Population Mobility Dynamics Estimated from Mobile Telephony Data

被引:35
作者
Doyle, John [1 ,2 ]
Hung, Peter [1 ]
Farrell, Ronan [2 ]
McLoone, Sean [3 ,4 ]
机构
[1] Natl Univ Ireland Maynooth, Maynooth, Kildare, Ireland
[2] Natl Univ Ireland Maynooth, Callan Inst Appl ICT, Maynooth, Kildare, Ireland
[3] Queens Univ Belfast, Energy Power & Intelligent Control Res Cluster, Sch Elect Elect Engn & Comp Sci, Belfast BT7 1NN, Antrim, North Ireland
[4] Natl Univ Ireland Maynooth, Dept Elect Engn, Maynooth, Kildare, Ireland
基金
爱尔兰科学基金会;
关键词
call detail records; imperfect trajectories; Markov chain; stationary distribution; population estimation; POSITIONING DATA; CELL PHONES; VARIABILITY; MODELS; SPACES;
D O I
10.1080/10630732.2014.888904
中图分类号
TU98 [区域规划、城乡规划];
学科分类号
0814 ; 082803 ; 0833 ;
摘要
In the last decade, mobile phones and mobile devices using mobile cellular telecommunication network connections have become ubiquitous. In several developed countries, the penetration of such devices has surpassed 100 percent. They facilitate communication and access to large quantities of data without the requirement of a fixed location or connection. Assuming mobile phones usually are in close proximity with the user, their cellular activities and locations are indicative of the user's activities and movements. As such, those cellular devices may be considered as a large scale distributed human activity sensing platform. This paper uses mobile operator telephony data to visualize the regional flows of people across the Republic of Ireland. In addition, the use of modified Markov chains for the ranking of significant regions of interest to mobile subscribers is investigated. Methodology is then presented which demonstrates how the ranking of significant regions of interest may be used to estimate national population, results of which are found to have strong correlation with census data.
引用
收藏
页码:109 / 132
页数:24
相关论文
共 70 条
[21]  
CSO, POP CLASS AR, V1
[22]   Characterization and optimization of the power consumption in wireless access networks by taking daily traffic variations into account [J].
Deruyck, Margot ;
Tanghe, Emmeric ;
Joseph, Wout ;
Martens, Luc .
EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING, 2012, :1-12
[23]  
Doyle J., 2011, IET IR SIGN SYST C
[24]  
Eagle Nathan, 2009, 2009 International Conference on Computational Science and Engineering (CSE), P144, DOI 10.1109/CSE.2009.91
[25]  
Eagle N., 2009, P AAAI SPRING S TECH
[26]  
Eagle N, 2009, LECT NOTES COMPUT SC, V5538, P342, DOI 10.1007/978-3-642-01516-8_23
[27]  
Edwards C, 2010, NEW ELECTRON, V43, P19
[28]  
Frias-Martinez E., 2011, Proceedings of the 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and IEEE Third International Conference on Social Computing (PASSAT/SocialCom 2011), P57, DOI 10.1109/PASSAT/SocialCom.2011.142
[29]   Understanding individual human mobility patterns [J].
Gonzalez, Marta C. ;
Hidalgo, Cesar A. ;
Barabasi, Albert-Laszlo .
NATURE, 2008, 453 (7196) :779-782
[30]  
Grinstead C.M., 1997, Introduction to Probability, Second Revised Edition (GNU version)