Spatially embedded co-offence prediction using supervised learning

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.

[1]  Kent A. Harries,et al.  Mapping Crime: Principle And Practice , 1999 .

[2]  Jure Leskovec,et al.  Friendship and mobility: user movement in location-based social networks , 2011, KDD.

[3]  R. Boba.  Crime analysis and crime mapping , 2005 .

[4]  Wilpen L. Gorr,et al.  Introduction to crime forecasting , 2003 .

[5]  Gang Chen,et al.  Evaluating geo-social influence in location-based social networks , 2012, CIKM.

[6]  Dino Pedreschi,et al.  Human mobility, social ties, and link prediction , 2011, KDD.

[7]  Yves Zenou,et al.  Working Paper No . 601 , 2003 Social Networks and Crime Decisions : The Role of Social Structure in Facilitating Delinquent Behavior , 2003 .

[8]  D. Cressey,et al.  Principles of Criminology. , 1947 .

[9]  George E. Tita,et al.  THE ECOLOGY OF GANG TERRITORIAL BOUNDARIES , 2012 .

[10]  A. Reiss,.  Co-Offending and Criminal Careers , 1988, Crime and Justice.

[11]  F. Weerman.  Co-Offending as Social Exchange Explaining. Characteristics of Co-Offending , 2003 .

[12]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[13]  Ron Kohavi,et al.  The Case against Accuracy Estimation for Comparing Induction Algorithms , 1998, ICML.

[14]  Srinivasan Parthasarathy,et al.  Local Probabilistic Models for Link Prediction , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[15]  Megan C. Kurlychek,et al.  Department of Justice Office of Justice Programs Office of Juvenile Justice and Delinquency Prevention Juvenile Accountability Incentive Block Grants Program Shay Bilchik , Administrator August 1999 From the Administrator , 1999 .

[16]  Uwe Glässer,et al.  Understanding the link between social and spatial distance in the crime world , 2012, SIGSPATIAL/GIS.

[17]  Brent Snook,et al.  Individual differences in distance travelled by serial burglars , 2004 .

[18]  Christopher J. Sullivan,et al.  INVESTIGATING THE STABILITY OF CO-OFFENDING AND CO-OFFENDERS AMONG A SAMPLE OF YOUTHFUL OFFENDERS* , 2008 .

[19]  G. A. Avanesov,et al.  The Principles of Criminology , 1982 .

[20]  Richard Frank,et al.  Exploring the structural characteristics of social networks in a large criminal court database , 2013, 2013 IEEE International Conference on Intelligence and Security Informatics.

[21]  Cecilia Mascolo,et al.  Exploiting place features in link prediction on location-based social networks , 2011, KDD.

[22]  D V Canter,et al.  Identifying the residential location of rapists. , 1994, Journal - Forensic Science Society.

[23]  Nitesh V. Chawla,et al.  New perspectives and methods in link prediction , 2010, KDD.

[24]  Lada A. Adamic,et al.  Friends and neighbors on the Web , 2003, Soc. Networks.

[25]  M. Felson.  Crime and nature , 2006 .

[26]  Carlo Morselli,et al.  Inside Criminal Networks , 2008 .

[27]  Martin Ester,et al.  Co-offending Network Mining , 2011, Counterterrorism and Open Source Intelligence.

[28]  Jon M. Kleinberg,et al.  The link-prediction problem for social networks , 2007, J. Assoc. Inf. Sci. Technol..

[29]  Shashi Shekhar,et al.  Environmental Criminology , 2008, Encyclopedia of GIS.

[30]  Mohammad Al Hasan,et al.  Link prediction using supervised learning , 2006 .

[31]  Martin Ester,et al.  CrimeWalker: a recommendation model for suspect investigation , 2011, RecSys '11.

[32]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[33]  Donald E. Brown,et al.  Criminal incident prediction using a point-pattern-based density model , 2003 .