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.

翻译：分析空间变化效应在地理分析中至关重要。然而，由于地理空间数据的复杂性和非线性，准确捕捉并解释这种变化性颇具挑战。本文提出一个集成框架，融合局部空间加权方案、可解释人工智能（XAI）以及前沿机器学习技术，旨在弥合传统地理分析模型与通用机器学习方法之间的差距。通过对合成数据集的测试，该框架通过阐明空间变异性，验证了其在地理回归与分类中提升预测可解释性与准确性的能力。该框架显著提高了预测精度，为理解空间现象提供了一种新颖方法。