Literature on how cities get connected through networks of firms has been increasing in recent years. In particular, advanced producer service (APS) firms are being widely used to build intra-firm linkages to establish urban networks. In contrast to studies applying intra-firm networks, this study proposes an alternative strategy to build urban networks based on inter-firm service provision relationships during the process of initial public offering (IPO) in which APS firms – including securities, law firms and accounting firms – provide professional services for firms aiming to be publicly listed. Based on service provision connections between APS firms and their clients, this study provides fresh insights on urban networks in China. The results show that Beijing, Shenzhen and Shanghai strategically hold dominant positions within Chinese urban networks and they are the lead command and financial centres within the country. Particularly, Beijing has overwhelmingly more influence over other cities. The urban networks are embedded in China’s unique institutional context where market and state power together have shaped these networks. Since the urban network is built up based on real economic linkages, the findings might have further implications for policy-making and could contribute to ongoing debates regarding financial centres in China. It implies that connections between firms based on real economic activities can be an effective way to construct urban networks in future research.

The business connections between Beijing, Hong Kong and Shanghai and other major world cities are investigated using the interlocking network model based upon the location strategies of advanced producer service firms. This approach emphasises non-hierarchical relations between cities. A key new finding is that city-dyad analysis enhances the prominence of these China cities compared with simple ranking by total global network connectivity. This suggests that Hong Kong, Shanghai and Beijing have developed more strategically important roles in the world city network than previously understood. Yet the geographies of these links are distinctive, with Shanghai shown to be better connected to the more important world cities such as London and New York than Beijing; and Beijing is found to be better connected to political world cities such as Washington and Brussels, and to other Pacific Asian cities, than Shanghai. The results are interpreted as suggestions for developing a new research programme.

The location patterns and organizational networks of both advanced producer services (APS) and cultural industries have attracted extensive attention in geography and other related disciplines. However, most research on these two sectors has examined each one in isolation, without paying attention on how they are engaged with each other. Drawing on a network analysis of the inter-firm service provision relationships between 245 cultural firms and their APS providers during the firms’ public listing processes in mainland China, this paper presents a pilot study of the functional interactions between cultural industries and APS from a geographical perspective. Our purpose is to expand the research on these two economic sectors from the simple mapping and ranking of their individual industrial activities to an investigation of the city-based spatial relationships between them. The outcome reveals that while the leading cultural firms and their APS intermediaries have demonstrated similar location patterns across major Chinese cities, the spatial interactions and connections between them are much more complicated than their co-location tends to suggest. This paper enriches our understanding of the functions of local clusters and trans-local networks in the establishment of inter-industrial linkages between the different sectors of knowledge economies. The paper also sheds light on the impacts of institutional context on the (spatial) development of cultural industries in a transitional economy.

On the basis of patent transaction data in 2015, spatial pattern of interurban technology transfer network in China was portrayed by integrating big data mining, social network, and GIS, from the perspectives of nodal strength and centrality, linkage intensity, and modular divisions. Then, its key influencing factors were identified as well using the Negative Binominal Regression Analysis. Some findings were ontained as follows. First of all, the intensity of interurban technology transfers in China is not well distributed with obvious polarization. Those cities with higher-level technology transfers are concentrated in the three urban clusters, namely, the Yangtze River Delta, the Pearl River Delta and Beijing-Tianjin-Hebei urban agglomeration. Secondly, a typical core-periphery structure with hub-and-spoke organization is evidently observed, which consists of several hubs and the majority of cities with far lower technology transfers. Beijing, Shenzhen, Shanghai and Guangzhou are acting as the pivot of the technology transfer network and playing a critical role in aggregating and dispersing technology flows. Thirdly, technology linkage intensities of urban pairs appear to be significantly uneven with hierarchies, centralizing in the three edges from Beijing to Shanghai, from Shanghai to Guangzhou and Shenzhen, and from Beijing to Guangzhou and Shenzhen, which shapes a triangle pattern. Fourthly, the technology transfer network is divided into four communities or plates, with prominent reflexivity and spillover effects, which is resulted from geographical proximity and technological complementary. Last but not least, spatial flows of technology are co-organized by a variety of spatial diffusion modes such as hierarchical diffusion, contact diffusion and leapfrog diffusion, owing to economic and administrative powers. They are greatly influenced by urban economic scale, foreign linkage, policy making, as well as multiple proximity factors related to geographical, technological, social and industrial proximities.

基于2015年专利交易数据,融合数据挖掘、社会网络、空间分析等方法,从节点、关联、模块及影响因素4个方面揭示中国城际技术转移的空间格局及其影响因素：① 技术转移整体强度偏低,空间极化严重,长三角、珠三角、京津冀城市群成为技术转移的活跃地带。② 北京、深圳、上海、广州是全国技术转移网络的“集线器”,发挥城际技术流的集散枢纽和中转桥梁作用,中西部大部分城市处于网络边缘,整个网络发育典型的核心—边缘式和枢纽—网络式结构。③ 技术关联的空间层级和马太效应凸显,形成以北京、上海、广深为顶点的“三角形”技术关联骨架结构,技术流集聚在东部地带经济发达的城市之间和具有高技术能级的城市之间,中西部技术结网不足,呈现碎片化。④ 技术转移网络形成明显的四类板块（子群）,具明显自反性和溢出效应,其空间聚类既有“近水楼台先得月”式块状集聚,也有“舍近求远”式点状“飞地”镶嵌。⑤ 城际技术流呈现等级扩散、接触扩散、跳跃扩散等多种空间扩散模式,其流向表现出经济指向性和行政等级指向性特征。⑥ 城市经济发展水平、对外开放程度、政策支持等主体属性和地理、技术、社会、产业邻近性的城市主体关系均会影响其技术转移强度。

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.

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。

Based on the global patent cooperation data in WIPO database, we construct global science and technology innovation network of Shanghai from 2010 to 2019 and the global science and technology innovation network of 32 first level science and technology cities in the world in 2019. On this basis, we use social network analysis and functional polycentric iterative analysis to explore the evolution and status of Shanghai's participation in global science and technology cooperation. The results show that: in the past 10 years, Shanghai has played a relatively important role in the global science and technology innovation network, but it has not improved its position in the science and technology innovation network to a great extent. Among the 32 cities in the world, Shanghai ranks the sixth in the strength of innovation connection, and the first in the breadth of innovation connection. At present, the science and technology innovation connection between Shanghai and other cities can be characterized as wide diversity but not in-depth. Ranking the 32 cities through polycentric iterative analysis, we can find that the innovation status of each city in the global science and technology innovation local network is characterized by polycentricity high-low distribution; Shanghai, London and Rotterdam are in the third level; among the top six cities in the innovation strength connection, Shanghai has become "marginal", and there is a big gap between Shanghai and the top five cities.

Under the background of China's innovation-driven development strategy, the innovation elements in urban development have become more important than ever. The research on urban innovation networks based on innovative enterprise contact data has become an important topic in today's urban development. Based on the kernel density estimation and the chain network model, this paper will study the enterprise layout and the urban innovation network structure based on the information of the high technology enterprise headquarters and branches in China's top 1000 innovation capacity in 2018. The research shows that: (1) High technology enterprises are mainly concentrated in China's three major coastal cities and some midwest central cities; (2) China's urban innovation network generally presents a diamond-shaped structure. Moreover the urban's abilities of innovation is significantly different and hierarchical, and the innovative connections between cities are mainly formed by hierarchical diffusion; (3) The structure of urban innovation networks vary greatly among different industries. According to industry attributes, innovative cities can be divided into technological innovation city, industrial innovation city and service innovation city. The research aims to promote the transformation and upgrading of traditional industries, realize the development of urban innovation, and provide scientific reference for the development of targeted development models for innovative cities.

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。

In the age of globalizing science and technology, urban economic development increasingly rests on knowledge production and knowledge flows. Inter-city scientific collaborations, as the most potent aspect of modern knowledge production, are more and more frequent, which produces some of the highest quality science. However, there is a paucity of analysis of the world city system from the knowledge flows perspective. Using highly cited papers data from the Web of Science database in 2017, this study applies social network analysis, a Bayesian-inference weighted stochastic block model (WSBM), the dominant flow analysis and spatial interaction model to explore the topological structure, spatial pattern and influencing factors of global inter-city scientific collaboration. Results show that the science hotspots are highly concentrated in three regions: North America, East Asia and Western Europe, and the whole network is dominated by a tri-polar world. The two seemingly paradoxical trends, star-shaped and triangulated structure, coexist in the global inter-city knowledge flows network. The spatial pattern of inter-city collaboration network forms a quadrilateral graph with four vertexes in Western Europe, North America, East Asia and Australia, particularly on the trans-Atlantic axis between North America and Western Europe. The network has a distinctive multicore-periphery structure, which can be divided into five categories: global core, macro-region core, strong semi-periphery, semi-periphery, and periphery, and identifies New York, London, Boston, San Francisco-San Jose, Washington, Los Angeles, Paris and Beijing as eight global core cities and forty-one macro-region cores. The network is characterized as hierarchical “hub-and-spoke” structures, and the hierarchy of the network is obvious, New York, Beijing and Jeddah are dominant nodes in the three subnetworks. In addition, the gravity model indicates the spatial distance impedes inter-city scientific collaboration, while the amount of science output and the number of urban residents, the number of world-class universities, institutional proximity and social proximity have positive and significant effect on inter-city scientific collaboration. In order to further our understanding of world city network, this paper calls for more attention to inter-city knowledge flows.

在科技全球化时代,城市之间的知识流动日益频繁,成为当代知识生产的重要特征。然而,鲜有从知识流动的视角开展全球城市体系的研究。基于2017年的高被引论文合作数据,采用社会网络分析方法和空间计量模型系统地刻画了全球城际科研合作网络的拓扑结构和空间格局及其影响因素。研究发现：① 拓扑结构呈现出以北美、欧洲和亚太城市的三极格局,轴辐式和分布式结构特征并存。② 世界城市科研合作网络形成以北美、西欧、东亚和澳大利亚为顶点的四边形格局。③ 多核心-边缘结构显现,纽约、伦敦和北京等8个城市为全球核心,芝加哥等41个城市为区域核心。④ 全球城市科研合作网络的等级层次特征显著,纽约、北京和吉达分别是三大子网的主导型城市。⑤ 空间相互作用模型表明地理距离阻碍了城市间的科研合作,城市的科研规模、人口规模、世界一流大学数量、制度邻近性和社会邻近性促进了城际联系。

The patent transfer provides an important indication of technology flows and knowledge diffusion across space. Drawing on patent transfer data, we modeled intercity technology transfer networks in the Guangdong–Hong Kong–Macau Greater Bay Area, a city region special for its “one country, two systems” structure, in the periods 2007–2011 and 2012–2016. We then explored the evolutionary characteristics of the networks and further examined the impact of, and interaction between, different forms of proximities in relation to technology transfer over time. Our results show that some kinds of proximities (institutional, cognitive, and social) are able to promote technology transfers, while others (geographical and cultural) do not exert significant impacts. Of the latter category, geographical proximity can, however, indirectly affect technology transfer by acting on the proximity of other dimensions (institutional, cognitive, and social). For instance, cognitive proximity can compensate for the lack of geographical proximity and social proximity frequently accompanies geographical proximity—and both relationships are reinforced over time. In contrast, the interrelatedness of geographical and institutional proximities have transformed from a relation of substitution to complementarity.

Interurban technology transfer becomes an essential channel for regions or cities to obtain external knowledge. Based on patent transaction data among cities during 2005-2015, this study investigates the interurban technology transfer network of Northeast China, aiming to explore spatial evolution of technology transfer network in this region from national to local perspectives based on social network analysis (SNA). A negative binomial regression analysis further reveals the factors of interurban technology transfer network. The results of the study are as follows: (1) From the national perspective, the interurban technology transfer network of Northeast China presents a core-periphery structure. The spatial pattern of "divergence in the northeast region" and "convergence in the coastal areas" has been formed. (2) From the local perspective, the technology transfer network of Northeast China shows a centripetal contraction situation, and its four hubs, namely, Harbin, Changchun, Shenyang and Dalian, play the role of technology gatekeeper. The interurban technology transfer flows present the characteristic of strengthening nationalization and weakening localization, which are more likely to emerge between the Northeast-Southeast China rather than among the Northeast China. (3) Both path-dependence and path-creation exist in the spatial dynamics of intercity technology flows in Northeast China. From the national perspective, technology flows from Northeast China to the Beijing-Tianjin-Hebei, Yangtze River Delta and Pearl River Delta urban agglomerations with Beijing, Shanghai and Shenzhen as the core respectively, while the local intercity technology transfer in Northeast China presents a mixed diffusing mode including hierarchical, contagious and jump diffusions. In addition, the local network mainly focuses on intra-provincial technology flows which centered on Haibin, Changchun, Shenyang and Dalian. (4) Some drivers, such as geographical proximity, the similarity of industrial structure, economic differences, the similarity of innovation capability, technology absorptive capacity, foreign direct investment, are evidenced to play a significant or determining role in interurban technology transfer of Northeast China.

从全国—本地视角,以东北三省为研究区,基于2005-2015年的专利权转移数据,融合社会网络、GIS空间分析和计量方法,定量刻画东北三省技术转移网络的空间演化规律。结果显示：① 全国视角下东北三省城际技术转移网络呈现“核心—边缘”等级层次性结构,形成了专利技术由东北辐散向全国沿海辐合的空间格局。② 本地视角下东北三省技术转移网络呈现出向心收缩结网态势,“哈长沈大”四大核心城市在本地网络中扮演“技术守门者”角色。技术转移表现出“强全国化,弱本地化”特征。③ 东北三省城际技术流动既存在路径依赖,也不断涌现路径创造。全国视角下,技术转移以东北三省核心城市为流源,基本流向以北京、上海和深圳分别为枢纽的京津冀、长三角和珠三角城市群。本地城际技术转移以哈尔滨、长春、沈阳、大连为集散中心,集中于省内转移,呈现等级、接触和跳跃式混合扩散空间模式。④ 地理距离接近度、产业结构相似度、经济水平差异度、创新能力相似度、技术吸收能力、外商直接投资对东北三省城际技术转移存在一定影响。

Inter-urban linkage is traditional research field of urban geography. With the increasing importance of innovation in city, inter-urban linkage of innovation has aroused the interesting of numerous sholars, some of which have examined the field through direct surveyed approach by co-author published papers or co-author patents granted, however, this approach is limited because it lacks data of the inter-urban and the rusults of survey may not present the comprensive inter-urban innovation situation of the cities. Therefore, we employ a indrect approach, using revised gravity model to map the pattern of inter-urban innovation linkage of Chinese major cities. China takes constructing the innovation country as the core strategy, and urban innovation as the core contents of national innovation system, so urban innovation linkage is an important part of China’s national innovation system. However, a number of issues, such as the current sitation of urban innovaiton linkage, and the pattern and laws of inter urban innovation have rarely been studied. This article will try to study the inter urban innovation linkage among major Chinese cities so as to find innovation source cities and innovation nodes cities in urban innovation system and the general pattern of the inter urban innovation, to promote the complementary and optimization of urban innovation function and to plan the circle of China urban innovation. Based on the review of the literatures of innovation linkage and theoretical analysis, through establishing a set of measureement of index, this article defines ourward innovation linkage of scale and measures innovation linkage and innovation pattern among Chinese major cities. The research shows: 1) the general pattern of urban innovation linkage in East China is stronger and that in West China is weak, and a "Golden Triangle innovation linkage" pattern has formed in the coastal area of China, which takes Shanghai, Nanjing and Hangzhou as the vertex, while Beijing-Tianjin and Guangzhou-Shenzhen as two points. 2) the city innovation linkage presents obvious hierarchy, the cities, such as Beijing, Shanghai, Guangzhou, Shenzhen, Tianjin and Chongqing have national innovation influence with extensive innovative linkage with the other cities in China, while cities, such as Nanjing, Hangzhou, Wuhan, Zhengzhou, Jinan, Qingdao, Dalian and Xi'an have regional innovation influence. 3) in terms of the East Coastal main economic circle in China, the Zhujiang River Delta economic circle has the strongest internal innovation linkage, but less outward innovation radiation; the Changjiang River Delta economic circle has very strong internal innovation linkage with strong external innovation linkage with the cities of Huan Bohai economic circle, while the cities of Beijing, Tianjin and Tangshan have very strong innovation linkage, and with strong outward radiation to the Changjiang River Delta economic circle. This article examines the general innovation linkage pattern among Chinese major cities considering two important elements of distance among cites and scale of urban innovation, but some elements, such institution and policies which may influence the innovation linkage have not been examined, it will be put consideration in future studies.

对国内外城市创新联系综述及理论分析的基础上,通过一组测度指标,界定了城市外向创新联系规模,采用引力模型,测度了中国主要城市间的创新联系强度及格局。研究表明：中国主要城市创新联系格局基本为东强西弱,东部地区城市创新联系格局显现出以上海、南京、杭州为顶角,以北京、天津,以广州、深圳为2个底角的创新联系“金三角”。城市创新联系在空间上呈现明显的等级性：北京、上海、广州、深圳、天津、重庆等与中国的许多城市有广泛的创新联系,具有全国创新影响力;南京、杭州、武汉、郑州、济南、青岛、大连、西安等成为地区性的城市创新联系节点,具有区域性的创新影响力。在创新联系较强的东部沿海主要的经济圈,珠江三角洲经济圈城市间创新联系最强,但外向辐射力有限;长江三角洲经济圈内部创新联系较强,并与环渤海经济圈有较强的创新联系, 环渤海经济圈内部北京、天津、唐山具有较强的创新联系,外向辐射以长江三角洲的城市为主。对中国创新联系格局规律的揭示,更进一步强化了中国创新城市体系中城市的作用,并为规划与建立中国创新都市圈提供依据。

Knowledge-intensive business services (KIBS) are widely perceived as being important drivers of technological progress and innovation. They generally depend on knowledge exchanges and, therefore, geographical proximity to markets, customers and suppliers would be expected to be a critical factor in their performance. This paper investigates how the innovation performance of German KIBS firms is related to their distance and size from the nearest city. The analysis largely conforms to a textbook type of spatial urban hierarchy and, indeed, finds that there are very strong distance-decay and city size effects, and these also vary according to the innovation type.

This article explored the exploratory spatial data analysis(ESDA) and spatial econometric model to check the spatial distribution of KIBS as well as its spatial dynamics factors across Guangdong Province. There are four main findings. First, it is described that the spatial distribution of KIBS which shows that cities in the Pearl River Delta(PRD) have the highest level across Guangdong Province, though there are discrepancy among different sectors in KIBS.Second, there is a spatial positive correlation relationship and spatial uneven distribution of KIBS among cities of Guangdong Province, which has a location of general dispersion and local concentration across this region. Third, most cities in PRD are KIBS hot spots with a high value agglomeration area, while the rest other cities in the eastern, western and northern of Guangdong are located incold area of KIBS, and the diffusion effect which come from the high level area(the PRD)remains limited to these adjacent cities, this lead to the regional lock-up effect of these cities to be low-low concentration for a couple of years. Fourth, a number of dynamic factors were used to interpret the spatial distribution of KIBS across Guangdong Province, result shows that urbanization, industrialization, ICT, economic openness and transport facilities play a positive role in promoting the development of KIBS, amongst which urbanization factor is the most important factor.While market demand factor has a negative influence on the development of knowledge intensive business service. Therefore, promoting urbanization level, improving the level of new industrial development and popularization of ICT infrastructure construction, increasing foreign investment, and revising the negative effect of market demand, all can effectively promote the development of KIBS in Guangdong Province.

以转型中的经济大省广东为例,运用探索性空间数据分析法和空间常系数模型,分析广东省知识密集型服务业(KIBS)发展水平的地区差异、空间关联特征及其动力机制,研究结论表明：① 广东省各地级市KIBS产出总体上以珠三角城市最高,但各行业存在空间差异;② 广东省地级市KIBS呈空间正相关特点,但空间结构极不平衡,呈“总体分散、局部集中”分布格局,珠三角内诸城市属于KIBS热点区域,是高值集聚区,粤东西北的大部分城市是位于冷点区域,4 a间没有突破低值集聚的现状,也揭示了珠三角地区内相邻城市的KIBS具有竞争性,尚未形成有效的扩散-协同的发展格局;③ 广东省KIBS空间格局的动力因子分析表明：城市化、工业化、信息化、经济外向度和交通通达性均对KIBS发展有促进作用,而市场需求对KIBS发展产生负向影响。

The comprehensive regionalization of Chinese human geography is based on the rules governing regional differentiation of Chinese physical geography factors. Based on regional differences and similarities in human factors, this study divides the whole country into two levels of relatively independent, complete and organically linked human geographic units. As a fundamental, comprehensive, cutting-edge, practical and important task, the comprehensive regionalization of human geography highlights the characteristics, regional and sub-regional features, complexity and variety of spatial differences between factors of Chinese human geography. It is capable of promoting the development of human geography based on local conditions, providing basic scientific support to national and local development strategies, such as the Belt and Road Strategy, new urbanization and environmental awareness, and creating a sound geopolitical environment in key areas. Using results from existing physical and human geography zoning studies, and in accordance with the principles of synthesis, dominant factors, the relative consistency of the natural environment, the relative consistency of economic and social development, the consistency of the regional cultural landscape, the continuity of spatial distribution and the integrity of county-level administrative divisions, and taking as its basis the division of human geography into 10 major factors (natural condition, the economy, population, culture, ethnicity, agriculture, transportation, urbanization, the settlement landscape and administrative divisions), this paper constructs an index system for the comprehensive regionalization of Chinese human geography through a combination of top-down and bottom-up zoning and spatial clustering analysis. In this study, Chinese human geography is divided into eight regions (first level) and 66 sub-regions (second level). The eight human geography regions are (I) Northeast China, (II) North China, (III) East China, (IV) Central China, (V) South China, (VI) Northwest China, (VII) Southwest China, and (VIII) Qinghai and Tibet. This zoning proposal fills gaps in studies involving the non-comprehensive regionalization of Chinese human geography. Each human geography region and sub-region has different topographical, climatic, ecological, population, urbanization, economic development, settlement landscape, regional cultural and ethno-religious attributes. This proposal on the comprehensive regionalization of Chinese human geography dovetails closely with previous studies on comprehensive regionalization in Chinese physical geography, Chinese economic zoning, and Chinese agriculture zoning. It shows that, under the dual roles of nature and humans, there are certain rules of regional differentiation that govern the comprehensive regionalization of Chinese human geography.

中国人文地理综合区划是以中国自然地理要素的地域分异规律为基础,充分考虑全国人文要素的地域分异性和相似性,将全国划分为不同空间层级、相对独立完整、并具有有机联系的特色人文地理单元。是一项基础性、综合性、前沿性与实用性的重要工作。通过人文地理综合区划,将突出中国人文地理要素的地域分异性、地方特色性和复杂多样性,因地制宜地推动人文地理学科发展,为面向国家和地方发展战略需求推动“一带一路”建设、新型城镇化和生态文明建设、为营造重点地区良好的地缘政治环境提供基础性的科学支撑。本文在吸纳已有的各类自然和人文地理区划成果的基础上,按照综合性、主导性、自然环境相对一致性、经济社会发展相对一致性、地域文化景观一致性、空间分布连续性与县级行政区划完整性等原则,以自然、经济、人口、文化、民族、农业、交通、城镇化、聚落景观和行政区划10大要素为基础划分依据,构建中国人文地理综合区划指标体系,采用自上而下与自下而上相结合的区划思路和空间聚类分析方法,将中国人文地理划分为东北人文地理大区Ⅰ、华北人文地理大区Ⅱ、华东人文地理大区Ⅲ、华中人文地理大区Ⅳ、华南人文地理大区Ⅴ、西北人文地理大区Ⅵ、西南人文地理大区Ⅶ和青藏人文地理大区Ⅷ共8个人文地理大区和66个人文地理区。该区划方案填补了中国人文地理没有综合区划的空白。各个人文地理大区和各个人文地理区之间呈现出不同的地形地貌属性、气候属性、生态属性、人口属性、城镇化属性、经济发展属性、聚落景观属性、地域文化属性和民族宗教属性。中国人文地理综合区划方案与已经划出的中国自然地理综合区划、中国经济区划、中国农业区划等有较大程度的吻合性,体现出在自然与人文要素双重作用下的中国人文地理综合区划的地域分异规律。

<p>The article explored the spatial pattern and determinants of Chinese urban innovative capabilities based on Gini index, trend surface analysis, spatial panel data model methods using urban patents data during 2003 to 2013. The results show that: 1) Spatial pattern of Chinese urban capabilities is highly agglomerated in center cities in three coastal metropolitan areas and regional center cities inland. The hot spots of innovation are highly agglomerated in regions around Beijing, Shanghai, and Shenzhen. Innovation abilities are spreading to inland cities with the time goes, although high innovative cities still agglomerated in coastal region. The Gini index of three patent output have decreased since 2011. 2) The technology level decreases with the invention patent, design patent, utility-patent, while the agglomeration level proxy by the Gini index, rises in sequence. 3) Spatial correlation of three kinds of patents is all significantly positive, and the correlation has been strengthened especially for appearance patent and utility patent. The correlation of appearance patent, utility patent and invention patent decrease in turn which indicates that lower technology can be spread and spillover more easily. The spatial trend surface analysis shows that there are high east and low west trend of innovative abilities, the north-south trend is not obvious, except for the utility patent show the inverse U shape of “high middle and low end” trend. 4) The results of spatial panel econometric models show that there are significant spillovers among urban innovative capabilities. The main influential factors include government support, industrial foundation, higher education sources, innovation input and economic openness, in which the influences of government support and higher education resources have been reinforced. The results show that dependent and independent variables have significant spatial dependence, indicates that urban abilities are heavily affected by the surrounding areas, the higher innovative surrounding areas can promote local innovative abilities. The spatial lag effect denotes that the high education resources and industrial foundation of neighboring cities have positive effects on cities innovation output, while the government support of neighboring cities have negative effect on urban innovation output. 5) Therefore, to promote urban innovation capabilities, government should still put forward the concept of innovation-driven concept, try to attract and nurture innovative enterprises; second, to promote urban higher education qualities and manufacture foundations, encourage enterprises to promote R&D input, and encourage the cooperation between industry, school and research; third, government should induce the innovation cooperation among cities, regions, and universities, drive the free flow of talents and innovative elements and promote the innovative spillovers among cites.</p>

基于2003~2013年城市专利数据采用基尼系数、趋势面分析、空间动态面板数据模型等方法探讨了中国城市技术创新能力的空间分布和影响因素。研究发现：① 中国创新能力高的城市高度集聚在沿海三大区域及内地的区域中心城市,随着时间推移,创新能力在空间上呈现扩散的趋势。城市技术创新能力的空间相关性逐渐增强,推动了创新的区域扩散和空间溢出。② 发明专利、外观专利和实用新型专利的创新水平依次降低,空间集聚程度依次提高,空间相关性依次提高。③ 固定效应面板数据的空间滞后模型和空间Durbin模型的计量结果发现,城市技术创新能力存在显著的空间溢出效应,邻近城市技术创新能力的提升有助于提升该市的创新能力。政府支持、工业基础、高等教育资源、创新投入、经济外向度显著影响城市技术创新能力水平的提升,且政府支持和城市高等教育资源对城市技术创新能力的影响出现增强趋势。

Based on the data released by UNESCO on international student mobility in tertiary education from 2001 to 2015, this paper draws on network science to construct a multidimensional weighted directed network heterogeneity model. This model combines the GIS spatial analysis method and the negative binomial regression model to study spatiotemporal evolution and factors influencing international student mobility networks. The results are as follows. First, in terms of the evolutionary characteristics of time series, the linkages of international student mobility networks have increased from 4921 to 9137, and its average weighted degree centrality has rapidly increased from 8004 to 20,834, which indicates that both the number of international students and the choices of overseas routines are mounting. Second, in terms of the topological structure, it has evolved from a single core comprising the United States into dual cores comprising the United States and China. Gradually, Asian countries have become the main body of international student mobility networks, while the centrality of European countries has reduced. Thirdly, in terms of spatial structure, the international student mobility network has a significant core-periphery structure and hierarchical characteristics. The countries in the core, strong semi-periphery and semi-periphery alternate with countries from other tiers. The spatial patterns of the international student mobility network "from east to west, and from south to north" are changing. China and Australia have become the new regional centers of international student mobility networks, meaning that regionalization has become an important trend. Finally, regression analysis shows that the flow of international students correlates positively with the quality of higher education in destination countries, the number of students in colleges and universities, the per capita GDP, the historical links connection between the destination country and the country of origin, and the level of globalization. Geographical distance and linguistic proximity have a significant negative correlation with the flow of international students, indicating that the impediment of language is rapidly weakening, while geographical distance still hinders student mobility.

基于联合国教科文组织公布的全球高等学校留学生流动数据,借鉴网络科学,利用社会网络分析方法和负二项回归模型,对2001—2015年全球留学网络时空演化及其影响因素进行研究。主要结论为：① 时序演化特征上,全球留学网络关系流从4921个增加到9137个,平均加权度从8004迅速增加到20834,表明全球留学网络不仅学生数量在增加,且留学路径数量也在不断增加。② 拓扑结构上,从以美国为单一核心,演变成以美国、中国为双核心的拓扑结构,亚洲国家逐渐成为全球留学网络的主体,欧洲国家的中心性逐渐萎缩。③ 空间结构上,全球留学网络具有明显的等级层次性,美国是全球性主导型节点,德国、捷克是区域性主导型节点,次级主导型节点从25个增加到32个,全球留学网络“东向西、南到北”的地理空间格局正在发生变化,中国、澳大利亚等国成为新的区域性中心,区域化成为全球留学网络一个重要趋势。④ 回归分析发现,留学目的地国高等教育质量、高校在校生数量、人均GDP、留学目的地国与留学生来源国间的历史联系及全球化水平与留学生流量呈显著正相关关系,地理距离、语言临近性与留学生流量呈显著负相关关系,表明语言差异的阻抗作用在迅速减弱,地理距离仍然是阻碍学生流动的一个重要因素。

Productivity across European regions is related to three types of networks that mediate R&D-related knowledge spillovers: trade, co-patenting and geographical proximity. Both our panel and instrumental variable estimations for European regions suggest that network relations are crucial sources of R&D spillovers, but with potentially different features. Both import and co-patenting relations affect local productivity directly, but spillovers from innovation-leading regions are effective only when they are import-mediated and when recipient regions have a solid knowledge base. From a policy perspective, this may frustrate recent European policy initiatives, such as Smart Specialization, which are designed to benefit all regions in Europe.