Local effects of pattern interactions in driving urbanization

• A local pattern interaction (LPI) model is developed to examine spatial association and interaction. • Compared with traditional models, LPI model improves explanatory power of local patterns of variables. • LPI integrates geocomplexity, locally stratified power, and interaction effects to uncover localized mechanisms of urbanization drivers. • LPI identifies local interactions of economic variables and patterns as key drivers of urbanization. Spatial association and spatial interaction are fundamental to understanding geographical phenomena and regional development disparities, with broad applicability across disciplines. Existing spatial heterogeneity analysis face significant challenges in capturing pattern interactions and local variability. This study develops a local pattern interaction (LPI) model that integrates local complexity patterns or geocomplexity of spatial data, the interaction of patterns, and their locally varied power of determinants (PD). LPI is implemented in assessing the PD of local variables and pattern interactions on the spatial distributions of urbanization using statistical data, remote sensing imagery, and open geospatial data. The results show that LPI effectively identifies the local PD of interactions involving the geocomplexity patterns of urbanization-related explanatory variables. Model performance is evaluated by comparison with the optimal-parameters geographical detector (OPGD), a widely used spatial heterogeneity–based PD identification model. The model validation shows that LPI provides advantages over OPGD by capturing spatially varying interaction patterns and local effects, whereas OPGD assesses only global interaction effects. For example, the LPI-derived PD for the interaction between total retail sales and the geocomplexity pattern of tertiary-industry output averages 0.610 [0.336,0.783], indicating critical spatial variation in both local PD values and their significance, while the OPGD-derived PD yields a single global estimate of 0.537 (p < 0.01). This research advances theoretical understanding of spatial association and interaction, while providing an innovative analytical tool and decision-support capability for regional development, urban planning, and resource allocation.