Residents, Employees and Visitors: Effects of Three Types of Ambient Population on Theft on Weekdays and Weekends in Beijing, China

Abstract

ObjectivesThe residential population of an area is an incomplete measure of the number of people that are momentarily present in the area, and of limited value as an indicator of exposure to the risk of crime. By accounting for the mobility of the population, measures of ambient population better reflect the momentary presence of people. They have therefore become an alternative indicator of exposure to the risk of crime. This study considers the heterogeneity of the ambient population by distinguishing residents, employees and visitors as different categories, and explores their differential impact on thefts, both on weekdays and weekends. Methods We analyze one-year of police recorded thefts across 2104 1 km 2 grid cells in a central area in Beijing, China. Controlling for the effects of attractiveness, accessibility, and guardianship, we estimate a series of negative binominal models to investigate the differential effects of the three groups (residents, employees and visitors) in the ambient population on crime frequencies, both on weekdays and during weekends and holidays. Results Overall, larger ambient populations imply larger theft frequencies. The effect of visitors is stronger than the effects of residents and employees. The effects of residents and employees vary over the course of the week. On weekdays, the presence of residents is more important, while the reverse holds true during weekends and holidays. Discussion The effects of ambient population on thefts vary by its composition in terms of social roles. The larger role of visitors is presumably because in addition to being potential victims, residents and employees may also exercise informal social control. In addition, they spend more time indoors than where risk of theft is lower, while visitors might spend more time outdoors and may also bring about greater anonymity and weaken informal social control.

Modeling the Effect of Streetscape Environment on Crime Using Street View Images and Interpretable Machine-Learning Technique

“…Green spaces such as trees and grassland can facilitate residents’ outdoor activities and thus promote informal monitoring of the area, thereby retraining the occurrence of crimes [ 1 , 23 , 24 , 25 ]. In addition, people on the street may act as potential victims, and this factor is usually associated with high crime rates [ 11 , 12 ]. Apart from pedestrians directly related to the people on the street, this study also considered the proportions of sidewalks and buildings, as these two elements are also associated with human activity [ 1 ].…”

“…On the one hand, crowds on the street provide suitable targets for street crimes such as pickpocketing. Therefore, streets with more people are usually associated with higher street crime rates because the plentiful crime opportunities attract potential offenders [ 11 , 12 , 13 ]. On the other hand, some scholars argue that people on the street can act as guardians and deter potential offenders from committing crimes.…”

Can a Twitter-Based Measure of Racial Heterogeneity Explain Neighborhood Crime Differences? Proposing an Ambient Population Measure of Social Disorganization

“…Similarly, Wo et al (2022) tagged Twitter user's home block group based on where they sent the most tweets and found that block groups with higher proportions of local users tended to have lower counts of violent crime and property crime. Using mobile phone data, Song et al (2021) estimated where people live and work to create ambient population measures for residents, employees, and visitors, with their results also suggesting that ambient populations composed of visitors are more criminogenic in their nature. As noted, the one case we know of that sought to study the racial composition of the ambient population measured individual-level race as an extension of the neighborhood's racial composition without drawing additional information from the user's activity (Gu et al 2022).…”

“…First, we considered the role of neighborhood visitors because outside visitors should be more concentrated in non-residential areas and they are likely to be a direct source of ambient heterogeneity. Furthermore, past research has shown that geographies with higher proportions of visitors in the ambient population tend to experience more crime (Song et al 2021;Tucker et al 2021;Wo et al 2022), so if ambient heterogeneity and visitation are highly correlated it is possible the association between ambient heterogeneity and crime is spurious. To estimate the visiting population of each neighborhood, we calculated the percentage of Twitter users in each tract who had an estimated home location within that tract.…”

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

“…This is because urban populations are characterized by persistent fluctuations, which is exemplified in residents' daily mobility patterns as they traverse diverse locations and are consequently exposed to varying degrees of crime risk (Kwan, 2012). A significant body of research indicates that a larger ambient population includes more potential targets and provides greater opportunities for crime (Song et al, 2021;Lan et al, 2019). And some studies turn their focus toward the impact of different groups' activities on crime.…”