2014 SpatiallyEmbeddedCoOffencePredi

• (Tayebi et al., 2014) ⇒ Mohammad A. Tayebi, Martin Ester, Uwe Glässer, and Patricia L. Brantingham. (2014). “Spatially Embedded Co-offence Prediction Using Supervised Learning.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623353

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Cited By

• http://scholar.google.com/scholar?q=%222014%22+Spatially+Embedded+Co-offence+Prediction+Using+Supervised+Learning
• http://dl.acm.org/citation.cfm?id=2623330.2623353&preflayout=flat#citedby

Quotes

Author Keywords

• Co-offence prediction; data mining; link prediction; social network

Abstract

Crime reduction and prevention strategies are essential to increase public safety and reduce the crime costs to society. Law enforcement agencies have long realized the importance of analyzing co-offending networks --- networks of offenders who have committed crimes together --- for this purpose. Although network structure can contribute significantly to co-offence prediction, research in this area is very limited. Here we address this important problem by proposing a framework for co-offence prediction using supervised learning. Considering the available information about offenders, we introduce social, geographic, geo-social and similarity feature sets which are used for classifying potential negative and positive pairs of offenders. Similar to other social networks, co-offending networks also suffer from a highly skewed distribution of positive and negative pairs. To address the class imbalance problem, we identify three types of criminal cooperation opportunities which help to reduce the class imbalance ratio significantly, while keeping half of the co-offences. The proposed framework is evaluated on a large crime dataset for the Province of British Columbia, Canada. Our experimental evaluation of four different feature sets show that the novel geo-social features are the best predictors. Overall, we experimentally show the high effectiveness of the proposed co-offence prediction framework. We believe that our framework will not only allow law enforcement agencies to improve their crime reduction and prevention strategies, but also offers new criminological insights into criminal link formation between offenders.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 SpatiallyEmbeddedCoOffencePredi	Martin Ester Mohammad A. Tayebi Uwe Glässer Patricia L. Brantingham			Spatially Embedded Co-offence Prediction Using Supervised Learning				10.1145/2623330.2623353		2014