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Recent urbanization has coincided with the enrichment of geotagged data, suchas street view and point-of-interest (POI). Region embedding enhanced by thericher data modalities has enabled researchers and city administrators tounderstand the built environment, socioeconomics, and the dynamics of citiesbetter. While some efforts have been made to simultaneously use multi-modalinputs, existing methods can be improved by incorporating different measures of'proximity' in the same embedding space - leveraging not only the data thatcharacterizes the regions (e.g., street view, local businesses pattern) butalso those that depict the relationship between regions (e.g., trips, roadnetwork). To this end, we propose a novel approach to integrate multi-modalgeotagged inputs as either node or edge features of a multi-graph based ontheir relations with the neighborhood region (e.g., tiles, census block, ZIPcode region, etc.). We then learn the neighborhood representation based on acontrastive-sampling scheme from the multi-graph. Specifically, we use streetview images and POI features to characterize neighborhoods (nodes) and usehuman mobility to characterize the relationship between neighborhoods (directededges). We show the effectiveness of the proposed methods with quantitativedownstream tasks as well as qualitative analysis of the embedding space: Theembedding we trained outperforms the ones using only unimodal data as regionalinputs.

Learning Neighborhood Representation from Multi-Modal Multi-Graph: Image, Text, Mobility Graph and Beyond