When artificial intelligence models are asked to explain themselves in the spatial sciences, the results have often been less than transparent. A new study published in the journal Machine Learning with Applications introduces a method called Spatially Adjusted SHAP, or SA-SHAP, that promises to bridge a long-standing divide between classical