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 ?

Forecasting crime levels in different neighborhoods.

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• Law enforcement planningQuestion: Where are the frequent crime routes ?

• 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

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Crime pattern theory Routine activity theory and Crime Triangle

Courtsey: http://www.popcenter.org/learning/60steps/index.cfm?stepnum=8

Crime Event: Motivated offender, vulnerable victim (available at an appropriate location and time), absence of a capable guardian.

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)

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Spatial Frequent Pattern Mining

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Process of discovering interesting, useful and non-trivial patterns from spatial data.

Illustrative Frequent Patterns: Regional Co-location

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Larceny, Bars and Assaults

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.

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).

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Illustrative Frequent Patterns: K Main Routes

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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

K- Main Routes K- Main Routes / CrimeStat ellipses

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Illustrative Frequent Patterns: Crime Outbreaks

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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.

Vandalism Crime Outbreaks around Bars. Alcohol crime outbreaks around bars.

Legend: (a) Risk Region Represented by Red Circle; (b) Black stars (*) represent Bars

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Crime Outbreaks to Regional Crime Patterns

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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

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Lincoln, NE crime dataset: Case study Is bar closing a generator for crime related CSTP ?

Observation: Crime peaks around bar-closing!

Bar locations in Lincoln, NE

Does Crime Peak around bar closing ?

Questions

Bar closing Increase(Larceny,vandalism, assaults)

Saturday Night Increase(Larceny,vandalism, assaults)

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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