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An Approach for the Police Districting Problem Using Artificial Intelligence

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Foundations of Intelligent Systems (ISMIS 2018)

Abstract

Police patrols are usually assigned to a restricted zone where they have to serve and protect the law. This feature not only results in routine tasks, such imposing traffic tickets, but also there are other important tasks, like assisting in accidents or riot control, that need to be covered.

An efficient traffic Police patrol location and a schedule assignment across the streets of a city or in a road network ensure that the traffic Police comply with their functions.

How to distribute these patrols in the city is a complicated task that needs experience and a deep analysis of traffic and Police data. In this work, we present a method that uses artificial intelligence to analyse these data and propose how to distribute the Police patrols reacting to events that are monitored in real-time for a better service to the citizens.

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References

  1. Schulenberg, J.L.: Systematic social observation of police decision-making: the process, logistics, and challenges in a Canadian context. Qual. Quant. 48(1), 297–315 (2014)

    Article  Google Scholar 

  2. Kula, S., Guler, A.: Smart public safety: application of mobile electronic system integration (MOBESE) in Istanbul. In: Gil-Garcia, J.R., Pardo, T.A., Nam, T. (eds.) Smarter as the New Urban Agenda. PAIT, vol. 11, pp. 243–258. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-17620-8_13

    Chapter  Google Scholar 

  3. Pereira Basilio, M., Pereira, V., Gomes Costa, H.: Review of the literature on multicriteria methods applied in the field of public security. Univers. J. Manag. 5(12), 549–562 (2017)

    Article  Google Scholar 

  4. Camacho-Collados, M., Liberatore, F., Angulo, J.M.: A multi-criteria Police Districting Problem for the efficient and effective design of patrol sector. Eur. J. Oper. Res. 246(2), 674–684 (2015)

    Article  Google Scholar 

  5. Liberatore, F., Camacho-Collados, M.: A comparison of local search methods for the multicriteria police districting problem on graph. Math. Probl. Eng. 3, 1–13 (2016)

    Article  Google Scholar 

  6. Adler, N., Hakkert, A.S., Raviv, T., Sher, M.: The traffic police location and schedule assignment problem. J. Multi-Criteria Decis. Anal. 21, 315–333 (2014)

    Article  Google Scholar 

  7. Daglar, M., Argun, U.: Crime mapping and geographical information systems in crime analysis. Int. J. Hum. Sci. 13(1), 2208–2221 (2016)

    Google Scholar 

  8. Radhakrishnan, S., Milani, V.: Implementing geographical information system to provide evident support for crime analysis. Procedia Comput. Sci. 48, 537–540 (2015)

    Article  Google Scholar 

  9. Curtin, K.M., Hayslett-McCall, K., Qui, F.: Determining optimal police patrol areas with maximal covering and backup covering location models. Netw. Spat. Econ. 10(1), 125–145 (2010)

    Article  MathSciNet  Google Scholar 

  10. Leuzzi, F., Del Signore, E., Ferranti, R.: Towards a pervasive and predictive traffic police. In: Leuzzi, F., Ferilli, S. (eds.) TRAP 2017. AISC, vol. 728, pp. 19–35. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75608-0_3

    Chapter  Google Scholar 

  11. Ferilli, S., Redavid, D.: A process mining approach to the identification of normal and suspect traffic behavior. In: Leuzzi, F., Ferilli, S. (eds.) TRAP 2017. AISC, vol. 728, pp. 37–56. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75608-0_4

    Chapter  Google Scholar 

  12. Rodriguez-Jimenez, J.M.: Detecting criminal behaviour patterns in Spain and Italy using formal concept analysis. In: Leuzzi, F., Ferilli, S. (eds.) TRAP 2017. AISC, vol. 728, pp. 57–68. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75608-0_5

    Chapter  Google Scholar 

  13. Bernaschi, M., Celestini, A., Guarino, S., Lombardi, F., Mastrostefano, E.: Unsupervised classification of routes and plates from the Trap-2017 dataset. In: Leuzzi, F., Ferilli, S. (eds.) TRAP 2017. AISC, vol. 728, pp. 97–114. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75608-0_8

    Chapter  Google Scholar 

  14. Bernaschi, M., Celestini, A., Guarino, S., Lombardi, F., Mastrostefano, E.: Traffic data: exploratory data analysis with Apache Accumulo. In: Leuzzi, F., Ferilli, S. (eds.) TRAP 2017. AISC, vol. 728, pp. 129–143. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75608-0_10

    Chapter  Google Scholar 

  15. Fumarola, F., Lanotte, P.F.: Exploiting recurrent neural networks for gate traffic prediction. In: Leuzzi, F., Ferilli, S. (eds.) TRAP 2017. AISC, vol. 728, pp. 145–153. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75608-0_11

    Chapter  Google Scholar 

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Acknowledgments

Author wants to thank Police Inspector Lucas López and Police Officer Fernando Arribas from Mijas PD, for their comments about the methodology.

Special thanks to Dr. I.P. Cabrera from University of Málaga for her suggestions.

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Correspondence to José Manuel Rodríguez-Jiménez .

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Rodríguez-Jiménez, J.M. (2018). An Approach for the Police Districting Problem Using Artificial Intelligence. In: Ceci, M., Japkowicz, N., Liu, J., Papadopoulos, G., Raś, Z. (eds) Foundations of Intelligent Systems. ISMIS 2018. Lecture Notes in Computer Science(), vol 11177. Springer, Cham. https://doi.org/10.1007/978-3-030-01851-1_14

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  • DOI: https://doi.org/10.1007/978-3-030-01851-1_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01850-4

  • Online ISBN: 978-3-030-01851-1

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