Abstract:
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Research on 'neighborhood effects' often focuses on linking features of social contexts or exposures to health, educational, and criminological outcomes. Traditionally, individuals are assigned a specific neighborhood, frequently operationalized by the census tract of residence, which may not contain the locations of routine activities. In order to better characterize the many social contexts to which individuals are exposed as a result of the spatially-distributed locations of their routine activities and to understand the consequences of these socio-spatial exposures, we have developed the concept of ecological networks. Ecological networks are two-mode networks that indirectly link individuals through the spatial overlap in their routine activities. This presentation focuses on statistical methodology for understanding the structure underlying ecological network. In particular, we propose a Bayesian bilinear mixed effects models that allows for third-order dependence patterns in the interactions between individuals and the places they visit. We illustrate our methodology using activity pattern and sample survey data from Columbus, OH.
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