多要素流视角下黄河“几”字弯都市圈空间网络结构及影响因素研究

doi:10.12118/j.issn.1000-6060.2022.286

摘要/Abstract

摘要：

基于交通流、物流、资金流与信息流等多元数据，运用联系强度、空间结构指数、凝聚子群、二次指派程序（Quadratic assignment procedure，QAP）等方法，对黄河“几”字弯都市圈空间网络“点-线-面”特征及影响因素进行分析。结果表明：（1）各城市交通流、物流、资金流、信息流与综合流的联系强度差异明显，综合流呈现以太原市与呼和浩特市为核心的“两核多中心”发展态势。（2）城市网络联系显示物流与资金流的联系更为紧密，同时各要素流网络结构空间非均衡特征明显，总体呈现东强西弱、北强南弱的特征。（3）空间结构指数表明不同要素流视角下黄河“几”字弯都市圈均呈现较为明显的多中心结构，同时不同要素流视角下的凝聚子群均呈现地理邻近性特征。（4）经济全球化、交通效率、产业结构、地理邻近性等因素显著影响都市圈多要素联系网络。

关键词: 黄河“几”字弯都市圈, 多要素联系网络, 空间结构, QAP回归分析

Abstract:

This paper takes 21 cities in Jiziwan (riverway bend with the shape of Chinese character “几”) Metropolitan Area of the Yellow River as the study area. Multidimensional data is used such as traffic flow, logistics, capital flow and information flow. The study also uses methods such as connection strength, spatial structure index, cohesive subgroup and QAP regression analysis to analyze the “point-line-plane” characteristics of the spatial network structure and the factors that influence Jiziwan Metropolitan Area of the Yellow River. The results show the following: (1) From the perspective of urban nodes, the connection strength of traffic flow, logistics, fund flow, information flow and comprehensive flow of the cities in Jiziwan Metropolitan Area of the Yellow River are quite different. In general, the comprehensive flow exhibits the development trend of “two cores and a multiple centers” with Taiyuan City and Hohhot City as the cores. (2) From the perspective of different connection flow networks, the connection strength of logistics and capital flow is closer. At the same time, the spatial nonequilibrium characteristics of the network structure of each feature flow are obvious. In general, it is strong in the east and weak in the west, and strong in the north and weak in the south. (3) The spatial structure indexes show that Jiziwan Metropolitan Area of the Yellow River exhibits a relatively obvious polycentric structure from the perspective of multidimensional feature flow, and the cohesive subgroups under different feature flow perspectives all show the characteristics of geographic proximity. (4) QAP regression results show that economic globalization, traffic efficiency, industrial structure and geographic proximity have a significant impact on the multidimensional feature connection network of Jiziwan Metropolitan Area of the Yellow River, while the economic development level has the least impact, indicating that it is not the dominant factor in the flow of factors in this area.

Key words: Jiziwan Metropolitan Area of the Yellow River, multi-dimensional feature connection network, spatial structure, QAP regression analysis

曹晨, 黄贤金. 多要素流视角下黄河“几”字弯都市圈空间网络结构及影响因素研究[J]. 干旱区地理, 2023, 46(6): 993-1003.

CAO Chen, HUANG Xianjin. Spatial network structure and its influencing factors of Jiziwan Metropolitan Area of the Yellow River from the perspective of multi-dimensional feature flow[J]. Arid Land Geography, 2023, 46(6): 993-1003.

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层级	交通流	物流	资金流	信息流	综合流
第一层级	呼和浩特市、包头市	太原市	太原市、呼和浩特市、银川市、包头市	太原市、呼和浩特市	太原市、呼和浩特市
第二层级	乌兰察布市、银川市、太原市、大同市	大同市、临汾市、吕梁市、忻州市、榆林市、呼和浩特市	大同市、鄂尔多斯市、榆林市	鄂尔多斯市、银川市、忻州市、包头市、大同市	银川市、包头市、大同市
第三层级	中卫市、乌海市、巴彦淖尔市、庆阳市、鄂尔多斯市	庆阳市、银川市、延安市	忻州市、延安市、临汾市、庆阳市、乌兰察布市、巴彦淖尔市	榆林市、乌兰察布市、临汾市、巴彦淖尔市、吕梁市	忻州市、榆林市、临汾市、鄂尔多斯市、吕梁市、乌兰察布市、庆阳市、延安市
第四层级	吴忠市、临汾市、忻州市、石嘴山市、榆林市、延安市、白银市、吕梁市	包头市、鄂尔多斯市、白银市、乌兰察布市、朔州市、吴忠市	白银市、吕梁市、朔州市、吴忠市、乌海市	朔州市、乌海市、延安市	巴彦淖尔市、朔州市、白银市、吴忠市、乌海市
第五层级	朔州市、阿拉善盟	巴彦淖尔市、中卫市、乌海市、石嘴山市、阿拉善盟	石嘴山市、中卫市、阿拉善盟	阿拉善盟、石嘴山市、吴忠市、庆阳市、中卫市、白银市	中卫市、石嘴山市、阿拉善盟

网络连接度等级	城市要素流网络连接度（归一化数据）					城市对数量					比例/%
网络连接度等级	交通流	物流	资金流	信息流	综合流	交通流	物流	资金流	信息流	综合流	交通流	物流	资金流	信息流	综合流
弱	0.0000~0.0426	0.0000~0.0516	0.0000~0.1083	0.0000~0.0403	0.0033~0.0586	57	73	68	127	77	50.00	34.76	32.38	61.06	36.67
较弱	0.0426~0.0994	0.0516~0.1114	0.1083~0.2154	0.0403~0.1146	0.0586~0.1235	34	48	67	40	61	29.82	22.86	31.90	19.23	29.05
中	0.0994~0.2454	0.1114~0.2223	0.2154~0.3501	0.1146~0.2108	0.1235~0.2075	16	51	40	24	44	14.04	24.29	19.05	11.54	20.95
较强	0.2454~0.5963	0.2223~0.4671	0.3501~0.6126	0.2108~0.4625	0.2075~0.3444	6	30	29	10	19	5.26	14.29	13.81	4.81	9.05
强	0.5963~1.0000	0.4670~1.0000	0.6126~1.0000	0.4625~1.0000	0.3444~0.6949	1	8	6	7	9	0.88	3.81	2.86	3.37	4.29

影响因素	回归系数
影响因素	交通流	物流	资金流	信息流	综合流
P	0.1392^**	-0.1560	-0.0586	0.2348^***	0.0806^*
T	-0.4328^***	-0.0720	-0.2167^***	-0.3304^***	-0.3087^***
E	-0.1656^**	0.0389	0.0104	-0.0376	-0.0326
I	0.0256	-0.2423^**	-0.1831^*	-0.1137^*	-0.1792^**
S	0.3523^***	-0.2614^*	0.0295	0.1085	0.0217
G	-0.1486^*	0.7759^***	0.4379^***	0.3077^***	0.4966^***
R²	0.274	0.419	0.295	0.378	0.409
调整后R²	0.266	0.412	0.286	0.371	0.402
观察项个数	420	420	420	420	420
随机置换次数	5000	5000	5000	5000	5000