This Research Bulletin has been published in Urban Geography, 28 (1), (2007), 74-91.
Please refer to the published version when quoting the paper.
In urban geography, the traditional way of researching inter-city relations has been through analyses of ‘national urban systems’. Typically using non-relational data from national censuses, varying populations of cities were used to define the ‘urban system’ as a ‘national urban hierarchy’. Models such as ‘the law of the primate city’ and ‘the rank size rule’ were used to describe this ‘hierarchy’ as if the rest of the world did not exist ( Taylor, 2004)1. Thus, when the worldwide positions of cities were compared, there was an inherent scalar choice (i.e. territorial states) that pre-determined the outcome of such a comparison.
Almost a decade ago, in a special issue of Urban Geography on the United States urban system, Knox (1996) already highlighted – albeit somewhat implicit – the problems with such an approach. It was argued that the importance of transnational relations was not confined to a limited set of ‘global cities’: ‘medium cities’ have just as much need to respond to globalization trends as their larger neighbours. As a consequence, it would be wrong to reify second-rung cities as ‘sub-global’, ‘regional’ or ‘national’ from the outset of the analysis. The classic example of this error among United States cities is Miami: Grosfoguel (1995) and Nijman (1996, 1997), for instance, convincingly revealed that Miami cannot be properly understood in the context of a ‘national urban system’.
On a more abstract level, the problem with using ‘national urban systems’ for comparing the position of cities boils down to the a priori choice of an ‘appropriate’ scale of analysis. All geographical studies are, of course, imbued with issues of scale: choosing scales of analysis, comparing outputs at different scales and describing constructions of scale are all common practices by geographers. Although the scalar choice itself is often left implicit in a study, analyses and interpretations thereof are heavily influenced by this initial areal circumscription. Urban geography has been no exception, whereby the nation-state has unsurprisingly defined the pivotal scale in almost all analyses of urban systems. This is what frameworks such as ‘the law of the primate city’ and ‘the rank size rule’ clearly reveal: the study of cities was nationalized, so that inter-city relations were territorially bounded to the severe detriment of properly understanding major cities. Especially within the framework of a new global scale of production and business servicing, however, and the ensuing rise of a world city network (WCN), the existence of areal subsets such as ‘national urban systems’ (or larger subsets such as a ‘European city system’ for that matter) cannot just be assumed or asserted: it has to be shown that nation-states or traditional historical world regions still define a valid patterning of inter-city relations (Taylor and Derudder, 2004). Previous analyses of the WCN have indeed shown strong regional patterns (e.g., Taylor et al., 2002b; Derudder et al., 2003), but they are rather more complicated than simple delineation by continent, let alone by nation-state. Under conditions of contemporary globalization, it is therefore highly problematic to assume, for instance, that United States cities can be adequately understood as a national urban system. Regionality may be an important influence in the spatial patterning of the WCN, but the scales on which these regional tendencies unfold are far more complex than often assumed. Put simply: one cannot contain urban networks within bounded spaces, and their analysis should therefore not be truncated from the outset.
This paper has two related purposes, one methodological and the other empirical. The first purpose is to discuss and illustrate how the position of cities can be compared in the context of the WCN. Potential data sources in this context should, of course, preferably contain (1) non-territorially bounded and (2) relational information, but it has been abundantly argued that there has long been a severe deficit as regards such data. In recent years, however, this evidential crisis has been averted through two separate and distinctive solutions (Derudder, 2006), i.e. through analysing worldwide corporate organisation (e.g., Alderson and Beckfield, 2004; Taylor, 2004; Derudder and Taylor, 2005) and describing the infrastructure that has enabled that organisation to go global (e.g., Smith and Timberlake, 2001; Malecki, 2002)2. We develop the latter approach by introducing an untapped airline data source that overcomes specific flaws previously identified. Using this data source, our second purpose is to demonstrate empirically how United States cities can be compared within the overall context of a global network of cities. In this respect, our analysis is similar to a research report published by the Brookings Institute. In this report, Taylor and Lang (2005) use information on the global location strategies of service firms to assess the position of US cities in the context of the WCN. We will compare our results with those presented in the Taylor and Lang (2005) report.
This paper has three main sections. First, we present a discussion of the empirical approaches that can be taken for comparing the position of cities in the context of an overarching WCN. We argue that although information on worldwide airline networks is potentially a prime data source, previous datasets were hampered by inadequate information coverage. In the second section, we present a new data source that is able to overcome some of the previous problems in this context. This detailed dataset is then used in the third section to assess and compare the major relations of a selection of major United States cities.
II. DATA SOURCES IN THE STUDY OF THE WORLD CITY NETWORK
II.1. The Requirement of Relational Data
In parallel with Gottmann’s (1989, p. 62) observation that “[d]ependence on a network (...) has become the general rule for the majority of substantial cities anywhere,” it has become commonplace to study major cities in the context of a WCN ( Taylor, 2004). Meyer (2003) contends that the theoretical underpinnings of this body of research have remained undeveloped, and observes that this theoretical lacuna needs to be addressed if scholars are to make greater progress in understanding the WCN. Meyer (2003) suggests that this can be done through a deepening and a refinement of existing political-economic and world-systems approaches, while Smith (2003) has taken a different approach altogether by suggesting that the theoretical study of world cities and their networks can be made through an engagement with the literatures of actor-network theory and non-representational theory 3 .
Although there is thus still a lot of work to do in the theoretical study of the WCN, it can be noted that authors have increasingly stressed the importance of a relational stance. It is now generally acknowledged that major cities are increasingly (re)produced by what flows through them (people, goods, information, knowledge, money, and cultural practices) rather than what is fixed within them, and it is therefore the presence of a vast bundle of global relations that lies at the root of WCN formation. As Taylor (2004, p. 42), following Castells (1996), puts it: cities “operate in a contemporary space of flows that enables them to have a global reach when circumstances require such connections.” From an empirical point of view, the consequences of this clear-cut relational standpoint are self-evident. Since all measurement and data should be the products of theory, empirical analyses of the WCN should reflect the relational perspective that lies at the root of its conceptualization. If we wish to take forward the view of world cities as a process (re)produced by global networking and connectivity, it is vital that relational data are sought after. This is most clearly explained by Smith and Timberlake (1995a,b), who stress the importance of constructing and analysing a variety of databases that take the form of ‘cities in global matrices’. However, although most seminal theoretical contributions thus consider worldwide inter-city relations to be of crucial importance, it can be noted that there has been a paucity of data detailing inter-city flows at the global level (e.g., Smith and Timberlake, 1995a,b, 2001, 2002; Taylor, 1997, 1999; Alderson and Beckfield, 2004; Derudder and Taylor, 2005).
Existing data sources in this field of research are generally inadequate because in most cases data on flows between cities are conspicuous by their absence, and with few exceptions empirical WCN research has thus neglected actual relations and linkages between world cities. Recently, however, there have been a number of efforts to rectify this situation. Generally speaking, these newly compiled databases have been premised upon two different foundations, which may be labelled the corporate organization and the infrastructure solutions respectively (Derudder, 2006). (1) The corporate organization approach to the construction of global inter-city matrices is premised on the observation that inter-city relations are primarily created by firms that pursue global strategies and are thus prime world city agents. The most elaborate examples of this corporate organization approach are the research pursued by the Globalization and World Cities Group and Network (GaWC, http://www.lboro.ac.uk/gawc , e.g., Beaverstock et al., 1999, 2000; Taylor et al., 2002a,b, 2004; Derudder et al., 2003), and two papers by Alderson and Beckfield (2004, 2006). (2) The research pursued in the direction of the infrastructure approach, in contrast, starts from the observation that advanced telecommunication and transportation infrastructures are unquestionably tied to key cities in the world economy. For instance, the most important cities in the world economy also harbour the most important airports. In addition, extensive fibre backbone telecommunications networks that support the Internet have been rolled out across the globe and have been predominantly deployed within and between major cities, creating a planetary infrastructure web on which the global economy has come to depend almost as much as physical transport networks (Moss and Townsend, 2000; Rutherford et al., 2004). These enabling telecommunication and transportation networks are the fundaments on which the connectivity within the WCN is built, and it is therefore not surprising that the geographical structure of these networks has been used to invoke a spatial imagery of the WCN. Examples include the WCN based on air traffic flows between cities (e.g., Cattan, 1995; Keeling, 1995; Kunzmann, 1998; Rimmer, 1998; Smith and Timberlake, 2001, 2002; O’Connor, 2003; Matsumoto, 2004; Derudder and Witlox, 2005b; Zook and Brunn, 2006), and those based on postal flows, telephone calls, and internet linkages (Marek, 1992; Warf, 1995; Graham and Marvin, 1996; Hanley, 2004). It is a new source of this second form of relational data solution that we employ in this study.
II.2. The Weakness of Previous Airline Data Sources
The use of air travel data for analysing worldwide inter-city flows has been largely attributed to the fact that airline data are comparatively easy to get hold of, while air transport is traditionally organized through cities rather than through states. More detailed appraisals of the usefulness of airline data are provided in Smith and Timberlake (2001, 2002) and Keeling (1995), who presents five interrelated arguments for why airline linkages are a suitable empirical source for assessing the WCN:
However, despite the obvious appeal of this data source, it can be noted that previous airline analyses have been hampered by inadequate data (Beaverstock et al., 2000; Derudder and Witlox, 2005a,b). Although airline data have some obvious advantages over other information (e.g., the limited number of assumptions to acquire network data4), some of the previously employed data sources and frameworks for analysis have not fulfilled their potential. Here we engage in a brief overview of biases that result from the use of standard airline data sources. It is not our intention to embark on an exhaustive overview of all airline analyses to date, but rather to sketch out the main obstacles in this context.
A first major obstruction in the translation of airline data into urban analyses is induced by the lack of origin/destination information in the databases. Most airline data record the individual legs of a trips separately rather than the trip as a whole, so that in case of stopovers a significant number of real inter-city links are replaced by two or more links that reflect airline corporate strategy (hubs) rather than relationships between key cities. Furthermore, the lack of origin/destination information makes geographically detailed assessments of the WCN difficult because direct connections become less likely once one goes down to less connected cities. To see how the lack of origin/destination data distorts a WCN analysis, we can refer to Keeling’s (1995) world city map, which is based on an analysis of the dominant linkages in the global airline network. This map was created from a matrix of scheduled air service involving 266 cities with populations exceeding one million. Only non-stop and direct flights between 2 cities, however, were taken into account, so that the measures used are not necessarily a reflection of actual inter-city relations. It is likely that in such an analysis the importance of cities that function as airline hubs, such as Amsterdam (KLM) and Frankfurt (Lufthansa), is overestimated at the expense of the likes of Brussels and Berlin. Furthermore, direct links between, say, Brussels and Rio de Janeiro cannot be measured because passengers are likely to go through São Paulo to make the trip. Relations between second-tier cities are hence difficult to measure with a data source that only contains single leg and direct trips. As a consequence, the analyses by Keeling (1995), Smith and Timberlake (2001, 2002) and Matsumoto (2004) are biased towards first-tier cities and important hub cities5.
A second obstacle to a clear-cut translation of air transport databases into transnational inter-city analyses can be traced to the fact that some of these data sources incorporate a subtle form of state-centrism. Despite their global aspirations, most analyses are based on databases that contain information on international flows. This bias towards inter-state rather than trans-state flows tends to undervalue relations between key cities that are situated in large and/or significant nation-state. Rimmer (1998), for instance, bases his analysis on data on international passengers, which downgrades United States world cities in particular since connections such as Los Angeles– New York and Chicago– New York are not included. Hence, Chicago only appears on one of Rimmer’s maps as a ‘fourth-level’ link to Toronto, while Dublin appears on all maps because of its ‘first-level’ link with London. Nobody would argue that Dublin is more important than Chicago as a world city; it only appears to be when one relies on international rather than global data. Another example of this problem is found in Smith and Timberlake (2002), who faced the absence of information on the volume of air passenger traffic between Hong Kong and London. This important global link did not feature in pre-1997 ICAO databases because a London-to-Hong Kong connection was considered to be ‘national’ ( Hong Kong was a British colony until 1997). Admittedly, Smith and Timberlake circumvent the London–Hong Kong problem by making an estimate, while the relegation of United States cities was tackled through the introduction of another data source that contained information on the major routes in the United States (i.e. data provided by the Air Transport Association in Washington, D.C.). Although this sidestepping of the most pressing holes in their initial database results in one of the most refined databases to date, in general this problem stays in place for major cities in a number of countries such as Canada, China and Brazil. A database detailing global rather than international air passenger flows would overcome the problems associated with the introduction of this state-centrism.
A third obstacle to the straightforward application of air transport statistics arises from the observation that such data measure general flow patterns. Since airline statistics are unable to differentiate between specific flows within the various linkages, it is doubtful that the specific flows that define the WCN can straightforwardly be deduced from such data. The latter is, of course, a more general problem that pertains to the variety of theoretical WCN conceptualizations (Derudder, 2006), but in this paragraph reference is made to specific, overarching distortions such as tourism. In a mapping of the European urban hierarchy based on air passenger flows, Kunzmann (1998) lists 14 airports that are secondary to the big three (London, Paris, and Frankfurt), including Munich, Milan, Madrid, and Palma de Mallorca. However, the high ranking of the latter merely reflects its role as one of the most popular holiday destinations in Europe; nobody would argue that it is a major world city. While it is likely that most researchers would agree that destinations such as Palma de Mallorca should be omitted from the analysis, such data manipulation becomes increasingly difficult when non-world city processes intersect with world city-formation. In his initial formulation of ‘The World City Hypothesis’, John Friedmann (1986, p. 74) maintained that the major driving forces behind world city growth were found in a limited number of rapidly expanding sectors. Although Friedmann identified world cities as major tourist destinations, it seems that tourism is merely an ancillary function, since
“[m]ajor importance attaches to corporate headquarters, international finance, global transport and communications; and high level business services, such as advertising, accounting, insurance, and legal services. An ancillary function of world cities is ideological penetration and control. New York and Los Angeles, London and Paris, and to a lesser degree Tokyo are centres for the production and dissemination of information, news, entertainment and other cultural artefacts.”
Hence, although it is clear that cities such as New York, London, Los Angeles, and Tokyo have become major tourist attractions in their own right, this is a secondary function at best, so that questions can be raised on the unmeasured number of tourists in the various databases. We agree that trying to single out the tourist functions may be perceived by some as a questionable move, but it seems nonetheless clear that existing clear-cut tourist destinations should be omitted from the data. Thus yes we would omit Parma de Mallorca, but Las Vegas is a major metropolis we might not want to drop, and this is certainly the case with Miami, despite it being a major tourist destination. Irrespective of the potential controversy over this point, we maintain that airline linkages reflect myriad processes of which world city formation is only one element, so that deducing the WCN from airline databases is not a straightforward matter. We concede that this problem may be the hardest to overcome, since we have no clear procedures for estimating the amount of contributing to world city formation within overall air travel; such a procedure would at the same time be open for debate and depend on which conceptualization of the WCN was being employed.
A final impediment to the use of airline data in WCN analyses is that the statistics that can be derived from them are not necessarily available and/or analysed within an appropriate relational framework. Airline data are not necessarily provided in the relational form presupposed by WCN research. Cattan (1995), for instance, has determined the global importance of 90 major European cities in terms of their international exchanges. She presents an assessment of the ‘European urban hierarchy’ based on the computation of various attribute measures, such as the number of international flights, the rate of international travel per head of population, the percentage of international traffic in overall traffic, and the number of direct international links. While these measures provide good proxies for ranking the connectivity of cities, they do not provide information on how overall connectivity can be disentangled into spatial patterns. As a consequence, while such an analysis may convey the hierarchical tendencies between the major European cities in the context of the WCN, the broader geography of this part of the network remains obscure. Only data in the form of inter-city matrices can unravel the spatiality behind overall connectivity.
III. A NEW DATA SOURCE
In the previous section, we have argued that, although information on the spatiality of worldwide airline networks is potentially a prime data source for comparing the position of cities in the context of an emerging WCN, previous analyses have not always been able to live up to their inherent potential. Ideally, air transport-based assessments of a city’s major connections should not suffer from the drawbacks outlined above. That is, data should (1) detail global rather than international flows in order to overcome the state-centrism problem; (2) contain information on origin/destination travel (i.e. the full trip), thereby avoiding an overestimation of the importance of locations acting as hubs; (3) comprise all passengers travelling on a specific origin-destination pair, which means that in principle as many as possible airlines servicing a specific connection will have to be included; and (4) pertain to real passenger movements.
In this section, we describe the construction of an airline-based, geographically detailed inter-city matrix that is able to circumvent and/or overcome some of the previously identified problems. After a brief introduction on the content and the manipulation of the initial dataset, we explain in what respect this new dataset is able to overcome the limitations highlighted in the previous section. The database employed here – the so-called ‘MIDT’-database (MIDT = Marketing Information Data Transfer) – contains information on bookings made through Global Distribution Systems (GDS) such as Galileo, Sabre, Worldspan, Amadeus, Topas, Infini, and Abaccus. GDS are electronic platforms used by travel agencies to manage airline bookings (i.e., the selling of seats on flights offered by different airlines), hotel reservations, and car rentals. Using a GDS-based database therefore implies that bookings made directly with an airline are sometimes excluded from the system. Airlines choose this direct booking option to avoid commissions charged by travel agencies. Direct bookings via the Internet are estimated to cost an airline $1, while bookings at travel agents cost an estimated $10 (Goetzl, 2000). Southwest, EasyJet, Virgin, and Ryanair, which are particularly low-cost carriers, have many direct sales, and consequently, their flights do not feature prominently in GDS-based databases. However, as late as 1999, 80% of all reservations continued to be made through GDS (Miller, 1999). This suggests that although our data source might provide a biased picture of airline transport the GDS level of coverage is still remains impressive. We proceed on the assumption that the spatiality of the reservations made by direct bookings did not differ fundamentally from that for reservations made through GDS at the time of our data.
With the cooperation of an airline, we were able to obtain a partial MIDT database that covers the period from January to August 2001 and contains information on a total of 3 753 100 trips, representing the movement of 547 410 397 passengers . Each MIDT record is made up of an entire airline trip, and comprises information on the IATA-airport codes of origin/destination, the air carrier, the connecting airports (if any), and the number of passengers. Airlines purchase the MIDT database for a variety of reasons, the most important of which is its ability to forecast demand. It is also a helpful tool for assessing the market share and the competitive position of an airline in a specific geographical area. In the context of our research, however, the database is used to construct a global inter-city matrix. The first step in the creation of this matrix (for more details: Derudder and Witlox, 2005b) is to transform the information because we are mainly interested in the total volume of passenger flows between cities. To achieve this, we relabelled the airport codes as city codes. These city codes are needed to compute meaningful intercity measures because a number of cities have more than one major airport. The particular airport used by a passenger is not important in this context because, for recording the London– New York relation, it is irrelevant whether a flight goes from Heathrow to JFK or from Gatwick to Newark. Next, we created a global intercity matrix that focuses on the most important cities in the world economy. That is, we omitted key holiday destinations and less important cities. For this, we used the tentative world city list compiled by GaWC, which contains 315 cities and includes the capital cities of all but the smallest states and numerous other cities of economic importance (Taylor et al., 2002a). Nine of these 315 cities were excluded either because they had no airport (e.g., Bonn and Kawasaki) or because the airport was not serviced in the period under consideration because of political instability (e.g., Kabul). This reconfiguration produced a 306 x 306 matrix that quantifies the relations between the most important cities in the world economy. It is this database we will use in the next section to compare the position of United States cities in a global space of flows.
How is this MIDT-database able to overcome some of the problems highlighted above? First, as this database contains origin/destination information, the overrating of the connectivity of airline hubs and first-tier world cities is minimized, which allows assessing the relational patterns in the lower rungs of the WCN in more detail (e.g., the downsizing of the importance of hub cities such as Amsterdam and Frankfurt). Second, the MIDT-based database does not distinguish between national and international flows, and can therefore be thought of as a truly transnational inter-city matrix. The New York–Chicago link is appropriately treated in the same way as the New York–Toronto link, which further reduces the underestimation of second-tier cities in large and/or significant nation-states. Third, reconfiguring the database by using GaWC’s detailed world city list excludes obvious holiday destinations, which results in a (modest) redirection away from tourism-induced biases. Fourth, this database contains relational information in a single, transnational framework. This allows overall connectivities to be disentangled into spatial patterns, while analyses of areal subsets can be carried out in the context of an overarching WCN, as suggested by Taylor and Derudder (2004).
Although this MIDT-based intercity matrix is able to overcome and/or circumvent some of the problems that have been associated with the use of airline data in WCN analyses (Derudder and Witlox, 2005a,b), a number of problems remain for future research. The main problem is that it remains largely impossible to discern flows generated within the context of the WCN from ‘other’ flows, while the precise meaning of WCN flows is itself subject to a variety of meanings. Either way, it is clear that the importance of the New York–Miami route and particularly the New York–Las Vegas route, for instance, suggests more than a business link. The linkages related to obvious holiday destinations such as Palma de Mallorca have been deleted from the database, but this manipulation only works for airports that are obviously not world city airports. Furthermore, although problems with the availability of global origin/destination data addresses the undervaluation of second-tier world cities, airline data cannot avoid undervaluing a second-tier city that is close to a major world city. For example, a passenger travelling from Rotterdam to New York is likely to depart from Amsterdam because of (1) the short distance between Rotterdam and Amsterdam (less than 50 miles) and (2) the importance of Amsterdam’s Schiphol airport. This bias is exacerbated by the overall tendency to underestimate the connectivity between nearby cities because of the availability of other modes of transportation. Elaborate high-speed rail networks, for instance, may be an alternative to short-haul flights, which results in an underestimation of the importance of some short-distance intercity links, Brussels-Paris, for instance. An overall solution to this underestimation problem may be to omit some cities from the database. Although this implies a further reduction in the geographical detail of the database, ensuing analyses would be more meaningful and robust. A final drawback of the dataset described in this paper is that because it only covers a single period, it cannot be used to analyse the evolution of the WCN. Thus, unlike Smith and Timberlake (2002), we cannot track changes over time.
IV. CONNECTIVITY PROFILES OF SELECTED UNITED STATES CITIES
In this section, we use the MIDT-based 306 x 306 matrix to assess how United States cities are connected within and without the United States. Furthermore, we will compare our results with the results of global service connectivity analysis of Taylor and Lang (2005) and the airline analysis of Smith and Timberlake (2002)6. Following Taylor (2001), we will term the geographical scope of a city’s major connections as an ‘urban hinterworld’. Our framework for analysis consists of a total of 23 United States cities, i.e. all cities that were identified in Taylor and Lang (2005) as having at least one fifth of London’s total global service connectivity: Atlanta, Boston, Charlotte, Chicago, Cleveland, Dallas, Denver, Detroit, Houston, Indianapolis, Kansas City, Los Angeles, Miami, Minneapolis, New York, Philadelphia, Pittsburgh, Portland, San Diego, San Francisco, Seattle, St. Louis and Washington, D.C. Hence, our list includes all major cities of economic importance, and also comprises the most important airline hubs (perhaps with the exception of Las Vegas and Orlando)7.
Table 1 orders the 23 United States cities in terms of total enplaned passengers in the period under investigation. It can be seen that New York, Los Angeles and Chicago are the three most important cities in terms of the number of enplaned passengers, with the Los Angeles- New York and the Chicago- New York air links as most important connections (over one million passengers per annum). Clearly, these three cities (together with San Francisco) are the United States leaders in global connectivity, a finding that runs parallel with Abu-Lughod’s (1999) leading triad. Other important nodes are Atlanta, Miami, Washington, Dallas, and Boston. At the bottom of Table 1, we find Kansas City, Cleveland, Pittsburgh, Indianapolis, and Charlotte. It is worthwhile to note that the ranking based on the volume of airline passenger transport is almost completely identical to the ranking produced in Taylor and Lang (2005) and Smith and Timberlake (2002).
When looking at the major connections of the 23 selected United States cities, it can be seen that the majority of the cities - Atlanta, Cleveland, Dallas, Denver, Detroit, Indianapolis, Kansas City, Minneapolis, Pittsburgh, Portland, San Diego, Seattle, and St. Louis - have an ‘all-American’ top 10. In other words, although our database contains information on transnational connections, domestic connections remain by far the most significant for most cities. An important exception is Miami, which only has two United States cities ( New York and Atlanta) amongst its top 10 connections. The most important non-United States connection in the top 10 is London. To be able to assess this in more detail, we carried out a twofold analysis on the data. First, for each city, a ranking was made of the most important connections in terms of real passenger movements. Note again that, in the case of a flight that comprises multiple connections, our dataset records the trip as a whole rather than the individual sections of the trip. As a consequence, our information avoids the problem where a significant number of ‘real’ inter-city links are replaced by two or more links that reflect airline corporate strategy. Second, we produced this ranking for non-United States connections only. Thus, while the first ranking indicates the global airline connectivity between all city-pairs (Table 2), the second ranking only focuses on the flights outside the United States (Table 3).
It is, of course, impossible to comment on the hinterworlds of all 23 United States cities, and we will therefore restrict the discussion to three pairs of United States cities. These pairs were selected, following Taylor and Lang (2005), to show as much variety and difference as possible. First, we contrast New York with Pittsburgh, i.e. the United States city with the highest global connectivity ( New York is ranked 2nd after London) and the lowest connectivity ( Pittsburgh is ranked 120th). Second, we compare two cities for which we can assume some interesting ‘regional’ tendencies: Los Angeles (Pacific Rim) and Miami (Latin-America). Finally, we consider two second-rung United States cities that may be thought to be similar in their relational patterns: Houston (ranked 62nd) and Seattle (ranked 68th).
As expected, the hinterworlds of New York and Pittsburgh show quite different patterns (Table 2). The Pittsburgh hinterworld is extremely United States-focused, with all of the top 15 connections situated in the United States (which supports Taylor and Lang, 2005). The primary connection from Pittsburgh is to New York. It accounts for 230,000 passengers during the period under investigation, which means that Pittsburgh is ranked only 32nd in New York’s hinterworld. In short: whereas Pittsburgh’s hinterworld can be considered as being very “regional”, New York’s hinterworld is far more global. Although the dominance of United States cities in New York’s hinterworld is still quite noticeable, with important connections to Los Angeles, Chicago, Boston, Miami, Atlanta, and San Francisco of around one million passengers, important non-United States cities can be noted as well. Clearly, London is very well connected to New York, ranking between Los Angeles and Chicago (Table 2). Thus, ‘hierarchy’ and ‘regionality’ seem to be closely intertwined in the WCN: less important cities are not only less connected, on average their connections are also more ‘regional’.
We can expect that the hinterworlds of Los Angeles and Miami may be particularly interesting for their ‘regional’ profiles. On the one hand, based on earlier studies employing data on the global distribution of the headquarters of global services firms (Derudder et al., 2003; Taylor, 2004), it has been suggested that Los Angeles is well-connected to Pacific Rim cities in both Asia and Australia. Miami, on the other hand, has previously been portrayed as unusual world city: the “most foreign city” in the USA (Nijman, 1997), the city that “breaks the rules” (Nijman, 1996), a contingent political (CIA) creation (Grosfoguel, 1995), with totally distinctive connections (Taylor and Walker, 2001). In Table 2, the Los Angeles claim is not noticeably supported: the city is most strongly linked to other United States cities, and its hinterworld pattern is not that different from New York – with the exception that Los Angeles serves as the main airport for the west of the United States (Seattle, San Jose, Phoenix), whereas New York does so for the east (Boston, Miami, Atlanta). The situation of Miami is much clearer. It has only four United States cities among its top 15 connections – New York, Atlanta, Los Angeles, and Washington. Unmistakably, Miami functions as a gateway city to Latin America, or even as ‘extra-mural capital’ of Latin America (Brown et al., 2002). Its top air connections are to the capital cities of Venezuela, Argentina, the Bahamas, Colombia, Mexico, Peru, Haiti, Brazil, and the Dominican Republic. In short, Miami clearly performs a crucial world city role in connecting Latin American cities to major North American cities.
The hinterworlds of Houston and Seattle (Table 2) are in a sense quite similar, despite being from very different parts of the United States. Both have only two non-United States cities in their top 15 important airline connections, London for both plus Mexico City for Houston and Tokyo for Seattle suggesting extra-United States regional biases. But in general, Houston and Seattle are very United States-oriented in their connections so that neither has a hinterworld that is very different from less important cities such as Pittsburgh.
To circumvent the dominance in domestic flights in Table 2, we also discuss the top 15 non- United States cities for the six aforementioned United States cities (Table 3). In terms of total passengers enplaned, the figures are of course much smaller. Whereas the top 15 connections to Pittsburgh equals about 1.1 million passengers in our dataset, its top 15 non-United States connections totals only 1/5th of this gross figure. In Table 3, it can be noted that the most dominant non- United States city is London. In three out of six cases, London is ranked in first place, for two other cities it is ranked second. Although the intensities of the connections are not the same, New York and Pittsburgh now have quite similar hinterworlds (Table 3). They both have seven (six of which are identical) European cities in their top 15 connections, but Pittsburgh has three more non-United States North American links in its list. For Los Angeles, the aforementioned regional bias towards the Pacific Rim now does become very apparent. Six of the top 15 non-United States connections point to cities in East Asia and the Pacific ( Taipei, Tokyo, Hong Kong, Seoul, Sydney, and Bangkok). The Miami case does not bring forward any additional new insights, given the large number of non-United States connections in the overall top 15 of most important airline connections. For Houston and Seattle, a somewhat different pattern emerges as hinted in Table 2. Houston’s non-United States connections are mainly to cities within Latin America, whereas Seattle is more focused towards cities in East Asia and the Pacific Rim (Table 3).
In this paper, we tried to show how the position of cities in an ‘urban system’ can be compared empirically under conditions of contemporary globalization in which the so-called ‘national urban system’ is an inadequate model. Hence, we evaluated the major transnational connections of United States cities in the context of an emerging world city network. After having identified airline data as a prime data source for such an analysis, we argued that previous airline-based studies used less-than-ideal data. The data employed here is able to sidestep some of these problems: we introduced a new data source that provides origins and destinations of airline passengers. Applying this data to leading United States cities, we have shown distinctively different ways these cities link to other cities within and without the United States of America. This is the first time United States air passenger flows have been shown shorn of airline corporate hub policy distortions, with the result that the findings are relatively close to previous research comparing United States cities in terms of their global service connectivities.
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* Correspondence concerning this article should be addressed to Frank Witlox, Ghent University, Department of Geography, Krijgslaan 281 (S8), B-9000 Gent, Belgium; telephone: +32 (0)9 264.45.53 ; fax: +32 (0)9 264.49.85; e-mail: firstname.lastname@example.org.
iWe would like to thank Jan Nijman, Robert Lake and three anonymous referees for their remarks on an earlier draft of this paper. Thanks also to Lomme Devriendt for part of the MIDT data handling. This research work is funded by the Scientific Research Foundation–Flanders, Research Project G.0214.04.
1. There have, however, been some (modest) attempts to look at the world-system of cities since the mid-80s (e.g. Chase-Dunn, 1985; Ettlinger and Archer, 1987). Furthermore, there were of course a few geographers devoted to ‘port geography’, the study of cities through which commodities flowed across the world-economy, but these researchers constituted a non-influential and very small minority of scholars.
2. Although data on corporate organisation and infrastructure networks have dominated the empirical world city literature, there have recently been quite a few other studies that focus on different types of inter-city linkages such as migrant flows (e.g. Short and Kim, 1999; Beaverstock et al., 2004; Benton-Short et al., 2005).
3. It is clear that Meyer (2003) and Smith (2003) take very different approaches, and it is therefore very hard to directly compare their observations. In this paper, we employ the notion of a WCN as a shorthand terminology for emphasizing that key cities in the global economy derive their status from a position in a great variety of networks.
4. GaWC-studies, for instance, construct relational data out of data on the organizational geography of corporate activities of producer service firms. However, this computation of relational data out of attribute-based indicators implies some far-reaching assumptions on the organizational structure of such firms (for an overview, Derudder and Taylor, 2005).
5. It is, however, important to note that it might equally be argued that a city’s hub function plays a formative role in turning a city into a world city. Miami’s role as a hinge between Anglo-America and Latin America (see below), for instance, is being reflected in its role as the main airline hub between the two regions. A plain origin/destination dataset will depreciate this essential function. However, taken together, we believe that information on actual origins and destinations is able to present a fuller grasp of the spatiality of the WCN.
6. The airline-based study of Smith and Timberlake (2002) features far fewer cities, but does have the advantage of being based on formal network analysis. The latter is more consistent with the logic of using relational data, albeit that the results are likely to be similar to our mere rankings. For a further elaboration on relevant techniques in this context see Smith and Timberlake (1998 2001, 2002). One of our avenues for future research consists of carrying out such a network analysis.
7. The absence of cities such as Las Vegas and Orlando in the Taylor and Lang (2005) report (and hence our paper) does not imply that the centrality of these cities should be considered as exclusively national and/or tourist-driven. One of the referees rightly pointed out that both cities support massive convention centres that help fill hotels in off season. In this respect, an enormous tourism infrastructure may - somewhat ironically - exactly be what specific business conventions need. Robert Lang and Paul Knox are developing a study that looks at what impact Las Vegas has on the US economy in spite of its relatively low ranking as a world city under the GaWC model. Such a detailed case-study will be a welcome addition to large scale empirical researches that exclusively focus on ‘classical’ measures of transnational urban connectivity.
Table 1: GLOBAL CONNECTIVITIES OF U.S. CITIES BASED ON AIRLINE DATA
Table 2: THE TOP 15 CITIES IN THE HINTERWORLDS OF SIX U.S. CITIES
Non-U.S. cities are shown in italics
Table 3: THE TOP 15 NON-U.S. CITIES IN THE HINTERWORLDS OF SIX U.S. CITIES
Edited and posted on the web on 14th September 2005; last update 23rd March 2007
Note: This Research Bulletin has been published in Urban Geography, 28 (1), (2007), 74-91