Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
38 tokens/sec
GPT-4o
59 tokens/sec
Gemini 2.5 Pro Pro
41 tokens/sec
o3 Pro
7 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Visual Arrangements of Bar Charts Influence Comparisons in Viewer Takeaways (2108.06370v1)

Published 13 Aug 2021 in cs.HC

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 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.

User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (6)
  1. Cindy Xiong (15 papers)
  2. Vidya Setlur (31 papers)
  3. Benjamin Bach (31 papers)
  4. Kylie Lin (2 papers)
  5. Eunyee Koh (36 papers)
  6. Steven Franconeri (12 papers)
Citations (27)