The article discusses a method for choosing important factors in spatial regression models. They use bivariate splines to handle spatial smoothing and a double penalization technique for variable selection. The method can find significant factors and deal with spatial issues in complex regions. They also develop a formula to estimate the accuracy of their results. The researchers show through simulations and real data that their approach works well.