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Information Journal Paper

Title

Bayesian Approach for Modelling of Spatio-Temporal Crime Data

Pages

  435-448

Abstract

 Introduction Studying crime data has become one of the essential topics in the world due to its connection with human security. Analyzing this type of data can effectively prevent future crimes and identify spatial patterns and factors that facilitate the commission of crimes to control crime-prone areas. Most of the time, crime data has a spatio-temporal structure that causes the formation of different spatio-temporal patterns. Therefore, spatio-temporal monitoring of crime data is essential in identifying factors that cause crime and preventing crime. An important issue in many cities is related to crime events, and the spatio-temporal Bayesian approach leads to identifying crime patterns and hotspots. In Bayesian analysis of spatio-temporal crime data, there is no closed form for posterior distribution because of its non-Gaussian distribution and the existence of latent variables. In this case, we face challenges such as high dimensional parameters, extensive simulation and time-consuming computation in applying MCMC methods. Material and Methods In this paper, we apply INLA to analyze crime data in Colombia. To describe the above concepts, a three-stage hierarchical model is considered. The advantages of this method can be the estimation of criminal events at a specific time and location and exploring unusual patterns in places. Results and Discussion The Bayesian analysis of crime data is usually performed as Bayesian infer ence of pure spatial or temporal patterns. However, such spatial or temporal Bayesian analyses are not suitable for crime data. In this article, in a case study, Bayesian hierarchical spatio-temporal analysis of crime data in Colombia was discussed using the INLA approach, which considers spatio-temporal dependence and makes the model more flexible in detecting unusual patterns. Exploratory data analysis is also discussed, detecting areas with unusual behaviour over time. Four different models were fitted to the data, and the best model that includes spatio-temporal interaction was selected using the DIC criterion. The research results identify the most important centre of crime in the Kennedy area of Bogotá, , as well as the highest crime rate in the time frame. Then, hierarchical spatio-temporal Bayesian analysis of these data was done with the INLA approach. Conclusion The advantage of using this Bayesian approach is that it includes the effects of spatio-temporal correlation in the model and makes the model flexible in detecting areas with abnormal behaviour over time and in different places. For this purpose, four different models, including side effects and spatio-temporal combination, were fitted to the crime data. The best model, including the spatio-temporal interaction effect, was proposed using the deviance information criterion. The comprehensive and scientific comparison of the two Bayesian methods INLA and the MCMC algorithm in terms of accuracy, speed and even accessibility and convenient use for researchers requires independent scientific and practical research because, for example, the various methods of sampling in the MCMC algorithms and sometimes its different methods in INLA make it difficult to compare accuracy. How to use parallel calculations in the application of these two methods is also effective in comparing the speed, and simply comparing the outputs cannot express the advantage of one method over the other.

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    APA: Copy

    Mohammadian Mosammam, A., ABBASI, E., & Mateu, J.. (2023). Bayesian Approach for Modelling of Spatio-Temporal Crime Data. JOURNAL OF STATISTICAL SCIENCES, 16(2 ), 435-448. SID. https://sid.ir/paper/1021895/en

    Vancouver: Copy

    Mohammadian Mosammam A., ABBASI E., Mateu J.. Bayesian Approach for Modelling of Spatio-Temporal Crime Data. JOURNAL OF STATISTICAL SCIENCES[Internet]. 2023;16(2 ):435-448. Available from: https://sid.ir/paper/1021895/en

    IEEE: Copy

    A. Mohammadian Mosammam, E. ABBASI, and J. Mateu, “Bayesian Approach for Modelling of Spatio-Temporal Crime Data,” JOURNAL OF STATISTICAL SCIENCES, vol. 16, no. 2 , pp. 435–448, 2023, [Online]. Available: https://sid.ir/paper/1021895/en

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