Spatial regression is an analytical technique used to model relationships between variables while accounting for spatial dependencies and spatial autocorrelation in the data. It is crucial for accurately understanding patterns and making predictions in geographically-referenced datasets, where traditional regression methods may fail due to spatial effects.