100+ datasets found
  1. h

    ARCHITRAVE [map visualization : data & software]

    • heidata.uni-heidelberg.de
    application/gzip, pdf
    Updated Oct 22, 2021
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    Hendrik Ziegler; Hendrik Ziegler; Alexandra Pioch; Alexandra Pioch (2021). ARCHITRAVE [map visualization : data & software] [Dataset]. http://doi.org/10.11588/DATA/AT1QUR
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    pdf(241144), application/gzip(914689)Available download formats
    Dataset updated
    Oct 22, 2021
    Dataset provided by
    heiDATA
    Authors
    Hendrik Ziegler; Hendrik Ziegler; Alexandra Pioch; Alexandra Pioch
    License

    https://heidata.uni-heidelberg.de/api/datasets/:persistentId/versions/2.0/customlicense?persistentId=doi:10.11588/DATA/AT1QURhttps://heidata.uni-heidelberg.de/api/datasets/:persistentId/versions/2.0/customlicense?persistentId=doi:10.11588/DATA/AT1QUR

    Time period covered
    1685 - 1723
    Area covered
    Italy, Versailles, France, Netherlands, Germany, Spain, Paris, France, Poland, France, Belgium
    Dataset funded by
    DFG-ANR
    Description

    The dataset includes cartographic visualization data and software designed, implemented, and published for the ARCHITRAVE research project website. The research focused on the edition, executed in German and French, of six travelogues by German travelers of the Baroque period who visited Paris and Versailles. The edited texts are published in the Textgrid repository. For all further information on the content and objectives of the research, please refer to the website (https://architrave.eu/) and given literature. Three visualizations were created for the website: the travel stops of five of the travelers on their way to Paris and Versailles the sites in Europe mentioned in the six travelogues the sites in Paris described by the six travelers The visualizations were implemented with Leaflet.js. The dataset contains scripts for data crunching processed geodata scripts for leaflet.js License README

  2. Multibeam Sonar Data Visualization Map

    • noaa.hub.arcgis.com
    Updated Mar 15, 2022
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    NOAA GeoPlatform (2022). Multibeam Sonar Data Visualization Map [Dataset]. https://noaa.hub.arcgis.com/maps/6795496737cf451d8fa4d5306b60889e
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    Dataset updated
    Mar 15, 2022
    Dataset provided by
    National Oceanic and Atmospheric Administrationhttp://www.noaa.gov/
    Authors
    NOAA GeoPlatform
    Area covered
    Description

    This map contains multibeam sonar survey data collected during the 2021 field project. This file supports the New Technology and the Search for Historic Shipwrecks StoryMap created by the National Oceanic and Atmospheric Administration (NOAA) National Centers for Coastal Ocean Science (NCCOS) and Office of National Marine Sanctuaries (ONMS). The StoryMap can be viewed here. The StoryMap was funded through NOAA's Office of Ocean Exploration and Research. More information on the project can be found here. All project files are stored in the NOAA National Centers for Environmental Information.

  3. d

    Data from: California State Waters Map Series--Offshore of Santa Cruz Web...

    • datasets.ai
    • data.usgs.gov
    • +2more
    55
    Updated Sep 21, 2024
    + more versions
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    Department of the Interior (2024). California State Waters Map Series--Offshore of Santa Cruz Web Services [Dataset]. https://datasets.ai/datasets/california-state-waters-map-series-offshore-of-santa-cruz-web-services
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    55Available download formats
    Dataset updated
    Sep 21, 2024
    Dataset authored and provided by
    Department of the Interior
    Area covered
    Santa Cruz, California
    Description

    In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California’s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of “lands” from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay’s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service–Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Offshore of Santa Cruz map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and photographic imagery; these “ground-truth” surveying data are available from the CSMP Video and Photograph Portal at https://doi.org/10.5066/F7J1015K. The “seafloor character” data layer shows classifications of the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. The “potential habitats” polygons are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Representative seismic-reflection profile data from the map area is also include and provides information on the subsurface stratigraphy and structure of the map area. The distribution and thickness of young sediment (deposited over the past about 21,000 years, during the most recent sea-level rise) is interpreted on the basis of the seismic-reflection data. The geologic polygons merge onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery seafloor-sediment and rock samplesdigital camera and video imagery, and high-resolution seismic-reflection profiles. The information provided by the map sheets, pamphlet, and data catalog has a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues. Web services were created using an ArcGIS service definition file. The ArcGIS REST service and OGC WMS service include all Offshore of Santa Cruz map area data layers. Data layers are symbolized as shown on the associated map sheets.

  4. I

    Interactive Map Creation Tools Report

    • datainsightsmarket.com
    doc, pdf, ppt
    Updated Jun 28, 2025
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    Data Insights Market (2025). Interactive Map Creation Tools Report [Dataset]. https://www.datainsightsmarket.com/reports/interactive-map-creation-tools-1418201
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    pdf, doc, pptAvailable download formats
    Dataset updated
    Jun 28, 2025
    Dataset authored and provided by
    Data Insights Market
    License

    https://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The interactive map creation tools market is experiencing robust growth, driven by increasing demand for visually engaging data representation across diverse sectors. The market, estimated at $2.5 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $7.8 billion by 2033. This expansion is fueled by several key factors. The rising adoption of location-based services (LBS) and geographic information systems (GIS) across industries like real estate, tourism, logistics, and urban planning is a major catalyst. Businesses are increasingly leveraging interactive maps to enhance customer engagement, improve operational efficiency, and gain valuable insights from geospatial data. Furthermore, advancements in mapping technologies, including the integration of AI and machine learning for improved data analysis and visualization, are contributing to market growth. The accessibility of user-friendly tools, coupled with the decreasing cost of cloud-based solutions, is also making interactive map creation more accessible to a wider range of users, from individuals to large corporations. However, the market also faces certain challenges. Data security and privacy concerns surrounding the use of location data are paramount. The need for specialized skills and expertise to effectively utilize advanced mapping technologies may also hinder broader adoption, particularly among smaller businesses. Competition among established players like Mapbox, ArcGIS StoryMaps, and Google, alongside emerging innovative solutions, necessitates constant innovation and differentiation. Nevertheless, the overall market outlook remains positive, with continued technological advancements and rising demand for data visualization expected to propel growth in the coming years. Specific market segmentation data, while unavailable, can be reasonably inferred from existing market trends, suggesting a strong dominance of enterprise-grade solutions, but with substantial growth expected from simpler, more user-friendly tools designed for individuals and small businesses.

  5. d

    Map Visualization example of RHESSys output at Coweeta subbasin18

    • search.dataone.org
    • hydroshare.org
    Updated Dec 5, 2021
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    YOUNG-DON CHOI (2021). Map Visualization example of RHESSys output at Coweeta subbasin18 [Dataset]. https://search.dataone.org/view/sha256%3Adb7dc4af52f6fea7e9846426b46bdf389ebb3f1de051d27277947144c7958640
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    Dataset updated
    Dec 5, 2021
    Dataset provided by
    Hydroshare
    Authors
    YOUNG-DON CHOI
    Area covered
    Description

    Map Visualization example of RHESSys output at Coweeta subbasin18

  6. a

    ArcGIS Pro: Mapping and Visualization

    • hub.arcgis.com
    Updated May 3, 2019
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    State of Delaware (2019). ArcGIS Pro: Mapping and Visualization [Dataset]. https://hub.arcgis.com/documents/delaware::arcgis-pro-mapping-and-visualization/explore?path=
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    Dataset updated
    May 3, 2019
    Dataset authored and provided by
    State of Delaware
    Description

    Discover how to display and symbolize both 2D and 3D data. Search, access, and create new map symbols. Learn to specify and configure text symbols for your map. Complete your map by creating an effective layout to display and distribute your work.

  7. s

    Visualization & Mind Maps Plugin Statistics

    • system3.md
    Updated Feb 9, 2025
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    System 3 (2025). Visualization & Mind Maps Plugin Statistics [Dataset]. https://system3.md/observatory/categories/visualization-mindmaps
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    Dataset updated
    Feb 9, 2025
    Dataset authored and provided by
    System 3
    Description

    Download statistics and trends for 127 plugins in the Visualization & Mind Maps category

  8. f

    Construction of an Emotion-Oriented Color Scheme Model for Maps

    • figshare.com
    application/x-rar
    Updated Jun 15, 2025
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    茜妮 胡 (2025). Construction of an Emotion-Oriented Color Scheme Model for Maps [Dataset]. http://doi.org/10.6084/m9.figshare.29321927.v1
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    application/x-rarAvailable download formats
    Dataset updated
    Jun 15, 2025
    Dataset provided by
    figshare
    Authors
    茜妮 胡
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The model applies manifold learning to reduce the dimensionality of high-dimensional map color data derived from the Map Emotion Dataset, thereby extracting emotional color manifold features, then emotional color constraints for maps on color feature indicators was introduced to genetic algorithm framework to generate color schemes that align with the thematic and emotional requirements of maps. This model offers a structured framework for enhancing the emotional expressiveness of cartographic products while maintaining consistency with established color design principles.

  9. f

    Madrid cycle track: visualizing the cyclable city

    • tandf.figshare.com
    • datasetcatalog.nlm.nih.gov
    pdf
    Updated Jun 3, 2023
    + more versions
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    Gustavo Romanillos; Martin Zaltz Austwick (2023). Madrid cycle track: visualizing the cyclable city [Dataset]. http://doi.org/10.6084/m9.figshare.3830241.v2
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    pdfAvailable download formats
    Dataset updated
    Jun 3, 2023
    Dataset provided by
    Taylor & Francis
    Authors
    Gustavo Romanillos; Martin Zaltz Austwick
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Madrid
    Description

    Maps are currently experiencing a paradigm shift from static representations to dynamic platforms that capture, visualize and analyse new data, bringing different possibilities for exploration and research. The first objective of this paper is to present a map that illustrates, for the first time, the real flow of casual cyclists and bike messengers in the city of Madrid. The second objective is to describe the development and results of the Madrid Cycle Track initiative, an online platform launched with the aim of collecting cycling routes and other information from volunteers. In the framework of this initiative, different online maps are presented and their functionalities described. Finally, a supplemental video visualizes the cyclist flow over the course of a day.

  10. 3D Visualisation Map (Individualised models)

    • data.gov.hk
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    data.gov.hk, 3D Visualisation Map (Individualised models) [Dataset]. https://data.gov.hk/en-data/dataset/hk-landsd-openmap-3d-visualisation-map-individualised-models
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    Dataset provided by
    data.gov.hk
    Description

    The 3D Visualisation Map (Individualised models) are a set of digital data of 3D models featuring geometry models and texture maps to represent the geometrical shape, appearance and position of different types of ground objects, including building, infrastructure, vegetation, site, waterbody, terrain and generic (others). The dataset covers the whole territory of Hong Kong. You can click the link below to access the 3D Visualisation Map (https://3d.map.gov.hk/).

  11. f

    Recommendations for the suitable contents of the geospatial datasets...

    • plos.figshare.com
    xls
    Updated Jun 15, 2023
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    Timo Rantanen; Harri Tolvanen; Meeli Roose; Jussi Ylikoski; Outi Vesakoski (2023). Recommendations for the suitable contents of the geospatial datasets presenting the distribution of languages including the benefits of each, and our solutions (selected in the case study) concerning the Uralic languages. [Dataset]. http://doi.org/10.1371/journal.pone.0269648.t001
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    xlsAvailable download formats
    Dataset updated
    Jun 15, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Timo Rantanen; Harri Tolvanen; Meeli Roose; Jussi Ylikoski; Outi Vesakoski
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Recommendations for the suitable contents of the geospatial datasets presenting the distribution of languages including the benefits of each, and our solutions (selected in the case study) concerning the Uralic languages.

  12. e

    ARCHITRAVE [map visualization : data & software] - Dataset - B2FIND

    • b2find.eudat.eu
    Updated Aug 15, 2025
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    (2025). ARCHITRAVE [map visualization : data & software] - Dataset - B2FIND [Dataset]. https://b2find.eudat.eu/dataset/6d6dc9a3-4144-5fca-b68f-8fa9de27d2e3
    Explore at:
    Dataset updated
    Aug 15, 2025
    Description

    The dataset includes cartographic visualization data and software designed, implemented, and published for the ARCHITRAVE research project website. The research focused on the edition, executed in German and French, of six travelogues by German travelers of the Baroque period who visited Paris and Versailles. The edited texts are published in the Textgrid repository. For all further information on the content and objectives of the research, please refer to the website (https://architrave.eu/) and given literature. Three visualizations were created for the website:

  13. f

    Data from: NDS: an interactive, web-based system to visualize urban...

    • tandf.figshare.com
    mp4
    Updated May 31, 2023
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    Yu Lan; Elizabeth Delmelle; Eric Delmelle (2023). NDS: an interactive, web-based system to visualize urban neighborhood dynamics in United States [Dataset]. http://doi.org/10.6084/m9.figshare.14484512.v1
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    mp4Available download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    Taylor & Francis
    Authors
    Yu Lan; Elizabeth Delmelle; Eric Delmelle
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    United States
    Description

    NDS is an interactive, web-based system, for the visualization of multidimensional neighborhood dynamics across the 50 largest US Metropolitan Statistical Areas (MSAs) from 1980 to 2010 (http://neighborhooddynamics.dreamhosters.com). Four different visualization tools are developed: (1) an interactive time slider to show neighborhood classification changes for different years; (2) multiple interactive bar charts for each variables of each neighborhood; (3) an animated neighborhood’s trajectory and sequence cluster on a self-organizing map (SOM) output space; and (4) a synchronized visualization tool showing maps for four time stamps at once. The development of this interactive online platform for visualizing dynamics overcomes many of the challenges associated with communicating changes for multiple variables, across multiple time stamps, and for a large geographic area when relying upon static maps. The system enables users to select and dive into details on particular neighborhoods and explore their changes over time.

  14. a

    2019 MEGABITESS Visualization Demonstration Map

    • megabitess-tga.hub.arcgis.com
    Updated Jun 25, 2020
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    Tennessee Geographic Alliance (2020). 2019 MEGABITESS Visualization Demonstration Map [Dataset]. https://megabitess-tga.hub.arcgis.com/maps/f306a9309b3446898430c9626bf6c8d0
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    Dataset updated
    Jun 25, 2020
    Authors
    Tennessee Geographic Alliance
    Area covered
    Description

    This map depicts the locations of ovitraps from the 2019 MEGA:BITESS cohort. Data needs filtered to remove No Data (-9999)

  15. d

    Example of Map Visualization with GIS tool stack in CyberGIS-Jupyter for...

    • search.dataone.org
    • hydroshare.org
    • +1more
    Updated Dec 5, 2021
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    Young-Don Choi (2021). Example of Map Visualization with GIS tool stack in CyberGIS-Jupyter for Water (CJW) [Dataset]. https://search.dataone.org/view/sha256%3A2f412289327e290f57b2b9caabc7aaf08aa739ee47bd9e76715565e6d78484d4
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    Dataset updated
    Dec 5, 2021
    Dataset provided by
    Hydroshare
    Authors
    Young-Don Choi
    Area covered
    Description

    These is an examples to test Data Processing Kernel in CyberGIS-Jupyter for water. The 2_map_visualization folder is an example of an interactive map visualization which is the high-level visualization using PyViz tools as post-processing of environmental modeling. For this example, we used the following PyViz tools: - geopandas (https://geopandas.org/), cartopy (https://scitools.org.uk/cartopy/), geoviews (https://geoviews.org/), and holoviews (https://holoviews.org/)

  16. Multibeam Sonar Data Visualization Map

    • noaa.hub.arcgis.com
    Updated Mar 15, 2022
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    NOAA GeoPlatform (2022). Multibeam Sonar Data Visualization Map [Dataset]. https://noaa.hub.arcgis.com/maps/6795496737cf451d8fa4d5306b60889e
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    Dataset updated
    Mar 15, 2022
    Dataset provided by
    National Oceanic and Atmospheric Administrationhttp://www.noaa.gov/
    Authors
    NOAA GeoPlatform
    Area covered
    Description

    This map contains multibeam sonar survey data collected during the 2021 field project. This file supports the New Technology and the Search for Historic Shipwrecks StoryMap created by the National Oceanic and Atmospheric Administration (NOAA) National Centers for Coastal Ocean Science (NCCOS) and Office of National Marine Sanctuaries (ONMS). The StoryMap can be viewed here. The StoryMap was funded through NOAA's Office of Ocean Exploration and Research. More information on the project can be found here. All project files are stored in the NOAA National Centers for Environmental Information.

  17. D

    Interactive Map Creation Tools Market Report | Global Forecast From 2025 To...

    • dataintelo.com
    csv, pdf, pptx
    Updated Jan 7, 2025
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    Dataintelo (2025). Interactive Map Creation Tools Market Report | Global Forecast From 2025 To 2033 [Dataset]. https://dataintelo.com/report/global-interactive-map-creation-tools-market
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    pdf, pptx, csvAvailable download formats
    Dataset updated
    Jan 7, 2025
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Interactive Map Creation Tools Market Outlook




    The global market size for Interactive Map Creation Tools was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 3.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 13.5% during the forecast period. The primary growth factors for this market include the increasing need for advanced geospatial data visualization, the rise of smart city initiatives, and the growing demand for real-time location-based services.




    One of the key growth drivers is the increasing demand for geospatial analytics across various sectors such as urban planning, transportation, and environmental monitoring. As urbanization accelerates, city planners and government authorities are turning to interactive mapping tools to visualize complex data sets that help in making informed decisions. These tools assist in laying out city infrastructures, optimizing traffic routes, and planning emergency response strategies. The trend towards smart cities further amplifies the need for such sophisticated tools, which can handle dynamic and interactive data layers in real-time.




    The transportation sector also finds significant utility in interactive map creation tools. With the surge in smart transportation projects globally, there is a mounting need to integrate real-time data into interactive maps for efficient route planning, traffic management, and logistics operations. Such tools not only aid in reducing congestion and travel times but also contribute to making transportation systems more sustainable. Additionally, interactive maps are becoming vital for managing fleets in logistics, enhancing the efficiency of delivery networks and reducing operational costs.




    Environmental monitoring is another critical application area driving market growth. With increasing concerns about climate change and natural disasters, there is a heightened need for tools that can provide real-time environmental data. Interactive maps enable organizations to monitor various environmental parameters such as air quality, water levels, and wildlife movements effectively. These tools are instrumental in disaster management, helping authorities to visualize affected areas and coordinate relief operations efficiently.




    Regionally, North America has been the dominant market for interactive map creation tools, driven by the high adoption of advanced technologies and significant investments in smart city projects. Europe follows closely, with countries like Germany and the UK leading the charge in urban planning and environmental monitoring initiatives. The Asia Pacific region is expected to witness the fastest growth, fueled by rapid urbanization and increasing investments in infrastructure development. Emerging economies in Latin America and the Middle East & Africa are also exploring these tools to address urbanization challenges and improve municipal services.



    In addition to the regional growth dynamics, the emergence of Custom Digital Map Service is revolutionizing the way organizations approach geospatial data. These services offer tailor-made mapping solutions that cater to the unique needs of businesses and government agencies. By providing highly customizable maps, these services enable users to integrate specific data layers, adjust visual styles, and incorporate branding elements, thereby enhancing the utility and appeal of the maps. As the demand for personalized mapping solutions grows, Custom Digital Map Service is becoming a vital component in sectors such as urban planning, logistics, and tourism, where tailored insights can drive strategic decisions and improve operational efficiency.



    Component Analysis




    In the Interactive Map Creation Tools market, the component segment is divided into Software and Services. The Software segment comprises products such as GIS software, mapping platforms, and data visualization tools. This segment holds a significant share of the market, fueled by the rising need for sophisticated software solutions that can handle vast amounts of geospatial data. Advanced mapping software offers features like real-time data integration, multi-layer visualization, and high customization capabilities, making it an indispensable tool for various industries.




    The increasing complexity

  18. Dataset: Rainbow color map distorts and misleads research in hydrology –...

    • zenodo.org
    txt
    Updated Mar 29, 2022
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    Michael Stoelzle; Michael Stoelzle; Lina Stein; Lina Stein (2022). Dataset: Rainbow color map distorts and misleads research in hydrology – guidance for better visualizations and science communication [Dataset]. http://doi.org/10.5281/zenodo.5145746
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    txtAvailable download formats
    Dataset updated
    Mar 29, 2022
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Michael Stoelzle; Michael Stoelzle; Lina Stein; Lina Stein
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The rainbow color map is scientifically incorrect and hinders people with color vision deficiency to view visualizations in a correct way. Due to perceptual non-uniform color gradients within the rainbow color map the data representation is distorted what can lead to misinterpretation of results and flaws in science communication. Here we present the data of a paper survey of 797 scientific publication in the journal Hydrology and Earth System Sciences. With in the survey all papers were classified according to color issues. Find details about the data below.

    • year = year of publication (YYYY)
    • date = date (YYYY-MM-DD) of publication
    • title = full paper title from journal website
    • authors = list of authors comma-separated
    • n_authors = number of authors (integer between 1 and 27)
    • col_code = color-issue classification (see below)
    • volume = Journal volume
    • start_page = first page of paper (consecutive)
    • end_page = last page of paper (consecutive)
    • base_url = base url to access the PDF of the paper with /volume/start_page/year/
    • filename = specific file name of the paper PDF (e.g. hess-9-111-2005.pdf)

    Color classification is stored in the col_code variable with:

    • 0 = chromatic and issue-free,
    • 1 = red-green issues,
    • 2= rainbow issues and
    • bw= black and white paper.

    See more details (e.g., sample code to analyse the survey data) on https://github.com/modche/rainbow_hydrology

    Paper: Stoelzle, M. and Stein, L.: Rainbow color map distorts and misleads research in hydrology – guidance for better visualizations and science communication, Hydrol. Earth Syst. Sci., 25, 4549–4565, https://doi.org/10.5194/hess-25-4549-2021, 2021.

  19. d

    06_COVID-19 Cases Dyanmic Map Visualization

    • search.dataone.org
    Updated Mar 6, 2024
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    Spatial Data Lab (2024). 06_COVID-19 Cases Dyanmic Map Visualization [Dataset]. http://doi.org/10.7910/DVN/VABWVR
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    Dataset updated
    Mar 6, 2024
    Dataset provided by
    Harvard Dataverse
    Authors
    Spatial Data Lab
    Description

    This case study includes multiple workflows, visualizing global countries' COVID-19 cases as dynamic maps, such as HTML, GIF, and MP4.

  20. d

    Rose Swanson Mountain Data Collation and Citizen Science

    • search.dataone.org
    • borealisdata.ca
    Updated Dec 28, 2023
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    Sun, Xiaoqing (Sunny) (2023). Rose Swanson Mountain Data Collation and Citizen Science [Dataset]. http://doi.org/10.5683/SP3/FSTOUQ
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    Dataset updated
    Dec 28, 2023
    Dataset provided by
    Borealis
    Authors
    Sun, Xiaoqing (Sunny)
    Description

    This study focuses on the use of citizen science and GIS tools for collecting and analyzing data on Rose Swanson Mountain in British Columbia, Canada. While several organizations collect data on wildlife habitats, trail mapping, and fire documentation on the mountain, there are few studies conducted on the area and citizen science is not being addressed. The study aims to aggregate various data sources and involve citizens in the data collection process using ArcGIS Dashboard and ArcGIS Survey 123. These GIS tools allow for the integration and analysis of different kinds of data, as well as the creation of interactive maps and surveys that can facilitate citizen engagement and data collection. The data used in the dashboard was sourced from BC Data Catalogue, Explore the Map, and iNaturalist. Results show effective citizen participation, with 1073 wildlife observations and 3043 plant observations. The dashboard provides a user-friendly interface for citizens to tailor their map extent and layers, access surveys, and obtain information on each attribute included in the pop-up by clicking. Analysis on classification of fuel types, ecological communities, endangered wildlife species presence and critical habitat, and scope of human activities can be conducted based on the distribution of data. The dashboard can provide direction for researchers to develop research or contribute to other projects in progress, as well as advocate for natural resource managers to use citizen science data. The study demonstrates the potential for GIS and citizen science to contribute to meaningful discoveries and advancements in areas.

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Hendrik Ziegler; Hendrik Ziegler; Alexandra Pioch; Alexandra Pioch (2021). ARCHITRAVE [map visualization : data & software] [Dataset]. http://doi.org/10.11588/DATA/AT1QUR

ARCHITRAVE [map visualization : data & software]

Related Article
Explore at:
pdf(241144), application/gzip(914689)Available download formats
Dataset updated
Oct 22, 2021
Dataset provided by
heiDATA
Authors
Hendrik Ziegler; Hendrik Ziegler; Alexandra Pioch; Alexandra Pioch
License

https://heidata.uni-heidelberg.de/api/datasets/:persistentId/versions/2.0/customlicense?persistentId=doi:10.11588/DATA/AT1QURhttps://heidata.uni-heidelberg.de/api/datasets/:persistentId/versions/2.0/customlicense?persistentId=doi:10.11588/DATA/AT1QUR

Time period covered
1685 - 1723
Area covered
Italy, Versailles, France, Netherlands, Germany, Spain, Paris, France, Poland, France, Belgium
Dataset funded by
DFG-ANR
Description

The dataset includes cartographic visualization data and software designed, implemented, and published for the ARCHITRAVE research project website. The research focused on the edition, executed in German and French, of six travelogues by German travelers of the Baroque period who visited Paris and Versailles. The edited texts are published in the Textgrid repository. For all further information on the content and objectives of the research, please refer to the website (https://architrave.eu/) and given literature. Three visualizations were created for the website: the travel stops of five of the travelers on their way to Paris and Versailles the sites in Europe mentioned in the six travelogues the sites in Paris described by the six travelers The visualizations were implemented with Leaflet.js. The dataset contains scripts for data crunching processed geodata scripts for leaflet.js License README

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