Jing Yuan
Yu Zheng
Xing Xie
ABSTRACT
The development of a city gradually fosters different functional regions, such as educational areas and business districts. In this paper, we propose a framework (titled DRoF) that Discovers Regions of different Functions in a city using both human mobility among regions and points of interests (POIs) located in a region. Specifically, we segment a city into disjointed regions according to major roads, such as highways and urban express ways. We infer the functions of each region using a topic-based inference model, which regards a region as a document, a function as a topic, categories of POIs (e.g., restaurants and shopping malls) as metadata (like authors, affiliations, and key words), and human mobility patterns (when people reach/leave a region and where people come from and leave for) as words. As a result, a region is represented by a distribution of functions, and a function is featured by a distribution of mobility patterns. We further identify the intensity of each function in different locations. The results generated by our framework can benefit a variety of applications, including urban planning, location choosing for a business, and social recommendations. We evaluated our method using large-scale and real-world datasets, consisting of two POI datasets of Beijing (in 2010 and 2011) and two 3-month GPS trajectory datasets (representing human mobility) generated by over 12,000 taxicabs in Beijing in 2010 and 2011 respectively. The results justify the advantages of our approach over baseline methods solely using POIs or human mobility.
Supplemental Material
- J. Antikainen. The concept of functional urban area. Findings of the Espon project, 1(1), 2005.Google Scholar
- S. Bednarz et al. Geography for Life: National Geography Standards. 1994.Google Scholar
- D. Blei. Introduction to probabilistic topic models. Communications of the ACM, 2011. Google ScholarDigital Library
- D. Blei, A. Ng, and M. Jordan. Latent dirichlet allocation. The Journal of Machine Learning Research, 3:993--1022, 2003. Google ScholarDigital Library
- R. Estkowski. No Steiner point subdivision simplification is NP-Complete. In Proc. 10th Canadian Conf. Computational Geometry. Citeseer, 1998.Google Scholar
- Y. Ge, C. Liu, H. Xiong, and J. Chen. A taxi business intelligence system. In Proc. KDD '11, pages 735--738, 2011. Google ScholarDigital Library
- Y. Ge, H. Xiong, A. Tuzhilin, K. Xiao, M. Gruteser, and M. Pazzani. An energy-efficient mobile recommender system. In Proc. KDD '10, pages 899--908, 2010. Google ScholarDigital Library
- C. Karlsson. Clusters, functional regions and cluster policies. JIBS and CESIS Electronic Working Paper Series (84), 2007.Google Scholar
- L. Lam, S. Lee, and C. Suen. Thinning methodologies-a comprehensive survey. IEEE Transactions on pattern analysis and machine intelligence, 14(9):869--885, 1992. Google ScholarDigital Library
- W. Liu, Y. Zheng, S. Chawla, J. Yuan, and X. Xing. Discovering spatio-temporal causal interactions in traffic data streams. In Proc. KDD '11, pages 1010--1018, 2011. Google ScholarDigital Library
- D. Mimno and A. McCallum. Topic models conditioned on arbitrary features with dirichlet-multinomial regression. In Uncertainty in Artificial Intelligence, pages 411--418, 2008.Google Scholar
- A. Pozdnoukhov and C. Kaiser. Space-time dynamics of topics in streaming text. In Proc. LBSN '11, pages 8:1--8:8, 2011. Google ScholarDigital Library
- G. Qi, X. Li, S. Li, G. Pan, Z. Wang, and D. Zhang. Measuring social functions of city regions from large-scale taxi behaviors. In IEEE PERCOM Workshops, pages 384--388, 2011.Google Scholar
- P. Rousseeuw. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20:53--65, 1987. Google ScholarDigital Library
- L. Shapiro and G. Stockman. Computer Vision. Prentice Hall, 2001. Google ScholarDigital Library
- R. R. Vatsavai, E. Bright, C. Varun, B. Budhendra, A. Cheriyadat, and J. Grasser. Machine learning approaches for high-resolution urban land cover classification: a comparative study. In Proc COM.Geo '11, pages 11:1--11:10, 2011. Google ScholarDigital Library
- M. Wand and M. Jones. Kernel smoothing, volume 60. Chapman & Hall/CRC, 1995.Google Scholar
- Z. Yin, L. Cao, J. Han, C. Zhai, and T. Huang. Geographical topic discovery and comparison. In Proc. WWW'11, pages 247--256, 2011. Google ScholarDigital Library
- J. Yuan, Y. Zheng, X. Xie, and G. Sun. Driving with knowledge from the physical world. In Proc. KDD '11, pages 316--324, 2011. Google ScholarDigital Library
- J. Yuan, Y. Zheng, L. Zhang, X. Xie, and G. Sun. Where to find my next passenger. In Proc. Ubicomp '11, pages 109--118, 2011. Google ScholarDigital Library
- Y. Zheng, Y. Liu, J. Yuan, and X. Xie. Urban computing with taxicabs. In Proc. Ubicomp '11, pages 89--98, 2011. Google ScholarDigital Library
- Y. Zheng and X. Zhou. Computing with spatial trajectories. Springer-Verlag New York Inc, 2011. Google ScholarDigital Library
Index Terms
- Discovering regions of different functions in a city using human mobility and POIs
Recommendations
Urban Computing: Concepts, Methodologies, and Applications
Special Section on Urban ComputingUrbanization's rapid progress has modernized many people's lives but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in ...
Deep ROI-Based Modeling for Urban Human Mobility Prediction
Rapidly developing location acquisition technologies have provided us with big GPS trajectory data, which offers a new means of understanding people's daily behaviors as well as urban dynamics. With such data, predicting human mobility at the city level ...
Transfer Urban Human Mobility via POI Embedding over Multiple Cities
Survey Paper, Special Issue on Urban Computing and Smart Cities and Regular PaperRapidly developing location acquisition technologies provide a powerful tool for understanding and predicting human mobility in cities, which is very significant for urban planning, traffic regulation, and emergency management. However, with the ...
Comments