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In the built environment, places such as retail outlets and public sites are embedded in the spatial context formed by neighboring places. We define the sets of these symbiotic places in the proximity of a focal place as the place's “place niche”, which conceptually represents the features of the local environment. While current literature has focused on pairwise spatial colocation patterns, we represent the niche as an integrated feature for each type of place, and quantify the niches' variation across cities. Here, with point of interest (POI) data as an approximation of places in cities, we propose representation learning models to explore place niche patterns. The models generate two main outputs: first, distributed representations for place niche by POI category (e.g. Restaurant, Museum, Park) in a latent vector space, where close vectors represent similar niches; and second, conditional probabilities of POI appearance of …