Shaping and Re-Shaping Tourism Areas: A Network Approach

Shaping and Re-Shaping Tourism Areas: A Network Approach

Vincenzo Asero (University of Catania, Italy), Simona Gozzo (University of Catania, Italy) and Venera Tomaselli (University of Catania, Italy)
DOI: 10.4018/978-1-5225-1054-3.ch014

Abstract

Defining the boundaries of tourism destinations has been long recognised as a problem in tourism research. The authors aim to define the spatial configuration of tourism areas including different destinations within a same region. Tourist mobility is employed as a methodological criterion to reveal the network relationships among destinations and explain how tourism areas are being shaped and reshaped. The study combines Network Analysis methods and multinomial logistic regression models, in an approach to processing the data of a sampling survey, carried out in Sicily. The results show that the network structures among destinations affect the shape and dimension of tourism areas. Useful evidence for the spatial planning of tourism regions and destination management strategies are derived.
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Introduction

A tourism destination is a specific geographical area within which tourists enjoy different types of holiday. According to Pike (2008), the destination is the spatial unit of analysis in any modelling of a tourism system. Many scholars highlight the fact that destination is a problematic concept (e.g., Framke, 2002; Saraniemi & Kylänen, 2011), one which can be used in different ways and across a varying range of spatial scale (e.g., Beaumont & Dredge, 2010; Saarinen, 2014). Others recognise that destinations are complex dynamic systems (Baggio, 2008, 2014; Baggio, Scott, & Cooper, 2010). Bieger et al. (2009) noticed that the size of a destination influences the Destination Management Organization (DMO) functions, activities and budgeting. More recently, Pearce (2014) observed that the conceptualization of destinations has practical implications for destination management, since viewing a destination as an open or a closed system, instead of as an entity complete in itself, affects DMO actions. The geographical dimension of tourism destinations is often associated with blurred boundaries, since they can encompass either a single location or a network of different places (Dredge, 1999). In this regard, Richie and Crouch (2003) distinguished different levels and types of destinations, where the biggest are countries and macro-regions, and the smallest are cities and single places (e.g., theme parks). Wall (1997) suggested a classification in terms of points, lines and areas based on spatial characteristics. Following Porter (1998), a destination can be considered as a tourism cluster, since it encompasses a geographical concentration of interconnected firms and institutions. By contrast, in the perspective of Massey (1994), destinations are networks of social relations instead of areas with boundaries. The spatial dimension of a destination can therefore be defined according to cooperation among different locations or strategic partnerships among firms and public entities, instead of administrative conditions.

Buhalis (2000) noted that a destination is also a subjective concept, perceived by tourists on the basis of different elements such as their travel itineraries. Similarly, Jenkins et al. (2011) observed that tourists are more likely to define a destination in relation to their journey, specifically in terms of attractions, services, travel time and entry and departure points. Klepers and Rozite (2010) found that tourists, while travelling, do not notice administrative boundaries.

There is a debate in tourism literature over whether the geographic boundaries of a destination are functional or administrative, fixed or fluid (Pearce & Schänzel, 2013). Recently, Beritelli, Bieger, and Laesser (2014) proposed a dynamic viewpoint, affirming that a destination is a space of ‘variable geometry’, since it is the playground of different supply networks activated by tourist movements. The analysis of the spatial distribution of tourists, therefore, could be an evaluation criterion of the interconnections among various destinations within an area, defining a tourism system (Carlsen, 1999; Cross, Borgatti, & Parker, 2002). As Burnes and Novelli (2008) observed, understanding tourist mobility among multiple localities could explain how destinations are being shaped and re-shaped. As a consequence, the mapping and modelling of tourists’ movements are suitable for understanding the dimension of a tourism system.

Key Terms in this Chapter

Tourist Mobility: Spatial movement of tourists defining routes among destinations.

Network Analysis: Methodological approach to analyse the relational patterns among entities.

Ego-Network: The concept indicates the amount of all the nodes (here, destinations) to which an ego/node is directly connected and includes all of the ties among nodes in a network.

CONCOR: A block-modelling method to group nodes (here, destinations) into blocks based on structural equivalence.

Accommodation Establishments: Units providing short-term or short-stay accommodation services. Here, they are classified into two groups, including hotels and similar establishments and other collective establishments.

Structural Equivalence: Mathematical property of two nodes having the same ties with themselves, each other, and to and from all other nodes in the network.

Density Matrix: A matrix showing intra- and/or inter-block relations measured by density scores. Density is based on the number of ties, as a proportion of the maximum possible number of ties among nodes in a network.

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