Since the reform and opening-up policy, the accelerated urbanization rate has triggered extensive construction of new towns, leading to architectural homogenization and environmental quality degradation. As urban development transitions toward a “quality improvement” paradigm, there is an urgent need to synergistically enhance the health performance of human settlements through the optimization of public space environments. The purpose of this study is to explore the impact of the built environment of urban streets on residents’ perceptions. In particular, in the context of rapid urbanization, how to improve the mental health and quality of life of residents by improving the street environment. Changbai Island Street in the Heping District of Shenyang City was selected for the study. Baidu Street View images combined with machine learning were employed to quantify physical characterizations like street plants and buildings. The ‘Place Pulse 2.0’ dataset was utilized to obtain data on residents’ perceptions of streets as beautiful, safe, boring, and lively. Correlation and regression analyses were used to reveal the relationship between physical characteristics such as green visual index, openness, and pedestrians. It was discovered that the green visual index had a positive effect on perceptions of it being beautiful and safe, while openness and building enclosure factors influenced perceptions of it being lively or boring. This study provides empirical data support for urban planning, emphasizing the need to focus on integrating environmental greenery, a sense of spatial enclosure, and traffic mobility in street design. Optimization strategies such as increasing green coverage, controlling building density, optimizing pedestrian space, and enhancing the sense of street enclosure were proposed. The results of the study not only help to understand the relationship between the built environment of streets and residents’ perceptions but also provide a theoretical basis and practical guidance for urban space design.

Urban vitality acts as a key driver of sustainable urban development, while the built environment serves as its physical foundation. However, spatial heterogeneity in urban landscapes leads to imbalanced impacts of economic, social, and environmental factors on vitality. Therefore, it is essential to investigate the underlying principles governing vitality impacts imposed by diverse components of the built environment at the spatial level. This study synthesized multi-source remote sensing data alongside geospatial datasets aiming to quantify vitality and built environment indicators across Shenyang, China. We applied Ordinary Least Squares (OLS) regression for collinearity diagnosis and Multi-scale Geographically Weighted Regression (MGWR) to model spatial heterogeneity impacts at the planning-unit level. The regression factor analysis yielded three primary conclusions: (1) Functional Mixture Degree, Bus Stop Density, and Subway Station Density demonstrated a statistically significant positive correlation with urban vitality. (2) FAR (Floor Area Ratio), Vegetation Coverage, Commercial Facility Density, and Road Density exhibited differentiated effects in core areas versus peripheral areas. (3) Public Facility Density and Bus Stop Density showed a negative correlation trend with vitality levels in Industrial Functional Zones. We propose a geospatial analysis framework that leverages remote sensing to decode spatially heterogeneous built environment–vitality linkages. This approach supports precision urban renewal planning by identifying location-specific interventions. Geospatial big data and MGWR offer replicable tools for analyzing urban sustainability. Future work should integrate real-time sensor data to track vitality dynamics.

Background: Research on spatial imagery as perceived by humans is an important frontier for deepening the theoretical understanding of Tourism Destination Image and promoting sustainable urban development. Significance: This study, from the perspective of tourists, explores the correlation mechanism between the cognitive image and affective image of urban space. This is of great significance for enhancing the overall spatial quality of cities, promoting the integration of the man–land relationship, and driving the sustainable development of tourism. Method: In this study, we took Harbin as the case site, collected 89,375 reviews and 23,561 review images of 488 scenic spots on the Mafengwo and Ctrip platforms, and constructed a multimodal dataset. We classified the image scenes with the help of the Places365-CNN model. We then extracted text emotional features by utilizing the SnowNLP deep learning algorithm. We constructed a map of the spatial influence mechanism acting on cognitive image and emotion through MGWR. Results: The experimental results showed that in the level of Pleasure, the five indicators NHS, HPA, RPA, PDS and WRV had significant spatial correlations with urban sentiment. In the level of Arousal, the three indicators PD, MaSD and WRV showed significant spatial characteristics. Conclusions: This study reveals the influence mechanism of urban spatial perception elements on tourists’ emotions. It not only deepens the understanding of the Tourism Destination Image theory, but also provides a practical path based on the optimization of perception scenarios for the improvement of urban space, which has important implications for regional sustainable development.

Under the backdrop of urban renewal, harmonizing transit-oriented development (TOD) with urban renewal to maximize rail value has emerged as a critical focus in contemporary planning. Based on this, this paper proposes the node–place–revenue (NPR) model, which constructs evaluation indexes from the three dimensions of the node, place, and revenue. It determines the weights of each index by using expert scoring and the Analytic Hierarchy Process (AHP). Taking Shenyang Metro Line 1 as an example, the study first used the model to measure the node value, place value, and revenue value of each sample TOD station area. Secondly, K-means clustering analysis was used to form a spatial classification of five station areas. Finally, this paper proposes one differentiated urban renewal strategy for each type of station area. It is found that (1) the NPR model classifies stations into five categories: stress and high revenue, balanced, unbalanced node, unbalanced place, and dependence and low revenue and (2) the differentiated urban renewal strategies for each type of station area can be explored in terms of precise decongestion, node upgrading, function expansion, endogenous optimization, and infill quality improvement. This paper examines the economic driving effect of Shenyang Metro Line 1 stations on the renewal of the surrounding areas from the perspective of the economic balance of payments, providing a new reference for Shenyang-rail-transit-guided urban renewal work.

In people-centered urban planning, enhancing the well-being of residents and tourists is one of the core objectives. Tourist emotion serves not only as a key indicator of the tourism experience but also indirectly reflects the quality of a city’s public spaces and built environment. In recent years, user-generated content has provided abundant data for understanding human emotional responses in urban environments, while deep learning models offer new technological pathways for extracting spatial–emotional associations from such data. However, existing research lacks a systematic evaluation of emotion analysis models from an urban spatial perspective and their application to uncover the relationship between emotional distribution and spatial characteristics in specific urban contexts. Based on a dataset of 9419 manually annotated travel reviews from Harbin, this study developed a multi-level evaluation framework and conducted a systematic comparison of seven emotion analysis models. This study then screened for the optimal model combinations based on two dimensions—spatial location and emotion polarity—to create a model matching matrix for mapping Harbin’s emotion map. Subsequently, a regression analysis was performed to examine the relationship between emotions and built environment elements. The results show that the ERNIE model demonstrated the best overall performance. Road density, green space density, and accommodation facility density were positively correlated with emotion, while POI diversity showed a negative correlation. This study demonstrates that emotion analysis technology can serve as a valuable analytical tool for identifying spatial patterns of sentiment, thereby offering empirical support for optimizing spatial design parameters and advancing a more people-centered approach to urban development.