Through the multi-stage hierarchical Bayesian model and Markov Chain Monte Carlo methods, Bayesian statistics can be used in dependent spatial data analysis, including disease mapping in small areas, disease clustering, and geographical correlation studies. Recently, Bayesian spatial models have been developed with many types, which have made considerable progress in data analysis. This paper introduces several approaches that have been fully developed and applied, such as BYM model,joint model, semi-parameter model, moving average model and so on. Recently,many studies focused on the comparison work through Deviance Information criterion. Those results show that BYM model and MIX model of semi-parameter model could obtain better results. As more research going on, Bayesian statistics will have more space in applications of spatial epidemiology.