Advertisement

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
Chapter
Part of the Human Dynamics in Smart Cities book series (HDSC)

Abstract

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.

Keywords

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

Notes

Acknowledgements

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).

References

  1. Ahas, R., Aasa, A., Mark, Ü., Pae, T., & Kull, A. (2007). Seasonal tourism spaces in Estonia: Case study with mobile positioning data. Tourism Management, 28(3), 898–910.CrossRefGoogle Scholar
  2. Ahas, R., Aasa, A., Silm, S., & Tiru, M. (2010a). Daily rhythms of suburban commuters’ movements in the Tallinn metropolitan area: case study with mobile positioning data. Transportation Research Part C: Emerging Technologies, 18(1), 45–54.CrossRefGoogle Scholar
  3. Ahas, R., Silm, S., Järv, O., Saluveer, E., & Tiru, M. (2010b). Using mobile positioning data to model locations meaningful to users of mobile phones. Journal of Urban Technology, 17(1), 3–27.CrossRefGoogle Scholar
  4. Balk, D., & Yetman, G. (2004). The global distribution of population: evaluating the gains in resolution refinement. New York: Center for International Earth Science Information Network (CIESIN), Columbia University.Google Scholar
  5. Ball, P. (2010). Predicting human activity. Nature, 465(7299), 692.CrossRefGoogle Scholar
  6. Barabási, A.-L. 2010. Bursts: the hidden patterns behind everything we do, from your e-mail to bloody crusades: Penguin.Google Scholar
  7. Bhaduri, B., Bright, E., Coleman, P., & Urban, M. L. (2007). LandScan USA: a high-resolution geospatial and temporal modeling approach for population distribution and dynamics. GeoJournal, 69(1–2), 103–117.CrossRefGoogle Scholar
  8. Birenboim, A., & Shoval, N. (2015). Mobility research in the age of the smartphone. Annals of the American Association of Geographers, 106(2), 283–291.Google Scholar
  9. Candia, J., González, M. C., Wang, P., Schoenharl, T., Madey, G., & Barabási, A.-L. (2008). Uncovering individual and collective human dynamics from mobile phone records. Journal of Physics A: Mathematical and Theoretical, 41(22), 224015.CrossRefGoogle Scholar
  10. Csáji, B. C., Browet, A., Traag, V. A., Delvenne, J.-C., Huens, E., Van Dooren, P., et al. (2013). Exploring the mobility of mobile phone users. Physica A: Statistical Mechanics and its Applications, 392(6), 1459–1473.CrossRefGoogle Scholar
  11. Cho, E., Myers, S. A, & Leskovec, J. (2011). Friendship and mobility: user movement in location-based social networks. In Paper read at Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1082–1090). San Diego, CA: ACM.Google Scholar
  12. Deville, P., Linard, C., Martin, S., Gilbert, M., Stevens, F. R., Gaughan, A. E., et al. (2014). Dynamic population mapping using mobile phone data. Proceedings of the National Academy of Sciences, 111(45), 15888–15893.CrossRefGoogle Scholar
  13. Dobson, J. E., Bright, E. A., Coleman, P. R., Durfee, R. C., & Worley, B. A. (2000). LandScan: a global population database for estimating populations at risk. Photogrammetric Engineering and Remote Sensing, 66(7), 849–857.Google Scholar
  14. Girardin, F., Vaccari, A., Gerber, A., Biderman, A., & Ratti, C. (2009). Towards estimating the presence of visitors from the aggregate mobile phone network activity they generate. In International Conference on Computers in Urban Planning and Urban Management.Google Scholar
  15. Gonzalez, M. C., Hidalgo, C. A., & Barabási, A.-L. (2008). Understanding individual human mobility patterns. Nature, 453(7196), 779–782.CrossRefGoogle Scholar
  16. Harvey, J. T. (2002a). Estimating census district populations from satellite imagery: some approaches and limitations. International Journal of Remote Sensing, 23(10), 2071–2095.CrossRefGoogle Scholar
  17. Harvey, J. T. (2002b). Population estimation models based on individual TM pixels. Photogrammetric Engineering and Remote Sensing, 68(11), 1181–1192.Google Scholar
  18. International Telecommunication Union. (2014). World Telecommunication Development Conference (WTDC-2014): Final Report. (ITU, Dubai, United Arab Emirates).Google Scholar
  19. International Telecommunication Union. (2015). ICT facts and figures—the world in 2015. (http://www.itu.int/en/ITU-D/Statistics/Documents/facts/ICTFactsFigures2015.pdf last accessed on February 6, 2016).
  20. Isaacman, S., Becker, R., Cáceres, R., Martonosi, M., Rowland, J., Varshavsky, A., Willinger, W. (2012). Human mobility modeling at metropolitan scales. In Proceedings of the 10th International Conference on Mobile systems, applications, and services (pp. 239–252). ACM.Google Scholar
  21. Kang, C., Liu, Y., Ma, X., & Wu, L. (2012). Towards estimating urban population distributions from mobile call data. Journal of Urban Technology, 19(4), 3–21.CrossRefGoogle Scholar
  22. Pei, T., Sobolevsky, S., Ratti, C., Shaw, S.-L., Li, T., & Zhou, C. (2014). A new insight into land use classification based on aggregated mobile phone data. International Journal of Geographical Information Science, 28(9), 1988–2007.CrossRefGoogle Scholar
  23. Ranjan, G., Zang, H., Zhang, Z.-L., & Bolot, J. (2012). Are call detail records biased for sampling human mobility? ACM SIGMOBILE Mobile Computing and Communications Review, 16(3), 33–44.CrossRefGoogle Scholar
  24. Ratti, C., Frenchman, D., Pulselli, R. M., & Williams, S. (2006). Mobile landscapes: using location data from cell phones for urban analysis. Environment and Planning B: Planning and Design, 33(5), 727–748.CrossRefGoogle Scholar
  25. Reades, J., Calabrese, F., & Ratti, C. (2009). Eigenplaces: analysing cities using the space–time structure of the mobile phone network. Environment and Planning B: Planning and Design, 36(5), 824–836.CrossRefGoogle Scholar
  26. Sevtsuk, A., & Ratti, C. (2010). Does urban mobility have a daily routine? Learning from the aggregate data of mobile networks. Journal of Urban Technology, 17(1), 41–60.CrossRefGoogle Scholar
  27. Schwanen, T., & Kwan, M.-P. (2008). The internet, mobile phone and space-time constraints. Geoforum, 39(3), 1362–1377.CrossRefGoogle Scholar
  28. Shanghai Bureau of Statistics. 2014. 2014 年上海市国民经济和社会发展统计公报 [Shanghai Economic and Social Development Statistical Bulletin 2014]. http://www.stats-sh.gov.cn/sjfb/201502/277392.html (last accessed 15 February 2016).
  29. Silm, S., & Ahas, R. (2010). The seasonal variability of population in Estonian municipalities. Environment and Planning A, 42(10), 2527–2546.CrossRefGoogle Scholar
  30. Soto, V., & Frías-Martínez E. (2011). Automated land use identification using cell-phone records. In Proceedings of the 3rd ACM International Workshop on MobiArch (​pp. 17–22). ACM.Google Scholar
  31. Stevens, F. R., Gaughan, A. E., Linard, C., & Tatem, A. J. (2015). Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data. PLoS ONE, 10(2), e0107042.CrossRefGoogle Scholar
  32. Vieira, M. R., Frias-Martinez, V., Oliver, N & Frias-Martinez, E. (2010). Characterizing dense urban areas from mobile phone-call data: Discovery and social dynamics. In Paper read at Social Computing (SocialCom), 2010 IEEE Second International Conference on.Google Scholar
  33. Xu, Y., Shaw, S.-L., Zhao, Z., Yin, L., Fang, Z., & Li, Q. (2015). Understanding aggregate human mobility patterns using passive mobile phone location data: a home-based approach. Transportation, 42(4), 625–646.CrossRefGoogle Scholar
  34. Xu, Y., Shaw, S.-L., Zhao, Z., Yin, L., Lu, F., Chen, J., et al. (2016). Another tale of two cities: understanding human activity space using actively tracked cellphone location data. Annals of the American Association of Geographers, 106(2), 489–502.Google Scholar
  35. Yuan, Y., Raubal, M., & Liu, Y. (2012). Correlating mobile phone usage and travel behavior—a case study of Harbin, China. Computers, Environment and Urban Systems, 36(2), 118–130.CrossRefGoogle Scholar
  36. Zhao, Z., Shaw, S.-L., Xu, Y., Lu, F., Chen, J., & Yin, L. (2016). Understanding the bias of call detail records in human mobility research. International Journal of Geographical Information Science​30(9), 1738–1762.Google Scholar

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

Personalised recommendations