University of Chicago

zed.uchicago.edu

Urban Crime Prediction: The data, ethics, and biases of predicting events

Rotaru, V., Huang, Y., Li, T. et al. 

Event-level prediction of urban crime reveals a signature of enforcement bias in US cities. Nature Human Behavior 6, 1056–1068 (2022).

https://doi.org/10.1038/s41562-022-01372-0

Harper Lectures

February 15 2023

AI Awakens

Machines can reach false or dangerous conclusions and human review is essential for responsible AI.

-- De-Arteaga et al.

It is not clear that humans are very effective at overseeing algorithms.

Unless humans get better at overcoming their own conscious and unconscious biases, manual oversight runs the risk of making bias problems worse.

-- Green et al.

Human-in-the-loop is not necessarily better

The Problem of Free Will

Predictive Policing

Broad patterns are easily predictable

Hotspots?

2 weeks, Chicago, 2017

property crime

violent crime

Some historical patterns are predictive

• Predictions cannot be self-fulfilling prophecies
• Transparency
• Explore the interaction between crime, social factors and enforcement

• Ability to predict law enforcement response to crime as well
• Analyze if such reponses reveal existant policy biases

Lets Predict Crime

• No manual selection of factors
• No creation of "lists"
• Uses only de-identified data

Patterns from event logs

ID,Case Number,Date,Block,IUCR,Primary Type,Description,Location Description,Arrest,Domestic,Beat,District,Ward,Community Area,FBI Code,X Coordinate,Y Coordinate,Year,Updated On,Latitude,Longitude,Location\\
8316800,HT550945,08/11/2011 11:00:00 AM,086XX S MARQUETTE AVE,1120,DECEPTIVE PRACTICE,FORGERY,RESIDENCE,false,false,0423,004,7,46,10,1195654,1848294,2011,02/04/2016 06:33:39 AM,41.738615478,-87.558741896\\
8316805,HT550781,10/20/2011 05:00:00 AM,056XX S ABERDEEN ST,0890,THEFT,FROM BUILDING,RESIDENCE,false,false,0712,007,16,68,06,1169943,1867457,2011,02/04/2016 06:33:39 AM,41.791797599,-87.652385205\\
8316806,HT550706,10/20/2011 05:45:00 AM,079XX S LOOMIS BLVD,031A,ROBBERY,ARMED: HANDGUN,STREET,false,false,0612,006,21,71,03,1168370,1852331,2011,02/04/2016 06:33:39 AM,41.750323974,-87.658588247\\
8316811,HT539324,10/12/2011 12:23:52 PM,003XX E 75TH ST,2027,NARCOTICS,POSS: CRACK,SMALL RETAIL STORE,true,false,0323,003,6,69,18,1179641,1855355,2011,02/04/2016 06:33:39 AM,41.758372192,-87.61719416\\
8316822,HT551031,10/19/2011 02:00:00 AM,071XX W DICKENS AVE,0910,MOTOR VEHICLE THEFT,AUTOMOBILE,SIDEWALK,false,false,2512,025,36,25,07,1127877,1913161,2011,02/04/2016 06:33:39 AM,41.918027518,-87.805606689\\
8316824,HT551032,10/20/2011 12:00:00 AM,034XX N NATCHEZ AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,false,1632,016,36,17,26,1132429,1922272,2011,02/04/2016 06:33:39 AM,41.94295109,-87.788669409\\
8316825,HT549690,10/19/2011 12:51:00 PM,079XX S ADA ST,2820,OTHER OFFENSE,TELEPHONE THREAT,APARTMENT,false,false,0612,006,21,71,26,1168711,1852015,2011,02/04/2016 06:33:39 AM,41.749449482,-87.657347764\\
8316826,HT549865,10/19/2011 06:00:00 AM,011XX N LEAMINGTON AVE,0810,THEFT,OVER \$500,RESIDENTIAL YARD (FRONT/BACK),false,false,1531,015,37,25,06,1141821,1907155,2011,02/04/2016 06:33:39 AM,41.9012995,-87.754523767\\
8316827,HT550963,09/01/2011 04:00:00 PM,079XX S LOOMIS BLVD,0610,BURGLARY,FORCIBLE ENTRY,RESIDENCE-GARAGE,false,false,0612,006,21,71,05,1168380,1851969,2011,02/04/2016 06:33:39 AM,41.749330381,-87.658562005\\
8316838,HT548010,10/17/2011 03:20:00 PM,055XX N KEDZIE AVE,0820,THEFT,\$500 AND UNDER,"SCHOOL, PUBLIC, GROUNDS",true,false,1712,017,40,13,06,1154047,1936545,2011,02/04/2016 06:33:39 AM,41.981712678,-87.708829703\\
8316839,HT551049,10/20/2011 08:50:00 AM,102XX S AVENUE N,0430,BATTERY,AGGRAVATED: OTHER DANG WEAPON,STREET,false,false,0432,004,10,52,04B,1201166,1837763,2011,02/04/2016 06:33:39 AM,41.709579698,-87.538903651\\
8316871,HT549680,10/19/2011 01:03:00 PM,044XX N BROADWAY,0460,BATTERY,SIMPLE,DEPARTMENT STORE,false,false,2313,019,46,3,08B,1168460,1929880,2011,02/04/2016 06:33:39 AM,41.963123126,-87.65601675\\
8316872,HT551071,10/19/2011 03:10:00 PM,053XX S CALUMET AVE,0810,THEFT,OVER \$500,RESIDENCE,false,false,0234,002,3,40,06,1179390,1869685,2011,02/04/2016 06:33:39 AM,41.797700881,-87.617676981\\
8316873,HT551063,10/20/2011 11:10:00 AM,003XX E 47TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,SIDEWALK,true,false,0222,002,3,38,18,1178980,1873925,2011,02/04/2016 06:33:39 AM,41.809345175,-87.619051287\\
8316874,HT550901,10/20/2011 09:11:00 AM,033XX W OGDEN AVE,2022,NARCOTICS,POSS: COCAINE,POLICE FACILITY/VEH PARKING LOT,true,false,1024,010,24,29,18,1154489,1891024,2011,02/04/2016 06:33:39 AM,41.856790413,-87.708424071\\
8316875,HT549739,10/19/2011 01:30:00 PM,002XX E GARFIELD BLVD,0820,THEFT,\$500 AND UNDER,CTA BUS,false,false,0232,002,3,40,06,1178645,1868596,2011,02/04/2016 06:33:39 AM,41.794729551,-87.620442108\\
8316880,HT549802,10/19/2011 12:00:00 PM,011XX W WILSON AVE,0460,BATTERY,SIMPLE,COLLEGE/UNIVERSITY GROUNDS,false,false,2311,019,46,3,08B,1167612,1930696,2011,02/04/2016 06:33:39 AM,41.96538061,-87.659110921\\
8316881,HT431449,08/04/2011 11:00:00 AM,027XX W CHICAGO AVE,0820,THEFT,\$500 AND UNDER,STREET,false,false,1313,012,26,24,06,1157782,1905211,2011,02/04/2016 06:33:39 AM,41.895654523,-87.69595021\\
8316882,HT549162,10/19/2011 06:42:00 AM,105XX S WESTERN AVE,0610,BURGLARY,FORCIBLE ENTRY,TAVERN/LIQUOR STORE,false,false,2211,022,19,72,05,1162255,1834721,2011,02/04/2016 06:33:39 AM,41.702128701,-87.681485145\\
8316884,HT544972,10/16/2011 04:30:00 AM,103XX S HALSTED ST,1310,CRIMINAL DAMAGE,TO PROPERTY,SMALL RETAIL STORE,false,false,2232,022,34,73,14,1172807,1836192,2011,02/04/2016 06:33:39 AM,41.705939683,-87.642803521\\
8316886,HT549777,10/19/2011 02:10:00 PM,014XX W PRATT BLVD,0850,THEFT,ATTEMPT THEFT,SMALL RETAIL STORE,false,false,2431,024,49,1,06,1165565,1945281,2011,02/04/2016 06:33:39 AM,42.005446228,-87.666219555\\
8316887,HT551046,10/20/2011 11:10:00 AM,050XX N WINTHROP AVE,2820,OTHER OFFENSE,TELEPHONE THREAT,RESIDENCE,false,false,2033,020,48,3,26,1167955,1933907,2011,02/04/2016 06:33:39 AM,41.974184283,-87.657756697\\
8316889,HT550997,10/20/2011 09:10:00 AM,041XX N DICKINSON AVE,1121,DECEPTIVE PRACTICE,COUNTERFEITING DOCUMENT,STREET,true,false,1624,016,45,15,10,1142513,1926900,2011,02/04/2016 06:33:39 AM,41.955468935,-87.751489799\\
8316890,HT532649,10/07/2011 11:46:00 PM,062XX S VERNON AVE,2092,NARCOTICS,SOLICIT NARCOTICS ON PUBLICWAY,SIDEWALK,true,false,0313,003,20,42,26,1180324,1863694,2011,02/04/2016 06:33:39 AM,41.781239632,-87.614435596\\
8316893,HT551023,10/20/2011 02:00:00 AM,081XX S STEWART AVE,0810,THEFT,OVER \$500,STREET,false,false,0622,006,21,44,06,1175042,1850896,2011,02/04/2016 06:33:39 AM,41.746239973,-87.634181801\\
8316894,HT550772,10/20/2011 07:10:00 AM,048XX N TALMAN AVE,1320,CRIMINAL DAMAGE,TO VEHICLE,STREET,false,false,2031,020,40,4,14,1157831,1931974,2011,02/04/2016 06:33:39 AM,41.969093078,-87.695038453\\
8316898,HT551055,10/16/2011 09:00:00 AM,018XX S LAFLIN ST,1365,CRIMINAL TRESPASS,TO RESIDENCE,APARTMENT,false,false,1222,012,25,31,26,1166665,1891065,2011,02/04/2016 06:33:39 AM,41.856651049,-87.663730374\\
8316899,HT550695,10/20/2011 05:30:00 AM,122XX S HALSTED ST,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE PORCH/HALLWAY,false,false,0524,005,34,53,14,1173210,1823636,2011,02/04/2016 06:33:39 AM,41.671475169,-87.641696924\\
8316901,HT549052,10/19/2011 12:01:00 AM,064XX S DR MARTIN LUTHER KING JR DR,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0312,003,20,42,18,1180027,1862432,2011,02/04/2016 06:33:39 AM,41.777783389,-87.615563072\\
8316902,HT550988,10/20/2011 10:25:00 AM,066XX S KENNETH AVE,0486,BATTERY,DOMESTIC BATTERY SIMPLE,RESIDENCE,false,false,0833,008,13,65,08B,1147854,1860166,2011,02/04/2016 06:33:39 AM,41.772241474,-87.733568892\\
8316909,HT550957,10/20/2011 04:45:00 AM,051XX S MONITOR AVE,2825,OTHER OFFENSE,HARASSMENT BY TELEPHONE,RESIDENCE,false,true,0811,008,23,56,26,1138234,1869952,2011,02/04/2016 06:33:39 AM,41.79927468,-87.768598031\\

variables: <location,category>

~3000 location tiles

~9000 variables

~40 million binary interactions

~ 1 billion possible models of binary interaction

The Problem:

• Predicting crime sufficiently ahead of time to be actionable
• Prediction precise enough in time and space to be actionable
• Use ONLY data that is realistically and cheaply available

Prediction Performance

"crime forecast"

93% accuracy

87% AUC

~70% specificity at ~80% sensitivity

Chicago Predictive Performance

Boston Districts: B2 B3 C1

Jan 1 2021 -  July 22 2022

Total # of events: 7419

Example: Boston MA, USA

Property:

Other_Larceny-Larceny_from_MV-Auto_Theft-Residential_Burglary-Robbery-Commercial_Burglary    

Violent:

Aggravated_Assault-Rape_&_Attempted-Homicide                                                 

Mean AUC

Property crime: 81%

Violent crime:    84%

Spatial tiles:

0.0028 deg latitude, 0.0019 deg longitude

0.2 miles across

(100 x 300 yds)

Time-period:

Training:                      Jan 1 2021 - March 31 2022

Out-of-sample test:  Apr 1 2022 - July 22 2022

Prediction Performance

sensitivity     0.88
ppv             0.91

10 crimes

Raise 11 flags

8 correct flags

3 false positives

2 false negatives

Mean AUC

Property crime: 81%

Violent crime:    84%

Spatial tiles:

0.003 deg latitude, 0.003 deg longitude

0.25 miles across

Time-period:

Training:                      Jan 1 2016 - Dec 31 2018

Out-of-sample test:  Jan 1 2019 - April 1 2019

Philadelphia

sensitivity   0.90
ppv           0.87

100 crimes

Raise 103 flags

90 correct flags

13 false positives

10 missed

3 day ahead prediction

Jan 1 2019

to

April 1 2019

Triangles: actual events

heatmap: predicted risk 3 days ahead

Triple homicide incident

Jan 7 2019

https://www.inquirer.com/crime/kensington-triple-shooting-homicide-philadelphia-police-20190107.html

Triangles: actual events

heatmap: predicted risk 3 days ahead

The Underlying Math

Not based on standard "Deep Learning"

• Forecasting rare events in  multi-variable stochastic evolution requires new modeling architecture​​

• Learn local "activation functions" as symbolic probabilistic transducers​

• Assemble these local predictors into a "fractal net"

Applies to any rare/extreme event phenomena

Ishanu Chattopadhyay, Yi Huang, James Evans et al. Deep Learning Without Neural Networks: Fractal-nets for Rare Event Modeling, 26 October 2020, PREPRINT (Version https://doi.org/10.21203/rs.3.rs-86045/v1

Digital Twin

Signature of enforcement

inequity

Results corroborated in signature observed in raw data