- Linear regression (simple, GLM) is able to capture some spatial variation with a relatively simple model
- Linear regression could be applied to data where general/regional trends are more important to identify than local behavior
- The local polynomial/GLM method is more complex and less interpretable but offers greater potential to capture local behavior
- A local polynomial/GLM model could be applied to data with a larger number of covariates where local variation is desired
- GAM offers the greatest amount of model flexibility at the cost of having to manually identify different combinations of models
- GAM could be applied for datasets with a small number of covariates and/or where the approximate desired relationship of the data to covariates is already known
- Spatial models (Kriging, hierarchical) best capture spatial effects and interpretability of the spatial data but are more computationally expensive
- Spatial models could be applied for spatial data where detailed local behavior is desired