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.

1. 990 Spring Garden St, 5th Floor
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7 Misconceptions about Predictive Policing

2. Adele Zhang
HunchLab Product Specialist
azhang@azavea.com
Jeremy Heffner
HunchLab Product Manager
jheffner@azavea.com

4. 55 people
using geodata
to do stuff that matters

5. B Corporation
• Civic/Social impact
• Donate share of profits
Research-Driven
• 10% Research Program
• Academic Collaborations
• Open Source
• Open Data

7. 7 Common Misconceptions
About Predictive Policing

8. Predictive Missions
• Determines high risk areas each shift
• Intelligently allocates patrol resources
• Uses multiple data sets to ‘explain’ patterns

9. Types of Information
Event Geographic
CalculatedTemporal

10. HunchLab automatically produces target areas called missions. Color
represents the primary risk in each mission area.

11. 7 Common Misconceptions
About Predictive Policing

12. MISCONCEPTION #1:
Predictive Policing is like
Minority Report in real life

13. “Pre-crime policing tech isn’t just real, it’s now
ubiquitous.”
–Jack Smith IV

15. Source: 20th Century Fox

17. MISCONCEPTION #2:
Predictive Policing can predict
individual crimes

18. Source: IBM

22. MISCONCEPTION #3:
Predictive Policing is just
rebranding existing technologies

23. “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

24. 2002!

27. Retrospective Analysis (Hotspots)
Assumption

28. Predictive Analysis
Learned?

29. • 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.)

30. • 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

31. We hold back the most recent 90 days of data…
1 Year 3 Years
Several
Months
Warm-up
Variables
Training
Examples
Testing
Examples

32. 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

34. 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

37. MISCONCEPTION #4:
Predictive Policing is unwarranted
government surveillance

38. “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

39. “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

40. Types of Information
Event Geographic
CalculatedTemporal

41. 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

42. Example Data Types
Weather Population Density
Location of BarsSchool Schedules

45. MISCONCEPTION #5:
Predictive Policing will worsen the
bias already present in policing

46. Credit: Department of Justice

47. Source: National Crime Victimization Survey, Bureau of Justice Statistics,

48. 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

49. Example Deployment
101 100 2
2 2 50
1 1 1

50. Example Deployment
101 100 2
2 2 50
1 1 1

51. 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

52. 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.

53. 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.

55. Example Deployment
101 100 2
2 2 50
1 1 1

56. Example Deployment
101 100 80
2 2 50
1 1 1

57. 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.

58. Example Deployment
101 100 80
2 2 50
1 1 1

59. 101 100 2
2 2 50
1 1 1

60. 101 100 2
2 2 50
1 1 1

61. 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

62. 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

63. 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

64. 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

65. Source: The Police Foundation

66. MISCONCEPTION #6:
Predictive Policing will
undermine civil liberties

67. “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

69. “Yet big data invites provocative questions about
whether such predictive tips should factor into the
reasonable suspicion calculus.”

70. .01 .0003 .0004
.02 .003 .003
.0042 .0002 .01

71. “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

72. MISCONCEPTION #7:
Predictive Policing will lead to
crime reduction

73. “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

74. Crime Reduction

75. Predictive Accuracy
Usable Software
Effective Tactics+
Crime Reduction=

76. 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

77. Usable Software

78. Effective Tactics

79. 7 Common Misconceptions
About Predictive Policing

80. 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