Acta Geographica Sinica ›› 2021, Vol. 76 ›› Issue (8): 1951-1964.doi: 10.11821/dlxb202108010

• Urban and Human Health • Previous Articles     Next Articles

The spatial heterogeneity distribution of Chinese urban nursing homes and socio-economic driving factors

JIANG Lei1(), CHEN Xingyu1, ZHU Hong2,3()   

  1. 1. School of Economics, Zhejiang University of Finance and Economics, Hangzhou 310018, China
    2. School of Geography and Remote Sensing, Research Center for Human Geography and Urban Development in Southern China, Guangzhou University, Guangzhou 510006, China
    3. Guangdong Provincial Center for Urban and Migration Studies, Guangzhou 510006, China
  • Received:2020-01-13 Revised:2021-01-15 Online:2021-08-25 Published:2021-10-25
  • Contact: ZHU Hong E-mail:lei_jiang@zufe.edu.cn;zhuhong@gzhu.edu.cn
  • Supported by:
    Natural Science Foundation of Guangdong Province, China(2019A1515012102);National Natural Science Foundation of China(41701146);National Natural Science Foundation of China(41601133);National Natural Science Foundation of China(41971184);National Natural Science Foundation of China(41901170)

Abstract:

Over the recent decades, China has become an ageing society and how to best take care of the elderly has been in heated debate. Nursing homes have been considered as an effective way to solve the problems associated with the care of the elderly in China. To address these problems, it is of great significance to better understand the spatial distribution of nursing homes in Chinese cities and investigate why their distribution differs in space. This study used crawler technology to obtain the number of nursing homes in 285 Chinese cities by September, 2019, and applied a geo-visualization technique to map their spatial distributions. A novel spatially stratified heterogeneity method (named geographical detector) was employed to uncover the socio-economic driving factors of these nursing homes. The following findings were obtained: (1) The spatial distribution of the number of nursing homes is similar to that of the elderly population in the investigated cities, indicating that there is a close relationship between them. (2) The results of the factor detector test showed that the urban elderly population, urban economic development level, fiscal expenditure, the number of employees joining urban basic pension insurance, and the area of green land is closely related to the number of nursing homes in Chinese cities. Of these five socio-economic driving factors, fiscal expenditure and the level of economic development are the main drivers. (3) The results of the interaction detector test showed that the interaction effects of pairwise factors on nursing homes are stronger than the effect of individual factor. This indicates that the spatial heterogeneity of the number of nursing homes is affected by multiple factors. Moreover, the interactions between the elderly population factor and four other driving factors are the strongest determinants for the development of the number of nursing homes of Chinese cities. Finally, several relevant policies are proposed to promote the increase of nursing homes in Chinese cities based on the main findings.

Key words: ageing, elderly, nursing home, spatial stratified heterogeneity, geographical detector, China