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Cross-Cultural Comparison of Urban Green Space through Crowdsourced Big Data: A Natural Language Processing and Image Recognition Approach

Author

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  • Shuhao Liu

    (Graduate School of Horticulture, Chiba University, Chiba 271-8510, Japan)

  • Chang Su

    (School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Junhua Zhang

    (Graduate School of Horticulture, Chiba University, Chiba 271-8510, Japan)

  • Shiro Takeda

    (Graduate School of Horticulture, Chiba University, Chiba 271-8510, Japan)

  • Jiarui Liu

    (School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China)

  • Ruochen Yang

    (Graduate School of Horticulture, Chiba University, Chiba 271-8510, Japan)

Abstract

Understanding the relationship between environmental features and perceptions of urban green spaces (UGS) is crucial for UGS design and management. However, quantifying park perceptions on a large spatial and temporal scale is challenging, and it remains unclear which environmental features lead to different perceptions in cross-cultural comparisons. This study addressed this issue by collecting 11,782 valid social media comments and photos covering 36 UGSs from 2020 to 2022 using a Python 3.6-based crawler. Natural language processing and image recognition methods from Google were then utilized to quantify UGS perceptions. This study obtained 32 high-frequency feature words through sentiment analysis and quantified 17 environmental feature factors that emerged using object and scene recognition techniques for photos. The results show that users generally perceive Japanese UGSs as more positive than Chinese UGSs. Chinese UGS users prioritize plant green design and UGS user density, whereas Japanese UGS focuses on integrating specific cultural elements. Therefore, when designing and managing urban greenspace systems, local environmental and cultural characteristics must be considered to meet the needs of residents and visitors. This study offers a replicable and systematic approach for researchers investigating the utilization of UGS on a global scale.

Suggested Citation

  • Shuhao Liu & Chang Su & Junhua Zhang & Shiro Takeda & Jiarui Liu & Ruochen Yang, 2023. "Cross-Cultural Comparison of Urban Green Space through Crowdsourced Big Data: A Natural Language Processing and Image Recognition Approach," Land, MDPI, vol. 12(4), pages 1-27, March.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:4:p:767-:d:1109841
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    References listed on IDEAS

    as
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    Cited by:

    1. Sonja Wilhelm Stanis & Emily Piontek & Shuangyu Xu & Andrew Mallinak & Charles Nilon & Damon M. Hall, 2024. "Residents’ Perceptions of Urban Greenspace in a Shrinking City: Ecosystem Services and Environmental Justice," Land, MDPI, vol. 13(10), pages 1-17, September.
    2. Ruochen Ma & Katsunori Furuya, 2024. "Social Media Image and Computer Vision Method Application in Landscape Studies: A Systematic Literature Review," Land, MDPI, vol. 13(2), pages 1-22, February.

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