Cited By
View all- Samsel FScott WMoreland K(2024)A New Default Colormap for ParaViewIEEE Computer Graphics and Applications10.1109/MCG.2024.338313744:4(150-160)Online publication date: 20-Aug-2024
Rainbow Colormaps: What are They <italic>Good</italic> and <italic>Bad</italic> for?
Author: 
Khairi RedaAuthors Info & Claims
Khairi RedaAuthors Info & ClaimsPages 5496 - 5510
Abstract
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 <monospace><uri>https://osf.io/xjhtf</uri></monospace>
References
[1]
L. D. Bergman, B. E. Rogowitz, and L. A. Treinish, “A rule-based tool for assisting colormap selection,” in Proc. 6th Conf. Vis., 1995, pp. 118–125.
[2]
B. Berlin and P. Kay, Basic Color Terms: Their Universality and Evolution. Berkeley, CA, USA: Univ. of California Press, 1991.
[3]
M. Borkin et al., “Evaluation of artery visualizations for heart disease diagnosis,” IEEE Trans. Vis. Comput. Graph., vol. 17, no. 12, pp. 2479–2488, Dec. 2011.
[4]
D. Borland and A. Huber, “Collaboration-specific color-map design,” IEEE Comput. Graph. Appl., vol. 31, no. 4, pp. 7–11, Jul./Aug. 2011.
[5]
D. Borland and R. M. T. Ii, “Rainbow color map (still) considered harmful,” IEEE Comput. Graph. Appl., vol. 27, no. 2, pp. 14–17, Mar./Apr. 2007.
[6]
C. Brewer, “Spectral schemes: Controversial color use on maps,” Cartography Geographic Informat. Syst., vol. 24, no. 4, pp. 203–220, 1997.
[7]
C. A. Brewer, “Guidelines for selecting colors for diverging schemes on maps,” Cartographic J., vol. 33, no. 2, pp. 79–86, 1996.
[8]
A. Buja et al., “Statistical inference for exploratory data analysis and model diagnostics,” Philos. Trans. Roy. Soc. A Math., Phys. Eng. Sci., vol. 367, no. 1906, pp. 4361–4383, 2009.
[9]
R. Bujack, T. L. Turton, F. Samsel, C. Ware, D. H. Rogers, and J. Ahrens, “The good, the bad, and the ugly: A theoretical framework for the assessment of continuous colormaps,” IEEE Trans. Vis. Comput. Graph., vol. 24, no. 1, pp. 923–933, Jan. 2018.
[10]
F. Crameri, G. E. Shephard, and P. J. Heron, “The misuse of colour in science communication,” Nat. Commun., vol. 11, no. 1, pp. 1–10, 2020.
[11]
A. Dasgupta, J. Poco, B. Rogowitz, K. Han, E. Bertini, and C. T. Silva, “The effect of color scales on climate scientists’ objective and subjective performance in spatial data analysis tasks,” IEEE Trans. Vis. Comput. Graph., vol. 26, no. 3, pp. 1577–1591, Mar. 2020.
[12]
G. Derefeldt, T. Swartling, U. Berggrund, and P. Bodrogi, “Cognitive color,” Color Res. Appl., vol. 29, no. 1, pp. 7–19, 2004.
[13]
D. J. Field, A. Hayes, and R. F. Hess, “Contour integration by the human visual system: Evidence for a local “association field,” Vis. Res., vol. 33, no. 2, pp. 173–193, 1993.
[14]
I. Golebiowska and A. Coltekin, “Rainbow dash: Intuitiveness, interpretability and memorability of the rainbow color scheme in visualization,” IEEE Trans. Vis. Comput. Graph., vol. 28, no. 7, pp. 2722–2733, Jul. 2020.
[15]
D. A. Green, “A colour scheme for the display of astronomical intensity images,” Bull. Astronomical Soc. India, vol. 39, pp. 289–295, Jun. 2011.
[16]
M. Harrower and C. A. Brewer, “Colorbrewer. org: An online tool for selecting colour schemes for maps,” Cartogr. J., vol. 40, no. 1, pp. 27–37, 2003.
[17]
D. G. Hays, E. Margolis, R. Naroll, and D. R. Perkins, “Color term salience,” Amer. Anthropologist, vol. 74, no. 5, pp. 1107–1121, 1972.
[18]
J. Heer and M. Stone, “Color naming models for color selection, image editing and palette design,” in Proc. SIGCHI Conf. Hum. Factors Comput. Syst., 2012, pp. 1007–1016.
[19]
H. Hofmann, L. Follett, M. Majumder, and D. Cook, “Graphical tests for power comparison of competing designs,” IEEE Trans. Vis. Comput. Graph., vol. 18, no. 12, pp. 2441–2448, Dec. 2012.
[20]
L. Huang and H. Pashler, “A boolean map theory of visual attention,” Psychol. Rev., vol. 114, no. 3, 2007, Art. no.
[21]
A. D. Kalvin, B. E. Rogowitz, A. Pelah, and A. Cohen, “Building perceptual color maps for visualizing interval data,” in Proc. Hum. Vis. Electron. Imag. V, 2000, pp. 323–336.
[22]
N. Kaye, A. Hartley, and D. Hemming, “Mapping the climate: Guidance on appropriate techniques to map climate variables and their uncertainty,” GeoSci. Model Develop., vol. 5, no. 1, pp. 245–256, 2012.
[23]
A. Light and P. J. Bartlein, “The end of the rainbow? color schemes for improved data graphics,” Eos, Trans. Amer. GeoPhys. Union, vol. 85, no. 40, pp. 385–391, 2004.
[24]
Y. Liu and J. Heer, “Somewhere over the rainbow: An empirical assessment of quantitative colormaps,” in Proc. CHI Conf. Hum. Factors Comput. Syst., 2018, Art. no.
[25]
K. Moreland, “Diverging color maps for scientific visualization,” in Proc. Int. Symp. Vis. Comput., 2009, pp. 92–103.
[26]
K. Moreland, “Why we use bad color maps and what you can do about it,” Electron. Imag., vol. 2016, no. 16, pp. 1–6, 2016.
[27]
T. Munzner, Visualization Analysis and Design, Boca Raton, FL, USA: CRC Press, 2014.
[28]
P. Nardini, M. Chen, R. Bujack, M. Bottinger, and G. Scheuermann, “A testing environment for continuous colormaps,” IEEE Trans. Vis. Comput. Graph., vol. 27, no. 2, pp. 1043–1053, Feb. 2020.
[29]
G. V. Paramei, “Singing the russian blues: An argument for culturally basic color terms,” Cross-Cultural Res., vol. 39, no. 1, pp. 10–38, 2005.
[30]
P. S. Quinan, L. Padilla, S. H. Creem-Regehr, and M. Meyer, “Examining implicit discretization in spectral schemes,” in Computer Graphics Forum, vol. 38. Hoboken, NJ, USA: Wiley, 2019, pp. 363–374.
[31]
K. Reda, P. Nalawade, and K. Ansah-Koi, “Graphical perception of continuous quantitative maps: The effects of spatial frequency and colormap design,” in Proc. CHI Conf. Hum. Factors Comput. Syst., 2018, Art. no.
[32]
K. Reda and M. E. Papka, “Evaluating gradient perception in color-coded scalar fields,” in Proc. IEEE Vis. Conf., 2019, pp. 271–275.
[33]
K. Reda, A. A. Salvi, J. Gray, and M. E. Papka, “Color nameability predicts inference accuracy in spatial visualizations,” in Computer Graphics Forum, vol. 40. Hoboken, NJ, USA: Wiley, 2021, pp. 49–60.
[34]
K. Reda and D. A. Szafir, “Rainbows revisited: Modeling effective colormap design for graphical inference,” IEEE Trans. Vis. Comput. Graph., vol. 27, no. 2, pp. 1032–1042, Feb. 2021.
[35]
B. E. Rogowitz and A. D. Kalvin, “The which blair project: A quick visual method for evaluating perceptual color maps,” in Proc. Visualization, 2001, pp. 183–556.
[36]
B. E. Rogowitz, A. D. Kalvin, A. Pelah, and A. Cohen, “Which trajectories through which perceptually uniform color spaces produce appropriate colors scales for interval data?,” in Proc. Color Imag. Conf., 1999, pp. 321–326.
[37]
B. E. Rogowitz and L. A. Treinish, “Data visualization: The end of the rainbow,” IEEE Spectr., vol. 35, no. 12, pp. 52–59, Dec. 1998.
[38]
B. E. Rogowitz et al., “How not to lie with visualization,” Comput. Phys., vol. 10, no. 3, pp. 268–273, 1996.
[39]
S. Silva, B. S. Santos, and J. Madeira, “Using color in visualization: A survey,” Comput. Graph., vol. 35, no. 2, pp. 320–333, 2011.
[40]
A. E. Skelton, G. Catchpole, J. T. Abbott, J. M. Bosten, and A. Franklin, “Biological origins of color categorization,” Proc. Nat. Acad. Sci. USA, vol. 114, no. 21, pp. 5545–5550, 2017.
[41]
S. Smart, K. Wu, and D. A. Szafir, “Color crafting: Automating the construction of designer quality color ramps,” IEEE Trans. Vis. Comput. Graph., vol. 26, no. 1, pp. 1215–1225, Jan. 2019.
[42]
M. Sun, L. Hu, X. Xin, and X. Zhang, “Neural hierarchy of color categorization: From prototype encoding to boundary encoding,” Front. Neurosci., vol. 15, no. 679627, 2021, Art. no.
[43]
D. A. Szafir, “Modeling color difference for visualization design,” IEEE Trans. Vis. Comput. Graph., vol. 24, no. 1, pp. 392–401, Jan. 2018.
[44]
K. M. Thyng, C. A. Greene, R. D. Hetland, H. M. Zimmerle, and S. F. DiMarco, “True colors of oceanography: Guidelines for effective and accurate colormap selection,” Oceanography, vol. 29, no. 3, pp. 9–13, 2016.
[45]
C. Tominski, G. Fuchs, and H. Schumann, “Task-driven color coding,” in Proc. 12th Int. Conf. Inf. Vis., 2008, pp. 373–380.
[46]
S. van der Walt and N. Smith, “Matplotlib colormaps,” 2015. Accessed: Apr. 20, 2022. [Online]. Available: https://bids.github.io/colormap/
[47]
S. VanderPlas and H. Hofmann, “Spatial reasoning and data displays,” IEEE Trans. Vis. Comput. Graph., vol. 22, no. 1, pp. 459–468, Jan. 2016.
[48]
S. VanderPlas and H. Hofmann, “Clusters beat trend!? testing feature hierarchy in statistical graphics,” J. Comput. Graphical Statist., vol. 26, no. 2, pp. 231–242, 2017.
[49]
M. Q. Wang Baldonado, A. Woodruff, and A. Kuchinsky, “Guidelines for using multiple views in information visualization,” in Proc. Work. Conf. Adv. Vis. Interfaces, 2000, pp. 110–119.
[50]
C. Ware, “Color sequences for univariate maps: Theory, experiments and principles,” IEEE Comput. Graph. Appl., vol. 8, no. 5, pp. 41–49, Sep. 1988.
[51]
C. Ware,” Information Visualization: Perception for Design. New York, NY, USA: Elsevier, 2012.
[52]
C. Ware, T. L. Turton, R. Bujack, F. Samsel, P. Shrivastava, and D. H. Rogers, “Measuring and modeling the feature detection threshold functions of colormaps,” IEEE Trans. Vis. Comput. Graph., vol. 25, no. 9, pp. 2777–2790, Sep. 2019.
[53]
H. Wickham, D. Cook, H. Hofmann, and A. Buja, “Graphical inference for infovis,” IEEE Trans. Vis. Comput. Graph., vol. 16, no. 6, pp. 973–979, Nov./Dec. 2010.
[54]
J. Winawer, N. Witthoft, M. C. Frank, L. Wu, A. R. Wade, and L. Boroditsky, “Russian blues reveal effects of language on color discrimination,” Proc. Nat. Acad. Sci. USA, vol. 104, no. 19, pp. 7780–7785, 2007.
[55]
L. Zhou and C. D. Hansen, “A survey of colormaps in visualization,” IEEE Trans. Vis. Comput. Graph., vol. 22, no. 8, pp. 2051–2069, Aug. 2016.
Index Terms
- Rainbow Colormaps: What are They
Good andBad for?
Index terms have been assigned to the content through auto-classification.
Recommendations
Naturalness Preserved Image Enhancement Using
a Priori Multi-Layer Lightness StatisticsThe enhancement of non-uniformly illuminated images often suffers from over-enhancement and produces unnatural results. This paper presents a naturalness preserved enhancement method for non-uniformly illuminated images, using <italic>a priori</italic> ...
Rainbow Colormaps Are Not All Bad
Some 15 years ago, Visualization Viewpoints published an influential article titled Rainbow Color Map (Still) Considered Harmful (Borland and Taylor, 2007). The paper argued that the “rainbow colormap’s characteristics of confusing the viewer, obscuring ...
Laplacian colormaps: a framework for structure-preserving color transformations
Mappings between color spaces are ubiquitous in image processing problems such as gamut mapping, decolorization, and image optimization for color-blind people. Simple color transformations often result in information loss and ambiguities, and one wishes ...
Comments
Information & Contributors
Information
Published In
1077-2626 © 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.
Publisher
IEEE Educational Activities Department
United States
Publication History
Published: 10 November 2023
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 06 Jan 2025
Other Metrics
Citations
Cited By
View all- Samsel FScott WMoreland K(2024)A New Default Colormap for ParaViewIEEE Computer Graphics and Applications10.1109/MCG.2024.338313744:4(150-160)Online publication date: 20-Aug-2024