Earlier in the week I was bloggin’ about extreme time scales and various uses of spirals in data visualisation. This morning I thought about it a little more and realised the attraction of extreme scales, like the entire lifetime of our planet, or the size of the solar system, is in large part just that it’s fun. I start my own dataviz talks with Gelman & Unwin’s 6 objectives, which I think are helpful in framing the many uses of images (for a statistician, anyway – we were trained that there is only one use of a graph and that is to check for outliers / normality briefly before it is deleted!), although I get the impression (and I would be happy to be corrected by better informed dataviz hipsters (I use the term with only the very mildest form of offense)) that those objectives are generally looked upon with some disdain as Johnnies-come-lately in a design community that has had its own goals for a much longer time. In this application, we are appealing to GU2, “conveying the sense of the scale and complexity of a dataset”. In the original paper, G&U give network graphs as an example, because they convey an overall impression but little or not concrete information, so people like me tend not to approve. I like the data to be retrievable by the viewer. But why not, if it effectively sets the scene?
A couple of unorthodox examples spring to mind: scale reconstructions of the solar system and Stamen+Nasdaq on high-frequency trading. If you wipe out the extremities with a super-log scale then you lose the fun too. (OK, it’s a sitting duck of an ugly example, but still!) Another good one is the Washington Post on Flight MH370.
And then consider two popular visualizations, US Gun Deaths and CarbonVisuals NYC. In each case, they rely on the emotional impact of the sudden acceleration or amplification of values, and they get that in very different ways. As we learnt from Haydn, the impact of the Surprise only really works the first time, but it stays fun for years afterwards.