Title: A Data Science Approach to Understanding Residential Water Contamination in Flint,
Abstract: When the residents of Flint learned that lead had contaminated their water
system, the local government made water-testing kits available to them free of
charge. The city government published the results of these tests, creating a
valuable dataset that is key to understanding the causes and extent of the lead
contamination event in Flint. This is the nation's largest dataset on lead in a
municipal water system.
In this paper, we predict the lead contamination for each household's water
supply, and we study several related aspects of Flint's water troubles, many of
which generalize well beyond this one city. For example, we show that elevated
lead risks can be (weakly) predicted from observable home attributes. Then we
explore the factors associated with elevated lead. These risk assessments were
developed in part via a crowd sourced prediction challenge at the University of
Michigan. To inform Flint residents of these assessments, they have been
incorporated into a web and mobile application funded by \texttt{Google.org}.
We also explore questions of self-selection in the residential testing program,
examining which factors are linked to when and how frequently residents
voluntarily sample their water.