TY - JOUR T1 - Bayesian Approach for Modelling Spatial–Temporal Crime Data TT - کاربست رهیافت بیزی در مدل‌سازی فضایی-زمانی داده‌ها‌ی جرم JF - JSS JO - JSS VL - 16 IS - 2 UR - http://jss.irstat.ir/article-1-795-en.html Y1 - 2023 SP - 435 EP - 448 KW - Integrated Nested Laplace Approximation KW - Bayesian Statistics KW - Spatial-temporal Statistics. N2 - 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. M3 10.52547/jss.16.2.435 ER -