In fast-paced and high-pressure modern urban environments, the increase in chronic and psychological diseases significantly threatens public physical and mental health. The community environment is a basic unit of residents’ lives and has direct and indirect effects on their health. This study examined the daily activities of residents in 12 communities in Fuzhou City and analysed the influencing mechanism among the community environment, daily activities, and residents’ health by constructing a structural equation model. We identified the direct and indirect effects of the community environment on residents’ health and provided a reference for the construction of community environments to promote residents’ health. The natural environment had the greatest direct impact on residents’ health, and the physical environment had the greatest indirect and comprehensive impact. Daily activities had a mediating effect on the relationship between the community environment, particularly physical, and residents’ health.

Introduction: Extreme heat events intensify health risks among vulnerable populations, raising concerns regarding climate justice. However, most existing assessments remain at the citywide scale and seldom examine inequities across different types of outdoor cooling spaces. Methods: This study integrates the Exposure–Sensitivity–Adaptive Capacity (VSD) framework with the dimensions of distributive, recognition, and procedural justice to construct a climate justice assessment model. Outdoor cooling spaces were classified into linear and areal forms. Using Gulou District in Fuzhou as the case study, we developed a multi-source indicator system based on remote sensing imagery, street-view data, points of interest, and demographic statistics. The entropy weight method was used to determine indicator weights, and K-means clustering was applied to identify climate injustice space types. Results: The findings show that 37.18% of linear cooling spaces and 44.45% of areal cooling spaces face significant climate injustice risks. High-risk areas are concentrated in dense built-up zones, aging neighborhoods, and peripheral areas with limited public services. Cluster analysis identified three distinct deficit categories: distributional justice deficit, recognitional justice deficit, and systemic justice deficit, reflecting overlapping vulnerabilities and uneven adaptive capacities. Discussion: These results highlight the need for differentiated interventions to reduce spatial inequities. Key actions include enhancing shading along traffic corridors, improving service accessibility in aging neighborhoods, and strengthening adaptive resources in systemic deficit areas. The proposed framework offers an evidence base for equity-oriented urban governance and supports resilient urban planning and public health strategies under extreme heat.

The purpose of this study is to explore renewal strategies for urban community public spaces by analysing the needs of different categories of older adults according to their interactions. The research sample comprises ten communities located in downtown Fuzhou City. By using a questionnaire-based quantitative design supported by field investigation, interviews, and virtual reality (VR)-assisted scenario elicitation, ten communities in the central urban area of Fuzhou were investigated in this study. Older adults were divided into five categories—dynamic self-care older adults, static self-care older adults, dynamic assisted older adults, static assisted older adults, and static nursing older adults—while community public spaces in enclosed, semi-open, and open spatial systems were analysed. As a standardized visual aid, VR-assisted panoramic scenes were used to help respondents to understand spatial functions and evaluate their needs more consistently. The findings highlight significant differences and interconnections in the spatial needs of different categories of older adults. Therefore, this paper proposes three hybrid scenarios—dynamic interaction, pairing companionship, and organisational interaction. Corresponding spatial renewal strategies are developed to resolve discrepancies between supply and demand. The objective is to promote communication and mutual companionship among older adults in urban communities, thus providing valuable insights and guidance for the renewal of public spaces in the context of an ageing population.

Rapid urbanization, population aging, and increasing physical and mental health challenges highlight the built environment's importance in promoting public health. Although healing environments are widely discussed, research on the spatial planning of healing resources at the urban scale remains limited, focusing mostly on specific spaces or micro-scale interventions. Given the growing demand for equitable and effective health-supportive environments, developing systematic, data-driven approaches for healing resource planning is necessary. This study explores spatial distribution mechanisms of urban healing resources to optimize their layout from a planning perspective. Using Fuzhou, China, as a case study, point of interest (POI) data identified urban functional resources and classified healing resources into four types: Physical health-oriented, Nature sensory, Cultural artistic, and Social interactive. A grid-based spatial framework (1 km × 1 km) was applied, and ID3 decision tree models were constructed to analyze factors influencing each healing resource type's distribution. City-wide layout prediction and population-based optimization were further conducted. Results revealed clear differences in spatial distribution patterns of the four healing resource types. While Physical health-oriented resources showed relatively comprehensive coverage, Nature sensory, Cultural artistic, and Social interactive resources exhibited notable spatial gaps, particularly in peripheral and rapidly developing areas. After population-based optimization, 128 grids with urgent demand for healing resource provision were identified. These findings demonstrate the value of integrating POI big data and interpretable machine learning into urban spatial planning. The proposed framework provides practical support for optimizing healing resource layout within the “15-minute community life circle” and offers a methodological reference for healthy city planning.