PyGALAX: An Open-Source Python Toolkit for Advanced Explainable Geospatial Machine Learning

2026-01-31

AI Summary

This study addresses the challenge of non-stationarity in geospatial data by proposing GALAX, a novel framework that integrates automated machine learning (AutoML) with explainable artificial intelligence (XAI). GALAX automatically optimizes model architectures and leverages SHAP-based explanations to effectively capture spatial heterogeneity. The method introduces configurable kernel functions and an automated bandwidth selection mechanism, significantly enhancing the flexibility and robustness of geographically weighted modeling. Experimental results demonstrate that GALAX outperforms traditional geographically weighted regression in both regression and classification tasks, while providing transparent insights into spatial relationships at both global and local scales. The framework is released as an open-source Python package, facilitating its adoption across diverse application domains.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Graph-based Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

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.

Problem

• spatial non-stationarity
• geospatial machine learning
• explainable AI
• spatial heterogeneity
• AutoML

Innovation

• AutoML
• XAI
• spatial heterogeneity
• SHAP
• geospatial machine learning