Identifying Hot Lines of Urban Spatial Structure Using Cellphone Call Detail Record Data

The rapid growth of cell phone users in cities enable the cell phone towers spread all over urban area in past years. The user call logs, which refer to users movement trajectory in urban area, can provide an opportunity to understand urban spatial structure. As the extraction of more popular channel of human movement in urban area, the hot lines highlighted the spatial morphology of human flows in urban structure. In this paper, we propose popularity index that utilizes diversity and density index of channel to identify the hot lines based on cell phone call detail record dataset. The density of cell phone users that travel across one channel and the diversity of travel behaviors from different cell phone users refer to one channel has been combined to infer the level of popularity index for each channel. In the case study, a call detail record dataset that generated from the users of an anonymous telecom in Wuhan has been applied to identify the hot lines. The results showed the effectiveness of our approach and can be used as references for more explicitly representing urban dynamics to support urban plan applications.

[1]  Ramón Cáceres,et al.  A Tale of One City: Using Cellular Network Data for Urban Planning , 2011, IEEE Pervasive Computing.

[2]  Nathan Eagle,et al.  Place-Based Attributes Predict Community Membership in a Mobile Phone Communication Network , 2013, PloS one.

[3]  L. Venkata Subramaniam,et al.  Mining GPS data to determine interesting locations , 2011, IIWeb '11.

[4]  Dietmar Bauer,et al.  Inferring land use from mobile phone activity , 2012, UrbComp '12.

[5]  Soong Moon Kang,et al.  Structure of Urban Movements: Polycentric Activity and Entangled Hierarchical Flows , 2010, PloS one.

[6]  Xing Xie,et al.  Towards mobile intelligence: Learning from GPS history data for collaborative recommendation , 2012, Artif. Intell..

[7]  Nicholas Jing Yuan,et al.  Segmentation of Urban Areas Using Road Networks , 2012 .

[8]  Xing Xie,et al.  Learning travel recommendations from user-generated GPS traces , 2011, TIST.

[9]  Antti Vasanen Functional Polycentricity: Examining Metropolitan Spatial Structure through the Connectivity of Urban Sub-centres , 2012 .

[10]  Kara M. Kockelman,et al.  Travel Behavior as Function of Accessibility, Land Use Mixing, and Land Use Balance: Evidence from San Francisco Bay Area , 1997 .

[11]  Xing Xie,et al.  Discovering regions of different functions in a city using human mobility and POIs , 2012, KDD.

[12]  Shan Jiang,et al.  Discovering urban spatial-temporal structure from human activity patterns , 2012, UrbComp '12.

[13]  Prashant Krishnamurthy,et al.  Location Affiliation Networks: Bonding Social and Spatial Information , 2012, ECML/PKDD.

[14]  K. Small,et al.  URBAN SPATIAL STRUCTURE. , 1997 .

[15]  Yuanyuan Tian,et al.  Event-based social networks: linking the online and offline social worlds , 2012, KDD.

[16]  Xianfeng Huang,et al.  Identifying Spatial Structure of Urban Functional Centers Using Travel Survey Data: A Case Study of Singapore , 2013, COMP '13.