• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology

Article

Cohesion index for investigate criminal organizations in criminal big databases
  • VIEWS 803
  • PDF 154

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v15i16.2042

Year: 2022, Volume: 15, Issue: 16, Pages: 750-759

Original Article

Cohesion index for investigate criminal organizations in criminal big databases

Humberto Troncoso Espinosa Fredy1*

1Department of Industrial Engineering, Faculty of Engineering, Universidad del BíoBío, Concepción, 4030000, Chile

*Corresponding Author
Email: [email protected]

Received Date:10 November 2020, Accepted Date:23 March 2022, Published Date:02 May 2022

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Background: The disarticulation of criminal organizations reduces the rates of victimization and insecurity in the population. One of the main characteristics of a criminal organization is the cohesion among its members. This investigation proposes a cohesion index that allows identifying quickly in Big databases individuals suspect to belong to criminal organizations and focus investigation resources on them. Methods: To obtain the index, we analyze the information provided by the Criminal Analysis and Investigative Focus System from the Regional Public Prosecutor’s Office in Biobío and the Investigation Police of Chile. Findings: The results show that the suspects with a higher cohesion index and a higher number of criminal cases commit crimes associated with criminal organizations.

Keywords: Criminal Cohesion; Crimes Against Property; Criminal Organization; Criminal Databases

References

  5. Kramer RC. State-Organized Crime, International Law and Structural Contradictions. Critical Criminology. 2016;24(2):231–245. Available from: https://doi.org/10.1007/s10612-015-9306-3
  6. Leverso J, Matsueda RL. Gang Organization and Gang Identity: An Investigation of Enduring Gang Membership. Journal of Quantitative Criminology. 2019;35(4):797–829. Available from: https://dx.doi.org/10.1007/s10940-019-09408-x
  7. Papachristos AV. The Importance of Cohesion for Gang Research, Policy, and Practice. Criminology & Public Policy. 2013;12(1):49–58. Available from: https://dx.doi.org/10.1111/1745-9133.12006
  8. Bruinsma G, Bernasco W. Criminal groups and transnational illegal markets. Crime, Law and Social Change. 2004;41:79–94. Available from: https://dx.doi.org/10.1023/b:cris.0000015283.13923.aa
  9. Xu J, Marshall B, Kaza S, Chen H. Analyzing and Visualizing Criminal Network Dynamics: A Case Study. Intelligence and Security Informatics. 2004;3073:359–377. Available from: https://doi.org/10.1007/978-3-540-25952-7_27
  10. Cressey DR. Criminal organization: Its elementary forms. Newyork. Harper & Row. 1972.
  11. Buneci B. Resilience of Criminal Organizations. SCIENTIA MORALITAS-International Journal of Multidisciplinary Research. 2021;6(1):113–118. doi: 10.5281/zenodo.5136361
  12. Oatley GC. Themes in data mining, big data, and crime analytics. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2021;p. e1432. Available from: https://doi.org/10.1002/widm.1432
  13. Hassani H, Huang X, Silva ES, Ghodsi M. A review of data mining applications in crime. Statistical Analysis and Data Mining: The ASA Data Science Journal. 2016;9:139–154. Available from: https://dx.doi.org/10.1002/sam.11312
  14. Espinosa FHT. Prediction of Recidivism in Thefts and Burglaries Using Machine Learning. Indian Journal of Science and Technology. 2020;13(06):696–711. Available from: https://dx.doi.org/10.17485/ijst/2020/v13i06/149853
  15. Chen H, Chung W, Xu JJ, Wang G, Qin Y, Chau M. Crime data mining: a general framework and some examples. Computer. 2004;37(4):50–56. Available from: https://dx.doi.org/10.1109/mc.2004.1297301
  16. Pourhabibi T, Ong KL, Kam BH, Boo YL. DarkNetExplorer (DNE): Exploring dark multi-layer networks beyond the resolution limit. Decision Support Systems. 2021;146:113537. Available from: https://dx.doi.org/10.1016/j.dss.2021.113537
  17. Troncoso F, Weber R. A novel approach to detect associations in criminal networks. Decision Support Systems. 2020;128:113159. Available from: https://dx.doi.org/10.1016/j.dss.2019.113159
  18. Ficara A, Cavallaro L, Curreri F, Fiumara G, Meo PD, Bagdasar O, et al. Criminal networks analysis in missing data scenarios through graph distances. PLOS ONE. 2021;16(8):e0255067. Available from: https://dx.doi.org/10.1371/journal.pone.0255067
  19. Sarvari H, Abozinadah E, Mbaziira A, Mccoy D. Constructing and Analyzing Criminal Networks. In: 2014 IEEE Security and Privacy Workshops. IEEE. 2014.
  20. Wijeratne S, Balasuriya L, Doran D, Sheth A. Word embeddings to enhance twitter gang member profile identification. IJCAI Workshop on Semantic Machine Learning. 2016;p. 18–24. Available from: https://arxiv.org/abs/1610.08597
  21. Ferrara E, Meo PD, Catanese S, Fiumara G. Detecting criminal organizations in mobile phone networks. Expert Systems with Applications. 2014;41(13):5733–5750. Available from: https://dx.doi.org/10.1016/j.eswa.2014.03.024
  23. Catanese S, Ferrara E, Fiumara G. Forensic analysis of phone call networks. Social Network Analysis and Mining. 2013;3:15–33. Available from: https://dx.doi.org/10.1007/s13278-012-0060-1
  25. Benson JS, Decker SH. The organizational structure of international drug smuggling. Journal of Criminal Justice. 2010;38(2):130–138. Available from: https://dx.doi.org/10.1016/j.jcrimjus.2010.01.001
  26. Giménez-Salinas A, RL, L DELC. Existe un perfil de delincuente organizado? Exploración a partir de una muestra española. Revista Electrónica de Ciencia . 2011;13:1–32. Available from: https://dialnet.unirioja.es/servlet/articulo?codigo=3671727
  27. Hashimi S, Bouchard M, Morselli C, Ouellet M. A method to detect criminal organizations from police data. Methodological Innovations. 2016;9:205979911562274. Available from: https://dx.doi.org/10.1177/2059799115622749
  28. Carrington PJ, Mastrigt SBv. Co-offending in Canada, England and the United States: a cross-national comparison. Global Crime. 2013;14(2-3):123–140. Available from: https://dx.doi.org/10.1080/17440572.2013.787926
  29. Mastrigt SBv, Farrington DP. Co-Offending, Age, Gender and Crime Type: Implications for Criminal Justice Policy. British Journal of Criminology. 2009;49(4):552–573. Available from: https://dx.doi.org/10.1093/bjc/azp021
  31. Framis AGS, Ibáñez LDlC, Espada LR, Espinosa MdJ. La medición y evaluación de la criminalidad organizada en España: ¿Misión Imposible? Revista Española de Investigación Criminológica. 2009;7(9):1–28. Available from: https://dx.doi.org/10.46381/reic.v7i0.41
  32. Al-Chalabi HS, Lundberg J, Wijaya A, Ghodrati B. Downtime analysis of drilling machines and suggestions for improvements. Journal of Quality in Maintenance Engineering. 2014;20(4):306–332. Available from: https://dx.doi.org/10.1108/jqme-11-2012-0038
  33. Pascual R, Castillo GD, Louit D, Knights P. Business-oriented prioritization: A novel graphical technique. Reliability Engineering & System Safety. 2009;94:1308–1313. Available from: https://dx.doi.org/10.1016/j.ress.2009.01.013
  34. Serrano J, Organizado JC. Una aproximación al fenómeno. Gaceta internacional de ciencias forenses. 2015;p. 23–30. Available from: https://dialnet.unirioja.es/servlet/articulo?codigo=4962237

Copyright

© 2022 Fredy. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Published By Indian Society for Education and Environment (iSee)

  • 03 May 2022

Year: 2022, Volume: 15, Issue: 16

Article Metrics


Post Graduate Fellowship in Research Communication

More articles

DON'T MISS OUT!

Subscribe now for latest articles and news.