IDEAS home Printed from https://ideas.repec.org/a/pal/palcom/v10y2023i1d10.1057_s41599-023-02456-6.html
   My bibliography  Save this article

Intercity asymmetrical linkages influenced by Spring Festival migration and its multivariate distance determinants: a case study of the Yangtze River Delta Region in China

Author

Listed:
  • Jinping Lin

    (Hanshan Normal University)

  • Kangmin Wu

    (Guangdong Academy of Sciences)

Abstract

Understanding intercity linkage patterns is of great importance to understanding urbanization. With advancements in transportation, communication technology, and the availability of big data, the “death of distance” concept has gained significant attention. This paper analyzes the asymmetric spatial intercity linkage network in China’s economically developed YRDR based on big data derived from Spring Festival (SF) migration. The aim is to explore the determinants of these linkages considering multivariate distance factors. The findings indicate a notable pattern of asymmetry in the intercity linkage network of the YRDR between core and non-core cities. The spatial decay effect of geographic distance on intercity asymmetry linkage is observed. Despite technological advancements, geographic distance remains the most influential and decisive factor in determining intercity asymmetric linkages. While other attribute distances also play a positive role, their effects become complex when controlling for geographic distance. Understanding these attribute distances is essential in comprehending the decay effect. This study contributes to the empirical investigation of the “death of distance” debate and provides a practical analytical framework for analyzing the drivers of intercity linkage patterns. It enhances our understanding of intercity spatial linkages within the context of urbanization in China and offers valuable insights for formulating development policies in the YRDR.

Suggested Citation

  • Jinping Lin & Kangmin Wu, 2023. "Intercity asymmetrical linkages influenced by Spring Festival migration and its multivariate distance determinants: a case study of the Yangtze River Delta Region in China," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-10, December.
    • Handle: RePEc:pal:palcom:v:10:y:2023:i:1:d:10.1057_s41599-023-02456-6
    DOI: 10.1057/s41599-023-02456-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41599-023-02456-6
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/s41599-023-02456-6?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. Eric Sheppard, 2002. "The Spaces and Times of Globalization: Place, Scale, Networks, and Positionality," Economic Geography, Taylor & Francis Journals, vol. 78(3), pages 307-330, July.
    2. Birke, Daniel & Swann, G.M. Peter, 2010. "Network effects, network structure and consumer interaction in mobile telecommunications in Europe and Asia," Journal of Economic Behavior & Organization, Elsevier, vol. 76(2), pages 153-167, November.
    3. Zheng, Yilin & Lu, Ming & Li, Jiewei, 2022. "Internal circulation in China: Analyzing market segmentation and integration using big data for truck traffic flow," Economic Modelling, Elsevier, vol. 115(C).
    4. Bailey, Michael & Farrell, Patrick & Kuchler, Theresa & Stroebel, Johannes, 2020. "Social connectedness in urban areas," Journal of Urban Economics, Elsevier, vol. 118(C).
    5. Jiao, Jingjuan & Wang, Jiaoe & Jin, Fengjun, 2017. "Impacts of high-speed rail lines on the city network in China," Journal of Transport Geography, Elsevier, vol. 60(C), pages 257-266.
    6. Tuba Bircan & Emre Eren Korkmaz, 2021. "Big data for whose sake? Governing migration through artificial intelligence," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-5, December.
    7. Tom Broekel & Pierre-Alexandre Balland & Martijn Burger & Frank Oort, 2014. "Modeling knowledge networks in economic geography: a discussion of four methods," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 53(2), pages 423-452, September.
    8. Jae Sik Jeon, 2020. "Moving away from opportunity? Social networks and access to social services," Urban Studies, Urban Studies Journal Limited, vol. 57(8), pages 1696-1713, June.
    9. Skyler J. Cranmer & Philip Leifeld & Scott D. McClurg & Meredith Rolfe, 2017. "Navigating the Range of Statistical Tools for Inferential Network Analysis," American Journal of Political Science, John Wiley & Sons, vol. 61(1), pages 237-251, January.
    10. Xu, Helian & Cheng, Long, 2016. "The QAP weighted network analysis method and its application in international services trade," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 448(C), pages 91-101.
    11. Shao, Shuai & Tian, Zhihua & Yang, Lili, 2017. "High speed rail and urban service industry agglomeration: Evidence from China's Yangtze River Delta region," Journal of Transport Geography, Elsevier, vol. 64(C), pages 174-183.
    12. Narisra Limtanakool & Tim Schwanen & Martin Dijst, 2009. "Developments in the Dutch Urban System on the Basis of Flows," Regional Studies, Taylor & Francis Journals, vol. 43(2), pages 179-196.
    13. Bindong Sun & Jie Lin & Chun Yin, 2021. "How does commute duration affect subjective well-being? A case study of Chinese cities," Transportation, Springer, vol. 48(2), pages 885-908, April.
    14. Chen, Yanguang, 2015. "The distance-decay function of geographical gravity model: Power law or exponential law?," Chaos, Solitons & Fractals, Elsevier, vol. 77(C), pages 174-189.
    Full references (including those not matched with items on IDEAS)

    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. Lianyue Feng & Helian Xu & Gang Wu & Wenting Zhang, 2021. "Service trade network structure and its determinants in the Belt and Road based on the temporal exponential random graph model," Pacific Economic Review, Wiley Blackwell, vol. 26(5), pages 617-650, December.
    2. Mundt, Philipp, 2021. "The formation of input–output architecture: Evidence from the European Union," Journal of Economic Behavior & Organization, Elsevier, vol. 183(C), pages 89-104.
    3. Wu, Rong & Li, Yingcheng & Wang, Shaojian, 2022. "Will the construction of high-speed rail accelerate urban land expansion? Evidences from Chinese cities," Land Use Policy, Elsevier, vol. 114(C).
    4. Sun, Xinyu & Yan, Sen & Liu, Tao & Wang, Jiayin, 2023. "The impact of high-speed rail on urban economy: Synergy with urban agglomeration policy," Transport Policy, Elsevier, vol. 130(C), pages 141-154.
    5. You He & Alex de Sherbinin & Guoqing Shi & Haibin Xia, 2022. "The Economic Spatial Structure Evolution of Urban Agglomeration under the Impact of High-Speed Rail Construction: Is There a Difference between Developed and Developing Regions?," Land, MDPI, vol. 11(9), pages 1-17, September.
    6. Wenqian Ke & Wei Chen & Zhaoyuan Yu, 2017. "Uncovering Spatial Structures of Regional City Networks from Expressway Traffic Flow Data: A Case Study from Jiangsu Province, China," Sustainability, MDPI, vol. 9(9), pages 1-16, August.
    7. Ziyang Chen & Xiao Feng & Ziwen He, 2022. "A Key to Stimulate Green Technology Innovation in China: The Expansion of High-Speed Railways," IJERPH, MDPI, vol. 20(1), pages 1-21, December.
    8. Liu, Liwen & Zhang, Ming, 2018. "High-speed rail impacts on travel times, accessibility, and economic productivity: A benchmarking analysis in city-cluster regions of China," Journal of Transport Geography, Elsevier, vol. 73(C), pages 25-40.
    9. Duan, Liaoliao & Sun, Weizeng & Zheng, Siqi, 2020. "Transportation network and venture capital mobility: An analysis of air travel and high-speed rail in China," Journal of Transport Geography, Elsevier, vol. 88(C).
    10. Tian, Meng & Wang, Yiwei & Wang, Yiran, 2023. "High-speed rail network and urban agglomeration economies: Research from the perspective of urban network externalities," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).
    11. Wang, Qi & Lu, Shaokai, 2022. "The influence of hybrid accessibility on tourism economy in prefecture-level cities: Evidence from China's high-speed rail network," Journal of Transport Geography, Elsevier, vol. 104(C).
    12. Huang, Yan & Zong, Huiming, 2020. "The spatial distribution and determinants of China’s high-speed train services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 142(C), pages 56-70.
    13. Yue Fu & Long Xue & Yixin Yan & Yao Pan & Xiaofang Wu & Ying Shao, 2021. "Energy Network Embodied in Trade along the Belt and Road: Spatiotemporal Evolution and Influencing Factors," Sustainability, MDPI, vol. 13(19), pages 1-29, September.
    14. Niu, Fangqu & Xin, Zhongling & Sun, Dongqi, 2021. "Urban land use effects of high-speed railway network in China: A spatial spillover perspective," Land Use Policy, Elsevier, vol. 105(C).
    15. Shanlang Lin & Ziyang Chen & Ziwen He, 2021. "Intra-City Industrial Collaborative Agglomeration, Inter-City Network Connectivity and Green Technology Innovation," Sustainability, MDPI, vol. 13(16), pages 1-25, August.
    16. Liu, Zhengying & Zhao, Pengjun & Liu, Qiyang & Cui, Yanzhe & Yang, Yuan & Liu, Juan & Li, Buhui & Li, Jingwei, 2023. "Exploring the spatial characteristics of the human mobility network in rural settings of China's Greater Bay Area," Journal of Transport Geography, Elsevier, vol. 112(C).
    17. Liu, Xueli & Jiang, Chunxia & Wang, Feng & Yao, Shujie, 2021. "The impact of high-speed railway on urban housing prices in China: A network accessibility perspective," Transportation Research Part A: Policy and Practice, Elsevier, vol. 152(C), pages 84-99.
    18. Meng, Xuechen & Lin, Shanlang & Zhu, Xiaochuan, 2018. "The resource redistribution effect of high-speed rail stations on the economic growth of neighbouring regions: Evidence from China," Transport Policy, Elsevier, vol. 68(C), pages 178-191.
    19. Li, Xiang & Cheng, Zhonghua, 2022. "Does high-speed rail improve urban carbon emission efficiency in China?," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    20. Lan, Xiujuan & Hu, Zheneng & Wen, Chuanhao, 2023. "Does the opening of high-speed rail enhance urban entrepreneurial activity? Evidence from China," Socio-Economic Planning Sciences, Elsevier, vol. 88(C).

    More about this item

    Statistics

    Access and download statistics

    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:pal:palcom:v:10:y:2023:i:1:d:10.1057_s41599-023-02456-6. 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: https://www.nature.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.