Abstract: Color is frequently used to encode values in visualizations. For color encodings to be effective, the mapping between colors and values must preserve important differences in the data. However, most guidelines for effective color choice in visualization are based on either color perceptions measured using large, uniform fields in optimal viewing environments or on qualitative intuitions. These limitations may cause data misinterpretation in visualizations, which frequently use small, elongated marks. Our goal is to develop quantitative metrics to help people use color more effectively in visualizations. We present a series of crowdsourced studies measuring color difference perceptions for three common mark types: points, bars, and lines. Our results indicate that peoples’ abilities to perceive color differences varies significantly across mark types. Probabilistic models constructed from the resulting data can provide objective guidance for designers, allowing them to anticipate viewer perceptions in order to inform effective encoding design.
Below are the supplemental materials for "Modeling Color Difference for Visualization Design." The data captures our intermediate models of color difference perceptions for data visualizations modeled over three different mark types: diagonally symmetric marks (e.g., points in a scatterplot), elongated marks (e.g., bars in a bar chart), and asymmetric elongated marks (e.g., lines in a line graph). The data and infrastructures can be used to replicate and extend the models presented in the paper.