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The Scale Effect of Street View Images and Urban Vitality Is Consistent with a Gaussian Function Distribution

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  • Jie Yin

    (School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
    Key Laboratory of Earth Surface System and Human-Earth Relations of Ministry of Natural Resources of China, Shenzhen Graduate School, Peking University, Shenzhen 518055, China)

  • Ran Chen

    (School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
    Key Laboratory of Earth Surface System and Human-Earth Relations of Ministry of Natural Resources of China, Shenzhen Graduate School, Peking University, Shenzhen 518055, China)

  • Ruixiang Zhang

    (School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
    Key Laboratory of Earth Surface System and Human-Earth Relations of Ministry of Natural Resources of China, Shenzhen Graduate School, Peking University, Shenzhen 518055, China)

  • Xiang Li

    (School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
    Key Laboratory of Earth Surface System and Human-Earth Relations of Ministry of Natural Resources of China, Shenzhen Graduate School, Peking University, Shenzhen 518055, China)

  • Yuan Fang

    (Key Laboratory of Earth Surface System and Human-Earth Relations of Ministry of Natural Resources of China, Shenzhen Graduate School, Peking University, Shenzhen 518055, China)

Abstract

Street view images are often used to assess the impact of the built environment on urban vitality from an eye-level perspective. However, the influence of analyzing scale on the accuracy of assessment results is often ignored. To find the appropriate scale, we need to quantify the scale effect of street view images and urban vitality. Therefore, in this study, Shenzhen is selected as the study area. Urban vitality is characterized by Weibo check-in records. VGG19 is employed to learn the features of street view images and performs regression analysis of the image features and Weibo check-in records in different sizes of grids (100 m, 150 m, …, 1000 m). Finally, we fit the scale effect of their correlation via a function. We find that as the scale increases, the correlation between street view images and urban vitality tends to increase and then decrease, which is consistent with the distribution law of a Gaussian function. This study provides a basis for selecting appropriate scales of the correlation between street view images and urban vitality.

Suggested Citation

  • Jie Yin & Ran Chen & Ruixiang Zhang & Xiang Li & Yuan Fang, 2025. "The Scale Effect of Street View Images and Urban Vitality Is Consistent with a Gaussian Function Distribution," Land, MDPI, vol. 14(2), pages 1-14, February.
    • Handle: RePEc:gam:jlands:v:14:y:2025:i:2:p:415-:d:1592829
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    References listed on IDEAS

    as
    1. Zhongshan Huang & Bin Wang & Shixian Luo & Manqi Wang & Jingjing Miao & Qiyue Jia, 2024. "Integrating Streetscape Images, Machine Learning, and Space Syntax to Enhance Walkability: A Case Study of Seongbuk District, Seoul," Land, MDPI, vol. 13(10), pages 1-20, September.
    2. Sarjala, Satu, 2019. "Built environment determinants of pedestrians’ and bicyclists’ route choices on commute trips: Applying a new grid-based method for measuring the built environment along the route," Journal of Transport Geography, Elsevier, vol. 78(C), pages 56-69.
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