Dynamic Property of a Tourism Destination Network

Yabuta, Masahiro; Scott, Noel

doi:10.3727/108354211x13154360475934

Cited by 5 publications

(1 citation statement)

References 6 publications

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“…Our goal is to show that our procedure produces clusters that are able to explain the tourist features of a region. The resulting geographical regions (clusters) have to have the following features [15,16] 1. clusters have to represent a tourist destination network in a well-defined area, where a small number of tourism sites are located and visitors can access them by a one-day trip; 2. clusters have to be a tourism complex, which includes various multiple tourism attractions (accommodations, restaurants, amusement parks etc. ).…”

Section: Introductionmentioning

confidence: 99%

Clustering Local Tourism Systems by Threshold Acceptance

Andria¹,

Tollo

2015

Applications of Evolutionary Computation

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Abstract. Despite the importance of tourism as a leading industry in the development of a country's economy, there is a lack of criteria and methodologies for the detection, promotion and governance of local tourism systems. We propose a quantitative approach for the detection of local tourism systems that are optimal with respect to geographical, economic, and demographical criteria. To this end, we formulate the issue as an optimization problem, and we solve it by means of Threshold Acceptance, a meta-heuristic algorithm which does not require us to predefine the number of clusters and also does not require all geographic areas to belong to a cluster.

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Section: Introductionmentioning

confidence: 99%

Clustering Local Tourism Systems by Threshold Acceptance

Andria¹,

Tollo

2015

Applications of Evolutionary Computation

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Structural Implications of Destination Value System Networks

Stienmetz

Fesenmaier

2017

Information and Communication Technologies in Tourism 2017

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Detection of local tourism systems by threshold accepting

2015

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Despite the importance of tourism as a leading industry in the development of a country’s economy, there is a lack of criteria and methodologies for the detection, promotion, and governance of local tourism systems. We propose a quantitative approach for the detection of local tourism systems the size of which is optimal with respect to geographical, economic, and demographical criteria: we formulate the problem as an optimisation problem and we solve it by a metaheuristic approach; then we compare the obtained results with standard clustering approaches and with an exact optimisation solver. Results show that our approach requires low computational times to provide results that are better than other clustering techniques and than the current approach used by local authorities

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Dynamic Property of a Tourism Destination Network

Cited by 5 publications

References 6 publications

Clustering Local Tourism Systems by Threshold Acceptance

Clustering Local Tourism Systems by Threshold Acceptance

Structural Implications of Destination Value System Networks

Detection of local tourism systems by threshold accepting

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