地级市尺度下中国物流企业空间格局演化特征及影响因素

doi:10.12118/j.issn.1000-6060.2025.095

Abstract

Abstract:

Logistics enterprises play a crucial role in a regional economy, with their distribution directly affecting its resource allocation and market competitiveness. The study of the evolution characteristics of the spatial pattern of logistics enterprises in prefecture-level cities and their influencing factors can help reveal the formation mechanism of logistics enterprises’ agglomeration. Based on the geographic location data of logistics enterprises in prefecture-level cities in China from 2006 to 2023, we use spatial analysis methods, such as kernel density analysis, standard deviation ellipse, average nearest neighbor, and spatial autocorrelation, to obtain the spatiotemporal pattern and evolution characteristics of logistics enterprises at prefecture-level city scale. The factors affecting the spatial pattern of logistics enterprises and their spatial differentiation are analyzed using multi-scale geographically weighted regression models. The results reveal that (1) The spatial distribution of logistics enterprises in China has always maintained the agglomeration characteristics, and its spatial pattern has experienced the “one-core-driven, multi-point agglomeration” to “multiple cores” and then gradually to the “dual-core” evolutionary process, with corridor diffusion and neighborhood diffusion effects. (2) Logistics enterprise development has a significant positive spillover effect, as a faster development of a city can drive the development of neighboring cities. Underdeveloped cities around a developed city experiencing a “siphon effect” in the “siphon tide” of low-lying areas would experience a significant negative spillover effect. (3) The number of employments in the tertiary industry and the total amount of imports and exports are the main influencing factors of the spatial pattern of logistics enterprises. Moreover, the total amount of imports and exports and the number of foreign-funded enterprises are global influencing factors, while the number of employments in the tertiary industry and GDP per capita are local influencing factors.

Key words: logistics enterprises, spatial evolution, influencing factors, multi-scale geographically weighted regression, prefecture-level city, industrial agglomeration

JI Xiaofeng, LI Zixin, CAO Rui, LI Wu, CHEN Fang. Spatial pattern evolution characteristics and influencing factors of logistics enterprises in China at prefecture-level city scale[J].Arid Land Geography, 2026, 49(1): 176-185.

Figures/Tables 10

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年份	椭圆中心经度/°E	椭圆中心纬度/°N	长轴长度/km	短轴长度/km	长短轴差距/km	转角/(°)
2006	115.0129	30.5001	1137.3493	678.1388	459.2105	11.3497
2009	115.0120	30.7920	1136.2869	699.4086	436.8783	10.4809
2012	115.3627	31.6742	1097.2123	706.1017	391.1106	12.1632
2016	115.5566	32.2317	1056.2653	681.2854	374.9799	12.1285
2020	115.5574	32.2692	1015.2783	672.8005	342.4778	13.9228
2023	115.1534	32.2529	1045.1275	723.0230	322.1045	10.5968

年份	平均观测距离/km	最近邻比率	Z值	分布类型
2006	975.2468	0.1425	-441.0534	显著聚集
2009	812.1787	0.1420	-528.3189	显著聚集
2012	719.0645	0.1421	-597.4309	显著聚集
2016	479.3788	0.1296	-831.6884	显著聚集
2020	373.5720	0.1217	-1015.6757	显著聚集
2023	630.1272	0.1435	-686.7394	显著聚集

年份	全局Moran’s I指数	Z值
2006	0.0668	11.5576
2009	0.1324	18.3909
2012	0.1026	13.9922
2016	0.0587	8.5120
2020	0.0761	10.2908
2023	0.0584	8.0763

维度	变量	指标
经济因素	地区经济水平	地区生产总值/10⁴元
经济因素	经济发展水平	地方财政收入总额/10⁴元
社会因素	交通发展水平	道路网密度/km·km^-2
社会因素	劳动力水平	第三产业就业人数/10⁴人
市场因素	居民消费能力	人均GDP/元
开放程度	国际贸易水平	进出口总额/10⁴元
开放程度	对外开放水平	外资企业数量/个

变量	OLS模型		GWR模型		MGWR模型
变量	系数	VIF	Mean	带宽	Mean	带宽
地区生产总值	0.00001^**	19.67	-	-	-	-
地方财政收入总额	0.00004^*	18.54	-	-	-	-
道路网密度	-73.27181	7.12	0.013	74	0.364	154
第三产业就业人数	1.21569	4.97	0.392^***	74	0.361^***	66
人均GDP	0.43760^**	4.38	0.166^***	74	0.106^***	86
进出口总额	0.00002^***	2.61	0.666^***	74	0.472^***	286
外资企业数量	-1.01783^***	1.26	0.221^***	74	-0.206^***	183
AICc	4157.667		163.599		162.389
R²	0.885		0.924		0.930
调整后R²	0.883		0.912		0.920

Spatial pattern evolution characteristics and influencing factors of logistics enterprises in China at prefecture-level city scale

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