IDEAS home Printed from https://ideas.repec.org/a/kap/transp/v47y2020i4d10.1007_s11116-019-09977-7.html
   My bibliography  Save this article

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

Author

Listed:
  • Mengyao Ren

    (Ministry of Land and Resources
    Harbin Institute of Technology (Shenzhen)
    Harbin Institute of Technology (Shenzhen))

  • Yaoyu Lin

    (Harbin Institute of Technology (Shenzhen)
    Harbin Institute of Technology (Shenzhen))

  • Meihan Jin

    (Harbin Institute of Technology (Shenzhen)
    Harbin Institute of Technology (Shenzhen))

  • Zhongyuan Duan

    (Shenzhen Urban Transport Planning Center)

  • Yongxi Gong

    (Harbin Institute of Technology (Shenzhen)
    Harbin Institute of Technology (Shenzhen))

  • Yu Liu

    (Peking University)

Abstract

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.

Suggested Citation

  • Mengyao Ren & Yaoyu Lin & Meihan Jin & Zhongyuan Duan & Yongxi Gong & Yu Liu, 2020. "Examining the effect of land-use function complementarity on intra-urban spatial interactions using metro smart card records," Transportation, Springer, vol. 47(4), pages 1607-1629, August.
  • Handle: RePEc:kap:transp:v:47:y:2020:i:4:d:10.1007_s11116-019-09977-7
    DOI: 10.1007/s11116-019-09977-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11116-019-09977-7
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s11116-019-09977-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. John R. Roy & Jean-Claude Thill, 2004. "Spatial interaction modelling," Advances in Spatial Science, in: Raymond J. G. M. Florax & David A. Plane (ed.), Fifty Years of Regional Science, pages 339-361, Springer.
    2. Kang, Chaogui & Ma, Xiujun & Tong, Daoqin & Liu, Yu, 2012. "Intra-urban human mobility patterns: An urban morphology perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1702-1717.
    3. Peter Widhalm & Yingxiang Yang & Michael Ulm & Shounak Athavale & Marta González, 2015. "Discovering urban activity patterns in cell phone data," Transportation, Springer, vol. 42(4), pages 597-623, July.
    4. Cervero, Robert, 1996. "Mixed land-uses and commuting: Evidence from the American Housing Survey," Transportation Research Part A: Policy and Practice, Elsevier, vol. 30(5), pages 361-377, September.
    5. Lee, Ho-Sang, 2009. "The networkability of cities in the international air passenger flows 1992–2004," Journal of Transport Geography, Elsevier, vol. 17(3), pages 166-175.
    6. Chengjin Wang & César Ducruet, 2014. "Transport corridors and regional balance in China: the case of coal trade and logistics," Post-Print halshs-01069149, HAL.
    7. Chengjin Wang & César Ducruet, 2014. "Transport corridors and regional balance in China : The case of coal trade and logistics," Post-Print hal-03246955, HAL.
    8. Fangzhou Li & Zhiming Feng & Peng Li & Zhen You, 2017. "Measuring directional urban spatial interaction in China: A migration perspective," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-19, January.
    9. Lee, Keumsook & Jung, Woo-Sung & Park, Jong Soo & Choi, M.Y., 2008. "Statistical analysis of the Metropolitan Seoul Subway System: Network structure and passenger flows," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(24), pages 6231-6234.
    10. Lenormand, Maxime & Bassolas, Aleix & Ramasco, José J., 2016. "Systematic comparison of trip distribution laws and models," Journal of Transport Geography, Elsevier, vol. 51(C), pages 158-169.
    11. James E. Anderson, 2011. "The Gravity Model," Annual Review of Economics, Annual Reviews, vol. 3(1), pages 133-160, September.
    12. Wang, Chengjin & Ducruet, César, 2014. "Transport corridors and regional balance in China: the case of coal trade and logistics," Journal of Transport Geography, Elsevier, vol. 40(C), pages 3-16.
    13. Morton O’Kelly & Michael Niedzielski & Justin Gleeson, 2012. "Spatial interaction models from Irish commuting data: variations in trip length by occupation and gender," Journal of Geographical Systems, Springer, vol. 14(4), pages 357-387, October.
    14. Xinqi Zheng & Tian Xia & Xin Yang & Tao Yuan & Yecui Hu, 2013. "The Land Gini Coefficient and Its Application for Land Use Structure Analysis in China," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-10, October.
    15. Ibeas, Ángel & Cordera, Ruben & dell’Olio, Luigi & Coppola, Pierluigi, 2013. "Modelling the spatial interactions between workplace and residential location," Transportation Research Part A: Policy and Practice, Elsevier, vol. 49(C), pages 110-122.
    16. Baltagi, Badi H. & Egger, Peter & Pfaffermayr, Michael, 2003. "A generalized design for bilateral trade flow models," Economics Letters, Elsevier, vol. 80(3), pages 391-397, September.
    17. Yu Liu & Zhengwei Sui & Chaogui Kang & Yong Gao, 2014. "Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-11, January.
    18. S. Veenstra & T. Thomas & S. Tutert, 2010. "Trip distribution for limited destinations: a case study for grocery shopping trips in the Netherlands," Transportation, Springer, vol. 37(4), pages 663-676, July.
    19. Filippo Simini & Marta C. González & Amos Maritan & Albert-László Barabási, 2012. "A universal model for mobility and migration patterns," Nature, Nature, vol. 484(7392), pages 96-100, April.
    20. Choukroun, Jean-Marc, 1975. "A general framework for the development of gravity-type trip distribution models," Regional Science and Urban Economics, Elsevier, vol. 5(2), pages 177-202, May.
    21. John R. Roy, 2004. "Spatial Interaction Modelling," Advances in Spatial Science, Springer, number 978-3-540-24807-1.
    22. Cordera, Rubén & Sañudo, Roberto & dell’Olio, Luigi & Ibeas, Ángel, 2018. "Trip distribution model for regional railway services considering spatial effects between stations," Transport Policy, Elsevier, vol. 67(C), pages 77-84.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen, Wendong & Chen, Xuewu & Cheng, Long & Liu, Xize & Chen, Jingxu, 2022. "Delineating borders of urban activity zones with free-floating bike sharing spatial interaction network," Journal of Transport Geography, Elsevier, vol. 104(C).
    2. Jin, Meihan & Wang, Menghan & Gong, Yongxi & Liu, Yu, 2022. "Spatio-temporally constrained origin–destination inferring using public transit fare card data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yang, Xiping & Fang, Zhixiang & Xu, Yang & Yin, Ling & Li, Junyi & Lu, Shiwei, 2019. "Spatial heterogeneity in spatial interaction of human movements—Insights from large-scale mobile positioning data," Journal of Transport Geography, Elsevier, vol. 78(C), pages 29-40.
    2. Tei, Alessio & Ferrari, Claudio, 2018. "PPIs and transport infrastructure: Evidence from Latin America and the Caribbean," Journal of Transport Geography, Elsevier, vol. 71(C), pages 204-212.
    3. Zhang, Yujiang & Feng, Guorui & Zhang, Min & Ren, Hongrui & Bai, Jinwen & Guo, Yuxia & Jiang, Haina & Kang, Lixun, 2016. "Residual coal exploitation and its impact on sustainable development of the coal industry in China," Energy Policy, Elsevier, vol. 96(C), pages 534-541.
    4. Song, Yunting & Wang, Nuo, 2019. "Exploring temporal and spatial evolution of global coal supply-demand and flow structure," Energy, Elsevier, vol. 168(C), pages 1073-1080.
    5. Xie, Qiwei & Hao, Jingjing & Li, Jingyu & Zheng, Xiaolong, 2022. "Carbon price prediction considering climate change: A text-based framework," Economic Analysis and Policy, Elsevier, vol. 74(C), pages 382-401.
    6. Meead Saberi & Taha H. Rashidi & Milad Ghasri & Kenneth Ewe, 2018. "A Complex Network Methodology for Travel Demand Model Evaluation and Validation," Networks and Spatial Economics, Springer, vol. 18(4), pages 1051-1073, December.
    7. Wang, Wenya & Fan, L.W. & Zhou, P., 2022. "Evolution of global fossil fuel trade dependencies," Energy, Elsevier, vol. 238(PC).
    8. Mattia Cai, 2021. "Doubly constrained gravity models for interregional trade estimation," Papers in Regional Science, Wiley Blackwell, vol. 100(2), pages 455-474, April.
    9. Wang, Wenya & Fan, Liwei & Li, Zhenfu & Zhou, Peng & Chen, Xue, 2021. "Measuring dynamic competitive relationship and intensity among the global coal importing trade," Applied Energy, Elsevier, vol. 303(C).
    10. Chen, Wendong & Chen, Xuewu & Cheng, Long & Liu, Xize & Chen, Jingxu, 2022. "Delineating borders of urban activity zones with free-floating bike sharing spatial interaction network," Journal of Transport Geography, Elsevier, vol. 104(C).
    11. Feng, Lin & Yuan, Liwei, 2017. "A developmental model on quantifying urban policy effectiveness in port city relations," MPRA Paper 81037, University Library of Munich, Germany.
    12. Justin Berli & Mattia Bunel & César Ducruet, 2018. "Sea-Land Interdependence in the Global Maritime Network: the Case of Australian Port Cities," Networks and Spatial Economics, Springer, vol. 18(3), pages 447-471, September.
    13. Zhang, Qiang & Yan, Kai & Yang, Dong, 2021. "Port system evolution in Chinese coastal regions: A provincial perspective," Journal of Transport Geography, Elsevier, vol. 92(C).
    14. Justin Berli & Mattia Bunel & César Ducruet, 2018. "Sea-Land Interdependence in the Global Maritime Network: the Case of Australian Port Cities," Post-Print hal-01806692, HAL.
    15. Yuexiang Yang & Xiaoyu Zheng & Zhen Sun, 2020. "Coal Resource Security Assessment in China: A Study Using Entropy-Weight-Based TOPSIS and BP Neural Network," Sustainability, MDPI, vol. 12(6), pages 1-15, March.
    16. Teng Ma & Kenji Takeuchi, 2016. "Controlling SO2 emissions in China: A panel data analysis of the 11th Five-Year Plan," Discussion Papers 1609, Graduate School of Economics, Kobe University.
    17. Cui, Shana & Pittman, Russell & Zhao, Jian, 2018. "Restructuring the Chinese Freight Railway: Two Scenarios," MPRA Paper 88407, University Library of Munich, Germany.
    18. Meead Saberi & Hani S. Mahmassani & Dirk Brockmann & Amir Hosseini, 2017. "A complex network perspective for characterizing urban travel demand patterns: graph theoretical analysis of large-scale origin–destination demand networks," Transportation, Springer, vol. 44(6), pages 1383-1402, November.
    19. Šveda, Martin & Madajová, Michala Sládeková, 2023. "Estimating distance decay of intra-urban trips using mobile phone data: The case of Bratislava, Slovakia," Journal of Transport Geography, Elsevier, vol. 107(C).
    20. Li, Tianjiao & Wang, Anjian & Xing, Wanli & Li, Ying & Zhou, Yanjing, 2019. "Assessing mineral extraction and trade in China from 1992 to 2015: A comparison of material flow analysis and exergoecological approach," Resources Policy, Elsevier, vol. 63(C), pages 1-1.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:transp:v:47:y:2020:i:4:d:10.1007_s11116-019-09977-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.