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Crime Data Mining

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

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Crime Data Mining

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  1. Crime Data Mining Team 22 Rami Alghamdi & RitikaJhangiani

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

  3. How? • Cluster the following Four points with (x, y) representing locations into tow clusters (K=2). Initial cluster centers are: A1(2,10), A4(5,8). • () = • Iteration 1 • Recalculate the means of the new clusters: • Cluster 1 = (2, 10) • Cluster 2 = ((2 + 8 + 5)/3 , (5 + 4 + 8)/3) = (5, 5.6)

  4. Recalculate the distance • () = • Iteration 2 • Clusters did not change after the second iteration! New mean!

  5. Baltimore County Robbery 'Hot Spots' • Using K-Means with K=10 Clusters

  6. Baltimore County Robbery 'Hot Spots' • Using K-Means with K=31 Clusters

  7. Crime Prediction: Space-Time Clustering • http://www.youtube.com/watch?v=CO2mGny6fFs

  8. References • 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 • 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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