Commuting, Voting, and the Off-Label Use of Data with Elaine McVey

Commuting, voting, and the off-label use of data

The modern practice of data science often involves approaching datasets differently than in traditional statistics. Two analyses will be presented, both of which use combinations of public datasets in expected and "off-label" ways. One addresses the need to simulate realistic commuting patterns, and the other evaluates the effects of North Carolina voting site decisions. In the process of telling the stories of these two data science projects, we'll have an opportunity to reflect on the nature of data science.