Spatial Variation Relationship between Floating Population and Residential Burglary: A Case Study from ZG, China

Abstract

With the rapid development of China's economy, the demand for labor in the coastal cities continues to grow. Due to restrictions imposed by China's household registration system, a large number of floating populations have subsequently appeared. The relationship between floating populations and crime, however, is not well understood. This paper investigates the impact of a floating population on residential burglary on a fine spatial scale. The floating population was divided into the floating population from other provinces (FPFOP) and the floating population from the same province as ZG city (FPFSP), because of the high heterogeneity. Univariate spatial patterns in residential burglary and the floating population in ZG were explored using Moran's I and LISA (local indicators of spatial association) models. Furthermore, a geographically weighted Poisson regression model, which addressed the spatial effects in the data, was employed to explore the relationship between the floating population and residential burglary. The results revealed that the impact of the floating population on residential burglary is complex. The floating population from the same province did not have a significant impact on residential burglary in most parts of the city, while the floating population from other provinces had a significantly positive impact on residential burglary in most of the study areas and the magnitude of this impact varied across the study area.

Integrative Analysis of Spatial Heterogeneity and Overdispersion of Crime with a Geographically Weighted Negative Binomial Model

“…The number of residential burglaries in each PSMA varied from 9 to 3547, as shown in Figure 1. Following previous studies [24,25,[35][36][37], the variables selected for this research are shown in Table 1, as well as their descriptive statistics. The number of burglaries was adopted as the dependent variable in this study.…”

“…GWPR has been employed to explore the relationship between crime and related risk factors when the response variable was the number of crimes [24]. As Xu and Huang indicated, using GWPR to model count data was only a temporary solution, which was mainly restricted by the available software GWR4 [44].…”

“…According to the type of response variable, GWR has evolved into different versions, such as geographic weight Gaussian regression, geographic weight Poisson regression (GWPR), and geographic weight logistic regression (GWLR). Crime data are usually reported in the form of the number of criminal cases, which can be used as the response variable without any transformation in the GWPR model [24,25].…”

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

“…Social environment characteristics including registration heterogeneity, percentage rental, divorce rate, and population are revealed by census data at the neighborhood level. Ethnic heterogeneity has been widely tested in many Western countries, but registration heterogeneity is chosen in the context of China because of the significant impact of household registration systems on population mobility and population heterogeneity [32,72], and the diverse influence of various registered population groups on urban crime [73]. Specifically, registration heterogeneity is measured by an index of qualitative variation using one minus the sum of the squared percentages of three household registration categories: (1) registration in ZG city, (2) registration outside ZG city but in the same province, and (3) registration in other provinces.…”

Journey-to-Crime Distances of Residential Burglars in China Disentangled: Origin and Destination Effects

“…However, racial differences in most Chinese cities are not as large as in American cities. The most pertinent group difference in the Chinese setting exists between local residents and nonlocal residents [44,45]. Offenders that belong to the local resident population are generally more familiar with the area than nonlocal migrants who might have recently arrived in the city and we thus expect local offenders to travel over longer distances than nonlocal offenders.…”