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How 2D and 3D Built Environment Impact Urban Vitality: Evidence from Overhead-Level to Eye-Level Urban Form Metrics

Author

Listed:
  • Yi Peng

    (School of Architecture, Southwest Jiaotong University, Chengdu 611756, China)

  • Xu Cui

    (School of Architecture, Southwest Jiaotong University, Chengdu 611756, China)

  • Bingjie Yu

    (School of Architecture, Southwest Jiaotong University, Chengdu 611756, China)

  • Runze Liu

    (School of Architecture, The Chinese University of Hong Kong, Sha Tin, Hong Kong SAR, China)

  • Hong Li

    (Changsha Planning and Design Institute Co., Ltd., Changsha 410126, China)

Abstract

The built environment is the key to creating vibrant urban spaces that contribute to the health and sustainability of cities. Studies have demonstrated that a reasonable built environment helps to stimulate urban vitality. Nevertheless, there are limitations to the understanding that three-dimensional (3D) built environment indicators from the ‘human perspective’ can substantially affect urban vitality. This study provides an empirical analysis of Xi’an, a city with both traditional historical blocks and a modern city landscape. By applying the ordinary least square model and the geographically weighted regression model, this study explores the impacts of the two-dimensional (2D) and 3D built environments on urban vitality. Results show: (1) the urban vitality exhibits significant spatial and temporal difference characteristics; (2) the 3D built environment exerts a greater influence on urban vitality than 2D; (3) taking weekdays for instance, the indicators of green space and road space (e.g., normalized difference vegetation index (−0.092), green view index (−0.104), road density (−0.021), and enclosure (−0.089)) are negatively correlated with urban vitality, while the indicators of building space and mixed function (e.g., building density, floor area ratio, points of interest (POI) mixing degree, and 3D mixing degree) present a positive effect. To improve urban vitality, the study provides suggestions from the perspective of 3D and human perception, which will contribute to the meticulous practice of urban design.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jlands:v:14:y:2025:i:5:p:1026-:d:1651373
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    References listed on IDEAS

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    1. Wang, Shaojian & Shi, Chenyi & Fang, Chuanglin & Feng, Kuishuang, 2019. "Examining the spatial variations of determinants of energy-related CO2 emissions in China at the city level using Geographically Weighted Regression Model," Applied Energy, Elsevier, vol. 235(C), pages 95-105.
    2. Lu, Yi & Sarkar, Chinmoy & Xiao, Yang, 2018. "The effect of street-level greenery on walking behavior: Evidence from Hong Kong," Social Science & Medicine, Elsevier, vol. 208(C), pages 41-49.
    3. Xiaodong Xu & Xinhan Xu & Peng Guan & Yu Ren & Wei Wang & Ning Xu, 2018. "The Cause and Evolution of Urban Street Vitality under the Time Dimension: Nine Cases of Streets in Nanjing City, China," Sustainability, MDPI, vol. 10(8), pages 1-19, August.
    4. Li, Shaoying & Lyu, Dijiang & Huang, Guanping & Zhang, Xiaohu & Gao, Feng & Chen, Yuting & Liu, Xiaoping, 2020. "Spatially varying impacts of built environment factors on rail transit ridership at station level: A case study in Guangzhou, China," Journal of Transport Geography, Elsevier, vol. 82(C).
    5. Shiwei Lu & Chaoyang Shi & Xiping Yang, 2019. "Impacts of Built Environment on Urban Vitality: Regression Analyses of Beijing and Chengdu, China," IJERPH, MDPI, vol. 16(23), pages 1-16, November.
    6. Gao, Ming & Fang, Congying, 2025. "Deciphering urban cycling: Analyzing the nonlinear impact of street environments on cycling volume using crowdsourced tracker data and machine learning," Journal of Transport Geography, Elsevier, vol. 124(C).
    7. 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.
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