Landscape Preferences of Recreational Walkways in Urban Green Spaces: Bada Shanren Meihu Scenic Area, China

Chengling Zhou, Jinlin Teng, Chunqing Liu (), Yiyin Zhang, Bingjie Ouyang, Tian Zeng, Huimin Gong and Cheng Zhang
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Chengling Zhou: College of Forestry, Jiangxi Agricultural University, Nanchang 330045, China
Jinlin Teng: College of Forestry, Jiangxi Agricultural University, Nanchang 330045, China
Chunqing Liu: College of Forestry, Jiangxi Agricultural University, Nanchang 330045, China
Yiyin Zhang: College of Forestry, Jiangxi Agricultural University, Nanchang 330045, China
Bingjie Ouyang: College of Forestry, Jiangxi Agricultural University, Nanchang 330045, China
Tian Zeng: College of Forestry, Jiangxi Agricultural University, Nanchang 330045, China
Huimin Gong: College of Forestry, Jiangxi Agricultural University, Nanchang 330045, China
Cheng Zhang: College of Forestry, Jiangxi Agricultural University, Nanchang 330045, China

Sustainability, 2025, vol. 17, issue 22, 1-22

Abstract: Urban greenway trails serve as a vital link between urban populations and the natural environment, playing a key role in enhancing quality of life and promoting physical and mental well-being. We propose an interpretable machine learning framework applied to 424 geotagged footprint images from the Bada Shanren Meihu Scenic Area in China. Our main findings are as follows: (1) The key factors influencing trail landscape preferences include the Water Visibility Index (WVI), Building Landscape Index (BVI), Freedom Index, and Greenery Visibility Index (GVI). (2) For WVI, SHAP values significantly increase around the 0.05 threshold. BVI has a critical threshold of 0.17, with a strong influence below it and a reduced effect above it. The Freedom variable shows an inverse relationship, with minimal contribution below 0.21 and a sharp increase above this threshold. GVI maintains high SHAP values at lower levels (GVI ≤ 0.66), but its predictive utility decreases at higher values. (3) Landscape preferences are significantly positively correlated with naturalness, wildness, WVI, and openness, with water landscapes being the strongest driver. In contrast, artificial factors, V_Low, and H_Purple significantly suppress preferences. This suggests that human intervention and certain color tones may reduce the attractiveness of the landscape.

Keywords: interpretable machine learning; urban greenway trails; landscape preferences; ELO rating (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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