Spatiotemporal differentiation and influencing mechanism of urban land development intensity in the Yangtze River Delta

Qingke YANG; Xuejun DUAN; Zhifeng JIN; Lei WANG; Yazhu WANG

doi:10.18402/resci.2020.04.11

Journals >Resources Science >Volume 42 >Issue 4 >Page 723 > Article

Resources Science
Vol. 42, Issue 4, 723 (2020)

Spatiotemporal differentiation and influencing mechanism of urban land development intensity in the Yangtze River Delta

Qingke YANG¹, Xuejun DUAN^2、*, Zhifeng JIN^3、4, Lei WANG², and Yazhu WANG²

Author Affiliations

¹School of Public Administration, Nanjing University of Finance & Economics, Nanjing 210023, China

²Nanjing Institute of Geography and Limnology, CAS, Nanjing 210008, China

³School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China

⁴Jiangsu Institute of Land Surveying and Planning, Nanjing 210017, China

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DOI: 10.18402/resci.2020.04.11 Cite this Article

Qingke YANG, Xuejun DUAN, Zhifeng JIN, Lei WANG, Yazhu WANG. Spatiotemporal differentiation and influencing mechanism of urban land development intensity in the Yangtze River Delta[J]. Resources Science, 2020, 42(4): 723 Copy Citation Text

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Fig. 1. A theoretical framework of urban land development intensity

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Fig. 2. Spatial distribution of urban land development intensity

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Fig. 3. Local indicators of spatial association (LISA) of urban land development intensity

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类型	指标选取	测算方法	作用方向
自然本底条件	可开发用地比重（Dland）/%	运用空间减法来计算：可开发用地比重= 1 - 不可建设土地面积（包括不可建设的湖泊河流水面、不可建设的陡岩石区等）比重-基本农田保护面积比重-已开发用地比重	+/-
社会发展水平	城镇化率（DUrban）/%	城镇人口/常住总人口	+
社会发展水平	交通可达性（Traffic）/km	依靠主要交通干线,各评价单元到中心城市（上海、南京、杭州、苏州、宁波）的时间和空间距离,具体测算方法参照文献[34]。	+
经济发展动力	人均GDP（PGDP）/(万元/人)	人均地区生产总值	+
	地均固定资产投资（FInvest）/(万元/km²)	固定资产投资总额/城市土地面积	+
	产业发展高级度（Indust）	高级度指数（IH）= 用于度量第二、三产业相对第一产业转移的效应（θ₁）+ 第二产业向第三产业转移的效应（θ₂）^[25]	-
城市竞争能力	地均财政支出（Finance）/(万元/km²)	一般公共预算支出/城市土地面积	+
城市竞争能力	城市行政等级（Admin）	作为虚拟变量,按照城市行政级别高低,对县（县级市）、地级市、副省级、直辖市城市分别赋值1、2、3、4	+

Table 1. Factors affecting urban land development intensity

指标	2000	2005	2010	2015
Moran’s I	0.439	0.340	0.397	0.394
Z(I)	36.549	31.926	34.525	34.082
E(I)	0.003	0.002	0.002	0.002

Table 2. Estimation of Moran’s I of urban land development intensity

	Dland	DUrban	Traffic	PGDP	FInvest	Indust	Finance	Admin
Dland	1
DUrban	0.069	1
Traffic	0.164	0.154	1
PGDP	0.135	0.522	0.164	1
FInvest	-0.106	0.019	0.026	0.161	1
Indust	0.347	0.521	0.033	0.179	0.287	1
Finance	-0.403	0.147	-0.114	0.201	0.081	0.158	1
Admin	0.137	0.168	0.084	0.073	0.260	0.142	0.047	1

Table 3. Coefficient matrix of influencing factors

自变量	2000		2005		2010		2015
自变量	系数	Sig.	系数	Sig.	系数	Sig.	系数	Sig.
常数项	-0.048	0.166	-0.101	0.122	-0.155	0.040	-0.188	0.010
Dland	0.154	0.000	0.104	0.024	0.096	0.106	0.079	0.225
Durban	0.163	0.035	0.126	0.074	0.204	0.001	0.065	0.312
Traffic	0.125	0.046	0.145	0.075	0.215	0.003	0.226	0.004
PGDP	0.787	0.011	1.121	0.000	0.518	0.000	0.495	0.000
Finvest	0.973	0.047	0.435	0.025	0.425	0.003	0.343	0.001
Indust	0.079	0.477	0.134	0.312	0.266	0.082	0.218	0.059
Finance	-0.150	0.790	0.053	0.883	-0.369	0.020	-0.004	0.963
Admin	0.102	0.019	0.054	0.317	0.187	0.000	0.224	0.000
R²	0.678		0.625		0.712		0.652
调整R²	0.614		0.529		0.681		0.608
F-statistics	19.183		27.308		39.344		32.732
Sig.	0.000		0.000		0.000		0.000

Table 4. Regression results of oridinary least squares (OLS) model

自变量	2000			2005			2010			2015
自变量	Model 1	Model 2	Model 3	Model 1	Model 2	Model 3	Model 1	Model 2	Model 3	Model 1	Model 2	Model 3
常数项	-0.207	-0.148**	-0.219	-0.020	-0.146	-0.316*	-0.215	-0.229*	-0.141**	-0.318	-0.331*	-0.379**
Dland	0.135***	0.155**	0.138***	-0.015	0.170***	0.130**	-0.025*	0.316**	0.115**	0.071	0.198**	0.086
Durban	0.212***		0.171**	0.317***		0.132*	0.361***		0.209***	0.196**		0.072**
Traffic	0.173**		0.124**	0.355***		0.151*	0.351***		0.218**	0.324***		0.231**
PGDP		1.007**	0.769**		1.338**	1.127**		0.661***	0.515*		0.563**	0.501
Finvest		0.926*	0.937*		0.429***	0.431**		0.503**	0.441**		0.313**	0.348
Indust		0.222**	0.092		0.186	0.140		0.515**	0.302**		0.299**	0.224**
Finance	0.510		0.164	0.208*		0.059	0.028**		0.089***	0.108*		0.012
Admin	0.103*	0.124	0.102	0.115*	0.097**	0.058	0.200**	0.163**	0.188	0.348**	0.209***	0.228***
Lambda	-0.233	-0.181	-0.150	-0.422**	-0.017	-0.001	-0.239	-0.204	-0.224	-0.142	-0.117	-0.014
R²	0.608	0.606	0.653	0.535	0.693	0.723	0.662	0.718	0.788	0.767	0.722	0.755
LOG L	106.686	106.549	112.252	70.896	90.347	94.935	75.818	84.107	97.158	90.769	78.165	84.077
LR Test	0.237	-0.013	-0.006	0.232	0.039	0.045	0.279	-0.011	-0.021	0.004	0.007	0.040
AIC	-201.602	-193.202	-207.650	-130.939	-169.841	-173.015	-140.781	-157.360	-177.463	-106.684	-145.475	-151.300
SC	-188.002	-187.803	-185.990	-116.499	-155.401	-151.355	-126.341	-142.919	-155.802	-92.244	-131.035	-129.640

Table 5. Regression results of structural equation modeling (SEM)

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