Browning Christopher R, Pinchak Nicolo P, Calder Catherine A
Department of Sociology, The Ohio State University, Columbus, Ohio 43210, USA.
Department of Statistics and Data Sciences, The University of Texas at Austin, Austin, Texas 78712, USA.
Annu Rev Criminol. 2021 Jan;4:99-123. doi: 10.1146/annurev-criminol-061020-021551. Epub 2020 Sep 22.
This review outlines approaches to explanations of crime that incorporate the concept of human mobility-or the patterns of movement throughout space of individuals or populations in the context of everyday routines-with a focus on novel strategies for the collection of geographically referenced data on mobility patterns. We identify three approaches to understanding mobility-crime linkages: () Place and neighborhood approaches characterize local spatial units of analysis of varying size with respect to the intersection in space and time of potential offenders, victims, and guardians; () person-centered approaches emphasize the spatial trajectories of individuals and person-place interactions that influence crime risk; and () ecological network approaches consider links between persons or collectivities based on shared activity locations, capturing influences of broader systems of interconnection on spatial- and individual-level variation in crime. We review data collection strategies for the measurement of mobility across these approaches, considering both the challenges and promise of mobility-based research for criminology.
本综述概述了将人类流动性概念纳入犯罪解释的方法，即在日常活动背景下个人或人群在空间中的移动模式，并重点关注收集有关流动性模式的地理参考数据的新策略。我们确定了三种理解流动性与犯罪联系的方法：（1）地点和邻里方法，根据潜在犯罪者、受害者和监护人在空间和时间上的交集，对不同规模的局部空间分析单位进行特征描述；（2）以个人为中心的方法，强调影响犯罪风险的个人空间轨迹和人地互动；（3）生态网络方法，基于共享活动地点考虑个人或群体之间的联系，捕捉更广泛的互联系统对犯罪空间和个体层面变化的影响。我们回顾了在这些方法中测量流动性的数据收集策略，同时考虑了基于流动性的研究对犯罪学的挑战和前景。
