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Vis Inf  2020, Vol. 4 Issue (4): 1-10    DOI: 10.1016/j.visinf.2020.09.004
论文     

采用散点图的交互式数据可视化方法评估

Quang Vinh Nguyena,b,Natalie Millerc,David Arnessc,Weidong Huangd,Mao Lin Huange,Simeon Simoffa,b
aMARCS Institute, Western Sydney University, Australia bSchool of Computer, Data and Mathematical Sciences, Western Sydney University, Australia cSchool of Psychology, Western Sydney University, Australia dFaculty of Transdisciplinary Innovation, University of Technology, Sydney, Australia eSchool of Software, Faculty of Engineering & IT, University of Technology, Sydney, Australia
Evaluation on interactive visualization data with scatterplots
Quang Vinh Nguyena,b,Natalie Millerc,David Arnessc,Weidong Huangd,Mao Lin Huange,Simeon Simoffa,b
aMARCS Institute, Western Sydney University, Australia bSchool of Computer, Data and Mathematical Sciences, Western Sydney University, Australia cSchool of Psychology, Western Sydney University, Australia dFaculty of Transdisciplinary Innovation, University of Technology, Sydney, Australia eSchool of Software, Faculty of Engineering & IT, University of Technology, Sydney, A
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摘要: 散点图和散点图矩阵方法已被广泛用于显示统计图形和揭示多元数据中蕴含的模式。最近一项称为可链接散点图的技术,为交互式可视探索提供了一种有趣的思路,它根据需要提供一组必要的绘图面板,可对这些面板进行交互、链接和涂刷。 本文介绍了一项采用混合模型设计的对照研究,以评估在使用顺序散点图(一次显示一个散点图)、多重散点图(可以指定和显示多个散点图和并行散点图(通过散点图矩阵显示所有散点图)时可视探索的有效性和用户体验。研究结果表明,使用多重散点图可视化能达到更高的精度,特别是与并行散点图相比。与较为简单的顺序散点图相比,虽然多重散点图技术在完成任务时稍显费时,但是其结果更准确。此外,多重散点图技术也是本项研究中最受欢迎和体验最佳的技术。


关键词: 多元数据可视化')" href="#">

多元数据可视化多维数据可视化散点图散点图矩阵对照研究     

Abstract: Scatterplots and scatterplot matrix methods have been popularly used for showing statistical graphics and for exposing patterns in multivariate data. A recent technique, called Linkable Scatterplots, provides an interesting idea for interactive visual exploration which provides a set of necessary plot panels on demand together with interaction, linking and brushing. This article presents a controlled study with a mixed-model design to evaluate the effectiveness and user experience on the visual exploration when using a Sequential-Scatterplots who a single plot is shown at a time, Multiple-Scatterplots who number of plots can be specified and shown, and Simultaneous-Scatterplots who all plots are shown as a scatterplot matrix. Results from the study demonstrated higher accuracy using the Multiple-Scatterplots visualization, particularly in comparison with the Simultaneous-Scatterplots. While the time taken to complete tasks was longer in the Multiple-Scatterplots technique, compared with the simpler Sequential-Scatterplots, Multiple-Scatterplots is inherently more accurate. Moreover, the Multiple-Scatterplots technique is the most highly preferred and positively experienced technique in this study. Overall, results support the strength of Multiple-Scatterplots and highlight its potential as an effective data visualization technique for exploring multivariate data.
Key words: Multivariate data visualization    Multidimensional data visualization    Scatterplots    Scatterplot matrix    Controlled study
出版日期: 2020-12-01
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Quang Vinh Nguyen
Natalie Miller
David Arness
Weidong Huang
Mao Lin Huang
Simeon Simoff

引用本文:

Quang Vinh Nguyen, Natalie Miller, David Arness, Weidong Huang, Mao Lin Huang, Simeon Simoff. Evaluation on interactive visualization data with scatterplots. Vis Inf, 2020, 4(4): 1-10.

链接本文:

http://www.zjujournals.com/vi/CN/10.1016/j.visinf.2020.09.004        http://www.zjujournals.com/vi/CN/Y2020/V4/I4/1

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