An Examination of Spatial Differences between Migrant and Native Offenders in Committing Violent Crimes in a Large Chinese City

Abstract

Immigrants and natives are generally comparable in committing violent crimes in many Western cities. However, little is known about spatial differences between internal migrant offenders and native offenders in committing violence in contemporary urban China. To address this gap, this research aims to explore spatial variation in violent crimes committed by migrant and native offenders, and examine different effects of ambient population, crime attractors, crime generators, and offender anchor points on these crimes. Offender data, mobile phone data, and points-of-interest (POI) data are combined to explain the crime patterns of these offenders who committed offenses and were arrested from 2012 to 2016 in a large Chinese city by using box maps and negative binomial regression models. It is demonstrated that migrant and native violent crimes vary enormously across space. Ambient population is only positively related to migrant violent crimes. Crime attractors and generators have more significant and stronger correlations with migrant violent crimes, while offender anchor points have a stronger association with native violent crimes. The results reveal that migrant offenders tend to be attracted by larger amounts of people and more affected by crime attractors and generators than native offenders.

Burglaries and entry controls in gated communities

“…House for sale, rental house and proportion of floating population have positive relationships with gated community burglary. This finding is consistent with those of Bursik and Grasmick (1999), Curran (1998), Feng et al (2019), Jiang et al (2017), Koehle (2013), Kubrin and Weitzer (2003), Li (2017), Liu et al (2018), Long et al (2017), Ma (2001), Song et al (2018) and Wang et al (2017), which suggested that these factors may boost crime by weakening the informal social control of communities. Based on the standardised coefficients, rental house had a larger impact (higher coefficients) than house for sale and proportion of floating population.…”

“…Since the distribution of the dependent variable is over-dispersed, the negative binomial (NB) regression should be used to estimate the impact of entry control levels while controlling for the other community characteristics (Feng et al, 2019; Hilbe, 2011; Osgood, 2000; Song et al, 2018). Osgood (2000) gave a systematic demonstration on how to use the NB model to deal with crime rates.…”

“…‘Housing price’ may present benefits for burglars (Gibbons and Machin, 2008; Tita et al, 2006). ‘House for sale’, ‘Rental house’ and ‘Floating population’ may influence the crime level by affecting the informal social control of gated communities (Bursik and Grasmick, 1999; Curran, 1998; Feng et al, 2019; Koehle, 2013; Kubrin and Weitzer, 2003; Li, 2017; Liu et al, 2018; Long et al, 2017; Payne, 2010).…”

“…In China, a floating population refers to people who live away from their Hukou (the Chinese household registration system established in the 1950s) location (Shen and Huang, 2003). Most of the floating population moved from rural to urban areas or from underdeveloped to developed areas to obtain employment (Feng et al, 2019; Liu et al, 2018; Long et al, 2017). Some research suggests that the floating population is associated with more crime (Curran, 1998; Li, 2017).…”

The Spatial Heterogeneity of Factors of Drug Dealing: A Case Study from ZG, China

“…There are more opportunities to meet potential drug buyers around the hotels. Therefore, hotels with poor management are often hot spots of illicit drug activities, which have a positive impact on drug-related crime [12,44]. As shown in Figure 4e, the influence of hotels in the downtown areas is lower than that in the suburbs, which may be due to the fact that there are many other suitable drug dealing places in the city center, such as restaurants, bars, and parks, etc.…”