7 misconceptions about predictive policing webinar

990 Spring Garden St, 5th FloorPhiladelphia, PA 19123

215.925.2600info@azavea.comwww.azavea.com

7 Misconceptions about Predictive Policing

Adele ZhangHunchLab Product Specialistazhang@azavea.com

Jeremy HeffnerHunchLab Product Managerjheffner@azavea.com

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

215.925.2600info@azavea.com

Adele ZhangHunchLab Product Specialistazhang@azavea.com

Jeremy HeffnerHunchLab Product Managerjheffner@azavea.com