Zone2Vec: Distributed Representation Learning of Urban Zones

Abstract

A metropolis consists of zones segmented by major roads. People travel between zones to conduct social activities. To analyze the characteristics of the entire city, we can explore regions' features and find region-wise latent relationships. In this paper, we propose a semantic associated zone embedding (SAZE) method using distributed representation learning. SAZE can generate zone embeddings which extract more comprehensive characteristics of each zone and fit for many urban computing tasks, rather than the task-oriented methods. To feed our SAZE, we not only consider the connections between zones via trajectory, but also embed its intrinsic properties. Furthermore, we apply SAZE to two tasks, zone classification and zone clustering visualization, respectively. For each task, we compare SAZE with other state-of-the-art baseline methods and the results have demonstrated the advantage of our model over the several methods.

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