1. Data Mining University Crime Presenters: Stuart King Konrad Kuczynski Ying Liu

2. Data Sources a) FBI Uniform Crime Reporting campus crime statistics for 669 schools from 1995 thru 2007 (except 2004):  1995-2005: http://www.securityoncampus.org/crimestats/index.html  2006-2007: http://www.fbi.gov/ucr/cius2006/offenses/standard_links/universities_colleges.html http://www.fbi.gov/ucr/cius2007/offenses/standard_links/universities_colleges.html b) City Crime Data:  1999-2007: http://www.fbi.gov/ucr/ucr.htm c) State Poverty:  1999-2007: http://www.census.gov/hhes/www/saipe/county.html

3. Data Mining Tasks Cluster/Classify 1: Prediction of missing population values Used for improving both charting and anomaly detection of Per Capita crime data 2: Prediction of crime trends Can be used by universities to allocate resources for the coming year Outlier Detection 3: Detect anomalies in crime Can be used by universities to target “root cause and prevention” projects

4. Data Preprocessing University crime data cleanup  669 universities with 971 names  136 universities were removed because they reported less than 5 years of data  If a year of crime data was missing, a fabricated record of averages was added Matching City, State, and Zip code information for each University Post cleaned and merged data sets:  5,869 records of crime data for 533 universities with City/State/Zip code added  1,712 records of crime data for 249 universities with the following added: City/State/Zip, City Crime Data, State Poverty Data Additionally, Per Capita and  values were calculated

5. Data Mining Data used for each task St.StPov % Pop.Vio.Murd.RapeRobAsltPrptyBurgTheftCarArson 1G| P %%%%% 2G U∆ P S∆U∆ C∆ U∆ C∆ U∆ C∆ U∆ C∆ U∆ C∆ U∆ C∆ U∆ C∆ U∆ C∆ U∆ C∆ U∆ C∆ U∆ C∆ 3% U∆ % U∆ % U∆ % U∆ % U∆ % U∆ % U∆ % U∆ % U∆ 1: Population cluster/classify 2: Crime Trend cluster/classify 3: Anomaly Detection G: Grouping = Clustering P: Prediction = Classifying % Per capita values ∆ Difference values | | Absolute values U University C City S State

6. Data Mining Algorithm used for each task TaskMiningAlgorithm 1: Population Prediction ClusteringEM ClassificationDecorate using J48 2: Crime Trend Prediction ClusteringEM ClassificationJ48 3: Crime Anomalies Outlier DetectionDBSCAN with minPoints=1

7. Visualizations Summary charts and graphs for Per Capita and Clustering data Interactive Map showing cluster changes for 533 Universities Interactive Map showing predicted clusters for 249 Universities Interactive Map showing where 355 outliers occurred Interactive Charts showing values for outliers http://www.cse.msu.edu/~kingstua/Team3