乌鲁木齐市物流企业区位时空演化、影响因素和发展策略研究

doi:10.12118/j.issn.1000-6060.2023.574

干旱区地理 ›› 2024, Vol. 47 ›› Issue (7): 1252-1262.doi: 10.12118/j.issn.1000-6060.2023.574

• 区域发展 • 上一篇

乌鲁木齐市物流企业区位时空演化、影响因素和发展策略研究

林秋平¹^,²(), 李松芮¹(), 杨上广³, 王云云¹

1.新疆财经大学工商管理学院，新疆乌鲁木齐 830012
2.新疆财经大学新疆企业发展研究中心，新疆乌鲁木齐 830012
3.华东理工大学商学院，上海 200237

收稿日期:2023-10-13 修回日期:2023-11-07 出版日期:2024-07-25 发布日期:2024-07-30
通讯作者: 李松芮（1994-），男，硕士研究生，主要从事物流与供应链管理研究. E-mail: lisongrui2021@163.com
作者简介:林秋平（1981-），硕士，女，副教授，主要从事物流产业经济研究. E-mail: lqpxjcd@foxmail.com
基金资助:
国家社科基金面上项目(20BGL020);新疆维吾尔自治区普通高等学校人文社会科学基地新疆企业发展研究中心项目(XJEDU2023J007);新疆财经大学研究生科研创新项目(XJUFE2023K048)

Spatiotemporal evolution, influencing factors, and development strategies of logistics enterprise location in Urumqi City

LIN Qiuping¹^,²(), LI Songrui¹(), YANG Shangguang³, WANG Yunyun¹

1. College of Business Administration, Xinjiang University of Finance & Economics, Urumqi 830012, Xinjiang, China
2. Xinjiang Enterprise Development Research Center, Xinjiang University of Finance & Economics, Urumqi 830012, Xinjiang, China
3. Business School, East China University of Science and Technology, Shanghai 200237, China

Received:2023-10-13 Revised:2023-11-07 Published:2024-07-25 Online:2024-07-30

摘要/Abstract

摘要：

物流企业的空间布局不仅可以改变现有的物流组织形式，对重塑地区的产业空间格局也会产生重大影响。基于2006—2022年乌鲁木齐市物流企业空间数据，在对乌鲁木齐市物流企业区位时空演变分析的基础上，运用地理探测器和多尺度地理加权回归进一步探究物流企业区位选择的影响因素及其空间异质性，并提出优化物流企业空间布局的发展策略。结果表明：（1）乌鲁木齐市物流企业存在集聚分布特征，呈现出由“一主轴、一核心”向“三主核、两副核”演变的空间格局。（2）乌鲁木齐市地区间物流企业发展存在显著的正向溢出效应。（3）物流企业区位选择的影响因素中，物流企业数解释力度的均值为66%、地区GDP为48%、人口密度为49%、物流园区距离为28%；其中物流企业数和物流园区距离是空间异质性因素且其系数在空间上变化较大，地区GDP是具有正向影响的全局变量，人口密度是具有负向影响的全局变量。研究结果不仅拓展了企业区位理论的研究，而且丰富了研究案例，同时可以为乌鲁木齐市进行物流产业规划和高质量建设丝绸之路经济带商贸物流中心提供理论依据和实践参考。

关键词: 物流企业, 空间演变, 多尺度地理加权回归, 影响因素, 乌鲁木齐市

Abstract:

The spatial layout of logistics enterprises can change the existing form of logistics organization and significantly affect the reshaping of the industrial spatial pattern of a region. Based on the spatial data of logistics enterprises in Urumqi City of Xinjiang, China, from 2006 to 2022, and based on the analysis of the spatiotemporal evolution of logistics enterprise locations in Urumqi, this study explores the influencing factors and spatial heterogeneity of logistics enterprise location selection using geographic detectors and multiscale geographically weighted regression and proposes development strategies to optimize the spatial layout of logistics enterprises. Research shows that: (1) Logistics enterprises in Urumqi have agglomeration distribution characteristics, presenting a spatial pattern of evolution from “one main axis, one core” to “three main cores, two secondary cores”. (2) A significant positive spillover effect occurs in developing logistics enterprises between districts in Urumqi City. (3) Among the factors influencing the location selection of logistics enterprises, the average number of logistics enterprises is 66%, the regional GDP is 48%, the population density is 49%, and the distance between logistics parks is 28%. The number of logistics enterprises and the distance between logistics parks are spatial heterogeneity factors, and their coefficients vary significantly. The regional GDP is a global variable with a positive impact, whereas population density is a global variable with a negative impact. The results expand the enterprise location theory and enrich research cases. Simultaneously, it can provide a theoretical basis and practical reference for Urumqi’s logistics industry planning and high-quality Silk Road Economic Belt commercial logistics center construction.

Key words: logistics enterprises, spatial evolution, multi-scale geographical weighted regression, influence factors, Urumqi City

林秋平, 李松芮, 杨上广, 王云云. 乌鲁木齐市物流企业区位时空演化、影响因素和发展策略研究[J]. 干旱区地理, 2024, 47(7): 1252-1262.

LIN Qiuping, LI Songrui, YANG Shangguang, WANG Yunyun. Spatiotemporal evolution, influencing factors, and development strategies of logistics enterprise location in Urumqi City[J]. Arid Land Geography, 2024, 47(7): 1252-1262.

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年份	最近邻比率	Z值
2006	0.157	-73.598
2010	0.158	-78.845
2014	0.166	-91.230
2018	0.154	-102.485
2022	0.127	-130.423

年份	Global Moran’s I	Z值
2006	0.434	36.066
2010	0.473	31.317
2014	0.521	35.568
2018	0.570	36.292
2022	0.602	38.111

维度	指标	单位	符号	定义与解释
交通条件	火车站的距离	km	X₁	到最近火车站的距离（取对数）
	飞机场的距离	km	X₂	到最近飞机场的距离（取对数）
	高速路出入口距离	km	X₃	到最近高速路出入口的距离（取对数）
	道路长度	km	X₄	网格内道路总长度（取对数）
集聚因素	物流企业数	家	X₅	网格内物流企业数量
	工业园区距离	km	X₆	到最近工业园区的距离（取对数）
	农业园区距离	km	X₇	到最近农业园区的距离（取对数）
	科技园区距离	km	X₈	到最近科技园区的距离（取对数）
社会经济	国内生产总值（GDP）	10⁸元·km^-2	X₉	网格内GDP的均值
	人口密度	人·km^-2	X₁₀	网格内人口密度的均值
政府政策	物流园区距离	km	X₁₁	到最近物流园区的距离（取对数）
	保税区距离	km	X₁₂	到最近保税区的距离（取对数）

影响因素		X₁	X₂	X₃	X₄	X₅	X₆	X₇	X₈	X₉	X₁₀	X₁₁	X₁₂
2017—2019年	q值	0.128	0.181	0.111	0.139	0.690	0.124	0.089	0.258	0.521	0.566	0.303	0.168
	排名	9	6	11	7	1	10	12	5	3	2	4	8
2020—2022年	q值	0.100	0.164	0.086	0.094	0.633	0.139	0.073	0.223	0.444	0.420	0.259	0.142
	排名	10	6	11	9	1	8	12	5	2	3	4	7

变量	MGWR模型				OLS模型
	2017—2019年		2020—2022年		2017—2019年		2020—2022年
	Mean	BW	Mean	BW	Coef	VIF	Coef	VIF
物流企业数	1.359^***	55	0.753^***	55	0.576^***	3.16	0.753^***	2.60
GDP	0.289^**	898	0.403^**	973	0.597^***	5.93	0.403^***	6.55
人口密度	-0.221^**	868	-0.377^**	872	-0.316^***	8.47	-0.377^***	5.21
物流园区距离	-0.023^***	57	-0.059^***	57	-0.078^***	1.58	-0.059^***	1.71
RSS	193.508		209.624		282.537		296.065
AICc	1337.050		1415.986		1578.786		1625.135
R²	0.805		0.788		0.715		0.701
调整后R²	0.791		0.773		0.714		0.700

乌鲁木齐市物流企业区位时空演化、影响因素和发展策略研究

Spatiotemporal evolution, influencing factors, and development strategies of logistics enterprise location in Urumqi City

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