IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i15p11857-d1208619.html
   My bibliography  Save this article

Identification and Prediction Network Analysis Based on Multivariate Data of Urban Form: A Case Study of Shenzhen, China

Author

Listed:
  • Zeyang Yu

    (School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China)

  • Yuan Huang

    (School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Yang Wang

    (School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

The rapid growth of urban populations has resulted in a scarcity of land, thus making sustainable urban development an urgent matter. Although Shenzhen has implemented land policies and optimized its functional layouts, these measures have inadvertently contributed to a shortage of available land for development. The city’s exponential population growth and expansive urban expansion have outpaced the supply of land. This study endeavors to identify urban commercial patterns by employing multiple data sources and applying machine learning and network analysis to predict future commercial areas. The results demonstrated that the identification of commercial points of interest and analysis of land surface temperature distributions made Futian district the primary area for ongoing commercial development, while also revealing a positive correlation between these two datasets. By leveraging network analysis to thoroughly examine this data, Bao’an district was highlighted as the future focal point for Shenzhen’s commercial sector, with 22 core nodes identified in total. Finally, by assessing the network centrality within the spatial networks, and utilizing clustering algorithms to categorize nodes into groups, the economic clustering pattern was determined as the predominant model for Shenzhen’s commercial growth. This research represents a significant contribution to the realm of sustainable urban development and presents a valuable framework for other cities to adopt.

Suggested Citation

  • Zeyang Yu & Yuan Huang & Yang Wang, 2023. "Identification and Prediction Network Analysis Based on Multivariate Data of Urban Form: A Case Study of Shenzhen, China," Sustainability, MDPI, vol. 15(15), pages 1-19, August.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:15:p:11857-:d:1208619
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/15/11857/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/15/11857/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lai, Yani & Tang, Bosin & Chen, Xiangsheng & Zheng, Xian, 2021. "Spatial determinants of land redevelopment in the urban renewal processes in Shenzhen, China," Land Use Policy, Elsevier, vol. 103(C).
    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. Lin Jiang & Yani Lai & Ke Chen & Xiao Tang, 2022. "What Drives Urban Village Redevelopment in China? A Survey of Literature Based on Web of Science Core Collection Database," Land, MDPI, vol. 11(4), pages 1-16, April.
    2. Dinghuan Yuan & Jiaxin Li & Qiuxiang Li & Yang Fu, 2024. "Tripartite Evolutionary Game and Policy Simulation: Strategic Governance in the Redevelopment of the Urban Village in Guangzhou," Land, MDPI, vol. 13(11), pages 1-21, November.
    3. Cao, Kexin & Deng, Yu & Wang, Wenxue & Liu, Shenghe, 2023. "The spatial heterogeneity and dynamics of land redevelopment: Evidence from 287 Chinese cities," Land Use Policy, Elsevier, vol. 132(C).
    4. Lingyan Huang & Shanshan Xiang & Jianzhuang Zheng, 2022. "Fine-Scale Monitoring of Industrial Land and Its Intra-Structure Using Remote Sensing Images and POIs in the Hangzhou Bay Urban Agglomeration, China," IJERPH, MDPI, vol. 20(1), pages 1-21, December.
    5. Huang, Xinxin & Wang, Haijun & Xiao, Fentao, 2022. "Simulating urban growth affected by national and regional land use policies: Case study from Wuhan, China," Land Use Policy, Elsevier, vol. 112(C).
    6. Kassouri, Yacouba & Alola, Andrew Adewale, 2022. "Towards unlocking sustainable land consumption in sub-Saharan Africa: Analysing spatio-temporal variation of built-up land footprint and its determinants," Land Use Policy, Elsevier, vol. 120(C).
    7. Hanxue Wei & Lucien C. Wostenholme & John I. Carruthers, 2021. "Planning and Markets at Work: Seattle under Growth Management and Economic Pressure," Sustainability, MDPI, vol. 13(14), pages 1-18, July.
    8. Guiwen Liu & Cheng Li & Taozhi Zhuang & Yuhan Zheng & Hongjuan Wu & Jian Tang, 2022. "Determining the Spatial Distribution Characteristics of Urban Regeneration Projects in China on the City Scale: The Case of Shenzhen," Land, MDPI, vol. 11(8), pages 1-27, July.
    9. Chen, Yang & Zhang, Xiaoling & Chau, K.W. & Yang, Linchuan, 2022. "How the institutional change in urban redevelopment affects the duration of land redevelopment approval in China?," Land Use Policy, Elsevier, vol. 119(C).
    10. Liu, Xiping & Zhang, Xiaoling & Sun, Wen, 2022. "Does the agglomeration of urban producer services promote carbon efficiency of manufacturing industry?," Land Use Policy, Elsevier, vol. 120(C).
    11. Salihoğlu, Tayfun & Albayrak, Ayşe Nur & Eryılmaz, Yaşasın, 2021. "A method for the determination of urban transformation areas in Kocaeli," Land Use Policy, Elsevier, vol. 109(C).
    12. Yani Lai & Lin Jiang & Xiaoxiao Xu, 2021. "Exploring Spatio-Temporal Patterns of Urban Village Redevelopment: The Case of Shenzhen, China," Land, MDPI, vol. 10(9), pages 1-26, September.
    13. Yuanyuan Huang & Lizhen Wei & Guiwen Liu & Wenjing Cui & Fangyun Xie & Xun Deng, 2022. "“Inspiring” Policy Transfer: Analysis of Urban Renewal in Four First-Tier Chinese Cities," Land, MDPI, vol. 12(1), pages 1-31, December.

    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:jsusta:v:15:y:2023:i:15:p:11857-:d:1208619. 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.