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.

中文翻译：

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Du Bois 环绕条形图：可视化具有不成比例值的分类数据

我们提出了一种可视化技术，Du Bois 包裹条形图，其灵感来自 WEB Du Bois 的工作。Du Bois 包裹条形图通过将大条形包裹在特定阈值之上，实现更好的大到小条形比较。我们首先展示了两个众包实验，比较了包装条形图和标准条形图，以评估 (1) 包装条形图在帮助参与者识别和比较值方面的好处；(2) 数据的特性最适合包裹条。在使用真实世界数据集的第一项研究 (n=98) 中，我们发现包装条形图在识别和估计条形之间的比率方面具有更高的准确性。在一项包含 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通过熵和 H-spread 测量不成比例时，参与者使用包裹条形图始终更准确。最后，