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Social Network Analysis and Qualitative Interviews for Assessing Geographic Characteristics of Tourism Business Networks

Fig 1

The Surselva-Gotthard tourism business collaboration network displayed in two ways: a) Force-directed layout where nodes that are more connected to one another cluster together in space, and b) geo-located in the three towns Andermatt, Disentis, and Sedrun. Each node is a business actor. Lines indicate self-described collaborative links between actors. Colour indicates clusters of actors that tend to collaborate more with one another than with those in other groups [56]. Modularity values are 0.33 (region), 0.14 (Andermatt), 0.12 (Disentis), and 0.17 (Sedrun). Collaboration clusters tend to be associated with geographic proximity. Nodes are sized by Cluster Centrality (ci), and the density distribution of ci is indicated in the grey bar. ci = (liii) / (1 + Hi) where: Hi = −∑jpij ln pij; pij = lij/li; li = number of links of node i (i.e., 'degree'); lij = number of i's links to cluster j; and ii = number of links to nodes of a different cluster. A node is central to a cluster if it is highly connected and most of its connections are within its own cluster (as opposed to a different cluster). Visualised with VibrantData (http://vibrantdata.io.

Fig 1

doi: https://doi.org/10.1371/journal.pone.0156028.g001