Du Bois Wrapped Bar Chart: Visualizing categorical data with disproportionate values

TL;DR

The paper introduces Du Bois wrapped bar charts, a visualization method that improves comparison of large and small categorical data values, supported by crowdsourcing experiments showing increased accuracy.

Contribution

It presents a novel wrapped bar chart technique inspired by W.E.B Du Bois, with empirical evidence demonstrating its effectiveness for disproportionate data.

Findings

Wrapped bar charts improve accuracy in value identification.

Participants perform better with wrapped bars on disproportionate data.

Guidelines for effective use of wrapped bar charts are provided.

Abstract

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.