An Ensemble Framework for Explainable Geospatial Machine Learning Models
This work addresses the problem of improving interpretability and accuracy in geospatial machine learning for researchers and practitioners in geographic analysis, though it appears incremental as it builds on prior methods like GeoShapley.
The paper tackled the challenge of capturing and interpreting spatial variability in geospatial data by proposing an ensemble framework that merges local spatial weighting with explainable AI and machine learning, which was verified to enhance interpretability and predictive accuracy in tests on synthetic datasets compared to existing methods like GWR, MGWR, and GeoShapley.
Analyzing spatially varying effects is pivotal in geographic analysis. However, accurately capturing and interpreting this variability is challenging due to the increasing complexity and non-linearity of geospatial data. Recent advancements in integrating Geographically Weighted (GW) models with artificial intelligence (AI) methodologies offer novel approaches. However, these methods often focus on single algorithms and emphasize prediction over interpretability. The recent GeoShapley method integrates machine learning (ML) with Shapley values to explain the contribution of geographical features, advancing the combination of geospatial ML and explainable AI (XAI). Yet, it lacks exploration of the nonlinear interactions between geographical features and explanatory variables. Herein, an ensemble framework is proposed to merge local spatial weighting scheme with XAI and ML technologies to bridge this gap. Through tests on synthetic datasets and comparisons with GWR, MGWR, and GeoShapley, this framework is verified to enhance interpretability and predictive accuracy by elucidating spatial variability. Reproducibility is explored through the comparison of spatial weighting schemes and various ML models, emphasizing the necessity of model reproducibility to address model and parameter uncertainty. This framework works in both geographic regression and classification, offering a novel approach to understanding complex spatial phenomena.