IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v13y2024i11p1771-d1508418.html
   My bibliography  Save this article

A Study on the Influence Mechanisms of Neighborhood Vitality and the Characteristics of Spatial and Temporal Differentiation in the Urban Fringe Areas of Wuhan City

Author

Listed:
  • Yan Long

    (School of Art and Design, Wuhan University of Science and Technology, Wuhan 430065, China)

  • Siyu Hu

    (School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China)

  • Zhengyuan Lu

    (School of Urban Design, Wuhan University, Wuhan 430072, China)

  • Lianghao Cheng

    (School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China)

  • Cairui Zhen

    (School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China)

  • Jingmei Shao

    (School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China)

  • Yuqiao Zheng

    (School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China)

  • Xuehui Zhou

    (School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China)

  • Jin Li

    (School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China)

  • Yudi Lin

    (School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China)

  • Shu Zhang

    (School of Art and Design, Wuhan University of Science and Technology, Wuhan 430065, China)

  • Yue Wang

    (School of Art and Design, Wuhan University of Science and Technology, Wuhan 430065, China)

  • Tianyue Luo

    (Wuhan Planning & Design Institute (Wuhan Transportation Development Strategy Institute), Wuhan 430010, China)

  • Haijuan Zhao

    (School of Urban Design, Wuhan University, Wuhan 430072, China
    Wuhan Design Consultation Group Co., Ltd., Wuhan 430023, China)

  • Xuejun Liu

    (School of Urban Design, Wuhan University, Wuhan 430072, China
    Research Center for Digital City, Wuhan University, Wuhan 430072, China)

Abstract

Achieving effective integration of urban–rural relationships and promoting the flow of resources between urban and rural areas in megacities are a key priority in the development of China’s new urbanization efforts. As a transitional zone between urban and rural areas, the urban fringe is the frontier of urban–rural integration. The specific research object of this paper is the urban fringe areas of Wuhan City. This paper quantifies the neighborhood vitality of the fringe areas by the short-stay visitors in the fringe areas and selects the 5D elements of the built environment and social media data from multiple sources to construct the indicator system assessing the neighborhood vitality of the urban fringe areas. This paper analyzes the spatial distribution characteristics of neighborhood vitality and its influencing factors in urban fringe areas and investigates the connection between neighborhood vitality and its influencing factors through the application of the multi-scale geographically weighted regression (MGWR) model. Based on the regression results, relevant planning recommendations are made on how to enhance the vitality of neighborhoods in urban fringe areas. The results show that the index system constructed by “5D” elements of built environment and social media data can well explain the spatial distribution of neighborhood vitality in urban fringe areas. Among the influencing factors, the absolute value of the correlation coefficient of network exposure is the largest, followed by road density and functional density. Thanks to the different bandwidths given by MGWR to the influencing factors, the global influencing factors are only two indicators—development intensity and functional mixing degree—while the other influencing factors are all local, and the influence degree of different regions is different, so it is necessary to analyze and put forward different planning suggestions accordingly.

Suggested Citation

  • Yan Long & Siyu Hu & Zhengyuan Lu & Lianghao Cheng & Cairui Zhen & Jingmei Shao & Yuqiao Zheng & Xuehui Zhou & Jin Li & Yudi Lin & Shu Zhang & Yue Wang & Tianyue Luo & Haijuan Zhao & Xuejun Liu, 2024. "A Study on the Influence Mechanisms of Neighborhood Vitality and the Characteristics of Spatial and Temporal Differentiation in the Urban Fringe Areas of Wuhan City," Land, MDPI, vol. 13(11), pages 1-36, October.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:11:p:1771-:d:1508418
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/13/11/1771/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/13/11/1771/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Yunzi Yang & Yuanyuan Ma & Hongzan Jiao, 2021. "Exploring the Correlation between Block Vitality and Block Environment Based on Multisource Big Data: Taking Wuhan City as an Example," Land, MDPI, vol. 10(9), pages 1-23, September.
    3. Daniel P. McMillen, 2004. "Geographically Weighted Regression: The Analysis of Spatially Varying Relationships," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(2), pages 554-556.
    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. 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. Wang, Wenjun & Pan, Lin & Yuan, Ning & Zhang, Sen & Liu, Dong, 2015. "A comparative analysis of intra-city human mobility by taxi," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 420(C), pages 134-147.
    3. Janka Lengyel & Stéphane Roux & François Sémécurbe & Stéphane Jaffard & Patrice Abry, 2023. "Roughness and intermittency within metropolitan regions - Application in three French conurbations," Environment and Planning B, , vol. 50(3), pages 600-620, March.
    4. Yi Peng & Xu Cui & Bingjie Yu & Runze Liu & Hong Li, 2025. "How 2D and 3D Built Environment Impact Urban Vitality: Evidence from Overhead-Level to Eye-Level Urban Form Metrics," Land, MDPI, vol. 14(5), pages 1-23, May.
    5. Jie Li & Kun Jia & Yanxu Liu & Bo Yuan & Mu Xia & Wenwu Zhao, 2021. "Spatiotemporal Distribution of Zika Virus and Its Spatially Heterogeneous Relationship with the Environment," IJERPH, MDPI, vol. 18(1), pages 1-14, January.
    6. Lihua Xu & Huifeng Xu & Tianyu Wang & Wenze Yue & Jinyang Deng & Liwei Mao, 2019. "Measuring Urban Spatial Activity Structures: A Comparative Analysis," Sustainability, MDPI, vol. 11(24), pages 1-17, December.
    7. 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.
    8. Fei Han & Xinqi Zheng & Peipei Wang & Dongya Liu & Minrui Zheng, 2022. "Effects of Meteorological Factors and Air Pollutants on COVID-19 Transmission under the Action of Control Measures," IJERPH, MDPI, vol. 19(15), pages 1-19, July.
    9. Wenbo Chen & Fuqing Zhang & Saiwei Luo & Taojie Lu & Jiao Zheng & Lei He, 2022. "Three-Dimensional Landscape Pattern Characteristics of Land Function Zones and Their Influence on PM 2.5 Based on LUR Model in the Central Urban Area of Nanchang City, China," IJERPH, MDPI, vol. 19(18), pages 1-18, September.
    10. 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.
    11. Rezapour, Shabnam & Baghaian, Atefe & Naderi, Nazanin & Sarmiento, Juan P., 2023. "Infection transmission and prevention in metropolises with heterogeneous and dynamic populations," European Journal of Operational Research, Elsevier, vol. 304(1), pages 113-138.
    12. Tang, Jinjun & Liu, Fang & Wang, Yinhai & Wang, Hua, 2015. "Uncovering urban human mobility from large scale taxi GPS data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 140-153.
    13. Zhongxun Zhang & Kaifang Shi & Zhiyong Zhu & Lu Tang & Kangchuan Su & Qingyuan Yang, 2022. "Spatiotemporal Evolution and Influencing Factors of the Rural Natural Capital Utilization Efficiency: A Case Study of Chongqing, China," Land, MDPI, vol. 11(5), pages 1-29, May.
    14. Xing Gao & Keyu Zhai, 2021. "Spatial Mechanisms of Regional Innovation Mobility in China," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(1), pages 247-270, July.
    15. Qian, Chen & Li, Weifeng & Duan, Zhengyu & Yang, Dongyuan & Ran, Bin, 2021. "Using mobile phone data to determine spatial correlations between tourism facilities," Journal of Transport Geography, Elsevier, vol. 92(C).
    16. Zhang, Shanqi & Yang, Yu & Zhen, Feng & Lobsang, Tashi & Li, Zhixuan, 2021. "Understanding the travel behaviors and activity patterns of the vulnerable population using smart card data: An activity space-based approach," Journal of Transport Geography, Elsevier, vol. 90(C).
    17. Chin-Yu Hsu & Yu-Ting Zeng & Yu-Cheng Chen & Mu-Jean Chen & Shih-Chun Candice Lung & Chih-Da Wu, 2020. "Kriging-Based Land-Use Regression Models That Use Machine Learning Algorithms to Estimate the Monthly BTEX Concentration," IJERPH, MDPI, vol. 17(19), pages 1-14, September.
    18. 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.
    19. Xin Li & Yongsheng Qian & Junwei Zeng & Xuting Wei & Xiaoping Guang, 2021. "The Influence of Strip-City Street Network Structure on Spatial Vitality: Case Studies in Lanzhou, China," Land, MDPI, vol. 10(11), pages 1-17, October.
    20. Yanjun Wang & Zixuan Liu & Yawen Wang & Peng Dai, 2025. "Research on Spatial Differentiation of Housing Prices Along the Rail Transit Lines in Qingdao City Based on Multi-Scale Geographically Weighted Regression (MGWR) Analysis," Sustainability, MDPI, vol. 17(9), pages 1-21, May.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    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:gam:jlands:v:13:y:2024:i:11:p:1771-:d:1508418. 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.