Visual analytics of bike-sharing data based on tensor factorization

Visual analytics of bike-sharing data based on tensor factorization
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基于张量分解的共享单车数据可视化分析

DOI:
10.1007/s12650-017-0463-1
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发表时间:
2018-02
影响因子:
1.7
通讯作者:
Lin Hai
Lin Hai
中科院分区:
计算机科学4区
文献类型:
--
作者:
Yan Yuyu;Tao Yubo;Xu Jin;Ren Shuilin;Lin Hai

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摘要近年来,自行车共享系统在全球范围内发展迅猛。了解城市地区的用户活动是非常宝贵的,特别是对于自行车再平衡和城市规划。然而,由于共享单车数据的稀疏性和不连续性,很难直接从共享单车数据中获取用户活动模式。近年来,人们探索了许多方法来可视化用户活动模式。以往的许多方法侧重于直观地呈现时空分布。本文基于共享单车数据的空间、时间和用户信息构建了一个张量,并利用张量分解提取潜在的用户活动模式。为了方便用户分析和理解这些模式,设计了一个可视化分析系统,从空间、时间和用户维度交互式地探索这些模式,并在城市/城市之间比较这些模式。我们通过实际数据集的案例研究证明了我们系统的有效性。图形抽象
AbstractBike-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.Graphical abstract
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