There is a lack of research on obesity that uses primary data and fine-grained information on neighborhoods. I use primary data for 367 participants in Detroit to examine neighborhood predictors of obesity. These data were supplemented with public data. I considered multilevel and spatial modeling, but the data lent itself best to ordinary least squares (OLS) regressions. I find that socioeconomic factors, the built environment, transportation usage, and perceptions of neighborhoods are important predictors of obesity. Importantly, litter is associated with higher levels of obesity. Planners can take measures to reduce litter and collaborate with other policy-makers to encourage less driving, though drawing direct lines of causality is complicated.
Keywords: fast food; litter; obesity; transportation.