Establishing an age-friendly environment at the community level is essential for promoting healthy aging. This study focused on the relationship between older adults and the community environment through their levels of satisfaction within it. We measured their physical activity (PA) in the community environment and three variables of community-level satisfaction: community environment (SCE), community social infrastructure (SSI), and community street networks (SSN). We analyzed 108 older adult participants in Suzhou using mediation analysis and multiple linear regression to investigate the relationship between physical activity and the community environment. The results of the mediation effect model showed that SCE, SSI, and SSN all affected the physical functions of older adults via the total amount of physical activity (TPA); SSI and SSN affected older adults’ physical functions by affecting the total duration of moderate-intensity physical activity (MPA) and vigorous-intensity physical activity (VPA). In addition, SSI and SSN are related to the types of community facilities, street space quality, and accessibility. Our study provides valuable insights into optimizing aging-friendly neighborhoods through moderate-to-vigorous-intensity PAs at both the facility and street space levels.

The acoustic environment of residential areas is critical to the health of the residents. To reveal the impact of the acoustic environment on people’s mental health and create a satisfactory acoustic setting, this study took a typical old residential area in Harbin as an example, conducted a field measurement and questionnaire survey on it, and took typical acoustic sources as the research object for human body index measurement. The relationship between heart rate (HR), skin conductivity level (SCL), physiological indicators, semantic differences (SD), and psychological indicators was studied. The sound distribution in the old community was obtained, determining that gender, age, and education level are significant factors producing different sound source evaluations. Music can alleviate residents’ psychological depression, while traffic sounds and residents’ psychological state can affect the satisfaction evaluation of the sound environment. There is a significant correlation between the physiological and psychological changes produced by different sounds. Pleasant sounds increase a person’s HR and decrease skin conductivity. The subjects’ HR increased 3.24 times per minute on average, and SCL decreased 1.65 times per minute on average in relation to hearing various sound sources. The SD evaluation showed that lively, pleasant, and attractive birdsongs and music produced the greatest HR and SCL changes, and that the sound barrier works best when placed 8 m and 18 m from the road.

The transit-oriented development (TOD) model provides benefits to urban areas in terms of transportation, the economy, society, and the environment. Given the complexity and limitations arising from the various backgrounds, objectives, and other aspects of previous studies, empirical research on specific cases has often failed to fully analyze the influence of built environment indicators on TOD performance. This paper systematically reviews and analyzes related empirical studies conducted worldwide up until June 2023. The correlations between built environment indicators and their impacts on TOD performance indicators are visualized and measured. General patterns of the influences of built environment indicators on TOD performance are summarized using social network analysis (SNA), and the key indicators are identified via cluster analysis. Finally, by analyzing the key indicators such as diversity, density, design, and distance to transit stations, as well as their associated built environment indicators, the built environment characteristics that ensure TOD performance are thoroughly examined. These insights provide a better understanding of the influences of the built environment on TOD performance, thus offering guidance for the development and application of the TOD model.

Community streets play a crucial role in promoting healthy aging and encouraging active behaviors among older adults. This study focuses on two types of activities of older adults: walking and social interaction. We explored the relationship between physical environmental factors and different activity types using a multiple linear regression model. Eye-level green visibility (GSV_N) was used to represent the diversity of facilities (DF_N), while betweenness (AB_N) accounted for the mixed degree of functions (P_Ni), enhancing model stability. The results indicate that community environmental factors, both individually (R² = 0.390) and in combination (R² = 0.409), have significant effects on active behaviors. Notably, the combined variable model demonstrated greater stability. Moreover, the model’s explanatory power for social interaction (R² = 0.567) was much higher than for walking behavior (R²= 0.137), underscoring the stronger influence of combined variables on social engagement. The results suggest that creating age-friendly urban environments requires a holistic approach that improves infrastructure, green visibility, and functional diversity to promote both mobility and social interaction. These insights provide valuable guidance for designing inclusive urban spaces that support active aging.

Traditional Jiangnan water towns in China serve as important cultural heritage sites and tourist destinations. Existing studies have revealed a potential connection between environmental elements and spatial perception in these towns. However, there remains a lack of research systematically investigating whether and how these environmental elements influence subjective evaluation indicators, such as spatial attractiveness, and the mechanisms underlying the interactions between these elements. To further understand these mechanisms, we used Nanxun Old Town as our study site, employed computer vision techniques to perform semantic segmentation on street-view images, extracted the visual proportions of environmental elements, and conducted quantitative correlation analysis with subjective attractiveness evaluations. The findings indicate that different environmental elements in water towns shape spatial imagery in diverse ways, thereby influencing perceived attractiveness. Firstly, though space-defining elements such as buildings and water generally contribute positively to perceived attractiveness, their proportions should be controlled within a reasonable range to maintain a spatial scale that aligns with the traditional imagery of water towns. Secondly, foreground elements like boats and lanterns, although occupying a smaller proportion, can effectively enhance the space when properly combined. Finally, the influence of elements such as bridges and buildings depends on the specific viewing distance and angle. These findings, based on an interpretable analytical framework, reveal that the effects of environmental elements on spatial attractiveness are context-dependent and nonlinear, varying with their proportions, combinations, and perspectives. This approach offers a more comprehensive understanding of the mechanisms by which environmental elements shape spatial attractiveness, providing a scientific foundation for regulating key visual components and optimizing spatial composition for sustainable traditional water town environment management.

Jiangnan water towns, as distinctive cultural landscapes in China, are confronting the dual challenge of surging tourist flows and imbalances in spatial distribution. Research on pedestrian dynamics has so far offered narrow coverage of influencing factors and limited insight into underlying mechanisms, falling short of a systemic perspective and an interpretable theoretical framework. This study uses Nanxun Ancient Town as a case study to address this gap. Pedestrian trajectories were captured using temporarily installed closed-circuit television (CCTV) cameras within the scenic area and extracted using the YOLOv8 object detection algorithm. These data were then integrated with quantified environmental indicators and analyzed through Random Forest regression with SHapley Additive exPlanations (SHAP) interpretation, enabling quantitative and interpretable exploration of pedestrian dynamics. The results indicate nonlinear and context-dependent effects of environmental factors on pedestrian dynamics and that tourist flows are jointly shaped by multi-level, multi-type factors and their interrelations, producing complex and adaptive impact pathways. First, within this enclosed scenic area, spatial morphology—such as lane width, ground height, and walking distance to entrances—imposes fundamental constraints on global crowd distributions and movement patterns, whereas spatial accessibility does not display its usual salience in this context. Second, perceptual and functional attributes—including visual attractiveness, shading, and commercial points of interest—cultivate local “visiting atmospheres” through place imagery, perceived comfort, and commercial activity. Finally, nodal elements—such as signboards, temporary vendors, and public service facilities—produce multi-scale, site-centered effects that anchor and perturb flows and reinforce lingering, backtracking, and clustering at bridgeheads, squares, and comparable nodes. This study advances a shift from static and global description to a mechanism-oriented explanatory framework and clarifies the differentiated roles and linkages among environmental factors by integrating video-based trajectory analytics with machine learning interpretation. This framework demonstrates the applicability of surveillance and computer vision techniques for studying pedestrian dynamics in small-scale heritage settings, and offers practical guidance for heritage conservation and sustainable tourism management in similar historic environments.