to offer localized content not only improves user experience but also drives higher conversion rates, thereby boosting revenue streams.

The versatility of IP Geo-Location Services in compliance and regulatory adherence is another driving force. With the advent of stringent data protection regulations such as GDPR and CCPA, organizations are required to ensure that data is processed and stored in compliance with regional laws. Geo-location services aid in this by providing accurate data on where IP addresses are originating from, thus helping organizations avoid hefty fines and legal repercussions. This compliance capability is crucial for multinational companies operating across different jurisdictions.

Regional outlook for the IP Geo-Location Service market indicates significant growth across various geographies. North America is expected to hold a dominant position due to the early adoption of advanced technologies and the presence of key market players. Europe is projected to grow steadily, driven by stringent data protection laws and increasing awareness about cybersecurity. The Asia Pacific region is anticipated to witness the highest growth rate, fueled by rapid digital transformation and increasing internet penetration. Latin America and the Middle East & Africa are also expected to contribute to market growth, albeit at a slower pace compared to other regions.

Component Analysis

When dissecting the IP Geo-Location Service market by component, we find that it is broadly segmented into software and services. The software segment encompasses various solutions, including IP location databases, API services, and SDKs, which are integral for real-time IP address tracking and analysis. The growing need for accurate and reliable geo-location data is driving the demand for advanced software solutions. These solutions are essential for businesses to enhance their cybersecurity measures, improve customer targeting, and comply with regulatory requirements.

The services segment, on the other hand, includes professional services such as consulting, training, and support, as well as managed services. Professional services are crucial for organizations to understand and implement geo-location technologies effectively. They offer expertise in setting up, configuring, and maintaining IP geo-location systems, ensuring optimal performance and compliance. Managed services provide ongoing support, monitoring, and management of geo-location solutions, allowing businesses to focus on their core operations while ensuring their geo-location needs are met.

The software segment is expected to hold a larger market share due to the increasing adoption of advanced geo-location solutions across various industries. The rise of cloud computing and the growing preference for SaaS (Software as a Service) models are also contributing to the growth of the software segment. These solutions offer scalability, flexibility, and cost-effectiveness, making them an attractive option for businesses of all sizes.

However, the services segment is anticipated to grow at a significant rate during the fore

Mosaics are published as ArcGIS image serviceswhich circumvent the need to download or order data. GEO-IDS image services are different from standard web services as they provide access to the raw imagery data. This enhances user experiences by allowing for user driven dynamic area of interest image display enhancement, raw data querying through tools such as the ArcPro information tool, full geospatial analysis, and automation through scripting tools such as ArcPy.Image services are best accessed through the ArcGIS REST APIand REST endpoints (URL's). You can copy the OPS ArcGIS REST API link below into a web browser to gain access to a directory containing all OPS image services. Individual services can be added into ArcPro for display and analysis by using Add Data -> Add Data From Path and copying one of the image service ArcGIS REST endpoint below into the resultant text box. They can also be accessed by setting up an ArcGIS server connectionin ESRI software using the ArcGIS Image Server REST endpoint/URL. Services can also be accessed in open-source software. For example, in QGIS you can right click on the type of service you want to add in the browser pane (e.g., ArcGIS REST Server, WCS, WMS/WMTS) and copy and paste the appropriate URL below into the resultant popup window. All services are in Web Mercator projection.For more information on what functionality is available and how to work with the service, read the Ontario Web Raster Services User Guide. If you have questions about how to use the service, email Geospatial Ontario (GEO) at geospatial@ontario.caAvailable Products:ArcGIS REST APIhttps://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/Image Service ArcGIS REST endpoint / URL'shttps://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/GEO_Imagery_Data_Service_2013to2017/ImageServerhttps://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/GEO_Imagery_Data_Service_2018to2022/ImageServer https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/GEO_Imagery_Data_Service_2023to2027/ImageServerWeb Coverage Services (WCS) URL'shttps://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2013to2017/ImageServer/WCSServer/https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2018to2022/ImageServer/WCSServer/https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2023to2027/ImageServer/WCSServer/Web Mapping Service (WMS) URL'shttps://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2013to2017/ImageServer/WMSServer/https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2018to2022/ImageServer/WMSServer/https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2023to2027/ImageServer/WMSServer/Metadata for all imagery products available in GEO-IDS can be accessed at the links below:South Central Ontario Orthophotography Project (SCOOP) 2023North-Western Ontario Orthophotography Project (NWOOP) 2022Central Ontario Orthophotography Project (COOP) 2021South-Western Ontario Orthophotography Project (SWOOP) 2020Digital Raster Acquisition Project Eastern Ontario (DRAPE) 2019-2020South Central Ontario Orthophotography Project (SCOOP) 2018North-Western Ontario Orthophotography Project (NWOOP) 2017Central Ontario Orthophotography Project (COOP) 2016South-Western Ontario Orthophotography Project (SWOOP) 2015Algonquin Orthophotography Project (2015)Additional Documentation:Ontario Web Raster Services User Guide (Word)Status:Completed: Production of the data has been completed Maintenance and Update Frequency:Annually: Data is updated every yearContact:Geospatial Ontario (GEO), geospatial@ontario.ca

This dataset was generated by the Remote Sensing Group of the TU Wien Department of Geodesy and Geoinformation (https://mrs.geo.tuwien.ac.at/), within a dedicated project by the European Space Agency (ESA). Rights are reserved with ESA. Open use is granted under the CC BY 4.0 license. With the recently published Sentinel-1 Global Backscatter Model (S1GBM) Version 1.0, we provide a new perspective on Earth's land surface through normalised microwave backscatter maps from Sentinel-1's Synthetic Aperture Radar (SAR) observations. This first extension of the S1GBM, V1.1, providing an additional set of normalised mosaics covering the northern and southern polar zones and sea ice regions. V1.1 ingests Medium-resolution data (GRDM) from Sentinel 1's Extra Wide (EW) swath mode in HV- and HH-polarisation, at a pixel sampling of 40m. To reflect cold and warm conditions in the high latitudes, and in particular to capture the varying snow pack extents along Greenland's coastline, data collections are set to the months January and July of the period 2016-17, respectively. Processing, normalisation- and mosaicking methods, and publication terms follow with minor adaptions the existing V1.0 dataset publication. We invite developers from the broader user community to exploit this novel data resource and to integrate S1GBM parameters in models for various variables of land cover, soil composition, or vegetation structure. Please be referred to our peer-reviewed article at Nature Scientific Data for details, generation methods, and an in-depth dataset analysis. In this publication, we demonstrate – as an example of the S1GBM's potential use – the mapping of permanent water bodies and evaluate the results against the Global Surface Water (GSW) benchmark. Dataset Record The HH and HV mosaics are sampled at 40 m pixel spacing, georeferenced to the Equi7Grid and divided into six continental zones (Antarctica, Asia, Europe, North America, Oceania, South America), which are further divided into square tiles of 300 km extent ("T3"-tiles). With this setup, the S1GBM consists of about 2000 tiles over six continents, for HH and HV each, totaling to a compressed data volume of about 140 TB.Please not that the collections for July over Antarctica suffer from a small number of tile-gaps. The tiles' file-format is a LZW-compressed GeoTIFF holding 16-bit integer values, with tagged metadata on encoding and georeference. Compatibility with common geographic information systems as QGIS or ArcGIS, and geodata libraries as GDAL is given. In this repository, we provide each mosaic as tiles that are organised in a folder structure per continent. With this, 24 zipped dataset-collections per continent per January and July are available for download. Web-Based Data Viewer In addition to this data provision here, there is a web-based data viewer set up at the facilities of the Earth Observation Data Centre (EODC) under http://s1map.eodc.eu/. It offers an intuitive pan-and-zoom exploration of the full S1GBM VV and VH mosaics. It has been designed to quickly browse the S1GBM, providing an easy and direct visual impression of the mosaics. Code Availability We encourage users to use the open-source Python package yeoda, a datacube storage access layer that offers functions to read, write, search, filter, split and load data from the S1GBM datacube. The yeoda package is openly accessible on GitHub at https://github.com/TUW-GEO/yeoda. Furthermore, for the usage of the Equi7Grid we provide data and tools via the python package available on GitHub at https://github.com/TUW-GEO/Equi7Grid. More details on the grid reference can be found in https://www.sciencedirect.com/science/article/pii/S0098300414001629. Acknowledgements This study was partly funded by the project "Development of a Global Sentinel-1 Land Surface Backscatter Model", ESA Contract No. 4000122681/17/NL/MP for the European Union Copernicus Programme. The computational results presented have been achieved using the Vienna Scientific Cluster (VSC). We further would like to thank our colleagues at TU Wien and EODC for supporting us on technical tasks to cope with such a large and complex data set. Last but not least, we appreciate the kind assistance and swift support of the colleagues from the TU Wien Center for Research Data Management.

WMS AND WMTS geo-services which allow the display of the reference cartographic base in a scale of 1:25,000 (2023 data), i.e. the cartographic preparation derived from the BDTRE (Reference Territorial Data Bank of Entities).

The Human Geography Dark Map (World Edition) web map provides a detailed world basemap with a dark monochromatic style and content adjusted to support human geography information. Where possible, the map content has been adjusted so that it observes WCAG contrast criteria.This basemap, included in the ArcGIS Living Atlas of the World, uses 3 vector tile layers:Human Geography Dark Label, a label reference layer including cities and communities, countries, administrative units, and at larger scales street names.Human Geography Dark Detail, a detail reference layer including administrative boundaries, roads and highways, and larger bodies of water. This layer is designed to be used with a high degree of transparency so that the detail does not compete with your information. It is set at approximately 50% in this web map, but can be adjusted.Human Geography Dark Base, a simple basemap consisting of land areas in a very dark gray only.The vector tile layers in this web map are built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.Learn more about this basemap from the cartographic designer in A Dark Version of the Human Geography Basemap.Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layers referenced in this map.

This is a sub-set of the data from the Coordinated Canyon Experiment (CCE) that specifically pertains to the published paper, Paull et al., 2018 Powerful turbidity currents driven by dense basal layers Nat Comms 9. doi: 10.1038/s41467-018-06254-6. The full data set is available at http://get.iedadata.org/doi/324762. The CCE was set up to measure the passage of sediment gravity flows down Monterey Canyon. Raw velocity data and raw backscatter data associated with three flow events are provided. The events took place on January 15th 2016, September 1st 2016 and November 24th 2016. The data from the first two events was collected from six moorings MS-1 (300 m water depth), MS-2 (500 m water depth), MS-3 (800 m water depth), MS-4 (1285 m water depth), MS-5 (1449 m water depth), and MS-7 (1850 m water depth), and from a Seafloor Instrument Node (SIN) at 1840 m water depth. Data for the November 24th 2016 event from the MS-1 moorings is also provided. The moorings held downward-looking ADCPs positioned 65 to 70 m above the seafloor. In the SIN, the ADCP’s were at the seafloor looking upwards. All instruments were synced initially on the same time base. MS-1 broke loose during the January 15th 2016 event and the ADCP kept recording while the mooring traveled down canyon and ultimately surfaced. The ADCP at MS-4 did not collect data during the September 1st 2016 event. File names start with the mooring name followed by the type and frequency of ADCP, and the date of the event. Files contain velocity and backscatter data over time. Times are UTC, except for the SIN data from the January 15th 2016 event, which is in local time (UTC is 7 hours ahead). Velocities are reported in millimeters per second and backscatter is reported in the output units of the ADCP, Echo Intensity Units (EIC), which are proportional to decibels. Files were converted to ASCII text format from the RDI proprietary format. Funding was provided by The David and Lucile Packard Foundation, Natural Environment Research Council (NE/K011480/1), U.S. Geological Survey, and Ocean University of China.

ESYS plc and the Department of Geomatic Engineering at University College London (UCL) have been funded by the British National Space Centre (BNSC) to develop a web GIS service to serve geographic data derived from remote sensing datasets. Funding was provided as part of the BNSC International Co-operation Programme 2 (ICP-2).

Particular aims of the project were to:

1. use Open Geospatial Consortium (OGC, recently renamed from the OpenGIS Consortium) technologies for map and data serving;

2. serve datasets for Europe and Africa, particularly Landsat TM and Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) data;

3. provide a website giving access to the served data;

4. provide software scripts, etc., and a document reporting the data processing and software set-up methods developed during the project.

ICEDS was inspired in particular by the Committee on Earth Observing Satellites (CEOS) CEOS Landsat and SRTM Project (CLASP) proposal. An express intention of ICEDS (aim 4 in the list above) was therefore that the solution developed by ESYS and UCL should be redistributable, for example, to other CEOS members. This was taken to mean not only software scripts but also the methods developed by the project team to prepare the data and set up the server. In order to be compatible with aim 4, it was also felt that the use of Open Source, or at least 'free-of-cost' software for the Web GIS serving was an essential component. After an initial survey of the Web GIS packages available at the time , the ICEDS team decided to use the Deegree package, a free software initiative founded by the GIS and Remote Sensing unit of the Department of Geography, University of Bonn , and lat/lon . However the Red Spider web mapping software suite was also provided by IONIC Software - this is a commercial web mapping package but was provided pro bono by IONIC for this project and has been used in parallel to investigate the possibilities and limitations opened up by using a commercial package.

http://iceds.ge.ucl.ac.uk/

This module uses the Project Eddie Module Assessing the Risk of Invasive Species Using Community Science Data developed by Heard, M. J. (2023) to create a deeper understanding of big geospatial data and volunteered geographic information (VGI) by embedding and Implementing the Project EDDIE Module on Assessing the Risk of Invasive Species Using Community Science Data or Other Project Eddue Modules into a cross-cutting curriculum for undergraduate students. Students in the upper-division courses completed the module while adapting it for a campus and community Global Citizen Science Month and Earth Day activity.

We deployed 200 CARTHE surface drifters drogued at 0.60 m depth. The sampling rate was set to every 10 minutes, giving theoretical battery life expectancy of more than 3 months. The GPS signal was very good despite constant difficult weather (winds and waves). The deployments followed a radiator pattern consisting of 4 12km-long lines aligned with the wind and separated by 3.5 km. We dropped 4 nodes of 3 drifters per line, separated by 0.5km, 1 km, and 2 km. This set up was chosen to provide synoptic and isotropic sampling of the submesoscale range and minimize the deployment time from a single ship. It took 3 to 4 hours at full speed (8 to 12 knots) to complete a deployment depending on conditions. The _location for each deployment was determined based on analysis of the most recent SSH and SST fields available, as well as shipboard ADCP plots, TSG transects, and weather forecasts (ECMWF).

Mexico Exports: FR: TM: Other Made Up Textile Articles, Set, Worn Clothing data was reported at 0.018 USD mn in Dec 2024. This records a decrease from the previous number of 0.042 USD mn for Nov 2024. Mexico Exports: FR: TM: Other Made Up Textile Articles, Set, Worn Clothing data is updated monthly, averaging 0.023 USD mn from Jan 2021 (Median) to Dec 2024, with 47 observations. The data reached an all-time high of 0.078 USD mn in Jun 2022 and a record low of 0.003 USD mn in Mar 2024. Mexico Exports: FR: TM: Other Made Up Textile Articles, Set, Worn Clothing data remains active status in CEIC and is reported by National Institute of Statistics and Geography. The data is categorized under Global Database’s Mexico – Table MX.JA012: Exports: by Country and Commodity: by 2 Digit HS Code: National Institute of Statistics and Geography.

InteractFen (I5Nf) is one of two automatic energy balance stations in Kobbefjord that was setup in 2011. Data from the station is found under Meteorology (air temperature, relative humidity, radiation, wind, air pressure), Soil Properties (water fraction, ground water level, soil temperatures, electrical conductivity) and Snow Properties (snow temperatures, snow depht). The station is placed in the fen (40 m.a.s.l.) close to the SoilFen (Meteorology, Soil Properties), EddyFen and Methane stations (Flux Monitoring). The SnowProperties dataset from the station consists of snow depth (Campbell, SR50A) and snow temperatures (thermocouples, 107T). All data is logged every 30 minutes (UTC-3 West Greenlandic Winter Time, CR1000X). Data comments: The station has suffered from power problems from 2015-2020 causing data gaps in autumn/winter. In June 2018 air temperature, relative humidity and wind sensors were added to the station setup. Snow depth: Snow free periods set to 0 since 2015. Since 2018, SR50A showed many false values. These values have been manually deleted. Snow temperatures: when sensor is not covered with snow, the measured temperature is an expression of the air temperature instead. In 2020-2022 power problems caused data gaps. The station is part of a network of similar energy balance stations. In Kobbefjord a comparable station is placed at the heath (I6Nh). A snow survey in Kobbefjord is done every year in coorporation with ClimateBasis Nuuk. Data from the manually snow survey is found under ClimateBasis Nuuk - Snow Properties. Contact the ClimateBasis Nuuk manager for more details.

Statistical analyses and maps representing mean, high, and low water-level conditions in the surface water and groundwater of Miami-Dade County were made by the U.S. Geological Survey, in cooperation with the Miami-Dade County Department of Regulatory and Economic Resources, to help inform decisions necessary for urban planning and development. Sixteen maps were created that show contours of (1) the mean of daily water levels at each site during October and May for the 2000-2009 water years; (2) the 25th, 50th, and 75th percentiles of the daily water levels at each site during October and May and for all months during 2000-2009; and (3) the differences between mean October and May water levels, as well as the differences in the percentiles of water levels for all months, between 1990-1999 and 2000-2009. The 80th, 90th, and 96th percentiles of the annual maximums of daily groundwater levels during 1974-2009 (a 35-year period) were computed to provide an indication of unusually high groundwater-level conditions. These maps and statistics provide a generalized understanding of the variations of water levels in the aquifer, rather than a survey of concurrent water levels. Water-level measurements from 473 sites in Miami-Dade County and surrounding counties were analyzed to generate statistical analyses. The monitored water levels included surface-water levels in canals and wetland areas and groundwater levels in the Biscayne aquifer. Maps were created by importing site coordinates, summary water-level statistics, and completeness of record statistics into a geographic information system, and by interpolating between water levels at monitoring sites in the canals and water levels along the coastline. Raster surfaces were created from these data by using the triangular irregular network interpolation method. The raster surfaces were contoured by using geographic information system software. These contours were imprecise in some areas because the software could not fully evaluate the hydrology given available information; therefore, contours were manually modified where necessary. The ability to evaluate differences in water levels between 1990-1999 and 2000-2009 is limited in some areas because most of the monitoring sites did not have 80 percent complete records for one or both of these periods. The quality of the analyses was limited by (1) deficiencies in spatial coverage; (2) the combination of pre- and post-construction water levels in areas where canals, levees, retention basins, detention basins, or water-control structures were installed or removed; (3) an inability to address the potential effects of the vertical hydraulic head gradient on water levels in wells of different depths; and (4) an inability to correct for the differences between daily water-level statistics. Contours are dashed in areas where the locations of contours have been approximated because of the uncertainty caused by these limitations. Although the ability of the maps to depict differences in water levels between 1990-1999 and 2000-2009 was limited by missing data, results indicate that near the coast water levels were generally higher in May during 2000-2009 than during 1990-1999; and that inland water levels were generally lower during 2000-2009 than during 1990-1999. Generally, the 25th, 50th, and 75th percentiles of water levels from all months were also higher near the coast and lower inland during 2000–2009 than during 1990-1999. Mean October water levels during 2000-2009 were generally higher than during 1990-1999 in much of western Miami-Dade County, but were lower in a large part of eastern Miami-Dade County.

Public Law (P.L.) 94-171, enacted in 1975, directs the U.S. Census Bureau to make special preparations to provide redistricting data needed by the 50 states. It specifies that within a year following Census Day (by April 1, 2011), the Census Bureau must send the governor and legislature in each state the data they need to redraw districts for the United States Congress and state legislature. The Census 2010 Redistricting Data Program was set up to afford state officials an opportunity to define the small areas for which they wish to receive census population totals for redistricting purposes. Officials then could receive data for voting districts (e.g., election precincts, wards, state house and senate districts) in addition to standard census geographic areas, such as counties, cities, census tracts, and blocks. State participation in defining areas is voluntary and nonpartisan. There are four map types that support the 2010 Census Redistricting Data (Public Law [P.L.] 94-171) program. Each of these large format map types is produced in Adobeâ s portable document format (PDF). These georeferenced PDF files were created in compliance with the OGC PDF Geo-registration Encoding Best Practice Version 2.2 (OGC project document reference number OGC 08-139r2). They will also be available through the U.S. Census Bureau Map Products web site. In addition to the maps, other geographic products include the State Redistricting Data (P.L.94-171) Shapefiles and the 2010 Census Block Assignment Files, which provide census block relationships to voting districts, state legislative districts, school districts, and congressional districts. All four map types are produced in a set for each county or statistically equivalent entity (school district maps for the District of Columbia, Florida, Hawaii, Maryland, Nevada, and West Virginia are state-based). Each map set consists of one or more numbered parent sheets which cover the entire county. If necessary, separate inset sheets show areas of dense features at a larger scale. Inset areas are identified with letters. If the set has more than one parent sheet, an index sheet is also included which depicts the arrangement of the parent sheets and inset areas in relation to the county boundary and selected major features. All of the parent sheets within a county are produced at the same scale, while maps for adjacent counties may be at different scales. The objective of each map type is to use the smallest number of sheets while preserving legibility of geographic entity names and feature identifiers. The physical size of the county and the density of features also affect the number of parent sheets and insets.

This National Geographic Style Map (World Edition) web map provides a reference map for the world that includes administrative boundaries, cities, protected areas, highways, roads, railways, water features, buildings, and landmarks, overlaid on shaded relief and a colorized physical ecosystems base for added context to conservation and biodiversity topics. Alignment of boundaries is a presentation of the feature provided by our data vendors and does not imply endorsement by Esri, National Geographic or any governing authority.This basemap, included in the ArcGIS Living Atlas of the World, uses the National Geographic Style vector tile layer and the National Geographic Style Base and World Hillshade raster tile layers.The vector tile layer in this web map is built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layers referenced in this map.