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Predicting per capita violent crimes in urban areas: an artificial intelligence approach

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Abstract

A major challenge facing all law-enforcement organizations is to accurately and efficiently analyze the growing volumes of crime data in order to extract useful knowledge for decision makers. This is an increasingly important task, considering the fast growth of urban populations in most countries. In particular, to reconcile urban growth with the need for security, a fundamental goal is to optimize the allocation of law enforcement resources. Moreover, optimal allocation can only be achieved if we can predict the incidence of crime within different urban areas. To answer this call, in this paper we propose an artificial intelligence system for predicting per capita violent crimes in urban areas starting from socio-economic data, law-enforcement data and other crime-related data obtained from different sources. The proposed framework blends a recently developed version of genetic programming that uses the concept of semantics during the search process with a local search method. To analyze the appropriateness of the proposed computational method for crime prediction, different urban areas of the United States have been considered. Experimental results confirm the suitability of the proposed method for addressing the problem at hand. In particular, the proposed method produces a lower error with respect to the existing state-of-the art techniques and it is particularly suitable for analyzing large amounts of data. This is an extremely important feature in a world that is currently moving towards the development of smart cities.

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Notes

  1. In this work, the GNU Scientific Library is used http://www.gnu.org/software/gsl/.

References

  • Castelli M, Vanneschi L, Silva S (2013) Prediction of high performance concrete strength using genetic programming with geometric semantic genetic operators. Expert Syst Appl 40(17):6856–6862

    Article  Google Scholar 

  • Castelli M, Silva S, Vanneschi L (2015a) A c++ framework for geometric semantic genetic programming. Genet Program Evol Mach 16(1):73–81

    Article  Google Scholar 

  • Castelli M, Vanneschi L, Felice MD (2015b) Forecasting short-term electricity consumption using a semantics-based genetic programming framework: The south italy case. Energy Econ 47:37–41

    Article  Google Scholar 

  • Castelli M, Vanneschi L, Popovič A (2015c) Parameter evaluation of geometric semantic genetic programming in pharmacokinetics. Int J Bio-Inspir Comput 1–9 (to appear)

  • Cho H, Seo YW, Vijaya Kumar B, Rajkumar R (2014) A multi-sensor fusion system for moving object detection and tracking in urban driving environments. In: Robotics and Automation (ICRA), 2014 IEEE International Conference on, pp 1836–1843

  • Doulaverakis C, Konstantinou N, Knape T, Kompatsiaris I, Soldatos J (2011) An approach to intelligent information fusion in sensor saturated urban environments. In: Intelligence and Security Informatics Conference (EISIC), 2011 European, pp 108–115

  • Findlay M (1999) The Globalization of Crime. Cambridge University Press, Cambridge

  • Haykin S (1998) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall PTR, Upper Saddle River

    MATH  Google Scholar 

  • Haykin S (1999) Neural networks: a comprehensive foundation. Prentice Hall, Upper Saddle River

    MATH  Google Scholar 

  • Hoffmann L (2009) Multivariate isotonic regression and its algorithms. Wichita State University, College of Liberal Arts and Sciences, Department of Mathematics and Statistics, Wichita, Kansas

  • Keijzer M (2003) Improving symbolic regression with interval arithmetic and linear scaling. In: Proceedings of the 6th European Conference on Genetic Programming, Springer-Verlag, Berlin, Heidelberg, EuroGP’03, pp 70–82

  • Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge

    MATH  Google Scholar 

  • Krawiec K, Lichocki P (2009) Approximating geometric crossover in semantic space. GECCO ’09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation. ACM, Montreal, pp 987–994

  • Krawiec K, O’Reilly UM (2014) Behavioral programming: A broader and more detailed take on semantic gp. In: Proceedings of the 2014 Conference on Genetic and Evolutionary Computation, ACM, New York, GECCO ’14, pp 935–942

  • Moraglio A, Mambrini A (2013) Runtime analysis of mutation-based geometric semantic genetic programming for basis functions regression. In: Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation, ACM, New York, NY, USA, GECCO ’13, pp 989–996

  • Moraglio A, Krawiec K, Johnson CG (2012) Geometric semantic genetic programming. In: Coello Coello CA, Cutello V, Deb K, Forrest S, Nicosia G, Pavone M (eds) Parallel Problem Solving from Nature, PPSN XII (part 1), Springer, Berlin, Lecture Notes in Computer Science, vol 7491, pp 21–31

  • Redmond M, Baveja A (2002) A data-driven software tool for enabling cooperative information sharing among police departments. Eur J Oper Res 141(3):660–678

    Article  MATH  Google Scholar 

  • Schölkopf B, Smola A (2002) Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Adaptive computation and machine learning

  • Seber G, Wild C (2003) Nonlinear Regression. Wiley, Wiley Series in Probability and Statistics

  • Stratton N (1993) Birth of an information network. FBI Law Enforc Bull 62(2):1–22

    Google Scholar 

  • Vanneschi L, Castelli M, Silva S (2014a) A survey of semantic methods in genetic programming. Genet Programm Evol Mach 15(2):195–214

    Article  Google Scholar 

  • Vanneschi L, Silva S, Castelli M, Manzoni L (2014b) Geometric semantic genetic programming for real life applications. In: Genetic Programming Theory and Practice XI, Springer New York, pp 191–209

  • Weisberg S (2005) Applied linear regression. Wiley Series in Probability and Statistics. Wiley, Hoboken

    Book  Google Scholar 

  • Weka Machine Learning Project (2014) Weka. http://www.cs.waikato.ac.nz/~ml/weka

  • Z-Flores E, Trujillo L, Schuetze O, Legrand P (2014) Evaluating the effects of local search in genetic programming. In: Tantar AA, et al (eds) EVOLVE—A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V, Springer, Berlin, no. 288 in Advances in Intelligent Systems and Computing, pp 213–228

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Correspondence to Mauro Castelli.

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Castelli, M., Sormani, R., Trujillo, L. et al. Predicting per capita violent crimes in urban areas: an artificial intelligence approach. J Ambient Intell Human Comput 8, 29–36 (2017). https://doi.org/10.1007/s12652-015-0334-3

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  • DOI: https://doi.org/10.1007/s12652-015-0334-3

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