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Visual analytics of bike-sharing data based on tensor factorization

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Abstract

Bike-sharing systems have grown tremendously worldwide in the recent years. Understanding the user activities in urban areas is invaluable, especially for bike rebalance and urban planning. However, it is difficult to directly capture the user activity patterns from the bike-sharing data due to its sparse and discontinuous characteristics. In the recent years, many methods have been explored to visualize the user activity patterns. Many previous methods focused on visually presenting the temporal and spatial distribution directly. In this paper, we construct a tensor based on the spatial, temporal, and user information of the bike-sharing data, and employ tensor factorization to extract latent user activity patterns. To facilitate the users to analyze and understand these patterns, a visual analytics system is designed to interactively explore these patterns from the spatial, temporal, and user dimensions and compare these patterns in/between cities. We demonstrate the effectiveness of our system via case studies with real-word datasets.

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Acknowledgements

This work was partially supported by National Natural Science Foundation of China no. 61472354 and 61672452, NSFC-Guangdong Joint Fund no. U1611263, and the Fundamental Research Funds for the Central Universities. Map data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org.

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Correspondence to Yubo Tao.

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Yan, Y., Tao, Y., Xu, J. et al. Visual analytics of bike-sharing data based on tensor factorization. J Vis 21, 495–509 (2018). https://doi.org/10.1007/s12650-017-0463-1

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