This chapter provides an applied introduction to model two types of point-based geospatial data using Bayesian methods. Unlike frequentist inference, Bayesian inference describes unknown statistical parameters with a prior distribution. With this foundation, Bayesian approach provides a valuable alternative to analyze geospatial data. We begin the chapter by introducing the basic concepts and benefits of Bayesian inference and survey four selected Bayesian models and methods, including Bayesian spatial interpolation, spatial epidemiology/disease mapping, Bayesian hierarchical models, and Bayesian spatial autoregressive models, for their applications in geospatial data analysis. Then we discuss some popular software packages to perform Bayesian analysis. We conclude the chapter by encouraging geospatial researchers and practitioners to add Bayesian methods in their toolboxes.