Efficient Region Embedding with Multi-View Spatial Networks: A Perspective of Locality-Constrained Spatial Autocorrelations

Urban regions are places where people live, work, consume, and entertain. In this study, we investigate the problem of learning an embedding space for regions. Studying the representations of regions can help us to better understand the patterns, structures, and dynamics of cities, support urban planning, and, ultimately, to make our cities more livable and sustainable. While some efforts have been made for learning the embeddings of regions, existing methods can be improved by incorporating locality-constrained spatial autocorrelations into an encode-decode framework. Such embedding strategy is capable of taking into account both intra-region structural information and inter-region spatial autocorrelations. To this end, we propose to learn the representations of regions via a new embedding strategy with awareness of locality-constrained spatial autocorrelations. Specifically, we first construct multi-view (i.e., distance and mobility connectivity) POI-POI networks to represent regions. In addition, we introduce two properties into region embedding: (i) spatial autocorrelations: a global similarity between regions; (ii) top-k locality: spatial autocorrelations locally and approximately reside on top k most autocorrelated regions. We propose a new encoder-decoder based formulation that preserves the two properties while remaining efficient. As an application, we exploit the learned embeddings to predict the mobile checkin popularity of regions. Finally, extensive experiments with real-world urban region data demonstrate the effectiveness and efficiency of our method.