Skip to main content

Social Network Analysis in Predictive Policing

  • Chapter
  • First Online:
Social Network Analysis in Predictive Policing

Part of the book series: Lecture Notes in Social Networks ((LNSN))

Abstract

Police departments have long used crime data analysis to assess the past, but the recent advances in the field of data science have introduced a new paradigm, called predictive policing which aims to predict the future. Predictive policing as a multidisciplinary approach brings together data mining and criminological theories which leads to crime reduction and prevention. Predictive policing is based on the idea that while some crime is random, the majority of it is not. In predictive policing crime patterns are learnt from historical data to predict future crimes.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. R. Boba, Crime Analysis and Crime Mapping (Sage, Thousand Oaks, 2013)

    Google Scholar 

  2. U. Brandes, T. Erlebach, Network Analysis: Methodological Foundations. Lecture Notes in Computer Science/Theoretical Computer Science and General Issues (Springer, Heidelberg, 2005)

    Google Scholar 

  3. P.L. Brantingham, M. Ester, R. Frank, U. Glässer, M.A. Tayebi, Co-offending network mining, in Counterterrorism and Open Source Intelligence, ed. by U.K. Wiil (Springer, Vienna, 2011), pp. 73–102

    Chapter  Google Scholar 

  4. M. Carlo, Inside Criminal Networks (Springer, New York, 2009)

    Google Scholar 

  5. D. Chakrabarti, C. Faloutsos, Graph mining: laws, generators, and algorithms. ACM Comput. Surv. 38 (1), Article 2 (2006)

    Google Scholar 

  6. J. Chen, O.R. Zaïane, R. Goebel, Detecting communities in social networks using max-min modularity, in Proceedings of SIAM International Conference on Data Mining (SDM’09) (2009), pp. 978–989

    Google Scholar 

  7. L.C. Freeman. Visualizing social networks. J. Soc. Struct. 1 (1), 4 (2000)

    Google Scholar 

  8. U. Glässer, M.A. Taybei, P.L. Brantingham, P.J. Brantingham, Estimating possible criminal organizations from co-offending data. Public Safety Canada (2012)

    Google Scholar 

  9. J. Han, M. Kamber, Data Mining: Concepts and Techniques (Morgan Kaufmann, San Francisco, 2006)

    MATH  Google Scholar 

  10. R.V. Hauck, H. Atabakhsh, P. Ongvasith, H. Gupta, H. Chen, Using Coplink to analyze criminal-justice data. Computer 35 (3), 30–37 (2002)

    Article  Google Scholar 

  11. S. Kaza, H. Chen, Effect of inventor status on intra-organizational innovation evolution, in Proceedings of the 42nd Hawaii International Conference on System Sciences (HICSS’09) (2009), pp. 1–10

    Google Scholar 

  12. D. Kempe, J. Kleinberg, É. Tardos, Influential nodes in a diffusion model for social networks. Autom. Lang. Program. 3580, 1127–1138 (2005)

    MathSciNet  MATH  Google Scholar 

  13. D. Liben-Nowell, J. Kleinberg, The link prediction problem for social networks, in Proceedings of the 12st ACM international conference on Information and knowledge management (CIKM’03) (2003), pp. 556–559

    Google Scholar 

  14. J.M. McGloin, A.R. Piquero, On the relationship between co-offending network redundancy and offending versatility. J. Res. Crime Delinq. 47 (1), 63–90 (2009)

    Article  Google Scholar 

  15. J.M. McGloin, C.J. Sullivan, A.R. Piquero, S. Bacon, Investigating the stability of co-offending and co-offenders among a sample of youthful offenders. Criminology 46 (1), 155–188 (2008)

    Article  Google Scholar 

  16. C. Moselli, T. Gabor, J. Kiedrowski, The factors that shape organized crime. Public Safety Canada (2010)

    Google Scholar 

  17. A.J. Reiss Jr., Co-offending and criminal careers. Crime Justice 10, 117–170 (1988)

    Article  Google Scholar 

  18. A.J. Reiss Jr., D.P. Farrington, Advancing knowledge about co-offending: results from a prospective longitudinal survey of london males. J. Crim. Law Criminol. 82, 360–395 (1991)

    Article  Google Scholar 

  19. R.B. Santos, Crime Cnalysis with Crime Mapping (Sage, Thousand Oaks, 2012)

    Google Scholar 

  20. Serious and organized crime (2015). Retrieved from http://www.rcmp-grc.gc.ca/soc-cgco/index-eng.htm

  21. M.N. Smith, P.J.H. King, Incrementally visualising criminal networks, in Proceedings of the Sixth International Conference on Information Visualisation (IV’02) (2002), pp. 76–81

    Google Scholar 

  22. M.A. Tayebi, U. Glässer, Investigating organized crime groups: a social network analysis perspective, in Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM’12) (2012), pp. 565–572

    Google Scholar 

  23. M.A. Tayebi, L. Bakker, U. Glässer, V. Dabbaghian, Locating central actors in co-offending networks, in Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining (ASONAM’11) (2011), pp. 171–179

    Google Scholar 

  24. M.A. Tayebi, R. Frank, U. Glässer, Understanding the link between social and spatial distance in the crime world, in Proceedings of the 20nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS’12) (2012), pp. 550–553

    Google Scholar 

  25. M.A. Tayebi, M. Ester, U. Glässer, P.L. Brantingham, Spatially embedded co-offence prediction using supervised learning, in Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’14) (2014), pp. 1789–1798

    Google Scholar 

  26. C. Uchida, A National Discussion on Predictive Policing: Defining Our Terms and Mapping Successful Implementation Strategies (National Institute of Justice, Washington, 2012)

    Google Scholar 

  27. T.W. Valente, Social Networks and Health: Models, Methods, and Applications (Oxford University Press, Oxford, 2010)

    Book  Google Scholar 

  28. S. Wasserman, K. Faust, Social Network Analysis: Methods and Applications (Cambridge University Press, New York, 1994)

    Book  MATH  Google Scholar 

  29. O.W. Wilson, Police Administration (McGraw-Hill, New York, 1963)

    Google Scholar 

  30. J.J. Xu, H. Chen, Untangling criminal networks: a case study. Intell. Secur. Inform. 2665, 958–958 (2003)

    Google Scholar 

  31. J.J. Xu, H. Chen, CrimeNet explorer: a framework for criminal network knowledge discovery. ACM Trans. Inf. Syst. 23 (2), 201–226 (2005)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Tayebi, M.A., Glässer, U. (2016). Social Network Analysis in Predictive Policing. In: Social Network Analysis in Predictive Policing. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-41492-8_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-41492-8_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41491-1

  • Online ISBN: 978-3-319-41492-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics