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Foresight and the Future of Crime: Advancing Environmental Scanning Approaches

Abstract

This chapter introduces the environmental scanning and Early Warning Systems (EWS) framework for crime detection and prevention in a predictive policing context using foresight methodologies such as weak signals , facilitating factors, horizon scanning, and leading indicators. The philosophy of the environmental scanning system is based on the principle that crime is a business and thus can be analysed in much the same way as a business. As such, both PESTLE and SWOT analyses can be part of the assessment of Organised Crime Group (OCG) activities. For law enforcement to combat crime, an efficient and effective environmental scanning system that utilises strategic early warning is necessary. The strategic early warning system takes into account the STARC criteria (Specificity, Timeliness, Accuracy, Relevance and Clarity), acknowledging bias in the system and ensuring that the number of false alarms stay at a minimum. This chapter consists of three parts. First, the analysis begins by exploring the ‘environmental scanning’ dimensions of foresight, relating to the future of crime. Second, the analysis continues by examining a series of models relevant to undertaking environmental scanning. Third, the chapter finishes with some overall conclusions, which bring together several of the foresight and future crime -related insights examined throughout.

Keywords

  • Environmental scanning methodology
  • Early warning
  • Future crime
  • Foresight
  • Predictive policing weak signals
  • Horizon scanning

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Notes

  1. 1.

    Publicly available details of the project can be found via its website of www.epoolice.eu.

  2. 2.

    EU Commission (2014).

  3. 3.

    See, for instance, Dixon (2016) see also Andrew (2004); Svendsen (2012a, b), p. 103.

  4. 4.

    See as characterised in Svendsen (2015a, b), p. 58.

  5. 5.

    Ratcliffe (2016 [2ed.]), p. 64.

  6. 6.

    Ibid., p. 3.

  7. 7.

    Perry et al. (2013) for further recent insights see also discussion in Ratcliffe (2016), p. 220, col. 1.

  8. 8.

    While much work can be cited here, this literature notably includes, e.g., Ansoff (1975), pp. 21–33; Martinet (2010), pp. 1485–1487; Schoemaker et al. (2013), pp. 815–824; Lockwood (2013); see also the fifty-five structured analytic techniques as detailed in Heuer and Pherson (2014 [2ed.]).

  9. 9.

    Ansoff (1975), pp. 21–33.

  10. 10.

    Ibid.

  11. 11.

    Ibid.

  12. 12.

    Ibid., p. 23.

  13. 13.

    See for recent valuable insights, e.g., Silver (2012).

  14. 14.

    Ansoff (1975) pp. 21–33.

  15. 15.

    See also, e.g., Svendsen (2012a), p. 143, 150; see also references to ‘soft law’ and ‘low levels of legalisation’ concepts in sources, e.g., Jojarth (2009), esp. p. 14 and 272; see also ‘2.8—Soft law’ in Dixon (2013 [7ed.]), p. 52.

  16. 16.

    See these criteria as given in Hall and Citrenbaum (2012), p. 81.

  17. 17.

    Perry et al. (2013).

  18. 18.

    Ratcliffe (2016), p. 41.

  19. 19.

    Europol (2013) see also Edwards (2013).

  20. 20.

    Criminal Intelligence Service Canada (2007). Another valuable document worth citing here is: Quarmby (2003).

  21. 21.

    See also, e.g., Svendsen (2012b).

  22. 22.

    Criminal Intelligence Service Canada (2007). p. 6.

  23. 23.

    Ibid., p. 8.

  24. 24.

    ‘Ibid., p. 11.

  25. 25.

    See, e.g., EU Council (2012), p. 10.

  26. 26.

    Criminal Intelligence Service Canada (2007), p. 12.

  27. 27.

    Silver (2012); see also as discussed earlier in this chapter.

  28. 28.

    Criminal Intelligence Service Canada (2007), p. 13.

  29. 29.

    Ibid., p. 14.

  30. 30.

    Ibid., p. 18.

  31. 31.

    Ibid., pp. 20–21; see also further discussion of the ‘traffic-light’ system and its use, below.

  32. 32.

    Criminal Intelligence Service Canada (2007), p. 19.

  33. 33.

    Ibid., p. 7.

  34. 34.

    Criminal Intelligence Ibid., p. 7.

  35. 35.

    ‘Ibid., pp. 7–8.

  36. 36.

    Ibid., p. 22.

  37. 37.

    ‘Ibid., p. 22.

  38. 38.

    Ibid., p. 23.

  39. 39.

    Svendsen (2015a), p. 59.

  40. 40.

    Mayer-Schönberger and Cukier (2013), p. 14.

  41. 41.

    See, e.g., Clark (2012).

  42. 42.

    Ibid., p. 13.

  43. 43.

    See also Symon and Tarapore (2015), pp. 4–11; Meserole (2016).

  44. 44.

    See, e.g., as discussed in detail in Svendsen (2015a), pp. 58–73, and Svendsen (2015b), pp. 105–123.

  45. 45.

    Gaub (2014), p. 82; see also insights and discussions in Gray (2014).

  46. 46.

    See also insights from (UK HMG 2013).

  47. 47.

    Klerks and Kop (2008), p. 20.

  48. 48.

    See also insights in Svendsen (2015b), pp. 105–123.

  49. 49.

    For example, see Danmarks Radio (DR) News (14 April 2014); DR News (10 April 2014); DR News (14 March 2014).

  50. 50.

    Albanese (2011).

  51. 51.

    Perry et al. (2013), p. 97.

  52. 52.

    Ibid.

  53. 53.

    Perry et al. (2013), p. 97.

  54. 54.

    See, for instance, relating to the ‘lack of opportunity’, Ratcliffe (2016), p. 163.

  55. 55.

    Ratcliffe (2016), p. 15.

  56. 56.

    See for these sorts of issues and challenges, e.g., I.R. Porche, III (2014), insights as discussed throughout Clark (2012).

  57. 57.

    See these criteria in Hall and Citrenbaum, Intelligence Collection (USA: Praeger Security International—PSI 2012), p. 81.

  58. 58.

    Perry et al. (2013).

  59. 59.

    Gaub (2014), p. 81.

  60. 60.

    Dupont and Reckmeyer (2012), p. 46.

  61. 61.

    See, e.g., inter alia, Grabo (2004); Habegger (2009).

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Kruse, M., Svendsen, A.D.M. (2017). Foresight and the Future of Crime: Advancing Environmental Scanning Approaches. In: Larsen, H., Blanco, J., Pastor Pastor, R., Yager, R. (eds) Using Open Data to Detect Organized Crime Threats. Springer, Cham. https://doi.org/10.1007/978-3-319-52703-1_4

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