Measuring univariate effects in the interaction of geographical patterns

作者：Peng Luo, Yang Li, Yongze Song, Ziqi Li, Liqiu Meng · 发表于：International Journal of Geographical Information Systems · 年份：2025 · DOI：10.1080/13658816.2025.2526042 · 被引用次数：13 · 研究领域：Spatial and Panel Data Analysis、Regional Economics and Spatial Analysis、Regional Economic and Spatial Analysis

Understanding the relationships between geographical variables is a fundamental task in spatial analysis. However, existing spatial methods often underperform in scenarios involving nonlinear relationships and complex interactions among geographical variables. Identifying the relationships between individual variables (i.e. univariate effect) within multiple interacting variables remains challenging and long-lasting. In this study, we propose a novel model—Geographical Pattern Interaction (GPI)—based on the premise that the spatial pattern of a response variable emerges from the interaction of spatial patterns in explanatory variables. GPI leverages decision trees and Shapley value explanations to quantify both global and local univariate effects by measuring the alignment between the spatial distribution of the target variable and those of the predictors. Through simulation experiments, we demonstrate GPI’s superior performance compared to traditional regression-based spatial explanation methods. Notably, the GPI framework is stable across varying spatial scales and sample sizes, making it particularly suitable for spatial explanation tasks under small data and multi-scale conditions. A case study on homelessness risk in Australia demonstrates GPI’s ability to reveal nonlinear spatial associations and interaction effects. By capturing overlooked pattern similarities and interactions, GPI offers an interpretable and transferable tool for analyzing complex spatial relationship...