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Bayesian Approach for Modelling Spatial–Temporal Crime Data
Ali Mohammadian mosammam * , Jorge Mateu
Abstract:   (401 Views)

An important issue in many cities is related to crime events, and 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 existence of latent variables. In this case, we face different challenges such as high dimensional parameters, extensive simulation and time-consuming computation in applying MCMC methods. In this paper, we use INLA to analyze crime data in Colombia. The advantages of this method can be the estimation of criminal events at a specific time and location and exploring unusual patterns in places.

Keywords: Integrated Nested Laplace Approximation, Bayesian Statistics, Spatial-temporal Statistics.
Full-Text [PDF 4205 kb]   (171 Downloads)    
Type of Study: Research | Subject: Spatial Statistics
Received: 2022/03/3 | Accepted: 2023/03/1
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مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences

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