I wondered how the Transparency International data I visualized here (and also discussed here) would behave in a GapMinder-style animation. So I poured the data into a Google motion chart. You can check out the results here.
As I mentioned the other day, one of the notable anomalies in this dataset is Georgia. Among countries whose CPI (Corruption Perception Index) rankings are most volatile (according to TI), it stands out as a hopeful data point moving in the right direction.
In these two frames, you can see Georgia pulling away from its neighbors between 2004 and 2008.
The motion chart is an interesting way to observe the anomaly, but I didn’t find it to be a useful way to discover it. In the earlier example, I made a stack of sparklines, sorted by volatility, and then eyeballed the trends looking for exceptions.
To approximate that method using the motion chart, I started with this view:
Plotting volatility against itself produces the same sorted view I had in my spreadsheet. I figured I’d select the cluster of most-volatile countries, then watch them bubble up and down. But the points overlapped too much to select all the ones I wanted.
Next I plotted volatility against rank, which doesn’t really make sense but had the effect of spreading out the points so I could select more of them:
That helped a bit, but I still couldn’t easily grab, e.g., the most-volatile third of the list.
Does this mean that motion charts work better for displaying patterns than for discovering them? Not necessarily. I think it all depends on the data, the patterns you think you’re looking for, and the patterns you don’t know you’re looking for. With more lenses — and more easily interchangeable lenses — our exploratory and explanatory powers will grow.