Data MiningData Mining University Crime University Crime

Presenters: Stuart King

Konrad Kuczynski

Ying Liu

Data Sourcesa) 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

Data Mining Tasks

• Cluster/Classify1: 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 Detection3: Detect anomalies in crime

Can be used by universities to target “root cause and prevention” projects

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

Data Mining• Data used for each task

St. StPov%

Pop. Vio. Murd. Rape Rob Aslt Prpty Burg Theft Car Arson

1 G | |

P | | % % % % % % % % % %

2 G U∆ U∆ U∆ U∆ U∆ U∆ U∆ U∆ 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

Data Mining• Algorithm used for each task

Task Mining Algorithm

1: Population

Prediction

Clustering EM

Classification Decorate using J48

2: Crime

Trend

Prediction

Clustering EM

Classification J48

3: Crime

Anomalies

Outlier Detection DBSCAN with minPoints=1

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