  Spatial documentation is exponentially increasing given the availability of
Big IoT Data, enabled by the devices miniaturization and data storage capacity.
Bayesian spatial statistics is a useful statistical tool to determine the
dependence structure and hidden patterns over space through prior knowledge and
data likelihood. Nevertheless, this modeling class is not well explored as the
classification and regression machine learning models given their simplicity
and often weak (data) independence supposition. In this manner, this systematic
review aimed to unravel the main models presented in the literature in the past
20 years, identify gaps, and research opportunities. Elements such as random
fields, spatial domains, prior specification, covariance function, and
numerical approximations were discussed. This work explored the two subclasses
of spatial smoothing global and local.
