Search Results (3)

With the rapid advancement of urbanization in China, the demand for community-based elderly care facilities (CECFs) has been increasing. One pressing challenge is the question of how to provide CECFs that not only meet the health needs of the elderly but also make efficient use of limited urban land resources. This study addresses this issue by adopting an integrated multi-method research framework that combines multi-objective optimization (MOO) algorithms, Spearman rank correlation analysis, ensemble learning methods (Random Forest combined with SHapley Additive exPlanations (SHAP), where SHAP enhances the interpretability of ensemble models), and Self-Organizing Map (SOM) neural networks. This framework is employed to identify optimal building configurations and to examine how different architectural parameters influence key daylight performance indicators—Useful Daylight Illuminance (UDI) and Daylight Factor (DF). Results indicate that when UDI and DF meet the comfort thresholds for elderly users, the minimum building area can be controlled to as little as 351 m² and can achieve a balance between natural lighting and spatial efficiency. This ensures sufficient indoor daylight while mitigating excessive glare that could impair elderly vision. Significant correlations are observed between spatial form and daylight performance, with factors such as window-to-wall ratio (WWR) and wall thickness (WT) playing crucial roles. Specifically, wall thickness affects indoor daylight distribution by altering window depth and shading. Moreover, the ensemble learning models combined with SHAP analysis uncover nonlinear relationships between various architectural parameters and daylight performance. In addition, a decision support method based on SOM is proposed to replace the subjective decision-making process commonly found in traditional optimization frameworks. This method enables the visualization of a large Pareto solution set in a two-dimensional space, facilitating more informed and rational design decisions. Finally, the findings are translated into a set of practical design strategies for application in real-world projects. Full article

The construction of community elderly care facilities (CECF) is pivotal for promoting healthy aging and “aging in place” for older people. This study focuses on the low utilization rates of community elderly care facilities in the Dongcheng and Xicheng Districts, core areas of Beijing. The explainable machine learning method is used to analyze data across three dimensions: the elderly’s individual attributes, characteristics of the community elderly care station (CECS), and features of the built environment around CECS and subdistrict, to identify the important factors that influence the usage frequency of overall CECS and its different functional spaces, and also the correlation between factors and usage frequency of CECS. It shows that the most important factors are the features of CSCF, including the degree of space acceptance and satisfaction with services provided, which influence the usage frequency of nine functional spaces (R² ≥ 0.68) and overall (R² = 0.56). In addition, older people’s individual factors, such as age and physical condition, significantly influence the usage of specific spaces such as rehabilitation therapy rooms and assistive bathing rooms. The influence of built environment characteristics is relatively low, with factors such as the density of bus stations and housing prices within the subdistrict and the mean distance from CECF to the nearest subway stations being more important. These findings provide a reference for the construction of indoor environments, management of service quality, and optimal site selection for future community elderly care facilities. Full article

Aging in place has been proposed in response to increasing aging worldwide. Recently, many community elderly care facilities (CECFs) in China have been built to meet the desire of the elderly to age in place and help them live better in familiar environments. This study instituted a correlation evaluation system between the elderly’s quality of life (QOL) and their satisfaction with CECFs. It assessed the QOL of the elderly in urban communities in Shenzhen and identified the issues of CECFs that led to the elderly’s low QOL. Firstly, a hypothesis of the relationship between QOL and CECF satisfaction was proposed on the basis of previous studies. The QOL–CECF model was verified and tested by structural equation modeling (SEM). It was found that the functional setting, planning layout, and operational management affect the QOL of the elderly. Secondly, this study investigated the issue of CECFs in three types of communities, namely, urbanized village communities, affordable housing communities, and commercial housing communities. A factor-based analysis revealed the intrinsic linkages between and extracted the composite factors of CECF indicators in the three types of communities, revealing the CECF issues that led to low QOL using a QOL–CECF satisfaction analysis. Lastly, this study proposed differentiated strategies for CECFs in urbanized village communities and affordable and commercial housing communities. This study can provide differentiated strategies for CECFs in various types of communities to effectively improve the QOL of the elderly and promote the sustainability of CECFs. Full article