Paper Info
Title
Understanding Urban Dynamics via Context-Aware Tensor Factorization with Neighboring Regularization
Abstract
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.
Year
DOI
Venue
2020
10.1109/TKDE.2019.2915231
IEEE Transactions on Knowledge and Data Engineering
Keywords
DocType
Volume
Urban areas,Data models,Global Positioning System,Trajectory,Public transportation,Sociology
Journal
32
Issue
ISSN
Citations 
11
1041-4347
3
PageRank 
References 
Authors
0.37
0
5
Name
Order
Citations
PageRank
Jingyuan Wang112417.40
Junjie Wu255147.60
Ze Wang3185.03
Fei Gao4263.52
Zhang Xiong51069102.45