Data and code

Aligning with the United Nations’ Sustainable Development Goals, the focus on creating safe, sustainable cities and enhancing the wellbeing of individuals across all age groups has become a central aspect of urban planning and environmental management. The environments we live in significantly influence our thoughts, emotions, and interactions with the world around us. Therefore, it is vital to explore how people perceive their surroundings and how these perceptions differ among various social groups, particularly in relation to social inequities in environmental exposure. Additionally, there is an ongoing need to address how improvements in the physical environment can enhance wellbeing and reduce these disparities within urban spaces. As such, our study aims to unveil the social inequity of neighborhood visual environment across different social/vulnerable groups (i.e., White, Black, Asian, Hispanic, low-income, low-educated, and unemployed) via crowdsourced street view imageries and computer vision and further examine which built environmental features that are associated with people’s visual perception towards the surrounding environment via multi-model machine learning methods, with the pilot study in Los Angeles County. Neighborhoods with a high concentration of Black, Hispanic, and low-income, low-educated and unemployed populations have a higher level of boring and depressive perception while a lower level of beautiful, liveable, safe, and wealthy perception. The most important actual built environment features positively associated with the neighborhood soundness include the density of canopy, followed by the density of multiple units, the distance to CBD, and car commuting to destinations, regardless of social groups. The perceived visual environment in the neighborhoods with a high concentration of Black, Hispanic, and low-educated groups can be not well explained by actual built environment features, reflecting the possibility that other underlying variables (e.g., personal preference, place attachment or other subjective factors) confound their perception on surrounding environment. The conceptual framework and analytical workflow used in this study can be readily applied in cross-disciplinary studies more broadly, to guide through urban planning, urban design, and healthy city initiatives with place-based evidence.