  Visualizations often encode numeric data using sequential and diverging color
ramps. Effective ramps use colors that are sufficiently discriminable, align
well with the data, and are aesthetically pleasing. Designers rely on years of
experience to create high-quality color ramps. However, it is challenging for
novice visualization developers that lack this experience to craft effective
ramps as most guidelines for constructing ramps are loosely defined qualitative
heuristics that are often difficult to apply. Our goal is to enable
visualization developers to readily create effective color encodings using a
single seed color. We do this using an algorithmic approach that models
designer practices by analyzing patterns in the structure of designer-crafted
color ramps. We construct these models from a corpus of 222 expert-designed
color ramps, and use the results to automatically generate ramps that mimic
designer practices. We evaluate our approach through an empirical study
comparing the outputs of our approach with designer-crafted color ramps. Our
models produce ramps that support accurate and aesthetically pleasing
visualizations at least as well as designer ramps and that outperform
conventional mathematical approaches.
