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Static and Dynamic Approaches of a Drug Trafficking Network

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Crime Prevention in the 21st Century

Abstract

This study aims to highlight the advantages of social network analysis (SNA) in the analysis of organisations in any illicit market related to organised crime. Using a cocaine trafficking network investigated in Spain as a sample, we explore the possibilities of analysing the network in both a static and dynamic context. Organisational dimensions such as structure, main players and subgroups are analysed through the static approach. With regard to its individual dimension, we analyse certain individual attributes of group members together with their position in the network. Finally, the network’s dynamic behaviour, in one of its simplest expressions, unearths the advantages of using SNA as a tool to predict the most relevant players at the start of an investigation and to design ways to neutralise and dismantle the network.

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Notes

  1. 1.

    Spanish police reports include ample data of each investigation and the characteristics of each organisation and its members. In particular, each police report contains the techniques used by the agents in the criminal investigation: entry and search, telecommunications interception, asset seizure, photographic reconnaissance, interrogation, shadowing, etc. During the data collecting and analyses process, the identity of each subject under investigation was anonymous, thus their right to privacy was safeguarded.

  2. 2.

    Borgatti, S.P., Evertt, M. G. & Freeeman, L.C. (<CitationRef CitationID="CR12" >2002</Citation Ref>).

  3. 3.

    These measures are not the same as those provided in Gimenez-Salinas (<CitationRef CitationID="CR12" >2014</Citation Ref>) because the members included in both networks are different. This article only includes the members that collaborate with the organisation on a permanent basis.

  4. 4.

    The police reports show that JPPM is the retail group leader, SNRM is the wholesaler group leader and JPPM is the importer group leader.

  5. 5.

    The method used is a core peripheral analysis that predicts the degree of coreness or closeness to each actor’s core. This method exposes two types of nodes, namely a cohesive subgraph (the core) in which actors are connected to each other in some maximal sense, and a class of player that is loosely connected to the cohesive subgraph by a lack of maximal cohesion with the core (Borgatti & Evertt, <CitationRef CitationID="CR12" >1999</Citation Ref>). Actors in the core can coordinate their actions and those in the periphery cannot.

  6. 6.

    This category was created after checking the police files and comparing the information in them with the information about their role in the organisation. Leadership includes group leaders according to police interpretation and role information; coordination includes secondary positions in the organisation and management roles; and operation includes all other roles: transportation, debt collection, etc.

  7. 7.

    Snijders et al. (<CitationRef CitationID="CR47" >2008</Citation Ref>).

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Acknowledgements

We would like to thank the judicial police unit of the Spanish Guardia Civil for giving us access to data. We are also very grateful to all those who reviewed this chapter for their comments and valuable suggestions.

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Authors and Affiliations

  1. Instituto de Ciencias Forenses y de la Seguridad, Universidad Autonoma de Madrid, Madrid, Spain

    Andrea Giménez-Salinas Framis & Sonia Fernández Regadera

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  1. Andrea Giménez-Salinas Framis
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  2. Sonia Fernández Regadera
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Correspondence to Andrea Giménez-Salinas Framis .

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Editors and Affiliations

  1. School of Criminology and Criminal Justice, Griffith University, Brisbane, Queensland, Australia

    Benoit LeClerc

  2. Università Cattolica del Sacro Cuore-Transcrime, Milano, Italy

    Ernesto U. Savona

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Framis, A.GS., Regadera, S.F. (2017). Static and Dynamic Approaches of a Drug Trafficking Network. In: LeClerc, B., Savona, E. (eds) Crime Prevention in the 21st Century. Springer, Cham. https://doi.org/10.1007/978-3-319-27793-6_13

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  • DOI: https://doi.org/10.1007/978-3-319-27793-6_13

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