Centrality in global shipping network basing on worldwideshipping areas

Zhenfu Li • Mengqiao Xu • Yanlei Shi

� Springer Science+Business Media Dordrecht 2014

Abstract Port and maritime studies dealing with

containerization have observed close correlation

between global liner shipping and world trade, and

centrality in global shipping network (GSN) may

change as the situation of world economy and trade

changes. Meanwhile, the influence that shipping areas

have on the GSN is much greater than any single port,

and connections between these shipping areas affect

the structure of the GSN. This paper wishes to

understand the dynamic changing of the centrality in

the GSN during the period from 2001 to 2012, which

sees both booms and depressions in world economy

and liner shipping. The paper divides global shipping

into 25 areas from geographical perspective, and

presents an analysis of each shipping area’s position in

the GSN through indicators of centrality. The results

reveal that to a large extent Europe is always in the

center of the GSN from 2001 to 2012, but its central

position is declining. Additionally, mapping the

centrality distribution of those shipping areas in the

latest year confirms their current positions in the GSN.

Keywords Shipping area � Liner shipping �Global shipping network � Network analysis �Centrality

Introduction

Global shipping is among the most important back-

bones of world trade and commodity train, which can

be explained by the fact that about 90 % of world trade

volumes are transported by shipping. Thus shipping, a

derived demand of world trade, has become a useful

looking glass for analyzing the global economy.

Global shipping network (GSN), mainly comprising

of vessels and ports as well as sea cargoes, has become

a reality (Rodrigue and Notteboom 2010). To better

serve the global trade, GSN is developing in a dynamic

adjustment way, e.g. container ship maximization,

hub-spoke shipping lines.

With a large number of ports and complicated

distribution of shipping lines, GSN, to some degree,

presents some characteristics of a complex network.

Definition of complex networks in geography can be

easily understood through the example of transport

networks. Routes of transport networks (Sen et al.

2003; Ferber et al. 2005) lay on the two-dimensional

surface of the globe, thus confined by geography. To

be specific, for the global shipping system, viewing the

ports as nodes and shipping routes or shipping

connections as links, the global shipping system is

actually a complex network (Tian et al. 2007; Mu and

Chen 2009). Many researchers had attempted to study

the complex nature of GSN basing on the complex

network theory.

According to the different network coverage, the

current GSN studies can be divided into three

Z. Li � M. Xu (&) � Y. Shi

Dalian Maritime University, Dalian, Liaoning, China

e-mail: stephanie1996@sina.com

123

GeoJournal

DOI 10.1007/s10708-014-9524-3

categories. That is, studies on the intra-area transpor-

tation of a specific shipping area (Robert et al. 2005;

Xu et al. 2007; Ducruet et al. 2010a, b), studies on the

shipping network constructed by several most power-

ful shipping companies (Tian et al. 2007; Wu et al.

2008; Mu and Chen 2009) which are of significant

importance to the global shipping industry, such as

Maersk Line, and studies interested in the GSN

constructed by container vessel’s moving paths world-

wide (Hu and Zhu 2009; Pablo et al. 2010; Ducruet

and Notteboom 2012). A common finding in most of

those studies is the existence of central ports (or nodes)

in the GSN and its centrality is evolving over time. For

example, Ducruet and Notteboom (2012) argued that,

Singapore was the most central port both in 1996 and

2006 by means of betweenness centrality measures,

while Pablo et al. (2010) argued that, Panama canal,

Suez canal, Shanghai port and Singapore port were the

top four central nodes in 2007. While the evolving

nature of the centrality in the GSN is well agreed in

current studies, little has been done investigating its

evolving process and rule in geography based on a

consecutive study period which is over a decade.

Meanwhile, most current studies regard ports as nodes

of the shipping network and shipping links between

them as edges, but studies focusing on the centrality in

the GSN from a perspective of shipping areas is scarce.

Due to the fact that the global container deployment of

a shipping company mainly depends on the trade

volumes among different shipping areas, however,

global shipping tends to think highly of the centrality

of shipping areas in the GSN and the connections

between them.

Two main questions addressed in this paper: Firstly,

what is the dynamic evolving process of the centrality

in the GSN from the perspective of both regional and

global level since the 21st century began? Secondly,

could it bring something new to the centrality studies

in the GSN from the perspective of shipping areas

rather than ports? To answer the stated questions, the

remainder of this paper is organized as follows:

In the data and method section, we confirms the

definition of shipping areas and its division in the

world, and presents the container deployment con-

nections among them from the period 2001 to 2012,

while introducing the centrality measures for mea-

suring the centrality of these shipping areas. In the

results sections, analysis on the changing topological

structure of the GSN as a whole by using some

global level indices and the results on the changing

centrality of all shipping areas from the period 2001

to 2012 was presented, together with some analysis

on the distribution of the centrality in the GSN and

geographic maps of the world showing centrality

scores of all shipping areas for the year 2012. In the

discussion section, we provide further analysis on

the dominance changing of the most central ship-

ping area in the GSN. The paper ends with a

discussion of the research outcomes for further

centrality studies on the GSN.

Data and method

So far, there has not been an agreed division of world

shipping areas. Meanwhile, service areas and defini-

tions of shipping routes of different shipping compa-

nies are different, as well as the division of shipping

areas. Physical geography is one of the original and

significant factors that lead to the emergence of the

GSN and its structural development, and due to the

very fact that cargo vessels travels under restrictions of

the distribution of costal line, sea and ocean in the

Table 1 Shipping areas in the world

Number Shipping area Number Shipping area

1 Australasia 14 North America Gulf

Coast

2 Black Sea 15 North America

West Coast

3 Caribbean 16 North Atlantic

4 Central America 17 North/South Pacific

5 East Africa 18 Red Sea

6 Europe 19 Baltic

7 Far East 20 South America East

Coast

8 Indian Ocean 21 South America

North Coast

9 Indian

subcontinent

22 South America

West Coast

10 Mediterranean 23 Southern Africa

di11 Mid East 24 St Lawrence

Seaway

12 North Africa 25 West Africa

13 North America

East Coast

Own elaboration based on CI-Online data

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world, it is reasonable to divide shipping areas in the

world from the geographic perspective.

The concept of shipping area in this paper belongs

to maritime facade, including coast areas and major

seas and oceans in global shipping. First, coast areas

comprising of local ports are the starting and destina-

tion points of shipping activities, such as North

America East Coast and North America West Coast.

Secondly, oceans and some crucial seas that serve the

global container shipping and making the cargo

shipping convenient among some shipping areas, are

specifically important to understand the geographic

distribution of container shipping routes in the world

and the changing centrality in the GSN. For example,

Red sea and Baltic sea areas. This is not difficult to

understand if we refer to some meaningful studies on

the network structure of global shipping and its

centrality which define ports as the nodes, while Suez

canal and Panama canal are also included for better

understanding (Pablo et al. 2010; Ducruet and Notte-

boom 2012). Therefore, 25 shipping areas were taken

into consideration in this paper (Table 1), together

with a geographic map of these shipping areas

(Fig. 1).

Data source and processing

An analysis of centrality in GSN which regards

shipping areas as nodes and shipping connections

between them as links requires knowledge of shipping

companies’ container deployment among world ship-

ping areas, or ships’ moving trajectories in the world.

Our study is based on Containerisation International,1

a database providing main carriers’ (the top 100 in

TEU capacity) container deployment statistics among

world shipping areas and updating monthly. Each year

from the period 2001 to 2012, the combined TEU

capacity of the top 100 container carriers is more than

96 % of total capacity in the world, for example, 97 %

in 2012.2 Meanwhile, Limitation of the database is that

the statistics is based on the ships’ capacity in TEU

rather than the real container cargo volume, with the

latter reflecting global trade structure more accurately.

Basing on shipping practices and shipping sched-

ules of main container carriers, CI-Online divides

global shipping into 58 shipping areas, and provides

the container deployment statistics among these

shipping areas. Among them, there are some shipping

areas mainly focusing on intra-area shipping without

any connections with others. Such as some long shore

transport areas like North America East Coastal

shipping, some intra shipping areas like Intra Medi-

terranean. However, the purpose of this paper is to

investigate centrality in the GSN, which is more

concerned with the connections among different

shipping areas, and the isolated shipping areas can

hardly occupy a central place in the GSN. Therefore,

without those isolated shipping areas, 25 interdepen-

dent shipping areas are considered in this study.

To measure the centrality of each shipping area and

the GSN as a whole, we construct topology graphs of

Fig. 1 Shipping areas in the

world. Source own

elaboration based on

Mapinfo software. Note for

clearness, oceans and seas

are represented by small

solid circles

1 http://www.ci-online.co.uk/.2 Alphaliner. http://www.alphaliner.com/.

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the GSN in each year from the period 2001 to 2012.

Figure 2 shows the topology graph of 2012, where the

existence of an edge between two shipping areas

depends on the container deployment between them.

In this paper, two shipping areas are also connected if

they belong to the same liner service or circulation,

although they are not directly connected.

To illustrate how this topology graph is made, let us

take a direct liner circulation from Far East to Europe

of COSCON as an example. Sequential ports of call in

this circulation are TAO (Qingdao), SHA (Shanghai),

NGB (Ningbo), YTN (Yantian), HKG (Hong Kong),

NSH (Nhava Sheva), SIN (Singapore), SZC (Szcze-

cin), PIR (Piraeus), SPE (La Spezia), GOA (Genova),

BCN (Barcelona), VLC (Valencia), PIR (Piraeus),

SZC, SIN, HKG and TAO, and the vessel name is

HANJIN YANTIAN, with a TEU capacity of

7471TEU. Where, TAO, SHA, NGB, YTN, HKG

and SIN belong to Far East, NSH and GOA to India

subcontinent, and SZC, PIR, SPE, BCN and VLC to

Europe, thus making shipping areas of Far East, Indian

Subcontinent, Indian Ocean, Red Sea, Mediterranean

and Europe connected with each other. The reason

why Indian Ocean, Red Sea and Mediterranean are

included in the circulation is because container

shipping among Far East, India subcontinent and

Europe are connected through them. Then, links

among all shipping areas in the GSN in each year

are confirmed by combining all direct liner circula-

tions, thus the binary graph of GSN is achieved (e.g.

Fig. 2).

Methods

Because this paper wishes to describe the position of

every shipping area in the GSN and their dynamic

changes from the period 2001 to 2012, it needs to

calculate each shipping area’s centrality value in each

year through some proper centrality measurements.

For one thing, the resulting topology graphs of inter

shipping area links in each year (e.g. Fig. 2) can be

analyzed by usual network centrality measures. For

another, with similar research purposes of this paper,

many current studies on GSN investigating the

position of sea ports and the overall structure of the

GSN have approved the usefulness of degree and

betweenness centrality measures (Pablo et al. 2010;

Ducruet et al. 2012; Ducruet and NOTTEBOOM

2012). As a result, degree, betweenness and closeness

measures are applied in this paper and the detailed

explanations are as follows.

The degree of a point k, denoted by CD(pk), is the

number of edges directly connected to it, representing

its connectivity ability in the network. For undirected

networks it can be computed as

Australasia

Black Sea

CaribbeanCentral America

East Africa

Europe

Far East

Indian Ocean

Indian subcontinent

Mediterranean

Mid East

North Africa

North America East Coast

North America Gulf Coast

North America West Coast

North Atlantic

North/South Pacific

Red Sea

Baltic

South America East Coast

South America North Coast

South America West Coast

Southern Africa

St Lawrence Seaway

West Africa

Fig. 2 Topology graph of the GSN in 2012. Source own elaboration based on CI-Online data and Ucinet software

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CD pkð Þ ¼Xn

i¼1

a pi; pkð Þ ð1Þ

where n is the number of points in the network,

a(pi,pk) = 1 if and only if pi and pk are directly

connected, 0 otherwise. In the case of GSN, the degree

of a shipping area can directly describe its position in

the GSN from a local perspective. That is, the higher

degree value of a shipping area is, the more central

position it lies in the GSN.

Betweenness centrality of point k is based on the

frequency with which the point falls between pairs of

other points on the shortest paths connecting them.

Betweenness is useful as an index of the potential

ability of controlling the communication in a network.

The betweenness centrality of point k (CE(pk)) is

defined as:

CE ¼Xn

i

Xn

j

bij pkð Þ; i 6¼ j 6¼ k; i\j ð2Þ

where bij(pk) is the probability that point pk falls on a

randomly selected shortest path linking pi with pj,

which is defined as:

bij pkð Þ ¼gij pkð Þ

gij

ð3Þ

where gij and gij(pk) are the number of shortest paths

linking pi and pj, and those contain pk, respectively. In

the case of the GSN in this paper, the betweenness of a

shipping area is sum of all possible shortest paths of

the graph passing through it, and can be interpreted as

a level of intermediacy or in-betweenness that con-

necting shipping areas within the networks from a

global perspective.

Closeness centrality of a point, denoted by CC(pk),

is inversely proportion to the sum of distances from

this point to others within the network, and it can

reflect its communication efficiency and represents its

independence in the network. It is defined as:

CC pkð Þ ¼1Pn

i¼1 d pi; pkð Þ ð4Þ

where d(pi,pj) is the number of edges in the shortest

path linking pi and pk. In the case of the GSN, the

closeness of a shipping area is the reciprocal of the

sum of distances (number of edges in the shortest path)

between other shipping areas and it. This measure can

reflect the reachability of a shipping area and can be

interpreted as a level of convenience that sea cargo be

transported between this shipping area and the others

in the GSN.

It has been proved that the central point in a star or a

wheel has the maximum degree in a binary graph

(Freeman 1979), betweenness and closeness centrality,

which are n - 1, (n2 - 3n ? 2)/2 and 1n�1

respec-

tively. Generally, these three index are normalized as

C0D pkð Þ ¼

Pn

i¼1a pi;pkð Þ

n�1, C

0B pkð Þ ¼ 2CB pkð Þ

n2�3nþ2and C0C pkð Þ ¼

n�1Pn

i¼1d pi;pkð Þ

, ranging from 0 to 1. A star or wheel, of any

size will have a center point with C0D pkð Þ ¼C0B pkð Þ ¼ C0C pkð Þ ¼ 1. Moreover, normalized indica-

tors make it easier and more reasonable to analyze

different centrality values horizontally and vertically,

such as comparing the degree, betweenness and

closeness centrality of a specific shipping area in a

specific year, and understanding the centrality chang-

ing dynamics for all shipping areas from the period

2001 to 2012.

Results

Topological structure changing of the GSN

In the early 21st century, global liner shipping had

went through a boom time during the period from 2004

to 2007 and also a depression since the end of 2008,

causing the topological structure changing of the GSN

(Table 2).

First, from a perspective of connections among

shipping areas, it is confirmed that GSN is a small-

world network with short average path length and

high degree of clustering, which is similar to current

GSN studies regarding ports as the nodes (Ducruet

and Zaidi 2012). And after more than a decade of

development, the GSN has an even higher average

clustering coefficient in 2012 than 2001, which are

0.795 and 0.741 respectively. Second, from a

perspective of network efficiency (diameter and

average distance), the GSN has two main develop-

ing stages from 2001 to 2012. During the period

2004 to 2007, more direct services of liner shipping

among world shipping areas are available than

before which is resulted from the economic boom

in the world, thus the GSN achieves higher trans-

portation efficiency. However, the efficiency in GSN

has become lower since the global economy

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depression in the end of 2008. To reduce economic

loss caused by insufficient container cargoes in the

world, shipping companies reallocated their con-

tainer capacity in the world, resulting in container

shipping between a few shipping areas needs more

intermediary shipping areas than before. Third, from

a perspective of network complexity (Beta), com-

pared with 2001, the number of links in the GSN

increases slightly in 2012.

Centrality of global shipping areas

The centrality of shipping area in the GSN can be

approached at the local and global levels. Degree

centrality is a local level measure counting for each

shipping area the number of connections to others.

Betweenness centrality is a global level measure

summing for each shipping area the number of its

positions on the shortest possible paths within the

entire network. Closeness centrality is also a global

level measure inversely proportional to the distance

of a shipping area to all others in the GSN. Degree

centrality is a measure of connectivity, while

betweenness and closeness centrality can be

regarded as a measure of dependency and accessi-

bility, respectively. It is because of this common

view on the interpretation of centrality that they are

considered the most appropriate measures to repre-

sent the position of all shipping areas in the GSN.

The hypothesis is that central shipping area will

have a high degree centrality and betweenness

centrality as well as closeness centrality, due to

their role as local center, bridges and hubs in the

GSN. Thus, make it relatively easy to understand

the dynamic evolving process of the centrality in

the GSN.

Degree centrality of global shipping areas

Shipping areas with highly developed maritime trade,

are more likely to start business with more other

shipping areas and establish direct liner shipping with

them. As a result, these shipping areas become the

most active ones in the GSN, and possess higher

degree centrality. An interesting finding of this paper

is that the relationship between shipping areas’ rank

and their value of relative degree centrality C0D is

linear in each year (Fig. 3), which shows the degree

centrality values of world shipping areas are almost

evenly distributed on the interval from 0 to 1 in each

year during the period 2001 to 2012. And there also

exits a gap in the rank-size distribution of degree

centrality in each year, which often lies between the

top three or four values and the rest, thus dividing the

shipping areas into two layers according to this.

The two-layer distribution of global shipping areas’

C0D values can be further understood when looking at

the changes in the ranking of all the 25 shipping areas

Fig. 3 Rank-size distribution of degree centrality. Note for the

sake of clearness, we only show the distribution for the year

2001 and 2012

Table 2 Topological properties changing of the GSN

Diameter Average

distance

Average clustering

coefficient

Beta Diameter Average

distance

Average clustering

coefficient

Beta

2001 3 1.58 0.741 5.2 2007 2 1.557 0.769 5.32

2002 2 1.537 0.770 5.56 2008 3 1.563 0.772 5.4

2003 3 1.63 0.761 4.92 2009 3 1.563 0.777 5.4

2004 2 1.557 0.767 5.32 2010 3 1.61 0.746 4.8

2005 2 1.573 0.759 5.12 2011 3 1.583 0.791 5.24

2006 2 1.573 0.776 5.12 2012 3 1.58 0.795 5.28

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from 2001 to 2012 (Fig. 4). For one thing, ranking of

the top three shipping areas is relatively stable, which

are Europe, Mediterranean and Far East (black lines in

Fig. 4). Therefore, combined with the results showed

by Fig. 3, Europe, Mediterranean and Far East are

regarded as the first layer and the others as the second.

For another, ranking of the last five shipping areas in

the second layer is generally stable, and they are North

Africa, St Lawrence Seaway, Black Sea, North

Atlantic and Baltic Ocean (purple lines in Fig. 4).

While others in this layer are of much fluctuation

(green, orange and blue lines in Fig. 4), such as

shipping areas with rising C0D values like Southern

Africa and South America North Coast, and those with

declining C0D values like North America West Coast

and Australasia.

Take a closer look at the most central shipping areas

lying in the first layer (Fig. 5). First, Europe and Far

East as well as Mediterranean (except for the year

2010) are always in the first layer from 2001 to 2012,

and also the North America East Coast before 2008.

Therein to, Europe has maintained the highest C0Dvalue ranging from 0.917 to 1. Second, the C0D value of

Mediterranean declined dramatically in 2010 while

Far East surpassed it and ranked the second since then.

The decline of Mediterranean can be explained by the

bad European economy as the 2008 economic crisis

continues, and container cargo volume in Europe

decreased, especially countries bordering the Medi-

terranean like Greece, France and Italy, thus resulting

Fig. 4 Rank change of

shipping areas’ degree

centrality

Fig. 5 Degree centrality change of the top four shipping areas

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in a significant reduction in container cargo transpor-

tation via Mediterranean. On the contrary, the C0Dvalue of Far East went up in 2010, and it is not because

Far East did not get affected by the crisis, but relatively

lesser than Mediterranean and Europe. That is, Far

East’s loss compensated by the gain. And the reason

why the degree centrality of North America East Coast

declined since 2008 is that its container cargo volume

began to reduce, which is directly resulted from

American economic crisis.

Besides, C0D values of all shipping areas in 2012 are

showed by a geographical map in Fig. 6. Compared

with 2001, shipping areas with a raised C0D value are

Black Sea, Caribbean, Central America, Far East,

Indian Ocean, Indian subcontinent, Mid East, South

America East Coast, South America North Coast,

Southern Africa and West Africa, and those with a

declined C0D value are Australasia, East Africa, North

America East Coast, North America Gulf Coast, North

America West Coast, North/South Pacific, Red Sea

and St Lawrence Seaway.

Betweenness centrality of global shipping areas

Shipping areas with high betweenness centrality serve

as the medium, and transship sea cargoes between

pairs of other shipping areas which are not directly

connected. The relative betweenness centrality values

(C0B) of all shipping areas from 2001 to 2012 show

such an interesting finding that only a few shipping

areas possess comparatively large C0B values, while the

majority very small and even equal to zero (Fig. 7).

The very fact of the uneven distribution of between-

ness centrality among world shipping areas can be

further demonstrated by its fitted power low distribu-

tion in each year (Table 3). Meanwhile, the between-

ness centrality distribution also shows an obvious

hierarchy of core, semi-core and periphery, consisting

of shipping areas with C0B value higher than 0.15,

0.05–0.15 and below 0.05, respectively. To be

specific, the core layer only includes Europe, the

semi-core layer Mediterranean, Far East and North

the top ten in value CD

Europe 0.917

Far East 0.875

Mediterranean 0.833

North America East Coast 0.667

Southern Africa 0.583

Indian subcontinent 0.583

North America Gulf Coast 0.542

South America East Coast 0.542

Caribbean 0.542

Central America 0.542

,

Fig. 6 World shipping areas’ degree centrality in 2012

Fig. 7 Rank-size distribution of betweenness centrality. Note

for the sake of clearness, we only show the distribution for the

year 2001 and 2012

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America East Coast (before 2007), and periphery layer

the rest shipping areas. That is, Europe severs as a

medium in the GSN, making it convenient for indirect

connections between some shipping areas. For exam-

ple, sea cargoes transported between Black Sea and

North America East Coast are often transshipped in

European ports. Thus we can make a conclusion that

most shipping areas are unable to influence the

communication between pairs of other shipping areas

except for Europe. And it is not difficult to understand

if thinking about the central position of many

European ports, such as Rotterdam, Antwerp, Piraeus,

Terneuzen, Hamburg, le havre and Bremerhaven,

which are of higher betweenness centrality than most

of other ports in the GSN (Pablo et al. 2010).

Take a closer look at the core and semi-core layers

(Fig. 8). With a C0B value ranging from 0.192 to 0.295,

Europe always rank the first during the period 2001 to

2012. With a C0B value ranging from 0.044 to 0.139,

Mediterranean had maintained its second position

until surpassed by Far East in 2010, whose C0B value

ranges from 0.059 to 0.136. As for the North America

East Coast, we can see a clear descending trend of its

C0B value, which ranges from 0.031 to 0.107, and it was

surpassed by Far East in 2007. It is worth noting that

both the C0B value of Europe and Far East reached the

maximum in 2010. Although global economy was still

depressed in 2010, the relatively high economic

growth rate of emerging markets like China, Brazil

and South Africa played an important role in main-

taining global liner shipping market. Consequently a

large amount of new container ships have been sent to

shipping routes between Far East and South America,

Far East and Southern Africa, and also between

Europe and Southern Africa, Europe and West Africa.

Though, of course, many of these ships come from

ship orders before 2008 when liner shipping market

was still booming, but delivered in 2010.

Besides, C0B values of all shipping areas in 2012 are

showed by a geographical map in Fig. 9. Compared

with 2001, betweenness centrality of Europe and Far

East rose, while Mediterranean and North America

East Coast declined.

Closeness centrality of global shipping areas

A shipping area with higher closeness centrality tends

to have a smaller sum of distance to others, and is more

independent as well as much easily reached by other

shipping areas in the GSN, due to less intermediary

shipping areas it needs to establish shipping connec-

tions with others. One important finding is that almost

all shipping areas are of high closeness centrality, thus

making the GSN as a whole possessing high commu-

nication efficiency. To be specific, the relative value of

closeness centrality (C0C) of almost all shipping areas

are higher than 0.5 in each year during the period

2001–2012, which indicates that container transpor-

tation between almost all pairs of shipping areas can be

Fig. 8 Betweenness centrality change of core and semi-core

shipping areas

Table 3 Fitted power low distribution of betweenness

centrality

Year a Correlation R2 Year a Correlation R2

2001 1.768 0.917 2007 1.887 0.925

2002 1.803 0.894 2008 1.608 0.820

2003 1.851 0.928 2009 1.764 0.774

2004 1.765 0.944 2010 1.902 0.877

2005 1.854 0.949 2011 1.857 0.845

2006 1.939 0.960 2012 1.872 0.854

The relative fitted power law based on C(v) * r-a where

C(v) is the centrality values, r is the rank and a is related to the

degree of concentration. The higher value of a means the

network is more concentrated

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achieved directly or depending on one other interme-

diary shipping area. For example, container transpor-

tation between East Africa and North America West

Coast can be available by transshipping in Far East

ports like Singapore and Shanghai.

However, distribution of closeness centrality

among world shipping areas is uneven. The rank and

closeness centrality distribution in each year is well

fitted by power low distribution (Table 4 and Fig. 10),

although the concentration is much lower than the

betweenness centrality (smaller a value). To be

specific, the C0C values of most shipping areas are

between 0.5 and 0.7, and only a few are higher than

0.7. Generally, Europe, Far East and Mediterranean

are higher than 0.8, North America East Coast, India

subcontinent and Southern Africa are between 0.7 and

0.8, and the rest areas are between 0.5 and 0.7. During

the period from 2001 to 2012, Southern Africa and

West Africa have an obvious rising trend in closeness

centrality, while North America East Coast, North

America West Coast, Australasia are just the opposite.

For example, Southern Africa surpassed North Amer-

ica East Coast in 2008 and even ranked the third in

2010.

Take a closer look at shipping areas possessing

extremely high C0C value, which are above 0.8

(Fig. 11). In this paper, a shipping area with a C0Cvalue equaling 0.8 means its sum of distances to all

other shipping areas is 30, in other words, the distance

the top ten in value

Europe 0.224

Far East 0.118

Mediterranean 0.093

North America East Coast 0.045

Indian subcontinent 0.031

Southern Africa 0.019

Caribbean 0.016

Central America 0.016

West Africa 0.013

Mid East 0.010

CB

,

Fig. 9 World shipping areas’ betweenness centrality in 2012

Fig. 10 Rank-size distribution of closeness centrality. Note for

the sake of clearness, we only show the distribution for the year

2001 and 2012

Table 4 Fitted power low distribution of closeness centrality

Year a Correlation

R2Year a Correlation

R2

2001 0.188 0.939 2007 0.187 0.945

2002 0.200 0.948 2008 0.191 0.891

2003 0.215 0.894 2009 0.189 0.876

2004 0.198 0.954 2010 0.180 0.900

2005 0.198 0.972 2011 0.194 0.900

2006 0.199 0.970 2012 0.196 0.900

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between it and eighty percents of the shipping areas in

the GSN is 1. During the period 2001–2012, the C0Cvalue of Europe was always higher than 0.9 and even

reached 1 during global liner shipping booms from

2004 to 2007, Far East and Mediterranean are higher

than 0.8 except the latter for 2010. Besides, their

closeness centrality changing trends are very similar to

degree centrality, such as the significant decrease of

Mediterranean and relatively rise of Far East in 2010,

and also the overall declining trend of North America

East Coast. Generally, the reason why Europe and Far

East can be easily reached by other shipping areas is

that they possess more hub ports than others which

play an important role in connecting liner shipping

among different shipping areas in the world, such as

Rotterdam, Antwerp, Singapore and Shanghai port.

And high closeness centrality of Mediterranean can be

better understood if considering its geographic loca-

tion. Connecting with Suez Canal and Europe, it has

the densest shipping lines in the world.

Besides, C0C values of all shipping areas in 2012 are

showed by a geographical map in Fig. 12. Compared

with 2001, shipping areas with a raised C0C value are

Black Sea, Caribbean, Central America, Far East,

Indian subcontinent, Mid East, South America East

Coast, South America North Coast, Southern Africa

and West Africa, and those with a declined C0C value

are Australasia, East Africa, Indian Ocean, North

America East Coast, North America Gulf Coast, North

America West Coast, North Atlantic, North/South

Pacific, Red Sea and St Lawrence Seaway.

Discussion

As the results of degree, betweenness and closeness

centrality show that world shipping areas are not

developing in imbalance, especially the extremely

uneven distribution of betweenness centrality

(Fig. 13), where we can see an obvious central

Fig. 11 Closeness centrality change of top shipping areas

the top ten in value

Europe 0.923

Far East 0.889

Mediterranean 0.857

North America East Coast 0.750

Southern Africa 0.706

Indian subcontinent 0.706

North America Gulf Coas 0.686

South America East Coast 0.686

Caribbean 0.686

Central America 0.686

CC

,

Fig. 12 World shipping areas’ closeness centrality in 2012

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tendency in the GSN. And it is no doubt that Europe is

of most central position in the GSN due to its highest

relative value of degree centrality and betweeness

centrality as well as closeness centrality from 2001 to

2012. However, to what extent does Europe dominate

the connectivity and dependency as well as efficiency

of the GSN as a whole? Or to what degree is GSN

structurally centralized? Therefore, the central posi-

tion of Europe is further analyzed in this section by

measures of central position dominance (CPD)3

(Fig. 14).

First, CPD in GSN is obvious in degree and

closeness centrality, ranging from 0.518 to 0.623 and

0.579 to 0.739 during the period from 2001 to 2012,

respectively, but not in betweenness centrality which

ranges from 0.174 to 0.280. Second, there is a slight

declination of CPD in both degree and closeness

centrality in 2012 when comparing with 2001. There-

fore, it can be conclude that Europe is really able to

affect the GSN largely on connectivity and reachabil-

ity, but not quite powerfully control shipping connec-

tions between pairs of shipping areas within the GSN.

In fact, there is no single shipping area serving as an

absolute intermediary in the GSN because of the

prevalent existence of hub ports in word shipping

areas, which is resulting from the widely adopted hub

and spoke routes in global liner shipping. These ports

serve as intermediaries among several shipping areas

in local network of global shipping, such as Singapore

and Hong Kong, as well as Shanghai port in the Far

East, Dubai port in the Middle East, New York port in

North America East Coast and Durban pot in the

Southern Africa.

Generally, the dynamic changing of CPD of Europe

in degree and closeness centrality is similar. Europe

had seen its rising position in the GSN since 2001 and

reached the peak in 2006, then declined in spite of a

brief rebound in 2010. And this can be better

understood if we think about the global shipping

market during the period from 2001 to 2012. First, that

Europe enjoyed unprecedented central position in the

GSN in 2006 can be explained by two main factors. On

one hand, as major container routes in global shipping,

container cargo volumes in routes between North

America East Coast, North America West Coast, Far

East and Europe increased rapidly in 2006 which is

resulted from the global shipping boom started in

2004. On another hand, container capacities on these

routes were growing due to more larger container

vessels put into practice, e.g. the first container ship

Fig. 13 World shipping areas’ concentration basing on

betweenness centrality

Fig. 14 CPD in the GSN

3 A measure of dominance of the most central point (also called

graph centrality) by Freeman is CX ¼Pn

i¼1CX pnð Þ�CX pið Þð Þ

maxPn

i¼1CX pnð Þ�CX p

ið Þð Þwhere CX is defined according to degree, betweenness and

closeness respectively, CX(p*) is the largest centrality value

associated with any point in the graph under investigation, and

maxPn

i¼1 CX p�ð Þ � CX pið Þð Þ is the difference in centrality

between the most central point and all others in a star or a wheel.

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with a 11000 TEU capacity, Emma Maersk was put

into route from Europe to Far East in 2006, thus the

position of Europe was further enhanced. Second,

compared with less developed areas like South

America and Southern Africa, developed areas like

Europe and North America were much affected by the

2008 economic crisis, resulting in dramatic reductions

in container cargo volumes between them and CPD

declination of Europe. Third, currently, since major

economies, European and North American countries

are still slowly recovering, container cargo volume

growth in East–West routes (mainly among Europe,

North America and Far East) have also slowed down.

On the contrary, due to the relatively stable growth of

container cargo volume on South-North routes (e.g.

Far East–South America West Coast) and regional

routes (e.g. long shore shipping within the Far East),

more container capacities have been shifted to them.

Besides, along with the development of Far East ports

such as Shanghai, Ningbo and Busan, the CPD of

Europe is further declined to some degree.

Conclusion

Looking at GSN from perspective of world shipping

areas brings interesting and novel results for its

structure studies. Beyond the traditional shipping

network studies confining the nodes of shipping

network to ports, this paper creatively constructed

the GSN which regards shipping areas as its nodes and

connections between them as its edges, and carried out

a comprehensive study on the centrality changing

dynamics of world shipping areas from 2001 to 2012,

in a macro viewpoint. Based on the assumption that

degree centrality, betweenness centrality and close-

ness centrality are key indicators of the position of

shipping areas in the GSN, major findings are as

follows. First, there is a two-layer distribution of

global shipping areas’ degree centrality, although

distribution of C0D value on an interval from 0 to 1 is

relatively even. And the first layer consists of the top

three shipping areas, namely Europe (0.917–1) and

Mediterranean (0.542–0.917) as well as Far East

(0.792–0.875), and the rest are included in the second

layer. Second, only a few shipping areas possess

comparatively large C0B values while the majority very

small and even equal to zero. The rank and

betweenness centrality distribution also shows an

obvious hierarchy of core, semi-core and periphery,

with the core layer only consists of Europe (0.192–

0.295) and the semi-core layer consists of Mediterra-

nean, Far East and North America East Coast (before

2007). Third, during the period 2001–2012, the

relative value of closeness centrality (C0C) of almost

all shipping areas are higher than 0.5 in each year.

While only Europe, Mediterranean and Far East

possess a C0C value higher than 0.8, C0C values of the

rest shipping areas’ lie in an interval between 0.5 and

0.7. After some further discussion, an important

finding is that, although it had the highest degree and

betweenness, as well as closeness centrality from 2001

to 2012, the CPD of Europe has declined and is being

shifted to Far East gradually.

Meanwhile, there are also limitations in the

centrality study of the GSN of this paper. For one

thing, internal shipping within shipping areas are not

considered here, but in fact, some short sea routes have

seen rapid growth in their container cargo volumes,

e.g. intra Far East and intra Mediterranean. For

another, specific amount of container capacity

deployed in those shipping routes among world

shipping areas and also directions are not considered

in this paper, which may lead to different results from

a directed weighted network of global shipping.

Nevertheless, studying GSN centrality from the per-

spective of world shipping areas is of great significant

and can complement researches on the structure of

global shipping. Therefore, further research in this

field can be carried out in many aspects. First, deep

studies on division of global shipping areas should be

done with consideration of both geography and real

development in global shipping industry. Second,

centrality studies in the directed weighted network of

global shipping should be achieved by many other

proper tools from graph theory and complex network

theory.

Acknowledgments Authors are grateful to anonymous

reviewers for their useful comments on this paper. This

research benefited from the financial support of National

Natural Science Foundation of China and National Social

Science Foundation of China.

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