Uncovering the Relationships Between Phone Communication Activities and Spatiotemporal Distribution of Mobile Phone Users

  • Yang Xu
  • Shih-Lung Shaw
  • Feng Lu
  • Jie Chen
  • Qingquan Li
Part of the Human Dynamics in Smart Cities book series (HDSC)


In recent years, call detail records (CDRs) have been widely used to study various aspects of urban and human dynamics. One assumption implicitly made in many existing studies is that people’s phone communication activities could represent spatiotemporal distribution of the population, or at least of the mobile phone users. By using a mobile phone data set which consists of CDRs plus other cellphone-related logs (e.g., cellular handover and periodic location update), we derive two cellphone usage indicators (volume of calls/messages [\(V\)] and number of active phone users [\(N\)]) as well as the spatiotemporal distribution of mobile phone users, and evaluate their relationships through correlation and regression analysis. We find that the correlations between the number of mobile phone users and each of the two cellphone usage indicators remain high and stable during the day time and in early evening (i.e., 07:00–21:30). However, their relationships revealed by the regression models vary greatly throughout a day. Researchers therefore should be cautious when using mobile phone communication activities to quantify certain aspects of urban dynamics. Our regression analyses suggest that the log-transformation model performs better than the simple linear regression model (in predicting phone user distribution) when the independent variable (\(V\) or \(N\)) is fixed. Also, we find that \(N\) serves as a better independent variable than \(V\), which is affected more by individual “burst” of phone communication activities, when explaining spatiotemporal distribution of mobile phone users. A 3-fold cross validation suggests that CDRs can be used along with other data sources (e.g., land use) to deliver more robust estimation of phone user distributions, which potentially facilitate dynamic projection of urban population distributions.


Call detail records Cellphone usage Space-time Population distribution Urban dynamics 



This research was jointly supported by the Alvin and Sally Beaman Professorship and Arts and Sciences Excellence Professorship of the University of Tennessee, Natural Science Foundation of China (41231171, 41371377, 41501486, 91546106, 41571431), Key Program of the Chinese Academy of Science (ZDRW-ZS-2016-6-3), and Beijing Key Laboratory of Urban Spatial Information Engineering (2014101).


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Yang Xu
    • 1
  • Shih-Lung Shaw
    • 2
    • 3
  • Feng Lu
    • 4
  • Jie Chen
    • 4
  • Qingquan Li
    • 5
  1. 1.Department of Land Surveying and Geo-InformaticsThe Hong Kong Polytechnic UniversityKowloonHong Kong
  2. 2.Department of GeographyUniversity of TennesseeKnoxvilleUSA
  3. 3.Guangzhou Institute of GeographyGuangzhouPeople’s Republic of China
  4. 4.State Key Laboratory of Resources and Environmental Information SystemInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesBeijingPeople’s Republic of China
  5. 5.Shenzhen Key Laboratory of Spatial Smart Sensing and ServicesShenzhen UniversityShenzhenPeople’s Republic of China

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