Restoring campus pedestrian spaces is vital for enhancing college students’ mental well-being. This study objectively and thoroughly proposed a reference for the optimization of restorative campus pedestrian spaces that are conducive to the mental health of students. Eye-tracking technology was employed to examine gaze behaviors in these landscapes, while a Semantic Difference questionnaire identified key environmental factors influencing the restorative state. Additionally, this study validated the use of virtual reality (VR) technology for this research domain. Building height difference (HDB), tree height (HT), shrub area (AS), ground hue (HG), and ground texture (TG) correlated significantly with the restorative state (ΔS). VR simulations with various environmental parameters were utilized to elucidate the impact of these five factors on ΔS. Subsequently, machine learning models were developed and assessed using a genetic algorithm to refine the optimal restorative design range of campus pedestrian spaces. The results of this study are intended to help improve students’ attentional recovery and to provide methods and references for students to create more restorative campus environments designed to improve their mental health and academic performance.

The sports environment plays a crucial role in shaping the physical and mental well-being of individuals engaged in sports activities. Understanding how environmental factors and emotional experiences influence sports perceptions is essential for advancing public health research and guiding optimal design interventions. However, existing studies in this field often rely on subjective evaluations, lack objective validation, and fail to provide practical insights for design applications. To address these gaps, this study adopts a data-driven approach. Quantitative data were collected to explore the visual environment of badminton courts using eye-tracking technology and a semantic differential questionnaire. The relationships between environmental factors—such as illuminance (IL), height (Ht), roof saturation (RSa), roof slope (RS), backwall saturation (BSa), and natural materials proportion on the backwall (BN)—and sports perception (W) were analyzed. Furthermore, this study identifies the best-performing machine learning model for predicting sports perception, which is subsequently integrated with a genetic algorithm to optimize environmental design thresholds. These findings provide actionable insights for creating sports environments that enhance user experience and support public health objectives.

Outdoor sports spaces in residential areas must provide safe and comfortable conditions for daily exercise, yet in severe cold regions their use is constrained by wind, solar exposure, thermal stress, noise, and air quality. Using six sites in Harbin, China, this study developed an Analytic Hierarchy Process (AHP)-based evaluation system covering the wind, light, acoustic, thermal, and air environments. Candidate indicators were screened using a user questionnaire, expert AHP judgments were used to derive weights, and site performance was quantified through field monitoring, site surveys, and ENVI-met simulation. The indicator weights, in descending order, were wet-bulb globe temperature (14.96%), pedestrian-level wind speed (13.67%), building layout conducive to air circulation (13.55%), pedestrian-level wind vortices and still-air zones (12.79%), sunlight exposure and lighting effectiveness (12.67%), shading rate of hard ground surfaces and structures (12.15%), reflectivity of pavement and covering materials (11.37%), and outdoor noise level (8.83%). Overall scores differed across the six sites. An AHP-weighted performance-shortfall analysis, calculated relative to the ideal Grade 5, identified outdoor noise, shading, building-layout-related air circulation, and sunlight/lighting performance as the four largest cross-site improvement needs. The resulting framework supports the diagnosis and renewal of outdoor sports spaces in cold-region residential areas. The empirical results represent the monitored summer and autumn transitional scenarios rather than year-round performance.