Clustering Residential Burglaries Using Modus Operandi and Spatiotemporal Information

To identify series of residential burglaries, detecting linked crimes performed by the same constellations of criminals is necessary. Comparison of crime reports today is difficult as crime reports traditionally have been written as unstructured text and often lack a common information-basis. Based on a novel process for collecting structured crime scene information, the present study investigates the use of clustering algorithms to group similar crime reports based on combined crime characteristics from the structured form. Clustering quality is measured using Connectivity and Silhouette index (SI), stability using Jaccard index, and accuracy is measured using Rand index (RI) and a Series Rand index (SRI). The performance of clustering using combined characteristics was compared with spatial characteristic. The results suggest that the combined characteristics perform better or similar to the spatial characteristic. In terms of practical significance, the presented clustering approach is capable of clustering cases using a broader decision basis.

[1]  Jessica Woodhams,et al.  Linking serial residential burglary: comparing the utility of modus operandi behaviours, geographical proximity, and temporal proximity , 2010 .

[2]  Ray Bull,et al.  Linking Different Types of Crime Using Geographical and Temporal Proximity , 2011 .

[3]  Shane D. Johnson,et al.  Who commits near repeats? A test of the boost explanation , 2004 .

[4]  Jitendra Malik,et al.  Normalized Cuts and Image Segmentation , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Veselka Boeva,et al.  Comparison of Clustering Approaches for Gene Expression Data , 2013, SCAI.

[6]  Martin Boldt,et al.  Detecting serial residential burglaries using clustering , 2014, Expert Syst. Appl..

[7]  Camille Roth,et al.  Natural Scales in Geographical Patterns , 2017, Scientific Reports.

[8]  Ickjai Lee,et al.  Crime analysis through spatial areal aggregated density patterns , 2011, GeoInformatica.

[9]  William M. Rand,et al.  Objective Criteria for the Evaluation of Clustering Methods , 1971 .

[10]  Donald E. Brown,et al.  A decision model for spatial site selection by criminals: a foundation for law enforcement decision support , 2003, IEEE Trans. Syst. Man Cybern. Part C.

[11]  Brent Snook,et al.  Linkage analysis in cases of serial burglary: comparing the performance of university students, police professionals, and a logistic regression model , 2010 .

[12]  Douglas B. Kell,et al.  Computational cluster validation in post-genomic data analysis , 2005, Bioinform..

[13]  D V Canter,et al.  Linking commercial burglaries by modus operandi: tests using regression and ROC analysis. , 2002, Science & justice : journal of the Forensic Science Society.

[14]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[15]  Pietro Perona,et al.  Self-Tuning Spectral Clustering , 2004, NIPS.

[16]  Craig Bennell,et al.  The impact of data degradation and sample size on the performance of two similarity coefficients used in behavioural linkage analysis. , 2010, Forensic science international.

[17]  Craig Bennell,et al.  Between a ROC and a hard place: a method for linking serial burglaries by modus operandi , 2005 .

[18]  Rui Xu,et al.  Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.

[19]  Guiyun Zhou,et al.  A web-based geographical information system for crime mapping and decision support , 2012, 2012 International Conference on Computational Problem-Solving (ICCP).

[20]  M. Maguire,et al.  Intelligence Led Policing, Managerialism and Community Engagement: Competing Priorities and the Role of the National Intelligence Model in the UK , 2006 .

[21]  Yi Peng,et al.  Evaluation of clustering algorithms for financial risk analysis using MCDM methods , 2014, Inf. Sci..

[22]  Robert E. Tarjan,et al.  Graph Clustering and Minimum Cut Trees , 2004, Internet Math..

[23]  Jerry H. Ratcliffe,et al.  The Hotspot Matrix: A Framework for the Spatio‐Temporal Targeting of Crime Reduction , 2004 .

[24]  P. Jaccard.  THE DISTRIBUTION OF THE FLORA IN THE ALPINE ZONE.1 , 1912 .

[25]  Craig Bennell,et al.  Addressing problems with traditional crime linking methods using receiver operating characteristic analysis , 2009 .

[26]  Catherine A. Sugar,et al.  Finding the Number of Clusters in a Dataset , 2003 .

[27]  Brian J. Reich,et al.  Partially supervised spatiotemporal clustering for burglary crime series identification , 2015 .

[28]  John Zeleznikow,et al.  Decision support systems for police: Lessons from the application of data mining techniques to “soft” forensic evidence , 2006, Artificial Intelligence and Law.

[29]  S. Chainey,et al.  GIS and Crime Mapping: Chainey/GIS and Crime Mapping , 2005 .

[30]  Wim Bernasco,et al.  Same-Offender Involvement in Repeat and Near Repeat Burglaries , 2008 .

[31]  Ray Bull,et al.  The psychology of linking crimes: A review of the evidence , 2007 .

[32]  Ying Cai,et al.  Spatial and temporal distribution and statistic method applied in crime events analysis , 2011, 2011 19th International Conference on Geoinformatics.

[33]  P. Rousseeuw.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[34]  Yi Peng,et al.  Evaluation of Classification Algorithms Using MCDM and Rank Correlation , 2012, Int. J. Inf. Technol. Decis. Mak..

[35]  Satu Elisa Schaeffer,et al.  Graph Clustering , 2017, Encyclopedia of Machine Learning and Data Mining.

[36]  Derek Johnson.  The space/time behaviour of dwelling burglars: Finding near repeat patterns in serial offender data , 2013 .