Previous studies have demonstrated that the built environment has a significant impact on individual subjective well-being (SWB). However, the majority of these studies primarily examined the impacts of community-level environments on subjective well-being, with limited exploration of the influence of multi-scale environments. This study addresses this gap by utilizing questionnaire surveys and built environment data to examine the effects of built environments at the housing, neighborhood, and community scales on subjective well-being through a multiple regression model (specifically, a hierarchical regression). The results show that environmental variables at the housing scale, neighborhood scale, and community scale can significantly improve the explanatory power of life satisfaction (LS). The findings reveal that the explanatory power of environmental variables on life satisfaction exhibits a diminishing trend from proximate to distal scales. It was found that, in terms of the housing scale, the housing construction environment and quality and the increase in per capita housing area positively contribute to residents’ life satisfaction. At the neighborhood scale, a comfortable environment and enhanced facilities are conducive to improving residents’ life satisfaction, whereas the evaluation of property management and services is linked to reduced life satisfaction. At the community scale, the increase in building density (BD), functional mix density (FMD), road intersection density (RID), and bus stop density (BSD) is not conducive to enhancing residents’ life satisfaction. However, higher bus line density (BLD) is positively correlated with the improvement of residents’ life satisfaction. These findings suggest that in urban community planning and management, attention should be paid not only to housing-scale environmental elements but also to neighborhood-scale environmental elements, and they emphasize the rational planning of community scale environments to enhance residents’ subjective well-being.

This research explores the environmental safety challenges faced by pocket parks in the context of urban aging within Chinese cities. It systematically analyzes visual elements that influence the elderly’s perception of environmental safety by applying interpretable machine learning techniques. By integrating panoramic image semantic segmentation and explainable AI models (e.g., SHAP and PDP), the study transforms subjective environmental perception into measurable indicators and constructs an environmental safety perception model using the LightGBM algorithm. Results indicate that sufficient pedestrian areas and moderate crowd activities significantly enhance safety perception among the elderly. Conversely, the presence of cars emerges as the most substantial adverse factor. Natural elements, such as vegetation and grass, exhibit nonlinear effects on safety perception, with an optimal threshold range identified. The research further elucidates the intricate synergies and constraints among visual elements, underscoring that the highest perceived safety arises from the synergistic combination of positive factors. This study deepens the understanding of environmental perception among the elderly and offers a data-driven framework and practical guidelines for urban planners and designers. It holds significant theoretical and practical implications for advancing the refined and human-centered renewal of urban public spaces.

Clarifying the mechanisms by which the micro-scale built environment influences urban vitality is an important scientific challenge, to guide precise urban planning in the context of urban renewal. In this study, we quantify the intensity of human activities through Baidu heat maps, analyze social interaction patterns using social media check-in data, and integrate built environment elements such as road network topology, 3D building morphology, and the spatial distribution of points of interest (POIs). A machine learning technique combining a real-encoded Accelerated Genetic Algorithm-Projective Pathfinding Model (RAGA-PPM) and Shapley Additive Projection for Interpretability (SHAP) for Interpretability Analysis (IPA) was used to investigate the nonlinear mechanisms of 17 factors affecting urban vitality in Macau Peninsula, China. Firstly, the explanatory power of the built environment for comprehensive vitality was significantly better than the other dimensions. Two factors, population vitality and microblogging check-in vitality, contributed the most to the composite vitality value. Secondly, road network density was the most important built environment factor affecting urban vitality in Macau Peninsula (SHAP = 0.025). Finally, the impacts of built environment factors on urban vitality showed nonlinearities, and the threshold effects of the core factors (road network density, spatial fractal dimension, and openness to the sky) showed a consistent neighborhood-level pattern. This study establishes a framework for micro-vitality mechanisms in high-density cities, addressing the limitations of traditional methods in modeling complex nonlinear relationships. The methodological integration of RAGA-PPM and SHAP advances the innovative paradigm of applying interpretable machine learning to the study of urban form.

In order to effectively identify the shortcomings and potential health risks in the construction of healthy cities and achieve sustainable development, relevant improvement strategies have been formulated. According to the National Healthy City Evaluation Index System, with the concept of “Making improvements is more important than reaching standards”, the healthy city construction levels of the first batch of 13 cities in East China were evaluated by combining entropy weight and linear coefficient weighting from the five dimensions of environment, population, society, service, and culture, based on the data of statistical yearbooks, bulletins, and government websites. The results show that Suzhou, Jiading, Wuxi, Hangzhou, Ningbo, Tongxiang, and Zhenjiang are in the first-grade group, Xiamen, Yantai, Jinan, and Weihai are in the second-grade group, and Yichun and Ma’anshan are in the third-grade group. There is also more significant heterogeneity in the healthy environment indicator among the 13 cities; at the same time, there are specific differences in the healthy culture indicator, and there are slight differences in the indicators of healthy population, society, and service. The study reveals the gaps and problems in the construction of healthy cities. It proposes constructive ideas for promoting follow-up improvement of “making up for shortcomings and strengthening the weaknesses”.

Urban perception research has long focused on global metropolises, but has overlooked many cities with complex functions and spatial structures, resulting in insufficient universality of existing theories when facing diverse urban contexts. This study constructed an analytical framework that integrates street scene images and interpretable machine learning. Taking Zhengzhou City as the research object, it extracted street visual elements based on deep learning technology and systematically analyzed the formation mechanism of multi-dimensional urban perception by combining the LightGBM model and SHAP method. The main findings of the research are as follows: (1) The urban perception of Zhengzhou City shows a significant east–west difference with Zhongzhou Avenue as the boundary. Positive perceptions such as safety and vitality are concentrated in the central business district and historical districts, while negative perceptions are more common in the urban fringe areas with chaotic built environments and single functions. (2) The visibility of greenery, the openness of the sky and the continuity of the building interface are identified as key visual elements affecting perception, and their directions and intensifies of action show significant differences due to different perception dimensions. (3) The influence of visual elements on perception has a complex mechanism of action. For instance, the promoting effect of greenery visibility on beauty perception tends to level off after reaching a certain threshold. The research results of this study can provide quantitative basis and strategic reference for the improvement in urban space quality and humanized street design.

Urban building color research has long been anchored in the “dominant-color” paradigm, which describes only the basic attributes of the most prevalent color and overlooks multi-color compositional relationships, thereby failing to reach evaluative dimensions such as color combination quality and spatial order. This study proposes a Fundamental–Compositional–Spatial (FCS) evaluation framework for building color quality, organizing ten indicators into three hierarchical layers: fundamental attributes, compositional structure, and spatial association. Using the Macao Special Administrative Region as an empirical case and drawing on building façade color data extracted from 8163 street-view sampling points, we systematically quantify the city-wide building color quality. Results show that (1) at 76.8% of the sampling points the dominant-color share lies within only 13–21%, so the dominant color holds no absolute areal advantage, and there is a significant intrinsic tension between colorfulness and harmony (r = −0.363) within the compositional structure; (2) Macao’s building colors are dominated by warm hues (warm-to-cool ratio ≈ 4.5:1), with saturation and value forming a systematic co-variation between a “dark-yet-colored” and a “bright-yet-colorless” mode, and color contrast exhibiting pronounced positive spatial autocorrelation (Moran’s I = 0.456); and (3) clustering based on the six C+S-layer indicators identifies four color-quality types—Subdued-Transitional (38.1%), Vibrant-Fragmented (13.5%), Dark-Harmonious (45.6%), and Monotonous-Clustered (2.7%)—whose spatial distribution is broadly consistent with the city’s historical construction strata. The study demonstrates that a multi-dimensional color-evaluation approach based on street-view big data can effectively transcend the limitations of dominant-color analysis and provides an operational technical pathway for fine-grained cognition and differentiated governance of urban color.