On the Readability of Abstract Set Visualizations
On the Readability of Abstract Set Visualizations
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DOI：
10.1109/tvcg.2021.3074615
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发表时间：
2021-01
影响因子：
5.2
通讯作者：
Mark Wallinger;Benjamin N. Jacobsen;S. Kobourov;M. Nöllenburg
中科院分区：
文献类型：
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作者：
Mark Wallinger;Benjamin N. Jacobsen;S. Kobourov;M. Nöllenburg
SET系统被用来对在许多情况下自然产生的数据进行建模：社交网络有社区，音乐家有流派，病人有症状。准确反映底层集合系统中的信息的可视化使识别集合元素、集合本身以及集合之间的关系成为可能。在静态环境中，例如平面媒体或信息图表，有必要在没有交互帮助的情况下捕获这些信息。考虑到这一点，我们考虑了三种不同的中等大小集合数据系统，LineSets、EulerView和MetroSets，并报告了受控人类受试者实验的结果，比较了它们的有效性。具体地说，我们在时间和误差方面评估了覆盖基于静态集合的任务频谱的任务的性能。我们还收集和分析了三种不同可视化系统的定性数据。我们的结果包括统计上的显著差异，这表明MetroSet的性能和可扩展性更好。
Set systems are used to model data that naturally arises in many contexts: social networks have communities, musicians have genres, and patients have symptoms. Visualizations that accurately reflect the information in the underlying set system make it possible to identify the set elements, the sets themselves, and the relationships between the sets. In static contexts, such as print media or infographics, it is necessary to capture this information without the help of interactions. With this in mind, we consider three different systems for medium-sized set data, LineSets, EulerView, and MetroSets, and report the results of a controlled human-subjects experiment comparing their effectiveness. Specifically, we evaluate the performance, in terms of time and error, on tasks that cover the spectrum of static set-based tasks. We also collect and analyze qualitative data about the three different visualization systems. Our results include statistically significant differences, suggesting that MetroSets performs and scales better.