IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i2p1518-d1034082.html
   My bibliography  Save this article

Assessment of Influence Mechanisms of Built Environment on Street Vitality Using Multisource Spatial Data: A Case Study in Qingdao, China

Author

Listed:
  • Mingyi Li

    (College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China)

  • Jinghu Pan

    (College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China)

Abstract

Street vitality is a significant indicator of a city’s capacity for sustainable development. Significant progress has been made on the basis of measurements of a single indicator of street vitality, but few studies have used multisource data to measure street vitality in a comprehensive way. In this study, in order to explore the multidimensional vitality characteristics of streets, streets were taken as the analysis unit, and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) evaluation model with combined weights was used to identify the spatial pattern of streets vitality from social, economic, and cultural dimensions using multisource spatial data such as Baidu heat map, Meituan store rating, and cultural facilities points of interest in the main urban area of Qingdao City, China. Using a Multiscale Geographically Weighted Regression (MGWR) model, the spatial correlations and differences between street built environment components and multidimensional street vitality were examined, to reveal the influence mechanism of street vitality creation in each street. The study found that the comprehensive vitality of the streets in the main urban area of Qingdao City exhibits the spatial differentiation features of “weak east–west, strong central, multicenter, cluster type”. Furthermore, although commercial and public services are essential for enhancing street vitality and attracting crowds, a very high degree of functional mix has not resulted in a high degree of street vitality. Lastly, high spatial heterogeneity between built environment factors and street vitality necessitates considering the functional positioning and development basis of the street, tailoring to local conditions and policies, considering the street’s vitality development status and development needs, complementing strengths, promoting coordinated development, and releasing and enhancing the street’s vitality. Therefore, it is essential to explore street vitality and its influencing mechanisms to improve people’s quality of life and promote sustainable urban development.

Suggested Citation

  • Mingyi Li & Jinghu Pan, 2023. "Assessment of Influence Mechanisms of Built Environment on Street Vitality Using Multisource Spatial Data: A Case Study in Qingdao, China," Sustainability, MDPI, vol. 15(2), pages 1-22, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1518-:d:1034082
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/2/1518/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/2/1518/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Reid Ewing & Guang Tian & JP Goates & Ming Zhang & Michael J Greenwald & Alex Joyce & John Kircher & William Greene, 2015. "Varying influences of the built environment on household travel in 15 diverse regions of the United States," Urban Studies, Urban Studies Journal Limited, vol. 52(13), pages 2330-2348, October.
    2. Runde Fu & Xinhuan Zhang & Degang Yang & Tianyi Cai & Yufang Zhang, 2021. "The Relationship between Urban Vibrancy and Built Environment: An Empirical Study from an Emerging City in an Arid Region," IJERPH, MDPI, vol. 18(2), pages 1-20, January.
    3. Xin Lao & Hengyu Gu, 2020. "Unveiling various spatial patterns of determinants of hukou transfer intentions in China: A multi‐scale geographically weighted regression approach," Growth and Change, Wiley Blackwell, vol. 51(4), pages 1860-1876, December.
    4. A. Stewart Fotheringham & Wenbai Yang & Wei Kang, 2017. "Multiscale Geographically Weighted Regression (MGWR)," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 107(6), pages 1247-1265, November.
    5. Wanshu Wu & Ziying Ma & Jinhan Guo & Xinyi Niu & Kai Zhao, 2022. "Evaluating the Effects of Built Environment on Street Vitality at the City Level: An Empirical Research Based on Spatial Panel Durbin Model," IJERPH, MDPI, vol. 19(3), pages 1-24, January.
    6. Soofi, Ehsan S. & Retzer, Joseph J., 1992. "Adjustment of importance weights in multiattribute value models by minimum discrimination information," European Journal of Operational Research, Elsevier, vol. 60(1), pages 99-108, July.
    7. Wanshu Wu & Xinyi Niu & Meng Li, 2021. "Influence of Built Environment on Street Vitality: A Case Study of West Nanjing Road in Shanghai Based on Mobile Location Data," Sustainability, MDPI, vol. 13(4), pages 1-23, February.
    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. Kai Zhao & Jinhan Guo & Ziying Ma & Wanshu Wu, 2023. "Exploring the Spatiotemporal Heterogeneity and Stationarity in the Relationship between Street Vitality and Built Environment," SAGE Open, , vol. 13(1), pages 21582440231, February.
    2. Hengyu Gu & Hanchen Yu & Mehak Sachdeva & Ye Liu, 2021. "Analyzing the distribution of researchers in China: An approach using multiscale geographically weighted regression," Growth and Change, Wiley Blackwell, vol. 52(1), pages 443-459, March.
    3. Li Yue & Hongbo Zhao & Xiaoman Xu & Tianshun Gu & Zeting Jia, 2022. "Quantifying the Spatial Fragmentation Pattern and Its Influencing Factors of Urban Land Use: A Case Study of Pingdingshan City, China," Land, MDPI, vol. 11(5), pages 1-15, May.
    4. Zhenbao Wang & Xin Gong & Yuchen Zhang & Shuyue Liu & Ning Chen, 2023. "Multi-Scale Geographically Weighted Elasticity Regression Model to Explore the Elastic Effects of the Built Environment on Ride-Hailing Ridership," Sustainability, MDPI, vol. 15(6), pages 1-22, March.
    5. Tongwen Wang & Ya Li & Haidong Li & Shuaijun Chen & Hongkai Li & Yunxing Zhang, 2022. "Research on the Vitality Evaluation of Parks and Squares in Medium-Sized Chinese Cities from the Perspective of Urban Functional Areas," IJERPH, MDPI, vol. 19(22), pages 1-23, November.
    6. Han Zou & Ruichao Liu & Wen Cheng & Jingjing Lei & Jing Ge, 2023. "The Association between Street Built Environment and Street Vitality Based on Quantitative Analysis in Historic Areas: A Case Study of Wuhan, China," Sustainability, MDPI, vol. 15(2), pages 1-23, January.
    7. Pengyu Zhu & Jiarong Li & Kailai Wang & Jie Huang, 2024. "Exploring spatial heterogeneity in the impact of built environment on taxi ridership using multiscale geographically weighted regression," Transportation, Springer, vol. 51(5), pages 1963-1997, October.
    8. Wanshu Wu & Ziying Ma & Jinhan Guo & Xinyi Niu & Kai Zhao, 2022. "Evaluating the Effects of Built Environment on Street Vitality at the City Level: An Empirical Research Based on Spatial Panel Durbin Model," IJERPH, MDPI, vol. 19(3), pages 1-24, January.
    9. Yanzhao Wang & Jianfei Cao, 2023. "Examining the Effects of Socioeconomic Development on Fine Particulate Matter (PM2.5) in China’s Cities Based on Spatial Autocorrelation Analysis and MGWR Model," IJERPH, MDPI, vol. 20(4), pages 1-23, February.
    10. Singleton, Patrick A. & Park, Keunhyun & Lee, Doo Hong, 2021. "Varying influences of the built environment on daily and hourly pedestrian crossing volumes at signalized intersections estimated from traffic signal controller event data," Journal of Transport Geography, Elsevier, vol. 93(C).
    11. Liu, Yan & Wang, Siqin & Xie, Bin, 2019. "Evaluating the effects of public transport fare policy change together with built and non-built environment features on ridership: The case in South East Queensland, Australia," Transport Policy, Elsevier, vol. 76(C), pages 78-89.
    12. Wang, Xiaoxi & Zhang, Yaojun & Yu, Danlin & Qi, Jinghan & Li, Shujing, 2022. "Investigating the spatiotemporal pattern of urban vibrancy and its determinants: Spatial big data analyses in Beijing, China," Land Use Policy, Elsevier, vol. 119(C).
    13. Qikang Zhong & Bo Li & Tian Dong, 2024. "Building sustainable slow communities: the impact of built environments on leisure-time physical activities in Shanghai," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-21, December.
    14. Shichao Lu & Zhihua Zhang & M. James C. Crabbe & Prin Suntichaikul, 2024. "Effects of Urban Land-Use Planning on Housing Prices in Chiang Mai, Thailand," Land, MDPI, vol. 13(8), pages 1-13, July.
    15. Jin, Peizhen & Mangla, Sachin Kumar & Song, Malin, 2021. "Moving towards a sustainable and innovative city: Internal urban traffic accessibility and high-level innovation based on platform monitoring data," International Journal of Production Economics, Elsevier, vol. 235(C).
    16. Chunfang Zhao & Yingliang Wu & Yunfeng Chen & Guohua Chen, 2023. "Multiscale Effects of Hedonic Attributes on Airbnb Listing Prices Based on MGWR: A Case Study of Beijing, China," Sustainability, MDPI, vol. 15(2), pages 1-21, January.
    17. Yu, Haitao & Peng, Zhong-Ren, 2019. "Exploring the spatial variation of ridesourcing demand and its relationship to built environment and socioeconomic factors with the geographically weighted Poisson regression," Journal of Transport Geography, Elsevier, vol. 75(C), pages 147-163.
    18. Li Gao & Mingjing Huang & Wuping Zhang & Lei Qiao & Guofang Wang & Xumeng Zhang, 2021. "Comparative Study on Spatial Digital Mapping Methods of Soil Nutrients Based on Different Geospatial Technologies," Sustainability, MDPI, vol. 13(6), pages 1-19, March.
    19. Faizeh Hatami & Jean-Claude Thill, 2022. "Spatiotemporal Evaluation of the Built Environment’s Impact on Commuting Duration," Sustainability, MDPI, vol. 14(12), pages 1-19, June.
    20. Li, Mengya & Kwan, Mei-Po & Hu, Wenyan & Li, Rui & Wang, Jun, 2023. "Examining the effects of station-level factors on metro ridership using multiscale geographically weighted regression," Journal of Transport Geography, Elsevier, vol. 113(C).

    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:2:p:1518-:d:1034082. 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.