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Scientific and Technological Advances in Law Enforcement Intelligence Analysis

  • Randy BorumEmail author
Chapter
Part of the Advanced Sciences and Technologies for Security Applications book series (ASTSA)

Abstract

Powered by advances in computing technology, a range of professions and business enterprises have moved toward a more science-driven approach to operations. Law enforcement has been no exception. In fact, the modern day idea of intelligence-led policing (ILP) emerged in the UK in the 1990s as the country was pushing all government services to operate on more of a data-informed, business process or managerial model. This trend led to the development of a British “National Intelligence Model” (NIM), which by 2002, was formally adopted as policy for law enforcement agencies nationwide. “The NIM followed the government policy of using a business process model to deal with crime control and employed the ILP philosophy to introduce intelligence into virtually all aspects of the policing business plan” (p. 311).

ILP has surged in popularity among US law enforcement agencies, although what exactly ILP means in an operational sense, and how it is implemented varies considerably (Ratcliffe J, Intelligence-led policing. Routledge, New York, 2016). At the most basic level, ILP is commonly defined as “the collection and analysis of information related to crime and conditions that contribute to crime, resulting in actionable intelligence products intended to aid law enforcement in developing tactical responses to threats and/or strategic planning related to emerging or changing threats” (Carter DL, Carter JG, Crim Justice Policy Rev 20(3):310, 2009, p. 317). Although sometimes broadly considered, ILP has become widely used. It is not a “program,” but more of a philosophy and approach for meeting a law enforcement agency’s mission objectives. This chapter will not focus primarily on the programmatic aspects of ILP, but on scientific and technological advances that have enhanced and accelerated the intelligence analysis process for policing applications.

Keywords

Intelligence led policing National intelligence model Law enforcement Data 

References

  1. Ackoff, R. L. (1989). From data to wisdom. Journal of Applied Systems Analysis, 16(1), 3–9.Google Scholar
  2. Akhgar, B., Saathoff, G. B., Arabnia, H. R., Hill, R., Staniforth, A., & Bayerl, P. S. (2015). Application of big data for national security: A practitioner’s guide to emerging technologies. Waltham: Butterworth-Heinemann.Google Scholar
  3. Allen, G., & Chan, T. (2017). Artificial intelligence and national security (Vol. 132). Cambridge, MA: Belfer Center for Science and International Affairs.Google Scholar
  4. Artner, S., Girven, R. S., & Bruce, J. B. (2016). Assessing the value of structured analytic techniques in the US intelligence community (No. RR-1408-OSD). Santa Monica: RAND National Defense Research Institute.CrossRefGoogle Scholar
  5. Babuta, A. (2017). Big data and policing: An assessment of law requirements, expectations and priorities. In RUSI occasional paper. London: Royal United Services Institute for Defence and Security Studies. Retrieved from https://rusi.org/sites/default/files/201709_rusi_big_data_and_policing_babuta_web.pdf
  6. Bichler, G. (2019). Understanding criminal networks: A research guide. Oakland: University of California Press.CrossRefGoogle Scholar
  7. Braga, A., Papachristos, A., & Hureau, D. (2012). Hot spots policing effects on crime. Campbell Systematic Reviews, 8(8), 1–96.Google Scholar
  8. Brantly, A. F. (2018). When everything becomes intelligence: Machine learning and the connected world. Intelligence and National Security, 33(4), 562–573.  https://doi.org/10.1080/02684527.2018.1452555.CrossRefGoogle Scholar
  9. Brenner, W., Zarnekow, R., & Wittig, H. (2012). Intelligent software agents: Foundations and applications. New York: Springer Science & Business Media.Google Scholar
  10. Bright, D., Koskinen, J., & Malm, A. (2019). Illicit network dynamics: The formation and evolution of a drug trafficking network. Journal of Quantitative Criminology, 35(2), 237–258.CrossRefGoogle Scholar
  11. Bruce, J. B., & George, R. (2015). Professionalizing intelligence analysis. Journal of Strategic Security, 8(3), 1–23.CrossRefGoogle Scholar
  12. Brumfield, E. (2014). Armed drones for law enforcement: Why it might be time to re-examine the current use of force standard. McGeorge Law Review, 46, 543–572.Google Scholar
  13. Burcher, M., & Whelan, C. (2018). Social network analysis as a tool for criminal intelligence: Understanding its potential from the perspectives of intelligence analysts. Trends in Organized Crime, 21(3), 278–294.  https://doi.org/10.1007/s12117-017-9313-8.CrossRefGoogle Scholar
  14. Burcher, M., & Whelan, C. (2019). Intelligence-led policing in practice: Reflections from intelligence analysts. Police Quarterly, 22(2), 139–160.  https://doi.org/10.1177/1098611118796890.CrossRefGoogle Scholar
  15. Bureau of Justice Assistance. (2005, September). Intelligence-led policing: The new intelligence architecture. Washington, DC: Bureau of Justice Assistance. Retrieved from: https://www.ncjrs.gov/pdffiles1/bja/210681.pdf
  16. Carter, D. L. (2009). Law enforcement intelligence: A guide for state, local, and tribal law enforcement agencies. Washington, DC: US Department of Justice, Office of Community Oriented Policing Services.Google Scholar
  17. Carter, D. L., & Carter, J. G. (2009). Intelligence-led policing: Conceptual considerations for public policy. Criminal Justice Policy Review, 20(3), 310.CrossRefGoogle Scholar
  18. Chan, J., & Moses, L. B. (2017). Making sense of big data for security. British Journal of Criminology, 57(2), 299–319.  https://doi.org/10.1093/bjc/azw059.CrossRefGoogle Scholar
  19. Chang, W., Chen, E., Mellers, B., & Tetlock, P. (2016). Developing expert political judgment: The impact of training and practice on judgmental accuracy in geopolitical forecasting tournaments. Judgment and Decision making, 11(5), 509–526.Google Scholar
  20. Chen, H., Chung, W., Qin, Y., Chau, M., Xu, J. J., Wang, G., Zheng, R., & Atabakhsh, H. (2003, May). Crime data mining: an overview and case studies. In Proceedings of the 2003 annual national conference on Digital government research (pp. 1–5). Digital Government Society of North America.Google Scholar
  21. Clark, R. M. (2019). Intelligence analysis: A target-centric approach (6th Ed.). Washington, DC: CQ Press.Google Scholar
  22. Custers, B., & Vergouw, B. (2015). Promising policing technologies: Experiences, obstacles and police needs regarding law enforcement technologies. Computer Law & Security Review, 31(4), 518–526.CrossRefGoogle Scholar
  23. Dell EMC. (2014). The digital universe of opportunities: Rich data and the increasing value of the internet of things. Hopkinton: Dell EMC.Google Scholar
  24. Dörfler, T., Stollenwerk, E., & Schibberges, J. (2019). Uncovering covert structures: Social network analysis and terrorist organizations. London: SAGE.CrossRefGoogle Scholar
  25. Drezewski, R., Sepielak, J., & Filipkowski, W. (2015). The application of social network analysis algorithms in a system supporting money laundering detection. Information Sciences, 295, 18–32.  https://doi.org/10.1016/j.ins.2014.10.015.CrossRefGoogle Scholar
  26. Eldridge, C., Hobbs, C., & Moran, M. (2018). Fusing algorithms and analysts: Open-source intelligence in the age of ‘Big Data’. Intelligence and National Security, 33(3), 391–406.  https://doi.org/10.1080/02684527.2017.1406677.CrossRefGoogle Scholar
  27. Engberts, B., & Gillissen, E. (2016). Policing from above: Drone use by the police. In B. Custers (Ed.), The future of drone use (pp. 93–113). The Hague: TMC Asser Press.CrossRefGoogle Scholar
  28. Faizal Bin Abdul Rahman, M. (2017). Why it won’t displace police analysts artificial intelligence. RSIS Commentary, 109, 1–3.Google Scholar
  29. Ferreira, J., João, P., & Martins, J. (2012). GIS for crime analysis: Geography for predictive models. Electronic Journal of Information Systems Evaluation, 15(1), 36.Google Scholar
  30. Ferrell, W. R. (1994). Discrete subjective probabilities and decision analysis: Elicitation, calibration and combination. In G. Wright & P. Ayton (Eds.), Subjective probability (pp. 411–451). Chichester: Wiley.Google Scholar
  31. Ganor, B. (2019). Artificial or human: A new era of counterterrorism intelligence? Studies in Conflict and Terrorism, 0(0), 1–20.  https://doi.org/10.1080/1057610X.2019.1568815.CrossRefGoogle Scholar
  32. Gentry, J. A. (2016). The “professionalization” of intelligence analysis: A skeptical perspective. International Journal of Intelligence and CounterIntelligence, 29(4), 643–676.CrossRefGoogle Scholar
  33. Global Justice Information Sharing Initiative. (2003). The national criminal intelligence sharing plan. Washington, DC: Global Justice Information Sharing Initiative, U.S. Department of Justice.Google Scholar
  34. Global Justice Information Sharing Initiative. (2007). Law enforcement analyst certification standards. Washington, DC: Global Justice Information Sharing Initiative, U.S. Department of Justice.Google Scholar
  35. Haas, T. C., & Ferreira, S. M. (2015). Federated databases and actionable intelligence: Using social network analysis to disrupt transnational wildlife trafficking criminal networks. Security Informatics, 4(1), 1–14.  https://doi.org/10.1186/s13388-015-0018-8.CrossRefGoogle Scholar
  36. Hardyns, W., & Rummens, A. (2018). Predictive policing as a new tool for law enforcement? Recent developments and challenges. European Journal on Criminal Policy and Research, 24(3), 201–218.  https://doi.org/10.1007/s10610-017-9361-2.CrossRefGoogle Scholar
  37. Hare, N., & Coghill, P. (2016). The future of the intelligence analysis task. Intelligence and National Security, 31(6), 858–870.  https://doi.org/10.1080/02684527.2015.1115238.CrossRefGoogle Scholar
  38. Harper, W. R., & Harris, D. H. (1975). The application of link analysis to police intelligence. Human Factors, 17(2), 157–164.CrossRefGoogle Scholar
  39. Harrison, M., Walsh, P. F., Lysons-Smith, S., Truong, D., Horan, C., & Jabbour, R. (2018). Tradecraft to standards—Moving criminal intelligence practice to a profession through the development of a criminal intelligence training and development continuum. Policing: A Journal of Policy and Practice, 1–13.  https://doi.org/10.1093/police/pay053.
  40. Hassani, H., Huang, X., Silva, E. S., & Ghodsi, M. (2016). A review of data mining applications in crime. Statistical Analysis and Data Mining: The ASA Data Science Journal, 9(3), 139–154.CrossRefGoogle Scholar
  41. Herchenrader, T., & Myhill-Jones, S. (2015). GIS supporting intelligence-led policing. Police Practice and Research, 16(2), 136–147.CrossRefGoogle Scholar
  42. Heuer, R. J. (1999). Psychology of intelligence analysis. Washington, DC: Center for the Study of Intelligence, Central Intelligence Agency.Google Scholar
  43. Heuer, R. J., & Pherson, R. H. (2010). Structured analytic techniques for intelligence analysis. Washington, DC: CQ Press.Google Scholar
  44. Hoadley, D. S., & Lucas, N. J. (2018). Artificial intelligence and national security. Washington, DC: Congressional Research Service.Google Scholar
  45. Hu, J. (2019). Big data analysis of criminal investigations. In 2018 5th international conference on systems and informatics, ICSAI 2018 (pp. 649–654).  https://doi.org/10.1109/ICSAI.2018.8599305.CrossRefGoogle Scholar
  46. Jentner, W., Sacha, D., Stoffel, F., Ellis, G., Zhang, L., & Keim, D. A. (2018). Making machine intelligence less scary for criminal analysts: Reflections on designing a visual comparative case analysis tool. The Visual Computer, 34(9), 1225–1241.  https://doi.org/10.1007/s00371-018-1483-0.CrossRefGoogle Scholar
  47. Leng, J., & Li, G. (2018). Big data-driven predictive policing innovation. Advances in Engineering Research, 163, 123–127.  https://doi.org/10.2991/iceesd-18.2018.19.CrossRefGoogle Scholar
  48. Li, J., & Wang, A. G. (2015). A framework of identity resolution: Evaluating identity attributes and matching algorithms. Security Informatics, 4(1).  https://doi.org/10.1186/s13388-015-0021-0.
  49. Lim, K. (2016). Big data and strategic intelligence. Intelligence and National Security, 31(4), 619–635.CrossRefGoogle Scholar
  50. Llinas, J., Rogova, G., Barry, K., Hingst, R., Gerken, P., & Ruvinsky, A. (2017). Reexamining computational support for intelligence analysis: A functional design for a future capability. Autonomy and Artificial Intelligence: A Threat or Savior? (April 2018), 13–46.  https://doi.org/10.1007/978-3-319-59719-5_2
  51. Manning, P. (2008). The technology of policing: Crime mapping, information technology, and the rationality of crime control. New York: New York University Press.Google Scholar
  52. Marrin, S. (2016). Improving intelligence studies as an academic discipline. Intelligence and National Security, 31(2), 266–279.CrossRefGoogle Scholar
  53. Marrin, S., & Clemente, J. D. (2006). Modeling an intelligence analysis profession on medicine. International Journal of Intelligence and CounterIntelligence, 19(4), 642–665.CrossRefGoogle Scholar
  54. McCue, C. (2015). Data mining and predictive analysis: Intelligence gathering and crime analysis. Oxford/Waltham: Butterworth-Heinemann.CrossRefGoogle Scholar
  55. Mellers, B., Stone, E., Atanasov, P., Rohrbaugh, N., Metz, S. E., Ungar, L., Bishop, M., Horowitz, M., Merkle, E., & Tetlock, P. (2015). The psychology of intelligence analysis: Drivers of prediction accuracy in world politics. Journal of Experimental Psychology: Applied, 21(1), 1–14.Google Scholar
  56. Mishra, N., & Shelke, P. (2015). Data mining – A necessity for crime detection. International Journal on Recent and Innovation Trends in Computing and Communication, 3(2), 291–294. Retrieved from http://www.ijritcc.org
  57. Perera, H., Udeshini, S., & Munasinghe, M. (2014, November). Criminal short listing and crime forecasting based on modus criminal short listing and crime.  https://doi.org/10.13140/RG.2.1.5149.1287
  58. Petit, N. (2018). Artificial intelligence and automated law enforcement: A review paper. SSRN Electronic Journal, 1(1974), 1–12.  https://doi.org/10.2139/ssrn.3145133.CrossRefGoogle Scholar
  59. Pike, T. (2019, May). Computational tools to support analysis and decision making, 23.  https://doi.org/10.1117/12.2518693.
  60. Pramanik, M. I., Lau, R. Y. K., Yue, W. T., Ye, Y., & Li, C. (2017). Big data analytics for security and criminal investigations. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 7(4), 1–19.  https://doi.org/10.1002/widm.1208.CrossRefGoogle Scholar
  61. Ratcliffe, J. H. (2004). Crime mapping and the training needs of law enforcement. European Journal on Criminal Policy and Research, 10(1), 65–83.CrossRefGoogle Scholar
  62. Ratcliffe, J. (2016). Intelligence-led policing. New York: Routledge.CrossRefGoogle Scholar
  63. Regens, J. L. (2019). Augmenting human cognition to enhance strategic, operational, and tactical intelligence. Intelligence and National Security, 34(5), 673–687.  https://doi.org/10.1080/02684527.2019.1579410.CrossRefGoogle Scholar
  64. Rossmo, D. K. (2012). Recent developments in geographic profiling. Policing: A Journal of Policy and Practice, 6(2), 144–150.CrossRefGoogle Scholar
  65. Rossmo, D. K., & Velarde, L. (2008). Geographic profiling analysis: Principles, methods and applications. In S. Chainey & L. Thompson (Eds.), Crime mapping case studies: Practice and research (pp. 35–43). New York: Wiley.Google Scholar
  66. Rowley, J. (2007). The wisdom hierarchy: Representations of the DIKW hierarchy. Journal of Information Science, 33(2), 163–180.CrossRefGoogle Scholar
  67. Sparrow, M. K. (1991). The application of network analysis to criminal intelligence: An assessment of the prospects. Social Networks, 13(3), 251–274.CrossRefGoogle Scholar
  68. Straub, J. (2014). Unmanned aerial systems: Consideration of the use of force for law enforcement applications. Technology in Society, 39, 100–109.CrossRefGoogle Scholar
  69. Tecuci, G., Schum, D. A., Marcu, D., & Boicu, M. (2014). Computational approach and cognitive assistant for evidence-based reasoning in intelligence analysis. International Journal of Intelligent Defence Support Systems, 5(2), 146.  https://doi.org/10.1504/ijidss.2014.059976.CrossRefGoogle Scholar
  70. Tetlock, P. E., & Gardner, D. (2016). Superforecasting: The art and science of prediction. London: Random House.Google Scholar
  71. Ungar, L., Mellers, B., Satopää, V., Tetlock, P., & Baron, J. (2012, October). The good judgment project: A large scale test of different methods of combining expert predictions. AAAI technical report FS-12-06: Machine Aggregation of Human Judgment, 37–42.Google Scholar
  72. Van der Hulst, R. C. (2009). Introduction to Social Network Analysis (SNA) as an investigative tool. Trends in Organized Crime, 12(2), 101–121. https://doi.org/10.1007/s12117-008-9057-6.
  73. Wang, F. (2012). Why police and policing need GIS: An overview. Annals of GIS, 18(3), 159–171.CrossRefGoogle Scholar
  74. Warner, M. (2002). Wanted: A definition of “intelligence”. Studies in Intelligence, 46(3), 15–22.Google Scholar
  75. Weston, C., Bennett-Moses, L., & Sanders, C. (2019). The changing role of the law enforcement analyst: Clarifying core competencies for analysts and supervisors through empirical research. Policing and Society.  https://doi.org/10.1080/10439463.2018.1564751.
  76. Xu, J., & Chen, H. (2005). Criminal network analysis and visualization. Communications of the ACM, 48(6), 100–107.CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.School of InformationUniversity of South FloridaTampaUSA

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