Affective Color Scales for Colormap Data Visualizations

TL;DR

This paper demonstrates that colormaps can be designed to maintain spatial detail through lightness contrast while conveying emotional connotations, emphasizing the importance of data-aware design in affective visualization.

Contribution

It introduces a method for creating colormaps with strong lightness contrast that preserve spatial details and communicate affective connotations, considering data-dependent color frequency effects.

Findings

Affective connotations depend on color scales and color frequency in the data.

It is possible to design colormaps with both spatial clarity and emotional communication.

Data-aware design enhances the effectiveness of visualizations.

Abstract

Research on affective visualization design has shown that color is an especially powerful feature for influencing the emotional connotation of visualizations. Associations between colors and emotions are largely driven by lightness (e.g., lighter colors are associated with positive emotions, whereas darker colors are associated with negative emotions). Designing visualizations to have all light or all dark colors to convey particular emotions may work well for visualizations in which colors represent categories and spatial channels encode data values. However, this approach poses a problem for visualizations that use color to represent spatial patterns in data (e.g., colormap data visualizations) because lightness contrast is needed to reveal fine details in spatial structure. In this study, we found it is possible to design colormaps that have strong lightness contrast to support spatial vision while communicating clear affective connotation. We also found that affective connotation depended not only on the color scales used to construct the colormaps, but also the frequency with which colors appeared in the map, as determined by the underlying dataset (data-dependence hypothesis). These results emphasize the importance of data-aware design, which accounts for not only the design features that encode data (e.g., colors, shapes, textures), but also how those design features are instantiated in a visualization, given the properties of the data.