Analyzing spatially varying effects is pivotal in geographic analysis. However, accurately capturing and interpreting this variability is challenging due to the increasing complexity and non-linearity of geospatial data. Recent advancements in integrating Geographically Weighted (GW) models with artificial intelligence (AI) methodologies offer novel approaches. However, these methods often focus on single algorithms and emphasize prediction over interpretability. The recent GeoShapley method integrates machine learning (ML) with Shapley values to explain the contribution of geographical features, advancing the combination of geospatial ML and explainable AI (XAI). Yet, it lacks exploration of the nonlinear interactions between geographical features and explanatory variables. Herein, an ensemble framework is proposed to merge local spatial weighting scheme with XAI and ML technologies to bridge this gap. Through tests on synthetic datasets and comparisons with GWR, MGWR, and GeoShapley, this framework is verified to enhance interpretability and predictive accuracy by elucidating spatial variability. Reproducibility is explored through the comparison of spatial weighting schemes and various ML models, emphasizing the necessity of model reproducibility to address model and parameter uncertainty. This framework works in both geographic regression and classification, offering a novel approach to understanding complex spatial phenomena.

翻译：分析空间变化效应在地理分析中至关重要。然而，由于地理空间数据日益增长的复杂性和非线性，准确捕捉并解释这种变异性具有挑战性。近年来，将地理加权模型与人工智能方法相结合的最新进展提供了新颖的途径。然而，这些方法通常侧重于单一算法，并强调预测而非可解释性。近期提出的GeoShapley方法将机器学习与Shapley值相结合，以解释地理特征的贡献，推动了地理空间机器学习与可解释人工智能的结合。然而，该方法缺乏对地理特征与解释变量之间非线性交互作用的探索。为此，本文提出一种集成框架，将局部空间加权方案与可解释人工智能及机器学习技术相融合，以弥补这一不足。通过在合成数据集上的测试，并与地理加权回归、多尺度地理加权回归及GeoShapley方法进行比较，验证了该框架通过阐明空间变异性，能够提升模型的可解释性与预测准确性。通过比较不同空间加权方案与多种机器学习模型，探讨了模型的可复现性，强调了解决模型与参数不确定性对模型可复现性的必要性。该框架适用于地理回归与分类任务，为理解复杂的空间现象提供了一种新方法。