Journal of Geographical Systems

, Volume 17, Issue 2, pp 157–185 | Cite as

Mobile phone usage in complex urban systems: a space–time, aggregated human activity study

Original Article


The present study aims to demonstrate the importance of digital data for investigating space–time dynamics of aggregated human activity in urban systems. Such dynamics can be monitored and modelled using data from mobile phone operators regarding mobile telephone usage. Using such an extensive dataset from the city of Amsterdam, this paper introduces space–time explanatory models of aggregated human activity patterns. Various modelling experiments and results are presented, which demonstrate that mobile telephone data are a good proxy of the space–time dynamics of aggregated human activity in the city.


Mobile phone Human activity Land use Urban dynamics 

JEL Classification

R14 R00 R15 O18 



This research is funded by the Urban Regions in the Delta programme, Netherlands Organisation for Scientific Research (NWO) and by the Dutch Ministry of Infrastructure and the Environment (RWS). The authors would also like to acknowledge the support of John Steenbruggen for his help with data acquisition.

Supplementary material (430 kb)
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  1. ADUML (1991) Plan de Developpement Urbain de la Communication. Agence de Developpement d’Urbanisme de la Metropole Lilloise, 2 place du Concert, F 59043, Lille, FranceGoogle Scholar
  2. Ahas R, Mark Ü (2005) Location based services—new challenges for planning and public administration? Futures 37(6):547–561CrossRefGoogle Scholar
  3. Anselin L (1995) Local indicators of spatial association – LISA. Geogr Anal 27(2):93–115CrossRefGoogle Scholar
  4. Arribas-Bel D (2014) Accidental, open and everywhere: emerging data sources for the understanding of cities. Appl Geogr 49:45–53CrossRefGoogle Scholar
  5. Batty M (1997a) The computable city. Int Plan Stud 2(2):155–173CrossRefGoogle Scholar
  6. Batty M (1997b) Virtual geography. Futures 29(4/5):337–352CrossRefGoogle Scholar
  7. Batty M (2010) The pulse of the city. Environ Plan B Plan Des 37(4):575–577CrossRefGoogle Scholar
  8. Becker RA, Caceres R, Hanson K, Loh JM, Urbanek S, Varshavsky A, Volinsky C (2011) A tale of one city: using cellular network data for urban planning. Pervasive Comput 10(4):18–26CrossRefGoogle Scholar
  9. Bisker S, Gross M, Carter D, Paulos E, Kuznetsov S (2010) Personal, public: using DIY to explore citizen-led efforts in urban computing. In: CHI 2010 proceedings, Atlanta, GeorgiaGoogle Scholar
  10. Caceres N, Wideberg J, Benitez F (2008) Review of traffic data estimations extracted from cellular networks. IET Intel Transp Syst 2(3):179–192CrossRefGoogle Scholar
  11. Calabrese F, Di Lorenzo G, Ratti C (2010) Human mobility prediction based on individual and collective geographical preferences. In: Intelligent transportation systems (ITSC), 2010 13th international IEEE conference on. IEEE, pp 312–317Google Scholar
  12. Calabrese F, Smoreda Z, Blondel VD, Ratti C (2011) Interplay between telecommunications and face-to-face interactions: a study using mobile phone data. PLoS ONE 6(7):e20814CrossRefGoogle Scholar
  13. Calabrese F, Diao M, Di Lorenzo G, Ferreira J Jr, Ratti C (2013) Understanding individual mobility patterns from urban sensing data: a mobile phone trace example. Trans Res C Emerg Technol 26:301–313CrossRefGoogle Scholar
  14. Calabrese F, Ferrari L, Blondel VD (2014) Urban sensing using mobile phone network data: a survey of research. ACM Comput Surv (CSUR) 47(2):25CrossRefGoogle Scholar
  15. Candia J, Gonzalez MC, Wang P, Schoenharl T, Madey G, Barabási A-L (2008) Uncovering individual and collective human dynamics from mobile phone records. J Phys A Math Theor 41(22):224015CrossRefGoogle Scholar
  16. Castells M (1996) The rise of the network society. Blackwell, OxfordGoogle Scholar
  17. CBS (2012) Land use dataset. Centraal Bureau voor de Statistiek (Statistics Netherlands), Den HaagGoogle Scholar
  18. Evans-Cowley J (2010) Planning in the real-time city: the future of mobile technology. J Plan Lit 25(2):136–149CrossRefGoogle Scholar
  19. Fridstrøm L (1999) Econometric models of road use, accidents, and road investment decisions, vol II. Institute of Transport Economics, OsloGoogle Scholar
  20. Graham S (1997) Cities in the real-time age: the paradigm challenge of telecommunications to the conception and planning of urban space. Environ Plan A 29(1):105–127CrossRefGoogle Scholar
  21. Graham S, Healey P (1997) Relational concepts of space and place: issues for planning theory and practice. Eur Plan Stud 7(5):623–646CrossRefGoogle Scholar
  22. Graham S, Marvin S (1996) Telecommunications and the city. Routledge, London, New YorkCrossRefGoogle Scholar
  23. Graham S, Marvin S (2001) Splintering urbanism. Routledge, London, New YorkCrossRefGoogle Scholar
  24. Jacobs-Crisioni C, Rietveld P, Koomen E, Tranos E (2014) Evaluating the impact of land-use density and mix on spatiotemporal urban activity patterns: an exploratory study using mobile phone data. Environ Plan A 46(11):2769–2785CrossRefGoogle Scholar
  25. KNMI (2012) Daily weather data of the Netherlands. Royal Netherlands Meteorological Institute, De BiltGoogle Scholar
  26. Koetse MJ, Rietveld P (2009) The impact of climate change and weather on transport: an overview of empirical findings. Transp Res Part D Transp Environ 14(3):205–221CrossRefGoogle Scholar
  27. Lambiotte R, Blondel VD, de Kerchove C, Huens E, Prieur C, Smoreda Z, Van Dooren P (2008) Geographical dispersal of mobile communication networks. Phys A 387:5317–5532CrossRefGoogle Scholar
  28. Licoppe C, Diminescu D, Smoreda Z, Ziemlicki C (2008) Using mobile phone geolocalisation for ‘socio-geographical’ analysis of co-ordination, urban mobilities, and social integration patterns. Tijdschrift voor Economische en Sociale Geografie 99(5):584–601CrossRefGoogle Scholar
  29. Louail T, Lenormand M, García Cantú O, Picornell M, Herranz R, Frias-Martinez E, Ramasco JJ, Barthelemy M (2014) From mobile phone data to the spatial structure of cities. arXiv:1401.4540
  30. Massey D (1992) Politics and space/time. New Left Rev 196:65–84Google Scholar
  31. Miller HJ (2010) The data avalanche is here. Shouldn’t we be digging? J Reg Sci 50(1):181–201CrossRefGoogle Scholar
  32. NDW (2012) National data warehouse for traffic estimation. Accessed 28 Dec 2014
  33. 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. Int J Geogr Inf Sci 28(9):1988–2007CrossRefGoogle Scholar
  34. Ratti C, Pulselli RM, Williams S, Frenchman D (2006) Mobile Landscapes: using location data from cell phones for urban analysis. Environ Plan B 33(5):727–748CrossRefGoogle Scholar
  35. Reades J, Calabrese F, Ratti C (2009) Eigenplaces: analyzing cities using the space-time structure of the mobile phone network. Environ Plan B 36(5):824–836CrossRefGoogle Scholar
  36. Sassen S (1991) The global city. New York, London, Tokyo. Princeton University Press, Princeton, New JerseyGoogle Scholar
  37. Sevtsuk A, Ratti C (2010) Does urban mobility have a daily routine? Learning from the aggregate data of mobile networks. J Urban Technol 17(1):41–60CrossRefGoogle Scholar
  38. Soja E (1989) Postmodern geographies. Verso, LondonGoogle Scholar
  39. Song C, Koren T, Wang P, Barabási A-L (2010a) Modelling the scaling properties of human mobility. Nat Phys 6:818–823CrossRefGoogle Scholar
  40. Song C, Qu Z, Blumm N, Barabási A-L (2010b) Limits of predictability in human mobility. Science 327:1018–1021CrossRefGoogle Scholar
  41. Steenbruggen J, Tranos E, Nijkamp P (2014) Data from mobile phone operators: a tool for smarter cities? Telecommun Policy (in press)Google Scholar
  42. Thrift N (1996) Inhuman geographies: landscapes of speed, light and power. In: Thrift N (ed) Spatial formation. Sage, London, pp 256–311Google Scholar
  43. Toole JL, Ulm M, Gonzalez MC, Bauer D (2012) Inferring land use from mobile phone activity. In: UrbComp ‘12, Beijing, China, pp 1–8Google Scholar
  44. Townsend AM (2000) Life in the real-time city: mobile telephones and urban metabolism. J Urban Technol 7(2):85–104CrossRefGoogle Scholar
  45. Tranos E (2013) The geography of the internet: cities, regions and the internet infrastructure in Europe. New horizons in regional science. Edward Elgar, CheltenhamCrossRefGoogle Scholar
  46. Tranos E, Nijkamp P (2014) Urban and regional analysis and the digital revolution: challenges and opportunities. In: Derruder B, Conventz S, Thierstein A, Witlox F (eds) Hub cities in the knowledge economy. Ashgate, Surrey, pp 145–162Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.School of Geography, Earth and Environmental SciencesUniversity of BirminghamBirminghamUK
  2. 2.Department of Spatial EconomicsVU University AmsterdamAmsterdamThe Netherlands

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