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

1. Using Data Mining Techniques to Improve Efficiency in Police Intelligence Dr Rick Adderley

2. Recent Riots London

3. Recent Riots Birmingham

4. Recent Riots Manchester

5. 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.

6. 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.

7. How can the provision of intelligence be strengthened and improved in the light of severe cuts? Providing Operational Intelligence

8. Retired Police Inspector FLINTS Business Intelligence Company 2003 Policing and Security EU Research Projects PhD – Offender Profiling and Crime Trend Analysis Introduction

9. Validated Examples Interview Lists Early Detection of Crime Series Modelling Forensic Recovery Automatic Identification of Priority & Prolific Offenders Introduction

10. 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:

11. Data Mining Tools in Policing

12. 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

13. Offenders Learn from Offenders Additional Complication

14. 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

15. MLP Training Set Testing Set 1. Interview Lists

16. 1. Interview Lists Crime BCU Billy Smith  Beat PostCode

17. 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

18. Spate of Burglaries/Robberies in an Area Are They Linked Who May Be Responsible 2. Early Detection of Crime Series

19. Self Organising Map 2. Early Detection of Crime Series

20. 2. Early Detection of Crime Series

21. 2. Early Detection of Crime Series

22. 2. Early Detection of Crime Series Each Cluster will Contain Crimes That Are Similar

23. Model Current Offenders Overlay Onto Crime Map 2. Early Detection of Crime Series

24. 2. Early Detection of Crime Series

25. Northamptonshire Forensic Science Department Dr John Bond Motivation:- 3. Modelling Forensic Recovery

26. 3. Modelling Forensic Recovery

27. 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

28. CRISP-DM www.the-modeling-agency.com/crisp-dm.pdf Naïve Bayes Algorithm Q-Prop Neural Network Algorithm 3. Modelling Forensic Recovery

29. 3. Modelling Forensic Recovery Algorithm Results 10 Fold Cross Validation on 28,490 Volume Crime Records

30. 3. Modelling Forensic Recovery Results Using Live Data

32. 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%

33. 3. Modelling Forensic Recovery How Good are the Models?

34. 3. Modelling Forensic Recovery Every Northamptonshire CSI 50 Random Crimes Assess whether a Forensic Sample would be collected

35. 3. Modelling Forensic Recovery Every Northamptonshire CSI 50 Random Crimes Assess whether a Forensic Sample would be collected 41% Accuracy

36. Which Offenders are Causing most Harm Including Cross Border Offenders Force Priorities Harm Matrix 4. Priority & Prolific Offenders

37. 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

38. Automated Process: 4. Priority & Prolific Offenders

39. 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

40. 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

41. 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

42. Benefits: Every Offender is Scored Objective Scoring Defendable Repeatable Process Can Be Frequently Run Offenders’ Scores Updated Current 4. Priority & Prolific Offenders

43. 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

44. Offender Networks: 4. Priority & Prolific Offenders

45. Questions?

46. Using Data Mining Techniques to Improve Efficiency in Police Intelligence Dr Rick Adderley www.a-esolutions.com