Wednesday, March 31, 2010

Education and the elections by region: the Hispanic Southwest.

Now I set about the continuation of my task to separate the relationship between education and elections down into regions.
One of the first things to be mentioned about this is that separating the United States into regions is never a cut and dried task. Parts of California obviously have much more in common with parts of Oregon than they do with the Arizona/Mexico border, and yet I will be grouping all of California and Arizona together. I think that most of the groupings I made make sense, but I might have to look at the results closely before I decide. I am grouping states together based not just on demographics, but also on the number of counties that state has, which can skew results wildly. Texas and California have similar demographics, but it doesn't show up in a scatterplot because the 200 small, rural, white counties that Texas has make it look like they are quite different.

So I put together California, Nevada, Arizona, and New Mexico, and this is what I got:

The first thing to notice is that in opposition to the national map, this area has only two counties over the 30% mark that voted for McCain, and both of those were only barely over the 30% mark, and only narrowly voted for McCain. This might be significant, or it might be caused by the area's larger counties. In an another state, where Orange County would be two or three counties, one of them might be much more wealthy, educated and Republican. But because of California's large counties, Orange County combines many different demographics.
At least, that is one theory.
Other than that, We have kind of the expected points: highly educated urban areas in the upper right, minority communities (mostly Hispanic) in the lower right, and rural, white counties in the lower left.
As I have pointed out before, counties make a bad unit sometimes. I specifically marked San Diego and Los Angeles Counties, because while they are not outliers, it is important to remember that most of the population in these data points is in only a few of the data points.

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