Spatial Frequent Pattern Mining for Crime Analysis

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: www.startribune.com 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 ?

Scientific Domain: Environmental Criminology Routine activity theory and Crime Triangle Crime pattern theory Courtsey: http://www.popcenter.org/learning/60steps/index.cfm?stepnum=8 Courtsey: http://www.popcenter.org/learning/60steps/index.cfm?stepNum=16 Courtsey: www.amazon.com 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

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

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. 20.1 % Shown by blue polygon area). N

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

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

Number of Crime Outbreaks : By Crime Type, Lincoln 2007

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).

Spatio-temporal Frequent patterns: Cascading Patterns

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!

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 12-015), URL: http://www.cs.umn.edu/tech_reports_upload/tr2012/12-015.pdf 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: 265-272 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. 2010 Aug 15;18:1910-1918 12 12