I like bubble charts. It seems many others don't. This is a quick review of bubble charts as a tool, with thoughts about why I like them, why others don't, and how best to use them.
At Aventine, our focus is marketing analytics, but we have a strong commitment to the related discipline of Information (or Data) Visualization, InfoVis for short. By using good InfoVis techniques, we are able to create better graphical presentations of the analyses we do for clients.
I've watched with some interest recurring discussions in InfoVis circles about the use of bubble charts, including one recently on a site we like, Junk Charts, panning a graphic in the Financial Times using bubbles. By and large, the InfoVis experts hate bubble charts, feeling people can't do a good job distinguishing differences of bubble size.
The interesting, parallel development is that motion charts, which are just a type of animated bubble chart, are getting increasing attention thanks to Google and a remarkable presentation at a TED Conference in 2006. These motion charts are getting traction despite the criticisms.
So how to reconcile the appeal that attracts Google with the concerns of the InfoVis mavens? Simple. Follow one rule, which is to never use bubbles to display the principal dimension of your analysis. Instead, use them to add a categorization variable to an analysis that is principally concerned with one or two other variables.
Perhaps the single best
example of good use of bubbles is a scatterplot demonstrating some relationship between the x
and y variables. The bubbles are added to
demonstrate some size metric to help show whether the relationship holds as
well for big or small entities of analysis.
Below is an illustration of this. For a number of financial services companies (this is a very old chart), the price-to-book ratio is plotted against the book value of equity. One of the clear messages of this analysis is that it is difficult for large companies to maintain high price-to-book ratios. The bubbles represent market capitalization, which can be directly obtained by multiplying the x and y values. But even though strictly speaking the bubbles do not add new information in the bubbles, being able to easily see the magnitudes of the differences in size helps the viewer sort through the relative advantages and disadvantages of the competitors in terms of size and valuations.
Another good use of bubbles is to help prioritize decisions coming from diagnostic-type analyses. Below is a classic growth-vs-profitability matrix.
The core analysis helps direct attention to business units that are doing
well or not so well; adding the bubble size helps prioritize attention and
action.
One of the nice things about bubbles is that they can easily be color coded to show an additional categorization variable. Below the colors identify action to be taken with the business units on the basis of the analysis.
The chart is another example of color coding to show the relative competitive positions of different sub-sectors within financial services.
I find these charts very useful, and my experience is that the help our clients quickly assimilate the essence of the analyses.
So why don't some like bubble charts? It can be difficult for readers to compare the sizes of bubbles more accurately than bigger, smaller, a little bit bigger, much smaller, etc. As a result, it would be a mistake to rely on bubbles to convey more important differences that those rough, relative judgements. But if the bubbles are used to display the primary dimensions or data of the analysis, that is exactly what they are being used to do.
I won't dwell on examples of poor uses of bubble charts because others have already done this well. The various posts on the Junk Charts site form a great primer on what not to do.
So avoid those pitfalls, and you'll have a nice tool in your graphics repertoire.
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