地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (9): 1575-1585.doi: 10.12082/dqxxkx.2021.200607

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中国居民预期寿命及其影响因素的空间差异分析

张子威1,2,3,4(), 黄秋昊1,2,3,5,*(), 陆羽1,2,3,4, 李满春1,2,3, 陈振杰1,2,3, 李飞雪1,2,3   

  1. 1. 南京大学地理与海洋科学学院,南京210023
    2. 自然资源部国土卫星遥感应用重点实验室,南京 210023
    3. 江苏省地理信息科学与技术重点实验室,南京 210023
    4. 自然资源部海岸带开发与保护重点实验室,南京 210023
    5. 江苏省地理信息资源开发与利用协同创新中心,南京 210023
  • 收稿日期:2020-10-15 出版日期:2021-09-25 发布日期:2021-11-25
  • 通讯作者: *黄秋昊(1977— ),男,江苏无锡人,副教授,硕士生导师,主要从事土地利用与GIS方面研究。 E-mail: qhhuang@nju.edu.cn
  • 作者简介:张子威(1995— ),男,山东德州人,硕士生,研究方向为GIS与健康地理分析。E-mail: mg1827085@smail.nju.edu.cn
  • 基金资助:
    国家自然科学基金面上项目(41571082)

Analysis of Life Expectancy and the Spatial Differences of Its Influencing Factors of Chinese Residents

ZHANG Ziwei1,2,3,4(), HUANG Qiuhao1,2,3,5,*(), LU Yu1,2,3,4, LI Manchun1,2,3, CHEN Zhenjie1,2,3, LI Feixue1,2,3   

  1. 1. School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
    2. Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, Nanjing 210023 China
    3. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing 210023, China
    4. The Key Laboratory of the Coastal Zone Exploitation and Protection, Ministry of Natural Resources, Nanjing 210023, China
    5. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
  • Received:2020-10-15 Online:2021-09-25 Published:2021-11-25
  • Supported by:
    National Natural Science Foundation of China(41571082)

摘要:

良好的健康和人类福祉是联合国提出的可持续发展目标之一,提高人口预期寿命是迈向此目标的重要一步。由于中国城市在自然环境和社会发展方面有所差异,因此理解不同城市居民的预期寿命主要受何种因素的影响是制定城市公共卫生策略的关键。本研究基于2015年中国286个城市的有效数据,利用探索性回归、普通最小二乘回归、地理加权回归筛选与预期寿命最相关的影响因素并探索其空间差异,再通过二阶聚类将城市分类,以针对性地提出每类城市政策建议。结果显示:① 经济发展,教育条件和医疗设施条件对预期寿命有显著的积极影响,平均海拔和环境污染则具有负面影响;② 东南地区的经济发展对当地居民的预期寿命影响程度更大;东北和西南地区的医疗设施条件对其居民预期寿命促进程度更高;北部地区的教育条件对当地居民预期寿命影响比其他地区更高;平均海拔对西部地区居民预期寿命的影响最大;西北地区居民的预期寿命则更易受到环境污染带来的负面影响;③ 根据空间差异将城市分为3类,其居民预期寿命关键影响因素依次是经济发展和环境污染、教育条件、医疗设施,每类城市的城市管理者应重点关注不同因素来提升居民的预期寿命。

关键词: 公共卫生政策, 预期寿命, 影响因素, 空间差异, 探索性回归, 地理加权回归, 聚类分析, 中国城市

Abstract:

Good Health and Human Well-being is one of The Sustainable Development Goals proposed by the United Nations, and increasing the life expectancy is a significant step towards this goal. Due to differences in the natural environment and social development of Chinese cities, understanding the factors that affect life expectancy in different regions is the key to formulate urban public health policy. Based on the data of 286 cities in China in 2015, this paper used exploratory regression, ordinary least squares, and geographically weighted regression to screen out the most relevant influencing factors to life expectancy and explore their spatial differences. Then, the two-step cluster analysis was used to make targeted policy recommendations for each type of cities. The results show that: (1) Economic development, educational conditions, and medical facilities had a significant positive impact on life expectancy, while average altitude and environmental pollution had a negative impact; (2) Compared with other regions, economic development in the southeast region had a greater impact on local life expectancy; medical facilities in the northeast and southwest regions had a higher degree of promotion of life expectancy for its residents; education conditions in the northern region had a higher impact on the life expectancy of local residents; average altitude had the greatest impact on the life expectancy of residents in the West region; The life expectancy of residents in the northwest region was more susceptible to the negative impact of environmental pollution than in other regions; (3) Cities were divided into three categories based on spatial differences, and the key factors affecting the life expectancy are economic development and environmental pollution, educational conditions, and medical facilities in order. City managers in each category of cities should pay attention to different factors to increase their life expectancy.

Key words: public health policy, life expectancy, influencing factor, spatial difference, exploratory regression, geographic weighted regression, cluster analysis, Chinese cities