Advertisement

Understanding Crime with Computational Topology

  • Patricia L. Brantingham
  • Paul J. Brantingham

Abstract

Environmental criminology began as a novel addition to criminology in the 1970s by calling for a shift in focus from offenders exclusively to the multidisciplinary exploration of criminal events. This involved the study and analysis of crimes, crime sequences, clusters of crimes, and the patterns yielded by them. This analysis always considered people (offenders, victims, and observers or guardians), locations where crimes occurred (convergence settings, crime niches, crime attractors, and more generally people attractors), and how people moved about between locations (home, daily activity nodes, and occasional trip end points). The mix of people, places, situations, attractions, and routines helps shape crime.

Keywords

Organize Crime Routine Activity Silk Road Criminological Theory Routine Activity Theory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alexander, C. (1965a). “A City is Not a Tree, Part I.” Architectural Forum 122 (No.1, April):58–62.Google Scholar
  2. Alexander, C. (1965b). “A City is Not a Tree, Part II.” Architectural Forum 122 (No.2, May):58–62.Google Scholar
  3. Andresen, M. A., & Felson, M. (2010). The impact of co-offending. British Journal of Criminology, 50(1), 00–81.CrossRefGoogle Scholar
  4. Anonymous. (2012). Europe establishes cybercrime fighting unit. Information Management Journal, 40 (S), 10.Google Scholar
  5. Bichler, G., Christie-Merrall, J., & Sechrest, D. (2011). Examining juvenile delinquency within activity space: Building a context for offender travel patterns. Journal of Research in Crime and Delinquency 4S, 472–506.CrossRefGoogle Scholar
  6. Bichler, G., Malm, A., & Enriquez, J. (2010). Magnetic facilities: Identifying the convergence settings of juvenile delinquents. Crime & Delinquency, 0011128710382349.Google Scholar
  7. Brantingham, P. L. (2011). Computational criminology. 2011 European Intelligence and Security Informatics Conference, Athens, Greece.Google Scholar
  8. Brantingham, P. L., & Brantingham, P. J. (1975). Residential burglary and urban form. Urban Studies, 12, 273–284.CrossRefGoogle Scholar
  9. Brantingham, P. L., & Brantingham, P. J. (1978a). Notes on the geometry of crime. Specialized Criminological Topics Symposium, International Sociological Association, Stockholm.Google Scholar
  10. Brantingham, P. L., & Brantingham, P. J. (1978b). Chicago sociology and the geometry of crime. Dallas: American Society of Criminology.Google Scholar
  11. Brantingham, P. L., Brantingham, P. J., & Fister, R. L. (1979). Mental maps of crime in a Canadian city. Cincinnati: Academy of Criminal Justice Sciences.Google Scholar
  12. Brantingham, P. L., & Brantingham, P. J. (1980a). Mental maps, simplicial complexes, and crime. Oklahoma City: Academy of Criminal Justice Sciences.Google Scholar
  13. Brantingham, P. L., & Brantingham, P. J. (1980b). Mobility, notoriety, and crime: A study in the crime patterns of urban nodal points. San Francisco: American Society of Criminology.Google Scholar
  14. Brantingham, P. L., & Brantingham, P. J. (1981). Notes on the geometry of crime. In P. J. Brantingham & P. L. Brantingham (Eds.), Environmental criminology. Beverly Hills, CA: Sage Publications, 27–54.Google Scholar
  15. Brantingham, P. J., & Brantingham, P. L. (Eds.) (1981). Environmental criminology. Beverly Hills, CA: Sage Publications.Google Scholar
  16. Brantingham, P. J., & Brantingham, P. L. (1984). Patterns in crime. New York: Macmillan Publishing Company.Google Scholar
  17. Brantingham, P. L., & Brantingham, P. J. (1993). Environment, routine and situation: Toward a pattern theory of crime. Advances in Criminological Theory, 5, 259–294.Google Scholar
  18. Brantingham, P. L., Brantingham, P. J., & Verma, A. (1992). Crime analysis through point set and algebraic topology. New Orleans: American Society of Criminology.Google Scholar
  19. Brantingham, P. L., Brantingham, P. J., Vajihollahi, M., & Wuschke, K. (2009). A topological technique for crime analysis at multiple scales of aggregation. In D. Weisburd, W. Bernasco, & G. Bruinsma (Eds.), Putting crime in its Place: Units of analysis in spatial crime research. New York, NY: Springer-Verlag, 87–107.CrossRefGoogle Scholar
  20. Brantingham, P. J., & Brantingham, P.L. (2013). The theory of target search. In F. Cullen & P. Wilcox (Eds.), The Oxford handbook of criminological theory. New York, NY: Oxford University Press, 535–553.Google Scholar
  21. Brenner, S. W. (2006). Cybercrime jurisdiction. Crime, Law and Social Change, 46, 189–206.CrossRefGoogle Scholar
  22. Brunswik, E. (1939). The conceptual focus of some psychological systems. Journal of Unified Science (Erkenntnis), 8, 36–49.Google Scholar
  23. Brunswik, E. (1952). The conceptual framework of psychology. (International Encyclopedia of Unified Science, Volume 1, Number 10.) Chicago, IL: University of Chicago Press.Google Scholar
  24. Bryant, R., & Bryant, S. (2014). Policing digital crime. Aldershot, UK: Ashgate, 209.Google Scholar
  25. Calderoni, F. (2014). Identifying mafia bosses from meeting attendance. In A. J. Masys (Ed.), Networks and network analysis for defence and security. New York, NY: Springer, 27–48.CrossRefGoogle Scholar
  26. Carlsson, G. (2009). Topology and data. Bulletin of the American Mathematical Society, 46, 255–308.CrossRefGoogle Scholar
  27. Cerri, A., Fabio, B. D., Ferri, M., Frosini, P., & Landi, C. (2013). Betti numbers in multidimensional persistent homology are stable functions. Mathematical Methods in the Applied Sciences, 36, 1543–1557.CrossRefGoogle Scholar
  28. Chen, H., Chung, W., Qin, J., Reid, E., Sageman, M., & Weimann, G. (2008). Uncovering the dark Web: A case study of Jihad on the Web. Journal of the American Society for Information Science and Technology, 59, 1347–1359.CrossRefGoogle Scholar
  29. Chen, H. (2012). Dark web. New York, NY: Springer.CrossRefGoogle Scholar
  30. Cohen, L. E., & Felson, M. (1979). Social change and crime rate trends: A routine activity approach. American Sociological Review, 44, 588–608.CrossRefGoogle Scholar
  31. Doxiadis, C. A. (1970). Ekistics, the science of human settlements. Science, 170, 393–404.CrossRefGoogle Scholar
  32. Erb, K. P. (2014). The end of the (Silk) Road for BitCoin as IRS, Fed Agencies make arrests. Forbes on-line 02/01/2014. Accessed 20 April 2014.Google Scholar
  33. Everett, C. (2009). Moving across to the dark side. Network Security, 9, 10–12.CrossRefGoogle Scholar
  34. Felson, M. (1986). Predicting crime potential at any point on the city map. In R. M. Figlio, S. Hakim and G.F. Rengert, Metropolitan Crime Patterns. Monsey, New York: Criminal Justice Press, 127–136.Google Scholar
  35. Felson, M. (1987). Routine activities and crime prevention in the developing metropolis. Criminology, 25, 911–931.CrossRefGoogle Scholar
  36. Felson, M. (1994) Crime and everyday life: Insight and implications for society. Thousand Oaks, CA: Pine Forge Press.Google Scholar
  37. Felson, M. (2003). The process of co-offending. Crime Prevention Studies, 16, 149–168.Google Scholar
  38. Felson, M. (2006a). Crime and nature. Thousand Oaks, CA: Sage Publications.Google Scholar
  39. Felson, M. (2006b). The ecosystem for organized crime. Helsinki: European Institute for Crime Prevention and Control, affiliated with the United Nations (HEUNI).Google Scholar
  40. Felson, M. (2008). Routine activity approach. In R. Wortley & L. Mazerolle (Eds.), Environmental criminology and crime analysis. London: Willan Publishing, 70–77.Google Scholar
  41. Felson, M., & Boba, R. L. (2010). Crime and everyday life, 4th ed. Thousand Oaks, CA: Sage.CrossRefGoogle Scholar
  42. Figlio, S. Hakim, & G. F. Rengert (Eds.), Metropolitan crime patterns. Monsey, NY: Criminal Justice Press.Google Scholar
  43. Goff, M. (2011). External betti numbers of Vietoris-Rips complexes. Discrete & Computational Geometry, 46, 132–155.CrossRefGoogle Scholar
  44. Gwern.net (2014) Silk road theory and practice. http://gwerm.net/Silk%20Road. Accessed 28 April 2014.Google Scholar
  45. Holt, T. J., & Bossler. A. M. (2009). Examining the applicability of lifestyle-routine activities theory for cybercrime victimization. Deviant Behavior, 30, 1–25.CrossRefGoogle Scholar
  46. Home Office. (2013). Crime against businesses: Headline findings from the 2012 Commercial Victimisation Survey. London: National Statistics Office.Google Scholar
  47. Internet World Stats: Usage and Population Statistics (2014). http://internetworldstats.com/marketing. Accessed 04 March 2014.
  48. Johnson, S. D., Bowers, K. J., Birks, D. J., & Pease, K. (2009). Predictive mapping of crime by ProMap: Accuracy, units of analysis, and the environmental backcloth. In D. Weisburd, W. Bernasco, & G. Bruinsma (Eds.), Putting crime in its place: Units of analysis in spatial crime research. New York, NY: Springer-Verlag, pp. 171–198.CrossRefGoogle Scholar
  49. Kigerl, A. (2012). Routine activity theory and the determinants of high cybercrime countries. Social Science Computer Review, 30, 470–486.CrossRefGoogle Scholar
  50. Lowe, J. C., & Moryadas, S. (1975). The geography of movement. Boston, MA: Houghton Mifflin.Google Scholar
  51. Martin, J. (2013). Lost on the Silk Road: Online drug distribution and the ‘cryptomarket’. Criminology and Criminal Justice, 1748895813505234.Google Scholar
  52. Masys, A.J. (2014). Networks and network analysis for defence and security. New York, NY: Springer.CrossRefGoogle Scholar
  53. McGuire, M. (2007). Hypercrime: The new geometry of harm. New York, NY: Routledge-Cavendish.Google Scholar
  54. Mohler, G. O., Short, M. B., Brantingham, P. J. II, Schoenberg, F. P., & Tita, G. E. (2011). Self-exciting point process modeling of crime. Journal of the American Statistical Association, 106, 100–108.CrossRefGoogle Scholar
  55. Perreault, S. (2013). Police-reported crime statistics in Canada, 2012. Ottawa, ON: Statistics Canada. Statistics Canada catalogue no. 85-002-x.Google Scholar
  56. Ratcliffe, J. H. (2002). Aoristic signatures and the spatio-temporal analysis of high volume crime patterns. Journal of Quantitative Criminology, 18, 23–43.CrossRefGoogle Scholar
  57. Ratcliffe, J. H. (2006). A temporal constraint theory to explain opportunity-based spatial offending patterns. Journal of Research in Crime and Delinquency, 43, 261–291.CrossRefGoogle Scholar
  58. Rengert, G. F., Lockwood, B., & McCord, E. S. (2012). The edge of the community: Drug dealing in a segregated environment. In M. A. Andresen & J. B. Kinney (Eds.), Patterns, prevention, and geometry of crime. London and New York: Routledge.Google Scholar
  59. Reyns, B. W., Henson, B., & Fisher, B. S. (2011). Being pursued online: Applying cyberlifestyle-routine activities theory to cyberstalking victimization. Criminal Justice and Behavior, 38, 1149–1169.CrossRefGoogle Scholar
  60. Rossmo, D. K. (1999). Geographic profiling. Boca Raton, FL: CRC press.CrossRefGoogle Scholar
  61. Rossmo, D. K., Lu, Y., & Fang, T. B. (2012). Spatial-temporal crime paths. In M. A. Andresen & J. B. Kinney (Eds.), Patterns, prevention and geometry of crime. London and New York: Routledge, pp. 16–42.Google Scholar
  62. Song, J., Spicer, V., & Brantingham, P.L. (2013). The edge effect: Exploring high crime zones near residential neighborhoods. Proceedings 2013 IEEE International Conference on Intelligence and Security Informatics. 2013, 245–250.CrossRefGoogle Scholar
  63. Soudijn, M. R. J., & Zegers, B. C. H. T. (2012). Cybercrime and virtual offender convergence settings. Trends in Organized Crime, 15, 111–129.CrossRefGoogle Scholar
  64. UK Fights Cybercrime. (2012). Computer Fraud & Security 2012, 2 February, 1, 3.Google Scholar
  65. van Vliet, W. (1983). Exploring the fourth environment: An examination of the home range of city and suburban teenagers. Environment and Behavior, 15, 567–588.CrossRefGoogle Scholar
  66. Wiles, P., & Costello, A. (2000). The “road to nowhere”: The evidence for travelling criminals. London: Research, Development and Statistics Directorate, Home Office. Home Office Research Study 207.Google Scholar
  67. Xu, J., & Chen, H. (2008). The topology of dark networks. Communications of the ACM, 51, 58–65.CrossRefGoogle Scholar
  68. Yar, M. (2005). The novelty of “cybercrime”: An assessment in light of routine activity theory. European Journal of Criminology, 2, 407–427.CrossRefGoogle Scholar
  69. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.CrossRefGoogle Scholar

Copyright information

© Patricia L. Brantingham and Paul J. Brantingham 2015

Authors and Affiliations

  • Patricia L. Brantingham
  • Paul J. Brantingham

There are no affiliations available

Personalised recommendations