Ryan Gabriel, Mosca Abigail, Chang Remco, Wu Eugene
IEEE Trans Vis Comput Graph. 2018 Aug 20. doi: 10.1109/TVCG.2018.2865264.
When inspecting information visualizations under time critical settings, such as emergency response or monitoring the heart rate in a surgery room, the user only has a small amount of time to view the visualization "at a glance". In these settings, it is important to provide a quantitative measure of the visualization to understand whether or not the visualization is too "complex" to accurately judge at a glance. This paper proposes Pixel Approximate Entropy (PAE), which adapts the approximate entropy statistical measure commonly used to quantify regularity and unpredictability in time-series data, as a measure of visual complexity for line charts. We show that PAE is correlated with user-perceived chart complexity, and that increased chart PAE correlates with reduced judgement accuracy. 'We also find that the correlation between PAE values and participants' judgment increases when the user has less time to examine the line charts.
在诸如应急响应或手术室心率监测等时间紧迫的场景下检查信息可视化时，用户仅拥有少量时间“一眼”查看可视化内容。在这些场景中，提供可视化的定量度量以了解该可视化是否过于“复杂”而无法一眼准确判断是很重要的。本文提出了像素近似熵（PAE），它采用通常用于量化时间序列数据中的规律性和不可预测性的近似熵统计度量，作为折线图视觉复杂性的一种度量。我们表明PAE与用户感知的图表复杂性相关，并且图表PAE的增加与判断准确性的降低相关。我们还发现，当用户检查折线图的时间较少时，PAE值与参与者判断之间的相关性会增加。
