Neighborhoods in Space-Time Contexts

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This research project will advance the way in which neighborhoods are defined in urban social science research. The definition of a socioeconomic neighborhood is an important and central issue across many research domains. Neighborhoods serve as the organizational units to frame empirical research that has examined a wide array of issues, including the ability of social networks to produce collective efficacy in a spatial context, the relationships between concentrated poverty traps and violence, spatial sorting and segregation, and the role of neighborhoods as seeds for wider urban economic development. Despite this importance, consensus has not yet emerged regarding how to operationalize neighborhoods in practice. Moreover, the role of spatial structure largely has been ignored in existing approaches to neighborhood definition. This project will produce new methods for neighborhood identification and analysis that draw on recent developments in geographic information science and spatial statistics. These new methods will enhance the urban social science toolkit enabling researchers to carry out empirical work that can be replicated, thereby moving urban policy research onto stronger analytic foundations. The new methods and analytics will be implemented in a publicly available open-source longitudinal neighborhood analysis package. The project will provide training to a post-doctoral research associate and will include a partnership between academia and industry, with applications to commercial market segmentation and analysis.This research project will advance understanding of the spatial dimensions of neighborhoods. The project will focus on three central aims. First, a number of underexamined issues associated with defining neighborhoods in spatial and temporal contexts will be investigated. These issues are related to the lack of a spatially explicit approach to neighborhood definition in the current literature and the impacts of ignoring the dynamics of spatial clustering and heterogeneity. The second aim is to develop new methods for neighborhood delineation and analysis that address these issues. These new methods will include local measures to identify hot-spots of neighborhood change within individual urban areas as well as global measures that summarize the overall amount of spatial change in a given metropolitan area. The third area of activity will be the implementation of the new methods for neighborhood delineation in an open-source framework to provide social scientists with a platform of flexible, scalable, and advanced spatio-temporal clustering methods that support replication and enhance the existing infrastructure of social science research on urban dynamics.