Jove
Visualize
联系我们

相关概念视频

Bar Graph01:07

Bar Graph

16.0K
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...
16.0K
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
Ogive Graph01:07

Ogive Graph

5.6K
An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
5.6K
Time-Series Graph00:54

Time-Series Graph

4.3K
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...
4.3K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Reflections on Visualizing the COVID-19 Pandemic for the Public.

IEEE computer graphics and applications·2026
Same author

Anatomy of a Swedish population-scale network.

Scientific reports·2025
Same author

Empowering Communities: Tailored Pandemic Data Visualization for Varied Tasks and Users.

IEEE computer graphics and applications·2025
Same author

Visual digital intermediaries and global climate communication: Is climate change still a distant problem on YouTube?

PloS one·2025
Same author

Immigrant-critical alternative media in online conversations.

PloS one·2023
Same author

The Long COVID experience from a patient's perspective: a clustering analysis of 27,216 Reddit posts.

Frontiers in public health·2023
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关实验视频

Updated: Jun 13, 2025

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

11.4K

通过可排序的节点链路布局改善了图形集群的视觉突出性.

Nora Al-Naami, Nicolas Medoc, Matteo Magnani

    IEEE transactions on visualization and computer graphics
    |September 11, 2024
    PubMed
    概括
    此摘要是机器生成的。

    可排序的节点链路图显著改善了图表中的集群识别. 用户可以使用这些图表准确且快速识别集群,而不是以力导向布局,特别是在较不明显的集群中.

    更多相关视频

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    487
    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

    1.0K

    相关实验视频

    Last Updated: Jun 13, 2025

    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

    11.4K
    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    487
    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

    1.0K

    科学领域:

    • 图形可视化的图形可视化
    • 数据分析数据分析
    • 信息可视化 信息可视化

    背景情况:

    • 图形模型关系,集群可视化有助于在各种领域的洞察力发现.
    • 强力导向布局提高了集群可见性,但缺乏直观的节点排序.
    • 矩阵布局提供了排序,但缺乏节点链接比喻.

    研究的目的:

    • 研究节点排序对可排序节点链接图中的集群视觉突出性的影响.
    • 为了比较可排序的节点链接图的有效性与最先进的力定向图表布局算法.

    主要方法:

    • 众包受控实验 众包受控实验 众包受控实验
    • 对射线图,弧形图和对称弧形图的评估.
    • 与"Linlog"",Backbone"和"sfdp"的强力导向布局进行比较.

    主要成果:

    • 用户通过可排序的节点链接图来更准确,更快地计算集群.
    • 这一优势在集群分离性和/或紧性较低的情况下更为明显.
    • 可排序的节点链路图表超过了经过测试的强力定向算法.

    结论:

    • 节点链路图中的节点排序增强了集群可视化.
    • 可排序的节点链路图为集群识别提供了一个更有效的替代力量定向布局.
    • 这些发现对于需要清晰的图形集群表示的应用程序至关重要.