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