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Evidence-Based Policing: What We Know, How We Know It

Evidence-Based Policing: What We Know, How We Know It. SIPR Annual Lecture Scottish Police College 1 st October, 2009 Lawrence Sherman University of Cambridge. Fire. Suicide. Predictable? Risk Factors Odds per million Yes/No or Uncertainty?. Preventable? Guideline Threshold

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Evidence-Based Policing: What We Know, How We Know It

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  1. Evidence-Based Policing:What We Know, How We Know It SIPR Annual Lecture Scottish Police College 1st October, 2009 Lawrence Sherman University of Cambridge

  2. Fire

  3. Suicide

  4. Predictable? Risk Factors Odds per million Yes/No or Uncertainty? Preventable? Guideline Threshold Policy for Action Known Effect of Action? Looking Backward

  5. Fire Me? Heather? 1992 Electrician? Suicide Simmons Family Leicestershire Police CC Matt Baggott Who To Blame?

  6. The Shame of Blame • Blame assumes certainty: --prediction --prevention • Blame narrows research question: --one case --not 99.99999% of cases --whodunit, not why it happened --not HOW to prevent such harm in future --not how to analyze SYSTEMS for improvement (learning) • All that blame prevents is fixing the problem itself

  7. Looking Forward:Success Stories • Airplane Crashes  down • Auto accident deaths  down • Fire Deaths  down • Making homes fireproof ? Evidence, Not Blame

  8. Not This Kind But This Kind What Kind of Evidence?

  9. Evidence-Based Policing (1998)http://www.policefoundation.org/pdf/Sherman.pdf

  10. What Is Evidence-Based Policing? A decision-making process that uses reliable, unbiased quantitative evidence on prediction and prevention as a primary criterion for • Setting goals • Setting priorities • Making policies (patterns) • Making decisions (cases) • Managing compliance • Assessing results • Improving policies

  11. Police using Evidence “dashboard”-- to steer policy, guide decisions turn-by-turn, sometimes U-turns! EBP: A DRIVER Process

  12. DRIVER Process • Diagnosis—local evidence on the problem • Response—reviews of published evidence • Implementation-- local evidence • Value-added—actual versus predicted • Evaluation—Is this the best we can do? • Revision—start all over again

  13. Sherman: 1998

  14. Sir Iain Chalmers Founder, Cochrane Collaboration-NHS Not just a preference Human Rights Issue ------------------------------------- “Policy makers and practitioners who intervene in the lives of other people not infrequently do more harm than good.” Evidence-Based Medicine: 1992

  15. Hall of Shame:Dr. Benjamin Spock, Pediatrician

  16. How To Kill Babies: use untested theory, not facts

  17. Theory-Based Practice

  18. David Hume’s Response Scottish Enlightenment: Insight from Edinburgh be more skeptical about what you think you know, and how you think you know it.

  19. Testing the Theory: Back to Sleep

  20. Post Hoc Ergo Propter Hoc? After this, then because of this? Inferring Cause From Trend?

  21. Did Program Cause Crime Drop?

  22. Or would it have dropped anyway? • “Natural” Trend • “History” • Other factors • “Spurious” explanations All ruled out by randomized controlled trials (RCTs) or other more rigourous tests

  23. Randomized Controlled Trial RCT:COMPARISON or NET difference

  24. British Invention 1948 TB Cure test Sir Austin Hill 1 million medical RCTs since then “Gold Standard” for what works Other research designs biased Randomized Controlled Trials

  25. Three Examples 1. Murder 2. Hot Spots 3. Domestic Common Assault

  26. MAPPAs • Multi-Agency Prevention Partnership Agreements • Clinical Prediction: How Accurate? False negatives? • Statistical forecasting: more accurate • Prevention: How effective?

  27. Forecasting Murder • JOURNAL OF THE ROYAL STATISTICAL SOCIETY • Berk, Sherman, et al, 2009 • Philadelphia Probation Cases • 300-400 murders per year • 1.5 million population • Rate = 14 X Scotland’s

  28. Risk of Murder byAge at Time of Crime (Phila)

  29. Berk (2006) Risk of Future Murder By Age of First Adult Disposition

  30. High Risk (2%) Neither High nor Low Risk (38%) Low Risk (60%)

  31. High Risk 2% vs. Bottom 60% Two Years From Forecast Date Charges for Any Offence 8 X more Charges Serious Offence 10 X more Charges Murder or Attempt 75 X more

  32. Average Number of Charges for ANY Offense Within Two Years of Probation Start 18.1 High Neither Low 7.75 2.25

  33. Average Number of Charges for SERIOUS Crimes Within Two Years of Probation Start 3.13 High Neither Low 1.28 0.30

  34. Average Charges for MURDER or Attempted Murder Within Two Years of Probation Start .375 High Neither Low .033 .005

  35. 3 points 1. If we need extra resources for extreme risk, they could come from risk-based assignment policies 2. Low Intensity for low-risk cases frees up officers for high intensity with high-risk cases 3. Tradeoff is uncertain, imperfect, but optimal

  36. MAPPA:Multi-Agency Prevention Partnership Agreements • Clinical to Statistical Prediction • False Positive--False Negative Ratio • What Works? • What Evidence?

  37. Second Example:HOT SPOTS Managing the high concentration of crime and disorder in a small number of high-risk but very small micro-locations

  38. Reprise: Why Predict? • Prediction is Key To Prevention • Efficiency focuses on high risk, not low • “Peak probability,” not “valley” (average)

  39. (Distribution of Violent Offenses in Tokyo) Peaks and Valleys of Crime

  40. 15-Year Trajectory of Blocks(Seattle: Weisburd, et al, 2004)

  41. Greater Manchester Police: Tactical Experiments and Strategic Testing Extending the US Hot Spots Patrol Experiments

  42. Uniformed Police Patrol:Putting Police Where the Crime Is 3% of street addresses 50% or more of all crime at certain times Yet no police agency directs 50% patrol to 3% of addresses Deterrent theory of patrol says they should Experimental evidence shows it works Displacement hypothesis disproven so far Sherman and Weisburd, 1995, Minneapolis

  43. Optimal Patrol Time Per Hot Spot: 10-15 Minutes (Minneapolis) Minutes police Present  Minutes to first crime after Police leave the Hot Spot

  44. Effects on Crime and Disorder Maximized by 14-15 Minute Stops: Koper (1995) Curve Effect On Crime And Disorder Length of Stop Source: Koper (1995)

  45. Experimental Hot Spots Improved Relative to Control Hot Spots Change In Crime

  46. Research Design: GMP • Identify 200 Hot Spots small enough so that one police car can be seen from anywhere in the hot spot • Make each hot spot far enough from other hot spots so police cars in one cannot be seen in other hot spots • Add a “displacement cushion” around each hot spot to see if crime moves around the corner • Assign 100 hot spots to 2 hours daily of extra PC patrol • Assign 100 hot spots to standard PC coverage (no directed patrols • Compare before-after crime trends  

  47. Randomized Controlled Trial RCT:COMPARISON or NET difference

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