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

作者: Luo, Y (Luo, Yun); Su, SL (Su, Shiliang)

來源出版物: INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 卷: 136 文獻号: 104315 DOI: 10.1016/j.jag.2024.104315 Published Date: 2025 FEB

摘要: 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) algorithms1, 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 nonstationarity to the fore.

作者關鍵詞: Spatially explicit modeling; Machine learning; Spatiotemporal random forest; Spatiotemporal stacking tree; Ensemble learning; Spatiotemporal non-stationarity; Nonlinearity

KeyWords Plus: GEOGRAPHICALLY WEIGHTED REGRESSION

地址: [Luo, Yun; Su, Shiliang] Wuhan Univ, Sch Resource & Environm Sci, Urban Comp & Visualizat Lab, Wuhan, Peoples R China.

[Su, Shiliang] Hubei Luojia Lab, Wuhan, Peoples R China.

通訊作者地址: Su, SL (通訊作者)，129 Luoyu Rd, Wuhan, Hubei, Peoples R China.

電子郵件地址: shiliangsu@whu.edu.cn

影響因子：7.6