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

Abstract

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.

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Notes

  1. 1.

    The initial data was provided at a 6-digit post code level (CBS 2012) and then aggregated to the GSM areas.

  2. 2.

    The data only includes mobile phone usage for 11 months for 2010 and one month is excluded due to co-linearity.

References

  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, France

  2. Ahas R, Mark Ü (2005) Location based services—new challenges for planning and public administration? Futures 37(6):547–561

    Article  Google Scholar 

  3. Anselin L (1995) Local indicators of spatial association – LISA. Geogr Anal 27(2):93–115

    Article  Google Scholar 

  4. Arribas-Bel D (2014) Accidental, open and everywhere: emerging data sources for the understanding of cities. Appl Geogr 49:45–53

    Article  Google Scholar 

  5. Batty M (1997a) The computable city. Int Plan Stud 2(2):155–173

    Article  Google Scholar 

  6. Batty M (1997b) Virtual geography. Futures 29(4/5):337–352

    Article  Google Scholar 

  7. Batty M (2010) The pulse of the city. Environ Plan B Plan Des 37(4):575–577

    Article  Google 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):18–26

    Article  Google 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, Georgia

  10. Caceres N, Wideberg J, Benitez F (2008) Review of traffic data estimations extracted from cellular networks. IET Intel Transp Syst 2(3):179–192

    Article  Google 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 312–317

  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):e20814

    Article  Google 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 26:301–313

    Article  Google 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):25

    Article  Google 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):224015

    Article  Google Scholar 

  16. Castells M (1996) The rise of the network society. Blackwell, Oxford

    Google Scholar 

  17. CBS (2012) Land use dataset. Centraal Bureau voor de Statistiek (Statistics Netherlands), Den Haag

    Google Scholar 

  18. Evans-Cowley J (2010) Planning in the real-time city: the future of mobile technology. J Plan Lit 25(2):136–149

    Article  Google Scholar 

  19. Fridstrøm L (1999) Econometric models of road use, accidents, and road investment decisions, vol II. Institute of Transport Economics, Oslo

  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):105–127

    Article  Google Scholar 

  21. Graham S, Healey P (1997) Relational concepts of space and place: issues for planning theory and practice. Eur Plan Stud 7(5):623–646

    Article  Google Scholar 

  22. Graham S, Marvin S (1996) Telecommunications and the city. Routledge, London, New York

    Book  Google Scholar 

  23. Graham S, Marvin S (2001) Splintering urbanism. Routledge, London, New York

    Book  Google 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):2769–2785

    Article  Google Scholar 

  25. KNMI (2012) Daily weather data of the Netherlands. Royal Netherlands Meteorological Institute, De Bilt

    Google 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):205–221

    Article  Google 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 387:5317–5532

    Article  Google 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):584–601

    Article  Google 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 196:65–84

    Google Scholar 

  31. Miller HJ (2010) The data avalanche is here. Shouldn’t we be digging? J Reg Sci 50(1):181–201

    Article  Google Scholar 

  32. NDW (2012) National data warehouse for traffic estimation. http://www.ndw.nu/pagina/en/78/database/79/real-time_traffic_data/. 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):1988–2007

    Article  Google 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):727–748

    Article  Google 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):824–836

    Article  Google Scholar 

  36. Sassen S (1991) The global city. New York, London, Tokyo. Princeton University Press, Princeton, New Jersey

    Google 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):41–60

    Article  Google Scholar 

  38. Soja E (1989) Postmodern geographies. Verso, London

    Google Scholar 

  39. Song C, Koren T, Wang P, Barabási A-L (2010a) Modelling the scaling properties of human mobility. Nat Phys 6:818–823

    Article  Google Scholar 

  40. Song C, Qu Z, Blumm N, Barabási A-L (2010b) Limits of predictability in human mobility. Science 327:1018–1021

    Article  Google Scholar 

  41. Steenbruggen J, Tranos E, Nijkamp P (2014) Data from mobile phone operators: a tool for smarter cities? Telecommun Policy (in press)

  42. Thrift N (1996) Inhuman geographies: landscapes of speed, light and power. In: Thrift N (ed) Spatial formation. Sage, London, pp 256–311

    Google Scholar 

  43. Toole JL, Ulm M, Gonzalez MC, Bauer D (2012) Inferring land use from mobile phone activity. In: UrbComp ‘12, Beijing, China, pp 1–8

  44. Townsend AM (2000) Life in the real-time city: mobile telephones and urban metabolism. J Urban Technol 7(2):85–104

    Article  Google 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, Cheltenham

    Book  Google 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 145–162

    Google Scholar 

Download references

Acknowledgments

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.

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Correspondence to Emmanouil Tranos.

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Appendices

Appendix 1

figurea

Average car flow in Amsterdam motorways per hour in 2010. Source (NDW 2012)

Appendix 2

Estimation of (4) based on OLS

Time Land use Non-working days Working days Land use Non-working days Working days
00 Habitants (ln) 0.082*** 0.083*** Industrial (share) −1.037*** −1.246***
  (40.24) (60.56) (−20.39) (36.44)
01 0.080*** 0.070*** −1.746*** −2.282***
  (39.35) (50.78) (−34.32) (66.35)
02 0.066*** 0.045*** −2.523*** −3.394***
  (32.42) (32.87) (−49.33) (99.17)
03 0.059*** 0.024*** −3.376*** −4.388***
  (28.75) (17.81) (−66.37) (128.27)
04 0.053*** 0.007*** −3.716*** −4.491***
  (25.99) (4.78) (−73.06) (131.32)
05 0.038*** −0.002* −3.794*** −3.248***
  (18.58) (−1.75) (−74.6) (95)
06 0.020*** 0.016*** −3.490*** −1.079***
  (9.97) (11.52) (−68.62) (31.56)
07 0.020*** 0.048*** −2.671*** 0.803***
  (9.88) (35.15) (−52.52) (23.48)
08 0.041*** 0.070*** −1.662*** 2.078***
  (19.89) (51.05) (−32.48) (60.68)
09 0.068*** 0.085*** −0.558*** 2.841***
  (33.1) (61.99) (−10.91) (83.1)
10 0.087*** 0.092*** 0.134*** 3.103***
  (42.69) (67.14) (2.63) (90.74)
11 0.099*** 0.094*** 0.501*** 3.191***
  (48.14) (69.03) (9.8) (93.3)
12 0.103*** 0.096*** 0.677*** 3.120***
  (50.25) (70.17) (13.25) (91.23)
13 0.102*** 0.096*** 0.652*** 3.181***
  (50.06) (70.25) (12.75) (93.03)
14 0.101*** 0.096*** 0.541*** 3.173***
  (49.28) (70.45) (10.58) (92.98)
15 0.101*** 0.096*** 0.518*** 3.096***
  (49.18) (70.26) (10.13) (90.57)
16 0.099*** 0.098*** 0.432*** 2.929***
  (48.57) (72.09) (8.44) (86.26)
17 0.100*** 0.100*** 0.379*** 2.549***
  (48.93) (73.48) (7.42) (75.07)
18 0.100*** 0.099*** 0.307*** 1.887***
  (49) (73.07) (6.01) (55.59)
19 0.102*** 0.099*** 0.282*** 1.415***
  (49.86) (73.02) (5.51) (41.59)
20 0.107*** 0.106*** 0.354*** 1.167***
  (52.15) (78.18) (6.88) (34.3)
21 0.109*** 0.112*** 0.178*** 1.021***
  (53.46) (81.85) (3.49) (29.93)
22 0.102*** 0.105*** −0.121** 0.608***
  (49.89) (77.18) (−2.37) (17.79)
23 0.094*** 0.096*** −0.583*** −0.088**
  (45.7) (70.41) (−11.4) (2.57)
00 Railways (share) 1.869*** 1.653*** Business (share) −1.465*** −1.860***
  (14.04) (18.54) (−26.08) (49.33)
01 1.465*** 0.785*** −2.502*** −3.183***
  (11.01) (8.77) (−44.41) (83.86)
02 0.534*** −0.261*** −3.372*** −4.349***
  (3.99) (−2.93) (−59.71) (115.12)
03 −0.073 −1.269*** −3.815*** −5.132***
  (−0.55) (−14.24) (−67.91) (135.84)
04 −0.242* −1.242*** −4.529*** −5.526***
  (−1.82) (−13.94) (−80.61) (146.2)
05 −0.576*** −0.922*** −4.737*** −5.289***
  (−4.33) (−10.35) (−84.33) (139.53)
06 −0.287** −0.249*** −4.706*** −3.633***
  (−2.15) (−2.79) (−83.59) (96.34)
07 −0.253* 1.378*** −3.508*** −0.546***
  (−1.9) (15.44) (−62.45) (14.47)
08 0.092 2.773*** −2.015*** 1.480***
  (0.68) (31.09) (−35.56) (39.17)
09 0.850*** 3.574*** −0.739*** 2.431***
  (6.35) (40.11) (−13.09) (64.46)
10 1.747*** 3.866*** 0.167*** 2.681***
  (13.06) (43.43) (2.95) (71.13)
11 2.297*** 4.077*** 0.620*** 2.795***
  (17.16) (45.76) (10.97) (74.11)
12 2.550*** 4.097*** 0.848*** 2.712***
  (19.06) (45.98) (15.02) (71.88)
13 2.689*** 4.202*** 0.847*** 2.758***
  (20.1) (47.19) (15) (73.18)
14 2.725*** 4.178*** 0.724*** 2.785***
  (20.36) (47.03) (12.81) (73.98)
15 2.719*** 4.239*** 0.606*** 2.741***
  (20.32) (47.61) (10.74) (72.75)
16 2.732*** 4.307*** 0.615*** 2.616***
  (20.42) (48.69) (10.9) (69.86)
17 2.786*** 4.486*** 0.655*** 2.501***
  (20.82) (50.71) (11.59) (66.77)
18 2.739*** 4.211*** 0.408*** 2.128***
  (20.47) (47.6) (7.23) (56.83)
19 2.623*** 3.562*** 0.210*** 1.522***
  (19.6) (40.17) (3.72) (40.53)
20 2.519*** 3.314*** −0.004 1.026***
  (18.73) (37.38) (−0.06) (27.33)
21 2.547*** 3.213*** 0.042 0.893***
  (19.04) (36.13) (0.74) (23.7)
22 2.409*** 2.966*** −0.276*** 0.583***
  (18) (33.35) (−4.89) (15.5)
23 2.103*** 2.474*** −0.941*** −0.300***
  (15.72) (27.78) (−16.66) (7.98)
00 Motorways (share) −0.974*** −1.584*** City centre (share) 1.533*** 1.166***
  (−8.48) (−20.59) (52.2) (58.84)
01 −3.754*** −4.354*** 0.888*** 0.231***
  (−32.68) (−56.19) (30.21) (11.57)
02 −4.893*** −6.067*** 0.479*** −0.477***
  (−42.37) (−78.71) (16.23) (24.04)
03 −5.713*** −7.045*** 0.184*** −0.890***
  (−49.73) (−91.43) (6.28) (44.91)
04 −6.086*** −7.468*** −0.134*** −1.200***
  (−52.99) (−96.96) (4.55) (60.51)
05 −6.078*** −6.590*** −0.490*** −1.683***
  (−52.91) (−85.56) (16.68) (84.87)
06 −5.678*** −2.110*** −1.039*** −0.890***
  (−49.43) (−27.4) (35.39) (44.92)
07 −4.058*** 1.100*** −0.773*** 0.607***
  (−35.33) (14.27) (26.33) (30.58)
08 −2.276*** 4.073*** 0.163*** 1.825***
  (−19.69) (52.82) (5.52) (91.98)
09 −0.707*** 4.796*** 1.099*** 2.518***
  (−6.12) (62.28) (37.22) (127.03)
10 0.453*** 4.814*** 1.794*** 2.810***
  (3.92) (62.53) (60.76) (141.79)
11 1.162*** 4.938*** 2.196*** 2.958***
  (10.06) (64.11) (74.34) (149.16)
12 1.515*** 4.991*** 2.397*** 2.992***
  (13.12) (64.8) (81.15) (150.91)
13 1.580*** 5.048*** 2.459*** 3.021***
  (13.68) (65.56) (83.27) (152.5)
14 1.543*** 5.086*** 2.450*** 3.019***
  (13.36) (66.18) (82.97) (152.63)
15 1.450*** 5.231*** 2.437*** 3.024***
  (12.56) (67.93) (82.53) (152.65)
16 1.479*** 5.420*** 2.445*** 3.022***
  (12.8) (70.88) (82.8) (153.51)
17 1.607*** 5.718*** 2.440*** 3.047***
  (13.92) (74.77) (82.62) (154.76)
18 1.516*** 5.084*** 2.369*** 2.974***
  (13.13) (66.48) (80.24) (151.06)
19 1.221*** 3.684*** 2.229*** 2.759***
  (10.58) (48.07) (75.48) (139.83)
20 1.162*** 2.754*** 2.166*** 2.632***
  (10.01) (35.93) (72.97) (133.4)
21 0.941*** 2.495*** 2.138*** 2.599***
  (8.15) (32.48) (72.41) (131.32)
22 0.531*** 1.986*** 1.983*** 2.436***
  (4.6) (25.83) (67.16) (123.14)
23 −0.224* 0.765*** 1.684*** 2.004***
  (−1.94) (9.94) (57.02) (101.21)
00 Retail (share) 2.031*** 1.249*** Outer city centre (share) 1.351*** 1.310***
  (60.65) (54.87) (43.52) (62.8)
01 1.566*** 0.605*** 0.809*** 0.533***
  (46.76) (26.47) (26.05) (25.39)
02 1.173*** −0.021 0.291*** −0.202***
  (34.85) (−0.94) (9.33) (9.68)
03 0.686*** −0.642*** −0.188*** −0.823***
  (20.49) (−28.27) (6.05) (39.44)
04 0.279*** −1.386*** −0.620*** −1.230***
  (8.32) (−61.01) (19.97) (58.95)
05 −0.306*** −2.617*** −0.942*** −1.342***
  (−9.13) (−115.19) (30.34) (64.33)
06 −1.408*** −2.153*** −1.029*** −0.696***
  (−42.03) (−94.76) (33.16) (33.39)
07 −1.346*** −0.238*** −0.549*** 0.467***
  (−40.21) (−10.48) (17.69) (22.36)
08 −0.639*** 0.990*** 0.363*** 1.524***
  (−18.91) (43.49) (11.63) (72.94)
09 0.410*** 1.918*** 1.222*** 2.064***
  (12.17) (84.39) (39.15) (98.93)
10 1.341*** 2.464*** 1.805*** 2.294***
  (39.82) (108.45) (57.87) (109.99)
11 1.968*** 2.773*** 2.131*** 2.403***
  (58.45) (122.02) (68.29) (115.16)
12 2.441*** 2.994*** 2.263*** 2.427***
  (72.51) (131.68) (72.52) (116.29)
13 2.688*** 3.120*** 2.252*** 2.415***
  (79.82) (137.5) (72.17) (115.81)
14 2.826*** 3.174*** 2.188*** 2.403***
  (83.94) (139.94) (70.14) (115.42)
15 2.949*** 3.238*** 2.129*** 2.428***
  (87.61) (142.65) (68.25) (116.43)
16 2.994*** 3.279*** 2.127*** 2.469***
  (88.93) (145.27) (68.17) (119.19)
17 2.953*** 3.320*** 2.155*** 2.531***
  (87.73) (147.09) (69.09) (122.17)
18 2.762*** 3.157*** 2.135*** 2.590***
  (82.04) (139.87) (68.45) (125.05)
19 2.519*** 2.857*** 2.119*** 2.543***
  (74.83) (126.31) (67.91) (122.48)
20 2.401*** 2.677*** 2.133*** 2.518***
  (70.93) (118.32) (68) (121.28)
21 2.279*** 2.540*** 2.113*** 2.506***
  (67.7) (111.8) (67.74) (120.38)
22 2.148*** 2.363*** 1.974*** 2.341***
  (63.81) (104.19) (63.26) (112.42)
23 1.950*** 2.046*** 1.671*** 1.938***
   (57.92) (90.05) (53.57) (92.99)
   t_sub0 −0.321***   March 0.158***
    (−58.7)    −36.14
   t_0_5 −0.185***   April 0.151***
    (−47.91)    −34.23
   t_5_10 −0.132***   May 0.081***
    (−49.63)    −18.43
   t_15_20 0.079***   June −0.019***
    (28.21)    −3.87
   t_above20 0.186***   July −0.267***
    (44.03)    −51.22
   r 0   August −0.277***
    (0.06)    −55.55
   s 0.037***   September −0.096***
    (5.48)    −20.84
   area_ln 0.648***   October −0.041***
    (573.58)    −9.4
   January 0.284***   November Omitted
    (45.35)   Constant −8.586***
   February 0.233***    −576.32
    (46.86)   R-squared 0.7
      N 1,874,207
  1. t test in parentheses, variables starting with t indicate temperature dummies, r is rain, s is snow and area_ln is the natural logarithm of the area of the GSM cell; months indicate dummies for the different months

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Tranos, E., Nijkamp, P. Mobile phone usage in complex urban systems: a space–time, aggregated human activity study. J Geogr Syst 17, 157–185 (2015). https://doi.org/10.1007/s10109-015-0211-9

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Keywords

  • Mobile phone
  • Human activity
  • Land use
  • Urban dynamics

JEL Classification

  • R14
  • R00
  • R15
  • O18