Examining the effect of land-use function complementarity on intra-urban spatial interactions using metro smart card records

  • Mengyao Ren
  • Yaoyu Lin
  • Meihan Jin
  • Zhongyuan Duan
  • Yongxi GongEmail author
  • Yu Liu


Spatial interaction is an important phenomenon that reflects the human–land relationship and has long been a core topic in multiple fields, such as urban planning, transportation planning, commodity trade, and epidemic prevention. However, as an underlying cause of spatial interaction, function complementarity has been ignored by existing research for a long time. At the same time, the increase in Big Data of travel behavior provides an opportunity to model spatial interactions in detail. In this paper, we proposed three types of land-use function complementarity indices according to the spatiotemporal characteristics of human mobility. These complementarity indices are introduced to spatial interaction to improve the gravity model. We also examined the effects of land function complementarity on intra-urban spatial interaction using smart card records of metro system for different time periods and directions. The results showed that all models could be improved by introducing the land-use function complementarity indices, but the models with a single travel pattern and clear direction were explained more by the complementary indices. The indices we propose in this paper could be used for predicting spatial flow and trip distribution, and also could be considered as factors in researches about transportation and land-use planning.


Spatial interaction Land-use function complementarity Gravity model Smart card data 



Funding was provided by National Natural Science Foundation of China (Grant Nos. 41830645, 41771169, 41371169, 41625003 and 41571145), the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources (Grant No. KF-2016-02-031), and the Smart Guangzhou Spatio-temporal Information Cloud Platform Construction (Grant No. GZIT2016-A5-147).

Compliance with ethical standards

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.


  1. Anderson, J. E.: The gravity model. Annu. Rev. Econ. 3, 133–160 (2011)Google Scholar
  2. Axhausen, K.W., Gärling, T.: Activity-based approaches to travel analysis: conceptual frameworks, models, and research problems. Transp. Rev. 12(4), 323–341 (1992)Google Scholar
  3. Baltagi, B.H., Egger, P., Pfaffermayr, M.: A generalized design for bilateral trade flow models. Econ. Lett. 80(3), 391–397 (2003)Google Scholar
  4. Birkin, M., Clarke, G., Clarke, M., Culf, R.: Using spatial models to solve difficult retail location problems. In: Stillwell, J., Clarke, G. (eds.) Applied GIS and Spatial Analysis, pp. 35–54. Willey, England (2004)Google Scholar
  5. Cascetta, E., Russo, F.: Calibrating aggregate travel demand models with traffic counts: estimators and statistical performance. Transportation 24(3), 271–293 (1997)Google Scholar
  6. Carey, H.C.: Principles of Social Science. Lippincott, Philadelphia (1858)Google Scholar
  7. Cervero, R.: Mixed land-uses and commuting: evidence from the American housing survey. Transp. Res. A 30(5), 361–377 (1996)Google Scholar
  8. Cervero, R., Kockelman, K.: Travel demand and the 3ds: density, diversity, and design. Transp. Res. D. 2(3), 199–219 (1997)Google Scholar
  9. Choukroun, J.M.: A general framework for the development of gravity-type trip distribution models. Reg. Sci. Urban Econ. 5(2), 177–202 (1975)Google Scholar
  10. Corbusier, L.: The Athens Charter. Grossman Publishers, New York (1973)Google Scholar
  11. Cordera, R., Sañudo, R., Dell’Olio, L., Ibeas, Á.: Trip distribution model for regional railway services considering spatial effects between stations. Transp. Policy 67, 77–84 (2018)Google Scholar
  12. Dai, T., Jin, F.: Spatial interaction and network structure evolvement of cities in terms of china’s rail passenger flows. Chin. Geogr. Sci. 18(3), 206–213 (2008)Google Scholar
  13. Dark, S.J., Bram, D.: The modifiable areal unit problem (MAUP) in physical geography. Prog. Phys. Geogr. 31(5), 471–479 (2007)Google Scholar
  14. Derudder, B., Witlox, F., Taylor, P.J.: U.S. cities in the world city network: comparing their positions using global origins and destinations of airline passengers. Urban Geogr. 28(1), 74–91 (2007)Google Scholar
  15. Evans, S.P.: A relationship between the gravity model for trip distribution and the transportation problem in linear programming. Transp. Res. 7(1), 39–61 (1973)Google Scholar
  16. Fotheringham, A. S., O’Kelly, M. E.: Spatial interaction models: formulations and applications. Kluwer Academic Publishers, Dordrecht (1988)Google Scholar
  17. Gao, Q.L., Li, Q.Q., Yue, Y., Zhuang, Y., Chen, Z.P., Kong, H.: Exploring changes in the spatial distribution of the low-to-moderate income group using transit smart card data. Comput. Environ. Urban Syst. 72, 68–77 (2018)Google Scholar
  18. Gao, S., Liu, Y., Wang, Y., Ma, X.: Discovering spatial interaction communities from mobile phone data. Trans. GIS 17(3), 463–481 (2013)Google Scholar
  19. Gong, Y., Liu, Y., Lin, Y., Yang, J., Duan, Z., Li, G.: Exploring spatiotemporal characteristics of intra-urban trips using metro smartcard records. In: 2012 20th International Conference on Geoinformatic, pp. 1–7. IEEE, HK, China (2012)Google Scholar
  20. Gong, Y., Lin, Y., Duan, Z.: Exploring the spatiotemporal structure of dynamic urban space using metro smart card records. Comput. Environ. Urban Syst. 64, 169–183 (2017)Google Scholar
  21. González, M.C., Hidalgo, C.A., Barabási, A.L.: Understanding individual human mobility patterns. Nature 453(7196), 779–782 (2008)Google Scholar
  22. Han, X.P., Hao, Q., Wang, B.H., Zhou, T.: Origin of the scaling law in human mobility: hierarchy of traffic systems. Phys. Rev. E Stat. Phys. Plasmas Fluids 83(3), 036117 (2011)Google Scholar
  23. Hincks, S., Wong, C.: The spatial interaction of housing and labour markets: commuting flow analysis of North West England. Urban Stud. 47(3), 620–649 (2010)Google Scholar
  24. Hufnagel, L., Brockmann, D., Geisel, T.: Forecast and control of epidemics in a globalized world. Proc. Natl. Acad. Sci. USA 101(42), 15124–15129 (2004)Google Scholar
  25. Ibeas, Á., Cordera, R., Dell’Olio, L., Coppola, P.: Modelling the spatial interactions between workplace and residential location. Transp. Res. Pt. A Policy Pract. 49(1), 110–122 (2013)Google Scholar
  26. Isserman, A.M.: The location quotient approach to estimating regional economic impacts. J. Am. Inst. Plan. 43(1), 33–41 (1977)Google Scholar
  27. Kang, C., Ma, X., Tong, D., Liu, Y.: Intra-urban human mobility patterns: an urban morphology perspective. Phys. A 391(4), 1702–1717 (2012)Google Scholar
  28. Kim, H., Yang, I., Choi, K.: An agent-based simulation model for analyzing the impact of asymmetric passenger demand on taxi service. KSCE J. Civ. Eng. 15(1), 187–195 (2011)Google Scholar
  29. Krings, G., Calabrese, F., Ratti, C., Blondel, V. D.: Urban gravity: a model for intercity telecommunication flows. J. Stat. Mech. Theory Exp. 2009, L07003 (2009)Google Scholar
  30. Lee, H.S.: The networkability, of cities in the international air passenger flows 1992–2004. J. Transp. Geogr. 17(3), 166–175 (2009)Google Scholar
  31. Lee, K., Jung, W.S., Park, J.S., Choi, M.Y.: Statistical analysis of the metropolitan Seoul subway system: network structure and passenger flows. Phys. A 387(24), 6231–6234 (2008)Google Scholar
  32. Lee, K., Park, J.S., Jung, W.S., Choi, M.Y.: Master equation approach to the intra-urban passenger flow and application to the metropolitan Seoul subway system. J. Phys. A Math. Theor. 44(11), 2345–2367 (2011)Google Scholar
  33. Lenormand, M., Bassolas, A., Ramasco, J.J.: Systematic comparison of trip distribution laws and models. J. Transp. Geogr. 51, 158–169 (2016)Google Scholar
  34. Li, F., Feng, Z., Li, P., You, Z.: Measuring directional urban spatial interaction in China: a migration perspective. PLoS ONE 12(1), e0171107 (2017)Google Scholar
  35. Liu, Y., Sui, Z., Kang, C., Gao, Y.: Uncovering patterns of inter-urban trip and spatial interaction from social media check-in data. PloS One 9(1), e86026 (2014)Google Scholar
  36. Liu, X., Gong, L., Gong, Y., Liu, Y.: Revealing travel patterns and city structure with taxi trip data. J. Transp. Geogr. 43, 78–90 (2015a)Google Scholar
  37. Liu, Y., Liu, X., Gao, S., Gong, L., Kang, C., Zhi, Y., Chi, G.H., Shi, L.: Social sensing: a new approach to understanding our socioeconomic environments. Ann. Assoc. Am. Geogr. 105(3), 512–530 (2015b)Google Scholar
  38. Liu, X., Kang, C., Gong, L., Liu, Y.: Incorporating spatial interaction patterns in classifying and understanding urban land use. Int. J. Geogr. Inf. Sci. 30(2), 334–350 (2016)Google Scholar
  39. Lovelace, R., Birkin, M., Cross, P., Clarke, M.: From big noise to big data: toward the verification of large data sets for understanding regional retail flows. Geogr. Anal. 48(1), 59–81 (2016)Google Scholar
  40. Newing, A., Clarke, G.P., Clarke, M.: Developing and applying a disaggregated retail location model with extended retail demand estimations. Geogr. Anal. 47(3), 219–239 (2014)Google Scholar
  41. Niedzielski, M.A., O’Kelly, M.E., Boschmannc, E.E.: Synthesizing spatial interaction data for social science research: validation and an investigation of spatial mismatch in Wichita, Kansas. Comput. Environ. Urban Syst. 54, 204–218 (2015)Google Scholar
  42. O’Kelly, M.E., Niedzielski, M.A., Gleeson, J.: Spatial interaction models from Irish commuting data: variations in trip length by occupation and gender. J. Geogr. Syst. 14(4), 357–387 (2012)Google Scholar
  43. O’Sullivan, S., Morral, J.: Walking distances to and from light-rail transit stations. Transp. Res. Rec. 1538(1), 19–26 (1996)Google Scholar
  44. Ravenstein, E.G.: The laws of migration. J. Stat. Soc. Lond. 48(2), 167–235 (1885)Google Scholar
  45. Roy, J. R., Thill, J. C.: Spatial interaction modelling. Pap. Reg. Sci. 83(1), 339–361 (2004)Google Scholar
  46. Sen, A., Smith, T.E.: Gravity Models of Spatial Interaction Behavior. Springer, Berlin (1995)Google Scholar
  47. Shaw, S.L., Xin, X.: Integrated land use and transportation interaction: a temporal GIS exploratory data analysis approach. J. Transp. Geogr. 11(2), 103–115 (2003)Google Scholar
  48. Simini, F., González, M.C., Maritan, A., Barabási, A.L.: A universal model for mobility and migration patterns. Nature 484(7392), 96-100 (2012)Google Scholar
  49. Sonis, M., Hewings, G.J.D.: Regional competition and complementarity: comparative advantages/disadvantages and increasing/diminishing returns in discrete relative spatial dynamics. In: Batey, P.W.J., Friedrich, P. (eds.) Regional competition, pp. 139–158 . Springer, Berlin, Heidelberg (2000)Google Scholar
  50. Stouffer, S.A.: Intervening opportunities: a theory relating mobility and distance. Am. Sociol. Rev. 5(6), 845–867 (1940)Google Scholar
  51. Taylor, J., Catalano, G., Walker, D.: Measurement of the world city network. Urban Stud. 39(13), 2367–2376 (2002)Google Scholar
  52. Tobler, W.: Spatial interaction patterns. J. Environ. Sci. 6(4), 1 (1975)Google Scholar
  53. Tong, D., Tao, L., Guicai, L.I., Lei, Y.U.: Empirical analysis of city contact in Zhujiang (pearl) River Delta, China. Chin. Geogr. Sci. 24(3), 384–392 (2014)Google Scholar
  54. Tsutsumi, M., Tamesue, K.: Intraregional flow problem in spatial econometric model for origin-destination flows. Environ. Plan. B Plan. Des. 39(6), 1006–1015 (2012)Google Scholar
  55. Ullman, E. L.: Geography as spatial interaction. In: Interregional Linkages: Proceedings of the Western Committee on Regional Economic Analysis, pp. 63–71. University of California Press, Berkeley (1954)Google Scholar
  56. Veenstra, S.A., Thomas, T., Tutert, S.I.A.: Trip distribution for limited destinations: a case study for grocery shopping trips in the Netherlands. Transportation 37(4), 663–676 (2010)Google Scholar
  57. Wang, C., Ducruet, C.: Transport corridors and regional balance in China: the case of coal trade and logistics. J. Transp. Geogr. 40, 3–16 (2014)Google Scholar
  58. Widhalm, P., Yang, Y., Ulm, M., Athavale, S., González, M.C.: Discovering urban activity patterns in cell phone data. Transportation 42(4), 597–623 (2015)Google Scholar
  59. Wilson, A.G.: A statistical theory of spatial distribution models. Transp. Res. 1(3), 253–269 (1967)Google Scholar
  60. Wilson, A.G.: The use of entropy maximising models in the theory of trip distribution, mode split and route split. J. Transp. Econ. Policy 3, 108–126 (1969)Google Scholar
  61. Wu, W., Zhang, W., Jin, F., Yu, D.: Spatio-temporal analysis of urban spatial interaction in globalizing China—a case study of Beijing–Shanghai corridor. Chin. Geogr. Sci. 19(2), 126–134 (2009)Google Scholar
  62. Xiao, Y., Wang, F.H., Liu, Y., Wang, J.E.: Reconstructing gravitational attractions of major cities in China from air passenger flow data, 2001–2008: a particle swarm optimization approach. Prof. Geogr. 65(2), 265–282 (2013)Google Scholar
  63. Yu, J.P.: Comparative advantage and trade complementarity between China and other Asian economies. World. Econ. 5, 33–40 (2003)Google Scholar
  64. Yan, X.Y., Zhao, C., Fan, Y., Di, Z., Wang, W.X.: Universal predictability of mobility patterns in cities. J. R. Soc. Interface 11(100), 20140834 (2014)Google Scholar
  65. Zheng, X., Xia, T., Yang, X., Yuan, T., Hu, Y.: The land Gini coefficient and its application for land use structure analysis in China. PLoS ONE 8(10), e76165 (2013)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Key Laboratory of Urban Land Resources Monitoring and SimulationMinistry of Land and ResourcesShenzhenPeople’s Republic of China
  2. 2.Shenzhen Key Laboratory of Urban Planning and Decision MakingHarbin Institute of Technology (Shenzhen)ShenzhenPeople’s Republic of China
  3. 3.School of ArchitectureHarbin Institute of Technology (Shenzhen)ShenzhenPeople’s Republic of China
  4. 4.Shenzhen Urban Transport Planning CenterShenzhenPeople’s Republic of China
  5. 5.Institute of Remote Sensing and Geographical Information SystemsPeking UniversityBeijingPeople’s Republic of China

Personalised recommendations