This course aims to provide an introductory exploration of Bayesian spatial statistics, with the goal of equipping students with the necessary computational skills to analyze geographically represented data. Bayesian inference has become one of widely utilized statistical methods for addressing spatial and temporal dependency in the fields of geo-informatics, environmental science, and social sciences. Throughout the course, we will focus on Bayesian modeling techniques that are specifically designed for spatial-temporal data in geography and related scientific disciplines. The lectures will be organized around three main themes: the fundamental concepts of Bayesian inference, generalized hierarchical models, and conditional autoregressive models. It is expected that students have a prior background in statistics, including regression analysis as well as a basic understanding of statistical computing. The course will involve substantial computational work, primarily utilizing the R programming language. Additionally, students are also encouraged to possess a background in geographic information systems (GIS).