Power of Criminal Attractors: Modeling the Pull of Activity Nodes

Frank, Richard; Dabbaghian, Vahid; Reid, Andrew A.; Singh, Sujata; Cinnamon, Jonathan; Brantingham, Patricia L.

doi:10.18564/jasss.1734

Cited by 28 publications

(14 citation statements)

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“…All have been confirmed to be crime attractors from previous studies and from crime data in the region [8,9,14]. However these results show that these attractors do not consist of a single activity node but rather contribute to high traffic along the routes they are located on, raising the attractiveness of the surrounding area.…”

Section: Discussionmentioning

confidence: 55%

A Computational Model for Predicting the Location of Crime Attractors on a Road Network

Iwanski

Frank

Reid

et al. 2012

2012 European Intelligence and Security Informatics Conference

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Crime Pattern Theory argues that offenders often commit their crimes at major criminal attractors or along the routes that lead to these attractors from other activity nodes in their awareness space. Without knowledge about each offender's awareness space, however, it is not possible to know the nodes they travel to. In this paper, it is assumed that each offender commits their crime along the way to a particular end-destination that they frequent. It thus follows that, for each crime location of an offender, there is an activity path nearby which starts at the offender's home location and ends at a nearby activity node. Since the activity node is not known, but the crime and home locations of offenders are, the method presented in this paper determines the locations of the attractors by statistically extending the path along the road-network from the offender's home location to their crime location. Each offender's movement pattern is based on probabilities assigned to each road segment that are determined by examining the angle of the road in relation to an offender's journey from home, and how frequently the road segment is taken by commuters on the road network. After generating paths for all offenders, attractor locations are identified by calculating the most frequently travelled nodes in the network. The aim of the model is to investigate the influence that crime attractors and other major activity nodes have on the locations of crimes to better understand the target selection behaviour of known offenders.

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Section: Discussionmentioning

confidence: 55%

A Computational Model for Predicting the Location of Crime Attractors on a Road Network

Iwanski

Frank

Reid

et al. 2012

2012 European Intelligence and Security Informatics Conference

Self Cite

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“…Several important properties of the observed chaos are reproduced in the space y(n) and the evolution in time y(n) → y(n + 1) follows the unknown dynamics given by Eq. (16). The deterministic behavior of x(n) → x(n + 1) assures the deterministic behavior of the substituting dynamics.…”

Section: Observed Chaotic Datamentioning

confidence: 91%

“…14,15 However, quantitative analysis of criminal data based on chaos theory and tools is still an open issue. In addition, determining how crime behaves in space 16 or space-time 17 following a dynamical perspective is an important topic in crime research.…”

Section: Introductionmentioning

confidence: 99%

Multifractal Approach to the Analysis of Crime Dynamics: Results for Burglary in San Francisco

Melgarejo

Neira

2017

Fractals

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This paper provides evidence of fractal, multifractal and chaotic behaviors in urban crime by computing key statistical attributes over a long data register of criminal activity. Fractal and multifractal analyses based on power spectrum, Hurst exponent computation, hierarchical power law detection and multifractal spectrum are considered ways to characterize and quantify the footprint of complexity of criminal activity. Moreover, observed chaos analysis is considered a second step to pinpoint the nature of the underlying crime dynamics. This approach is carried out on a long database of burglary activity reported by 10 police districts of San Francisco city. In general, interarrival time processes of criminal activity in San Francisco exhibit fractal and multifractal patterns. The behavior of some of these processes is close to 1/f noise. Therefore, a characterization as deterministic, high-dimensional, chaotic phenomena is viable. Thus, the nature of crime dynamics can be studied from geometric and chaotic perspectives. Our findings support that crime dynamics may be understood from complex systems theories like self-organized criticality or highly optimized tolerance.

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“…() assumed that offenders were travelling to shopping malls and stopping along the way to commit crimes, Frank et al . () lifted that assumption and focused on calculating the locations of such criminal (not crime) attractors. They showed that it was possible to calculate the location of these attractors from data about offender's home and crime locations.…”

Section: Related Workmentioning

confidence: 99%

The strongest does not attract all but it does attract the most – evaluating the criminal attractiveness of shopping malls using fuzzy logic

Mago¹,

Frank

Reid

et al. 2013

Expert Systems

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Crime attractors are locations (e.g. shopping malls) that attract criminally motivated offenders because of the presence of known criminal opportunities. Although there have been many studies that explore the patterns of crime in and around these locations, there are still many questions that linger. In recent years, there has been a growing interest to develop mathematical models in attempts to help answer questions about various criminological phenomena. In this paper, we are interested in applying a formal methodology to model the relative attractiveness of crime attractor locations based on characteristics of offenders and the crime they committed. To accomplish this task, we adopt fuzzy logic techniques to calculate the attractiveness of crime attractors in three suburban cities in the Metro Vancouver region of British Columbia, Canada. The fuzzy logic techniques provide results comparable with our real-life expectations that offenders do not necessarily commit significant crimes in the immediate neighbourhood of the attractors, but travel towards it, and commit crimes on the way. The results of this study could lead to a variety of crime prevention benefits and urban planning strategies.

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Power of Criminal Attractors: Modeling the Pull of Activity Nodes

Cited by 28 publications

References 0 publications

A Computational Model for Predicting the Location of Crime Attractors on a Road Network

A Computational Model for Predicting the Location of Crime Attractors on a Road Network

Multifractal Approach to the Analysis of Crime Dynamics: Results for Burglary in San Francisco

The strongest does not attract all but it does attract the most – evaluating the criminal attractiveness of shopping malls using fuzzy logic

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