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INTEGRATING FACET ANALYSIS AND GEOGRAPHICAL INFORMATION SYSTEMS

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INTEGRATING FACET ANALYSIS AND GEOGRAPHICAL INFORMATION SYSTEMS Powered By Docstoc
					Core Functionality of
               iOPS
  Developed with the Metropolitan Police Service.
                           (New Scotland Yard)


  Freya Newman,
  MSc
  Centre for Investigative Psychology
  The University of Liverpool, UK
  www.i-psy.com
Comparative Case Analysis

    Q1: Can I link undetected
     crimes together?

    Q2: I have an offender with a
     particular offending style.
     What other crimes is he
     good for?
Q1: Can I link undetected
crimes to a common offender?




    Burglaries
Structural Analysis of
Behaviours




                         Posed
                          (42)
Behavioural similarity of
crimes
Identify crime series

    All the same offender
Q2: I have an offender with a particular offending
style. What other crimes is he/she good for?




                                              Burglaries
Posed & distraction?




                       Burglaries
Posed & distraction




                      Crime series
Suspect Prioritisation
   Who dunnit?
       Geography AND Behaviour to prioritise
        suspects
Geography
   Prioritises offenders by the location of
    their (home) base(s)
   iOPS does this with Dragnet
         Geographical ‘profiling’ system
         Developed at CIP
         Integrated within iOPS
Principles of Dragnet
   Offenders tend commit crimes close to
    home
   As distance from home to crime
    increases
    Probability of committing the crime
    decreases
        Distance decay
Source, Professor Canter, iOPS presentation, September 2004
Our Example



         Known
       offenders?
              X = Prioritised offenders
                = Known offenders
Our example     = Crimes in series
Prioritisation table

 Offender                              MO
 ID       Address      Probability     Match
 124      Location A 0.28574311864     0

 427      Location B 0.27038233898     0
 427      Location C 0.26035169492     0

 226      Location D 0.25577861017     0

 48       Location E   0.23282991525   0

 124      Location F   0.22445984746   0.3
 124      Location G 0.21932662712     0
              X = Prioritised offenders
                = Known offenders
Our example     = Crimes in series
MO Matching

Behaviours in                       Behaviours of
crimes                              known offenders




     climb sharp   smoke defecate
Prioritisation table

 Offender                              MO
 ID       Address      Probability     Match
 124      Location A 0.28574311864     0

 427      Location B 0.27038233898     0
 427      Location C 0.26035169492     0

 226      Location D 0.25577861017     0

 48       Location E   0.23282991525   0

 124      Location F   0.22445984746   0.3
 124      Location G 0.21932662712     0
Social Network Analysis
Offender ID   Address      Probability     MO Match
124           Location A   0.28574311864   0
427           Location B   0.27038233898   0
427           Location C   0.26035169492   0
226           Location D   0.25577861017   0
48            Location E   0.23282991525   0
124           Location F   0.22445984746   0.3
124           Location G   0.21932662712   0

				
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posted:4/7/2010
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