論文 / 著書情報
Article / Book Information
題目(和文)

クルーズネットワーク構造の時空間変化

Title(English)

Spatial and temporal changes in the cruise network structure

著者(和文)

伊東弘人

Author(English)

Hirohito Ito

出典(和文)

学位:博士（工学）,学位授与機関:東京工業大学,
報告番号:甲第12224号,
授与年月日:2022年9月22日,
学位の種別:課程博士,
審査員:花岡 伸也,髙橋 邦夫,屋井 鉄雄,室町 泰徳,瀬尾 亨,杉下 佳辰

Citation(English)

Degree:Doctor (Engineering),
Conferring organization: Tokyo Institute of Technology,
Report number:甲第12224号,
Conferred date:2022/9/22,
Degree Type:Course doctor,
Examiner:,,,,,

学位種別(和文)

博士論文

Type(English)

Doctoral Thesis

Powered by T2R2 (Tokyo Institute Research Repository)SPATIAL AND TEMPORAL CHANGES IN
THE CRUISE NETWORK STRUCTURE（クルーズネットワーク構造の時空間変化）

by
HIROHITO ITO

Submitted to the Department of Transdisciplinary Science and Engineeringin Partial Fulfillment of the Requirements of the Degree of
DOCTOR OF ENGINEERING

Dissertation Committee:
Professor, Shinya Hanaoka
Professor, Tetsuo Yai
Professor, Kunio Takahashi
Associate Professor, Yasunori Muromachi
Associate Professor, Toru Seo
Assistant Professor, Kashin Sugishita
Department of Transdisciplinary Science and Engineering
School of Environment and Society
TOKYO INSTITUTE OF TECHNOLOGYABSTRACTThe cruise industry has experienced continuous growth since the late 1960s. While theglobal financial crisis of 2008-09 had a major impact on maritime cargo shipping, thecruise industry continued to experience an increase in passenger numbers. Cruisedemand is characterized as supply-driven. Ports develop facilities first, and then cruise
ships can call at the ports. Therefore, under conditions of uncertain future cruisedemand, ports are required to make decisions that require port investments. Conversely,
the lack of cruise ships coming to some ports after the opening of port facilities has
become a problem.
These undesirable conditions may be due to the supply-driven characteristics of cruisedemand under the lack of information in the cruise market. If there were a little lessuncertainty about future demand for cruise ships, it might reduce the number of portsthat do not receive cruise ships after they are put into service. The situation at sea,
where many cruise ships operate between different ports, cannot be understood in termsof just one cruise ship itinerary. By taking the trajectory of all cruise ships navigating ina given area as a network, and by getting a bird's eye view, and understanding the
growth areas and timing where cruise lines are deploying ships and developing routes, it
may be possible to avoid a situation where cruise ships do not arrive after a port has
been built. The purpose of this dissertation is to understand the spatial and temporalchanges in the structure of the cruise network using automatic identification system(AIS) data.
Characteristics of spatial and temporal changes in the structure of the cruise network
were identified. First, cruise lines continued to operate under the contingency of theterminal high altitude area defense missile (THAAD) disruption of shipping routes, butwith a reduced number of ports of call for punctuality. Second, cruise ships continued to
be deployed in the Caribbean and Mediterranean, while Asia closed its cruise port earlyin the outbreak of coronavirus disease (COVID-19). Third, the Northeast Asian cruise
network has tended to develop routes for mega- and small-size ships, and for all ship
sizes, the quality of community connections tended to improve over time. Finally,seasonality played a role in the cruise industry. Cruise lines took advantage ofseasonality to change their deployment areas.

ACKNOWLEDGMENTSFirst and foremost, I am extremely grateful to my supervisors, Professor Shinya
Hanaoka, and Assistant Professor Kashin Sugishita for their invaluable advice,continuous support, and patience during my study. Without their assistance anddedicated involvement in every step of the process, this dissertation would have neverbeen accomplished. I would like to thank you very much for your support andunderstanding over these past three years.
I would like to thank my committee members, Professor Tetsuo Yai, Professor Kunio
Takahashi, Associate Professor Yasunori Muromachi, and Associate Professor Toru Seo.
Their encouraging words and thoughtful, detailed feedback have been very important tome. I am grateful to Senior Lecturer Tomoya Kawasaki, who moved to the University ofTokyo while I was in the first year of my Ph.D. program, for his appropriate guidance.
Thank you to all the members of the Hanaoka Laboratory. It is their kind help andsupport that have made my study and life at the Tokyo Institute of Technology awonderful time. Furthermore, I would also like to thank everyone at Central Consultant
Inc. for their tremendous support.
Finally, I would like to express my gratitude to my parents, Makoto Ito and Satoe, mywife, Fumie, and my daughters, Aoi and Yuho. Without their tremendous understanding
and encouragement over the past few years, it would be impossible for me to complete
my study. I dedicate this dissertation to my family.

TABLE OF CONTENTS
1. INTRODUCTION ................................................................................ 1
1.1.
1.2.
1.3.
1.4.
1.5.

What is the cruise industry? ................................................................................ 1
Introduction ....................................................................................................... 10
Literature review ............................................................................................... 13
Objectives .......................................................................................................... 17
Dissertation outline ........................................................................................... 23

2. CRUISE NETWORK IN CASE OF ROUTE DISRUPTION IN
NORTHEAST ASIA ................................................................................. 25
2.1.
2.2.
2.3.
2.4.
2.5.

Introduction ....................................................................................................... 25
Literature review ............................................................................................... 27
Methodology ..................................................................................................... 29
Results and discussion....................................................................................... 33
Chapter conclusion ............................................................................................ 44

3. THE CRUISE INDUSTRY AND THE COVID-19 OUTBREAK .. 46
3.1.
3.2.
3.3.
3.4.

Introduction ....................................................................................................... 46
Methodology ..................................................................................................... 47
Results and discussion....................................................................................... 49
Chapter conclusion ............................................................................................ 57

4. SPATIAL AND TEMPORAL CHANGES IN THE CRUISE
NETWORK STRUCTURE IN NORTHEAST ASIA ............................ 59
4.1.
4.2.
4.3.
4.4.
4.5.
4.6.

Introduction ....................................................................................................... 59
Literature review ............................................................................................... 61
Methodology ..................................................................................................... 63
Results ............................................................................................................... 69
Discussion ......................................................................................................... 84
Chapter conclusion ............................................................................................ 86

5. CHANGES IN THE GLOBAL CRUISE NETWORK DUE TO
SEASONALITY ........................................................................................ 88
5.1.
5.2.

Introduction ....................................................................................................... 88
Literature review ............................................................................................... 89

5.3.
5.4.
5.5.
5.6.

Methodology ..................................................................................................... 91
Results ............................................................................................................... 94
Discussion ....................................................................................................... 108
Chapter conclusion .......................................................................................... 111

6. CONCLUSIONS ............................................................................... 113
6.1.
6.2.

Summary ......................................................................................................... 113
Limitations and future works .......................................................................... 116

APPENDICES ......................................................................................... 118
REFERENCES........................................................................................ 123

LIST OF FIGURES
Figure 1-1 Global cruise population ......................................................................... 2
Figure 1-2 Percentage of cruise population by country ............................................ 3
Figure 1-3 Trends in maximum and average cruise ship sizes (Gross Tonnage) ...... 4
Figure 1-4 Trends in maximum and average cruise ship sizes (Passengers) ............ 4
Figure 1-5 What is a cruise network?........................................................................ 7
Figure 1-6 Dissertation framework ......................................................................... 24
Figure 2-1 Total number of voyages by country from 2014 to 2016 ...................... 25
Figure 2-2 Business continuity plan ........................................................................ 29
Figure 2-3 Concept of cruise business continuity plan ........................................... 30
Figure 2-4 Major cruise ports in Northeast Asia ..................................................... 33
Figure 2-5 Trends in the number of Arrival and Departure at the top 10 ports ....... 33
Figure 2-6 Trends in the number of calls at the top 10 ports .................................. 34
Figure 2-7 Ship movement (COSTA SERENA) ..................................................... 35
Figure 2-8 Ship movement (GLORY SEA) ............................................................ 36
Figure 2-9 Ship movement (COSTA ATLANTICA) .............................................. 37
Figure 2-10 Ship movement (SKYSEA GOLDEN ERA) ...................................... 38
Figure 2-11 Ship movement (CHINESE TAISHAN) ............................................. 39
Figure 2-12 Five ship movements ........................................................................... 40
Figure 2-13 Top 3 trends in itinerary patterns by ship ............................................ 41
Figure 2-14 Trends in the average number of sailing days each itinerary .............. 42
Figure 2-15 Trends in the percentage of itineraries calling at multiple ports ......... 42
Figure 3-1 Cruise area classification ....................................................................... 48
Figure 3-2 Number of port calls per week by cruise area ....................................... 49
Figure 3-3 Number of port calls for each port from January to March ................... 53
Figure 3-4 COVID-19 infection rate for groups accepting and reducing ships inMarch .............................................................................................................. 54
Figure 3-5 Passenger capacity of cruise ships infected with COVID-19 ................ 56
Figure 4-1 Location of ports ................................................................................... 64
Figure 4-2 Network structure .................................................................................. 70
Figure 4-3 Mega-size ship network......................................................................... 73
Figure 4-4 Large-size ship network ........................................................................ 74
Figure 4-5 Mid-size ship network ........................................................................... 75
Figure 4-6 Small-size ship network ........................................................................ 76
Figure 4-7 Degree centrality ................................................................................... 78

Figure 4-8 Degree distribution ................................................................................ 79
Figure 4-9 Degree centralization ............................................................................. 79
Figure 4-10 Community detection .......................................................................... 82
Figure 4-11 Visualizing the 2019 Northeast Asian cruise network ......................... 83
Figure 4-12 Modularity ........................................................................................... 83
Figure 5-1 Number of nodes and edges .................................................................. 94
Figure 5-2 Average degree and average clustering coefficient ............................... 95
Figure 5-3 Density and degree centralization ......................................................... 96
Figure 5-4 Degree centrality (Mega-size) ............................................................... 98
Figure 5-5 Degree centrality (Large-size) ............................................................... 99
Figure 5-6 Degree centrality (Mid-size)................................................................ 100
Figure 5-7 Degree centrality (Small-size) ............................................................. 101
Figure 5-8 Monthly degree of the top 50 ports with the highest degree ............... 103
Figure 5-9 Degree distribution .............................................................................. 104
Figure 5-10 Coefficient of variation of monthly degrees for the top 50 ports ...... 105
Figure 5-11 Percentage of port calls by season at each latitude ............................ 106
Figure 5-12 Latitude distance distribution ............................................................ 107
Figure 5-13 Box plot of latitude distance by ship size .......................................... 108

LIST OF TABLES
Table 1-1 Investment in cruise terminals (Concessions) ........................................... 5
Table 1-2 Development of private islands................................................................. 6
Table 1-3 Cruise industry data published by CLIA................................................. 11
Table 1-4 Previous studies on the conditions required for cruise ports .................. 14
Table 1-5 Previous studies on the conditions required for cruise itineraries .......... 15
Table 2-1 Top 5 Cruise ships by number of bookings to Jeju port in 2017............. 32
Table 3-1 Estimated number of cruise passengers landing and COVID-19 cases .. 50
Table 3-2 COVID-19 infection rates between countries with arrival and departure
ports and countries with only ports of call ...................................................... 52
Table 3-3 Major events stopping the movement of cruise ships ............................. 55
Table 3-4 Itinerary of cruise ships infected with COVID-19 .................................. 57
Table 4-1 Interpretation of network indicators from the cruise industry perspective......................................................................................................................... 67
Table 5-1 Interpretation of network indicators from the cruise industry perspective......................................................................................................................... 93
Table 5-2 Deployment areas in summer and winter by ship size .......................... 109

1. INTRODUCTION
1.1. What is the cruise industry?
1.1.1. History of the cruise industry
The cruise industry started with regular passenger ship services. From the mid-19th
century liner services supported long-distance passenger transportation between
continents, particularly between Europe and North America. The need to accommodatea large number of passengers of different socio-economic statuses for at least a week led
to the emergence of specific ship designs radically different from cargo ships where
speed and comfort were paramount. The emergence of the cruise industry can be traced
to the demise of the ocean liner in the 1960s as it was replaced by fast jet services forwhich it could not compete. The availability of a fleet of liners whose utility was nolonger commercially justifiable incited their reconversion to form the first fleet of cruise
ships (Rodrigue and Notteboom, 2013).
Historically, cruise services were offered by ‘regular steamship lines’ and ‘transatlantic
companies,’ and are currently provided by ‘cruise lines.’ Regular steamship companies
were lines that operated year-round from US mainland ports such as New York, New
Orleans, and later Miami, carrying both cruise passengers and cargo (or only
passengers) throughout the Caribbean on designated shipping routes. For example,
Quebec, United Fruit, Ward (USA), and Furness (UK) fall into this category. As impliedby the term, transatlantic companies provided passenger services between North
American and European ports, such as Cunard. The term ‘cruise lines’ is used mainly ina post-1965 context, describing those companies such as Norwegian Cruise Line in
1966, Royal Caribbean International in 1968, and Carnival Cruise Lines in 1972, whichhave remained the large cruise lines (Lawton and Butler, 1987; Garin, 2005).
1.1.2. Cruise market trends
Since the late 1960s, the cruise industry has witnessed uninterrupted growth, except fora temporary negative growth during the 1974-75 oil crisis. While the global financial
crisis of 2008-09 had a major impact on maritime cargo shipping, cruise lines and cruise
ports continued to experience an increase in passenger numbers. It did so even when an
unfortunate event, the COSTA CONCORDIA loss, created the most sustained period ofnegative publicity for the cruise industry (Pallis, 2015). The cruise industry has
1expanded since the 1990s. The global cruise population increased from 3.8 million in1990 to 29.7 million in 2019 (Cruise Lines International Association [CLIA], 2011;2019; 2020a). The average annual growth rate for the last 30 years is 7.37%.
Global cruise population (1,000)

30,00025,00020,00015,00010,000
5,000

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019

0
Year

Figure 1-1 Global cruise population
Source: Cruise Market Watch (2022)
1.1.3. Cruise market expanding globally
The global cruise market has expanded from the US-centric market to Europe, followedby Asia and other countries (ROW). In 1990, 93% of the world cruise population was
America; by 2000, 91% was America as the European population grew. The U.S. cruise
population dropped to 53% in 2010 and less than half by 2019. Recently, the cruise
population in China has increased. In 2006, the first year of the Chinese cruise market,
the cruise population was 20,000 (Wang, 2017). In 2016, the number reached 2.1
million, and China became the world’s second-largest cruise market, following theUnited States (CLIA, 2016). Thus, the cruise market is expanding worldwide.

2

1990
5%

2000
3%
4%

2%
5%

5%

3%

3%

United States

United States

10%

UK & Ireland

UK & Ireland
Germany

Germany

FranceFrance

Japan

Japan

93%

91%

2010

2019
United States

UK & Ireland
23%

Germany

1%3%Germany

France5%1%2%United States

UK & Ireland

21%

53%

48%

Japan

6%
2%

Italy
Spain

6%
8%

Italy

China

3%
1%

ROW

2%

France
Japan
Italy
Spain

9%

7%

China
ROW

Figure 1-2 Percentage of cruise population by country
Source: CLIA (2011, 2016, 2019, 2020b)
1.1.4. Deployment of mega cruise ships
Cruise lines are deploying larger ships to meet the growing demands of cruise
passengers. The first dedicated cruise ships began to appear in the 1970s and could
carry about 1,000 passengers. By the 1980s, cruise ships had emerged that could
transport more than 2,000 passengers to take advantage of economies of scale. The
current large cruise ship, the OASIS OF THE SEAS, was deployed by Royal Caribbean
in 2009. This ship is a super-large with a total tongue of 226,838 GT, a passenger
capacity of 6,360, and a crew of 2,165. There are plans to introduce mega-sized vesselsof 230,000 GT or more after 2022. In terms of average gross tonnage, it was 5,723 GT
in 1990, but by 2019 it was 38,907 GT. Including deployment of new ships, this figure
shows to grow to 44,957 GT by 2027.

3

250,000

45,00040,000200,000

35,00030,000150,000

25,00020,000100,000

15,00010,00050,000

5,000
0
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
2016
2018
2020
2022
2024
2026

0

Maximum gross tonnage (GT)Average gross tonnage (GT)50,000

Average gross tonnage

Maximum gross tonnage

Figure 1-3 Trends in maximum and average cruise ship sizes (Gross Tonnage)
Source: IHS Maritime ship database (2022)
Note: Estimated after 2020.

1,400

8,000

1,200

7,000
6,000

1,000

5,000

800

4,000
600

3,000

400

2,000

200

1,000
0
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
2016
2018
2020
2022
2024
2026

0

Maximum passenger capacity

Average passenger capacity

In terms of maximum passenger capacity, the number has increased from 2,020 in 1990
to 6,654 in 2019. Including the future deployment of new ships, the plan is for the 6,988
mega ships to emerge after 2022. In terms of average passenger capacity, the number
has increased from 216 in 1990 to 1,174 in 2019. Including the future deployment of
new ships, the number is expected to increase to 1,317 by 2027.

Average passenger capacity

Maximum passenger capacity

Figure 1-4 Trends in maximum and average cruise ship sizes (Passengers)
Source: IHS Maritime ship database (2022)
Note: Estimated after 2020.
1.1.5. Oligopolistic market with cruise line alliances
The global cruise market is an oligopolistic market with alliances such as Carnival
Corporation & plc, Royal Caribbean Cruises Ltd., and Norwegian Cruise Line Holding
4Ltd. These three alliances account for more than 80% of the global passenger share in2014 (OECD, 2015). In the container shipping market, three major shipping alliances(2M, THE Alliance, and Ocean Alliance) were formed in 2017. These three alliances
account for 80% of the global container market (xChange Solutions GmbH, 2019). The
purpose of the alliance is lower prices and service coverage, which is almost the samefor both cruise lines and container liners.
In the case of the cruise industry, cruise lines invest in passenger terminals in popular
ports and develop private islands to differentiate their ports of call from competitors. In
the cruise terminal concession, for example, Terminal D (Palacruceros) in Barcelona,
Spain, is operated by Carnival Corporation & plc. Rome Cruise Terminal in
Civitavecchia, Italy, is jointly operated by three cruise lines (Royal Caribbean Cruises
Ltd., Costa Crociere, and MSC Crociere Spa).
Table 1-1 Investment in cruise terminals (Concessions)
Terminals

Cruise lines

Terminal D (Palacruceros)in Barcelona, Spain

Carnival Corporation & plc.

Rome Cruise Terminal in
Civitavecchia, Italy

Royal Caribbean Cruises Ltd., Costa
Crociere, MSC Crociere Spa

Marseille Provence Cruise
Terminal In Marseille,
FranceCosta Crociere, MSC Crociere Spa,Louis Cruises

Galveston, US

Royal Caribbean International,
Carnival Cruise Lines, CH2MHILLPort Everglades, US

Royal Caribbean International

Cagliari, Italy

Royal Caribbean Cruises Ltd.

Kasadasi, Turkey

Royal Caribbean Cruises Ltd.,
Global Ports Holding (GPH)

Source: Pallis (2015)
The major cruise lines have built private ports on islands reserved for their exclusive
use, such as CocoCay (Royal Caribbean), Half Moon Cay (Holland), Castaway Cay(Disney), Princess Cay (Princess), and Great Stirrup Cay (Norwegian). These private
facilities are all within one cruise day from the homeports of Florida, offering the optionof short three to four days cruises (Rodrigue and Notteboom, 2013). Thus, cruise lines
design itineraries that give preference to passenger terminals and private islands inwhich they have invested themselves.
5Table 1-2 Development of private islands
Private islands

Cruise lines

CocoCay

Royal Caribbean Cruises Ltd.

Half Moon Cay

Holland America Line

Castaway Cay

Disney Cruise Line

Princess Cay

Princess Cruises

Great Stirrup Cay

Norwegian Cruise Line

Source: Pallis (2015)
1.1.6. A cruise itinerary is a network
The cruise industry sells itineraries, not a destination (Rodrigue and Notteboom, 2013).
The design of new itineraries is always performed with the idea that the cruise industry
sells itineraries rather than single ports/destinations (Esteve-Perez and Garcia-Sanchez,2018). Each cruise ship itinerary is a combination of ports of arrival and departure andports of call, forming a single network. Each such itinerary becomes a larger network
over time. Furthermore, by overlapping multiple cruise ship networks, an even larger
cruise network is constructed.
There are two types of itineraries for cruise products: ‘Closed itineraries’ and ‘Open
itineraries.’ ‘Closed itineraries’ only have one homeport because the itinerary starts andends at this port; in this case, the itinerary is a closed loop. ‘Open itineraries’ have two
homeports – itinerary starts and ends at different ports. Ports of call are then needed tocomplete the itinerary (Esteve-Perez and Garcia-Sanchez, 2018). In practical terms, aseven-day cruise itinerary normally represents a loop with the same beginning andending home port, and usually three to five ports of call in between (Marti, 1990).
Cruise ports that make up these itineraries are divided into three categories: (a) home (orturnaround) ports, (b) ports of call, and (c) hybrid ports (Marti, 1990). Each typeembraces different characteristics and levels of required investment. Homeport means
where passengers embark or disembark at the beginning or end of their cruise,
respectively. Port of call means the port is part of an itinerary; passengers are at the port
for only the duration of their port call. A hybrid port means serving as both ports andports of call. Itinerary design characteristics should also keep in mind these differencesin cruise port types. Thus, by viewing the itinerary as a network, the operational andcommercial characteristics of the cruise line may be understood.
6

Network of one ship

Networks of one ship

Networks of all ships(One itinerary per cruise ship)(Multiple itineraries per cruise ship)(Multiple networks on all cruise ships)

Arrival and departure port

Arrival and departure port

CloseditineraryC cruise ship
Port of call
Arrival port

Port of call
Departure port

Port of call
Arrival port

Port of call
Arrival anddeparture

Departure port

B cruise shipOpen
itineraryA cruise ship
Port of callPort of callPort of callPort of callFigure 1-5 What is a cruise network?
1.1.7. Supply-driven markets evolve over time
The cruise market is ‘supply-driven,’ with supply coming first and demand following.
Vogel and Oschmann (2012) explained that cruise demand has always been ‘supplyled,’ starting with the invention of leisure cruising by passenger shipping lines whosescheduled transatlantic services were losing passengers to the airlines. Similarly,
Rodrigue and Notteboom (2013) analyzed that the cruise industry works in a ‘supply
push mechanism’ as cruise lines aim to generate demand for cruises by providing new
products (itineraries) with a larger and more diversified range of ships. Cruise lines
strategically create demand by offering new cruise ships and new itineraries. On thecontrary, container shipping companies deploy their ships to ports where demand forcontainerized cargo appears (or will). In other words, demand comes first, and supply
follows. Thus, the movement of cruise ships could provide insight into the operationaland commercial characteristics of cruise lines.
1.1.8. Fragility and vulnerability of cruise industry changes itineraries
The cruise business has vulnerable characteristics of being quickly stopped by (a)natural disasters such as hurricanes and storm surges (Travel Agent Central, 2018); (b)political factors such as border blockades, travel bans, and economic sanctions (Reuters,2019); (c) violence and conflict such as war, terrorism, and piracy (Maritime Cyprus,2016); (d) port labor strikes such as wage wars, union unification, and port blockades(Cruise Hive, 2019); (e) community opposition such as environmental protection, overtourism (CNN, 2021); and (f) infectious diseases such as norovirus, SARS, MERS, and
COVID-19 (McCarter, 2009; Vivancos et al., 2010; Seatrade Cruise News, 2015; Fisher
7et al., 2018; Ito et al., 2020). On the other hand, container transport, as a logistics
activity that supports production in factories, distribution sales in stores, and the daily
lives of consumers, cannot be easily stopped. Therefore, an analysis comparing theoperational and commercial characteristics of cruise lines under contingency conditions
with those under normal conditions is significant.
1.1.9. Different ship sizes form different itineraries
Cruise lines and cruise ships are valued in categories based on ship concept, quality ofservice, and ticket prices (luxury, premium, casual, etc.). Competition and cooperation
occur between two or more rival ports in a given market. The competition is more
intense between ports of the same category (Pallis, 2015). However, each ratingcompany has different criteria, and each ship's category rating is different. Bjornsen(2003) gives examples of the differences in cruise duration, ticket price, and ship sizes
in different categories. Contemporary is an itinerary of 3 to 7 days, 100-200 USD per
day per person, operating mega- and large-size ships. Premium is an itinerary of 7 to 14
days, 150-500 USD per day per person, operating large- and mid-size ships. Luxury is
an itinerary of 7 days and upwards, 600-3,000 USD per day per person, operating midand small-size ships (Gibson, 2012). Thus, small-size cruise ships typically offer better
service and higher ticket prices.
The itinerary varies with the size of the ship. In particular, the itineraries for larger ships(mass cruise tourism) tend to be more stable than for smaller ships. However, thestability in the sailing schedule is not only linked to shipping size, but also the strategiesof the cruise operators regarding cruise product, branding, targeted customer base,pricing, and cost and technical considerations related to the ship operations (Bagis andDooms, 2014). Each cruise line has different target customers and different ship size
strategies. So, not all cruise lines necessarily aim to increase the size of their ships. In
the container transport industry, shippers need is cheap and fast transportation.
Therefore, generally, all container liners aim to make their container ships larger. So, itmakes sense to understand the differences in operational and commercial characteristics
by cruise ship size.
1.1.10. Seasonality changes itineraries around the globe
Seasonality plays a key role in the cruise industry (Charlier, 1999; Charlier and
McCalla, 2006). Cruise lines are attempting to optimize the utilization of their assets
year-round by repositioning to take advantage of the seasonality of cruise markets. The
8

Caribbean is dominantly serviced during the winter while the Mediterranean
experiences a summer peak season. The two markets are not functioning independently
but are interconnected operationally, through the repositioning of ship units to cope withvariations in seasonal demand among the geographical markets. The seasonality of
Alaska, Bermuda, and Canada/ New England is also evident (Rodrigue and Notteboom,2012). The seasonality pattern of cruise destinations is conditioned not only by weather
and market demand constraints but also by other neighboring destination regions(Esteve-Perez and Garcia-Sanchez, 2019). The characteristic of frequent changes incruise ship deployment based on seasonality is not found in container transport.
Therefore, it is necessary to understand the seasonal differences between the operationaland commercial characteristics of the cruise industry.

91.2. Introduction
1.2.1. Background
Cruise demand has the characteristic of being supply-driven. In other words, supplyside initiatives such as port development are planned first, followed by demand in theform of cruise ships and passengers. The history of the cruise industry, which has been
growing steadily since the late 1960s, shows that this supply-driven approach tostimulating demand has been successful in expanding the market. Conversely, supplydriven port development does not lead to success in all ports. Some ports haveexperienced problems with cruise ships not coming after the passenger terminals andother facilities have been put into service. Accommodating a cruise ship in a port can be
expected to generate economic benefits through the consumption of passengers andcrew members, in addition to the expenses of the cruise lines such as port entry fees andwharves. The port needs to be improved and a passenger terminal should beconstructed. Therefore, understanding the port and ship operations in a given area isimportant to avoid wasting investment in port improvements and consequently, cruise
ships not coming to the ports.
In addition, the shutdown of cruise ship operations due to emergencies leads touncertain future demand for cruise ports. The cruise industry is characterized by its
fragility and vulnerability to the closure of ports and disruption of shipping routes due
to a variety of factors that could bring cruise ship operations to a halt. For example, inNortheast Asia, the terminal high altitude area defense missile (THAAD) event halted
cruise ship calls from China to South Korea in March 2017, disrupting all China toSouth Korea routes, including between the ports of Shanghai and Jeju, where cruise shiptraffic was busiest at the time, and has not yet recovered at this time. Also worldwide,cruise ship operations have been suspended since March 2020, when the outbreak ofcoronavirus disease (COVID-19) continued to spread. In addition to these politicalfactors and infectious diseases, cruise ship operations are frequently suspended due tovarious factors such as natural disasters, conflicts, and over-tourism. Frequent
shutdowns of cruise ships lead to lost employment opportunities as well as lost
economic benefits at the ports. Therefore, understanding the changes in the spatial andtemporal structure of the cruise network during a contingency is essential to
implementing proactive measures to ensure that cruise operations do not come to a halt
during a contingency.

101.2.2. Problems
The problem in the supply-driven cruise industry is that there is not enough information
available to decide on whether to invest in port development. Data on the cruiseindustry worldwide and in major ocean regions is published by CLIA. Table 1-3 lists the2019 reports published by CLIA before the COVID-19 infection. The information
contained in these reports can be divided into three categories: cruise ship movements,
passenger numbers and characteristics, and economic impact. Cruise ship movements
are the number of cruise ships deployed worldwide and in major ocean areas and thenumber of port calls by port. Passenger information is the cruise population and the age,
number of cruise days, and destinations of passengers by residence. The economic
impact is the number of direct and indirect benefits and jobs created by the cruiseindustry.
Table 1-3 Cruise industry data published by CLIA
RegionData
Source (e.g. quotation)
Passenger capacity, Number of2019 State of the Industry (CLIA,
ships,
Deployment,
Economic
2019b)
impactPassenger Volume (from source
2019 Global Market Report (CLIA,
markets, by destination/ trade route),
Global2019a)
Average duration, Average age
Direct cruise sector expenditures,
The Economic Contribution of the
Passenger and crew spending,International
CruiseIndustry
Economic contribution, Output,
Globally in 2019 (CLIA, 2020a)
Income, Employment
Passenger volume (from source 2019 North American Market Reportmarkets, by destination/ trade route), (CLIA, 2020c), 2019 South American
North,
Average duration, Average age
Market Report (CLIA, 2020d)
andDirect cruise sector expenditures, The Economic Contribution of theSouth
America Passenger and crew spending, International Cruise Industry in the
Economic contribution, Output, United States in 2019 (CLIA, 2020e),
Income, Employment
in Canada in 2019 (CLIA, 2021)
Passenger volume (from source
2019 Europe Market Report (CLIA,
Europemarkets, by destination/ trade route),
2020f)
Average duration, Average age
Passenger volume (from source
2019 Asia Market Report (CLIA Asia,
markets, by destination/ trade route),
2020)
Average duration, Average age
Asia
Number of ships, Cruise ship2019 Asia Cruise Deployment andsegments, Length of cruises,
Capacity – Cruise Industry Report
Capacity, Total cruises & voyages,(CLIA Asia, 2019)
Port calls by country, and port
Source: CLIA (2019ab, 2020acdef), CLIA Asia (2019, 2020)11

Thus, there is too little information about the cruise industry for ports that are planningtheir facilities. In particular, there is no information available for ports to know the areas
and timing of potential growth, where cruise lines can deploy cruise ships, develop
routes, and enhance the function of hub ports as bases. In other words, the problem maybe that ports are required to make decisions on port development under conditions of
uncertain future cruise demand. In the cruise industry, which is characterized as supplydriven, reducing the uncertainty of future demand may prevent unnecessary port
investments. Reducing uncertainty about future demand requires an understanding of
the network structure of specific sea areas.
There are many previous studies on cruise lines' port selection factors and theirapproach to itinerary design. Marti (1990), Manning (2006), Wang et al. (2014),
Castillo-Manzano et al. (2014), Gui and Russo (2011), and Lekakou et al. (2009)
suggest that port specifications, transportation access, and tourism resources arenecessary conditions for ports to be selected by cruise lines. Rodrigue and Notteboom(2013), Marti (1990), Pallis (2015), and Rodrigue et al. (2013) identify conditions for
cruise line itinerary design in terms of maximizing revenues, minimizing costs, andimproving passenger satisfaction. Why do some ports not receive cruise ships eventhough they meet the requirements indicated in these previous studies? Other possible
causes include spatial and temporal changes in cruise lines' strategies, seasonal factorsthat cause ships to shift to distant areas, political factors, outbreaks of infectious
diseases, and other contingencies that close ports or disrupt shipping routes. The port
selection behavior of cruise lines, influenced by these macroeconomic conditions andtheir interactions through competitive relationships, cannot be determined simply byanalyzing the itineraries of each (single) cruise ship.
Analyzing the movements of all cruise ships operating in a given area as a network
would provide a better understanding of the spatial and temporal structural changes in
the cruise network. Specifically, some network science methods can measure temporaland spatial changes in the number of ports and routes in a sea area, the number of routes
per port, the percentage of port groups in a triangular relationship, hub ports playing acentral role, and port groups (communities) with dense route connections. This allows
each port to infer the best timing and location for port development. For the cruiseindustry, which is characterized as supply-driven, the movements of all cruise ships
operating in a particular area are tracked by AIS data and viewed as a network. Then,
using network science methods and other techniques, the results of the analysis of12spatial and temporal changes in the structure of the cruise network may help to reducesome of the uncertainty of future demand. This is significant because it may reduce thenumber of ports that do not receive cruise ships after they are put into service.

1.3. Literature review
The cruise industry, which has the characteristic of being supply-driven, requires adecision on the need for port development under conditions of uncertainty about future
cruise demand at the time of port planning. Therefore, it is important to analyze thespatial and temporal changes in the structure of the cruise network, as this will improve
the accuracy of decisions on the need for port improvements. Analyzing the movementof all cruise ships operating in a given area as a network would provide a more realistic
understanding of the port selection behavior of cruise lines based on the actual numberof ports and routes, the existence of hub ports, the connections between ports, and the
response under contingency conditions, as well as the market environment andcompetitive relationships. This could reduce the uncertainty of future cruise demand andreduce the number of ports that do not receive cruise ships after they are put into
service.
1.3.1. Port selection conditions
Ports selected by cruise lines are required to have many features, specifications,attractions, etc. For example, the port's key natural and cultural assets are the port, port
facilities, location access to other destinations and the homeport, security, infrastructure(vehicles, well-trained multilingual guides, and coordinators, etc.), provisioning (local
supply of food, drink, and clean water), port costs (dockage fees, etc.), and marketing(the variety of itineraries available for passenger selecting), etc. are necessary(Manning, 2006). In addition, the attractiveness of tourist destinations (Wang et al.,2014), ports located in densely populated areas, with large airports nearby, ports that use
their quays with ferries rather than using them for container ships, and a minimum water
depth are necessary (Castillo-Manzano et al., 2014). When cruise lines add new ports ofcall to their itineraries, it is important to ensure the quality of security in addition to port
charges to improve the tourism experience offered and the impact on profitability in the
short and medium term. Other factors such as customs and immigration handling,anchorage facilities, proximity to airports, cab and coach services, and quality port ofcall sightseeing and shopping areas are also necessary (Gui and Russo, 2011). In
addition, Lekakou et al. (2009) noted that cruise lines place a high priority on security
13and safety, yet require the convenience and reliability of air routes.
Table 1-4 Previous studies on the conditions required for cruise ports
Port selection factors

Source•Site and situation, physical or cultural qualities

Marti, 1990•••

Natural and cultural assets, port facilities
Location access to destinations and homeport
Security, infrastructure, provisioning, port costs

Manning, 2006•

Tourism attractions

Wang et al., 2014

•
•
•
•

Located in populous areas, closer to large airports
Ports not specialized in container traffic
Sharing facilities with ferries traffic
Minimum depth of water

Castillo-Manzano
et al., 2014

•
•
•
•
•
•

Port entry characteristics
Customs and immigration handlingDocking and anchorage facilities
Service and port area infrastructure
Airports, taxi fleets, coach services
High quality shore excursions and shopping area

Gui and Russo,
2011

•
•
•

Significance of air connections
Reliability of air transports
Availability of international airport

Lekakou et al.,2009

Source: Marti (1990), Manning (2006), Wang et al. (2014), Castillo-Manzano et al.
(2014), Gui and Russo (2011), Lekakou et al. (2009)
1.3.2. Factors to consider in the itinerary design
Cruise lines also consider a variety of matters when designing their itineraries. For
example, cruise ship operators constantly focus on developing new ports to develop
products that can differentiate them from their competitors, and they determine thenumber of ports they can call depending on the location of the port of embarkation andthe port of call, the ship's speed, and the number of voyage days (Marti, 1990). They
also design itineraries by considering four factors: sailing speed, port of embarkation,
voyage duration, and tourist destinations (Rodrigue et al., 2013). Cruise lines also wantto build itineraries in a way that includes different ports. Cruise lines also want to build
itineraries that include different ports, because the different attractions of each port offerdifferent opportunities for different experiences. And in the process of seeking new
ports of call, cruise lines consider the geopolitical factors and political stability of theport of call and the level of security in the port and after tourism, to provide a safe andcomfortable itinerary (Pallis, 2015) Cruise lines need to develop more competitive
cruise products and, at the same time, to minimize operating costs constantly
14

considering how to optimize the deployment of their cruise fleet in terms of minimizing
and maximizing revenues. Other factors that influence cruise ship operators' deployment
strategies and itinerary design include the seasonality of cruise demand, optimal cruise
vacation periods, the balance between sailing and anchoring times, the presence of wellknown ports of call, and customer satisfaction. At the same time, cruise lines consider
port anchoring capacity, accessibility at sea, port-to-port distances between ports of call,and connectivity with air service (Rodrigue and Notteboom, 2013).
Table 1-5 Previous studies on the conditions required for cruise itineraries
Itinerary design factors

Source

•
•
•
•
•
•
•
•

Minimizing operating costs and/or maximizing revenue
Seasonality in demand, the optimal duration
Balance between sailing time and shore time
Existence of ‘must see’ destinations
Overall guest satisfaction
Berthing capacity and nautical accessibility
Distance between ports of call
Synchronization with air transfers

RodrigueandNotteboom,
2013•

Vessel operating speed, embarkation port, voyage duration,
spatial pattern of destination ports
Location of embarkation ports, relative to destination ports
Vessel speed and the number of days

Marti, 1990

Seeking new destinations consider the geopolitical factors andinstitutional stability
Security level of ports and tourist hinterlands to provide“secure-comfortable” itineraries

Pallis, 2015

Mix of must-see ports, marquee ports and discovery ports
Must-see locations/marquee ports are world-famous ports
Discovery ports are not world-famous

Rodrigue etal., 2013

•
•
•
•
•
•
•

Source: Rodrigue and Notteboom (2013), Marti (1990), Pallis (2015), Rodrigue et al.
(2013)
1.3.3. Cruise sector analysis using AIS data
Few studies have used Automatic Identification System (AIS) data for analysis in cruise
shipping. Tichavska and Tovar (2015) used AIS data to measure the pollution status of
exhaust gas from cruise ships calling at the Las Palmas Port in the Canary Islands.
Vicente-Cera et al. (2019, 2020a) arranged the cruise ship’s operating hours, repair
times, and berthing times, estimated seawater pollution status by cruise ships, andassessed environmental pressures related to global cruise traffic along their paths basedon AIS data. Vicente-Cera et al. (2020b) used AIS data to aggregate cruise ship calling15

patterns at European ports and evaluated the diversity of cruise ship calls at each port.
Ito et al. (2020) organized the port call patterns before and after the suspension of cruise
ship operations by COVID-19 and analyzed the relationship between cruise ship
operations and the spread of infection at the port of call using AIS data.
1.3.4. Network science techniques for the cruise sector
Few studies have analyzed cruise ship networks using network science techniques.
Tsiotas et al. (2018) showed the double role of the cruise network, which is composedof the profit-driven strategies of cruise companies and port authorities, using data fromthe 2013 itineraries of Costa Cruises and MSC Cruises in the Mediterranean cruise
market. Jeon et al. (2019) investigated the centrality of cruise ports in the Asian cruise
shipping market while proposing the hubs and authorities centrality (HACC) metric as adirectional synthesis of the hubs centrality and authorities centrality to explore cyclicaland directional characteristics of centrality in the cruise shipping network. In a recent
cruise network study, Kanrak and Nguyen (2021) revealed that the cruise shipping
network is scale-free using itinerary data from Asian and Australian cruise network
websites. Lopez Rodriguez et al. (2021) suggested that Caribbean ports are the mostimportant concerning hub and authority centrality, using 2018 itineraries for each cruiseline from the sites for 902 ports in the Caribbean and Northern Europe.
1.3.5. Originality
Thus, Marti (1990), Manning (2006), Wang et al. (2014), Castillo-Manzano et al.
(2014), Gui and Russo (2011), and Lekakou et al. (2009) have examined cruise line port
selection behavior in terms of port specifications and necessary conditions for ports,such as transportation access and tourism resources, but not as a network. Rodrigue andNotteboom (2013), Marti (1990), Pallis (2015), and Rodrigue et al. (2013) also analyze
cruise line port selection behavior from an itinerary perspective, including revenue
maximization, cost minimization, and passenger satisfaction, but not as a network.
Tichavska and Tovar (2015) and Vicente-Cera et al. (2019, 2020a) analyze cruise ship
movements using AIS data for purposes such as measuring the environmental impact ofships, but not for changes in network structure. Furthermore, Tsiotas et al. (2018), Jeonet al. (2019), Kanrak and Nguyen (2021), and Lopez Rodriguez et al. (2021) analyzeeach port as a network to understand its position and role in the region but do not
analyze changes in the network structure.

16

Therefore, the actual movements of cruise ships are tracked by AIS data, and theaggregated data is captured as a network to analyze the spatial and temporal changes inthe structure of the cruise network based on the number of ports and routes in aparticular sea area, the existence of hub ports, connections between ports, and responses
under contingency situations.
Analyzing the movements of all cruise ships operating in a given area as a network will
enable a more realistic understanding of the situation in the area, including the numberof ports and routes, the existence of hub ports, connections (communities) among ports,
and emergency responses, compared to analyzing the movements of a single cruise shipon an itinerary-by-itinerary basis. This enables us to grasp the situation of the sea area
based on the actual situation. This allows us to understand the growing areas and timing
in which cruise lines will increase their deployment of cruise ships, develop routes,
strengthen the functions of hub ports, etc., and continue to invest in the future. This will
reduce the uncertainty of future cruise demand and eliminate ports that will not accept
cruise ships once they are in service. This is the significance and originality ofanalyzing in a network.

1.4. Objectives
In the supply-driven cruise industry, many ports invest huge amounts of money in theconstruction of port facilities such as passenger terminals in anticipation of future
demand. Each port learns from previous studies on port selection factors and itinerary
design conditions by cruise lines so as not to waste these investments. However, someports are not used after construction. Why are some ports not used after service, eventhough the cruise ship meets the factors that call at the port and the design conditions of
the itinerary? A possible problem with this question is that the cruise industry on the
supply side does not have sufficient information to decide whether to invest in port
improvements.
Other possible causes include spatial and temporal changes in cruise lines' strategies,
seasonal factors that cause ships to shift to distant areas, political factors, outbreaks ofinfectious diseases, and other contingencies that close ports or disrupt shipping routes.
The movement of cruise ships influenced by these macroeconomic conditions and theirinteractions through competitive relationships cannot be understood simply byanalyzing the itineraries of each (single) cruise ship. Analyzing the movements of all17

cruise ships operating in a given area as a network would provide a better understandingof the network structure in a given area.
The following are examples of what can be learned by aggregating the movements of all
cruise ships operating in a given area as a network, rather than the itineraries of a single
cruise ship.
・ The number of nodes represents the number of ports where cruise ships havecalled, and provides an understanding of the progress being made in port
development and port of call development in the sea area. The higher the numberof nodes, the greater the number of ports where cruise ships call and the greater
the diversity of ports of call.
・ The number of edges represents the number of routes that cruise ships havesailed between ports, indicating the progress made by cruise lines in developing
routes in the sea area. The higher the number of edges, the more routes cruise
ships are navigating, meaning that the routes are more developed.
・ The average degree indicates the number of routes in service per port andindicates the multidirectional nature of the shipping routes in the sea area. The
higher the average degree, the more routes are connected per port, meaning thatthe routes are more multidirectional.
・ Density represents the ratio of the number of actual routes to the number ofroutes that can be served between ports in the region and indicates the progressthat cruise lines are making in developing routes with the development of ports
in the region. If the number of ports is constant and the density is high, it meansthat the development of routes by cruise lines is advanced. If the development ofroutes is more advanced than the development of ports, the density is higher.
・ Degree centrality represents the number of routes served by each port, indicatingthat ports with high degree centrality are hub ports that play a central role withroutes connecting to many ports. A port with a high degree centrality means that
it is a hub port with a large number of route connections to neighboring ports.
・ Degree centralization represents the degree of bias in the number of routes
18

connecting to a port in a sea area, with areas with a high degree centralization
being heterogeneous (routes are biased toward hub ports). A high degree ofdegree centralization means that the development of routes by cruise lines isbiased toward hub ports.
・ Community is the same group of ports that are closely connected by routes, andthe quality of community division can be measured by modularity. The higher themodularity of the sea area, the higher the quality of the division of thecommunity that closely connects the ports.
・ The average clustering coefficient represents, for example, the percentage of
cruise ships that have routes in service between Port B and Port C that also haveroutes in service between Port A and Port C. However, the cruise ships that sailbetween each port are not necessarily the same ship or the same itinerary. A sea
area with a high average clustering coefficient has many identical itineraries
connected by triangular routes (A→B→C→A), or cruise ships of competing
carriers are in service but are connected by routes. A higher average clustering
coefficient indicates a more triangular route, i.e., an itinerary consisting of two
ports of call for one arrival and departure port, or a network with many routes
that are not connected to the same itinerary but appear to be triangular. Areas
with high average clustering coefficients may tend to have more short cruise
itineraries with only two ports of call and a short number of days compared toareas with low average clustering coefficients.
・ Diameter represents the maximum number of routes leading from one port toanother without considering connections on the same itinerary, and waters withlonger diameters tend to have different cruise lines and ships calling different
ports from each other. Direct connections between ports in the itinerary are nottaken into account, but the longer the diameter of the sea area, the longer thereach where the ports are connected.
・ The average shortest path length represents the minimum number of routes thatlead from one port to another without considering connections on the sameitinerary, and areas with shorter average shortest path lengths tend to have tocompete for shipping lines and ships calling only at similar ports. Directconnections between ports in the itinerary are not considered, but the shorter the19average shortest path length, the shorter the number of reaches that the ports areconnected to.
This study analyzes not a single itinerary, but a network of all ports and ships operating
in a given area. All cruise ship movements are analyzed as a network of aggregated AIS
data. This may allow us to understand the changes in the network structure over time,
geography, seasonality, and under contingency, reflecting the impact of market
conditions, competitive relationships, and other factors.
Whether or not cruise ships call at a port after the port has been developed is not only
determined by the response of cruise lines to the requirements and itinerary designconcepts of the port, but is also affected by the movements of all cruise ships operatingin the entire sea area, including those attracted by the port in question, and thedevelopment of surrounding ports. The movements of all cruise ships operating in aparticular sea area are tracked by AIS data and viewed as a network, and the results ofanalysis of spatial and temporal changes in the structure of the cruise network can be
useful information for understanding the state of the entire sea area. By using several
methods of network science, the number of ports and routes in a sea area, the number ofroutes per port, the percentage of port groups in a triangular relationship, hub ports
playing a central role, and port groups (communities) with dense route connections canbe measured in time and space, so that suitable port development timing and location
can be inferred. This could lead to more efficient port development, etc., by reducing
the uncertainty of future demand related to the port in question and possibly reducing
the risk of cruise ships not coming to the port after it is in service.
Why would a network analysis reduce the number of ports that do not receive cruise
ships? This is because, by capturing the trajectories of all cruise ships navigating in thearea as a network and measuring the areas of growth and timing, it is possible to preventa situation where cruise ships do not arrive after the port is developed. This is notpossible with the movement of a single cruise ship or the allocation behavior of a single
cruise ship company. Growth areas are places (or communities) where cruise lines are
increasing the number of ships and developing routes, and if a port is planned to be
developed in such an area, it is more likely that cruise ships will continue to call at theport after it is put into service than in a non-growth area. In addition, the timing ofgrowth is a time when cruise lines are deploying ships and developing routes, andplanning port development at a time when the market is growing makes it more likely
20

that cruise ships will arrive after the port is put into service than at a time when themarket is stagnant.
The network indicators of these growing locations and timing indicate that the numberof nodes, edges, average degree, density, etc. are all high and increasing. In addition, theregion has hub ports with routes connecting to ports in many directions and is in theprocess of acquiring more routes. Conversely, the average clustering coefficient,diameter, average shortest path length, and degree centralization are determined by thecharacteristics of the geographical situation in which each port is located and thecharacteristics of the cruise ships it targets. Therefore, the purpose of the dissertation isto understand the spatial and temporal changes in the structure of the cruise network. To
this end, the movements of cruise ships are tracked by AIS data, and the aggregated data
are viewed as a network to understand the spatial and temporal changes in the structureof the cruise network based on the actual situation of the number of ports and routes in aparticular sea area, the existence of hub ports, connections between ports, and responses
under emergencies.
To this end, this dissertation proposes the following four sub-objectives.
l

Sub-objective 1 (SO1): To clarify the differences in the structure of the cruise
network between normal and contingency situations. The THAAD event, which has
been stopping routes between China and South Korea since March 2017, is a casestudy.
SO1 proposes a method to analyze the differences in cruise line operational andcommercial connections between ports under normal and contingency
conditions using AIS data. This is because the cruise industry is characterized as
fragile and vulnerable. A case in point is the THAAD event that has stopped
cruise ship calls from China to South Korea since March 2017 (Crew center,2017). Before the THAAD event, port-to-port connections between Shanghai,Jeju, and Hakata were the most frequent in Northeast Asia. However, after
March 2017, cruise ship calls from China to South Korea suddenly stopped, andthe popular shipping route between Shanghai and Jeju disappeared. The
structure of the cruise network is analyzed using AIS data to determine how it
differs between ports during normal and contingency periods.

21lSub-objective 2 (SO2): To observe changes in the cruise networks during the earlystages of the COVID-19 outbreak, such as port closures. The relationship between
the cruise industry and the COVID-19 outbreak is analyzed.
SO2 proposes a network-wide impact of port closures in one area. The
movement of cruise ships has the potential to be a major trigger of coronavirus
disease (COVID-19) outbreaks. The U.S. Centers for Disease Control andPrevention (CDC) banned cruise ship operations in waters surrounding theUnited States on March 14, 2020 (CDC, 2020a). Since the area around the
Caribbean Sea from Florida is the center of the global cruise market, this rule
has resulted in the cessation of cruise ship operations around the world. The
ports of Shanghai at the end of January 2020 and Japan and Taiwan at the
beginning of February 2020 had already refused to allow cruise ships to call attheir ports (Cruise Industry News, 2020a, 2020b, 2020c, 2020d). The reason was
to prevent the spread of the COVID-19 infection by cruise passengers aroundthe ports. In other words, these countries wondered if there might be somerelationship between the landing of cruise passengers and the spread of infectionin port cities. How the closure of the port affects the entire cruise network isanalyzed by observing cruise ship operations from January to March 2020.

l

Sub-objective 3 (SO3): To identify changes in spatial and temporal changes in the
structure of the cruise network by ship size. A case study is the Northeast Asiancruise market 2014-2019, which has experienced rapid growth in recent years.
SO3 proposes a method to understand the spatial and temporal changes in the
structure of the cruise network. How connections between ports in the cruise
network vary geographically and temporally with ship size are analyzed. As acase study, the focus is on Northeast Asia, where the cruise market has been
expanding rapidly in recent years. By using some network science techniques, it
may be possible to analyze changes in spatial and temporal changes in the
structure of the cruise network in Northeast Asia by ship size.

lSub-objective 4 (SO4): To understand how seasonality changes the structure of thecruise network by ship size around the world, both spatially and temporally. A casestudy is the global cruise network in 2019.

22

SO4 proposes a method for analyzing seasonal geographic deployment area
differences in the spatial and temporal structure of the cruise network. AIS data
for all cruise ships operating worldwide in 2019 is divided into four seasons andanalyzed for geography, seasonality, etc. of the cruise network structure using
several network science methods. Through this analysis, annual patterns ofspatial and temporal structural change in the cruise network are elucidated byship type.
These results may lead to more efficient port development, etc., as they may reduce theuncertainty of future demand related to the port in question and reduce the risk of cruise
ships not coming to the port after it is in service.

1.5. Dissertation outline
The rest of this dissertation is organized as follows.
Chapter 2 presents an analysis of changes in the cruise network in Northeast Asia during
the route disruption due to the THAAD event using AIS data from 2016 to 2018.
Chapter 3 analyzes the changes in the global cruise network during the early stages of
the COVID-19 outbreak, such as port closures using AIS data from January to March
2020.
Chapter 4 describes an analysis of the spatial and temporal changes in the structure of
the cruise network in Northeast Asia, using AIS data from 2014 to 2019.
Chapter 5 analyzes the changes in the global cruise network due to seasonality, using
AIS data from all cruise ships worldwide in 2019.
Finally, Chapter 6 concludes the dissertation. The overall picture of the dissertation is asfollows.

23

Chapter 1
Introduction
・Background, Literature review
・Objectives etc.

Emergency situations

Temporal, geographic, andseasonal(Sub objective 1)(Sub objective 3)

Chapter 2
Cruise network in case of routedisruption in Northeast Asia

Chapter 4
Spatial and temporal changes in
the cruise network structure in
Northeast Asia

(Sub objective 2)(Sub objective 4)

Chapter 3
The cruise industry and the
COVID-19 outbreak

Chapter 5
Changes in the global cruise
network due to seasonality

Chapter 6
Conclusion
・Limitations
・Future works etc.
Figure 1-6 Dissertation framework242. CRUISE NETWORK IN CASE OF ROUTEDISRUPTION IN NORTHEAST ASIA
2.1. Introduction
Around 3,300 Chinese passengers refused to leave the COSTA SERENA at Jeju port inSouth Korea on March 11, 2017, in a spontaneous protest against South Korea’s plans to
install the THAAD missile system in the country in response to North Korea’s missile
development programs (Crew center, 2017). A few days after the event, cruise ships
from China stopped calling South Korea, and even after more than five years, cruise
ships have not yet gone to South Korea.
The route between China and South Korea played an important role in the Northeast
Asian cruise industry. Figure 2-1 shows the total number of voyages by partnercountries in China and South Korea from 2014 to 2016. There were 1,336 voyages
between the Chinese port and the Korean port. For China, the number of shipping routes
connected to South Korea accounted for 37% of all routes. For South Korea, the numberof shipping routes connected to China accounted for 47% of all routes. The disruptionof the shipping route, which occupied such a large share, should have had a great impact
on the port selection behavior of the cruise line.

China, 334 ,9%Taiwan, 68 ,2%

Hong Kong,
32 , 1%

Taiwan, 16 ,
1%

Korea
, 338 ,
12%

Hong Kong,
341 , 9%Japan, 1,537 ,43%Japan, 1,119 ,39%

Korea, 1,336 ,37%China, 1,336 ,47%[China][South Korea]
Figure 2-1 Total number of voyages by country from 2014 to 2016
Source: IHS Maritime AIS data (2020)
Understanding the port selection behavior of cruise lines in emergencies, which is25different from normal times, is important in building a risk-resistant cruise market
where cruise operations do not stop even if the port is closed or the route is interrupted.
By analyzing an itinerary constructed by connecting multiple ports rather than a single
port, it may be possible to understand the port selection behavior of cruise lines in amore realistic emergency.
There are many previous studies on cruise lines' port selection factors and theirapproach to itinerary design. Marti (1990), Manning (2006), Wang et al. (2014),
Castillo-Manzano et al. (2014), Gui and Russo (2011), and Lekakou et al. (2009) foundthat port specifications, transportation access, and tourism resources are necessary
conditions for ports to be selected by cruise lines. Rodrigue and Notteboom (2013),
Marti (1990), Pallis (2015), and Rodrigue et al. (2013) identify conditions for cruiseline itinerary design in terms of maximizing revenues, minimizing costs, and improving
passenger satisfaction. However, all of the previous studies are aimed at port selection
behavior and itinerary design during normal times, not in an emergency.
The purpose of this chapter is to see how major port interruptions affect cruise lines’
port selection behavior. An example of a major port disruption would be the March2017 THAAD event. AIS data is used to track the movements of cruise ships before andafter the THAAD event. Cruise ships featured in this chapter are several ships scheduled
to call South Korea in the “2017 Jeju Port Booking List” published before the THAAD
event. Further, time-series itinerary data for arrival and departure ports and ports of callis generated.
Our research question is “When cruise lines cannot visit desired ports due to sometrouble, what are their priorities, and how do they recover? How does cruise line’s port
selection behavior under these risks change over time?” In particular, this chapter asks
three questions. (1) Immediately after the THAAD event, what is the priority of cruiselines to keep their cruise business? (2) How long does it take to recover to an original
operating level? (3) How does the cruise market in Northeast Asia change because of
the THAAD event? In this chapter, the analysis is based on the BCP concept proposedby the Cabinet Office, Government of Japan (2013). Because the BCP is a concept for
integrated business continuity and disaster recovery planning for efficient and effective
resuming and recovery of critical operations after being disrupted.
The rest of this chapter is organized as follows. Section 2.2 presents a literature review.
26

Section 2.3 outlines methodologies such as AIS data. Section 2.4 presents the results
and discussion of the chapter. Finally, section 2.5 provides conclusions.

2.2. Literature review
2.2.1. Creation of cruise products
Rodrigue and Notteboom (2013b) argue that the cruise industry sells itineraries, notdestinations. The cruise industry is challenged to develop competitive cruise packages
but at the same time, they have to optimize the deployment of their cruise ship fleet
given minimizing operating costs and/or maximizing revenue per passenger slot.
Esteve-Perez and Garcia-Sanchez (2018) mention that cruise lines focus on seeking new
ports to meet demand in an attempt to create differentiated products based on the portsthat compose the itinerary. Further, Vogel and Oschmann (2012) explain that cruisedemand has always been “supply-led”, starting with the invention of leisure cruising bypassenger shipping lines whose scheduled transatlantic services were losing passengers
to the airlines. Similarly, Rodrigue and Notteboom (2013b) analyze that the cruiseindustry works in a “supply push mechanism” as cruise lines aim to generate demandfor cruises by providing new products (itineraries) with a larger and more diversified
range of ships.
Bagis and Dooms (2014) point out that the cruise industry continually needs tointroduce new itineraries and ships with new amenities and destinations as well as
redeploy older and smaller vessels in their itineraries. Then, Pallis (2015) explains thatcruise lines wish to create itineraries that include ports of different sizes, as each type of
port provides different types of experiences by blending different types of attractivenessand permitting future passengers to select from among various options to access thedeparting port. In a set of ports of call, there is a mix of “must-see” ports/marquee ports
and discovery ports; each type differs according to the tourism attractiveness of the port.
“Must-see” locations/marquee ports are world-famous ports that are necessary for every
itinerary. Discovery ports are not world-famous but offer the sense of discovering an
unknown treasure (Rodrigue et al, 2013a). In addition, Marti (1990) looks at four
components – vessel operating speed, embarkation port, voyage duration, and thespatial pattern of destination ports – set the bounds on cruise-ship itineraries.
2.2.2. Port of call selection
The selection of the cruise port place is a very important and complex problem.
27

Manning (2006) suggests that the main influencing factors include the key natural andcultural assets of the port, port facilities, location access to other destinations and thehomeport, security, infrastructure (vehicles, well-trained multilingual guides, andcoordinators, etc.), provisioning (local supply of food, drink, and clean water), port
costs (dockage fees, etc.), and marketing (the variety of itineraries available forpassenger selecting). The location of embarkation ports, relative to destination ports,
along with vessel speed and the number of days allocated to complete each round-trip
voyage also governs how many ports can be visited. The norm in most popular cruisingareas on a seven-day voyage is three to four different ports of call (Marti, 1990). Bagis
and Dooms (2014) point out that cruise ports compete within the limits of certain
geographic regions. These limits are mainly shaped according to the location of theregional homeports.
When choosing and adding a new port of call to the itinerary, cruise lines consider the
improvement to the tourist experience offered and the effect on profitability over boththe short and medium term. In addition to port charges, cruise lines are strongly
concerned about the quality and security issues of their passengers. Their requirements
often include a wide range of dedicated infrastructures and services such as port entry
features, customs, and immigration handling, docking and anchorage facilities, service
and port area infrastructure, airports, taxi fleets, coach services, and high-quality shore
excursions and shopping area (Gui and Russo, 2011). Lekakou et al. (2009) insist that
security and safety are major issues in the contemporary transport industry. The
significance of air connections and reliability of air transport are expected results, as
they are related to the leading “availability of international airport” criterion. Wang et al.
(2014) showed that “tourism attractions” was the most considerable issue taken into
consideration when a cruise ship is selecting a port of call location. Castillo-Manzano etal. (2014) study the determinants that affect the capacity of ports to attract cruise ships
in Spain. The conclusion was that the likelihood of having cruise traffic was linked toports located in populous areas and close to large airports, ports not specialized incontainer traffic but share facilities with ferries traffic, and ports having a minimum
depth of water.

28

2.3. Methodology
Since the end of March 2017, cruise ships from China to Korea have not called yet.
Therefore, this chapter analyzes the movement of the cruise ship before and after the
THAAD event. The year before the THAAD event from April 2016 to March 2017
(FY2016) occurred, is defined as “Before.” After THAAD is defined as April 2017 toMarch 2019 (FY 2017 to FY2018).
2.3.1. What is the BCP concept?
Figure 2-2 shows the BCP concept proposed by the Cabinet Office, the Japanese
government. This Business Continuity Plan (BCP) is a plan describing the policy, systems,
procedures, etc. by which enterprises can avoid suspension of their critical business or
can recover the critical business quickly if it is interrupted, even when contingencies arise,
including natural disasters such as major earthquakes, communicable disease pandemics,
terrorist acts, serious accidents, disruption of supply chains and abrupt changes in abusiness environment, or they can recover business quickly if their business is interrupted.
For example, if a large-scale disaster occurs, demand for certain products and services
will increase compared with normal times, or demand for one company’s products andservices will temporarily increase if other companies in the same business are hit by thedisaster. Therefore, the operating rate may exceed 100% in response to such an increasein demand. Sahebjamnia et al. (2015) propose the BCP concept as a novel framework for
integrated business continuity and disaster recovery planning for efficient and effective
resuming and recovering of critical operations after being disrupted.

Figure 2-2 Business continuity plan
Source: The Cabinet Office, the Japanese government (2013)29

Figure 2-3 depicts an image of a recovery curve of the THAAD event regarding the BCP
concept. The horizontal axis indicates time, and the vertical axis indicates operating level.
The time indicates before and after the THAAD event, and the operating level indicatesthe degree of business continuity for the cruise line in Northeast Asia.

Operating level

100%

THAAD
▼ How long was recovery?
← Ideal recovery curve(early recovery)50%
How much damage?

← Actual recovery curve

How to recover early?
0%

FY2016FY2017FY2018Time

Figure 2-3 Concept of cruise business continuity plan
2.3.2. What indicator should be used to evaluate the operating level?
What indicator should be used to evaluate the operating level for the Northeast Asiancruise lines? In simple terms, the operating level in the "cruise market" will be calculatedby the number of arrivals and departures of cruise ships and the number of passengers
demanded by cruise ships. However, the operating level defined by the "cruise line" will
be different from the operating level in the "cruise market". This is because the operatinglevel for the "cruise line" is not just the number of passengers or the number of port calls,
but profitability and passenger satisfaction.
Lekakou et al. (2009) point out that passenger satisfaction is only one of the major reasonsfor a cruise line to select and change itineraries. Rodrigue et al. (2013a) analyze that cruiselines are challenged to develop competitive cruise packages but at the same time, they
have to optimize the deployment of their cruise ship fleet given minimizing operating
costs and/or maximizing revenue per passenger slot. According to Bagis et al. (2014), tomaximize the commercial potential and utilization of the ship assets, the selection of portsof call and itineraries are pondered carefully. Yet, Gui et al. (2011) suggest when choosing
and adding a new port of call to the itinerary, cruise lines consider the improvement to
the tourist experience offered and the effect on profitability over both the short andmedium term. Based on this literature, the operating level is defined as a situation wherea cruise line can achieve both high profitability and high passenger satisfaction.

30

Based on this definition, how should the operational level of Northeast Asian cruise lines
be evaluated? From the viewpoint of minimizing operating costs, the optimization ofsailing distances in designing an itinerary is a key question because fuel costs have amajor impact on the total shipping costs and fuel consumption has an exponential
dependence on sailing speed (Esteve-Perez and Garcia-Sanchez, 2018). On the other hand,in terms of maximizing revenue, the main sources of cruise revenue are ticket sales,
passenger transportation to and from the ships, on-board sales, cancellation fees, as well
as sales of insurance, and pre-and post-tours (Vogel, 2012). Furthermore, from a customer
satisfaction perspective, there are differences in preferences between first-time cruisers,
who tend to demand more intensive port itineraries, and more experienced cruisers, who
appear to prefer a more relaxed itinerary with more days at sea (Cartwright and Baird,
1999; Lingard, 2002; Haller, 2005).
Based on such preliminary research, the realization of the operating level in Northeast
Asia can be measured by, for example, the following indicators. (1) Cost minimization:
port distance, ship speed. (2) Maximizing revenue: ticket prices, pre-, and post-tour sales.
(3) Passenger satisfaction: number of ports of call per itinerary. In this chapter, the numberof ports of the call per itinerary is adopted as an indicator to measure the recovery of theoperating level, taking into account the limitations of data. According to the Shanghai
International Cruise Business Institute (2017), 90% of Chinese cruise passengers are newcustomers. In other words, since most passengers are first cruisers, it is defined that anitinerary with more ports of call per trip is more satisfying than an itinerary with fewer
ports of call.
In this chapter, the operating level is defined as the percentage of itineraries that call at
more than one port. The operating level (OL) of the cruise market in Northeast Asia
includes the total number of trips (Nm) for services calling at multiple ports (m) and thenumber of trips (Ns) for services calling at only a single port (s). It is the number divided
by the number of itineraries. The formula is as follows.
𝑁!
𝑂𝐿 =
(1)
𝑁! + 𝑁"
2.3.3. Movement of cruise ship using AIS data
There is no data on the calling routes of cruise ships in Northeast Asia. The annual reports
compiled by CLIA (2018; 2019) are based on plans and not actual data. In addition,although the data on the number of calls by cruise ships at each port is publicized, there31

is no data on the network on where to come from and where to go. Therefore, changes in
the cruise market in Northeast Asia are analyzed by organizing data on ship movements
using AIS data. This aggregated data was picked up from cruise ships around the world
registered as “Passenger (Cruise) Ship” in a database called IHS Maritime Sea-web(2020), and the data were organized using International Maritime Organization (IMO)
ship identification number. AIS was introduced to enhance safety at sea, improvenavigation efficiency and protect marine environments. IMO International Convention
for Safety of Life at Sea (SOLAS) requires AIS to be fitted onboard international
voyaging ships above 299GT.
Jeju port in Korea is the busiest port in Northeast Asia before the THAAD event. Table
2-1 is a compilation of the “2017 Jeju Port Booking List” published by Jeju Special SelfGoverning Province on February 28, 2017. This booking list is a list of cruise ships thatcruise lines have pre-booked and have been allowed to use the berths of Jeju Port. The
data consists of the following items. Date/ Ship's name/ Arrival time/ Departure time/
Former port/ Next port/ Berthing time. At this time, 525 cruise ships are to call at Jeju
port by the end of 2017. The COSTA SERENA operated by Costa Cruises in Italy is 82
times, the GLORY SEA operated by Diamond Cruise in China is 81 times, the COSTAATLANTICA operated by Costa Cruises in Italy is 75 times, the SKYSEA GOLDENERA operated by the SkySea Cruises in China is 72 times, and the CHINESE TAISHAN
operated by Bohai Cruise in China is 55 times. This chapter tracks the movement of these
five cruise ships before and after the THAAD event. Incidentally, these five ships accountfor 70% of Jeju port’s 2017 bookings.
Table 2-1 Top 5 Cruise ships by number of bookings to Jeju port in 2017
No.

Ship Name

Number of Calls

1
2
3
4
5

COSTA SERENAGLORY SEACOSTA ATLANTICASKYSEA GOLDEN ERACHINESE TAISHAN
Others
Total

82
81
75
72
55
160
525

Source: Jeju Port Booking List (2017)32

Cumulative
percentage
16%
31%
45%
59%
70%
70%
100%

2.4. Results and discussion
2.4.1. The number of arrival and departure, port of calls before and after THAAD
Figure 2-4 shows the location of major cruise ports in Northeast Asia.
DalianTianjin

Korea

YantaiQingdao
Lianyungang
Shanghai

China
ShekouHaikouJapan
Tokyo/Yokohama

Busan

HakataOsaka/Kobe
Jeju
SaseboNagasakiKagoshima

NingboNaha
XiamenHirara(Miyakojima)
Keelung Ishigaki

Hong Kong

TaiwanFigure 2-4 Major cruise ports in Northeast Asia
Figure 2-5 shows the number of arrival and departure by the port in Northeast Asia. In
FY2016, Shanghai port top with more than 500 times, followed by Tianjin port, Hong
Kong (SAR), and Keelung with more than 100 times. It can be seen that many cruise
ships depart from Shanghai port before the THAAD event. After the THAAD event,while the number of arrival and departure at Shanghai and Tianjin ports in northern
China dropped, the number at Keelung port, Tokyo and Yokohama ports, and Xiamen
and Shekou ports increase. In other words, after the THAAD event, the arrival anddeparture ports tend to move south.
600
500

400
300
200
100
0

FY2016FY2017FY2018Figure 2-5 Trends in the number of Arrival and Departure at the top 10 ports
Figure 2-6 shows the number of ports of call by port in Northeast Asia in FY2016. Jeju
33

port in Korea tops with more than 500 times, followed by Hakata port in Japan withmore than 300 times, and Nagasaki port in Japan with more than 200 times. It can beseen that many cruise ships visit Jeju port before the THAAD event. After the THAAD
event, the number of calls at Korea’s Jeju and Busan ports drops sharply, while thenumber of calls at Japan’s Okinawan archipelagos Naha, Ishigaki, and Hirara ports
increased. In other words, it can be seen that the port of call tends to move southward
after the THAAD event.
600
500
400
300
200
100

0

FY2016FY2017FY2018Figure 2-6 Trends in the number of calls at the top 10 ports
2.4.2. Movement of the five cruise ships
The movement of the five cruise ships booked at Jeju port before the THAAD event isanalyzed. Here, the location of the itinerary, which is a combination of the country
where the port of arrival and departure and the port of call is located, and the trend ofchanges in the number of times are analyzed.
a) COSTA SERENAIn Figure 2-7, the front side shows the departure and arrival ports and ports of call, andthe numbers in the figure show the number of quarterly itineraries for the three years
from FY2016 to FY2018, from April 2016 to March 2019. The bottom rows show thenumber of itineraries and itinerary patterns per quarter. The COSTA SERENA goes toboth a port in Japan and a port in Korea from the Shanghai port in FY2016. Some of theitineraries include a port for Korea only and a port for Japan only. However, after the
THAAD event, the COSTA SERENA does not change the arrival and departure from/toShanghai port in FY2017 but changed it to a pattern that calls only at a single port inJapan. In FY2018, it is gradually improving its itinerary to call at two or more ports inJapan. The bottom row shows the number of arrival and departure per quarter. There is
no change between FY2016 and FY2017 before and after the THAAD event, indicating34

that the sailing days do not change. However, the number gradually decreases in
FY2018, indicating that the sailing days extend. In addition, the other bottom row
shows the number of itinerary patterns per quarter. There is no change between FY2016
and FY2017 before and after the THAAD event, indicating that the itinerary patterns do
not change. However, the number gradually increase in FY2018, indicating that they are
seeking for new ports to create a new itinerary.
▼ THAAD
Arrival & Departure
North
China

East
China

Ports of Call

1
2
3
Japan
TianjinJapan Japan
QingdaoJapan
Korea Japan
Korea
Korea Korea
Korea Korea Japan
Shanghai
Korea Japan Japan
Japan
Japan Japan
Japan Japan Japan
Total number of itineraries

Q1

19
1
1

BEFOREFY2016
Q2 Q3

14
3
2
1
1

2

23

21

Q4

Q1

FY2017
Q2 Q3

13
6

6
2

5

2
4
2

21
1

19
3

20
2

24

16

22

22

22

Number of Itineraries
AFTER
FY2018Q4 Q1 Q2 Q3 Q4
1
4
1

10
6
2
18

5
8
1
14

8
10
1
19

9
11

3
7

20

16

Figure 2-7 Ship movement (COSTA SERENA)b) GLORY SEAThe GLORY SEA goes to two ports in Japan and Korea from Shanghai and Qingdao
ports in China in FY2016. However, from FY2017 to FY2018, the GLORY SEA’s
arrival and departure ports change to Qingdao and Dalian, and after that, she operates
ocean-only itineraries with no ports of call and shift to Lianyungang. According toSeatrade Cruise News (2019) on March 16, 2019, “the Glory Sea, owned by Diamond
Cruise International Co., Limited, was placed under arrest by Shanghai Maritime Court
due to unpaid crew wages”. Following this news, her service has remained suspended.
The lower row of the chart showing the number of arrival and departure for this shipeach quarter cannot be analyzed due to variations in the numbers.

35

▼THAAD
Arrival & Departure

Ports of Call

1
2
3
Q1
Japan
North
DalianJapan JapanChina(At sea)
TianjinJapan
Korea
Korea Japan
Korea Korea Japan
Qingdao
Japan
Japan Japan(At sea)Japan
Lianyungang
East(At sea)
China
Korea JapanJapan
ShanghaiJapan China
China
Japan
NingboJapan Japan(At sea)Wenzhou JapanSouth(At sea)
HaikouChina(Outside Northeast Asia)
Total number of itineraries
0

BEFOREFY2016
Q2 Q3

3
1

3
5

11

Q4

1
4
1
3

FY2017
Q1 Q2 Q3
5
6
1
2
2
1
1

5
1

4

Number of Itineraries
AFTER
FY2018Q4 Q1 Q2 Q3 Q4

5

4

1

8

1
9
4

7
2

3
1

1
1
1

2
1
1

9

14

16

15

17

3
14

12
17
29

3
2
18

2

1

17

3

0

Figure 2-8 Ship movement (GLORY SEA)c) COSTA ATLANTICA
The COSTA ATLANTICA goes to two ports in Japan and Korea from Tianjin port in
FY2016. However, in FY2017, the arrival and departure port shifted from Tianjin port
to Shanghai port and then back to Tianjin port for a short time, but soon shift to Shekou
and Xiamen ports in southern China in FY2018. The bottom line of the chart shows thenumber of arrivals and departures per quarter. Although the number of arrival anddeparture increased in the first quarter immediately after the THAAD event, there wasno significant change from FY2016 to FY2018, indicating that the sailing days does notchange.

36

Arrival & Departure

North
China

East
China

South
China

TaiwanPorts of Call

1
2
3
4
Korea
Korea Japan
Korea Japan Japan Japan
Korea Korea
Tianjin
Korea Korea Japan
Korea Korea Korea
Japan
Japan Japan
Korea
Korea JapanJapan
ShanghaiJapan Japan
Japan Japan Japan China(At sea)
Japan
Japan Japan
Xiamen(At sea)(Outside Northeast Asia)
Japan
Japan Japan
HongKong
Shekou(At sea)(Outside Northeast Asia)
Keelung
Japan Korea Japan
Total number of itineraries

Number of Itineraries▼THAAD
BEFOREAFTER
FY2016FY2017FY2018Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
3
2
12 13
7
5
1
1
1
1
4
9
8
1
5
1
5 19 11
1
3
1
1
1
6
1
4
3
1
2
1
2
5
5
4
2
6
1
1
1
6
6
3
11
1
6 10
5
1
18 18 11 15 22 17 18 12 19 19 21 20

Figure 2-9 Ship movement (COSTA ATLANTICA)d) SKYSEA GOLDEN ERA
The SKYSEA GOLDEN ERA goes to two ports in Japan and Korea from the Shanghai
port in FY2016. However, in FY2017, along the way, she shifts to Xiamen and Shekou
ports, but she keeps the arrivals and departures from Shanghai port and shifts her port ofcall to one of Japan’s ports. The bottom line shows the number of arrival and departure
per quarter. No significant change from FY2016 to FY2018, indicating that the sailingdays do not change. According to Cruise Industry News (2018), “Royal Caribbean
Cruises and Ctrip announced that they were ending the SkySea Cruise Line joint
venture, which had carried well over 200,000 Chinese passengers since launching its
service in 2015. TUI AG’s Marella Cruises agreed to purchase the SKYSEA GOLDEN
ERA, with delivery expected in December 2018. After the sale of her, it is expected that
SkySea will wind down its business operations before the end of 2018.”

37

▼THAAD
Arrival & Departure

East
China

South
China
Korea

Ports of Call

1
2
3
Q1
Japan
QingdaoJapan Japan
Korea
Korea Japan
11
Korea Korea Japan
5
Korea Japan Japan
3
ShanghaiJapan
1
Japan Japan
Japan Japan Japan(At sea)
NingboJapan
Japan
XiamenJapan Japan(Outside Northeast Asia)
Japan
Shekou(At sea)
Busan
Japan Japan
Total number of itineraries
20

BEFOREFY2016
Q2 Q3

1
8
1
4
1
1
1

1
10
3
5
2

Q4

11
1
2
3
3

FY2017
Q1 Q2 Q3
2
2
1

6
3

7
5
1

Number of Itineraries
AFTER
FY2018Q4 Q1 Q2 Q3 Q4

14
5

8
3

13

1

1

2

2
4
1

1
1
1

4
2
2
5
3

17

21

20

1
17

19

10

20

20

15

0

0

Figure 2-10 Ship movement (SKYSEA GOLDEN ERA)e) CHINESE TAISHANThe CHINESE TAISHAN sails from many ports to wander in FY2016, and her ports ofcall at that time are two ports in Japan and Korea. However, in the first half of 2017, she
fixes Xiamen port as an arrival and departure port, but one year later, she shifts to
Dalian, Qingdao, Tianjin, Yantai, and Weihai ports in FY2018. During this time, herport of call is only one port in Japan. The bottom line of the chart shows the number ofarrivals and departures per quarter. Although the number of arrival and departure
increased in the first quarter immediately after the THAAD event, there is no significant
change from FY2016 to FY2018, indicating that the sailing days does not change.

38

▼THAAD
Arrival & Departure

North
China

East
China

South
China

Ports of Call

1
2
Korea
Korea Japan
Korea Japan
DalianJapan
Japan Japan(At sea)Japan
TianjinJapan Japan
Korea Korea
Yantai
Korea JapanJapan
WeihaiJapan
Korea
Korea Japan
Korea Korea
Qingdao Russia Korea
Japan
Japan Japan(At sea)
Korea Japan
NingboKorea Japan
Japan
Japan
Japan Japan
Xiamen(At sea)
China
Haikou (At sea)
Total number of itineraries

3

Japan

Q1
1
5
2

BEFOREFY2016
Q2 Q3
1

Q4

Q1

FY2017
Q2 Q3

Number of Itineraries
AFTER
FY2018Q4 Q1 Q2 Q3 Q4

4
1
1

11
1

5
3
1
12

1
10

2
1
1

1
3

Japan
Japan

2
2

2
11

2

1
1
1

Japan

1
6
1
4

16

20

4
1

2

1

15

5

19
20

15
5
2
1

7
8

23

15

0

0

14

18

14

16

Figure 2-11 Ship movement (CHINESE TAISHAN)
Thus, it is found that the port selection behavior is significantly different for each cruise
ship. The COSTA SERENA and the SKYSEA GOLDEN ERA have a fixed itineraryboth before and after the THAAD event at Shanghai port. On the other hand, the
GLORY SEA and the CHINESE TAISHAN have unfixed itineraries both before andafter the THAAD event. The COSTA ATLANTICA is easy to understand as cruise line’s
port selection behavior. Before the THAAD event, she is visiting Japan and Korea from
Tianjin port. However, as soon as the itinerary for Korea cannot be organized, the port
shifts from Tianjin port to Shanghai port. After arriving and departing from Shanghai
port for more than half a year, she moves further south to the Shekou and Xiamen ports.
In some cases, the number of sailing days is temporarily shortened immediately after
the interruption, and in some cases, it is extended more than one year after theinterruption. However, in general, there is no change before and after the THAAD
event.
2.4.3. Analysis of the recovery curve
Figure 2-12 shows the sum of the movements of these five cruise ships. In FY2016, the39year before the THAAD event, most of the itineraries visit two ports, Japan and Korea.
In the first quarter of FY2016, 72% of all itineraries for the five ships call at two ports
in Japan and Korea. 63% in the second quarter and 62% in the third quarter call at two
ports, Japan and Korea. However, in the first quarter of FY2017, 81% of all itineraries
were called at only one port in Japan. Since then, 70% in the second quarter and 69% in
the third quarter called at only one port in Japan. While the fourth quarter of Northeast
Asia is in winter, the numbers fluctuate as the cruise ship temporarily moves south, but
in the first quarter of 2018, still, 52% are in the second quarter. Even in the period, 55%called at one port in Japan.
The change occurs in the third quarter of FY2018. In the third quarter of 2018, a year
and a half has passed since the THAAD event stopped cruise ship calls from China to
Korea. Calls to two Japanese ports exceed the number of calls to one Japanese port.
46% of all itineraries call at two ports in Japan. And in the fourth quarter, 56% called at
two ports in Japan. In the previous chapter, the operating level for a cruise line wasdefined as one that offers both high profitability and high passenger satisfaction. In the
Northeast Asian cruise market, where most first cruisers are located, it is more attractive
to have more ports of call than a cruise package that has fewer ports of call per itinerary.
Percentage of calls▼THAAD
Arrival &Departure

Port of callpatterns
Korea

China

Japan
&
Korea
Japan
OthersPorts of Call
1
2
3
4
Korea
Korea Korea
Korea Korea Korea
Korea Korea Japan
Korea Japan Japan Japan
Korea Japan Japan
Korea Japan
Japan
Japan Japan
Japan Japan Japan(At sea)(Outside Northeast Asia)
Total

Q1
3%
4%
11%
7%
72%
4%

100%

BEFOREFY2016Q2
Q3
11% 10%
4%
1%
3%
4%
1%
6%
7%
63% 62%
9% 11%
1%
3%
1%
1%
1%
100%

100%

AFTER
Q4
3%

Q1

FY2017
Q2
Q3

Q4

Q1

FY2018
Q2
Q3

70%
24%
1%
3%
1%

69%
20%

52%
23%
1%
12%
12%

55%
16%
1%
17%
11%

33%
46%

22%
56%

6%
5%

30%
20%
3%
24%
23%

7%
14%

22%

100%

100%

100%

100%

100%

100%

100%

Q4

2%
5%
39%
22%
6%

81%
16%

23%

2%

100%

100%

Figure 2-12 Five ship movements
Figure 2-13 shows changes in the best three itineraries for each of the five cruise ships.
Before the THAAD event, all cruise ships call at Jeju port in FY2016 and have anotheritinerary at a Japanese port. The COSTA SERENA departs from Shanghai port for theitinerary in FY2016, calling at Jeju port first, and then calling at a single port in Japan(Hakata port, Nagasaki port, Kagoshima port). However, in FY2017 after THAAD, it
does not call at Jeju Port, but only at one port in Japan, and in FY2018 it changes to anitinerary calling at two ports, Hakata Port and Nagasaki Port. The GLORY SEA also40

calls at another port in Japan (Sasebo port, Shimonoseki port) based on Jeju port in
FY2016, but since FY2017 after THAAD, it is re-arranged to call at only one port inJapan. The COSTA ATLANTICA also calls at another port in Japan (Hakata port,
Nagasaki port) based on Jeju Port in FY2016, but since FY2017 after THAAD, it ischanged to an itinerary that calls only at one port in Japan. In FY2018, the port of call isswitched to the Okinawa archipelago in the southern part of Japan (Naha port, Hirara
port). The SKYSEA GOLDEN ERA is also located at Jeju port before THAAD andcalls at another Japanese port (Hakata port, Sasebo port). However, after THAAD, it
skips Jeju port and returns to one of the Japanese ports (Hakata port and Sasebo port,
Naha port). Before the THAAD event, the CHINESE TAISHAN is also an itinerarycalling at Jeju port and one of the Japanese ports (Sasebo port, Hakata port).
In other words, before the THAAD event, Jeju port is a marquee port for cruise ships
from China that can call Japan and Korea. However, no itinerary can be completed only
at Jeju port. It can be said that the pattern of calling at Jeju port plus another Japaneseport is a typical itinerary in Northeast Asia. However, after the THAAD event, it isfound that they cannot call at Jeju port, so they choose a single port in Japan andgradually shift to an itinerary to call at two ports in Japan. And it turns out that themarquee ports in Japan are Hakata port, Nagasaki port, Sasebo port, and so on.
Ship
Name
COSTA
SERENA
GLORY
SEA
COSTA
ATLANTICASKYSEAGOLDENERA
CHINESETAISHANFY2016
Top Arrival andDeparture

1
2
3
1
2
3
1
2
3
1
2
3
1
2
3

Ports of Call

Shanghai Jeju / Hakata
Shanghai Jeju / Nagasaki
Shanghai Jeju / Kagoshima
Shanghai Jeju / Sasebo
Shanghai Jeju / Shimonoseki
Qingdao Jeju / Sasebo
Tianjin Jeju / Hakata
Tianjin Jeju / Nagasaki
Shanghai Jeju / Hakata
Shanghai Jeju / Hakata
Shanghai Jeju / Sasebo
Shanghai Jeju / Kagoshima / Nagasaki
Haikou (At sea)
Qingdao Jeju / Sasebo
Qingdao Jeju / Hakata

FY2017Itinerary Arrival and%Departure

Ports of Call

28% Shanghai Hakata
13% Shanghai Nagasaki
10% Shanghai Kagoshima
31% Haikou (Out of Northeast Asia)
15% Qingdao Sasebo
15% Haikou (At sea)
42% Shanghai Hakata
13% Shanghai Nagasaki
10% Shanghai Kagoshima
24% Shanghai Sasebo
13% Shanghai Hakata
10% Xiamen Naha
27% Xiamen Hirara
10% Xiamen Naha / Hirara
8% Xiamen Nakagusuku / Hirara

FY2018Itinerary Arrival and%Departure

Ports of Call

29% Shanghai Nagasaki / Hakata
19% Shanghai Nagasaki
15% Shanghai Hakata
25% Lianyungang Sasebo
17% Lianyungang Nagasaki
16% Lianyungang (At sea)
17% Shekou (Out of Northeast Asia)
13% Shekou (At sea)
10% Shekou Naha / Hirara
24% Shanghai Sasebo
8% Shanghai Hakata
8% Xiamen Naha
55% Qingdao Hakata
18% Tianjin Sasebo / Hakata
16% Dalian Hakata

Itinerary
%

9%
7%
5%
21%
13%
11%
27%
24%
20%
23%
23%
9%
15%
15%
13%

Figure 2-13 Top 3 trends in itinerary patterns by ship
Figure 2-14 shows the average number of sailing days for each itinerary for five ships.
Although there is a slight change in Q3 of FY2017, there is no significant change before
and after the THAAD event. The average number of sailing days over three years is 5.3
days (4.3 nights). According to Sun (2014), the average cruise duration in the Chinese
market is 4.5 nights. The analysis in the previous section shows that the number of ports41

of call is reduced from a few ports to a single port immediately after the THAAD event.
Reducing the number of ports of call without changing the sailing days puts the highest
priority on keeping the schedule.
Sailing days each itinerary

7.0
6.0
5.0

4.0
3.0
2.0
1.0
0.0

Q1

Q2

Q3

Q4

Q1

Q2

FY2016Q3Q4Q1FY2017Q2Q3Q4FY2018Figure 2-14 Trends in the average number of sailing days each itinerary
Figure 2-15 shows the operating level, as measured by the percentage of itineraries
calling at multiple ports including the “At sea” itinerary where the cruise ship itself isthe destination. Before the THAAD event, this percentage is about 80% in FY2016.
However, it decreases to less than 20% immediately after the THAAD event. After that,it increases gradually, more than half in the third quarter of FY2018, and it exceeds 70%
in the fourth quarter. In other words, it takes one and a half years to recover from thesituation before the THAAD event.

Operating level

100%

50%

0%
Q1

Q2

Q3

Q4

Q1

Q2

FY2016Q3

FY2017

Q4

Q1

Q2

Q3

Q4

FY2018Figure 2-15 Trends in the percentage of itineraries calling at multiple ports
Our research question is “When cruise lines cannot visit desired ports due to sometrouble, what are their priorities, and how do they recover? How does cruise line’s port
selection behavior under these risks change over time?” In particular, three questions arefocused on in this chapter. (1) Immediately after the THAAD event, what is the priorityof the cruise line to keep their cruise business? (2) How long does it take to recover tothe original operating level? (3) How does the cruise market in Northeast Asia change42because of the THAAD event?
Consequently, (1) immediately after the trouble, cruise lines prioritized reducing thenumber of ports of call and keeping schedules. In other words, this prioritizes keeping
sailing days. During this period, it maintained its itinerary to call one of the marqueeports (Hakata port, Nagasaki port, or Sasebo port) from Shanghai port. (2)Subsequently, gradually adding new ports to their itinerary, based on the marquee ports,
was piloted. It took nearly a year and a half to finally create the next best itinerary. (3)Due to the THAAD event, the centers of the cruise market in Northeast Asia shiftedfrom Shanghai port in eastern China to Shekou port and Xiamen port in southern China,
Keelung port in Taiwan, and Tokyo/Yokohama ports in Japan. With this shift, thecenters of call shifted to the Okinawa archipelago (Naha port, Hirara port, Ishigaki port)
in southern Japan and out of Northeast Asia (Southeast Asia, etc.).
According to Henry (2012), from a cruise ship company’s perspective, planning takes
place some 2-3 years before an actual voyage. In this chapter, the next good itinerarytakes about one and a half years. Keeping the schedule is one of the most important
things in the cruise business (Bagis and Dooms, 2014). This chapter also concludes thatmany cruise lines reduce port calls and kept sailing days shortly after the THAAD
event. In addition, an operating level for a cruise line is a combination of a marquee portthat is popular with passengers and a discovery port that expects discoveries (Rodrigue
et al., 2013a). This time, just after the THAAD event, there was only a single port, but a
two-port itinerary was being made gradually. When cruise lines consider port selection,
they do not think about the port of call but about the itinerary (Rodrigue andNotteboom, 2013b). In this chapter, itinerary units shifted from Shanghai port in eastern
China to areas such as southern China (Shekou, Xiamen), Taiwan (Keelung), and Japan(Tokyo, Yokohama).
There are three limitations of this chapter. One limitation is that this chapter targets the
top five ships booked at Jeju Port, but it is necessary to analyze more ships in the future.
Second, this chapter uses three years from FY2016 to FY2018 to draw the recovery
curve, but the line stops during recovery. In the future, it is necessary to extend theanalysis a little longer. Third, the chapter chose the number of ports of call as anindicator of the operating level for drawing a recovery curve. However, an operatinglevel cannot be defined solely by the number of ports of call. For example, various
indicators such as port distance and ship speed for cost minimization, ticket prices and43

pre- and post-tour fees for maximizing revenue, and attractiveness of tourist
destinations for customer satisfaction can be adopted. In the future, it is necessary to
draw a recovery curve using these various indicators.

2.5. Chapter conclusion
In this chapter, the movement of the cruise ship before and after the THAAD event was
analyzed using AIS data for the period from FY2016 to FY2018. The target cruise ships
were the top five ships with the highest number of berth bookings at Jeju port during theyear 2017.
Consequently, immediately after the THAAD event, to keep the sailing days, that is, toadhere to the schedule strictly, the number of ports of call was reduced to a single port,
which is a marquee port, and the number of port calls was reduced and the ship
continued to operate. Then, while gradually testing discovery ports, itinerary patterns
were increased, and after one and a half years, the next best itinerary was created. Dueto the THAAD event, the centers of the cruise market in Northeast Asia shifted from
Shanghai port in eastern China to Shekou and Xiamen ports in southern China, Keelung
port in Taiwan, and Tokyo/Yokohama ports in Japan. With this shift, the centers of callshifted to the Okinawa archipelago in southern Japan and out of Northeast Asia
(Southeast Asia, etc.).
Chinese cruise passengers have driven the growth of the Northeast Asian cruise market.
Its main itinerary from China involves two countries, Japan and Korea. Jeju port in
Korea, located very close to Shanghai port, has extremely high geographic potential.
The itinerary that cannot visit Jeju port gradually moved south and shifted to Japan.
According to Cruise Industry News, “Royal Caribbean Cruises and Ctrip announced
that SkySea will wind down its business operations before the end of 2018.” In addition,according to Seatrade Cruise News (2018), “In April 2019, the NORWEGIAN JOY willreposition from China to Seattle to offer seven-day voyages to Alaska.” Consequently,
the market in Northeast Asia has shrunk.
While our writing this report, a new difficulty emerged: the new Coronavirus.
According to the World Health Organization (WHO, 2020a), “The Chinese authorities
identified a new type of coronavirus (novel coronavirus, nCoV (After this statement, it
is named COVID-19.)), which was isolated on 7 January 2020. According to44

information conveyed to the WHO by Chinese authorities on 11 and 12 January, 41
cases of novel coronavirus infection have been preliminarily diagnosed in Wuhan City.
Symptom onset of the 41 confirmed nCoV cases ranges from 8 December 2019 to 2
January 2020.” Namely, COVID-19 occurred in 2019, just two years after the THAAD
event in 2017. Yet again, the following year, the cruise ship from China stopped.
This chapter focused on the THAAD event. The disruption of popular ports of call, such
as Jeju port, caused cruise lines to struggle to create itineraries, causing the cruise
market to move south and shrink. The recovery curve for the operating level confirmedthat THAAD affected 70% of the itinerary of the five ships and that the original
itinerary took one and a half years. In the future, the Business Continuity Planning(BCP) measures should be considered to shift this recovery curve upward (minimallydamaging) and to the left (early recovery).

45

3. THE CRUISE INDUSTRY AND THE COVID-19
OUTBREAK
3.1. Introduction
Cruise ship movements can be a major trigger of the COVID-19 outbreaks. In Australia,
the cruise ship, the Ruby Princess became the largest COVID-19 epicenter. When the
Ruby Princess arrived at the Port of Sydney in New South Wales on March 19, 2020,
approximately 2,700 passengers disembarked. On arrival, 130 passengers and crew
members with flu-like symptoms were tested for the new virus. However, the officials
of New South Wales allowed the other passengers to disembark before the test results
were available. The next day, four people tested positive. The infection continued,
however, among the passengers who had disembarked, and the number rose to 162 by
March 27 (Reuters, 2020).
Signs that cruise ships may become a source of infection had already appeared in earlyFebruary. The largest cluster of COVID-19 cases outside mainland China occurred on
board the Diamond Princess, which was quarantined in the port of Yokohama, Japan on
February 3 (WHO, 2020b). On March 6, cases of COVID-19 were identified on theGrand Princess off the coast of California; the ship was subsequently quarantined. By
March 17, confirmed cases of COVID-19 had been associated with at least 25
additional cruise ships (CDC, 2020b). The two cruise ships mentioned above werereportedly successful in isolating the infected individuals on board, to prevent
transmission to local communities.
The cruise industry has been suspended due to infectious diseases such as norovirus,
SARS, and MERS (McCarter, 2009; Vivancos et al., 2010; Seatrade Cruise News, 2015;
Fisher et al., 2018). Frequent outages due to infectious diseases may cause problems
such as reduced economic effects and loss of employment not only on cruise lines but
also around ports. Analyzing the link between the cruise industry and COVID-19 isimportant for the resumption of safe cruise operations in the future. The purpose of thischapter is to analyze the relationship between the cruise industry and the COVID-19
outbreak.

46

An analysis is attempted from two perspectives. The first analysis focuses on therelationship between the estimated number of cruise passengers landing and the numberof COVID-19 cases. The second analysis focuses on the characteristics of cruise ships
infected with COVID-19. For the first analysis, the global movement of ocean cruise
ships is tracked using AIS data, in service from January to March 2020, and estimate thenumber of cruise passengers landing in each country. Then, the number of cruise
passengers landing and the number of COVID-19 cases are compared, and therelationship between the movement of cruise ships and the COVID-19 outbreak isanalyzed. In the second analysis, characteristics of ship size, onboard services, anditineraries are compared from a list of cruise ships infected with CDC's COVID-19.
With this, the characteristics of cruise ships infected with COVID-19 are analyzed.
COVID-19 is said to be transmitted by human-to-human contact. The transportationindustry is not limited to cruise ships but includes airplanes, railroads, buses, and otherships too. Thus, cruise ships are not the only source of COVID-19 transmission. Using
AIS data to track cruise ships, the number of cruise passengers landing in each country
is estimated. The estimates differ from the actual figures based on the capacity of theships (occupancy rate 100%).
The rest of this chapter is organized as follows. Section 3.2 outlines methodologies suchas AIS data. Section 3.3 presents the results and discussion of the chapter. Finally,
Section 3.4 provides conclusions.

3.2. Methodology
Using AIS data, the global movement of ocean cruise ships in service from January to
March is tracked. All ocean cruise ships registered as “Passenger/Cruise” on the ship
registration database IHS Maritime Sea-web (2020) are identified. A total of 392 ocean
cruise ships were in operation. The movement data are based on the date and time when
the cruise ship enters the port. After obtaining movement data of the cruise ships at eachport, the data is divided into ten cruise areas (Appendix 1). Next, using ship data fromthe IHS Maritime (2020), ship sizes and itinerary characteristics are compared from the
CDC's list of cruise ships infected with COVID-19. With this, the characteristics of
cruise ships infected with COVID-19 are analyzed.
The first analysis focuses on the relationship between the estimated number of cruise
47

passengers landing and the number of COVID-19 cases. AIS data is used to track the
global movement of ocean cruise ships in service from January to March and estimate
the number of cruise passengers landing in each country. All ocean cruise ships
registered as “Passenger/Cruise” on the ship registration database are identified, IHS
Maritime Sea-web (2020). A total of 392 ocean cruise ships were in operation fromJanuary to March 2020. Data is obtained using the International Maritime Organization(IMO) ship identification number allotted by the IMO. Using three months of AIS data,
the time and location of the ocean cruise ship deployments are extracted. The purpose of
the AIS is to enhance safety at sea, improve navigation efficiency, and protect marine
environments. The International Convention for Safety of Life at Sea (SOLAS) requires
AIS to be fitted on board all international voyaging ships above 299 gross tonnages(GT).
After obtaining movement data of the cruise ships at each port, the data is divided into
ten main regions (Figure 3-1): North America, Caribbean, South America, Pacific
Ocean, Oceania, Asia, Middle East, and South Asia, Mediterranean, Northern Europe,and Africa. In this way, the number of deployments for all cruise ships in each region iscounted. The movement data are based on the date and time when the cruise ship enters
the port. The subsequent movement data, which is unintended for passenger
transportation, are removed from this analysis: movements within the port; same-day
movements; movements to refuel from the refueling ships; transportation of only crew
members; transport to change used berths; transfer to shipyards; and voyage in domestic
waters.

Northern Europe
North America
Mediterranean
Caribbean
AfricaMiddle East, Asia
South Asia

South America

Pacific Ocean

Oceania

Figure 3-1 Cruise area classification
The second analysis focuses on the characteristics of cruise ships infected with COVID48

19. Using ship data from the IHS Maritime (2020), ship sizes, onboard services, anditinerary characteristics are compared to the CDC's list of cruise ships infected with
COVID-19. With this, the characteristics of cruise ships infected with COVID-19 are
analyzed.

3.3. Results and discussion
3.3.1. Cruise passenger landing and the COVID-19 outbreak
Figure 3-2 shows the total number of calls per week for each cruising area. The
Caribbean has the largest number, followed by Oceania, South America, and North
America. In terms of the number of port calls, the Caribbean Sea is 400 to 450 times aweek from January to February, 300 times in the second week of March, andapproximately 50 times from the third week in March.
500

Number of port calls per week

450
400
350
300
250
200
150
100
50
0

Jan1-7

Jan8-14

Jan15-21

Jan22-28

Jan29-Feb4

Feb5-11

Feb19-25

Feb26-Mar3 Mar4-10

Mar11-17

Mar18-24

Mar25-31

Feb12-18

Figure 3-2 Number of port calls per week by cruise area
To observe the relationship between the number of cruise passengers landing and
COVID-19 transmission, the former is calculated using Equation (1).
𝐿# = & & 𝑥$,& 𝑁& 𝐶&(1)$ ∈ * ! &∈(Li represents the number of passengers landing in the country i. Pi is a port in the
country i. R is all services for cruise products. Nr is the number of port calls for service r(number of calls). Cr is the capacity of service r (the number of cruise ship passengers
49put into service r). xp,r is 1 if service r calls at port p, and 0 otherwise. The number of
cruise passengers landing by country from January to March 2020 and the number of
COVID-19 cases by country from December 2019 to April 15, 2020, are calculated asshown in Table 3-1. This chapter uses the capacity of cruise ships instead of actual
passengers of cruise ships due to data unavailability.
From January to March, the US had the largest number of cruise passengers (4.71
million), followed by Mexico (2.06 million), Bahamas (1.90 million), Australia(990,197), and the Canary Islands (906,978), New Zealand has 828,170, and Brazil has
800,283. Note that the number of cruise passengers landing is counted as three when
one passenger calls at three ports. Note also that this is not the net number of cruise
passengers. Table 3-1 shows the top 50 countries with the largest number of cruise
passengers landing from January to March. AIS data confirms cruise passengers landingin 129 countries.
Table 3-1 Estimated number of cruise passengers landing and COVID-19 cases

Source: European Centre for Disease Prevention and Control (2020)
a. The number of cruise passengers landing is from January 1, 2020, to March 31,
502020.
b. The number of COVID-19 cases is from December 2019 to April 15, 2020.
The US, which had a large number of cruise passengers landing, also had the largest
number of COVID-19 cases at 609,516. However, Mexico has the second largest
number of cruise passengers with 5,399 COVID-19 cases, while the Bahamas, with thethird largest, has only 49. In addition, Italy, which has a large number of COVID-19
cases, has the 10th largest number of cruise passengers, while Spain ranks 12th and the
UK ranks 45th. In other words, countries with the highest number of cruise passengers
did not necessarily have the highest number of COVID-19 cases.
In general, cruise passengers stay in the port city for a few days before boarding or
disembarking the ship. On the other hand, the passengers spend only a few hours at theport of call. According to Henry (2012), cruise passengers generally prefer to arrive at a
port by 8 am and leave before 6 pm (in the early evening). This allows maximum
daylight hours ashore and an opportunity to leave after breakfast and return for an
evening meal. In other words, the time spent by cruise passengers at the arrival anddeparture ports tends to be longer than the time spent at the port of call. Therefore, theinfection rate of COVID-19 in countries with arrival and departure ports and theinfection rate of COVID-19 in countries with only ports of call were analyzed
separately.
Unfortunately, AIS data can track ship movements, but not passenger movements.
Therefore, the Expedia cruise product search website (2020) was used. However, it
should be noted that this website does not include cruise products directly sold by some
cruise lines and travel agencies. Since our search was conducted in April, the results
were different from the ports where ships arrived and departed from January to March.
In particular, more countries in the Northern Hemisphere, which have the best season
for cruises from April, were included in the search results than countries in the Southern
Hemisphere. As of April 10, 18,501 items of ocean cruise products were sold. Using thissite, all cruise products are sorted by port of departure and arrival and by country, as
shown in Appendix 2. The US accounted for 43%, followed by Italy, Spain, Canada,
France, and Australia. In this analysis, these 30 countries as those with ports of arrivaland departure are defined.
The data in Table 3-1 are divided into two groups: one for countries with arrival and51

departure ports and those without ports. Then, the following two indicators are used forcomparison between the port of arrival and departure and the port of call. The firstindicator is the number of COVID-19 cases relative to the number of cruise passengers
landing. The results are shown in Table 3-2. For the country of arrival and departure, thefigure was 12.85% and for the country, only at the port of call, it was 1.50%. It was
found that the number of COVID-19 cases against the number of cruise passengers
landing at the country of arrival and departure was 11.36% points higher. The second
indicator is the COVID-19 infection rate to express the number of COVID-19 cases per
population. The COVID-19 infection rate in the country of arrival and departure was
0.057%, while that in the country of the port of call was 0.006%. It was found that the
COVID-19 infection rate in the country of arrival and departure tended to be 0.051%
points higher.
Table 3-2 COVID-19 infection rates between countries with arrival and departure ports
and countries with only ports of calla. The number of cruise passengers landing is from January 1, 2020, to March 31,
2020.
b. The number of COVID-19 cases is from December 2019 to April 15, 2020.
Lekakou et al. (2009) proposed that the convenience of an international airport is anecessary condition for cities with arrival and departure ports. Based on an analysis of
cruise home ports, Castillo-Manzano et al. (2014) suggested that the likelihood of
having cruise traffic was linked to the location of the port in populous areas and being
close to large airports. In other words, cities with ports of arrival and departure arecharacterized by proximity to an international airport. Therefore, it should be noted thatmany tourists are not passengers because of the international airport nearby.
There may be some relationship between the timing of the port call and the timing of
the COVID-19 outbreak expansion. Figure 3-3 shows the number of port calls by day
for the top 30 ports from January to March. A dark color-filled box indicates a day withmany port calls, while white means no port calls in a day. Most of the top 30 ports
accepted cruise ships until mid-March when CLIA announced that the cruise ships had
stopped operating (CLIA, 2020g). In particular, Caribbean ports such as Cozumel
52

(Mexico), Miami (US), and Nassau (Bahamas) continued to accept cruise ships.
No.

Port of Call

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30

Cozumel (Mexico)
Miami (US)
Nassau (Bahamas)
Port Everglades (US)Bridgetown (Barbados)
Ushuaia (Argentina)
Penang (Malaysia)
Philipsburg (St Maarten)
Georgetown (Cayman Is)
San Juan (Puerto Rico)
Port Canaveral (US)Port Rashid (UAE)Castries (St Lucia)
Sydney (Australia)
St John's (Antigua)
Key West (US)
Honolulu (US)1-7

January
8-14 15-21 22-28 29-4

February
5-11 12-18 19-25 26-3

March
4-10 11-17 18-24 25-31

Charlotte Amalie (Virgin Islands (US))

Santa Cruz (Canary Islands)
Fort-de-France (Martinique)
Oranjestad (Aruba)Montevideo (Uruguay)
Basseterre (St Kitts & Nevis)
Freeport (Bahamas)
Buenos Aires (Argentina)
Wickham's Cay (Virgin Islands (British))

Las Palmas (Canary Islands)
Melbourne (Australia)
Tauranga (New Zealand)
Coxen Hole (Honduras)

Figure 3-3 Number of port calls for each port from January to March
Source: IHS Maritime (2020)*Darker color indicates a higher number of port calls.
Given the 14-day incubation period, the number of COVID-19 cases in mid-April issuspected to be related to the acceptance of cruise ships in March. Therefore, COVID19 infection rates among countries that had reduced the acceptance of cruise ships inMarch and those that did not are compared. In the 129 countries in Table 1 where cruise
ships called from January to March, the number of cruise passengers landing in March
was arranged in descending order. These countries are then divided into two groups foranalysis. The top half of countries are defined as the group that continued to accept
cruise ships in March. On the other hand, the lower half of countries are defined as thegroup that had reduced the acceptance of cruise ships in March.
As a result, Figure 3-4 shows the COVID-19 infection rate of the former group was 0%in January-February but increased to 0.016% in March. By mid-April, it has increased
to 0.028%. Conversely, the COVID-19 infection rate of the latter group was flat at53

0.003% in February, 0.002% in March, and 0.006% in mid-April. The results show that
the COVID-19 infection rate in countries that had reduced the acceptance of cruise
ships in March was lower than that of the countries that continued to accept cruise ships
in March.
0.028%

COVID-19 Cases/Population

0.03%
0.03%
0.02%
0.02%

0.016%

0.01%
0.01%
0.00%

0.006%
0.003%

0.002%

0.000%
0.000%
January

0.000%
February

March

April 1-15

Country group that had reduced cruise ship acceptance in MarchCountry group that continued to accept cruise ships in March

Figure 3-4 COVID-19 infection rate for groups accepting and reducing ships in MarchAs shown in Figure 3-4, major cruise ports continued to accept cruise ships even until
mid-March. This indicates that the decision of stopping a cruise operation cannot bemade by each cruise line alone. Similarly, the suspension of port operations also cannot
be decided by each port individually. The following viewpoints can offer some reasons.
According to Bagis and Dooms (2014), the purpose of the cruise business is tomaximize profits by using ships with huge investments. Suspension of cruise ships willlead to reduced profits and, in the worst case, bankruptcy. According to Henry (2012),
general itinerary planning by a cruise line takes place some 2-3 years before an actual
voyage. If one port is closed, a cruise line cannot immediately call at another port. Dueto these circumstances, even if the risk of infection from a cruise ship is increasing, it is
not easy for a cruise line to take a management decision to suspend cruise operations.
Table 3-3 identifies areas where cruise travel was stopped following the stopping of
cruises to and from China on January 25 to prevent the spread of COVID-19 infectionin Wuhan, China. The events that occurred subsequently are summarized in Table 3-2.

54

Table 3-3 Major events stopping the movement of cruise ships
Date
January 25
February 3
February 611
February 27
March 8March 13

March 14-15

Event
Cruise ports closed in China (Cruise Industry News, 2020a)
Refusal to accept cruise ships in Saint Lucia and Dominica (The Australian,2020)
Cruise ports closed in Taiwan. Refusal to accept cruise ships in Japan and
Thailand (Cruise Industry News, 2020b, c, d)Refusal to accept cruise ships in Jamaica and Grand Cayman (TIME, 2020)
Cruise Lines International Association (CLIA) announces on March 8 "theadoption of additional enhanced screening measures in response to COVID-19”(CLIA, 2020h)
CLIA ocean-going cruise lines decide to voluntarily and temporarily suspend
cruise ship operations from US ports of call for 30 days as public health officials
and the US Government continue to address COVID-19. The temporary
suspension takes effect at midnight EDT on March 14, 2020. (CLIA, 2020g)
Cruise ports closed in the US, Canada, Australia, and New Zealand (Cruise
Industry News, 2020e)

Source: Cruise Industry News, 2020a; The Australian, 2020; Cruise Industry News,
2020b, 2020c, 2020d, 2020e; TIME, 2020; CLIA, 2020h; CLIA, 2020g
Even for the port, closing the port is also a difficult decision. One of the reasons for thisis the fierce competition between neighboring ports. Recently, the bargaining power on
the port side becomes weaker than that of the cruise line. Port closures could lead to theelimination of future port calls. According to Pallis et al. (2018), investment in ports bycruise lines is accelerating. In recent years, cruise lines have invested in ports and haveexclusive cruise terminals for their ships. The trend of privatization leads to a decrease
in interest in cruises on the port side. There is concern that the lack of interest in thecruise business on the part of the port led to the delay in the decision to close the port.
3.3.2. Characteristics of cruise ships with an infection on board and the COVID19 outbreak
According to the “Cruise ships affected in the US by COVID-19” (CDC, 2020b), thecruise ships listed in Appendix 3 made voyages discovered to be infected with COVID19. The characteristics of these cruise ships infected with COVID-19 from two
perspectives: the ship size and ship operation schedule, were analyzed.
Figure 3-5 shows the passenger capacities of cruise ships that were in operation between
January and March, which are arranged in order of size of the ship. A total of 594 ships
were analyzed, including river cruises. The median and average for all cruise ships are
312 and 1,238 passengers, respectively. The passenger capacity of all infected cruise
ships was above the median and average. Cruise ships infected with COVID-19 arelarge ships.
55

SYMPHONY OF THE SEAS

7,000
OASIS OF THE SEAS
6,000

LIBERTY OF THE SEAS

MSC MERAVIGLIANORWEGIAN BREAKAWAY

Passenger capacity

5,000

EXPLORER OF THE SEASCARNIVAL VISTANORWEGIAN BLISSPRIDE OF AMERICA

4,000

CELEBRITY REFLECTIONCROWN PRINCESSCARNIVAL VALORGRAND PRINCESS

3,000

CELEBRITY ECLIPSEDISNEY WONDERCARNIVAL IMAGINATIONCELEBRITY INFINITY

2,000

MAJESTY OF THE SEAS

RIVIERA

Average 1,238
1,000Median 312
0

0

100

200

300

400

500

600

700

Number of ships
Uninfected Ship

Infected Ships

Figure 3-5 Passenger capacity of cruise ships infected with COVID-19
The second indicator is a ship operation schedule (itinerary). Table 3-4 shows theitinerary of eight large cruise ships before the COVID-19 infection was confirmed.
These itineraries had several characteristics. The first is an itinerary that repeats one
week for seven nights and eight days. Second is the itinerary, where the arrival anddeparture port (home port) is fixed. Third, the port of call as a destination is also fixed.
These itineraries include private islands owned by each cruise line as ports of call.

56

Table 3-4 Itinerary of cruise ships infected with COVID-19
SYMPHONY OF THE SEAS

OASIS OF THE SEAS

Day1 2/15 Miami

US

Day1 2/9

Day3 2/17 Coxen Hole

Honduras

Day4 2/18 Puerto Coasta Maya

MSC MERAVIGLIA
US

Day1 2/9

Day4 2/12 Falmouth Port

Jamaica

MexicoDay6 2/14 Cozumel

Day5 2/19 Cozumel

MexicoDay7 2/21 Slaughter Harbour

Day1 2/1

GalvestonUS

Day3 2/11 Ocho Rios

Jamaica

Day3 2/3

CozumelMexicoMexicoDay4 2/12 Georgetown

Cayman Is lands

Day4 2/4

Belize City

BelizeDay8 2/16 Miami

US

Day5 2/13 Cozumel

MexicoDay8 2/8

Galveston

US

BahamasDay1 2/16 Miami

US

Day7 2/15 Ocean Cay

BahamasDay1 2/8

GalvestonUS

Day8 2/22 Miami

US

Day3 2/18 Labadee

Haiti

Day8 2/16 Miami

US

Day3 2/10 Cozumel

MexicoDay1 2/22 Miami

US

Day4 2/19 San Juan

Puerto Rico

Day1 2/16 Miami

US

Day4 2/11 Georgetown

Cayman Is lands

Day4 2/25 Philipsburg

St Maarten

Day5 2/20 St Thomas

Virgin Is lands (US)Day3 2/18 Coxen Hole

Honduras

Day5 2/12 Ocho Rios

Jamaica

Day5 2/26 San Juan

Puerto Rico

Day8 2/23 Miami

US

Day4 2/19 Belize City

BelizeDay8 2/15 Galveston

US

Day7 2/28 Slaughter Harbour

BahamasDay1 2/23 Miami

US

Day5 2/20 Puerto Coasta Maya

MexicoDay1 2/15 Galveston

US

Day8 2/29 Miami

US

Day6 2/28 Cozumel

MexicoDay8 2/23 Miami

US

Day3 2/17 Cozumel

MexicoDay1 2/29 Miami

US

Day8 3/1

Miami

US

Day1 2/23 Miami

US

Day4 2/18 Belize City

BelizeDay3 3/2

Coxen Hole

Honduras

Day1 3/2

Miami

US

Day3 2/25 Ocho Rios

Jamaica

Day8 2/22 Galveston

US

Day4 3/3

Puerto Coasta Maya

MexicoDay3 3/4

Philipsburg

St Maarten

Day5 2/27 Cozumel

MexicoDay1 2/22 Galveston

US

Day5 3/4

CozumelMexicoDay4 3/5

San Juan

Puerto Rico

Day8 3/1

Miami

US

Day3 2/24 Cozumel

MexicoDay7 3/6

Coco Cay

BahamasDay7 3/8

Miami

US

Day1 3/1

Miami

US

Day4 2/25 Georgetown

Cayman Is lands

Day8 3/7

Miami

US

Day1 3/8

Miami

US

Day4 3/4

Belize City

BelizeDay5 2/26 Ocho Rios

Jamaica

Day1 3/7

Miami

US

Day6 3/13 Cozumel

MexicoDay5 3/5

Coxen Hole

Honduras

Day8 2/29 Galveston

US

Day4 3/10 Basseterre

St Kitts & Nevis

Day8 3/15 Miami

US

Day8 3/8

Miami

US

Day1 2/29 Galveston

US

Day5 3/11 St Thomas

Virgin Is lands (US)Day5 3/4

Belize City

BelizeDay7 3/13 Coco Cay

BahamasDay6 3/5

CozumelMexicoDay8 3/14 Miami

US

Day8 3/7

GalvestonUS

NORWEGIAN BREAKAWAY

Day1 2/23 Galveston

US

Day1 2/8

Day3 2/25 Cozumel

MexicoDay4 2/26 Georgetown
Day8 3/1

Port Canaveral

Miami

CARNIVAL VISTAUSLIBERTY OF THE SEASMiami

NORWEGIAN BLISSEXPLORER OF THE SEAS

US

Day1 2/18 Port Canaveral

US

Day1 2/16 Miami

US

Day4 2/11 Wickham's Cay

Virg in Islan d s (British )Day3 2/20 Nassau

BahamasDay3 2/18 Nassau

BahamasCayman Is lands

Day5 2/12 San Juan

Puerto Rico

Day6 2/23 New York & New Jersey US

Day5 2/20 Nassau

BahamasGalvestonUS

Day7 2/14 Slaughter Harbour

BahamasDay8 2/25 Port Canaveral

US

Day6 2/21 Miami

US

Day1 3/1

GalvestonUS

Day8 2/15 Port Canaveral

US

Day1 2/25 Port Canaveral

US

Day1 2/21 Miami

US

Day4 3/4

Coxen Hole

Honduras

Day1 2/15 Port Canaveral

US

Day2 2/26 Slaughter Harbour

BahamasDay5 2/25 Oranjestad (Aruba)

Aruba

Day6 3/6

CozumelMexicoDay5 2/19 Georgetown

Cayman Is lands

Day3 2/27 Nassau

BahamasDay6 2/26 Kralendijk

BonaireDay8 3/8

GalvestonUS

Day6 2/20 Cozumel

MexicoDay6 3/1

New York & New Jersey

US

Day7 2/27 Willemstad West Bank

CuracaoDay1 3/8

GalvestonUS

Day8 2/22 Port Canaveral

US

Day8 3/3

Port Canaveral

US

Day10

3/1

Miami

US

Day3 3/10 Cozumel

MexicoDay1 2/22 Port Canaveral

US

Day1 3/3

Port Canaveral

US

Day1 3/1

Miami

US

Day4 3/11 Georgetown

Cayman Is lands

Day4 2/25 Wickham's Cay

Virg in Islan d s (British )Day2 3/4

Great Stirrup Cay

BahamasDay4 3/4

San Juan

Puerto Rico

Day5 3/12 Falmouth Port

Jamaica

Day5 2/26 Charlotte Amalie

Virgin Is lands (US)Day3 3/5

NassauBahamasDay5 3/5

St Thomas

Virgin Is lands (US)Day8 3/15 Galveston

US

Day8 2/29 Port Canaveral

US

Day6 3/8

New York & New Jersey

US

Day7 3/7

Freeport (Bahamas)

BahamasDay1 3/15 Galveston

US

Day1 2/29 Port Canaveral

US

Day8 3/10 Port Canaveral

US

Day8 3/8

Miami

US

Day7 3/21 South Sabine Point STS

US

Day4 3/3

Falmouth Port

Jamaica

Day1 3/10 Port Canaveral

US

Day1 3/8

Miami

US

Day8 3/22 Galveston

US

Day5 3/4

Georgetown

Cayman Is lands

Day2 3/11 Great Stirrup Cay

BahamasDay3 3/10 San Juan

Puerto Rico

Day1 3/22 Galveston

US

Day6 3/5

CozumelMexicoDay3 3/12 Nassau

BahamasDay4 3/11 Charlotte Amalie

Virgin Is lands (US)Day6 3/27 South Sabine Point STS

US

Day8 3/7

Port Canaveral

US

Day6 3/15 New York & New Jersey US

Day8 3/15 Miami

US

Day8 3/29 Galveston

US

Day1 3/7

Port Canaveral

US

Day11

Day6 3/12 Cozumel

MexicoDay8 3/14 Port Canaveral

US

3/20 Miami

US

Source: IHS maritime (2020)*Darker color indicates an itinerary infected with COVID-19.
The risk of infection on board a ship increases proportionately as the number ofpassengers increases. Cruise ships with an unspecified number of cruise passengers
replaced in a week have a higher infection rate than ships that do not have passengers
replaced for several weeks. In the case of a large cruise ship with many passengers
aboard, due to the limited number of persons in charge of inspection, there is apossibility that health inspection cannot be strict.

3.4. Chapter conclusion
The purpose of this chapter is to analyze the relationship between the cruise industry
and the COVID-19 outbreak. This from two perspectives is analyzed.
57

The first analysis focused on the relationship between the cruise ship movement and the
COVID-19 outbreak. Consequently, it was found that COVID-19 infection rates incountries that have ports of arrival and departure are higher than in countries with only
ports of call. In addition, COVID-19 infection rates in countries that continued to accept
cruise ships until March were higher than those in countries that did not. However,estimates of the number of cruise passengers landing in each country by AIS data wereused to track cruise ships. The estimated figures differ from the actual ones; thus, it isnecessary to get the actual data from the cruise lines. The second analysis focused on
the characteristics of cruise ships infected with COVID-19. From the CDC's list of
cruise ships infected with COVID-19, ship sizes and itinerary characteristics werecompared. The cruise ships infected with COVID-19 tended to be large. In addition,
most cruise ships were sailing from the same home port to the same port of call in a
week. It should be noted that, as mentioned earlier, the spread of COVID-19 infection is
not only caused by cruise ships, but also by differences in infection control measures ineach country and the effects of other modes of transportation such as airplanes.
The emergence of the modern cruise industry began in the late 1960s and early 1970s(Garin, 2005). The cruise industry has shown remarkable resilience in the face ofeconomic, social-political, and other crises. The global financial crisis of 2008-2009 hada serious impact on the maritime cargo shipping industry. However, the cruise industry
has continued to grow steadily. When the Costa Concordia loss (2012) created a periodof negative publicity for the cruise industry, the industry cruised “through the perfectstorm” (Peisley, 2012) and continued to generate more demand in large part due to thesuccessful marketing strategies developed by the cruise lines (Pallis, 2018). Vogel andOschmann (2012) explained that cruise demand has always been “supply-led” startingwith the invention of leisure cruising by passenger shipping lines whose scheduledtransatlantic services were losing passengers to the airlines. Similarly, Rodrigue andNotteboom (2013) analyzed that the cruise industry works in a “supply pushmechanism” as cruise lines aim to generate demand for cruises by providing new
products with a larger and more diversified range of ships. The impact of COVID-19 on
the cruise industry will be much stronger than any of the past difficulties. However, thecruise industry will grow again with a new supply-driven strategy as overcoming
difficulties in the past. The results of this chapter will be useful not only for academic
researchers but also for executives of the cruise industry and port officials.

58

4. SPATIAL AND TEMPORAL CHANGES IN THE
CRUISE NETWORK STRUCTURE IN NORTHEAST
ASIA
4.1. Introduction
Cruise demand has the characteristic of being supply-driven. In other words, supplyside initiatives such as port development are undertaken first, followed by the
emergence of demand in the form of cruise ships and passengers. Conversely, supplydriven port development does not lead to success in all ports. Some ports haveexperienced problems with cruise ships not coming after the passenger terminals andother facilities have been put into service. The cruise industry has proliferated since the1990s. The world cruise population increased from 3.8 million in 1990 to 29.7 million
in 2019 (CLIA, 2011; CLIA, 2019a). The average annual growth rate for the last 30
years is 7.37%. Furthermore, the global cruise industry has created huge economic
benefits. The cruise industry generated $72.0 billion in total direct expenditures and$154.5 billion in total output contributions worldwide over the year 2019. A vast
number of employment opportunities were also created, with a total income
contribution of $50.5 billion and a total employment contribution of 1.16 million
people, making it a huge industry (CLIA, 2020a).
While the accommodation of cruise ships at a port can be expected to generate
economic benefits, it also requires a huge investment to construct the berth and improve
the passenger terminal. Therefore, understanding the port selection behavior of cruiselines is important to avoid wasting investment in port improvements and consequently,
cruise ships not coming to the ports. In our view, the problem in the supply-driven
cruise industry is that there is not enough information available to decide on whether toinvest in port development. In other words, the problem is that ports are required tomake decisions on port development under conditions of uncertain future cruisedemand. In the cruise industry, which is characterized as supply-driven, reducing theuncertainty of future demand may prevent unnecessary port investments. Reducing
uncertainty about future demand requires an understanding of the principles of demandside cruise line port selection behavior.
59

The port selection behavior of cruise lines is influenced by various factors such as thepreferences of their target customers, quality of service, and size of their ships. The
quality of a cruise line’s service is classified into “cruise segments.” Cruise segmentclassifications differ among evaluation agencies, and there are no uniform standards.
Bjornsen (2003) gives examples of the differences in cruise duration, ticket price, andship sizes in different categories. Contemporary is an itinerary of 3 to 7 days, 100-200
USD per day per person, operating mega- and large-size ships. Premium is an itineraryof 7 to 14 days, 150-500 USD per day per person, operating large- and mid-size ships.
Luxury is an itinerary of 7 days and upwards, 600-3,000 USD per day per person,
operating mid- and small-size ships (Gibson, 2012). Thus, small-size cruise ships
typically offer better service and higher ticket prices. In particular, the size of the cruise
ship may be a limiting factor in port selection. In practical terms, the first step indeveloping a cruise port is to determine the size of the cruise ships to be anchored. In
the case of mega-size ships, there are many restrictions. For example, they need to be
based at a turnaround port that can handle large volumes of passengers quickly andefficiently. Additionally, they require ports that can physically handle such large ships
and where there are no impediments to berthing (Henry, 2012).
A network diagram can be depicted by tracking the movement of cruise ships as theynavigate between ports. The network describes the number and location of ports (nodes)that make up the cruise network and their connections (edges) due to cruise ship
movements. Network structures can be measured using various network science
methods, which is a powerful way to understand the structure of various networks ofdifferent types, such as technological, information, social, and biological networks(Newman, 2018). Barabási (2015) revealed that the architectures of networks thatemerge in different domains of science, nature, and technology are similar and are basedon the same organizational principles. If so, some network science methods may be able
to analyze cruise networks to understand their port selection behavior.
This chapter aims to understand the spatial and temporal changes in the structure of thecruise network in Northeast Asia. To this end, the movements of cruise ships are tracked
by AIS data, and the aggregated data are viewed as a network to understand t the spatial
and temporal changes in the structure of the cruise network based on the actual situationof the number of ports and routes in a particular sea area, the existence of hub ports,
connections between ports. AIS data (https://maritime.ihs.com) is used to track the
movement of cruise ships in Northeast Asia by size.
60

4.2. Literature review
Several previous studies have focused on cruise lines’ port selection behavior. Marti(1990) suggests that the geographic concepts of “site” and “situation” can contribute toa greater understanding of the cruise-ship port selection process. “Site,” a physicalfactor, holds great significance in the origin and evolution of cruise ports. “Situation” isa notion that can comprise either physical or cultural qualities. Manning (2006) explainsthat the main influencing factors for port selection include the key natural and cultural
assets of the port, port facilities, location access to other destinations and the homeport,
security, infrastructure, provisioning, port costs, and marketing. Gui and Russo (2011)
show that cruise lines’ requirements include a wide range of dedicated infrastructure andservices, as well as port area infrastructure, airports, taxi fleets, coach services, andshore excursions and shopping areas. Wang et al. (2014) analyze the factors that affectcruise lines’ port selections using the fuzzy-AHP method. The results show that“tourism attractions” are the most significant issue taken into consideration when acruise ship is selecting a port of call location. Castillo-Manzano et al. (2014) conclude
that the likelihood of having cruise traffic is linked to ports located in populous areasand closer to large airports.
Few studies have analyzed cruise ship networks using network science techniques.
Tsiotas et al. (2018) show the double role of the cruise network, which is composed ofthe profit-driven strategies of cruise companies and port authorities, using data from the2013 itineraries of Costa Cruises and MSC Cruises in the Mediterranean cruise market.
Jeon et al. (2019) investigate the centrality of cruise ports in the Asian cruise shipping
market while proposing the hubs and authorities centrality (HACC) metric as adirectional synthesis of the hubs centrality and authorities centrality to explore cyclicaland directional features of centrality in the cruise shipping network. In a recent cruise
network study, Kanrak and Nguyen (2021) reveal that the cruise shipping network is
scale-free using itinerary data from Asian and Australian cruise network websites.
Lopez Rodriguez et al. (2021) suggest that Caribbean ports are the most importantconcerning hub and authority centrality, using 2018 itineraries for each cruise line fromthe sites for 902 ports in the Caribbean and Northern Europe. To the best of ourknowledge, there are no cases of structural changes in cruise networks using network
science methods.
Moreover, few studies have used AIS data for analysis in cruise shipping. Tichavska and61

Tovar (2015) use AIS data to measure the pollution status of exhaust gas from cruise
ships calling at the Las Palmas Port in the Canary Islands. Vicente-Cera et al. (2019,2020a) arrange the cruise ship’s operating hours, repair times, and berthing times,
estimated seawater pollution status by cruise ships, and assess environmental pressuresrelated to global cruise traffic along their paths based on AIS data. Vicente-Cera et al.
(2020b) use AIS data to aggregate cruise ship calling patterns at European ports andevaluated the diversity of cruise ship calls at each port. Ito et al. (2020) organize theport call patterns before and after the suspension of cruise ship operations by COVID19 and analyze the relationship between cruise ship operations and the spread of
infection at the port of call using AIS data. However, there are no studies of network
analysis focusing on the structural changes of cruise networks using AIS data for cruise
ships for a longer period.

62

4.3. Methodology
4.3.1. Data
The Northeast Asian cruise area, which has expanded rapidly in recent years, waschosen as a case study. The CLIA officially began collecting data on the Northeast
Asian cruise population in 2012. The cruise population in Northeast Asia in 2012 wasapproximately 450,000, reaching 2.84 million by 2019 (CLIA Asia, 2013; 2020). China
is a source of demand for the fast-growing Northeast Asian cruise market. In 2006, thefirst year of the Chinese cruise market, the cruise population was 20,000 (Wang, 2017).
In 2016, the number reached 2.1 million, and China became the world’s second-largest
cruise market, following the United States (CLIA, 2016).
Data from international cruise itineraries (excluding domestic cruise itineraries) callingat ports in Northeast Asian countries (Japan, China, Hong Kong, Taiwan, and South
Korea) from 2014 to 2019 were used. Since some ports are not equipped with AIS data
receivers and itineraries for which AIS data cannot be obtained, the itineraries were
supplemented with brochures and other information from each cruise line. The divisionby the size of the cruise ship followed the classification criteria of CLIA Asia (2019).
The classification criteria are as follows. Information on cruise operators, based on ship
size, is provided in Appendix 4.
••••

Mega-size: Lower berth capacity of 3,500 or more OR GRT over 150,000
Large-size: Lower berth capacity of 2,000 to 3,500 AND GRT over 75,000
Mid-size: Lower berth capacity of 750 to 2,000 passengers
Small-size: Lower berth capacity under 750 passengers (*including Expedition
ships)The locations of the ports targeted in this chapter are shown in Figure 4-1.

63

OtaruHakodate
Aomori
TianjinHokkaido region

DalianQingdao

IncheonKanazawa
South
Korea Sakaiminato
Busan

Jeju

China

Shanghai

Hiroshima Kobe
Osaka

HakataSaseboNagasaki

Kochi
Kagoshima

Japan
Yokohama

Seto Inland Sea

Kyushu region

XiamenKeelung

Naha
IshigakiTaiwanGuangzhou
Hong Kong

Kaohsiung

Hong Kong

Figure 4-1 Location of ports
4.3.2. Measurements of network structure
The cruise passengers depart from the generating region, stop at each port of call to lookaround, and finally arrive at the destination region, which can be the same as thegenerating region. In the case of these looped routes, whether the order of port calls isclockwise or counterclockwise is of little significance. Kanrak and Nguyen (2021)
reported that the degree distributions for in-degree and out-degree were similar
observations. Therefore, the network was analyzed in an undirected graph. In addition,
since this chapter aims to understand the spatial and temporal changes in the structure of
the cruise network, it focuses on the connections between ports rather than the numberof port calls, so it is analyzed in an unweighted graph. Network analysis andvisualization were conducted using Gephi (the open graph Viz platform).
The maximum number of edges in a network is limited by the number of possible
different connections between nodes in the cruise network. The maximum number ofedges is therefore given by the number of pairs of nodes. A network with the maximum
number of edges, in which all possible pairs of nodes are connected by edges, is called acomplete network (Menczer et al., 2020). The density is the ratio of the actual numberof edges to the number of all possible edges in a graph. It is used to analyze the
network’s connectivity level. If the number of nodes is 𝑛 and the number of edges is𝑚, the network density 𝑑 is given as follows:

64

𝑑=

2𝑚
𝑛(𝑛 − 1)

(1)

The clustering coefficient of a node is the fraction of pairs of neighbors of the nodeconnected. The clustering coefficient 𝐶# of node 𝑖 is defined as follows: 𝜏(𝑖) is thenumber of triangles involved in 𝑖. The maximum possible number of triangles for 𝑖 isthe number of pairs formed by its 𝑘# neighbors. 𝐶# is defined only if the degree 𝑘# >1 because of the terms 𝑘# and 𝑘# − 1 in the denominator. A node must have at least
two neighbors for any triangle to be possible.

𝐶# =

2𝜏(𝑖)
𝑘# (𝑘# − 1)(2)

The clustering coefficient of the entire network is the average clustering coefficient 𝐶,
which is used to understand the formation of the triangular route. The formula is asfollows:+

1
𝐶 = & 𝐶#
𝑛

(3)

#,-

The average shortest path length is the average of the shortest network distances in the
network that can reach the other ports. The average shortest path length 〈𝑙〉 is asfollows: 𝑙#. is the shortest path length between nodes 𝑖 and 𝑗. The sum is over all
pairs of nodes, and it is divided by the number of pairs to compute the average.

〈𝑙〉 =∑#,. 𝑙#.
2 ∑#,. 𝑙#.
=𝑛𝑛(𝑛 − 1)
< =
2

(4)

The diameter 𝑙!/0 of a network is the maximum shortest-path length across all pairs of
nodes (i.e., the length of the longest shortest path in the network). The formula is asfollows:
𝑙!/0 = max𝑙#.
i, j

65

(5)

Degree centrality 𝑘# is assumed to be centered on a node with a higher degree among
the nodes in the network. Hub ports are detected with degree centrality. The degree of
node 𝑖 by 𝑘# is denoted. If the adjacency matrix of a network with 𝑛 nodes is 𝑎#. ,then degree centrality can be formulated as follows:++

𝑘# = & 𝑎#. = & 𝑎.#
.,-

(6)

.,-

Degree centralization 𝐶1 measures are based on a normalized variance in the degree
centrality to compare distinct networks based on their highest degree centralization
scores (Freeman, 1979; Krnc and Škrekovski, 2020). It can measure whether the degree
is biased toward a high node in the network. 𝑘!/2 is the highest degree centrality inthe network. 𝑘# denotes the degree of node 𝑖. The more concentrated the network, theless homogeneous it is:

𝐶1 =∑+#,-(𝑘!/0 − 𝑘# )max ∑+#,-(𝑘!/0 − 𝑘# )(7)

In network science, a community is defined as a group of nodes belonging to one groupand connected with a higher probability than the nodes belonging to other groups.
Community detection was performed using modularity optimization. Modularity is ameasure of the quality of the community partitioning results. Modularity 𝑄 is defined
as follows: 𝐿3 is the number of internal edges in community 𝑐, and 𝑘3 is the total
degree of nodes in community 𝑐. 𝐿 is the number of edges in the network.
1𝑘34
𝑄 = & D𝐿3 − F
𝐿
4𝐿

(8)

3

In this chapter, the Louvain algorithm for modularity optimization was used (Blondel etal., 2008). The Louvain algorithm is a heuristic algorithm for approximately
maximizing modularity over divisions of a network into any number of communities.
This is an agglomerative algorithm, which works by taking single nodes and joining
them into groups, then joining groups with other groups, and so forth, to find theconfiguration with the highest modularity (Newman, 2018). Calculated using Gephi.

66

Table 4-1 Interpretation of network indicators from the cruise industry perspective
Indicators
Number of
nodes

Meaning of indicators
Number of ports wherecruise ships have called

Number ofedges
Density

Number of cruise shiproutes navigated between
ports
The ratio of the actual
number of shipping routes
to the number of possible
connections between ports

Averageclustering
coefficient

The ratio of ports that haveshipping routes to andfrom one port to another.

Averageshortest path
lengthThe shortest path on the
network between one port
and another

Diameter

The longest path on the
network between one port
and another

Degreecentrality

Number of shipping routes
connecting to the port

Network characteristics
The higher the number of nodes, the moredeveloped and diversified the network of ports ofcall.
The higher the number of edges, the moredeveloped (multidirectional) the route is in the
network.
The higher the density, the higher the percentage
of development of shipping routes is in the
network. If the development of shipping routes ismore advanced than the development of ports,
density will be higher. (Progress in thedevelopment of new shipping routes by shippinglines)The higher the average cluster coefficient, themore triangular the network (i.e., a productconsisting of an arrival and departure port and two
ports of call), or a network with many routes thatare not connected but are triangulated.
Not considering connections on direct itineraries,
the longer the average shortest path length, thelonger the connections (paths) between ports on
the closest route, and the sparser the network.
It does not consider connections on direct
itineraries; the longer the diameter, the longer theconnection (path) between ports on the longest
route, and the sparser the network.
Ports with a higher degree centrality have moreshipping connections with neighboring ports. The
port is a hub port with multimodal shipping
routes.

67

Cruise lines' port selection behavior
A higher number of nodes indicatescharacteristics that promote the development of
new ports.
A high edge number indicates characteristics
that promote the creation of a new route.
If the density is high, new ports may not be
developed and itineraries (combinations ofports of call) may become rutted.

Characteristics of (changes in) shipping routes
in a given network, such as whether a shipper's
itinerary is open or closed, longer or shorter indays, etc.
Characteristics of (changes in) shipping routes
in a given network, such as whether a shipper's
itinerary is open or closed, longer or shorter indays, etc.
Characteristics of (changes in) shipping routes
in a given network, such as whether a shipper's
itinerary is open or closed, longer or shorter indays, etc.
It shows which ports the shipping lines operate
out of. Degree centrality can also understandthe strategies of the shipping companies, suchas whether or not they are promoting hubs inDegree
centralization

The ratio of degrees biased
toward the port with thehighest degree

Modularity

Quality of community(inter-port connections)divisioncertain ports.
The network as a whole can understandstrategies such as whether or not a group ofcarriers participating in a given network isconverting to a hub.
The higher the modularity, the higher the quality
From the perspective of which ports and whichof the network in terms of the division
ports belong to the same community,(boundaries) of communities (groups of ports) that modularity can understand the characteristics ofare connected among ports.
the port selection behavior of cruise lines.
Networks with a higher degree centralization are
more heterogeneous and biased toward the portwith the highest degree (hub port).

Source: Author

684.4. Results
4.4.1. Network structure
The changes in the number of nodes and edges over time were analyzed to examinechanges in the number of ports and routes by ship size in Figure 4-2 (a) and (b). The
number of nodes and edges was highest for the small-size ships and lowest for themega-size ships. The changing trend was an increasing number of nodes and edges forsmall- and mega-size ships, whereas that of large- and mid-size ships decreased around2017–2018. This indicates that small- and mega-size ships were driving growth in the
Northeast Asian cruise market. In Figure 4-2 (c) and (d), observations were made using
density and average clustering coefficients to understand changes in network density
and the presence of triangular connection patterns. As a result, small-size ships had
sparse networks and low triangular connectivity. The network of mega-size ships around2014 was dense, but it tended to become sparse over time. It can be said that mega-size
ships were dense in the early years of the market, but they gradually became sparse as
the choice of ports of call increased. Furthermore, changes in network size wereanalyzed using the average shortest path length and diameter indicators in Figure 4-2 (e)and (f). The network of small-size ships was longer than that of other sizes, both inaverage shortest path length and diameter. In particular, the diameter of small ships has
become increasingly longer since 2018.

69

(a) Number of nodes(b) Number of edges
250

Number of edges

Number of nodes

90
80
70
60
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20
10
0

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0

2014

2015

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2019

2014

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Year
Mega-size

Large-size

Mid-size

Small-sizeMega-size(c) Density

Average clustering coefficient

Density

0.3
0.2
0.1
0

Large-size

2015

Mega-size

2016

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2018

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0.5
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3
2
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10
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Year

Year
Large-size

2018(f) Diameter

4

2015

2017
Year

Diameter

Average shortest path lengthMid-size

0.6

2019

(e) Average shortest path length

Mega-size

2019

0.7

Year

2014

2018

(d) Average clustering coefficient

0.4

2014

2017
Year

Mid-size

Mega-size

Small-size

Large-size

Mid-size

Small-size

Figure 4-2 Network structure
4.4.2. Degree centrality
Hub ports by ship size were detected from Figure 4-3 to Figure 4–6 using degree
centrality to understand changes in the center of the Northeast Asian cruise network.
The nodes of the same color belong to the same country. The size of each node
represents its degree. The mega-sized network has grown with Shanghai, Japan's
Kyushu region, and South Korea's ports as hubs (Figure 4-3). In 2014, there were still
few ports with mega-size ships calling. Then, in 2015, the number of degrees inShanghai, Nagasaki, Hakata, and Jeju increased. In 2016, these ports were joined byHong Kong and Busan. From 2017, the number of degrees in Naha gradually increased.
In 2018, Shanghai’s degrees increased even more, as did the degree of the Kyushu
70region in Japan, such as Kagoshima, Nagasaki, and Hakata. Additionally, Jeju
disappeared from the network this year. In 2019, Busan, Keelung, and Naha grew as
hub ports. Thus, the group of hub ports where mega-size ships call had grown into a hub
port with Shanghai as the center, together with the surrounding port groups in Japan,
Korea, Taiwan, and Hong Kong. However, there are few operations in the northern partof Japan (Hokkaido region), and they are not extensive.
Figure 4-4 shows the growth of the large-size network with Yokohama and Busan as
hub ports. In 2014, the degree increased in Yokohama, Kobe, Busan, Jeju, andShanghai. There were also cruise ship calls to the Hokkaido region, located in northern
Japan. In 2015, the degree in Shanghai, Yokohama, and Busan increased. In 2016, inaddition to these port groups, the degree of Jeju, Keelung, Nagasaki, and Kagoshima
increased. In 2017, the degree of Busan, Shanghai, and Keelung increased further, as
well as for many Japanese ports (including Yokohama, Nagasaki, Kagoshima, Kobe,
Kochi, and Naha). In 2018, Tianjin’s degree increased, and in 2019, Yokohama andBusan grew into huge hubs while Shanghai declined. Thus, the network of large-size
ships consisted of Yokohama and Busan as hub ports, with multiple ports of callscattered around them. Ports of call were widely spread throughout Northeast Asia, withsome as far north as Japan. The situation was not as Shanghai-centric as the mega-size
network.
The mid-size network had grown with Shanghai and Jeju as hub ports until 2017, after
which the hub ports shifted to Keelung in Taiwan, Busan in South Korea, and Kyushu
region in Japan (Figure 4-5). In 2014, Yokohama and Shanghai were the hub ports, butboth still had low degree numbers. In 2015, several hub ports had emerged, mainly
Shanghai, and Jeju, Hakata, Kobe, and Keelung had also increased their degree. In
2016, Yokohama and Busan joined this hub port group. In 2017, Shanghai grew into aneven more massive hub port. Other ports such as Kobe, Hakata, Sasebo, and Naha also
increased. However, the situation changed in 2018. While Shanghai, Tianjin, and Jeju
declined, Hakata and Sasebo increased, and Keelung emerged as a hub port. In 2019,
the degree shrank at many ports. Thus, the mid-size network was on a downward trend,as the trend toward expansion from 2014 to 2017 changed dramatically in 2018. The
decline in the position of Shanghai and Jeju was noticeable.
In the small-size network, the development of Hiroshima and Kobe as hub ports can beseen in Figure 4-6. No major hub ports were found in 2014, but the following were71

relatively high: Kobe, Otaru, Busan, Jeju, Shanghai, Keelung, and Hong Kong. Several
hub ports have emerged around Japan since 2015. These hub ports are Hiroshima, Kobe,
and Nagasaki. In 2016, Hiroshima and Kobe formed a huge hub port among these ports,
followed by Jeju, Shanghai, and Keelung, which were gradually increasing. In 2017, in
addition to Hiroshima and Kobe, Nagasaki, Busan, and Hong Kong were growing hub
ports. It is also evident that cruise ship calls were operating over a wide area from the
Hokkaido region in northern Japan to southern China. In 2018, Hiroshima emerged as ahuge hub port. Moreover, numerous ports had emerged around Hiroshima. Furthermore,by 2019, an extremely large number of ports would emerge around Hiroshima, as well
as Kobe, Osaka, and Nagasaki, while Hakodate would be next in line in northern Japan.
Thus, it can be seen that the small-size network has grown with Hiroshima as its hub
port in Japan, with several sub-hub ports in the vicinity working in tandem with eachother. In the northern part of Japan, there were also signs of a hub port cluster forming
around Hakodate.

72

2014

2015

2016

2017

2018

2019

Figure 4-3 Mega-size ship network
73

2014

2015

2016

2017

2018

2019

Figure 4-4 Large-size ship network
74

2014

2015

2016

2017

2018

2019

Figure 4-5 Mid-size ship network
75

2014

2015

2016

2017

2018

2019

Figure 4-6 Small-size ship network76

Figure 4-7 shows the degree by the port to observe changes in the hub ports by shipsize. The legend lists the top 10 ports in 2019. Each country has a different color line.
Pink, light green, light blue, green, and orange colors represent Japan, China, South
Korea, Hong Kong, and Taiwan, respectively. The gray lines represent the degree ofports ranked 11th and lower in 2019. The mega- and small-size ship’s hubs remainunchanged, while the large- and mid-size ship’s hubs were gradually replaced. The
transition of hub ports by ship size is described below.
Since 2015, the mega-size network was continuously highest in Shanghai, indicatingthat the hub port was fixed in only Shanghai. In 2019, Shanghai, Busan, and Kagoshima
were higher. There was a temporary drop in the overall degree in 2017. It also showsthat Shanghai, Kagoshima, and Hakata have changed at the same time since 2017.
Large-size frequently swapped places in the rankings. Jeju was the highest in 2014 and2016, Yokohama in 2015 and 2018, and Busan in 2017 and 2019. In 2019, Busan was
the highest, followed by Yokohama and Keelung. For the mid-size, after the upward
trend from 2014 to 2017, a downward trend was evident from 2018 onwards. Shanghai
remained at a high level through 2017 but then fell sharply in 2018. This led to higher
degrees in 2019 for Busan, Keelung, and Hakata, but none of these ports had been on an
upward trend in recent years and remained flat. In terms of the small-size network,
Hiroshima and Kobe have continuously had high degrees since 2015. In particular,
Hiroshima’s degree was high since 2018, indicating that the port was present as a hub
port. In 2019, Japanese ports such as Hiroshima, Kobe, and Osaka ranked high. It also
shows that the number of ports in the grey lines with a degree of 10 or less was high anddensely populated.
The degree distribution is organized in Figure 4-8 to examine changes in the degree by
ship size. In this bar chart, the white bars represent 2014, the black bars represent 2019,
and the rest represent the degree distribution for each year. The red line in the figure
depicts the 2019 figures. The distribution bar charts for all sizes also had a shape with along tail to the right-hand side, with the highest bar around degree two. The bar thenshifts to the right-hand side over time. The mega-size was mostly degrees 2 and 4. The
number of degrees after 13 increased over time, indicating that the trend was toward
becoming a hub. Large-size was mostly in the range from 2 to 5, followed by degrees 10
and 13. In addition, ports with a higher degree of 27 were present. Mid-size had similarlevels of degrees with a wide range of degrees from 2 to 9. This is in contrast to a large
number of degrees 2 in the other sizes. The number of ports with higher degrees
77decreased over time. Small-size had higher degrees 2 and 4, followed by degrees 8 and13. Compared with the other sizes, the tails on the right-hand side were shorter,
indicating less obvious hub ports.
Mega-size
30

Shanghai25

Busan
Kagoshima

Degree20

Hakata15

Keelung10

Nagasaki

5

Hong Kong

Naha
Tianjin

0
2014

2015

2016
2017
Year

2018

2019

Yokohama
Osaka

Large-size
30

Busan

25

Yokohama
Keelung

Degree20

Kagoshima

15

Hakodate

10

Naha

5

Jeju

Shanghai
Kochi (Japan)

0
2014

2015

2016
2017
Year

2018

2019

Kobe
Osaka

Mid-size

Degree

30

Busan

25

Keelung

20

HakataShanghai

15

Kobe

10

Kagoshima

5

Nagasaki
Naha

0
2014

2015

2016

2017

2018

2019

Year

Dalian
SaseboSmall-size
30

Hiroshima

25

Kobe
Osaka

Degree

20

Busan

15

Hong Kong

10

Kagoshima

5

Nagasaki

KeelungShanghai

0
2014

2015

2016
2017
Year

2018

2019

Figure 4-7 Degree centrality
78

HakodateTokyoLarge-size

0.40

0.40

0.30

0.30

Frequency

Frequency

Mega-size

0.20
0.10
0.00

0.20
0.10
0.00

1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627
Degree
2014

2015

2016

2017

2018

2019

1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627
Degree2019(Line)

2014

2015

2016

2019

2019(Line)

0.40
Frequency

0.40
Frequency

2018

Small-size

Mid-size
0.50

0.30
0.20
0.10

0.30
0.20
0.10
0.00

0.00

1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627
Degree1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627
Degree
2014

2017

2015

2016

2017

2018

2019

2019(Line)

2014

2015

2016

2017

2018

2019

2019(Line)

Figure 4-8 Degree distribution
Figure 4-9 using a measure of degree centralization examines changes in network
uniformity (i.e., the relative impact of higher degree hub ports) with ship size. From2014 to 2018, the mega-size network was the highest. This is because the mega-size
network means a heterogeneous network with some huge hub ports versus some thatwere not. The large-size network was on a gradual but upward trend. However, it
declined from 2018 onward, with large-size having the highest values in 2019. Mid-size
was on an upward trend until 2016, and it had been declining since 2017. The small-size
network was at much lower levels indicating that it was more homogeneous than theother sizes.
Degree centralization

0.5
0.4
0.3
0.2
0.1
0
2014

2015

2016

2017

2018

2019

Year
Mega-size

Large-size

Mid-size

Small-size

Figure 4-9 Degree centralization

794.4.3. Community detection
Using a method of community detection based on modularity optimization, spatial andtemporal changes in the number of communities and geographic locations created byconnections between ports by ship size were observed (Figure 4-10). The mega-size
network witnessed a rapid increase in the number of communities from two to five from2014–2019. The mega-size network had two communities in 2014, one in northern
China, Japan, and Korea, and the other in southern China, Taiwan, and eastern Japan.
There were four communities in 2015–2016. The community in northern Japandisappeared in 2017, reducing the number of communities to three. The community innorthern Japan community again emerged in 2018, bringing the number of communities
back to four. Further, Tianjin and Kyushu regions in Japan formed a separate
community from Shanghai in 2018. Another community emerged in the Hong Kong andGuangzhou areas in 2019, bringing the number of communities to five.
The large-size network remained stable with four to five communities from 2014–2019.
The large-size already had five communities in 2014. Unlike the mega-sizedcommunities of the same year, these communities were geographically widespread,including northern Japan. From 2015 to 2017, the community was fixed in fourcommunities: “northern China, western Japan, and South Korea,” “southern China,
Hong Kong, Taiwan, and southern Japan,” “northern Japan,” and “eastern Japan.”
However, the community in the central part of Japan split in 2018, and since then, fivecommunities have emerged.
The mid-size, like the large-size, remained stable with four to five communities from2014 to 2019. The location of the boundaries dividing the mid-size communities was
also similar to that of the large-size. The similarity was also evident in 2018 when Jeju,
located near Shanghai, left the northern Chinese community and joined the Japanesecommunity. However, there were some differences between the mid- and large-size
communities. The mid-size had more nodes in northern China (near Dalian and
Qingdao), fewer nodes in northern Japan, and a new community emerged in southern
China from 2016 to 2017.
The small-size community had a different structural evolution from other sizedcommunities. The four main differences between the small size and the other sizes were
the number of communities, geographic spread of nodes in the same community,
location of the community boundaries, and areas where the nodes were concentrated.
80First, the number of small-size communities was already five in 2014, with sevenemerging in 2015. Even in 2019, there were six communities. Second, several nodes
located far away from each other were connected within the same community. For
example, one of the communities in 2014 (in orange) was characterized by thegeographic breadth of the community, with a node in northern Japan and a node insouthern China belonging to the same community. Third, other than the small size
network, there were two distinct communities, one centered in Shanghai and the other in
Keelung. However, because of their small size, Shanghai and Keelung belonged to thesame community in 2016, 2018, and 2019. Finally, many nodes have been continuously
concentrated in the Seto Inland Sea in western Japan since 2014.

81

Year

2014

2015

2016

2017

2018

2019

Mega-size

Community=4

Community=4

Community=3

Community=4

Community=5

Community=5

Community=4

Community=4

Community=4

Community=5

Community=5

Community=5

Community=4

Community=5

Community=5

Community=4

Community=4

Community=6

Community=5

Community=5

Community=6

Community=2

Large-size

Mid-size

Small-size

Community=5

Community=7

Figure 4-10 Community detection
Note: Nodes of the same color belong to the same community.
82

Further analysis visualizes the differences in the way ports are connected by ship size
(Figure 4-11). Nodes of the same color belong to the same community. The size of eachnode represents its degree. The network of mega-, large-, and mid-size ships had several
high-degree hubs with several smaller nodes around them, forming a “hub and spoke”
structure in each community, as well as connected edges across communities.
Conversely, the network of small-size ships tended to be homogeneous in the degree of
each node and had the smallest value of degree centralization. The appearance of somenodes being connected with some detours between them was also consistent with thelonger average shortest path lengths and diameters. In other words, the small-size
network was constructed by connecting ports “side by side.”
Mega-size

Large-size

Mid-size

Small-size
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0D.XQJ+XL]KRXFigure 4-11 Visualizing the 2019 Northeast Asian cruise network
The quality of the network’s community partitioning by ship size was analyzed using
the measure of modularity, which is shown in Figure 4-12. This implies that the cruise
network increases the quality of community division over time. In addition, themodularity of the small-ship network was the highest compared with other sizes,
although it has been declining since 2018. However, the lowest was the modularity ofmega-ships.
0.6

Modularity

0.5
0.4
0.3
0.2
0.1
0
2014

2015

2016

2017

2018

2019

Year
Mega-size

Large-size

Mid-size

Figure 4-12 Modularity

83

Small-size4.5. Discussion
This chapter aimed to understand the spatial and temporal changes in the structure of thecruise network in Northeast Asia. The spatial and temporal changes in the structure of
the cruise network varied with ship size. In particular, five key findings were found.
First, the number of nodes and edges in the mega- and small-sizes were growing fasterthan those in the other sizes in Northeast Asia. The small-size network had the highest
number of nodes and edges. Generally, small ships are operated by luxury cruise lines,
which target wealthy customers. Barron and Greenwood (2006) and Han and Hyun(2018) stated that the development of luxury cruise itineraries is critical to customer
satisfaction. Hwang and Han (2014) and Lee and Kim (2019) stated that luxury cruiselines need to constantly offer new cruise products. As pointed out in these studies, small
ships are always looking for new ports and routes, which leads to a large number ofnodes and edges. Bagis and Dooms (2014) also noted that the itineraries of larger ships
tend to be more fixed than those of smaller ships. The mega-sized network in thischapter may have the lowest number of nodes and edges, given the limited number ofports that can be called. Interestingly, however, the number of nodes and edges formega- and small-size networks has been on the rise in recent years. In other words, thegrowth of the cruise market in Northeast Asia in recent years was accomplished by two
ship sizes: mega- and small-size.
Second, the growth pattern of the small-size network differed from that of the othersizes. Specifically, the small size grew uniquely, with low network density and an
average clustering coefficient but a high average shortest path length and diameter. The
low density is due to the rapid increase in the number of nodes. Also, the low averageclustering coefficient is due to longer duration itineraries than other ship sizes, which
results in fewer triangular routes. Furthermore, the high average shortest path length anddiameter may be because there are fewer “hub and spoke” connections, and there aremany patterns in which ports are connected “side by side” compared with other ship
sizes.
Third, hub ports differed depending on the size of the ship. In 2019, the mega-size hub
was Shanghai; the large-size ones were Busan and Yokohama; the mid-size ones were
Busan and Keelung; the small-size ones were Hiroshima and Kobe. The mega- andsmall-size ship’s hubs remain unchanged, while the large- and mid-size ship’s hubs
84

were gradually replaced. In terms of port calls to peripheral ports, mainly hub ports, themega-, and mid-size networks were mainly to ports in the vicinity of Shanghai, Busan,
and Keelung, which are closer together. Meanwhile, the large- and small-size networks
were growing, with cruise ships operating over a wide area from northern Japan tosouthern China. Furthermore, as for the homogeneity of the network for hub ports, thesmall-size network was growing in a more homogeneous state than those of other sizes.
Fourth, Shanghai was a mega-size hub port. Shanghai was a hub port for large- andmid-size networks until 2017; however, since 2018, Shanghai was no longer a hub port
due to its rapidly decreasing degree centrality. At the time, many of the major cruiselines were the first to deploy their new mega-ships to Shanghai to target the rapidly
growing number of Chinese cruise passengers. This resulted in the existing large- andmid-size ships in Shanghai being displaced by the new mega-size ships and shifted to
Keelung, Hong Kong, Yokohama, and other ports. In the case of Princess Cruises, thedeployment of the mega-size ship MAJESTIC PRINCESS to Shanghai in 2017 shifted
the existing large-size ship, SAPPHIRE PRINCESS, from Shanghai to Keelung. As aresult, Shanghai was a hub port in the mega-ship network, but it significantly reduced
the number of degrees for large- and mid-size ships. This reflects the cruise lines’ port
selection behavior of launching large- or mid-size ships in the early stages of the cruise
market and then replacing them with mega-size ships when they are convinced that
there is sufficient passenger demand in that port.
Finally, modularity increased for all sizes, which indicates that the community structure
has become clearer over time. Moreover, the small-ship network had more communities
and different boundaries than the other sizes. The number of mega-, large-, and mid-size
ship communities was approximately four to five. On the contrary, only the small-ship
network had seven communities in 2015 and six in 2019. Many communities werelocated in Japan, and the boundaries between the communities differed from other ship
sizes. Unlike other sizes, in 2016, 2018, and 2019, small-ship networks had Shanghai inthe same community as Hong Kong and Keelung, and many ports were concentrated
around the Seto Inland Sea in Japan.
Our findings allow for a deeper analysis to clarify the structural changes in the cruise
network by considering the following data limitations. First, AIS data show the
movement of cruise ships but do not distinguish between arrival/departure ports andports of call. Therefore, this chapter could not analyze the data on an itinerary basis.
85

Second, AIS data do not provide the number of cruise passengers per route (edge).
Ideally, if a network diagram could depict not only the movement of cruise ships but
also the movement of cruise passengers, the process of evolution of the relationshipbetween supply and demand could be analyzed.

4.6. Chapter conclusion
In summary, by using AIS data and some network science methods, the spatial andtemporal structure of the cruise network were determined through observations ofchanges over time by ship size. The results from these data highlight the spatial andtemporal structure of the cruise network by ship size.
The mega-size ships choose Shanghai as their hub port and connect with nearby ports.
Over time, they formed a network of "hub and spoke." The number of nodes and edges
is small compared with other ship sizes, but that number has been growing rapidly inrecent years. This indicates that the development of ports in Northeast Asia has
eliminated restrictions on port facilities, passenger handling, and other factors. Cruises,
one of the tourism products, are supply-driven (Vogel and Oschmann, 2012; Rodrigue
and Notteboom, 2013). Future development of the cruise market for mega-ships inNortheast Asia will require synchronization of port development and deployment of
cruise ships by cruise lines.
Conversely, large-size ships used to have Shanghai and Jeju as their hub ports.
However, since 2017, they have shifted to Busan and Yokohama, which is characterized
by frequent changes in hub ports. Large-size ships operate over a wider area throughout
Northeast Asia than mega-size ships. Similarly, mid-size ships were characterized by ashift from Shanghai and Jeju as hub ports to Busan and Keelung after 2017. To add, theimpact of the deployment of new mega-size ships in Shanghai led to the replacement of
hub ports within each network as existing large and mid-size ships were pushed out.
This indicates that countries and ports need to closely monitor cruise ship deployment
behavior by major cruise lines such as Royal Caribbean Cruises, Costa Cruises, MSC
Cruises, and Princess Cruise Lines, which own three sizes of ships: mega-, large-, andmid-size ships.
Small-size ships have unique operational and commercial port selection characteristics.
In the network of small-size ships, the Japanese ports of Hiroshima and Kobe became86hub ports and grew higher over time. In addition, the number of nodes and edges in thesmall-size network is higher than for other ship sizes and has been increasing in recent
years. This indicates that luxury cruise lines operating small ships are successfully
developing new ports and routes to improve customer satisfaction. Furthermore, smallsize cruise ships operate on a side-by-side rather than the hub-and-spoke connection
found on other ship sizes.
More specifically, the increasing trend in the number of nodes and edges was observed
not only for small-size ships but also for mega-size ships, which indicates that the cruise
market in Northeast Asia has been diversifying in recent years. Further development ofports where small-size ships can call at ports of call and tourist attractions should befurther promoted, especially in Japan. Furthermore, it is necessary to create a market
where not only small-size ships but also mega-, large-, and mid-size ships operate. Thissituation will help customers with diverse needs to choose their favorite cruise ship
category. The government, ports, and cruise lines need to work together to diversify thecruise market.
Moreover, one characteristic of all ship sizes is the sudden disappearance of Jeju fromthe network in 2018. This phenomenon might be related to the Terminal High Altitude
Area Defense missile (THAAD) event in March 2017. China banned group travel toSouth Korea in retaliation for the planned deployment of THAAD by US forces inSouth Korea’s territory. This event hugely impacted the entire Korean cruise tourism
industry (Park, 2019). Since then, all cruise ships from China to South Korea havestopped. This suggests that the cruise network is fragile and is affected by a variety offactors. Given such vulnerability, governments need to actively promote cross-bordercooperation among ports. Specifically, ports must be prepared in advance to provide
backup in the event of an emergency, and a system must be established to ensure thatcruise ship operations do not come to a halt in the event of an emergency.
There are three challenges for future studies. The first is to expand the target cruise areafrom Northeast Asia to the world to clarify the geographical differences in the structuralchanges of the cruise network. The second is to conduct a more detailed analysis ofunits (e.g., monthly and quarterly) to show the seasonal differences in the structuralchanges of the network structure over time. The third is to understand the structure ofconnections between nodes based on their spatial characteristics, analyze the evolution
process of cruise networks, and apply it to future prediction models.
87

5. CHANGES IN THE GLOBAL CRUISE NETWORK DUE
TO SEASONALITY
5.1. Introduction
Seasonality plays a key role in the cruise industry (Charlier, 1999; Charlier and
McCalla, 2006). The characteristic of frequent changes in cruise ship deployment basedon seasonality may not be seen in other transports. In the case of air and containertransport, hubs are fixed throughout the year, based on the operator's business strategy.
Conversely, because demand for tourist destinations changes with the seasons, thecruise industry may also frequently change hub ports from season to season.
Seasonality is beneficial to both the cruise line and the tourist destination. For the cruiselines, customer satisfaction will be enhanced because they can introduce theirpassengers to tourist destinations at the time of year when the weather is most favorable
for them. The understanding of attractive tourism resources on a global scale due toseasonality also has the advantage that strategies can be drawn to further expand thecruise market. For destinations, this will also improve the image of the destination tovisiting cruise passengers, who will be more likely to return for a longer stay the nexttime they visit.
However, seasonality is not always desirable for ports. The varying number of cruise
ship calls per season exposes taxpayers to criticism for passenger terminals that are notused during off-season periods. It also creates job instability for employees in the cruiseindustry, as they may need to find other work during off-season periods. Understanding
the spatial and temporal changes in the structure of the cruise network on a global scale
will be necessary for the stability of underutilized passenger terminals and employment
during off-peak periods.
A network diagram can be depicted by tracking the movement of cruise ships as theynavigate between ports. The network describes the number and location of ports (nodes)that make up the cruise network and their connections (edges) due to cruise ship
movements. Network structures can be measured using various network science
methods, which is a powerful way to understand the structure of various networks of88

different types, such as technological, information, social, and biological networks(Newman, 2018). Barabási (2015) revealed that the architectures of networks thatemerge in different domains of science, nature, and technology are similar and are basedon the same organizational principles. If so, some network science methods could be
used to analyze changes in hub port and cruise network structure due to seasonality.
The purpose of this chapter is to understand how seasonality changes in the structure of
the cruise network. A case study is the global cruise network in 2019. To do so, track
seasonal movements of all cruise ships in the world with AIS data
(https://maritime.ihs.com) and set up network data. The spatial and temporal changes inthe structure of the cruise network are measured using the number of nodes and edges,
average degree, average clustering coefficient, density, and degree centralization. The
number of nodes and edges indicates the number of ports and routes where the cruise
ship sails. The average degree is the average number of routes (directions) at each port,
and the average clustering coefficient indicates the percentage of connected partner
ports that are connected. And density indicates the percentage of direct routeconnections to the complete graph. Furthermore, degree centralization indicates thehomogeneity of the network. The change in the hub port is measured by a measure ofdegree centrality.
The rest of this chapter is organized as follows. Section 5-2 presents a literature review.
Section 5-3 outlines methodologies such as AIS data and network structure
measurement. Section 5-4 presents the results of the chapter, which is discussed in
Section 5-5. Finally, the conclusions of the chapter are presented in Section 5-6.

5.2. Literature review
Several previous studies exist that have analyzed the impact of seasonality on the cruiseindustry. Seasonality plays a key role in the cruise industry (Charlier, 1999; Charlier and
McCalla, 2006). Cruise lines are attempting to optimize the utilization of their assets
year-round by repositioning to take advantage of the seasonality of cruise markets. The
Caribbean is dominantly serviced during the winter while the Mediterranean
experiences a summer peak season. The two markets are not functioning independently
but are interconnected operationally, particularly through the repositioning of ship units
to cope with variations in seasonal demand among the geographical markets. The
seasonality of Alaska, Bermuda, and Canada/ New England is also evident (Rodrigue
89and Notteboom, 2012). The seasonality pattern of cruise destinations is not only
conditioned by both weather and market demand constraints, but also by the seasonality
patterns of other neighboring destination regions (Esteve-Perez and Garcia-Sanchez,2019). The cruise ship tends to voyage in a stable temperature range of 10°C–20°C, flee
the winter and catch up with the summer and spring, and mainly stay in the temperature
maritime climate zone, the subtropical monsoon humid climate zone, and theMediterranean climate zone to form several branch networks (Li et al., 2021).
Few studies have analyzed cruise ship networks using network science techniques.
Tsiotas et al. (2018) showed the double role of the cruise network, which is composedof the profit-driven strategies of cruise companies and port authorities, using data fromthe 2013 itineraries of Costa Cruises and Mediterranean Shipping Company (MSC)
Cruises in the Mediterranean cruise market. Jeon et al. (2019) investigated the centralityof cruise ports in the Asian cruise shipping market while proposing the hub andauthority centrality metric as a directional synthesis of the hub centrality and authority
centrality to explore cyclical and directional features of centrality in the cruise shipping
network. In a recent cruise network study, Kanrak and Nguyen (2021) revealed that thecruise shipping network is scale-free using itinerary data from Asian and Australiancruise network websites. Lopez Rodriguez et al. (2021) suggested that Caribbean ports
are the most important for hub and authority centrality, using 2018 itineraries for eachcruise line from the sites of 902 ports in the Caribbean and Northern Europe.
Moreover, few studies have used AIS data for analysis in cruise shipping. Tichavska andTovar (2015) used AIS data to measure the pollution status of exhaust gas from cruise
ships calling at the Las Palmas Port in the Canary Islands. Vicente-Cera et al. (2019,2020a) arranged the cruise ship’s operating hours, repair times, and berthing times,
estimated seawater pollution status by cruise ships, and assessed environmentalpressures related to global cruise traffic along their paths based on AIS data. VicenteCera et al. (2020b) used AIS data to aggregate cruise ship calling patterns at European
ports and evaluated the diversity of cruise ship calls at each port. Ito et al. (2020)
organized the port call patterns before and after the suspension of cruise ship operations
owing to the coronavirus disease 2019 pandemic and analyzed the relationship between
cruise ship operations and the spread of infection at the port of call using AIS data.
To the best of our knowledge, no network science approach using AIS data has
elucidated the spatial and temporal changes in the structure of the cruise network over90

the seasons. In particular, no previous studies have focused on markets around theworld, not just major markets such as the Caribbean and Mediterranean, or changes in
hub ports, network structure, and cruise ship travel distances.

5.3. Methodology
5.3.1. Data
AIS data on ports of arrival for all cruise ships worldwide in 2019 is used. The divisionby the size of the cruise ship followed the classification criteria of CLIA Asia (2019).
The classification criteria are as follows.
•

Mega-size: Lower berth capacity of 3,500 or more OR GRT over 150,000•••

Large-size: Lower berth capacity of 2,000 to 3500 AND GRT over 75,000
Mid-size: Lower berth capacity of 750 to 2,000 passengers
Small-size: Lower berth capacity under 750 passengers (*including Expedition
ships)

5.3.2. Network structure measurement
The network is analyzed with an undirected graph and an unweighted graph. Network
analysis and visualization were conducted using Gephi (the open graph Viz platform).
A network is made up of components called nodes and direct connections between themcalled edges. An average degree is denoted by 〈𝑘〉. An average degree is a number
obtained by dividing the total degree 𝑘# of node 𝑖by the number of nodes 𝑛.
+

1〈𝑘〉 = & 𝑘#
𝑛

(1)

#,-

The clustering coefficient of a node is the fraction of pairs of neighbors of the nodeconnected. The clustering coefficient 𝐶# of node 𝑖 is defined as follows: 𝜏(𝑖) is thenumber of triangles involved in 𝑖. The maximum possible number of triangles for i isthe number of pairs formed by its 𝑘# neighbors. 𝐶# is defined only if the degree 𝑘# >1 because of the terms 𝑘# and 𝑘# − 1 in the denominator. A node must have at least
two neighbors for any triangle to be possible.

91

𝐶# =

2𝜏(𝑖)
𝑘# (𝑘# − 1)(2)

The clustering coefficient of the entire network is the average clustering coefficient 𝐶,
which is used to understand the formation of the triangular route. The formula is asfollows:+

1
𝐶 = & 𝐶#
𝑛

(3)

#,-

The density is the ratio of the actual number of edges to the number of all possibleedges in a graph. It is used to analyze the network’s connectivity level. If the number ofnodes is 𝑛 and the number of edges is 𝑚, the network density 𝑑 is given as follows:
2𝑚
(4)
𝑑=
𝑛(𝑛 − 1)Degree centralization 𝐶1 measures are based on a normalized variance in the degree
centrality to compare distinct networks based on their highest degree centralization
scores (Freeman, 1979; Krnc and Škrekovski, 2020). It can measure whether the degree
is biased toward a high node in the network. 𝑘!/2 is the highest degree centrality inthe network. 𝑘# denotes the degree of node 𝑖. The more concentrated the network, theless homogeneous it is:∑+#,-(𝑘!/0 − 𝑘# )𝐶1 =max ∑+#,-(𝑘!/0 − 𝑘# )(5)

Degree centrality 𝑘# is assumed to be centered on a node with a higher degree among
the nodes in the network. Hub ports are detected with degree centrality. the degree of
node 𝑖 by 𝑘# is denoted. If the adjacency matrix of a network with 𝑛 nodes is 𝑎#. ,then degree centrality can be formulated as follows:++

𝑘# = & 𝑎#. = & 𝑎.#
.,-

.,-

92

(6)

Table 5-1 Interpretation of network indicators from the cruise industry perspective
Indicators
Number of
nodes

Meaning of indicators
Number of ports wherecruise ships have called

Network characteristics
The higher the number of nodes, the more developed
and diversified the network of ports of call.

Number ofedges

Number of cruise shiproutes navigated between
ports
Number of shipping
routes per port (numberof directions)
The ratio of ports thathave shipping routes toand from one port toanother.

The higher the number of edges, the more developed(multidirectional) the route is in the network.

AveragedegreeDensity

The ratio of the actual
number of shipping
routes to the number ofpossible connections
between ports

Degreecentralization

The ratio of degrees
biased toward the portwith the highest degree

The higher the average degree, the more routes areserved per port and the denser the network.
(multidirectional shipping routes)
The higher the average cluster coefficient, the moretriangular the network (i.e., a product consisting of anarrival and departure port and two ports of call), or a
network with many routes that are not connected but
are triangulated.
The higher the density, the higher the percentage ofdevelopment of shipping routes is in the network. If
the development of shipping routes is more advanced
than the development of ports, density will be higher.
(Progress in the development of new shipping routes
by shipping lines)
Networks with a higher degree centralization are moreheterogeneous and biased toward the port with thehighest degree (hub port).

Degreecentrality

Number of shipping
routes connecting to theportPorts with a higher degree centrality have moreshipping connections with neighboring ports. The port
is a hub port with multimodal shipping routes.

Averageclustering
coefficient

Source: Author
93

Cruise lines' port selection behavior
A higher number of nodes indicatescharacteristics that promote the developmentof new ports.
A high edge number indicates characteristics
that promote the creation of a new route.
A high average degree indicates thecharacteristic of developing routes to various
directions with the base port as the axis.
Characteristics of (changes in) shipping
routes in a given network, such as whether ashipper's itinerary is open or closed, longer
or shorter in days, etc.
If the density is high, new ports may not be
developed and itineraries (combinations ofports of call) may become rutted.

The network as a whole can understandstrategies such as whether or not a group ofcarriers participating in a given network isconverting to a hub.
It shows which ports the shipping lines
operate out of. Degree centrality can also
understand the strategies of the shipping
companies, such as whether or not they arepromoting hubs in certain ports.

5.4. Results
5.4.1. Network structure
As shown in Figure 5-1, the number of nodes tended to be higher for smaller ships. The
small-size ships with the highest number of nodes had an increase in the number ofnodes at two different times: from April to May and from September to October. The
small-size ships were positioning themselves to change areas from south to north andfrom north to south during these two periods. The timing of these changes in the numberof nodes was generally similar for the other sizes.
Conversely, the change in the number of edges was different from the change in thenumber of nodes. The number of edges was higher for small-size ships in May-June and
September-October, but higher for mid-size ships in July-August. July-August, whenthe edge numbers for mid-size ships were high, was the right time to be deployed to
Alaska. The size with the lowest number of edges was the network of mega-size ships,
as was the number of nodes. In summary, the number of edges was high from May to
October and low from November to April, a trend common to all sizes.
600

Number of edges

Number of nodes

500
400
300
200
100
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Mega-size

Large-size

Mid-size

1,800
1,600
1,400
1,200
1,000
800
600
400
200
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Small-sizeMega-size

Large-size

Mid-size

Small-size

Figure 5-1 Number of nodes and edges
The average degree in Figure 5-2 was highest for mega- and large-size ships and lowest
for small-size ships. This result was opposite to the number of nodes and edges. The
trend of change was similar for mega- and large-size ships and similar for mid- andsmall-size ships. Mega- and large-size ships were lower from March to April and from
October to November. During this period, they were deployed in the Caribbean andMediterranean. Mid- and small-size ships remained high from May to around August
but did not increase after December.
The average clustering coefficient was also high for mega- and large-size ships, as was94the average degree. In particular, it was above 0.5 from December to January for megasize ships. Mega-size ships were deployed only in the Caribbean during this period.
Overall, the trend was high around June through September and falling around April
and November.
Average clustering coefficient

14

Average degree

12
10
8
6
4
2
0
Large-size

Mid-size

0.5
0.4
0.3
0.2
0.1
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Mega-size

0.6

Small-sizeMega-size

Large-size

Mid-size

Small-size

Figure 5-2 Average degree and average clustering coefficient
Similar to an average degree, density in Figure 5-3 also showed similar trends for megaand large-size ships and similar trends for mid- and small-size ships. Mega-size ships
had higher densities from December to January and June to July. Large-size ships had
higher densities in January or February and June to August. From December to
February, when the density of mega- and large-size ships was high, they were deployed
only in the Caribbean. In addition, the density of these sizes increased again from June
to August because they were deployed intensively in Alaska, the Mediterranean, andNorthern Europe.
Degree centralization was higher for mega- and large-size ships and lower for mid- andsmall-size ships, the same as average degree, average clustering coefficient, and density,
but differed in that the mega-size ship did not increase around June to August. It should
be noted that the figures for mega-size ships increased from December to January andfor large-size ships from May to September. The timing of the higher degree
centralization for these two sizes is exactly the opposite. Degree centralization formega-size ships was high from December to February because of the concentration of
deployments in Cozumel, Miami, and Nassau. Also, degree centralization for large-size
ships was high from May to September because of the concentration of deployments in
Alaska.

95

Degree centraization

Density

0.6

0.09
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0

0.5
0.4
0.3
0.2
0.1
0

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Mega-size

Large-size

Mid-size

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Small-sizeMega-size

Large-size

Mid-size

Small-size

Figure 5-3 Density and degree centralization
5.4.2. Degree centrality
Figure 5-4 plots the degree centrality of mega-size ships on a world map. Ports withlarger node circles indicate ports with a higher degree centrality. In other words, these
ports are called hub ports. One of the characteristics of the mega-ship network is that the
Caribbean has a high concentration of hub ports throughout the year. The mega-size
ships were deployed in the Caribbean and South America from December to March,
only in the Caribbean and Mediterranean in April, in Alaska and Northeast Asia from
May, and back to only the Caribbean and the Mediterranean again in October. No cruise
ships were crossing the Atlantic between June and August.
Large ships changed their deployment areas seasonally compared to mega ships (Figure5-5). The network of large-size ships is characterized by a large variation in Caribbean
degree. Caribbean degree centrality was high from November to April but contracted
the rest of the year. The Mediterranean showed an increase in degree centrality from
April, the Nordic and Baltic Sea were also higher from May, and then shrank from
November. Another characteristic of the large-size ship network is the movement
around Alaska. Alaska's degree centrality was high only during the five months from
May to September. In addition, degree centrality around Sydney, Australia, and New
Zealand was high for six months from November to April. Large ships were moving
east to west from March to April and October. No cruise ships were crossing theAtlantic between June and September.
The mid-size network tended to have a higher degree around Northern Europe, such as
the Baltic Sea, the Scandinavian, and the Mediterranean Sea (Figure 5-6). These regions
were highly degree-centric for the nine months from April to December. Conversely, theCaribbean had a high degree centrality for the six months from October to March, the96

opposite of the Mediterranean. From June to September, degree centrality around
Alaska also increased, but the numbers were lower than those for large-size ships.
Another characteristic is that the smaller degrees are scattered over a wider area thanthose of mega- and large-size ships. For example, numerous small degrees can be foundin South America, Africa, Australia, and Asia. Mid-size ships were moving east to west
from February to March and November to December. The north-south movement was inJanuary and November-December. No ships were crossing the Atlantic in July andAugust.
The network of small-size ships changed their deployment areas significantly dependingon the season (Figure 5-7). The highest degree centrality ports were concentrated in
Northern Europe, such as the Mediterranean, the Baltic Sea, and the Scandinavian.
Although these areas have degrees all year round, degree centrality was high during theeight months from April to November. In particular, around Scandinavia, despite thecooler temperatures in the region, the degree was also present in the winter months ofDecember through March. The characteristic of the small-size ship network is the highnumber of degrees in the Panama Canal during the five months from November toMarch. Since the Panama Canal is intended for crossings between the Pacific andAtlantic Oceans, many small cruise ships are likely engaging in what is known as
positioning, changing their deployment areas during this period. Another characteristicof the small-size ship network is the movement of ships from October to November.
Several edges can be seen crossing from north to south in October. Then, in November,a sudden high degree centrality appeared around Ushuaia (for Antarctic cruises),
Argentina, and the tip of South America. In both time and region, there was a highdegree centrality in Australia from December to March, followed by a shift in the centerof degree centrality to around Southeast Asia in March. Furthermore, from April to May
thereafter, the degree centrality in Northeast Asia around Japan increased. This meansthat the small ship group was gradually moving northward during the period from
December to May. East-west movements of small-size ships were from February to
March, while north-south movements were from April and from October to December.

97

Jan

Feb

Mar

Apr

Mediterranean
Caribbean

Caribbean

Caribbean

South America

South America

May

Jun
Northeast Asia

AlaskaCaribbean

South AmericaJul

AlaskaNortheast Asia

AlaskaMediterranean

Mediterranean

Northeast Asia

AlaskaMediterranean

Caribbean

Sep

Aug

Northern Europe

Northern Europe

Northern Europe

Caribbean

Caribbean

Oct

Mediterranean
Caribbean

Nov

Dec

AlaskaCaribbean

Mediterranean

Caribbean

Mediterranean

Mediterranean

Caribbean

Caribbean
South America

Figure 5-4 Degree centrality (Mega-size)

98

Jan

Feb

Mar

Canary Islands

Caribbean

Southeast Asia

Caribbean

South America

South America

Australia and New Zealand

May

Jun

Northern Europe
AlaskaMediterranean

Mediterranean

Sep

Aug

AlaskaCaribbean

Caribbean

Australia and New ZealandAustralia and New Zealand

Northern Europe

AlaskaMediterranean

Mediterranean
Caribbean

Jul

Northern Europe

Northern Europe
AlaskaSouth America

Australia and New Zealand

Northeast Asia

Northeast Asia

Southeast Asia

Caribbean

Southeast Asia

Apr

Caribbean

Caribbean

Oct

Mediterranean

Nov

Dec

Northern Europe
AlaskaMediterranean
Caribbean

Caribbean

Mediterranean

Mediterranean
Caribbean
Australia and New ZealandFigure 5-5 Degree centrality (Large-size)

99

Caribbean

Southeast Asia
Australia and New Zealand

Jan

Feb

Mar

Apr

Northern Europe

Northern Europe

Northeast Asia

Canary Islands
Caribbean

Caribbean
South America

Caribbean
South America

Australia and New Zealand

May

Southeast Asia

AlaskaJul

Caribbean

Aug
Northern Europe

AlaskaMediterranean

Mediterranean

Mediterranean

Caribbean

Caribbean

Caribbean

Oct

Northern Europe

Northern Europe
AlaskaMediterranean

Sep

Caribbean

Australia and New Zealand

Jun
Northern Europe

Northern Europe

Mediterranean
Canary Islands

Canary Islands

Canary Islands

Nov

Northern Europe

Dec
Northern Europe

Northern Europe

AlaskaMediterranean

Mediterranean
Caribbean

Mediterranean

Caribbean

Caribbean

Canary Islands
Caribbean
South America

Figure 5-6 Degree centrality (Mid-size)

100

Jan

Feb

Mar

Apr

Northern Europe

Northern Europe
Northeast Asia

Northeast Asia
Canary Islands

Mediterranean
Panama Canal
Caribbean

Panama Canal
Caribbean

Panama Canal
Caribbean

South America

May

Jun

Northern Europe

Northern Europe

AlaskaOct

South America

Dec

Northern Europe
Canary Islands

Mediterranean

Mediterranean

Mediterranean

Nov

New York

AlaskaPanama Canal
Caribbean

MediterraneanSoutheast Asia

South America

Figure 5-7 Degree centrality (Small-size)

101

Svalbard
Northern Europe

Mediterranean

Northern Europe
New York

Arctic Canada

AlaskaAlaskaMediterranean

Sep
Northern Europe

Aug
Svalbard

Northern Europe

MediterraneanSoutheast Asia

Australia and New Zealand

Jul

Northeast Asia
AlaskaPanama Canal
Caribbean

Southeast Asia

Panama Canal
Caribbean

South America

5.4.3. Seasonal variation in the degree
Seasonality could lead to frequent changes in hub ports. Figure 5-8 shows the 50 ports
with the highest annual total number of monthly degrees by ship size. The darker
colored areas indicate periods of high degree, and the white areas indicate periods of no
port calls. Most of the top ports for mega-size ships tended to have high degrees
throughout the year, and mid-size ships tended to be similar. Conversely, the top-ranked
ports for large- and small-size ships had low degrees or no port calls at certain times ofthe year. In other words, these networks vary widely with seasonality.
Hub ports for the mega-size network were mainly Caribbean ports such as Cozumel,
Miami, Nassau, Port Canaveral, Port Everglades, Philipsburg, and San Juan, andMediterranean ports such as Barcelona, Genoa, and Civitavecchia. These ports
continued to operate cruise ships throughout the year, but the Caribbean ports had
higher degrees from November through March, while the Mediterranean ports had
higher degrees from April through October.
Hub ports for the large-size network were also Caribbean ports such as Nassau, Miami,
Cozumel, Key West, Port Everglades, and Alaskan ports such as Ketchikan, Juneau,
Skagway, and Campbell River (Canada). Sydney (Australia) was also a hub port withcruise ship calls year-round. While these Caribbean ports had cruise ship calls yearround, the Alaskan ports only had cruise ship calls for six months, from May to October.
Hub ports for the mid-size network were Stockholm in the Baltic Sea; Piraeus,
Mykonos, and Heraklion in the Aegean Sea; Barcelona and Palma in the Mediterranean
Sea; Cozumel, Freeport (Bahamas), Nassau, and Miami in the Caribbean Sea. The midsize network was similar to the mega-size with many ports with cruise ship calls all year
round. Characteristically, Aegean hub ports such as Piraeus, Mykonos, and Heraklion
had high degrees around April to November.
Hub ports for the small-sized network were extensive, including Split and Dubrovnik on
the Adriatic, the Panama Canal, Piraeus on the Aegean, Juneau in Alaska, Civitavecchia,
and Valletta in the Mediterranean, and Kiel and Tromso in Northern Europe. Split andDubrovnik in the Adriatic and Piraeus in the Aegean, Civitavecchia, and Valletta in the
Mediterranean from April to October, the Panama Canal from October to April, Juneauin Alaska and Kiel and Tromso from May to September had a higher degree. The smallsize network was characterized by many ports with large seasonal degree fluctuations.
102

Port

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total

Port

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total

Port

1

Cozumel

87

69

73

56

43

53

50

55

50

56

71

75

738 Nassau

48

47

46

39

20

37

40

33

23

29

39

52

453 Stockholm

2

Miami

65

58

51

52

46

43

50

48

42

43

66

84

648 Miami

54

46

43

52

29

28

25

20

25

32

34

53

441 Piraeus

3

Nassau

59

49

64

57

44

48

48

52

35

39

36

72

603 Ketchikan

75

88

95

91

88

2

439 Mykonos

4

Port Canaveral

50

43

59

52

30

52

42

39

35

46

32

63

543 Juneau89

76 1008571

4425 Cozumel

5

Barcelona

17

15

22

45

50

42

43

55

64

65

42

14

474 Cozumel

20

21

25

25

27

16

6

Genoa

13

12

23

50

49

39

39

37

54

68

32

11

427 Skagway

71

85

82

85

59

2

7

Civitavecchia

6

9

22

50

41

53

54

45

52

24

11

367 Key West

45

42

36

42

20

23

24

23

29

32

29

8

Port Everglades

43

38

45

30

20

19

17

10

11

20

45

64

362 Port Everglades

59

59

54

45

2

9

9

8

7

16

9

Philipsburg

45

43

37

18

29

27

27

23

13

23

26

32

343 Sydney (Australia)

36

36

51

44

21

20

8

16

16

33

10 San Juan

15

19

23

23

31

36

31

37

24

37

25

40

341 Campbell River

49

75

72

72

82

4

11 Georgetown (Cayman Is)

42

35

29

27

22

28

23

27

20

28

31

27

339 Civitavecchia

25

50

34

42

48

47

69

32

12 St Thomas

35

34

23

22

30

25

24

19

16

32

28

41

329 Barcelona

14

26

37

23

26

26

33

54

13 Palma

8

8

9

24

36

40

42

52

46

39

16

2

322 Piraeus

4

26

36

27

32

35

37

40

14 Savona-Vado

12

12

15

32

36

27

23

23

35

40

23

5

283 Southampton

12

12

32

33

32

39

43

33

15 Southampton

1

5

10

24

45

31

35

39

44

25

16

6

281 Victoria (Canada)

3

45

55

50

50

43

9

16 Singapore

22

20

22

23

26

22

14

16

19

17

24

20

245 Georgetown (Cayman Is)

31

36

22

10

12

13

15

10

5

25

40

249 Long Beach

17

11

12

18

20

18

24

19

23

29

32

20

16

242 Venice

2

3

24

33

31

41

32

36

29

11

5

4

24

32

31

37

32

32

36

8

239 Valletta

3

22

31

29

27

36

33

32

8

21

27

30

28

26

29

31

12

19

2

New York & New Jersey

56

46

10

30

51

15

38

46

53

22

9

8

281 Bergen

4

8

19

33

48

24

15

277 Kiel Canal

2

7

32

37

18

13

267 Mariehamn

15

31

8

26

20

27

27

24

31

27

27

276 Nice

3

18

44

2

4

22

47

39

26

43

26

45

17

4

275 Gustavus

2

22

24

21

21

12

22

25

16

20

25

27

265 Campbell River

247 Venice

2

22

43

38

25

38

37

36

16

1

258 Malaga

1

4

221 Thira

4

17

34

37

42

38

34

28

16

2

252 Lisbon

3

1
1

20

18

11

11

6

173 San Juan

33

32

30

28

1

4

2

4

4

5

2

30

34

41

32

33

1

2

14

22

21

21

17

21

34

25
17

24

25

20

21

31

12

19

19

21

20

3

36

34

33

20

24

21

2

2
2

1

34 St John's (Antigua)

36

23

25

4

2

4

4

2

4

8

13

22

147 Mykonos

35 Naha

6

7

7

9

9

145 Kralendijk

32

28

24

18

142 Philipsburg

29

22

33

23

4

140 New York & New Jersey

4

3

7

6

14

17

20

133 Hilo

10

10

10

15

16

8

30

22

32

21
25

10

20

20

20

16

11

10

36 Copenhagen

2

17

33

36

31

16

7

37 Piraeus

2

11

24

20

26

22

18

38 Ketchikan

2

24

27

28

27

23

2

14

3

6

39 Castries

23

13

16

5

4

4

4

4

4

8

18

27

130 Castries

40 Valletta

8

8

10

20

16

8

9

9

9

17

7

7

128 Yakutat

41 Sydney (Australia)

18

20

19

6

6

12

21

25

127 Honolulu

42 Bridgetown

23

17

15

6

3

4

4

4

4

6

15

25

126 Livorno

43 Hong Kong

4

4

12

13

12

10

10

12

13

9

9

18

126 St John's (Antigua)

44 Johor

10

9

9

12

14

11

9

11

11

9

14

7

45 Nagasaki

12

5

12

11

6

21

10

7

5

10

14

11

46 Puerto Progreso

14

9

7

9

7

8

11

8

9

14

12

14

122 Long Beach

15

847 Great Stirrup Cay

4

8

5

8

7

10

12

10

8

6

24

17

119 Panama Canal

14

48 Santa Cruz de Tenerife

10

8

14

17

6

2

2

8

10

23

16

116 St Thomas

21

2

9

7

2

20

16

18

16

14

9

1

114 Genoa

2

14

17

14

21

18

21

6

49 Tianjin
50 Livorno

174

23

6

2

156

29

11

1

151

14

38

16

10

16

24

11

5

145

20

21

13

10

7

3

145

19

19

25

2

1

144

19

4

9

26

18

2

143

34

24

35

24

23

6

17

22

27

20

43

6

7

24

24

9

7

9

12

20

20

3

140

4

31

26

12

4

4

17

22

14

1

139

2

14

28

16

14

12

19

22

10

5

22

17

20

27

22

17

5

3
3

148

142
141

16

23

25

19

24

27

22

42

14

12

243 Barcelona

209 Southampton

7

5

20

29

18

27

19

31

12

20

28

21

237 Thira203 Ensenada

25

17

22

20

15

12

22

25

11

16

23

21

229 Reykjavik

2

9

20

34

43

23

3

1

198 Georgetown (Cayman Is)

30

27

25

15

11

14

13

14

10

23

20

22

224 Rhodes

1

5

15

15

16

21

17

27

16

2

135

12

197 Naples

2

4

4

16

23

29

29

34

28

30

19

6

224 Philipsburg

18

19

32

11

2

2

2

2

15

28

131

21

28

192 Funchal

37

26

37

32

5

2

2

4

2

6

35

26

214 Venice

5

8

1

5

9

15

16

13

8

11

128

36

28

5

187 Rhodes

4

12

20

23

34

31

23

28

20

17

212 San Juan

11

18

20

6

4

26

37

124

18

28

25

182 Aalesund

15

211 Singapore

16

13

15

12

7

4

1

4

9

19

23

123

34

7

3

6

11

15

34

27

16

8

27

20

11

35

26

2

22

27

18

2

5

2

10

15

22

2

138
2

137
135

2

6

19

19

43

43

31

22

11

179 Stavanger

2

12

13

25

31

41

20

22

25

13

5

6

174 Key West

16

15

18

19

18

13

19

20

12

17

19

20

206 Hamburg

3

26

17

7

20

28

10

6

2

119

2

171 Philipsburg

26

18

34

13

9

8

10

8

8

12

21

29

196 Siracusa

6

17

18

15

6

17

27

11

1

118

8

5

170 Castries

29

21

24

16

6

8

8

10

8

10

25

28

193 Kobe

17

20

6

2

7

11

13

7

8

114

21

32

170 Copenhagen

13

38

25

35

30

34

14

4

193 Monaco

5

20

21

11

25

15

14

3

114

20

28

168 Split

9

112

5

167 Lisbon

2

2

13

30

29

18

26

24

28

13

14

6

5

23

31

13

6

15

12

28

17

161 Arrecife de Lanzarote

29

26

27

25

4

2

2

4

2

9

23

16

23

13

5

9

6

9

7

12

9

31

27

24

34

34

12

12

23

31

25

17

209 Longyearbyen

15

17

8

13

11

18

20

15

13

183 Oban

2

10

20

19

13

24

11

13

23

28

181 Sitka

1

17

27

29

23

14

1

19

36

178 Aalesund

4

175 Copenhagen2412

175 Rovinj

5

10

29

20

172 St John's (Antigua)

10

16

22

11

15

26

16

27

18

18

31

9

7

28

28

17

27

25

23

7

3

7

17

28

157 Bridgetown

2

8

13

23

157 Rostock

16

18

26

13

9

153 Tromso

2

12

17

9

9

13

16

23

13

152 Port Everglades

21

12

12

17

8

11

8

9

8

17

1

5

22

18

151 Dover

7

5

18

18

11

28

29

29

19

2

34

32

36

21

148 Santa Cruz de Tenerife

27

14

25

23

6

4

2

4

25

35

165 Gustavia

7

10

9

9

3

20

12

12

2

12

4

1

14

16

20

34

15

7

9

17

16

7

19

8

1

2

2

2

11

25

25

166 Ketchikan

9

14

17

18

14

146 Cartagena (Colombia)

22

13

23

17

11

10

9

9

9

10

15

16

164 Lipari

1

6

24

14

25

16

17

25

13

3

144 Oranjestad (Aruba)

8

9

10

11

14

10

12

15

11

21

20

19

160 Palma

26

13

2

2

18

22

144 Bari

13

23

23

15

31

27

25

2

126 St Petersburg

2

27

34

26

39

13

2

143 Colon

124 Tallinn

2

31

34

23

37

13

3

143 Hamburg

19

17

8

2

3

4

26

22

16

140 Corfu

19

27

14

1

3

17

28

17

140 Las Palmas

27

16

24

22

14

22

1

4

4

4

4

7

14

21

138 San Juan

14

13

5

12

13

13

12

13

17

26

20

4

135 Willemstad

10

11

26

17

2

17

25

134 Kralendijk

9

12

2

2

18

22

10

13

11

8

11

11

12

14

12

156 Gibraltar

7

20

28

18

13

12

7

11

14

7

13

150 Tallinn

18

18

20

19

23

26

19

6

16

4

2

2

2

2

9

19

26

149 Genoa

14

9

10

11

9

10

12

8

15

24

149 Cadiz

1

12

12

11

10

10

12

13

16

11

15

143 Castries

16

8

13

13

11

12

12

12

18

12

10

142 Colon

22

24

149 Bridgetown

3
14

5

21

112
112
9

6

6

8

12

109

4

12

30

111

107

20

17

111

2

106
105

24

21

9

10

17

19

11

17

14

17

7

6

24

5

105

2

6

9

7

35

43

1

103

6

11

14

7

10

9

11

18

1

7

22

30

27

12

3

23

15

1

3

4

32

15

8

9

15

11

1

2

11

8

9

9

10

18

19

17

17

8

2

8

2

2

10

11

3

13

8

20

120

5

159 Quebec

Figure 5-8 Monthly degree of the top 50 ports with the highest degree

103

2

1

43

14

6

185 Corinth Canal

8

23

10

2

4

211
15

8

9

24

13

11

12

32

17

233

214 Livorno

13

29

14

6

30

10

113 Bridgetown

8

13

255 Dubrovnik

23

8

20

30

26

150 Zeebrugge

27

37

13

18

24

29

10

20

8

56

12

9

11

32

11

7

18

53

12

9

16

28

24 Hakata

10

19

37

10

7

13

31

16

10

22

25

8

11

19

18

33

20

13

26

22

15

22

13

15

12

7

17

23

20

17

12

19

33 Oranjestad (Aruba)

35

17

6

23

21

10

1

19

21

20

268 Civitavecchia

14

26

10

303 Bergen

17

38

12

9

174 Port Canaveral

151 Oranjestad (Aruba)

18

8

15

3

19

22

14

4

10

31

11

18

10

284 Augusta

9

23

20

32

11

25

5

31

16

28

37

27

14

22

36

4

15

22

35

16

13

6

53

31

10

32 La Spezia

36

15

19

23

8

27

27

18

1

29

23 Belize City

4

5

27

1

23

34

14

3

24

51

31

6

3

57

353 Palm Beach

32

36

40

6

35

164 Willemstad

39

287 Klickitat

36

165 Bari

7

27

34

22

342 Juneau

29

43

19

13

24

30

3

45

20

44

9

41

23

32

8

42

25

41

30

39

18

24

35

38

22

31

10

39

21

7

35

34

22

2

11

33

21

175 Thira

33

16

35

187 Seattle

10

288

2

354 Miami

20

26

304

5

293 Valletta

24

13

53

22

301 Civitavecchia

16

2

28

40

15

11

11

24

42

38

9

12

2

42

24

14

15

1

36

44

6

25

4

29

39

5

31 Seattle

4

45

36

7

30 Willemstad

4

22

30

6

165 Copenhagen

20

2

18

8

4

35

2

34

7

30

66

1

19

8

38

63

359 Piraeus

31

10

32

361 Panama Canal

34

26

10

34

2

28

31

13

27

16

32

23

19

29 Victoria (Canada)

35

25

29

12

9

45

30

15

21

4

60

30

19

29

6

63

24

16

26

172 Palma

50

23

11

29

2

54

18

17

20

31

28

31

13

32

37

312

8
39

22

22 Long Beach

39

7

17

21 Basseterre

35

3

27

47

27

47

362 Palma

30

1

53

375 Nassau

35

28 Skagway

45

39

36

27

41

61

37

25

45

42

3

172 Augusta

34

46

196 Vancouver (Canada)

173 Cartagena (Colombia)

20

325 Tromso

10

8

9

15

18

17

6

33

20

5

343

2

45

17

18

7

371 Dubrovnik

25

18

15

3

13

29

25

16

50

26

27

19

13

66

47

26

13

17

50

45

37

10

11

44

40

40

7

17

43

46

20

16

11

46

37

14

23

20

16

55

14

20 Shanghai

14

9

42

379 Barcelona

196 Dubrovnik

16

6

14

34

5

12

3

6

335 Kiel

14

14

377 Split

23

8

8

27

23

20

16

27

19

25

16

38

5

25

14

34

24

25

16

46

37

23

14

51

23

24

21

37

35

11

16

46

38

7

27 New Orleans

10

39

9

26 Palermo

424 Heraklion

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total

31

26

19 Marseille

173 Naples

45

Port

15

43

3

25 Juneau

15384 Freeport (Bahamas)

18 Naples4

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total

2

4

105

6

102
102

15

18

101

4

101

10

2

100

10

18

100

20

100

Seasonal changes in the degree distribution by ship size are shown in Figure 5-9. Unlike
the previous figure, this figure is not limited to the top 50 ports with the highest orderbut covers all ports where calls were made each month by ship size. As a result, several
hub ports with degrees over 70 for mega- and large-size ship networks have appeared.
However, the timing of the appearance of the hub ports was exactly the opposite: themega-size was from November to March, and the large-size was from April to
September. In the mid-size and small-sized networks, hub ports with a degree of around
60 appeared, but ports with a degree of 80% or more had an order of 10 or less.
Furthermore, for all sizes, the proportion of ports with a degree of 10 or less tended todecrease from June to August.
Large-size
1

0.8

0.8
Frequency

Frequency

Mega-size
1

0.6
0.4
0.2

0.6
0.4
0.2

0

0
1-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-100
DegreeJan Feb Mar Apr May Jun

Jul

Aug Sep Oct

1-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-100
DegreeNov Dec

Jan Feb Mar Apr May Jun

Aug Sep Oct

Nov Dec

Small-size

1

1

0.8

0.8
Frequency

Frequency

Mid-size

Jul

0.6
0.4
0.2

0.6
0.4
0.2

0

0
1-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-100
DegreeJan Feb Mar Apr May Jun

Jul

Aug Sep Oct

Nov Dec

1-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-100
DegreeJan Feb Mar Apr May Jun

Jul

Aug Sep Oct

Nov Dec

Figure 5-9 Degree distribution
Differences in the number of monthly degrees by ship size can be measured by acoefficient of variation. Figure 5-10 plots the coefficient of variation of the monthly
degrees for the 50 ports with the largest annual degrees by ship size. Figure 5-8 shows
the calculated coefficients of variation by ship size. As a result, the highest coefficientof variation was found in the small-size ship network, and conversely, the lowest in themega-size ship network. In the mega-size ship network, Caribbean ports such as St
John's (Antigua), Castries, and Bridgetown had high coefficients of variation. In
addition, the coefficient of variation for Southampton in the UK was also high. The
network of large-size ship networks had high coefficients of variation for the Caribbean
104

ports of San Juan, Port Everglades, and Willemstad, as well as the Nordic ports ofZeebrugge and Copenhagen. The mid-size ship networks had high coefficients ofvariation, including the Atlantic ports of Funchal, Las Palmas, and Arrecife deLanzarote, as well as Bergen in Northern Europe. Ports with high coefficients ofvariation in the network of small-size ships cover a wide area. Quebec in Canada had
the highest coefficient of variation, followed by Panama Canal, Reykjavik and Lisbon in
Europe, and St John's (Antigua) in the Caribbean.
Mega-size

Large-size

Coefficient of variation
0
0.5
1
CozumelMiami
NassauPort Canaveral
Barcelona
Genoa
Civitavecchia
Port Everglades
Philipsburg
San JuanGeorgetown…
St Thomas
Palma
Savona-Vado
Southampton
Singapore
New York & New…
NaplesMarseille
ShanghaiBasseterreLong Beach
Belize City
HakataJuneauPalermo
New Orleans
Skagway
Victoria (Canada)
Willemstad
SeattleLa Spezia
Oranjestad (Aruba)
St John's (Antigua)
Naha
Copenhagen
Piraeus
Ketchikan
Castries
Valletta
Sydney (Australia)
Bridgetown
Hong Kong
Johor
Nagasaki
Puerto Progreso
Great Stirrup Cay
Santa Cruz de…
TianjinLivorno0.439

Mid-size

Coefficient of variation
0
0.5
1
NassauMiami
Ketchikan
JuneauCozumelSkagway
Key West
Port Everglades
Sydney (Australia)
Campbell River
Civitavecchia
Barcelona
PiraeusSouthampton
Victoria (Canada)
Georgetown…
VeniceValletta
Dubrovnik
Vancouver (Canada)
SeattleThiraPort Canaveral
San JuanNaplesCartagena…
Augusta
Palma
Copenhagen
Bari
Willemstad
Oranjestad (Aruba)
Zeebrugge
Mykonos
Kralendijk
Philipsburg
New York & New…
Hilo
Castries
YakutatHonolulu
LivornoSt John's (Antigua)
St Petersburg
Tallinn
Long Beach
Panama Canal
St Thomas
Genoa
Bridgetown

0.497

Small-size

Coefficient of variation
00.5
1
Stockholm
PiraeusMykonos
CozumelHeraklion
Freeport (Bahamas)
Barcelona
NassauPalma
Miami
Palm Beach
BergenCivitavecchia
MariehamnDubrovnikLong Beach
VeniceThira
LivornoSouthampton
Ensenada
Georgetown…
NaplesFunchal
RhodesAalesundStavanger
Key West
Philipsburg
Castries
Copenhagen
Split
LisbonArrecife de…
Bridgetown
Rostock
TromsoPort Everglades
Dover
Santa Cruz de…
Cartagena…
Oranjestad (Aruba)
Bari
Colon
Hamburg
Corfu
Las Palmas
San JuanWillemstad
Kralendijk

0.447

0

Coefficient of variation
0.5
1

Split
Dubrovnik
Panama Canal
PiraeusJuneauKiel
TromsoCivitavecchia
Valletta
Klickitat
AugustaBergenKiel Canal
Nice
Gustavus
Campbell River
MalagaLisbonBarcelona
Thira
Reykjavik
RhodesPhilipsburg
VeniceSan JuanSingapore
Longyearbyen
Hamburg
Siracusa
Kobe
MonacoCorinth Canal
Oban
Sitka
AalesundCopenhagen
Rovinj
St John's (Antigua)
Ketchikan
Gustavia
Lipari
Palma
QuebecGibraltar
Tallinn
Bridgetown
Genoa
Cadiz
Castries
Colon

0.630

Figure 5-10 Coefficient of variation of monthly degrees for the top 50 ports
5.4.4. Seasonality of cruise ship movements
What size ship travels the furthest latitude distance throughout the year? The percentage
of port calls by quarter was measured by latitude in Figure 5-11. This allows us tounderstand the spatial and temporal changes in the structure of the cruise network
depending on the season. Note that in Figure 5-11, the north latitude is shown as a plus
and the south latitude as a minus.
Mega-size ships called more frequently in the second and third quarters at higher
northern latitudes, and more frequently in the first and fourth quarters as latitudes
105

moved south. And below 30°N, the number of port calls in the first and fourth quarters
exceeded the number of port calls in the second and third quarters. This trend, with 30
degrees north latitude as the boundary, was the same for large-, mid-, and small-size
ships.
However, the main difference between the mega-size ships and other sizes was thepercentage of the area near 80°N and near 60°S. Mega-size ships called near 80°N inthe second quarter and third quarter and a half, and near 60°S only in the first quarter.
Conversely, near 80°N, the large-size ships had a fourth-quarter call, while the mid andsmall-size ships also had a first and fourth-quarter call. Small-size ships, in particular,
had a similar percentage of port calls throughout all quarters. Around 60°S, mega-size
ships only had port calls in the first quarter, while large-size ships had port calls in thefirst and fourth quarters, and mid-size ships had port calls in the first, second, and fourth
quarters as well. In addition, small-size ships had port calls throughout all quarters.
Mega-size
20%

40%

Large-size
60%

80%

100%

80
70
60
50
40
30
20
10
-10
-20
-30
-40
-50
-60

0%

LatitudeLatitude

0%

1Q

2Q

3Q

20%

40%

4Q

1Q

Mid-size
20%

40%

80
70
60
50
40
30
20
10
-10
-20
-30
-40
-50
-60
1Q

2Q

3Q

80%

100%

2Q

3Q

80%

100%

4Q

Small-size
60%

80%

100%

0%

LatitudeLatitude

0%

60%

80
70
60
50
40
30
20
10
-10
-20
-30
-40
-50
-60

4Q

20%

40%

60%

80
70
60
50
40
30
20
10
-10
-20
-30
-40
-50
-60
1Q

2Q

3Q

4Q

Figure 5-11 Percentage of port calls by season at each latitude
Figure 5-12 shows the distribution of travel latitude distances for each ship size
throughout the year. Mega-size ships were highest at a latitude distance of 20, followed
by around 50. 110 was also high. Large-size ships were highest around latitude distance106

50, followed by those around 110. Mid-size ships were highest around latitude distances
10 to 20, followed by those around 130 and 70. Small-size ships were highest aroundlatitude distance 10 and were otherwise flat. Characteristically, many of the small ships
traveled long distances, as long as latitude distance 140.
Large-size

Frequency

Frequency

Mega-size
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0

0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0

10 20 30 40 50 60 70 80 90 100 110 120 130 140
Latitude distance

10 20 30 40 50 60 70 80 90 100 110 120 130 140
Latitude distance

Small-size

Frequency

Frequency

Mid-size
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
10 20 30 40 50 60 70 80 90 100 110 120 130 140
Latitude distance

0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
10 20 30 40 50 60 70 80 90 100 110 120 130 140
Latitude distance

Figure 5-12 Latitude distance distribution
Figure 5-13 is a box plot showing the maximum, mean, median, 50% distribution ofdata (75% distribution, 25% distribution), and minimum values of latitude distance foreach ship size. Maximum values were higher for small- and mid-size, but 75%
distribution was higher for mid-size ships. Averages were higher for large- and mid-size
ships, while mega- and small-size ships were equally low. Medians were also high forlarge- and mid-size ships, followed by mega-size ships, and lowest for small-size ships.
25% to 75% distributions showed that large-size ships were widely distributed, followedby small-size ships.

107

140

Latitude distance

120
100
80
60
40
20
0

Mega-size Large-size 1 Mid-size Small-size

Figure 5-13 Box plot of latitude distance by ship size

5.5. Discussion
The purpose of this chapter is to understand how seasonality changes the spatial andtemporal structure of the cruise network. The results of the chapter are summarized infive categories and described below.
First, the smaller the ship size, the higher the number of nodes and edges, and also thehighest at the positioning (moving the deployment area) timings of April-May and
September-October. This is because smaller ships have more port options due to fewer
constraints on port facilities and passenger operations. Conversely, average degree,
average clustering coefficient, density, and degree centralization had higher values forlarger ships. This is indicative of the tendency for larger ships to have more limited
ports of call and a higher degree of hub ports. Interestingly, while the average degree
and average clustering coefficient and density for mega- and large-size ships were highfrom May to September, degree centralization for the same period was high for largesize ships, while that for mega-size ships remained low. This is due to deployment to
Alaska; May through September is the best season for deployment to Alaska, with bothmega- and large-size ships moving. However, some mega-size ships have a low degree
centralization due to their inability to pass through the Panama Canal because of theirexcessive ship size (TIME, 2018). As a result, unlike large-size ships, mega-size ships
cannot become hubs in Alaska's major ports. In addition, for many indicators, the timingof the figure changes from April to May and from September to October. This may be
due to port selection behavior known as "positioning", where cruise ships move fromone deployment area to another.
Second, hub ports did not appear only in the Caribbean or Mediterranean but varied108

geographically depending on the season and the size of the cruise ship. Mega-size ships
had hub ports in the Caribbean, and South America (Brazil) in winter and theMediterranean, Alaska, and Northeast Asia in summer, while large-size ships had hub
ports in the Caribbean and Australia in winter and in Alaska, Northern Europe, and theMediterranean in summer. Mid-size ships had hub ports in and around the Caribbean
Sea, Canary Islands, and Scandinavia in winter and various locations in Northern
Europe, the Mediterranean, Adriatic sea, Aegean seas, and Alaska in summer, whilesmall-size ships had hub ports in the Panama Canal, South America (Argentina), andScandinavia in winter, and to the Adriatic and Aegean Seas, Alaska, Northern Europe,
Svalbard, and Arctic Canada in summer. As Rodrigue and Notteboom (2012) pointed
out, the Caribbean was dominantly serviced during the winter while the Mediterranean
experiences a summer peak season. This assertion was true only in the case of megasize ships.
Table 5-2 Deployment areas in summer and winter by ship size
Ship size

WinterSummerMega-

Caribbean, South America(Brazil)Mediterranean, Alaska, Northeast Asia

Large-

Caribbean, Australia

Alaska, Northern Europe, Mediterranean

Mid-

Caribbean, Canary Islands,
Scandinavia

Northern Europe, Mediterranean, Adriatic sea,
Aegean seas, Alaska

Small-

Panama Canal, South America(Argentina), Scandinavia

Adriatic sea, Aegean seas, Alaska, Northern
Europe, Svarbard, Arctic Canada

Third, the monthly changes in the number of degrees at hub ports in the small-size ship
network varied most significantly with the seasons. It was lower for mega-size ships,
followed by mid- and large-size ships, which did not fluctuate as much as small-size
ships. The reason for the large fluctuations in the degree of ports for small-size ships
from season to season stems from their deployment patterns, in which they move fromone popular port to another seasonally, depending on the climate, rather than calling
mainly at major ports, as mega- and large-size ships do. Interestingly, small-size ships
had higher Panama Canal degrees from December to February. This indicates that
small-size ships were crossing between the Atlantic and Pacific Oceans during thisperiod. In other words, small-size ships changed their deployment areas significantly
depending on the season, resulting in a higher degree of variability in the degree of each
port.
Fourth, there were no mega-size ships deployed around the Arctic region (above 80°N)
109

or around the Antarctic region (above 60°S), with the boundary around 30°N reflecting
differences in deployment due to seasonality. Using this as a boundary, the larger ships,
such as mega-size ships, had more distinct seasonal deployment latitudes, deploying
above 30°N during the second and third quarters and shifting to below 30°N (south) in
the fourth and first quarters. Conversely, the smaller ships showed more consistent
deployment throughout the year in all latitudes. Smaller ships, in particular, were
deployed in the fourth to first quarter, the winter period when the 80°N area is out of
season. Similarly, they were also deployed in the second to third quarters, the summer
months when the 60°S area was out of season. This means that warmth is notnecessarily the only factor in port selection behavior for cruise lines.
Finally, in terms of latitudinal distance, mega-size ships did not move much throughout
the year, but neither did small-size ships. Bagis and Dooms (2014) pointed out, that theitineraries of larger vessels (mass cruise tourism) tend to be more stable than that forsmaller vessels. In terms of ship movement (latitudinal distance) by ship size, mega-size
ships tended to be more stable (less mobile) throughout the year than other ship sizes.
They were correct in their assertion that mega-size ships were stable, but not necessarily
that small-size ships were not stable (moving). The distribution of the latitudinal travel
distances of the small-size ships shows that most of the small-size ships did not movethroughout the year, but some of them did move extremely long distances (Figure 5-12).
This situation can be seen in the degree centrality map in Figure 5-7. Some small-size
ships gathered in Alaska and North Europe in the summer, in South America andAustralia in the winter, and in the Panama Canal in the middle of the season.
Conversely, some small-size ships continued to be deployed to areas such as
Scandinavia and Scandinavia throughout the year. This means that ships classified under
the same small-size ship type also have separate characteristics.
Our findings allow for a deeper analysis to clarify changes in the spatial and temporalchanges in the structure of the cruise network due to seasonality, taking into account thefollowing data limitations. First, AIS data show the movement of cruise ships but do not
distinguish between arrival/departure ports and ports of call. Therefore, this chapter
could not analyze the data on an itinerary basis. Second, AIS data do not provide thenumber of cruise passengers per route. Ideally, if not only the movement of cruise ships
but also the movement of cruise passengers could be depicted in a network diagram, theprocess of change in the relationship between supply and demand due to seasonality
could be analyzed.
1105.6. Chapter conclusion
The results of this chapter revealed that seasonality played an important role in thecruise industry. In particular, seasonality influences the spatial and temporal changes inthe structure of the cruise network, resulting in different geographic relationships of hub
ports and changes in cruise networks depending on cruise ship size. The findings of thischapter are used to contribute through proposals to countries and cruise ports.
The positioning and repositioning of cruise ships by seasonality resulted in different
combinations of deployment areas and hub ports depending on the ship size. Changes incruise ship distribution areas due to seasonality could lead to problems for port
operations, such as fluctuations in employment opportunities during off-peak periods.
Therefore, countries and ports should promote cooperation with the countries and portsof the positioning partner based on the information on the combination of deployment
areas and hub ports that differ depending on the ship size as a result of this research. At
that time, the center of the world cruise market has been the Caribbean and theMediterranean, but it is necessary to consider that the cruise market will expand to
South America, Asia, Africa, etc. in the future.
In this chapter, mega-size ships were characterized by their positioning area being
limited to the Caribbean and Mediterranean Seas, their latitude distances being short,and their immobility. However, this fact is problematic due to physical restrictions, suchas restrictions on the size of the port where mega ships can call and restrictions on thesize of the Panama Canal. According to Cruise Industry News (2022), 23 new mega-size
ships, 6 MSC Cruises, 5 Royal Caribbean Cruises, 3 Princess Cruises, 2 Carnival
corporation, 2 Carnival China, 2 TUI Cruises, 1 Costa Cruises, 1 AIDA Cruises, and 1
Dream Cruises, will be deployed in the market over the seven years from 2021 to 2027.
Given the plans for many of these new mega-size ships to be deployed shortly,
governments and ports will need to develop ports and expand canals on a scale that canaccommodate mega-size ships.
There are three challenges for future studies. First, the small-size ships, which had beenbifurcated into those that move and those that do not, could be sorted by category(luxury type or expedition type), ice class level, etc., and then analyzed for seasonality,
which would allow for more realistic analysis. Second, since the movement of mega111

size ships may be gradually becoming more widespread due to the construction of port
facilities and the expansion of canals, the impact of port development on seasonality
will be analyzed in the future. Third, the impact of global warming and seasonality on
cruise operations will be analyzed. This is because global warming is expected to allow
cruise ships to operate in the Arctic Ocean during the summer months and, conversely,
to avoid cruise ship operations in areas of extreme heat.

112

6. CONCLUSIONS
6.1. Summary
The purpose of the dissertation is to understand the spatial and temporal changes in the
structure of the cruise network. By taking the trajectory of all cruise ships navigating ina given area as a network, and by getting a bird's eye view, and understanding the
growth areas and timing where cruise lines are deploying ships and developing routes, it
may be possible to avoid a situation where cruise ships do not arrive after a port has
been built. To this end, this paper proposes the following four sub-objectives, the resultsof which are as follows:lSub-objective 1 (SO1): To clarify the spatial and temporal changes in the cruise
network structure between normal and contingency situations. Case study of the
March 2017 THAAD event that halted routes between China and South Korea.

lSub-objective 2 (SO2): To observe changes in the connections between cruise ports
during the early stages of the COVID-19 outbreak, such as port closures. The
relationship between the cruise industry and the COVID-19 outbreak was analyzed.

lSub-objective 3 (SO3): To identify changes in the spatial and temporal changes inthe structure of the cruise network by ship size. The case study is the Northeast
Asian cruise market 2014-2019, which has experienced rapid growth in recent
years.

lSub-objective 4 (SO4): To understand how seasonality changes the spatial andtemporal structure in the cruise network by ship size around the world. The casestudy is the global cruise network in 2019.

Regarding sub-objective 1 (SO1), that cruise network structure differs between normal
and contingency conditions, which can be elucidated by tracking cruise ship movements
using AIS data, the following was found.
•

Immediately after the THAAD event, to keep the sailing days, that is, toadhere to the schedule strictly, the number of ports of call was reduced to a113

single port, which is a marquee port, and the number of port calls was reduced
and the ship continued to operate.
•

Then, while gradually testing discovery ports, itinerary patterns wereincreased, and after one and a half years, the next best itinerary was created.

•

Due to the THAAD event, the centers of the cruise market in Northeast Asia
shifted from Shanghai port in eastern China to Shekou and Xiamen ports insouthern China, Keelung port in Taiwan, and Tokyo/Yokohama ports in Japan.

Regarding sub-objective 2 (SO2), the spatial and temporal structure in the cruise
network changes due to port closures in the early stages of the COVID-19 outbreak,which revealed a relationship between the cruise industry and the COVID-19 outbreak,the following was found.
•

The number of cruise ship operations in Asia had declined since the end ofJanuary 2020, while those in Western countries had not changed.

•

Consequently, it was found that COVID-19 infection rates in countries thathave ports of arrival and departure are higher than in countries with only portsof call. To add, COVID-19 infection rates in countries that continued to accept
cruise ships until March were higher than those in countries that did not.

•The second analysis focused on the characteristics of cruise ships infected with
COVID-19. As a result, the cruise ships infected with COVID-19 were large.
Also, most cruise ships were sailing from the same home port to the same portof call in a week.

Regarding sub-objective 3 (SO3), that the spatial and temporal structure in the cruise
network changes by the size of the ship, which can be revealed by tracking cruise ship
movements using AIS data, the following was found.
•

The number of nodes and edges in the mega- and small-sizes was growing
faster than those in the other sizes in Northeast Asia. The small-size had thehighest number of nodes and edges, and new ports and routes were opened.

•

The growth pattern of the small-size differed from that of the other sizes. The
small size grew uniquely, with low network density and an average clustering
coefficient.

•

Hub ports differed depending on the size of the ship. In 2019, the mega-size
hub was Shanghai; the large-size ones were Busan and Yokohama; the mid114

size ones were Busan and Keelung, and the small-size ones were Hiroshima
and Kobe. The mega- and small- size hubs remain unchanged, while the largeand mid-size hubs were gradually replaced.
•

Shanghai was a mega-size hub port. It was also, until 2017, a hub port forlarge- and mid-size ports. However, after 2018, Shanghai was no longer a hub
port. At the time, many of the major cruise lines were the first to deploy their
new mega-size ships to Shanghai. This resulted in the existing large- and midsize ships in Shanghai being displaced by the new mega-size ships.

•

Modularity increased for all sizes, which indicates that the community
structure has become clearer over time. Moreover, the small-ship network had
more communities and different boundaries than the other sizes. Many
communities of the small-ship were located in Japan, and the boundaries
between the communities differed from other ship sizes.

Regarding sub-objective 4 (SO4), the difference in the spatial and temporal structuralchanges in the cruise network depending on the season by cruise lines can be clarified
by tracking cruise ships that change according to the four seasons, the following was
found.
•The smaller the ship size, the higher the number of nodes and edges, and also
the highest at the positioning (moving the deployment area) timings of AprilMay and September-October.

•

Hub ports did not appear only in the Caribbean or Mediterranean but varied
geographically depending on the season and the size of the cruise ship.

•

The monthly changes in the number of degrees at hub ports in the small-size
ship network varied most significantly with the seasons. It was lower formega-size ships, followed by mid- and large-size ships, which did not fluctuate
as much as small-size ships.

•

There were no mega-size ships deployed around the Arctic region (above
80°N) or around the Antarctic region (above 60°S), with the boundary around
30°N reflecting differences in deployment due to seasonality.

•

In terms of latitudinal distance, mega-size ships did not move muchthroughout the year, but neither did small-size ships. Mega-size ships tended tobe more stable (less mobile) throughout the year than other ship sizes. The
distribution of the latitudinal travel distances of the small-size ships shows thatmost of the small-size ships did not move throughout the year, but some of115

them did move extremely long distances.

6.2. Limitations and future works
This study has several limitations. Although AIS data provides information on cruise
ship movements, it is not possible to distinguish between arrival and departure ports andports of call, and thus itinerary analysis could not be performed in this study. Since AIS
data does not provide information on the number of cruise passengers per route (edge),
landing demand was estimated from the capacity of the ship.
There are five future works. As follows.
1. Cruise network prediction modeling by elucidating ‘Supply-Driven Mechanisms’
The results of the spatial and temporal structural changes in the cruise network inthis study can be used to clarify the mechanism of supply-driven unique to thecruise industry, such as how cruise demand develops after port development. This
will allow us to understand how the entire network grows through the developmentof ports (addition of nodes) and the opening of shipping routes (addition of edges),which may be used to build a model to predict the growth of the cruise network.
2. Impact of route disruptions, etc. using causal inference techniques
Using the findings of this study on spatial and temporal structural changes in thecruise network under contingency conditions, such as route disruptions due to
THAAD events and port closures due to the spread of COVID-19 infection, theimpact can be measured using causal inference and other methods. This may
enable specific studies of coordination among ports, such as preparing alternative
ports in advance.
3. Port infrastructure development of cruise ship movement data by size
To the network data on ship movements by size based on the AIS data constructedin this study, we added information on the specifications of each port, such as berthlength, water depths, and passenger terminals, as well as information on thefunctions of each port, such as whether the port is used as a home port, arrival anddeparture port, or port of call by each cruise line. By building a new network data,
it may be possible to develop efficient port development strategies, such as
specifications of port infrastructure required for different sizes of cruise ships, and116

gradual development of facilities to accommodate small-, large-, and mega-size
ships.
4. Competitive relationships among cruise lines considering differences in services
and clientele
By adding information on cruise lines' service level (service category) and clientele(wealthy class, nationality of passengers, etc.) to the network data on ship
movements by size based on AIS data constructed in this study, new network data
can be constructed, which will allow us to analyze the competitive relationships
among cruise lines and to better understand the strategies and principles of port
selection by cruise lines.
5. Impact of global warming on cruise industry seasonality
In this study, spatial and temporal cruise network structural changes due toseasonality were analyzed using AIS data from 2019. In the future, ship movement
data of all cruise ships operating worldwide will be traced back to the past to
capture changes in the cruise network structure over a long period. Then, bycomparing this data with indicators (temperature and other data) that show theeffects of global warming, etc., which have been changing over a long period, it
may be possible to analyze the effects of global warming, etc., on the actual
conditions of movement and migration of cruise ships at latitudes (north andsouth).

117

APPENDICES
Appendix 1 List of cruise areas and countries
Cruise area
North America
Caribbean Sea

South America
Pacific Ocean
Oceania

Asia
Middle East,
South Asia
Mediterranean
Sea
Northern
EuropeAfricaCountry
Canada, Costa Rica (West coast), El Salvador, Guatemala (West coast),
Mexico (West coast), Nicaragua, Panama (West coast), St Pierre andMiquelon, US (Northeast coast, West coast)
Antigua, Aruba, Bahamas, Barbados, Belize, Cayman Islands, Colombia,
Costa Rica (East coast), Cuba, Dominica, Dominican Republic, French
Guiana, Grenada, Guadeloupe, Guatemala (East coast), Guyana, Haiti,
Honduras, Jamaica, Martinique, Mexico (East coast), Montserrat,
Netherlands Antilles, Panama (East coast), Puerto Rico, St Kitts & Nevis,
St Lucia, St Vincent, Suriname, Trinidad & Tobago, Turks & Caicos
Islands, US (Southeast coast), Venezuela, Virgin Islands
Argentina, Brazil, Chile (West coast), Ecuador, Falkland Islands, Peru,
South Georgia, Uruguay
Chile (Around Easter islands), US (Around Hawaii islands)
American Samoa, Australia, Christmas Island, Cook Islands, Fiji, French
Polynesia, Guam, Kiribati, Marshall Islands, Micronesia, New Caledonia,
New Zealand, Norfolk Island, Northern Mariana Islands, Palau, Papua
New Guinea, Samoa, Solomon Islands, Tonga, Vanuatu
Brunei, Cambodia, China, Indonesia, Japan, Myanmar, Philippines,
Russia, Singapore, South Korea, Taiwan, Thailand, Timor, Vietnam
Bahrain, Egypt, India, Iran, Israel, Jordan, Kuwait, Maldives, Oman,
Pakistan, Qatar, Saudi Arabia, Sri Lanka, United Arab Emirates, Yemen
Albania, Algeria, Bulgaria, Croatia, Cyprus, Egypt, France (South coast),
Gibraltar, Greece, Israel, Italy (South coast), Lebanon, Libya, Malta,
Monaco, Montenegro, Romania, Russia (West coast), Slovenia, Spain(South coast), Syria, Tunisia, Turkey, Ukraine
Belgium, Channel Islands, Denmark, Estonia, Faroe Islands, Finland,
France (North coast), Germany, Greenland, Guernsey, Iceland, Ireland, Isleof Man, Jersey, Latvia, Lithuania, Netherlands, Norway, Poland, Portugal,
Russia (North coast, East coast), Spain, Sweden, UK
Angola, Benin, Cameroon, Canary Islands, Cape Verde Islands, Comoros,
Congo (Republic), Cote d'Ivoire, Djibouti, Eritrea, Gabon, Gambia,
Ghana, Guinea, Kenya, Liberia, Madagascar, Madeira, Mauritania,
Mauritius, Morocco, Mozambique, Namibia, Nigeria, Reunion, Senegal,
Seychelles, Sierra Leone, South Africa, Sudan, Tanzania, Togo, Western
SaharaSource: Author118

Appendix 2 Top 30 countries with ports of arrival and departure among cruise products
sold in early April

Appendix 3 Cruise ships affected in the US by COVID-19

119

Appendix 4 Cruise operators by ship size
Mega ship operators
Costa Crociere SpA
Dream Cruises Management Ltd
MSC Crociere SpA
NCL Bahamas Ltd
Princess Cruise Lines Ltd
Royal Caribbean Cruises Ltd

Large ship operators
Celebrity Cruises Inc
Costa Crociere SpA
Cunard Line Ltd
Holland America Line NV
NCL Bahamas Ltd
P&O Cruises
Princess Cruise Lines Ltd
Star Cruises

Midsize ship operators
Aida Cruises
Astro Ocean Cruise
Costa Crociere SpA
Cruise & Maritime Voyages Ltd
Crystal Cruises LLC
Diamond Cruise
Dream Cruises Management Ltd
Fred Olsen Cruise Lines Ltd
Holland America Line NV
Maritime Holdings Group Inc
MSC Crociere SpA
Nina Services Corp
NYK Cruises Co Ltd
Oceania Cruises Inc
P&O Cruises
Phoenix Reisen GmbHRoyal Caribbean Cruises Ltd
SkySea Cruises
Star Cruises
Viking Ocean Cruises Ltd
Yantai Bohai Ferry Int'l Ship

Source: Author

120

Small ship operators
Azamara Club Cruises
Hapag-Lloyd Kreuzfahrten GmbHJapan Cruise Line Inc
Mitsui Passenger
Noble Caledonia Ltd
Oceania Cruises Inc
P&O Cruises
Phoenix Reisen GmbH
Plantours & Partner GmbHPONANT
Princess Cruise Lines Ltd
Regent Seven Seas Cruises Inc
ROW Management Ltd
Seabourn Cruise Line Ltd
SeaDream Yacht Club Management
Semester at Sea
Sete Yacht Management SA
Silversea Cruises Ltd
Voyages of Discovery Ltd
Windstar Cruises LLC

Appendix 5 Implications and interpretation of results of network indicators from the cruise industry perspective
Indicators
Number ofnodes
Number ofedges
AveragedegreeDensity

Meaning ofindicators
Number of ports
where cruise ships
have called
Number of cruise
ship routes
navigated between
ports
Number of shipping
routes per port
(number of
directions)
The ratio of theactual number ofshipping routes tothe number ofpossibleconnections
between ports

Averageclustering
coefficient

The ratio of portsthat have shipping
routes to and fromone port to another.

Diameter

The longest path on
the network

Network characteristics
The higher the number of nodes, themore developed and diversified the
network of ports of call.
The higher the number of edges, themore developed (multidirectional) theroute is in the network.
The higher the average degree, themore routes are served per port andthe denser the network.
(Multidirectional shipping routes)
The higher the density, the higher thepercentage of development ofshipping routes is in the network. If
the development of shipping routes ismore advanced than the developmentof ports, density will be higher.
(Progress in the development of new
shipping routes by shipping lines)
The higher the average cluster
coefficient, the more triangular the
network (i.e., a product consisting ofan arrival and departure port and two
ports of call), or a network with manyroutes that are not connected but aretriangulated.
It does not consider connections on
direct itineraries; the longer the121

Understanding cruise lines'
port selection behavior
A higher number of nodes
indicates characteristics that
promote the development of newports.
A high edge number indicatescharacteristics that promote thecreation of a new route.

Implications for ports
Determination of the need for
new port development, etc.
Determination of proposals for
new routes connecting to newports, etc.

A high average degree indicatesthe characteristic of developing
routes to various directions withthe base port as the axis.
If the density is high, new ports
may not be developed anditineraries (combinations of portsof call) may become rutted.

Decisions on proposals for
new routes connecting to newports, etc.

Characteristics of (changes in)shipping routes in a given
network, such as whether ashipper's itinerary is open or
closed, longer or shorter in days,
etc.

Determination of the need for
new port development, etc.,
and proposals for new
shipping routes connecting to
new ports, etc.

Characteristics of (changes in)shipping routes in a given

Determination of the need for
new port development, etc.,

Determination of the need for
new port development, etc.,
and proposals for new
shipping routes connecting to
new ports, etc.

between one port
and anotherdiameter, the longer the connection(path) between ports on the longest
route, and the sparser the network.

Averageshortest path
lengthThe shortest path on
the network
between one port
and another

Degreecentralization

The ratio of degrees
biased toward theport with the highest
degreeNot considering connections on direct
itineraries, the longer the average
shortest path length, the longer theconnections (paths) between ports on
the closest route, and the sparser the
network.
The higher the degree centralization,
the more heterogeneous the network,with the degree biased toward the portwith the highest degree (hub port).

Degreecentrality

Number of shipping
routes connecting to
the port

Modularity

Quality ofcommunity (interport connections)division

network, such as whether ashipper's itinerary is open or
closed, longer or shorter in days,
etc.
Characteristics of (changes in)shipping routes in a given
network, such as whether ashipper's itinerary is open or
closed, longer or shorter in days,etc.
The network as a whole canunderstand strategies such aswhether or not a group of carriers
participating in a given network
is converting to a hub.

and proposals for new
shipping routes connecting to
new ports, etc.
Determination of the need for
new port development, etc.,
and proposals for new
shipping routes connecting to
new ports, etc.

It will serve as a basis forstrategic considerations, suchas one port aiming to becomea hub port, another aiming toconnect to various ports as a
spoke, etc.
Ports with a higher degree centrality
It shows which ports theIt will serve as a basis forhave more shipping connections with shipping lines operate out of. The strategic considerations, suchneighboring ports. The port is a hub
cruise line’s strategy is revealed, as one port aiming to become
port with multimodal shipping routes. such as whether or not they area hub port, another aiming topromoting hubs in certain ports.
connect to various ports as a
spoke, etc.
The higher the modularity, the higher From the perspective of which
Ports belonging to the same
the quality of the network in terms of ports and which ports belong tocommunity can work together
the division (boundaries) of
the same community, theto attract other ports.
communities (groups of ports) that are characteristics of the port
connected among ports.
selection behavior of shipping
companies are understood.

Source: Author

122

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