Spatio-temporal Dynamics of Population in Shanghai: A Case Study Based on Cell Phone Signaling Data

  • De WangEmail author
  • Weijing Zhong
  • Zhenxuan Yin
  • Dongcan Xie
  • Xiao Luo
Part of the Advances in Geographic Information Science book series (AGIS)


The analysis of spatial and temporal dynamic distribution of population is an important basis for recognizing people’s behavior patterns and urban spatial structure, allocating urban public infrastructures, and making emergency plans of public safety. Due to the lack of data on spatial and temporal dynamic distribution of population, research related to this area is limited in China. As cell phone becomes the most popular communication terminal, the spatial and temporal distribution of cell phone users should be able to reflect that of population accurately. Using datasets of cell phone signaling records and related data of Shanghai like land use, this study attempts to build an analytical framework on internal relations among population, time, and behavior, to recognize characteristics of spatial and temporal dynamic distribution of population in Shanghai. The results show as follows: (1) the density of population appears to be monocentric distribution, and this characteristic is more significant during the day compared to that at night. The spatial distribution experiences the central aggregation process during daytime and the decentralization to suburban area during nighttime. (2) People’s behaviors (like commuting, leisure, and consumption) could cause the spatial and temporal distribution of population change. The spatial mismatch between residences and workplaces as well as the high dependence on the central area result in unevenly distribution of population and form the central aggregating pattern. (3) The dependence level of consumption and leisure on central area is significantly higher than that of employment, especially in the suburban areas adjacent to the central area.


Cell phone signaling data Daytime and nighttime population Workplaces and residential places Land use Shanghai 


  1. Ahas, R., & Mark, Ü. (2005). Location based service-new challenges for planning and public administration. Futures, 37(6), 547–561.CrossRefGoogle Scholar
  2. Ahas, R., Aasa, A., Silm, S., & Tiru, M. (2010). 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. Akkerman, A. (1995). The urban household pattern of daytime population change. The Annals of Regional Science, 29(1), 1–16.CrossRefGoogle Scholar
  4. Bian, X., Chen, H., & Cao, G. (2013). Patterns of regional urbanization and its implications: An empirical study of the Yangtze River Delta region. Geographical Research, 32(12), 2281–2291.Google Scholar
  5. Calabrese, F., Colonna, M., Lovisolo, P., Parata, D., & Ratti, C. (2011). Real-time urban monitoring using cell phones: A case study in Rome. IEEE Transactions on Intelligent Transportation Systems, 12(1), 141–151.CrossRefGoogle Scholar
  6. Calabrese, F., Mi, D., Lorenzo, G., Ferreira, J., & Ratti, C. (2013). Understanding individual mobility patterns from urban sensing data: A mobile phone trace example. Transportation Research Part C: Emerging Technologies, 26, 301–313.CrossRefGoogle Scholar
  7. Deville, P., Linard, C., Martin, S., et al. (2014). Dynamic population mapping using mobile phone data. Proceedings of the National Academy of Sciences, 111(45), 15888–15893.CrossRefGoogle Scholar
  8. Diao, M., Zhu, Y., Ferreira, J., & Ratti, C. (2015). Inferring individual daily activities from mobile phone traces: A Boston example. Environment and Planning B: Planning and Design, 43(5), 1–21.Google Scholar
  9. Ding, L., Niu, X., & Song, X. (2015). Identifying the commuting area of Shanghai central city using mobile phone data. City Planning Review, 39(9), 100–106.Google Scholar
  10. Elvidge, C. D., Imhoff, M., Baugh, K. E., et al. (2001). Night-time lights of the world: 1994–1995. ISPRS Journal of Photogrammetry and Remote Sensing, 56, 81–99.CrossRefGoogle Scholar
  11. Foley, D. L. (1952). The daily movement of population into central business districts. American Sociological Review, 17(5), 538–543.CrossRefGoogle Scholar
  12. Foley, D. L. (1954). Urban daytime population: A field for demographic-ecological analysis. Social Forces, 32, 323–330.CrossRefGoogle Scholar
  13. Guo, C., Zhen, F., & Zhu, S. (2014). Progress and prospect of the application of smart phone LBS data in urban researches. Human Geography, 29(6), 18–23.Google Scholar
  14. Li, Y., Liu, H., & Tang, Q. (2015). Spatial-temporal patterns of China’s interprovincial migration during 1985–2010. Geographical Research, 34(6), 1135–1148.Google Scholar
  15. Liu, Y., Xiao, Y., Gao, S., Kang, C., & Wang, Y. (2011). A review of human mobility research based on location aware devices. Geography and Geo-Information Science, 27(4), 8–13+3+2.Google Scholar
  16. Liu, Y., Liu, X., Gao, S., et al. (2015). Social sensing: A new approach to understanding our socioeconomic environments. Annals of the Association of American Geographers, 105(3), 512–530.CrossRefGoogle Scholar
  17. Long, Y., Shen, Z., & Mao, Q. (2011). Retrieving individual attributes from aggregate dataset for urban micro-simulation: A preliminary exploration. Acta Geographica Sinica, 66(3), 416–426.Google Scholar
  18. Long, Y., Mao, M., Mao, Q., Shen, Z., & Zhang, Y. (2014). Fine-scale urban modelling and its opportunities in the “Big data” era: Methods, data and empirical studies. Human Geography, 29(3), 7–13.Google Scholar
  19. Niu, X., & Ding, L. (2015). Analyzing job-housing spatial relationship in Shanghai using mobile phone data: Some conclusions and discussions. Shanghai Urban Planning Review, (121), 39–43.Google Scholar
  20. Niu, X., Ding, L., & Song, X. (2014). Understanding urban spatial structure of Shanghai central city based on mobile phone data. Urban Planning Forum, 6, 61–67.Google Scholar
  21. Pan, Q., Jin, X., & Zhou, Y. (2013). Population change and spatiotemporal distribution of China in recent 300 years. Geographical Research, 32, 1291–1302.Google Scholar
  22. Qin, X., Wei, Y., Chen, W., & Duan, X. (2013). Population expansion and polycentric development of Nanjing city in a period of hyper-growth. Geographical Research, 32(4), 711–719.Google Scholar
  23. Rao, Y., Song, J., & Yu, W. (2015). Spatial pattern and mechanism of population growth in metropolitan Beijing. Geographical Research, 34(1), 149–156.Google Scholar
  24. 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
  25. Roddis, S. M., & Richardson, A. J. (1998). Construction of daytime activity profiles from households travel survey data. Washington DC: National Research Council.Google Scholar
  26. Sleeter, R., & Wood, N. (2006). Estimating daytime and nighttime population density for coastal communities in Oregon. 44th Urban and Regional Information Systems Association Annual Conference. Columbia, British.Google Scholar
  27. The Floating Population Service Management Department of national population and Family Planning Commission. (2011). Floating population in China: 2011. Beijing: China population publishing house.Google Scholar
  28. Vieira, M., Frias-Martinez, V., Oliver, N., & Frias-Martinez, E. (2010). Characterizing Dense Urban Areas from Mobile Phone-Call Data: Discovery and Social Dynamics, In Proceedings of 2010 IEEE Second International Conference (pp. 241–248). Minneapolis.Google Scholar
  29. Wang, L., Yang, Y., & Feng, Z. (2014). Prediction of China’s population in 2020 and 2030 on county scale. Geographical Research, 33, 310–322.CrossRefGoogle Scholar
  30. Wang, D., Wang, C., Xie, D., et al. (2015a). Comparison of retail trade areas of retail centers with different Hierarchical levels: A case study of East Nanjing Road, Wujiaochang, Anshan Road in Shanghai. Urban Planning Forum, (223), 51–61.Google Scholar
  31. Wang, D., Zhong, W., Xie, D., & Ye, H. (2015b). The application of cell phone signaling data in the assessment of urban built environment: A case study of Baoshan district in Shanghai. Urban Planning Forum, (225), 82–90.Google Scholar
  32. Wu, Z., Chai, Y., Dang, A., et al. (2015). Geography interact with big data: Dialogue and reflection. Geographical Research, 34, 2207–2221.Google Scholar
  33. Yang, C., & Ning, Y. (2015). Evolution of spatial pattern of inter-provincial migration and its impacts on urbanization in China. Geographical Research, 34(8), 1492–1506.Google Scholar
  34. Yang, Z., Long, Y., & Douay, N. (2015). Opportunities and limitations of big data applications to human and economic geography: The state of the art. Progress in Geography, 34(4), 410–417.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. Zhang, X., Chai, Y., Chen, Z., & Tan, Y. (2016). Analysis of spatial and temporal patterns of daily activities of suburban residents based on GPS data: A case study of the Shangdi-Qinghe area of Beijing. International Review for Spatial Planning and Sustainable Development, 4, 4–16.CrossRefGoogle Scholar
  37. Zhen, F., & Wang, B. (2015). Rethinking human geography in the age of big data. Geographical Research, 34(5), 803–811.Google Scholar
  38. Zhen, F., Qin, X., & Xi, G. (2015). The innovation of geography and human geography in the information era. Scientia Geographica Sinica, 35(1), 11–18.Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • De Wang
    • 1
    Email author
  • Weijing Zhong
    • 1
    • 2
  • Zhenxuan Yin
    • 3
  • Dongcan Xie
    • 1
  • Xiao Luo
    • 3
  1. 1.Collage of Architecture and Urban PlanningTongji UniversityShanghaiChina
  2. 2.Hanzhou City Planning and Design AcademyHangzhouChina
  3. 3.Shanghai Tongji Urban Planning and Design InstituteShanghaiChina

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