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

Abstract

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

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.

Figures/Tables 8

Tab. 1

Fig. 1

Tab. 2

Fig. 2

Tab. 3

Tab. 4

Tab. 5

Fig. 3

References 33

[1]	中国新闻网. 《中国商贸物流发展报告(2022年)》发布[EB/OL]. [2023-06-17]. https://www.chinanews.com/m/cj/2023/06-17/10026870.shtml.
	[China News Network. Release of the China Trade and Logistics Development Report (2022)[EB/OL]. [2023-06-17]. https://www.chinanews.com/m/cj/2023/06-17/10026870.shtml.]
[2]	Julie C. Concentration and decentralization: The new geography of freight distribution in US metropolitan areas[J]. Journal of Transport Geography, 2010, 18(3): 363-371.
[3]	Zhang J. An analysis of spatial distribution characteristics and influential factors of China’s A-Level logistics enterprises[J]. Science, Technology & Public Policy, 2019, 2(2): 273-293.
[4]	Sun B W, Li H M, Zhao Q Y. Logistics agglomeration and logistics productivity in the USA[J]. The Annals of Regional Science, 2018, 61(2): 273-293.
[5]	李国旗, 金凤君, 陈娱, 等. 基于物流热度的中国物流业空间格局[J]. 地理科学进展, 2015, 34(5): 629-637. doi: 10.11820/dlkxjz.2015.05.011
	[Li Guoqi, Jin Fengjun, Chen Yu, et al. Spatial patterns of logistics industry based on a geographic analysis of hotness degree[J]. Progress in Geography, 2015, 34(5): 629-637.] doi: 10.11820/dlkxjz.2015.05.011
[6]	Rivera L, Sheffi Y, Welsch R. Logistics agglomeration in the US[J]. Transportation Research Part A, 2014, 59(11): 222-238.
[7]	Woudsma C, Jakubicek P. Logistics land use patterns in metropolitan Canada[J]. Journal of Transport Geography, 2019, 88: 102381, doi: 10.1016/j.jtrangeo.2019.01.001
[8]	Zhang Y W, Kong J, Zhang Y, et al. Case study of stratification, spatial agglomeration, and unequal logistics industry development on western cities in China[J]. Journal of Urban Planning and Development, 2022, 148(2): 1-13.
[9]	张璐璐, 赵金丽, 宋金平. 京津冀城市群物流企业空间格局演化及影响因素[J]. 经济地理, 2019, 39(3): 125-133.
	[Zhang Lulu, Zhao Jinli, Song Jinping. Spatial evolution and influencing factors of logistics enterprises in Beijing-Tianjin-Hebei urban agglomeration[J]. Economic Geography, 2019, 39(3): 125-133.]
[10]	叶磊, 段学军. 基于物流企业的长三角地区城市网络结构[J]. 地理科学进展, 2016, 35(5): 622-631. doi: 10.18306/dlkxjz.2016.05.009
	[Ye Lei, Duan Xuejun. City network structure of the Yangtze River Delta region based on logistics enterprise network[J]. Progress in Geography, 2016, 35(5): 622-631.] doi: 10.18306/dlkxjz.2016.05.009
[11]	Dablanc L, Ross C. Atlanta: A mega logistics center in the Piedmont Atlantic Megaregion (PAM)[J]. Journal of Transport Geography, 2012, 24: 432-442.
[12]	程秀娟, 李晶晶, 杨洁辉, 等. 河南省物流业空间格局——基于百度地图和面板数据[J]. 人文地理, 2018, 33(5): 114-122.
	[Cheng Xiujuan, Li Jingjing, Yang Jiehui, et al. Spatial patterns of Henan logistics industry based on a geographic analysis of Baidu Maps and panel data[J]. Human Geography, 2018, 33(5): 114-122.]
[13]	陈治亚, 周于轶. 基于POI的物流业空间集聚特征分析——以浙江省为例[J]. 铁道科学与工程学报, 2022, 19(10): 2862-2872.
	[Chen Zhiya, Zhou Yuyi. Analysis of spatial agglomeration characteristics of logistics industry based on POI: Taking Zhejiang Province as an example[J]. Journal of Railway Science and Engineering, 2022, 19(10): 2862-2872.]
[14]	Woudsma C, Jakubicek P, Dablanc L. Logistics sprawl in North America: Methodological issues and a case study in Toronto[J]. Transportation Research Procedia, 2016, 12: 474-488.
[15]	Strale M. Logistics sprawl in the Brussels metropolitan area: Toward a socio-geographic typology[J]. Journal of Transport Geography, 2020, 88: 102372, doi: 10.1016/j.jtrangeo.2018.12.009.
[16]	Heitz A, Dablanc L, Olsson J, et al. Spatial patterns of logistics facilities in Gothenburg, Sweden[J]. Journal of Transport Geography, 2018, 88: 102191, doi: 10.1016/j.jtrangeo.2018.03.005.
[17]	Li G Q, Jin F J, Chen Y, et al. Location characteristics and differentiation mechanism of logistics nodes and logistics enterprises based on points of interest (POI): A case study of Beijing[J]. Journal of Geographical Sciences, 2017, 27(7): 879-896. doi: 10.1007/s11442-017-1411-7
[18]	张大鹏, 曹卫东, 姚兆钊, 等. 上海大都市区物流企业区位分布特征及其演化[J]. 长江流域资源与环境, 2018, 27(7): 1478-1489.
	[Zhang Dapeng, Cao Weidong, Yao Zhaozhao, et al. Study on the distribution characteristics and evolution of logistics enterprises in Shanghai metropolitan area[J]. Resources and Environment in the Yangtze Basin, 2018, 27(7): 1478-1489.]
[19]	千庆兰, 陈颖彪, 李雁, 等. 广州市物流企业空间布局特征及其影响因素[J]. 地理研究, 2011, 30(7): 1254-1261.
	[Qian Qinglan, Chen Yingbiao, Li Yan, et al. Spatial distribution of logistics enterprises in Guangzhou and its influencing factors[J]. Geographical Research, 2011, 30(7): 1254-1261.] doi: 10.11821/yj2011070011
[20]	王瑞, 蒋天颖, 王帅. 宁波市港口物流企业空间格局及区位选择[J]. 地理科学, 2018, 38(5): 691-698. doi: 10.13249/j.cnki.sgs.2018.05.006
	[Wang Rui, Jiang Tianying, Wang Shuai. Spatial pattern and location selection of port logistics enterprises in Ningbo[J]. Scientia Geographica Sinica, 2018, 38(5): 691-698.] doi: 10.13249/j.cnki.sgs.2018.05.006
[21]	朱慧, 周根贵. 国际陆港物流企业空间格局演化及其影响因素——以义乌市为例[J]. 经济地理, 2017, 37(2): 98-105.
	[Zhu Hui, Zhou Gengui. Spatial agglomeration evolution and influencing factors of logistics enterprises in international inland port: A case study of Yiwu City[J]. Economic Geography, 2017, 37(2): 98-105.]
[22]	潘方杰, 王宏志, 宋明洁, 等. 基于GIS的中国A级物流企业时空演变特征及其影响因素[J]. 长江流域资源与环境, 2020, 29(10): 2186-2199.
	[Pan Fangjie, Wang Hongzhi, Song Mingjie, et al. Study on the spatiotemporal evolutionary characteristics and the influencing factors of A-grade logistics companies in China based on GIS[J]. Resources and Environment in the Yangtze Basin, 2020, 29(10): 2186-2199.]
[23]	张圣忠, 柴廷熠. 西安市物流企业空间格局演化及影响因素分析[J]. 世界地理研究, 2021, 30(6): 1275-1285. doi: 10.3969/j.issn.1004-9479.2021.06.2020101
	[Zhang Shengzhong, Chai Tingyi. Spatial evolution and influencing factors of logistics enterprises in Xi’an[J]. World Regional Studies, 2021, 30(6): 1275-1285.] doi: 10.3969/j.issn.1004-9479.2021.06.2020101
[24]	曹卫东. 城市物流企业区位分布的空间格局及其演化——以苏州市为例[J]. 地理研究, 2011, 30(11): 1997-2007.
	[Cao Weidong. Spatial pattern and location evolution of urban logistics enterprises: Taking Suzhou as an example[J]. Economic Geography, 2011, 30(11):1997-2007.]
[25]	蒋天颖, 史亚男. 宁波市物流企业空间格局演化及影响因素[J]. 经济地理, 2015, 35(10): 130-138.
	[Jiang Tianying, Shi Ya’nan. The spatial pattern evolution and influencing factors of logistics enterprises in Ningbo[J]. Economic Geography, 2015, 35(10): 130-138.]
[26]	赵学伟, 张志斌, 冯斌, 等. 西北内陆中心城市物流企业空间分异及区位选择——以兰州市为例[J]. 干旱区地理, 2022, 45(5): 1671-1683.
	[Zhao Xuewei, Zhang Zhibin, Feng Bin, et al. Spatial differentiation and location choice of logistics enterprises in the central cities of inland northwest China: A case of Lanzhou City[J]. Arid Land Geography, 2022, 45(5): 1671-1683.]
[27]	沈静, 王少谷, 周楚平. 环境公正视角下广州市污染企业分布与区域人口社会特征的时空关系研究[J]. 地理研究, 2022, 41(1): 46-62. doi: 10.11821/dlyj020210547
	[Shen Jing, Wang Shaogu, Zhou Chuping. Spatio-temporal relations between distribution of polluting enterprises and regional sociodemographic characteristics in Guangzhou from the perspective of environmental justice[J]. Economic Geography, 2022, 41(1): 46-62.]
[28]	谢永琴, 魏晓晨. 北京物流企业空间布局演化及影响因素分析[J]. 陕西师范大学学报(自然科学版), 2019, 47(2): 106-115.
	[Xie Yongqin, Wei Xiaochen. The spatial distribution evolution and influence factor of logistics enterprises in Beijing[J]. Journal of Shaanxi Normal University (Natural Science Edition), 2019, 47(2): 106-115.]
[29]	李天宇, 陆林, 张海洲, 等. 长三角城市群A级物流企业空间演化特征及驱动因素[J]. 经济地理, 2021, 41(11): 157-166.
	[Li Tianyu, Lu Lin, Zhang Haizhou, et al. Evolution characteristics and driving factors of A-level logistics enterprises in the Yangtze River Delta urban agglomeration[J]. Economic Geography, 2021, 41(11): 157-166.]
[30]	Fotheringham S A, Yang W, Kang W. Multiscale geographically weighted regression (MGWR)[J]. Annals of the American Association of Geographers, 2017, 107(6): 1247-1265.
[31]	王劲峰, 徐成东. 地理探测器:原理与展望[J]. 地理学报, 2017, 72(1): 116-134. doi: 10.11821/dlxb201701010
	[Wang Jinfeng, Xu Chengdong. Geodetector: Principle and prospective[J]. Acta Geographica Sinica, 2017, 72(1): 116-134.] doi: 10.11821/dlxb201701010
[32]	王成金, 张梦天. 中国物流企业的布局特征与形成机制[J]. 地理科学进展, 2014, 33(1): 134-144. doi: 10.11820/dlkxjz.2014.01.015
	[Wang Chengjin, Zhang Mengtian. Spatial pattern and its mechanism of modern logistics companies in China[J]. Progress in Geography, 2014, 33(1): 134-144.] doi: 10.11820/dlkxjz.2014.01.015
[33]	周侃, 殷悦, 陈妤凡. 城市群水污染物排放的驱动因素及尺度效应[J]. 地理学报, 2022, 77(9): 2219-2235. doi: 10.11821/dlxb202209006
	[Zhou Kan, Yin Yue, Chen Yufan. Driving factors and scale effects of water pollutant discharge in the urban agglomeration[J]. Acta Geographica Sinica, 2022, 77(9): 2219-2235.] doi: 10.11821/dlxb202209006

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

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

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 33

Related Articles 15

Metrics

Comments

Recommended 0

[1]	CHU Jiaqi, JIANG Zhihui. Spatiotemporal evolution and influencing factors of green efficiency of agricultural water resources in Xinjiang [J]. Arid Land Geography, 2024, 47(7): 1231-1241.
[2]	LI Junjia, ZHAO Meifeng. Spatial evolution and influencing mechanism of high-quality development in ethnic minority areas of China [J]. Arid Land Geography, 2024, 47(3): 496-505.
[3]	TANG Yuting, DU Hongru. Spatial scope of community living circle and comprehensive measurement method of construction level: Urumqi City as an example [J]. Arid Land Geography, 2024, 47(2): 332-342.
[4]	PENG Ya, WANG Juanjuan, WANG Shanshan, TIAN Liulan, LIU Jie, WU Zhaopeng. Spatiotemporal pattern evolution of land use conflict in Urumqi City from the perspective of ecological security [J]. Arid Land Geography, 2024, 47(1): 81-92.
[5]	TIAN Liulan, WANG Shanshan, WU Zhaopeng. Construction of ecological security pattern in Urumqi based on multi-temporal remote sensing data [J]. Arid Land Geography, 2023, 46(7): 1155-1165.
[6]	NI Honghong, MA Qiang, BU Yuankun, YANG Xiaoxuan, LI Weizhong. Spatiotemporal distribution pattern evolution and influencing factors of forestry enterprises in Shaanxi Province [J]. Arid Land Geography, 2023, 46(12): 2098-2110.
[7]	LIANG Liang, GUO Xiaosong, CHEN Hanjie, XU Haofan, ZHOU Yanbo, XIE Shaowen, YANG Fen, WEI Chaoyang. Accumulation characteristics and ecological risks of heavy metals in green land soils in core urban area of Urumqi City [J]. Arid Land Geography, 2023, 46(11): 1868-1878.
[8]	SUI Lu, PU Chunling, LIU Zhiyou, BAI Pengfei, LIU Tao. Trade-off synergy of ecosystem service value in Urumqi City based on PLUS model [J]. Arid Land Geography, 2023, 46(1): 159-168.
[9]	CHENG Jing,WANG Peng,CHEN Hongxiang,HAN Yonggui. Geographical exploration of the spatial and temporal evolution of ecological risk and its influencing factors in semi-arid regions: A case of Yanchi County in Ningxia [J]. Arid Land Geography, 2022, 45(5): 1637-1648.
[10]	ZHAO Xuewei,ZHANG Zhibin,FENG Bin,GONG Weimin,MA Xiaomin,GUO Yan. Spatial differentiation and location choice of logistics enterprises in the central cities of inland northwest China: A case of Lanzhou City [J]. Arid Land Geography, 2022, 45(5): 1671-1683.
[11]	LI Anbei,WAN Yu,ZHANG Jun,LI Ruqi,Mangsuer AIRETI,LI Na. Circulation classification and cause analysis of the snowstorm case in Urumqi City from 1961 to 2019 [J]. Arid Land Geography, 2022, 45(2): 379-388.
[12]	ZHAO Keming,LI Na,LI Xia,SUN Mingjing,SHI Junjie,AN Dawei,PU Jie,ZHENG Bohua. Characteristic analysis of atmospheric diffusion conditions of winter foehn weather process in Urumqi City [J]. Arid Land Geography, 2021, 44(6): 1534-1544.
[13]	ZHAO Yongyu,Alimujiang KASIM,GAO Pengwen,LIANG Hongwu. Quantitative analysis of urban expansion and response factors in Urumqi City based on random forest algorithm and geographical detector [J]. Arid Land Geography, 2021, 44(6): 1729-1739.
[14]	MIAO Yunling,GONG Hengrui,WANG Jian,GE Yicheng,LI Ruqi. Diurnal variation characteristics of summer precipitation in Urumqi Cityfrom 2012 to 2019 [J]. Arid Land Geography, 2021, 44(5): 1222-1230.
[15]	WANG Yishan,ZHANG Fei,CHEN Rui,QI Yaxiao,LIU Changjiang. Comprehensive ecological security assessment: A case study of Urumqi City [J]. Arid Land Geography, 2021, 44(2): 427-440.