There are many good planetarium programs that you can access on laptops, but it is really not that much fun to lug them around on camping trips or even out on to the roof at night. But now, thanks to the iPhone (and the iPod Touch) there has been a great leap forward.

The iTunes AppStore now has a number of astronomy themed apps, including apps that tell you the distance to the Moon correct to a meter. But the most impressive of the lot has to be the ones that produce skycharts and let you search for and find stars, constellations, and deep sky objects at any time, from anywhere. There are four such available now: Starmap, GoSkyWatch, iAstronomica, and iStellar.

I have only tried Starmap so far, and it is incredible. The developer says that there is a PRO version in the works, but this one is already plenty good for me.

It is quite well known that, unlike amateur astronomers, professional astronomers are quite ignorant of the night sky. Really, if someone turns us around to face North, we might figure out where Polaris is, but that’s it. Oh, and we can usually find the Moon. And daytime, we can point to where the Sun is, provided it is not cloudy, which though it often is in New England. True story: I still haven’t set eyes on the star which formed the basis of my PhD thesis (α Triangulum Australis; in my defence, it is only visible from the southern hemisphere). But all that is in the past, now I can rediscover my amateur roots, now I am feeling pretty confident that I can find anything, even dear old α TrA, all I need to do is cross the Equator and point with my tricorder.

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.