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A STOCHASTIC DYNAMIC PROGRAMMING MODEL OF POLICE RESOURCE ALLOCATION FOR CRIME CONTROL
Keywords:
stochastic dynamic programming, police patrol, optimal allocation policy, regions, hotspotsDOI:
https://doi.org/10.17654/0974165825026Abstract
In this paper, we developed a non-homogeneous Poisson process-based stochastic dynamic programming model for efficient allocation of police resources with precision and effective crime control. Another important model developed alongside the stochastic dynamic programming model was the optimal decision model for allocating police resources to regions with probabilities of intercepting crimes. The two developed models were applied to crime and logistics data from Area Command, Enugu for optimal resource allocation. From this work, we conclude that for effective crime prevention and control, Area Command should allocate ten (10) patrols to the five regions of interest in this order: to arrive at the optimal allocation of scarce resources for effective crime interception and control.
Received: December 2, 2024
Revised: December 31, 2024
Accepted: January 23, 2025
References
M. Rosenshine, Contributions to a theory of patrol scheduling, Oper. Res. Q. 21 (1970), 99-106.
C. D. Hale, Police Patrol, Operations and Management, Prentice Hall, Upper Saddle River, 1980.
K. Chelst, An algorithm for deploying a crime directed (tactical) patrol force, Management Science 24 (1978), 1314-1327.
C. I. Tongo and A. B. Agbadudu, A dynamic programming model for optimal distribution of police patrol efforts in Nigeria: a case study of Lagos State, Int. J. Oper. Res. 8 (2010), 110-126.
O. Ogbereyivwe and O. S. Olaniyi, On optimal allocation of crime preventing patrol team using dynamic programming, International Journal of Mathematics and Statistics Invention 2 (2014), 7-17.
Y. Zhang and D. Brown, Police patrol districting method and simulation evaluation using agent-based model & GIS, Security Informatics 2 (2013), Art. No. 7.
A. Shapiro, Predictive policing for reform? Indeterminacy and Intervention in Big Data Policing, Surveillance and Society 17 (2019), 456-472.
M. Dewinter, C. Vandeviver, T. V. Beken and F. Witlox, Analysing the police patrol routing problem: a review, ISPRS Int. Journal of Geo-Information 9 (2020), 1-17.
K. M. Curtin, K. Hayslett-McCall and F. Qiu, Determining optimal police patrol areas with maximal covering and backup covering location models, Netw. Spat. Econ. 10 (2010), 125-145.
F. A. Graybill, D. C. Boes and A. M. Mood, Introduction to the Theory of Statistics, McGraw-Hill Book Company, New York, 1974.
C. Chat Field and J. V. Zidek, Modelling and Analysis of Stochastic Systems, Chapman and Hall, London, 1995.
H. O. Amuji, G. U. Ugwuanyim, C. J. Ogbonna, H. C. Iwu and O. B. Nwanyibuife, The usefulness of dynamic programming in course allocation in the Nigerian universities, Open Journal of Optimization 6 (2017), 176-186.
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