Detecting Dividing Lines in Turnout: Spatial Dependence and Heterogeneity in the 2012 US Presidential Election

US voters have been moving further and further apart, most notably in terms of partisanship. This trend has led to a strong geographic concentration of voters' preferences. We look at how turnout shows a similar pattern by jointly addressing two features of the data: spatial autocorrelation and heterogeneity of the observed units. Results obtained through a spatial lag regression tree procedure for the 2012 US presidential elections allow us to identify twelve groups of counties with similar characteristics. We find that (i) close counties behave similarly in terms of turnout; (ii) across various groups of counties, some variables have different statistical significance (or lack of it, such as household income and unemployment), and often different signs (such as the shares of adherents to congregations, Blacks, and Hispanics, and urban population). These results are useful for targeting geographically based groups in get out the vote operations.