Papers
Quantifing The Imbalance Of Spatial Distribution Of Elderly Service With Muti-Source Data
Pixin Gong School of Architecture and Art, North China University of Technology
Xiaoran Huang School of Architecture and Art, North China University of Technology
Chenyu Huang School of Architecture and Art, North China University of Technology
Marcus White Swinburne University of Technology
With the growing challenge of aging populations around the world, the study of the elderly service is an essential initiative to accommodate the particular needs of the disadvantaged communities and promote social equity. Previous research frameworks are very case-specific with limited evaluation indicators that cannot be extended to other scenarios and fields. Based on multi-source data and Geographic Information System (GIS), this paper quantifies and visualises the imbalance in the spatial distribution of elderly services in 218 neighbourhoods in Shijingshan District, Beijing, China. Mortality data were obtained, and the most contributing indicators to mortality were investigated by correlation analysis. Finally, mapping between other facility indicators to mortality rates was constructed using machine learning to further investigate the factors influencing the quality of elderly services at the community level. The conclusion shows that the functional density of transportation facilities, medical facilities, living services facilities, and the accessibility of elderly care facilities are most negatively correlated with mortality. The correlation conclusion is combined with a machine learning prediction model to provide future recommendations for the construction of unbalanced elderly neighbourhoods. This research offers a novel systematic method to study urban access to elderly services as well as a new perspective on improving social fairness. References. Bingqiu Yan and Xiaolu Gao and Michael Lyon. (2014). Modeling satisfaction amongst the elderly in different Chinese urban neighborhoods. Social Science & Medicine,118pp. 127-134. Junling Gao et al. (2017). Relationships between neighborhood attributes and subjective well-being among the Chinese elderly: Data from Shanghai. BioScience Trends, 11(5), pp. 516-523 Yanan Zhao and Pak-Kwong Chung. (2017). Neighborhood environment walkability and health-related quality of life among older adults in Hong Kong. Archives of Gerontology and Geriatrics, 73pp. 182-186. Fan Zhang et al. (2019). Assessing spatial disparities of accessibility to community-based service resources for Chinese older adults based on travel behavior: A city-wide study of Nanjing, China. Habitat International, 88pp. 101984-101984. Fan Zhang and Dezhi Li. (2019). Multiple Linear Regression-Structural Equation Modeling Based Development of the Integrated Model of Perceived Neighborhood Environment and Quality of Life of Community-Dwelling Older Adults: A Cross-Sectional Study in Nanjing, China. International Journal of Environmental Research and Public Health, 16(24), pp. 4933. Fan Zhang and Dezhi Li. (2019). How the Urban Neighborhood Environment Influences the Quality of Life of Chinese Community-Dwelling Older Adults: An Influence Model of “NE-QoL”. Sustainability, 11(20), pp. 5739-5739. Nicia del Rocío Santana-Berlanga et al. (2020). Instruments to measure quality of life in institutionalised older adults: Systematic review. Geriatric Nursing, 41(4), pp. 445-462. Dai, D. (2011). Racial/ethnic and socioeconomic disparities in urban green space accessibility: Where to intervene?. Landscape and Urban Planning, 102(4), 234-24. Puebla, J. G. . (1996). Accessibility in the european union: a comparative analysis by transportation mode. Estudios De Transportes Y Comunicaciones
Keywords: Elderly Service Facilities, Multi-Source Data, Machine Learning, Sdg 3, Sdg 10 , Sdg 11