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Urban Morphology Promotes Urban Vibrancy from the Spatiotemporal and Synergetic Perspectives: A Case Study Using Multisource Data in Shenzhen, China

Author

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  • Sijia Li

    (School of Resources and Environmental Sciences, Wuhan University, 129 Luoyu Road, Wuhan 430079, China)

  • Chao Wu

    (School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China)

  • Yu Lin

    (Hubei Institute of Land Surveying and Mapping, 199 Aomen Road, Wuhan 430010, China)

  • Zhengyang Li

    (Yunnan Provincial Mapping Institute, 223 Xichang Road, Kunming 650034, China)

  • Qingyun Du

    (School of Resources and Environmental Sciences, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
    Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
    Key Laboratory of Digital Mapping and Land Information Application Engineering, Ministry of Natural Resources, Wuhan University, Wuhan 430079, China
    Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China)

Abstract

Urban vibrancy is the key and the foundation for monitoring the status of urban spatial development, assisting in data-driven urban development planning and realizing sustainable urban development. Based on a dataset of multisource geographical big data, the understanding and analysis of urban vibrancy can be deepened with fine granularity. The working framework in this study focuses on the comprehensive perspective of urban morphology, which is decomposed into two dimensions (formality and functionality) and four elements (road, block, building, point of interest). The geographically and temporally weighted regression model was first applied to determine the spatiotemporal effect of the morphological metrics on vibrancy, and then, the geographical detector was employed from the perspective of spatially stratified heterogeneity to reveal the synergetic impacts. The following findings were revealed. (1) Dense street networks, small and medium-sized blocks, and the diversification and intensification of building and land use are beneficial to urban vibrancy. (2) Under the premise of adapting to local conditions, urban spaces combine multiple morphological metrics for the accomplishment of a full-region and all-time vibrancy. (3) The mixture of urban functions is worthy of attention for vibrancy growth because of its extraordinary synergy, not its capacity. Morphological metrics serve to foster and prolong urban vibrancy, adapt to urban sustainability, and contend against inefficient, disorderly urban sprawl. These findings provide significant implications for urban planners/designers and policymakers to optimize urban morphology, improve the vibrancy in large cities, and implement high-quality city planning.

Suggested Citation

  • Sijia Li & Chao Wu & Yu Lin & Zhengyang Li & Qingyun Du, 2020. "Urban Morphology Promotes Urban Vibrancy from the Spatiotemporal and Synergetic Perspectives: A Case Study Using Multisource Data in Shenzhen, China," Sustainability, MDPI, vol. 12(12), pages 1-24, June.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:12:p:4829-:d:370849
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    References listed on IDEAS

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    1. Fardin Kooshki & Abdollah Mollatabar & Leila Masumi, 2015. "The Local Community Planning (Case Study: Narmak Neighborhood of Tehran)," International Journal of Academic Research in Business and Social Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Business and Social Sciences, vol. 5(7), pages 171-178, July.
    2. Hao Wu & Hongzan Jiao & Yang Yu & Zhigang Li & Zhenghong Peng & Lingbo Liu & Zheng Zeng, 2018. "Influence Factors and Regression Model of Urban Housing Prices Based on Internet Open Access Data," Sustainability, MDPI, vol. 10(5), pages 1-17, May.
    3. van Lenthe, F. J. & Brug, J. & Mackenbach, J. P., 2005. "Neighbourhood inequalities in physical inactivity: the role of neighbourhood attractiveness, proximity to local facilities and safety in the Netherlands," Social Science & Medicine, Elsevier, vol. 60(4), pages 763-775, February.
    4. Alice Barreca & Rocco Curto & Diana Rolando, 2020. "Urban Vibrancy: An Emerging Factor that Spatially Influences the Real Estate Market," Sustainability, MDPI, vol. 12(1), pages 1-23, January.
    5. Tan, Ronghui & He, Qingsong & Zhou, Kehao & Xie, Peng, 2019. "The effect of new metro stations on local land use and housing prices: The case of Wuhan, China," Journal of Transport Geography, Elsevier, vol. 79(C), pages 1-1.
    6. Ann Dale & Chris Ling & Lenore Newman, 2010. "Community Vitality: The Role of Community-Level Resilience Adaptation and Innovation in Sustainable Development," Sustainability, MDPI, vol. 2(1), pages 1-17, January.
    7. Lingjun Tang & Yu Lin & Sijia Li & Sheng Li & Jingyi Li & Fu Ren & Chao Wu, 2018. "Exploring the Influence of Urban Form on Urban Vibrancy in Shenzhen Based on Mobile Phone Data," Sustainability, MDPI, vol. 10(12), pages 1-21, December.
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    Cited by:

    1. Jinyao Lin & Yaye Zhuang & Yang Zhao & Hua Li & Xiaoyu He & Siyan Lu, 2022. "Measuring the Non-Linear Relationship between Three-Dimensional Built Environment and Urban Vitality Based on a Random Forest Model," IJERPH, MDPI, vol. 20(1), pages 1-18, December.
    2. Jiangang Shi & Wei Miao & Hongyun Si & Ting Liu, 2021. "Urban Vitality Evaluation and Spatial Correlation Research: A Case Study from Shanghai, China," Land, MDPI, vol. 10(11), pages 1-15, November.
    3. Pei Zhang & Tao Zhang & Hiroatsu Fukuda & Moheng Ma, 2023. "Evidence of Multi-Source Data Fusion on the Relationship between the Specific Urban Built Environment and Urban Vitality in Shenzhen," Sustainability, MDPI, vol. 15(8), pages 1-24, April.
    4. 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).
    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. Tao Shen & Wenshiqi Zhou & Shuai Yuan & Liang Huo, 2024. "Spatiotemporal Characterization of the Three-Dimensional Morphology of Urban Buildings Based on Moran’s I," Sustainability, MDPI, vol. 16(15), pages 1-16, July.
    7. 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.

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