Spatial Frequent Pattern Mining for Crime Analysis

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Spatial Frequent Pattern Mining for Crime Analysis
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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 ?
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Scientific Domain Environmental Criminology
Routine activity theory and Crime Triangle
Crime pattern theory
Courtsey http//www.popcenter.org/learning/60step
s/index.cfm?stepnum8
Courtsey www.amazon.com
Courtsey http//www.popcenter.org/learning/60step
s/index.cfm?stepNum16

• 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
Process of discovering interesting, useful and
non-trivial patterns from spatial data.
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Illustrative Frequent Patterns Regional
Co-location

• Input Spatial Features, Crime Reports.
• Output RCP (e.g. lt (Bar, Assaults), Downtown gt)
• 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
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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
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K- Main Routes / CrimeStat ellipses
K- Main Routes
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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.

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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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Number of Crime Outbreaks By Crime Type,
Lincoln 2007
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Crime Outbreaks to Regional Crime Patterns

• Input Crime types involved in a large number of
  significant Crime Outbreaks (Slide 7s 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).
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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 ?

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Bar locations in Lincoln, NE
Questions

• Does Crime Peak around bar closing ?

• Observation Crime peaks around bar-closing!

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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/tr2012/1
  2-015.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 15181910-1918

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