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

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.

Application of machine learning in measurement of ageing and geriatric diseases: a systematic review

“…The suicide group had a more prominent history of depression, with the use of medicaments significantly higher. Body mass index, waist circumference, total cholesterol, and low-density lipoprotein level were lower in the suicide group Random Forest (AUC = 0.818, accuracy = 0.832, sensitivity = 0.600, specificity = 0.833, NPV = 0.999, PPV = 0.007) 29 Kasthurirathne et al [ 44 ] 2019 To build decision models caapble of predicting the need of advanced care for depression across patients 84,317 individuals from Primary Care Visit at Eskenazi Health, Indiana Mental Health ML model—Random forest decision models This study demonstrates the ability to automate screening for patients in need of advanced care for depression across (1) an overall patient population or (2) various high-risk patient groups using structured datasets covering acute and chronic conditions, patient demographics, behaviors, and past visit history AUC, optimal sensitivity, optimal specificity was reported for different patient groups 30 Zhang et al [ 45 ] 2022 To explore the spatial patterns of urban streetscape features and their associations with residents’ mental health by age and sex in Zhanjiang, China Study area where images are captured-Zhanjiang City, China Mental health data—813 patients suffering psychiatric disorders from hospitalization data of Guangdong Medial University Mental Health Image capturing- Baidu Street View physical features- Green View Index (GVI) Spatial distributions- Global Moran's I and hotspot analysis Deep learning methods—Fully Convolutional Network for semantic image segmentation 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 NA 31 Opoku et al [ 46 ] 2021 1) to investiagate the feasibility of predicting depression with human behaviours quantified from smart phone datasets 2) to identify behaviours that can influence depression Data of 629 participants collected in a longitudinal observational study with the Carat app in 6 months interval Smart phone datsets and self-reported 8-item Patient Health Questionnaire depression assessments Mental Health the relationship between the behavioral features and depression—correlation and bivariate linear mixed models (LMMs) ML models- RF, SVM with radial basis functio...…”

“…Except physical health, mental health has equal importance for the overall body condition of older adults. Many types of mental health-related issues among the older population, for example, predicting depression from smartphone data using supervised ML models [ 46 ], identifying patients with depressive symptoms using random forest decision models on primary care visits [ 44 ], suicide prediction model [ 43 ], analysing the effect of environmental factors on mental health [ 45 , 49 ], quantifying the psychotherapy content and its effect [ 47 ], and studying loneliness using social media data Twitter [ 48 ] and sentiment analysis were addressed by different studies. Most of the studies applied logistic regression, random forest, and deep learning models.…”

The effects of audio-visual perceptual characteristics on environmental health of pedestrian streets with traffic noise: A case study in Dalian, China

“…Izenberg and Fullilove (2016) investigated street contribution to community social cohesion, a condition of optimal health, and found that street wall permeability was a relevant element of street hospitality. Zhang et al (2022) focused on the relationships between streetscape characteristics and mental health, and noted a significant positive correlation between residents' mental health and the street enclosure. Many studies have highlighted walking behavior, street greening and diversified streetscape features as popular indicators of public health that are connected with pedestrian spaces.…”

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

“…These studies have overcome the limitations of traditional methods, such as difficult data collection and insufficient in-depth analysis of activity patterns and have provided a more comprehensive understanding of how people pass through and use space. However, current research methods on crowd activities in commercial streets mainly aim to analyze the correlation between crowd gathering areas [21,31] and the built environment [32]. Since location distribution does not reflect the details of crowd activities, such as activity types and trajectory characteristics, this limitation makes it difficult to determine behavioral choices and activity paths in space, thereby affecting the overall understanding of the utilization of commercial street spaces.…”