The Impact of Visual Elements in Street View on Street Quality: A Quantitative Study Based on Deep Learning, Elastic Net Regression, and SHapley Additive exPlanations (SHAP)

Urban street quality directly affects the daily lives of residents and the experiences of tourists, playing a crucial role in the sustainable development of cities. However, most studies either focus on a single demographic or lack interpretable data analysis. To address this, we propose a framework integrating deep learning, elastic net regression, and SHapley Additive exPlanations (SHAPs). Using street view images, we quantitatively assess street quality in Xi’an’s Mingcheng District, considering the perspectives of both residents and tourists. The framework assesses comfort, convenience, safety, and culture to determine street quality and explores influencing factors. The results indicate that high-quality streets are primarily located near major urban roads, tourist attractions, and commercial areas, while older residential areas in historic districts exhibit widespread low-quality streets. Building density significantly and negatively impacts street quality, whereas visibility of the sky and green coverage positively influences street quality. SHAP reveals that greenery can mitigate the negative effects of high building density and enhance street quality. This study provides actionable insights for enhancing urban street quality through data-driven, human-centered approaches, directly contributing to the Sustainable Development Goal 11 (Sustainable Cities and Communities) by promoting more livable, safe, inclusive, and sustainable urban environments.