Scientific and Technological Advances in Law Enforcement Intelligence Analysis
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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.
KeywordsIntelligence led policing National intelligence model Law enforcement Data
- Ackoff, R. L. (1989). From data to wisdom. Journal of Applied Systems Analysis, 16(1), 3–9.Google Scholar
- 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
- Allen, G., & Chan, T. (2017). Artificial intelligence and national security (Vol. 132). Cambridge, MA: Belfer Center for Science and International Affairs.Google Scholar
- 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
- Braga, A., Papachristos, A., & Hureau, D. (2012). Hot spots policing effects on crime. Campbell Systematic Reviews, 8(8), 1–96.Google Scholar
- Brenner, W., Zarnekow, R., & Wittig, H. (2012). Intelligent software agents: Foundations and applications. New York: Springer Science & Business Media.Google Scholar
- 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
- 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
- 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
- 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
- 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
- Clark, R. M. (2019). Intelligence analysis: A target-centric approach (6th Ed.). Washington, DC: CQ Press.Google Scholar
- Dell EMC. (2014). The digital universe of opportunities: Rich data and the increasing value of the internet of things. Hopkinton: Dell EMC.Google Scholar
- Faizal Bin Abdul Rahman, M. (2017). Why it won’t displace police analysts artificial intelligence. RSIS Commentary, 109, 1–3.Google Scholar
- 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
- 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
- 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
- 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
- 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.
- Heuer, R. J. (1999). Psychology of intelligence analysis. Washington, DC: Center for the Study of Intelligence, Central Intelligence Agency.Google Scholar
- Heuer, R. J., & Pherson, R. H. (2010). Structured analytic techniques for intelligence analysis. Washington, DC: CQ Press.Google Scholar
- Hoadley, D. S., & Lucas, N. J. (2018). Artificial intelligence and national security. Washington, DC: Congressional Research Service.Google Scholar
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- Pike, T. (2019, May). Computational tools to support analysis and decision making, 23. https://doi.org/10.1117/12.2518693.
- 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
- Tetlock, P. E., & Gardner, D. (2016). Superforecasting: The art and science of prediction. London: Random House.Google Scholar
- 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
- 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.
- Warner, M. (2002). Wanted: A definition of “intelligence”. Studies in Intelligence, 46(3), 15–22.Google Scholar
- 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.