Pearly Dhingra points me to this article, “The Geographic Distribution of Obesity in the US and the Potential Regional Differences in Misreporting of Obesity,” by Anh Le, Suzanne Judd, David Allison, Reena Oza-Frank, Olivia Affuso, Monika Safford, Virginia Howard, and George Howard, who write:

Data from BRFSS [the behavioral risk factor surveillance system] suggest that the highest prevalence of obesity is in the East South Central Census division; however, direct measures suggest higher prevalence in the West North Central and East North Central Census divisions. The regions relative ranking of obesity prevalence differs substantially between self-reported and directly measured height and weight.

And they conclude:

Geographic patterns in the prevalence of obesity based on self-reported height and weight may be misleading, and have implications for current policy proposals.

Interesting. Measurement error is important.

But, hey, what’s with this graph:

Who made this monstrosity? Ed Wegman?

I can’t imagine a clearer case for a scatterplot. Ummmm, OK, here it is:

Hmmm, I don’t see the claimed pattern between region of the country and discrepancy between the measures.

Maybe things will be clearer if we remove outlying Massachusetts:

Maryland’s a judgment call; I count my home state as northeastern but the cited report places it in the south. In any case, I think the scatterplot is about a zillion times clearer than the parallel coordinates plot (which, among other things, throws away information by reducing all the numbers to ranks).

P.S. Chris in comments suggests redoing the graphs with same scale on the two axes. Here they are:

It’s a tough call. These new graphs make the differences between the two assessments more clear, but then it’s harder to compare the regions. It’s fine to show both, I guess.