PyGALAX：一个用于高级可解释地理空间机器学习的开源Python工具包
PyGALAX: An Open-Source Python Toolkit for Advanced Explainable Geospatial Machine Learning
AI总结：
PyGALAX通过整合AutoML和XAI技术，提供了一个用于高级可解释地理空间机器学习的开源Python工具包，解决了空间非平稳性问题。
摘要：
PyGALAX是一个用于地理空间分析的Python工具包，集成了自动化机器学习（AutoML）和可解释人工智能（XAI）技术，用于分析回归和分类任务中的空间异质性。它能够自动选择和优化不同地理区域和情境下的机器学习模型，同时通过SHAP（SHapley Additive exPlanations）分析保持可解释性。PyGALAX建立在并改进了GALAX框架（Geospatial Analysis Leveraging AutoML and eXplainable AI）之上，该框架已被证明在性能上优于传统地理加权回归（GWR）方法。PyGALAX相对于原始GALAX框架的关键增强包括自动带宽选择和灵活的核函数选择，为跨多样数据集和研究问题的空间建模提供了更大的灵活性和鲁棒性。PyGALAX不仅继承了原始GALAX框架的所有功能，还将其包装成一个易于访问、可重复和易于部署的Python工具包，同时提供了额外的空间建模选项。它有效解决了空间非平稳性，并在全局和局部尺度上生成对复杂空间关系的透明见解，使高级地理空间机器学习方法对地理学、城市规划、环境科学等相关领域的研究人员和从业者更具可及性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
