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Using Data Mining Techniques to Improve Efficiency in Police Intelligence

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

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Using Data Mining Techniques to Improve Efficiency in Police Intelligence

  1. 1. Using Data Mining Techniques to Improve Efficiency in Police Intelligence<br />Dr Rick Adderley<br />
  2. 2. Recent Riots<br />London<br />
  3. 3. Recent Riots<br />Birmingham<br />
  4. 4. Recent Riots<br />Manchester<br />
  5. 5. Recent Riots – Policing Cuts<br />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.<br />The Mayor of London Boris Johnson has warned the government against cutting police numbers. <br />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.<br />
  6. 6. Policing Cuts<br />Welsh police voice fears over budget cuts<br />Views wanted on £134m police cuts in Greater Manchester<br />Home secretary defends cuts to policing budget<br />South Yorkshire Police chief warns of crime rise<br />Crime concerns over Surrey Police cuts<br />Claims that crime will rise in Surrey because of cuts to police funding have been rebutted by the force.<br />
  7. 7. How can the provision of intelligence be strengthened and improved in the light of severe cuts?<br />Providing Operational Intelligence<br />
  8. 8. Retired Police Inspector<br />FLINTS<br />Business Intelligence Company<br />2003<br />Policing and Security<br />EU Research Projects<br />PhD – Offender Profiling and Crime Trend Analysis<br />Introduction<br />
  9. 9. Validated Examples<br />Interview Lists<br />Early Detection of Crime Series<br />Modelling Forensic Recovery<br />Automatic Identification of Priority & Prolific Offenders<br />Introduction<br />
  10. 10. IBM SPSS Modeller (Clementine)<br />SAS Enterprise Miner<br />Insightful Miner<br />…or…<br />Data Mining Tools in Policing<br />All of the Validated Examples can be accomplished by using:<br />
  11. 11. Data Mining Tools in Policing<br />
  12. 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.<br />Data Mining<br />
  13. 13. Offenders Learn from Offenders<br />Additional Complication<br />
  14. 14. Problem Outline<br />Analysts Time Constraints<br />Examine Index Crime & Compare with:<br />Keyword Type Search<br />Personal Memory<br />Produce a List “Matching” Index Crime<br />1 to 2 Hours<br />10% to 15% Accurate<br />Interviewing Officers Refine List<br />1. Interview Lists<br />
  15. 15. MLP<br />Training Set<br />Testing Set<br />1. Interview Lists<br />
  16. 16. 1. Interview Lists<br />Crime<br />BCU<br />Billy Smith<br /><br />Beat<br />PostCode<br />
  17. 17. Results of Modelling<br />MLP<br />Takes Into Account Whole Range of Criminality<br />Improved Accuracy<br />75% to 85%<br />Independent Validation<br />Intelligence Unit Sergeants<br />Intelligence Unit Analyst<br />1. Interview Lists<br />
  18. 18. Spate of Burglaries/Robberies in an Area<br />Are They Linked<br />Who May Be Responsible<br />2. Early Detection of Crime Series <br />
  19. 19. Self Organising Map<br />2. Early Detection of Crime Series <br />
  20. 20. 2. Early Detection of Crime Series <br />
  21. 21. 2. Early Detection of Crime Series <br />
  22. 22. 2. Early Detection of Crime Series <br />Each Cluster will<br />Contain Crimes <br />That Are Similar<br />
  23. 23. Model Current Offenders<br />Overlay Onto Crime Map<br />2. Early Detection of Crime Series <br />
  24. 24. 2. Early Detection of Crime Series <br />
  25. 25. Northamptonshire Forensic Science Department<br />Dr John Bond<br />Motivation:-<br />3. Modelling Forensic Recovery<br />
  26. 26. 3. Modelling Forensic Recovery<br />
  27. 27. Northamptonshire Forensic Science Department<br />Dr John Bond<br />Motivation:-<br />Which Crimes Should be Attended First?<br />Which Crime Give Best Opportunity of Forensic Recovery?<br />3. Modelling Forensic Recovery<br />
  28. 28. CRISP-DM<br />www.the-modeling-agency.com/crisp-dm.pdf<br />Naïve Bayes Algorithm<br />Q-Prop Neural Network Algorithm<br />3. Modelling Forensic Recovery<br />
  29. 29. 3. Modelling Forensic Recovery<br />Algorithm Results<br />10 Fold Cross Validation on 28,490 Volume Crime Records<br />
  30. 30. 3. Modelling Forensic Recovery<br />Results Using Live Data<br />
  31. 31.
  32. 32. 3. Modelling Forensic Recovery<br />Gwent Police Trial<br />11,800 Volume Crime Records<br />10 Fold Cross Validation<br />Q-Prop Accuracy 81.79%<br />Naïve Bayes Accuracy 88.84%<br />
  33. 33. 3. Modelling Forensic Recovery<br />How Good are the Models?<br />
  34. 34. 3. Modelling Forensic Recovery<br />Every Northamptonshire CSI<br />50 Random Crimes<br />Assess whether a Forensic Sample would be collected<br />
  35. 35. 3. Modelling Forensic Recovery<br />Every Northamptonshire CSI<br />50 Random Crimes<br />Assess whether a Forensic Sample would be collected<br />41% Accuracy<br />
  36. 36. Which Offenders are Causing most Harm<br />Including Cross Border Offenders <br />Force Priorities<br />Harm Matrix<br />4. Priority & Prolific Offenders<br />
  37. 37. Which Offenders are Causing most Harm <br />Current Process:<br />Offender is “Nominated”<br />Scored Against Matrix<br />Placed on List<br />Infrequently Reviewed<br />Insufficient Time<br />20 Minutes to 2 Hours to Complete Scoring<br />4. Priority & Prolific Offenders<br />
  38. 38. Automated Process:<br />4. Priority & Prolific Offenders<br />
  39. 39. 4. Priority & Prolific Offenders<br />Offender<br />L2Offender<br />Offend Priority Nab<br />Live Priority Nab<br />Community Safety<br />Control Of Offenders<br />Reduce Crime<br />Total Prism Score<br />BEAT AREA CRIME<br />Eric Smith<br />20<br />10<br />6<br />0<br />0<br />62<br />98<br />Z2<br />Paul Jones<br />20<br />0<br />6<br />0<br />0<br />71<br />97<br />Z2<br />Mary Hands<br />20<br />10<br />6<br />4<br />0<br />54<br />94<br />Z1<br />John Fresh<br />20<br />0<br />0<br />20<br />20<br />30<br />90<br />Z2<br />Ali Khan<br />20<br />10<br />6<br />0<br />0<br />54<br />90<br />Z1<br />Ming Hu<br />20<br />10<br />6<br />4<br />0<br />50<br />90<br />Z1<br />Graham Zhu<br />20<br />10<br />6<br />0<br />0<br />50<br />86<br />Z1<br />Fred Brown<br />20<br />0<br />0<br />0<br />0<br />60<br />80<br />Z1<br />Sally Johns<br />20<br />12<br />6<br />0<br />0<br />40<br />78<br />Z1<br />David Green<br />20<br />0<br />0<br />16<br />10<br />30<br />76<br />Z2<br />Alison Blue<br />20<br />10<br />6<br />0<br />0<br />40<br />76<br />Z1<br />Tom Black<br />20<br />0<br />0<br />4<br />0<br />50<br />74<br />Z2<br />Vinny Smith<br />20<br />0<br />0<br />24<br />0<br />30<br />74<br />Z1<br />Saad Wang<br />20<br />12<br />6<br />0<br />0<br />36<br />74<br />Z1<br />Mendip Kaur<br />20<br />12<br />0<br />0<br />0<br />40<br />72<br />Z1<br />Brian Ling<br />20<br />0<br />0<br />0<br />0<br />52<br />72<br />Z1<br />Billy Smith<br />20<br />10<br />0<br />0<br />20<br />22<br />72<br />Z1<br />Ho Tu<br />20<br />10<br />6<br />0<br />0<br />36<br />72<br />Z1<br />Paul Wells<br />20<br />0<br />0<br />0<br />30<br />20<br />70<br />Z2<br />
  40. 40. 4. Priority & Prolific Offenders<br />Offender<br />A1<br />A2<br />A3<br />A4<br />A5<br />A6<br />A7<br />A8<br />A9<br />B1<br />B2<br />B3<br />B4<br />B5<br />B6<br />B7<br />B8<br />C1<br />C2<br />C3<br />C4<br />Total<br />Crimes<br />Num<br />BCUs<br />Offender<br />Latest BCU<br />Eric Smith<br />0<br />0<br />0<br />0<br />0<br />3<br />0<br />0<br />0<br />5<br />1<br />3<br />5<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />17<br />5<br />X1<br />Paul Jones<br />0<br />0<br />1<br />0<br />0<br />0<br />4<br />0<br />0<br />0<br />0<br />1<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />1<br />7<br />4<br />V3<br />Mary Hands<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />1<br />4<br />0<br />0<br />0<br />1<br />0<br />0<br />0<br />1<br />0<br />0<br />0<br />7<br />4<br />V2<br />John Fresh<br />0<br />0<br />1<br />0<br />0<br />1<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />1<br />0<br />0<br />0<br />1<br />4<br />4<br />V2<br />Ali Khan<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />1<br />0<br />1<br />0<br />1<br />0<br />1<br />0<br />0<br />4<br />4<br />V2<br />Ming Hu<br />0<br />4<br />0<br />0<br />1<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />5<br />0<br />0<br />10<br />3<br />U2<br />Jill King<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />4<br />0<br />0<br />0<br />0<br />1<br />0<br />0<br />0<br />3<br />0<br />0<br />0<br />0<br />8<br />3<br />W1<br />Fred Brown<br />0<br />0<br />0<br />2<br />0<br />0<br />0<br />0<br />0<br />3<br />2<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />7<br />3<br />Z1<br />Sally Johns<br />0<br />0<br />2<br />4<br />0<br />0<br />0<br />0<br />0<br />1<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />7<br />3<br />Z1<br />Lin Ho Pu<br />0<br />0<br />1<br />0<br />0<br />0<br />1<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />4<br />0<br />0<br />0<br />6<br />3<br />V2<br />Brian Ling<br />3<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />2<br />0<br />0<br />0<br />0<br />0<br />1<br />0<br />0<br />0<br />0<br />6<br />3<br />T1<br />Billy Smith<br />0<br />0<br />0<br />0<br />0<br />0<br />3<br />0<br />0<br />0<br />1<br />0<br />0<br />0<br />0<br />0<br />1<br />0<br />0<br />0<br />0<br />5<br />3<br />U2<br />Ho Tu<br />0<br />2<br />0<br />1<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />2<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />5<br />3<br />M3<br />Paul Wells<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />0<br />3<br />0<br />1<br />0<br />0<br />0<br />0<br />0<br />1<br />0<br />0<br />0<br />0<br />5<br />3<br />T2<br />
  41. 41. Motivation:<br />Sample Offender Test<br />Same Offender<br />4 BSU’s Where Offender Not Known<br />20 Minutes to 2 Hours<br />Scores From Low 100’s to High 400’s<br />Place / Not Place on List<br />Scores Not Related To Time Taken<br />4. Priority & Prolific Offenders<br />
  42. 42. Benefits:<br />Every Offender is Scored<br />Objective Scoring<br />Defendable<br />Repeatable<br />Process Can Be Frequently Run<br />Offenders’ Scores Updated<br />Current<br />4. Priority & Prolific Offenders<br />
  43. 43. Offender Networks:<br />The Data WILL Contain Networks<br />Which Networks Cause the Most Harm<br />Prioritisation<br />Scoring<br />Degrees of Freedom<br />Dependant Upon Priorities<br />4. Priority & Prolific Offenders<br />
  44. 44. Offender Networks:<br />4. Priority & Prolific Offenders<br />
  45. 45. Questions?<br />
  46. 46. Using Data Mining Techniques to Improve Efficiency in Police Intelligence<br />Dr Rick Adderley<br />www.a-esolutions.com<br />

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