Chapter 6: Interpreting the criminal environment

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Chapter 6 Interpreting the criminal environment
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Target selection

• Ive been set a performance management plan. It
  is updated yearly. (intelligence manager)
• I make my own decisions. I target the worst
  offenders. (analyst)
• Two New Zealand intelligence professionals,
  quoted from Ratcliffe, J.H. (2005) 'The
  effectiveness of police intelligence management
  A New Zealand case study', Police Practice and
  Research, 65, pp. 435-451.

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Copes seven key variables

• Nature of offence (the legal category of the
  crime)
• Location (space and place of crime)
• Time of offence
• Method of offence (modus operandi)
• Target details
• Victim characteristics
• Physical and social circumstances of the offence

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ViCAP

• Violent Criminal Apprehension Program
• After ten years, it was found that less than 10
  per cent of homicides were reported to ViCAP
• Original form had 189 questions

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Threat assessments

• National agencies such as SOCA, CISC and Europol
  use unclassified annual threat assessments to
  raise public awareness
• Law-enforcement-sensitive versions used to inform
  law enforcement priorities and other relevant
  initiatives (legislation, regulation or policy)

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Harm as a component of threat assessments

• Harm the adverse consequences of criminal
  activity
• Metropolitan Police have four types
• Social
• Negative physical, psychological or emotional
  consequences that cannot readily be expressed in
  cash terms (as in homicide and assault)
• Economic
• Negative effects on an individual, community,
  business, institution, government or country (in
  as theft, counterfeiting and fraud)
• Political
• Negative effects on the political stability of a
  community or institution (such as in corruption,
  loss of confidence in government or law
  enforcement)
• Indirect
• Secondary adverse consequences of criminal
  activities (such as environmental damage from
  clandestine drug labs)

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Offender self-selection

• Offender self-selection may be a more ethical
  approach to offender targeting
• Existing criminal triggers are used to identify
  more serious offenders
• Offenders bring police attention on themselves

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Self-selection example

• Traffic wardens in Huddersfield, Yorkshire
  compared cars illegally parked in disabled bays
  with nearby legally parked cars. Illegal cars
  were
• nearly 10 times more likely to be of immediate
  police interest
• at least 10 times more likely to be owned by
  someone with a criminal record, and
• more likely to be driven by someone with a
  history of traffic violations
• See Chenery et al. 1999

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Playing well with others

• Problems
• Information sharing is a US priority after 9/11
  but the organization of police departments
  militates against it
• Small agencies rarely have the resources to
  address wider concerns
• Memorandums of understanding are often convoluted
  and take time to organize and approve

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Playing well with others

• Potential solutions
• Informal networks spring up to work around
  bureaucratic hurdles
• Joint task forces allow access to data from
  various agencies
• Wide dissemination of products that are not
  case-sensitive can improve information sharing
• Liaison officers can overcome some problems

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Intelligence requirements

• Structured mechanisms that can aid information
  collation, especially when analysts collaborate
• Strategic Intelligence Requirements
• Tactical Intelligence Requirements

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Sheptyckis organizational pathologies

• Digital divide - caused by incompatible
  information systems between agencies
• Linkage blindness - where crime series cross
  agency boundaries
• Noise - low-quality information volume
  exacerbated by increased sharing
• Intelligence overload - lack of analytical
  capacity in the crime intelligence system
• Intelligence gaps - caused by criminals operating
  in the spaces between police agencies either
  hierarchically or geographically
• Duplication - caused by separate agencies keeping
  the same information on isolated systems
• Institutional friction - between agencies with
  different missions, structures
• and methodologies
• Intelligence hoarding and information silos -
  caused by retention of information until it is
  most beneficial to the information-holder
• Defensive data concentration - concentration of
  resources in one area to address a short-term
  problem creates other organizational pathologies
• Occupational subcultures - both intra-agency as
  well as interagency

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Sharing information 2005 forum ideas

• Become intelligence-led
• Police chiefs should work closely with analysts
• Co-locate analysis and intelligence functions
  close to decision-makers
• Articulate the analytical vision within the
  police department
• Make the case for integrated analysis
• Create integrated reporting mechanisms
• Develop informal information exchange mechanisms
• Consciously collect feedback and respond to
  criticisms
• Create an analysis users group
• Get over the whole security issue
• Develop technology solutions but do not fixate on
  them
• Be realistic about what can be achieved in your
  department

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Nine analytical techniques in the NIM

• Crime pattern analysis
• Network analysis
• Market profiles
• Demographic/social trend analysis
• Criminal business profiles
• Target profile analysis
• Operational intelligence assessment
• Risk analysis
• Results analysis

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Strategic thinking

• Aims for a more holistic view of the criminal
  environment
• Uses techniques rarely taught in analysis classes
• Futures wheels
• Competing hypothesis
• Force-field analysis
• Morphological analysis
• Ishikawa diagrams
• PESTEL(O)
• SWOT analysis
• Delphi analysis
• Scenario generation
• (for descriptions and examples of these
  techniques, see Heldon 2004 and Quarmby 2004)

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Futures work in crime analysis

• For future work within a strategic intelligence
  environment to succeed, there must be
• An identifiable decision-making system to
  support
• A will to think ahead in both the intelligence
  system and the decision system to be supported
• A will to apply the results in both the
  intelligence system and the decision system to be
  supported
• Neil Quarmby (2004 128-129)