This article develops a conceptual framework of neighborhood crime dynamics based on a synthesis of criminology and neighborhood change literatures which suggests that neighborhood decline can produce a nonlinear response in crime rates. The authors probe this relationship using a rich Detroit data set containing detailed, block-level information about housing, land, abandonment, population, schools, liquor outlets, and crime reports of various categories. Negative binomial models reveal that several neighborhood attributes are consistently associated with all types of crime (renter occupancy, population density, establishments with liquor licenses) while other attributes are only associated with particular types of crime. A simulation using estimated parameters suggests that processes of disinvestment and abandonment can generate a nonlinear pattern in the rate of growth in neighborhood crimes that vary in intensity by crime type. The authors explore the implications of their findings for anticrime strategies focusing on demolishing abandoned housing, "right-sizing" urban footprints, and regulating liquor-selling establishments.
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