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.

Understanding how different social groups perceive urban streets is essential for inclusive and sustainable urban design. This study proposes an interpretable and scalable machine learning framework that integrates Street View Images with subjective evaluations to examine perceptual differences between residents and tourists. Using data from Xi’an’s historic Mingcheng District, we collected perception ratings across five dimensions-safety, comfort, convenience, pleasure, and sociability-and analyzed how visual and environmental features shape these perceptions. The framework combines predictive modeling and explainable analysis to uncover both linear and nonlinear drivers of perception. The results show that tourists are more responsive to symbolic and aesthetic cues, while residents emphasize functional and comfort-related features. Key visual elements such as vegetation, building facades, and spatial openness exert different effects on the two groups. By revealing these perceptual disparities, the study provides actionable insights for perception-informed and equitable street design strategies that better address the needs of diverse urban users.

Traditional villages are vital carriers of cultural heritage and key foundations for rural revitalization and sustainable development, yet rapid urbanization increasingly threatens their survival, making it necessary to clarify their spatial distribution and driving mechanisms to support effective conservation and rational utilization. Yunnan Province, home to 777 nationally recognized traditional villages and the highest number in China, offers a representative context for such analysis. Methodologically, this study uses a 12 km × 12 km geographic grid (3005 cells) rather than administrative units. The count of catalogued traditional villages in each cell is taken as the dependent variable, and nine indicators selected from five dimensions (traffic accessibility, natural topography, climatic conditions, socioeconomic factors, and historical and cultural factors) serve as explanatory variables. Assuming that relationships between villages and their environment are spatially nonstationary and operate at multiple spatial scales, we combine spatial autocorrelation analysis with a multiscale geographically weighted regression (MGWR) model to detect clustering patterns and estimate location-specific coefficients and bandwidths. The results indicate that: (1) traditional villages in Yunnan exhibit significant clustering, with over 60% concentrated in Dali, Baoshan, Honghe, and Lijiang; (2) the spatial pattern follows a “more in the northwest, fewer in the southeast, dense in mountainous areas” distribution, shaped by both natural and socioeconomic factors; (3) natural geographic factors show the strongest associations, with sunshine duration and water availability strongly promoting village presence, while slope exhibits regionally differentiated effects; (4) socioeconomic development and transportation accessibility are generally negatively associated with village distribution, but in tourism-driven areas such as Dali and Lijiang, road improvements have facilitated protection and revitalization; and (5) historical and cultural factors, particularly proximity to nationally protected cultural heritage sites, contribute to spatial clustering and long-term preservation. The MGWR model achieves strong explanatory power (R² = 0.555, adjusted R² = 0.495) and outperforms OLS and standard GWR, confirming its suitability for analyzing the spatial mechanisms of traditional villages. Finally, the study offers targeted recommendations for the conservation and sustainable development of traditional villages in Yunnan.

가로경관의 아름다움은 주민들의 일상생활의 질에 영향을 줄 뿐만 아니라 관광객 유치의 중요한 요인이라고 할 수 있다. 본 연구는 주민과 관광객이 가로의 아름다움을 어떻게 인식하는지에 대한 차이를 탐구하기 위해 새로운 평가 방법을 제안한다. 서울시 중구, 강남구, 서초구, 송파구를 연구 대상으로 삼아, 가로경관 시각적 요소를 정량적으로 평가하기 위해 스트리튜 뷰와 의미론적 분할을 활용하였다. 일부 스트리튜 뷰에 대해 지원자를 모집하여 아름다움에 대한 인식 점수를 매겨 데이터셋을 만들었고, 이 데이터셋을 통해 연구 대상 지역의 모든 스트리튜 뷰의 아름다움 점수를 예측하는 랜덤 포레스트 모델을 훈련시켰다. 또한 시각적 요소가 아름다움 인식에 미치는 영향을 분석하였다. 본 연구 결과는 각 시각적 요소는 공간적으로 불균형하게 분포되어 있었으며, 가로의 아름다움에 대한 인식은 한국 주민과 중국 관광객 사이에 차이가 나타났다. 한국 주민은 현대적이고 상업적으로 번화한 환경에서 아름다움을 더 많이 느끼는 반면, 중국 관광객은 녹지와 역사문화가 어우러진 장소를 더 선호하는 것으로 나타났다. 탄력 네트워크 회귀분석 결과, 두 집단 모두 수목이 가장 강한 긍정적인 영향을 미치는 것으로 나타났으나, 건물과 벽은 부정적인 영향을 미치는 것으로 나타났다. 본 연구 결과는 가로경관 계획 및 설계에 참고 자료가 될 수 있으며, 가로 평가와 공간 최적화를 위한 과학적 근거를 제공함으로써 도시 관광산업 발전에도 기여할 수 있다고 생각된다.