论文标题
DU BOIS包装条形图：可视化不成比例的分类数据
Du Bois Wrapped Bar Chart: Visualizing categorical data with disproportionate values
论文作者
论文摘要
我们提出了一种可视化技术，杜波依斯包装了条形图，灵感来自W.E.B du Bois的作品。 Du Bois包装的条形图可以通过在一定的阈值上包装大小条，从而可以更好地比较大型小杆。我们首先提出了两个比较包装和标准条形图的众包实验，以评估（1）包装条的好处，以帮助参与者识别和比较值； （2）最适合包裹条的数据的特性。在使用现实世界数据集的第一项研究（n = 98）中，我们发现包裹的条形图在识别和估计条之间的比率方面提高了更高的准确性。在一项具有13个模拟数据集的后续研究（n = 190）中，我们发现，当某些类别值不成比例时，参与者始终使用包裹的条形图更加准确，如熵和H-Spread所测量。最后，在一项单位研究中，我们研究了参与者的经验和策略，从而为何时以及如何使用包裹的条形图提供了指南。
We propose a visualization technique, Du Bois wrapped bar chart, inspired by work of W.E.B Du Bois. Du Bois wrapped bar charts enable better large-to-small bar comparison by wrapping large bars over a certain threshold. We first present two crowdsourcing experiments comparing wrapped and standard bar charts to evaluate (1) the benefit of wrapped bars in helping participants identify and compare values; (2) the characteristics of data most suitable for wrapped bars. In the first study (n=98) using real-world datasets, we find that wrapped bar charts lead to higher accuracy in identifying and estimating ratios between bars. In a follow-up study (n=190) with 13 simulated datasets, we find participants were consistently more accurate with wrapped bar charts when certain category values are disproportionate as measured by entropy and H-spread. Finally, in an in-lab study, we investigate participants' experience and strategies, leading to guidelines for when and how to use wrapped bar charts.
