thE policing LAb @ Nyu

Bringing data science to public safety

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WHAT WE DO

The Policing Lab @ NYU uses the tools of data science to promote cost-effective public safety, with an awareness of both resource and social costs. We work with communities and law enforcement agencies to design studies that meet jurisdictions’ needs.

 

Communities and agencies interested in working with the Policing Lab can contact us at policinglab@nyu.edu.

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WHO WE ARE

The Policing Lab @ NYU coordinates the work of researchers across multiple disciplines and universities.

 

Jillian Carr

Assistant Professor of Economics

Krannert School of Management

Purdue University

Chris Dawes

Assistant Professor of Politics

New York University

Greg DeAngelo

Assistant Professor of Economics

College of Business and Economics

West Virginia University

Barry Friedman

Jacob D. Fuchsberg Professor of Law

Affiliated Professor of Politics

Director, Policing Project at NYU Law

New York University

 

Sharad Goel

Assistant Professor of Management Science and Engineering

Assistant Professor of Computer Science

Stanford University

Sanford Gordon

Professor of Politics

Affiliated Professor of Law

New York University

Anna Harvey

Professor of Politics

Affiliated Professor of Law

Director, Policing Lab @ NYU

New York University

Emily Owens

Associate Professor of Criminology, Law and Society

Associate Professor of Economics

University of California, Irvine

 

Ravi Shroff

Assistant Professor of Applied Statistics and Urban Informatics

New York University

Hye Young You

Assistant Professor 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

Abraham Navarrete

Ph.D. Student

Department of Politics

New York University

 

Stephanie Zonszein

Ph.D. Student

Department of Politics

New York University

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Current Projects

911 Call Analytics

A call to 911 triggers a chain of law enforcement responses, starting with a priority code being assigned to a call by a 911 call taker. Working with a large urban jurisdiction, we are analyzing whether assigned call priority codes are good predictors of actual risk to both officers and civilians, and exploring whether using an analytic tool to assign call priority can reduce this risk.

Traffic Stops

Some jurisdictions rely heavily on traffic stops as a crime deterrent, but the practice is often not welcomed by heavily stopped communities. Working with a mid-sized urban jurisdiction, we are analyzing the predictors of "productive" traffic stops and working to design a data-driven traffic stop policy that will target only those stops likely to produce real gains in public safety.

Real Time Crime Centers

Real Time Crime Centers (RTCCs) typically deploy analysts to search for information relevant to an incoming call for service, and then push that information to responding officers. Working with a large urban police department, we are using a quasi-experimental design to assess whether RTCCs increase officer and civilian safety.

911 Call Response Time

Estimating the effect of 911 call response time on the risk of civilian injury is typically confounded by the fact that more emergent calls are typically characterized by both a higher risk of civilian injury and shorter response times. Working with a large urban police force, we are exploiting the random assignment of 911 call takers to calls as a means to estimate the causal effect of shorter response times on the risk of civilian injury.  

De-Escalation

Understanding how interactions between officers and civilians can escalate may help to reduce use of force and risk of civilian and officer injury. Working with a mid-size urban police force, we are working to identify the predictors of escalation in officer-civilian interactions, and to estimate the effects of de-escalation training programs.

Gunfire Detection

Working with a mid-size suburban police force, we are looking at the effects of using gunfire detection sensors on crime and clearance rates. We are also evaluating the effects of using gunfire detection data to determine the deployment of social service resources.

Vehicle Pursuit

Working with adjacent jurisdictions that have altered their vehicle pursuit policies over time, we are working to estimate the effects of alternative vehicle pursuit policies on a variety of outcomes, including crime, clearance, and accident rates.

Civil Asset Forfeiture

Very little is known about about civil asset forfeiture. We are working to build a database that includes data on state-level civil asset forfeiture statutes, the amount and type of assets being seized, and incident-level data on traffic stops. 

Failure to Pay/Failure to Appear

Drivers who receive speeding tickets typically have a window within which they can plead guilty and pay a fine; alternatively they can go to court on a specified date. Many who receive such tickets neither pay nor appear in court, putting their licenses at risk. Working with a regional highway patrol force, we are using a natural experiment to assess whether a longer time to pay window can reduce failure to pay/failure to appear, particularly for lower income drivers.

Crowdsourcing Public Safety

Working with a large urban police department, we are developing an app that will allow the community to partner with the department to co-produce public safety. Through the app, the department and the community will be able to engage in a two-way information flow, easily sharing vital information that can promote both officer and civilian safety.  

911 Caller Satisfaction Survey

Callers to 911 often don't observe the law enforcement response to or resolution of their calls. Working with a large urban police force, we are analyzing whether incentivizing officers to reach out to 911 callers with information about how their call was resolved can improve callers' satisfaction with officers' response.

 
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