Albers Danielle, Correll Michael, Gleicher Michael
University of Wisconsin-Madison 1210 W Dayton St, Madison, WI, 53706, USA
Proc SIGCHI Conf Hum Factor Comput Syst. 2014;2014:551-560. doi: 10.1145/2556288.2557200.
Many visualization tasks require the viewer to make judgments about aggregate properties of data. Recent work has shown that viewers can perform such tasks effectively, for example to efficiently compare the maximums or means over ranges of data. However, this work also shows that such effectiveness depends on the designs of the displays. In this paper, we explore this relationship between aggregation task and visualization design to provide guidance on matching tasks with designs. We combine prior results from perceptual science and graphical perception to suggest a set of design variables that influence performance on various aggregate comparison tasks. We describe how choices in these variables can lead to designs that are matched to particular tasks. We use these variables to assess a set of eight different designs, predicting how they will support a set of six aggregate time series comparison tasks. A crowd-sourced evaluation confirms these predictions. These results not only provide evidence for how the specific visualizations support various tasks, but also suggest using the identified design variables as a tool for designing visualizations well suited for various types of tasks.
许多可视化任务要求观察者对数据的聚合属性进行判断。最近的研究表明，观察者能够有效地执行此类任务，例如高效地比较数据范围内的最大值或平均值。然而，这项研究也表明，这种有效性取决于显示的设计。在本文中，我们探讨了聚合任务与可视化设计之间的这种关系，以便为任务与设计的匹配提供指导。我们结合了感知科学和图形感知的先前结果，提出了一组影响各种聚合比较任务性能的设计变量。我们描述了这些变量的选择如何导致与特定任务相匹配的设计。我们使用这些变量来评估一组八种不同的设计，预测它们将如何支持一组六个聚合时间序列比较任务。一项众包评估证实了这些预测。这些结果不仅为特定可视化如何支持各种任务提供了证据，还建议将识别出的设计变量用作设计非常适合各种类型任务的可视化的工具。
