Du Bois Wrapped Bar Chart: Visualizing Categorical Data with Disproportionate Values

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.

Keywords
bar chart
graphical perception
user study
evaluation
Mechanical Turk
crowdsourcing
Information visualization
Authors
Alireza Karduni
University of North Carolina at Charlotte, Charlotte, NC, USA
Ryan Wesslen
University of North Carolina at Charlotte, Charlotte, NC, USA
Isaac Cho
North Carolina A&T State University, Greensboro, NC, USA
Wenwen Dou
University of North Carolina at Charlotte, Charlotte, NC, USA
DOI

10.1145/3313831.3376365

Paper URL

https://doi.org/10.1145/3313831.3376365

Video

Conference: CHI 2020

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2020.acm.org/)

Session: Perception of visualizations

Paper session
316A MAUI
5 items in this session
2020-04-29 09:00:00
2020-04-29 10:15:00
Japanese summary
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