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

Area Computation by the Alternative Coordinate Method01:24

Area Computation by the Alternative Coordinate Method

365
The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
365
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

944
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
944
Cluster Sampling Method01:20

Cluster Sampling Method

13.7K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
13.7K
Graphical Representation of Inequalities01:28

Graphical Representation of Inequalities

72
The graph of the equation where y equals x squared forms a curve known as a parabola. This curve acts as a boundary in the coordinate plane, dividing it into distinct regions based on the relative position of points.When the equality sign in the equation is replaced with an inequality—such as greater than, less than, greater than or equal to, or less than or equal to—the graphical representation changes from a single curve into a broader shaded area that signifies the set of all...
72
Graphs of Functions01:30

Graphs of Functions

70
Graphs of functions provide a visual representation of how output values change in response to varying inputs. Each point on the graph corresponds to an ordered pair, where the x-coordinate (independent variable) determines the horizontal position and the y-coordinate (dependent variable) determines the vertical position. Linear functions like y = x give a straight line, indicating a constant rate of change.Nonlinear functions display more complex behaviors. Even power functions generate...
70
Design Example: Maintaining Level of an Embankment01:19

Design Example: Maintaining Level of an Embankment

278
Constructing a roadway embankment over uneven terrain requires precise leveling to ensure stability and proper drainage. Surveyors use a leveling instrument and staff to calculate ground elevations and determine the required fill material at each point along the embankment alignment.The process begins by positioning a leveling instrument near a benchmark with a known elevation. A backsight reading establishes the instrument height, which serves as a reference for subsequent measurements. A...
278

You might also read

Related Articles

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

Sort by
Same author

Chat Modeling: Interaction-Enhanced Agent Framework for Visualizing Literature-Grounded Biological Structures.

IEEE transactions on visualization and computer graphics·2026
Same author

AIvaluateXR: An Evaluation Framework for On-Device AI in XR With Benchmarking Results.

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

MidSurfer: A Parameter-Free Approach for Mid-Surface Extraction From Segmented Volumetric Data.

IEEE computer graphics and applications·2025
Same author

Nanouniverse: Virtual Instancing of Structural Detail and Adaptive Shell Mapping.

IEEE transactions on visualization and computer graphics·2025
Same author

Bundling-Aware Graph Drawing Revisited.

IEEE transactions on visualization and computer graphics·2025
Same journal

Two-phase Impulse Fluid on Particle Flow Map.

IEEE transactions on visualization and computer graphics·2026
Same journal

FGO-SLAM++: Real-time Geometry-Aware Gaussian SLAM with Continuous Opacity Field.

IEEE transactions on visualization and computer graphics·2026
Same journal

Blue Noise Dithering for Reservoir-based Spatio-temporal Importance Resampling.

IEEE transactions on visualization and computer graphics·2026
Same journal

ROS-GS: Relightable Outdoor Scenes With Gaussian Splatting.

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

Related Experiment Video

Updated: Nov 30, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.2K

Multi-Level Area Balancing of Clustered Graphs.

Hsiang-Yun Wu, Martin Nollenburg, Ivan Viola

    IEEE Transactions on Visualization and Computer Graphics
    |November 17, 2020
    PubMed
    Summary
    This summary is machine-generated.

    We developed a new multi-level area balancing technique for clustered graphs. This method improves understanding of complex relationships in fields like life sciences and sociology by optimizing label placement.

    More Related Videos

    Modeling the Functional Network for Spatial Navigation in the Human Brain
    05:55

    Modeling the Functional Network for Spatial Navigation in the Human Brain

    Published on: October 13, 2023

    1.4K
    Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
    08:45

    Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

    Published on: October 24, 2012

    15.0K

    Related Experiment Videos

    Last Updated: Nov 30, 2025

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
    12:27

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

    Published on: February 15, 2017

    7.2K
    Modeling the Functional Network for Spatial Navigation in the Human Brain
    05:55

    Modeling the Functional Network for Spatial Navigation in the Human Brain

    Published on: October 13, 2023

    1.4K
    Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
    08:45

    Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

    Published on: October 24, 2012

    15.0K

    Area of Science:

    • Computer Science
    • Data Visualization
    • Graph Theory

    Background:

    • Clustered graphs are vital for modeling attribute-based groupings in complex datasets.
    • Effective visualization of these groupings is crucial for data analysis in fields like life sciences and sociology.
    • Current methods struggle to optimally arrange textual labels and packed graphs within limited screen space.

    Purpose of the Study:

    • To present a novel multi-level area balancing technique for laying out clustered graphs.
    • To enhance the comprehensive understanding of complex relationships represented by attribute-based groupings.
    • To improve the conveyance of attribute data through optimized visual arrangement.

    Main Methods:

    • Hierarchical partitioning of screen space using multi-level Voronoi tessellations.
    • Guiding textual label positions via a blend of constrained forces and centroidal Voronoi cell forces.
    • Focusing on area balancing to achieve uniform area allocation for each textual label.
    • Untangling general graphs to clustered graphs using textual label duplication and spanning-tree-like visual integration.

    Main Results:

    • Demonstrated the feasibility of the proposed multi-level area balancing technique through illustrative examples.
    • Evaluated the method's effectiveness by comparing it against conventional approaches.
    • Gathered positive feedback from domain experts on the clarity and utility of the visualizations.

    Conclusions:

    • The proposed technique effectively addresses the challenge of laying out clustered graphs with attribute-based groupings.
    • The method facilitates a more comprehensive understanding of complex relationships by optimizing visual representation.
    • This approach offers a significant improvement for data analysis requiring clear visualization of grouped data.