中国市级人口增长的多因素空间建模分析

doi:10.12082/dqxxkx.2018.180135

摘要/Abstract

摘要：

本文以中国市域单元为研究对象,利用1990-2010年人口普查数据,采用探索性空间数据分析的方法,分析了过去20 年间中国市级人口增长率的空间分布特征和多变量的空间依赖关系。使用空间计量模型和空间滤波后的经典线性回归模型分别探究了经济、气候、地形、社会文化等因素对中国市级人口增长率的影响。模型对比结果显示,经过空间滤波后的经典线性回归模型能够更好的模拟中国市级人口增长率的变化。在该模型中,经济因素是影响中国城市人口增长率的主要因素,例如代表城市经济发展水平的城市夜光指数密度。气候因素对人口增长率也有着不可忽视的作用,如七月热指数随着等级的提升对人口增长率有着越来越强的负向影响。研究结果表明：人口的区域增长模式是多要素综合作用的结果,在相关建模研究和政策制定中需要重点考虑经济发展水平和气候条件因素对人口增长趋势的不同影响。

关键词: 人口增长, 影响因素, 探索性空间数据分析, 空间自回归, 空间滤波

Abstract:

This paper is aimed at exploring the determinants of population growth in Chinese urban areas. With the method of exploratory spatial data analysis and the data of traditional population census between 1990 and 2010, we could delve into the spatial distribution characteristics of the population growth rate and the multivariable spatial dependency during the past twenty years in Chinese city-level. Based on a thorough interpretation of population data, we are able to discover an existing spatial dependency between different cities. Obviously, spatial relations should not be negligible, because the spatial dependency is much stronger within cities living in shorter distance. It is more reasonable to use spatial regression model for our work, therefore, we use spatial lag regression model, spatial error model and classical linear regression model with spatial filtering to explore the influences of economic factors, climate factors, sociocultural factors and topography factors on population growth rate. It is showed that the classical linear regression model with spatial filtering can simulate the urban population growth rate batter than other models in our outcomes. The findings also suggest that economy is the most pivotal factors in population growth, such as the total amount of economy reflected by density of urban nightlight index plays an important role in driving population growth. Meanwhile other factors are following as well. Climatic variation is another systematic and significant factor affecting the rates of urban population growth. Some weather-related movement appears. People are willing to leave the unpleasant places and move to the places with nice weather. For example, with the increase of July heat index, there is a more and more stronger negative impact on population growth. The research shows that Chinese population growth is a complex question. There is a comprehensive action of multi-factor in generating the model of regional population growth. It is necessary to consider the different effects of economic development and climate conditions on the population growth in the researches on corresponding modeling and formulation of policy.

Key words: population growth, influence factors, exploratory spatial data analysis, spatial regression, spatial filtering

芦蕊, 马廷. 中国市级人口增长的多因素空间建模分析[J]. 地球信息科学学报, 2018, 20(7): 939-946.DOI:10.12082/dqxxkx.2018.180135

LU Rui,MA Ting. Spatially Modeling of Multiple Factors for City-level Population Growth in China[J]. Journal of Geo-information Science, 2018, 20(7): 939-946.DOI:10.12082/dqxxkx.2018.180135

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统计量	值
Moran I statistic	0.338
E(I)	-0.003
Var(I)	0.001
Z(I)	10.850
P-value	0.000

变量名	含义	计算方法
NLDens	城市夜光密度	城市夜光指数之和除以城市面积
NLGrow	城市夜光指数的增长趋势	计算城市20年夜光指数的变化趋势
RLSum	城市道路总里程	城市铺装道路总长度
SIRGR	城市第二产业区位熵	城市第二产业从业人数百分比/城市从业人数占全国从业人数的百分比
TIRGR	城市第三产业区位熵	城市第三产业从业人数百分比/城市从业人数占全国从业人数的百分比
APMean	年降雨量	年平均降水量小于400 mm时值为1,否则值为0
JTMean	城市一月平均气温	1月日平均气温的算数平均值
HI	7月热指数	根据城市7月平均气温（T）和7月相对湿度（H）计算得出
APArea	少数民族自治区域	少数民族自治区域值为1,否则值为0
Patent	专利申请量	无
UENum	大学招生数量	无
CoastC	城市是否临海	临海值为1,否则值为0

解释变量	模型
解释变量	全模型	简化模型
NLDens	0.529***	0.543***
NLGrow	-0.106*	-0.107*
RLSum	0.231?	0.216?
SIRGRs	-0.026	-
TIRGR	0.209***	0.202***
APMean	0.468**	0.449**
JTMean	0.270***	0.274***
HIb	-0.106	-0.105
HIc	-0.545**	-0.535**
HId	-0.587**	-0.587***
APArea	0.280*	0.277*
Patent	-0.182**	-0.179***
UENum	0.167	-
CoastC	0.074	-
F-statistic	28.120***	36.01***
AIC	736.506	731.092

解释变量	空间统计模型
解释变量	LAG模型	SEM模型	SFOLS模型
NLDens	0.479***	0.645***	0.543***
NLGrow	-0.092*	-0.098*	-0.107**
RLSum	0.161	0.037	0.216?
TIRGR	0.219***	0.264***	0.202***
APMean	0.304*	0.368*	0.449***
JTMean	0.237***	0.326***	0.274***
HIb	-0.090	-0.092	-0.105
HIc	-0.439**	-0.511**	-0.535***
HId	-0.488**	-0.572**	-0.587***
APArea	0.226*	0.232?	0.277**
Patent	-0.178***	-0.166**	-0.180***
RHO	0.299	-	-
LAMBDA	-	0.318	-
LR test	20.385***	13.329***	-
ASE	0.061***	0.073***	-
Wald statistic	24.397***	19.165***	-
Log likelihood	-342.353	-345.882	-319.109
ML residual variance	0.421	0.428	-
LM test	12.001(0.000)	-	-
SSR	144.746	147.376	128.773
AIC	712.710	719.760	678.218

TEST	LAG	SEM
df	14.000	14.000
AIC	712.710	719.760
logLik	-342.350	-345.880