Understand users’ comprehension and preferences for composing information visualizations
Understand users’ comprehension and preferences for composing information visualizations
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了解用户对构建信息可视化的理解和偏好
DOI：
10.1145/2541288
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Michelle X. Zhou
中科院分区：
文献类型：
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作者：
Huahai Yang;Yunyao Li;Michelle X. Zhou
我们正在开发一种自动化可视化系统，帮助用户将两个或更多现有的信息图形组合在一起，形成一个集成的视图。为了为构建这样的系统奠定经验基础，我们设计并进行了两项关于Amazon Machine Turk的研究，以了解用户在不同条件下(如数据和任务)对复合可视化的理解和偏好。在研究1中，我们收集了1500多个文本描述，捕捉了大约500名参与者对给定信息图形的洞察力，这导致了一种面向任务的视觉洞察力分类。在研究2中，我们要求240名参与者根据他们对获得研究1中确定的给定视觉洞察力的适宜性来对复合可视化进行排名，这导致了对获得每种类型洞察力的视觉成分的用户偏好的排名。在这篇文章中，我们报告了我们两项研究的细节，并讨论了我们的众包研究方法和结果对人机界面驱动的可视化研究的更广泛的影响。
We are developing an automated visualization system that helps users combine two or more existing information graphics to form an integrated view. To establish empirical foundations for building such a system, we designed and conducted two studies on Amazon Mechanical Turk to understand users’ comprehension and preferences of composite visualization under different conditions (e.g., data and tasks). In Study 1, we collected more than 1,500 textual descriptions capturing about 500 participants’ insights of given information graphics, which resulted in a task-oriented taxonomy of visual insights. In Study 2, we asked 240 participants to rank composite visualizations by their suitability for acquiring a given visual insight identified in Study 1, which resulted in ranked user preferences of visual compositions for acquiring each type of insight. In this article, we report the details of our two studies and discuss the broader implications of our crowdsourced research methodology and results to HCI-driven visualization research.