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Article

A Social Network Analysis of Tourist Movement Patterns in Blogs: Korean Backpackers in Europe

1
Department of Hotel Management, College of Hotel and Tourism Management, Kyung Hee University, Seoul 02447, Korea
2
Department of Culture, Tourism & Contents, College of Hotel and Tourism Management, Kyung Hee University, Seoul 02447, Korea
*
Author to whom correspondence should be addressed.
Sustainability 2017, 9(12), 2251; https://doi.org/10.3390/su9122251
Submission received: 27 October 2017 / Revised: 18 November 2017 / Accepted: 1 December 2017 / Published: 5 December 2017
(This article belongs to the Special Issue Mobile Technology and Smart Tourism Development)

Abstract

:
Given recent developments in information and communication technology, the number of individual tourists enjoying free travel without the advice of travel agencies is increasing. Therefore, such tourists can visit more tourist destinations and create more complex movement patterns than mass tourists. These tourist movement patterns are a key factor in understanding tourist behavior and they contain various information that is important for tourism marketers. In this vein, this study aims to investigate tourist movement patterns in Europe. We acquired 122 data points from posts on the NAVER blog, which is the most famous social media platform in Korea. These data were transformed into matrix data for social network analysis and analyzed for centrality. The results suggest that Korean backpackers in Europe tend to enter Europe through London and Paris. Venezia and Firenze are also key cities.

1. Introduction

The movement of tourists is a key factor in tourism and also represents very important information in terms of understanding tourist behavior and the role of specific tourist destinations [1,2,3]. Specifically, because the number of individual tourists who do not rely on travel agencies and make their own travel decisions instead is increasing, tourist movement patterns are becoming more and more complicated. The development of the Internet and information and communication technology has made tourists smart and independent. Mobile devices have created an environment in which tourists can create and consume a great deal of information, as well as sharing information in real time [4]. Therefore, the guidebook and tour guide are being replaced by mobile devices. These new information-sharing activities are going on constantly, without any restrictions based on the physical environment. In this context, this study examines such individuals’ independent travel behavior in terms of sustainable tourism. Sustainable tourism has mostly been studied in terms of development in an attempt to determine how tourism destinations should be developed in harmony with local residents and the natural environment [5,6,7,8]. However, focusing on tourists can provide infinite information without environmental destruction and the accompanying tourism activities can also be viewed in a broader sense. Travelers who enjoy traveling independently using information obtained via the virtual space information are more concerned with maintaining harmony with local residents as compared to group tourists and seek local authenticity [9].
As mentioned above, the development of the Internet has strengthened the information for search among tourists. Because the number of individual tourists who do not rely on travel agencies and make their own decisions instead is increasing, tourist movement patterns are becoming more and more complicated.
Given this trend, tourists who spend more time at a destination find it worthwhile because it allows them time to seek new experiences, choose destinations based on their cultures and visit more tourist destinations in total [10]. Furthermore, platforms to help tourists who plan their trips alone are emerging. For instance, Fortune Korea 2014 introduced Stubby Planner, an innovative tool. Now, one million people use this platform to plan their trips to Europe annually.
According to Cohen [9], these tourists can be termed ‘noninstitutionalized tourists.’ They take trips freely and visit multiple tourist destinations. A typical example of a tourist with these characteristic is a backpacker. A backpacker is a young tourist who travels without the advice of a tour guide and is free to plan his or her own schedule [11,12]. Because of this freedom of movement, backpackers generate more complex movement patterns than tourists in large groups, such as mass tourists, who are generally moving on the same itinerary and these movement patterns may contain more useful information for tourism marketers and developers. Many researchers have studied such tourists’ movement patterns and suggested various implications [12,13,14,15,16]. However, despite the increasing the number of backpackers in Korea and the popularity of backpacking in Europe, there is still a lack of understanding of their movement patterns. Therefore, the present paper aims to investigate the movement patterns of Korean backpackers in Europe by using Social network analysis. First, we measure the movement patterns of Korean backpackers in Europe in 2015. In doing so, we attempt to identify key tourist cities in Europe from the perspective of Korean tourist and understand the connections between tourist cities. Second, we attempt to analyze the changes in Korean tourists’ movement patterns by comparing the 2015 data with the 2012 data. Finally, we suggest ways of developing of more efficient and productive tourist infrastructure.

2. Literature Review

2.1. Tourist Destination

Tourist destinations are a crucial factor in the tourism industry [17]. However, the concept of a tourist destination is vague and broad. For instance, an attraction, such as Disneyland, can be a tourist destination and a city, such as Rome, can also be a tourist destination. Therefore, it is important to clarify the definition of a ‘tourist destination’ in order to understand tourist movement patterns.
Leiper [2] discusses tourist destinations as one of the geographical elements of tourism. Leiper [2] defines a tourist destination as a location that can attract tourists to visit it. Thus, his definition of a tourist destination is well-characterized but also abstract. Lew and McKercher [18] argue that tourist destinations involve various factors, which can be divided into primary attributes and secondary attributes. Primary attributes are characteristics that are inherent to a tourist destination, such as its ecology and culture. Secondary attributes are characteristics that are created via development, such as hotels. These attributes combine to make a given tourist destination attractive to tourist destination [17]. However, the geographical discussions of tourist destinations remain vague.
The WTO [19] attempted to create a concrete definition of a tourist destination. According to the WTO [19], a local tourist destination can be defined as “a physical space that includes tourism products, such as support services and attractions and tourism resources. It has physical and administrative boundaries defining its management and images and perceptions defining its market competitiveness. Local destinations incorporate various stakeholders, often including a host community and can nest and network to form larger destinations. They are the focal point in the delivery of tourism products and the implementation of tourism policy ([18], p. 405)”. The purpose of the present study is to measure tourist movement patterns between various destinations in Europe. Additionally, because Europe is largely urban, many cities are represented among tourist destinations. Thus, the present study defines a tourist destination as a city, including the products and activities in that city that attract tourists.

2.2. Tourist Movement Patterns

From a geographical point of view, tourists who visit more than one tourist destination create spatial movement patterns. These tourist patterns can be global, national, or local [20] and can include a variety of information that is useful for tourism marketers [1,3,14]. A concrete understanding of the spatial movements of tourist can provide insights into tourist behavior, including the destination characteristics that are most attractive to tourists [21]. Lue, Crompton and Fesenmaire [11] examined the effect of the spatial patterns of attraction on tourist routes and conceptualized multi-destination trip behavior. Other researchers have focused on tourist movement patterns rather than the locations of tourist destinations. Specifically, Pearce [12] suggested that the direction of transportation development should be based on the analysis of tourist movement patterns in Europe. Shin [22] examined the movement of automotive tourists in Taiwan and found that they engaged in various movement patterns and that 16 tourist destinations all had different roles. Leung et al. [14] investigated the movement patterns of international tourists who visited Beijing, including the effect of 2008 Beijing Olympics on tourist movement patterns. According to his study, these tourists tended to visit famous traditional attractions and their movement patterns were concentrated in the central city area. In addition, tourists who visited during or after the 2008 Beijing Olympics showed extended movement patterns as compared to tourists who visited before the Olympics. On the other hand, the inherent characteristics of tourists can also influence their movement patterns. Lew and MaKercher [18] argue that tourists’ movement patterns reflect their consumption styles. Hwang et al. [13] also explored international tourists’ travel patterns within cites in the United States. According to his study, these patterns differ with tourists’ origins and levels of familiarity with the US. Specifically, Asian tourists tended to visit Los Angeles, Las Vegas and San Francisco, whereas European tourists and New Yorker tended to visit Orlando, Miami and New York, which had low levels of popularity among Asian tourists.
Cohen [9] distinguished between two types of tourist in terms of sociology. First, institutionalized tourists depend on a travel agency or guide. They enjoy passive tourism activities and moving about as ordered. On the other hand, noninstitutionalized tourists attempt to experience the culture of any tourist destinations they visit. They enjoy active tourism activities and novelty. Backpackers are an example of noninstitutionalized tourists: “backpackers (are) predominantly young travelers on extended holidays with a preference for budget accommodation, a flexible and informal travel itinerary and an emphasis on meeting people and participating in a range of activities ([23,24], p. 194)”. That is, backpackers are young tourists who do not move according to the travel agency’s schedule and are free to plan their own schedules. They have a strong motivation to escape from their daily lives [25], seek out unique sites and interact with the culture of tourist destinations they visit [9,26]. Therefore, backpackers can create their own routes and travel to more complex and remote tourist destinations than mass tourists. The development of information and communication technology has enabled tourists to share information, [4] and thus, a great deal of tourism information has been generated. As a result, tourists are becoming smarter and more able to travel independently by using the real-time personalized information provided at tourist destinations. In this context, the present study examines the movement patterns of backpackers.

2.3. Korean Backpackers

Pizam and Sussmann [16] explored tourist behaviors by nationality and found that Korean tourists preferred familiar places, rather than experiencing other cultures during overseas trips. Recently, however, several researchers have identified the characteristics of Korean tourists, especially backpackers in Korea, that are different characteristic from those mentioned above. Specifically, Park and Santos [27] examined memorable tourism experiences among Korean backpackers in Europe through interviews and found that they chose European backpacking trips to experience different cultures. These tourists reported that a unique experience was most important in terms of having a memorable trip. Bae and Chick [25] explored the characteristics of domestic Korean backpackers and found that Korea backpackers were mostly young people who wanted to take long trips during their school break and attempt to experience events that they could not experience in daily life. The number of young tourists has gradually increased [28] and this is reflected in the analysis of travel agency product sales. According to GTN [29], the sales volume for individual products, such as airline tickets and hotel products, is increasing rapidly as compared to package product sales. Among the seven biggest travel agencies in Korea, European airline tickets account for most of sales volume. Thus, one can infer that Korean backpackers prefer Europe as a destination for free travel.

3. Research Question

Tourist movement patterns, including spatial and temporal information, are key to understanding tourists [1,3,14]. In particular, backpackers’ movement patterns are more informative for understanding tourists’ behavior than those of package tourists because backpackers visit more various destinations and take longer trips [9,24,26]. The analysis of the spatial and temporal movement patterns of backpackers can help provide useful information about them. Consequently, this study aims to analyze the travel movement patterns of Korean backpackers in Europe.
Research Question 1. Which city was most central for Korean backpackers in Europe in 2015?
Research Question 2. Were there differences in city centrality for Korean backpackers in European networks between 2012 and 2015?

4. Method

4.1. Sample and Procedure

To analyze the movement patterns of Korean backpackers in Europe in 2015, this study collected secondary data. Specifically, this study was conducted using NAVER, one of the most popular portal and blog service sites, which has an approximate share of 71% in Korea [30]. For the network analyses, postings on the blogs might be useful for comparison with photo-based SNS services because they provide the entire itineraries of each backpacker regarding European tourism sites. The researcher searched for blog postings using the term ‘Europe backpacking route’. Posts written during the one-year period from 1 January to 31 December 2015 were included. Of the 717 blog posts that appeared in the search, we excluded postings that contained advertisements or recommendations or did not mention detailed information. This left 122 postings that contained actual routes including specific cities. Thirty-four postings from 2012 were collected in the same way. This study also collected data from 2010 and tried to compare the trend of backpacking trips after five years. However, the number of blog posts in 2010 was too small and those posts provided little information about specific routes. Therefore, this study adopted data from both 2015 and 2012 (Appendix A). From the 2015 data, 162 cities were identified and 129 cities were identified from the 2012 data. This study initially examined the top 20 cities Korean backpackers had the visited and found that Paris (France) was ranked first in 2015 and that London (UK) was ranked first in 2012 (Table 1 and Table 2).

4.2. Network Analysis

Tourists’ movement patterns can be examined through network analysis. Network analysis is a set of research procedures for identifying structures in systems based on the relationships among components [31] Network analysis can be used to describe the global level of structure because it examines system indicators such as centrality, connectedness, integrativeness and system density, as well as the potential clustering of the network into subgroups.
The basic network dataset is an n × n matrix S, where n equals the number of nodes in the network. A node might be an individual or a higher-level component, such as an organization or a nation, of which the system is composed. Each cell sij indicates the strength of the relationship between nodes i and j. In communication research, this relationship is generally defined by the frequency of communication between nodes [32,33,34,35]. To examine global corporate communications regarding the movement routes of tourists, this study used a 162 (cities) × 162 (cities) matrix for the 2015 network and a 129 (cities) × 129 (cities) matrix for the 2012 network. Each cell was weighted according to the frequency of moving from the city in the column to the city in the row. Therefore, nodes indicate European cities and links indicate the movements of Korean backpackers.
Next, using UCINET 6, degree centrality was calculated in order to determine which cities play significant roles in determining the movements of Korean backpackers in the European network. The key benefit of normalizing degree centrality in this way is that we can assess the relative centrality of two cities.
Degree centrality, in this study, indicates the number of co-visitors between two cities. Because centrality is a structural attribute of each node in the network, centrality identifies the central point based on many direct contacts with other points [34,36]. This study adopts the Freeman approach to calculating the degree centrality of each node, as well as the overall network degree centralization, because backpackers’ networks are significantly asymmetric. For non-symmetric data, the in-degree of a node u is the number of ties received by u, while the out-degree is the number of ties initiated by u [34,37]. As defined above, degree centrality is simply the number of nodes that a given node is connected to. In this case, out-degree centrality is the number of backpackers that have left from a given city. In-degree centrality is the number of backpackers that have traveled to a given city. A higher number of visitors does not simply increase the degree centrality score. Rather, to obtain a higher degree centrality score, a city must be linked with certain other cities. In other words, the way in which cities build relationships with other core cities is crucial in positioning them at the center of the network of Korean backpacker routes in Europe.
Eigenvector centrality is an ideal measure for those networks in which the tie strength between actors, rather than simply the presence or absence of a tie, is known [32,33,38]. It considers the strength of ties, including indirect social ties, among nodes. Thus, more central destinations can boost their centrality due to the inherent circularity involved in the calculation of the eigenvector centrality measure. This has the effect of making actors with strong ties to more central actors appear to be more central [32,33,38,39]. Betweenness centrality refers to the “share” of the shortest paths in a network that pass through a certain node [40]. Thus, betweenness centrality can be affected by the number of cities that are specifically co-linked specifically with two other cities. Finally, in this study, a high closeness value indicates that travelers leaving a city require the minimum steps to reach all other nodes. The lowest possible score occurs when the node has ties to every other node. Therefore, if a city is the most central in terms of closeness, it should allow travelers to quickly reach all other cities in Europe. Table 3 shows a summary of measurement methods mentioned above.

5. Results

The results reveal the structure of the network of Korean backpackers in Europe. Overall, Italian cities, such as Firenze, Venezia and Rome, are continually ranked in the top 20 in the list, regardless of year. Therefore, these cities play a key role for Korean backpackers in Europe. In addition, Paris (France), London (UK), Prague (the Czech Republic) and Interlaken (Switzerland) are also considered key cities. The specific results are described below.

5.1. Networks Structure of Korean Backpackers in Europe: Degree Centrality

Table 4 shows the centrality scores for the top 20 out of 162 cities in 2015. In this case, degree centrality indicates the total number of co-hyperlinked cities that one city shares with other cities. When the degree centrality analysis results for 2015 is compared with the results for 2012, differences emerge (Table 4). Specifically, in 2015, Firenze (109, 2nd in 2012) had the highest out-degree centrality, followed by Venezia (107, 4th in 2012), London (100, 1st in 2012), Paris (94, 7th in 2012) and Interlaken (92, 9th in 2012). The in-degree centrality rankings are significantly different from the out-degree centrality rankings. In 2015, Rome (112, 2nd in 2012) had the highest in-degree centrality, followed Paris (110, 1st in 2012), Venezia (108, 4th in 2012), Firenze (107, 3rd in 2012) and Prague (94, 7th in 2012). The interesting result here is the difference between London’s in-degree and out-degree centrality. London’s the out-degree score was quite high, while the in-degree score was low in both 2012 and 2015 (Table 5). Both the out-degree and in-degree centrality results show that Italian cities (i.e., Firenze, Venezia and Rome) rank at the top of the list in 2012 and 2015.

5.2. Networks Structure of Korean Backpackers in Europe: Eigenve Centrality

For 2015, the eigenvector centrality results were similar to those for degree centrality. Firenze had the highest eigenvector centrality (75.473), followed by Venezia (70.474), Rome (55.151), Interlaken (33.249) and Milano (31.685). However, the Italian cities (e.g., Milano and Pisa) moved up in the rankings as compared with Paris and London, which moved down in the rankings (Table 6). Additionally, comparing 2012 and 2015, the eigenvector centrality results are similar and in 2012 (Table 7), Firenze (1st in 2015) had the highest eigenvector score (67.063) in the rankings. However, there is a slight difference in the order in that in 2012, Firenze was followed by Rome (3rd in 2015), Venezia (2nd in 2015), Wien (7th in 2015) and Munich (13th in 2015).

5.3. Networks Structure of Korean Backpackers in Europe: Betweenness

Table 8 shows the results of the betweenness analysis for 2015. These results are somewhat different from those for the 2012 network (Table 9). Specifically, in 2015, Paris had the highest betweenness score (28.702, 1st in 2012), followed by Rome (18.345, 4th in 2012), Venezia (14.113, 5th in 2012), Prague (13.573, 3rd in 2012) and Interlaken (12.699, 10th in 2012). Norwegian cities that had not been identified in 2012 entered the top 20 and Budapest (25th in 2012) moved up 14 steps in the rankings.

5.4. Networks Structure of Korean Backpackers in Europe: Closeness Centrality

High closeness centrality indicates that when leaving from a node, a traveler must take only the minimum number of steps to reach all other nodes. The results showed that Paris had the highest closeness centrality (29.87), followed by Munich (29.22), Interlaken (29.16), Prague (29.06) and Rome (28.95). These results are similar to those for betweenness in that four (e.g., Paris, Interlaken, Prague and Rome) of the top five cities are the same (Table 10) and quite different from the results in 2012 (Table 11).

6. Discussion

This study explored the movement patterns of Korean backpackers in Europe through network analysis. The main contribution of network analysis is to offer broad pictures of recent dynamic relationships, which are critical in terms of understanding tourist behavior among European tourism sites. Furthermore, this study attempted to identify cities that are characteristic of the 2015 network as compared with the 2012 network. Network analysis provides various methods with which to investigate and compare movement patterns. Overall, Italian cities played a key role in 2015 when Korean backpackers travelled to Europe as compared to 2012.
The out-degree centrality values in the 2015 network indicate that certain core cities—Firenze (Italy), Venezia (Italy), London (UK), Paris (France) and Interlaken (Switzerland)—played a key role in the movement patterns of Korean backpackers in Europe. However, the order was slightly different than that for in-degree centrality. In particular, London (UK) had a significantly lower score for in-degree centrality than for out-degree centrality. This means that more tourists move from London to other cities than form other cities to London. In other words, Korean backpackers tend to choose London (UK) as their first city when they travel to Europe. In contrast, Rome (Italy) had an in-degree score that was higher than its out-degree score. This means that Korean backpackers tend to choose Rome (Italy) as their final city. Furthermore, as compared to 2012, in 2015, Italian cities were ranked more highly. Thus, Italian cities are playing a key role for Korean backpackers traveling in Europe. Among flights from Korea to Europe, there are 14 flights to Paris and 13 flights to either Rome or Milan. On the other hand, there are four flights to London, where Korean tourists have chosen to start their European trips [41]. Because the United Kingdom is geographically distant from other European countries in terms of location, backpackers may feel that travel there is relatively difficult.
The eigenvector centrality results for the 2015 network show that Firenze (Italy) had the highest score, followed by Venezia (Italy) and Rome (Italy). Unlike degree centrality, eigenvector centrality does not simply represent being connected to many other cities. Rather, it identifies cities that are connected to core cities. Firenze is linked to key cities such as Munich (Germany) and Salzburg (Austria). Therefore, Firenze (Italy) is the most influential city in that it is connected to the core cities. Moreover, the top three cities were all Italian cities (i.e., Firenze, Venezia and Rome). The results are the same for the 2012 network. In 2012, although the order is slightly different, the top three cities are all Italian (i.e., Firenze, Rome and Venezia). Thus, most Korean backpackers travelling to core cities tend to visit Italy and Italy is the most important tourist destination for Korean backpackers.
On the other hand, the betweenness results show that Paris (France) had the highest scores in both 2012 and 2015. Betweenness indicates the extent to which cities are not directly connected. For example, Paris (France) plays a role in connecting the overall cities in the network. That is, Korean backpackers are the most dependent on Paris (France) when traveling to other cities. In the case of Paris, the frequency of visits is high and the distance to London, which is the typical starting city for European travel, is also short. In addition, unlike UK cities that require air travel, Paris can be reached via a rail option called Eurostar. Eurostar’s London-Paris section has the highest number of sales and the top ranking in Korea [42]. Thus, Korean backpackers who have flown into Europe through London can be found traveling through Paris to other European cities. On the other hand, as compared to the 2012 network, in 2015, the role of Italian cities became more important. Specifically, Rome (4th, Italy) and Venezia (5th, Italy) moved up in the rankings. Paris (France) plays a central role for Korean backpackers in Europe and serves as a link between cities that are not directly connected.

7. Conclusions

This study investigated the routes of backpackers in Europe. In particular, it examined specific movement patterns between European cities. Tourists’ movement patterns are very important in understanding tourists because they contain a great deal of information [1,2,3]. This study identified the cities (e.g., Venezia, Paris, London, etc.) that are preferred by Korean backpackers, as well as the cities (e.g., Paris) that play a major role in tourists’ movement.
This study also has practical implications. First, this study identified the key cities for Korean backpackers in Europe. More specifically, Korean backpackers have traveled to Europe, mainly via London and have confirmed that they typically enter Continental Europe via Paris. Because backpackers in Korea likely prefer convenient transportation within Europe, destination marketers should design marketing that emphasizes convenient transportation in Europe. Second, a number of backpackers start from London, but London flights are fewer in number than those to Rome and Paris. Thus, it is suggested that Korean airline managers may be able to increase airline revenue by increasing the number of flights. In addition, Korean backpackers have been shown to move through Paris when traveling from London to continental Europe. This seems to be influenced by Eurostar and travel agency managers should be able to draw the attention of backpackers who do not typically rely on agencies by concentrating on those who purchase London-Paris Eurostar tickets. Lastly, as the era of smart tourism evolves, tourists’ movement patterns are becoming salient information. More specifically, as tourists’ accessibility to information increases, a variety of start-up companies are emerging that provide information on travel routes for tourists. It is suggested that, for businesses, this data on travel patterns can be useful.
Notably, this study has certain limitations, including that it inferred the factors that affect tourists’ movement patterns yet did not verify them empirically. Therefore, future studies can provide richer implications if they address the factors that influence travel routes and empirically verify the relationships between them. Moreover, this study collected information via specific blogs (i.e., NAVER blogs). Although NAVER has the highest share of Korean blogs, using their data cannot be generalized. Therefore, future research needs to collect information through multiple channels.

Acknowledgments

This work was supported by the Ministry of Education of the Republic of Korea and the (NRF-2016S1A3A2925146).

Author Contributions

All of the authors have contributed to the idea of this paper. Hee Chung Chung contributed to initiating and conceiving the topic of this paper and Yoonjae Nam contributed significantly to the methods, social network analysis, results and discussion sections. Namho Chung provided constructive advice to improve the manuscript.

Conflicts of Interest

The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript and in the decision to publish the results.

Appendix A

Table A1. The list of cities in 2015.
Table A1. The list of cities in 2015.
CodeCityCountyCodeCityCounty
1AdelbodenSwitzerland82LjubljanaSlovenia
2AmalfiItaly83LondonUnited Kingdom
3AmsterdamNetherlands84LuxembourgLuxembourg
4AnnecyFrance85LuzernSwitzerland
5AntibesFrance86LyonFrance
6AntwerpenBelgium87MadridSpain
7ArlesFrance88MalagaSpain
8AssisiItaly89ManchesterUnited Kingdom
9AthensGreece90MannheimGermany
10AvignonFrance91MarseilleFrance
11Bad IschlAustria92MelkAustria
12BambergGermany93Meteora (Kalabaka)Greece
13BarcelonaSpain94MilanoItaly
14BariItaly95MonacoMonaco
15BaselSwitzerland96Mont Saint MichelFrance
16BathUnited Kingdom97MontpellierFrance
17BergenNorway98MontreuxSwitzerland
18BerlinGermany99MoskvaRussia
19BernSwitzerland100MunichGermany
20BledSlovenia101MuranoItaly
21BodrumTurkey102MykonosGreece
22BonnGermany103MyrdalNorway
23BordeauxFrance104NapoliItaly
24BratislavaSlovakia105NiceFrance
25BrienzSwitzerland106NurembergGermany
26BrightonUnited Kingdom107OsloNorway
27BrugeBelgium108OxfordUnited Kingdom
28BrusselsBelgium109PairsFrance
29BudapestHungary110PamplonaSpain
30BuranoItaly111PamukkaleTurkey
31CambridgeUnited Kingdom112PisaItaly
32CannesFrance113PlitviceCroatia
33CappadociaTurkey114PompeiiItaly
34CapriItaly115PortoPortugal
35CascaisPortugal116PositanoItaly
36Cesky KrumlovCzech117PragueCzech
37Cingue TerreItaly118RastokeCroatia
38ColmarFrance119RhodesGreece
39CordobaSpain120RomeItaly
40CorkIreland121RondaSpain
41CotswoldUnited Kingdom122RothenburgGermany
42DresdenGermany123SaarbrückenGermany
43DublinIreland124SafranboluTurkey
44DubrovnikCroatia125SalernoItaly
45DüsseldorfGermany126SalzburgAustria
46EdinburghUnited Kingdom127SantoriniGreece
47EtretatFrance128SchwangauGermany
48FeldkirchAustria129SegoviaSpain
49FethiyeTurkey130SelcukTurkey
50FirenzeItaly131SevillaSpain
51FlamNorway132SienaItaly
52FontainebleauFrance133SintraPortugal
53FrankfurtGermany134SliemaMalta
54FüssenGermany135SorentoItaly
55GenevaSwitzerland136SpiezSwitzerland
56GivernyFrance137SplitCroatia
57GmundenAustria138St. GilgenAustria
58GosauAustria139StavangerNorway
59GranadaSpain140StockholmSweden
60GrindelwaldSwitzerland141StrasbourgFrance
61GudvangenNorway142StuttgartGermany
62Haag (Hague)Netherlands143SzentendreHungary
63HallstattAustria144TelcCzech
64HamburgGermany145TivoliItaly
65HeidelbergGermany146ToledoSpain
66HelsinkiFinland147ToulouseFrance
67HonfleurFrance148TrogirCroatia
68HvarCroatia149ValenciaSpain
69IbizaSpain150Vatican CityState della citta del vaticano
70InnsbruckAustria151VeneziaItaly
71InterlakenSwitzerland152VeronaItaly
72IstanbulTurkey153Versailles (Yvelines)France
73København (Copenhagen)Denmark154VossNorway
74Köln (Cologne)Germany155Warszawa (Warsaw)Poland
75Kraków (Krakow)Poland156WienAustria
76Kutná HoraCzech157WurzburgGermany
77LausanneSwitzerland158Zaanse SchansNetherlands
78LienzAustria159ZadarCroatia
79LilleFrance160ZagrebCroatia
80LisbonPortugal161ZermattSwitzerland
81LiverpoolUnited Kingdom162ZurichSwitzerland
Table A2. The list of cities in 2012.
Table A2. The list of cities in 2012.
CodeCityCountyCodeCityCounty
1Aix-en-ProvenceFrance66LondonUnited Kingdom
2AmalfiItaly67LuxembourgLuxembourg
3AmsterdamNetherlands68LuzernSwitzerland
4AntalyaTurkey69MadridSpain
5AntwerpenBelgium70ManchesterUnited Kingdom
6AranIreland71MarrakeshMorocco
7AssisiItaly72MarseilleFrance
8AthensGreece73Meteora (Kalabaka)Greece
9AugsbrugGermany74MilanoItaly
10AvignonFrance75MonacoMonaco
11BambergGermany76Mont Saint MichelFrance
12BarcelonaSpain77MunichGermany
13BariItaly78MuranoItaly
14BaselSwitzerland79NapoliItaly
15BerchtesgadenGermany80NiceFrance
16BergenNorway81NurembergGermany
17BerlinGermany82OdenseDenmark
18BernSwitzerland83OsloNorway
19BledSlovenia84OxfordUnited Kingdom
20BodrumTurkey85PairsFrance
21BordeauxFrance86PamukkaleTurkey
22BratislavaSlovakia87PisaItaly
23BrugeBelgium88PlitviceCroatia
24BrusselsBelgium89PompeiiItaly
25BudapestHungary90PortoPortugal
26BuranoItaly91PositanoItaly
27CannesFrance92PostoinaSlovenia
28CappadociaTurkey93PragueCzech
29CapriItaly94PulaCroatia
30CasablancaMorocco95RhodesGreece
31CascaisPortugal96RomeItaly
32CassisFrance97RondaSpain
33Cesky KrumlovCzech98RothenburgGermany
34Cingue TerreItaly99RotterdamNetherlands
35CordobaSpain100SafranboluTurkey
36CotswoldUnited Kingdom101SalisburyUnited Kingdom
37DresdenGermany102SalzburgAustria
38DublinIreland103SantoriniGreece
39DubrovnikCroatia104SegoviaSpain
40EdinburghUnited Kingdom105SelcukTurkey
41EtretatFrance106SevillaSpain
42FethiyeTurkey107SintraPortugal
43FirenzeItaly108SofiaBulgaria
44FrankfurtGermany109SorentoItaly
45FüssenGermany110SplitCroatia
46GalwayIreland111StockholmSweden
47GenevaSwitzerland112StrasbourgFrance
48GentBelgium113StuttgartGermany
49GlasgowUnited Kingdom114SyrosTurkey
50GranadaSpain115SzentendreHungary
51Haag (Hague)Netherlands116ToledoSpain
52HallstattAustria117UtrechtNetherlands
53HamburgGermany118ValenciaSpain
54HeidelbergGermany119Vatican CityState della citta del vaticano
55HonfleurFrance120VeneziaItaly
56HowthIreland121VeronaItaly
57InterlakenSwitzerland122WienAustria
58IstanbulTurkey123Wiltshire (Stonehenge)United Kingdom
59KøbenhavnDenmark124WindsorUnited Kingdom
60KölnGermany125WurzburgGermany
61KosGreece126Zaanse SchansNetherlands
62LausanneSwitzerland127ZagrebCroatia
63Le havreFrance128ZermattSwitzerland
64LisbonPortugal129ZurichSwitzerland
65LjubljanaSlovenia
Table A3. The list of cities by country in 2015.
Table A3. The list of cities by country in 2015.
CountyCityCodeNCountyCityCodeN
AustriaBad Ischl1111ItalyAmalfi220
Feldkirch48Assisi8
Gmunden57Bari14
Gosau58Burano30
Hallstatt63Capri34
Innsbruck70Cinque Terre37
Lienz78Firenze50
Melk92Milano94
Salzburg126Murano101
St. Gilgen138Napoli104
Wien156Pisa112
BelgiumAntwerpen63Pompeii114
Bruge27Positano116
Brussels28Rome120
CroatiaDubrovnik448Salerno125
Hvar68Siena132
Plitvice113Sorento135
Rastoke118Tivoli145
Split137Venezia151
Trogir148Verona152
Zadar159LuxembourgLuxembourg841
Zagreb160MaltaSliema1341
CzechCesky Krumlov364MonacoMonaco951
Kutná Hora76NetherlandsAmsterdam33
Prague117Haag62
Telc144Zaanse Schans158
DenmarkKøbenhavn731NorwayBergen177
FinlandHelsinki661Flam51
FranceAnnecy421Gudvangen61
Antibes5Myrdal103
Arles7Oslo107
Avignon10Stavanger139
Bordeaux23Voss154
Cannes32PolandKraków (Krakow)752
Colmar38Warszawa (Warsaw)155
Etretat47PortugalCascais354
Fontainebleau52Lisbon80
Giverny56Porto (Pôrto)115
Honfleur67Sintra133
Lille79RussiaMoskva (Moscow)991
Lyon86SlovakiaBratislava241
Marseille91SloveniaBled202
Mont Saint Michel96Ljubljana82
Montpellier97SpainBarcelona1312
Nice105Cordoba39
Pairs109Granada59
Strasbourg141Ibiza69
Toulouse147Madrid87
Versailles153Malaga88
GermanyBamberg1218Pamplona110
Berlin18Ronda121
Bonn22Segovia129
Dresden42Sevilla131
Düsseldorf45Toledo146
Frankfurt53Valencia149
Füssen54State della citta del vaticanoVatican City1501
Hamburg64SwedenStockholm1401
Heidelberg65SwitzerlandAdelboden113
Köln74Basel15
Mannheim90Bern19
Munich100Brienz25
Nuremberg106Geneva55
Rothenburg122Grindelwald60
Saarbrücken123Interlaken71
Schwangau128Lausanne77
Stuttgart142Luzern85
Wurzburg157Montreux98
GreeceAthens95Spiez136
Meteora (Kalabaka)93Zermatt161
Mykonos102Zurich162
Rhodes119TurkeyBodrum217
Santorini127Cappadocia33
HungaryBudapest292Fethiye49
Szentendre143Istanbul72
IrelandCork402Pamukkale111
Dublin43Safranbolu124
Selcuk130
United KingdomBath169
Brighton26
Cambridge31
Cotswold41
Edinburgh46
Liverpool81
London83
Manchester89
Oxford108
Table A4. The list of cities by country in 2012.
Table A4. The list of cities by country in 2012.
CountyCityCodeNCountyCityCodeN
AustriaHallstatt523LuxembourgLuxembourg671
Salzburg102MonacoMonaco751
Wien122MoroccoCasablanca302
BelgiumAntwerpen54Marrakesh71
Bruge23NetherlandsAmsterdam35
Brussels24Haag51
Gent48Rotterdam99
BulgariaSofia1081Utrecht117
CroatiaDubrovnik395Zaanse Schans126
Plitvice88NorwayBergen162
Pula94Oslo83
Split110PortugalCascais314
Zagreb127Lisbon64
CzechCesky Krumlov332Porto (Pôrto)90
Prague93Sintra107
DenmarkKøbenhavn592SlovakiaBratislava221
Odense82SloveniaBled193
FranceAix-en-Provence113Ljubljana65
Avignon10Postoina92
Bordeaux21SpainBarcelona129
Cannes27Cordoba35
Cassis32Granada50
Etretat41Madrid69
Honfleur55Ronda97
Le havre63Segovia104
Marseille72Sevilla106
Mont Saint Michel76Toledo116
Nice80Valencia118
Pairs85State della citta del vaticanoVatican City1191
Strasbourg112SwedenStockholm1111
GermanyAugsbrug915SwitzerlandBasel148
Bamberg11Bern18
Berchtesgaden15Geneva47
Berlin17Interlaken57
Dresden37Lausanne62
Frankfurt44Luzern68
Füssen45Zermatt128
Hamburg53Zurich129
Heidelberg54TurkeyAntalya49
Köln (Cologne)60Bodrum20
Munich77Cappadocia28
Nuremberg81Fethiye42
Rothenburg98Istanbul58
Stuttgart113Pamukkale86
Wurzburg125Safranbolu100
GreeceAthens85Selcuk105
Kos61Syros114
Meteora(Kalabaka)73United KingdomCotswold369
Rhodes95Edinburgh40
Santorini103Glasgow49
HungaryBudapest252London66
Szentendre115Manchester70
IrelandAran64Oxford84
Dublin38Salisbury101
Galway46Wiltshire (Stonehenge)123
Howth56Windsor124
ItalyAmalfi217
Assisi7
Bari13
Burano26
Capri29
Cingue Terre34
Firenze (Florence)43
Milano74
Murano78
Napoli (Naples)79
Pisa87
pompeii89
Positano91
Rome96
Sorento109
Venice120
Verona121

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Table 1. Frequencies of city in 2015.
Table 1. Frequencies of city in 2015.
RankCityCountryFrequency
1PairsFrance119
2RomeItaly112
3VeneziaItaly106
4FirenzeItaly103
5LondonUnited Kingdom102
6PragueCzech98
7InterlakenSwitzerland96
8MunichGermany70
9WienAustria70
10LuzernSwitzerland62
11BarcelonaSpain59
12BrusselsBelgium54
13SalzburgAustria54
14MilanoItaly51
15MadridSpain43
16AmsterdamNetherlands39
17FrankfurtGermany33
18BudapestHungary30
19Cesky KrumlovCzech28
20HallstattAustria26
Table 2. Frequencies of city in 2012.
Table 2. Frequencies of city in 2012.
RankCityCountryFrequency
1LondonUnited Kingdom37
2PairsFrance33
3MunichGermany32
4RomeItaly32
5FirenzeItaly28
6VeneziaItaly26
7PragueCzech23
8InterlakenSwitzerland22
9WienAustria20
10SalzburgAustria19
11AmsterdamNetherlands15
12BrusselsBelgium15
13MilanoItaly14
14NapoliItaly13
15FrankfurtGermany12
16BarcelonaSpain10
17LuzernSwitzerland10
18FüssenGermany9
19MadridSpain9
20NiceFrance7
Table 3. Conceptual and operationalization definition of measurement.
Table 3. Conceptual and operationalization definition of measurement.
The Name of MeasurementConceptual DefinitionOperationalization DefinitionReferences
Degree centralitythe central point based on many direct contacts with other pointsthe number of co-visitors between two citiesNam & Barnett [34], Scott [36]
Eigenvector centralityan ideal measure for those networks in which the tie strength between actorsthe number of cities that are specifically linked specifically with the central citiesBarnett et al. [32], Nam [33], Nam et al. [38]
Betweenness centralityto the “share” of the shortest paths in a network that pass through a certain nodethe number of cities that are specifically co-linked specifically with two other citiesBorgatti [40]
Closeness centralityThe lowest possible score occurs when the node has ties to every other nodeTravelers leaving a city require the minimum steps to reach all other nodesNam et al. [38]
Table 4. In-degree and out-degree centrality in the Korean backpackers’ network in 2015.
Table 4. In-degree and out-degree centrality in the Korean backpackers’ network in 2015.
RankCityCountryOut-DegreeIn-Degree
1FirenzeItaly109107
2VeneziaItaly107108
3LondonUnited Kingdom10032
4ParisFrance94110
5InterlakenSwitzerland9292
6PragueCzech9194
7RomeItaly85112
8WienAustria7271
9MunichGermany7068
10LuzernSwitzerland6464
11SalzburgAustria5655
12BarcelonaSpain5563
13BrusselsBelgium5457
14MilanoItaly5250
15AmsterdamNetherlands4040
16MadridSpain3644
17FrankfurtGermany3228
18BudapestHungary2930
19HallstattAustria2726
20Cesky KrumlovCzech2627
Table 5. Out-degree and in-degree in Korean backpackers’ network in 2012.
Table 5. Out-degree and in-degree in Korean backpackers’ network in 2012.
RankCityCountryOut-DegreeIn-Degree
1LondonUnited Kingdom3510
2FirenzeItaly2828
3MunichGermany2730
4VeneziaItaly2622
5RomeItaly2530
6WienAustria2520
7PairsFrance2431
8PragueCzech2321
9InterlakenSwitzerland2122
10SalzburgAustria1922
11AmsterdamNetherlands1410
12MilanoItaly1414
13NapoliItaly1313
14BrusselsBelgium1215
15BarcelonaSpain109
16LuzernSwitzerland1010
17FüssenGermany99
18MadridSpain99
19FrankfurtGermany810
20NiceFrance77
Table 6. Eigenvectors in Korean backpackers’ network in 2015.
Table 6. Eigenvectors in Korean backpackers’ network in 2015.
RankCityCountrynEigenvector
1FirenzeItaly75.473
2VeneziaItaly70.474
3RomeItaly55.151
4InterlakenSwitzerland33.249
5MilanoItaly31.685
6LuzernSwitzerland23.368
7WienAustria21.983
8PisaItaly20.692
9PragueCzech20.110
10BarcelonaSpain19.824
11PairsFrance17.378
12SalzburgAustria14.936
13MunichGermany14.634
14NapoliItaly12.426
15LondonUnited Kingdom12.012
16AssisiItaly10.448
17MadridSpain9.494
18BrusselsBelgium8.639
19Cesky KrumlovCzech8.379
20BernSwitzerland7.910
Table 7. Eigenvectors in Korean backpackers’ network in 2012.
Table 7. Eigenvectors in Korean backpackers’ network in 2012.
RankCityCountrynEigenvector
1FirenzeItaly69.063
2RomeItaly58.175
3VeneziaItaly57.271
4WienAustria40.816
5MunichGermany29.878
6PragueCzech29.387
7MilanoItaly27.127
8SalzburgAustria25.916
9NapoliItaly24.076
10InterlakenSwitzerland23.484
11PairsFrance21.449
12PisaItaly19.571
13ZurichSwitzerland18.210
14LuzernSwitzerland14.861
15FüssenGermany13.929
16Vatican CityVatican City13.277
17LondonUnited Kingdom12.660
18BarcelonaSpain10.643
19BrusselsBelgium10.638
20Cinque TerreItaly9.812
Table 8. Betweenness in Korean backpackers’ network in 2015.
Table 8. Betweenness in Korean backpackers’ network in 2015.
RankCityCountrynBetweenness
1ParisFrance28.702
2RomeItaly18.345
3VeneziaItaly14.113
4PragueCzech13.573
5InterlakenSwitzerland12.699
6MunichGermany11.130
7LondonUnited Kingdom9.362
8BarcelonaSpain9.206
9WienAustria6.871
10SpiezSwitzerland6.231
11BudapestHungary6.110
12MadridSpain5.730
13OsloNorway5.450
14BodrumTurkey4.944
15SantoriniGreece4.838
16StockholmSweden4.829
17BergenNorway4.821
18FlamNorway4.821
19GudvangenNorway4.821
20MyrdalNorway4.821
Table 9. Betweenness in Korean backpackers’ network in 2012.
Table 9. Betweenness in Korean backpackers’ network in 2012.
RankCityCountrynBetweenness
1PairsFrance32.139
2LondonUnited Kingdom16.015
3PragueCzech14.742
4RomeItaly14.429
5VeneziaItaly14.073
6BrusselsBelgium13.142
7MunichGermany12.887
8BarcelonaSpain11.357
9AmsterdamNetherlands10.955
10InterlakenSwitzerland9.584
11MadridSpain9.096
12AthensGreece8.686
13FrankfurtGermany7.815
14DublinIreland6.920
15LisbonPortugal6.234
16NiceFrance5.737
17FirenzeItaly5.518
18NapoliItaly5.421
19DubrovnikCroatia5.180
20WienAustria5.070
Table 10. Closeness centrality in Korean backpackers’ network in 2015.
Table 10. Closeness centrality in Korean backpackers’ network in 2015.
RankCityCountryinClosenessoutCloseness
1ParisFrance29.87028.445
2MunichGermany29.22026.524
3InterlakenSwitzerland29.16726.833
4PragueCzech29.06127.059
5RomeItaly28.95728.000
6VeneziaItaly28.75027.196
7LuzernSwitzerland28.44526.094
8BarcelonaSpain28.04926.393
9MilanoItaly27.90325.156
10LondonUnited Kingdom27.01326.264
11NiceFrance26.83325.394
12MadridSpain26.78925.801
13AthensGreece26.26421.611
14FrankfurtGermany26.26424.284
15BudapestHungary26.17925.926
16AmsterdamNetherlands25.84325.196
17BrusselsBelgium25.76025.556
18WienAustria25.67825.843
19StrasbourgFrance25.51525.156
20FirenzeItaly25.39425.394
Table 11. Closeness centrality in Korean backpackers’ network in 2012.
Table 11. Closeness centrality in Korean backpackers’ network in 2012.
RankCityCountryinClosenessoutCloseness
1Aix-en-ProvenceFrance-11.563
2AmsterdamNetherlands-15.293
3AntwerpenBelgium-13.734
4AugsburgGermany-13.704
5BudapestHungary-13.502
6Cesky KrumlovCzech-14.334
7CordobaSpain-12.32
8EtretatFrance-12.774
9LjubljanaSlovenia-0.775
10LuxembourgLuxembourg-0.040
11KalabakaGreece-0.051
12RondaSpain-0.058
13RotterdamNetherlands-0.060
14SelcukTurkey-0.048
15ZurichSwitzerland-0.058
16CappadociaTurkey16.4740.787
17PamukkaleTurkey14.0350.039
18FrankfurtGermany11.86314.936
19BrusselsBelgium11.85216.264
20BarcelonaSpain11.72216.060

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MDPI and ACS Style

Chung, H.C.; Chung, N.; Nam, Y. A Social Network Analysis of Tourist Movement Patterns in Blogs: Korean Backpackers in Europe. Sustainability 2017, 9, 2251. https://doi.org/10.3390/su9122251

AMA Style

Chung HC, Chung N, Nam Y. A Social Network Analysis of Tourist Movement Patterns in Blogs: Korean Backpackers in Europe. Sustainability. 2017; 9(12):2251. https://doi.org/10.3390/su9122251

Chicago/Turabian Style

Chung, Hee Chung, Namho Chung, and Yoonjae Nam. 2017. "A Social Network Analysis of Tourist Movement Patterns in Blogs: Korean Backpackers in Europe" Sustainability 9, no. 12: 2251. https://doi.org/10.3390/su9122251

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