The AstroStat Slog

All of a sudden, partially owing to a thought provoking talk about visualization by Felice Frankel at IIC, I recollected a book, The Grammar of Graphics by Leland Wilkinson (2nd Ed. – I partially read the 1st ed. and felt little of use several years ago because there seemed no link for visualization of data from astronomy.)

Both good and bad reviews exist but I don’t believe there’s a book this extensive to cover the grammar of graphics. Not many statisticians are handling images compared to computer vision engineers but at some points, all engineers and scientists must present their work into graphs and tables. By the same token, tongs are different, although alphabets are common. Often times, plots from scientist A cannot talk to scientist B (A \ne B). This communication discrepancy seems prevalent between astronomy and statistics.

Almost all chapters begin with the Greek or Latin origins of chapter names to reflect the common origins of lexicons in graphics regardless of subjects. Some chapters, on the contrary, tend to illuminate different practices/perspectives/interests in graphics between astronomers and statisticians:

• Chap. 6 [Scale]: Scaling by log transformation is meant to stabilize errors (Box-Cox transformation) in statistics; in contrast, in astronomy to impose a linear relationship between predictor and response which is manifested better in log scale.
• Chap. 7 [Statistics]: Discussion on error bars, bins, and histogram; although graphical tools are same but the objectives seem different (statistics – optimal binning: astronomy – enhancing signals in each bin).
• Chap 15. [Uncertainty]: Concepts of uncertainty; many words are associated with uncertainty, for example, variability, noise, incompleteness, indeterminacy, bias, error, accuracy, precision, reliability, validity, quality, and integrity.

Overall, the ideas are implored to be included adaptively in the astronomical data analysis packages for visualizing the analyzed products. Perhaps, it may inspire some astronomers to transform the ways of visualization. For instance, instead of histograms, in my opinion, box-plots, qq-plots, and scatter plots would shed improved information while maintaining the simplicity but except scatter plots, other summary plots are not commonly used in astronomy. A benefit from box plot and qq plot is checking gaussianity without sacrificing information from binning. However, there’s no golden rule which type or grammar of graphics is correct and shall be used . Only exists user preference.

Different disciplines maintain their ways of presenting graphics and expect that they can talk to viewers of other disciplines. No one fully reached that point, disappointingly. Extensive discussion and persuasion is required to deliver stories behind graphics to others.

As Felice Frankel pointed out the way of visualization could enhance recognition and understanding of deliberate delivering of information. To the purpose, a few interesting quotes from the book is replaced the conclusion of this post.

• The first ed. of this book, and Part 1 of the current ed., explicitly cautioned that the grammar of graphics is not a visualization system.
• We are surprised, nevertheless, to discover how little some visualization researchers in various fields know about the origins of many the of techniques that are routinely applied in visualization.
• The grammar of graphics determined how algebra, geometry, aesthetics, statistics, scales, and coordinates interact. In the world of statistical graphics, we cannot confuse aesthetics with geometry by picking a tree graphics to represent a continuous flow of migrating insects across a geographic field simply because we like the impression in conveys.
• If we must choose a single word to characterize the focus of modern statistics, it would be uncertainty (Stigler, 1983)
• … decision-makers need statistical tools to formalize the scenarios they encounter and they need graphical aids to keep them from making irrational decisions. … the use of graphics for decision-making under uncertainty is a relatively recent field. … We need to go beyond the use of error bars to incorporate other aesthetics in the representation of error. And we need research to assess the effectiveness of decision-making based on these graphics using a Bayesian yardstick.