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Determination of Seasonality Patterns in the Transport of Cruise Travellers Through Clustering Techniques

Published online by Cambridge University Press:  17 April 2019

Jeronimo Esteve-Perez*
Affiliation:
(Department of Naval Technology, Universidad Politécnica de Cartagena (UPCT), Paseo Alfonso XIII 52, 30203 Cartagena, Spain)
Antonio Garcia-Sanchez
Affiliation:
(Department of Economics, Universidad Politécnica de Cartagena (UPCT), Calle Real 3, 30201 Cartagena, Spain)
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Abstract

The tourist attraction of a cruise itinerary is composed of the on board experience plus the shore-based experience. Due to the positive dynamism of the cruise industry since the beginning of the twenty-first century, cruise lines are driven to innovate to create new experiences that help maintain high demand rates. From the point of view of creating new experiences on land, cruise lines move their vessels from one destination region to another to maximise the vessel's occupancy and to offer itineraries with a wider variety of shore-based attractions. These new itinerary designs lead to alterations in the seasonality patterns of the neighbouring regions. In this work, the 17 most important cruise ports located in the northeast quadrant of the Atlantic Ocean are analysed to find groups of ports with homogeneous seasonality patterns using clustering techniques. The analysis showed two different seasonality patterns. Consequently, some implications to improve the use of the ports of both clusters are included.

Information

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2019 
Figure 0

Figure 1. Northeast sector of the Atlantic Ocean highlighting the area of analysis.Source: Author's elaboration through Google (2018).

Figure 1

Figure 2. Example of a small-scale itinerary. Source: Authors using Google (2018).

Figure 2

Figure 3. Example of a medium-scale itinerary. Source: Authors using Google (2018).

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Figure 4. Example of a large-scale itinerary. Source: Authors using Google (2018).

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Table 1. Average yearly and cumulative cruise passenger movements of the ports of the sample during the period 2007–2016.

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Table 2. Classification of the ports in the sample according to the proposed sizes.

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Table 3. Monthly seasonal variation index of cruise passenger movements registered in each port of the sample during the period analysed.

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Figure 5. Dendrogram with clusters obtained and associated seasonality patterns of each cluster.

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Figure 6. Geographical position of each cluster inside the analysed area and associated seasonality patterns for each. Source: Authors, partially using Google (2018).

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Figure 7. Conceptual representation of the functions of obtained clusters. Source: Authors, partially using Google (2018).