Accessible Color Sequences for Data Visualization

TL;DR

This paper introduces a method for generating aesthetically pleasing and accessible color sequences for data visualization, balancing user preference with considerations for color vision deficiencies and print contrast.

Contribution

It presents a data-driven approach combining aesthetic preference modeling with accessibility constraints to produce optimal color sequences for visualization.

Findings

Generated color sequences meet aesthetic and accessibility criteria.

Sequences are suitable for various visualization types like scatter and line plots.

The approach balances visual appeal with inclusivity for color-vision deficiencies.

Abstract

Color sequences, ordered sets of colors for data visualization, that balance aesthetics with accessibility considerations are presented. In order to model aesthetic preference, data were collected with an online survey, and the results were used to train a machine-learning model. To ensure accessibility, this model was combined with minimum-perceptual-distance constraints, including for simulated color-vision deficiencies, as well as with minimum-lightness-distance constraints for grayscale printing, maximum-lightness constraints for maintaining contrast with a white background, and scores from a color-saliency model for ease of use of the colors in verbal and written descriptions. Optimal color sequences containing six, eight, and ten colors were generated using the data-driven aesthetic-preference model and accessibility constraints. Due to the balance of aesthetics and accessibility considerations, the resulting color sequences can serve as reasonable defaults in data-plotting codes, e.g., for use in scatter plots and line plots.