Using Data Mining Techniques to Improve Efficiency in Police Intelligence
Upcoming SlideShare
Loading in...5
×
 

Using Data Mining Techniques to Improve Efficiency in Police Intelligence

on

  • 1,033 views

This is presentation that was given by Dr Rick Adderley at the UK KDD Symposium on 29th September 2011.

This is presentation that was given by Dr Rick Adderley at the UK KDD Symposium on 29th September 2011.

Statistics

Views

Total Views
1,033
Views on SlideShare
1,032
Embed Views
1

Actions

Likes
0
Downloads
10
Comments
0

1 Embed 1

https://www.linkedin.com 1

Accessibility

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

Using Data Mining Techniques to Improve Efficiency in Police Intelligence Using Data Mining Techniques to Improve Efficiency in Police Intelligence Presentation Transcript

  • Using Data Mining Techniques to Improve Efficiency in Police Intelligence
    Dr Rick Adderley
  • Recent Riots
    London
  • Recent Riots
    Birmingham
  • Recent Riots
    Manchester
  • Recent Riots – Policing Cuts
    The chair of South Wales Police Federation has warned if "savage" cuts go ahead police will not be able to respond effectively to future riots.
    The Mayor of London Boris Johnson has warned the government against cutting police numbers.
    Mr Johnson said the case for cuts had been "substantially weakened" by the riots and that he opposed the Home Secretary's plans to reduce forces' budgets.
  • Policing Cuts
    Welsh police voice fears over budget cuts
    Views wanted on £134m police cuts in Greater Manchester
    Home secretary defends cuts to policing budget
    South Yorkshire Police chief warns of crime rise
    Crime concerns over Surrey Police cuts
    Claims that crime will rise in Surrey because of cuts to police funding have been rebutted by the force.
  • How can the provision of intelligence be strengthened and improved in the light of severe cuts?
    Providing Operational Intelligence
  • Retired Police Inspector
    FLINTS
    Business Intelligence Company
    2003
    Policing and Security
    EU Research Projects
    PhD – Offender Profiling and Crime Trend Analysis
    Introduction
  • Validated Examples
    Interview Lists
    Early Detection of Crime Series
    Modelling Forensic Recovery
    Automatic Identification of Priority & Prolific Offenders
    Introduction
  • IBM SPSS Modeller (Clementine)
    SAS Enterprise Miner
    Insightful Miner
    …or…
    Data Mining Tools in Policing
    All of the Validated Examples can be accomplished by using:
  • Data Mining Tools in Policing
  • To automatically interrogate one or more data sets with a view to providing information that will save time, reduce crime, deter offending and enhance dynamic business processes.
    Data Mining
  • Offenders Learn from Offenders
    Additional Complication
  • Problem Outline
    Analysts Time Constraints
    Examine Index Crime & Compare with:
    Keyword Type Search
    Personal Memory
    Produce a List “Matching” Index Crime
    1 to 2 Hours
    10% to 15% Accurate
    Interviewing Officers Refine List
    1. Interview Lists
  • MLP
    Training Set
    Testing Set
    1. Interview Lists
  • 1. Interview Lists
    Crime
    BCU
    Billy Smith

    Beat
    PostCode
  • Results of Modelling
    MLP
    Takes Into Account Whole Range of Criminality
    Improved Accuracy
    75% to 85%
    Independent Validation
    Intelligence Unit Sergeants
    Intelligence Unit Analyst
    1. Interview Lists
  • Spate of Burglaries/Robberies in an Area
    Are They Linked
    Who May Be Responsible
    2. Early Detection of Crime Series
  • Self Organising Map
    2. Early Detection of Crime Series
  • 2. Early Detection of Crime Series
  • 2. Early Detection of Crime Series
  • 2. Early Detection of Crime Series
    Each Cluster will
    Contain Crimes
    That Are Similar
  • Model Current Offenders
    Overlay Onto Crime Map
    2. Early Detection of Crime Series
  • 2. Early Detection of Crime Series
  • Northamptonshire Forensic Science Department
    Dr John Bond
    Motivation:-
    3. Modelling Forensic Recovery
  • 3. Modelling Forensic Recovery
  • Northamptonshire Forensic Science Department
    Dr John Bond
    Motivation:-
    Which Crimes Should be Attended First?
    Which Crime Give Best Opportunity of Forensic Recovery?
    3. Modelling Forensic Recovery
  • CRISP-DM
    www.the-modeling-agency.com/crisp-dm.pdf
    Naïve Bayes Algorithm
    Q-Prop Neural Network Algorithm
    3. Modelling Forensic Recovery
  • 3. Modelling Forensic Recovery
    Algorithm Results
    10 Fold Cross Validation on 28,490 Volume Crime Records
  • 3. Modelling Forensic Recovery
    Results Using Live Data
  • 3. Modelling Forensic Recovery
    Gwent Police Trial
    11,800 Volume Crime Records
    10 Fold Cross Validation
    Q-Prop Accuracy 81.79%
    Naïve Bayes Accuracy 88.84%
  • 3. Modelling Forensic Recovery
    How Good are the Models?
  • 3. Modelling Forensic Recovery
    Every Northamptonshire CSI
    50 Random Crimes
    Assess whether a Forensic Sample would be collected
  • 3. Modelling Forensic Recovery
    Every Northamptonshire CSI
    50 Random Crimes
    Assess whether a Forensic Sample would be collected
    41% Accuracy
  • Which Offenders are Causing most Harm
    Including Cross Border Offenders
    Force Priorities
    Harm Matrix
    4. Priority & Prolific Offenders
  • Which Offenders are Causing most Harm
    Current Process:
    Offender is “Nominated”
    Scored Against Matrix
    Placed on List
    Infrequently Reviewed
    Insufficient Time
    20 Minutes to 2 Hours to Complete Scoring
    4. Priority & Prolific Offenders
  • Automated Process:
    4. Priority & Prolific Offenders
  • 4. Priority & Prolific Offenders
    Offender
    L2Offender
    Offend Priority Nab
    Live Priority Nab
    Community Safety
    Control Of Offenders
    Reduce Crime
    Total Prism Score
    BEAT AREA CRIME
    Eric Smith
    20
    10
    6
    0
    0
    62
    98
    Z2
    Paul Jones
    20
    0
    6
    0
    0
    71
    97
    Z2
    Mary Hands
    20
    10
    6
    4
    0
    54
    94
    Z1
    John Fresh
    20
    0
    0
    20
    20
    30
    90
    Z2
    Ali Khan
    20
    10
    6
    0
    0
    54
    90
    Z1
    Ming Hu
    20
    10
    6
    4
    0
    50
    90
    Z1
    Graham Zhu
    20
    10
    6
    0
    0
    50
    86
    Z1
    Fred Brown
    20
    0
    0
    0
    0
    60
    80
    Z1
    Sally Johns
    20
    12
    6
    0
    0
    40
    78
    Z1
    David Green
    20
    0
    0
    16
    10
    30
    76
    Z2
    Alison Blue
    20
    10
    6
    0
    0
    40
    76
    Z1
    Tom Black
    20
    0
    0
    4
    0
    50
    74
    Z2
    Vinny Smith
    20
    0
    0
    24
    0
    30
    74
    Z1
    Saad Wang
    20
    12
    6
    0
    0
    36
    74
    Z1
    Mendip Kaur
    20
    12
    0
    0
    0
    40
    72
    Z1
    Brian Ling
    20
    0
    0
    0
    0
    52
    72
    Z1
    Billy Smith
    20
    10
    0
    0
    20
    22
    72
    Z1
    Ho Tu
    20
    10
    6
    0
    0
    36
    72
    Z1
    Paul Wells
    20
    0
    0
    0
    30
    20
    70
    Z2
  • 4. Priority & Prolific Offenders
    Offender
    A1
    A2
    A3
    A4
    A5
    A6
    A7
    A8
    A9
    B1
    B2
    B3
    B4
    B5
    B6
    B7
    B8
    C1
    C2
    C3
    C4
    Total
    Crimes
    Num
    BCUs
    Offender
    Latest BCU
    Eric Smith
    0
    0
    0
    0
    0
    3
    0
    0
    0
    5
    1
    3
    5
    0
    0
    0
    0
    0
    0
    0
    0
    17
    5
    X1
    Paul Jones
    0
    0
    1
    0
    0
    0
    4
    0
    0
    0
    0
    1
    0
    0
    0
    0
    0
    0
    0
    0
    1
    7
    4
    V3
    Mary Hands
    0
    0
    0
    0
    0
    0
    0
    0
    1
    4
    0
    0
    0
    1
    0
    0
    0
    1
    0
    0
    0
    7
    4
    V2
    John Fresh
    0
    0
    1
    0
    0
    1
    0
    0
    0
    0
    0
    0
    0
    0
    0
    0
    1
    0
    0
    0
    1
    4
    4
    V2
    Ali Khan
    0
    0
    0
    0
    0
    0
    0
    0
    0
    0
    0
    0
    1
    0
    1
    0
    1
    0
    1
    0
    0
    4
    4
    V2
    Ming Hu
    0
    4
    0
    0
    1
    0
    0
    0
    0
    0
    0
    0
    0
    0
    0
    0
    0
    0
    5
    0
    0
    10
    3
    U2
    Jill King
    0
    0
    0
    0
    0
    0
    0
    4
    0
    0
    0
    0
    1
    0
    0
    0
    3
    0
    0
    0
    0
    8
    3
    W1
    Fred Brown
    0
    0
    0
    2
    0
    0
    0
    0
    0
    3
    2
    0
    0
    0
    0
    0
    0
    0
    0
    0
    0
    7
    3
    Z1
    Sally Johns
    0
    0
    2
    4
    0
    0
    0
    0
    0
    1
    0
    0
    0
    0
    0
    0
    0
    0
    0
    0
    0
    7
    3
    Z1
    Lin Ho Pu
    0
    0
    1
    0
    0
    0
    1
    0
    0
    0
    0
    0
    0
    0
    0
    0
    0
    4
    0
    0
    0
    6
    3
    V2
    Brian Ling
    3
    0
    0
    0
    0
    0
    0
    0
    0
    0
    2
    0
    0
    0
    0
    0
    1
    0
    0
    0
    0
    6
    3
    T1
    Billy Smith
    0
    0
    0
    0
    0
    0
    3
    0
    0
    0
    1
    0
    0
    0
    0
    0
    1
    0
    0
    0
    0
    5
    3
    U2
    Ho Tu
    0
    2
    0
    1
    0
    0
    0
    0
    0
    0
    0
    2
    0
    0
    0
    0
    0
    0
    0
    0
    0
    5
    3
    M3
    Paul Wells
    0
    0
    0
    0
    0
    0
    0
    0
    3
    0
    1
    0
    0
    0
    0
    0
    1
    0
    0
    0
    0
    5
    3
    T2
  • Motivation:
    Sample Offender Test
    Same Offender
    4 BSU’s Where Offender Not Known
    20 Minutes to 2 Hours
    Scores From Low 100’s to High 400’s
    Place / Not Place on List
    Scores Not Related To Time Taken
    4. Priority & Prolific Offenders
  • Benefits:
    Every Offender is Scored
    Objective Scoring
    Defendable
    Repeatable
    Process Can Be Frequently Run
    Offenders’ Scores Updated
    Current
    4. Priority & Prolific Offenders
  • Offender Networks:
    The Data WILL Contain Networks
    Which Networks Cause the Most Harm
    Prioritisation
    Scoring
    Degrees of Freedom
    Dependant Upon Priorities
    4. Priority & Prolific Offenders
  • Offender Networks:
    4. Priority & Prolific Offenders
  • Questions?
  • Using Data Mining Techniques to Improve Efficiency in Police Intelligence
    Dr Rick Adderley
    www.a-esolutions.com