research-article

Measuring Categorical Perception in Color-Coded Scatterplots

Authors:

Ghulam Jilani Quadri,

Danielle Albers SzafirAuthors Info & Claims

CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

Article No.: 824, Pages 1 - 14

https://doi.org/10.1145/3544548.3581416

Published: 19 April 2023 Publication History

Abstract

Scatterplots commonly use color to encode categorical data. However, as datasets increase in size and complexity, the efficacy of these channels may vary. Designers lack insight into how robust different design choices are to variations in category numbers. This paper presents a crowdsourced experiment measuring how the number of categories and choice of color encodings used in multiclass scatterplots influences the viewers’ abilities to analyze data across classes. Participants estimated relative means in a series of scatterplots with 2 to 10 categories encoded using ten color palettes drawn from popular design tools. Our results show that the number of categories and color discriminability within a color palette notably impact people’s perception of categorical data in scatterplots and that the judgments become harder as the number of categories grows. We examine existing palette design heuristics in light of our results to help designers make robust color choices informed by the parameters of their data.

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References

[1]

Cary L Anderson and Anthony C Robinson. 2021. Affective congruence in visualization design: Influences on reading categorical maps. IEEE Transactions on Visualization and Computer Graphics (2021).

[2]

Lyn Bartram, Abhisekh Patra, and Maureen Stone. 2017. Affective color in visualization. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. 1364–1374.

Digital Library

[3]

Jörg Blasius and Michael Greenacre. 1998. Visualization of categorical data. Academic Press.

[4]

David Borland and Russell M Taylor Ii. 2007. Rainbow color map (still) considered harmful. IEEE Computer Graphics and Applications 27, 2 (2007), 14–17.

Digital Library

[5]

Michael Bostock, Vadim Ogievetsky, and Jeffrey Heer. 2011. D³ Data-Driven Documents. IEEE Transactions on Visualization and Computer Graphics 17, 12(2011), 2301–2309. https://doi.org/10.1109/TVCG.2011.185

Digital Library

[6]

Jason J Braithwaite, Derrick G Watson, Lucy Andrews, and Glyn W Humphreys. 2010. Visual search at isoluminance: Evidence for enhanced color weighting in standard sub-set and preview-based visual search. Vision Research 50, 14 (2010), 1414–1425.

[7]

Cynthia A Brewer. 1994. Guidelines for use of the perceptual dimensions of color for mapping and visualization. In Color hard copy and graphic arts III, Vol. 2171. SPIE, 54–63.

[8]

Roxana Bujack, Terece L Turton, Francesca Samsel, Colin Ware, David H Rogers, and James Ahrens. 2017. The good, the bad, and the ugly: A theoretical framework for the assessment of continuous colormaps. IEEE transactions on visualization and computer graphics 24, 1(2017), 923–933.

[9]

David Burlinson, Kalpathi Subramanian, and Paula Goolkasian. 2017. Open vs. closed shapes: New perceptual categories?IEEE Transactions on Visualization and Computer Graphics 24, 1(2017), 574–583.

[10]

Carto. 2022. Location Intelligence & GIS for cloud natives. https://carto.com/

[11]

Jarry HT Claessen and Jarke J Van Wijk. 2011. Flexible linked axes for multivariate data visualization. IEEE Transactions on Visualization and Computer Graphics 17, 12(2011), 2310–2316.

Digital Library

[12]

Pierre Dragicevic. 2016. Fair statistical communication in HCI. In Modern statistical methods for HCI. Springer, 291–330.

[13]

Michael Gleicher, Michael Correll, Christine Nothelfer, and Steven Franconeri. 2013. Perception of average value in multiclass scatterplots. IEEE Trans. on Visualization and Computer Graphics 19 (2013). https://doi.org/10.1109/TVCG.2013.183

Digital Library

[14]

Robert L Goldstone. 1995. Effects of categorization on color perception. Psychological Science 6, 5 (1995), 298–304.

[15]

Connor C Gramazio, David H Laidlaw, and Karen B Schloss. 2016. Colorgorical: Creating discriminable and preferable color palettes for information visualization. IEEE transactions on visualization and computer graphics 23, 1(2016), 521–530.

[16]

LeGrand H Hardy, Gertrude Rand, and M Catherine Rittler. 1945. Tests for the detection and analysis of color-blindness. I. The Ishihara test: An evaluation. JOSA 35, 4 (1945), 268–275.

[17]

Stevan Harnad. 2003. Categorical perception. (2003).

[18]

Steve Haroz and David Whitney. 2012. How capacity limits of attention influence information visualization effectiveness. IEEE Trans. on Visualization and Computer Graphics 18, 12 (2012). https://doi.org/10.1109/TVCG.2012.233

Digital Library

[19]

Lane Harrison, Fumeng Yang, Steven Franconeri, and Remco Chang. 2014. Ranking Visualizations of Correlation Using Weber’s Law. IEEE Trans. on Visualization and Computer Graphics 20 (2014). https://doi.org/10.1109/TVCG.2014.2346979

[20]

Mark Harrower and Cynthia A Brewer. 2003. ColorBrewer. org: an online tool for selecting colour schemes for maps. The Cartographic Journal 40, 1 (2003), 27–37.

[21]

Christopher G Healey. 1996. Choosing effective colours for data visualization. In Proceedings of Seventh Annual IEEE Visualization’96. IEEE, 263–270.

[22]

Jeffrey Heer and Maureen Stone. 2012. Color naming models for color selection, image editing and palette design. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 1007–1016.

Digital Library

[23]

Matt-Heun Hong, Jessica K Witt, and Danielle Albers Szafir. 2021. The Weighted Average Illusion: Biases in Perceived Mean Position in Scatterplots. IEEE Transactions on Visualization and Computer Graphics 28, 1(2021), 987–997.

Digital Library

[24]

Luke Jefferson and Richard Harvey. 2006. Accommodating color blind computer users. In Proceedings of the 8th international ACM SIGACCESS conference on Computers and accessibility. 40–47.

Digital Library

[25]

Edna L Kaufman, Miles W Lord, Thomas Whelan Reese, and John Volkmann. 1949. The discrimination of visual number. The American Journal of Psychology 62, 4 (1949), 498–525.

[26]

Matthew Kay and Jeffrey Heer. 2016. Beyond weber’s law: A second look at ranking visualizations of correlation. IEEE Trans. on Visualization and Computer Graphics 22 (2016). https://doi.org/10.1109/TVCG.2015.2467671

Digital Library

[27]

Younghoon Kim and Jeffrey Heer. 2018. Assessing effects of task and data distribution on the effectiveness of visual encodings. In Computer Graphics Forum, Vol. 37. Wiley Online Library, 157–167.

[28]

Gordon Kindlmann, Erik Reinhard, and Sarah Creem. 2002. Face-based luminance matching for perceptual colormap generation. In IEEE Visualization, 2002. VIS 2002. IEEE, 299–306.

[29]

Robert Kosara, Fabian Bendix, and Helwig Hauser. 2006. Parallel sets: Interactive exploration and visual analysis of categorical data. IEEE Transactions on Visualization and Computer Graphics 12, 4(2006), 558–568.

Digital Library

[30]

Peter Kovesi. 2015. Good colour maps: How to design them. arXiv preprint arXiv:1509.03700(2015).

[31]

Robin SS Kramer, Caitlin GR Telfer, and Alice Towler. 2017. Visual comparison of two data sets: do people use the means and the variability?Journal of Numerical Cognition 3, 1 (2017).

[32]

Sungkil Lee, Mike Sips, and Hans-Peter Seidel. 2012. Perceptually driven visibility optimization for categorical data visualization. IEEE Transactions on Visualization and Computer Graphics 19, 10(2012), 1746–1757.

[33]

Alexander Lex, Marc Streit, Christian Partl, Karl Kashofer, and Dieter Schmalstieg. 2010. Comparative analysis of multidimensional, quantitative data. IEEE Transactions on Visualization and Computer Graphics 16, 6(2010), 1027–1035.

Digital Library

[34]

Jing Li, Jean-Bernard Martens, and Jarke van Wijk. 2010. A model of symbol size discrimination in scatterplots. In ACM SIGCHI Conf. on Human Factors in Comp. Systems. 2553–2562. https://doi.org/10.1145/1753326.1753714

Digital Library

[35]

Alvin M Liberman, Katherine Safford Harris, Howard S Hoffman, and Belver C Griffith. 1957. The discrimination of speech sounds within and across phoneme boundaries.Journal of Experimental Psychology 54, 5 (1957), 358.

[36]

Sharon Lin, Julie Fortuna, Chinmay Kulkarni, Maureen Stone, and Jeffrey Heer. 2013. Selecting semantically-resonant colors for data visualization. In Computer Graphics Forum, Vol. 32. Wiley Online Library, 401–410.

[37]

Yang Liu and Jeffrey Heer. 2018. Somewhere over the rainbow: An empirical assessment of quantitative colormaps. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 1–12.

Digital Library

[38]

Jock Mackinlay, Pat Hanrahan, and Chris Stolte. 2007. Show me: Automatic presentation for visual analysis. IEEE Transactions on Visualization and Computer Graphics 13, 6(2007), 1137–1144.

Digital Library

[39]

Luana Micallef, Gregorio Palmas, Antti Oulasvirta, and Tino Weinkauf. 2017. Towards perceptual optimization of the visual design of scatterplots. IEEE Trans. on Visualization and Computer Graphics 23, 6 (2017), 1588–1599. https://doi.org/10.1109/TVCG.2017.2674978

Digital Library

[40]

Nathan Moroney. 2003. Unconstrained web-based color naming experiment. In Color imaging VIII: Processing, hardcopy, and applications, Vol. 5008. SPIE, 36–46.

[41]

Tamara Munzner. 2014. Visualization analysis and design. CRC press.

[42]

Pascal Nardini, Min Chen, Francesca Samsel, Roxana Bujack, Michael Böttinger, and Gerik Scheuermann. 2019. The making of continuous colormaps. IEEE Transactions on Visualization and Computer Graphics 27, 6(2019), 3048–3063.

[43]

Christine Nothelfer and Steven Franconeri. 2019. Measures of the benefit of direct encoding of data deltas for data pair relation perception. IEEE Trans. on Visualization and Computer Graphics 26, 1 (2019), 311–320. https://doi.org/10.1109/TVCG.2019.2934801

[44]

Christine Nothelfer, Michael Gleicher, and Steven Franconeri. 2017. Redundant encoding strengthens segmentation and grouping in visual displays of data.Journal of Experimental Psychology: Human Perception and Performance 43, 9(2017), 1667.

[45]

Swiss Federal Statistical Office. 2022. Swiss Federal Statistical Office. https://www.bfs.admin.ch/bfs/en/home.html

[46]

Travis E Oliphant. 2006. A guide to NumPy. Vol. 1. Trelgol Publishing USA.

[47]

Lace Padilla, P Samuel Quinan, Miriah Meyer, and Sarah H Creem-Regehr. 2016. Evaluating the impact of binning 2d scalar fields. IEEE Transactions on Visualization and Computer Graphics 23, 1(2016), 431–440.

Digital Library

[48]

WA Phillips and DFM Christie. 1977. Components of visual memory. The Quarterly Journal of Experimental Psychology 29, 1(1977), 117–133.

[49]

Edmur A Pugliesi and Mônica MS Decanini. 2011. Cartographic design of in-car route guidance for color-blind users. In International Cartographic Conference.

[50]

Ghulam Jilani Quadri, Jennifer Adorno Nieves, Brenton M Wiernik, and Paul Rosen. 2022. Automatic Scatterplot Design Optimization for Clustering Identification. IEEE Transactions on Visualization and Computer Graphics (2022).

Digital Library

[51]

Ghulam Jilani Quadri and Paul Rosen. 2021. Modeling the Influence of Visual Density on Cluster Perception in Scatterplots Using Topology. IEEE Trans. on Visualization and Computer Graphics (2021).

[52]

Ghulam Jilani Quadri and Paul Rosen. 2021. A survey of perception-based visualization studies by task. IEEE Transactions on Visualization and Computer Graphics (2021).

[53]

P Samuel Quinan, LM Padilla, Sarah H Creem-Regehr, and Miriah Meyer. 2019. Examining implicit discretization in spectral schemes. In Computer Graphics Forum, Vol. 38. Wiley Online Library, 363–374.

[54]

Khairi Reda, Pratik Nalawade, and Kate Ansah-Koi. 2018. Graphical perception of continuous quantitative maps: the effects of spatial frequency and colormap design. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 1–12.

Digital Library

[55]

Khairi Reda and Danielle Albers Szafir. 2020. Rainbows revisited: modeling effective colormap design for graphical inference. IEEE Transactions on Visualization and Computer Graphics 27, 2(2020), 1032–1042.

[56]

Terry Regier and Paul Kay. 2009. Language, thought, and color: Whorf was half right. Trends in cognitive sciences 13, 10 (2009), 439–446.

[57]

Katharina Reinecke, David R Flatla, and Christopher Brooks. 2016. Enabling designers to foresee which colors users cannot see. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. 2693–2704.

Digital Library

[58]

Ronald Rensink and Gideon Baldridge. 2010. The perception of correlation in scatterplots. Computer Graphics Forum 29, 3 (2010), 1203–1210. https://doi.org/10.1111/j.1467-8659.2009.01694.x

Digital Library

[59]

Ronald A Rensink. 2014. On the prospects for a science of visualization. In Handbook of human centric visualization. Springer, 147–175.

[60]

Bernice E Rogowitz and Alan D Kalvin. 2001. The" which blair project": A quick visual method for evaluating perceptual color maps. In Proceedings Visualization, 2001. VIS’01. IEEE, 183–556.

[61]

Bahador Saket, Alex Endert, and Cagatay Demiralp. 2018. Task-Based Effectiveness of Basic Visualizations. IEEE Trans. on Visualization and Computer Graphics (2018). https://doi.org/10.1109/TVCG.2018.2829750

[62]

Alper Sarikaya and Michael Gleicher. 2018. Scatterplots: Tasks, data, and designs. IEEE Trans. on Visualization and Computer Graphics1 (2018), 402–412. https://doi.org/10.1109/TVCG.2017.2744184

[63]

Karen B Schloss, Connor C Gramazio, Allison T Silverman, Madeline L Parker, and Audrey S Wang. 2018. Mapping color to meaning in colormap data visualizations. IEEE Transactions on Visualization and Computer Graphics 25, 1(2018), 810–819.

Digital Library

[64]

Karen B Schloss, Zachary Leggon, and Laurent Lessard. 2020. Semantic discriminability for visual communication. Ieee transactions on visualization and computer graphics 27, 2(2020), 1022–1031.

[65]

Karen B Schloss and Stephen E Palmer. 2011. Aesthetic response to color combinations: preference, harmony, and similarity. Attention, Perception, & Psychophysics 73, 2 (2011), 551–571.

[66]

Michael Sedlmair, Andrada Tatu, Tamara Munzner, and Melanie Tory. 2012. A taxonomy of visual cluster separation factors. Computer Graphics Forum 31, 3pt4 (2012), 1335–1344. https://doi.org/10.1111/j.1467-8659.2012.03125.x

Digital Library

[67]

Vidya Setlur and Maureen Stone. 2016. A linguistic approach to categorical color assignment for data visualization. IEEE Trans. on Visualization and Computer Graphics 22 (2016). https://doi.org/10.1109/TVCG.2015.2467471

Digital Library

[68]

Gaurav Sharma, Wencheng Wu, and Edul N Dalal. 2005. The CIEDE2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations. Color Research & Application: Endorsed by Inter-Society Color Council, The Colour Group (Great Britain), Canadian Society for Color, Color Science Association of Japan, Dutch Society for the Study of Color, The Swedish Colour Centre Foundation, Colour Society of Australia, Centre Français de la Couleur 30, 1 (2005), 21–30.

[69]

Galit Shmueli, Nitin R Patel, and Peter C Bruce. 2011. Data mining for business intelligence: Concepts, techniques, and applications in Microsoft Office Excel with XLMiner. John Wiley and Sons.

[70]

Samuel Silva, Beatriz Sousa Santos, and Joaquim Madeira. 2011. Using color in visualization: A survey. Computers & Graphics 35, 2 (2011), 320–333.

Digital Library

[71]

Stephen Smart and Danielle Albers Szafir. 2019. Measuring the Separability of Shape, Size, and Color in Scatterplots. In ACM SIGCHI Conf. on Human Factors in Comp. Systems. 669. https://doi.org/10.1145/3290605.3300899

Digital Library

[72]

Stephen Smart, Keke Wu, and Danielle Albers Szafir. 2019. Color Crafting: Automating the Construction of Designer Quality Color Ramps. IEEE Trans. on Visualization and Computer Graphics (2019). https://doi.org/10.1109/TVCG.2019.2934284

[73]

Inc. Statgraphics Technologies. 2022. Statgraphics19. https://www.statgraphics.com/

[74]

Maureen Stone. 2006. Choosing colors for data visualization. Business Intelligence Network 2 (2006).

[75]

Maureen Stone, Danielle Albers Szafir, and Vidya Setlur. 2014. An engineering model for color difference as a function of size. In Color and Imaging Conference, Vol. 2014. Society for Imaging Science and Technology, 253–258.

[76]

Danielle Albers Szafir. 2018. Modeling color difference for visualization design. IEEE Trans. on Visualization and Computer Graphics 24, 1 (2018), 392–401. https://doi.org/10.1109/TVCG.2017.2744359

[77]

Danielle Albers Szafir, Maureen Stone, and Michael Gleicher. 2014. Adapting color difference for design. In Color and Imaging Conference, Vol. 2014. Society for Imaging Science and Technology, 228–233.

[78]

Tableau. 2022. Tableau. https://www.tableau.com/

[79]

Paul Tol. 2012. Colour schemes. SRON Technical Note2.2(2012), SRON–EPS.

[80]

Bruce E Trumbo. 1981. A theory for coloring bivariate statistical maps. The American Statistician 35, 4 (1981), 220–226.

[81]

Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE.Journal of machine learning research 9, 11 (2008).

[82]

Yunhai Wang, Xin Chen, Tong Ge, Chen Bao, Michael Sedlmair, Chi-Wing Fu, Oliver Deussen, and Baoquan Chen. 2018. Optimizing Color Assignment for Perception of Class Separability in Multiclass Scatterplots. IEEE Trans. on Visualization and Computer Graphics (2018). https://doi.org/10.1109/TVCG.2018.2864912

Digital Library

[83]

Colin Ware. 1988. Color sequences for univariate maps: Theory, experiments and principles. IEEE Computer Graphics and Applications 8, 5 (1988), 41–49.

Digital Library

[84]

Colin Ware, Terece L Turton, Roxana Bujack, Francesca Samsel, Piyush Shrivastava, and David H Rogers. 2018. Measuring and modeling the feature detection threshold functions of colormaps. IEEE Transactions on Visualization and Computer Graphics 25, 9(2018), 2777–2790.

[85]

Yating Wei, Honghui Mei, Ying Zhao, Shuyue Zhou, Bingru Lin, Haojing Jiang, and Wei Chen. 2019. Evaluating perceptual bias during geometric scaling of scatterplots. IEEE Transactions on Visualization and Computer Graphics 26, 1(2019), 321–331.

[86]

Matt Whitlock, Stephen Smart, and Danielle Albers Szafir. 2020. Graphical perception for immersive analytics. In 2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR). IEEE, 616–625.

[87]

Fumeng Yang, Lane Harrison, Ronald Rensink, Steven Franconeri, and Remco Chang. 2018. Correlation Judgment and Visualization Features: A Comparative Study. IEEE Trans. on Vis. and Computer Graphics(2018). https://doi.org/10.1109/TVCG.2018.2810918

[88]

Achim Zeileis, Kurt Hornik, and Paul Murrell. 2009. Escaping RGBland: selecting colors for statistical graphics. Computational Statistics & Data Analysis 53, 9 (2009), 3259–3270.

Digital Library

[89]

Qian Zheng, Min Lu, Sicong Wu, Ruizhen Hu, Joel Lanir, and Hui Huang. 2022. Image-guided color mapping for categorical data visualization. Computational Visual Media(2022), 1–17.

[90]

Liang Zhou and Charles D Hansen. 2015. A survey of colormaps in visualization. IEEE Transactions on Visualization and Computer Graphics 22, 8(2015), 2051–2069.

Digital Library

Cited By

Wang ABorland DGotz D(2024)An empirical study of counterfactual visualization to support visual causal inferenceInformation Visualization10.1177/1473871624122943723:2(197-214)Online publication date: 7-Feb-2024
https://doi.org/10.1177/14738716241229437
Quadri GWang AWang ZAdorno JRosen PSzafir D(2024)Do You See What I See? A Qualitative Study Eliciting High-Level Visualization ComprehensionProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642813(1-26)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642813
Wang JLi XLi CPeng DWang AGu YLai XZhang HXu XDong XLin ZZhou JLiu XChen W(2024)AVA: An automated and AI-driven intelligent visual analytics frameworkVisual Informatics10.1016/j.visinf.2024.06.0028:2(106-114)Online publication date: Jun-2024
https://doi.org/10.1016/j.visinf.2024.06.002

Index Terms

Measuring Categorical Perception in Color-Coded Scatterplots
1. Human-centered computing
  1. Visualization
    1. Empirical studies in visualization

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CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

April 2023

14911 pages

ISBN:9781450394215

DOI:10.1145/3544548

Editors:
Albrecht Schmidt
LMU Munich, Germany60028717
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Kaisa Väänänen
Tampere University, Finland60011170
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Tesh Goyal
Google Research, USA60006191
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Per Ola Kristensson
University of Cambridge, UK60031101
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Anicia Peters
University of Namibia, Namibia60072704
,
Stefanie Mueller
Massachusetts Institute of Technology, USA60022195
,
Julie R. Williamson
University of Glasgow, UK60001490
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Max L. Wilson
University of Nottingham, UK60015138

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Published: 19 April 2023

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Cited By

Wang ABorland DGotz D(2024)An empirical study of counterfactual visualization to support visual causal inferenceInformation Visualization10.1177/1473871624122943723:2(197-214)Online publication date: 7-Feb-2024
https://doi.org/10.1177/14738716241229437
Quadri GWang AWang ZAdorno JRosen PSzafir D(2024)Do You See What I See? A Qualitative Study Eliciting High-Level Visualization ComprehensionProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642813(1-26)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642813
Wang JLi XLi CPeng DWang AGu YLai XZhang HXu XDong XLin ZZhou JLiu XChen W(2024)AVA: An automated and AI-driven intelligent visual analytics frameworkVisual Informatics10.1016/j.visinf.2024.06.0028:2(106-114)Online publication date: Jun-2024
https://doi.org/10.1016/j.visinf.2024.06.002
Shi CCui WLiu CZheng CZhang HLuo QMa X(2023)NL2Color: Refining Color Palettes for Charts with Natural LanguageIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332652230:1(814-824)Online publication date: 23-Oct-2023
https://dl.acm.org/doi/10.1109/TVCG.2023.3326522

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