Ondov Brian, Jardine Nicole, Elmqvist Niklas, Franconeri Steven
IEEE Trans Vis Comput Graph. 2018 Aug 20. doi: 10.1109/TVCG.2018.2864884.
Data are often viewed as a single set of values, but those values frequently must be compared with another set. The existing evaluations of designs that facilitate these comparisons tend to be based on intuitive reasoning, rather than quantifiable measures. We build on this work with a series of crowdsourced experiments that use low-level perceptual comparison tasks that arise frequently in comparisons within data visualizations (e.g., which value changes the most between the two sets of data?). Participants completed these tasks across a variety of layouts: overlaid, two arrangements of juxtaposed small multiples, mirror-symmetric small multiples, and animated transitions. A staircase procedure sought the difficulty level (e.g., value change delta) that led to equivalent accuracy for each layout. Confirming prior intuition, we observe high levels of performance for overlaid versus standard small multiples. However, we also find performance improvements for both mirror symmetric small multiples and animated transitions. While some results are incongruent with common wisdom in data visualization, they align with previous work in perceptual psychology, and thus have potentially strong implications for visual comparison designs.
数据通常被视为一组单一的值，但这些值常常必须与另一组进行比较。现有的有助于这些比较的设计评估往往基于直观推理，而非可量化的指标。我们在此工作基础上开展了一系列众包实验，这些实验使用了数据可视化中比较时经常出现的低级感知比较任务（例如，两组数据中哪个值变化最大？）。参与者在各种布局下完成这些任务：叠加布局、两种并列小倍数排列布局、镜像对称小倍数布局以及动画过渡布局。一种阶梯程序寻找导致每种布局具有同等准确性的难度级别（例如，值变化量）。正如先前的直觉所证实的，我们观察到叠加布局与标准小倍数布局相比具有较高的性能。然而，我们还发现镜像对称小倍数布局和动画过渡布局的性能也有所提升。虽然有些结果与数据可视化的常识不一致，但它们与感知心理学的先前研究一致，因此对视觉比较设计可能具有重要意义。
