A Multi-Objective Optimization and Decision Support Framework for Natural Daylight and Building Areas in Community Elderly Care Facilities in Land-Scarce Cities

Bibliographic Data

ID22034643
AuthorsFang Wen (0000-0001-5799-4569, North China University of Technology), Lu Zhang (0000-0002-8855-8492, North China University of Technology), Ling Jiang (0000-0002-8485-8893, North China University of Technology), Wenqi Sun (0000-0002-8857-2008, North China University of Technology), Tong Jin (0000-0002-5725-8008, North China University of Technology), Bing Zhang (0000-0002-3953-0290, North China University of Technology, corresponding author), Bo Zhang (0000-0003-0986-0018, North China University of Technology)
Year2025
Volume14
Issue7
Pages272
Publication date2025-07-10
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueISPRS International Journal of Geo-Information (JOURNAL)
Journal identifiersISSN: 2220-9964 • E-ISSN: 2220-9964
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi14070272
OpenAlexW4412161634
LanguageEN
References cited97

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

Business · Daylight · Economics · Environmental economics · Environmental planning · Environmental resource management · Geography · Building Energy and Comfort Optimization · Environmental Science

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