地理学报 ›› 2021, Vol. 76 ›› Issue (8): 1951-1964.doi: 10.11821/dlxb202108010

• 城市与人类健康 • 上一篇    下一篇

中国城市养老院的空间分布特征及其分异成因

姜磊1(), 陈星宇1, 朱竑2,3()   

  1. 1.浙江财经大学经济学院,杭州 310018
    2.广州大学地理科学与遥感学院 华南人文地理与城市发展研究中心,广州 510006
    3.广东省城市与移民研究中心,广州 510006
  • 收稿日期:2020-01-13 修回日期:2021-01-15 出版日期:2021-08-25 发布日期:2021-10-25
  • 通讯作者: 朱竑(1968-), 男, 甘肃临夏人, 博士, 教授, 博士生导师, 研究方向为社会文化地理学。E-mail: zhuhong@gzhu.edu.cn
  • 作者简介:姜磊(1983-), 男, 山东烟台人, 博士, 副教授, 主要从事经济地理学研究。E-mail: lei_jiang@zufe.edu.cn
  • 基金资助:
    广东省自然科学基金项目(2019A1515012102);国家自然科学基金项目(41701146);国家自然科学基金项目(41601133);国家自然科学基金项目(41971184);国家自然科学基金项目(41901170)

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 Published:2021-08-25 Online:2021-10-25
  • 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)

摘要:

中国正在快速步入老龄化社会,老年人的养老问题已经成为当今社会密切关注的焦点。养老院是解决中国养老问题的有效办法。因此,分析中国城市养老院的空间分布特征以及探索城市之间养老院数量差异的成因具有重要的现实意义。本文利用网络爬虫技术获取了2019年中国285个城市的养老院数量,并进行了地图化展示,然后从空间分异性视角出发,运用地理探测器方法对城市养老院数量的社会经济影响因素进行分析。结果表明:① 中国城市养老院数量的空间分布与城市老年人口分布非常相似。② 地理探测器方法的因子探测结果显示,老年人口数量、经济发展水平、公共财政支出、养老保险参保人数和公园绿地面积是影响城市养老院数量的主要因素。其中,城市财政支出能力和经济发展水平对于养老院的建设至关重要。③ 交互因子探测分析结果显示,5个影响因素的两两交互作用均大于单个因素的作用,说明城市养老院数量的空间分布是受多种因素共同作用的结果。其中,老年人口与其他4个变量的交互作用最强,说明老年人口和其他因素结合对城市养老院数量空间分布来说是最主要的影响因素。

关键词: 老龄化, 老年人, 养老院, 空间分异性, 地理探测器, 中国

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