A Testing Environment for Continuous Colormaps

Data-Driven Colormap Adjustment for Exploring Spatial Variations in Scalar Fields

Colormapping is an effective and popular visualization technique for analyzing patterns in scalar fields. Scientists usually adjust a default colormap to show hidden patterns by shifting the colors in a trial-and-error process. To improve efficiency, efforts have been made to automate the colormap adjustment process based on data properties (e.g., statistical data value or histogram distribution). However, as the data properties have no direct correlation to the spatial variations, previous methods may be insufficient to reveal the dynamic range of spatial variations hidden in the data. To address the above issues, we conduct a pilot analysis with domain experts and summarize three requirements for the colormap adjustment process. Based on the requirements, we formulate colormap adjustment as an objective function, composed of a boundary term and a fidelity term, which is flexible enough to support interactive functionalities. We compare our approach with alternative methods under a quantitative measure and a qualitative user study (25 participants), based on a set of data with broad distribution diversity. We further evaluate our approach via three case studies with six domain experts. Our method is not necessarily more optimal than alternative methods of revealing patterns, but rather is an additional color adjustment option for exploring data with a dynamic range of spatial variations.

Rainbow Colormaps: What are they good and bad for?

Guidelines for color use in quantitative visualizations have strongly discouraged the use of rainbow colormaps, arguing instead for smooth designs that do not induce visual discontinuities or implicit color categories. However, the empirical evidence behind this argument has been mixed and, at times, even contradictory. In practice, rainbow colormaps are widely used, raising questions about the true utility or dangers of such designs. We study how color categorization impacts the interpretation of scalar fields. We first introduce an approach to detect latent categories in colormaps. We hypothesize that the appearance of color categories in scalar visualizations can be beneficial in that they enhance the perception of certain features, although at the cost of rendering other features less noticeable. In three crowdsourced experiments, we show that observers are more likely to discriminate global, distributional features when viewing colorful scales that induce categorization (e.g., rainbow or diverging schemes). Conversely, when seeing the same data through a less colorful representation, observers are more likely to report localized features defined by small variations in the data. Participants showed awareness of these different affordances, and exhibited bias for exploiting the more discriminating colormap, given a particular feature type. Our results demonstrate costs and benefits for rainbows (and similarly colorful schemes), suggesting that their complementary utility for analyzing scalar data should not be dismissed. In addition to explaining potentially valid uses of rainbow, our study provides actionable guidelines, including on when such designs can be more harmful than useful. Data and materials are available at https://osf.io/xjhtf.