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Visual Preference Analysis and Planning Responses Based on Street View Images: A Case Study of Gulangyu Island, China

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  • Jingxiong Huang

    (School of Architecture and Civil Engineering, Xiamen University, Xiamen 361005, China
    School of Architecture, Tsinghua University, Beijing 100084, China)

  • Jiaqi Liang

    (School of Architecture and Civil Engineering, Xiamen University, Xiamen 361005, China
    Xiamen Key Laboratory of Integrated Application of Intelligent Technology for Architectural Heritage Protection, Xiamen 361005, China)

  • Mengsheng Yang

    (School of Architecture and Civil Engineering, Xiamen University, Xiamen 361005, China
    Xiamen Key Laboratory of Integrated Application of Intelligent Technology for Architectural Heritage Protection, Xiamen 361005, China)

  • Yuan Li

    (School of Architecture and Civil Engineering, Xiamen University, Xiamen 361005, China
    Xiamen Key Laboratory of Integrated Application of Intelligent Technology for Architectural Heritage Protection, Xiamen 361005, China)

Abstract

The features of a street environment play an essential role in human behavior, but predicting the preferred environment becomes challenging for city planning. This paper takes Gulangyu Island as an example and examines tourists’ visual preferences through street view images and a stated preference survey. Based on the findings, planning responses were proposed to provide references for improving tourists’ visual perception of the street’s environment. The results show that tourists’ preferences for the street environment are significantly affected by visual features. From highest to lowest are variety, the green view index, crowdedness, sky openness, and enclosure. The green view index, sky openness, and variety positively affect the visual utility, while crowdedness and enclosure have a negative effect. Among them, variety has the most potent positive effect on visual preference, while crowdedness has the most substantial negative effect. Moreover, there is a balance between green view and enclosure that is affected by green plants, and when the enclosure value is too high, the marginal effect of the green view index will be less effective. Last, the streets with high visual utility have an ideal natural environment, spacious roads, an open sky, and limited architecture.

Suggested Citation

  • Jingxiong Huang & Jiaqi Liang & Mengsheng Yang & Yuan Li, 2022. "Visual Preference Analysis and Planning Responses Based on Street View Images: A Case Study of Gulangyu Island, China," Land, MDPI, vol. 12(1), pages 1-15, December.
  • Handle: RePEc:gam:jlands:v:12:y:2022:i:1:p:129-:d:1021226
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    References listed on IDEAS

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    1. Yu Ye & Wei Zeng & Qiaomu Shen & Xiaohu Zhang & Yi Lu, 2019. "The visual quality of streets: A human-centred continuous measurement based on machine learning algorithms and street view images," Environment and Planning B, , vol. 46(8), pages 1439-1457, October.
    2. Sang Seup Kim & Jae-Song Lee & Dong Hak Lee & Yeol Choi, 2021. "Citizens’ Preference and Perception of Street Trees of Main Boulevards in Busan, South Korea," Sustainability, MDPI, vol. 13(6), pages 1-15, March.
    3. Boeing, Geoff, 2018. "Measuring the Complexity of Urban Form and Design," SocArXiv bxhrz, Center for Open Science.
    4. Kati Häfner & Ingo Zasada & Boris T. van Zanten & Fabrizio Ungaro & Mark Koetse & Annette Piorr, 2018. "Assessing landscape preferences: a visual choice experiment in the agricultural region of Märkische Schweiz, Germany," Landscape Research, Taylor & Francis Journals, vol. 43(6), pages 846-861, August.
    5. Vondolia, Godwin K. & Hynes, Stephen & Armstrong, Claire W. & Chen, Wenting, 2021. "Subjective well-being and stated preferences: Explorations from a choice experiment in Norway," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 91(C).
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    Cited by:

    1. Yiming Liu & Xiangxiang Pan & Qing Liu & Guicai Li, 2023. "Establishing a Reliable Assessment of the Green View Index Based on Image Classification Techniques, Estimation, and a Hypothesis Testing Route," Land, MDPI, vol. 12(5), pages 1-14, May.
    2. Tingjin Wu & Deqing Lin & Yi Chen & Jinxiu Wu, 2025. "Integrating Street View Images, Deep Learning, and sDNA for Evaluating University Campus Outdoor Public Spaces: A Focus on Restorative Benefits and Accessibility," Land, MDPI, vol. 14(3), pages 1-28, March.

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