Urban region representation learning via dual spatial contrasts

Region representation learning emerges as a new research paradigm to encode urban systems and facilitate geographic mapping. Recent studies have sought to reasonably introduce inductive biases, which refer to prior assumptions that guide model learning, from a geospatial perspective to improve the quality of region representations. However, there remain challenges in incorporating the spatial effects, e.g. spatial dependency and spatial heterogeneity, into inductive biases, as they are critical to the geographic awareness of region representations. In response, we developed a novel region representation learning framework, termed Region Graph Spatial Contrastive Learning (RGSCL), by leveraging building footprints and points of interest (POIs) along with prior spatial knowledge to derive region representations. Specifically, RGSCL first constructed multi-view region graphs with POIs, building footprints and their spatial proximity, to form a base representation space. Next, the algorithm adopted a contrastive learning mechanism with spatial effects to formulate a dual-spatial-contrast loss function to optimise the representation space. The dual-spatial-contrasts captured POI-building spatial dependency and the region’s spatial heterogeneity to compose semantics in region representations. Experimental results demonstrated that RGSCL improved performance in geographic mapping. This study offers new insights into GeoAI from the perspective of inductive biases with respect to spatial effects.