Different goals, different looks: Infovis and the Chris Rock effect

[This article was first published on Statistical Modeling, Causal Inference, and Social Science, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Seth writes:

Here’s my candidate for bad graphic of the year:

Screen shot 2011-07-03 at 7.20.48 PM.png

I [Seth] studied it and learned nothing. I have no idea how they assigned colors to locations. I already knew that there were more within-city calls than calls to individual distant locations — for example that there are more SF-SF calls than SF-LA calls. The researchers took a huge rich database and boiled it down to nothing (in terms of information value) — and I have a funny feeling they don’t realize how awful this is and what a waste.

I send it to you because it isn’t obvious how to do better — at least not obvious to them.

My reply:

My first reaction is to agree–I don’t get anything out of this graph either! But let me step back.

I think it’s best to understand this using the framework of my paper with Antony Unwin, by thinking of the goals that are satisfied by different sorts of graphs.

What does this graph convey? It doesn’t tell us much about phone calls, but it does tell us that some people can make colored maps with lots of lines. It also tells us that someone has a bunch of telephone call data. Even though the lines on the graph are difficult to interpret, they (correctly, I assume) convey that they come from a big database.

The graph also has the pleasant feature of revealing things we already knew. For example, the country is divided into many localities by color. I don’t know how this was done but I assume it was some clustering algorithm applied to the telephone call data. In any case, we see Arizona and New Mexico together–hey, that makes sense!–also Virginia paired with Maryland and Western Pennsylvania connected with West Virginia. These make sense too. We also see Alabama connected with Georgia rather than Mississippi (which is what I’d expect), but, hey, no algorithm is perfect. The map with all the lines also shows a bunch of coast-to-coast calls–that makes sense too–and it confirms our intuition that Minneapolis, Chicago, and Detroit are in the upper midwest, whereas Boston, New York, and Philadelphia are tightly packed in the northeast.

I call this the Chris Rock effect. Chris Rock says things we all know are true. But he says it so well that we get a shock of recognition, the joy of relearning what we already know, but hearing it in a new way that makes us think more deeply about all sorts of related topics. Sure, you might have already known that Denver is not near any other large city–but seeing it on this map of phone calls brings this fact to life in a way that maybe never happened in your previous experiences looking at U.S. maps.

It’s just like that famous map of Napoleon’s march into Russia. It didn’t tell you anything you didn’t already know, but it presented familiar knowledge in an attractive, unfamiliar format, Sort of like if your spouse sent you a valentine written in pig Latin. Good old “I love you” sounds that much better if you have to work for it a bit.

OK, back to goals. The graphs that Seth hates so much do their job in that they look unusual and draw the viewer in to look more carefully and rediscover familiar truth. After that, though, there’s not much more there, and it would be great if they could link to something more informative.

P.S. See Chris Volinsky’s comments below.

To leave a comment for the author, please follow the link and comment on their blog: Statistical Modeling, Causal Inference, and Social Science.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)