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.

This is a longitudinal/panel data set. Standard textbook methods are fixed, random, or mixed effects. R has a package called plm to handle these.
Alternatively, one can use least squares dummy variable (LSDV) estimation, where one includes dummies for region (LSDV1), year (LSDV2), or both (LSDV3). This method is equivalent to fixed effects, as Cameron and Trivedi (2005) explain.
