Urban Street Network Configuration and Property Crime: An Empirical Multivariate Case Study

In 21st-century American cities, urban crime remains a critical public safety concern influenced by complex social, political, and environmental structures. Crime is not randomly distributed and built-environment characteristics, such as street network configuration, impact criminal activity through spatial dependence effects at multiple scales. This study investigates the cross-sectional, multi-scale spatial effects of street network configuration on property crime across neighborhoods in Charlotte, North Carolina. Specifically, we examine whether the fundamental characteristics of a neighborhood’s street network contribute to variations in its property crime. Using a novel and granular spatial approach, incorporating spatial econometric models (SAR, CAR, and GWR), several street network characteristics, including density, connectivity, and centrality, within five nested buffer bands are measured to capture both local and non-local influences. The results provide strong and consistent evidence that certain characteristics of the neighborhood street network, such as connectivity and accessibility, significantly influence the occurrence of property crime. Impacts are also found to be spatially heterogenous, manifesting themselves at the mid-range scale rather than hyper-locally. The integration of comprehensive measures of street network configuration into spatially explicit models offers new opportunities for advancement in environmental criminology literature. Such spatial dynamics further contribute to urban safety policy by informing decision-makers so that they can ensure a defensively built environment design.