Automatic Y-axis Rescaling in Dynamic Visualizations
Automatic Y-axis Rescaling in Dynamic Visualizations
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DOI：
10.1109/vis49827.2021.9623319
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发表时间：
2021-09
期刊：
影响因子：
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通讯作者：
J. Fisher;Remco Chang;Eugene Wu
中科院分区：
文献类型：
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作者：
J. Fisher;Remco Chang;Eugene Wu
动画和交互式数据可视化动态地改变可视化中呈现的数据（例如，条形图）。随着数据的变化，y轴可能需要随着数据域的变化而重新缩放。每个轴重新调整可能会提高当前图表的可读性，但也可能使用户迷失方向。与静态可视化相比，有相当多的文献来帮助选择适当的y轴尺度，缺乏关于如何以及何时在动态可视化中使用重新缩放的指导。现有的可视化系统和库采用固定的全局y轴，或在每次数据更改时重新缩放。然而，专业的可视化，如数据新闻，并不采用这两种策略。相反，他们会根据分析任务和数据仔细手动选择何时重新缩放。为此，我们进行了一系列的Mechanical Turk实验，以研究动态轴重新缩放的潜力以及影响其有效性的因素。我们发现，适当的重新缩放政策是任务和数据依赖的，我们没有找到一个明确的政策选择，所有情况下。
Animated and interactive data visualizations dynamically change the data rendered in a visualization (e.g., bar chart). As the data changes, the y-axis may need to be rescaled as the domain of the data changes. Each axis rescaling potentially improves the readability of the current chart, but may also disorient the user. In contrast to static visualizations, where there is considerable literature to help choose the appropriate y-axis scale, there is a lack of guidance about how and when rescaling should be used in dynamic visualizations. Existing visualization systems and libraries adapt a fixed global y-axis, or rescale every time the data changes. Yet, professional visualizations, such as in data journalism, do not adopt either strategy. They instead carefully and manually choose when to rescale based on the analysis task and data. To this end, we conduct a series of Mechanical Turk experiments to study the potential of dynamic axis rescaling and the factors that affect its effectiveness. We find that the appropriate rescaling policy is both task- and data-dependent, and we do not find one clear policy choice for all situations.