摘要
基于1993—2015年全国31省区的老龄化率数据,采用基因表达式编程(GEP)算法建立老龄化率预测模型,预测了2016—2035年各省老龄化率,并讨论了未来老龄人口发展的空间差异演化特征。结果表明:第一,预测期内,我国老龄化率呈上升趋势,2020年和2035年全国老龄化率将分别达到12.59%和20.36%,且2020—2035年老龄化率增速高于2015—2020年老龄化率增速。第二,老龄化率在2020年之前总体上呈现从东北、东部、中部至西部逐渐递减态势;同时各省区老龄化率高低与其的经济发展水平有密切关联性。第三,全域空间关联分析表明,全国各省区老龄化率呈现显著的正空间自相关性,存在空间集聚效应,但自相关系数逐渐下降说明老龄化率的空间集聚效应不断减小;局域空间关联性则表明,部分省区老龄人口呈现空间离散特征,且存在这一特征的省区数量在逐渐增加。
As a new type of evolutionary algorithm,gene expression programming algorithm(GEP)has been widely used in many fields,but the research on population prediction is still very limited.This paper plans to use the proportion of the elderly population in 31 provinces from 1993 to 2015 as a time series,use the GEP algorithm to establish an ageing population proportion prediction model,predict the proportion of aged people in each province in 2016-2035,and compare it with the grey prediction model and the metabolic model and thus explore the evolutionary characteristics of spatial differences in the development of China's future aging population.First,research shows that the proportion of the aged population is on the rise.The GEP model predicts that the proportion of the aged population in the country will reach 12.59%in 2020,and the proportion of the aged population in the country will reach 19.36%in 2035.The proportion of the aged population will increase at a high rate from 2020 to 2035.Second,the proportion of the aged population as a whole shows that the Northeast,East,Central,and West gradually decline,and the proportion of aged people in each region of each province is related to the level of economic development in this province.Third,the global spatial correlation analysis shows that the distribution of aged population in each province across the country presents significant positive spatial autocorrelation,but the spatial distribution differences of the old-age population continue to decrease while in the analysis of local spatial correlation,most of the provinces the aged population shows a constant proliferation.
作者
逯进
李琳
郭志仪
LU Jin;LI Lin;GUO Zhi-yi(School of Economics,Qingdao University,Qingdao 266071,China;School of Economics,Lanzhou University,Lanzhou 730000,China)
出处
《东方论坛（青岛大学学报）》
2018年第4期1-11,共11页
Eastern Forum（JOURNAL OF QINGDAO UNIVERSITY）
关键词
基因表达式编程
老龄化率
预测
gene expression programming
proportion of aged population
prediction
