PyGALAX让地理空间机器学习自动建模且可解释，一键分析区域差异。
PyGALAX: An Open-Source Python Toolkit for Advanced Explainable Geospatial Machine Learning
- 融合AutoML与XAI，自动为不同区域选最优模型。
- 支持自动带宽与核函数选择，提升模型适应性。
- 适合地理、城规、环境等领域的研究者使用。
PyGALAX是一个用于地理空间分析的Python工具包，整合了自动化机器学习（AutoML）与可解释人工智能（XAI）技术，用于分析回归与分类任务中的空间异质性。它能自动为不同地理区域和情境选择并优化机器学习模型，同时通过SHAP（SHapley Additive exPlanations）分析保持可解释性。PyGALAX在原有GALAX框架（Geospatial Analysis Leveraging AutoML and eXplainable AI）基础上进行了改进，新增自动带宽选择与灵活核函数选择功能，显著提升了空间建模在多样化数据集和研究问题中的灵活性与鲁棒性。该工具不仅继承了原GALAX的所有功能，还将其封装为一个易于访问、可复现且便于部署的Python工具包，并提供额外的空间建模选项。它有效应对空间非平稳性问题，在全局与局部尺度上生成透明的复杂空间关系洞察，使高级地理空间机器学习方法惠及地理学、城市规划、环境科学等领域的研究人员与实践者。
原文摘要 · Abstract (English)
PyGALAX is a Python package for geospatial analysis that integrates automated machine learning (AutoML) and explainable artificial intelligence (XAI) techniques to analyze spatial heterogeneity in both regression and classification tasks. It automatically selects and optimizes machine learning models for different geographic locations and contexts while maintaining interpretability through SHAP (SHapley Additive exPlanations) analysis. PyGALAX builds upon and improves the GALAX framework (Geospatial Analysis Leveraging AutoML and eXplainable AI), which has proven to outperform traditional geographically weighted regression (GWR) methods. Critical enhancements in PyGALAX from the original GALAX framework include automatic bandwidth selection and flexible kernel function selection, providing greater flexibility and robustness for spatial modeling across diverse datasets and research questions. PyGALAX not only inherits all the functionalities of the original GALAX framework but also packages them into an accessible, reproducible, and easily deployable Python toolkit while providing additional options for spatial modeling. It effectively addresses spatial non-stationarity and generates transparent insights into complex spatial relationships at both global and local scales, making advanced geospatial machine learning methods accessible to researchers and practitioners in geography, urban planning, environmental science, and related fields.
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