吉林省人口老龄化时空分异特征及成因

doi:10.18306/dlkxjz.2018.03.006

摘要/Abstract

摘要：

本文基于地理信息软件ArcGIS 10.1,选取老年人口系数为人口老龄化指标,运用人口重心模型、空间自相关和半变异函数等分析方法,对吉林省第四次、第五次、第六次人口普查数据进行分析,探讨1990-2010年吉林省人口老龄化时空分异特征,并采用多元线性回归方法解释其形成原因。研究结果表明：①吉林省各县市人口老龄化差异明显,进程加快,重心由西北向东南方向移动,但移动速度减缓;②吉林省人口老龄化存在正的空间自相关性,在空间的集聚呈现先弱后强的变化趋势;③人口老龄化高—高区向东部集聚,低—低区向西部集聚的趋势比较明显;④总体上吉林省人口老龄化空间的结构化分异较为明显,随机性因子引起的空间异质性程度较弱,人口老龄化的空间差异主要体现在东—西方向上;⑤出生率始终是影响吉林省人口老龄化的主要因素,迁出率对人口老龄化发展起关键作用,人均GDP年对人口老龄化呈负相关,城市化率对人口老龄化的推动作用愈发显著。

关键词: 人口老龄化, 老年人口系数, 时空分异, 影响因素, 吉林省

Abstract:

Rapid population aging has a broad and profound impact on the sustainable development of China's economy, and the country has implemented some policies and measures to tackle the problem of rapid population aging. Using ArcGIS 10.1, this study analyzed the spatiotemporal differentiation and influencing factors of population aging in Jilin Province by selecting the coefficient of aged population as the indicator of aging population. Data from the fourth, fifth, and sixth population census of Jilin Province were analyzed by using quantitative methods such as population gravity model, spatial autocorrelation, semi-variant function, and multiple linear regression. The results indicate that: (1) The difference of population aging in the counties and cities of Jilin Province is significant, and the progress is accelerating. The barycenter of population aging in had been moving from northwest to southeast of the province, but at a decreasing speed; (2) Through global spatial autocorrelation analysis, the population aging of Jilin Province presents a positive and enhancing spatial autocorrelation characteristics and trend; (3) Local spatial autocorrelation analysis indicates that the high-value areas of population aging cluster in the east of the province and low-value areas cluster in the west; (4) The spatiotemporal differentiation of aging in Jilin Province was mainly caused by structural factors, while random factors contributed less to the overall variation. In the other words, the spatial heterogeneity caused by spatial autocorrelation is more intense than that caused by random factors in the population aging. The eastern-western difference is the primary contributor to the spatial differentiation of population aging. (5) The main structural factors influencing population aging in the province were birth rate, per capita GDP, migration rate, and urbanization rate. The birth rate and per capita GDP factors have negative impacts on the population aging, however, migration rate and urbanization rate have positive impacts on population aging. Birth rate is the leading factor, and migration rate plays a key role in prompting population aging. Urbanization rate is increasingly more significant for population aging.

Key words: population aging, coefficient of aging population, spatiotemporal differentiation, influencing factors, Jilin Province

梅林, 郭艳花, 陈妍. 吉林省人口老龄化时空分异特征及成因[J]. 地理科学进展, 2018, 37(3): 352-362.

Lin MEI, Yanhua GUO, Yan CHEN. Spatiotemporal characteristics and influencing factors of population aging in Jilin Province[J]. PROGRESS IN GEOGRAPHY, 2018, 37(3): 352-362.

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模型	非标准化系数		标准化系数	t	P	共线性统计量
模型	B		标准化系数	t	标准误差	容差	VIF
a₀	1.158	0.430		2.696	0.010
X₁	-0.106	0.042	-0.252	-2.546	0.015^**	0.820	1.219
X₂	0.295	0.073	0.478	4.026	0.000^***	0.569	1.758
X₃	0.041	0.019	0.258	2.201	0.034^**	0.583	1.716
X₄	0.041	0.060	0.123	0.689	0.495	0.251	3.990
X₅	-0.024	0.054	-0.065	-0.437	0.664	0.363	2.756
X₆	-0.052	0.033	-0.304	-1.566	0.125	0.213	4.694
X₇	0.036	0.011	0.354	3.404	0.002^***	0.744	1.345
X₈	-0.007	0.023	-0.060	-0.308	0.760	0.210	4.768

年份	Moran's I	E(I)	Z(I)	P值
1990年	0.462	-0.021	4.750	<0.001
2000年	0.416	-0.021	4.737	<0.001
2010年	0.480	-0.021	5.336	<0.001

参数	全方位			南—北		东北—西南			东—西			东南—西北
参数	D		R²		D	R²		D	R²	D	R²	D	R²
1990年	1.802	0.754		1.888	0.214	1.932	0.104		1.646	0.776		1.905	0.469
2000年	1.742	0.926		1.826	0.213	1.573	0.892		1.787	0.810		1.390	0.676
2010年	1.834	0.779		1.830	0.421	1.744	0.447		1.721	0.938		1.899	0.229

年份		1990年	2000年	2010年
变程/km	A	203.5	281.4	655.3
块金值	C₀	0.0146	0.0001	0.0233
基台值	C₀+C	0.0788	0.0384	0.1592
块金系数	C₀ /(C₀+C)	0.1852	0.0026	0.1463
拟合模型	Model	球体模型	指数模型	高斯模型
决定系数	R²	0.865	0.988	0.955

模型	非标准化系数		标准化系数	t	P	共线性统计量
模型	B		标准化系数	t	标准误差	容差	VIF
a₀	4.039	0.641		6.298	0.000
X₁	-0.298	0.108	-0.373	-2.771	0.009^***	0.466	2.146
X₂	-0.054	0.057	-0.098	-0.942	0.352	0.777	1.286
X₃	0.065	0.034	0.208	1.908	0.064^*	0.713	1.403
X₄	-0.162	0.068	-0.316	-2.387	0.022^**	0.482	2.075
X₅	-0.069	0.069	-0.202	-1.009	0.319	0.209	4.777
X₆	0.147	0.053	0.431	2.745	0.009^***	0.342	2.921
X₇	0.033	0.024	0.176	1.397	0.170	0.534	1.872
X₈	0.017	0.047	0.064	0.366	0.716	0.278	3.601