Color Crafting: Automating the Construction of Designer Quality Color Ramps

Abstract

Fig. 1. Our approach uses design mining and unsupervised clustering techniques to produce automatically generated color ramps that capture designer practices. The four choropleth maps shown above utilize color ramps generated from our approach. Developers select a single guiding seed color, shown in the squares below each map, to generate a ramp. We then fit curves capturing structural patterns in designer practices in CIELAB (bottom) to these seed colors to generate ramps (middle, seed colors indicated by a black dot). We embody this technique in Color Crafter, a web-based tool that enables designers of all ability levels to generate high-quality custom color ramps.Abstract-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.

Rainbows Revisited: Modeling Effective Colormap Design for Graphical Inference

“…However, more recent work suggests that assumptions around colormap design, such as the assertion that CIELAB matches perceived differences in data marks [52,54], may not hold in practice. A recent analysis of practices also found that designer colormaps often do not obey conventional guidelines [53]. Empirical evaluations of colormap performance point to conflicting results that often cannot be reconciled with the guidelines [41,42].…”

Better sensitivity to linear and nonlinear trends with position than with color

“…Many designer color tools, such as ColorBrewer, generate color ramps that transverse nonlinear paths through the perceptual color space Cyclical noise of 0 means the simulated data contained only random noise. (Smart et al, 2020). The diverging red-blue color palette used here is symmetrical, meaning that there is an equal number of colors on either side of the midpoint.…”

Color Nameability Predicts Inference Accuracy in Spatial Visualizations

“…We selected a set of 6 colormaps for our experiments representing various designs (see Figure 2). We first started with a corpus of 235 color scales that were collected from a variety of sources by Smart et al [SWS19]. We then selected three colormap pairs with increasing degrees of name variation and, by extension, increasing levels Curves show the running name salience (blue) and name distance (red) measurements across the scale.…”