选取全行业企业的投资数据,通过逐一计算海量企业股权联系的所有可能链接路径,提出了利用企业之间联系研究城市网络的新思路。对长三角地区进行实证研究,结果表明：有向网络中,上海、杭州与合肥是区域资本集散中心。其中,上海是整个区域的链接枢纽,对江苏的辐射影响尤为突出。无向网络中,上海与杭州的核心地位明显,但淮北、衢州等边缘性节点的网络层级也大幅提升。上海、杭州与合肥加速了整个区域的一体化进程,南京的网络地位有较大的提升空间。

Currently, existing articles about urban network from the perspective of firm relationship mainly use frims’ spatial and organizational structure to quantify intra-firm linkages, and then to study connections between cities. However, such research method neglects inter-firm linkages, especially those linkages among different-industry firms, resulting in thelack of authenticity in establishing urban network. Investment linkages between enterprises often bring the flow of institutions, knowledge and talents, which opens a new window for the study of inter-firm linkages. Therefore, this study uses investment data of all-industry enterprises published by ‘Tian Yan Cha’ website to propose an alternative way for establishing urban network. The data of ‘Tian Yan Cha’ website are authorized by the State Administration of Industry and Commerce, and have been widely used in business investigation and economic research. Through digging out all possible investment linkage routes between each two firms from massive data, this study chooses the Yangtze River Delta as the empirical strudy area in order to discover some new characteristics of urban network in node hierarchy and link under the support of real enterprises’ investment behavior. The study results show that in directed urban network, Shanghai, Hangzhou and Hefei are regional capital distribution centers. Among these cities, Shanghai is the linkage hub of the whole region, while Hangzhou and Hefei dominate their provincial cities respectively. One interesting phenomenon is that Shanghai produces strong effects on cities of Jiangsu Province, which indirectly weakens Nanjing’s network status. Nanjing's disadvantaged status is also related to its insufficient amount of data involved in the analysis, which implies that Nanjing’s firms prefer investing outside the Yangtze River Delta. In undirected urban network, cities’ intermediary effect vary dramatically, the strength of Shanghai and Hangzhou becomes more obvious. At the same time, the network status of some peripheral cities including Huaibei and Quzhou has been significantly promoted, and the former city has an energy tycoon firm producing large amount of investment linkages and the latter city is famous for its ‘family firms’. As the intermediary hub, Shanghai stimulates the integration process of the Yangtze River Delta while Hangzhou and Hefei mainly play the role of ‘bridge’ in connecting their provincial cities and Shanghai. There are nearly none prominent intermediary nodes in Jiangsu Province, which demonstrates that there is great room for Nanjing to improve its network status. There are 3 research topics deserve further attention. The first is the investment amount is more accurate in quantifying urban linkages than investment frequency. Secondly is study on single-industry firm investment may explain why gap exists between some city’s network status and their economic status. The last is that the massive firm investment data inevitably lead to magnitude differences, how to eliminate the influence of data quantity on the basis of guaranteeing network characteristics remains to be explored.

当前,学者普遍认同城市体系研究的重心从等级范式向网络范式转向。在大量的文献梳理基础上,阐述20世纪80年代以来中国城市体系研究范式的转向过程,并进一步梳理中国城市网络研究的进展、问题与关注重点。结果表明：2010年之后,网络成为中国城市体系研究的主流范式,等级体系文献数量呈波动递减趋势,全国和长三角成为热点关注区域;属性数据一定程度上弥补了早期城市网络研究中数据匮乏的状况,关系数据应成为未来研究的侧重点;不同研究视角及其所应用的数据存在着一定的局限性,多元视角的综合研究成为趋势。在此基础上,提出城市网络研究未来可能的突破点,包括：调整国家城市网络的研究框架、超越交通数据直接考察城市间的实体联系、测度企业外部的横向联系、有向加权网络分析和可视化、增加研究中的地理“意味”等。

At present, there is a consistency that the focus of urban system research has shifted from the hierarchical to network paradigm. Based on an extensive review of existing studies, this paper investigates the shift of paradigm of China's urban system research since the 1980s, and discusses the development situation, main problems, and key topics of related research on urban network. The results show that: (1) After 2010, the amount of literature about urban network has increased dramatically and become the mainstream of urban system research, while the number of studies on the hierarchy system decreased. And the existing studies mainly focus on the urban system at national level and in the Yangtze River Delta. (2) The attribute data to a certain extent compensate for the lack of data in the early urban network research. Meanwhile, although the gravity model was widely used in these studies, its suitability is gradually decreasing. (3) In contrast, the relationship data have attracted scholars' attention, which is mainly applied to the urban system research from three perspectives: enterprise organization, traffic flow and information flow. However, due to the limitations of different perspectives, a comprehensive research on multiple perspectives has gradually become imperative. (4) The identification analysis has been the dominant theme of urban network research. Visualization analysis and design for urban system is mainly supported by directed unprivileged networks and undirected weighted networks. (5) Five important aspects of urban network research in future include adjusting the research framework of national urban network, examining the physical connections between cities directly without traffic data, measuring the horizontal linkages between the outside enterprises, analyzing and visualizing the directed weighted network research, and intensifying the geospatial characteristics of urban network research.

信息流能够客观反映城市社会经济联系，对城市网络研究具有重要价值。基于2011、2014和2017年中国城市间信息关注度数据，利用社会网络分析法，从网络密度、中心度、城市联系等方面分析中国城市网络结构的演变特征及其驱动因素，研究表明：1）城市信息网络结构明显拓展但网络总体联系强度仍较低；省域内部信息联系强度高于跨区域联系，信息联系仍受行政区划的影响较大。2）网络节点中心度显著提高，热点区主要集中在东部沿海三大城市群；中西部部分区域核心城市如重庆、成都、郑州等成长为全国性核心城市，但中西部地区城市信息发展仍与东部地区差距较大。3）信息联系层级分布特征明显，并呈现局部区域联系紧密、区域一体化特征；核心网络由以北京、上海为核心的“轴-辐”结构演变为以“北京-上海-广深-成渝”为核心的“菱形”结构。4）信息化水平、经济发展基础、城市职能等互补性因素，网络营销、大事件效应等介入性因素及信息技术变革、城市群建设等高效性因素相互作用，共同促进信息网络结构优化重组。

Information flow can objectively reflect the social and economic linkages of cities, and is of great value to the study of urban networks. Based on the 2011, 2014 and 2017 intercity information attention data, we analyze the evolution characteristics and driving factors of urban network structure in China from the aspects of network density, centrality, urban linkages by the social network analysis method. It shows that:1) The structure of urban information network has been significantly expanded, but the overall strength of the network linkages is still low. The strength of urban linkages within provinces is far higher than that of cross-region, it is still heavily influenced by administrative territorial entity.2) The centrality of information network nodes has been significantly improved. The hot spots are mainly concentrated in the three major urban agglomerations along the eastern seaboard. Some central and western regional core cities such as Chongqing, Chengdu and Zhengzhou have grown into national core cities, but there is still a big gap between the mid-western and the eastern region of information development.3) The hierarchical distribution of urban information network is obvious, and it presents the characteristics of local area close connection and regional integration. The core network evolved from "axis-spoke" structure with Beijing and Shanghai as its core to a diamond structure with Beijing-Shanghai-Guangzhou,Shenzhen-Chengdu, Chongqing as its core.4) Complementary factors such as information level, the basis of economic development, urban functions, and the intervening factors of network marketing, and big event effect, as well as high-efficiency factors such as information technology reform, urban agglomeration construction are interacted to promote the optimization and reorganization of information network structure.

知识经济时代城市间的创新关系是新时代城市间相互作用关系的新内涵,研究者尝试采用各种方法探索城市间创新关系及其网络特征。然而,如何从理论上建构知识流动空间的城市间创新关系?如何设计更加合理的城市间创新网络模拟方法?这些问题却少有专门探讨。基于相关研究,本文提出了城市间创新关系构建的理论框架,认为城市间创新网络本质上是区别于“硬网络”的“软网络”,是一种主观的关系建构过程,需要经过异城创新主体间的点—点关系向城—城之间关系的尺度转换,这一转换过程容易发生夸大或偏离城市间客观存在的创新关系,对结果的精确度产生很大影响,应对关系建构给予充分理论论证;本文论述了4种城市间创新关系建构和网络模拟方法,包括科技成果异城合作的城市间无向网络构建方法、科技成果转让转移的城市间有向网络构建方法、高端人才跨城移动的城市间创新网络建构方法和创新企业机构多城分布的城市间创新网络建构方法,并运用相关数据进行了模拟试验与结果展示,来反映城市间创新关系的不同方面。本研究有助于推动从城市地理学视角和城市关系的维度探讨全球/区域的创新空间格局,为城市间创新网络研究提供理论和方法支撑。

The interactive relationships between cities in the knowledge economy era have attracted much attention. Researchers have applied a range of methods to explore intercity innovative relationships and associated network characteristics. It nevertheless remains unclear just how intercity innovative relationships can be theoretically constructed based on knowledge flow space and how further scientific simulation methods can be designed. Research questions in this area have rarely been explored in detail, an issue which has inevitably placed obstacles on further exploration. A framework for the theoretical construction of intercity innovative relationships is presented in this study; the basis for this research is that an intercity innovation network is essentially a 'soft network', distinct from a 'hard network'. These interconnections are founded on a subjective relationship construction process and therefore necessitate scale transformation from 'point-point' connections between innovative subjects in different cities with respect to 'city-city' interactions. At the same time, this transformation process is prone to exaggerations and deviations from objective intercity innovative relationships and therefore exerts considerable influence on the accuracy of results such that constructions must be entirely theoretical. Four construction methods for intercity innovative relationships and network simulation are summarized in this study, including an intercity undirected network based on cross-city co-operations between scientific and technological achievements, an intercity directed network based on the cross-city transfer of scientific and technological achievements, an intercity innovation network based on the cross-city flow of high-end talents, and an intercity innovation network based on the multi-city distribution of innovative enterprises and institutions. Simulation tests were then undertaken using relevant data to reflect aspects of these relationships. The results of this analysis are conducive to further exploration of global and regional innovative spatial patterns from the perspective of urban geography and intercity relationships and provide a theoretical and methodological foundation for further research on intercity innovation networks.

交通流是城市经济联系最显性的表现,为更清晰地识别城市网络结构提供了新视角。本研究采用高速公路联网收费数据作为交通流“关系数据”,综合图论、社会网络分析法（SNA）和GIS空间分析,以广东省为研究区域探索高速公路交通流网络空间结构特征,并利用QAP回归法探究影响网络形成的因素。结果表明：① 广东省高速公路已形成典型的网络式空间结构,具有“小世界”网络效应。其节点度值服从幂律无标度分布,交通出入量基本平衡,节点重要性自区域中心向外围递减;网络有向边权值等级差异显著,优势交通流具有“向心指向”特征和距离依赖性。② GDP、常住人口、社会消费品零售总额、县区间行驶距离等已成为影响广东省高速公路交通流网络形成的重要因素。其中人口因素是影响城市间交通流动的主要因素,网络形成受制于距离因素,消费因素逐渐成为刺激城市间交通流动的强大动力。③ 广东省高速公路交通流动模式分为邻近指向型、中心指向型和等级指向型三种,以邻近指向型为典型模式。研究结果对于揭示交通流动规律,丰富城市网络理论,推动道路合理规划和区域协调发展具有重要理论和实践意义。

Traffic flow is the most powerful indicator to map urban economic connections, which provides a new perspective to identify urban network spatial structure clearly. This study uses Guangdong's data to explore the spatial structure of expressway network based on theory of synthesized graph, social network analysis (SNA) and GIS spatial analysis. QAP regression method is used to explore the factors which influence the network characteristics. The results show that: (1) The Guangdong's expressway forms a typical network spatial structure with “small world” network effect. The out-degree and in-degree values of network nodes follow the power law scale-free distribution, traffic volume is basically balanced, and the importance of nodes decreases from regional center to periphery. There are significant differences in the weights of directed edges, and the dominant traffic flows have the characteristics of “centripetal direction” and “distance dependence”. (2) The results of QAP regression show that the character of Guangdong's expressway traffic flow network is significantly affected by GDP, resident population, total retail sales of consumer goods, and the distance. Population is the most powerful factor which affects inter-city traffic flows, the formation of network is restricted by distance, and the consumption factor is gradually becoming a powerful driving force to stimulate the inter-city traffic flows. (3) The traffic flow patterns of expressway in Guangdong are divided into three types, namely, proximity-oriented, central-oriented and hierarchical-oriented. The proximity-oriented traffic flow is typical, which is widely distributed in the province; the central-oriented traffic flow is concentrated in the Pearl River Delta urban agglomeration; and the hierarchical-oriented traffic flow is mainly distributed between Guangzhou and Qingyuan, and between Shenzhen and Shantou. The research results have important theoretical and practical significance for revealing the laws of traffic flow, and enriching urban network theory. Furthermore, it provides theoretical support for scientific road planning and coordinated regional development.

随着经济全球化和区域一体化的快速发展,城市间的交流日益密切,要素的流动性和互赖性促进了城市网络的形成,并成为一种新的区域组织模式和空间结构。本文基于2018年腾讯人口迁徙数据,构建了372×372关系数据矩阵,并从不同交通方式出发,系统刻画了中国城市网络格局。研究表明：① 网络关联度高的城市主要集中在胡焕庸线以东区域,尤其是长三角、珠三角、京津冀、成渝等城市群地区的聚集程度最高,成为中国城市网络格局的主控力量;而西北半壁的城市处于被支配地位。② 依据迁徙量,将中国城市网络划分为国家级、大区级、区域级、地方级和基座级网络。城市网络结构随交通方式而变化,当网络等级下移时,节点城市趋于增多,网络密度加大,但网络范围趋于缩小,网络等级与迁徙路径存在着密切的关联性,国家级网络与空运相关,区域级网络与铁路运输相呼应,地方级网络与汽车运输相关。③ 不同运输方式的经济时空距离决定了网络结构,是引致网络随路径不同而产生分异的基本因素。

With the rapid development of economic globalization and regional integration, the connection between cities is increasingly close. The mobility and interdependence of elements have promoted the formation of city network and become a new regional organization model and spatial structure. Based on Tencent's migration data, this paper constructs a 372×372 relational data matrix, and systematically depicts the city network pattern in China from different modes of transportation. The results show that cities with high network correlation degree are mainly concentrated in the area east of the Hu Huanyong Line, especially in the Yangtze River Delta, Pearl River Delta, Beijing-Tianjin-Hebei region, Chengdu-Chongqing region and other urban agglomeration areas, which have the highest concentration and become the main control power of China's city network pattern; and the cities in the northwest half are at a disadvantage status. According to the amount of migration, the Chinese city network is divided into national, large regional, regional, local and pedestal networks. The city network structure changes with the mode of transportation. When the network level moves down, the number of node cities tends to increase and the network density increases, but the network range tends to shrink. There is a close correlation between the network level and the migration path. National-level network related to air transportation, regional network related to railway transportation, and local network are relevant to automobile transportation. The economic space-time distance of different transportation modes determines the network structure, which is the basic factor that causes the network to differentiate with different paths.

迁徙流作为互联网时代的新产物,信息流、资本流、交通流等流空间的基本载体,能客观地反映城市间地理行为关系,对于刻画城市网络结构具有重要意义。基于地级以上城市的百度迁徙大数据,本文尝试从全域和净迁徙的视角探索研究中国城市网络结构特征,对其网络的层级、关联关系和影响因素等进行挖掘提取。研究发现：① 全国城市网络呈现出稳定的、层级明确的三棱锥四顶点“钻石型”结构,与主要城市群的经济规模空间分布相吻合;② 区域网络表现出向高级别行政中心集聚的“核心—外围”放射状结构;③ 以省会城市为核心的典型小世界特征比较凸出,小世界网络的可达性和连通性较高;周口、阜阳、赣州、上饶、重庆等作为主要劳动力输出型城市,深圳、东莞、广州、北京、上海等城市成为了主要的外来人口聚集地,并形成了对应的人口就近输送网络关系;④ 城市的行政地位、经济规模、交通枢纽建设、劳动力资源等因素都对其网络控制力和影响力起到了决定性作用。最后,研究结合中国城市网络结构特征及其主要影响因素,提出相关政策建议,以期为中国城市网络结构均衡发展与建设提供借鉴依据。

随着城市间经济联系日益密切,资本跨区域流动逐渐成为影响区域发展的关键因素,企业异地投资是资本流动的微观体现,探讨企业异地投资特征对于区域发展具有重要意义。为此,论文以1998—2018年中国上市企业异地投资数据为研究对象,构建中国异地投资网络,从全国和东中西3大地区角度分析异地投资网络的空间演化特征及影响因素。研究发现：全国和3大地区异地投资网络节点中心性空间集聚特征明显,主要集中在5大城市群地区;异地投资网络空间上表现出明显的层级结构、空间集聚性、路径依赖等特征;投资净流入和净流出城市仍主要位于东部地区,但投资活动存在向中部和西部发展的趋势;城市的经济发展水平、产业结构、金融环境等均对全国和东中西3大地区呈现差异化的影响。

基于企业网络联系构建城市网络研究是近年来的研究热点。以往研究多运用企业总部与分支机构的空间位置关系建立企业内部联系，以构建城市网络，缺少从企业间实际联系层面对城市网络的研究。本文利用企业间跨城市投融资关系建立城市联系并探究其驱动机制，为中国城市网络研究提供了新的思路。研究发现：①2013—2016年全国企业间跨城市资本流动数量不断增加，全国范围内投融资覆盖面越来越广，资本越发集中于大城市。②中国资本流动城市网络基本形成了以京津冀、长三角、珠三角、成渝地区以及武汉等城市为重要节点相互联系的空间结构，京深、京沪和深沪是网络中最重要的城市联系对。③长三角城市群内部联系最为密切，形成的城市网络最为复杂，资本活跃程度高；京津冀、珠三角城市群内部城市间联系相对较弱，“核心-边缘”结构分化较为严重。④中国城市网络嵌入在独特的制度环境中，市场和政府作用共同导致了城市网络空间结构演化。

In recent years, it has been an increasing number of studies on the construction of urban networks based on network of firms, in particular, using intra-firm links established by spatial location of headquarters and branches to build urban networks. Different from previous researches, the article builds interurban capital flow network based on the inter-city investment and financing relationship of companies, and finds that:1) The number of interurban capital flows among enterprises increases from 2013 to 2016, and the cover range of investment and financing is becoming wider. Capital is increasingly concentrated in large cities.2) Beijing-Tianjin-Hebei Region, Yangtze River Delta, Pearl River Delta, Chengdu-Chongqing area and Wuhan area stand out in the urban networks shaped by China’s capital flows. The capital flows among Beijing, Shanghai and Shenzhen are the most important connections in the network.3) Compared with the Beijing-Tianjin-Hebei Region and the Pearl River Delta, the internal links of the Yangtze River Delta are more complex and stronger, with a higher level of activity of capital.4) The interurban capital flow is embedded in China's unique institutional environment. The market and the government have influenced the evolution of spatial structure of urban network together.

知识经济与网络社会时代,知识合作网络成为理解城市创新过程的重要视角。在知识合作网络中,存在两种不同类型的合作模式,即“本地蜂鸣”和“全球管道”。本文以“蜂鸣—管道”模型的逻辑思想和理论假设为基础,探讨区域内和跨区域两种不同城市知识合作模式的特征及其对城市知识产出的影响。首先提出了蜂鸣与管道的测度方法;然后以“Web of Science”数据库为基础构建了中国20个城市群的知识合作网络,考察了网络中蜂鸣和管道的演化路径和组合特征,归纳出城市知识合作模式的不同类型;最后,通过计量模型揭示了蜂鸣和管道对城市知识产出的影响。结果显示：① 中国城市知识合作网络中的蜂鸣与管道表现出渐进积累和自我强化的空间演化特征;② 根据蜂鸣和管道的组合特征,可将城市划分为“网络型”“外向型”“孤岛型”“内向型”4类,不同类型的城市在知识网络中的空间分布规律和发展演进路径存在差异;③ 根据负二项回归模型发现,蜂鸣与城市知识产出呈倒“U”型关系;管道与城市知识产出显著正相关;此外,两种合作模式在促进城市知识生产的过程中存在互补效应。

以湖北省为研究对象,基于铁路客流的动态关系数据,采用城市网络连锁模型,以城市之间铁路客流的流动性和集聚性为研究切入点,辨析省域城市的网络关联强度和空间组织结构特征。研究表明：① 湖北省域城市网络结构初步形成,整体呈现“东强西弱,北低南高”的态势,省域“单中心”网络格局特征突出,武汉是链接鄂东、鄂中、鄂西三大区域的核心枢纽;② 中心城市指向性和地域邻近指向性特征明显,整体受省域中心城市武汉的影响较大,与之紧密联系的周边城市是构成武汉“1+8”都市圈的主体;③ 交通轴线是客流联系的主要方向,以“十堰—襄阳—荆门—武汉”、“恩施—宜昌—荆州—武汉”等城市为主体的“>”型城市带,其空间组织结构联系较为紧密,是推动湖北省经济发展和实施城镇化战略的重要载体,同时襄阳、宜昌在鄂西地区的枢纽作用日益凸显。最后基于实证研究结论,为省域空间规划的要义、趋势和取向提出了若干探讨。

构建层次分明、结构合理的科技合作网络对于推动京津冀协同创新具有非常重要的作用。本文从基于知识创新的科研合作网络和基于技术创新的技术合作网络的角度,使用合作论文和合作专利数据,采用社会网络分析法对2013—2018年京津冀地区地级及以上城市之间科技合作网络的演变特征进行了分析,并基于半参数估计和面板计量模型实证检验了影响京津冀78个城市对之间科技合作的主要因素。主要结论为：① 京津冀科研合作网络快速成长,呈现北京与天津双核引领,以京津为主轴、京保石为次轴的空间结构。② 京津冀逐渐形成了以北京为主中心,天津、石家庄为次中心,廊坊、保定、沧州为三级枢纽,其他城市为节点的技术合作网络结构。③ 空间距离越近的城市之间越容易产生科技合作,高铁开通带来的时空距离压缩可以减弱科技合作的空间衰减系数;相对于科研合作,技术合作对于空间距离更为敏感;技术邻近性是促进城市间科技合作的主要驱动因子,尤其是对技术合作的促进效应更为明显;经济发展差距越小的城市之间相对容易产生技术合作。

Building a hierarchical and reasonable structured science and technology cooperation network plays a very important role in promoting collaborative innovation in Beijing-Tianjin-Hebei region (BTH). This paper analyzes the evolutionary characteristics of the science and technology cooperation network between cities in the BTH during 2013-2018 from two perspectives: the research cooperation network based on knowledge innovation and the technology cooperation network based on technology innovation. This paper uses micro data of collaborative papers and collaborative patents and applies social network analysis to examine the evolutionary characteristics of the science and technology cooperation network between cities in the BTH from 2013-2018, and empirically tests the influence of 78 pairs of city in the study area on the science and technology cooperation based on panel econometric model factors. The main conclusions can be drawn as follows. First of all, the rapid growth of the research cooperation network of the BTH presents a spatial structure characterized as the twin cores of Beijing and Tianjin, with Beijing-Tianjin as the main axis and Beijing-Baoding-Shijiazhuang as the secondary axis. Second, the BTH gradually formed a technical cooperation network structure with Beijing as the main center, Tianjin and Shijiazhuang as secondary centers, Langfang, Baoding and Cangzhou as tertiary hubs, and other cities as nodes. Finally, small distance plays a positive role in science and technology cooperation between cities, and the compression of spatial and temporal distance brought by the operation of high-speed rail can weaken the spatial attenuation coefficient of science and technology cooperation. Compared with research cooperation, technical cooperation is more sensitive to spatial distance. Technological proximity is the main driving factor for promoting science and technology cooperation between cities, especially the promoting effect on technological cooperation is more obvious. The smaller the gap of economic development, the easier it is to generate science and technology cooperation cooperation between cities.

随着城市间关系趋向于网络化发展以及外部急性冲击和慢性压力的增加,城市网络结构韧性作为衡量区域韧性的重要手段,致力于评估城市网络系统在面对突发故障或扰动时,能够抵御、吸收和恢复原有网络特征和重要功能的能力。以中国346个地级及以上城市为研究对象,采用2017年百度指数、腾讯人口迁徙大数据等,在构建信息、交通、经济和综合城市联系网络的基础上,从城市节点和网络层面对其层级性和匹配性进行测度,并对中断场景下网络的传输性和多样性进行分析,进而针对各网络从不同视角提出网络结构优化策略和建议。结果表明：① 2017年中国346个地级及以上城市的信息、交通、经济和综合联系网络整体上呈现以“胡焕庸线”为界的“东密西疏”的分布格局,但空间结构各有特点。② 信息、交通和经济三大网络的层级性排名依次为经济>信息>交通网络。大城市及省会城市因其信息技术的快速创新与传播、产业经济的高度发展与集聚及交通基础设施的快速配备与完善在网络中具有更高的层级。③ 信息、交通、经济三大网络均具有异配性特征。信息网络中高权重节点与低权重节点间的跨区域交流机会更多,因而具有最高的异配韧性。④ 信息网络的传输与多样韧性稍高于经济网络,远高于交通网络。同一城市节点的故障或中断在影响网络传输性的同时,也会累及网络的多样性。

With the rapid development of information technology, the interweaving physical infrastructure and virtual urban networks show more abundant connotations. As one of the most effective indicators to measure regional resilience, urban network structure resilience focuses on the capacity of the urban network system to restore, maintain, or improve the original network characteristics and important functions when faced with external acute shock and chronic pressure. Therefore, evaluating the resilience of the urban network structure is of great significance for recognizing and understanding regional resilience. Taking 346 cities of China at the prefecture level and above as the research objects, this study constructed information, transportation, economic, and comprehensive connection networks based on the Baidu index, Tencent location services, and social statistics data. The resilience of the urban network was then measured and evaluated in four aspects: hierarchy, heterogeneity, transmissibility, and diversity. The last two properties are in interrupting simulation scenarios. Accordingly, the optimization strategies and suggestions for the network structure were put forward. Based on the results, the following conclusions can be drawn: (1) Although China's information, transportation, economic, and comprehensive networks among the 346 prefecture-level cities and above in 2017 presented a spatial pattern of "dense in the east and sparse in the west" with "Hu Huanyong Line" as the boundary, their spatial structures exhibit their own characteristics. (2) The hierarchy of information, transportation, and economic networks from strong to weak is in the following order: economic network > information network > transportation network. Large cities and provincial capital cities have a higher hierarchy in a network because of the rapid innovation and dissemination of information technology, the high development and agglomeration of industrial economy, and the rapid allocation and improvement of traffic infrastructures. (3) The three major networks of information, transportation, and economy are all heterogeneous. In the information network, there are more opportunities for cross-regional communication between high-weighted and low-weighted nodes, thereby exhibiting the highest heterogeneous resilience. (4) The transmissibility and diversity resilience of information network was slightly higher than those of the economic network and much higher than those of the transportation network. Failures or perturbations of a city have almost the same impact on network transmissibility and diversity. Cities that have a great impact on the resilience of China's urban network structure typically exhibit high centrality and control power. These cities not only have a higher level of economic development and a relatively sound transportation hub, but also exhibit superiority with regard to their geographical conditions and distribution of natural resources.

铁路客流是城市群内经济社会联系的重要反映,也是区域流空间的关键体现。本文依据流空间理论,采用复杂网络方法和2018年中国铁路班次OD数据,从全国尺度分析城市群社区结构特征,并从城市群尺度比较分析了5个主要城市群的空间结构、规模结构和网络节点结构。结果显示,从铁路客运班次联系来看,城市组团片区与国家规划方案中的城市群在范围上存在一定的空间错位。5个主要城市群具有不同的局部特征：① 长江三角洲城市群呈网络状的空间形态和相对集聚的规模分布,主要问题为局部节点的枢纽作用与规模的匹配性有待提高;② 珠江三角洲城市群呈放射状的空间形态和相对集聚的规模分布,主要问题为与全国其他城市群的联系相对薄弱;③ 京津冀城市群呈放射状的空间形态和相对集聚的规模分布,主要问题为存在边缘化的节点;④ 长江中游城市群呈组团状的空间形态和相对分散的规模分布,主要问题为内部结构较为松散;⑤ 成渝城市群呈哑铃状的空间形态和相对集聚的规模分布,主要问题为与全国其他城市群的联系薄弱。随着区域一体化进程,需要根据不同城市群网络结构发展阶段的差异,补足城市群内和城市群间的联系短板,促进规模位序结构与网络结构的耦合协调,推动建设层次分明、枢纽协作、网络化发展的客运服务网络,支撑城市群一体化发展。

当前,“流”空间视角的城市网络结构研究方兴未艾,而城市内部急需寻找到稳定的网络结构分析框架与形成机制模型。在此背景下,本文聚焦通勤和休闲两类有向人流网络,以南京为研究对象,利用中心性与控制力、社区划分、改进重力模型等方法,解析南京内部的人流网络结构特征及其影响因素。结果显示：① 对空间尺度的分解可以有效识别全域-局域两个层次的空间结构。南京江南主城的“单极化”发展态势明显,主要表现为江南主城是通勤流与休闲流的密集流入地,且集中了各类型引力中心,兼具地方服务和区域服务功能。② 改进重力模型对全域人口流动强度的拟合效果较好,人口数量和空间距离在很大程度上影响着人口流动,职住比和人均POI数量分别是通勤流、休闲流的方向调节器。重力模型低估了浦口区、高淳区等政策重点倾斜地区的人流集散能力。

At present, the research on urban network structure from the perspective of “flow space” is in the ascendant. Compared with the fruitful results at the regional scale, the stable network analysis framework and formation mechanism model of city-group have not yet been built. It is urgent to comprehensively use the urban regional network analysis method and expand it at the microscopic scale. At the same time, more and more attention has been paid to the city hierarchy and assortativity in the network context. In this research context, focusing on two types of directed flow of people: commute and recreation, this paper takes Nanjing as the study area, and uses the methods of centrality and control, community division and improved gravity model to analyze the characteristics and influencing factors of urban network association from the city-local level. The results show that: (1) The decomposition of spatial scale can effectively identify the city-local structure of commute and recreation. Nanjing presents a “unipolar” development trend of the main city to the south of the Yangtze River. The main performance is that the main city to the south of the Yangtze River attracts intensive commute and recreation flows, and it has all types of centers, providing both local and regional service. (2) The results show that the improved gravity model has a good fitting effect on the real flow of people in the whole area, and the number of residents and spatial distance limit affect the flow of residents to a great extent. The occupation residence ratio and per capita POI amount are the direction regulators of commuting flow and leisure flow respectively. The difference of functional energy levels of township streets determines the interpretation of gravity model to local pedestrian network. The gravity model underestimates the distribution capacity of people flows in Pukou district, Gaochun district and other key policy inclined areas.

A bourgeoning literature is stressing the crucial importance of agglomeration economies provided by large cities for personal, business and territorial development. One of the main reasons for the economic, demographic and social struggles of rural regions is exactly the lack of such agglomeration benefits. This paper explores the question of which broad strategies can make rural economies more competitive in the age of the 'urban triumph' by organising the presence of a higher level of agglomeration benefits in rural regions. Theoretically, three strategies are discerned: a) 'borrowing size' in infrastructural, transport and organisational networks to lap into' some of the benefits of surrounding or other metropolitan areas; b) urban concentration to develop a single, more attractive urban center in the rural region; and c) the formation of a strongly integrated network of complementary towns and cities in the rural region. The implementation of these strategies is often complicated, as we document for the rural province of Zeeland in the Netherlands. From an overarching perspective, it has been particularly internal fragmentation that has led to further marginalisation, peripheralisation and the missing of many opportunities to 'borrow size'. The strategies defined here show that rural regions are not unfortunate victims of the tendencies leading to the 'urban triumph', but have the potential to reap the benefits of agglomeration themselves.

利用2016年中国上市公司500强企业网络数据和隶属联系方法构建城市网络,解析了中国城市的网络地位分异格局,并定量测度了城市网络地位的影响因素,在城市网络“行为-结构-绩效”分析框架下解释了地位分异的动力机制。研究发现：正则等价性模型识别的15个正则等价块分化为首位者、经纪人、接收者和孤立者4个地位类型;地位间关系呈现“核心-半边缘-边缘”结构特征,其中首位者地位的正则等价块形成凝聚子群并占据网络权力的核心,经纪人和接收者地位的块位于半边缘位置,孤立者地位的块处于网络权力的边缘;市场潜力、政治资源、知识资本、区位条件和发展基础是中国城市网络地位的关键影响因素;规模偏好、择优选择、地理临近和路径依赖构成了城市网络地位分异的动力机制。城市网络的发展倾向于扩大城市间发展差距,中国城市化政策和城市治理体系需要做出相应调整。

This article aims to analyze the spatial structure and its determinants of network positions of cities to serve the purposes of developing urban network theory and improving urbanization policy in China in the new era. Data on headquarter and branch locations of the largest 500 listed companies in China in 2016 are subjected to ownership linkage model to approximate the urban network, and regular equivalence block techniques are employed to generalizing about the configuration of position of cities in the system, leading to 15 blocks and 4 positions, i.e. primacy, broker, snob and isolates. Then the influence factors are examined by using 2 broad types of econometric models, i.e. ordinal logit model and multinomial logit model, and the mechanism is explored under the “behavior-structure-performance” conceptual framework of urban network growth. 4 main findings are concluded. First, the 15 blocks identified from applying the regular equivalent criterion can be allocated to 4 positions: 3 blocks occupy what Burt terms the primacy position, 5 blocks occupy the broker position, 3 blocks occupy the snob position, the rest 4 blocks are in the isolation position. Second, the image matrix resulting from the density criterion to the block-model density table reveals that the relationship among the blocks presents a core-peri periphery-periphery characteristics, in which the primacy blocks are located in the core, forming a high densely clique and playing the role of “headquarter cities”, the broker and snob blocks are located in the outskirt, while the isolation blocks are in the periphery, playing the role of “branch plant cities”. Third, market potential, key resources, location conditions and development basis are the key factors affecting the position of China's urban network. The improvement of economic scale, population size, political power, knowledge capital and network facilities will greatly enhance the network position in China, increasing the odds of cities being in the primacy position while decreasing the probability in the snob and isolate position. Fourth, the expansion of urban network space in China is a process of preferential attachment, geographical proximity and path dependence, which will further strengthen the power of cities with traditional advantages such as larger economic scale, rich political resources, etc. Under the environment of network development, China’s urbanization policy and urban governance system need to be adjusted accordingly. The Chinese government must actively respond to the possibly increasing gap between cities under the network environment and promote the cooperation between cities on a larger spatial scale.

独角兽企业是新经济时代不同形式创新的集中组织和整合,其内部联系及空间组织反映了区域创新网络的格局和特征。论文基于独角兽企业内部联系,通过总部—分支机构联系模型构建中国城市创新网络,运用社会网络分析方法从网络层级、关联模式、网络节点3个层面探究新经济时代中国城市创新网络的空间结构和特征。研究表明：① 独角兽企业内部联系视角下中国城市创新网络呈现“1+3+N”的核心城市驱动发展格局,“北上杭深”组合领跑全局,空间极化现象明显;② 中国城市创新网络的空间结构整体呈菱形结构,且明显呈东强西弱的格局;强联系主要发生在核心节点城市之间,其他城市通过与重要节点城市连接进入网络,网络核心—边缘结构突出;③ 重要的节点城市在城市创新网络中展示出较强的权力和威望,但在网络中发挥的作用有一定差异。研究可为新时代中国城市创新网络的研究提供新的视角,文中构建的独角兽指数亦可为城市创新综合实力评估研究提供参考。

Unicorn company, a new term in today's business climate, is the integration of different types of innovations in the new era, and its internal relations and spatial organization reflect the pattern and characteristics of regional innovation networks. Consistent with the emerging entrepreneurship and the rapid development of the Internet, increasingly more unicorn companies appear in China, which provides an opportunity for us to catch a glimpse of China's innovation development through a new perspective. In this study, we used the ownership linkage model to map China's urban innovation network based on 164 Chinese unicorn companies' internal relations, and analyzed the spatial structure through the following social network analysis perspectives: urban hierarchy, association pattern, and city nodes. The "unicorn index" was developed as an indicator to evaluate the development and magnitude of a city's unicorn companies as well as its innovation environment and position in the network. Cities of high unicorn index values rank high in the urban innovation network hierarchy, which constitutes four tiers in a pyramid-shaped structure with 253 cities, only nine of which are in the top three tiers. Among them, Beijing, Shanghai, Hangzhou, and Shenzhen show their innovative activity and advanced development in the unicorn entrepreneurship. To analyze the association pattern, we calculated the network density, assortativity coefficient, and the degree correlation for the network. The results show that most cities are not closely linked with each other in this generally loose-association urban innovation network, while intensive connections only happen between the cities with high unicorn index, highlighting an intensive-connection diamond-shaped structure. To further examine each city node, we regard outdegree as its innovative influence and "power", indegree as its innovative attraction and "prestige", and eigenvector centrality as its connectivity. We found that Beijing and Shanghai still play leading roles in the innovation network, but Nanjing and Wuhan start to exhibit innovative attraction and potential. In conclusion, the development of urban innovation network is driven by four leading cities—Beijing, Shanghai, Hangzhou, and Shenzhen, showing a spatial polarization character. The network pattern highlights a diamond structure whose activity weakens from the east to the west. Cities having high unicorn index are strongly linked to each other and playing important roles while other cities enter the network by establishing links to those cities, shaping the core-periphery network structure. Moreover, those important cities enjoy power and prestige and have various influences in the innovation network. This study provides a new look into China's urban innovation network and some insights for policymakers to promote urban innovation. Also, the unicorn index can be considered in city innovation evaluation.

在我国创新驱动发展战略的背景下，城市发展中的创新要素变得空前重要，基于创新型企业联系数据研究城市创新网络成为当今城市发展的重要议题。选取2018年中国创新能力1000强的高新技术企业总部和分支机构信息，基于核密度分析法和连锁网络模型，对企业布局和城市创新网络结构进行研究。研究表明：（1）高新技术企业主要聚集于中国三大沿海城市群及部分中西部中心城市；（2）中国城市创新网络大致呈现出菱形结构，城市创新能力差异显著且具有等级性，城市间的创新联系主要通过等级扩散形成；（3）不同行业视角下的城市创新网络结构差异性显著，依据行业属性可将创新型城市划分为科技创新型城市、工业创新型城市和服务创新型城市。研究以期推动传统产业转型升级，实现城市创新发展，为创新型城市制定针对性发展模式提供科学参考。

采用2007、2012和2017年中国百强物流企业网络数据,运用世界城市网络研究的链锁网络模型方法,分析中国城市网络的等级结构、空间分异、空间联系及其演变特征。研究表明：基于物流企业数据的中国城市网络具有明显的层级特征,并呈现“两主多中心”的区域空间结构,网络联系的离散水平逐渐降低,其稳定性和均衡性有所增加,等级结构趋于合理;各节点城市组织能力空间分异明显,京津冀、长三角、珠三角的组织能力和介中心性始终处于较高水平,城市间物流联系逐渐增强,并有协同发展的趋势;城市网络联系中,长江沿线城市物流网络联系地位上升,上海为中心的T字型空间结构替代以北京为中心的核心网络结构,在全国尺度形成以胡焕庸线为界线,物流联系网络呈中东部密集、西部相对稀疏的空间格局,其演变过程呈现由等级网络联系特征向等级性与空间近邻性网络联系特征转变趋势。

Urban system and network are the hot topics of worldwide urban and regional research. There is a plenthora of literature on the urban network from the perspective of passenger, information and business flow. But there is a big gap in the urban network from the perspective freight flow. This article analyzed the hierarchical structure, spatial differentiation, spatial connection and evolution characteristics of Chinese urban networks using the interlocking network model, which is established by the theory of global urban networks. The network data of China's top 100 logistics enterprises in 2007, 2012 and 2017 is introduced into the model. Results indicated that the urban network of China based on logistics enterprise data was hierarchically characterized obviously. And presented a regional spatial structure of ‘two main and multi-center’. The discrete level of network connection was in gradually decreasing, its stability and balance were increasing, and the hierarchical structure tended to be reasonable. The spatial differentiation of organizational capacity of each node city was obvious, and the organizational capacity of Beijing-Tianjin-Hebei Region, the Yangtze River Delta and the Pearl River Delta were continuously at a high level in comparison. There was a trend of synergistic development among cities. In terms of urban network connection abd the linkage status of urban logistics networks along the Yangtze River rises, Shanghai, as the center of the T type space structure, is replaced with Beijing as the center of the core network structure. Taking Hu Line as the boundary, a dense spatial pattern in the East and relatively sparse in the west part had been formed. And it concluded that the spatial pattern evolved from hierarchical network connection to hierarchical and spatial proximity network connection.

论文通过对230家“新三板”文化产业挂牌企业总部—分支机构关联数据的收集、整理和分析,对文化产业视角下中国城市网络的空间结构特征进行研究,比较文化产业城市网络与其他类型城市网络的差异性,并探索影响文化产业城市网络空间格局的经济社会因素。结果表明：① 中国文化产业挂牌企业地理分布呈现出地域分散但数量集中的特点,城市网络空间分布高度不均衡,并未表现出常见的“菱形结构”;② 网络核心节点以东中部发达城市以及少数具有特殊资源的城市为主,多数城市的对外输出能力有限;③ 城市网络扩散以核心节点城市间的等级扩散为主,邻近城市间的扩散效应不明显,同时本地网络(即城市内部网络)对文化企业组织结构的贡献度低于跨地域网络;④ 不同类型文化产业在网络结构上表现出较为明显的差异,反映其市场需求和发展条件的不同;⑤ 地方政府作用和城市产业结构是影响中国文化产业企业布局和网络格局形成的重要因素,其他社会经济因素的作用则较为不明显,反映出中国文化产业的独特性。

In recent years, urban networks have become a focus of research in geography, urban studies, and other related disciplines. While previous research has examined the urban networks in China from different perspectives, the networks generated by cultural industries have remained a largely unexplored area. This article aims to fill this gap by presenting a comprehensive analysis of the spatial structures of the cultural industry-generated urban networks in China's mainland and compares the disparities between these networks and the urban networks created by other types of economic activities. Data of the headquarter-affiliation connections of 230 NEEQ (National Equities Exchange and Quotations)-listed cultural firms in 2017 were collected and analyzed using social network analysis method. In addition, the article also explores the economic and social factors that shape the formation of the spatial patterns of China's cultural industry urban networks drawing on the method of multiple linear regression (MLR). The outcomes reveal that: First, shaped by both market mechanisms and the interventions of local governments, the distribution of the NEEQ-listed cultural firms demonstrates a geographically dispersive, but quantitatively concentrated pattern in China. The spatial structure of the cultural industry urban networks is highly uneven, with most inter-city connections concentrated in the eastern part of the country. The cultural industry urban networks do not exhibit the typical diamond-shaped structure that has been observed in many other urban networks in China. Second, the core nodes of the cultural industry urban networks are mainly economically advanced metropolises in the eastern and central areas, as well as a few minor cities that possess some special local assets, including large film and television studios, unque natural or humanistic environment, and preferential tax policies. Most cities are receivers instead of exporters of cultural industry functions. Third, the expansion of the cultural industry urban networks is mainly between the core nodes of different hierarchies instead of between geographically proximate cities. The contribution of local (intra-urban) networks to the formation of intra-firm relationships of cultural industries is weaker than that of trans-local networks, which indicates that, compared with the advantages of geographical proximity, the complementarity of market or resources is the primary factor considered by cultural firms during their expansion. Fourth, the urban networks of different subsets of cultural industries have rather diverse spatial structures, which reflect the varied market demands and development conditions of different cultural industry activities. Fifth, according to the outcome of MLR analysis, the function of local governments and urban industrial structure are two important factors that shape the location strategy of cultural firms and the pattern of cultural industry urban networks in China, whereas the impacts of other economic and social factors are not significant. The article enriches our understanding of the spatial organization of cultural industries and the diversity of modern urban networks. It also sheds light on cultural industry development policies in the Chinese context.

结构特征与权力等级是城市网络的重要要素。基于“产业-区位”视角,通过对2012年中国汽车产业供应链体系分析,凝炼中国城市网络特征。研究发现：① 基于汽车产业供应链体系的中国城市网络表现出明显的“低密度-多核心、高聚类-少趋同”的结构特征。② 城市网络的结构特征与权力等级存在显著“悖论”,即城市节点的网络地位不仅取决于链接城市的数量,还需考虑关联网络的空间属性和资本容量。③ 城市网络权力等级中既包括上海、重庆等领导核心城市,也包括广州、芜湖等中心集约城市和苏州、成都等权力门户城市,说明转变中心性与转变控制力不仅能有效揭示中国城市网络节点的真实权力属性,也更符合经济现象的地理空间非均衡规律。④ 重庆、上海、天津、长春、北京、十堰等领导核心城市并未完全锁定中国六大汽车产业集聚区,其中长三角地区网络权力最突出,珠三角地区网络权力最弱。

Structural characteristics and power hierarchy are important elements of urban networks. This paper studied the selection of China's automobile industry supply chain system in 2012 by using "China automobile industry enterprise information Daquan", "China industrial enterprise database" and "China automobile supplier network", and analyzed the characteristics of China's urban network from the perspective of "industry-location". The results showed that: Firstly, based on the supply chain system of automobile industry, China's urban network showed obvious structural characteristics of "low density, multi-core, high clustering, less convergence". Secondly, there existed a "paradox" between the structural characteristics and power levels of cities in the urban network, which means that the network status depended not only on the number of linked cities, but also on the spatial attributes and capital capacity of the associated networks. Thirdly, the urban network power level included not only the leading core cities such as Shanghai and Chongqing, but also the central intensive cities such as Guangzhou and Wuhu, and the power gateway cities such as Suzhou and Chengdu. The result suggested that the "alter-based centrality" and "alter-based power" could not only effectively reveal the real power attribute of China's urban network nodes, but also kept more in line with the unbalanced law of geographical space of economic phenomena. Fourth, leading core cities, including Chongqing, Shanghai, Tianjin, Changchun, Beijing, Shiyan, etc., did not completely take over the six major automotive agglomerations in China, among which Yangtze River Delta region was at the highest level in the power hierarchy whereas the Pearl River Delta region was at the bottom. Finally, it should be noted that the supply chain system of automobile industry was only a special situation between "urban agents", and its research conclusions could not be infinitely copied and promoted, and could not replace the relevant conclusions of other factor flows.

高质量的人才跨越地理的流动影响和驱动着全球范围内的创新活动,正在重塑世界经济格局。研究基于全球高校留学生流动数据,构建加权有向网络模型,对2001—2015年全球人才流动网络复杂性时空演化进行全面刻画。主要结论如下：① 全球人才流动网络规模迅速扩大,网络发育具有明显的小世界性,两级分化显著。② 全球人才流动网络呈典型“金字塔结构”特征,基本呈“东向西,南到北”的地理格局,新兴国家作为人才吸纳国的角色逐渐上升。③ 全球人才流动网络核心-边缘结构显著,核心国家间存在较强的关系流,边缘国家间联系较弱,其人才主要流向了核心国家和半边缘国家。④ 全球人才流动网络分化成美国社团、欧盟社团、中国社团、南美和南非社团、马来西亚社团、独联体社团等6个凝聚子群。

High-quality talents are reshaping the world economic landscape by transcending geographic flows and driving global innovative activities. Based on the data of international student mobility, this paper draws on complex network theory to construct a multidimensional weighted directed network heterogeneity model. This model uses the GIS spatial analysis method to study spatiotemporal evolution of global talent mobility network complexity from 2001 to 2015. The results are as follows. First, the scale of the global talent mobility network is expanding rapidly, and the relationship is becoming closer and closer. In the network, both the number of talents and the choices of overseas routines are mounting. The network development has obvious small-world characteristics. However, the two levels of network differentiation are significant, and the difference is decreasing year by year. Second, the rank-size distributions of network nodes weighted degree of accession and weighted degree of output conform to the law of power distribution, showing a typical "pyramid structure" characteristics, reflecting that the global talent mobility network is controlled by a small number of pivotal node countries. The spatial patterns of the global talent mobility network are basically "from east to west, and from south to north", but the trend of mobile regionalization is gradually emerging, and the role of emerging countries attracting talents has gradually increased. Thirdly, the core-periphery structure of the global talent mobility network is remarkable. The countries in the core, strong semi-periphery and semi-periphery alternate with countries from other tiers. There is a strong mobility of relations between the core countries. The marginal countries are not connected with each other, or have weak links with each other. The talent of the marginal countries mainly flows to the core countries and semi-marginal countries. Finally, the global talent mobility network community has been significantly differentiated. The network has evolved into six associations, including American associations, EU associations, Chinese associations, South American and South African associations, Malaysian associations, and CIS associations. The scale of associations varies widely. Like the overall network, each community has a similar "pyramid structure" feature.