1. Spatial Frequent Pattern Mining for Crime Analysis

2. Application Questions
Crime analysis
Localizing frequent crime patterns, Opportunities for crime vary across space!
Question: Do downtown bars often lead to assaults more frequently ?
Law enforcement planning
Question: Where are the frequent crime routes ?
Courtsey:
Forecasting crime levels in different neighborhoods.
Predictive policing (e.g. forecast crime levels in different neighborhoods )
Question: What are the crime levels 1 hour after a football game within a radius of 1 mile ?

3. Scientific Domain: Environmental Criminology
Routine activity theory and Crime Triangle
Crime pattern theory
Courtsey:
Courtsey:
Courtsey:
Crime Event: Motivated offender, vulnerable victim (available at an appropriate location and time), absence of a capable guardian.
Key Message: Scientific underpinning
Crime Generators : offenders and targets come together in time place, large gatherings (e.g. Bars, Football games)
Crime Attractors : places offering many criminal opportunities and offenders may relocate to these areas (e.g. drug areas) 6

4. Spatial Frequent Pattern Mining
Process of discovering interesting, useful and non-trivial patterns from spatial data.

5. Illustrative Frequent Patterns: Regional Co-location
Input: Spatial Features, Crime Reports.
Output: RCP (e.g. < (Bar, Assaults), Downtown >)
Subsets of spatial features / Crime Types.
Frequently located in certain regions of a study area.
Larceny, Bars and Assaults
Q. Are downtown Bars likely to be more crime prone than others ?
Dataset: Lincoln, NE, Crime data (Winter ‘07), Neighborhood Size = 0.25 miles, Prevalence Threshold = 0.07
Observation : Bars in Downtown are more likely to be crime prone than bars in other areas (e.g % Shown by blue polygon area). N

6. Illustrative Frequent Patterns: K Main Routes
Input: Crime Reports, Road Network, K (# of Patrol Vehicles)
Output: K- Main Routes Taken by the Patrol Vehicles
Dataset: U.S. City (Southern U.S), K = 10 N
K- Main Routes
K- Main Routes / CrimeStat ellipses

7. Illustrative Frequent Patterns: Crime Outbreaks
Input: Crime Reports, Crime Types, Spatial Features (Bars)
Output: (a) Bars with more than usual crime activity, (b) Crime Types that are highly active around bars, (c) Regions (Crime Outbreaks) around Bars with high risk of crime. N
Vandalism Crime Outbreaks around Bars.
Alcohol crime outbreaks around bars.
Legend: (a) Risk Region Represented by Red Circle; (b) Black stars (*) represent Bars

8. Number of Crime Outbreaks : By Crime Type, Lincoln 2007

9. Crime Outbreaks to Regional Crime Patterns
Input: Crime types involved in a large number of significant Crime Outbreaks (Slide 7’s output)
Output: Regional co-location patterns between crime types involved in one or more outbreaks.
Dataset: Lincoln, NE, Crime data (2007), Neighborhood Size = 700 feet, Prevalence Threshold = 0.001
Observation : Bars in Downtown have a marginally higher chance (4.6%) to witness Alcohol as well as Vandalism related Crime Outbreaks (Center Polygon).

10. Spatio-temporal Frequent patterns: Cascading Patterns

11. Lincoln, NE crime dataset: Case study
Is bar closing a generator for crime related CSTP ? N
Bar locations in Lincoln, NE
Questions
Does Crime Peak around bar closing ?
Observation: Crime peaks around bar-closing!

12. References
S. Shekar, P.Mohan, D.Oliver, X. Zhou. Crime Pattern Analysis: A spatial frequent pattern mining approach. Department of Computer Science and Engineering, University of Minnesota, Twin-Cities, Tech Report (TR ), URL:
P.Mohan, S.Shekhar, J.A. Shine, J.P. Rogers, Z.Jiang, N. Wayant. A spatial neighborhood graph approach to Regional Colocation Pattern Discovery.
D. Oliver, A. Bannur, J.M. Kang, S.Shekhar, R. Bousselaire. A K-Main Routes Approach to Spatial Network Activity Summarization. ICDM Workshops 2010:
P. Mohan, S. Shekhar, J.A. Shine, J.P. Rogers. Cascading Spatio-temporal Pattern Discovery. In IEEE Transactions on Knowledge and Data Engineering, 2012, November (to Appear).
Jung I,Kulldorff M,Richard OJ,.  A spatial scan statistic for multinomial data .  Stat Med Aug 15;18: 12 12