Aging Population Spatial Distribution Discrepancy and Impacting Factor

Abstract

The phenomenon of population aging is gradually spreading around the world. Consequently, it is leading to unsustainable economic development due to the decline of the labor force. Therefore, many people identify the aging population from national and intercontinental levels, as it would not be possible to recognize specific population spatial distribution characteristics and impacting factors in a province or state because of the spatial and temporal differences. In this paper, Jiangsu Province was selected as the study area to represent its aging population’s spatial characteristics and to identify the spatial heterogeneity with impacting factor by Geographically Weighted Regression (GWR), as well as to determine the impacting situation by marginal effect. The results show the following: (1) The impact factor’s spatial heterogeneity from the cities in Jiangsu Province is small but occurs in the city groups, while the impacting situation is the same in the north, central and south city groups, showing a disparity among them. (2) There is a significant change in the impact factor’s influence from 2010 to 2020. (3) The social–economic factor negatively relates to the aging population in 2020, with an interval value of [−1.0585, −1.0632]. This finding indicates that the spatial heterogeneity of the aging population at the province level is not the same as that at the national level. Therefore, we need to consider the local situation more. These findings further provide an empirical basis for the province-level study of the aging population, which differs from the national level.

Study on the differences and influencing factors of spatial distribution of population aging at township scale: a case study of township research units in Anshun City, China

“…The spatial distribution characteristic of population aging at the township scale was influenced by complex and comprehensive factors. We referred to previous empirical research to identify the influencing factors ( 9 , 39 , 40 ) and the unique geographical characteristic factors of the karst mountainous areas ( 19 , 31 ). Based on the actual regional development, we selected 18 variables from four dimensions including natural factors, social factors, economic factors, and karst factors.…”

“…The spatial heterogeneity of the economic level represented by the per capita GDP of the township scale is small ( 16 ); therefore, the impact on OPD and OPP is not significant. The marginal effect of economic level on life expectancy also reduced the reliance on the economy for population aging ( 40 ). The coefficient of land for construction (CLC) of all townships displayed a significant positive correlation with OPD and OPP at p = 0.05 level ( Tables 5 , 6 ).…”

How Does the Spatial Structure of Urban Agglomerations Affect the Spatiotemporal Evolution of Population Aging?

“…There are two specific types of spatial autocorrelation analysis: overall spatial autocorrelation and local spatial autocorrelation. The first is the overall spatial autocorrelation analysis, which studies the geographic attribute values as a whole to determine whether they have spatial clustering characteristics [29][30][31]. The calculation formula is:…”

Spatial Distribution Pattern and Evolution Characteristics of Elderly Population in Wuhan Based on Census Data

“…There are abundant research results on the spatial distribution of the elderly population, and many scholars have conducted empirical studies on the spatial distribution characteristics of the elderly population in cities or counties [7][8][9][10][11][12]. The research scale is mostly at the macro or meso level [13][14][15][16][17][18], and the types of population data used in the research have tended to be diversified, mainly including population census data [19][20][21][22][23], population grid data [24,25], mobile signaling data [26][27][28], and YiChuxing data [29][30][31][32].…”