不同流视角下的贵州省空间网络结构及形成机制分析

doi:10.13249/j.cnki.sgs.2020.06.009

地理科学 ›› 2020, Vol. 40 ›› Issue (6): 939-947.doi: 10.13249/j.cnki.sgs.2020.06.009

不同流视角下的贵州省空间网络结构及形成机制分析

郑龙飞¹(), 顾伟男²^,³, 龙奋杰¹^,⁴^,*(), 张苏⁵

1. 清华大学建设管理系/清华大学恒隆房地产研究中心，北京 100084
2. 中国科学院地理科学与资源研究所/中国科学院区域可持续发展分析与模拟重点实验室，北京 100101
3. 中国科学院大学，北京 100049
4. 石家庄铁道大学经济管理学院，河北石家庄 050043
5. 贵州理工学院大数据学院，贵州贵阳 550003

收稿日期:2019-05-21 出版日期:2020-06-01 发布日期:2020-12-07
通讯作者: 龙奋杰 E-mail:tjulongfei@163.com;longfj@tsinghua.edu.cn
作者简介:郑龙飞（1995−），男，河南开封人，博士研究生，主要研究方向为大数据与区域经济学。E-mail: tjulongfei@163.com
基金资助:
国家社会科学基金项目（14AJY012）资助

The Urban Network Structure and Influential Mechanism of Guizhou Province from the Perspective of Various Flows

Zheng Longfei¹(), Gu Weinan²^,³, Long Fenjie¹^,⁴^,*(), Zhang Su⁵

1. Department of Construction Management and Hang Lung Center for Real Estate, Tsinghua University, Beijing 100084, China
2. Institute of Geographic Sciences and Natural Resources Research, Key Laboratory of Regional Sustainable Development Modeling, Chinese Academy of Sciences, Beijing 100101, China
3. University of Chinese Academy of Sciences, Beijing 100049, China
4. School of Economic & Management, Shijiazhuang Tiedao University, Shijiazhuang 050043, Hebei, China
5. School of Big Data, Guizhou Institute of Technology, Guiyang 550003, Guizhou, China

Received:2019-05-21 Online:2020-06-01 Published:2020-12-07
Contact: Long Fenjie E-mail:tjulongfei@163.com;longfj@tsinghua.edu.cn
Supported by:
National Social Science Foundation of China (14AJY012).

摘要/Abstract

摘要：

基于手机通话和高速公路收费等实测大数据，以贵州省县级行政区域为基本单元，采用C-value和T-value分析、优势流分析和网络中心性分析的方法，探究贵州省不同流空间下的控制结构、层级结构和核心?边缘结构及其网络的形成机制。结果表明：① 贵州省在信息流和物质流空间下的控制结构大致相似，但控制节点存在差异。② 在信息流和物质流空间下，贵州省都形成了省会?地市（州）?县（市）的层级化网络结构。③ 与物质流空间相比，信息流空间下贵州省空间结构的中心化趋势得到加强，区域差异进一步的扩大。④ 区域间物质流和信息流都随距离的延伸而不断衰减，且地理、文化、产业和组织距离对区域间物质流的阻碍作用显著大于信息流。经济发展水平、基础设施水平和人口规模等区域主体属性也会对区域间的要素联系强度产生影响。

关键词: 通信流, 交通流, 空间网络结构, 距离衰减, 贵州省

Abstract:

The flows of passengers, goods and information are important expressions of interaction between regions, and it is a new attempt to analyze the differences of urban network structure in the space of various flows. Based on the actual big data, such as mobile phone call detail records and highway toll data, the various flows between the county-level administrative areas of Guizhou Province are measured. This study comprehensively analyzes the control structure, hierarchical structure and core-periphery structure of urban network in Guizhou Province, through the methods of C-value and T-value analysis, dominant flow analysis and network centrality analysis. Then, the key factors influencing various flows between cities (counties) were identified, and the results show that: 1) The control structure of Guizhou Province in the space of information and material flows are similar, but there are differences in control nodes. 2) In the space of information and material flows, Guizhou Province has formed a hierarchical network structure of provincial city-prefecture-county. 3) Compared with the space of material flow, the degree of centralization of the spatial network structure of Guizhou Province in the space of information flow has been further strengthened, and the gaps between cities (counties) have been further expanded. 4) The material and information flows between regions will decrease significantly as distance increases, and the geographical, cultural, industrial and organizational distances have a greater hindrance to the material flows between regions than the information flows. The regional attributes, such as economic development level, infrastructure level and population size also have significant impacts on the various flows between cities (counties).

Key words: information flow, traffic flow, spatial structure, distance attenuation, Guizhou Province

中图分类号:

F127

郑龙飞, 顾伟男, 龙奋杰, 张苏. 不同流视角下的贵州省空间网络结构及形成机制分析[J]. 地理科学, 2020, 40(6): 939-947.

Zheng Longfei, Gu Weinan, Long Fenjie, Zhang Su. The Urban Network Structure and Influential Mechanism of Guizhou Province from the Perspective of Various Flows[J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(6): 939-947.

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参考文献 28

[1]	Long F J, Zheng L F, Song Z D High-speed rail and urban expansion: An empirical study using a time series of nighttime light satellite data in China[J]. Journal of Transport Geography, 2018. 72, 106- 118. doi: 10.1016/j.jtrangeo.2018.08.011
[2]	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.
[3]	Castells M The rise of the network society[J]. Contemporary Sociology, 1996. 38 (26): 389- 414.
[4]	董超, 修春亮, 魏冶基于通信流的吉林省流空间网络格局[J]. 地理学报, 2014. 69 (4): 510- 519. doi: 10.11821/dlxb201404007
	Dong Chao, Xiu Chunliang, Wei Ye Network structure of'space of flows'in Jilin Province based on telecommunication flows[J]. Acta Geographica Sinica, 2014. 69 (4): 510- 519. doi: 10.11821/dlxb201404007
[5]	王垚, 钮心毅, 宋小冬 “流空间”视角下区域空间结构研究进展[J]. 国际城市规划, 2017. 32 (6): 27- 33. doi: 10.22217/upi.2017.012
	Wang Yao, Niu Xinyi, Song Xiaodong Research progress of regional spatial structure under the perspective of space of flow[J]. Urban Planning International, 2017. 32 (6): 27- 33. doi: 10.22217/upi.2017.012
[6]	叶磊, 段学军, 吴威基于交通信息流的长三角地区网络空间结构及其效率研究[J]. 地理研究, 2016. 35 (5): 992- 1002.
	Ye Lei, Duan Xuejun, Wu Wei Spatial structure and efficiency of the urban network within the Yangtze River Delta based on traffic and information flow[J]. Geographical Research, 2016. 35 (5): 992- 1002.
[7]	Friedman T L. The world is flat: A brief history of the twenty-first century[M]. London: Macmillan, 2005.
[8]	金凤君, 王成金轴−辐侍服理念下的中国航空网络模式构筑[J]. 地理研究, 2005. 24 (5): 774- 784. doi: 10.3321/j.issn:1000-0585.2005.05.015
	Jin Fengjun, Wang Chengjin Hub-and-spoke system and China aviation network organization[J]. Geographical Research, 2005. 24 (5): 774- 784. doi: 10.3321/j.issn:1000-0585.2005.05.015
[9]	Wang J E, Mo H H, Wang F H Evolution of air transport network of China 1930-2012[J]. Journal of Transport Geography, 2014. 40, 145- 158. doi: 10.1016/j.jtrangeo.2014.02.002
[10]	冯兴华, 修春亮, 刘志敏, 等东北地区城市网络层级演变特征分析——基于铁路客运流视角[J]. 地理科学, 2018. 38 (9): 1430- 1438.
	Feng Xinghua, Xiu Chunliang, Liu Zhimin et al Characteristics of urban network hierarchy evolution based on the perspective of railway passenger transport in Northeast China[J]. Scientia Geographica Sinica, 2018. 38 (9): 1430- 1438.
[11]	Murca M C R, Hansman R J, Li L et al Flight trajectory data analytics for characterization of air traffic flows: A comparative analysis of terminal area operations between New York, Hong Kong and Sao Paulo[J]. Transportation Research Part C- Emerging Technologies, 2018. 97, 324- 347. doi: 10.1016/j.trc.2018.10.021
[12]	柯文前, 陈伟, 杨青基于高速公路流的区域城市网络空间组织模式——以江苏省为例[J]. 地理研究, 2018. 37 (9): 1832- 1847.
	Ke Wenqian, Chen Wei, Yang Qing Uncovering spatial organization patterns of regional city networks from expressway traffic flow data: A case study of Jiangsu province, China[J]. Geographical Research, 2018. 37 (9): 1832- 1847.
[13]	柯文前, 陈伟, 陆玉麒, 等基于高速公路流的江苏省城市网络空间结构与演化特征[J]. 地理科学, 2019. 39 (3): 405- 414.
	Ke Wenqian, Chen Wei, Lu Yuqi et al Extracting spatial structures and evolutionary characteristics of city networks in Jiangsu Province based on the expressway traffic flow[J]. Scientia Geographica Sinica, 2019. 39 (3): 405- 414.
[14]	王钊, 杨山, 龚富华, 等基于城市流空间的城市群变形结构识别——以长江三角洲城市群为例[J]. 地理科学, 2017. 37 (9): 1337- 1344.
	Wang Zhao, Yang Shan, Gong Fuhua et al Identification of urban agglomerations deformation structure based on urban-flow space: A case study of the Yangtze River Delta Urban Agglomeration[J]. Scientia Geographica Sinica, 2017. 37 (9): 1337- 1344.
[15]	孙阳, 姚士谋, 张落成中国沿海三大城市群城市空间网络拓展分析——以综合交通信息网络为例[J]. 地理科学, 2018. 38 (6): 827- 837.
	Sun Yang, Yao Shimou, Zhang Luocheng Spatial expansion of urban network for the three coastal agglomerations of China: A study based on integrated traffic information network[J]. Scientia Geographica Sinica, 2018. 38 (6): 827- 837.
[16]	Shanmukhappa T, Ho I W, Tse C K Spatial analysis of bus transport networks using network theory[J]. Physica A- Statistical Mechanics and Its Applications, 2018. 502, 295- 314. doi: 10.1016/j.physa.2018.02.111
[17]	冯兴华, 修春亮, 白立敏, 等基于公路交通流视角的吉林省城镇中心性及影响因素[J]. 经济地理, 2019. 39 (1): 64- 72.
	Feng Xinghua, Xiu Chunliang, Bai Limin et al Urban centrality and influencing factors in Jilin Province from the perspective of highway traffic flow[J]. Economic Geography, 2019. 39 (1): 64- 72.
[18]	郭卫东, 钟业喜, 冯兴华, 等长江中游城市群县域公路交通网络中心性及其影响因素[J]. 经济地理, 2019. 39 (4): 34- 42.
	Guo Weidong, Zhong Yexi, Feng Xinghua et al County-level highway network centrality of urban agglomerations and its influencing factors in the middle reaches of the Yangtze River[J]. Economic Geography, 2019. 39 (4): 34- 42.
[19]	Barnett G A, Chon B, Rosen D The structure of the Internet flows in cyber space[J]. Netcom, 2001. 15 (1/2): 61- 80.
[20]	金彪, 颜丽玲, 沙晋明, 等中国省域信息流、资金流强度的空间差异——以2015年京东手机交易为例[J]. 地理科学, 2018. 38 (2): 223- 232.
	Jin Biao, Yan Liling, Sha Jinming et al The spatial difference of the intensity of information and capital flow in Chinese provincial region: A case study of mobile phone transactions in Jingdong mall in 2015[J]. Scientia Geographica Sinica, 2018. 38 (2): 223- 232.
[21]	赵梓渝, 魏冶, 杨冉, 等中国人口省际流动重力模型的参数标定与误差估算[J]. 地理学报, 2019. 74 (2): 203- 221.
	Zhao Ziyu, Wei Ye, Yang Ran et al Gravity model coefficient calibration and error estimation: Based on Chinese interprovincial population flow[J]. Acta Geographica Sinica, 2019. 74 (2): 203- 221.
[22]	叶磊, 段学军, 欧向军基于交通信息流的江苏省流空间网络结构研究[J]. 地理科学, 2015. 35 (10): 1230- 1237.
	Ye Lei, Duan Xuejun, Ou Xiangjun The urban network structure of Jiangsu Province based on the traffic and information flow[J]. Scientia Geographica Sinica, 2015. 35 (10): 1230- 1237.
[23]	程利莎, 王士君, 杨冉基于交通与信息流的哈长城市群空间网络结构[J]. 经济地理, 2017. 37 (5): 74- 80.
	Cheng Lisha, Wang Shijun, Yang Ran The urban network structure of Ha-Chang Urban Agglomerations based on the traffic and information flow[J]. Economic Geography, 2017. 37 (5): 74- 80.
[24]	Liu X, Ben D, Wu K Measuring polycentric urban development in China: An intercity transportation network perspective[J]. Regional Studies, 2015. 50 (8): 1302- 1315.
[25]	陈伟, 刘卫东, 柯文前, 等基于公路客流的中国城市网络结构与空间组织模式[J]. 地理学报, 2017. 72 (2): 224- 241.
	Chen Wei, Liu Weidong, Ke Wenqian et al The spatial structures and organization patterns of China's city networks based on the highway passenger flows[J]. Acta Geographica Sinica, 2017. 72 (2): 224- 241.
[26]	龙奋杰, 郑龙飞, 石朗, 等基于公路大数据的黔中城市群一体化研究[J]. 浙江大学学报(工学版), 2019. 53 (2): 307- 314. doi: 10.3785/j.issn.1008-973X.2019.02.014
	Long Fenjie, Zheng Longfei, Shi Lang et al Regional integration of Qianzhong cities using highway big data[J]. Journal of Zhejiang University (Engineering Science), 2019. 53 (2): 307- 314. doi: 10.3785/j.issn.1008-973X.2019.02.014
[27]	刘承良, 管明明, 段德忠中国城际技术转移网络的空间格局及影响因素[J]. 地理学报, 2018. 73 (8): 1462- 1477.
	Liu Chengliang, Guan Mingming, Duan Dezhong Spatial pattern and influential mechanism of interurban technology transfer network in China[J]. Acta Geographica Sinica, 2018. 73 (8): 1462- 1477.
[28]	贺灿飞, 金璐璐, 刘颖多维邻近性对中国出口产品空间演化的影响[J]. 地理研究, 2017. 36 (9): 1613- 1626.
	He Canfei, Jin Lulu, Liu Ying How does multi-proximity affect the evolution of export product space in China[J]. Geographical Research, 2017. 36 (9): 1613- 1626.

市县	信息流		市县	物质流
市县	T-value	C-value	市县	T-value	C-value
贵阳市	1029128	0.0835	贵阳市	94769952	0.0238
遵义市	205129	0.1132	遵义市	44700927	?0.0092
清镇市	174153	?0.0323	清镇市	33111773	?0.0562
六盘水市	169367	0.0478	安顺市	17873098	0.0015
凯里市	160669	0.0662	凯里市	14766611	0.0026
安顺市	157199	0.1098	都匀市	14548557	0.0069
毕节市	150151	?0.0900	桐梓县	13869929	0.0073
大方县	148908	?0.1343	兴义市	12945096	?0.0065
织金县	141605	?0.1625	毕节市	11876469	0.0151
黔西县	131302	?0.1485	镇宁县	11580648	0.0074

	总通话流	总车流	总通话流?标准化	总车流? 标准化
网络中心势	13.88%	4.40%	14.02%	4.41%
节点中心度
最大	贵阳市	贵阳市	贵阳市	贵阳市
	(545566)	(48824900)	(261.39)	(145.26)
第二	遵义市	遵义市	遵义市	遵义市
	(109113)	(22797648)	(36.49)	(61.98)
第三	清镇市	清镇市	清镇市	清镇市
	(90996)	(17154754)	(27.16)	(43.93)
第四	六盘水市	安顺市	六盘水市	安顺市
	(89154)	(9128572)	(26.21)	(18.25)
第五	凯里市	凯里市	凯里市	凯里市
	(83291)	(7488388)	(23.19)	(13.00)
最小	万山特区	瓮安县	万山特区	瓮安县
	(4879)	(195886)	(?17.22)	(?10.33)

变量类型	变量	含义与测度
因变量	总通话	用市县间的通话时长表示(h)
	总车流	用市县间的总车流量表示(万辆)
自变量：市县间距离	地理距离	地理空间的距离, 用行政中心的直线距离表示(km)
	文化距离	生活习俗的相似程度, 用是否同属一个民族表示
	产业距离	产业结构的相似程度, 用产业结构相似系数的相反数表示^[28]
	组织距离	规章制度及行为的相似度, 用是否同属一个地市(州)表示
自变量：市县主体属性	经济发展	用人均GDP表示(元)
自变量：市县主体属性	人口规模	用年末常住人口数表示(万人)
	产业兴旺度	用规模以上工业总产值表示(亿元)
	基础设施	用固定资产投资额表示(亿元)
控制变量	居民收入	用城镇居民可支配收入表示(元)
	消费水平	用社会消费品零售总额表示(亿元)

	总通话	总车流	总通话	总车流
地理距离	?1.5940^***	?2.1730^***	?1.4800^***	?2.1110^***
	(0.0442)	(0.0786)	(0.0349)	(0.0714)
文化距离	0.1030^**	?0.4020^***	?0.0520	?0.5400^***
	(0.0484)	(0.0855)	(0.0343)	(0.0801)
产业距离	?0.0756^*	?0.3430^***	?0.0762^**	?0.2650^***
	(0.0440)	(0.0768)	(0.0316)	(0.0707)
组织距离	?0.9260^***	?0.9960^***	?1.3540^***	?1.4530^***
	(0.0801)	(0.1400)	(0.0571)	(0.1200)
经济发展水平			0.1760^***	0.3670^***
			(0.0445)	(0.1270)
人口规模			0.8400^***	0.0157
			(0.0658)	(0.1140)
产业兴旺度			?0.0252	0.1400^***
			(0.0203)	(0.0415)
基础设施水平			0.7690^***	0.7210^***
			(0.0445)	(0.1240)
控制变量	无	无	有	有
R²	0.465	0.344	0.810	0.601
样本数量	3081	3081	3081	3081

不同流视角下的贵州省空间网络结构及形成机制分析

The Urban Network Structure and Influential Mechanism of Guizhou Province from the Perspective of Various Flows

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