Theft from the person in urban China: assessing the diurnal effects of opportunity and social ecology

Song

et al. 2018

IJGI

Self Cite

Abstract: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.

Section: Conclusion and Discussioncontrasting

confidence: 53%

Section: Conclusion and Discussionmentioning

confidence: 99%

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

Xiao

Song

et al. 2018

IJGI

Self Cite

“…In response to hypothesis 1, the existence of crime geographical displacement is confirmed. Many empirical studies have confirmed the feasibility of many Western criminology theories in China [30,31,[33][34][35]. Our current research, which is probably the first investigation of crime geographical displacement in China, enriches the literature of the similarities shared by China and Western countries.…”

Section: Discussionsupporting

confidence: 52%

“…Our current study follows the existing research by adopting 200 m and seven days as the space and time bandwidth, respectively. As such, six spatial (same location, 200, 400, 600, 800, and 1000 m) and seven temporal (7,14,21,28,35,42, and >42 days) bandwidths are used and evaluated in the framework of a risk analysis.…”

Section: Repeat and Near-repeat Calculatormentioning

confidence: 99%

Crime Geographical Displacement: Testing Its Potential Contribution to Crime Prediction

Wang

Zhou

et al. 2019

IJGI

Self Cite

Crime geographical displacement has been examined in many Western countries. However, little is known about its existence, distribution, and potential predictive ability in large cities in China. Compared to the existing research, this study contributes to the current research in three ways. (1) It provides confirmation that crime geographical displacement exists in relation to burglaries that occur in a large Chinese city. (2) A crime geographical displacement detector is proposed, where significant displacements are statistically detected and geographically displayed. Interestingly, most of the displacements are not very far from one another. These findings confirm the inferences in the existing literature. (3) Based on the quantitative results detected by the crime geographical displacement detector, a crime prediction method involving crime geographical displacement patterns could improve the accuracy of the empirical crime prediction method by 7.25% and 3.1 in the capture rate and prediction accuracy index (PAI), respectively. Our current study verifies the feasibility of crime displacement for crime prediction. The feasibility of the crime geographical displacement detector and results should be verified in additional areas.

“…Crime research usually takes the number of crimes as a dependent variable, which is discrete, whereas the OLS model assumes that the dependent variable should be continuous. Therefore, the Poisson and negative binomial regression models have been adopted for crime modeling [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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

Chen

Xiao

et al. 2020

IJGI

Self Cite

Negative binomial (NB) regression model has been used to analyze crime in previous studies. The disadvantage of the NB model is that it cannot deal with spatial effects. Therefore, spatial regression models, such as the geographically weighted Poisson regression (GWPR) model, were introduced to address spatial heterogeneity in crime analysis. However, GWPR could not account for overdispersion, which is commonly observed in crime data. The geographically weighted negative binomial model (GWNBR) was adopted to address spatial heterogeneity and overdispersion simultaneously in crime analysis, based on a 3-year data set collected from ZG city, China, in this study. The count of residential burglaries was used as the dependent variable to calibrate the above models, and the results revealed that the GWPR and GWNBR models performed better than NB for reducing spatial dependency in the model residuals. GWNBR outperformed GWPR for incorporating overdispersion. Therefore, GWNBR was proven to be a promising tool for crime modeling.