让决策树学会空间思考，解释更稳定可靠。
SX-GeoTree: Self-eXplaining Geospatial Regression Tree Incorporating the Spatial Similarity of Feature Attributions
- 融合空间残差与解释一致性，在分裂时同时优化预测、空间分布和解释稳定性。
- 在福建县区GDP和西雅图房价任务中，解释一致性提升一倍，空间残差更均匀。
- 适合需要可解释性与空间规律的地理数据分析场景，如城市规划、经济评估。
决策树在表格预测中仍具核心地位，但难以捕捉空间依赖性，且解释局部不稳定。本文提出SX-GeoTree，一种自解释地理回归树，通过递归分裂时耦合三个目标：方差减少（MSE）、空间残差控制（全局莫兰指数），以及基于共识相似性网络的解释鲁棒性增强。该网络由地理加权回归（GWR）系数距离（刺激-响应相似性）和SHAP attribution距离（解释相似性）构建。将特征归因的局部Lipschitz连续性重构为网络社区保持问题，实现无需逐样本邻域搜索的空间一致解释。在两个典型任务中验证：福建83个县区的县级GDP预测，以及西雅图21,613个点位的房价预测。结果表明，SX-GeoTree预测精度与决策树相当（$R^{2}$差距小于0.01），同时显著改善残差空间均匀性，并使归因共识度翻倍（福建：0.19 vs 0.09；西雅图：0.10 vs 0.05）。消融实验显示莫兰指数与模块化项互补，任一移除均导致空间残差结构与解释稳定性下降。框架证明了超越几何邻近的空间相似性（通过GWR导出局部关系）可嵌入可解释模型，推动可信地理空间机器学习发展，并提供可迁移的领域感知可解释性模板。
原文摘要 · Abstract (English)
Decision trees remain central for tabular prediction but struggle with (i) capturing spatial dependence and (ii) producing locally stable (robust) explanations. We present SX-GeoTree, a self-explaining geospatial regression tree that integrates three coupled objectives during recursive splitting: impurity reduction (MSE), spatial residual control (global Moran's I), and explanation robustness via modularity maximization on a consensus similarity network formed from (a) geographically weighted regression (GWR) coefficient distances (stimulus-response similarity) and (b) SHAP attribution distances (explanatory similarity). We recast local Lipschitz continuity of feature attributions as a network community preservation problem, enabling scalable enforcement of spatially coherent explanations without per-sample neighborhood searches. Experiments on two exemplar tasks (county-level GDP in Fujian, n=83; point-wise housing prices in Seattle, n=21,613) show SX-GeoTree maintains competitive predictive accuracy (within 0.01 $R^{2}$ of decision trees) while improving residual spatial evenness and doubling attribution consensus (modularity: Fujian 0.19 vs 0.09; Seattle 0.10 vs 0.05). Ablation confirms Moran's I and modularity terms are complementary; removing either degrades both spatial residual structure and explanation stability. The framework demonstrates how spatial similarity - extended beyond geometric proximity through GWR-derived local relationships - can be embedded in interpretable models, advancing trustworthy geospatial machine learning and offering a transferable template for domain-aware explainability.
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