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.

学者们早就证实了零售地理学的集聚效应。多个商店的托管将鼓励客户在一次购物之旅中进行多次停留。犯罪等违法行为是否存在集聚效应？什么是吸引或产生犯罪的设施托管的可行衡量标准？这些问题从未在犯罪地理学中得到明确解决。许多研究通过设施数量来解释犯罪，但数量变量忽略了设施之间的规模变化。由于设施通常与夜间灯光相关，因此本研究使用珞珈 1-01 卫星夜灯和基于基尼系数的调节器来推断俄亥俄州辛辛那提的犯罪吸引器和发电机的聚集影响。结果表明，夜灯与设施有很强的空间相关性，它们可以有效地捕捉到大中型不同类型的犯罪吸引器和发生器集群。此外，负二项式回归模型通过控制代表社会混乱的潜在混杂变量来比较这些不同措施对犯罪的影响。结果表明，夜灯模型优于计数模型。这种优势在犯罪率高的地区更为明显。这无疑是一个令人鼓舞的结果，因为犯罪研究和犯罪预防都倾向于关注高犯罪率领域。总共，