Statistical Graphics and more

On the JMP blog you find a post which uses the same data source I took for my first attempt to visualize the web of the EU debt. I found it hard to really make a point with this data. Looking at the JMP post tells me I wasn’t all too bad. Let’s walk through their visualizations:

1. The Map
   Certainly necessary for someone in the US – who knows where Portugal, Spain and Austria are; or was it Australia ..?
   But seriously, scaling the very irregular shapes of the countries outline has more problems (on the perceptual side) as benefits.
2. The Heat Map
   This graph degrades the information to a binary information of being creditor or not – be it 1€ or 1,000,000,000€. The conclusion that Germany and France are in trouble because they lend to many other countries is not convincing as it only reflects their economical power.
3. The Tree Map
   As tree maps are “only” a different representation of a tree, i.e., a hierarchy, why would you visualize a matrix with it? I can’t really  follow the chain reaction of defaults which is interpreted from this tree map. Looks a bit along the lines “if all you have is a hammer, every problem looks like a nail”.

One thing that is really not well thought about the graphs are the different color schemes between the graphs.

In the end, I think someone really needs to get “the right” data and show us “the right” visualizations such that we understand what’s going on – but hurry up, Greece might be broke by then …