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

12.4K
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:
12.4K
Time-Series Graph00:54

Time-Series Graph

5.5K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
5.5K
Bar Graph01:07

Bar Graph

23.5K
A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
23.5K
Multiple Bar Graph01:07

Multiple Bar Graph

10.4K
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...
10.4K
Review and Preview01:13

Review and Preview

12.0K
Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
12.0K
Review and Preview01:10

Review and Preview

8.7K
In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
Percentiles are a type of fractile that partition data into...
8.7K

You might also read

Related Articles

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

Sort by
Same author

Augmented BindingNet dataset for enhanced ligand binding pose predictions using deep learning.

npj drug discovery·2026
Same author

Dual-Modal Safety Framework for Robotic-Assisted Bronchoscopy via Endoscopic Vision and Haptic Feedback.

The international journal of medical robotics + computer assisted surgery : MRCAS·2026
Same author

Controlled Synthesis of Cyclopenta-Fused B<sub>2</sub>N<sub>2</sub>-Pyrene and Diazaborepin: Structures and Photophysical Properties.

Organic letters·2026
Same author

Engineering and characterization of a novel PD-L1/VEGF bispecific antibody with enhanced VEGF-neutralizing capacity.

Biochemical and biophysical research communications·2026
Same author

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

IEEE transactions on visualization and computer graphics·2026
Same author

Enhancing Line Density Plots with Outlier Control and Bin-Based Illumination.

IEEE transactions on visualization and computer graphics·2026

Related Experiment Video

Updated: Mar 8, 2026

Rapid Analysis and Exploration of Fluorescence Microscopy Images
11:41

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Published on: March 19, 2014

12.8K

Line Graph or Scatter Plot? Automatic Selection of Methods for Visualizing Trends in Time Series.

Yunhai Wang, Fubo Han, Lifeng Zhu

    IEEE Transactions on Visualization and Computer Graphics
    |January 17, 2017
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an algorithm to automatically select the best visualization for time series data trends. It measures visual consistency to determine if line graphs or scatter plots best reveal trends, aligning with user preferences.

    More Related Videos

    ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
    05:12

    ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

    Published on: January 16, 2019

    12.0K
    Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
    10:58

    Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

    Published on: January 2, 2011

    10.5K

    Related Experiment Videos

    Last Updated: Mar 8, 2026

    Rapid Analysis and Exploration of Fluorescence Microscopy Images
    11:41

    Rapid Analysis and Exploration of Fluorescence Microscopy Images

    Published on: March 19, 2014

    12.8K
    ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
    05:12

    ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

    Published on: January 16, 2019

    12.0K
    Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
    10:58

    Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

    Published on: January 2, 2011

    10.5K

    Area of Science:

    • Data Visualization
    • Human-Computer Interaction
    • Time Series Analysis

    Background:

    • Line graphs are commonly used for time series data, but optimal visualization for trend clarity remains unaddressed.
    • Scatter plots are sometimes used for time series trends, but guidelines for choosing between methods are lacking.

    Purpose of the Study:

    • To develop an algorithm that automatically selects the most effective visualization (line graph or scatter plot) for displaying time series trends.
    • To provide objective criteria for choosing between line graphs and scatter plots based on trend clarity.

    Main Methods:

    • An algorithm was developed to measure the visual consistency between a time series trend (LOESS fit) and its representation in line graphs and scatter plots.
    • An empirical study with controlled experiments was conducted to compare algorithm performance with user choices.
    • Factor analysis was used to identify visual and data factors influencing visualization preference.

    Main Results:

    • The proposed algorithm demonstrates a high degree of correspondence with user preferences in selecting optimal time series visualizations.
    • Visual consistency measurement effectively predicts the best visualization for trend representation.
    • Factor analysis revealed significant effects of visual and data characteristics on user preference for line graphs versus scatter plots.

    Conclusions:

    • The developed algorithm offers an automated solution for selecting the best visualization method to reveal time series trends.
    • This work provides valuable insights into the factors influencing the choice of visualization for time series data.
    • Findings can guide the design of more effective data visualization tools for trend analysis.