Local pathways of association

Abstract

Spatial association reveals the interconnected nature of geographical phenomena, describing the interactions and influences of environmental variables across geographic space. Path analysis can explore complex causal relationships between variables by analyzing path coefficients. However, in large-scale studies, path analysis methods are often affected by local effects, which can influence the accuracy and reliability of the results. This study develops a local pathway association (LPA) model to analyze local effects of pathways among variables that integrates path analysis and local pathway coefficient estimations. The LPA model was employed to investigate the spatial heterogeneity of spatial associations between factors such as climate, soil, and vegetation on the Tibetan Plateau. Results indicate that the LPA model effectively reveals the spatial variation characteristics of local path coefficients between geographic variables, avoiding the underestimation or overestimation of global path coefficients in traditional path coefficient studies. The developed LPA model provides an effective technical tool for revealing spatial differences in path associations of large-scale spatial studies. The strong data compatibility of the LPA model allows for broad applicability across various disciplines and a deeper understanding of localized interactions and variations in complex geospatial and Earth systems.