Exploring the distribution and dynamics of functional regions using mobile phone data and social media data

How different functional regions in urban space are distributed and dynamically changing is determined by how their residents interact with them, which is crucial for urban managers to make urban planning decisions, respond to emergency quickly. Based on these, this paper proposed a novel approach for the probability based labelling individual activities which can be further used to explore the distribution of social land use at base tower station (BTS) level using a combination of multi-source spatiotemporal data, namely, call data and checkin data. We applied an experiment in Shenzhen, China, and the result is compared to Tencent Street View to demonstrate the effectiveness of the proposed approach to infer urban functional regions. _______________________________________________________ Jinzhou CAO State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, P. R. China Email: caojinzhou@whu.edu.cn Wei TU (Corresponding author) Key Laboratory for Geo-Environment Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation, Shenzhen University, Shenzhen 518060, P.R. China Email: tuwei@szu.edu.cn Qingquan LI (Corresponding author) Key Laboratory for Geo-Environment Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation, Shenzhen University, Shenzhen 518060, P.R. China. Email: liqq@szu.edu.cn Meng ZHOU Department of Geography, Hong Kong Baptist University, Hong Kong, P.R. China. Email: zhoumeng@life.hkbu.edu.hk Rui CAO School of Geodesy and Geometics, Wuhan University, Wuhan 430079, P. R. China Email: cr@whu.edu.cn CUPUM 2015 264-Paper

[1]  Andrew J. Viterbi,et al.  Error bounds for convolutional codes and an asymptotically optimum decoding algorithm , 1967, IEEE Trans. Inf. Theory.

[2]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[3]  K. Axhausen,et al.  Habitual travel behaviour: Evidence from a six-week travel diary , 2003 .

[4]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[5]  Xing Xie,et al.  Mining user similarity based on location history , 2008, GIS '08.

[6]  Alois Ferscha,et al.  Pervasive Computing , 2004, Lecture Notes in Computer Science.

[7]  Chaoming Song,et al.  Modelling the scaling properties of human mobility , 2010, 1010.0436.

[8]  Jr. G. Forney,et al.  Viterbi Algorithm , 1973, Encyclopedia of Machine Learning.

[9]  Albert-László Barabási,et al.  Limits of Predictability in Human Mobility , 2010, Science.

[10]  Margaret Martonosi,et al.  Identifying Important Places in People's Lives from Cellular Network Data , 2011, Pervasive.

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

[12]  Balázs Csanád Csáji,et al.  Exploring the Mobility of Mobile Phone Users , 2012, ArXiv.

[13]  Cecilia Mascolo,et al.  A Tale of Many Cities: Universal Patterns in Human Urban Mobility , 2011, PloS one.

[14]  Víctor Soto,et al.  Characterizing Urban Landscapes Using Geolocated Tweets , 2012, 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing.

[15]  Zhe Zhu,et al.  What's Your Next Move: User Activity Prediction in Location-based Social Networks , 2013, SDM.

[16]  Davy Janssens,et al.  Annotating mobile phone location data with activity purposes using machine learning algorithms , 2013, Expert Syst. Appl..

[17]  Zbigniew Smoreda,et al.  Unravelling daily human mobility motifs , 2013, Journal of The Royal Society Interface.

[18]  Carlo Ratti,et al.  Understanding individual mobility patterns from urban sensing data: A mobile phone trace example , 2013 .

[19]  Ling Bian,et al.  From traces to trajectories: How well can we guess activity locations from mobile phone traces? , 2014 .

[20]  Lun Wu,et al.  Intra-Urban Human Mobility and Activity Transition: Evidence from Social Media Check-In Data , 2014, PloS one.