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

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    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. 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.
    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.
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