Analysis of Burglary Hot Spots and Near-Repeat Victimization in a Large Chinese City

In recent years, various academic studies have proposed crime forecasting models based on the concept of repeat victimization. Some of them have been modelled from the area of differential equations and others from the perspective of spatio-temporal statistics, within the framework of point processes. These models have tended towards a certain sophistication in their formulation, which at times impedes understanding of the predictive mechanism and how it adapts to different realities. Predictive models that function well in one environment or society do not appear to do so in others. In this article, the possibility of crime forecasting for burglaries with forced entry in Catalonia is studied from the perspective of near repeat victimization on a larger territorial scale than is usual. To this effect, the explicative and predictive possibilities of this criminological theory are explored and a predictive system that does not require mathematical or statistical models is proposed. We found that a large part of the series of burglaries with forced entry in residences in Catalonia between 2014 and 2015 follow patterns of near repeat victimization. In addition, the average intensity of burglaries in space–time was high, as was the standard deviation. This system is adaptable to different environments and gives police forces the opportunity to improve preventative strategies and to optimize resources using standard tools. Last, the limitations of this approach are debated and new lines of investigation proposed that could increase its predictive capacity without abandoning the concept of repeat victimization.

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An Analysis of Near Repeat Victimization Patterns across Crime Types

Theory and Applications of GIS ◽

10.5638/thagis.18.129 ◽

2010 ◽

Vol 18 (2) ◽

pp. 129-138 ◽

Cited By ~ 2

Author(s):

George Kikuchi ◽

Mamoru Amemiya ◽

Takahito Shimada ◽

Tomonori Saito ◽

Yutaka Harada

Keyword(s):

Repeat Victimization ◽

Crime Types ◽

Near Repeat

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Near-repeat burglary patterns in Malmö: Stability and change over time

European Journal of Criminology ◽

10.1177/1477370817751382 ◽

2018 ◽

Vol 16 (1) ◽

pp. 3-17 ◽

Cited By ~ 9

Author(s):

Laura Hoppe ◽

Manne Gerell

Keyword(s):

Time Frame ◽

Repeat Victimization ◽

Full Dataset ◽

Local Police ◽

Complete Dataset ◽

Close Proximity ◽

Private And Public ◽

Repeat Burglary ◽

Near Repeat ◽

Individual Dataset

It is well established that previous crime events are valuable indicators for the prediction of future crime. Near-repeat burglaries are incidents that occur in close proximity in space and time to an initial burglary. The current study analyses near-repeat victimization patterns in Malmö, Sweden’s third-largest city. The data, provided by the local police, cover a six-year time frame from 2009 to 2014. The complete dataset, as well as each year’s individual dataset, was analysed using Ratcliffe’s Near Repeat Calculator version 1.3. Results reveal significant near-repeat victimization patterns. For the full dataset, an observed/expected ratio of 2.83 was identified for the first week after an initial incident and an area of 100 metres surrounding the original burglary. Separate analyses of each individual year reveal both similarities and differences between years. Some years manifest near-repeat patterns at longer spatial and temporal distances, indicating a need for further studies on the variability of near repeats. Preventive strategies that include both private and public actors need to be intensified and focused on the first two weeks after a burglary.

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A Near Repeat Examination of Economic Crimes

Crime & Delinquency ◽

10.1177/0011128718811927 ◽

2018 ◽

Vol 65 (9) ◽

pp. 1319-1340 ◽

Cited By ~ 2

Author(s):

Zachary A. Powell ◽

Jonathan A. Grubb ◽

Matt R. Nobles

Keyword(s):

Credit Card ◽

Police Department ◽

Policy Implications ◽

Repeat Victimization ◽

Future Research ◽

Increased Risk ◽

Risk Areas ◽

Repeat Examination ◽

Near Repeat ◽

Economic Crimes

Criminological inquiry has recently showcased increased attention toward the near repeat phenomenon, which suggests that following an initial criminal event, there is an increased risk for the occurrence of the same type of event spatially and temporally proximate to the initiating event. The majority of previous research tends focuses on the patterns related to violent and conventional property crimes but neglects to examine other economic-related crimes. Given the significant costs associated with this crime type, unpacking hidden patterns may be useful in identifying high-risk areas and deploying appropriate countermeasures. The current study builds on previous examinations of the near repeat phenomenon through the exploration of specific economic crimes (counterfeiting, credit card/ATM fraud, false pretense/swindling) using 6 years of crime data from the Fort Worth (Texas) Police Department. Although near repeat patterns were discovered across multiple analyses, the greatest risk across all analyses was for repeat victimization at the same location. A discussion of policy implications, limitations, and areas for future research are also provided.

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Stationary patterns and their selection mechanism of urban crime models with heterogeneous near-repeat victimization effect

European Journal of Applied Mathematics ◽

10.1017/s0956792516000206 ◽

2016 ◽

Vol 28 (1) ◽

pp. 141-178 ◽

Cited By ~ 8

Author(s):

YU GU ◽

QI WANG ◽

GUANGZENG YI

Keyword(s):

Neumann Boundary ◽

Urban Crime ◽

Repeat Victimization ◽

Selection Mechanism ◽

Linear Diffusion ◽

Stable Pattern ◽

Diffusion Systems ◽

Victimization Rate ◽

Pattern Formations ◽

Near Repeat

In this paper, we study two PDEs that generalize the urban crime model proposed by Short et al. (2008 Math. Models Methods Appl. Sci.18, 1249–1267). Our modifications are made under assumption of the spatial heterogeneity of both the near-repeat victimization effect and the dispersal strategy of criminal agents. We investigate pattern formations in the reaction–advection–diffusion systems with non-linear diffusion over multi-dimensional bounded domains subject to homogeneous Neumann boundary conditions. It is shown that the positive homogeneous steady state loses its stability as the intrinsic near-repeat victimization rate ε decreases and spatially inhomogeneous steady states emerge through bifurcation. Moreover, we find the wavemode selection mechanism through rigorous stability analysis of these non-trivial spatial patterns, which shows that the only stable pattern must have a wavenumber that maximizes the bifurcation value. Based on this wavemode selection mechanism, we will be able to predict the formation of stable aggregates of the house attractiveness and criminal population density, at least when the diffusion rate ε is around the principal bifurcation value. Our theoretical results also suggest that large domains support more stable aggregates than small domains. Finally, we perform extensive numerical simulations over 1D intervals and 2D squares to illustrate and verify our theoretical findings. Our numerics also demonstrate the formation of other interesting patterns in these models such as the merging of two interior spikes and the emerging of new spikes, etc. These non-trivial solutions can model the well-observed phenomenon of aggregation in urban criminal activities.

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