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990 Spring Garden St, 5th FloorPhiladelphia, PA 19123
7 Misconceptions about Predictive Policing
55 people using geodata
to do stuff that matters
B Corporation• Civic/Social impact• Donate share of profits
Research-Driven• 10% Research Program• Academic Collaborations• Open Source• Open Data
7 Common Misconceptions About
Predictive Policing
Predictive Missions• Determines high risk areas each shift
• Intelligently allocates patrol resources
• Uses multiple data sets to ‘explain’ patterns
Types of Information
Event Geographic
CalculatedTemporal
HunchLab automatically produces target areas called missions. Color represents the primary risk in each mission area.
7 Common Misconceptions About
Predictive Policing
MISCONCEPTION #1:Predictive Policing is likeMinority Report in real life
“Pre-crime policing tech isn’t just real, it’s now ubiquitous.”
–Jack Smith IV
Source: 20th Century Fox
MISCONCEPTION #2:Predictive Policing can predict individual crimes
Source: IBM
MISCONCEPTION #3:Predictive Policing is just rebranding existing technologies
“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
2002!
Retrospective Analysis (Hotspots)
Assumption
Predictive Analysis
Learned?
• 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.)
• 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
We hold back the most recent 90 days of data…
1 Year 3 Years Several Months
Warm-up Variables
Training Examples
Testing Examples
Cells ranked highest to lowest0% 100%
Percent of Patrol Area to Capture All Crimes
Average Crime Rank
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%0%
50%
100%
Percent of Crimes Captured vs. Percent of Patrol Area
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
MISCONCEPTION #4:Predictive Policing is unwarranted government surveillance
“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
“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
Types of Information
Event Geographic
CalculatedTemporal
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
Example Data Types
Weather Population Density
Location of BarsSchool Schedules
MISCONCEPTION #5:Predictive Policing will worsen the bias already present in policing
Credit: Department of Justice
Source: National Crime Victimization Survey, Bureau of Justice Statistics,
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
Example Deployment
101 100 2
2 2 50
1 1 1
Example Deployment
101 100 2
2 2 50
1 1 1
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
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.
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.
Example Deployment
101 100 2
2 2 50
1 1 1
Example Deployment
101 100 80
2 2 50
1 1 1
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.
Example Deployment
101 100 80
2 2 50
1 1 1
101 100 2
2 2 50
1 1 1
101 100 2
2 2 50
1 1 1
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
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
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
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
Source: The Police Foundation
MISCONCEPTION #6:Predictive Policing will undermine civil liberties
“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
“Yet big data invites provocative questions about whether such predictive tips should factor into the reasonable suspicion calculus.”
.01 .0003 .0004
.02 .003 .003
.0042 .0002 .01
“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
MISCONCEPTION #7:Predictive Policing will lead to crime reduction
“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
Crime Reduction
Predictive Accuracy
Usable Software
Effective Tactics+
Crime Reduction=
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.160
0.2
0.4
Theft of Motor Vehicles
Percent of Land Area
Perc
ent
of C
rimes
Cap
ture
d
Predictive Accuracy
Usable Software
Effective Tactics
7 Common Misconceptions About
Predictive Policing
Questions?
990 Spring Garden St, 5th FloorPhiladelphia, PA 19123
Adele ZhangHunchLab Product [email protected]
Jeremy HeffnerHunchLab Product [email protected]
