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Exploring the Influence of Urban Form on Urban Vibrancy in Shenzhen Based on Mobile Phone Data

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  • Lingjun Tang

    (Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources, Shenzhen 518034, China
    Shenzhen Municipal Planning & Land Real Estate Information Centre, Shenzhen 518034, China
    School of Resources and Environmental Sciences, Wuhan University, 129 Luoyu Road, Wuhan 430079, China)

  • Yu Lin

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

  • Sijia Li

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

  • Sheng Li

    (Shenzhen Municipal Planning & Land Real Estate Information Centre, Shenzhen 518034, China)

  • Jingyi Li

    (Shenzhen Municipal Planning & Land Real Estate Information Centre, Shenzhen 518034, China)

  • Fu Ren

    (School of Resources and Environmental Sciences, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
    Key Laboratory of GIS, Ministry of Education, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
    Key Laboratory of Digital Mapping and Land Information Application Engineering, National Administration of Surveying, Mapping and Geoinformation, 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
    Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210023, China)

Abstract

Urban vibrancy is an important indicator of the attractiveness of a city and its potential for comprehensive, healthy and sustainable development in all aspects. With the development of big data, an increasing number of datasets can be used to analyse urban vibrancy on fine spatial and temporal scales from the perspective of human perception. In this study, we applied mobile phone data as a proxy for local vibrancy in Shenzhen and constructed a comprehensive framework for the factors that influence urban vibrancy, especially in terms of urban morphology and space syntax. In addition, the popular geographically and temporally weighted regression (GTWR) method was used to explore the spatiotemporal relationships between vibrancy and its influencing factors. The spatial and temporal coefficients are presented through maps. The conclusions of this attempt to study urban vibrancy with urban big data have significant implications for helping urban planners and policy makers optimize the spatial layouts of urban functional zones and perform high-quality city planning.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:12:p:4565-:d:187477
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    References listed on IDEAS

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    2. Nuria Vidal Domper & Gonzalo Hoyos-Bucheli & Marta Benages Albert, 2023. "Jane Jacobs’s Criteria for Urban Vitality: A Geospatial Analysis of Morphological Conditions in Quito, Ecuador," Sustainability, MDPI, vol. 15(11), pages 1-19, May.
    3. 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.
    4. Xucai Zhang & Yeran Sun & Ting On Chan & Ying Huang & Anyao Zheng & Zhang Liu, 2021. "Exploring Impact of Surrounding Service Facilities on Urban Vibrancy Using Tencent Location-Aware Data: A Case of Guangzhou," Sustainability, MDPI, vol. 13(2), pages 1-23, January.
    5. Renyang Wang & Qingsong He & Lu Zhang & Huiying Wang, 2021. "Coupling Cellular Automata and a Genetic Algorithm to Generate a Vibrant Urban Form—A Case Study of Wuhan, China," IJERPH, MDPI, vol. 18(21), pages 1-15, October.
    6. Li, Shaodong & Chen, Pengfei & Hui, Fengming & Gong, Mengjie, 2024. "Evaluating urban vitality and resilience under the influence of the COVID-19 pandemic from a mobility perspective: A case study in Shenzhen, China," Journal of Transport Geography, Elsevier, vol. 117(C).
    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.
    8. Yuan Lai & Jiatong Li & Jiachen Zhang & Lan Yan & Yifeng Liu, 2022. "Do Vibrant Places Promote Active Living? Analyzing Local Vibrancy, Running Activity, and Real Estate Prices in Beijing," IJERPH, MDPI, vol. 19(24), pages 1-19, December.
    9. 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).
    10. Yun Han & Chunpeng Qin & Longzhu Xiao & Yu Ye, 2024. "The nonlinear relationships between built environment features and urban street vitality: A data-driven exploration," Environment and Planning B, , vol. 51(1), pages 195-215, January.
    11. Yuting Chen & Bingyao Jia & Jing Wu & Xuejun Liu & Tianyue Luo, 2022. "Temporal and Spatial Attractiveness Characteristics of Wuhan Urban Riverside from the Perspective of Traveling," Land, MDPI, vol. 11(9), pages 1-21, August.
    12. Yanyan Chen & Hanqiang Qian & Yang Wang, 2020. "Analysis of Beijing’s Working Population Based on Geographically Weighted Regression Model," Sustainability, MDPI, vol. 12(12), pages 1-16, June.
    13. Anqi Zhang & Weifeng Li & Jiayu Wu & Jian Lin & Jianqun Chu & Chang Xia, 2021. "How can the urban landscape affect urban vitality at the street block level? A case study of 15 metropolises in China," Environment and Planning B, , vol. 48(5), pages 1245-1262, June.
    14. Pengyu Ren & Yuanli Li & Kairui You, 2021. "Residents’ Demands for Urban Retail: Heterogeneity in Housing Structure Characteristics, Price Quantile, and Space," Land, MDPI, vol. 10(12), pages 1-17, December.
    15. Jing Wu & Xirui Chen & Shulin Chen, 2019. "Temporal Characteristics of Waterfronts in Wuhan City and People’s Behavioral Preferences Based on Social Media Data," Sustainability, MDPI, vol. 11(22), pages 1-37, November.
    16. Jinliu Chen & Haoqi Wang & Zhuo Yang & Pengcheng Li & Geng Ma & Xiaoxin Zhao, 2023. "Comparative Spatial Vitality Evaluation of Traditional Settlements Based on SUF: Taking Anren Ancient Town’s Urban Design as an Example," Sustainability, MDPI, vol. 15(10), pages 1-20, May.
    17. Hongyu Gong & Xiaozihan Wang & Zihao Wang & Ziyi Liu & Qiushan Li & Yunhan Zhang, 2022. "How Did the Built Environment Affect Urban Vibrancy? A Big Data Approach to Post-Disaster Revitalization Assessment," IJERPH, MDPI, vol. 19(19), pages 1-25, September.

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