crime data mining
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Crime Data MiningTeam 22Rami Alghamdi & Ritika Jhangiani
Baltimore County Police Hot Spot Analysis (Frequent Crime
locations) K-Means Clustering
CrimeStat
Crime Location
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)
How?
(2, 10) (5, 8) Point Distance Distance Cluster
A1 (2,10) 0 3.6 1 A2 (2,5) 5 4.2 2 A3 (8,4) 8.5 5 2A4 (5,8) 3.6 0 2
() = Iteration 2
Clusters did not change after the second iteration!
Recalculate the distance
(2, 10) (5, 5.6) Point Distance Distance Cluster
A1 (2,10) 0 5.3 1 A2 (2,5) 5 3.1 2 A3 (8,4) 8.5 3.4 2A4 (5,8) 3.6 2.4 2
New mean!
Using K-Means with K=10 Clusters
Baltimore County Robbery 'Hot Spots'
Using K-Means with K=31 Clusters
Baltimore County Robbery 'Hot Spots'
http://www.youtube.com/watch?v=CO2mGny6fFs
Crime Prediction: Space-Time Clustering
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.
References
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