  Analyzing spatial varying effect is pivotal in geographic analysis. Yet,
accurately capturing and interpreting this variability is challenging due to
the complexity and non-linearity of geospatial data. Herein, we introduce an
integrated framework that merges local spatial weighting scheme, Explainable
Artificial Intelligence (XAI), and cutting-edge machine learning technologies
to bridge the gap between traditional geographic analysis models and general
machine learning approaches. Through tests on synthetic datasets, this
framework is verified to enhance the interpretability and accuracy of
predictions in both geographic regression and classification by elucidating
spatial variability. It significantly boosts prediction precision, offering a
novel approach to understanding spatial phenomena.
