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Tseloni, Andromachi; Pease, Ken - Using Modeling to Predict and Prevent Victimization - 9783319031842 - V9783319031842
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Using Modeling to Predict and Prevent Victimization

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Description for Using Modeling to Predict and Prevent Victimization Paperback. Series: SpringerBriefs in Criminology. Num Pages: 88 pages, black & white illustrations. BIC Classification: JHBC; JKV. Category: (P) Professional & Vocational. Dimension: 234 x 156 x 4. Weight in Grams: 150.
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 ... Read more

Product Details

Format
Paperback
Publication date
2014
Publisher
Springer International Publishing AG Switzerland
Number of pages
88
Condition
New
Series
SpringerBriefs in Criminology
Number of Pages
80
Place of Publication
Cham, Switzerland
ISBN
9783319031842
SKU
V9783319031842
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15

About Tseloni, Andromachi; Pease, Ken
Professor Andromachi Tseloni teaches quantitative methodology for applied social research to undergraduate and especially graduate students. She is currently responsible for research generation and mentoring in the Division and contributes to the Research Committees of the Division and the School. She serves on the Editorial Board of Journal of Quantitative Criminology. Apart from single – dependent variable (preferably hierarchical) modeling ... Read more

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