Team 22 Rami Alghamdi & Ritika Jhangiani.  Baltimore County Police  Hot Spot Analysis (Frequent Crime locations)  K-Means Clustering Crime Location.

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Presentation transcript:

Team 22 Rami Alghamdi & Ritika Jhangiani

 Baltimore County Police  Hot Spot Analysis (Frequent Crime locations)  K-Means Clustering Crime Location

(2, 10)(5, 8) PointDistance Cluster A1(2,10) A2(2,5) A3(8,4)8.552 A4(5,8)3.602

(2, 10)(5, 5.6) PointDistance Cluster A1(2,10) A2(2,5) A3(8,4) A4(5,8) New mean!

 Using K-Means with K=10 Clusters

 Using K-Means with K=31 Clusters



 Mohler, George O., et al. "Self-exciting point process modeling of crime."Journal of the American Statistical Association (2011).  BBC Horizon 2013 The Age of Big Data  Ned Levine (2010). CrimeStat: A Spatial Statistics Program for the Analysis of Crime Incident Locations (v 3.3). Ned Levine & Associates, Houston, TX, and the National Institute of Justice, Washington, DC. July.