Mapping Color to Meaning in Colormap Data Visualizations

Abstract

To interpret data visualizations, people must determine how visual features map onto concepts. For example, to interpret colormaps, people must determine how dimensions of color (e.g., lightness, hue) map onto quantities of a given measure (e.g., brain activity, correlation magnitude). This process is easier when the encoded mappings in the visualization match people's predictions of how visual features will map onto concepts, their inferred mappings. To harness this principle in visualization design, it is necessary to understand what factors determine people's inferred mappings. In this study, we investigated how inferred color-quantity mappings for colormap data visualizations were influenced by the background color. Prior literature presents seemingly conflicting accounts of how the background color affects inferred color-quantity mappings. The present results help resolve those conflicts, demonstrating that sometimes the background has an effect and sometimes it does not, depending on whether the colormap appears to vary in opacity. When there is no apparent variation in opacity, participants infer that darker colors map to larger quantities (dark-is-more bias). As apparent variation in opacity increases, participants become biased toward inferring that more opaque colors map to larger quantities (opaque-is-more bias). These biases work together on light backgrounds and conflict on dark backgrounds. Under such conflicts, the opaque-is-more bias can negate, or even supersede the dark-is-more bias. The results suggest that if a design goal is to produce colormaps that match people's inferred mappings and are robust to changes in background color, it is beneficial to use colormaps that will not appear to vary in opacity on any background color, and to encode larger quantities in darker colors.

Color Crafting: Automating the Construction of Designer Quality Color Ramps

“…Correll et al [16] show how manipulating ramp structure can support uncertainty estimation. Schloss et al [66] found that the background color in a visualization affects the inferred color mapping between color ramps and data, depending on whether the color ramp varies in apparent opacity. Liu & Heer [44] evaluate how different design parameters, such as color name and perceptual distance, influence encoding accuracy for popular color ramps.…”

A Study of Colormaps in Network Visualization

“…Mapping data to visual channels (e.g., position, size, and color) can reveal unseen patterns and insights in the data. In many existing studies (e.g., [17,[19][20][21]), it is well documented that among the different visual channels, color is more powerful than size, shape, and orientation [22]. These studies also characterize the properties of a color scale that result in useful and effective colormaps [1].…”

“…The effectiveness of a colormap depends on the data and task at hand [2,26,31]; thus, one must consider the type of data in use before encoding to a certain colormap. Studies show that sequential colormaps best represent sequential data, whereas diverging data with a neutral or average midpoint is best suited for diverging colormaps [22]. Tominski et al [4] designed a color mapping function to support tasks like comparison, localization, or identification of data values.…”

“…These primarily represent the values with a linear ramping in the luminance [25] and are used to represent information that has an order. High data values are represented by dark colors and low values by light colors [22]. Light-to-dark progression dominates the scheme, as studies have shown that dark colors contain more bias.…”

Estimating Color-Concept Associations from Image Statistics

“…First, cognitive representations of color have strong categorical structure [5,38,52], which naturally maps to categories of data [8,15]. Second, people have rich semantic associations with colors called color-concept associations (e.g., a particular red associated with strawberries, roses, and anger) [17,30,33], which they use to interpret meanings of colors in visualizations [22,23,40,41,43]. Indeed, it is easier to interpret visualizations if semantic encoding between colors and concepts (referred to as color-concept assignments) match people's expectations derived from their color-concept associations [23,40,41].…”