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

Scatter Plot01:15

Scatter Plot

6.8K
The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:
6.8K
Relative Frequency Histogram01:14

Relative Frequency Histogram

5.4K
The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
5.4K
Multiple Bar Graph01:07

Multiple Bar Graph

5.1K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
5.1K
Residual Plots01:07

Residual Plots

4.6K
A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...
4.6K
Modified Boxplots00:57

Modified Boxplots

9.6K
A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
However, the box plot does not tell the reader about outliers - values that lie far from the center of the data. We can modify the standard box and whisker plot to identify the outliers and visualize the actual spread of the data in a sample.
Initially, we calculate the adjusted...
9.6K
Interpreting Run Charts01:25

Interpreting Run Charts

100
Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...
100

You might also read

Related Articles

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

Sort by
Same author

Probabilistic Inclusion Depth for Fuzzy Contour Ensemble Visualization.

IEEE transactions on visualization and computer graphics·2026
Same author

A Multimodal Framework for Understanding Collaborative Design Processes.

IEEE transactions on visualization and computer graphics·2026
Same author

Motif Simplification for BioFabric Network Visualizations: Improving Pattern Recognition and Interpretation.

IEEE transactions on visualization and computer graphics·2025
Same author

Visual explainable artificial intelligence for graph-based visual question answering and scene graph curation.

Visual computing for industry, biomedicine, and art·2025
Same author

Uncertainty-Aware Spectral Visualization.

IEEE transactions on visualization and computer graphics·2025
Same author

Understanding Collaborative Learning of Molecular Structures in AR with Eye Tracking.

IEEE computer graphics and applications·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: Jun 28, 2025

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

953

Comparative Evaluation of Animated Scatter Plot Transitions.

Nils Rodrigues, Frederik L Dennig, Vincent Brandt

    IEEE Transactions on Visualization and Computer Graphics
    |April 16, 2024
    PubMed
    Summary
    This summary is machine-generated.

    Animations help trace data points across scatter plot views. Rotations with orthographic cameras or staged depth axis expansion best preserve point traceability in multivariate data analysis.

    More Related Videos

    Testing Visual Sensitivity to the Speed and Direction of Motion in Lizards
    12:30

    Testing Visual Sensitivity to the Speed and Direction of Motion in Lizards

    Published on: December 14, 2006

    11.5K
    Automated Interactive Video Playback for Studies of Animal Communication
    07:21

    Automated Interactive Video Playback for Studies of Animal Communication

    Published on: February 9, 2011

    13.5K

    Related Experiment Videos

    Last Updated: Jun 28, 2025

    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

    953
    Testing Visual Sensitivity to the Speed and Direction of Motion in Lizards
    12:30

    Testing Visual Sensitivity to the Speed and Direction of Motion in Lizards

    Published on: December 14, 2006

    11.5K
    Automated Interactive Video Playback for Studies of Animal Communication
    07:21

    Automated Interactive Video Playback for Studies of Animal Communication

    Published on: February 9, 2011

    13.5K

    Area of Science:

    • Data Visualization
    • Human-Computer Interaction
    • Scientific Computing

    Background:

    • Multivariate data analysis often requires visualizing high-dimensional datasets.
    • Traditional methods like scatter plot matrices (SPLOMs) or grand tours can be challenging for tracking data points across different views.
    • Maintaining a mental map of data points during view transitions is crucial for effective analysis.

    Purpose of the Study:

    • To evaluate the effectiveness of different animation techniques for preserving data point traceability in multivariate scatter plots.
    • To compare spline- and rotation-based view transitions under ecologically valid conditions.
    • To determine if animation direction impacts task accuracy for tracing points and clusters.

    Main Methods:

    • Conducted a crowdsourced user study with a focus on ecological validity.
    • Evaluated various spline- and rotation-based animation techniques for scatter plot view transitions.
    • Assessed the ability of participants to trace individual points and clusters across different views.
    • Investigated the impact of rotation order (horizontal vs. vertical) on task performance.

    Main Results:

    • Rotations using an orthographic camera or staged depth axis expansion significantly improved individual point traceability compared to other methods.
    • A ranking of animation techniques for individual point traceability was established.
    • No significant differences were found in the traceability of clusters across different animation techniques.
    • Differences in animation direction were observed, suggesting potential confounds for future research.

    Conclusions:

    • Orthographic camera rotations and staged depth axis expansion are recommended for enhancing individual point traceability in multivariate scatter plot visualizations.
    • Current animation techniques show limited effectiveness in improving cluster traceability.
    • Further research is needed to understand the influence of animation direction and identify potential confounds.
    • The study data and animation framework (D3.js plug-in) are publicly available for reuse.