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

Iwanski, Natalia; Frank, Richard; Reid, Andrew A.; Dabbaghian, Vahid

doi:10.1109/eisic.2012.14

Cited by 9 publications

(2 citation statements)

References 8 publications

(12 reference statements)

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“…Though the distribution of crime was not the primary focus, the work of Iwanski et al (2012) is also notable for its treatment of the street network. Working with data for offenders' homes and offence locations, the work examined the hypothesis that crimes were committed while the offender was in the process of a longer journey.…”

Section: Introductionmentioning

confidence: 99%

Examining the Relationship Between Road Structure and Burglary Risk Via Quantitative Network Analysis

2014

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Objectives To test the hypothesis that the spatial distribution of residential burglary is shaped by the configuration of the street network, as predicted by, for example, crime pattern theory. In particular, the study examines whether burglary risk is higher on street segments with higher usage potential.Methods Residential burglary data for Birmingham (UK) are examined at the street segment level using a hierarchical linear model. Estimates of the usage of street segments are derived from the graph theoretical metric of betweenness, which measures how frequently segments feature in the shortest paths (those most likely to be used) through the network. Several variants of betweenness are considered. The geometry of street segments is also incorporated-via a measure of their linearity-as are several socio-demographic factors.Results As anticipated by theory, the measure of betweenness was found to be a highlysignificant predictor of the burglary victimization count at the street segment level for all but one of the variants considered. The non-significant result was found for the most localized measure of betweenness considered. More linear streets were generally found to be at lower risk of victimization.Conclusion Betweenness offers a more granular and objective means of measuring the street network than categorical classifications previously used, and its meaning links more directly to theory. The results provide support for crime pattern theory, suggesting a higher risk of burglary for streets with more potential usage. The apparent negative effect of linearity suggests the need for further research into the visual component of target choice, and the role of guardianship.

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Section: Introductionmentioning

confidence: 99%

Examining the Relationship Between Road Structure and Burglary Risk Via Quantitative Network Analysis

2014

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“…In addition, it can simply be used by law enforcement officers who work with large datasets. Iwanski et al [10] also tried to find the location of crime attractors on a road network. It is accepted that each offender commits an offense in a path which lies in between his/her home and a nearby activity location.…”

Section: Previous Studiesmentioning

confidence: 98%

Predicting Next Location of Twitter Users for Surveillance

Gunduz

Yavanoğlu

Sağıroğlu

2013

2013 12th International Conference on Machine Learning and Applications

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In this study a novel approach that uses location based social networks for next location prediction in the field of technical surveillance and digital forensics is proposed. With the help of proposed methodology, search area for the potential criminals will be narrowed so that the spent time, money and effort by the law enforcement officers will be minimized. After collecting enough past location information for Foursquare users, the whole data is trained by means of Artificial Neural Networks. After training process, predicting the next location of the wantedperson is carried out. Prediction process is made region-based, so it is tried to predict the region of the potential criminals' next geographical location. The experimental results have shown that the proposed approach and developed system might achieve the prediction goal with only 3% error rate, and proposed methodology can be used by law enforcement officers for forensic surveillance and similar criminal acts.

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