Visualizing Sets with Linear Diagrams
Visualizing Sets with Linear Diagrams
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DOI：
10.1145/2810012
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发表时间：
2015-12-01
影响因子：
3.7
通讯作者：
Chapman, Peter
中科院分区：
文献类型：
--
作者：
Rodgers, Peter;Stapleton, Gem;Chapman, Peter
本文提出了使用线性图优化集合可视化的第一个设计原则。这些原则通过评估图形特征对任务性能影响的实证研究得到证实。线性图使用直线段表示集合，线重叠对应于集合的交点。这项研究建立在最近的实证研究的基础上，该研究表明线性图可以优于著名的集合可视化技术，即欧拉图和维恩图。我们解决如何使用线性图最好地可视化重叠集的问题。为了解决这个问题，我们研究了线性图的哪些图形特征显着影响用户任务性能。为此，我们进行了七项众包实证研究，共涉及 1,760 名参与者。这些研究使我们能够确定以下设计原则，这对任务性能有很大帮助：使用最少数量的线段，在重叠的开始和结束处使用指导方针，以及绘制细线而不是粗条。我们还评估了以下不会显着影响任务性能的图形属性：颜色、方向和设置顺序。通过免费提供的软件实现，可以通过用户控制的图形选择自动绘制线性图，从而使结果变得栩栩如生。我们研究的一个重要结果是，用户现在能够自动创建有效的集合可视化，从而改善人机交互。
This paper presents the first design principles that optimize the visualization of sets using linear diagrams. These principles are justified through empirical studies that evaluate the impact of graphical features on task performance. Linear diagrams represent sets using straight line segments, with line overlaps corresponding to set intersections. This study builds on recent empirical research, which establishes that linear diagrams can be superior to prominent set visualization techniques, namely Euler and Venn diagrams. We address the problem of how to best visualize overlapping sets using linear diagrams. To solve the problem, we investigate which graphical features of linear diagrams significantly impact user task performance. To this end, we conducted seven crowdsourced empirical studies involving a total of 1,760 participants. These studies allowed us to identify the following design principles, which significantly aid task performance: use a minimal number of line segments, use guidelines where overlaps start and end, and draw lines that are thin as opposed to thick bars. We also evaluated the following graphical properties that did not significantly impact task performance: color, orientation, and set order. The results are brought to life through a freely available software implementation that automatically draws linear diagrams with user-controlled graphical choices. An important consequence of our research is that users are now able to create effective visualizations of sets automatically, thus improving human-computer interaction.