Reducing the social inequity of neighborhood visual environment in Los Angeles through computer vision and multi-model machine learning

Abstract

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. Our study aims to unveil the social inequity of citizens’ wellbeing, reflected by their perception on neighborhood environment (e.g., feeling of depression), 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. Specifically, we quantified the actual built environment in the 5D dimensions (i.e., density, diversity, design, distance, and destination) based on multiple sources; measured six types of neighborhood visual environment (i.e., perception of beautiful, safe, wealthy, liveable, boring and depressing) and the overall neighborhood soundness index by using computer vision technique and street view imageries collected from Mapillary; and unveiled the actual built environmental features that are associated with people's visual perception towards the surrounding environment via multi-model machine learning methods. Our pilot study in Los Angeles County finds that neighborhoods with higher concentrations of Black, Hispanic, low-income, low-educated, and unemployed populations are perceived as less beautiful, liveable, safe, and wealthy. The most important actual built environment features positively influencing human perception 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. Our key findings provide place-based evidence for the design and upgrading of the community environment that further affects people's daily activity and living style. Our framework and methods can be applied to cross-disciplinary studies, aiding urban planning and healthy city initiatives with place-based evidence.