In recent years, it has increasingly been recognized that due to the uncertain geographic context problem caused by daily human mobility, the residential population is too static to serve as a valid measure of the population at risk for criminal victimization. Various alternative measures have been suggested instead. Guided by the routine activity approach, this study furthers the concept of crime risk population and its measurement across space and time. Using exceptionally comprehensive data sets on population mobility and on theft from the person in a large city in China, we select the best indicator of the risk population from the following four candidates: residential population, subway ridership, taxi ridership, and mobile phone users. Controlling for the potentially confounding effects of offender and guardian presence, we show that on both weekdays and weekends, the best indicators of risk population vary over the course of the day. In the morning, residential population outperforms other measures. In the afternoon and evening, taxi ridership and phone users are better indicators. Although the mobile phone user base forms an arguably more representative measure of ambient population, during some periods taxi ridership is superior because it provides a better indicator of outdoor (as opposed to indoor) activities. In terms of practical applications to security policy and law enforcement, these findings can help identify crime hot spots by calculating accurate crime risks.

Research on journey-to-crime distance has revealed the importance of both the characteristics of the offender as well as those of target communities. However, the effect of the home community has so far been ignored. Besides, almost all journey-to-crime studies were done in Western societies, and little is known about how the distinct features of communities in major Chinese cities shape residential burglars’ travel patterns. To fill this gap, we apply a cross-classified multilevel regression model on data of 3763 burglary trips in ZG City, one of the bustling metropolises in China. This allows us to gain insight into how residential burglars’ journey-to-crime distances are shaped by their individual-level characteristics as well as those of their home and target communities. Results show that the characteristics of the home community have larger effects than those of target communities, while individual-level features are most influential. Older burglars travel over longer distances to commit their burglaries than the younger ones. Offenders who commit their burglaries in groups tend to travel further than solo offenders. Burglars who live in communities with a higher average rent, a denser road network and a higher percentage of local residents commit their burglaries at shorter distances. Communities with a denser road network attract burglars from a longer distance, whereas those with a higher percentage of local residents attract them from shorter by.

The relationship between burglary and socio-demographic factors has long been a hot topic in crime research. Spatial dependence and spatial heterogeneity are two issues to be addressed in modeling geographic data. When these two issues arise at the same time, it is difficult to model them simultaneously. A cross-comparison of three models is presented in this study to identify which spatial effect should be addressed first in crime analysis. The negative binominal model (NB), Bayesian hierarchical model (BHM) and the geographically weighted Poisson regression model (GWPR) were implemented based on a three-year residential burglary data set from ZG, China. The modeling result shows that both BHM and GWPR outperform NB as they capture either of the spatial effects. Compared to the NB model, the mean absolute deviation (MAD) of BHM and GWPR was decreased by 83.71% and 49.39%, the mean squared error (MSE) of BHM and GWPR was decreased by 97.88% and 77.15%, and the ${\mathrm{R}}_{\mathrm{d}}^2$ of BHM and GWPR was improved by 26.7% and 19.1%, respectively. In comparison with BHM and GWPR, BHM fits the data better with lower MAD, MSE and higher ${\mathrm{R}}_{\mathrm{d}}^2$ . The empirical analysis indicates that the percentage of renter population, percentage of people from other provinces, bus line density, and bus stop density have a significantly positive impact on the number of residential burglaries. The percentage of residents with a bachelor degree or higher, on the other hand, is negatively associated with the number of residential burglaries.

The segregation–crime relationship is a classic topic in sociology and crime geography, yet existing literature mainly focuses on the impact of racial segregation at the global scale. Little is known about the impact of local segregation of other socioeconomic characteristics such as education level, an important segregation factor for racially homogenous countries like China. Also unknown is their impact beyond the residential domain. Using the Baidu Map Location-Based Service population data set and court records in 863 local geographic units of the central urban area of Beijing during 2018 and 2019, this study uncovers the spatial pattern of segregation between people with and without a bachelor’s degree measured in the residential space and activity space and further investigates the influence of these two types of educational segregation and their interaction effects with social context on theft and violent crime. Results show less segregation in the activity space than in the residential space. Both types of segregation, however, significantly increase the risk of theft and violence, with activity space–based segregation more consequential. Moreover, the positive segregation–crime link is moderated by the local social context measured by the educational composition among residents and the ambient population. Compared with residential segregation, activity space–based segregation is more detrimental for places dominated by the less educated. Our results highlight the elevated influence of segregation on safety beyond the residential space, especially for areas clustered with the less educated ambient population.

Fear of crime can lead to lower satisfaction with life and subjective well-being. The indicators of fear of crime vary from the social and cultural context, and the hukou (household registration) status causes unequal rights between local hukou and non-local hukou residents in China. To improve people’s perception of safety, this study takes hukou as an indicator of social vulnerability and examines the relationship between hukou, perceived neighborhood conditions, and fear of crime in China. A binary logistic regression model was used to analyze the 1727 residents garnered from the 2016 Project on Public Safety in Guangzhou Neighborhoods (PPSGN) in Guangzhou, China. The results show that women, victimization experience, physical and social disorder, and neighborhood policing are associated with residents’ fear of crime. Although hukou status has no statistically significant effect on fear of crime, hukou status significantly moderates the influence of perceived neighborhood conditions on fear of crime. That is, perceived neighborhood conditions’ effects on fear are conditional on one’s hukou status: non-local hukou, perception of the social disorder has more of the detrimental effect on fear, and perception of social integration has less of the helpful effect on fear. In sum, this study adds to the international literature by revealing the conditional effect of the hukou on fear in a Chinese city.