The aging of the population is increasing the load on the healthcare system, and enhancing light physical activity among older adults can alleviate this problem. This study used medical examination data from 1773 older adults in Lanzhou city (China) and adopted the random forest model to investigate the effect of the built environment on the duration of light physical activity of older adults. The results showed that streetscape greenery has the most significant impact on older adults’ light physical activity; greenery can be assessed in a hierarchy of areas; population density and land-use mix only have a positive effect on older adults’ light physical activity up to a certain point but a negative effect beyond that point; and a greater distance to the park within 1 km is associated with a longer time spent on light physical activity. Therefore, we conclude that the built environment’s impact is only positive within a specific range. Changes in the intervention of environmental variables can be observed visually by calculating the relative importance of the nonlinearity of built environment elements with partial dependency plots. These results provide a reasonable reference indicator for age-friendly community planning.

Studies have proved that activity and fitness behaviors are closely related to the quality of life and health status of the elderly. However, different intensities of physical activity (PA)—walking, moderate PA, and vigorous PA—have different correlations with the built environment (BE). This study combines the high and low socioeconomic status (SES) of Guangzhou to establish two types of BE models. The physical activity time of 600 elderly people was collected from questionnaires. Through ArcGIS software, 300 m, 500 m, 800 m, and 1000 m buffer zones were identified, and the land use diversity, street design, population density, distance to destination, distance to public transportation—the five Ds of the BE—were measured. SPSS software was adopted to analyze the correlation between the BE and PA. Results: The PA of people living in low-SES areas was more dependent on the BE, whereas the correlation may be limited in high SES areas. Moreover, in low SES areas, walking was negatively correlated with street connectivity; moderate PA was positively correlated with street connectivity and the shortest distance to the subway station, but negatively correlated with the density of entertainment points of interest (POIs). Studying the relevant factors of the environment can propose better strategies to improve the initiative of the elderly to engage in PA.

A substantial body of research has linked the built environment to obesity risk in older adults, with physical activity reducing obesity risk. Most studies have focused on temperate and subtropical regions; however, results can vary due to different climate characteristics. This study examines Lanzhou, a representative of arid Northwestern China, to explore the nonlinear relationship between the built environment and physical activity among older adults, focusing on the role of green spaces. By using the XGBoost method, we analyze how green spaces and the 5D built environment affect physical activity levels among the obese and standard‐weight elderly populations in Lanzhou. Results indicate that green spaces significantly influence physical activity in both groups, with their effect surpassing other environmental and sociodemographic factors. Obese elderly individuals are more influenced by green space quality and accessibility, while standard‐weight individuals are more affected by the overall quantity and coverage of green spaces. Obese individuals also rely more on street greening compared with their standard‐weight counterparts. In addition, a diverse urban environment and appropriate public transportation distances are crucial for promoting physical activity among the elderly. Low temperatures inhibit physical activity more in obese elderly individuals. Our findings provide insights for policymakers, planners, and designers on optimizing green infrastructure to reduce obesity risks among the elderly in arid regions, ultimately enhancing the urban environment's effectiveness in promoting healthy aging.

Urban waterfront spaces are key venues for residents’ physical activity, and their spatial environment significantly impacts usage efficiency. Existing studies predominantly employ linear models and focus on single waterfront types, making it difficult to reveal differences across various types and the nonlinear mechanisms of influencing factors. To address this, this study investigates three types of waterfront spaces in Shenzhen—riverfront, lakeshore, and seafront spaces—integrating multi-source data and machine learning techniques to systematically analyze the differential impacts of the same elements on physical activity. The results indicate: (1) In terms of transportation accessibility, public transport is the most important factor for riverfront and lakeshore spaces, while road network accessibility is most critical for seafront spaces. (2) Regarding natural landscapes, the dominant factors are normalized difference vegetation index (NDVI) for riverfront spaces, green view index for lakeshore spaces, and distance to the shoreline for seafront spaces. (3) For facility services, the core factors are building density (riverfront), number of sports facilities (lakeshore), and number of leisure facilities (seafront). (4) The study further reveals nonlinear relationships and threshold effects of multiple elements. For instance, a turning point in physical activity intensity occurs when the distance to a subway station reaches 2–2.5 km. The green view index shows a threshold of 30% in the overall model, while dual-threshold phenomena are observed in the lakeshore and seafront models. (5) Synergistic effects between elements vary by waterfront type: in riverfront and seafront spaces, activity is more vibrant when areas are close to subway stations and have a low sky view index, whereas the opposite pattern is observed in lakeshore spaces. A combination of a high green view index and greater distance to the shoreline promotes activity in lakeshore spaces, while a high green view index combined with proximity to the shoreline has the most significant promotional effect in riverfront and seafront spaces. This study provides a scientific basis for health-oriented, precise planning and design of urban waterfront spaces.

The equitable allocation of mental health rehabilitation support spaces provides an important foundation for community living and social participation among persons with mental disabilities. Existing accessibility studies tend to focus on a single facility type or travel mode, with limited consideration of the relationships among rehabilitation needs, travel conditions, and spatial opportunities. Using the central urban area of Tianjin as a case study, we aggregated records of persons with mental disabilities at the residential community level and classified rehabilitation support spaces into four types: professional rehabilitation spaces, natural restorative spaces, social interaction spaces, and occupational participation spaces. The Gaussian-enhanced two-step floating catchment area (Gaussian E2SFCA) method was used to estimate accessibility by space type and comprehensive accessibility under public transport, walking, and car passenger travel scenarios. Moran’s I, bivariate LISA, Lorenz curves, the Gini coefficient, and screening for communities with high needs and low accessibility were combined to assess spatial clustering, overall distribution, and needs-based spatial equity. The results show that travel modes substantially reshape the spatial distribution of rehabilitation support opportunities. Public transport accessibility was higher in central areas and lower on the periphery, whereas high walking accessibility was confined to scattered patches around facilities. Car passenger travel reduced spatial disparities but access remained constrained by family caregiving capacity and financial resources. Sensitivity to travel constraints varied across space types, with the largest gaps in access observed for occupational participation and professional rehabilitation spaces, indicating that comprehensive accessibility cannot replace assessment by space type. Screening at the residential community level identified multiple units with high needs and low accessibility, revealing that overall equity assessments can obscure local mismatches between supply and demand. These findings indicate that transport improvements can reduce distance-related barriers but do not automatically achieve needs-based equity. The study provides spatial evidence to inform the allocation of mental health rehabilitation resources, the identification of priority areas, and the development of compensatory transport support policies.

Previous research has primarily focused on the restorative effects of environments on the general population, often overlooking the specific restorative capacity of urban settings for the disabled population. There is a lack of comprehensive investigation into the interaction between accessibility elements and urban restorativeness. This study, conducted in Shenzhen, Guangdong Province, China, categorizes streetscape accessibility elements for the disabled population and develops a recognition system based on an enhanced DeeplabV3+ framework. Semantic segmentation of streetscape accessibility elements was performed using 201,860 sampling points and 807,440 street view images. This study employed a combination of TrueSkill scoring, sentiment semantic analysis, LDA topic modeling, and LAB color clustering to quantify and visualize urban restorativeness. The impact of accessibility elements on urban restorativeness was explored using the XGBoost-SHAP model. Results indicate significant effects of architectural space constraints and high-density motor vehicle distribution on the safety of the disabled population’s mobility. The low pixel ratio of accessibility facilities and signs indicates insufficient infrastructure, while high landscape recognition rates exhibit significant spatial coverage heterogeneity. Detection rates for the disabled population in street views are nearly zero, highlighting a severe lack of inclusivity in pedestrian environments. Urban restorativeness exhibited a pattern of being higher in the south and east, and lower in the north and west. Among the accessibility elements, public green spaces (PGS) contributed the most to urban restorativeness, accounting for 25% of the impact, and the study elucidates the mechanisms through which various elements affect urban restorativeness. This absence stems from spatial competition, missing co-design, threshold effect conflicts, and color interference mechanisms. This research breaks away from traditional linear analytical frameworks and reveals the complex non-linear relationship between accessibility elements and urban restorativeness through the XGBoost-SHAP model, providing a quantitative decision-making tool for planning accessible environments in high-density cities.