17 Theory of graphical display

17.1 Design arguments

One of the main design arguments for the graphical display of information is data / ink maximization Tufte (1990). This is the idea that idea that as much of the “ink” (non-background pixels) of the plot as possible should be displaying data.

Data/ink maximalization has been criticized empirically. For example, Inbar, Tractinsky, and Meyer (2007) conducted a study with 87 undergraduates and found a clear preference for the non-maximized variations. Another line of argument discusses the “paradox of simplicity” Norman (2007), Eytam, Tractinsky, and Lowengart (2017), whereby we have a strong aesthetic preference for simplicity, but also want flexibility and maximum utility.

Bertin (1983)

17.1.1 Graphical critique

Wickham et al. (2010)

17.1.2 Theory of graphics

Cleveland (1987)
Cleveland and McGill (1984)
Cleveland and Devlin (1980)
Carswell (1992)
Cleveland and McGill (1986)
Magical thinking Diaconis (2006)

17.2 Implementation

17.2.1 Grammar of graphics

Wilkinson (2012)
Wilkinson (2013)
Wickham (2010)

17.2.2 Narative storytelling

Edward and Jeffrey (Segel and Heer (2010)) argue regarding the use of modern interactive tools in data narrative storytelling. They give seven canonical genres of narrative visulation.

17.3 Graph galleries and further reading

17.3.1 Further reading

Karl Broman on How to display data badly
Karl Broman Data Vizualization
Karl Broman 10 worst plots
Karl Broman Data visualization

17.3.2 Graph galleries

17.3.3 Historically famous graphics

17.3.4 Infographics in the media

Bertin, Jacques. 1983. Semiology of Graphics. University of Wisconsin press.

Carswell, C Melody. 1992. “Choosing Specifiers: An Evaluation of the Basic Tasks Model of Graphical Perception.” Human Factors 34 (5): 535–54.

Cleveland, William S. 1987. “Research in Statistical Graphics.” Journal of the American Statistical Association 82 (398): 419–23.

Cleveland, William S, and Susan J Devlin. 1980. “Calendar Effects in Monthly Time Series: Detection by Spectrum Analysis and Graphical Methods.” Journal of the American Statistical Association 75 (371): 487–96.

Cleveland, William S, and Robert McGill. 1984. “The Many Faces of a Scatterplot.” Journal of the American Statistical Association 79 (388): 807–22.

———. 1986. “An Experiment in Graphical Perception.” International Journal of Man-Machine Studies 25 (5): 491–500.

Diaconis, Persi. 2006. “Theories of Data Analysis: From Magical Thinking Through Classical Statistics.” Exploring Data Tables, Trends, and Shapes, 1–36.

Eytam, Eleanor, Noam Tractinsky, and Oded Lowengart. 2017. “The Paradox of Simplicity: Effects of Role on the Preference and Choice of Product Visual Simplicity Level.” International Journal of Human-Computer Studies 105: 43–55.

Inbar, Ohad, Noam Tractinsky, and Joachim Meyer. 2007. “Minimalism in Information Visualization: Attitudes Towards Maximizing the Data-Ink Ratio.” In Proceedings of the 14th European Conference on Cognitive Ergonomics: Invent! Explore!, 185–88.

Norman, Donald A. 2007. “Simplicity Is Highly Overrated.” Interactions 14 (2): 40–41.

Segel, Edward, and Jeffrey Heer. 2010. “Narrative Visualization: Telling Stories with Data.” IEEE Transactions on Visualization and Computer Graphics 16 (6): 1139–48.

Tufte, ER. 1990. “Data-Ink Maximization and Graphical Design.” Oikos, 130–44.

Wickham, Hadley. 2010. “A Layered Grammar of Graphics.” Journal of Computational and Graphical Statistics 19 (1): 3–28.

Wickham, Hadley, Dianne Cook, Heike Hofmann, and Andreas Buja. 2010. “Graphical Inference for Infovis.” IEEE Transactions on Visualization and Computer Graphics 16 (6): 973–79.

Wilkinson, Leland. 2012. “The Grammar of Graphics.” In Handbook of Computational Statistics, 375–414. Springer.

———. 2013. The Grammar of Graphics. Springer Science & Business Media.