Spatial data mining is used to find patterns in large datasets, especially those with spatial information. Researchers combined spatial data mining techniques with econometric modeling to improve predictions. By using Classification and Regression Trees (CART) and considering spatial attributes, they were able to reduce prediction errors caused by spatial autocorrelation. This approach helps to better understand the impact of spatial arrangement on variables in predictive models.