Using Open Data to Detect Organized Crime Threats - Factors Driving Future Crime (Hardcover, 1st ed. 2017)

This work provides an innovative look at the use of open data for extracting information to detect and prevent crime, and also explores the link between terrorism and organized crime. In counter-terrorism and other forms of crime prevention, foresight about potential threats is vitally important and this information is increasingly available via electronic data sources such as social media communications. However, the amount and quality of these sources is varied, and researchers and law enforcement need guidance about when and how to extract useful information from them. The emergence of these crime threats, such as communication between organized crime networks and radicalization towards terrorism, is driven by a combination of political, economic, social, technological, legal and environmental factors. The contributions to this volume represent a major step by researchers to systematically collect, filter, interpret, and use the information available. For the purposes of this book, the only data sources used are publicly available sources which can be accessed legally and ethically. This work will be of interest to researchers in criminology and criminal justice, particularly in police science, organized crime, counter-terrorism and crime science. It will also be of interest to those in related fields such as applications of computer science and data mining, public policy, and business intelligence.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Release date:	April 2017
First published:	2017
Editors:	Henrik Legind Larsen • Jose-Maria Blanco • Raquel Pastor Pastor • Ronald R. Yager
Dimensions:	235 x 155 x 18mm (L x W x T)
Format:	Hardcover
Pages:	282
Edition:	1st ed. 2017
ISBN-13:	978-3-319-52702-4
Categories:	Books > Social sciences > Sociology, social studies > Crime & criminology > General Books > Computing & IT > Applications of computing > Databases > Data mining
LSN:	3-319-52702-9
Barcode:	9783319527024

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