基于地理探测器和GWR模型的中国重点镇布局定量归因

doi:10.18306/dlkxjz.2020.10.008

Abstract

Abstract:

National key towns are the leader in the development of small towns. The formation of scientific and reasonable national key town layouts is of great significance for optimizing China's urbanization strategy. Taking the 1887 and 3675 national key towns published in 2004 and 2014 respectively as samples, this study used geodector and the geographically weighted regression (GWR) model to explore the changing characteristics of their distribution and analyze the influencing factors of national key town layouts and effects at the prefecture level. The results show that: 1) After the adjustment by the government, the balance of the layout and construction effect of national key towns is enhanced. The main gathering areas moved westward and northward, the distribution of cold spots and hotspots break through the "Hu Huanyong Line", and the degree of differentiation of economic radiation effect is weakened, reflecting the powerful influence of policy factors. In addition to the inter-county balance and regional preferential policies, the distribution of national key towns is also significantly affected by factors such as altitude, road network density, and resident population urbanization rate. 2) The combination of the factor detector, the GWR model, and the interaction detector can more accurately characterize the mode of action, direction, path, and intensity of the influencing factors. The layout of national key towns is not the result of the uniform, independent, and direct effect of the five significant factors, but the product of the synergistic effect of the interaction of the factors with spatial heterogeneity. 3) The synergy between the inter-county balance policy and other factors is the dominant force in the formation of the existing layout of the key towns; the effect of the regional preferential policy is overall positive, but the target areas need to be more accurately identified.

Key words: national key towns, quantitative attribution, Geodetector, geographically weighted regression, China

HAN Jing, RUI Yang, YANG Kun, LIU Wei, MA Teng. Quantitative attribution of national key town layout based on geodetector and the geographically weighted regression model[J].PROGRESS IN GEOGRAPHY, 2020, 39(10): 1687-1697.

Figures/Tables 9

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References 30

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样本	变量	重点镇认定前				重点镇认定后			DIFF(FU)-DIFF(BL)
样本	变量	处理组	控制组	差分值		处理组	控制组	差分值	DIFF(FU)-DIFF(BL)
2004年公布的全国重点镇	GDP	76.10	38.24	37.86^**		290.45	149.31	141.14^***	103.28
2004年公布的全国重点镇	非农就业率	47.21	25.80	21.41^*		84.61	41.14	43.47^**	22.06
2014年增补调整后的全国重点镇	GDP	39.82	2.09	37.73^*		30.5	26.07	4.43^*	-33.30
2014年增补调整后的全国重点镇	非农就业率	71.06	53.78	17.27^**	70.03		51.23	18.80^**	1.53

影响因子维度	自变量名称	计算方法	单位	预期作用方向
政策	县际均衡政策	统计年鉴获取	个	+
政策	区域倾斜政策	否=0;是=1	—	+
地理	海拔高度	ArcGIS栅格统计	m	-
	地形起伏度	ArcGIS栅格统计	m	-
	到省会城市中心距离	ArcGIS邻域分析	km	-
	公路网密度	ArcGIS密度分析	km/km²	+
经济	人均GDP	统计年鉴获取	万元	+
	二三产业产值比重	二三产业产值/GDP	%	+
	地方一般预算收入	统计年鉴获取	万元	+
	全社会固定资产投资	统计年鉴获取	万元	+
社会	人口密度	常住人口/行政区面积	人/km²	+
	非农化率	从事二三产业人口/总人口	%	+
	常住人口城镇化率	常住人口/总人口	%	+

影响因子维度	自变量名称	P值	显著性水平	q值	解释力排序
政策	县际均衡政策	<0.001	0.01	0.506	1
政策	区域倾斜政策	<0.001	0.01	0.038	4
地理	海拔高度	<0.001	0.01	0.082	3
	地形起伏度	0.101	—	0.061
	到省会城市中心距离	0.502	—	0.015
	公路网密度	0.004	0.01	0.139	2
经济	人均GDP	0.945	—	0.006
	二三产业产值比重	0.325	—	0.027
	地方一般预算收入	0.993	—	0.004
	全社会固定资产投资	0.992	—	0.003
社会	人口密度	0.382	—	0.001
	非农化率	0.464	—	0.058
	常住人口城镇化率	0.093	0.1	0.028	5

q=A∩B	A+B	比较结果	交互作用产生效应的类型	交互作用后的解释力排序
S₁∩S₂=0.536	S₁(0.506)+S₂(0.038)=0.544	A+B>q>A, B	双因子增强	3
S₁∩S₃=0.629	S₁(0.506)+S₃(0.082)=0.588	q>A+B	非线性增强	2
S₁∩S₄=0.646	S₁(0.506)+S₄(0.139)=0.645	q>A+B	非线性增强	1
S₁∩S₅=0.527	S₁(0.506)+S₅(0.028)=0.534	A+B>q>A, B	双因子增强	4
S₂∩S₃=0.096	S₂(0.038)+S₃(0.082)=0.120	A+B>q>A, B	双因子增强	9
S₂∩S₄=0.184	S₂(0.038)+S₄(0.139)=0.177	q>A+B	非线性增强	8
S₂∩S₅=0.060	S₂(0.038)+S₅(0.028)=0.066	A+B>q>A, B	双因子增强	10
S₃∩S₄=0.278	S₃(0.082)+S₄(0.139)=0.221	q>A+B	非线性增强	5
S₃∩S₅=0.197	S₃(0.082)+S₅(0.028)=0.110	q>A+B	非线性增强	7
S₄∩S₅=0.252	S₄(0.139)+S₅(0.028)=0.167	q>A+B	非线性增强	6

Quantitative attribution of national key town layout based on geodetector and the geographically weighted regression model

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