Regression analysis with spatially-varying coefficients using generalized additive models (GAMs)

Regression models for spatial data have attracted the attention of researchers from different fields given their widespread application. In this work we analyze the utility of generalized additive models (GAMs) as regression methods with spatially-dependent coefficients. Particularly, three different aspects of the regression analysis were addressed: model definition and estimation, testing spatial heterogeneity, and variable selection. Spatial heterogeneity was addressed through bootstrapping, while and algorithm using the Bayesian Information Criterion (BIC) was implemented for variable selection to reduce computation time. In addition, this study makes a comparison of GAMs with two of the most common methods for regression with spatially-varying coefficients: Geographically Weighted Regression (GWR) and Multiscale Geographically Weighted Regression (MGWR), using both synthetic and real data.