Spatial evolution and influencing factors of logistics enterprises in Xi'an

doi:10.3969/j.issn.1004-9479.2021.06.2020101

Abstract

Abstract:

Based on the micro geographic data of logistics enterprises in Xi'an, the spatial distribution and evolution characteristics of logistics enterprises in Xi'an from 1995 to 2019 are described by using the estimation of average nearest neighbor, standard deviation ellipse and kernel density. The distance relationship between logistics enterprises cluster area and city center is analyzed by using statistical method, and the main factors affecting the spatial distribution of logistics enterprises are analyzed by constructing the least square regression model. The results show that: the logistics enterprises in Xi'an have the trend of aggregation since 2000, and gradually develop from single core to dual core, and the third core is now emerging; the spatial distribution of logistics enterprises shows the obvious diffusion characteristics, and the evolution process extends along the direction of "Southwest-northeast", and the overall trend of "suburbanization" appears, concentrated in the location about 15km away from the city center; The number of logistics enterprises, resident population and total retail sales of social consumer goods in the last year are the main factors that affect the formation of spatial pattern of logistics enterprises in Xi'an, which have a positive effect on the layout and agglomeration of logistics enterprises.

Key words: logistics enterprises, spatial evolution, influencing factors, Xi 'an

摘要：

借助西安市物流企业微观地理数据，采用平均最近邻、标准差椭圆和核密度估计描述1995—2019年西安市物流企业空间分布及其演化特征，采用统计学方法分析物流企业聚集区与市中心的距离关系，并构建最小二乘回归模型分析影响物流企业空间分布的主要因素。结果表明：西安市物流企业从2000年开始出现聚集趋势，逐渐从单核发展为双核，现有出现第三核的趋势；物流企业空间分布呈现明显的扩散特征，演化过程沿着“西南-东北”方向延伸，整体呈现出“郊区化”趋势，集中分布于距市中心15km左右的位置；上一年度物流企业数量、常住人口及社会消费品零售总额是影响西安市物流企业空间格局形成的主要因素，对物流企业布局和集聚具有积极作用。

关键词: 物流企业, 空间演化, 影响因素, 西安

Shengzhong ZHANG, Tingyi CHAI. Spatial evolution and influencing factors of logistics enterprises in Xi'an[J]. World Regional Studies, 2021, 30(6): 1275-1285.

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.

Figures/Tables 10

Tab.1 Number of logistics enterprises in Xi'an by period

企业状态	1995年	2000年	2005年	2010年	2015年	2019年
在业/存续	17	85	297	685	2217	5946
注销	8	20	60	111	332	424
总计	25	105	364	796	2549	6370

Tab.2 Number and proportion of logistics enterprises in districts and counties of Xi'an

行政区划	企业数量	占比	行政区划	企业数量	占比
新城区	498	7.2%	阎良区	200	2.9%
碑林区	243	3.5%	临潼区	168	2.4%
莲湖区	605	8.8%	高陵区	278	4.0%
雁塔区	942	13.7%	鄠邑区	158	2.3%
灞桥区	998	14.5%	蓝田县	59	0.9%
长安区	375	5.4%	周至县	59	0.9%
未央区	2318	33.6%	合计	6901	100%

Fig.1 The central city of Xi'an

Tab.3 Analysis results of the average nearest neighbor of logistics enterprises in the central city of Xi'an

时间	观测距离/米	平均距离/米	最邻近比率	z 得分	p 值	分布情况
1995	1322.4757	1158.7675	1.141278	1.296190	0.194910	随机
2000	922.9732	1136.3297	0.812241	-3.645449	0.000267	比较聚集
2005	389.3010	748.5241	0.520092	-17.51620	0	聚集
2010	237.2625	523.4689	0.453250	-28.06633	0	显著聚集
2015	153.1529	374.1299	0.409358	-53.60969	0	显著聚集
2019	96.4211	289.3201	0.333268	-94.65416	0	显著聚集

Fig.2 Analysis of kernel density of logistics enterprises in the central city of Xi'an

Fig.3 Distribution standard deviation ellipse of logistics enterprises in the central city of Xi'an

Fig.4 Distribution statistics of logistics enterprises from the city center in the central city of Xi'an

Tab.4 Explanatory variable selection and variable description

因素	解释变量	变量代码	变量说明
生产因素	人均生产总值	PCGDP	衡量地区人民生活水平
生产因素	规模以上工业总产值	GIOV	反映一定时间内工业生产的总规模和总水平
消费因素	固定资产投资	IFA	反映企业建造和购置固定资产变化情况
	社会消费品零售总额	FSBC	地区商品零售收入，间接体现物流产业发展状况
	居民人均可支配收入	PCADI	反映地区消费水平情况
交通因素	公路里程	LH	反映物流运输中的基础设施建设情况
	货运周转量	FT）	衡量地区运输产业的综合性产量指标
	社会经营的载货汽车数	SOT	反映物流运输车辆总体情况
产业因素	邮电业务总量	PTV	反映邮政业务整体变化
	行业年新增固定资产	NIFA	交通运输、仓储及邮政业资产增长情况
	年在建项目数	CP	反映物流运输行业正在建设项目情况
社会因素	常住人口	PP	反映地区物流需求情况
	财政支出	FS	政府支持物流行业给予的财政支持
	上一年度企业数量	NUM	反映上一年度地区物流企业聚集数量

Tab.5 Correlation analysis of influencing factors

解释变量	PCGDP	GIOV	IFA	FSBC	PCADI	LH	FT
Pearson相关系数	$0 . 876 * *$	$0 . 851 * *$	$0 . 893 * *$	$0 . 915 * *$	$0 . 780 * *$	$0 . 673 * *$	$0 . 604 *$
解释变量	SOT	PTV	NIFA	CP	PP	FS	NUM
Pearson相关系数	$0 . 833 * *$	$0 . 916 * *$	$0 . 906 * *$	$0 . 699 * *$	$0 . 935 * *$	$0 . 901 * *$	$0 . 998 * *$

Tab.5 Correlation analysis of influencing factors

解释变量	PCGDP	GIOV	IFA	FSBC	PCADI	LH	FT
Pearson相关系数	$0 . 876 * *$	$0 . 851 * *$	$0 . 893 * *$	$0 . 915 * *$	$0 . 780 * *$	$0 . 673 * *$	$0 . 604 *$
解释变量	SOT	PTV	NIFA	CP	PP	FS	NUM
Pearson相关系数	$0 . 833 * *$	$0 . 916 * *$	$0 . 906 * *$	$0 . 699 * *$	$0 . 935 * *$	$0 . 901 * *$	$0 . 998 * *$

Tab.6 Regression model analysis results

变量	模型一	模型二	模型三	模型四	模型五	模型六	模型七	模型八	模型九	模型十
PCGDP	$2 . 015 * * *$
GIOV		$2 . 080 * * *$
IFA			$1 . 227 * * *$
FSBC				$1 . 743 * * *$
SOT					$1 . 646 * *$
PTV						$0 . 811 * * *$
NIFA							$0 . 680 * * *$
PP								$1 . 403 * * *$
FS									$2 . 882 *$
NUM										$1 . 020 * * *$
PCADI	$- 0.588$	$- 0.202$	$0 . 788 *$	$- 0.572$	$- 0.270$	$0.691$	$0 . 884 *$	$0.408$	$- 1.393$	$- 0.044$
LH	$0 . 419 *$	$0.320$	$0.375$	$0 . 385 *$	$1 . 004 * *$	$- 0 . 643 *$	$0 . 551 * *$	$- 0.399$	$1.026$	$- 0.072$
FT	$- 1 . 046 * *$	$- 1 . 348 * * *$	$- 1 . 436 * * *$	$- 0 . 693 * *$	$- 1 . 638 * * *$	$0.040$	$- 1 . 398 * * *$	$- 0.444$	$- 1.708$	$0.067$
CP	$0.004$	$- 0.100$	$- 0.188$	$- 0.020$	$0.143$	$0.128$	$0.176$	$- 0.162$	$0.180$	$0.031$
$R 2$	$0 . 960 * * *$	$0 . 945 * * *$	$0 . 965 * * *$	$0 . 975 * * *$	$0 . 918 * * *$	$0 . 962 * * *$	$0 . 955 * * *$	$0 . 979 * * *$	$0 . 909 * *$	$0 . 998 * * *$

Tab.6 Regression model analysis results

变量	模型一	模型二	模型三	模型四	模型五	模型六	模型七	模型八	模型九	模型十
PCGDP	$2 . 015 * * *$
GIOV		$2 . 080 * * *$
IFA			$1 . 227 * * *$
FSBC				$1 . 743 * * *$
SOT					$1 . 646 * *$
PTV						$0 . 811 * * *$
NIFA							$0 . 680 * * *$
PP								$1 . 403 * * *$
FS									$2 . 882 *$
NUM										$1 . 020 * * *$
PCADI	$- 0.588$	$- 0.202$	$0 . 788 *$	$- 0.572$	$- 0.270$	$0.691$	$0 . 884 *$	$0.408$	$- 1.393$	$- 0.044$
LH	$0 . 419 *$	$0.320$	$0.375$	$0 . 385 *$	$1 . 004 * *$	$- 0 . 643 *$	$0 . 551 * *$	$- 0.399$	$1.026$	$- 0.072$
FT	$- 1 . 046 * *$	$- 1 . 348 * * *$	$- 1 . 436 * * *$	$- 0 . 693 * *$	$- 1 . 638 * * *$	$0.040$	$- 1 . 398 * * *$	$- 0.444$	$- 1.708$	$0.067$
CP	$0.004$	$- 0.100$	$- 0.188$	$- 0.020$	$0.143$	$0.128$	$0.176$	$- 0.162$	$0.180$	$0.031$
$R 2$	$0 . 960 * * *$	$0 . 945 * * *$	$0 . 965 * * *$	$0 . 975 * * *$	$0 . 918 * * *$	$0 . 962 * * *$	$0 . 955 * * *$	$0 . 979 * * *$	$0 . 909 * *$	$0 . 998 * * *$

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