Inequality, Crime and the Floating Population in China

Lo, T. Wing; Jiang, Guoping

doi:10.1007/s11417-006-9000-1

Cited by 32 publications

(21 citation statements)

References 45 publications

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“…The positive sign of the size of the permanent population variable indicates that an increase in the size of the permanent population will also increase the likelihood of NMVT incidents. The density of the floating population was another important variable contributing to crime incidents, which is consistent with previous findings [19,43]. Two particular environmental variables also contributed to the occurrence of NMVT, the density of road intersections and the size of the built-up area.…”

Section: Relationship Between the Spatial Distribution Of Vehicle Thesupporting

confidence: 90%

Space–Time Analysis of Vehicle Theft Patterns in Shanghai, China

Mao

Dai

Ding

et al. 2018

IJGI

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To identify and compare the space–time patterns of vehicle thefts and the effects of associated environmental factors, this paper conducts a case study of the Pudong New Area (PNA), a major urban district in Shanghai, China’s largest city. Geographic information system (GIS)-based analysis indicated that there was a stable pattern of vehicle theft over time. Hotspots of vehicle theft across different time periods were identified. These data provide clues for how law enforcement can prioritize the deployment of limited patrol and investigative resources. Vehicle thefts, especially those of non-motor vehicles, tend to be concentrated in the central-western portion of the PNA, which experienced a dramatic rate of urbanization and has a high concentration of people and vehicles. Important factors contributing to vehicle thefts include a highly mobile and transitory population, a large population density, and high traffic volume.

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Section: Relationship Between the Spatial Distribution Of Vehicle Thesupporting

confidence: 90%

Space–Time Analysis of Vehicle Theft Patterns in Shanghai, China

Mao

Dai

Ding

et al. 2018

IJGI

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“…The prevalence of violent crimes among migrant offenders can be interpreted in a socioeconomic background. Looking back, income inequality inevitably elevates alongside the great success in economic revitalization in China [77][78][79]. Massive migration into the city because of income disparities makes it easier for internal migration to commit crimes [80].…”

Section: Discussionmentioning

confidence: 99%

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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“…In China, strong contrasts exist between East and West, rural and urban regions, and even between different districts of the same city. Unequal regional development may cause social instability that hinders sustainable development [1,2]. Therefore, it is important to measure the regional inequality of China.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Measurement of Regional Inequality in Mainland China during 2005–2010 Using DMSP/OLS Night Light Imagery and Population Density Grid Data

Yang

et al. 2015

Sustainability

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This study used the Night Light Development Index (NLDI) to measure the regional inequality of public services in Mainland China at multiple scales. The NLDI was extracted based on a Gini Coefficient approach to measure the spatial differences of population distribution and night light distribution. Population data were derived from the dataset of China's population density grid, and night light data were acquired from satellite imagery. In the multi-scale analysis, we calculated the NLDI for China as a whole, eight 13470The same pattern was observed from the provincial and prefectural analysis, suggesting that public services in Mainland China became more equal within the five-year period. A regression analysis indicated that provincial and prefectural regions with more public services per capita and higher population density had more equal public services.

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Inequality, Crime and the Floating Population in China

Cited by 32 publications

References 45 publications

Space–Time Analysis of Vehicle Theft Patterns in Shanghai, China

Space–Time Analysis of Vehicle Theft Patterns in Shanghai, China

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

Multi-Scale Measurement of Regional Inequality in Mainland China during 2005–2010 Using DMSP/OLS Night Light Imagery and Population Density Grid Data

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