  Spatial statistical models are commonly used in geographical scenarios to
ensure spatial variation is captured effectively. However, spatial models and
cluster algorithms can be complicated and expensive. This paper pursues three
main objectives. First, it introduces covariate effect clustering by
integrating a Bayesian Geographically Weighted Regression (BGWR) with a
Gaussian mixture model and the Dirichlet process mixture model. Second, this
paper examines situations in which a particular covariate holds significant
importance in one region but not in another in the Bayesian framework. Lastly,
it addresses computational challenges present in existing BGWR, leading to
notable enhancements in Markov chain Monte Carlo estimation suitable for large
spatial datasets. The efficacy of the proposed method is demonstrated using
simulated data and is further validated in a case study examining children's
development domains in Queensland, Australia, using data provided by Children's
Health Queensland and Australia's Early Development Census.
