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Using Open Data to Detect Organized Crime Threats

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Cover of 'Using Open Data to Detect Organized Crime Threats'

Table of Contents

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    Book Overview
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    Chapter 1 Organised Crime as a Framework Concept
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    Chapter 2 Measuring Organised Crime: Complexities of the Quantitative and Factorial Analysis
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    Chapter 3 Scanning of Open Data for Detection of Emerging Organized Crime Threats—The ePOOLICE Project
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    Chapter 4 Foresight and the Future of Crime: Advancing Environmental Scanning Approaches
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    Chapter 5 Big Data—Fighting Organized Crime Threats While Preserving Privacy
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    Chapter 6 Horizon Scanning for Law Enforcement Agencies: Identifying Factors Driving the Future of Organized Crime
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    Chapter 7 Macro-environmental Factors Driving Organised Crime
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    Chapter 8 Extracting Future Crime Indicators from Social Media
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    Chapter 9 Organised Crime, Wild Cards and Dystopias
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    Chapter 10 Operation Golf: A Human Trafficking Case Study
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    Chapter 11 Radicalization in a Regional Context: Future Perspective on Possible Terrorist Threats and Radicalization
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    Chapter 12 Why Do Links Between Terrorism and Crime Increase?
Attention for Chapter 11: Radicalization in a Regional Context: Future Perspective on Possible Terrorist Threats and Radicalization
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Chapter title
Radicalization in a Regional Context: Future Perspective on Possible Terrorist Threats and Radicalization
Chapter number 11
Book title
Using Open Data to Detect Organized Crime Threats
Published by
Springer International Publishing, April 2017
DOI 10.1007/978-3-319-52703-1_11
Book ISBNs
978-3-31-952702-4, 978-3-31-952703-1
Authors

Holger Nitsch

Editors

Henrik Legind Larsen, José María Blanco, Raquel Pastor Pastor, Ronald R. Yager

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 1 50%
Researcher 1 50%
Student > Postgraduate 1 50%
Readers by discipline Count As %
Computer Science 1 50%
Social Sciences 1 50%

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