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Understanding urban dynamics via context-aware tensor factorization with neighboring regularization

J. Wang, J. Wu, Z. Wang, F. Gao, and Z. Xiong

IEEE Transactions on Knowledge and Data Engineering (TKDE), 2019

Recent years have witnessed the world-wide emergence of mega-metropolises with incredibly huge populations. Understanding residents mobility patterns, or urban dynamics, thus becomes crucial for building modern smart cities. In this paper, we propose a Neighbor-Regularized and context-aware Non-negative Tensor Factorization model (NR-cNTF) to discover interpretable urban dynamics from urban heterogeneous data. Different from many existing studies concerned with prediction tasks via tensor completion, NR-cNTF focuses on gaining urban managerial insights from spatial, temporal, and spatio-temporal patterns. This is enabled by high-quality Tucker factorizations regularized by both POI-based urban contexts and geographically neighboring relations. NR-cNTF is also capable of unveiling long-term evolutions of urban dynamics via a pipeline initialization approach. We apply NR-cNTF to a real-life data set containing rich taxi GPS trajectories and POI records of Beijing. The results indicate: 1) NR-cNTF accurately captures four kinds of city rhythms and seventeen spatial communities; 2) the rapid development of Beijing, epitomized by the CBD area, indeed intensifies the job-housing imbalance; 3) the southern areas with recent government investments have shown more healthy development tendency. Finally, NR-cNTF is compared with some baselines on traffic prediction, which further justifies the importance of urban contexts awareness and neighboring regulations.

Understanding urban dynamics via context-aware tensor factorization with neighboring regularization
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@article{wang2019understanding,

  title={Understanding urban dynamics via context-aware tensor factorization with neighboring regularization},

  author={Wang, Jingyuan and Wu, Junjie and Wang, Ze and Gao, Fei and Xiong, Zhang},

  journal={IEEE Transactions on Knowledge and Data Engineering},

  year={2019},

  publisher={IEEE}

}