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Using Modeling to Predict and Prevent Victimization [electronic resource] / by Ken Pease, Andromachi Tseloni.

By: Pease, Ken [author.].
Contributor(s): Tseloni, Andromachi [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: SpringerBriefs in Criminology: 13Publisher: Cham : Springer International Publishing : Imprint: Springer, 2014Description: VIII, 80 p. 11 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319031859.Subject(s): Social sciences | Statistics | Criminology | Social Sciences | Criminology & Criminal Justice | Statistics for Social Science, Behavorial Science, Education, Public Policy, and LawDDC classification: 364 Online resources: Click here to access online
Contents:
Introduction -- Crime Concentration -- Preventing Repeat Victimization -- Predicting Frequent Victimization -- Preventing Recurring Victimization -- Conclusions.
In: Springer eBooksSummary: This work provides clear application of a new statistical modeling technique that can be used to recognize patterns in victimization and prevent repeat victimization. The history of crime prevention techniques range from offender-based, to environment/situation-based, to victim-based. The authors of this work have found more accurate ways to predict and prevent victimization using a statistical modeling, based around crime concentration and sub-group profiling with regard to crime vulnerability levels, to predict areas and individuals vulnerable to crime. Following from this prediction, they propose policing strategies to improve crime prevention based on these predictions. With a combination of immediate actions and longer-term research recommendations, this work will be of interest to researchers and policy makers in focused on crime prevention, police studies, victimology and statistical applications.
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Introduction -- Crime Concentration -- Preventing Repeat Victimization -- Predicting Frequent Victimization -- Preventing Recurring Victimization -- Conclusions.

This work provides clear application of a new statistical modeling technique that can be used to recognize patterns in victimization and prevent repeat victimization. The history of crime prevention techniques range from offender-based, to environment/situation-based, to victim-based. The authors of this work have found more accurate ways to predict and prevent victimization using a statistical modeling, based around crime concentration and sub-group profiling with regard to crime vulnerability levels, to predict areas and individuals vulnerable to crime. Following from this prediction, they propose policing strategies to improve crime prevention based on these predictions. With a combination of immediate actions and longer-term research recommendations, this work will be of interest to researchers and policy makers in focused on crime prevention, police studies, victimology and statistical applications.

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