Unraveling the influence of income-based ambient population heterogeneity on theft spatial patterns: insights from mobile phone big data analysis

Abstract

AbstractWhile previous research has underscored the profound influence of the ambient population distribution on the spatial dynamics of crime, the exploration regarding the impact of heterogeneity within the ambient population, such as different income groups, on crime is still in its infancy. With the support of mobile phone big data, this study constructs an index of ambient population heterogeneity to represent the complexity of the social environment. After controlling for the effects of total ambient population, nonlocal rate, transportation accessibility, crime attractors, and crime generators, this study employs a negative binomial regression model to examine the influence of ambient population heterogeneity and different income groups on the spatial manifestations of thefts. The findings indicate that ambient population heterogeneity significantly escalates the incidence of thefts, with middle and upper-middle-income groups acting as more attractive targets, whereas the higher-income group exerts a deterrent effect. The interaction analysis shows that increased population heterogeneity contributes to social disorder, thereby amplifying the attractiveness of the ambient population to perpetrators. These conclusions highlight the crucial role of ambient population heterogeneity in explaining crime dynamics and therefore enrich the routine activity theory.