Titlebook: Social Network Analysis in Predictive Policing; Concepts, Models and Mohammad A. Tayebi,Uwe Glässer Book 2016 Springer International Publis

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Introduction,economies” (., 2012). Based on the most recent report (Easton et al, ., 2014), the total cost of crime in Canada during 2012 is estimated as $81.5 billion, approximately 5.7 % of national income. Given such whopping costs, crime reduction and prevention strategies have become a top priority for law enforcement agencies.

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2190-5428 mal representation and algorithmic solutions of predictive p This book focuses on applications of social network analysis in predictivepolicing. Data science is used to identify potential criminal activity byanalyzing the relationships between offenders to fully understand criminalcollaboration patt

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Social Network Analysis in Predictive Policing, theories which leads to crime reduction and prevention. Predictive policing is based on the idea that while some crime is random, the majority of it is not. In predictive policing crime patterns are learnt from historical data to predict future crimes.

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Concluding Remarks,blems presented in this work opens the door for researchers in criminology and computer science fields to explore important issues pertaining to public safety, and facilitates more informed and deliberate adoption of predictive policing as a complement for existing policing methods.

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Organized Crime Group Detection,-West side: organizing crime in New York City, 1930–1950. Transaction Publishers, New Brunswick, 1994; van der Heijden, ., 1996; Fijnaut et al, Organized crime in the Netherlands. Kluwer Law International, The Hague, 1998; von Lampe, ., Feb 2015).

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Introduction,d crime mapping. Sage, Thousand Oaks, 2013). Crime imposes a substantial cost on society at individual, community, and national levels (McCollister et al, Drug Alcohol Depend 108(1):98–109, 2010). Criminality worldwide makes trillions of dollars yearly, turning crime into one of the world’s “top 20