With just about 30 years of rapid development, the Yangtze River Delta has gone through considerable social and economical development. As indicated in the statistic data disclosed in 2007, though only accounting for 1.05% of the country area and 6.66% of the population in China, the 15 cities (Figure 1) in the Yangtze River Delta (including 15 major cities: Shanghai, Nanjing, Suzhou, Wuxi, Changzhou, Nantong, Yangzhou, Zhenjiang, Taizhou, Hangzhou, Ningbo, Jiaxing, Huzhou, Shaoxing and Zhoushan,) had achieved 18.2% of the GDP, 24.0% of the country's financial revenues, and 44.8% of the actual volume of foreign capital use of China. Therefore, during the globalization supported by increasingly developed information technologies, and as a rising international urban agglomeration, the region should be included as one of the key research fields for the analysis of the intercity network relations. Between 1996 and 2005, the ratio of foreign trade to the GDP in Yangtze River Delta increased from 29.71% to 81.36% (Figure 2).
As the front of the country's policy - opening up to the outside world, Yangtze River Delta Region is bearing quite complicated relations among the degrees of labor division, coordination of the internal economies and the process of economic globalization.
Figure1: Fifteen Hinge Cities in the Yangtze River Delta.
Figure 2: The Ratio of Foreign Trade to the GDP in the Yangtze River Delta.
Cities in Network
For centuries it has been the spatial centrality of these cities– their size and dominance over a surrounding hinterland – that conferred upon their class in the urban hierarchy and economic significance in the urban system (Neal, 2008a; Neal, 2010). In recent decades, the regional economy, society, and culture have become integrated in a globe-spanning network of communication and exchange. Castells (1996) argued the ‘space of flows' are controlling the ‘spaces of places'. In recent, the contemporary concept of Regional City concept has caught people's attention, for example the ‘city network' (Camagni, 1993) or 'network city' (Batten, 1995). And F.G. van Oort et al (2007) summarized, such research has been concentrating on the significance of people's increasing flexibility and mobility and their changing dwelling preferences in the urban system (Renkow and Hoover, 2000; Van Ham, 2002). Based on the review of many scholars' work (Alderson and Beckfield, 2004; Beaverstock et al, 1999, 2000; Carroll, 2007; Neal, 2008b; Smith and Timberlake, 1993, 2001, 2002; Taylor 2001, 2004; Smith, 2007), Allen (2008) found that their concerning point is to get to the bottom of the complex network through which the cities many sustain, enhance or lose their capacity to influence and control what happened in the surroundings. The competitiveness of cities is changing, with stronger local competitive relations, in terms of space and time, as well as in cyclical downturns (Taylor et al, 2009), cities in networks and networks that only survive by force of collective complementarities (Thompson, 2003).
A network, topologically, comprises nodes and links that present the pattern of connection (Taylor, 2001). This graph theory approach, simple as it is, has been used in human geography to explain networks in transportation and communication (Taaffe et al, 1996; Taylor, 2001). International airline flights have been employed for cosmopolitan cities to outline a global transportation network (Keeling, 1995; Neal, 2008a; Neal, 2010), and studies on new electronic communication linkages have been also carried out as well (Graham and Marvin, 1996; Rimmer, 1998; Taylor, 1999; Taylor, 2001).
Information in City Network
Castells (1996) compared the typical concern for “spaces of places” (e.g., cities) with the transnational relocation of modern people, commodities, and particularly, information, which, according to him, is “spaces of flows.” Giddens (1990) also pointed out that globalization is an important symbol of information age, and it closely connects with long-distance regional social relationship in the world. It means that the occurance of local events may closely associate with remaining areas. This kind of revelant information across borders has led to transient accumulation of value, ideology, opinion and technology within global context (Giddens, 1990). From their views that information exchange is one type of space of flows, it can be argued that the space has been developed and redeveloped with the advances of technology.
In a global economy with productive infrastructure progressively depends on information channels, more and more cities and regions are becoming nodes in network as critical agents for economic progress (Zhou, 2007). The geographical definition of city is united with a systematic application of Science Citation Index (SCI) to generate a reliable list of important research centers appraised based on the research results registered under the address of the authors ' institution (Matthiessen, Schwarz and Find, 2006). During 2002-2004, an analysis covering 74 urban units and representing the world largest scientific centers was measured by the results of SCI-registered papers in scientific, medical and engineering area (Matthiessen and Schwarz, 1999; Matthiessen et al., 2002). While focusing on inter-city links, co-authorship was adopted as an indicator between Matthiessen and other authors from different cities, which presented two analytical steps, they argue the interactive pattern of co-authorship as an indicator mirrors the flows of ideas and reflects attraction patterns as well as the traditions of co-operation (Matthiessen, Schwarz and Find, 2006).
As we have seen, information and knowledge had been taken as a key factor of modern economy. The terms should be classified for further discussion, under spatial context in particular, despite that they seem to be used interchangeably.
News as Information in City Network
Daily business news reported by a city's newspapers provides a continuous source of information on what a given editor thinks are the salient news stories of the day for a given readership, and the city's business community. Just as Beaverstock, Smith and Taylor et al (2000) summarized, by recording place mentioned in a sample of business news stories, one can derive a surrogate measure of a city's external relations. Content analysis of city newspapers has been used by Pred (1980) in his classic study of changing hierarchical patterns among nineteenth century U.S. cities, and the credibility of such analysis for contemporary U.S. cities has been affirmed by Taylor (1997). The key point is that newspapers can be looked at as a potential source for monitoring changes in intercity relations in terms of related place mentioned as a quantitative estimate of business salience, and the research in this paper is also similar to the work of Matthiessen (2006) who used co-authorship for authors from different cities as an indicator of net work.
Based on the events from intercity economic news, this study is to investigate the network among the cities in Yangtze River Delta. We look on inter-city economic news as one type of the spatial network between cities. The intercity economic events are taken as methods for information flow measurement based on literature metrology. Moreover inspections and verifications have been made to the relation of information flow and office flow confined by set condition of headquarters and sub-companies, and so are the nodes of information flow with the major innovative cities.
This research is based on the above theory that the economic news could be looked as information flow between cities. Specifically, this paper considers: i) economic news networks as being taken from the China economic news database to analyze inter-city information flows and ii) comparison between the information flows and that of office network based on the location of headquarters and affiliates, iii) comparison between the nod o sity of information and the strength of innovation.
To accomplish the above analysis, ideas was borrowed from the analysis methods on urban networks by related scholars such as Pred and Beaverstock, etc. to make analyses from the viewpoint of regional economic information relations. That is, based on the intercity economic news events, to study the frequenc ies of mutually - related events of the cities within the Yangtze River Delta. And the information relations of intercity economic events are taken as the measuring methods on information flows based on literature metrology.
When coming to the methods in this paper, Ti,j is defined as the flow data of the connected city “j” in the connecting city “i” (amount of economic news to be quoted); and with out directiv e feature being considered, (Ti,j+Tj,i) stands for the intensity of economic relation between “i” and “j”. Then calculate the total volume of network “flow” of the spatial units of “i” and “j”:
Where, Li,j represents the influence degree of total network relation extent, between the spatial units “i” and “j”, bearing on the spatial unit of “i”; and Oi, Di represent the total heading amounts and the amounts of contents to be quoted respectively. As for the Li,j value in city “i”, the focuses of the study lie in object cities bearing relatively greater relations with the city “i”, especially those ranking the top 2 among the other cities by Li,j values of city “i”.
In addition, to describe the node hierarchies in the networ s, Ni is defined to represent the influence status of the regional node “i” on other cities in the urban relation networks.
After that the maximum value of Ni in the city system, which is defined as Nmax. is calculated, the related influent ial degree of any city in such urban system can be further determined. Centesimal grade is applied in scoring, and the value range s between 0 and 100.
The main purposes of such formulas are to describe the intercity network relations in Yangtze River Delta, to analyze the extents of mutual functions among the cities and set the hierarchies of the regional nodes from the viewpoints of networks.
This paper takes China Economic News Database which linked by the Financial and Economic Database of the libarary database of Tongji University as the collection source of data and utilizes the designated word frequency distribution of the named city to acquire ralated information. The inquiry processes for searching subject word of city name are: i) Find out all economic news with the city name (i.e. Nanjing) in the headings in the literature library; ii) For the city to be quoted (i.e. Hangzhou), screen out the coverages with the target city name (i.e. Hangzhou) included in the contents quoted; iii) Create a data set for the information relation “Nanjing — Hangzhou”. The number of coverages focused on the target cities can basically reflect the relation intensity of information correlation between different cities. The following example is from searching among cities.
[Heading] Nanjing to Build 4 Cross-provincial or intercity Underground Railways
[Content] … The line-side program of the Nanjing- Hangzhou Intercity Railway has been determined and Riverside Railway is now under positive preparation. 5 years will be taken for the accomplishment of the two railways, to make Nanjing the center of the intercity railways.…
In the above - mentioned example, the city information in the heading is a bold “Nanjing”, and its content includes the target city “Hangzhou” in bold type as well. Therefore, this news is identified in the statistics of literature amount as a coverage of event in which Nanjing is taken as a subject and Hangzhou as the related city. As for the source of the sample data in the study of information flow, the intercity related information should be made as objective as possible. In the practical operations, some geographical name information such as “West Nanjing Road” and “North Suzhou River Road” are filtered. With this method, the quantity of intercity economic news from 1993 to 2007 selected as samples of the study is up to 18,093 pieces.
Analysis on Intercity Network Structure
Intercity Related “Flow”
The intercity information flows are unbalanced according to the Figure 3. As seen from city news events, the information relations between Nanjing and Shanghai are the greatest (with the sum of TNJ-SH and TSH-NJ as 2,223), followed by Hangzhou and Shanghai (with the sum of THZ-SH and TSH-HZ as 1,980). In other sample data, Zhoushan has the least economic relations with other cities, no relations with 4 of the other 14 cities (Figure 3).
Figure 3: Spatial Distribution of the News Relations Among the 15 Cities in the Yangtze River Delta.
Table 1: Ti,j among 15 Cities in the Yangtze River Delta. Notes: The rows of the table represent the cities quoted in the headings while the ranks represent that quoted in the contents. Ti,j is indicated in the table of row i and rank j.
Table 2: Li,j among 15 Cities in the Yangtze River Delta. Notes: The sum of each row is 1.00 and Li,j is indicated in the table of row i and rank j.
In the Figure 3, Table 1 and Table 2, it can be seen that Shanghai is acting as the core in information relations among the 15 cities. As analyzed in the primary relations (largest flow) of economy news with other cities for each city, among the 15 primary relations, Shanghai is involved in 14 of them, which proves that the other 14 cities are all taking Shanghai as the primary relation city (Table 2). And in the spatial distribution of “Flows”, SH appears to be the absolute center in Yangtze River Delta. Furthermore, by taking into the second largest relations into account, Nanjing and Hangzhou also have relatively higher degree of network relations. In the distribution of “flows” of secondary network relations, the two cities are mainly bearing close relations with other major cities in the same province, that is, the secondary object cities of Nanjing are the other 7 cities in Jiangsu Province and those of Hangzhou are the other 4 cities in Zhejiang except Zhoushan. In terms of the spatial distibution, the information relations are characterized by that Shanghai is in the absolute core, and inner-provincial relations either in Zhejiang province or in Jiangsu province are higher than cross-provincial relations.
Nodes of Network
The related influence level of the concentration ratio of the economic news in the region is calculated based on the samples, and the corresponding rankings are also made. The result shows that the node sequence in Yangtze River Delta is characterized by obvious polarization (Figure 4, Figure 5). Obviously, the power-law distribution suits the spatial networks in the Yangtze River Delta just as other regions that have analyzed by Watts (2006) and Gonzalez (2008).
In addition, when calculated by primacy ratio indexes, the primacy ratio is as high as 2.53 in aspect of network influence level based on the city information linkage, which is far beyond the primacy ratio of Shanghai in aspect of GDP in 2005. Moreover, it can be noticed that Suzhou ranks the 2nd in GDP, only the 4th in Pa value though (Table 3).
The polarization of spatial levels is quite obvious. After mapping the Pa data nodes of Pa values of the 15 cities in Yangtze River Delta for analyzing the spatial distribution status, the polarization is clear that Shanghai is on the top, followed by Nanjing and Hangzhou, and then Suzhou, Wuxi, Ningbo, and cities such as Zhoushan, Taizhou, Yangzhou and Zhenjiang in the weak end.Figure 4: The Relation of Pa and Rank of Pa in the Yangtze River Delta.
Table 3: Pa, Ni, ΣOi, ΣDi and GDP in the Yangtze River Delta.
It can be argued that the intercity relation is generally of a network type in global urban areas, especially with the steady developments of transportation and communication technology, the communications of production factors (capitals and technologies etc.) and information can be arranged in mobile spaces beyond the obstruction of physical spaces. As analyzed on the features of the related information flows among the cities in Yangtze River Delta, the most important core city Shanghai takes a relatively obvious leading role, and cities blocked for geographical factors such as Nantong, Yangzhou, Taizhou on the north bank of the Yangtze River and the relatively weaker island city Zhoushan (Figure 5).
Figure 5: Information Relation Degree of Spatial Distribution of Network Nodes.
Comparison of Different Relation Flow
Comparison in Relation Flow between Office and Information
GaWC group describes the internal structure of large advanced productive service firms by analyzing the "regional relation between headquarters and affiliates” so as to further reflect the internal and external communications. Through analyzing the set condition (location and relation therein) of headquarters and affiliates of these companies, the distribution of intra-urban office flow in each area is observed and studied.
As there are flaws in the data, the research used the ownership links between firms to calculate the company relations between the areas within the Yangtze River Delta and to make a comparison in economic information flow among the cities. The company relations of each city are respectively calculated on the basis of Wanfang enterprise database during research (Table 4).
Table 4: Distribution of Relation between Headquarterts and Affiliates of Cities in the Yangtze River Delta. Notes: The data in rows of the table represents cities where affiliates located, and the data in ranks stands for the cities the where headquarters located.
It can be found that with a total number of 549, the companies with a headquarter in Suzhou and affiliates in Shanghai boast the largest office relations of the Yangtze River Delta, followed by the companies with headquarters in Wuxi and Ningbo and affiliates in Shanghai, with respective number of 468 in Wuxi and 406 in Ningbo; in additon, in the sample data, the companies set up subsidiary companies in Zhoushan have the least office relations, which have no relation at all with 5 cities of the rest 14 cities.
In order to comprehensively describe the polycentricity feature of the Yangtze River Delta, the study also respectitvely analyzes the regional distribution characteristics of headquarters and affiliates. As shown by the regional distribution of headquarters in the sample data, Shanghai (1243) is obviously higher than all the other cities, followed by Hangzhou (888), Suzhou (867), Wuxi (836), Nanjing (512) and Changzhou (512); the city with least headquarters is Zhoushan (51). It can be seen from the regional distribution of affiliates that Shanghai (3311) has obviously greater advantages, and Nanjing (945), Hangzhou (795) and Suzhou (604) are next to it; other cities left have a quantity of affiliates lower than 500, and Zhoushan (36) again is the lowest in quantity. Since the affiliate quantity of production offices owned by a city in other cities represents the capacity of the city in providing services in field of sales, scientific research, procurement, etc, the fact that the quantity of affiliates is much higher than that of headquarters in Shanghai indicates that the place mainly served as a productive service provider in the regional office relations (Table 5).
Table 5: Headquarter Quantity (ΣOi) and Affiliate Quantity (ΣDi) of Cities in the Yangtze River Delta.
With regard to spatial distribution, information relations among regions within the Yangtze River Delta are quite similar to the office relations, both of them are characterized by the outstanding central position of Shanghai, relations among cities within each province are larger than those among inter-provincial cities, and the like, inter-provincial relations among Zhenjiang, Nantong, Yangzhou and Taizhou in Jiangsu province, and Jiaxing, Huzhou, Shaoxing and Zhoushan are obvious of the lowest level.The essential difference between the two spatial relations is: the information relation intensity between Hangzhou and Shaoxing belongs to the top 5 percent of the connectivity while the office connectivity is at Grade 2 (top 25 percent); the office relation intensity of Shanghai and Wuxi lists in the top 5 percent, but their information relation is at Grade 2 (top 25 percent); additionally, the information relation between Nanjing and Ningbo is also higher than the corresponding office relation.
Linear estimation is carried out to flow values (Ti,j + Tj,i) of information and office relations among regions for detecting the relativity of both relations. According to analysis of flow values, we can find that information relation is correlated with the height of inter-regional flow data with the pearson correlation of 0.908 and a two-tailed test of 0.000, which indicates that with regard to the flow, information relations among regions in the Yangtze River Delta are highly synchronous with flow relations of inter-regional offices (Figure 6).
Figure 6: Comparison of Relation Flow between Information (left) and Office (right) in the Yangtze River Delta.
Also, linear estimation is implemented to node values of inter-regional information and company relations. According to the analysis of 15 pairs of node values (Ni), node value correlativity between information and company relations is more noticeable, with the pearson correlation up to 0.987 and a two-tailed test of 0.000 (Sig.). In terms of node performance, the linkage of information nodes among areas in the Yangtze River Delta can better reflect that of the company relations.
Verifications of Network Nodes and Regional Innovation Capacity
As the data of co-authorship between authors from different innovation units, which has been utilized by Matthiessen etc, is not available for the research, converted data of the patents in 15 cities is adopted to measure the innovative strength of each city. From the Table 6, the 5 cities with the largest number of patents in 2005 are Shanghai, Hangzhou, Ningbo, Suzhou and Nanjing and they are also the very 5 cities with the largest Ni, which indicates the influence level of node for economic news of city “i”. And the ratio of Shanghai's patents to that of the whole 15 cities is 0.319, which is close to that of Ni, of 0.354. The greatest difference between these two kinds of data is that Zhoushan, whose performance of Ni indicated by the information network is significantly stronger than that of patents. Zhoushan's information linkage to other cities that might has something to do with its location since it is an island city.
To verify the reliability of the data, curve estimation and related analysis are carried out on the integrated Ni values and patents of the 15 cities, of which linear estimation method is applied in the curve estimation to prove a quite high correlativity with the correlation coefficient of 0.974 and the error level of 0.000 in two-tailed test (Figure 7).
Figure 7: Comparison of Nodosity between Information (left) and Patents (right) in the Yangtze River Delta.
According to the above results, the nodosity of information flow can be considered as the target for the regional innovative capacity, in a reliable and typical way. Moreover, the function of nodes of the innovative centers is relevant to the performance of economic news.
Our data use indirect measures of flows through its particular focus upon relations among cities through intercity economic events. The economic events and analyses of intercity relations present a first look at the configuration of city network of Yangtze River Delta. The results are derived from estimating the importance of intercity economic events which relates to each of the above flows between cities. Hence this is a project to begin the task of a comprehensive study on city network formation in Yangtze River Delta in space of flows. Clearly there are many other ways to analyze these data; here we just present the basic structures.
With great orientation features analyzed in the inter-regional relation networks on the economic event information, linkages in the developed core cities express obvious greater relation flows than those in periphery cities. Shanghai is the most important node city and performed as the core city of the region and a rising international metropolis; Nanjing and Hangzhou also tend to be quite strong in network connectivity. What's more, the power-law distribution suits the spatial networks of economic event information in the Yangtze River Delta.
Through comparing the office relations among different areas with city event relations, correlation coefficient between the node data of the news and firms have shown the quite favorable linear fitting between them. And it is believed that the role of cities and regions in the linkage of offices can be indicated by news information. Similar situation occurs when it comes to the nodosity of economic-news-network and the patents of each city, which indicates that, the innovative capacity may be larger in a city with more obvious centralization of economic information. As information and knowledge indicated by the economic news from the cities jointly forms a kind of intercity network, and creative industries can be looked upon in particular as the drivers of metropolitan growth.
Alderson, A. S. and Beckfield, J. (2004) “Power and Position in the World City System American”, Journal of Sociology, 109 (4), pp. 811-851.
Batten, D. (1995). “Network Cities: Creative Urban Agglomerations for the 21st Century”, Urban Studies, 32, pp. 313-27.
Beaverstock, J. V., Hoyler, M., Pain, K. and Taylor, P. J. (2001) Comparing London and Frankfurt as World Cities: A Relational Study of Contempory Urban Change. London: Anglo-German Foundation for the Study of Industrial Society.
Beaverstock, J.V., Smith, R. G., Taylor, P., Walker, D. and Lorimer, H. (2000) “Globalization and world cities: some measurement methodologies.” Applied Geography 20 (1): pp. 43-63.
Beaverstock, J.V., Smith, R.G. and Taylor, P.J. (2000) “World City Network: A New Metageography?” Environment and Planning (A) 31: pp. 187-192.
Camagni, R.P. (1993) "From city hierarchy to city network: reflections about an emerging paradigm" In Structure and Change in the Space Economy, edited by T.R. Lakshmanan and P. Nijkamp, pp. 66-87. Berlin: Springer-Verlag.
Carroll, W. K. (2007) “Global Cities in the Global Corporate Network”, Environment and Planning (A), 39, pp. 2297 - 2323.
Castells, M. (1996) The Rise of the Network Society. Blackwell: Oxford.
Evert, M. (2008) “Stein's 'Regional City' concept revisited: Critical Mass and Complementarity in Contemporary Urban Networks”, The Town Planning Review, 79, pp. 485-507.
F. G. van Oort, M. J. Burger and O. Raspe. (2007) “On the Economic Foundation of the Urban Network Paradigm: Spatial Integration, Functional Integration and Economic Complementarities within the Dutch Randstad”, GaWC Research Bulletin 243.
Giddens, A. (1999) Runaway world. Cambridge: Polity Press.
Graham, S. and Marvin, S. (1996) Telecommunications and the City. London: Routledge.
Gonzalez, M.C., Hidalgo, A.C. and Barabasi, A.L. (2008) “Understanding Individual Mobility Patterns”, Nature 553 (7 196), pp. 697-822.
Hall, P. and Pain, K. (2006) The Polycentric Metropolis: Learning from Mega-City Regions in Europe. London: Earthscan.
Allen, J. (2008) “Powerful City Networks: More than Connections, Less than Domination and Control”, GaWC Research Bulletin 270.
Keeling, D.J. (1995) “Transportation and the world city paradigm" In World Cities in a World-System, edited by P.L. Knox and P.J. Taylor, pp. 115-31. Cambridge, UK: Cambridge University Press.
Matthiessen, C.W. and Schwarz, A.W. (1999) “Scientific Centres in Europe: an Analysis of Research Strength and Patterns of Specialisation Based on Bibliometric Indicators”, Urban Studies, 36, pp. 453-477.
Matthiessen, C.W., Schwarz, A.W. and Find, S. (2002) “The Top-level Global Research System, 1997-1999: Centers, Networks and Nodality, An analysis Based on Bibliometric Indicators”, Urban Studies, 39, pp. 903- 9 27.
Neal, Z. P. (2008a) "The Space of What Flows? Rethinking Connections and Connectivity in City Networks”, GaWC Research Bulletin 281.
Neal, Z. P. (2008b) "The Duality of World Cities and Firms: Comparing Networks, Hierarchies and Inequalities in the Global Economy", Global Networks, 8, pp. 94-115.
Neal, Z. P. (20 10) "The Space of What Flows? Rethinking Connections and Connectivity in City Networks ", Urban Studies, (in press).
Pred, A. (1980) Urban Growth and City Systems in the United State s, 1840-1860. Hutchinson, London.
Renkow, M. and Hoover, D.M. (2000) "Commuting, Migration and Rural-urban Population Dynamics ", Journal of Regional Science, 40, pp. 261 – 287.
Rimmer, P.J. (1998) "Transport and telecommunication among world cities ", in Globalization and the World of Large Cities Eds Lo, F-C, Yeung Y-M (United Nations University Press, Tokyo)
Smith, D.A. and Timberlake, M. (1993) "World cities: A Political Economy/Global Network Approach ", Research in Urban Sociology, 3, pp. 181-207.
Smith, D.A. and Timberlake, M. (200 1) "World City Networks and Hierarchies, 1977- 19 97: An Empirical Analysis of Global Air Travel Links ", American Behavioural Scientist, 44, pp. 1656- 16 78.
Smith, D.A. and TimberlakE, M. (2002) “Hierarchies of dominance among world cities: a network approach" In Global Networks, Linked Cities, edited by Sassen. S, pp. 117–141. London: Routledge.
Smith, R.G. (2007) “Place as network" in Companion Encyclopedia of Geography 2nd Edition, edited by Douglas I, Huggett R and Perkins C, pp. 57-69. London: Routledge.
Taaffe, E.J. Gauthier, H.L. and O'Kelly, M.E. (1996) Geography of Transportation. New York: Prentice Hall.
Taylor, P.J. (1999) "So-called 'World Cities ': The Evidential Structure within a Literature ", Environment and Planning (A), 31 (11), pp. 1901-1904.
Taylor, P.J. (2001) "Specification of the World City Network", Geographical Analysis, 33 (2), pp. 181-194.
Taylor, P.J. (2004) World City Network: a global urban analysis, London, Routledge.
Taylor, P.J. et al. (2009) "Measuring the World City Network: New Results and Developments”, GaWC Research Bulletin 300.
Thompson, G.F. (2003) Between Hierarchies and Markets: The Logic and Limits of Networked Forms of Organization. Oxford: Oxford University Press.
Van Ham, M. (2002) Job Access, Workplace Mobility, and Occupational Achievement. Delft: Eburon.
Watts, D. (2003) Six Degrees: The Science of a Connected Age. W. W. Norton & Company: New York.
Zhou, Z. (2007) "Global City Regions: The Region Space Foundation that the Global City Develops", Tianjin Social Science, 1: pp. 69-81.
* Supported by National Natural Science Foundation of China (SN: 50678117) and Shanghai Science and Technology Committee (SN: 200806013).