Analyses of Crime Patterns in NIBRS Data Based on a Novel Graph Theory Clustering Method: Virginia as a Case Study

This paper suggests a novel clustering method for analyzing the National Incident-Based Reporting System (NIBRS) data, which include the determination of correlation of different crime types, the development of a likelihood index for crimes to occur in a jurisdiction, and the clustering of jurisdictions based on crime type. The method was tested by using the 2005 assault data from 121 jurisdictions in Virginia as a test case. The analyses of these data show that some different crime types are correlated and some different crime parameters are correlated with different crime types. The analyses also show that certain jurisdictions within Virginia share certain crime patterns. This information assists with constructing a pattern for a specific crime type and can be used to determine whether a jurisdiction may be more likely to see this type of crime occur in their area.

[1]  Eman Abdu,et al.  Clustering Categorical Data Using Data Summaries and Spectral Techniques , 2009 .

[2]  Lynn A. Addington,et al.  Rape Co-occurrence: Do Additional Crimes Affect Victim Reporting and Police Clearance of Rape? , 2008 .

[3]  Cun-Quan Zhang,et al.  A new multimembership clustering method , 2007 .

[4]  Ira Assent,et al.  CLICKS: an effective algorithm for mining subspace clusters in categorical datasets , 2005, KDD '05.

[5]  Roger N. Shepard,et al.  Additive clustering: Representation of similarities as combinations of discrete overlapping properties. , 1979 .

[6]  James J. Nolan,et al.  Methods for Understanding and Analyzing NIBRS Data , 1999 .

[7]  Gang Wang,et al.  Crime data mining: a general framework and some examples , 2004, Computer.

[8]  Renée J. Miller,et al.  Scalable clustering of categorical data and applications , 2004 .

[9]  Howard N. Snyder.  The Overrepresentation of Juvenile Crime Proportions in Robbery Clearance Statistics , 1999 .

[10]  Ying Xu,et al.  Clustering gene expression data using a graph-theoretic approach: an application of minimum spanning trees , 2002, Bioinform..

[11]  Jon M. Kleinberg,et al.  Clustering categorical data: an approach based on dynamical systems , 2000, The VLDB Journal.

[12]  Margaret J. Sylvia,et al.  National Archive of Criminal Justice Data , 2013 .

[13]  Linda E. Saltzman,et al.  Applying NIBRS Data to the Study of Intimate Partner Violence: Massachusetts as a Case Study , 1999 .

[14]  Joshua Zhexue Huang,et al.  Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values , 1998, Data Mining and Knowledge Discovery.

[15]  Yi Li,et al.  COOLCAT: an entropy-based algorithm for categorical clustering , 2002, CIKM '02.

[16]  Christopher S. Dunn,et al.  NIBRS Data Available for Secondary Analysis , 1999 .

[17]  Johannes Gehrke,et al.  CACTUS—clustering categorical data using summaries , 1999, KDD '99.

[18]  Sudipto Guha,et al.  ROCK: a robust clustering algorithm for categorical attributes , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

[19]  Cun-Quan Zhang,et al.  A new clustering method and its application in social networks , 2011, Pattern Recognit. Lett..

[20]  Ronen Feldman,et al.  The Data Mining and Knowledge Discovery Handbook , 2005 .

[21]  Rainer Fuchs,et al.  Analysis of temporal gene expression profiles: clustering by simulated annealing and determining the optimal number of clusters , 2001, Bioinform..