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

Abstract

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

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.

Figures/Tables 7

Fig. 1

Tab. 1

Tab. 2

Fig. 2

Tab. 3

Fig. 3

Tab. 4

References 30

[1]	姚世谋, 汤茂林, 陈雯, 等. 区域与城市发展论[M]. 第2版. 合肥: 中国科学技术大学出版社, 2009: 222-223.
	[Yao Shimou, Tang Maolin, Chen Wen, et al. Theory of regional and urban development[M]. 2^nd ed. Hefei: University of Science and Technology of China Press, 2009: 222-223.]
[2]	Castells M. Grassrooting the space of flows[J]. Urban Geography, 1999, 20(4): 294-302. doi: 10.2747/0272-3638.20.4.294
[3]	邱坚坚, 刘毅华, 陈浩然, 等. 流空间视角下的粤港澳大湾区空间网络格局--基于信息流与交通流的对比分析[J]. 经济地理, 2019, 39(6): 7-15.
	[Qiu Jianjian, Liu Yihua, Chen Haoran, et al. Urban network structure of Guangdong-Hong Kong-Macao Greater Bay Area with the view of space of flows: A comparison between information flow and transportation flow[J]. Economic Geography, 2019, 39(6): 7-15.]
[4]	李苑君, 吴旗韬, 吴康敏, 等. “流空间”视角的电子商务快递物流网络结构研究--以珠三角城市群为例[J]. 地域研究与开发, 2021, 40(2): 20-26.
	[Li Yuanjun, Wu Qitao, Wu Kangmin, et al. Structure of e-commerce express logistics network from the perspective of flow space: Take the Pearl River Delta urban agglomeration as example[J]. Areal Research and Development, 2021, 40(2): 20-26.]
[5]	黄沣爵, 杨滔. 珠三角城市空间联系及社会网络--基于“流空间”的分析[J]. 热带地理, 2022, 42(3): 422-430. doi: 10.13284/j.cnki.rddl.003461
	[Huang Fengjue, Yang Tao. Exploring the spatial structure of Pearl River Delta: Based on “space of flows”[J]. Tropical Geography, 2022, 42(3): 422-430.] doi: 10.13284/j.cnki.rddl.003461
[6]	周慧玲, 王甫园. 基于修正引力模型的中国省际旅游者流空间网络结构特征[J]. 地理研究, 2020, 39(3): 669-681. doi: 10.11821/dlyj020190021
	[Zhou Huiling, Wang Fuyuan. Research on structure characteristics of the inter-provincial tourist flow spatial network in China based on the modified gravity model[J]. Geographical Research, 2020, 39(3): 669-681.] doi: 10.11821/dlyj020190021
[7]	程雪兰, 方叶林, 苏雪晴, 等. 中国东部沿海5大城市群旅游流网络结构空间分布特征研究[J]. 地理科学进展, 2021, 40(6): 948-957. doi: 10.18306/dlkxjz.2021.06.005
	[Cheng Xuelan, Fang Yelin, Su Xueqing, et al. Spatial distribution characteristics of network structure of tourism flow in five major urban agglomerations of coastal China[J]. Progress in Geography, 2021, 40(6): 948-957.] doi: 10.18306/dlkxjz.2021.06.005
[8]	陈伟, 修春亮, 柯文前, 等. 多元交通流视角下的中国城市网络层级特征[J]. 地理研究, 2015, 34(11): 2073-2083. doi: 10.11821/dlyj2015110006
	[Chen Wei, Xiu Chunliang, Ke Wenqian, et al. Hierarchical structures of China’s city network from the perspective of multiple traffic flows[J]. Geographical Research, 2015, 34(11): 2073-2083.] doi: 10.11821/dlyj2015110006
[9]	陈维肖, 刘玮辰, 段学军. 基于“流空间”视角的铁路客运空间组织分析--以长三角城市群为例[J]. 地理研究, 2020, 39(10): 2330-2344. doi: 10.11821/dlyj020190573
	[Chen Weixiao, Liu Weichen, Duan Xuejun. Spatial organization evolution of railway passenger transportation in the perspective of “space of flow”: A case study of the Yangtze River Delta urban agglomeration[J]. Geographical Research, 2020, 39(10): 2330-2344.] doi: 10.11821/dlyj020190573
[10]	姚文萃, 周婕, 陈虹桔, 等. 基于互联网公共信息流的区域网络空间结构研究[J]. 经济地理, 2017, 37(10): 10-16.
	[Yao Wencui, Zhou Jie, Chen Hongju, et al. Spatial structure of regional network based on internet public information flow[J]. Economic Geography, 2017, 37(10): 10-16.]
[11]	王启轩, 张艺帅, 程遥. 信息流视角下长三角城市群空间组织辨析及其规划启示--基于百度指数的城市网络辨析[J]. 城市规划学刊, 2018(3): 105-112.
	[Wang Qixuan, Zhang Yishuai, Cheng Yao. Spatial organization of the Yangtze River Delta urban agglomeration and its implications on planning from the perspective of information flow: Analysis of city network based on Baidu index[J]. Urban Planning Forum, 2018(3): 105-112.]
[12]	李艳, 孙阳, 陈雯. 反身性视角下信息流空间建构与网络韧性分析: 以长三角百度用户热点搜索为例[J]. 中国科学院大学学报, 2021, 38(1): 62-72. doi: 10.7523/j.issn.2095-6134.2021.01.009
	[Li Yan, Sun Yang, Chen Wen. Construction of space of information flows and assessment of network resilience from reflexive perspective: A case study of Baidu index in Yangtze River Delta[J]. Journal of University of Chinese Academy of Sciences, 2021, 38(1): 62-72.] doi: 10.7523/j.issn.2095-6134.2021.01.009
[13]	董琦, 甄峰. 基于物流企业网络的中国城市网络空间结构特征研究[J]. 人文地理, 2013, 28(4): 71-76.
	[Dong Qi, Zhen Feng. The study on spatial structure characteristics of China’s city network based on the logistics enterprise network[J]. Human Geography, 2013, 28(4): 71-76.]
[14]	李苑君, 吴旗韬, 张玉玲, 等. 中国三大城市群电子商务快递物流网络空间结构及其形成机制研究[J]. 地理科学, 2021, 41(8): 1398-1408. doi: 10.13249/j.cnki.sgs.2021.08.011
	[Li Yuanjun, Wu Qitao, Zhang Yuling, et al. Spatial structure and formation mechanism of e-commerce express logistics network in the three major urban agglomerations of China[J]. Scientia Geographica Sinica, 2021, 41(8): 1398-1408.] doi: 10.13249/j.cnki.sgs.2021.08.011
[15]	刘望保, 石恩名. 基于ICT的中国城市间人口日常流动空间格局--以百度迁徙为例[J]. 地理学报, 2016, 71(10): 1667-1679. doi: 10.11821/dlxb201610001
	[Liu Wangbao, Shi Enming. Spatial pattern of population daily flow among cities based on ICT: A case study of “Baidu Migration”[J]. Acta Geographica Sinica, 2016, 71(10): 1667-1679.] doi: 10.11821/dlxb201610001
[16]	孙桂平, 韩东, 贾梦琴. 京津冀城市群人口流动网络结构及影响因素研究[J]. 地域研究与开发, 2019, 38(4): 166-169, 180.
	[Sun Guiping, Han Dong, Jia Mengqin. Structure and influencing factors of human mobility network in Beijing-Tianjin-Hebei urban agglomeration[J]. Areal Research and Development, 2019, 38(4): 166-169, 180.]
[17]	胡刚钰, 黄建中, 李峰清. 厦漳泉大都市区空间网络结构分析--基于人流活动特征的LBS大数据分析[J]. 现代城市研究, 2019(11): 13-19.
	[Hu Gangyu, Huang Jianzhong, Li Fengqing. Spatial network structure analysis of Xia-Zhang-Quan metropolitan area: LBS big data analysis based on crowds’ activities characteristics[J]. Modern Urban Research, 2019(11): 13-19.]
[18]	钟业喜, 冯兴华, 文玉钊. 长江经济带经济网络结构演变及其驱动机制研究[J]. 地理科学, 2016, 36(1): 10-19. doi: 10.13249/j.cnki.sgs.2016.01.002
	[Zhong Yexi, Feng Xinghua, Wen Yuzhao. The evolvement and driving mechanism of economic network structure in the Changjiang River Economic Zone[J]. Scientia Geographica Sinica, 2016, 36(1): 10-19.] doi: 10.13249/j.cnki.sgs.2016.01.002
[19]	郑蔚, 许文璐, 陈越. 跨区域城市群经济网络的动态演化--基于海西、长三角、珠三角城市群分析[J]. 经济地理, 2019, 39(7): 58-66, 75.
	[Zheng Wei, Xu Wenlu, Chen Yue. Dynamic evolution of economic network within inter-regional urban agglomerations: Based on the urban agglomerations of west coast of Taiwan Straits, Yangtze River Delta and Pearl River Delta[J]. Economic Geography, 2019, 39(7): 58-66, 75.]
[20]	叶珊珊, 曹明明, 胡胜. 关中平原城市群经济联系网络结构演变及对经济增长影响研究[J]. 干旱区地理, 2022, 45(1): 277-286.
	[Ye Shanshan, Cao Mingming, Hu Sheng. Evolution of economic connection network structure in the Guanzhong Plain City Cluster and its impact on economic growth[J]. Arid Land Geography, 2022, 45(1): 277-286.]
[21]	王少剑, 高爽, 王宇渠. 基于流空间视角的城市群空间结构研究--以珠三角城市群为例[J]. 地理研究, 2019, 38(8): 1849-1861. doi: 10.11821/dlyj020180635
	[Wang Shaojian, Gao Shuang, Wang Yuqu. Spatial structure of the urban agglomeration based on space of flows: The study of the Pearl River Delta[J]. Geographical Research, 2019, 38(8): 1849-1861.] doi: 10.11821/dlyj020180635
[22]	王逸舟, 王海军, 张彬, 等. 基于多维要素流视角的城市群网络结构及影响因素分析--以武汉城市圈为例[J]. 经济地理, 2021, 41(6): 68-76.
	[Wang Yizhou, Wang Haijun, Zhang Bin, et al. Analysis on the network structure of urban agglomeration and its influencing factors based on the perspective of multi-dimensional feature flow: Taking Wuhan urban agglomeration as an example[J]. Economic Geography, 2021, 41(6): 68-76.]
[23]	魏向前. 黄河“几”字弯都市圈协同治理的动因、约束与前瞻[J]. 北方民族大学学报, 2022(1): 152-160.
	[Wei Xiangqian. Motivation, restriction and prospect of coordinated governance of Jiziwan Metropolitan Area of the Yellow River[J]. Journal of North Minzu University, 2022(1): 152-160.]
[24]	刘传明, 曾菊新. 县域综合交通可达性测度及其与经济发展水平的关系: 对湖北省79个县域的定量分析[J]. 地理研究, 2011, 30(12): 2209-2221.
	[Liu Chuanming, Zeng Juxin. The calculating method about the comprehensive transport accessibility and its correlation with economic development at county level: The statistical analysis of 79 counties in Hubei Province[J]. Geographical Research, 2011, 30(12): 2209-2221.]
[25]	Hanssens H, Derudder B, Van Aelst S, et al. Assessing the functional polycentricity of the mega-city-region of Central Belgium based on advanced producer service transaction links[J]. Regional Studies, 2013, 48(12): 1939-1953. doi: 10.1080/00343404.2012.759650
[26]	刘骁啸, 吴康. 功能疏解背景下京津冀中部核心区产业投资网络演化研究[J]. 地理科学进展, 2020, 39(12): 1972-1984. doi: 10.18306/dlkxjz.2020.12.002
	[Liu Xiaoxiao, Wu Kang. Change of industrial investment networks in the central core area of the Beijing-Tianjin-Hebei region under the background of non-capital function dispersal[J]. Progress in Geography, 2020, 39(12): 1972-1984.] doi: 10.18306/dlkxjz.2020.12.002
[27]	孙宇, 彭树远. 长三角城市创新网络凝聚子群发育机制研究--基于多值ERGM[J]. 经济地理, 2021, 41(9): 22-30.
	[Sun Yu, Peng Shuyuan. Development mechanism of cohesive subgroups urban innovation networks in the Yangtze River Delta: Based on the valued ERGM[J]. Economic Geography, 2021, 41(9): 22-30.]
[28]	高鹏, 何丹, 宁越敏, 等. 长江中游城市群社团结构演化及其邻近机制--基于生产性服务企业网络分析[J]. 地理科学, 2019, 39(4): 578-586. doi: 10.13249/j.cnki.sgs.2019.04.007
	[Gao Peng, He Dan, Ning Yuemin, et al. Community structure and proximity mechanism of city clusters in middle reach of the Yangtze River: Based on producer service firms’network[J]. Scientia Geographica Sinica, 2019, 39(4): 578-586.] doi: 10.13249/j.cnki.sgs.2019.04.007
[29]	冯颖, 侯孟阳, 姚顺波. 中国粮食生产空间关联网络的结构特征及其形成机制[J]. 地理学报, 2020, 75(11): 2380-2395. doi: 10.11821/dlxb202011008
	[Feng Ying, Hou Mengyang, Yao Shunbo. Structural characteristics and formation mechanism of spatial correlation network of grain production in China[J]. Acta Geographica Sinica, 2020, 75(11): 2380-2395.] doi: 10.11821/dlxb202011008
[30]	汪永生, 李玉龙, 郑绍杰. 长三角城市群空间网络结构特征研究[J]. 统计与决策, 2022, 38(6): 69-74.
	[Wang Yongsheng, Li Yulong, Zheng Shaojie. Study on the spatial network structure of urban agglomeration in Yangtze River Delta[J]. Statistics and Decision, 2022, 38(6): 69-74.]

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

要素流	空间结构指数
交通流	0.9403
物流	0.8478
资金流	0.8145
信息流	0.8977
综合流	0.8915

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

Spatial network structure and its influencing factors of Jiziwan Metropolitan Area of the Yellow River from the perspective of multi-dimensional feature flow

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 7

References 30

Related Articles 8

Metrics

Comments

Recommended 0

[1]	YANG Yu, SONG Futie, ZHANG Jie. Spatial structure characteristics and influencing factors of financial network of China based on geodetectors [J]. Arid Land Geography, 2023, 46(9): 1524-1535.
[2]	SU Hang, GU Jiao, ZHAO Jinli. Spatial structure evolution of urban information network in the Yellow River Basin from multi-scale perspective [J]. Arid Land Geography, 2023, 46(7): 1206-1216.
[3]	CAO Kaijun, SHANG Ning, WANG Mimi. Spatial distribution pattern and accessibility analysis of the key rural tourism villages in the five northwestern provinces of China [J]. Arid Land Geography, 2023, 46(7): 1196-1205.
[4]	GAO Zi-yi, ZHANG Hai-feng. Spatial structure of the retail industry in Xining City based on POI data [J]. Arid Land Geography, 2019, 42(5): 1195-1204.
[5]	LIANG Feng, LI Qianguo, SHI Peiji, ZHANG Xuebin, CHEN Jiayin. Evolution characteristics and its driving factors of urban functional land in Zhangye City [J]. Arid Land Geography, 2019, 42(2): 414-432.
[6]	ZHAO Xin-zheng, LI Meng-xue, LI Qiu-ping, LI Tong-sheng, RUI Yang. Population distribution and polycentric spatial structure of Chinese megacities above the vice-provincial level [J]. , 2017, 40(2): 415-423.
[7]	BAO Xiang-ping，XUE Dong-qian，GUO Rui-bin. Difference of tourism cultural industry strength and spatial structure construction of tourism cultural industry in Shaanxi Province [J]. , 2015, 38(1): 190-198.
[8]	JI Jue,,GAO Xiaolu. Identification of spatial organization and spatial morphology of urban agglomeration in North Slope of Tianshan Mountains [J]. , 2012, 35(04): 687-694.