The synthetic control method is a widely used technique for estimating causal effects in comparative case studies involving panel data. In many applications, observation units correspond to spatial entities such as municipalities, cities, or regions. Despite recent advancements, few studies have addressed how to estimate causal effects using synthetic control when spatial confounding and spatial structures in the outcomes are present. We estimate causal effects by introducing a spatially augmented version of the synthetic control method that leverages spatial information in the data to enhance the interpretability of the estimates and reduce their variability. We adopt a Bayesian regression framework that penalizes the selection of more distant control units, within a semiparametric model designed to account for unobserved spatial confounding. Our simulation results suggest that a spatially augmented synthetic control method outperforms classical approaches when outcome variables exhibit spatial correlation.