A spatial modelling framework for equitable and efficient elderly care facility allocation in urban China
- 1School of Design and Architecture, Zhejiang University of Technology
Abstract
Rapid population ageing in China has significantly intensified the demand for equitable and efficient allocation of elderly care facilities. However, local governments continue to rely on simplistic ratio-based indicators, while advanced GIS-based spatial accessibility models remain rarely adopted in practice, resulting in a persistent implementation gap between academic research and real-world planning. This study reviews four mainstream accessibility algorithms and identifies low-resolution elderly population data as the primary barrier hindering the production of reliable results from advanced models. To address the distinctive characteristics of Chinese urban areas—particularly low data resolution and inconsistent elderly density patterns across old and new districts—we propose an integrated assessment framework based on the High-Resolution Gaussian 2SFCA method. This framework enhances the spatial resolution of demand estimates, provides a more context-sensitive treatment of service thresholds, and generates policy-interpretable assessment outputs. Application in two contrasting urban districts of Hangzhou shows that, compared with conventional approaches, the framework is less affected by aggregation bias in dense built-up areas and better able to distinguish between bed shortages and excessive service radii in low-density zones. By generating high-resolution and visually interpretable outputs, the framework helps narrow the implementation gap between academic spatial modelling and practical decision-making, providing local governments in data-constrained settings with an effective tool to support the implementation of aging-in-place policies.
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BibTeX
@article{dai2026spatial,
title = {{A spatial modelling framework for equitable and efficient elderly care facility allocation in urban China}},
author = {Xiaoling Dai and Lu Huang and Xinyu Zhou and Wenbo Yu},
journal = {Scientific Reports},
year = {2026},
month = jul,
doi = {10.1038/s41598-026-62251-6},
url = {https://doi.org/10.1038/s41598-026-62251-6},
note = {CC BY},
}
RIS
TY - JOUR
TI - A spatial modelling framework for equitable and efficient elderly care facility allocation in urban China
AU - Xiaoling Dai
AU - Lu Huang
AU - Xinyu Zhou
AU - Wenbo Yu
PY - 2026
DA - 2026/07/01
JO - Scientific Reports
DO - 10.1038/s41598-026-62251-6
UR - https://doi.org/10.1038/s41598-026-62251-6
AB - Rapid population ageing in China has significantly intensified the demand for equitable and efficient allocation of elderly care facilities. However, local governments continue to rely on simplistic ratio-based indicators, while advanced GIS-based spatial accessibility models remain rarely adopted in practice, resulting in a persistent implementation gap between academic research and real-world planning. This study reviews four mainstream accessibility algorithms and identifies low-resolution elderly population data as the primary barrier hindering the production of reliable results from advanced models. To address the distinctive characteristics of Chinese urban areas—particularly low data resolution and inconsistent elderly density patterns across old and new districts—we propose an integrated assessment framework based on the High-Resolution Gaussian 2SFCA method. This framework enhances the spatial resolution of demand estimates, provides a more context-sensitive treatment of service thresholds, and generates policy-interpretable assessment outputs. Application in two contrasting urban districts of Hangzhou shows that, compared with conventional approaches, the framework is less affected by aggregation bias in dense built-up areas and better able to distinguish between bed shortages and excessive service radii in low-density zones. By generating high-resolution and visually interpretable outputs, the framework helps narrow the implementation gap between academic spatial modelling and practical decision-making, providing local governments in data-constrained settings with an effective tool to support the implementation of aging-in-place policies.
SN - 2045-2322
ER -