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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 on theme: "Team 22 Rami Alghamdi & Ritika Jhangiani.  Baltimore County Police  Hot Spot Analysis (Frequent Crime locations)  K-Means Clustering Crime Location."— Presentation transcript:

1 Team 22 Rami Alghamdi & Ritika Jhangiani

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

3 (2, 10)(5, 8) PointDistance Cluster A1(2,10)0 3.61 A2(2,5)5 4.22 A3(8,4)8.552 A4(5,8)3.602

4 (2, 10)(5, 5.6) PointDistance Cluster A1(2,10)0 5.31 A2(2,5)5 3.12 A3(8,4)8.53.42 A4(5,8)3.6 2.4 2 New mean!

5  Using K-Means with K=10 Clusters

6  Using K-Means with K=31 Clusters

7  http://www.youtube.com/watch?v=CO2mGny6fFs http://www.youtube.com/watch?v=CO2mGny6fFs

8  Mohler, George O., et al. "Self-exciting point process modeling of crime."Journal of the American Statistical Association 106.493 (2011).  BBC Horizon 2013 The Age of Big Data http://www.youtube.com/watch?v=CO2mGny6fFs http://www.youtube.com/watch?v=CO2mGny6fFs  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.

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