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Risk-based policingevidence-based cr...

Caplan, Joel M., (1980-)

Risk-based policingevidence-based crime prevention with big data and spatial analytics /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Risk-based policingLeslie W. Kennedy, Joel M. Caplan, Eric L. Piza.
其他題名:	evidence-based crime prevention with big data and spatial analytics /
作者:	Kennedy, Leslie W.
其他作者:	Caplan, Joel M.,
出版者:	Oakland, Ca. :University of California Press,c2018.
面頁冊數:	1 online resource (xii, 153 p.) :ill., maps.
標題:	Crime prevention
電子資源:	http://www.degruyter.com/isbn/9780520968349
ISBN:	9780520968349$q(electronic book)

Risk-based policingevidence-based crime prevention with big data and spatial analytics /
Kennedy, Leslie W.

Risk-based policingevidence-based crime prevention with big data and spatial analytics /[electronic resource] :Leslie W. Kennedy, Joel M. Caplan, Eric L. Piza. - 1st ed. - Oakland, Ca. :University of California Press,c2018. - 1 online resource (xii, 153 p.) :ill., maps.

Includes bibliographical references and index.

Introduction to risk and big data -- Evolution of modern policing -- Policing in the new era of public safety and law enforcement -- Risk-based policing and action -- The theory of risky places -- High-risk target areas and priority places -- Case studies of Colorado Springs, Glendale, Newark, and Kansas city : the role of police in risk-based policing -- Facilitators and impediments to designing, implementing, and evaluating risk-based policing strategies : insights from completed researcher-practitioner partnerships -- The roles of multiple stakeholders in risk-based policing : case studies of Jersey city and Atlantic city -- People make risk-based policing and data actionable.

"Risk-based policing is the latest advancement in the long history of policing innovations, where research and planning have combined to manage crime risks, prevent crime, and enhance public safety. In Risk-Based Policing the authors share case studies from different agencies to demonstrate how focusing police resources at risky places, based on smart uses of data and strong analytical work, can address the worst effects of disorder and crime while improving public safety and community relations. Topics include the role of big data; the evolution of modern policing; dealing with high-risk targets; designing, implementing, and evaluating risk-based policing strategies; and the role of multiple stakeholders in risk-based policing. Case studies explore cities such as Colorado Springs, Glendale, Newark, Kansas City, and others. The book also demonstrates how Risk Terrain Modeling (RTM) can be extended to offer a more comprehensive view of prevention and deterrence"--Provided by publisher.

ISBN: 9780520968349$q(electronic book)

LCCN: 2018015036Subjects--Topical Terms:

244564
Crime prevention

LC Class. No.: HV7432 / .K46 2018

Dewey Class. No.: 363.2/3028557

Risk-based policingevidence-based crime prevention with big data and spatial analytics /
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300 $a 1 online resource (xii, 153 p.) : $b ill., maps.
504 $a Includes bibliographical references and index.
505 0 $a Introduction to risk and big data -- Evolution of modern policing -- Policing in the new era of public safety and law enforcement -- Risk-based policing and action -- The theory of risky places -- High-risk target areas and priority places -- Case studies of Colorado Springs, Glendale, Newark, and Kansas city : the role of police in risk-based policing -- Facilitators and impediments to designing, implementing, and evaluating risk-based policing strategies : insights from completed researcher-practitioner partnerships -- The roles of multiple stakeholders in risk-based policing : case studies of Jersey city and Atlantic city -- People make risk-based policing and data actionable.
520 $a "Risk-based policing is the latest advancement in the long history of policing innovations, where research and planning have combined to manage crime risks, prevent crime, and enhance public safety. In Risk-Based Policing the authors share case studies from different agencies to demonstrate how focusing police resources at risky places, based on smart uses of data and strong analytical work, can address the worst effects of disorder and crime while improving public safety and community relations. Topics include the role of big data; the evolution of modern policing; dealing with high-risk targets; designing, implementing, and evaluating risk-based policing strategies; and the role of multiple stakeholders in risk-based policing. Case studies explore cities such as Colorado Springs, Glendale, Newark, Kansas City, and others. The book also demonstrates how Risk Terrain Modeling (RTM) can be extended to offer a more comprehensive view of prevention and deterrence"--Provided by publisher.
588 0 $a Online resource; title from digital title page (viewed on January 07, 2019).
650 0 $a Crime prevention $3 244564
650 0 $a Crime analysis $x Data processing. $3 610079
700 1 $a Caplan, Joel M., $d 1980- $3 906468
700 1 $a Piza, Eric L., $d 1981- $3 906469
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