This paper on the spatial relationship between the built environment (BE) and urban gun crime finds that attractors vary by city and socio-economic status.
The objective of this paper is to improve understanding of the spatial relationship of the built environment (BE) with urban gun crime and how the influence of environmental features varies across geographic location and socioeconomic context. The study finds that attractors vary by city and socio-economic status, indicating that the unique underlying environmental context of each city facilitates firearms crime differently. Specifically, the authors analyze associations between incidents of reported firearms violence and distance to built environment features, accounting for neighborhood socio-economic status. This study compares reported crime data with the outputs from a Monte Carlo Simulation using the Network Cross-K Function for Stochastic Spatial Events on street networks. Where data is available, the researchers examine ten features—transit stations, universities/colleges, convenience stores, gas stations, liquor licenses, alcohol outlets, tobacco retailers, lodging, restaurants, and schools—across diverse metropolitan areas to uncover features that exert “attractive” or “repellent” influence on firearms violence. The authors present results from four U.S. cities and examine how results vary by socio-economic status of census tracts. Attractive features include tobacco retailers in Pittsburgh; hotels/motels, alcohol outlets, and restaurants in New Orleans; and rail transit stations in Los Angeles. The authors uncover localized attractive and repellent relationships within the lowest and highest socio-economic areas and also identify several firearms crime repellents, including universities/colleges and public/private schools. (Published Abstract Provided)
- Assessing the Effectiveness of Programs To Prevent and Counter Violent Extremism
- Evaluation of ForenSeq Signature Prep Kit B on Predicting Eye and Hair Coloration as Well as Biogeographical Ancestry by Using Universal Analysis Software (UAS) and Available Web-tools
- Forensic Human Identification Using Skin Microbiomes