Visual Arrangements of Bar Charts Influence Comparisons in Viewer Takeaways

Cindy Xiong*, Vidya Setlur, Benjamin Bach, Eunyee Koh, Kylie Lin, Steven Franconeri

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

Well-designed data visualizations can lead to more powerful and intuitive processing by a viewer. To help a viewer intuitively compare values to quickly generate key takeaways, visualization designers can manipulate how data values are arranged in a chart to afford particular comparisons. Using simple bar charts as a case study, we empirically tested the comparison affordances of four common arrangements: vertically juxtaposed, horizontally juxtaposed, overlaid, and stacked. We asked participants to type out what patterns they perceived in a chart and we coded their takeaways into types of comparisons. In a second study, we asked data visualization design experts to predict which arrangement they would use to afford each type of comparison and found both alignments and mismatches with our findings. These results provide concrete guidelines for how both human designers and automatic chart recommendation systems can make visualizations that help viewers extract the 'right' takeaway.

Original languageEnglish (US)
Pages (from-to)955-965
Number of pages11
JournalIEEE Transactions on Visualization and Computer Graphics
Volume28
Issue number1
DOIs
StatePublished - Jan 1 2022

Keywords

  • Comparison
  • bar charts
  • natural language interaction
  • perception
  • recommendation systems
  • visual grouping

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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