Spatial-Temporal Variation of Population Aging: A Case Study of China’s Liaoning Province
With the rapid increase of the aging population in China, research on the aging phenomenon has become critical. Studying population aging from a spatial perspective is of vital practical significance. Based on China’s census data from 1990 to 2010, this study establishes the indicator system of agin...
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| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/5436061 |
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| author | Dunyi Guan Lei Lei Zenglin Han |
| author_facet | Dunyi Guan Lei Lei Zenglin Han |
| author_sort | Dunyi Guan |
| collection | DOAJ |
| description | With the rapid increase of the aging population in China, research on the aging phenomenon has become critical. Studying population aging from a spatial perspective is of vital practical significance. Based on China’s census data from 1990 to 2010, this study establishes the indicator system of aging level and chooses the entropy method, standard deviational ellipse, and spatial autocorrelation as main methods. Taking Liaoning province of China as an example, the study measures the aging level of its 100 districts and counties, analyzing the overall spatial movement degree, and elucidates the temporal-spatial evolution of aging development. It further discusses the spatial correlational characteristics between each county unit. The results reveal that, first, the spatial distribution of aging in Liaoning shows an unceasingly intensifying northeast-southwest pattern. County areas along the Shenyang-Dalian line show a stronger effect of aging of the whole province. Second, the average score of aging in all county areas is on the increase; the degree of aging shows an increasing trend, but the speed of aging tends to slow down. Population aging in Liaoning shows an obvious spatial heterogeneity, which is mainly manifested by regional differences, urban-rural differences, and minority differences. Third, Moran’s I index of aging level first increases and then decreases; the values are all positive. The overall spatial pattern has a positive correlation; the spatial agglomeration effect is first enhanced and then weakened. The hot spots are distributed in the surrounding counties of Shenyang and Dalian, showing a distribution pattern of two cores; the cold spots gradually form a binary structure represented by Chaoyang-Huludao and Yingkou-Panjin. The study showed the positive spatial correlation of the aging level between the county units, thereby paving the way for future research on the balanced distribution. Population aging studies will contribute to optimize the population structure and promote a virtuous circle of economy. The study showed the positive spatial correlation of the aging level between the county units, thereby paving the way for future research on the balanced distribution. Population aging studies will contribute to optimize the population structure and promote a virtuous circle of economy. |
| format | Article |
| id | doaj-art-4c365847553f44e2889000089cbfcaec |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-4c365847553f44e2889000089cbfcaec2025-02-03T05:49:34ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/54360615436061Spatial-Temporal Variation of Population Aging: A Case Study of China’s Liaoning ProvinceDunyi Guan0Lei Lei1Zenglin Han2School of Geography, Liaoning Normal University, Dalian, ChinaSchool of Geography, Liaoning Normal University, Dalian, ChinaMarine Economics and Sustainable Development Research Center, Liaoning Normal University, Dalian, ChinaWith the rapid increase of the aging population in China, research on the aging phenomenon has become critical. Studying population aging from a spatial perspective is of vital practical significance. Based on China’s census data from 1990 to 2010, this study establishes the indicator system of aging level and chooses the entropy method, standard deviational ellipse, and spatial autocorrelation as main methods. Taking Liaoning province of China as an example, the study measures the aging level of its 100 districts and counties, analyzing the overall spatial movement degree, and elucidates the temporal-spatial evolution of aging development. It further discusses the spatial correlational characteristics between each county unit. The results reveal that, first, the spatial distribution of aging in Liaoning shows an unceasingly intensifying northeast-southwest pattern. County areas along the Shenyang-Dalian line show a stronger effect of aging of the whole province. Second, the average score of aging in all county areas is on the increase; the degree of aging shows an increasing trend, but the speed of aging tends to slow down. Population aging in Liaoning shows an obvious spatial heterogeneity, which is mainly manifested by regional differences, urban-rural differences, and minority differences. Third, Moran’s I index of aging level first increases and then decreases; the values are all positive. The overall spatial pattern has a positive correlation; the spatial agglomeration effect is first enhanced and then weakened. The hot spots are distributed in the surrounding counties of Shenyang and Dalian, showing a distribution pattern of two cores; the cold spots gradually form a binary structure represented by Chaoyang-Huludao and Yingkou-Panjin. The study showed the positive spatial correlation of the aging level between the county units, thereby paving the way for future research on the balanced distribution. Population aging studies will contribute to optimize the population structure and promote a virtuous circle of economy. The study showed the positive spatial correlation of the aging level between the county units, thereby paving the way for future research on the balanced distribution. Population aging studies will contribute to optimize the population structure and promote a virtuous circle of economy.http://dx.doi.org/10.1155/2020/5436061 |
| spellingShingle | Dunyi Guan Lei Lei Zenglin Han Spatial-Temporal Variation of Population Aging: A Case Study of China’s Liaoning Province Complexity |
| title | Spatial-Temporal Variation of Population Aging: A Case Study of China’s Liaoning Province |
| title_full | Spatial-Temporal Variation of Population Aging: A Case Study of China’s Liaoning Province |
| title_fullStr | Spatial-Temporal Variation of Population Aging: A Case Study of China’s Liaoning Province |
| title_full_unstemmed | Spatial-Temporal Variation of Population Aging: A Case Study of China’s Liaoning Province |
| title_short | Spatial-Temporal Variation of Population Aging: A Case Study of China’s Liaoning Province |
| title_sort | spatial temporal variation of population aging a case study of china s liaoning province |
| url | http://dx.doi.org/10.1155/2020/5436061 |
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