Effects of Point Size and Opacity Adjustments in Scatterplots

要旨

Systematically changing the size and opacity of points on scatterplots can be used to induce more accurate perceptions of correlation by viewers. Evidence points to the mechanisms behind these effects being similar, so one may expect their combination to be additive regarding their effects on correlation estimation. We present a fully-reproducible study in which we combine techniques for influencing correlation perception to show that in reality, effects of changing point size and opacity interact in a non-additive fashion. We show that there is a great deal of scope for using visual features to change viewers’ perceptions of data visualizations. Additionally, we use our results to further interrogate the perceptual mechanisms at play when changing point size and opacity in scatterplots.

著者
Gabriel Strain
University of Manchester, Manchester, United Kingdom
Andrew J. Stewart
University of Manchester, Manchester, United Kingdom
Paul A. Warren
University of Manchester, Manchester, United Kingdom
Caroline Jay
University of Manchester, Manchester, United Kingdom
論文URL

doi.org/10.1145/3613904.3642127

動画

会議: CHI 2024

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2024.acm.org/)

セッション: Data Visualization: Charts

314
5 件の発表
2024-05-13 23:00:00
2024-05-14 00:20:00