Testing Indicators of Risk Populations for Theft from the Person across Space and Time: The Significance of Mobility and Outdoor Activity

Song, Guangwen; Liu, Lin; Bernasco, Wim; Xiao, Luzi; Zhou, Suhong; Liao, Weiwei

doi:10.1080/24694452.2017.1414580

Cited by 50 publications

(47 citation statements)

References 57 publications

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“…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: 53%

Crime Geographical Displacement: Testing Its Potential Contribution to Crime Prediction

Wang

Liu

Zhou

et al. 2019

IJGI

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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.

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Section: Discussionsupporting

confidence: 53%

Crime Geographical Displacement: Testing Its Potential Contribution to Crime Prediction

Wang

Liu

Zhou

et al. 2019

IJGI

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“…Descriptive statistics and correlations of census unit characteristics (counts of crime generators and % nonlocals) The data of the locations of mobile phone users in this research was provided by a major mobile phone service provider in China that has a market share of 22.5% (Chong et al 2015). Song et al (2018a) used the mobile phone users' data from the same provider to measure ambient populations, and argued that there are no major systematic differences between the customers of different mobile phone providers, which suggest that our sample of mobile phone users is representative for the general population in ZG City. Like Xu et al (2016) using mobile phone data of 1 day to measure activities, our mobile phone data contains tracking information of the routes taken by the mobile phones on the network hour by hour on Wednesday, December 28, 2016, a fairly regular day in ZG city.…”

Section: Tablementioning

confidence: 99%

Crime Feeds on Legal Activities: Daily Mobility Flows Help to Explain Thieves’ Target Location Choices

et al. 2019

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Objective According to routine activity theory and crime pattern theory, crime feeds on the legal routine activities of offenders and unguarded victims. Based on this assumption, the present study investigates whether daily mobility flows of the urban population help predict where individual thieves commit crimes. Methods Geocoded tracks of mobile phones are used to estimate the intensity of population mobility between pairs of 1616 communities in a large city in China. Using data on 3436 police-recorded thefts from the person, we apply discrete choice models to assess whether mobility flows help explain where offenders go to perpetrate crime. Results Accounting for the presence of crime generators and distance to the offender's home location, we find that the stronger a community is connected by population flows to where the offender lives, the larger its probability of being targeted. Conclusions The mobility flow measure is a useful addition to the estimated effects of distance and crime generators. It predicts the locations of thefts much better than the presence of crime generators does. However, it does not replace the role of distance, suggesting that offenders are more spatially restricted than others, or that even within their activity spaces they prefer to offend near their homes.

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“…An alternative measurement of the population at risk is the ambient population in the existing research [48]. To capture the ambient population, diverse data sources have been applied, including remote sensing dataset, travel survey data, workday census data, Twitter data, mobile phone data, taxi ridership data, and subway ridership data [40,[49][50][51][52][53]. For example, population density and population change are estimated from mobile phone users' locations in a recent study to discern the effect of ambient population on snatch-and-run offenses [54].…”

Section: Ambient Populationmentioning

confidence: 99%

“…Bernasco and Block have verified the spatial influence of crime generators, crime attractors, and offender anchor points on robberies ascribed to all kinds of robbers at the block level in Chicago [39]. Studies in ZG city, China show that ambient population, resident's daily activities, and the locations of amenities affect the pattern of theft from the person across time and space [40][41][42]. As Brantingham and Brantingham point out, crimes do not take place randomly or uniformly across social groups [43].…”

Section: Introductionmentioning

confidence: 97%

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

Feng

Liu

Long

et al. 2019

IJGI

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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.

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Testing Indicators of Risk Populations for Theft from the Person across Space and Time: The Significance of Mobility and Outdoor Activity

Cited by 50 publications

References 57 publications

Crime Geographical Displacement: Testing Its Potential Contribution to Crime Prediction

Crime Geographical Displacement: Testing Its Potential Contribution to Crime Prediction

Crime Feeds on Legal Activities: Daily Mobility Flows Help to Explain Thieves’ Target Location Choices

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

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