IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v14y2025i1p120-d1563039.html
   My bibliography  Save this article

Research on the Spatial Network Connection Characteristics and Influencing Factors of Chengdu–Chongqing Urban Agglomeration from the Perspective of Flow Space

Author

Listed:
  • Yangguang Hao

    (School of Architecture, Southwest Jiaotong University, Chengdu 611756, China)

  • Zhongwei Shen

    (School of Architecture, Southwest Jiaotong University, Chengdu 611756, China)

  • Jiexi Ma

    (School of Architecture, Southwest Jiaotong University, Chengdu 611756, China)

  • Jiawei Li

    (School of Architecture, Southwest Jiaotong University, Chengdu 611756, China)

  • Mengqian Yang

    (School of Art, Yunnan Normal University, Kunming 650500, China)

Abstract

Urban Agglomerations (UAs), as the primary form of China’s new urbanization and an essential spatial unit for promoting coordinated regional development, play a crucial role in measuring the sustainable and healthy development of urban clusters through the assessment of spatial network connections among cities within the UAs. Taking the 16 prefecture-level cities of the Chengdu-Chongqing Urban Agglomeration (CCUA) as the research subject, this study constructs six types of element flow networks, including population flow, logistics, and information flow. Employing network visualization analysis, the Self-Organizing Maps (SOM) neural network machine learning models, and Quadratic Assignment Procedure (QAP) relational regression models, the research analyzes the spatial network characteristics of the CCUA from the perspective of multi-dimensional element flows and explores the influencing factors of the UA’s connectivity pattern. The results indicate that: The various element flows within the CCUA exhibit a bipolar spatial network characteristic with Chengdu and Chongqing as the poles. In the element network grouping features, a multi-centered group differentiation structure is presented, and the intensity of internal element flow varies. Based on the results of the SOM neural network machine learning model, the connectivity capabilities of cities within the CCUA are divided into five levels. Among them, Chengdu and Chongqing have the strongest comprehensive connectivity capabilities, showing a significant difference compared to other cities, and there is an imbalance in the connectivity capabilities between cities. In terms of the influencing factors of the urban connectivity pattern within the CCUA, the differences in permanent population size and urbanization rates have a significant negative impact on the information flow network, technology flow network, and capital flow network. The differences in the secondary industrial structure and public budget expenditures have a significant positive impact on the intensity of inter-city element flows, and the differences in per capita consumption expenditures have a significant negative impact, collectively influencing the formation of the spatial connectivity pattern of the CCUA. The findings of this study can provide a scientific basis for the construction and optimization of the spatial connectivity pattern of the CCUA.

Suggested Citation

  • Yangguang Hao & Zhongwei Shen & Jiexi Ma & Jiawei Li & Mengqian Yang, 2025. "Research on the Spatial Network Connection Characteristics and Influencing Factors of Chengdu–Chongqing Urban Agglomeration from the Perspective of Flow Space," Land, MDPI, vol. 14(1), pages 1-22, January.
  • Handle: RePEc:gam:jlands:v:14:y:2025:i:1:p:120-:d:1563039
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/14/1/120/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2073-445X/14/1/120/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Matsumoto, Hidenobu, 2004. "International urban systems and air passenger and cargo flows: some calculations," Journal of Air Transport Management, Elsevier, vol. 10(4), pages 239-247.
    2. Qiaowen Lin & Mengyu Xiang & Lu Zhang & Jinjiang Yao & Chao Wei & Sheng Ye & Hongmei Shao, 2021. "Research on Urban Spatial Connection and Network Structure of Urban Agglomeration in Yangtze River Delta—Based on the Perspective of Information Flow," IJERPH, MDPI, vol. 18(19), pages 1-20, September.
    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. Sujuan Li & Xiaohui Zhang & Xueling Wu & Erbin Xu, 2022. "Exploration of Urban Network Spatial Structure Based on Traffic Flow, Migration Flow and Information Flow: A Case Study of Shanxi Province, China," Sustainability, MDPI, vol. 14(23), pages 1-18, December.
    2. Alexander, D.W. & Merkert, R., 2021. "Applications of gravity models to evaluate and forecast US international air freight markets post-GFC," Transport Policy, Elsevier, vol. 104(C), pages 52-62.
    3. Jinzhao Song & Qing Feng & Xiaoping Wang & Hanliang Fu & Wei Jiang & Baiyu Chen, 2018. "Spatial Association and Effect Evaluation of CO 2 Emission in the Chengdu-Chongqing Urban Agglomeration: Quantitative Evidence from Social Network Analysis," Sustainability, MDPI, vol. 11(1), pages 1-19, December.
    4. Gong, Qiang & Wang, Kun & Fan, Xingli & Fu, Xiaowen & Xiao, Yi-bin, 2018. "International trade drivers and freight network analysis - The case of the Chinese air cargo sector," Journal of Transport Geography, Elsevier, vol. 71(C), pages 253-262.
    5. Van Asch, Thomas & Dewulf, Wouter & Kupfer, Franziska & Cárdenas, Ivan & Van de Voorde, Eddy, 2020. "Cross-border e-commerce logistics – Strategic success factors for airports," Research in Transportation Economics, Elsevier, vol. 79(C).
    6. Chen, Jieh-Haur & Wei, Hsi-Hsien & Chen, Chih-Lin & Wei, Hsin-Yi & Chen, Yi-Ping & Ye, Zhongnan, 2020. "A practical approach to determining critical macroeconomic factors in air-traffic volume based on K-means clustering and decision-tree classification," Journal of Air Transport Management, Elsevier, vol. 82(C).
    7. Ma, Wen & Fang, Zhuoqiong & Zhang, Xiangfeng, 2023. "Comparative analysis of structural characteristics of China's 18 typical urban agglomerations based on flows of various elements," Ecological Modelling, Elsevier, vol. 479(C).
    8. B. Derudder & F. Witlox, 2005. "An Appraisal of the Use of Airline Data in Assessing the World City Network: A Research Note on Data," Urban Studies, Urban Studies Journal Limited, vol. 42(13), pages 2371-2388, December.
    9. Herman L. Boschken, 2008. "A Multiple-perspectives Construct of the American Global City," Urban Studies, Urban Studies Journal Limited, vol. 45(1), pages 3-28, January.
    10. Katrin Oesingmann, 2022. "The determinants of air cargo flows and the role of multinational agreements: An empirical comparison with trade and air passenger flows," The World Economy, Wiley Blackwell, vol. 45(8), pages 2370-2393, August.
    11. Tsui, Wai Hong Kan & Fung, Michael Ka Yiu, 2016. "Analysing passenger network changes: The case of Hong Kong," Journal of Air Transport Management, Elsevier, vol. 50(C), pages 1-11.
    12. 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.
    13. Shengdong Nie & Hengkai Li, 2023. "Analysis of Construction Networks and Structural Characteristics of Pearl River Delta and Surrounding Cities Based on Multiple Connections," Sustainability, MDPI, vol. 15(14), pages 1-26, July.
    14. Jiawei Wu & Wei Sun, 2023. "Regional Integration and Sustainable Development in the Yangtze River Delta, China: Towards a Conceptual Framework and Research Agenda," Land, MDPI, vol. 12(2), pages 1-20, February.
    15. Matsumoto, Hidenobu & Domae, Koji, 2019. "Assessment of competitive hub status of cities in Europe and Asia from an international air traffic perspective," Journal of Air Transport Management, Elsevier, vol. 78(C), pages 88-95.
    16. Gizem Kaya & Umut Aydın & Burç Ülengin, 2023. "A Comparison of Forecasting Performance of PPML and OLS estimators: The Gravity Model in the Air Cargo Market," EKOIST Journal of Econometrics and Statistics, Istanbul University, Faculty of Economics, vol. 0(39), pages 112-128, December.
    17. Zhang Weiyang & Derudder Ben, 2016. "Approximating actual flows in physical infrastructure networks: the case of the Yangtze River Delta high-speed railway network," Bulletin of Geography. Socio-economic Series, Sciendo, vol. 31(31), pages 145-160, March.
    18. Nguyen, Quang Hai, 2024. "Modeling the volatility of international air freight: A case study of Singapore using the SARIMAX-EGARCH model," Journal of Air Transport Management, Elsevier, vol. 117(C).
    19. Fildes, Robert & Wei, Yingqi & Ismail, Suzilah, 2011. "Evaluating the forecasting performance of econometric models of air passenger traffic flows using multiple error measures," International Journal of Forecasting, Elsevier, vol. 27(3), pages 902-922, July.
    20. Loo, Becky P.Y. & Li, Linna & Psaraki, Voula & Pagoni, Ioanna, 2014. "CO2 emissions associated with hubbing activities in air transport: an international comparison," Journal of Transport Geography, Elsevier, vol. 34(C), pages 185-193.

    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:gam:jlands:v:14:y:2025:i:1:p:120-:d:1563039. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.