  We present an approach to estimate distance-dependent heterogeneous
associations between point-referenced exposures to built environment
characteristics and health outcomes. By estimating associations that depend
non-linearly on distance between subjects and point-referenced exposures, this
method addresses the modifiable area-unit problem that is pervasive in the
built environment literature. Additionally, by estimating heterogeneous
effects, the method also addresses the uncertain geographic context problem.
The key innovation of our method is to combine ideas from the non-parametric
function estimation literature and the Bayesian Dirichlet process literature.
The former is used to estimate nonlinear associations between subject's
outcomes and proximate built environment features, and the latter identifies
clusters within the population that have different effects. We study this
method in simulations and apply our model to study heterogeneity in the
association between fast food restaurant availability and weight status of
children attending schools in Los Angeles, California.
