GWRBoost: A Geographically Weighted Gradient Boosting Framework for Enhanced Explainable Quantification of Spatially Varying Relationships

Spatial processes are fundamental to understanding complex patterns and dynamics across diverse systems but remain challenging to model due to the competing demands of predictive accuracy and interpretability. Traditional spatial regression techniques such as geographically weighted regression (GWR) provide interpretable, location-specific coefficients yet systematically underfit complex, high-dimensional, and nonlinear spatial relationships prevalent in modern geospatial data sets. In contrast, machine learning methods achieve higher prediction accuracy but sacrifice explicit quantification of spatially varying relationships and compatibility with spatial model evaluation criteria. To address these limitations, we propose GWRBoost, an ensemble learning framework that integrates gradient boosting with GWR models to simultaneously improve model performance and enhance the interpretability of coefficient estimates across space. GWRBoost uses a stage-wise residual passing mechanism to leverage spatial dependence and optimize model performance, globally preserving intrinsically interpretable, location-specific linear coefficients that support coefficient-based local explanations. We demonstrate the effectiveness of GWRBoost through synthetic and empirical case studies including regional, national, and global scales in the domains of computational social science, ecology, and public health. Our results show that GWRBoost consistently outperforms traditional global regression models and leads spatially varying methods, delivering superior predictive accuracy and more precise estimation of spatially heterogeneous relationships. Importantly, GWRBoost retains compatibility with model selection metrics, enabling robust comparative evaluation. GWRBoost provides a scalable, interpretable, and generalizable approach for spatial process modeling, bridging the gap between accuracy and explainability, and offering significant value to a broad range of spatial disciplines.