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 frequent occurrence of extreme heat events has notably affected human's living environment, and a considerable number of studies have reported that green space is an efficient measure by investigating the correlation between green space and land surface temperature (LST). However, spatiotemporal effects of green space on LST still remain unclear. In this study, green space patterns (e.g., core, islet, perforation, edge, loop, bridge, and branch) were identified through morphological spatial pattern analysis (MSPA). Moreover, the effects of green space pattern on LST in three periods were investigated through three kinds of models. As indicated by the results: (1) the geographically and temporally weighted regression model exhibited the optimal performance compared with other two models. (2) in general, the core, the edge, the bridge, and the branch significantly contributed to cooling, and the islet hindered cooling. However, the perforation and the loop exerted significant dual nature effects with the similar quantity of the negative and positive coefficients, showing relatively complex impact mechanism. (3) the intensity of the effect of the respective MSPA class varied across the study area. The core had the most substantial effect, which distributed in the south and middle corners. (4) the result suggested that a neighborhood scale in China, which was 960 m in this study, served as a basic unit in green space management. The spatiotemporal non-stationarity of the effects of green space morphological patterns on LST provided important insights into urban thermal environment improvement through urban green space planning and design.

To date, most studies on building environments and walking behavior have utilized top-down approaches (e.g., big data or social media data) yet lack bottom-up approaches to verify their findings. Therefore, this study divided urban streets into three main settings (community streets, waterfront paths, and urban greenways) and collected data from a sample of 411 urban residents in Chengdu via an online questionnaire to examine the impact of street environmental factors on their choice of walking path. It was found that: (1) people with higher levels of education preferred streets with water bodies as walking paths; (2) the environmental quality of the physical and aesthetic aspects both had an impact on residents’ choices, and the aesthetic environmental quality had a stronger impact; (3) the impact of most infrastructures on community streets was stronger than on other streets; (4) residents were more concerned about the environmental quality of waterfront paths and urban greenways. Based on these findings, three design patterns for residents’ preferred street environments are proposed.

Land use types other than specialized athletic fields provide a variety of jogging environments, addressing the shortage of urban fitness facilities and promoting urban health as well as sustainability. Currently, there is limited research comparing the differences in jogging support among various land use types, which can assist decision-makers in setting priorities and targeted strategies for urban renewal, especially in urban built-up areas with limited land resources. Initially, spatial information, statistical data, and recommendation reason text were extracted from recommended routes in mobile fitness apps and categorized into six land use types. Subsequently, spatial potential was measured through descriptive statistics, buffer area analysis, spatial autocorrelation analysis, and line density analysis. Environmental preferences were gauged by text analysis using jieba word segmentation and grouped word frequency calculation. Finally, the measurement results of different land uses were compared, including scale differences, spatial differentiation, environmental perception, and environmental elements. The research found that streets, residential areas, campuses, parks, and greenways possess significant potential to support jogging, particularly streets. These types of land use exhibit varying spatial potentials and attractions in environmental preferences. Targeted recommendations have been proposed to support the renewal of urban built-up areas and research in related fields.

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.

Informal green spaces (IGSs) play an essential role in enhancing urban well-being by offering restorative environments, yet the impact of visitor behaviors on perceived restorativeness (PR) remains underexplored. This study investigates how different spatio-temporal behaviors influence PR in IGS, providing urban planners with actionable insights to optimize these spaces for better user experiences. Using a visitor-employed photography (VEP) survey and post-visit PR assessments, K-means clustering was applied to identify distinct visitor behavior patterns. Correlation analysis further explored the relationships between these patterns and PR; the results reveal three unique clusters of visitor behaviors—fast, extensive exploration; moderate, focused exploration; and slow, thorough exploration—each showing distinct impacts on PR. Visitors who engage in rapid, broad exploration perceive larger, navigable spaces as more restorative, while those focusing on specific or in-depth exploration emphasize psychological aspects like escape and fascination. These behavioral patterns demonstrate varying strengths in their association with restorative experiences; This study underscores the importance of integrating spatio-temporal behavior data with PR assessments, highlighting how the physical and psychological features of IGS influence visitor experiences. These findings offer critical insights for designing and managing IGS to accommodate diverse user needs and promote urban well-being.