Using Modeling to Predict and Prevent Victimization

  • Ken Pease
  • Andromachi Tseloni

Part of the SpringerBriefs in Criminology book series (BRIEFSCRIMINOL, volume 13)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Ken Pease, Andromachi Tseloni
    Pages 17-27
  3. Ken Pease, Andromachi Tseloni
    Pages 29-44
  4. Ken Pease, Andromachi Tseloni
    Pages 45-61
  5. Ken Pease, Andromachi Tseloni
    Pages 63-64
  6. Back Matter
    Pages 65-80

About this book


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.


Crime Prediction Crime Prevention Police Crime Response Repeat Victimization Victim Characteristics Victimology

Authors and affiliations

  • Ken Pease
    • 1
  • Andromachi Tseloni
    • 2
  1. 1.Department of Social SciencesLoughborough UniversityLeicestershireUnited Kingdom
  2. 2.Department of Social SciencesLoughborough UniversityLeicestershireUnited Kingdom

Bibliographic information

  • DOI
  • Copyright Information The Author(s) 2014
  • Publisher Name Springer, Cham
  • eBook Packages Humanities, Social Sciences and Law
  • Print ISBN 978-3-319-03184-2
  • Online ISBN 978-3-319-03185-9
  • Series Print ISSN 2192-8533
  • Series Online ISSN 2192-8541
  • About this book