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

  • Mauro Castelli
  • Raul Sormani
  • Leonardo Trujillo
  • Aleš Popovič
Original Research


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.


Evolutionary Computation Crime Prediction Urban Security Semantics Local Search 


  1. 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):6856–6862CrossRefGoogle Scholar
  2. Castelli M, Silva S, Vanneschi L (2015a) A c++ framework for geometric semantic genetic programming. Genet Program Evol Mach 16(1):73–81CrossRefGoogle Scholar
  3. 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 47:37–41CrossRefGoogle Scholar
  4. 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) Google Scholar
  5. 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 1836–1843Google Scholar
  6. 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 108–115Google Scholar
  7. Findlay M (1999) The Globalization of Crime. Cambridge University Press, CambridgeGoogle Scholar
  8. Haykin S (1998) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall PTR, Upper Saddle RiverzbMATHGoogle Scholar
  9. Haykin S (1999) Neural networks: a comprehensive foundation. Prentice Hall, Upper Saddle RiverzbMATHGoogle Scholar
  10. Hoffmann L (2009) Multivariate isotonic regression and its algorithms. Wichita State University, College of Liberal Arts and Sciences, Department of Mathematics and Statistics, Wichita, KansasGoogle Scholar
  11. 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 70–82Google Scholar
  12. Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, CambridgezbMATHGoogle Scholar
  13. 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 987–994Google Scholar
  14. 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 935–942Google Scholar
  15. 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 989–996Google Scholar
  16. 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 21–31Google Scholar
  17. Redmond M, Baveja A (2002) A data-driven software tool for enabling cooperative information sharing among police departments. Eur J Oper Res 141(3):660–678CrossRefzbMATHGoogle Scholar
  18. Schölkopf B, Smola A (2002) Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Adaptive computation and machine learningGoogle Scholar
  19. Seber G, Wild C (2003) Nonlinear Regression. Wiley, Wiley Series in Probability and StatisticsGoogle Scholar
  20. Stratton N (1993) Birth of an information network. FBI Law Enforc Bull 62(2):1–22Google Scholar
  21. Vanneschi L, Castelli M, Silva S (2014a) A survey of semantic methods in genetic programming. Genet Programm Evol Mach 15(2):195–214CrossRefGoogle Scholar
  22. 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 191–209Google Scholar
  23. Weisberg S (2005) Applied linear regression. Wiley Series in Probability and Statistics. Wiley, HobokenCrossRefGoogle Scholar
  24. Weka Machine Learning Project (2014) Weka.
  25. 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 213–228Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Mauro Castelli
    • 1
  • Raul Sormani
    • 3
    • 4
  • Leonardo Trujillo
    • 5
  • Aleš Popovič
    • 2
  1. 1.NOVA IMSUniversidade Nova de LisboaLisbonPortugal
  2. 2.Faculty of EconomicsUniversity of LjubljanaLjubljanaSlovenia
  3. 3.Consorzio Milano RicercheMilanItaly
  4. 4.Department of Computer Science, Systems and CommunicationUniversity of Milano-BicoccaMilanItaly
  5. 5.Tree-Lab, Posgrado en Ciencias de la IngenieríaInstituto Tecnológico de TijuanaTijuanaMexico

Personalised recommendations