During Bush v. Gore, there was a ton of freely available data about that election. I did a quick-and-dirty graph showing that Gore won the most populous counties by wide margins and Bush won most of the rest of the counties by wide margins.
For this course, I am going to revisit that analysis with the 2012 election data and maybe the 2008 election data.
In the United States, there is a strong correlation between population density and voting for the Democratic candidate for president.
The 2012, by-county results are available through The Guardian newspaper website. The 2008 election data is available for purchase through Dave Leip’s Election Data Store. The US Census Data website has information available about the population and land area of each U.S. county.
My first web search for related data was:
This search turns up several articles about a scatterplot by Conor Sen relating the Cook Partisan Voting Index (PVI) plotted against population density based on 2012 data. There are related heat-maps by others from the same data.
That search also turns up a paper by Jowei Chen of the University of Michigan and Jonathan Rodden of Stanford University about why compact voting districts are bad for Democrats. That paper focuses mostly on the shapes of voting districts in Florida and how they have be gerrymandered to make those in population-dense areas very compact while those in less populated areas are tentacled and sprawling and how this results in a higher number of Republican representatives than is warranted by overall population numbers.
A related aspect that shows up in this search is that on specific issues, like transit infrastructure, the congressional voting record is strongly correlated with the population density of the congressperson’s district. This effect is a second-order effect, however. The vote of a congressperson will likely be entwined with what the party as a whole wants as much as (or even more than) their constituents want.
I will, of course, being me, use Common Lisp for all of this. I suspect that I will use Fare-CSV for ingesting CSV data. If I have to parse TIGRE data, I will likely rely on some blend of esrap and CL-EWKB or custom geometry code. For plotting, I will likely rely on Vecto but may also try out some of the other libraries like adw-charting or finally get around to making my own multi-backend charting library.