Speakers
Description
According to the World Health Organisation (WHO), the global population aged 60 years and above will account for 16.67% of the total population by 2030. 80% of the elderly population is expected to reside in developing countries by 2035(World Health Organization, 2024). As the world’s largest developing country, China is experiencing an unprecedented aging crisis. As of 2023, the elderly population aged 60 years and above in China had reached 297 million, accounting for 21.1% of the total population. High-density urban areas are facing even more severe aging issues(Ji et al., 2023). The reasonable layout of elderly care institutions directly influence the quality of life and well-being of the elderly(Cheng and Cui, 2020). However, under the backdrop of rapid urbanisation, spatial resources in high-density urban areas are becoming increasingly constrained.(Lee and Chan, 2008). How to plan and allocate elderly care institutions within limited space has become a key issue that urgently needs to be addressed in urban development.
Existing research on the optimisation of elderly care institution layouts primarily focused on solving current issues and tended to optimise such layouts with a single objective. This study aims to reveal the spatial differences in the accessibility of elderly care institutions in Jianghan District, Wuhan, and to build a multi-objective optimisation model based on future elderly population distribution forecasts. The model will focus on maximising equity, maximising efficiency and minimising construction costs to scientifically optimise the location and service capacity of elderly care institution and ensure the optimal allocation of institutional elderly care resources.
This study predicts the elderly population distribution at a 100 m × 100 m scale in Jianghan District, Wuhan in 2035 under natural growth conditions, based on Baidu Huiyan data and mortality data from the seventh national census. Accessibility is calculated using a Three-Step Floating Catchment Area (3SFCA) method, and equity of the potential elderly care institution layout is measured by minimising the sum of the accessibility variances of all demand points. The gravity p-median model is employed to optimise efficiency by minimising total weighted travel costs. The total construction cost of elderly care facilities is quantified, aiming to minimise construction costs. Finally, street-level residential communities are considered as potential locations for elderly care facilities, and layout plans are iteratively optimised using the multi-objective optimization algorithm (NSGA-II) to improve the spatial distribution of elderly care institutions in high-density urban areas.
The results of the study indicate that, the elderly population distribution in Jianghan District will show significant spatial differences by 2030. High-density areas will have higher demand for elderly care, while low-density areas will have more dispersed demand. The accessibility of existing elderly care facilities shows a significant disparities between urban centre and suburban areas. The optimised layout of elderly care institutions is more balanced, with a noticeable increase in service coverage and a reduction in service gaps. The total weighted travel cost is reduced to achieve a balance between equity and efficiency in the layout.
The conclusions of this study are as follows: (1) It reveals the spatial distribution characteristics of the elderly population in high-density urban areas and the changing trends in future elderly care facility demand. (2) It identifies potential supply–demand mismatches in the existing layout of elderly care institutions, particularly in high- density areas. (3) It validates the applicability of multi-objective optimisation algorithms (NSGA-II) in complex urban facility planning to provide a scientific theoretical reference and practical guidance for elderly care institution planning in high-density urban areas through the integrated optimisation of equity, efficiency and cost.
References
World Health Organization (2024) Ageing and Health. [Online] available at: https://www.who.int/zh/news-room/fact-sheets/detail/ageing-and-health?
Ji, Yin, Li, Zhou (2023) Exploring the influence path of high-rise residential environment on the mental health of the elderly. Sustainable Cities and Society, 98, p.104808.
Chen, Peng, Liang, Gan, Xu, Xiang (2024) Association between community walkability and hypertension: Evidence from the Wuhan Chronic Disease Cohort Study. Environmental Research, 263, p.120071.
Cheng, Cui (2020) Spatial optimization of residential care facility configuration based on the integration of modified immune algorithm and GIS: A case study of Jing’an district in Shanghai, China. International Journal of Environmental Research and Public Health, 17(21), p.8090.
Lee, Chan (2008) Factors affecting urban renewal in high-density city: Case study of Hong Kong. Journal of Urban Planning and Development, 134(3), pp.140-148.
Tao, Cheng, Dai, Rosenberg (2014) Spatial optimization of residential care facility locations in Beijing, China: maximum equity in accessibility. International journal of health geographics, 13, pp.1-11.
| Keywords | population prediction; elderly care institutions; accessibility; multi-objective optimization; NSGA-II |
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| Best Congress Paper Award | Yes |
