The neighborhood effects literature represents a wide span of the social sciences broadly concerned with the influence of spatial context on social processes. From the study of segregation dynamics, the relationships between the built environment and health outcomes, to the impact of concentrated poverty on social efficacy, neighborhoods are a central construct in empirical work. From a dynamic lens, neighborhoods experience changes not only in their socioeconomic composition, but also in spatial extent; this latter source of change, however, has been largely neglected in the extant literature. In this paper, we discuss the development of novel, spatially explicit approaches to the study of longitudinal neighborhood dynamics using the scientific Python ecosystem.

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