Effects of ambient population with different income levels on the spatio-temporal pattern of theft: A study based on mobile phone big data

Recent studies focus on how to accurately measure ambient population and its effects on crimes. However, limited attention has been paid to the internal heterogeneity of ambient population composition and its temporal variation. To fill these gaps, controlling for indicators of ambient population, social disorganization, crime generators and attractors, we model the effects of the share of low income, middle income, upper-middle income and high income of ambient population at the community level in the whole and specific time periods on weekdays and weekends. Results suggest that while all income groups of the ambient population contribute to theft at the community level, their contributions are not uniform. At different periods of both weekdays and weekends, there is no single income group that consistently predicts theft. Furthermore, the magnitude of the impact of each income group fluctuates with time. Our results highlight the heterogeneous segments of the ambient population, coupled with the dynamic human mobility patterns, structure the convergence of the crime triangle – motivated offenders, potential targets, and guardianship – in nuanced yet crucial ways.