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7 misconceptions about predictive policing webinar

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Over the last few years, Predictive Policing has become more common in police departments around the world. With the rising interest in crime forecasting tools, important questions concerning ethics, privacy and fairness have been raised. We know that there are some misconceptions when it comes to the topic, and we want to dispel some of the common myths about Predictive Policing.

We invite you to join us as we walk through 7 Misconceptions of Predictive Policing. In this webinar, we aim to discuss some of the charged rhetoric and beliefs that surround the term. Also, we will highlight the some of the diverse crime modeling concepts that are used to make robust, predictions when forecasting crime.

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7 misconceptions about predictive policing webinar

  1. 1. 990 Spring Garden St, 5th Floor Philadelphia, PA 19123 215.925.2600 info@azavea.com www.azavea.com 7 Misconceptions about Predictive Policing
  2. 2. Adele Zhang HunchLab Product Specialist azhang@azavea.com Jeremy Heffner HunchLab Product Manager jheffner@azavea.com
  3. 3. 55 people using geodata to do stuff that matters
  4. 4. B Corporation • Civic/Social impact • Donate share of profits Research-Driven • 10% Research Program • Academic Collaborations • Open Source • Open Data
  5. 5. 7 Common Misconceptions About Predictive Policing
  6. 6. Predictive Missions • Determines high risk areas each shift • Intelligently allocates patrol resources • Uses multiple data sets to ‘explain’ patterns
  7. 7. Types of Information Event Geographic CalculatedTemporal
  8. 8. HunchLab automatically produces target areas called missions. Color represents the primary risk in each mission area.
  9. 9. 7 Common Misconceptions About Predictive Policing
  10. 10. MISCONCEPTION #1: Predictive Policing is like Minority Report in real life
  11. 11. “Pre-crime policing tech isn’t just real, it’s now ubiquitous.” –Jack Smith IV
  12. 12. Source: 20th Century Fox
  13. 13. MISCONCEPTION #2: Predictive Policing can predict individual crimes
  14. 14. Source: IBM
  15. 15. MISCONCEPTION #3: Predictive Policing is just rebranding existing technologies
  16. 16. “Crime analysts and police departments say the same thing: The new, predictive maps just repackage old intelligence. One criminologist called it “old wine in new bottles.”” – Excerpt from ‘Minority Report’ Is real – And It’s Really Reporting Minorities on Mic
  17. 17. 2002!
  18. 18. Retrospective Analysis (Hotspots) Assumption
  19. 19. Predictive Analysis Learned?
  20. 20. • Crime predictions based on: – Baseline crime levels • Similar to traditional hotspot maps – Near repeat patterns • Event recency (contagion) – Risk Terrain Modeling • Proximity and density of geographic features • Points, Lines, Polygons (bars, bus stops, etc.) – Collective Efficacy • Socioeconomic indicators (poverty, unemployment, etc.)
  21. 21. • Crime predictions based on: – Routine Activity Theory • Offender: proximity and concentration of known offenders • Guardianship: police presence (AVL / GPS) • Targets: measures of exposure (population, parcels, vehicles) – Temporal cycles • Seasonality, time of month, day of week, time of day – Recurring temporal events • Holidays, sporting events, etc. – Weather • Temperature, precipitation
  22. 22. We hold back the most recent 90 days of data… 1 Year 3 Years Several Months Warm-up Variables Training Examples Testing Examples
  23. 23. Cells ranked highest to lowest 0% 100% Percent of Patrol Area to Capture All Crimes Average Crime Rank 0% 50% 100% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Percent of Crimes Captured vs. Percent of Patrol Area
  24. 24. Example Areas Under ROC Curve 94.5% Robbery 93.0% Residential Burglary 95.6% Gun Crimes 93.8% DWI 95.3% Aggravated Assault --% Homicide 93.5% Larceny from Vehicle 91.2% Vehicle Accidents 91.7% Trespassing 92.1% Simple Assault
  25. 25. MISCONCEPTION #4: Predictive Policing is unwarranted government surveillance
  26. 26. “Miami police say HunchLab is basically an enhanced version of PredPol, because it adds other relevant elements to crime data — like weather, social media and school calendars.” –Excerpt from Non Fiction: Miami Looking to Adapt ‘Pre-Crime’ Fighting System
  27. 27. “Cops are using software programs that use algorithms to analyze surveillance, GPS coordinates, and crime data to pinpoint specific areas where, and specific people who, might at some point commit a crime.” –Peter Moskowitz, The Future of Policing Is Here, and It’s Terrifying
  28. 28. Types of Information Event Geographic CalculatedTemporal
  29. 29. Data Type Explaination id Unique event ID Datetimefrom when the event started datetimeto when the event ended class the type of crime point x, point y geocoded location reporttime the time the event was reported address the address of the event lastupdated when the record was last updated
  30. 30. Example Data Types Weather Population Density Location of BarsSchool Schedules
  31. 31. MISCONCEPTION #5: Predictive Policing will worsen the bias already present in policing
  32. 32. Credit: Department of Justice
  33. 33. Source: National Crime Victimization Survey, Bureau of Justice Statistics,
  34. 34. The quality of making judgments that are free from discrimination. Comes from the Old English faeger meaning “pleasing, attractive.” term: fairness Practices may be discriminatory if they have a disproportionate adverse impact on members of a protected class. term: theory of disparate impact
  35. 35. Example Deployment 101 100 2 2 2 50 1 1 1
  36. 36. Example Deployment 101 100 2 2 2 50 1 1 1
  37. 37. Example Deployment 101 100 2 2 2 50 1 1 1 101 100 2 2 2 50 1 1 1 101 100 2 2 2 50 1 1 1
  38. 38. Example Deployment 101 100 2 2 2 50 1 1 1 101 100 2 2 2 50 1 1 1 101 100 2 2 2 50 1 1 1 If deploying to an area increases events, then we form a feedback loop.
  39. 39. Example Deployment 101 100 2 2 2 50 1 1 1 101 100 2 2 2 50 1 1 1 101 100 2 2 2 50 1 1 1 If deploying to an area increases events, then we form a feedback loop. Using officer-initiated events to identify areas is a bad idea.
  40. 40. Example Deployment 101 100 2 2 2 50 1 1 1
  41. 41. Example Deployment 101 100 80 2 2 50 1 1 1
  42. 42. Percentage of unreported violent crime victimizations not reported because the victim believed the police would not or could not help doubled from 1994 to 2010 Over 20% of unreported violent victimizations against persons living in urban areas were not reported because the victim believed the police would not or could not help From 2006 to 2010, the highest percentages of unreported crime were among household theft (67%) and rape or sexual assault (65%) victimizations.
  43. 43. Example Deployment 101 100 80 2 2 50 1 1 1
  44. 44. 101 100 2 2 2 50 1 1 1
  45. 45. 101 100 2 2 2 50 1 1 1
  46. 46. 75 30 2 2 2 60 1 1 1 101 100 2 2 2 50 1 1 1 80 60 2 2 2 40 1 1 1
  47. 47. 75 30 2 2 2 60 1 1 1 101 100 2 2 2 50 1 1 1 80 60 2 2 2 40 1 1 1
  48. 48. 75 30 2 2 2 60 1 1 1 101 100 2 2 2 50 1 1 1 80 60 2 2 2 40 1 1 1
  49. 49. 75 30 2 2 2 60 1 1 1 101 100 2 2 2 50 1 1 1 80 60 2 2 2 40 1 1 1
  50. 50. Source: The Police Foundation
  51. 51. MISCONCEPTION #6: Predictive Policing will undermine civil liberties
  52. 52. “There are widespread fears among civil liberties advocates that predictive policing will actually worsen relations between police departments and black communities.” —Excerpt from Policing the Future Source: Whitney Curtis for The Marshall Project
  53. 53. “Yet big data invites provocative questions about whether such predictive tips should factor into the reasonable suspicion calculus.”
  54. 54. .01 .0003 .0004 .02 .003 .003 .0042 .0002 .01
  55. 55. “St Louis County Police Officer: “Being in the box alone was not a good enough reason to stop someone. “Does the data give me grounds to stop just because they’re walking around? No.” —Excerpt from Policing the Future, Maurice Chammah & Mark Hansen, The Marshall Project
  56. 56. MISCONCEPTION #7: Predictive Policing will lead to crime reduction
  57. 57. “By placing your officers in the right place at the right time, you will reduce crime in your community.” —Donald Summers, PredPol CEO in Predictive Policing: Seeing The Future
  58. 58. Crime Reduction
  59. 59. Predictive Accuracy Usable Software Effective Tactics+ Crime Reduction=
  60. 60. 0 0.2 0.4 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 PercentofCrimesCaptured Percent of Land Area Theft of Motor Vehicles Predictive Accuracy
  61. 61. Usable Software
  62. 62. Effective Tactics
  63. 63. 7 Common Misconceptions About Predictive Policing
  64. 64. Questions? 990 Spring Garden St, 5th Floor Philadelphia, PA 19123 215.925.2600 info@azavea.com Adele Zhang HunchLab Product Specialist azhang@azavea.com Jeremy Heffner HunchLab Product Manager jheffner@azavea.com

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