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.

翻译：决策树在表格数据预测中仍占据核心地位，但存在两大局限：(i) 难以捕捉空间依赖性；(ii) 无法生成局部稳定（鲁棒）的解释。本文提出SX-GeoTree——一种自解释地理空间回归树，其在递归分裂过程中整合了三个耦合目标：不纯度降低（均方误差MSE）、空间残差控制（全局莫兰指数Moran's I），以及通过基于(a)地理加权回归（GWR）系数距离（刺激-响应相似性）与(b)SHAP归因距离（解释相似性）构建的共识相似性网络进行模块度最大化，以实现解释鲁棒性。我们将特征归因的局部Lipschitz连续性重构为网络社区保持问题，从而无需逐样本邻域搜索即可可扩展地强制实现空间连贯的解释。在两个典型任务上的实验（福建省县级GDP，n=83；西雅图点状房价，n=21,613）表明，SX-GeoTree在保持有竞争力的预测精度（与决策树的R²差距在0.01以内）的同时，改善了残差的空间均匀性，并将归因共识度提升了一倍（模块度：福建0.19对比0.09；西雅图0.10对比0.05）。消融实验证实莫兰指数项与模块度项具有互补性；移除任一指标均会同时损害空间残差结构与解释稳定性。该框架展示了如何通过GWR衍生的局部关系将空间相似性——超越几何邻近性——嵌入可解释模型，推动了可信地理空间机器学习的发展，并为领域感知的可解释性提供了可迁移的模板。