Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Plotting of Topographic Maps01:29

Plotting of Topographic Maps

310
Topographic maps represent the Earth's surface features using contour lines, which connect points of equal elevation to create a two-dimensional representation of three-dimensional terrain. Creating a topographic map requires a systematic approach.Begin by plotting a scaled grid and marking intersections corresponding to the survey's elevation data points. Assign elevation values at these intersections to build the base map. Next, determine contour levels using a consistent contour interval,...
310
Levels of Use of a GIS01:29

Levels of Use of a GIS

229
Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
229
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

173
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
173
Design Example: Alignment of a Road Line Using GIS01:17

Design Example: Alignment of a Road Line Using GIS

224
The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
224
Thematic Layering in GIS01:30

Thematic Layering in GIS

194
In the past, planning projects such as schools or public facilities required extensive manual effort to gather and compile data. Information such as property boundaries, soil characteristics, road networks, zoning regulations, and flood zones had to be sourced individually from courthouses, utility providers, and registry offices. Assembling these datasets into a coherent format often took several months, delaying project timelines.The introduction of Geographic Information Systems (GIS)...
194
Manipulation and Analysis01:21

Manipulation and Analysis

205
GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
205

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Exploring MLLMs Perception of Network Visualization Principles.

IEEE transactions on visualization and computer graphics·2026
Same author

Visualization Tasks for Unlabeled Graphs.

IEEE transactions on visualization and computer graphics·2026
Same author

How Scale Breaks "Normalized Stress" and KL Divergence: Rethinking Quality Metrics.

IEEE transactions on visualization and computer graphics·2026
Same author

F<sup>2</sup>Stories: A Modular Framework for Multi-Objective Optimization of Storylines with a Focus on Fairness.

IEEE transactions on visualization and computer graphics·2025
Same author

Bundling-Aware Graph Drawing Revisited.

IEEE transactions on visualization and computer graphics·2025
Same author

GraphTrials: Visual Proofs of Graph Properties.

IEEE transactions on visualization and computer graphics·2025
Same journal

MesoSplats: Texture Synthesis with Gaussian Splatting.

IEEE transactions on visualization and computer graphics·2026
Same journal

GLLA: A Unified Force-Directed Graph Layout Framework Supporting Local Adjustments.

IEEE transactions on visualization and computer graphics·2026
Same journal

Multi-Perception Crowd: Learning to combine entity and implicit perception for diverse crowd simulation.

IEEE transactions on visualization and computer graphics·2026
Same journal

Hiding in Plain Sight: Camouflaging Real-world Objects.

IEEE transactions on visualization and computer graphics·2026
Same journal

RTF2Mesh: Restricted Tangent Face Based Mesh Compression With Neural Displacement Fields.

IEEE transactions on visualization and computer graphics·2026
Same journal

Practical Occluder Generation for Mobile Games.

IEEE transactions on visualization and computer graphics·2026
See all related articles

Related Experiment Video

Updated: Dec 5, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.9K

MetroSets: Visualizing Sets as Metro Maps.

Ben Jacobsen, Markus Wallinger, Stephen Kobourov

    IEEE Transactions on Visualization and Computer Graphics
    |October 14, 2020
    PubMed
    Summary
    This summary is machine-generated.

    MetroSets visualizes complex set systems using an intuitive metro map metaphor. This flexible online tool represents set elements as stations and subsets as lines, aiding data comprehension.

    More Related Videos

    Visualizing Methane-Cycling Microbial Dynamics in Coastal Wetlands
    07:26

    Visualizing Methane-Cycling Microbial Dynamics in Coastal Wetlands

    Published on: January 31, 2025

    693
    Using Generative Art to Convey Past and Future Climate Transitions
    06:10

    Using Generative Art to Convey Past and Future Climate Transitions

    Published on: March 31, 2023

    1.3K

    Related Experiment Videos

    Last Updated: Dec 5, 2025

    Trajectory Data Analyses for Pedestrian Space-time Activity Study
    16:14

    Trajectory Data Analyses for Pedestrian Space-time Activity Study

    Published on: February 25, 2013

    13.9K
    Visualizing Methane-Cycling Microbial Dynamics in Coastal Wetlands
    07:26

    Visualizing Methane-Cycling Microbial Dynamics in Coastal Wetlands

    Published on: January 31, 2025

    693
    Using Generative Art to Convey Past and Future Climate Transitions
    06:10

    Using Generative Art to Convey Past and Future Climate Transitions

    Published on: March 31, 2023

    1.3K

    Area of Science:

    • Computer Science
    • Data Visualization
    • Graph Theory

    Background:

    • Visualizing complex set systems is challenging.
    • Existing methods lack intuitive representations for hypergraphs.
    • Metro map metaphors offer a novel approach to data representation.

    Purpose of the Study:

    • Introduce MetroSets, a novel online tool for visualizing set systems.
    • Develop a flexible pipeline for generating metro map representations of hypergraphs.
    • Evaluate the effectiveness of the MetroSets pipeline on real-world datasets.

    Main Methods:

    • Model set systems as hypergraphs (vertices V, hyperedges S).
    • Employ a 4-step pipeline: hypergraph support construction, optimization, layout, and schematization.
    • Utilize metro map layout algorithms for visualization.
    • Implement and evaluate multiple algorithms for each pipeline stage.

    Main Results:

    • MetroSets generates metro map visualizations of set systems.
    • The pipeline optimizes for properties like octolinearity, monotonicity, and edge uniformity.
    • A functional prototype with preset configurations is available.
    • Quantitative evaluation demonstrates the impact of pipeline stages on map properties.

    Conclusions:

    • MetroSets provides a flexible and intuitive method for visualizing set systems.
    • The proposed pipeline effectively generates optimized metro map representations.
    • The tool and evaluation offer valuable insights for data visualization research.