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Investigating the Association between Streetscapes and Mental Health in Zhanjiang, China: Using Baidu Street View Images and Deep Learning

Author

Listed:
  • Anjing Zhang

    (Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Henan University, Ministry of Education, Kaifeng 475004, China
    Department of Geography, University of Zurich, 8057 Zurich, Switzerland)

  • Shiyan Zhai

    (Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Henan University, Ministry of Education, Kaifeng 475004, China
    College of Geography and Environmental Science, Henan University, Kaifeng 475004, China)

  • Xiaoxiao Liu

    (Department of Community Health Science, Cumming School of Medicine, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada)

  • Genxin Song

    (Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Henan University, Ministry of Education, Kaifeng 475004, China
    College of Geography and Environmental Science, Henan University, Kaifeng 475004, China)

  • Yuke Feng

    (Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Henan University, Ministry of Education, Kaifeng 475004, China
    College of Geography and Environmental Science, Henan University, Kaifeng 475004, China)

Abstract

Mental health is one of the main factors that significantly affect one’s life. Previous studies suggest that streets are the main activity space for urban residents and have important impacts on human mental health. Existing studies, however, have not fully examined the relationships between streetscape characteristics and people’s mental health on a street level. This study thus aims to explore the spatial patterns of urban streetscape features and their associations with residents’ mental health by age and sex in Zhanjiang, China. Using Baidu Street View (BSV) images and deep learning, we extracted the Green View Index (GVI) and the street enclosure to represent two physical features of the streetscapes. Global Moran’s I and hotspot analysis methods were used to examine the spatial distributions of streetscape features. We find that both GVI and street enclosure tend to cluster, but show almost opposite spatial distributions. The Results of Pearson’s correlation analysis show that residents’ mental health does not correlate with GVI, but it has a significant positive correlation with the street enclosure, especially for men aged 31 to 70 and women over 70-year-old. These findings emphasize the important effects of streetscapes on human health and provide useful information for urban planning.

Suggested Citation

  • Anjing Zhang & Shiyan Zhai & Xiaoxiao Liu & Genxin Song & Yuke Feng, 2022. "Investigating the Association between Streetscapes and Mental Health in Zhanjiang, China: Using Baidu Street View Images and Deep Learning," IJERPH, MDPI, vol. 19(24), pages 1-17, December.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:24:p:16634-:d:1000062
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    References listed on IDEAS

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    Cited by:

    1. Xinyu Hu & Yifan Ren & Ying Tan & Yi Shi, 2023. "Research on the Spatial and Temporal Dynamics of Crowd Activities in Commercial Streets and Their Relationship with Formats—A Case Study of Lao Men Dong Commercial Street in Nanjing," Sustainability, MDPI, vol. 15(24), pages 1-23, December.

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