Affective Color Scales for Colormap Data Visualizations

TL;DR

The paper addresses how to design colormaps that support fine spatial detail while conveying affective connotations. It systematically varies hue, lightness, and chroma across 16 color scales and applies them to real raster data, validating affective associations with eight emotions and revealing a two-dimensional affect space tied to valence and activation. Experiment 2 demonstrates the data-dependence hypothesis: affective connotations depend not only on color scales but also on how often colors appear in the visualization, with data-aware models providing superior predictions. The findings guide data-aware colormap design, highlighting practical implications for promoting engagement and interpretation in visualizations that depict spatial patterns.

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.

Figures (15)

• Figure 1: Visualizations of categorical data (bar chart) and continuous data (colormap) using colors that have (A) low or (B) high lightness contrast.
• Figure 2: Illustration of how color scales were generated in Color Crafter by adjusting lightness and chroma sliders (note, in Color Crafter the scale controlling chroma is labeled 'saturation'). The charts rightward of slider pairs show Color Crafter's charts of the 9-point color scale plotted in terms of lightness (L) and chroma (C) of CIELCh space. The inset in each plot shows an example colormap produced from that color scale.
• Figure 3: Example trials for rating color-emotion associations for colormaps.
• Figure 4: Mean association ratings for colormaps created from each of the 16 color scales and the eight affective concepts. The x-axis represents hue (Red, Yellow, Green, Blue) and the separate lines represent lightness (dark: dark gray, light: light gray) and chroma (high chroma: solid, low chroma: dashed). The mark color represents the middle color of the color scale used to construct the corresponding colormaps. Error bars represent +/- standard errors of the means (SEM).
• Figure 5: Summary of LMER model results for (A) Exp. 1 and (B) Exp. 2 (see Supplementary Material Tables \ref{['tab:exp1-lmer-results-2col']} and \ref{['tab:exp2-lmer-results-2col']} for the full output). The factors included lightness (L*), chroma (C*), red/green (a*), yellow/blue (b*), and interactions therein. Dots with $+$/$-$ symbols indicate significant effects with positive/negative beta weights, respectively ('ns' indicates not significant). Dot diameter is proportional to beta weight to represent effect size (diameter minimum was set to 1.5 pixels to ensure dot visibility).