Urban vitality, intricately connected to urban morphology, has long been a cornerstone of urban planning and design. The accelerated pace of urbanization has created abundant living and working spaces, but it has also brought about a series of issues such as traffic congestion, environmental pollution, insufficient public spaces, and uneven urban development, leading to a decline in urban vitality. The spatial distribution patterns of urban vitality and their influencing factors are diverse and vary across different cities, necessitating a multidimensional exploration of the relationship between urban vitality and the built environment. Utilizing the central urban area of Beijing as a case study, this research leverages multi-source urban spatial data to delineate the spatial characteristics of social, economic, cultural, and comprehensive vitality. Furthermore, a comprehensive set of built-environment indicators is developed across five dimensions to analyze their correlation with urban vitality. The results indicate: (1) There is a significant spatial clustering of various vitality types in Beijing, with a pronounced correlation between high-density population aggregation and vigorous economic activities. (2) Subdistricts exhibiting high social vitality display an “n”-shaped distribution around the Second Ring Road. In contrast, those with high economic vitality are concentrated along Chang’an Street and various district commercial centers. High cultural vitality subdistricts are distributed in a northwest–southeast trajectory from the Fourth Ring Road to the Second Ring Road, and subdistricts with high comprehensive vitality exhibit a concentric distribution radiating outwards from the center. (3) Social vitality is most closely related to comprehensive vitality, and the various vitality types in Beijing’s central urban area develop relatively evenly. (4) The built environment significantly affects all types of urban vitality. Factors such as floor area ratio, POI density, POI mixing degree, and intersection density are fundamental to enhancing urban vitality, whereas the greening rate somewhat inhibits it. (5) Future spatial planning should utilize the radiating effect of high-vitality subdistricts to optimize population distribution, enhance POI mixing, increase metro station density, and strengthen critical urban structures for synergistic economic and cultural development. This study provides a foundation and promotion strategies for optimizing the layout and enhancing vitality at the subdistrict scale within Beijing’s central urban area.

Public sub-health has emerged as a pressing concern in densely populated urban areas. The urban environment, with its innate ability to modulate public emotions, harbors a precious resource in the form of urban rivers, which provide a serene and verdant space. This study focuses on the Liangma River in Chaoyang District, Beijing, selecting two rivers with diverse landscape features as the subjects of research. By employing physiological feedback data in conjunction with a subjective questionnaire, the emotional impact of high-density urban riverside spaces on individuals is quantitatively analyzed. Electrocardiogram (ECG) data, eye movement data, and the positive–negative emotion scale (PANAS) are subjected to data analysis. The study reveals the following key findings: (1) The riverside landscape in high-density urban areas exerts a positive influence on emotional well-being. Individuals in more natural river settings experience greater levels of contentment and relaxation, while those in areas with a higher proportion of artificial elements exhibit increased excitement and happiness. Moreover, scenes characterized by a greater degree of greening have a more pronounced soothing effect on mood. (2) A specific correlation between visual characteristics and emotional fluctuations is observed. The waterfront side of the trail exerts a stronger spatial attraction, and a higher proportion of blue and green spaces significantly contributes to stress relief. (3) The utilization of human-induced engineering technology, which captures emotional changes through physiological feedback, demonstrates a higher level of accuracy and is well-suited for small-scale studies. These findings highlight the potential of arranging diverse types of waterfront footpath landscapes in high-density urban areas and approaching waterfront landscape design and transformation from a novel perspective centered on health intervention. Such efforts hold promise for alleviating the daily pressures faced by the general public and fostering the development of a “healthy city”.

In rapidly urbanizing areas, there is a notable aggregation of vitality in high-density urban environments, accompanied by an increasing discrepancy between the supply and demand of urban green space (UGS). This study presented an integrated framework comprising a model for UGS supply-demand coupling coordination and a measure of urban vitality. Using downtown Beijing as a case study, the Gini coefficient assessed UGS supply-demand disparities across different vitality types. The study examined how UGS supply and demand factors interact with urban vitality, revealing the impact of UGS supply-demand imbalances on various dimensions of vitality and the UGS mismatches experienced by different vitality groups. The study showed that: (1) 63.29% of central Beijing’s areas had low UGS supply-demand coordination, with 39.23% experiencing UGS mismatches; (2) UGS supply and demand were significantly correlated with urban vitality spatial distribution; (3) these factors significantly impacted urban comprehensive vitality; (4) and there were notable UGS distribution disparities among vitality groups, with economic vitality group perceiving the greatest inequity (Gini = 0.311), followed by social vitality (Gini = 0.289) and cultural vitality group (Gini = 0.247). These findings offer valuable insights for a more refined assessment and enhancement of UGS, aiming to achieve balanced, high-quality, and sustainable urban development.

Sustainable Development Goal 11 emphasizes inclusive, resilient, and sustainable cities, in which high-quality urban green space (UGS) is essential. However, traditional two-dimensional (2D) metrics, such as Green Coverage Ratio (GCR), fail to capture the vertical complexity of UGS, leading to incomplete assessments in planning practice. With accelerating densification and land scarcity restricting horizontal expansion, sustainable UGS growth faces critical challenges. To address this gap, we developed a Three-Dimensional Green Quantity (3DGQ) mapping framework and an integrated 2D-3D assessment approach for urban green-space evaluation. We leveraged global open-source satellite data-including spaceborne LiDAR, canopy height, canopy coverage, and optical vegetation information-to retrieve growing-season Leaf Area Index (LAI) using a machine-learning model, and introduced the Three-Dimensional Green Coverage Ratio (3DGCR) to enhance 3DGQ performance assessment. Applying this framework to Beijing's central urban area, the tuned Random Forest model achieved an R² of 0.76, RMSE of 0.51, and MAE of 0.30 m²/m² for LAI retrieval, supporting 3DGQ mapping at resolutions as fine as 1 m. Results revealed a spatial divergence between 2D and 3D metrics: 3DGCR showed a "low-south, high-north" gradient, contrasting with the 2D "low-center, high-periphery" pattern. At the subdistrict level, 38.4% of units were classified as 2D-3D green imbalance under the coupling coordination framework. We identified four "2D-3D matching patterns" to provide spatial guidance for targeted UGS regeneration. Moreover, the equity assessment unveiled a "double inversion" between spatially and population-weighted results; specifically, 66.7% of subdistricts had lower population-weighted Gini coefficients for 3DGCR than for GCR. Our results further suggest that vertical UGS structural optimization in high-density cores may function as an "equity amplifier." These findings demonstrate that integrated 2D-3D assessment can uncover hidden urban green deficits and inequities overlooked by planar indicators, providing a diagnostic basis for structural optimization, equity-oriented planning, and sustainable urban green-space governance.

Mountain cities with complex topographies have always been highly vulnerable areas to global environmental change, prone to geological hazards, climate change, and human activities. Exploring and analyzing the vulnerability of coupling systems in mountain cities is highly important for improving regional resilience and promoting sustainable regional development. Therefore, a comprehensive framework for assessing the spatial vulnerability of mountain cities is proposed. A vulnerability assessment index system is constructed using three functional systems, ecological protection, agricultural production, and urban construction. Subsequently, the BP neural network and the genetic algorithm (GA) are combined to establish a vulnerability assessment model, and geographically weighted regression (GWR) is introduced to analyze the spatial influence of one-dimensional systems on the coupling system. Linzhou, a typical mountain city at the boundary between China’s second- and third-step terrains, was selected as a case study to demonstrate the feasibility of the framework. The results showed that the vulnerability of the ecological protection system was highly aggregated in the east–central region, that of the agricultural production system was high in the west, and that of the urban construction system was low in the central region and high in the northwestern region. The coupling system vulnerability was characterized by multispatial distribution. The complex topography and geomorphology and the resulting natural hazards are the underlying causes of the vulnerability results. The impact of ecological and urban systems on the coupling system vulnerability is more prominent. The proposed framework can serve as a reference for vulnerability assessments of other similar mountain cities with stepped topographies to support the formulation of sustainable development strategies.

The research is grounded in the perspective of urban historical landscape (HUL), exploring the connections between cultural heritage and a broader urban context, as well as the general public and communities. It also focuses on small cultural heritage sites (SCHSs) in urban areas that have been overlooked in previous studies. By integrating various types of data, an assessment framework and methodology comprising six dimensions and 24 indicators were established and applied to the empirical research of 30 SCHSs in the Beijing section of the Grand Canal. The empirical research demonstrated the operability, effectiveness, and flexibility of the HUL assessment for SCHSs. The research findings are as follows. (1) The method provides differentiated recommendations for the formulation of tailored policies and planning management schemes based on heritage types, conservation levels, and the urban districts in which they are located. (2) The comprehensive quality of the open spaces where SCHSs are situated is critical for the cognition of the general public and community residents. (3) The overall conservation of the community areas containing SCHSs is highly significant, and the linkage between social development levels and cultural resources enhances public cognition of the SCHSs. (4) Cluster analysis offers guidance for the refined improvement of different SCHSs. The research aims to establish an action-oriented assessment framework, with a dimensional framework responding to the requirements of HULs and allowing for indicator flexibility. This study is significant for supporting the conservation and utilization of SCHSs in urban areas and for promoting their sustainable development.