Rainbows Revisited: Modeling Effective Colormap Design for Graphical Inference

Reda, Khairi; Szafir, Danielle Albers

doi:10.1109/tvcg.2020.3030439

Cited by 38 publications

(45 citation statements)

References 59 publications

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“…The above results partially-but not entirely-confirm recent work, which found a positive effect for color name variation [RS21]. However, this work extends those earlier results in several impor-tant ways.…”

Section: Nameability Predicts Accuracy In Graphical Inferencesupporting

confidence: 91%

Color Nameability Predicts Inference Accuracy in Spatial Visualizations

Reda¹,

Salvi²,

Gray³

et al. 2021

Preprint

Self Cite

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Color encoding is foundational to visualizing quantitative data. Guidelines for colormap design have traditionally emphasized perceptual principles, such as order and uniformity. However, colors also evoke cognitive and linguistic associations whose role in data interpretation remains underexplored. We study how two linguistic factors, name salience and name variation, affect people's ability to draw inferences from spatial visualizations. In two experiments, we found that participants are better at interpreting visualizations when viewing colors with more salient names (e.g., prototypical 'blue', 'yellow', and 'red' over 'teal', 'beige', and 'maroon'). The effect was robust across four visualization types, but was more pronounced in continuous (e.g., smooth geographical maps) than in similar discrete representations (e.g., choropleths). Participants' accuracy also improved as the number of nameable colors increased, although the latter had a less robust effect. Our findings suggest that color nameability is an important design consideration for quantitative colormaps, and may even outweigh traditional perceptual metrics. In particular, we found that the linguistic associations of color are a better predictor of performance than the perceptual properties of those colors. We discuss the implications and outline research opportunities. The data and materials for this study are available at https://osf.io/asb7n

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Section: Nameability Predicts Accuracy In Graphical Inferencesupporting

confidence: 91%

Color Nameability Predicts Inference Accuracy in Spatial Visualizations

Reda¹,

Salvi²,

Gray³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…More specifically, we expect the effects to hold in smooth (e.g., scalar fields) and discrete representations (e.g., choropleths). H1 was proposed by Reda and Szafir [RS21], who found support for it. However, they were unable to uniquely attribute the observed benefits to linguistic associations or to perceptual factors (color name variation was highly correlated with perceptual discriminability in their setup).…”

Section: Hypothesis Developmentmentioning

confidence: 90%

Color Nameability Predicts Inference Accuracy in Spatial Visualizations

Reda

Salvi

Gray

et al. 2021

Computer Graphics Forum

Self Cite

View full text Add to dashboard Cite

Color encoding is foundational to visualizing quantitative data. Guidelines for colormap design have traditionally emphasized perceptual principles, such as order and uniformity. However, colors also evoke cognitive and linguistic associations whose role in data interpretation remains underexplored. We study how two linguistic factors, name salience and name variation, affect people's ability to draw inferences from spatial visualizations. In two experiments, we found that participants are better at interpreting visualizations when viewing colors with more salient names (e.g., prototypical ‘blue’, ‘yellow’, and ‘red’ over ‘teal’, ‘beige’, and ‘maroon’). The effect was robust across four visualization types, but was more pronounced in continuous (e.g., smooth geographical maps) than in similar discrete representations (e.g., choropleths). Participants' accuracy also improved as the number of nameable colors increased, although the latter had a less robust effect. Our findings suggest that color nameability is an important design consideration for quantitative colormaps, and may even outweigh traditional perceptual metrics. In particular, we found that the linguistic associations of color are a better predictor of performance than the perceptual properties of those colors. We discuss the implications and outline research opportunities. The data and materials for this study are available at https://osf.io/asb7n

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“…Third, our study focuses on a single visual task -exploring gradual spatial variations. We would like to extend this framework to support more applicable analytical tasks in the future, such as quantity estimation [24], value comparison [2], feature detection [7], and graphical inference [40]. Fourth, colormap design for dynamical exploration of multi-scale data has been addressed in several previous works [49], [50] that introduced interactive factors (e.g., viewing direction) into the categorical color design.…”

Section: Discussionmentioning

confidence: 99%

“…R2: Colormap preservation of the default colormap. Scientists often prefer to encode data with familiar colormaps [5], [11], [14], e.g., the rainbow colormap is still popular despite its tendency to introduce misleading patterns [39], [40]. Our domain expert E6 expressed that her user experience working with a familiar colormap can improve the efficiency of her data analysis, and unexpected changes in the appearance of a colormap may reduce efficiency.…”

Section: Requirements For Colormap Adjustmentmentioning

confidence: 99%

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

Qiong

Zhao

Wang

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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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.

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Rainbows Revisited: Modeling Effective Colormap Design for Graphical Inference

Cited by 38 publications

References 59 publications

Color Nameability Predicts Inference Accuracy in Spatial Visualizations

Color Nameability Predicts Inference Accuracy in Spatial Visualizations

Color Nameability Predicts Inference Accuracy in Spatial Visualizations

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

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