Perceptions of built environment and health outcomes for older Chinese in Beijing: A big data approach with street view images and deep learning technique

Abstract Built environment attributes have been demonstrated to be associated with various health outcomes. However, most empirical studies have typically focused on objective built environmental measures. Still, perceptions of the built environment also play an important role in health and may complement studies with objective measures. Some built environment attributes, such as liveliness or beauty, are difficult to measure objectively. Traditional methods to assess perceptions of the built environment, such as questionnaires and focus groups, are time-consuming and prone to recall bias. The recent development in machine deep learning techniques and big data of street view images, makes it possible to assess perceptions of the built environment with street view images for a large-scale study area. By using online free Tencent Street View (TSV) images, this study assessed six perceptual attributes of the built environment: wealth, safety, liveliness, depression, bore and beauty. These attributes were associated with both the physical and the mental health outcomes of 1231 older adults in 48 neighborhoods in the Haidian District, Beijing, China. Results show that perceived safety was significantly associated with both the physical and mental health outcomes. Perceived depression and beauty were significant related to older adults' mental health, while perceived wealth, bore and liveliness were significantly related to their physical health. The findings carry important policy implications and hence contribute to the development of healthy cities. It is urgent to improve residents' positive perceptions and decrease their negative perceptions of the built environment, especially in neighborhoods that are highly populated by older adults.