  We combine fine-grained spatially referenced census data with the vote
outcomes from the 2016 US presidential election. Using this dataset, we perform
ecological inference using distribution regression (Flaxman et al, KDD 2015)
with a multinomial-logit regression so as to model the vote outcome Trump,
Clinton, Other / Didn't vote as a function of demographic and socioeconomic
features. Ecological inference allows us to estimate "exit poll" style results
like what was Trump's support among white women, but for entirely novel
categories. We also perform exploratory data analysis to understand which
census variables are predictive of voting for Trump, voting for Clinton, or not
voting for either. All of our methods are implemented in Python and R, and are
available online for replication.
