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

Shuhao Liu, Chang Su, Junhua Zhang, Shiro Takeda, Jiarui Liu and Ruochen Yang ()
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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

Land, 2023, vol. 12, issue 4, 1-27

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.

Keywords: environmental features; urban green space; social media; natural language processing; image recognition; cross-cultural comparisons; cultural elements (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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