SpatioTemporal Random Forest and SpatioTemporal Stacking Tree: A novel spatially explicit ensemble learning approach to modeling non-linearity in spatiotemporal non-stationarity

Abstract

A wide variety of spatially explicit modeling algorithms has recently mushroomed in geoinformation research. These algorithms establish local models with data from spatially confined subsets, thereby offering a new impetus for addressing the issue of spatiotemporal non-stationarity. However, a significant challenge persists in literature that local models are primarily predicated on linear assumptions, limiting their capacity to capture the non-linear relationships prevalent in real-world geographical phenomena. This study remedies this gap through proposing a novel approach that integrates the bagging and stacking approaches of ensemble learning into the spatially explicit modeling framework. We specifically develop the SpatioTemporal Random Forest (STRF) and SpatioTemporal Stacking Tree (STST) algorithms11Python package link: https://github.com/46319943/GeoRegression., which capture and interpret the non-linearity in the spatial and temporal context more effectively. Additionally, we introduce the ‘local importance score’ and ‘spatiotemporally accumulated local effects’ as novel interpretable metrics for visualizing and unraveling the dynamics of non-stationarity in spatial analyses. Simulation and real data experiments validate that the STRF and STST outperform over traditional spatially explicit modeling algorithms to a large content. This study contributes to the methodological innovation of spatially explicit modeling by bringing the nonlinearity in spatiotemporal non-stationarity to the fore.