Agglomerative Effects of Crime Attractors and Generators on Street Robbery? An Assessment by Luojia 1-01 Satellite Nightlight. Issue 2 (7th February 2022)

• Record Type: Journal Article
• Authors: Liu, Lin; Zhou, Hanlin; Lan, Minxuan
• Is Part Of: Annals of the American Association of Geographers. Volume 112:Issue 2(2022)
• Page Start: 350
• Page End: 367
• Publication Date: 2022-02-07
• Subjects: agglomerative effects -- crime attractor -- crime generator -- Luojia 1-01 nightlight -- street robbery; 集聚效应 -- 犯罪诱因 -- 犯罪成因 -- 珞珈1-01夜灯 -- 街头抢劫。; efectos aglomeradores -- atractor del crimen -- generador de crimen -- luz nocturna Luojia 1-01 -- robo callejero; Geography -- Periodicals; Environmental sciences -- Periodicals; Geography; Electronic journals; Periodicals; 550
• DOI: 10.1080/24694452.2021.1933888
• Languages: English
• ISSNs: 2469-4452

Abstract

Abstract : Scholars have long confirmed the agglomerative effect in retail geography. The colocation of multiple shops would encourage customers to make multiple stops in a single shopping trip. Does the agglomerative effect exist in illegal activities such as crime? What is a viable measurement of the colocation of facilities that attract or generate crime? These questions have never been explicitly addressed in crime geography. Numerous studies explain crime by the count of facilities, but the count variable ignores the size variation among the facilities. Because facilities are typically associated with lights at night, this study uses Luojia 1-01 satellite nightlight and a Gini coefficient–based adjuster to infer the agglomerative impact of crime attractors and generators in Cincinnati, Ohio. Results show that nightlights have a strong spatial correlation with the facilities and they can effectively capture large-sized and moderate-sized clusters of diverse types of crime attractors and generators. Additionally, negative binomial regression models compare the impacts of these different measures on crime by controlling potential confounding variables representing social disorganization. Results show that the nightlight models outperform the count models. Such advantage is more pronounced in areas where the crime rates are high. This is certainly an encouraging outcome, because both crime research and crime prevention tend to focus on high-crime areas. In sum, this preliminary study on the possible agglomerative effect in illegal activities reveals that nightlight data can effectively measure the agglomeration of crime attractors and generators and that the agglomerative nightlight explains crime better than the popular measure of facility counts.