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.

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 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.

Urban vitality provides a human-activity-based lens for evaluating how existing urban land supports everyday use and is therefore relevant to sustainable urban regeneration in compact cities. This study examines block-level activity patterns and their spatial correlates in the Macau Peninsula using Baidu Heat Map raster imagery from two sampled Mondays and two sampled Saturdays in summer 2020, together with POIs, road networks, building data, vegetation, waterfront conditions, public transport, and socioeconomic indicators for 159 blocks. We treat the Baidu-derived measure as relative activity intensity rather than an absolute population count or calibrated population density. Because the archived raster captures lack independently verifiable cross-acquisition calibration metadata, temporal interpretation is limited to relative spatial configurations within the sampled profiles rather than absolute changes in population presence. We applied spatial mapping, Getis-Ord Gi* statistics, and Geographic Detector factor and interaction analyses. A formal block-level comparison based on within-acquisition fractional ranks showed very strong spatial similarity between the sampled workday and rest-day profiles (Pearson r = 0.980; Spearman’s rho = 0.979; both p < 0.001), indicating persistent relative spatial ordering without implying equal absolute activity levels. Archived ArcGIS sensitivity outputs further show that Gi* clustering was examined under multiple neighborhood specifications rather than a single undocumented default setting. Degree of adjacency function mixing had the highest single-factor q-values in the aggregated sampled profiles (0.461 for workdays and 0.538 for rest days), and its interaction with spatial compactness had the highest joint q-values (0.798 and 0.823). These results identify a consistent spatial association between neighboring functional context, urban form, and relative activity intensity within the observed sample. From a sustainability perspective, the findings support area-based coordination of complementary functions, accessibility, spatial form, and time-specific demand. In contrast, the temporal and spatial conclusions are deliberately bounded by the relative, block-based analytical framework and the 2020 sampling context.