Resumen
The ambient population has been regarded as an important indicator for analyzing or predicting thefts. However, the literature has taken it as a homogenous group and seldom explored the varied impacts of different kinds of ambient populations on thefts. To fill this gap, supported by mobile phone trajectory data, this research investigated the relationship between ambient populations of different social groups and theft in a major city in China. With the control variables of motivated offenders and guardianship, spatial-lag negative binominal models were built to explore the effects of the ambient populations of different social groups on the distribution of theft. The results found that the influences of ambient populations of different social groups on the spatial distribution of theft are different. Accounting for the difference in the ?risk?benefit? characteristics among different activity groups to the offenders, individuals from the migrant population are the most likely to be potential victims, followed by suburban and middle-income groups, while college, affluent, and affordable housing populations are the least likely. The local elderly population had no significant impact. This research has further enriched the studies of time geography and deepened routine activity theory. It suggests that the focus of crime prevention and control strategies developed by police departments should shift from the residential space to the activity space.