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

Anjing Zhang; Shiyan Zhai; Xiaoxiao Liu; Genxin Song; Yuke Feng

doi:10.3390/ijerph192416634

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

Int J Environ Res Public Health. 2022 Dec 11;19(24):16634. doi: 10.3390/ijerph192416634.

Authors

Anjing Zhang^{1

2}, Shiyan Zhai^{1

3}, Xiaoxiao Liu⁴, Genxin Song^{1

3}, Yuke Feng^{1

3}

Affiliations

¹ Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Henan University, Ministry of Education, Kaifeng 475004, China.
² Department of Geography, University of Zurich, 8057 Zurich, Switzerland.
³ College of Geography and Environmental Science, Henan University, Kaifeng 475004, China.
⁴ Department of Community Health Science, Cumming School of Medicine, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada.

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.

Keywords: deep learning; mental health; street view image; streetscapes.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Aged
China / epidemiology
City Planning
Deep Learning*
Environment Design*
Female
Humans
Male
Mental Health
Residence Characteristics

Grants and funding

This research was funded by the Science and Technology Department of Henan Province [grant number 222102320397]; The National Natural Science Foundation of China [grant number 42171294]; Key scientific research projects of colleges and universities in Henan Province [grant number 21A170007]; The National Experimental Teaching Demonstrating Center of Henan University [grant number 2020HGSYJX004].