  We propose the spatial-temporal aggregated predictor (STAP) modeling
framework to address measurement and estimation issues that arise when
assessing the relationship between built environment features (BEF) and health
outcomes. Many BEFs can be mapped as point locations and thus traditional
exposure metrics are based on the number of features within a pre-specified
spatial unit. The size of the spatial unit--or spatial scale--that is most
appropriate for a particular health outcome is unknown and its choice
inextricably impacts the estimated health effect. A related issue is the lack
of knowledge of the temporal scale--or the length of exposure time that is
necessary for the BEF to render its full effect on the health outcome. The
proposed STAP model enables investigators to estimate both the spatial and
temporal scales for a given BEF in a data-driven fashion, thereby providing a
flexible solution for measuring the relationship between outcomes and spatial
proximity to point-referenced exposures. Simulation studies verify the validity
of our method for estimating the scales as well as the association between
availability of BEFs' and health outcomes. We apply this method to estimate the
spatial-temporal association between supermarkets and BMI using data from the
Multi-Ethnic Atherosclerosis Study, demonstrating the method's applicability in
cohort studies.
