Learning dynamic relational heterogeneity for spatiotemporal prediction with geographical meta-knowledge
Kaiqi Chen, Xiaoyong Tan, Min Deng, Kaiyuan Lei, Wentao Yang, Huimin Liu, Cheng Hong Huang · International Journal of Geographical Information Systems · 2025
Spatial heterogeneity presents significant challenges in improving the spatiotemporal prediction (STP) performance of geographical phenomena. This study identified two primary interpretations of spatial heterogeneity: variable heterogeneity, which refers to spatially uneven distributions of variables, and relational heterogeneity, which involves spatial variations in the relationships between variables. Within STP models, relational heterogeneity is a significant factor that influences performance; however, it is often overlooked by most studies that primarily focus on spatiotemporal dependency or on variable heterogeneity. The dynamics of relational heterogeneity and spatial invariance components require further investigation for predictive learning. Therefore, in this study, we developed a novel Geographical Meta-Learning Neural Network (GeoMetaNet) to address these issues. GeoMetaNet consists of a global component for spatial invariance learning and a local component for dynamic relational heterogeneity learning. In the local component, a meta-learning strategy adjusts the model parameters in different regions with various geographical environments, subject to the effects of spatiotemporal dependency and geographical similarity. We evaluated GeoMetaNet’s performance in predicting cellular traffic in Milan, demonstrating it outperformed several state-of-the-art STP models across 15 STP tasks. We analyze GeoMetaNet’s effectiveness in learning dynamic relational heterogeneity and explore geographical meta-knowledge utility for downstream mining tasks.