GaWC Research Bulletin 310

This Research Bulletin has been published in Urban Studies, 47 (9), (2010), 1861-1877.

doi:10.1177/0042098010372682

Please refer to the published version when quoting the paper.

(Z)

Pathways of Change: Shifting Connectivities in the World City Network, 2000-08

B. Derudder^*, P.J. Taylor^**, P. Ni^***, A. De Vos^*, M. Hoyler^**, H. Hanssens^*, D. Bassens^*, J. Huang^****, F. Witlox^*, W. Shen^***** and X. Yang^****

Abstract

This is an empirical paper that measures and interprets changes in intercity relations at the global scale in the period 2000-2008. We draw on the network model devised by the Globalization and World Cities (GaWC) research group to measure global connectivities for 132 cities across the world in 2000 and 2008. The measurements for both years are adjusted so that a coherent set of services/cities is used. A range of statistical techniques is used to explore these changes at the city level and the regional scale. The most notable changes are (i) the general rise of connectivity in the world city network, (ii) the loss of global connectivity of US and Sub-Saharan African cities (Los Angeles, San Francisco and Miami in particular), and (iii) the gain in global connectivity of South Asian, Chinese and Eastern European cities (Shanghai, Beijing and Moscow in particular).

Introduction

This is an empirical paper that measures and interprets changes in intercity relations at the global scale in the period 2000-2008. To this end, we employ the network model of intercity relations detailed in Taylor (2001). This network model has been the fundamental tool for the quantitative researches of the Globalization and World Cities research network (GaWC, http://www.lboro.ac.uk/gawc), and is based on advanced producer service (APS) firms ‘interlocking' cities through their worldwide distributions of offices. The rationale for establishing GaWC was that research on globalized urbanization has long been hampered by data deficiencies. More specifically, in our empirical research, we have focused on one particular criticism of this literature in the 1980s and 90s: a severe empirical deficit as regards intercity relations (e.g. Smith and Timberlake, 1995a,b; Taylor, 1997, 1999; Beaverstock et al., 2000a,b). This evidential crisis has been averted in the last decade through detailed analyses of transnational intercity relations. Two separate and distinctive solutions to this problem have been developed in the literature (Derudder, 2006): (i) analyzing worldwide corporate organization (e.g. Taylor et al., 2002b; Derudder et al., 2003; Alderson and Beckfield, 2004; Wall and van der Knaap, 2009) and (ii) describing the infrastructure that has enabled that organization to go global (e.g., Smith and Timberlake, 2001; Malecki, 2002; Derudder and Witlox, 2008; Devriendt et al., 2008).

A first major application of the GaWC model was the measurement (Taylor et al., 2002a) and subsequent empirical analysis (Taylor et al., 2002b; Derudder et al., 2003) of the world city network (WCN) in the year 2000. In practice, the analyses were based on information on the (importance of the) presence of 100 leading APS firms in 315 cities for the year 2000¹. Continuing GaWC's decade-long concern for mapping the global economy through the networking practices of APS firms in cities, in 2007 we joined forces with the Global Urban Competitiveness Project (GUCP) at the Chinese Academy of Social Sciences (CASS) to carry out a new large-scale data collection exercise for 2008 (Taylor et al., 2009a,b; 2010). We garnered information on 175 office networks of firms across 525 cities in the first half of 2008. This implies that we have detailed cross-sectional snapshots of the WCN in 2000 and 2008, and the purpose of this paper is to report and interpret the changes that have occurred in this period.

The remainder of the argument is developed in two main sections. First, the paper briefly provides a summary of GaWC's world city network (WCN) model, i.e. its conceptual rationale and data requirements. We also describe the problems involved in comparing the 2000 and 2008 data collection exercises, and detail how we have transformed the data to deal with these issues. The second section discusses the results at the network, the city and the regional level respectively, after which the paper is concluded with an overview of our major findings and some avenues for further research.

Data and methodology

Conceptual Rationale of the GaWC Model

Drawing on Saskia Sassen's (1995, 2001) work on place and production in an increasingly globalized economy, GaWC has undertaken a theoretically grounded endeavor of data acquisition for measuring WCN-formation. Sassen's research emphasizes the self-accelerative transformation of the economic bases of cities from manufacturing to business services. This conversion can be traced back to the observation that a growing number of manufacturing and service industries, unable to cope with the accelerated pace of structural change and the increasing pressure for product innovation on their own, are becoming more and more dependent on specialized business services, such as financial services, accountancy, management consultancy, advertising, etc. In most, if not all of these advanced producer services (APS) firms, clients purchase customized knowledge, expertise and skills. APS firms have increasingly become multinational firms in their own right as they look for a foreign presence in an international market to service existing clients and find new ones (see Aharoni and Nachum, 2000; Warf, 2001; Harrington and Daniels, 2006). Sassen's (1991, p. 126) basic argument in relation to cities, now, is that a number of metropolitan centers have secured “a particular component in their economic base” which gives them a “specific role in the current phase of the world economy”: they have become prime centers for the production and consumption of business services in the organization of global capital. As locales for service innovations in such areas as multi-jurisdictional law and new financial instruments, these metropolitan centers constitute concentrations of information and knowledge necessary for new service productions by business service firms.

For the purpose of our large-scale empirical analysis, the salient point is that business services are in and of themselves an indispensable production factor that has a growth potential of its own, as opposed to other domains of service sector growth that is the strict result of derived demand in other sectors. The reason for this is that such corporate service firms have benefited immensely from the technological advances in computing and communications that have allowed them to broaden the geographical distribution of their service provision: service firms have always clustered in cities to provide such services to their clients, but under conditions of contemporary globalization, multiple offices are required in major cities around the world to provide a seamless service, thereby protecting global brand integrity by keeping all work in-house (see Figure 1). Each firm has its own locational strategy – which cities to have offices in, what size and functions those offices will be, and how the offices will be organized. It is the work done in these offices that ‘interlock' various cities in projects that require multiple office inputs. Thus the intercity relations in these servicing practices are numerous electronic communications – information, instruction, advice, planning, interpretation, strategy, knowledge, etc., some teleconferencing as required, and probably travel for face-to-face meetings at a minimum for the beginning and end of a given project. These are the working flows that combined across numerous projects in many firms constitute the world city network (WCN) as specified in the GaWC model (Taylor, 2001, 2004).

Figure 1: Deloitte advertisement at Schiphol Airport.

Model Specification

The GaWC specification of the WCN can be formally represented by a matrix V_ij defined by n cities x m firms, where v_ij is the 'service value' of city i to firm j. This service value is a standardized measure of the importance of a city to a firm's office network, which depends upon the size and functions of an office or offices in a city. The global network connectivity GNC_a of city a in this interlocking network is defined as follows:

The conjecture behind conceiving the product of service values as a surrogate for actual flows of inter-firm information and knowledge between cities is that the more important the office, the more connections there will be with other offices in a firm's network. The limiting case is a city that shares no firms with any other city so that all of its service value products in equation (1) are 0 and it has no connectivity. To make GNC measures manageable in our use below (i.e. independent from the number of firms/cities), we express connectivities as proportions of the largest computed connectivity in the data, thus creating a scale from 0 to 1.

Data Gathering

Precise specification guides our data collection: data are required on the city office networks of large professional, financial and creative service firms. These exercises in data collection are described in detail in Taylor et al. (2002a) for the year 2000 and in Taylor et al. (2009b) for the year 2008, and will be summarized here as it is the input to our subsequent analysis.

In 2000, global APS firms were defined as firms with offices in 15 or more different cities, including at least one in each of the prime globalization regions: northern America, western Europe and Pacific Asia. Firms meeting this criterion were selected from rankings of leading firms in different service sectors. The other key criterion was purely practical - whether adequate information could be found on the firm's website. In the event 100 firms were identified in six sectors: 18 in accountancy, 15 in advertising, 23 in banking/finance, 11 in insurance, 16 in law, and 17 in management consultancy. Selecting cities was much more arbitrary and was based upon previous GaWC experience in researching global office networks. Capital cities of all but the smallest states were included plus many other important cities in larger states. A total of 315 cities were selected. The end result is a 315 cities x 100 firms matrix of 31,500 service values.

In both data gatherings, assigning service values to city/firm-pairs focused on two features of a firm's office(s) in a city as shown on their corporate websites: first, the size of office (e.g. number of practitioners), and second, their extra-locational functions (e.g. regional headquarters). Information for every firm was simplified into service values ranging from 0 to 5 as follows. The city housing a firm's headquarters was scored 5, a city with no office of that firm was scored 0. An 'ordinary ' or 'typical' office of the firm resulted in a city scoring 2. With something missing (e.g. no partners in a law office), the score reduced to 1. Particularly large offices were scored 3 and those with important extra-territorial functions (e.g. regional headquarters) scored 4. All such assessments were made firm by firm.

Consistency in the Data Gathering Exercises

The only longitudinal GaWC analyses to date are by Taylor et al. (2003) for 2000-2001 changes and Taylor and Aranya (2008) for 2000-2004 changes (for an interpretation of the patterns of change documented in the latter paper, see Orozco-Pereira & Derudder, 2010). Both papers take the 2000 data gathering as a starting point, and basically look at how the office networks of the 100 service firms have shifted in 2000-2001 and 2000-2004 respectively. However, particularly in Taylor and Aranya (2008), it became clear that this approach would turn out to be increasingly problematic as the time period becomes longer. The reason for this is that t he comparison of city connectivities over time requires a certain consistency in the data structure. However, the dynamic nature of the global economy implies that the relevance of invoking the geography of the office networks of the initial APS firms becomes increasingly problematic as time passes. For instance, in Taylor and Aranya (2008), of the 100 firms used in 2000, 20 had to be deleted because the firms were liquidated, had merged with other firms in the data, or simply because the authors were unsure of the comparability of new data with old data (see also Orozco-Pereira & Derudder, 2010). Since measurement of differences should represent changing urban geographies rather than data collection change, it was clear that this approach was not tenable in the long run: ever-lower number of firms would reduce the robustness of the dataset, while changes in the sectoral composition of the dataset may influence the results. Furthermore, new firms may enter the fray as their global presence/importance rises. The rationale behind the altered data gathering strategy should be understood in light of these problems. The new methodology allows for a more flexible approach: using a predefined number of firms from each sector (e.g. 75 in financial services and 25 in law) selected via independent rankings of their importance as detailed above. This puts the data collection on a new and sounder footing: there will be statistical robustness in future research as the same large number of firms is used for each sector, while we will include the leading firms from each sector.

However, for the present case, special modifications need to be made to compare the 2000 and 2008 measurements. This requires maintaining as much consistency as possible in the data structure. In terms of cities, consistency is relatively straightforward in that we initially only focused on the 307 cities that feature in both analyses and have a GNC ≠ 0 for both 2000 and 2008 (Baghdad, for instance, had zero connectivity in 2000 and is excluded). Of these 307 cities, we only retained the 132 cities with a GNC of at least one fifth of the most connected city in either year. GNCs were then recomputed based on these 132 so that a coherent set of inter-city relations is being compared. One major consequence of this decision is that the GNC measures and rankings used in the remainder of this paper may be slightly different than those reported in other GaWC publications.

In terms of firms, our main concern was that changes in the sectoral composition of the dataset might influence the results. For instance, in Taylor and Aranya (2008), the number of firms in the accountancy sector was almost reduced by a half between 2000 and 2004. As a consequence, this implies an increased influence of, say, law and management consultancy in dictating network structures. To avoid this situation in the present analysis, the measurements for the year 2000 were adjusted so that the same ‘service mix' is used as in 2008 (see Table 1). This was especially relevant given the larger number of financial services firms in the 2008 data gathering (75 out of 175), which would – in relative terms – artificially boost the connectivity levels of clear-cut international financial centres such as New York when comparing unadjusted GNC levels in 2000 with GNC levels in 2008. To deal with this potential problem, the connectivity measurements in 2000 were transformed to match the 2008 sectoral distribution (i.e. the last column of Table 1). For instance, the connectivity generated in the office networks of the 15 advertising firms in the 2000 data gathering now makes up 25/175 = 14,3% of a city's revised 2000 GNC rather than the initial 15/100 = 15%, while the connectivity generated in the office networks of the 23 + 11 = 34 financial/banking/insurance firms in the 2000 data gathering now makes up 75/175 = 42,9% of a city's revised GNC rather than the initial 34/100 = 34%. Once again, as a consequence of this transformation, the GNC figures in this paper may somewhat differ from those reported in other GaWC publications.

Table 1: Sectoral distribution of GaWC data gatherings in 2000 and 2008.

Figures 2 and 3 illustrate the WCN in 2000 and 2008 respectively as a pattern of nodes (city codes are given in an Appendix to this paper). The 132 cities included in both figures have a global network connectivity of at least one fifth of the most connected city in either 2000 (London) or 2008 (New York). Obviously this is an arbitrary cut off point, it has been chosen because it still provides us with a large number of cities that are distributed across all parts of the settled world. The next section provides a detailed account of the major changes in the WCN between 2000 and 2008.

Figure 2: GNC for 132 cities in 2000.

Figure 3: GNC for 132 cities in 2008.

Changes in the WCN

Preliminary Analysis: Shifting Ranks, Rising Connectivities

Table 2 presents an overview of the 20 cities with the largest global network connectivity (GNC) in 2000 and 2008, and the major changes in ranks in this time period. Although New York and London change positions, the most notable feature is the stability at the apex of the WCN: London, New York, and Hong Kong remain the most connected cities with NY-LON as the undisputed dominant dyad, and Paris, Singapore and Tokyo follow, albeit with different rankings. Below the top 6, there have been some major changes with 8 cities entering the 14 positions between 6 and 20: cities such as Chicago, Los Angeles and Amsterdam lose out in favour of the likes of Shanghai, Beijing and Seoul in an ‘east-west swap'. More specifically, the plummeting of US cities and the concomitant rise of Chinese cities is a more fundamental feature of this analysis. This obviously points to an overarching ‘world-regional' trend, as the 20 most connected cities in 2000 included 5 North American cities and 5 Asian cities, whereas in 2008 only 3 North American cities (New York, Toronto, and Chicago) make the top 20 as opposed to 9 Asian cities. It has frequently been suggested that the world-system is in the midst of a major geographical transformation from ‘West' to ‘East' (e.g. Arrighi, 1994, 2007; Frank, 1998), and these changes – even just before the current financial crisis got underway – suggest that this shift is indeed unfolding in terms of urban connectivity.

Merely considering (notable) changes in ranks is, however, not the best approach when discussing WCN change. An exclusive focus on ranks implicitly entails the adoption of a competitive approach to studying urban change. This approach can, for instance, be observed in Friedmann's (1995, p. 23) statement that world cities are “driven by relentless competition, struggling to capture ever more command and control functions that comprise their very essence,” even to the degree that “[c]ompetitive angst is built into world city politics.” In the GaWC approach, however, firms not cities are the agents of change and this means that the ‘essence' of intercity relations is interurban cooperation within office networks of globalized service firms rather than crude interurban competition for capital, resources, knowledge, etc,… (Beaverstock et al., 2001). This is not to say that there is no competition between cities (see Begg, 1999, p. 807), but in this paper's argument, the cooperation process is prioritized because it entails the basic reproduction of the intercity relations: cities exist in city networks and networks can only exist through collective complementarities (Taylor, 2004). This position is consistent with general organization theory wherein competition and hierarchy are deemed to be different from network and cooperation (Powell, 1990; Thompson, 2003; for an application to cities see Taylor et al., 2010). All this implies that, from our perspective, change is much more than a matter of cities ‘rising' or ‘falling'. This can be readily observed when looking at Chicago in Table 2: in the period under investigation, the city has retained more or less the same overall level of GNC, but it nonetheless loses 12 places in the ranking because other cities have become relatively more connected. In other words: rather than some cities dropping in the ‘ranking' per se, the first notable feature of our analysis is the overall rise of connectivity in the WCN in the period 2000-2008. This is evident from a number of related indicators: the average connectivity in the WCN has risen from 0,20 to 0,22, while in 2008 the number of cities with a connectivity larger than 20% of the leading city has risen from 110 to 125. Overall, 179 out of 307 cities are more connected to the WCN at large than they were in 2000. This indicates that the globalization of services has been a dynamic and growing economic sector expanding offices in many cities and extending office networks to new cities in the period under investigation. Although the NY-LON dyad still dominates the network, its structure has become more horizontal between 2000 and 2008 indicating a worldwide diffusion of globalization processes. The result has been an increasingly integrated world city network.

Change in the WCN

Figure 4 shows the geography of global urban connectivity change for the most connected cities in the WCN, with rising connectivities for 97 out of 132 cities. The regional pattern suggested in Table 2 is confirmed, with connectivity losses for Western European, Australasian and especially North American cities, and connectivity gains in other parts of the world in general and specifically in Eastern Europe and Pacific Asia/China.

Figure 4: Absolute GNC change for 132 cities.

The degree of change in absolute global network connectivity is useful for observing some of the most notable shifts in the WCN, but it has some severe limitations as a way of understanding change. This is because GNC_a is a closed number system that distorts the measurement of change. However much more connected it becomes, the leading city cannot show additional connectivity through its GNC_a measure of unity. In more general terms, there is a problem of possible underestimation of change at the higher ends of the scale. This problem consists of two components: (i) a measurement problem in that higher ranked cities have less leeway to increase their connectivity because they are nearer the limit of the measurement scale (i.e. a city with a GNC_a of 0,95 can only increase its connectivity with 0,05) and (ii) a conceptual problem in that the markets of higher ranked cities are closer to saturation in that they have less leeway to acquire more/larger/more important offices (i.e. a city where all major service firms have a major office can hardly become more important in the office networks of these firms). We therefore developed an alternative way of measuring change, which takes into account both problems.

The measurement problem is tackled by generating standardized measures of sectoral connectivity change SCC_a. To this end, we first compute standardized global network connectivities SGNC_a for both 2000 and 2008 as follows:

where

and

For both cross-sections, t his produces an open number sequence pivoting on zero. Second, change in connectivity is first measured by comparing both standardized global network connectivity SGNC_a:

And, third, for analysis of the distribution of change, CC_a is further standardized to obtain our standardized measure of connectivity change SCC_a, which is defined as:

where

and

This leaves us with the conceptual problem of market saturation (see also Orozco-Pereira and Derudder, 2010). Figure 5 presents a scatter plot in which the Y axis represents SCC in the period 2000-08 and the X axis GNC in 2000 for each of the 132 cities. While the figure provides an immediate and compelling picture of the most upwardly and downwardly mobile cities, it also shows that there is indeed a small but statistically significant negative correlation (r = -0,21) between both indicators. The straightforward solution to the ensuing interpretation problem is to use the standardized residuals from this regression SRESID_a as our actual measures of change. In other words, SRESID_a measures can be understood as the actual level of SCC after accounting for the possible underestimation of change in major service centers because of small but statistically significant processes of market saturation.

Figure 5: GNC in 2000 versus and SCC in 2000-2008 for 132 cities.

This double transformation of GNC_a change into SRESID_a measures produces an open number sequence pivoting on zero. Figure 6 plots the distribution of SRESID_a, which conforms to a standard normal distribution in that its average is 0, its standard deviation equals 1, while the application of a Kolmogorov-Smirnov test reveals that this distribution can indeed be considered as a normal distribution. As a consequence, SRESID_a can basically be understood as a z-score, which implies that the measures can be interpreted as such (e.g., cities with an absolute value of SRESID_a = 2 have witnessed exceptional connectivity change). However, it needs to be emphasized that SRESID_a needs to be interpreted as a relative measure (i.e. relative vis-à-vis the entire distribution): it is possible that, in the face of an overall rise of connectivity in the WCN, a city that has been gaining in connectivity in recent years has a negative SRESID_a value because other cities in the distribution have – on average – been gaining more connectivity (after taking into account the effect of the initial level of connectivity in 2000). Referring back to the Chicago example, for instance, this implies that the city will have a fairly substantial negative value for SRESID_a despite retaining the same overall level of connectivity.

Figure 6: SRESID for 132 cities in 2000-2008.

Using the transformed measurements for 2000 and 2008 as input to the model allows for straightforward assessments of change in the WCN between 2000 and 2008. Figure 7 and Table 3 summarize the changing geography of global urban connectivity through this standardized measurement of change. Figure 6 plots the SRESID_a of cities in their approximate geographical position, while Table 3 features the 10 cities that have witnessed the largest changes (both positive and negative).

Table 3: Major positive/negative values of SRESID in 2000-2008.

Figure 7: Standardized residuals for 132 cities in 2000-2008.

With this standardized measurement summarized in Table 3 and Figure 7, the relative decline of Western European, Australasian and especially North American cities becomes very clear, with not a single city in the latter region keeping up with change in the rest of the distribution in general and Pacific Asia in particular: the 10 cities with the largest relative connectivity decline are all located in Northern America and Germany (plus Hamilton and Nassau as offshore banking centres). In addition to the substantive interpretation of this pattern being an overall indicator of the ‘reOrientation' of the global economy, there is also another – and perhaps less gloomy – possible reading of this trend. That is, it is simply possible that cities with long established service offices in Western Europe and Northern America are declining relatively (= standing still in comparison with the rest of the world) while other parts of world are catching up (see the Chicago example). Moreover, the USA is a special case with its cities already being reported as under-represented in the world city network in 2000 (Taylor and Lang, 2004) and 2004 (Taylor and Aranya, 2008). The 2008 results show a continuation and perhaps even an accentuation of this trend. This may in part be a result of the US home market for advanced producer services being far greater than for any other country, which has two key effects. First, foreign firms find it hard to penetrate the market and tend to represent clients through just a New York office. Second, US service firms have less reason to gamble on global expansion – compare a large Chicago company with a large Seoul company: the former can make better profits through domestic expansion; the latter can only expand through new cross-border work.

Shanghai and Beijing have witnessed the most substantial connectivity gains in the period 2000-2008. Although perhaps not surprising, this is in a way an important finding: although all cities in Pacific Asia in general and China in particular have become more connected in the period under investigation, size does seem to matter in that especially the major cities in this region have become the principal gateways for the channelling of transnational flows of capital, goods, knowledge and people (see also the connectivity gains of Moscow in the context of the Former Soviet Union). China's evolution towards capitalism has been fast but in a way also gradual through the continuing imprint of the Party-state, resulting in a state-led transformation of the economy towards a unique variety of capitalism (see Ma, 2002, p. 1546). Within this context of enduring state-control, these results support the idea that China is now being opened up not only through the well-established gateway of Hong Kong, but also through Beijing and Shanghai. The latter cities are thereby developing along complementary lines, respectively as a political centre and as the mainland's premier business and financial centre (see Lai, 2009).

Conclusions

This paper has employed a network model of intercity relations based on advanced producer service firms ‘interlocking' world cities through their worldwide distributions of offices. Based on data on the location strategies of producer services firms in 2000 and 2008, we have highlighted the major changes that have been taking place in the WCN during this time period. A little thought might lead to the idea that these findings are not actually very surprising: what makes New York and London so important in the world economy is their distinctiveness as massive global service centres, while the rising connectivities of Shanghai and Beijing are in line with commonsensical expectations. But such reasoning remains conjecture, to be convincing such a notion needs empirical verification in a broad comparative study. The value of the measurement exercise described here is that it can make such verification possible. In conclusion, we have tried to enhance insight into globalization through the depiction of the changing geography of the world city network. Very much in the spirit of a number of other papers in this special issue (e.g. Alderson et al., 2010; Matthiessen et al., 2010; Mahutga et al., 2010; Pirie, 2010), we have not restricted 'globalization forces' to just a limited set of 'world cities' but have incorporated a very large number of cities into a single global urban analysis. Contemporary globalization is not an end-product in itself but an on-going bundle of processes. We cannot know what future scenario will come to pass but we do know that we will not be able to assess such changes unless we have a good empirical understanding of the contemporary world city network.

When garnering the data for 2008, we obviously could not fully realize that this research was generating instant history. Given the usual lag time of about one year from preparing and carrying out data collection to the beginning of analyses, it seemed a reasonable assumption that we would have the latest, up-to-date results on the shape of the world city network. But much has happened since the new GaWC data were collected in the first half of 2008: place-based public finance has had to come to the rescue of network-based private finance. The nationalization (part, full or implicit) of financial services firms has brought territories and their boundaries back to the centre stage of the world economy at the expense of networks and their flows. Put another way, the relations between cities and states have seemingly been readjusted in the latter's favour. Neo-liberal globalization may be collapsing but firms operating through global spaces of flows have not been eliminated; we cannot now know what form this emerging globalization will take. So what we have been presenting here are the latest but not up-to-date results on the world city network. This can be interpreted as measurement of the world city network prior to the effects of the current geoeconomic transition being realized: it is the ‘before' position to be compared to subsequent research on the ‘after' position in, say, 2010 (Taylor et al., 2009a).

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APPENDIX

List of Abbreviations

NOTES

* Ben Derudder, Anneleen De Vos, Heidi Hanssens, David Bassens, Frank Witlox, Department of Geography, Ghent University, Belgium; email: ben.derudder@ugent.be

** Peter J. Taylor, Michael Hoyler, Department of Geography, Loughborough University, UK

*** Pengfei Ni, Chinese Academy of Social Sciences, PRC

**** Jin Huang, Xiaolan Yang, Beijing University of Post and Telecommunication, PRC

***** Wei Shen, ESSCA École de Management, Angers, France

1. This data gathering was repeated in 2001 and 2004 (Taylor et al., 2003; Taylor and Aranya, 2008), but it became increasingly clear that a new way for gathering data was necessary to maintain a consistency in the data structure for fruitful longitudinal analysis.

2. In practice, a number of overlapping criteria were used to select cities. All cities with a population of more than 2 million inhabitants were included, which led to the consideration of far more cities located in China, India, Pakistan and Iran. We also included a ‘second city' of all but the smallest states plus other important cities in larger states. The latter selection was in part based on a systematic comparison with the airline data presented in Derudder and Witlox (2005). For instance, the most connected city in the global airline networks that is not included in our dataset is Nice, one of the major tourist centres and a leading resort on the French Riviera.