César Notteboom

Ducruet

Kateryna Gruchevska

Intra vs. extra-regional connectivity of the Black Sea port system

Intra-vs. extra-regional connectivity of the Black Sea port system

Kateryna Gruchevska, Theo E.

Several empirical studies on maritime network analysis can be found in the extant literature.

However, [START_REF] Ducruet | Centrality and vulnerability in liner shipping networks: revisiting the Northeast Asian port hierarchy[END_REF] argue that these studies are scarce and quite fragmented.

Moreover, shipping connectivity literature shows an absence of a systematized comprehensive analysis of the maritime network of the Black Sea. This chapter addresses shipping dynamics in the Black Sea with a specific focus on internal and external connectivity dynamics, with reference to the wider field of network analysis. Our central research question is whether the foreland regionalization and globalization of Black Sea shipping networks also depends on a certain degree of intra-regional connectivity. In transport geography, intermediacy and centrality [START_REF] Fleming | Spatial characteristics of transportation hubs: centrality and intermediacy[END_REF] well describe the spatial qualities that enhance the traffic levels of transportation hubs and hence indicate which places are favorably located within transport systems. Centrality focuses on the port vicinity being a point of origin and destination of traffic to/from adjacent areas. At the same time intermediacy focuses on the port being a point of transit between different systems of circulation. Intermediate ports/airports as defined by [START_REF] Fleming | Spatial characteristics of transportation hubs: centrality and intermediacy[END_REF] are the enroute intermediate locations between important origins and destinations chosen as way stops, route conjunctions, break-in-bulk points, gateways etc.

Yet, this chapter wishes to introduce another network-related concept, namely vulnerability, which better describes how certain port nodes grow at the expense of others, the latter becoming peripheral [START_REF] Fraser | Peripherality in the global container shipping network: the case of the Southern African container port system[END_REF] or dependent upon external hubs [START_REF] Ducruet | Centrality and vulnerability in liner shipping networks: revisiting the Northeast Asian port hierarchy[END_REF]. While the centralization (or concentration) level of port systems is a classic approach in transport studies [START_REF] Notteboom | Traffic inequality in seaport systems revisited[END_REF], it had been only recently applied to shipping networks in particular regions (see a comparative perspective in [START_REF] Mareï | The regionalization of maritime networks: Evidence from a comparative analysis of maritime basins[END_REF] or at the global scale (Ducruet and Notteboom, 2012b). This chapter goes one step further, by relating the internal organization of a given regional network, here the Black Sea, taking into account the external dynamics taking place in the vicinity and in the rest of the world.

Nevertheless, it goes beyond the analysis of the maritime forelands of individual ports [START_REF] Wang | The geographical connectedness of Chinese seaports with foreland markets: A new trend?[END_REF]. We complement this quantitative analysis of shipping data with qualitative information about past and current projects on a local or national scale, and compare our results with other data sources (for other analyses of transnational port systems, see Chapters 5, 9, 11, 19, 23, and 24 on Taiwan Strait, the Arctic, the Mediterranean, the USSR, Northwest Africa, and the Indian subcontinent, respectively).

First we will set the scene of the Black Sea container port system, by assembling and synthesizing complementary data and knowledge about Black Sea ports and shipping routes, such as by using the liner shipping connectivity and logistics performance index developed by UNCTAD. Then, we will perform a network analysis of the Black Sea region looking at traffic shifts and topological dynamics from the late 1970s up to the present day. Finally, we conclude with the main findings of the chapter and outline the possible future research avenues.

General profile of the Black Sea container port system

Notwithstanding the fact that the Black Sea region accounts for only 2.5% of global seaborne trade, (while the leading European trade region -the North Sea-accounts for about 17%) it is an important area of development due to its geographical size and resource/consumption base. The Black Sea container port system is among the world's fastest growing markets with a cargo growth rate in 2014 of 6% p.a., and a CAGR (1998-2014) of 14%. In the same period, the global CAGR amounted to 9%, South Asia 11%, Africa 11% and Western Europe 5% (UNCTAD). The Black Sea container port system consists of 11 ports with annual container traffic of about 2.6 mln. TEU (2014).

What became clear in recent decades is the growing share of container traffic in the Black Sea region (Figure 8.1), which traditionally specializes in bulks, as a heritage of the former Soviet/ socialist period. In the late 1970s, container traffic represented no more than 2.5% of the total, followed by 5.0% one decade later, 13.4% in the late 1990s, and 25.7% in 2008. Such a trend mainly occurred at the expense of bulks, with solid bulks and liquid bulks declining from 29. 0% and 53.3% in 1977 to 19.4% and 35.9% in 2008, respectively. This reflects a growing diversification of Black Sea ports, as reflected by world trends [START_REF] Ducruet | Multilayer dynamics of complex spatial networks: the case of global maritime flows (1977-2008)[END_REF] but at the same time, the direct effects of liner shipping strategies and actions performed in the region.

[Figure 8.1]

The main container lines serving the Black Sea are MSC, Maersk, Zim, Arkas, Emc; a detailed overview of which is presented in Table 8.1. The peculiarity of the Black Sea is that only few ports are called at directly by shipping lines: Constanta (Romania), Odessa and Illyichevsk (Ukraine), and since 2011 also Yuzhnyi (Ukraine) and Nororossyisk (Russia). As from 2003, the size of the container vessels entering the Black Sea grew from 2500 TEU up to 10,000 TEU at the present time. A significant restriction on vessel size in the Black Sea is the Bosporus Strait on account of its physical constraints. Thus, container shipping lines can deploy vessels of up to 10,000 TEU for the Black Sea, while vessels of up to 18,000 TEU are already calling at ports in the Mediterranean. Furthermore, shipping economics would not allow for much bigger ship sizes because of the current stagnation in demand -in 2014 and 2015 the total regional container demand dropped by 1-2% per annum.

Shipping lines call at the Black Sea basin mainly in a shared calling pattern (Table 8.2). There are only two (out of four previously existing) direct services left between the ports of the Black Sea and the Far East: the Asia-East Med Express service of Zim/OOCL and joint Ocean three/Yang Min service. The changes in the calling patterns accrued during 2015 and 2016 due to the decreased Black Sea demand and low freight rates. Other weekly services are the Ecumed service (Maersk Line, South America, annual capacity of 150,000 TEU) and a Maersk service connecting the Indies and the Middle East to the Black Sea.

[Table 8.1] [Table 8.2] These services involve line-bundling operations with calls at 3 to 4 Black Sea ports. Roughly, half of the current containers handled in the Black Sea ports are shipped using direct calls, while the other half is feedered from hub ports in the East Med such as Istanbul, Piraeus and to a lesser extent Thessaloniki, Gioia Tauro etc. The Mediterranean ports play an increasing role in the Far East-Black Sea trade. As from the mid-1990s, the Mediterranean witnessed an active development of hub-feeder container systems as well as short sea shipping networks driven by the growth of the container volumes serving the southern part of Europe [START_REF] Notteboom | Concentration and the formation of multi-port gateway regions in the European container port system: an update[END_REF].

Connectivity shifts of the Black Sea port system

In this section, we use the Lloyd's List Intelligence (LLI) database to analyze and map the traffic level and connectivity of Black Sea ports in the last 40 years or so. Such a long period allows us to grasp essential features on how the network had actually evolved across different geopolitical and technological phases, from limited containerization to the era of mega-ships. The daily movements of fully cellular containerships were aggregated each year during the month of June and transformed into global and regional port-to-port adjacency matrices. Inter-port links are defined as follows: ports are connected when they belong to the voyage of the same vessel during the 30 years of circulation. The amount of flows (traffic volume) in such a weighted, but undirected, network corresponds to the product between call frequency and vessel capacity measured in deadweight tons (DWTs). We then apply conventional graph-theoretical measures to the matrix, or graph, in order to extract its topological properties, reveal the geographic distribution of connectivity, and map the port hierarchy.

Traffic distribution and network topology

One first step toward understanding past and current dynamics affecting the Black Sea port system is to compare total vessel traffic with the topological size (nodes, links) of the regional network (Figure 8.5). Missing years directly reflect the low containerization rate of the region in the initial period as well as the political instability and transition of the early 1990s due to the collapse of the USSR in 1991 and Eastern Block dismantlement (see also Chapter 19 for a more in-depth analysis of the impact of USSR collapse on related shipping networks). After such a major shock, especially from the mid-1990s onwards, we observe a fast and continuous traffic growth despite slight fluctuations. Traffic volumes and network size grew in parallel with each other, with the gradual containerization of Black Sea ports. This evolution reflects the increasing concentration of shipping traffic among Black Sea ports (cf. Gini coefficient), but at the same time, a decreasing share of the largest ports (cf.

Herfindahl index or HHI) probably due to the polycentricism of the system and the emergence of small and medium-sized ports.

[Figure 8.5] The spread of containerization, however, tended to reinforce existing (or to create new) inequalities within the region, which are best visible at country level (Figure 8.6). After a period of minimal traffic , the main players on the Black Sea scene became Ukraine, Romania and Bulgaria, namely the Western sub-region closest to Western Europe.

In comparison, and given their relatively shorter coastline and fewer numbers of ports, Russia, Georgia and Turkey remained rather peripheral in the system, in both absolute and relative terms. These ports serve relatively narrow hinterlands and are at a certain remove from the core European markets; therefore, their traffic is limited. One exception is Russia, which rapidly and recently acquired a sizeable portion of the total Black Sea container traffic. Some explanatory factors include transit trade for the Russian Federation itself through third countries, such as Ukraine; port expansion in Russia, such as in Novorossiysk; and conjectural phenomena, such as the Sochi Olympic Games in 2012 fostering traffic growth to build and cater for the new villages. In addition, the somewhat protectionist Russian attitude in terms of trade favored domestic ports over others to serve Russian hinterlands.

[Figure 8.6] Externally, the connectivity of Black Sea ports as a whole went through a similar trend of growth (Figure 8.7,left). Despite some fluctuations, the number of links and nodes connecting the Black Sea with the rest of the world increased somewhat gradually and in parallel until the late 1990s, notwithstanding a noticeable decline in the number of ports after 1990. After 1998, a second phase was marked by rapid growth, despite some gaps in 2001-2003, 2006, and 2013. In the early 2000s, the freshly built Black Sea republics were still in a shaky economic state, namely undergoing rejuvenation after the early 1990s. The case of 2013 is better explained by the delayed impacts of the 2008-2009 global financial crisis in addition to local geopolitical conflicts leading to currency devaluation, high inflation, and less demand for containers. In terms of traffic volume and share (Figure 8.7, right), extraregional vessel tonnage increased in similar ways than connectivity, albeit with an earlier take-off, namely in the mid-1990s. Following the collapse of the USSR, many Black Sea ports in the new republics were forced to diversify their traffic and adopt new technologies (i.e. containerization) to adapt to global standards and compensate the amputation of their formerly vast hinterlands [START_REF] Thorez | La difficile mutation des transports maritimes dans la C.E.I[END_REF]. Each port gateway used to operate separately in the Soviet era, connecting the USSR with the outside world, which explains the very high share of extra-regional traffic in the total Black Sea traffic in the early period, around 90%.

For the same reasons cited above, post-Soviet ports strove to develop and multiply their external connections while increasing their mutual links. This resulted in a sharp decline of extra-regional traffic share from around 80% in the late 1990s to about 50% in 2006, the lowest value. During the last decade, however, extra-regional traffic has boomed and reached nearly 70% in 2015. As mentioned earlier, the role of Turkish ports as intermediate nodes ensuring the Black Sea's connectivity is well revealed by Turkey's rapidly growing share in total extra-regional traffic, from around 10% in the mid-1990s to around 60% in 2015.

[Figure 8.7]

The geographic distribution of Black Sea ports' maritime forelands brings complementary elements to the understanding of their global and regional connectivity (Figure 8.8). At the level of world regions or continents (left figure), Europe takes the lion's share of total extraregional traffic with 77.4% on average over the whole period. This share had been relatively stable, except for a decline in the period 2004-2011, with an average of 65.3%. Geographic proximity to Europe, in addition to the fact that Western Europe had long played a crucial role for socialist trade either for transit or real commerce, is the main explanatory factor.

The Americas as a whole never played any significant role for Black Sea container shipping, nor did Oceania (zero traffic), so that Europe is only followed by Asia and Africa. Although geographic proximity also explains, at least in part, the heavier weight of West Asia (i.e. Near East, Middle East, Indian subcontinent) compared with East Asia, the latter became more important than the first during the period 2004-2013 despite physical distance. Yet, the recent period was clearly impacted by a fall in demand (i.e. 15% decline in Black Sea-Asia trades) in 2015 and again in 2016 [START_REF] Gerden | Black Sea port volumes decline deepens[END_REF][START_REF] Ugurlu | The new Black Sea port[END_REF]. Africa as a whole, despite a relatively low traffic share (3.4% on average), maintained its share in the recent decade (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016) with a 5.6% average, probably due to increased transshipment through Egyptian ports such as Alexandria, Damietta, and Port Said. When zooming into Europe (right figure), we see that South Europe in general had always been dominant compared with North Europe, the latter reaching a peak in 1994 (28.1% of Black Sea's total European traffic) but only during the 1984-2001 period. Thus, there has been a shift of Black Searelated transshipment activities from North to South Europe over time. An important change in regional foreland distribution is the drastic shift from Southwest Europe dominance to East Med dominance. Southwest Europe accounted for 62.9% on average of the Black Sea's total European traffic in the period 1977-2004, while the East Med reached 72.9% during the rest of the period, with 81.0% in 2016. Such an inversion is directly attributable to the new competitive position and attractiveness of East Med ports for transshipment activities in the Med and the Black Sea. It is also driven by the Turkish effect, as mentioned above.

[Figure 8.8]

Multiple linkage analysis

Within any regional port system, it is fundamental to understand the evolution of port hierarchy but also the way in which shipping connectivity spatially spreads among port nodes. To achieve such a goal, we defined eight sub-periods and for each of them we calculated the sum of vessel traffic by port. In addition, we mapped a simplified shipping network made up of the two largest flows of each port in terms of traffic volume. The loss of information is thus counterbalanced by a better view of the network's backbone, with the hypothesis that the multiple linkage analysis can reveal which hubs dominate the port system and the system's degree of centralization (Figure 8.9). The size of nodes corresponds to total vessel traffic including extra-regional traffic.

[Figure 8.9] Before analyzing the results, it is important to note that over the entire period, we found no evidence of a centralization process in the Black Sea. The Gamma Index, which corresponds to the proportion of actual links in the total maximum possible number of links among connected ports, did not show any particular trend. The index oscillates around 40% over time, although from 2010 onwards the average index went down to 32-33%. This slight decrease suggests a simplification of the network in recent years, but it remains rather moderate. Such a result indicates that the Black Sea port system might be more externally than internally centralized, due to the absence of a hub-and-spokes configuration in the region. Based on the 8 periods under consideration, the constantly connected top 5 ports (i.e. Constanta, Odessa, Chornomorsk, Varna, and Mariupol) exhibit a very high and significant relationship (R² = 0.8945) between their average traffic size and the standard deviation of their traffic growth rates, thereby depicting a well-integrated region or range [START_REF] Lemarchand | Regional integration and maritime range[END_REF].

Apart from providing a clear picture of the port hierarchy, Figure 8.10 sheds more light on the evolution of the dominant shipping linkages within the Black Sea. While Constanta and Odessa are connected only externally in the first period (1977)(1978)(1979)(1980)(1981)(1982)(1983)(1984), Mariupol appears as a pivotal port for the other periods. However, Mariupol sustained its activity in the two subsequent periods, but without being connected internally to the region. North-South linkages in the west part of the region then dominate, notwithstanding the emergence of East-West transversals in subsequent periods. Constanta appears as the largest and best connected port, but without centralizing the whole network, as a number of dominant linkages connect Eastern ports, and a number of secondary nodes do not depend on Constanta for their connectivity. Geographic proximity has some visible influence on the sub-regionalization of the Black Sea, between East and West, but remains moderate. As time passes, the network becomes more and more complex and does not exhibit any particular pattern. Perhaps this relates to the absence of a single or twin-hub system internally. It could also reveal, indirectly, the growing dependence of Black Sea ports upon external Med hubs, such as in Turkey.

Conclusion

This chapter investigated the changing internal and external trends affecting the past and current evolution of the Black Sea port system and liner shipping network. Due to a low containerization rate and a peripheral situation from major container shipping routes in the 1970s-1990s, this region exhibited a rather peculiar pattern at the time, characterized by low internal connectivity, extraverted flows, and small traffic and network size. If the gradual and rapid increase of traffic volumes (especially from the mid-1990s) inevitably resulted in growing concentration around a few large ports, network analysis did not confirm the emergence of a centralized system, or hub-and-spokes configuration, within the Black Sea itself. More likely is the growing dependence of these large ports upon external transshipment hubs such as in Turkey, for instance, the latter having absorbed the lion's share of the Black Sea's external connectivity in the past decade or so. Our main findings thus suggest that the Black Sea port system does not have a clear structure, due to the absence of a hub-and-spokes configuration locally, and therefore a mismatch between port traffic volume and port centrality, except for Constanta, which took on hub functions for some time until 2008. Thus, contradictory forces characterize the region, between regional integration and network centralization, due to its polycentric structure.

Based on historical analysis and recent developments in the region we conclude that the outlook for Black Sea port connectivity does not seem set to change drastically. Given the fact that (i) the Black Sea remains a remote area; (ii) the economic situation in the region in the near future is expected to gradually grow and (iii) it is hard to estimate the future impact of the new Silk Road development on the Black Sea ports, we forecast a moderate growthstable scenario. When compared with existing literature on Black Sea ports, this chapter provides mixed evidence, such as the observed growth of direct calls vs. feeder [START_REF] Gouvernal | The divergence of regionalization: the challenges of the Mediterranean ports of Europe[END_REF], which is reflected in the gradual increase of the LSCI index but not in the other analyses. Indirectly, our results at least partially confirm the findings of [START_REF] Notteboom | Container port competition in Europe[END_REF] in relation to the fast growth of South European ports, with our chapter bringing new evidence about the regional shift from West Med to East Med as the Black Sea's main external connector. The work by [START_REF] Ducruet | The worldwide maritime network of container shipping: Spatial structure and regional dynamics[END_REF] on the global shipping network underlined the emergence of nodal regions around the ports of Constanta, Izmir, and Ambarli, the first being also considered in this chapter a major node but not yet the principal hub of the Black Sea, which remains more polycentric. Our findings also support those of Seoane et al. (2013) due to that West-East shift in external connectivity; one explanation for this in recent years being the suspension of 2 out of 4 direct Far-East-Black Sea services (2015)(2016). These elements might explain why [START_REF] Li | Centrality in global shipping network basing on worldwide shipping areas[END_REF] observed a decline in the Black Sea's global shipping centrality despite its overall traffic growth. The absence of traffic concentration internally had been already underlined by Gruchevska and [START_REF] Grushevska | An economic and institutional analysis of multi-port gateway regions in the Black Sea basin[END_REF], with the notable exception of Novorossiysk port, so our study is in line with their work, though adding new evidence from a relational (network) perspective beyond traffic volumes taken in isolation. All in all, this study shows us how important the geographical scale of analysis is when it comes to shipping flows and networks. The Black Sea in itself might not (yet) be a well-organized system; its internal dynamics cannot be understood without taking into account the centralization of its own flows through external hubs. This contribution ties in well with academic literature on the evolution of the vulnerability and hub-dependency of shipping networks [START_REF] Ducruet | Hub dependence in constrained economies: The case of North Korea[END_REF], especially when looking at the impact of both technological and geopolitical factors affecting trade and logistics.

Since 2001 ,

 2001 Black Sea ports have attracted global terminal operators. The first entrants were HHLA in Odessa port (2001) and NCC in Novorossiysk port (2002). A second wave of entrants included DPW in Constanta (2004) and NCC in Illichivsk (2005). The last big inflow of global terminal operators included APMT in Poti (2011) and a joint involvement of CMA-CGM in Odessa, together with a local terminal operator (2008). In the coming years two new green field ports in the Black Sea are expected to start operations, namely in Anaklia in Georgia and Taman port in the Russian Federation.

Figure 8 .

 8 Figure 8.2 provides an overview of container throughput in Black Sea ports. Last year's traffic decline in Black Sea ports is in sharp contrast to the strong growth witnessed by Piraeus and Turkish deep-sea ports near the Sea of Marmara. This demonstrates that shipping lines increasingly prefer a hub-feeder model in the Med to service the Black Sea area instead of direct deep-sea calls in the Black Sea. Constanta's container throughput fell sharply from 1.38 million TEU in 2008 to 0.59m TEU in 2009. 2015 witnessed a stabilization in port traffic with limited growth of up to 0.68m TEU. Early on in its development, Constanta was very much seen as the transshipment gateway for the Black Sea and registered an incidence of around 75% in 2008. However, the crisis of 2008 hit the regional economies and the shipping lines that likewise motivated the later change in their liner services in search of cost-efficient logistic solutions. A number of direct services from the Far East into the Black Sea region were cancelled, negatively affecting Constanta's transshipment volumes. As a result, by 2012 almost three-quarters of the volumes handled at the port consisted of local import and export containers, with the remaining quarter being transshipment.

Figure 8 . 1 :

 81 Figure 8.1: Ports of the Black Sea container port system

Table 1 . Top 10 container lines calling Black Sea (full containers)

 1

			TEU (2014)	share, % (2013)	share, % (2014)
	MSC	446,620	445,133	28%	28%
	MAERSK	393,080	359,568	24%	23%
	CMA CGM	193,683	170,397	12%	11%
	ZIM	166,172	193,159	10%	12%
	ARKAS	127,969	136,889	8%	9%
	EMC	66,951	62,941	4%	4%
	CSAV	61,250	52,929	4%	3%
	CSCL	58,583	59,411	4%	4%
	HL	50,201	71,481	3%	5%
	YML	42,630	32,384	3%	2%
	Total	1,607,139	1,584,292	100%	100%

Source: Ports of Ukraine

Acknowledgements

The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement n. [313847] "World Seastems".


The local determinants of interregional shipping flows

The analysis of the determinants of shipping flows includes a wide array of approaches, which is mainly the focus of transport and maritime economics as well as operations research. The majority of related studies take the firm as the core unit of analysis, trying to unravel the determinants of port selection and shipping network design from a company perspective [START_REF] Tiwari | Shippers' port and carrier selection behaviour in China: a discrete choice analysis[END_REF][START_REF] Tongzon | Port choice and freight forwarders[END_REF], using either quantitative or qualitative methodologies. While "location" is often cited as one of the numerous determinants, space in itself is considered in very basic ways, such as Euclidian or nautical distance between ports as a complementary proxy for time and cost parameters in various models [START_REF] Wilmsmeier | International Maritime Transport Costs. Market Structures and Network Configurations[END_REF]). This, for example, is the case of numerous studies using the gravity model to explain the intensity and distribution of trade flows among countries and regions of the world, but often overlooking the maritime mode [START_REF] Guerrero | Explaining international trade flows with shipping-based distances[END_REF]. Another focus on shipping flows mainly describes the topology of the network made of port nodes and their mutual linkages [START_REF] Ducruet | The mutual specialization of port regions connected by multiple commodity flows in a maritime network[END_REF], with reference to general models of networks (see [START_REF] Kaluza | The complex network of global cargo ship movements[END_REF], as a means to detect industry-specific influences such as the emergence of huband-spokes systems and global transport players in various contexts [START_REF] Mccalla | The geographical hierarchy of container shipping networks in the Caribbean basin and Mediterranean Sea[END_REF].

Overall, the local, socio-economic background of shipping flows is much neglected in contemporary literature. The role of the local economy in shaping flows has been investigated in the case of port cities and regions [START_REF] Ducruet | Regions and material flows: Investigating the regional branching and industry relatedness of port traffic in a global perspective[END_REF], but without a relational or network perspective (see also Chapters 13 and 17 on the relationships between shipping, inland accessibility, and regional development, respectively). Most of the time, scholars preferably look at the economic impacts of ports on local economies [START_REF] Ng | Port geography at the crossroads with human geography: between flows and spaces[END_REF][START_REF] Bottasso | Ports and regional development: A spatial analysis on a panel of European regions[END_REF]. This chapter thus adopts a different perspective, by raising the question on how the intensity, distribution, specialization, and topology of shipping flows is determined not only by operational but also territorial factors. Its main objective is not to underestimate the role of supply chain actors and their main requirements (i.e. cost, time, performance, reliability, etc.) but to provide a more balanced view on the influence of demand factors on the pattern of global shipping networks. The relational or network perspective with regard to territorial factors implies that not only port nodes and their interlinkages are considered, but their surrounding socio-economic environment, which is barely mentioned in related studies, except for a few where roads networks [START_REF] Tavasszy | A strategic network choice model for global container flows: Specification estimation and application[END_REF] or land use and other territorial features [START_REF] Nelson | Travel time to major cities: A global map of accessibility[END_REF] are added as complementary indicators. Strangely enough, this approach is new and necessitates a number of methodological choices in terms of database construction and variable selection.

First, a global matrix of inter-port vessel movements was constructed for the year 2008 by aggregating the daily flows of most of the world's merchant fleet, based on original shipping data provided by Lloyd's List Intelligence for the months of March, June, September, and December. Such vessels were classified under six main categories (i.e. containers, general cargo, liquid bulks, solid bulks, passengers, and vehicles), while their carrying capacity in deadweight tons (DWT) was attributed to every port of call and inter-port link, the total amount of traffic being a product of ship capacity and call frequency. Links between ports can be either direct calls through a one-stop voyage, or indirect calls with intermediary stops in between, along the whole and uninterrupted vessel voyage. In the meantime, port nodes were aggregated with each other at the level of subnational administrative regions in order to work on a broader unit of analysis (the port region). This allowed collecting supplementary data for the year 2008/2009 on socio-economic activities, population, productivity, etc. from various sources ranging from national accounts and transport ministries to broader online databases such as the OECD Territorial Database or Eurostat.

Lastly, network-specific indicators were calculated using the TULIP software on port region centrality but also link centrality, as well as other classic indicators on traffic concentration and specialization. Most of the time, regional indicators such as those on employment and regional gross product (RGDP) were transformed into location quotients and the share of national total to take into account the national and global context and also to make local economies comparable using relative rather than absolute figures (see Appendix 14.1 for a full list and description of link variables).

The main novelty of the present analysis is thus to provide a joint analysis of regional and shipping indicators at global level for no less than nearly 300 port regions situated in about 40 developed and developing countries. In addition, and as seen in the following sections, it shifts the focus from node level (port region) to link level (inter-port region), creating new variables that translate similarities or gaps in terms of socio-economic and traffic structure by port region pairs. The remainder of this chapter are organized as follows: the second section deals with port regions as nodes in a global maritime network, where we investigate the degree of affinity between regional specialization and traffic specialization, as a first step towards the understanding of the local determinants of shipping flows. The third section adopts a complementary perspective based on the concepts of homophily and assortativity in maritime networks (and more specifically in spatial networks), namely how port regions of similar or contrasted profiles connect through certain types of shipping flows. The fourth section gives more importance to the distance parameter, or friction of trades, by applying a gravity model to interregional (long-distance) shipping flows and contrasting the results with observed flows. In the conclusion we discuss the outcomes of this research and its contribution to existing regional and transport studies, while examining possible transfers in terms of decision-making support.

Traffic structure, regional specialization, and port region centrality

In this section we examine the interplay between the socio-economic and traffic variables that characterize network nodes, in our case subnational port regions. A Principal Component Analysis (PCA) was applied to two different global datasets, which mainly differ by number of employment categories; the first dataset comprises 273 regions based on three main economic sectors, while the second includes fewer regions (222) but it details seven economic sectors (agriculture, industry, construction, trade, finance, public, and other services). For each analysis, we retained only the four main load factors (or principal components), which concentrate 54.8% of total variance for the first sample with 25 variables; and 52.2% (as above) for the second sample with 28 variables. Such a result in itself suggests that the sample under study is characterized by heterogeneity; as traffic, network and local economic indicators have complex relationships and accordingly their explanation can be similarly complicated.

Yet, the analysis based on 273 port regions reveals a number of interesting trends that validate earlier works on port regions (see [START_REF] Ducruet | Regions and material flows: Investigating the regional branching and industry relatedness of port traffic in a global perspective[END_REF], for a review and empirical test based on port tonnage). As seen in Figure 14.1, the main trend (F1) is an opposition between two different types of port regions. Positive scores underline a concomitant importance of traffic volume (DWT), concentration, network centrality, geographic reach, demographic and economic size and concentration, population density, RGDP per capita, with a specialization in the tertiary sector and in container as well as vehicle flows, the most valued commodities. This is the typical profile of bigger and richer port regions with a dominant position in the maritime network and concentrating the majority of maritime flows. Thus, this profile is a combination of mass or size effect (large urban nodes) and specialization effect (high-valued cargo, economic productivity and prosperity). The opposite profile is characterized by a combined specialization in solid bulks, general cargoes in the primary sector and, to a lesser extent, in liquid bulks and passenger traffic in the secondary sector, with a slightly more pronounced unemployment rate compared with the national average. Such a profile corresponds to regions being more peripheral at national and global scale in terms of size and situation, based on more traditional or labor-intensive activities and less valued cargoes.

The second factor (F2) provides a complementary trend whereby regions specialized in the primary sector can also be very central in the network and have far-reaching spatial connectivity, especially because solid and liquid bulk traffics, unlike liner shipping, are often operated through direct origin-destination and long-distance flows. In turn, the opposite profile (positive F2 score) is specialized in more valuable traffics (general cargo, containers, vehicles), and tends to have a very crucial importance within its national territory in terms of population, productivity and traffic concentration, with a more tertiary profile, but a lower centrality in the global shipping network and higher unemployment rate. Four types of port regions can thus be defined based on the crossing of the first two components: large urban regions specialized in services and containers/vehicles (top-right quadrant); larger and longer traffic nodes with a strong centrality in the shipping network (bottom-right); primary sector regions specialized in bulk traffics (bottom-left); and general cargo regions with higher unemployment rate and industrial specialization (top-left).

Although it excludes numerous regions situated in developing countries where disaggregated data on employment categories is not available, the analysis of 222 port regions provides additional evidence on such trends. While the main oppositions are very similar to the former analysis, one can observe that the top-right quadrant remains defined by a profile of large urban regions concentrating economic wealth, population and container traffic, but also and more specifically, financial and insurance activities as well as "other services". These core economic regions are thus also the most advanced in terms of shipping technology and capital-intensive activities. The same opposition occurs with the bottom-left quadrant with port regions specialized in agricultural activities, solid and liquid bulks and passengers, but also public services and manufacturing activities to a lesser extent.

Interestingly, centrality indicators remain grouped in the bottom-right quadrant, but together with the specialization in "distributive trade, repairs, transport and accommodation" that concentrates most of the port, logistics and transport-related activities.

[Figure 14.1] A complementary analysis is proposed based on six quantiles applied to selected key indicators to reveal how regional and shipping indicators, which are standardized by the average of each indicator for the comparison between indicators, statistically fit with each other (Figure 14.2). When taking demographic population as the dependent variable, we see that most of the shipping indicators increase in relation to the number of inhabitants from the lower to the higher quantile, especially betweenness centrality that exhibits the widest gaps among quantiles. Thus, again here more populated regions tend to be more central in the network (betweenness centrality, degree centrality, clustering coefficient) and to exert longer-distance shipping linkages on average. Very similar results were obtained based on RGDP as dependent variable, with the exception of the average kilometric length of shipping linkages, which, albeit fuzzier than those based on population, still exhibit the highest value for richest regions. Overall, the same goes for population density and RGDP per capita taken as location quotients (no figures because of space constraint). This means that not only the port handling itself but its socio-economic and demographic performance regarding the rest of the host country has a clear and noticeable influence on traffic intensity, interaction range and network centrality. In turn, port regions that are more specialized, based on the Herfindahl-Hirschman Index (HHI), or where traffic structure is more concentrated around fewer cargo types, tend to be more populated and richer.

[Figure 14.2]

Maritime linkages, homophily and assortativity

This approach consisted in further understanding how maritime linkages are influenced by the socio-economic characteristics of the connected regions. However, since the unit of analysis becomes the inter-port link itself, it is difficult to conceptualize the territorial embedding of this link in two regions simultaneously. The chosen solution has been to reason in terms of homophily and assortativity, two relatively common approaches in network analysis, in particular social networks for the first and complex networks for the second. Homophily in social networks, notably those that are spatially embedded [START_REF] Wong | A spatial model for social networks[END_REF], is defined by stronger connectivity probability between nodes sharing certain common features than between contrasted nodes. Assortativity is a parent concept, with a noticeable difference that the same probability is based on the size of nodes. In other words, a network will exhibit assortative mixing when nodes of similar size (i.e. based on degree centrality) are better connected with each other than nodes of dissimilar size, in the latter case the network being more disassortative [START_REF] Newman | Assortative mixing in networks[END_REF]. Empirical studies showed, for instance, that airline networks get more disassortative as the network grows, due to deregulation fostering the emergence of hub-and-spokes configurations [START_REF] Barrat | The effects of spatial constraints on the evolution of weighted complex networks[END_REF].

In this chapter, both concepts target a better understanding of maritime flows in relation to local socio-economic development.

While the empirical testing of such ideas in a maritime context does not yet exist, it faces a number of conceptual and methodological issues. At conceptual level, it is relatively straightforward to test assortativity since maritime transport, in particular container shipping, adopted similar configurations to airlines in the mid-1990s, namely the establishment of transshipment hub ports at strategic locations. Yet, the whole shipping industry does not follow this network design since it is also based on coastal and short-sea shipping, direct origin-destination flows, such as for bulk shipping, as well as itineraries, such as for cruise shipping, which do not depend on hubs. In terms of homophily, the extent to which similar port regions will be (a) more strongly connected and (b) connected by specific types of flows is not an easy question. In particular, maritime transport had increasingly become one element only of wider value chains [START_REF] Robinson | Ports as elements in value-driven chain systems: the new paradigm[END_REF], which means that it does not constitute a complete system on its own, if land transport and logistics between the true origins and destinations of flows are not included. Maritime transport is thus a segment connecting regions scattered across the world at different phases of value creation, from raw materials to manufactured goods. This leads us to question the relevance of testing homophily on the sole basis of maritime flows (given that many port regions are transit nodes, not the production or consumption markets, e.g. [START_REF] Ducruet | Regions and material flows: Investigating the regional branching and industry relatedness of port traffic in a global perspective[END_REF]. In addition, our maritime network based on vessel movements is an artefact from which it is difficult to assess whether port regions belonging to the same shipping route truly trade with each other.

Despite such issues, it has been possible to measure in relatively simple ways how homophily and assortativity determine (or not) the topological and spatial distribution of the global maritime network. First, each interregional shipping link is characterized by a number of variables describing its own profile, such as traffic volume, geographic length (orthodromic distance), number of handled commodity types and HHI for their capacities and various centralities (betweenness, clustering, strength). Second, the territorial embedding of each link is measured by socio-economic differentials among region pairs. One group of such variables is a ratio (i.e. gap index, 1 being completely similar size and 0being very dissimilar size) between the minimum and the maximum value (i.e. population, RGDP), and another looks at the correlation (Pearson) between the employment structure of the connected regions. Such gaps and correlations thus become attributes of the link itself and are compared with traffic/network variables. Two complementary analyses are proposed, namely a multivariate analysis of combined traffic and socio-economic indicators on links, and descriptive statistics based on quantiles.

The Principal Components Analysis (PCA) is a first approach to test the possibility for global maritime flows to exhibit homophily and assortativity (Figure 14.3). In other words, are similar port regions more connected with each other than dissimilar port regions, and through similar or dissimilar types of shipping flows? Given the vast number of links under scrutiny (22,578), our PCA results provided a large number of principal components with an eigenvalue over 1, i.e. fourteen components for all 41 variables included (75.4% of cumulated variance), and eight components for 22 variables excluding certain redundant variables (69.5% of cumulated variance). Our discussion on such results will, however, mainly focus on the first four components, reaching about half of the total cumulated variance in both cases (42.1% for all variables and 47.1% for selected variables), to highlight the main groupings and oppositions at stake. In terms of traffic characteristics, we observed that shipping links carrying bigger traffic volumes (DWTs) are generally more diversified (number of cargo types), with a tendency to specialize in containers, but shorter in terms of kilometric length. This is in line with former works showing that most of the world's container traffic concentrates over links of 500 kilometers or less [START_REF] Ducruet | The worldwide maritime network of container shipping: Spatial structure and regional dynamics[END_REF], while container flows are specific in a sense that they present the greatest overlap with other traffic types in the global network, while most diversified links are also shorter [START_REF] Ducruet | Network diversity and maritime flows[END_REF]. This explains why such bigger links are less international on average (i.e. more domestic). When it comes to region pairs in terms of traffic, there is a clear assortativity of the network since bigger links preferentially connect port regions of similar traffic weight, commodity diversity, network position (i.e. degree centrality, clustering coefficient on links) and geographic reach. In turn, links of smaller traffic volumes tend to connect more distant regions, to be more specialized (higher HHI), and to have a more crucial position in the network (strength links, betweenness centrality links), with a pronounced specialization in passengers, general cargo, and solid bulk. In terms of homophily when focusing on the demographic and socio-economic similarity of connected regions, bigger links tend to concentrate in more productive regions (higher RGDP) as well as between regions of similar demographic size (higher gap index) and economic structure to a lesser extent. These results are meaningful in the sense that they empirically demonstrate that the spatial distribution of the global shipping network is far from being randomly organized. On the contrary, spatial structures greatly influence the intensity and profile of shipping flows, notwithstanding noticeable oppositions between bigger and smaller flows. Based on demographic (population) and economic productivity (RGDP) gaps among region pairs, it appeared that connected regions of similar weight share a similar network centrality, as mentioned above, while being connected through less crucial links (i.e. lower betweenness centrality links and strength index), and at the same time more densely connected with their neighbors (clustering coefficient). The gap of hub dependence (vulnerability) is narrower between regions of similar economic weight (RGDP), which is less apparent from a demographic perspective. This means that the economic productivity of connected regions is perhaps a better indicator than population to highlight similarities in network connectivity. Finally, results were compared based on the similarity (Pearson correlation coefficient) between the respective employment structure of port regions, for 3 and 7 economic sectors. In the case of 3 sectors, connected regions with a similar employment structure (higher quantiles) tend to have a wider gap (lower gap index) in most centrality indicators (i.e. betweenness centrality gap, degree centrality gap, betweenness centrality links) and to be connected through more diversified traffic linkages (i.e. lower HHI).

This means that economic differentiation, as a potential complementarity between connected regions, fosters similarity in terms of network performance and specialization in terms of commodity structure. Such results underline that socio-economic homophily is associated with centrality disassortativity, and vice-versa. When it comes to 7 sectors, results are similar only for the betweenness centrality links, since the opposite is shown for all other gaps and link characteristics. One likely reason is the restricted availability of 7sector data for regions in developing countries: connected regions in more developed (OECD) countries show a slight tendency to share both homophily and assortativity. In other words, such connected regions are more similar and less complementary to each other. In addition, more specialized links (i.e. higher HHI) connect regions with wider socio-economic and demographic gaps (i.e. lower gap index). Thus, more diversified links (i.e. lower HHI) are found between regions of similar size and productivity (higher gap index). These trends confirm previous research showing that the most diversified maritime links actually concentrated between port cities with similar total port traffic size [START_REF] Ducruet | Network diversity and maritime flows[END_REF]. In this analysis, diversified links are also seen to concentrate between demographically and economically similar regions.

[Figure 14.4]

Gravity model

Maritime networks are driven by forces related to connectivity between ports (i.e. the nodes). As noted by [START_REF] Lam | Dynamics of liner shipping network and port connectivity in supply chain systems: analysis on East Asia[END_REF], shipping networks interconnect different kinds of ports, while the role of these ports within the network also affects their competitive position within global supply chains. On the other hand, maritime network development prioritizes main nodes, creating a hierarchy that influences port competitiveness and its role in respect of the main shipping routes, and additionally serving major hinterlands (e.g. [START_REF] Ducruet | Centrality and vulnerability in liner shipping networks: revisiting the Northeast Asian port hierarchy[END_REF]. This scenario is connected with the possibility of studying maritime flows as driven by the port traffic volume, in which main distribution nodes (i.e. hubs) can have an advantage in comparison with minor nodes (i.e. minor ports, or satellites on the network). Moreover, the presence of frictions (e.g. transport and logistics costs) that differ in relation to both "distance" and "specific route" issues is also a characteristic of the current maritime network.

The spatial interaction -within a given network-between nodes that partially compete through their traffic volume and different costs depending on network design naturally calls for the use of a spatial interaction model, such as gravity.

Gravity models are a specific kind of spatial interaction analysis in which the flow between two "nodes" -and the related network-carries "hidden costs" (i.e. frictions) that affect flow distribution with respect to an ideal or optimal scenario. Gravity models are often used in regional economics (e.g. Karemada et al., 2010;[START_REF] Burger | On the specification of the gravity model of trade: zeros, excess zeros and zero-inflated estimation[END_REF] and related disciplines (e.g. [START_REF] Khadarooa | The role of transport infrastructure in international tourism development: A gravity model approach[END_REF], with several applications in transportation science (e.g. [START_REF] Simini | A universal model for mobility and migration patterns[END_REF][START_REF] Anas | Discrete choice theory, information theory and the multinomial logit and gravity models[END_REF]. Concerning maritime studies, gravity models were applied to hinterland flows connecting specific ports (e.g. [START_REF] Ferrari | Measuring the quality of port hinterland accessibility: The Ligurian case[END_REF][START_REF] Guerrero | Deep-sea hinterlands: Some empirical evidence of the spatial impact of containerization[END_REF].

As suggested by [START_REF] Haynes | Gravity and Spatial Interaction Models[END_REF], a general gravity model appears as:

𝑇𝑖𝑗=𝐴𝑖 𝑂𝑖 𝐷𝑗 𝐵𝑗 (𝑑𝑖𝑗) (1)
in which T equals the "interactions (i.e., maritime traffic volume)" between regions i and j; O and D are the "production" and "destination" flows; while A and B are "balancing constraints" for the origin or the destination, respectively. If A and B are used simultaneously, the gravity model is called "doubly" constrained, while if only one of them is used the model can be "production" (in the case of A) or "destination" (in the case of B) constrained.

In general terms, where:𝐴 𝑖 𝑗 𝐵 𝑗 𝐷 𝑗 𝑓 𝑑 𝑖𝑗 ∑ 1

(2)

𝐵 𝑗 𝑖 𝐴 𝑖 𝑂 𝑖 𝑓 𝑑 𝑖𝑗 ∑ 1 (3)
A and B should be solved interactively in order to identify the constraints related to the inand out-flows of origin and destination. (𝑑𝑖𝑗) represents the effect of the physical distance between regions in the spatial interaction. Often f(𝑑𝑖𝑗)=𝑑 i𝑗 β where β shows the sensitivity to the distance of the studied variables and the effect of potential frictions. In our case, Euclidian distance has been used between port regions attracting different kind of cargoes.

This choice is linked to the main differences in the organization of the shipping services that is actually affecting the shipping distance (e.g. transshipment, not direct call services).

In order to evaluate spatial interaction between the maritime regions, SIMODEL software has been used. This software allows the estimation of the gravity model, comparing the actual T ij and the predicted T' ij . As said, the application of either A or B (or both) changes the model; in particular when both are applied traffic between each node is kept constant, whereas if only one is applied, the traffic generated by each origin remains constant both in the observed (T ij ) and the predicted (T' ij ) matrix, while the traffic attracted by each destination is redistributed among them.

In order to solve the model, a hypothesis on the f(d ij ) is necessary; in accordance with main literature (e.g. [START_REF] Ferrari | Measuring the quality of port hinterland accessibility: The Ligurian case[END_REF], we used a power function equal to (𝑑 i𝑗 β ). In such a model, the parameter β represents the "probability" for the estimating values to be equal to the real value, therefore, representing the relation between the distance and the "generalised costs" (i.e. frictions that affect the model) that impact in the development of the network.

Since distance should have a decay effect, the parameter β is expected to be negative.

Moreover, β<-1 means costs have an effect more than proportional in respect to distance, while if -1<β<0 costs are less proportional than distance. When β is equal to zero there are no frictions and the distribution has no connections with the distance. Another interesting outcome is the standard deviation (e) that represents a goodness of fit in the model.

Moreover, the standard deviation also provides information on the redistribution among the regions of total traffic and containers only (TEU).

The analysis targets the NUTS2 regions belonging to our database, representing the maritime network. The main results provide a comparison of NUTS2 interactions along three main shipping routes (e.g. Transpacific, Transatlantic, and Far East-Europe) for the discussion on long-distance trade excluding short-sea and coastal shipping. In fact, the gravity model only takes into account the regions belonging to the above listed route, as representative examples of the differences within the current maritime network. Table 14.1 shows the results of the analyses for the production model and the doubly-constrained model.

[Table 14.1]

The analysis underlines how "frictions" act differently among the main trade routes while distance impacts on the distribution of commercial flows. Moreover, the standard deviation (e) -that is a proxy for the degree of redistribution-is higher in the transatlantic matrix in comparison with the other routes, demonstrating a difference between predicted and observed values. In particular, higher values of both β and e for the Europe-America routes can be explained by a different traffic organization (e.g. fewer main nodes and different use of transshipment) in respect to the other two main routes, or including Asian trade.

Moreover, the high value of the standard deviation highlights a great difference among predicted and observed values. This issue underlines how some regions are able to attract more than what they might be, generating agglomeration effects at port regions. In general Asian and American regions seem to have a better distribution pattern than European ones, Figure 14.5, for instance, compares the results related to the trade routes of American and European regions. In the figure, the axes represent the difference between the expected and real values of the studied routes per region (i.e. the European graph represents the differences characterising the European-Asia routes and European-Americas route). The fact that the majority of the regions are more or less concentrated around the origin means that expected and actual values do not differ too much (even if differences can be underlined).

Thus, outliers in both graphs have specific explanations depending on the quarter to which they belong. For instance, ES70 (i.e. Canary Islands) and ES61 (i.e. Andalusia), representing two outliers located on the first quarter of "Europe", demonstrate an unexpected flow from/to these regions in both the EU-Asia and transatlantic routes. Similarly, there is an imbalance situation in the ES52 (i.e. the Community of Valencia) outlier belonging to the fourth quarter of the same graph, but only for the EU-Asia route, while it has a negative difference in the transatlantic route. These outliers are often related to big hub ports (e.g.

Algeciras, Valencia) that provoke a distortion to the "estimated" connections, deviating flows that would otherwise be "naturally" and more directly reaching other regions. Similar cases occur in the "Americas" graph, with regions hosting port hubs (e.g. California, South Carolina, and Florida) that became regional pivotal nodes. Interestingly, among the outliers there are also some few regions that register either heavy underperforming (e.g. Balearic Islands) or over-performing values (e.g. Alaska), not for structural reasons but for demographical and market specialisation (e.g. bulk and cruise) characteristics. Nevertheless, Figure 14.5 demonstrates how the shipping network reflects not only distance effects but also organisational effects (e.g. transshipment) that promote both scale and scope economies to overcome the distance factor.

[Insert Figure 14.5 about here]

Conclusion

This chapter underlines how contemporary maritime flows remain very much linked to the economic structure of the regions that they help to interconnect, contributing to existing literature on the impact of the local economy of port regions and maritime flows, an area so far insufficiently researched. Preliminary findings from current research underline how it is possible through the study of maritime networks to differentiate regions according to their role within the network and with their economic characteristics. The PCA -and related analyses-showed how regional centrality in respect to specific traffic flows (e.g. liquid bulk, container, general cargo) depends on the regional environment (e.g. population, sectoral specialization, economic productivity), demonstrating how the international transport network is still shaped by local strategies and policies. Moreover, links carrying bigger traffic volumes are generally more diversified (in terms of lower HHI), with a tendency to specialize in containerized cargo, but shorter in terms of kilometric length. On the other hand, the gravity model showed that the main commercial routes are differently affected by distanceand transport-related costs. These findings showed how the different organization on main routes is still affecting main trade volumes among different world regions. The differences between real and expected values estimated by the gravity model also underline an unbalanced situation among the flows that is not fully explained by distance and is principally due to specialization and agglomeration effects (especially in the container business) in the port regions and in their neighboring regions too.

Moreover, even if the assumption related to distance (i.e. Euclidian distance) seems quite reasonable considering the differences between various maritime transport sectors, further research could improve the overall findings of the gravity model. In fact, nautical distance is only partially representative for all those kinds of traffic that do not use direct calls (e.g. containers) or, generally speaking, do not load/unload completely at every port of call (e.g. general cargo, passengers). As a matter of fact, the complexity of the freight distributionand the related network-leads to a low representation of the connectivity using simple nautical distance within the region and for this reason Euclidian distance has been chosen.

Nevertheless, further investigations could try to overcome this issue, differentiating the distance used for different kinds of services (e.g. container, bulk).

Thus, even if further research needs to be done, the findings of this chapter shed new light on the understanding of maritime network development and characteristics. Eventually, as potential extensions of the current research, one could further differentiate the effects among different traffic categories in order to identify specific patterns that characterise the different shipping markets and their network development.

[

  Insert Figure 14.3 about here] A complementary analysis consisted in classifying links according to six quantiles to check the mutual influences between territorial and logistical elements in the network (Figure 14.4, standardized by the average of each indicator for the comparison between indicators).

Figure 14 . 1 :Figure

 141 Figure 14.1: Principal Component Analysis of 273 port regions (top) and 222 port regions (bottom)

Table 17 .1: Gravity model results

 17

Acknowledgements

The authors would like to thank Ali El Hosni for his help with the database construction. The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement n. [313847] "World Seastems".