标题: Explaining the longevity characteristics in China from a geographical perspective: A multi-scale geographically weighted regression analysis

作者: Yang, RF (Yang, Renfei); Ren, F (Ren, Fu); Ma, XY (Ma, Xiangyuan); Zhang, HW (Zhang, Hongwei); Xu, WX (Xu, Wenxuan); Jia, P (Jia, Peng)

来源出版物: GEOSPATIAL HEALTH 卷: 16 期: 2 文献号: 1024 DOI: 10.4081/gh.2021.1024 出版年: 2021

摘要: Longevity is a near-universal human aspiration that can affect moral progress and economic development at the social level. In rapidly developing China, questions about the geographical distribution and environmental factors of longevity phenomenon need to be answered more clearly. This study calculated the longevity index (LI), longevity index for females (LIF) and longevity index for males (LIM) based on the percentage of the long-lived population among the total number of elderly people to investigate regional and gender characteristics at the county level in China. A new multi-scale geographically weighted regression (MGWR) model and four possible geographical environmental factors were applied to explore environmental effects. The results indicate that the LIs of 2838 counties ranged from 1.3% to 16.3%, and the distribution showed obvious regional and gender differences. In general, the LI was high in the East and low in the West, and the LIF was higher than the LIM in 2614 counties (92.1%). The MGWR model performed well explaining that geographical environmental factors, including topographic features, vegetation conditions, human social activity and air pollution factors have a variable influence on longevity at different spatial scales and in different regions. These findings enrich our understanding of the spatial distribution, gender differences and geographical environmental effects on longevity in China, which provides an important reference for people interested in the variations in the associations between different geographical factors.

作者关键词: Longevity; geographical environmental factors; county level; multi-scale geographically weighted regression; influence scale; China

地址: [Yang, Renfei; Ren, Fu; Ma, Xiangyuan; Jia, Peng] Wuhan Univ, Sch Resource & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Peoples R China.

[Ren, Fu] Wuhan Univ, Minist Educ, Key Lab GIS, Wuhan, Peoples R China.

[Zhang, Hongwei] Wuhan Univ, Elect Informat Sch, Wuhan, Peoples R China.

[Xu, Wenxuan] Nanjing Univ, Sch Geog & Oceanog Sci, Nanjing, Peoples R China.

[Jia, Peng] Wuhan Univ, Int Inst Spatial Lifecourse Epidemiol ISLE, Wuhan, Peoples R China.

通讯作者地址: Ren, F (通讯作者)，Wuhan Univ, Sch Resource & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Peoples R China.

电子邮件地址: renfu@whu.edu.cn

影响因子：3.169