Measuring Categorical Perception in Color-Coded Scatterplots

Abstract

Scatterplots commonly use color to encode categorical data. However, as datasets increase in size and complexity, the efficacy of these channels may vary. Designers lack insight into how robust different design choices are to variations in category numbers. This paper presents a crowdsourced experiment measuring how the number of categories and choice of color encodings used in multiclass scatterplots influences the viewers' abilities to analyze data across classes. Participants estimated relative means in a series of scatterplots with 2 to 10 categories encoded using ten color palettes drawn from popular design tools. Our results show that the number of categories and color discriminability within a color palette notably impact people's perception of categorical data in scatterplots and that the judgments become harder as the number of categories grows. We examine existing palette design heuristics in light of our results to help designers make robust color choices informed by the parameters of their data. CCS CONCEPTS• Human-centered computing → Empirical studies in visualization.

An empirical study of counterfactual visualization to support visual causal inference

“…Task-based analyses are a common approach, in which researchers explore and summarize how to design visualizations to aid understanding for specific tasks, such as low-level graphical perception 22,23 and color design. 24,25 In addition to task-based studies, researchers have also investigated how to assess and model users' understanding and ability to communicate visualizations through evaluating study strategy design, 26,27 and visual quality measures. 28,29 Interdisciplinary insights have also been proposed, such as Bae et al's assessment of how curiosity and play in physicalizations improve data visualization literacy in children's education.…”