新疆物流企业空间布局多尺度演化特征及影响因素研究

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Multi-scale evolution characteristics and influencing factors of spatial layout of logistics enterprises in Xinjiang

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doi:10.12118/j.issn.1000-6060.2024.404

Abstract:

The National Development and Reform Commission’s “14th Five-Year Plan for the Construction of a Modern Circulation System” emphasizes “improving the regional logistics service network”, while the State Council’s “Overall Plan for the China (Xinjiang) Pilot Free Trade Zone” proposes “building a comprehensive logistics hub connecting Europe and Asia”. Therefore, studying the spatial layout of Xinjiang logistics enterprises is of great strategic significance to improving the regional logistics service network and building the Xinjiang Pilot Free Trade Zone. Using the geographic coordinate data of Xinjiang logistics enterprises from 1992 to 2022, this study examines the geographical spatial distribution across different scales. This study explores the spatial heterogeneity of formation mechanisms and influencing factors at county and grid levels. The results indicate that: (1) Xinjiang logistics enterprises exhibit agglomeration, forming an overall spatial pattern of a “single axis, single main core, and multiple sub-cores”, while locally developing into a primary core with multiple sub-cores.(2) At the county level, logistics enterprise hotspots and sub-hotspots remain concentrated in northern Xinjiang, constrained by the northern slope of the Tianshan Mountains. However, at a finer grid scale, the sub-hotspots eventually expand beyond this geographical barrier. (3) E-commerce parks, comprehensive bonded zones, and population density significantly influence the spatial layout of logistics enterprises across different scales. Additionally, natural environmental factors can interact nonlinearly and should not be overlooked. (4) Xinjiang should prioritize logistics park development, enhance port functionality, and drive economic growth. In response to local influencing factors, such as e-commerce, industrial parks, ports, and comprehensive bonded zones, logistics plans should be expressed based on local conditions. The research deepened the enterprise location theory and provided theoretical support and practical guidance for the construction of Xinjiang’s Eurasian logistics hub and the core area of the Silk Road Economic Belt.

Key words: logistics enterprises, multi scale, spatial layout, evolutionary characteristics, influencing factors, Xinjiang

LI Songrui, LIN Qiuping, YANG Shangguang. Multi-scale evolution characteristics and influencing factors of spatial layout of logistics enterprises in Xinjiang[J].Arid Land Geography, 2025, 48(4): 739-752.

Fig. 1

Tab. 1

Fig. 2

Tab. 2

Fig. 3

Tab. 3

Tab. 4

Interaction detection results of different influencing factors"

因素	县域	网格	因素	县域	网格	因素	县域	网格	因素	县域	网格
X₁∩X₂	BE	BE	X₃∩X₅	BE	BE	X₅∩X₁₂	BE	BE	X₈∩X₁₅	NE	NE
X₁∩X₃	BE	BE	X₃∩X₆	BE	BE	X₅∩X₁₃	BE	BE	X₈∩X₁₆	NE	NE
X₁∩X₄	BE	BE	X₃∩X₇	BE	BE	X₅∩X₁₄	BE	NE	X₉∩X₁₀	BE	BE
X₁∩X₅	BE	BE	X₃∩X₈	BE	NE	X₅∩X₁₅	BE	NE	X₉∩X₁₁	BE	BE
X₁∩X₆	BE	BE	X₃∩X₉	BE	BE	X₅∩X₁₆	NE	BE	X₉∩X₁₂	BE	BE
X₁∩X₇	BE	BE	X₃∩X₁₀	BE	BE	X₆∩X₇	BE	BE	X₉∩X₁₃	BE	BE
X₁∩X₈	NE	NE	X₃∩X₁₁	BE	BE	X₆∩X₈	BE	NE	X₉∩X₁₄	NE	NE
X₁∩X₉	BE	BE	X₃∩X₁₂	BE	BE	X₆∩X₉	BE	BE	X₉∩X₁₅	NE	NE
X₁∩X₁₀	BE	BE	X₃∩X₁₃	BE	BE	X₆∩X₁₀	BE	BE	X₉∩X₁₆	NE	NE
X₁∩X₁₁	BE	BE	X₃∩X₁₄	NE	NE	X₆∩X₁₁	BE	BE	X₁₀∩X₁₁	BE	BE
X₁∩X₁₂	BE	BE	X₃∩X₁₅	BE	NE	X₆∩X₁₂	BE	BE	X₁₀∩X₁₂	BE	BE
X₁∩X₁₃	BE	BE	X₃∩X₁₆	NE	BE	X₆∩X₁₃	BE	BE	X₁₀∩X₁₃	BE	BE
X₁∩X₁₄	NE	NE	X₄∩X₅	BE	BE	X₆∩X₁₄	BE	NE	X₁₀∩X₁₄	NE	NE
X₁∩X₁₅	NE	NE	X₄∩X₆	BE	BE	X₆∩X₁₅	BE	NE	X₁₀∩X₁₅	NE	NE
X₁∩X₁₆	NE	BE	X₄∩X₇	BE	BE	X₆∩X₁₆	BE	NE	X₁₀∩X₁₆	NE	NE
X₂∩X₃	BE	BE	X₄∩X₈	BE	NE	X₇∩X₈	BE	NE	X₁₁∩X₁₂	BE	BE
X₂∩X₄	BE	BE	X₄∩X₉	BE	BE	X₇∩X₉	BE	BE	X₁₁∩X₁₃	BE	BE
X₂∩X₅	BE	BE	X₄∩X₁₀	BE	BE	X₇∩X₁₀	BE	BE	X₁₁∩X₁₄	BE	NE
X₂∩X₆	BE	BE	X₄∩X₁₁	BE	BE	X₇∩X₁₁	BE	BE	X₁₁∩X₁₅	NE	BE
X₂∩X₇	BE	BE	X₄∩X₁₂	BE	BE	X₇∩X₁₂	BE	BE	X₁₁∩X₁₆	NE	BE
X₂∩X₈	BE	NE	X₄∩X₁₃	BE	BE	X₇∩X₁₃	BE	BE	X₁₂∩X₁₃	BE	BE
X₂∩X₉	BE	BE	X₄∩X₁₄	BE	NE	X₇∩X₁₄	BE	NE	X₁₂∩X₁₄	NE	NE
X₂∩X₁₀	BE	BE	X₄∩X₁₅	BE	NE	X₇∩X₁₅	BE	NE	X₁₂∩X₁₅	BE	BE
X₂∩X₁₁	BE	BE	X₄∩X₁₆	NE	BE	X₇∩X₁₆	NE	NE	X₁₂∩X₁₆	NE	BE
X₂∩X₁₂	BE	BE	X₅∩X₆	BE	BE	X₈∩X₉	BE	NE	X₁₃∩X₁₄	NE	BE
X₂∩X₁₃	BE	BE	X₅∩X₇	BE	BE	X₈∩X₁₀	BE	NE	X₁₃∩X₁₅	BE	BE
X₂∩X₁₄	NE	NE	X₅∩X₈	BE	NE	X₈∩X₁₁	BE	NE	X₁₃∩X₁₆	NE	BE
X₂∩X₁₅	NE	BE	X₅∩X₉	BE	BE	X₈∩X₁₂	BE	NE	X₁₄∩X₁₅	NE	NE
X₂∩X₁₆	NE	BE	X₅∩X₁₀	BE	BE	X₈∩X₁₃	BE	NE	X₁₄∩X₁₆	NE	NE
X₃∩X₄	BE	BE	X₅∩X₁₁	BE	BE	X₈∩X₁₄	BE	NE	X₁₅∩X₁₆	NE	NE

Tab. 4

Tab. 5

Fig. 4

Tab. 6

Fig. 5

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年份	莫兰指数	Z值	P值	年份	莫兰指数	Z值	P值
1992	0.214	3.783	0.000	2008	0.254	4.785	0.000
1994	0.194	3.436	0.001	2010	0.268	5.056	0.000
1996	0.233	4.142	0.000	2012	0.240	4.552	0.000
1998	0.139	2.518	0.012	2014	0.212	3.993	0.000
2000	0.122	2.209	0.027	2016	0.139	2.572	0.010
2002	0.141	2.552	0.011	2018	0.129	2.377	0.017
2004	0.193	3.479	0.001	2020	0.161	2.918	0.004
2006	0.221	4.041	0.000	2022	0.186	3.363	0.001

年份	莫兰指数	Z值	年份	莫兰指数	Z值
1992	0.167	7.290	2008	0.131	6.017
1994	0.161	7.103	2010	0.138	6.219
1996	0.151	6.691	2012	0.141	6.212
1998	0.139	5.839	2014	0.136	5.968
2000	0.138	5.620	2016	0.129	5.423
2002	0.132	5.441	2018	0.134	5.581
2004	0.130	5.577	2020	0.141	5.944
2006	0.127	5.574	2022	0.141	6.072

一级指标	二级指标	单位	符号	县域q值	网格q值
集聚因素	物流企业距离	km	X₁	0.050	0.114^***
	批发市场距离	km	X₂	0.104^**	0.196^***
	工业园区距离	km	X₃	0.227^***	0.215^***
	农业园区距离	km	X₄	0.272^***	0.204^***
	电商园区距离	km	X₅	0.322^***	0.352^***
经济因素	经济发展水平（灯光数据均值）	-	X₆	0.439^***	0.209^***
	人口密度	人·km^-2	X₇	0.448^***	0.301^***
交通因素	交通干线可达性（5级）	-	X₈	0.171^***	0.006
	火车站距离	km	X₉	0.192^***	0.178^***
	机场距离	km	X₁₀	0.142^***	0.149^***
政策因素	物流园区距离	km	X₁₁	0.282^***	0.256^***
	口岸距离	km	X₁₂	0.292^***	0.210^***
	综合保税区距离	km	X₁₃	0.412^***	0.317^***
自然环境因素	山地面积占比	%	X₁₄	0.031	0.011
	沙漠、戈壁面积占比	%	X₁₅	0.101^**	0.032^**
	年结冰时间占比	%	X₁₆	0.060	0.077^***

变量	OLS模型		GWR模型		MGWR模型
变量	系数	VIF	均值	带宽	均值	带宽
常数项	-	-	-0.013^**	53	-0.047^***	44
电商园区距离	-0.177^**	2.02	0.024^**	53	0.023^**	79
经济发展水平	0.404^***	3.95	0.079^**	53	0.236^**	104
人口密度	0.155	3.80	0.300^**	53	0.040^**	106
口岸距离	0.142	3.38	0.283^**	53	0.397^***	44
综合保税区距离	-0.374^***	3.82	-0.771^**	53	-0.918^***	44
AICc	222.344		153.930		132.178
RSS	43.077		16.784		15.134
R²	0.601		0.845		0.860
调整后R²	0.582		0.812		0.837

变量	OLS模型		GWR模型		MGWR模型
变量	系数	VIF	均值	带宽	均值	带宽
常数项	-	-	-0.896^***	53	-1.063^***	43
工业园区距离	-0.130^*	3.66	0.026^***	53	0.104^***	43
电商园区距离	-0.277^***	4.42	-0.393^***	53	-0.257^***	43
人口密度	0.440^***	1.16	0.242^***	53	-0.175^***	43
物流园区距离	0.274^***	6.98	-0.159^***	53	-0.331^***	43
综合保税区距离	-0.260^***	3.50	-0.689^***	53	-0.828^***	43
AICc	846.484		207.262		154.745
RSS	206.053		24.225		21.382
R²	0.442		0.934		0.942
调整后R²	0.434		0.920		0.930

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[1]	LI Weilu, ZHANG Mingdou. Coordinated study of urban population agglomeration and land ecological resilience in the Yellow River Basin [J]. Arid Land Geography, 2025, 48(4): 728-738.
[2]	YANG Xiaoya, YU Kunxia, LI Zhanbin, LI Peng, LIU Yonggang, MO Shuhong, YANG Jianhong. Regional differences and threshold of ecological base flow in the Qinling Mountains-Loess Plateau region [J]. Arid Land Geography, 2025, 48(3): 380-390.
[3]	WANG Fuhong, XIA Yong. Spatio-temporal pattern evolution and influencing factors of main crops production in arid region: A case of Xinjiang [J]. Arid Land Geography, 2025, 48(3): 444-454.
[4]	ZHANG Jie, QU Jianjun, CHEN Hai, SHI Jinxin, MA Yuhe, LIU Di. Spatial and temporal evolution and influencing factors of ecological security pattern based on circuit theory: A case of Loess Plateau in northern Shaanxi [J]. Arid Land Geography, 2025, 48(3): 494-505.
[5]	FU Xiao, HUANG Yingmin. Spatial pattern and influencing factors of high-quality development in China’s old revolutionary areas from the perspective of main functions [J]. Arid Land Geography, 2025, 48(3): 517-527.
[6]	LI Mengyuan, PANG Jiapeng, LI Huan. Coordinated development of comprehensive accessibility of provincial typical tourism distribution centers and their spatial relationship: A case of Xinjiang [J]. Arid Land Geography, 2025, 48(3): 539-548.
[7]	ZHU Yiting, ZHAO Chen, CAO Kaijun, LIANG Guixian, YAO Hao. Influencing factors of recreation satisfaction in national mining park [J]. Arid Land Geography, 2025, 48(3): 549-558.
[8]	Jianiya YERKEN, HOU Jiannan, LIU Sibo. Spatio-temporal characterization of tourism climate comfort in Xinjiang prefectures and cities in the last 30 years [J]. Arid Land Geography, 2025, 48(2): 212-222.
[9]	TANG Yandong, ZANG Cuiping, YU Yunpeng, YU Qinglin. Developmental characteristics and influencing factors of gullies based on fractal theory in eastern Gansu Province [J]. Arid Land Geography, 2025, 48(2): 223-233.
[10]	GUO Jiali, DU Hongru. Network pattern of oasis cities in Xinjiang from the perspective of multiple flows [J]. Arid Land Geography, 2025, 48(2): 323-332.
[11]	LI Gang. Practice of high quality development in Xinjiang: Construction and measurement of evaluation system [J]. Arid Land Geography, 2025, 48(1): 143-152.
[12]	LIU Hui, SUN Siao, FANG Chuanglin, ZHOU Di, BAO Chao. Spatiotemporal evolutionary patterns and influencing factors of water use in Xinjiang from 1990 to 2020 [J]. Arid Land Geography, 2024, 47(9): 1451-1461.
[13]	ZHANG Lu, SUN Meiping, YAN Xin, WANG Weisheng, FAN Ruiyi. Investigation and evaluation of surface solar radiation on the north slope of Kunlun Mountains based on multi-source data [J]. Arid Land Geography, 2024, 47(8): 1304-1313.
[14]	ZHU Lei, LI Yannan, XU Jiahui, HU Jing, ZHU Fang, LIANG Mangmang. Spatial distribution pattern and causes of ice and snow tourism in China [J]. Arid Land Geography, 2024, 47(8): 1399-1410.
[15]	SHI Yudong, WANG Shengjie, ZHANG Mingjun, ZHU Chenggang, CHE Yanjun. Spatial distribution characteristics of stable hydrogen and oxygen isotopes in surface waters on the northern slope of the Kunlun Mountains [J]. Arid Land Geography, 2024, 47(7): 1127-1135.