Discovering the joint influence of urban facilities on crime occurrence using spatial co-location pattern mining

He, Zhanjun; Deng, Min; Xie, Zhen; Wu, Liang; Chen, Zhanlong; Pei, Tao

doi:10.1016/j.cities.2020.102612

Cited by 47 publications

(25 citation statements)

References 45 publications

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“…The criminal activities in these cities have been found to concentrate or cluster in some areas and give rise to some hotspots [4][5][6]. Similar to the findings of studies conducted in Western countries, the distribution of crimes or hotspots in some Chinese cities displays hourly, daily, weekly and seasonal variation, and some factors that may have impacts on the spatial and temporal pattern of crime have also been verified [7][8][9][10]. The applicability of western criminological theories, such as social disorganization theory [11] and routine activities theory [12], have also been demonstrated by studies conducted in Chinese context [13][14][15].…”

Section: Introductionsupporting

confidence: 66%

Understanding the Spatiotemporal Pattern of Crimes in Changchun, China: A Bayesian Modeling Approach

et al. 2021

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Chinese cities have been undergoing extraordinary changes in many respects during the process of urbanization, which has caused crime patterns to evolve accordingly. This research applies a Bayesian spatiotemporal model to explore and understand the spatiotemporal patterns of crime risk from 2008 to 2017 in Changchun, China. The overall temporal trend of crime risk, the effects of land use covariates, spatial random effects, and area-specific differential trends are estimated through a Bayesian spatiotemporal model fitted using the Integrated Nested Laplace Approximation (INLA). The analytical results show that the regression coefficient for the overall temporal trend of crime risk changed from significantly positive to negative after the land use variables are incorporated into the Bayesian spatiotemporal model. The covariates of road density, commercial and recreational land per capita, residential land per capita, and industrial land per capita are found to be significantly associated with crime risk, which relates to classic theories in environmental criminology. In addition, some areas still exhibit significantly increasing crime risks compared with the general trend even after controlling for the land use covariates and the spatial random effects, which may provide insights for law enforcement and researchers regarding where more attention is required since there may be some unmeasured factors causing higher crime trend in these areas.

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Section: Introductionsupporting

confidence: 66%

Understanding the Spatiotemporal Pattern of Crimes in Changchun, China: A Bayesian Modeling Approach

et al. 2021

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“…These noises will bring challenges to 3D models visualization, splitting, spatial analysis, object extraction and 3D printing etc. [ 3 , 4 , 5 ]. Therefore, it is very significant to eliminate noises in triangulated meshes.…”

Section: Introductionmentioning

confidence: 99%

Mesh Denoising via Adaptive Consistent Neighborhood

Guo

Song

Han

et al. 2021

Sensors

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In this paper, we propose a novel guided normal filtering followed by vertex updating for mesh denoising. We introduce a two-stage scheme to construct adaptive consistent neighborhoods for guided normal filtering. In the first stage, we newly design a consistency measurement to select a coarse consistent neighborhood for each face in a patch-shift manner. In this step, the selected consistent neighborhoods may still contain some features. Then, a graph-cut based scheme is iteratively performed for constructing different adaptive neighborhoods to match the corresponding local shapes of the mesh. The constructed local neighborhoods in this step, known as the adaptive consistent neighborhoods, can avoid containing any geometric features. By using the constructed adaptive consistent neighborhoods, we compute a more accurate guide normal field to match the underlying surface, which will improve the results of the guide normal filtering. With the help of the adaptive consistent neighborhoods, our guided normal filtering can preserve geometric features well, and is robust against complex shapes of surfaces. Intensive experiments on various meshes show the superiority of our method visually and quantitatively.

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“…The same is probably true for offenders, as offending generally takes up only a small part of their routine activities [18]. A growing body of research has shown that the various environmental factors affect offenders' crime location choices [10,[19][20][21][22][23][24][25][26][27]. Simultaneously, according to the geometry of crime in crime pattern theory [10,28], offenders are more likely to commit offenses within their awareness spaces because they know these areas.…”

Section: Introductionmentioning

confidence: 99%

Do Migrant and Native Robbers Target Different Places?

Long¹,

Liu²

2021

IJGI

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The spatial pattern of crime has been a central theme of criminological research. Recently, the spatial variation in the crime location choice of offenders by different population groups has been gaining more attention. This study addresses the issue of whether the spatial distribution of migrant robbers’ crime location choices is different from those of native robbers. Further, what factors contribute to such differences? Using a kernel density estimation and the discrete spatial choice modeling, we combine the offender data, POI data, and mobile phone data to explain the crime location choice of the street robbers who committed offenses and were arrested from 2012 to 2016 in ZG City, China. The results demonstrate that the crime location choices between migrant robbers and native robbers have obvious spatial differences. Migrant robbers tend to choose the labor-intensive industrial cluster, while native robbers prefer the old urban areas and urban villages. Wholesale markets, sports stadiums, transportation hubs, and subway stations only affect migrant robbers’ crime location choices, but not native robbers’. These results may be attributable to the different spatial awareness between migrant robbers and native robbers. The implications of the findings for criminological theory and crime prevention are discussed.

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Discovering the joint influence of urban facilities on crime occurrence using spatial co-location pattern mining

Cited by 47 publications

References 45 publications

Understanding the Spatiotemporal Pattern of Crimes in Changchun, China: A Bayesian Modeling Approach

Understanding the Spatiotemporal Pattern of Crimes in Changchun, China: A Bayesian Modeling Approach

Mesh Denoising via Adaptive Consistent Neighborhood

Do Migrant and Native Robbers Target Different Places?

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