A Bounded Measure for Estimating the Benefit of Visualization: Case Studies and Empirical Evaluation
Min Chen, Alfie Abdul-Rahman, Deborah Silver, Mateu Sbert
备注：
Following the SciVis 2020 reviewers' request for more explanation and clarification, the origianl article, "A Bounded Measure for Estimating the Benefit of Visualization, arxiv:2002.05282", has been split into two articles, on "Theoretical Discourse and Conceptual Evaluation" and "Case Studies and Empirical Evaluation" respectively. This is the second article
Many visual representations, such as volume-rendered images and metro maps, feature a noticeable amount of information loss. At a glance, there seem to be numerous opportunities for viewers to misinterpret the data being visualized, hence undermining the benefits of these visual representations. In practice, there is little doubt that these visual representations are useful. The recently-proposed information-theoretic measure for analyzing the cost-benefit ratio of visualization processes can explain such usefulness experienced in practice, and postulate that the viewers' knowledge can reduce the potential distortion (e.g., misinterpretation) due to information loss. This suggests that viewers' knowledge can be estimated by comparing the potential distortion without any knowledge and the actual distortion with some knowledge. In this paper, we describe several case studies for collecting instances that can (i) support the evaluation of several candidate measures for estimating the potential distortion distortion in visualization, and (ii) demonstrate their applicability in practical scenarios. Because the theoretical discourse on choosing an appropriate bounded measure for estimating the potential distortion is yet conclusive, it is the real world data about visualization further informs the selection of a bounded measure, providing practical evidence to aid a theoretical conclusion. Meanwhile, once we can measure the potential distortion in a bounded manner, we can interpret the numerical values characterizing the benefit of visualization more intuitively.
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我们所面临的视觉交流量正在迅速增加，对处理、理解和生成视觉表示的技能需求很高。特别是专注于计算机图形和可视化的学生可以从更多样化的视觉素养教育中受益，因为他们毕业后经常需要为广大群众进行图形表示。我们提出的教学方法将基本的设计思维原则融入传统的可视化和图形教育。我们的课程受到了《Dear Data》一书的启发，这本书是IEEE VIS 2017闭幕式上热烈讨论的主题。本文概述了我们为期12周的教学实验，并总结了附带的问卷调查和访谈提取的结果。特别是，我们提供了可视化新手的创作过程和痛点的见解，讨论了可视化任务和设计思维之间的相互作用，并最终得出一般的视觉素养教育的设计启示。
