Difference between revisions of "Tourism Network Analysis"

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For a full discussion with all references see:
For a full discussion with all references see:
Baggio, R. (2013). Complexity, Network Science & Tourism. Innsbruck: IFITT Education Group. Online at: http://www.ifitt.org/home/view/the_ifitt_eTourism_curriculum.
[http://wiki.ifitt.org/images/3/31/Complexity%2C_Network_Science_and_Tourism_%28document%29.pdf Baggio, R. (2013). Complexity, Network Science & Tourism. Innsbruck: IFITT Education Group]

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Tourism and tourism systems can be defined in many ways, but, even if there is scarce agreement on possible definition, a tourism system, like many other economic and social systems, is undoubtedly a complex dynamic system. Among the many possible ways to study a complex system, its representation as a network is especially effective. Complex networks, in fact, can well represent the intricate interactions between the different components, are interesting models, and provide a sound basis for the researchers to study their structural and dynamic characteristics.

Network science has provided in the last years numerous tools for studying the structure and the dynamic behavior of many complex systems present in nature, technology and society. Most studies have dealt so far with networks where vertices correspond to single elements or subsystems, and edges indicate interactions or relationships between vertices. Moreover, a significant number of systems can be treated as composite assemblies of interacting networks that may combine in multiple ways and generate systems whose properties cannot be simply inferred by combining those of their constituents and, often, depend on the strength of the topological coupling between the different components.

Tourism networks topology

Modern network analysis methods are increasingly used in tourism studies and have shown to be able to provide scholars and practitioners with interesting outcomes. They have dealt mainly with tourism destinations or communities. The main objectives of this line of research is to apply these methods in order to better understand the structural characteristics of a tourism destination as it emerges from the “spontaneous” arrangements of the links between the different stakeholders, independently from preconceived ideas typically based on traditional divisions by type of business or geographical location.

Tourism Networks 1.png

Figure 1: Elba island and its destination network

More specifically the main topological characteristics of a tourism destination network have been measured. The destination network is generally composed of the main stakeholders (tourism firms, groups, consortia, associations, public bodies etc.) and the informational, economic or business relationships that connect them. It has been found that a scale-free topology exists. This means that there are a few nodes with many connections, acting as hubs, and many nodes with a limited number of links. This is common to many other systems. The destinations examined have also a very low density of connections and low clusterization, that is not many well defined communities (groups of nodes with more links between them than to other nodes of the network) can be identified. By using network topological measures important or critical stakeholders in a destination have been recognized. They turn out to be located in the core of the network and form an influential assembly able to influence the governance of the system.

These are important result, because weaknesses (or differences) in the connectivity patterns of the destination can be more objectively and independently characterized. There is also a significant managerial implication. It is well known, in fact, that there is a need for a destination to be a collaborative environment in order to ensure a balanced and sustainable growth. When the destination shows good cohesiveness (high local density of links) the whole system can achieve better outcomes. This can now have a natural measure in the metrics of the destination network, and the composition of the collaborative groups possibly found can give destination managers much better information for designing effective policies and governance practices.

Dynamic processes

Network topology ha an important influence on the unfolding of processes such as information or knowledge diffusion, or patterns of tourists’ movements. Many studies have described these processes with the objective of optimizing them by looking at the possibility of intervening on the structural organization of the destination. The final objective is to provide a deeper knowledge of the evolutionary paths and the dynamic behavior of the systems and study them in order to find the best governance styles for ensuring a sustainable growth of the systems and all their components. One of the advantages of a network representation is that numerical simulations can be performed with reasonable ease. They allow to conduct experiments when it would not otherwise be feasible for theoretical or practical reasons. Different configurations can be designed and several dynamic processes simulated. This allows to better understand how these configurations affect the behavior of the whole destination system or its reaction to processes such as information and knowledge diffusion.

Tourism Networks 2.png

Figure2: Diffusion of information simulated on different networks

Information and knowledge flows are relevant determinants of the system’s wellbeing. Overall efficiency, innovation and development are strongly influenced by them, and the way in which the spread occurs shape the speed by which individual actors perform and plan their future. A used way to study this problem is based on an analogy with the diffusion of a disease. Yet, differently from standard epidemiological models, it has been demonstrated that the structure of the network is highly influential in determining the basic unfolding of the process.

Internet and the Web

The World Wide Web is one of the most studied environments by network scientists. The relative ease with which it is possible to collect large amounts of data obviously facilitates the task. Therefore, a wealth of interesting outcomes have been published that concern the general structure of the Web and of many of its subnetworks, crawling & search strategies, the discovering of communities, the classification and organization of information (highlighting importance, relevance, ranking) and the sociology of content creation, techniques for efficiently mining for data, the behavior of users as they traverse the Web, or the detection of spam. The idea is that with a “good” Web model it is easier (or at all possible) to prove formal properties of algorithms and models, detect peculiar region of the Web, predict its evolution and highlight new phenomena.

A particular focus, then, has been given to the wide and varied world of Web 2.0 and the social media. Here important matters have been examined such as the patterns of (virtual) acquaintance formation, the role of influencers (individuals or organizations), the transmission of ideas and opinions and their influence on the choices consumers make, the determinants of trust and prestige, or the identification of communities.

Network analysis methods have been applied also to the virtual network of the websites present in a destination. The results have allowed to gauge the level of utilization of advanced communication technologies and measure the usage (or the waste) of important resources, universally considered crucial in a globalized market. Moreover, the topological similarity between the real and the virtual components has been assessed thus leading to the conjecture that the webspace of a tourism destination can be a faithful representation of its real structure. More recently, the same techniques have been employed to show the strong structural integration of real and virtual elements in a destination so that the idea of a tourism digital business ecosystem can be better explored.


The general framework of network science provides a sound basis for the study of the dynamic properties of tourism destination systems described in terms of its networked components and of the linkages that connect them. The study of the structural properties of a destination is able to provide insights into the functioning of the system both from a static and a dynamic point of view.

Many studies have confirmed the essence of the outcomes described here, reassessing the usefulness at both theoretical and practical level of network analytic methods to study issues concerning governance, social capital, decision-making, collective action or demand and supply patterns using cases from different parts of the World.

It must be noted, however, that the use of quantitative measurement for the assessment of network properties has little meaning without a physical interpretation which may only come from the outcomes of more traditional qualitative investigations. For the scholar, this can greatly help in confirming these models. For those interested or involved in managing a destination, the combination of both traditional qualitative evaluations and quantitative measurements can give more strength to the decisions made and better inform the actions and policies needed. As a final point, it is important to remark that a more rigorous establishment of methodological tools such as those briefly described here, can be a powerful way to help a transition towards a less undisciplined set of theories and models in the tourism arena, and that this can be greatly beneficial for a good understanding of the structure and behavior of these systems and their components, so important in today’s social and economic setting.

Reading suggestion

For a full discussion with all references see:

Baggio, R. (2013). Complexity, Network Science & Tourism. Innsbruck: IFITT Education Group

Further readings


NB: all the papers cited here can be found online (full text) with a Google search, at least in their pre-print version.


Amaral, L. A. N., & Ottino, J. M. (2004). Complex networks. European Physical Journal B, 38, 147-162.

Barabási, A. L. (2011). The network takeover. Nature Physics, 8(1), 14.

Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Network analysis in the social sciences. Science, 323(5916), 892-895.

Brandes, U., Robins, G., McCranie, A. N. N., & Wasserman, S. (2013). What is network science? Network Science, 1(01), 1-15.

Crutchfield, J. P. (2011). Between order and chaos. Nature Physics, 8(1), 17-24.

Hendler, J., Shadbolt, N., Hall, W., Berners-Lee, T., & Weitzner, D. (2008). Web science: an interdisciplinary approach to understanding the web. Communications of the ACM, 51(7), 60-69.

Newman, M. (2008). The physics of networks. Physics Today, 61(11), 33-38.

Newman, M. E. J. (2011). Communities, modules and large-scale structure in networks. Nature Physics, 8(1), 25-31.

Vespignani, A. (2011). Modelling dynamical processes in complex socio-technical systems. Nature Physics, 8(1), 32-39.


Baggio, R. (2008). Symptoms of complexity in a tourism system. Tourism Analysis, 13(1), 1-20.

Baggio, R., Scott, N., & Cooper, C. (2010). Improving tourism destination governance: a complexity science approach. Tourism Review, 65(4), 51-60.

Baggio, R., Scott, N., & Cooper, C. (2010). Network science - a review focused on tourism. Annals of Tourism Research, 37(3), 802–827.

Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., & Hwang, D. U. (2006). Complex networks: Structure and dynamics. Physics Reports, 424(4), 175-308.

Castellano, C., Fortunato, S., & Loreto, V. (2009). Statistical physics of social dynamics. Reviews of Modern Physics, 81(2), 591.

da Fontoura Costa, L., Oliveira, O. N., Travieso, G., Rodrigues, F. A., Villas Boas, P. R., Antiqueira, L., Viana, M. P., & Correa Rocha, L. E. (2011). Analyzing and modeling real-world phenomena with complex networks: a survey of applications. Advances in Physics, 60(3), 329-412.

da Fontoura Costa, L., Rodrigues, A., Travieso, G., & Villas Boas, P. R. (2007). Characterization of complex networks: A survey of measurements. Advances in Physics, 56(1), 167-242.

Easley, D. & Kleinberg, J. (2010) Networks, Crowds, and Markets: Reasoning about a Highly Connected World. Cambdridge: Cambridge University Press, 2010. (Chapter 13: The Structure of the Web).

Lancichinetti, A., Kivelä, M., Saramäki, J., & Fortunato, S. (2010). Characterizing the community structure of complex networks. PloS one, 5(8), e11976.



Gleick, J. (1987). Chaos: Making a New Science. New York: Viking.

Mitchell, M. (2009). Complexity: A Guided Tour. Oxford: Oxford University Press.

Strogatz, S. H. (2003). Sync: The Emerging Science of Spontaneous Order. New York: Hyperion.

Barabási, A.-L. (2002). Linked: The New Science of Networks. Cambridge, MA: Perseus.

Watts, D. J. (2003). Six Degrees: The Science of a Connected Age. New York: Norton.

General books on networks

Newman, M. E. J. (2010). Networks - An introduction. Oxford: Oxford University Press.

Easley, D., & Kleinberg, J. (2010). Networks, Crowds, and Markets: Reasoning about a Highly Connected World. Cambridge: Cambridge University Press.

Barrat, A., Barthélémy, M., & Vespignani, A. (2008). Dynamical Processes on Complex Networks. Cambridge: Cambridge University Press.