Abstract
The entire drug trafficking database of the London Metropolitan Police Department comprising 144 variables and some 1,120 records representing arrests for illegal drug activities in the 32 boroughs of London was analyzed utilizing the constraint satisfaction (CS) artificial neural network (ANN) method developed in the previous chapter. A detailed analysis of the resulting analysis has shown each area of London and their drug activities, associated drug felons and suspects, and profile of activity that can be used to guide police in focusing their limited manpower into areas and kinds of individuals most characteristic of association with certain crimes. By utilizing these methods, profiling has been lifted from an arguably subjective mode to one objectively determined by sophisticated mathematical means and represented in the CS ANNs.
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- 1.
In English law, the word conviction refers to the outcome of a criminal prosecution which concludes in a judgment or finding that the defendant is guilty of the crime charged. The term summary conviction refers to the consequence of a trial before a court or magistrate, without a jury, which generally involves a minor offense.
- 2.
The word offense is synonymous with the word crime in English law.
References
Buscema, M. (1998). Constraint satisfaction neural networks. Substance Use & Misuse, 33(2), 389–408.
Buscema, M. (2004, May). Genetic doping algorithm (GenD). Theory and applications. Expert Systems, 21(2), 63–79.
Buscema, M., & Sacco, P. L. (2010). Auto-contractive maps, the H function, and the maximally regular graph (MRG): A new methodology for data mining. In V. Capecchi, et al. (Eds.), Applications of mathematics in models, artificial neural networks and arts,10.1007/978-90-481-8581-8_11. London: Springer.
Kruskal, J. B. (1956). On the shortest spanning subtree of a graph and the traveling salesman problem. Proceedings of the American Mathematical Society, 7(1), 48–50.
Rumelhart, D. E., Smolensky, P., McClelland, J. L., & Hinton, G. E. (1986). Schemata and sequential thought processes in PDP models. In J. L. McClelland & D. E. Rumelhart (Eds.), PDP, exploration in the microstructure of cognition (Vol. II). Cambridge, MA: The MIT Press.
Software
Buscema, M. (2007a). Contractive maps. Software for programming auto contractive maps (Semeion Software #15, v. 2), Rome.
Buscema, M. (2007b). Constraints satisfaction networks. Software for programming non linear auto-associative networks (Semeion Software #14, v. 10), Rome.
Buscema, M. (2008a). MST. Software for programming Trees from artificial networks weights matrix (Semeion Software #38, v 5), Rome.
Buscema, M. (2008b). Pst cluster. Software for clustering based on Gend algorithm (Semeion Software #34, v5.2).
Massini, G. (2007). Tree visualizer. Software to draw and manipulate tree graph (Semeion Software #40, v. 3), Rome.
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Appendix
Appendix
Next, we show the tables relating to each borough when is consider “place of residence” and “place of arrest” and the activated variables with respective values.
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Intraligi, M., Buscema, M. (2013). Application of the Constraint Satisfaction Network. In: Buscema, M., Tastle, W. (eds) Intelligent Data Mining in Law Enforcement Analytics. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4914-6_14
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DOI: https://doi.org/10.1007/978-94-007-4914-6_14
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