Public Safety Lab

 
 
 
 

Bringing data science to public safety

 
 
nyu_stacked_color.png
shutterstock_260476700.jpg

WHAT WE DO

The Public Safety Lab uses the tools of data science and social science to support communities’ efforts to improve public safety outcomes.

For example, many relatively minor offenses are easy to detect and interdict. Many more serious offenses are much harder to detect and interdict. We may then overpunish relatively minor offenses, and underpunish more serious offenses. The Public Safety Lab works with communities and law enforcement agencies to achieve more productive allocations of public safety resources.

Communities and agencies interested in working with the Public Safety Lab can contact us at publicsafetylab@nyu.edu.

shutterstock_681723346.jpg

WHO WE ARE

The Public Safety Lab coordinates the work of researchers across multiple disciplines and universities.

 

Mike Cafarella

Associate Professor of Computer Science and Engineering

University of Michigan

Chris Dawes

Associate Professor of Politics

New York University

Sharad Goel

Assistant Professor of Management Science and Engineering

Stanford University

Sanford Gordon

Professor of Politics

New York University

 

Anna Harvey

Director, Public Safety Lab

Professor of Politics

New York University

Dean Knox

Assistant Professor of Politics

Princeton University

Jonathan Mummolo

Assistant Professor of Politics and Public Affairs

Princeton University

Murat Mungan

Professor of Law

George Mason University

 

Daniel Neill

Associate Professor of Computer Science and Public Service

New York University

Ravi Shroff

Assistant Professor of Applied Statistics and Urban Informatics

New York University

Hye Young You

Assistant Professor of Politics

New York University

 
 

Public Safety Lab Data Science Team

 

Tinghao Li

M.S. in Data Science

Center for Data Science

New York University

Yurui Mu

M.S. in Data Science

Center for Data Science

New York University

Fu Shang

M.S. in Data Science

Center for Data Science

New York University

Liwei Song

M.S. in Data Science

Center for Data Science

New York University

Yueqiu Sun

M.S. in Data Science

Center for Data Science

New York University

Ruofan Wang

M.S. in Data Science

Center for Data Science

New York University

 

Tingyan Xiang

M.S. in Data Science

Center for Data Science

New York University

Xue Yang

M.S. in Data Science

Center for Data Science

New York University

Binqian Zeng

M.S. in Data Science

Center for Data Science

New York University

Wei Zhang

M.S. in Data Science

Center for Data Science

New York University

Yidi Zhang

M.S. in Data Science

Center for Data Science

New York University

 

Public Safety Lab Doctoral Students

 

Abraham Aldama

Ph.D. Student

Department of Politics

New York University

Antonella Bandiera

Ph.D. Student

Department of Politics

New York University

Ryan Fackler

Ph.D. and J.D. Student

Department of Economics

University of Pennsylvania

New York University School of Law

Nicholas Haas

Ph.D. Student

Department of Politics

New York University

Niklas Loynes

Ph.D. Student

Department of Politics

University of Manchester

Taylor Mattia

Ph.D. Student

Department of Politics

New York University

Mateo Vasquez

Ph.D. Student

Department of Politics

New York University

Stephanie Zonszein

Ph.D. Student

Department of Politics

New York University

shutterstock_547175098.jpg

Current Projects

 

911 Call Analytics

Calls to 911 in large urban jurisdictions are typically first assigned to a responding service (e.g., police, medical, fire), and are then assigned priority codes determining the speed and nature of the service's response. These human-assigned call and priority codes may not be optimal. For example, calls involving mental health issues may be assigned to policing agencies when they might more appropriately be assigned to medical professionals. Calls assigned to policing agencies may be assigned priority codes that are influenced by call taker biases. Working with a large urban policing agency, we are exploiting the as-if random assignment of 911 calls to call takers to evaluate both the process by which call and priority codes are assigned to calls, and the causal effects of call and priority codes on outcomes.

911 Response Field Experiment

Many policing agencies seek to improve their relationships with the communities they police, yet struggle to find effective means to do so. Working with a large urban policing agency, we are developing a randomized controlled trial of a platform that pushes an SMS-based survey to recent 911 callers, asking them to provide feedback on how they were treated by responding officers. Responding patrol officers may then access their average survey ratings, as well as those of the other officers in their unit, through a web-based interface. The platform may both incentivize responding officers to treat 911 callers with greater professionalism, and increase caller satisfaction with the police response to their call. More generally, the platform may provide an effective means for agencies to receive real time feedback from the communities they police.

Prosecutorial Discretion Project

Prosecutors play a vital role in the criminal justice system. After an arrest, prosecutors have numerous choices to make regarding whether and how to pursue prosecution of an offense. In many jurisdictions, prosecutors use this discretion to pursue prosecutions of subfelony violations to the full extent of the criminal law. In these jurisdictions, cases involving subfelony offenses typically consume a large share of law enforcement, prosecutorial, and judicial resources. Yet it is unclear whether this practice reduces offender recidivism; the practice may actually result in increased recidivism, and/or the escalation of minor offending into more serious offending. Working with a large urban district attorney's office, and exploiting the as-if random assignment of assistant district attorneys to subfelony cases, we are investigating the impact on offender recidivism of the use of prosecutorial discretion to reduce the incidence of prosecution for subfelony offenses.

Pretrial Detention Project

The problem of overincarceration has received considerable attention of late. The focus of this attention, however, has largely been on state and federal prison systems. Often overlooked are local jails. In any given year, approximately 11 million Americans will be detained in a local jail, often because they lack the funds to post bail. Yet we know little about the consequences of this widespread practice of pretrial detention. It is possible that longer periods of pretrial detention actually increase offender recidivism, and/or escalate petty offending into more serious offending. Our data science team is crawling the daily jail rosters of over 1,000 county jails, recording defendant-level data on pretrial detention and offender recidivism, as well as jail-level data on daily jail capacity, and merging these data with crawled defendant-level criminal case and incarceration records. Using these data we will then estimate the impact of the length of pretrial detention on offender recidivism, leveraging daily variation in local jail capacity.

Identifying Sex Trafficking

Sex trafficking is a crime of extraordinary violence visited largely upon vulnerable girls and young women. Its frequency is likely severely underreported, given the isolation within which its victims are kept. Working with a multi-university team, we are extracting structured content from a very large corpus of online commercial sex ads and provider reviews crawled over a period of several years. We are then looking for features in this extracted content that are suggestive of the presence of sex trafficking, and that may assist law enforcement agencies in identifying newly posted ads and reviews associated with trafficking. Initial results suggest that urban public school district calendars may be a good indicator of the presence of sex trafficking, highlighting for agencies the importance of investigating online sex ads posted on days when high school-aged girls are not in school, but adults are at work.

Policing for Revenue

There is widespread concern about the deployment of law enforcement resources to generate revenue for fiscally stressed jurisdictions. Yet rigorous evidence of the practice has been elusive. Working with a large regional highway patrol agency, we are exploiting a discontinuity in the allocation of revenue received from traffic citations to explore the consequences of fiscal incentives for both law enforcement and driver behavior. We find that traffic safety enforcement increases in the towns wherein the regional government receives more citation revenue. We further find that these fiscal incentives increase roadway safety in the high-revenue jurisdictions, but decrease roadway safety in the low-revenue jurisdictions. Finally, we find that these fiscal incentives have negative economic consequences for cited drivers in the high-revenue jurisdictions.

 

Criminal Defendant Equity Project

Ensuring that criminal defendants are treated equally before the law is one of the most important imperatives of criminal justice institutions. Yet judges’ retention incentives may lead to the unequal treatment of defendants, even in appellate cases. Working with extensive data crawled from public access websites, including the texts of every criminal opinion issued by New York State's intermediate appellate courts between 2003 and 2017,  and demographic and criminal history data on all criminal defendants processed by New York State's Department of Corrections since the 1970s, we are estimating the impact of judicial retention incentives (both reelection and reappointment) on the treatment of criminal defendants in intermediate appellate court cases.

Firearm Trafficking Analytics

Several websites host online firearms markets. Many sellers on these sites are not federally licensed firearms dealers. Federal law and most state laws do not require these private firearms sellers to conduct background checks on potential buyers. Even when state statutes do require background checks for purchases from private online dealers, as in New York State, these sales are difficult to monitor and track. As a result, it is relatively easy in these states for those prohibited from purchasing firearms to do so via online firearms markets. Working with the Regional Gun Violence Research Consortium, we are developing a project to identify and measure the incidence of online firearms trafficking, and to evaluate the efficacy of state and local regulations to reduce this incidence.

Civil Asset Forfeiture Project

Civil asset forfeiture allows law enforcement agencies to seize property upon the suspicion of criminal activity. While the practice may deter some criminal activity, it may also divert agency resources away from other productive pursuits, and may impose economic costs unevenly. We are collecting data on the amounts and types of assets being seized and forfeited by state and local law enforcement agencies, whether forfeitures are contested, demographic data on the subjects of asset forfeiture, and cross-state and within-state variation in civil asset forfeiture statutes. Using these data we are building predictive models of the incidence and effects of civil asset forfeiture.

Community Policing

Although "community policing" has become a popular catchphrase, little is known systematically about whether community policing can improve communities' trust in their local law enforcement agencies. Working with a large urban police force in South America, we are analyzing whether randomly assigned town hall meetings bringing together residents and officers to discuss policing strategies can improve trust in law enforcement. 

Social Networks and Violent Crime

Recent work has suggested that violent criminal behavior is often perpetrated through closely knit social networks, networks that facilitate overlapping forms of crime often associated with high rates of gun violence (e.g. drugs, guns, sex trafficking). This work suggests that identifying social networks with the potential to either encourage or discourage violent criminal behavior may be a promising policing strategy. Working with two large urban police forces in South America, we are analyzing whether randomized police interventions delivered through socially central individuals can reduce participation in violent criminal activity.

Domestic Violence Policing

Law enforcement agencies in developing countries often fail to appropriately respond to domestic violence complaints. Working with a state policing agency in India, we are conducting a randomized controlled trial of several interventions designed to increase the efficacy of the police response to domestic violence complaints, including randomized officer training in alternative dispute resolution and random assignment of paralegals to assist in case development.

shutterstock_630604613.jpg

Public Safety Lab

Workshop on Data-Driven Criminal Justice Reform

Friday October 19th 2018 9 AM-4:30 PM

Saturday October 20th 2018 9 AM-1 PM

Lester Pollack Colloquium Room

Furman Hall, New York University School of Law

 

Data science and social science may offer opportunities to support communities’ efforts to pursue criminal justice reform. For example, many relatively minor offenses are easy to detect and interdict. Many more serious offenses are much harder to detect and interdict. As a consequence we may overpunish relatively minor offenses, and underpunish more serious offenses. Data science and social science strategies may allow us to achieve more socially productive allocations of criminal justice resources.

This workshop will convene university researchers along with representatives from public agencies and nonprofit organizations to engage in a broad-ranging conversation on data-driven criminal justice reform.

Space is limited and registration is required; please rsvp here if you wish to attend. Rsvps will close when capacity is reached.

Friday, October 19

8:30-9:00: Light breakfast available

9:00-9:15: Opening remarks

9:15-10:45: Do we overpolice minor offenses?

A significant share of law enforcement effort is directed at policing relatively minor offenses, such as infractions, violations, and misdemeanors. Law enforcement personnel are often asked to respond to situations that might better be handled by medical or social service professionals. How are these resource allocation decisions made? Are they effective? Are they equitable? What consequences do they have? Are there better ways to make these decisions? What kinds of data would we need to answer these questions?

Wesley Bell, Democratic Candidate for St. Louis County Prosecutor

Jenn Rolnick Borchetta, Deputy Director, Impact Litigation, The Bronx Defenders

Captain Clay Farmer, North County Police Cooperative, St. Louis County

Ravi Shroff, Assistant Professor of Applied Statistics and Urban Informatics, New York University

11:00-12:30: Do we underpolice more serious offenses?

Many more serious offenses, such as gun violence, gun trafficking, drug trafficking, and sex trafficking, are difficult to detect and/or seemingly intractable. Recent developments in data science may offer tools to help law enforcement agencies address these more serious offenses. How do these predictive policing tools work? Are they effective? Are they equitable? What consequences do they have? What kinds of data would we need to answer these questions? 

Anna Harvey, Professor of Politics and Director, Public Safety Lab, New York University

Justin McCrary, Paul J. Evanson Professor of Law, Columbia Law School

Daniel Neill, Associate Professor of Computer Science and Public Service, New York University

Chief Lori Pollock, Crime Control Strategies, New York Police Department

12:30-1:30: Lunch

1:30-3:00: Is pretrial decision making optimal? 

A number of important decisions are made during the period between arrest and the disposition of a defendant’s case. For example, prosecutors make decisions over whether to pursue prosecution, the nature and count of charges brought, bail and detention recommendations, and plea offers. Judges make bail decisions, rule on pretrial motions, and approve or disapprove of plea bargains. How are these decisions made? What consequences do they have? What kinds of data-driven strategies might assist prosecutorial decision making? Do bail algorithms offer the promise of more effective bail decisions? What kinds of data would we need to answer these questions?

Sharad Goel, Assistant Professor of Management Science and Engineering, Executive Director, Stanford Computational Policy Lab, Stanford University

Scott Levy, Special Counsel to the Criminal Defense Practice, The Bronx Defenders

Jens Ludwig, McCormick Foundation Professor of Social Service Administration, Law, and Public Policy; Director, University of Chicago Crime Lab

Maria McKee, Principal Analyst, Office of San Francisco District Attorney

3:15-4:30: Access to counsel

Jurisdictions vary widely in their practices with respect to providing counsel to the indigent. What factors affect access to counsel? How does access to counsel affect outcomes? How does the quality of counsel affect outcomes? Are there data-driven strategies to promote more effective access to counsel? What kinds of data would we need in order to answer these questions? 

Amanda Agan, Assistant Professor of Economics and Affiliated Professor of Criminal Justice, Rutgers University

Andrew Davies, Director of Research, New York State Office of Indigent Legal Services

Miguel de Figueiredo, Associate Professor of Law and Terry J. Tondro Research Scholar, University of Connecticut

Saturday, October 20

8:30-9:00: Light breakfast available

9:00-10:15 Incarceration and reentry 

Incarceration is expensive, both in terms of its direct financial costs and in terms of the indirect costs it imposes on defendants post-release. Is incarceration an effective strategy to achieve deterrence? Is it equitable? What consequences does it have? Are there more cost-effective alternatives that can achieve both greater deterrence and greater reentry success? What kinds of data would we need to answer these questions?

Monica Deza, Assistant Professor of Economics, Hunter College

Elizabeth Glazer, Director, Mayor’s Office of Criminal Justice, New York City

Jennifer Skeem, Mack Distinguished Professor of Social Welfare and Public Policy, University of California, Berkeley

10:30-11:45 Transparency and accountability

Recent media attention directed at police shootings, instances of officer misconduct, and civilian complaints has raised questions about the availability and accuracy of law enforcement data. What kinds of data should law enforcement agencies report to the communities they police? What consequences, both positive and negative, might follow from reporting these data? How would we know?

Cynthia Conti-Cook, Staff Attorney, Criminal Special Litigation Unit, Legal Aid Society, New York City

Captain Clay Farmer, North County Police Cooperative, St. Louis County

Jonathan Mummolo, Assistant Professor of Politics and Public Affairs, Princeton University

Anita Ravishankar, Fellow, The Lab @ DC 

12:00-1:00: Working Lunch: Developing strategies for data-driven criminal justice reform

In this final session we will review the most promising strategies identified to promote data-driven criminal justice reform, discuss potential partnerships, and develop a roadmap for future research.

Chris Dawes, Associate Professor of Politics, New York University

Sharad Goel, Assistant Professor of Management Science and Engineering, Executive Director, Stanford Computational Policy Lab, Stanford University

Jonathan Mummolo, Assistant Professor of Politics and Public Affairs, Princeton University

Daniel Neill, Associate Professor of Computer Science and Public Service, New York University

 
shutterstock_621089699 (1).jpg