  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.
