Modelling spatio-temporal data with repeated measurements I would like to perform regression on an environmental dataset. The covariates are in the following form

I realize that the 4 repeated measures for each region are dependent because they come from the same region. For this reason, a model with mixed effects should be used. I also suspect that the data is spatially and temporally correlated.
I am not sure what models I should try on my dataset first. I am open to anything from very advanced state-of-the-art  models (preferred) to the more simple models that are taught in textbooks. I would appreciate suggestions from those that are familiar with spatio-temporal data.

One possible approach here is to use a mixed effects model with random intercepts for Region to handle the within-region correlations.
In R, the glmmTMB package can handle several different correlation structures:

*

*Heterogeneous unstructured

*Heterogeneous Toeplitz

*Heterogeneous compound symmetry

*Heterogeneous diagonal

*AR(1)

*Ornstein-Uhlenbeck

*Spatial exponential

*Spatial Gaussian

*Spatial Matern

*Reduced rank

https://cran.r-project.org/web/packages/glmmTMB/vignettes/covstruct.html
