不同流要素视角下沿海港口城市体系的网络空间联系

doi:10.13249/j.cnki.sgs.2022.11.002

Abstract

Abstract:

With the rapid development of globalization, information and communication technology and high-speed railways, the frequency of element flow has greatly increased, and the comprehensive connection of multiple flow perspectives has become the key to the development of port cities and regions. With the help of GIS, Matlab and Gephi and other technical means, different flow elements are used to analyze the network connection and spatial variation characteristics of China’s coastal port city system in 2006 and 2016. The results show that: 1) In the 5 flow element networks, the total number of element connections between cities has increased and the increase degree has been larger, and the degree of concentration and dispersion and the characteristics of regional distribution have changed significantly. The cyberspace structure of the port city system is transforming from a ‘point axis’ model to a ‘networked’ model. 2) The hub cities with different flow elements have a high degree of overlap, but the network structure has obvious differences. Logistics and passenger flow present the network characteristics of coexistence of ‘group development’ and ‘hub-spoke’. The capital flow and technology flow have developed significant ‘core-periphery’ and hierarchical spatial patterns. Information flow presents a balanced network development model. 3) Different flow elements describe the port city system at different levels, but they are closely related. Logistics and passenger flow affect each other and are cause and effect. The flow of capital and technology has a positive effect on logistics. The impact of information flow on logistics has obvious lag; the port city system has changed from a single flow element to a comprehensive development of multiple flow elements, and the connections between port cities are more diversified, and the interaction and integration with external flow elements have been realized.

Key words: multi element flow, urban network, flow space, port city system

CLC Number:

K902

Qin Yafeng, Guo Jianke. Network Spatial Connection of Coastal Port City System from the Perspective of Multiple Flows[J].SCIENTIA GEOGRAPHICA SINICA, 2022, 42(11): 1867-1878.

Figures/Tables 9

Table 1

Table 2

Data information and sources

流要素	类型	信息	构建原则	来源
物流	中国各港口间、中国始发至全球其他港口的船期表	集装箱船只起讫港口、出发和抵达时间、船名和航次	从中筛选出中国沿海各港口城市间（直达及中转）的船期信息，并以此为依据构建物流网络	《中国航务周刊》（ http://csg.wuliujia2018.com/）
客流	各城市中列车时速高于120 km/h的列车，也即以K、T、Z、D、C、G开头的列车	车次、始发站、终到站、运行时间	从中筛选出中国沿海各港口城市间的列次信息，并以此为依据构建客流网络	《全国铁路列车时刻表》（ https://www.12306.cn/index/）、《中国交通统计年鉴》（ https://www.mot.gov.cn/tongjishuju/）
资金流	中国城市间城际控股型（含全资）的企业间投资联系数据	企业名称、注册时间、注册资本、注册地址、投资企业、投资资本、投资地址等	从中筛选出中国沿海各港口城市企业间的投资联系数据信息，以此为依据构建资金流网络	启信宝（ www.qixin.com/）^①(①“启信宝”是从全国企业信用信息公示系统、中国法院裁判文书网、中国执行信息公开网等100家网站提取官方数据。通过企业名称、人名、品牌、联系方式、网址、专利等企业信息关键字或者组合关键字多维度锁定目标企业。)
技术流	各城市之间关于专利、科技^{成果及科技报告的单位合作联系数据}	成果类型、发表成果时间、发^{表成果作者单位、单位地址等}	采取一定规则^②(②选取有合作关系的数据，汇总并进行加权。即参与每篇专利等的作者群，如为两两联系，则分别计为1/2；3个联系的数据，分别计为1/3，以此类推，由此得出港口城市网络联系矩阵。)对相关专利、成果和报告进行量化。从中筛选出中国沿海各港口城市间技术联系数据信息，以此为依据构建技术流网络	万方数据知识平台^{（ www.wanfangdata.com.cn）}
信息流	“两两城市之间”的信息流^{通数据（剔除了无效和干扰信息）}	时间、词条内容（积极/消极）、^地址等	从中筛选出中国沿海各港口^{城市间信息联系数据，以此为依据构建信息流网络}	百度搜索（ www.baidu.com）^③(③百度拥有目前世界上最大的中文信息库,并使用了高性能的“网络蜘蛛”程序自动在互联网中搜索信息, 能在极短的时间内收集到最大数量的互联网信息。)界面

Table 2

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港口城市群	港口
注：未含港澳台数据。
环渤海	天津、丹东、大连、营口、锦州、唐山、沧州、秦皇岛、烟台、威海、青岛、日照
长三角	上海、苏州、宁波、连云港、舟山、南京、南通、盐城、温州、常州、无锡、嘉兴、台州、泰州、扬州、镇江
珠三角	广州、深圳、珠海、汕头、惠州、东莞、中山、阳江、江门、茂名
东南沿海	福州、莆田、泉州、厦门
西南沿海	湛江、北海、钦州、防城、海口

	流要素类型	流要素中心	流要素副中心	排名前20%城市所占比重/%	H指数
注：1.流要素中心、流要素副中心和排名20%的城市均依据其加权度中心性界定；2.H指数是指赫希曼–赫芬达尔指数，用于测量港口城市各流要素的集中与分散程度；3.未含港澳台数据。
2006年	物流	上海	深圳、宁波	71.3	0.057
	客流	上海	南京、无锡	47.3	0.038
	资金流	深圳	上海、宁波	74.1	0.075
	技术流	上海	深圳、广州	73.5	0.113
	信息流	上海、南京、宁波	苏州、深圳	31.0	0.003
2016年	物流	上海、宁波	深圳、青岛	83.6	0.078
	客流	上海、南京	无锡、常州	60.7	0.032
	资金流	上海、深圳、广州	苏州、南京	73.8	0.081
	技术流	深圳、上海	广州、南京	77.7	0.068
	信息流	上海、南京、深圳	广州、南通	24.7	0.003

Network Spatial Connection of Coastal Port City System from the Perspective of Multiple Flows

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冲击程度排序	物流对其他流要素冲击		其他流要素对物流冲击
冲击程度排序	2006年	2016年	2006年	2016年
注：未含港澳台数据。
1	客流	客流	客流	资金流
2	资金流	技术流	信息流	技术流
3	技术流	资金流	技术流	客流
4	信息流	信息流	资金流	信息流

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