César Ducruet
email: ducruet@parisgeo.cnrs.fr

Sung-Woo Lee

FRONTLINE SOLDIERS OF GLOBALISATION PORT-CITY EVOLUTION AND REGIONAL COMPETITION

Keywords: Containerisation, Globalisation, Port city, Relative concentration index

This paper is an empirical attempt to measure the relative concentration of port-city functions in the context of globalisation. It reviews a number of urban and port issues regarding their complementary and contradictory aspects about the evolution of port cities. The main purpose is to verify how port function is more or less important to local economies, compared to other functions, through a temporal and global approach. Based on a matrix of port-city centrality and intermediacy, the main indicators available for international comparison are urban population and container throughput. An analysis of 653 places between 1970 and 2005 period is provided, using the relative concentration index proposed by Vallega. The appropriate geographical scale to measure the relative evolution of port cities at a global level is discussed. Results tend to question previous models which consider functional and spatial separation between the city and its port as an ineluctable process. The port-city evolution appears to be gradual rather than linear or chaotic, and in many cases largely influenced by regional factors and local strategies.

INTRODUCTION

The nature of the relationships between ports and port cities is an old question with few answers. Ports have become complex entities with various locations and functions that tend to exceed the responsibility of the port authority. Coastal cities may be torn between their maritime functions which are determined by specific maritime foreland trade relationships [START_REF] Brocard | Les relations fonctionnelles entre le port et la ville[END_REF][START_REF] Pearson | Port cities and intruders[END_REF], and their other functions, that are dependent on predominantly land based urban system relationships. The equilibrium between these functions is becoming increasingly unbalanced. Thus, "many port cities (…) stand among the most environmentally degraded cities in the world" (OECD 2004).

These distinctions are reflected in the literature, where port and urban studies are often separate and distinct [START_REF] Reeves | Studying the Asian port city[END_REF][START_REF] Banister | Transport and urban development[END_REF]. Considering port functions as one of many urban functions, like manufacturing and retailing, is one approach to the examination of the specificity of ports within urban systems. Over the last 40 years port sites have expanded [START_REF] Lee | A study of port performance related to port backup area in ESCAP region[END_REF], and may be considered as following and contributing larger urban trends which include the shift of activities outside city centres [START_REF] Bird | The major seaports of the United Kingdom[END_REF]. Manufacturing, commercial and sports activities have relocated in a significant way to the city's peripheral areas. The difference is that ports are responding to international market and technological changes, while the new pattern of urban functions is better explained by local conditions, such as changing accessibility and population distributions. Similar difficulties are encountered when trying to measure the relationships between the size and growth trajectories of port activity with those of the broader urban economy. In India, for example, the linear correlation between demographic size and port total throughput has diminished dramatically between 1911 and 1981 [START_REF] Kidwai | Port cities in a national system of ports and cities: A geographical analysis of India in the 20 th century[END_REF], due to the emergence of specialised ports away from big cities (e.g. New Mangalore, Haldia, Kandla). In Europe, the growth rates of added value were identical for port cities and non-port cities between 1975and 1985, but widened in favour of non-port cities between 1985and 1994[START_REF] Lever | Regional economic growth and port activities in European cities[END_REF]). In China on the other hand port-centred coastal industrial growth is widening the disparities with inland cities [START_REF] Wang | La gouvernance des ports et la relation ville-port en Chine[END_REF].

The gaps in the literature on port-city relationships are particularly extensive in that with an urban focus, with the important exception of waterfront redevelopment. Urban planners and geographers tend to see the port as some separate and alien activity [START_REF] Bird | Of central places, cities and seaports[END_REF][START_REF] Bird | Centrality and cities[END_REF][START_REF] Boyer | Les ports et l'organisation urbaine et régionale[END_REF]. For port specialists there is recognition that maritime activity frequently takes place in an urban milieu, but the nature of that milieu is rarely considered [START_REF] Goss | Economic policies and seaports: The economic functions of seaports[END_REF]. Distinctions between the ports of Le Havre and Rotterdam rarely consider the contrasting urban environments. Traffic related to the city itself have become in many cases a residual share of total port activity (UNCTAD 1985). Furthermore, most of these portfocussed studies are limited in scale, usually applying to small regional sets or individual case studies. Finally, the lack of data on port and urban functions has restrained comparative studies to the national level [START_REF] Slack | Port services, ports and the urban hierarchy[END_REF][START_REF] O'connor | Australian ports, metropolitan areas and trade-related services[END_REF][START_REF] Gripaios | Ports and their influence on local economies: a UK perspective[END_REF], but this has been partly overcome on a Europe-Asia scale [START_REF] Ducruet | European port-city interface and its Asian application[END_REF].

This paper seeks to confront these lacunae head on. It takes a global approach to the question of port-urban relations. It begins by presenting a typology of port-urban relations and then proposes a means of transcribing every type into simple quantitative expressions by using the relative concentration index proposed by [START_REF] Vallega | Fonctions portuaires et polarisations littorales dans la nouvelle régionalisation de la Méditerranée, quelques réflexions[END_REF]. The second and third sections raise important methodological issues, such as the pertinence of urban population and container traffic in transcribing city and port functions, and the dimension of the regional area within which port cities shall be compared. By measuring the degree to which port cities are dependent on port or other urban functions it is possible to obtain a global snapshot of the situation. The paper goes on to examine regional and temporal patterns, focusing on the largest port cities. This paper is seen as establishing a base upon which further investigation of port-city relations can be undertaken. [START_REF] Ducruet | Approche comparée du développement des villes-ports à l'échelle mondiale: problèmes théoriques et méthodologiques[END_REF] has produced a matrix of port-city relationships based on the concepts of centrality and intermediacy that had been developed by [START_REF] Fleming | Spatial characteristics of transportation hubs: centrality and intermediacy[END_REF]. This matrix provides a useful starting point (see Figure 1). The matrix considers that centrality is an urban functional measure, while intermediacy is an essentially maritime-based measure.

PORT-CITY RELATIONS

A matrix of port-city relations

Figure 1: A matrix of port-city relations

Source: modified from [START_REF] Ducruet | Approche comparée du développement des villes-ports à l'échelle mondiale: problèmes théoriques et méthodologiques[END_REF] One diagonal illustrates a progression from a 'coastal town' (e.g.: a small coastal port and village) to the 'global hub port city' where the two functional dimensions are of equal relative importance (e.g.: New York, Tokyo, Hong Kong, Singapore as business centers and major ports). The second diagonal shows the most imbalanced situations: from the port hub, with limited centrality (e.g.: Freeport, Gioia Tauro, Laem Chabang, Salalah) to the general city with limited intermediacy (e.g. Stockholm, Tunis, Baltimore, Calcutta). Where the two diagonals cross Ducruet uses the term the cityport, first employed by Hoyle and Pinder (1992), which is an intermediate condition. The model overcomes the difficulty of creating a single definition of the 'port city', as few places manage to keep a balanced combination of centrality and intermediacy. More likely are unbalanced profiles such as the gateway which is subdued to its hinterland and develops few activities apart from heavy industry and logistics (e.g. Le Havre, Genoa, Rotterdam); the maritime city where port functions are efficient in spite of an important urban environment (e.g. Barcelona, Marseilles, Capetown, Buenos Aires); the urban port which has some importance in the urban system but with limited port activity (e.g. Incheon, Bordeaux); and finally, the outport which is usually dependent on nearby cities and whose port functions do not act as a mechanism for developing its own urban economy (e.g. Fos, Felixstowe, Apapa).

It is important to recognise that port-city relationships vary over time. This is hinted at in some general urban evolutionary models. [START_REF] Murphey | On the evolution of the port city[END_REF], for example, suggests that the port city is destined to become a general city through successive stages of economic diversification. A first stage is defined by a high dependence of the urban economy on maritime and port functions. The second marks the attraction of additional activities (e.g. industrial) and the third corresponds to the development of a service economy, allowing the city to release itself from port dependence [START_REF] Charlier | Ports en évolution, espaces en mutation[END_REF]). In the final stage of their evolutionary model, [START_REF] Pesquera | Sustainable development strategies for cities and ports[END_REF] define the post-industrial period by the interdependency between urban metropolitan and port logistic functions.

Measuring port-city dependence

There are no universally accepted measures of urban development. Economic criteria, such as percentages employed in particular economic activities vary significantly across the development spectrum. Simpler measures employing demographic data offer better prospectives, since population totals are recognised as indicators of overall economic weight [START_REF] Pumain | Pour une théorie évolutive des villes[END_REF]. However, great care must be applied in inferring relationships between population size and economic functions (Carriou 2002). Nevertheless, because population size is such an important dimension of urban development and because it is a parameter that is widely available it was used in this study as a surrogate for centrality.

Operationalising this measure for this study still presented numerous problems. The population of local administrative units may not reflect the true extent of the urban environment. Administrative boundaries are often mismatched [START_REF] Verlaque | Inductions portuaires: Le sas sétois[END_REF]). The solution adopted here is to use data from the contiguous urbanised area. These data are drawn from the global urban data base 'Geopolis' [START_REF] Moriconi-Ebrard | Geopolis: Pour comparer les villes du monde[END_REF] which provides data on agglomerations over 100,000 from 1950 to 1990. These data were supplemented for cities under 100,000 and for the period after 1990 from other sources, such as the 'World Gazetteer' [START_REF] Helders | The world gazetteer[END_REF] and others [START_REF] Brinkhoff | Cities and agglomerations of the world[END_REF][START_REF] Lahmeyer | Population statistics[END_REF].

Port cities located in the same morphological urban area have been agglomerated into a single unit: Seattle-Tacoma, Los Angeles-Long Beach, Tokyo-Yokohama, Oakland-San Francisco, Osaka-Kobe. Ports located within the urban area of a distant city have been attributed to this city: Incheon-Seoul, Port Klang-Kuala Lumpur, Keelung-Taipei, Fremantle-Perth, Callao-Lima, Haydarpasa-Istanbul, etc. While total port traffic, or lists of port equipment and the spatial extent of port infrastructures have been used to assess port hierarchies, container throughput has been selected as the dimension that best captures the status of ports in a global economy. We are aware of its limits. Although it embraces an increasing proportion of maritime trade, many ports depend greatly on bulk shipments. Furthermore container totals tend to be inflated because of transhipment, which containers are double counted.

Container data have several advantages because they are comparable at a world scale, using the same units of measurement (the twenty foot equivalent unit -the TEU). Moreover, they are intimately tied to globalisation and the rise of intermodality in supply chains.

Competition in the container business is particularly keen, and is monitored closely by portcities seeking to expand their roles. Of all goods by ports, general cargoes and full containers are most valued in terms of interaction and potential benefits for the local economy [START_REF] Charlier | Sur le concept de tonnages pondérés en économie portuaire. L'exemple du northern range[END_REF].

THE RELATIVE CONCENTRATION INDEX

The relative concentration index was used by [START_REF] Vallega | Fonctions portuaires et polarisations littorales dans la nouvelle régionalisation de la Méditerranée, quelques réflexions[END_REF] to reveal how Mediterranean port regions and their related human settlements are organised. The author divided the regional share of throughput by the regional share of population among the total area. The resulting value is high for trading regions (e.g.: gateway or 'pivotal' regions), low for densely populated regions (e.g.: those remote from major shipping routes) and close to 1 for regions having a similar urban and port importance. This index is similar to the ones used by [START_REF] Vigarié | Géographie de la circulation[END_REF], to measure countries' maritime dependence (number of merchant marine tons per inhabitant), and [START_REF] Kenyon | Elements in inter-port competition in the United States[END_REF], to measure the relative importance of transit function and urban magnitude amongst US port cities (number of general cargo tons per inhabitant). It is a close relative of the location quotient that has been used extensively in economic studies of urban activity. We propose applying the relative concentration index to determine the type of relation between port and urban functions. It represents the first time it has been applied using container traffic at a world scale and over a period of time. However, it raises a series of methodological questions: geographical limits of the referential area, thresholds of the resulting values to determine the type of port-city relations.

The geographical framework

An important question is over which geographic scale should relative concentration measure be applied? Using the global scale would mean that all cities and ports are competing at this level, which is not perfectly true in all cases, even though countries and regions can be seen as competing within "one (and only one) global market" [START_REF] Song | Port co-opetition in concept and practice[END_REF]. There is a different logic according to urban functions and port functions (Table 1). On one side, in the urban hierarchy, it can be argued that only global cities are competing at a global scale, and secondary cities are only concerned by their regional and national urban system. For cities, therefore, the global scale is relevant only for those which are capable of competing for international finance, major companies' headquarters and tourism more appropriately reflected by air transport flows. On the other hand, for the port hierarchy, even the so-called global ports are in fact competing with their neighbouring competitors within a regional area.

Ports compete locally as well as regionally against other ports because they serve the same hinterland inland areas.

The application of the relative concentration index then becomes very complex according to the functions involved. The limits of regional areas are difficult to fix, as the radiance area of cities and ports varies according to their rank and throughout time. For example, some cities may have a stable relative position in their national urban system, but ports are submitted to very unstable levels of throughput, depending on a variety of factors such as the strategies of port authorities and the level of accessibility. In the case of Liverpool, does it compete only with British ports, or within the Atlantic Arc, or the Northern Range? Gothenburg in Sweden is often placed within the Northern Range (Le Havre-Hamburg) although the city is competing with Stockholm on a national scale. Busan is Korea's major port but its role increasingly is to connect main trunk lines between Asia and the other major poles, rivalling Chinese, Taiwanese and Japanese ports [START_REF] Lee | A study of port performance related to port backup area in ESCAP region[END_REF].

Source: own realization

The RCI calculation is based on regional areas defined by Containerisation International Yearbook as a 'trading region' where ports and cities compete beyond their national boundaries (Figure 2). As showed in Table 1, this geographical scale is the most convenient when analysing urban and port dynamics simultaneously.

The functional framework

We propose to illustrate the meaning of the expected results in a systematic way, by relating the index to the matrix of port-city relations described in Figure 1. As the transcription of port-city types into quantitative values is an essential component of the methodology, the definition of the thresholds needs careful attention.

As showed in Figure 3, the necessary limit to help distinguish how different are port cities from each other has to be an arbitrary number for several reasons. First, previous scholars have never specified how many times port and urban functions should be relatively concentrated to establish a realistic typology. Second, several extreme values coming from high port traffic without much urban development (e.g., Gioia Tauro) hamper the application of usual classification methods to determine the thresholds, because RCI values do not follow a normal statistical distribution.

For any scale, any period and any port city, the values of the relative concentration index (RCI) which are close to "1" (between 0.75 and 1.25) express an equilibrium between the port and the city. This means that container traffic and urban population are of equal importance locally compared to the other nodes of the regional area. Thus, the coastal town, the cityport and the port metropolis share the same logic of port-city relationships, though they differ in terms of absolute size. The other thresholds are difficult to fix because other types than the cityport have no universally recognised definition. For example, how can hubs and gateways, general cities and maritime cities be distinguished from the cityport? Basically, any value higher than 1.25 will be interpreted as port specialisation and any value lower than 0.75 will be considered urban magnitude. It is thus proposed to consider that hubs and general cities, situated at both ends of the port-city spectrum, are concentrating respectively more than three times container traffic and population (above 3 and under 0.33). Consequently, gateways and outports are considered of being in the same category (between 1.25 and 2.99), like maritime cities and urban ports (between 0.33 and 0.74). Those types are different in terms of size but not in terms of function. In such respect, the nine types are changed into only five possible categories of port-city relationships.

Figure 3: Methodology of RCI measurement

Source: own realization

Table 2 shows to what extent the application of Figure 3 gives a relatively coherent distribution over time. The cumulated frequency is very close to the theoretical values, on a 20 percent basis for each of the five categories, even though it tends to lean towards urban magnitude. Thus, although the boundaries between the chosen categories may not perfectly satisfy the quantitative transcription of the conceptual framework, they are kept to enable a homogenous and global comparison. Source: own realization

The temporal framework

On a time basis, RCI will change according to multiple factors such as urban growth and uneven port activity due to port competition (exogenous factors) and strategy (endogenous factor). This is more difficult to interpret as a wide variety of possibilities emerge from the framework. The observation of RCI change can help to address a number of implications, as showed in Figure 4.

Interpreting changes of RCI scores is difficult because it is the result of simultaneous phenomena: urban constraint and spatial growth, port expansion and competition, congestion at the port-city interface, lack of space, logistic costs, uneven industrial growth and location, natural disasters and geopolitical change.

The decrease of RCI can be explained by the reducing importance of port functions for the local economy. In developing countries, urban growth has strongly affected port concentration, while in developed countries, the limited nautical accessibility of inner quays and the lack of space for urban development have forced modern terminals to shift outwards.

In both cases, it also means that port activity has become less competitive for several reasons including handling costs and distance to ships and markets. What is more difficult to address is the fact that in advanced economies, some cities have refined and enhanced their port activity beyond the physical handling function to broader services in the tertiary sector. Thus, although port traffics decrease, port functions as a whole are kept through maritime insurance, banking, ship management, chartering and brokering. Moreover, some port cities may develop alternative strategies by supporting their port functions rather than redeveloping them through systematic urban-oriented waterfront schemes [START_REF] Charlier | The regeneration of old port areas for new port uses[END_REF].

Figure 4: Logics of port-city spatial and functional evolution

Source: adapted from [START_REF] Hoyle | The port-city interface: trends, problems, and examples[END_REF] and [START_REF] Murphey | On the evolution of the port city[END_REF].

The increase of RCI values at a final stage may reveal a successful port policy, resulting in the attraction of shipping lines, port operators and freight forwarders for cargo handling and distribution, sometimes beyond the needs of the city itself. The improvement of the efficiency of cargo handling operations and the betterment of the management of flows at the port-city interface could allow in some cases to overcome congestion and restore port dynamics within the port city, creating a new pattern. This can also reflect the absence of strong urban dynamics like in advanced economies, where urban growth is rather slow compared to developing countries. Such argument reinforces the need to compare port cities within homogenous areas instead of at a world scale which would embrace very dissimilar issues.

RELATIVE URBAN-PORT CONCENTRATION WITHIN 653 PORT CITIES

Preliminary outcomes

A total of 653 port cities has been collected from Containerisation International Yearbook between 1970 and 2005. Total port container traffic represents 80 (1980), 84 (1985), 94 (1990), 92 (1995), 100 (2000) and 93 (2005) percent of the world total according to Drewry Shipping Consultants [START_REF] Rodrigue | The geography of transport systems[END_REF]. Coastal urban population represents 76 percent of the world total in 1990 calculated by [START_REF] Noin | La population des littoraux du monde[END_REF]. The number of port cities varies because new ports emerge and others decline over time (Table 3). Another reason is the unevenness of statistics. At the end, 653 different port cities constitute the database.

In table 2, the correlation between city size and port traffic is increasing until 1990, and declines since then. It is assumed that containerisation has spread among the existing urban hierarchy, according to a hierarchical diffusion process [START_REF] Saint-Julien | La diffusion spatiale des innovations[END_REF]. Because of limiting factors such as lack of space for port expansion and rising handling costs, container traffics have developed in non-urban locations for facilitating the concentration of shipping lines, allowing the formation of hub-feeder networks and backed by new port policies throughout the world. Thus, the relationship between urban and port hierarchy is put in question. The same phenomenon is showed differently in Figure 5, with three major trends.

First, the most populated port cities (over 1 million inhabitants) are increasing their dominance among the port system, from 53 percent to 73 percent of total container traffic.

Second, intermediate port cities (200,000 to 1 million inhabitants) show a decline from 25 percent to 9 percent and third, the less populated port cities (0 to 199,000 inhabitants) rise from 13 percent (1975) to 17 percent. Due to economies of scale, large port cities may easily keep their eminent and powerful position. On the other hand, the increase of small port cities may reflect new opportunities to raise their economy (De Langen 1998) and new port projects permitted by the 'challenge of peripheral ports' [START_REF] Hayuth | Containerization and the load center concept[END_REF]) as a mean to limit port concentration and solve environmental problems in old port cities. Although this shows the importance of global port cities among the world hierarchy, some ports operated within vast urban areas were in fact forced to shift their main activity towards suburban areas, like in the case of New York (Rodrigue 2003). The limitations of this analysis in inferring individual trends shall be overcome by looking at the evolution of RCIs.

General trends of RCI evolution

Based on Figure 3, the total number of port cities is redistributed among the five different port-city types. It appears that for every type of port city, the share of port cities remaining in the same category is higher than the share of port cities shifting to different categories (Table 4). On an average basis, general cities and hubs are the most stable, what confirms their situation at the extremities of the port-city spectrum. As stated by the aforementioned models of port-city evolution, the general city is an ultimate stage when port functions are not likely to increase [START_REF] Murphey | On the evolution of the port city[END_REF]). The hub symbolises the inadequacy of modern logistics and urban development [START_REF] Stern | Developmental effects of geopolitically located ports[END_REF][START_REF] Fujita | The role of ports in the making of major cities: self-agglomeration and hubeffect[END_REF]. This broad figure is very important to address the general mechanisms of port-city evolution. 1970-1975 1975-1980 1980-1985 1985-1990 1990-1995 1995-2000 2000-2005 Average MARITIME CITY HUB 0.0 0.0 1.6 1.9 0.0 0.0 0.0 0.5 HUB GENERAL CITY 0.0 0.0 1.9 0.0

Source: own realization

Another important aspect is that the most important changes happen between close types of port cities (e.g., cityport to gateway or gateway to hub). It means that port-city evolution is a gradual phenomenon rather than a sudden disrupt. This is confirmed by the lowest values attributed to changes occurring between very dissimilar types (e.g., hub to general city and general city to hub). Although transport players, like shipping lines, have a growing influence on port activity and, in turn, can indirectly modify the spatial pattern of port networks, they are still dependent on the regional structure of spatial systems. Also, the effects of technological change (e.g., growing size of container ships) are gradually affecting ports and cases of disrupt evolution are a very minority among port-city trajectories. Such trends are confirmed by [START_REF] Lemarchand | The container traffic by ports and ranges: between scale and random, there is governance[END_REF] in his comparison of the size and growth rates of port throughputs. He concludes that although recently created container ports generate massive cargo volumes, they also have unstable and important growth variations due to the absence of a sufficient economic base and local community that would stabilize their traffics.

Inversely, large ports in developed countries have lower but more stable growth rates because of a higher maturity in local governance which help them depending less on their intermediacy.

Finally, cityports are the most likely to see their profile modified because their equilibrium is a very unstable state. Among port-city types, cityports have the lowest share of stability on an average basis (37.5 percent), while they have the highest percentage of change to another profile (23.4 percent become maritime cities and 22.0 percent become gateways).

Moreover, cityports are among the lowest shares of transformation into hub ports and general cities. Once one function has become dominant, the port city is not likely to go back to a symbiotic state, but follows a trajectory of either urban diversification or port specialisation.

Oppositely, the transformation of the cityport goes through successive stages rather than through sudden disrupt. The total share of port cities reaching the category of cityports has been one of the largest until 1995, and then became the lowest, illustrating the changes in global transportation, with ports and shipping lines seeking efficient logistics rather than depending on local economies.

Individual trajectories

As it is beyond the scope of this paper to analyse in detail every single trajectory, Table 5 focuses on the most populated places. The evolution of individual RCIs is a good indicator of how some port cities match general trends and, notably, how they contradict or not existing models of port-city evolution. A possible explanation is that those cities have been the inevitably dominant nodes of ports and urban systems until the dimension of their urban area has become a constraint for port concentration, reflected in the Japan case by the increase of cargo handling charges. For Liverpool, 'this was due to the loss of its function of being the primary port for the UK's exports as the majority of goods switched to being directed to the continental Europe' [START_REF] Cheshire | Urban problems in Western Europe: an economic analysis[END_REF]. A few other cities show a growth and decline in a more complex way: Montreal, Jacksonville, Virginia, San Juan, Dubai, Alexandria, Kaohsiung, Cebu, Douala, Port Elizabeth, Santos and Leixoes-Porto. Although their RCI score returned to its original value after a period of growth, their concentration of container traffic has not declined dramatically.

A second group of port cities is marked at the contrary by a continuous increase of

CONCLUSION

This paper has applied the relative concentration index to a sample of 653 port cities for the period covering the emergence, spread and growth of containerisation . It has proposed to calculate RCI from the population of cities and the container throughput of ports, at the scale of trade regions. One advantage of the method is the trans-scalar dimension of the results, which give evidence about simultaneous phenomena on a local, regional and global scales. In this respect, it allows international comparison from identical concepts and overcomes the usual difficulty to interpret simultaneously the global and local forces of contemporary trends. Using the Relative Concentration Index is one possible and fruitful way to overcome the separate study of port and urban dynamics through international comparison.

Based on such methodology, the database allows a verification of previous models of portcity evolution.

First of all, there is no universal evolution of port-city relationships. The trends of port-city separation described by [START_REF] Murphey | On the evolution of the port city[END_REF] based on colonial Asian cities and the model of [START_REF] Hoyle | The port-city interface: trends, problems, and examples[END_REF] based on European port cities are confirmed on a world scale, but several cases do not match. Highly urbanised port cities may have seen their port function decline over time, but many of them have managed to overcome the difficulties of port competition and urban growth, thanks to efficient planning policies and exceptional locational advantages, as seen in the Asian consolidation model of [START_REF] Lee | Interaction between city and port in Asian hub port cities[END_REF] based on Hong Kong and Singapore. However, although there is a common achievement in avoiding port decline, addressing a new model of port-city relationships is difficult, because there is a subtle combination of local and regional factors during the adaptation to globalisation.

Second, port-city evolution appears to be gradual, and there are only few examples of sudden change like from general city to hub port and vice-versa. In terms of policy implications, this evidence argues in favour of long-term urban and port strategies. This also has an academic implication, referring to the works of [START_REF] Rodrigue | Transportation and spatial cycles: Evidence from maritime systems[END_REF] on transportation and spatial cycles applied to maritime systems. The redistribution of flows between different types of port cities over time indicates important regional shifts within the world system but, also, shows the ability of local players in inserting efficiently their place in evermore complex trading and logistic networks. Although containerisation has spread globally, the continuous growth of world trade and maritime traffics shall not blur the very uneven adaptation of local and regional structures to the global pattern. Thus, this study could have highlighted different stages of port-city evolution based on the somewhat contradictory intervening of three temporalities: the long term of cities, the short term of maritime networks and the middle term of ports. 'Successful' port cities are not necessarily those who increase their traffics in absolute numbers or create an attractive waterfront, but those who manage to sustain an equilibrium between different temporalities, different functions and different scales. In this respect, further research on port cities shall focus on the broader issue of the resilience of spatial systems in globalisation processes.

Figure 2 :

 2 Figure 2: Port regions of the world

Figure 5 :

 5 Figure 5: Evolution of world container throughput by city size, 1970-2005 (Unit: %)

  RCI scores. The most regular evolutions are those of Los Angeles-Long Beach, Shanghai, Shenzhen, Canton, Tianjin, Ningbo, Dalian, Qingdao, Fuzhou, Zhongshan, Taichung, Tanjung Perak-Surabaya, Haiphong, Ho Chi Minh City, Belawan-Medan, Colombo, Brisbane, Valencia, Hamburg, Odessa, Cartagena, Barranquilla, Vitoria and Tema. The majority of Chinese cities is revelatory of their recent role for container trade, with the end of Hong Kong, Busan and Kaohsiung's monopoly in this sector and the considerable economic growth stemming from the Open Door Policy. Others could have profited from new opportunities like the European enlargement for Hamburg's hinterland, and the proximity of a major sea corridor like Gibraltar, Panama and Malacca. Another category shows a similar but more uneven trend: Hong Kong, Singapore, Busan, Houston, Miami-Port Everglades, Vancouver BC, Helsinki, Antwerp, Auckland, Durban, Salvador and Montevideo. Most of them have avery peculiar situation favouring intermediacy, but the lack of space is altering their trajectory as hub or hinterland ports. In addition to those cases, a last possibility as showed in Figure4is the growth of RCI after a period of decline: New York-New Jersey, Incheon-Seoul, Callao-Lima, Algiers, Casablanca, Rotterdam, Bremen-Bremerhaven, St Petersburg, Piraeus-Athens, Dublin and Shuwaikh. Those port cities have implemented a number of measures in order to avoid losing their port competitiveness, such as the attraction of hub functions in Piraeus, the extension of hinterland connections, developing strategy and institutional change in New York's port authority, the Europoort project in Rotterdam, the 'Pentaport' project in Incheon combining free-trade zones, new towns, new airport and container port facilities.

Table 1 : Geographical scales for port-city concentration measurement

 1

	Scale	Urban concentration Advantage Inconvenient	Port concentration Advantage Inconvenient
			May ignore the
	Provincial	Immediate rivalry between closely connected cities	country capital city; with a coherent administrative boundaries do not fit	No	Small number of ports
			urban system
	National	Covers the territorial planning area	Ignores the close foreign cities	Covers the national maritime and port policy framework	Countries with only one port; countries with several maritime borders
	Macro-regional	Includes cross-border competition among cities	Ignores physical disrupts (e.g. sea or mountain); area's limits are subject to multiple criteria	Area of intense competition for gateway and hub functions	Cut from the pendulum services (interregional trade and foreland of global ports)
	World	Includes the global city paradigm	Too much hierarchical at the expense of smaller cities	Includes the global regular maritime services	Doesn't fit with port competition reality

Table 2 : Cumulated frequency of RCI values, 1970-2005 (Unit: %) 1970 1975 1980 1985 1990 1995 2000 2005

 2

	0.00 < 0.32 20	19	23	27	25	29	30	31
	0.33 < 0.74 38	36	47	43	44	47	50	49
	0.75 < 1.24 51	58	59	57	59	63	60	57
	1.25 < 2.99 72	75	77	75	75	77	74	74
	3.00 <	100 100 100 100 100 100 100 100

Table 3 : Population and container throughput, 1970-2005

 3

			1970	1975	1980	1985	1990	1995	2000	2005
	Correlation coefficient	0.126	0.582	0.587	0.592	0.485	0.422	0.405	0.389
	Total
	container throughput	4,393,305 14,676,815 34,985,348 52,475,730 85,632,458 138,700,101 233,466,385 300,514,048
	(TEUs)
	Total	urban
	population	127,816	173,046	314,770	362,094	458,427	442,680	640,823	665,180
	(000s inhab.)
	No of port cities	72	110	256	322	368	403	481	470
	Sources: own realization based on Containerisation International, Geopolis, World Gazetteer, Populstat,
					Citypopulation, port authorities.

Table 4 : Port-city evolution, 1970-2005 (Unit: % change by type of port city)

 4

	From	To

Table 5 : RCI evolution at the world's most populated port cities

 5 A first group of cities matches previous models as they show a continued loss of RCI along the period. Thus, those large cities gradually develop urban functions to the expense of port functions. Mumbai, Manila, Karachi, Port Klang-Kuala Lumpur, Chittagong, Davao, Osaka-Kobe, Kitakyushu, Hakata-Fukuoka, Jeddah, Dammam, Chicago, Milwaukee, Baltimore, Oakland-San Francisco, Boston, Philadelphia, Toronto, Tampa, Richmond VA, Belem, Manaus and Port Harcourt are in this category. Among them, port competitiveness has faced the lack of space and accessibility for further port growth, resulting in the precedence taken by urban strategies over port activities, as seen in several US port cities (e.g., Boston's Charlestown Navy Yard redevelopment). Another group of cities show a similar trend, although less regular: Seattle-Tacoma, Portland OR, Buenos Aires, Port-au-Prince, Melbourne, Adelaide, Fremantle-Perth, Cochin, Barcelona, Bilbao, Marseilles, Lisbon, Amsterdam, Copenhagen-Malmö and Stockholm. This sub-category is seeking to maintain port functions although they have become less important for the local economy, but cannot avoid the declining trend. For example,'Port Vell' in Barcelona and 'Euroméditerranée' in

	AREA	PORT CITY	1970	1975	1980	1985	1990	1995	2000	2005	AREA	PORT CITY	1970	1975	1980	1985	1990	1995	2000	2005
	NEAS	Tokyo-Yokohama	0.41	0.43	0.59	0.81	0.61	0.48	0.40	0.28 CARI	Havana								0.19
	NAEC	New York-New Jersey		1.42	1.01	0.99	0.59	0.46	0.63	0.75 EMBS	Izmir					0.29 0.76 0.49 0.81
	NEAS	Incheon-Seoul				0.11	0.04		0.07	0.07 NEAS	Ishikariwan Shinko								0.02
	INSU	Mumbai (Bombay)				1.03		0.72	0.31	0.10 NEAS	Qingdao				0.18 0.36 0.83 1.94 3.08
	SEAS	Tanjung Priok-Jakarta		0.07	0.17	0.23	0.25	0.25	0.30	0.28 NEAS	Nanjing					0.09 0.17
	NEAS	Osaka-Kobe	1.52	1.10	0.77	1.21	1.13	0.49	0.49	0.37 NAWC	Portland OR		0.60 0.58 0.47 0.44 0.77 0.26 0.26
	SEAS	Manila		0.87	0.51	0.56	0.42	0.33	0.45	0.27 WEAF	Dakar					0.85		1.11
	NAWC Los Angeles-Long Beach 0.39	0.41	0.73	0.91	1.00	1.02	1.14	1.38 SCBA Copenhagen-Malmo	0.24 0.29 0.50 0.26 0.83 0.74 0.33 0.22
	NEAS	Shanghai				0.20	0.24	0.42	1.08	1.36 SAEC	Belem					0.42 0.47 0.33 0.20
	INSU	Kolkata (Calcutta)			0.11	0.21	0.27	0.16	0.12	0.15 NAWC	Vancouver BC		0.72 0.73 0.68 0.75 0.99 1.29 1.40
	SAEC	Buenos Aires			0.89		0.65	0.37	1.12	0.43 CARI	Maracaibo				0.05			0.02 0.04
	SAEC	Rio de Janeiro			0.18	0.23	0.15	0.24	0.25	0.26 SEAS	Belawan-Medan				0.11 0.23 0.28 0.29
	INSU	Karachi				1.48	0.99	0.97	0.70	0.58 CARI	Barranquilla					0.02		0.07 0.08
	EMBS	Haydarpasa-Istanbul				0.05	0.10	0.16	0.07	0.06 CARI	San Juan		1.93 1.59 2.33 2.75 1.73 1.62 1.86
	SEAS	Bangkok			0.56	0.66	0.65	0.45	0.26	0.22 NEAS	Zhongshan							0.61 0.66
	WEAF	Lagos-Apapa			0.78	0.99	1.11	0.74	0.37		EMBS	Beirut			0.38 0.15		0.52 0.38 0.46
	NAEC	Chicago	0.05	0.01		0.00					MERS	Dubai			0.47 1.92 2.95 3.24 2.89 2.18
	NEAS	Nagoya	1.36	0.24	0.37	0.61	0.74	0.62	0.52	0.42 NEAS	Hiroshima							0.15 0.15
	NAEC	Milwaukee	0.02	0.01	0.01	0.00					SEAS	Haiphong						0.20 0.26 0.36
	NEAS	Hong Kong	1.50	2.82	3.36	4.17	4.97	6.27	5.13	5.35 OCEA	Brisbane				0.58 0.68 0.68 0.75 0.78
	NEAS	Keelung-Taipei		3.08	1.49	2.07	1.64	1.00	0.57	0.43 MERS Shuwaikh (Kuwait City)			0.44 0.22 0.08 0.12 0.13
	SAWC	Callao-Lima				0.31	0.22		0.30	0.46 SOAF	Maputo			0.04 0.06		0.24 0.08 0.10
	NAEC	Baltimore		0.43	1.73	1.49	0.67	0.48	0.29	0.37 WEAF	Conakry				0.29		0.56 0.41
	NAWC Oakland-San Francisco	1.41	1.32	1.23	0.92	0.77	0.83	0.51	0.51 CARI	Port-au-Prince			0.12 0.11 0.12 0.09 0.08
	NEAS	Guangzhou (Canton)					0.12		0.47	0.86 SAEC	Montevideo			0.46 0.23 1.46 1.92 2.14 1.82
	NAEC	Boston	0.41		0.38	0.37	0.21	0.15	0.09	0.10 WEAF	Douala			0.68 2.51 1.55 1.77 1.38 0.69
	INSU	Chennai (Madras)				0.67	0.41	0.56	0.58	0.56 NEAS	Sendai						0.00 0.19 0.12
	NEAS	Tianjin			0.03	0.26	0.30	0.34	0.58	0.84 SCBA	Stockholm		0.29 0.37 0.09 0.22 0.13 0.09 0.08
	NAEC	Philadelphia	2.26	0.61	0.91	1.01	0.38	0.32	0.14	0.10 WMIP	Leixoes-Porto	0.45 0.38 0.55 0.47 0.54 0.45 0.35 0.34
	SEAS Port Klang-Kuala Lumpur			1.99	2.13	1.37	1.54	1.55	1.42 SAEC	Santos		1.00 8.99 12.72 13.24 11.66 6.52 8.91
	NAEC	Toronto	0.14	0.13	0.02		0.00			0.03 NOAF	Tunis				0.25			0.03
	SEAS Ho Chi Minh City (Saigon)							0.27	0.52 SEAS	Makassar						0.23 0.27 0.25
	NOAF	Algiers					0.18		0.06	0.23 NAEC	Virginia	11.58 4.97 3.83 3.51 4.50 3.91 3.67 4.13
	NEAS	Ningbo						0.39	0.39	0.90 MERS	Dammam			2.47 1.65 1.29 0.75 0.41 0.32
	NAGC	Houston		0.87	0.70	0.82	1.03	1.16	1.27	1.21 SAEC	Manaus				1.36			0.71 0.65
	SEAS	Singapore	1.00	3.18	5.93	5.59	7.30	8.13	7.99	6.29 WMIP	Marseilles			1.90 1.80 0.61 1.09 0.71 0.74
	NAEC	Miami-Port Everglades	1.71	0.31	0.50	0.72	1.09	1.56	1.39	1.47 SEAS	Penang		0.82 1.79 2.33 1.63 1.43 1.32 0.80
	WMIP	Barcelona		0.90	0.38	0.41	0.49	0.48	0.53	0.47 NEAS	Fuzhou						0.44 0.60 0.68
	SCBA	St Petersburg			0.19		0.13	0.10	0.30	0.51 WMIP	Valencia			0.74 1.10 1.24 1.36 1.39 1.83
	WEAF	Abidjan			1.12	1.84	1.41	2.02	1.91	0.95 SAEC	Vitoria							0.82 0.90
	NAWC	San Diego			0.01	0.01	0.01	0.01		0.04 NWEU	Clydeport	0.15 0.20 0.16		0.07 0.02
	NWEU	Liverpool	0.77	0.39	0.22	0.19	0.28	0.34	0.21	0.08 WEAF	Port Harcourt			4.96 0.27 0.24 0.10 0.16
	NEAS	Busan			2.17	3.13	3.21	3.16	4.07	4.01 NWEU	Amsterdam	0.17 0.09 0.11 0.11 0.07 0.07 0.02 0.02
	OCEA	Sydney-Port Botany	0.60	0.88	0.86	0.89	0.43	0.85	1.22	0.67 OCEA	Fremantle-Perth	1.04 0.93 0.50 0.70 0.83 0.76 0.56 0.76
	EMBS	Alexandria			0.28	0.47	0.36	0.39	0.48	0.34 INSU	Cochin			3.80 2.31 1.00 1.15 1.13 1.04
	INSU	Yangon				0.02	0.03	0.34	0.14		NWEU	Tyne			0.02 0.01 0.01 0.01 0.02 0.02
	SOAF	Cape Town			0.82	0.74	0.53	0.70	0.59	0.57 INSU	Visakhapatnam				0.02		0.29 0.18 0.14
	NEAS	Kitakyushu			0.69	0.91	0.90	0.81	0.23	0.18 WEAF	Tema			0.08 0.31 0.78 1.23 10.43
	NEAS	Hakata-Fukuoka				0.50	0.44	0.44	0.28	0.23 NWEU Rotterdam-Europoort 1.21 2.90 3.16 3.10 3.02 2.86 2.85 4.07
	SEAS Tanjung Perak-Surabaya					0.38	0.42	0.54	0.73 NEAS	Masan-Changwon								0.06
	WMIP	Naples		0.21	0.36		0.20	0.21	0.16	0.16 SAEC	Rosario								0.06
	NAWC	Seattle-Tacoma	2.44	3.01	3.17	3.26	3.00	3.15	1.60	1.67 SEAS	Davao				0.79 0.39		0.26 0.27
	SAEC	Porto Alegre							0.01	0.08 NAGC	New Orleans	1.00 1.16 1.57 2.07 2.16 1.55 1.13 1.19
	EMBS	Piraeus-Athens	0.59	0.39	0.61	0.64	0.86	0.86	1.46	1.27 NEAS	Shenzhen						1.06 8.51 14.12
	NOAF	Casablanca			0.41	0.42	0.59	0.34	0.65		WMIP	Seville					0.13 0.13 0.10 0.11
	OCEA	Melbourne	1.31	1.39	1.24	1.21	1.45	1.23	0.91	1.11 NEAS	Ulsan							0.48 0.43
	INSU	Chittagong					2.03		1.50	1.54 MERS	Mina Zayed			0.16 0.22 0.30 0.79 0.39
	NAEC	Montreal	2.03	1.06	1.34	1.74	1.49	1.22	1.40	1.27 NWEU Bremen-Bremerhaven 1.39 1.57 1.67 1.64 1.49 1.37 1.51 1.73
	SAEC	Recife			0.05	0.14	0.15		0.10	0.25 SOAF	Port Elizabeth		1.05 1.23 0.80 0.89 1.10 1.22 1.01
	NEAS	Wuhan						0.03			SEAS	Cebu			0.78 1.12 0.75 0.41 0.71
	INSU	Colombo			2.92	3.42	3.81	4.38	8.51	7.11 SCBA	Helsinki	0.41 0.54 1.30 1.05 2.10 2.11 1.55 1.49
	SAEC	Salvador			0.17	0.37 0.32	0.33	0.37	0.47 NAEC	Jacksonville		10.64		1.78 1.58 5.92 2.83 2.36
	SAWC	Guayaquil			0.32	1.61	2.01		0.95	0.92 EMBS	Odessa					0.19 0.34		0.41
	NWEU	Hamburg	0.22	0.50	0.82	0.85	1.06	1.13	0.83	1.27 SAEC	Natal							0.06
	NEAS	Dalian			0.01		0.29	0.42 0.73	1.10 WMIP	Bilbao		3.29 0.66 0.60 0.86 0.85 0.69 0.57
	SAEC	Fortaleza					0.29	0.21	0.30	0.22 NWEU	Antwerp	1.29 1.15 1.48 1.93 1.64 1.79 2.72 3.30
	SOAF	Durban		0.97	1.63	1.88	1.68	1.79	2.43	2.23 OCEA	Auckland	0.79 0.50 0.71 1.18 1.59 1.63 1.23 3.95
	CARI	Santo Domingo							0.01	0.02 OCEA	Adelaide	0.50 0.87		0.15 0.23 0.25 0.30 0.28
	NEAS	Taichung			0.03	0.06	0.24	0.42	1.23	1.02 EMBS	Haifa	3.61 3.20 4.70 4.23 15.65 5.86 3.56 2.93
	MERS	Jeddah			2.07	1.65	0.93	0.65	0.53	0.51 NAEC	Richmond VA					0.37 0.28 0.19 0.17
	NAEC	Cleveland OH			0.01						NWEU	Dublin			0.33 0.23 0.26 0.26 0.28 0.33
	EAIO	Dar-es-Salaam			0.22	0.39	0.41	0.31	0.26	0.26 NEAS	Shantou							0.06
	WMIP	Lisbon		0.59	0.37	0.32	0.50	0.29	0.23	0.30 CARI	Cartagena			0.07 0.15 0.20 0.38 0.82 1.26
	NEAS	Kaohsiung		3.38	5.21		7.59	5.82	6.75	5.99 SAEC	Maceio							0.04
	NAGC	Tampa				0.03	0.02	0.02	0.02	0.01
							Source: own realization

ACKNOWLEDGEMENTS

Authors would like to express their profound gratitude towards Professor Brian Slack, Concordia University, for his in-depth revision of the paper and his very helpful comments.


César Ducruet
email: ducruet@parisgeo.cnrs.fr

Theo Notteboom
email: theo.notteboom@ua.ac.be

THE WORLDWIDE MARITIME NETWORK OF CONTAINER SHIPPING: SPATIAL STRUCTURE AND REGIONAL DYNAMICS

Keywords: liner shipping, network analysis, nodal regions, port hierarchy, spatial change

Port and maritime studies dealing with containerization have observed traffic concentration and dispersion throughout the world. Globalization, intermodal transportation, and technological revolutions in the shipping industry have resulted in both network extension and rationalization. However, lack of precise data on inter-port relations prevent the application of wider network theories to global maritime container networks, which are often examined through case studies of specific firms or regions. This paper presents an analysis of the global liner shipping network in 1996 and 2006, a period of rapid change in port hierarchies and liner service configurations. While it refers to literature on port system development, shipping networks, and port selection, it is one of the only analyses of the properties of the global container shipping network. The paper analyzes the relative position of ports in the global network through indicators of centrality. The results reveal a certain level of robustness in the global shipping network. While transhipment hub flows and gateway flows might slightly shift among nodes in the network, the network properties remain rather stable in terms of the main nodes polarizing the network and the overall structure of the system. Additionally, mapping the changing centrality of ports confirms the impacts of global trade and logistics shifts on the port hierarchy and indicates that changes are predominantly geographic.

INTRODUCTION

Maritime networks are among the oldest forms of spatial interaction. Port hierarchies and the spatial pattern of maritime linkages can be considered as illustrations of wider ongoing processes, such as the regionalization and globalization of trade patterns and business cycles, thus revealing a certain political economy of the world [START_REF] Vigarié | La mer et la géostratégie des nations[END_REF]. [START_REF] Lewis | A maritime response to the crisis in area studies[END_REF] argue that the meta-geography of the world system would be better understood from the maritime looking glass of basins, seas, and oceans. Following decades of adaptation and diffusion since the emergence of containerization, the global maritime container shipping network has become a reality [START_REF] Frémont | Global maritime networks: The case of Maersk[END_REF][START_REF] Rodrigue | Foreland-based regionalization: Integrating intermediate hubs with port hinterlands[END_REF]. The technological revolution of containerization has gradually produced new forms of relationships among countries, regions, and port cities, backed by a continuous pressure on transport costs [START_REF] Limao | Infrastructure, geographical disadvantage, transport costs, and trade[END_REF] and an increasing power of shipping alliances and large carriers [START_REF] Sys | Is the container liner shipping industry an oligopoly?[END_REF][START_REF] Slack | Fifty years of organisational change in container shipping: Regional shifts and the role of family firms[END_REF]. Investigating such changes would complement the lack of evidence about the spatial patterns of commodity chains (Leslie and Reimer, 1999), because ports compete not as individual places that handle ships but as crucial links within global supply chains [START_REF] Notteboom | Structural changes in logistics: how will port authorities face the challenge?[END_REF][START_REF] Hall | Shifting proximities: The maritime ports sector in an era of global supply chains[END_REF].

While the main shipping routes and ports are well described in a number of studies, the structure and evolution of the global maritime network itself has not been fully documented.

More extensive is the research on global airline networks due to their closer overlap with systems of cities (Guimera et al., 2005;[START_REF] Choi | Comparing world city networks: A network analysis of Internet backbone and air transport intercity linkages[END_REF]Derudder and Witlox, 2009).

Despite the local dereliction of port-city linkages in recent decades, maritime transport remains absolutely necessary for globalization. Its crucial weight in world trade volumes (90%) makes it a useful looking glass for analyzing the global economy and its geographic architecture. In parallel, the spatial design of maritime transport not only follows trade demand but also possesses its own practical arrangements and network configurations, which also evolve over time. The concentration and regional polarization of flows by load centers and intermediate hubs toward other secondary ports are typical examples of such configurations. It is thus important to evaluate the respective influence of technological factors (e.g. carriers and infrastructures, industry changes) and territorial factors (e.g. geographic and trade proximities, socio-economic developments) in the formation of shipping networks, port hierarchies, and maritime regions.

The remainder of this paper is organized as follows. Section 2 introduces the concept of port system and reviews the mechanisms shaping port competition, port selection, and port concentration, while describing the specificity and complexity of liner service networks. In Section 3, data on vessel movements (1996 and 2006) and the methodology for analyzing the global liner service network are presented, together with some results on the structure and geographic coverage of this network. Section 4 provides a closer look at the port hierarchy based on centrality measures and the geographic pattern of nodal maritime regions. The paper ends with a discussion of the research outcomes for further analysis of the global economy and its networks. The aggregate outcome of port choice and supply chain decisions leads to a specific distribution of cargo flows in port systems. The search for regularities in the development of port hierarchies has mostly been done from a continental perspective considering ports as heads of land-based transport corridors willing to extend their hinterland coverage. Early works provided spatial models [START_REF] Taaffe | Transport expansion in underdeveloped countries: A comparative analysis[END_REF][START_REF] Rimmer | The search for spatial regularities in the development of Australian seaports 1861-1961/2[END_REF][START_REF] Ogundana | Patterns and problems of seaport evolution in Nigeria[END_REF] suggesting a trend towards an increasing level of cargo concentration in port systems. The concepts of maritime range [START_REF] Vigarié | Les grands ports de commerce de la Seine au Rhin[END_REF] and port system [START_REF] Robinson | Modelling the port as an operational system: A perspective for research[END_REF] originally comprised a set of adjacent seaports in close proximity that were interdependent through land and sea freight flows. However, most scholars have continued focusing primarily on hinterlands, due to the development of intermodalism and logistic chains around ports [START_REF] Van Klink | The port network as a new stage in port development: The case of Rotterdam[END_REF][START_REF] Robinson | Ports as elements in value-driven chain systems: The new paradigm[END_REF] and the higher cost of land transport versus sea transport [START_REF] Notteboom | Container shipping and ports: An overview[END_REF]. The nature and performance of traffics is often explained by the situation of ports within land-based transport and urban systems (Ducruet et al., 2010c).

PORT SYSTEMS AND MARITIME NETWORKS

Port choice and the hierarchy in port systems

Although the development of peripheral ports [START_REF] Hayuth | Containerization and the load center concept[END_REF] and offshore hubs has a maritime purpose for cargo distribution toward secondary ports [START_REF] Slack | The challenge of peripheral ports: An Asian perspective[END_REF][START_REF] Notteboom | The peripheral port challenge in container port systems[END_REF], their emergence has been interpreted from the hinterland perspective of a port regionalization process leading to the formation of a regional load center network [START_REF] Notteboom | Port regionalization: Towards a new phase in port development[END_REF]. There remain important local deviations from general models of port system development due to path dependency and contingency (Notteboom, 2006a(Notteboom, , 2009a)).

The definition of port systems has often been limited to coastal morphology, geographic proximity, and administrative boundaries (Ducruet et al., 2009a(Ducruet et al., , 2009b)). Never have port systems been defined and delineated from the maritime perspective of inter-port linkages.

This raises the question of whether physical factors and geographic proximity still play a role in the current spatial patterns of container shipping circulations. The concepts of maritime region and port region, which remain rather descriptive and vague in the literature [START_REF] Ducruet | Port regions and globalization[END_REF], may benefit from the application of similar methods used by studies of other global networks (see [START_REF] Derudder | The cliquishness of world cities[END_REF], allowing for the definition of coherent groups of ports as well as the identification of leader ports. A close look at the current organization of liner shipping networks is necessary before applying specific network analytical tools.

Design and operation of liner service networks

The development of liner shipping in the last 30 years has exceeded the growth of world trade volumes. The activity of this very dynamic branch of maritime transport is measured in Figure 1 based on annual container port throughputs. Besides continuous growth in throughput volumes, we also observe a parallel increase in the concentration in the global port system, notwithstanding slight decreases in recent years, notably after the 2008 financial crisis that directly affected traffic volumes and distributions. Despite those cyclical changes, liner shipping remains built on a series of specific network configurations. 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Source: own realization based on Containerisation International

Container shipping features a complex combination of end-to-end services, line-bundling services, and pendulum services, which are connected to form extensive shipping networks.

Port hierarchy in the container business is intrinsically linked to shipping lines' design of these liner service networks in terms of service variables such as service frequency, vessel capacity, fleet mix, vessel speed, and the number and order of port calls [START_REF] Fagerholt | Designing optimal routes in a liner shipping problem[END_REF]Notteboom 2006c). Liner service design is a function not only of carrier-specific operational factors (i.e. lower costs) but also of shippers' needs (e.g. transit time) and willingness to pay for a better service.

In the last two decades, increased cargo availability has led carriers and strategic alliances among them to reshape their liner shipping networks through the introduction of new types of liner services on the main east-west trade lanes (see Figure 2). active in securing (semi)dedicated terminal capacity in the strategic locations within their liner service networks. Figure 3 gives an overview of the strategic ports in the worldwide liner network of Maersk Line. Shipping lines also rely on horizontal integration through operating agreements (e.g. vessel sharing agreements, slot chartering agreements, consortia and strategic alliances) and mergers and acquisitions. Alliance structures (cf. Grand Alliance, New World Alliance, and CYKH) provide its members easy access to more loops or services with relatively low-cost implications and allow them to share terminals. 4 The average vessel size increased from 1,155 TEU in 1987 to 1,581 TEU ten years later, 2,417 TEU in 2007and 2,618 TEU in 2009(UNCTAD, 2009). In 2006, Maersk Line introduced the Emma Maersk of around 13,500 TEU capacity, the first vessel to move far beyond the 10,000 TEU mark. The total fleet in late 2009 counted 39 vessels in the range of 10,000-15,500 TEU, and another 168 vessels of above 10,000 TEU unit capacity were on order (Source: Alphaliner, www.alphaliner.com) In the last few decades, extensive hub-feeder container systems and short-sea shipping networks came into existence to cope with increasing volumes and to connect to other port ranges [START_REF] Rodrigue | Foreland-based regionalization: Integrating intermediate hubs with port hinterlands[END_REF]. The economics of transhipment and relay/interlining have resulted in the establishment of intermediate hubs with terminals owned, in whole or in part, by carriers or port operators. In some cases, intermediate hubs were developed within offshore locations often on small islands with an implicit local cargo base [START_REF] Rodrigue | Foreland-based regionalization: Integrating intermediate hubs with port hinterlands[END_REF]. The development of offshore hubs did not exclude transhipment activities at traditional gateway ports such as in the Western Mediterranean port system, where the distinction between hub ports and gateway ports has become blurred [START_REF] Gouvernal | Dynamics of change in the port system of the western Mediterranean[END_REF].

The position of pure transhipment hubs is generally more unstable than that of pure gateway ports: once traffic volumes for the gateway ports are sufficient, hubs are bypassed and might even become redundant [START_REF] Wilmsmeier | Determinants of liner shipping network configuration: A two region comparison[END_REF]. The location of transhipment hubs remains important, because they lower the deviation distance to/from main trunk lines [START_REF] Zohil | The MED rule: The interdependence of container throughput and transhipment volumes in the Mediterranean ports[END_REF]. There remains a subtle combination between centrality (proximity to origin/destination markets) and intermediacy (insertion in carrier networks) in nearly every port [START_REF] Fleming | Spatial characteristics of transportation hubs: Centrality and intermediacy[END_REF].

METHODOLOGY AND LINER SHIPPING NETWORK CHARACTERISTICS

In their recent review of the scientific literature on maritime network analysis, Ducruet et al. (2010a) stress the scarcity and fragmentation of empirical studies, which are categorized by four main approaches:

 Geographic coverage of carrier networks: regional or global distribution of the port networks for individual shipping companies based on service data (e.g. Coscon, Maersk) revealing their strategic choices [START_REF] Rimmer | China's extra-and intra-Asian liner shipping connections', 1990-2000[END_REF][START_REF] Frémont | Global maritime networks: The case of Maersk[END_REF]Bergantino and Veenstra, 2007);  Network connectivity: characteristics of a given network based on its topology, with reference to spatial analysis and graph theory, such as the pioneering study of [START_REF] Joly | La structuration des réseaux de circulation maritime[END_REF] showing the tripolar organisation of the global maritime system based on Reeds zones, and other works on a regional level [START_REF] Mccalla | Hierarchical network structure as seen in container shipping liner services in the Caribbean basin[END_REF]Ducruet et al. 2010b);  Network efficiency: modeling of port selection processes and search for the optimal location, for instance, of a transhipment hub lowering overall shipping costs (Zeng and Yang, 2002;Song et al., 2005;[START_REF] Tai | Analysis of hub port choice for container trunk lines in East Asia[END_REF];  Complex networks: description of the network' hierarchical structure on a global level comparing its properties with general models of small-world and scale-free networks [START_REF] Deng | Worldwide marine transportation network: Efficiency and container throughput[END_REF][START_REF] Hu | Empirical analysis of the worldwide maritime transportation network[END_REF][START_REF] Kaluza | The complex network of global cargo ship movements[END_REF].

This paper wishes to further the interpretation of network structure, port hierarchy, and the dynamics influencing them. It gives paramount importance to the visualization of the network as a whole and of emerging regional patterns. This is based on a rarely used data source on daily vessel movements, which is more precise than service data and therefore more representative of the reality and complexity of liner shipping.

Data overview

The methodology used for building the global liner network defines an inter-port connection by the circulation of vessels between the ports through a 365-day sequence of port calls. Thus, nodes (vertices) in the network are the ports, and links (edges) in the network are the connections realized by vessel movements (Table 1). The years 1996 and 2006 were chosen, because 1996 marked the emergence of post-panamax vessels (e.g. the Regina Maersk of 6140 TEU was introduced in 1996) and the start of strategic alliances formation among shipping lines; 2006 saw the introduction of the first 10,000+ TEU vessels in a period of rapid container growth mainly triggered by the China effect in the world economy. Data was obtained from Lloyd's Marine Intelligence Unit (LMIU)5 that ensures most of the world fleet for all types of vessels. The obtained database covers approximately 92% and 98% of the world's fleet of container vessels in 1996 and 2006, respectively. Interestingly, the capacity and size of the fleet as well as the number of vessel movements have grown faster than the number of ports and operators, while the average vessel capacity has grown from 1906 TEU to 2413 TEU. Such evidence confirms the observed limitations for ports accommodating evergrowing vessels and traffic, which remain in the hands of horizontally and vertically integrated companies. ). In the GDL, Le Havre and Tokyo are never connected by a direct link, whereas, in the GAL, this connection might occur inside a pendulum or round-the-world service. The GAL is the overlap of all individual complete graphs created by the circulation of each vessel.

These two dimensions of the same reality (GDL and GAL) may exhibit distinct features in terms of network structure and port hierarchy. In order to reveal the structural properties of the two graphs for each year of observation, we apply conventional measures derived from graph theory, which were originally applied to transport networks by [START_REF] Kansky | The structure of transportation networks[END_REF] and from complex systems theory, referring to the works of [START_REF] Barabasi | Emergence of scaling in random networks[END_REF] and Watts and Strogatz (1998). This set of measures provides clear evidence about the nature of the network based on topological properties (see [START_REF] Ducruet | Graph theory: Measures and indices[END_REF] for a review of network measures).

One limitation of the data is that it ignores how many full or empty containers were truly handled by ships and ports. In reality, some vessels may not be fully loaded, since their passage in a port does not always include stevedoring activities (e.g. a port visit in the framework of bunkering activities). However, with reference to the observation made by Joly (1999), the linear correlation in our data between vessel traffic and port throughput 6 is very 6 Source: Containerisation International significant: about 88% and 87% of total variance is explained by the regression in 1996 and 2006, respectively. This verifies the good fit and quality of the LMIU data source with official port statistics for analyzing container ports and their position in liner shipping networks.

Network structure

Table 2 highlights important differences between the GDL and GAL approaches and between the two years of observation. In terms of network size, the GDL has fewer links than the GAL, which includes numerous indirect connections among ports, thus making it about 5 times larger (edges) and 12 to 13 times longer than the GDL for the same number of ports (vertices). In the GDL, the most central port in terms of maximum degree value connects about 18 to 19% of all ports; in the GAL, it connects 48 to 51%. Such differences in size have a strong influence on other network properties. Indeed, the GAL has about 5 to 6 times greater density, connectivity, and lattice degree compared to the GDL.

More robust measures proposed by physics complement such findings by revealing the polarized or scale-free structure of the GDL with power-law exponents higher than one (-1.35 in 1996 and -1.29 in 2006): few ports concentrate a large number of links (high degree centrality), while most ports have a limited number of links with other ports. Due to its higher density, the GAL is more likely to be a small-world network: higher average clustering coefficients (0.74 and 0.73), higher transitivity (0.40 and 0.43), lower power-law exponents (-0.62 and -0.65), and smaller diameters (4 and 5) than the GDL indicate the tendency for a given port to have its direct neighbors connected to each other, thus forming tightly connected communities. Thus, the GDL is more representative of hub ports dominating secondary ports, whereas the GAL represents densely connected maritime regions. Consequently, the GAL is more efficient than the GDL, because the inclusion of indirect links facilitates the circulation of flows in the graph, as reflected by its shorter average path length. Our results are similar to those of [START_REF] Hu | Empirical analysis of the worldwide maritime transportation network[END_REF] based on 2006 service data, both for the GDL (power-law exponent of -1.7, average clustering coefficient of 0.4) and the GAL (average clustering coefficient of 0.7). Finally we observe that the network exhibits positive assortative mixing (correlation between the degree centrality of ports and the average degree centrality of their direct neighbours), which confirms that high degree ports tend to connect to high degree ports. This is corroborated by the rich-club coefficients: the density of links per node among higher degree ports is two times higher than the same density (Beta index) among all ports.

Similarly, the proportion of traffic among higher degree ports in their total traffic is very strong (i.e. about 95%).

Despite their fundamental differences in size and structure, the two networks share similar evolutionary paths. Network structure has remained somewhat resilient to the aforementioned industry changes (and their spatial consequences), as seen with the stable connectivity (gamma index) and clustering coefficients. However, both networks have become more complex (cf. higher values for alpha and beta indices) due to the multiplication of nodes and edges, resulting in better efficiency as illustrated by the decreased average path length. One important trend that is only visible in the GDL is the decrease of the power-law coefficient, which seems to contradict the higher polarization of shipping networks for individual shipping companies as a result of service rationalization and a reduction of port calls per liner service.

Source: realized by authors based on LMIU data

This decrease may be interpreted as the combined influence of bottom-up and top-down retroactions. Bottom-up phenomena include congestion issues at the port-urban interface and regional integration processes. On a local level, large ports face important limitations in terms of lack and cost of available land for further expansion as well as congestion and bottleneck effects at terminals situated within dense urban environments. Port-city separation and the shift of modern terminals outside urban areas may be avoided in some cases through efficient planning policies [START_REF] Lee | A tale of Asia's world ports: The spatial evolution in global hub port cities[END_REF]. On a regional level, trade growth has multiplied the number of intra-regional shipping connections, thus making the network denser and more evenly distributed. This is particularly true in emerging economies where maritime transport plays a crucial role (e.g. China, India, Brazil, and Middle East).

Top-down retroactions are found at the level of the competition among shipping lines. A number of shipping lines seek differentiation and competitive advantage by fully or partially controlling (semi)dedicated terminal facilities. However, this spatial concentration at the company level does not necessarily result in higher cargo concentration at port system level since individual shipping lines often opt for different locations to set up their hub ports (Cullinane and Khana, 1999;[START_REF] Frémont | Northern European range: Shipping line concentration and port hierarchy[END_REF]. Traffic thus becomes relatively more balanced among several hubs rather than one mega-hub. Even when dedicated hubs are developed, shipping lines can still follow a risk-spreading strategy over different ports in view of offering more routing options to shippers. However, there are important variations in the position of individual routes and ports, as demonstrated in the next sections.

Geographic coverage of the network

The interplay between distance and flow intensity is depicted in Figure 4, where most traffic occurs across relatively short distances. This trend is more obvious in the GDL approach, where 78 to 79% of worldwide vessel traffic occurs over links of 500km or less. Links of 100km or less support more than half of worldwide traffic in both years. Strong traffic links are likely to occur among adjacent seaports serving shared hinterlands (e.g.

Antwerp/Rotterdam in the Benelux area) or acting as dual hubs (e.g. Busan/Gwangyang in South Korea), which often receive multiple calls for the same vessels or liner services. The share of links shorter than 500km is much lower in the GAL, because it includes many long-distance and high-density indirect maritime links between world ports such as Le Havre-Tokyo and New York-Singapore. There is a noticeable increase in the traffic share of the 3000 to 5000km edges, caused mainly by the growing importance of trans-Pacific relations.

Continued globalization and technological progress in the maritime industry are likely to be responsible for the increased share of the longest links (over 5000km) from 7% in 1996 to almost 10% in 2006. The increased share of shortest links (from 51% in 1996 to 55% in 2006) illustrate that long-distance links remain inferior to the number and weight of intra-regional linkages (i.e. short-sea shipping or hub-and-feeder services with a high sailing frequency).

N.B. values represent determination coefficients (%) of the power-law lines

A look at the spatial distribution of the heaviest direct links provides interesting findings regarding the network's geographic coverage (Figure 5). The top 100 links represent 52% and 39% of total worldwide vessel traffic in 1996 and 2006, respectively. They connect primarily neighboring ports and remain intra-regional rather than interregional. 8 There is a clear dominance of three main poles: Asia, Europe, and North America. In each pole, a small number of ports constitute the backbone (i.e. East Asian corridor, North European range, and US East and West coasts). In 1996, only Buenos Aires, Santos, Jeddah, and Colombo stand out as main ports outside of these poles. The strongest inter-regional links run between Asia and the two other large poles, with Japan (i.e. Tokyo) and Singapore acting as turntables across the Pacific and the Indian Ocean, respectively. Most other inter-regional links generate less traffic, while some regions remain isolated (e.g. South Africa and Australia). The pattern in 2006 is similar, but there is an intensification of intra-regional links at the expense of interregional links. Busan has taken over as the key bridge between East Asia and North America, and Trans-Atlantic links has disappeared from the top 100 list. Such changes in the network structure and geographic coverage should also be analyzed from the perspective of port hierarchies.

CHANGING PORT HIERARCHIES

Centrality of ports in the network

The centrality of ports in the network can be approached at the local and global levels. Degree centrality is a local level measure counting for each port the number of connections to other ports. Betweenness centrality is a global level measure summing for each port the number of its positions on the shortest possible paths within the entire network. Degree centrality is a measure of connectivity, while betweenness centrality can be regarded as a measure of accessibility. The hypothesis is that hub ports will have both a high degree centrality and a high betweenness centrality, due to their role as inter-regional pivots in the global network.

As defined by [START_REF] Fleming | Spatial characteristics of transportation hubs: Centrality and intermediacy[END_REF], hub ports are those that welcome mother vessels for redistributing cargoes to satellite (and often secondary) ports via feeder vessel services.

Figure 6 visualizes the GDL and the port hierarchy. At first sight, the geography of the network appears similar over time, with Asia-Pacific centered on the Singapore-Busan axis and Europe-Atlantic with the Le Havre-Hamburg range. Surprisingly, large North American and Japanese ports are poorly represented despite their traffic volume due to their lack of hub/feeder activities. Inherent to the data, gateway (hinterland) functions of seaports are not included in the analysis. Results indicate that Singapore is the most central port of the global system, which echoes its rank at the top of throughput hierarchy in official statistics. 10 The very high centrality of the Suez and Panama canals underlines the strong vulnerability of the global network, but they are not taken into account in the following analyses. Conversely, the position of some formerly central ports has lowered significantly, as in the cases of Los Angeles, Houston, New York, Melbourne, Bilbao, North European range ports, Tokyo-Yokohama, Kaohsiung, and even Singapore. In contrast with recent literature [START_REF] Yap | Container shipping services and their impact on container port competitiveness[END_REF], Hong Kong has maintained and even increased its position in the network. This trend is thus a good illustration of the globalization process with the shift of production from mature to emerging economies. Changes in betweenness centrality scores follow a similar geographic pattern with more drastic gaps among ports.

10 Appendix 1 provides detailed centrality scores for the top 25 ports. We clearly see the strong effects of hub strategies at Kingston, Gioia Tauro, Dubai, and Busan as well as the emergence of large load centers in South Brazil and China. There is a clear North-South divide illustrating emerging economies and differentiating ports according to local and global changes in trade routes and port selection.

Polarization and nodal regions

The method applied originally by [START_REF] Nystuen | A graph theory interpretation of nodal regions[END_REF] to telephone flows among Washington State cities in the U.S. has been extensively applied to many transport networks [START_REF] Cattan | Barrier effects: The case of air and rail flows[END_REF][START_REF] Grubesic | Global airline networks and nodal regions[END_REF][START_REF] Van Nuffel | Even important connections are not always meaningful: On the use of a polarization measure in a typology of European cities in air transport networks[END_REF], but this is the first time that it has been applied to maritime transport. The method allows for delimiting so-called nodal regions by focusing on the strongest associations between city pairs, which are believed to reflect the hierarchy of central places in which subordinate nodes (satellites) are under the influence of independent (dominant) nodes through a transitive principle: an independent city also dominates the satellites of its satellites. Due to its higher average clustering coefficient (see Table 2), this algorithm is applied to the GAL to reduce the likelihood that geographic proximity is the main explanatory factor behind the delimitation of nodal regions.

A small number of nodal regions form the world maritime system and tend to merge or to split with each other over time (see Figure 8). Indeed, the global network is highly polarized by a few large entities concentrating 58% and 69% of all ports in 1996and 2006, respectively. Singapore and Hong Kong's combined nodal regions include 39% in 1996and 50% in 2006 of all ports, while Hamburg and Rotterdam maintain their 19% share. This complements the sole indicators of centrality and better highlights the increasing influence of Asia on the world economy. Hong Kong is the independent port of the largest region centred on Asia due to its role as a gateway for South China and centrally located hub in East Asia. Hong Kong's ramifications 12 remain focused on East Asia in 1996, except the link with Los Angeles, but they extend much further in 2006 with the inclusion of important Caribbean and Mediterranean ports. Despite their traffic size, other large Northeast Asian ports remain under Hong Kong's influence due to double calls and hub dependence [START_REF] Yap | Container shipping services and their impact on container port competitiveness[END_REF]. Some of them have also extended their influence, such as Busan and Shanghai, while Taiwanese and Japanese ports have seen a significant reduction in their influence.

Comparatively, Singapore possessed a widely diversified tributary area in 1996 due to its pivotal role between Europe and Asia, as reflected by its ramifications covering Southeast Asia, South Asia, the Middle East, and large parts of the Mediterranean. This pattern did not change in 2006, notwithstanding the increase in the number of ports under its influence. In fact, a number of large subordinate ports such as Incheon, Surabaya, and Port Klang extended their own tributary areas in response to the overwhelming dominance of Singapore and Hong Kong. The extensions of the latter two ports toward the Mediterranean reflect the importance of the Europe-Asia trade link with a continuous and regular alignment of transhipment hubs during the last two decades. The port hierarchy does not always overlap traffic volumes due the dominant gateway function of some ports compared with their limited transhipment activities (e.g. Antwerp, Bremerhaven, Le Havre, Shenzhen, and Tokyo).

Although European ports appear as subordinates of Asian ports in 1996, this is no longer the case in 2006, as the core region has split in two between Europe and Asia. This change can be interpreted in two ways. On the one hand, each region has reinforced its internal connectivity, making it a distinct entity stemming from regional integration forces. On the other hand, the so-called global shift (especially in the manufacturing sector) has placed Asia at the forefront of the global scene, thus relegating Europe and the rest of the world to the periphery.

A closer look at the geographic coverage of Rotterdam and Hamburg, the two main ports of the European nodal region, reveals their respective specialization. In 1996, Rotterdam primarily covered the British Isles, Iceland, the Iberian Peninsula, and the Canary Islands, while Hamburg turned toward Scandinavia and the Baltic regions, with Antwerp as a large

12 Appendix 2 provides a graphic visualization of the largest regions. The analysis confirms the strong influence of geography and distance on the distribution of traffic, showing the dominance of intraregional links and demonstrating good applicability of the gravity model for estimating inter-port traffic. As in previous analyses of other global inter-city networks, maritime linkages retain an important regional dimension [START_REF] Derudder | The cliquishness of world cities[END_REF], but there is a striking absence and decline of transatlantic linkages as already verified in the global pattern of airline networks [START_REF] Cattan | Le monde au prisme des réseaux aériens[END_REF]. The overarching importance of the Asia-Pacific area in the maritime network is best illustrated by delineating the ramifications of nodal regions. This was also made evident when mapping the changing centrality of ports. While the Old World (Atlantic, Northern hemisphere) versus the New World (Asia-Pacific, Southern hemisphere) would be a too simplistic interpretation of our results, the role of a changing world geography cannot be ignored. As such, analyzing the global liner shipping network provides a useful and necessary complement to the study of globalization and regionalization processes, which are often approached through other types of global networks.

Further research in this field may benefit from the inclusion of land-based networks (e.g. road, rail) as a means of considering hinterland accessibility. Currently, a worldwide database of vessel movements over the contemporary period (1890s-2010s) for all types of vessels is being built to expand the analysis of the global maritime network's dynamics.

Graph of all links (GAL)

  Traffic flows through ports are a physical outcome of route and port selection by the relevant actors in the chain. The most relevant service-related and cost factors explaining port selection by the main players of the transport chain (e.g. shippers, ocean carriers, and forwarders) are identified in the scientific literature on port choice 3 . Port choice becomes a function of the overall network cost and performance. Notteboom (2009b) groups the factors together in the demand profile of the port, the supply profile of the port, and the market profile of the port. Typical port choice criteria include factors such as: (a) Physical and technical port infrastructure, including nautical accessibility (e.g. draft); (b) Terminal infrastructure and equipment, hinterland accessibility, and intermodal offer; (c) Geographical location vis-à-vis the main shipping lanes and the hinterland; (d) Port efficiency expressed as port turnaround time, terminal productivity, and cost efficiency; (e) Interconnectivity of the port (sailing frequency of deep-sea and feeder shipping services); (f) Reliability, capacity, frequency, and cost of inland transport services; (g) Quality and cost of auxiliary services such as pilotage, towage, and customs; (h) Efficiency and cost of port management and administration (e.g. port dues); (i) Availability, quality, and cost of logistic value-added activities (e.g. warehousing) and port community systems; (j) Port security/safety and environmental profile; and (k) Port reputation.

Figure 1 :

 1 Figure 1: World port throughput and concentration, 1970-2009

Figure 2 :Figure 3 :

 23 Figure 2: Typical examples of liner services on trade routes in relation to Europe

Figure 4 :

 4 Figure 4: Edge and traffic distribution over distance in 1996 and 2006

Figure 5 :

 5 Figure 5: Top hundred direct maritime links in 1996 and 2006

Figure 7

 7 Figure 7 reveals noticeable changes between 1996 and 2006. The lowered centrality of Houston and Port Everglades to Kingston, Jamaica in the Caribbean is a good example of the impact of hub-and-spoke strategies. Several ports have strengthened their positions based on their gateway functions, such as Santos, Brazil and Shanghai, China. In East Asia and the Mediterranean, an increasing number of ports have high connectivity (e.g. Gwangyang, Port Klang, Xiamen, Shenzhen in Asia; Marsaxlokk, Gioia Tauro in the Mediterranean), but this growth has not altered the established position of established pivotal hubs (e.g. Singapore, Busan, Algeciras, Gioia Tauro) and gateway ports (e.g. Barcelona, Valencia in Spain).

Figure 6 :

 6 Figure 6: Visualization of the global liner shipping network in 1996 and 2006

Figure 7 :

 7 Figure 7: Changes in throughput and centrality, 1996-2006

  subordinate. Despite the stability in the number of subordinates and geographic coverage in 2006, Rotterdam extended to Africa and the Black Sea, while Hamburg and Antwerp reached across the Atlantic.Geographic proximity and regional integration seem to explain the delineation of most nodal regions, except for the giant Asian region, and despite the absence of a comparable transatlantic region. Physical geography that reinforces the internal connectivity of basins may also be responsible for the limited North-South linkages across Europe. Several secondary nodal regions remained in 2006, such as the one centered on New York, which integrated the region comprising Kingston and Rio Haina. Another important region expanded in 2006: the one from Santos primarily bound to Brazilian ports but embracing Venezuela and some Caribbean ports. One may interpret such changes under the context of NAFTA and MERCOSUR arrangements due to growing North-South trade among the Americas. Some nodal regions have become detached from large ones, such as the region of Lisbon, with strong links to the Azores, the region of Constantza in the Black Sea, the regions of Izmir and Ambarli in Turkey, as well as Puerto Barrios and Veracruz in Central America. Others have remained rather stable in their size and geographic distribution, such as the West Mediterranean range polarized by Barcelona, Trieste in the Adriatic and the West African range polarized by Abidjan, despite a drop in the number of its subordinates that were caught by Algeciras on one side and the Asian region on the other. The independent port has shifted in some regions: Buenaventura has replaced Callao as the head of the Latin American west coast. Conversely, some formerly detached regions have been integrated within a larger one, such as most of Africa and the Mediterranean basin shifting to the Asian region as well as the Belem and Puerto Cabello regions shifting under the influence of Santos (Brazil). Such phenomena depict the expansion of ocean carrier networks, making the world system increasingly interconnected.

Figure 8 :

 8 Figure 8: Nodal maritime regions of the world in 1996 and 2006

Table 1 : Overview of the database on vessel movements, 1996-2006

 1 The global network was modelled based on vessel characteristics, ports of call, and vessel movements. The first result is a global network composed of weighted and non-directed links between ports, which can be analyzed in two different ways. On the one hand, vessel circulations create a graph of direct linkages (GDL) based on the successive ports of calls (i.e.

	1996	2006	2006/1996

from port A to port B and from port B to port C). On the other hand, it can be argued that two ports are also connected if they belong to the same liner service or loop, although they are not adjacent calls; a graph of all linkages (GAL) thus adds indirect linkages (i.e. from port A to port C

Table 2 : Topological properties of the global maritime network

 2

			Graph of direct	Graph of all
	Index	Measure	linkages (GDL)	linkages (GAL)
			1996	2006	1996	2006
		No. vertices	910	1205	910	1205
		No. edges	5,666	9,829 28,510 51,057
		Max. degree	165	226	437	610
		Average degree	12.787 17.027 64.178 87.521
	Network size	Total length (000s km)	5,159 10,813 71,835 130,927
		Traffic density (TEU/km)	331	407	125	183
		Max. edge length (km)	10,012 10,018 10,018 10,018
		Average edge length (km)	1,008	1,227 2,900	2,997
		Diameter	9	8	4	5
	Cycles	Cyclomatic number	4,757	8,625 27,601 49,853
	Lattice degree Alpha index	0.005	0.012 0.033	0.069
	Complexity	Beta index	6.226	8.156 31.329 42.370
	Connectivity	Gamma index	0.014	0.014 0.069	0.070
	Scale-free	Power-law coefficient	-1.351 -1.293 -0.624	-0.647
	Small-world	Average clustering coefficient (local) Transitivity (global)	0.540 0.266	0.545 0.744 0.266 0.404	0.734 0.435
	Efficiency	Average shortest path length	3.253	3.189 2.230	2.219
	Assortativity	Average nearest neighbours degree	0.419	0.430 0.376	0.277
	Rich-club	Topological rich-club coefficient Weighted rich-club coefficient	1.920 0.948	2.159 1.820 0.954 0.946	1.929 0.920

Table 3 : Estimated maritime traffic flows based on gravity model

 3

			Direct linkages	All linkages
	Unit	Type of observed traffic	(GDL)	(GAL)
			1996	2006	1996 2006
	Links	Port throughput Weighted degree	0.145 0.184 0.259 0.308 0.285 0.309 0.397 0.354
	Ports	Port throughput Weighted degree	0.533 0.620 0.570 0.615 0.458 0.514 0.414 0.687

See Murphy et al.(1992),[START_REF] Murphy | A comparative analysis of port selection factors[END_REF],[START_REF] Malchow | A disaggregate analysis of factors influencing port selection[END_REF],[START_REF] Tiwari | Shippers' port and carrier selection behavior in China: A discrete choice analysis[END_REF],[START_REF] Nir | Port choice behaviour -from the perspective of the shipper[END_REF],[START_REF] Chou | Comparison of two models for port choice[END_REF],[START_REF] Song | A competitive analysis of Chinese container ports using the analytic hierarchy process[END_REF],Barros and Athanassiou (2004),[START_REF] Guy | Port selection and multicriteria analysis: An application to the Montreal-New York alternative[END_REF] and[START_REF] Wiegmans | Port and terminal selection by deep-sea container operators[END_REF]

http://www.seasearcher.com/lmiu/index.htm(Accessed October 2010)

The distance parameter of the gravity model is the orthodromic distance in kilometres (i.e. taking into account the sphericity of the Earth) between ports.

We have calculated the share of intra-regional traffic versus total traffic at the level of 25 maritime LMIU regions, which increased from 53% to 55% in the GDL and from 27% to 31% in the GAL.

http://philcarto.free.fr/ (Accessed October 2010)

ACKNOWLEDGEMENTS

Authors are grateful to Liliane Lizzi and Igor Lugo at Géographie-Cités for their help on the cartography and data calculation respectively. Thanks also go to the three anonymous reviewers for their useful comments as well as to the journal editor Dr. Ali Rogers. This research benefited from the financial support of Marie Curie EIF-FP6 and the Reintegration Grant (ERG).

Appendix 1: Top 25 most central ports in 1996 and 2006

Graph of direct links (GDL)