Friday, February 8, 2013

Rutgers-Newark professors receive $500,000 grant from National Institute of Justice

Rutgers-Newark professors receive $500,000 grant from National Institute of Justice [ Back to EurekAlert! ] Public release date: 7-Feb-2013
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Contact: Helen Paxton
paxton@andromeda.rutgers.edu
973-353-5262
Rutgers University

Grant used to conduct risk terrain modeling research

Have you ever wondered why some venues seem rife with criminal activity, while others not so much? Wouldn't it be nice to peer into a crystal ball to see which streets and neighborhoods you should avoid because they possess most, if not all, of the elements favorable to nefarious individuals? Research being conducted by Dr. Leslie Kennedy, Dr. Joel Caplan, and Dr. Eric Piza of the Rutgers Center on Public Security (RCPS) at the School of Criminal Justice in Newark, New Jersey, may provide such foretelling.

A theoretically grounded and empirically based approach called "risk terrain modeling" (RTM) uses technology to predict where crime will likely happen. RTM marries historical information about crime hotspots with a variety of factors endemic to threatening areas (e.g., building vacancies, population density, area illumination, proximity to ex-offenders' residences) to create a map that highlights locations at greatest risk for crime. Armed with a reliable picture of regions that are prone to attract criminals, public safety professionals can make informed decisions on how best to deploy resources to help quell disturbances and offenses before they actually occur.

With a two-year, $500,000 grant from the National Institute of Justice, Kennedy, Caplan, and Piza will be using RTM to assist police agencies in Arlington, Texas; Chicago, Illinois; Colorado Springs, Colorado; Glendale, Arizona; Kansas City, Missouri; and Newark, New Jersey. RTM will help these agencies define high-risk environments and measure the extent to which allocating police patrols to those areas affects the frequency of new crime events.

"Risk terrain modeling is a smarter way to address public safety issues," comments Kennedy. "It is far more effective and efficient to be proactive than reactive to threatening situations."

Kennedy, Caplan, and Piza have focused on RTM research for the past four years, with much of the results of their research posted on RCPS's website at www.rutgerscps.org to make it more readily accessible to end users. "Our research has global impact," notes Caplan. "More than 5,000 free downloads of manuals on RTM have been made available to individuals of both police and non-police disciplines residing on nearly every continent."

In addition to the free downloads, the researchers conduct webinars on RTM, have partnered with the Rutgers Division of Continuing Studies in New Brunswick, New Jersey, to offer public safety professionals courses on the subject, and are developing software to automate the procedures for RTM.

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Rutgers-Newark professors receive $500,000 grant from National Institute of Justice [ Back to EurekAlert! ] Public release date: 7-Feb-2013
[ | E-mail | Share Share ]

Contact: Helen Paxton
paxton@andromeda.rutgers.edu
973-353-5262
Rutgers University

Grant used to conduct risk terrain modeling research

Have you ever wondered why some venues seem rife with criminal activity, while others not so much? Wouldn't it be nice to peer into a crystal ball to see which streets and neighborhoods you should avoid because they possess most, if not all, of the elements favorable to nefarious individuals? Research being conducted by Dr. Leslie Kennedy, Dr. Joel Caplan, and Dr. Eric Piza of the Rutgers Center on Public Security (RCPS) at the School of Criminal Justice in Newark, New Jersey, may provide such foretelling.

A theoretically grounded and empirically based approach called "risk terrain modeling" (RTM) uses technology to predict where crime will likely happen. RTM marries historical information about crime hotspots with a variety of factors endemic to threatening areas (e.g., building vacancies, population density, area illumination, proximity to ex-offenders' residences) to create a map that highlights locations at greatest risk for crime. Armed with a reliable picture of regions that are prone to attract criminals, public safety professionals can make informed decisions on how best to deploy resources to help quell disturbances and offenses before they actually occur.

With a two-year, $500,000 grant from the National Institute of Justice, Kennedy, Caplan, and Piza will be using RTM to assist police agencies in Arlington, Texas; Chicago, Illinois; Colorado Springs, Colorado; Glendale, Arizona; Kansas City, Missouri; and Newark, New Jersey. RTM will help these agencies define high-risk environments and measure the extent to which allocating police patrols to those areas affects the frequency of new crime events.

"Risk terrain modeling is a smarter way to address public safety issues," comments Kennedy. "It is far more effective and efficient to be proactive than reactive to threatening situations."

Kennedy, Caplan, and Piza have focused on RTM research for the past four years, with much of the results of their research posted on RCPS's website at www.rutgerscps.org to make it more readily accessible to end users. "Our research has global impact," notes Caplan. "More than 5,000 free downloads of manuals on RTM have been made available to individuals of both police and non-police disciplines residing on nearly every continent."

In addition to the free downloads, the researchers conduct webinars on RTM, have partnered with the Rutgers Division of Continuing Studies in New Brunswick, New Jersey, to offer public safety professionals courses on the subject, and are developing software to automate the procedures for RTM.

###


[ Back to EurekAlert! ] [ | E-mail | Share Share ]

?


AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert! system.


Source: http://www.eurekalert.org/pub_releases/2013-02/ru-rpr020713.php

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