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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
  2. Altmetric Badge
    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 10: Operation Golf: A Human Trafficking Case Study
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Readers on

mendeley
4 Mendeley
Summary
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Chapter title
Operation Golf: A Human Trafficking Case Study
Chapter number 10
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_10
Book ISBNs
978-3-31-952702-4, 978-3-31-952703-1
Authors

Bernie Gravett

Editors

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

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 50%
Student > Bachelor 1 25%
Student > Ph. D. Student 1 25%
Readers by discipline Count As %
Computer Science 2 50%
Social Sciences 1 25%
Unknown 1 25%

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