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Compositional Data Analysis in E-Tourism Research

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Handbook of e-Tourism

Abstract

Compositional Data (CoDa) contain information about the relative importance of parts of a whole, which the researcher deems more interesting than overall size or volume. In web mining, for instance, the relative frequency of a term is normally given more importance than absolute frequency, which mostly tells about web size, in other words, the sheer volume of online content. Many research questions in e-tourism are either related to the distribution of a whole or relative importance: How do the most salient contents in hotel Facebook accounts relate to hotel characteristics? What are the dominant topics on TripAdvisor comments about fish freshness in seafood restaurants? How does the relative popularity of search terms in Google relate to destination market share?

In CoDa, most of the basic statistical notions, such as center, variation, association, and distance, are flawed unless they are re-expressed by means of logarithms of ratios. The appeal of log-ratios is that once they are computed, standard statistical methods can be used. On the other hand, since one part can only increase in relative terms if some other(s) decrease, statistics need to be multivariate.

This chapter uses an example based on TripAdvisor hotel reviews from one of the most visited cities worldwide, Barcelona, focusing on what users complain about, to illustrate the main multivariate exploratory and descriptive tools in CoDa, including imputation of zeros prior to computing the log-ratios, multivariate outlier detection, principal component analysis, cluster analysis, and multivariate data visualization tools. The use of CoDaPack, a popular CoDa freeware, is described in a step-by-step fashion.

To appear in: Zheng X, Fuchs M, Gretzel U, Höpken W (eds) Handbook of E-Tourism. Springer Nature.

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Acknowledgements

The authors acknowledge the support of the Catalan Government for the accreditation as Consolidated Research Groups TURESCO (2017 SGR 49) and COSDA (2017 SGR 656). This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness (Grant id.: TURCOLAB ECO2017-88984-R) and by the Spanish Ministry of Science, Innovation and Universities and FEDER (Grant id.: CODAMET RTI2018-095518-B-C21). Finally, authors also acknowledge the comments by Santi Thió-Henestrosa.

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Authors and Affiliations

  1. University of Lleida, Lleida, Spain

    Berta Ferrer-Rosell & Eva Martin-Fuentes

  2. University of Girona, Girona, Spain

    Germà Coenders

Authors
  1. Berta Ferrer-Rosell
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  2. Germà Coenders
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  3. Eva Martin-Fuentes
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Correspondence to Berta Ferrer-Rosell .

Editor information

Editors and Affiliations

  1. Department of Hospitality and Tourism Management, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA

    Zheng Xiang

  2. Department of Tourism Studies and Geography, Mid Sweden University, Östersund, Sweden

    Matthias Fuchs

  3. Annenberg School for Communication and Journalism, University of Southern California, Los Angeles, CA, USA

    Ulrike Gretzel

  4. Department of Business Informatics, University of Applied Sciences Ravensburg-Weingarten, Weingarten, Germany

    Wolfram Höpken

Section Editor information

  1. Annenberg School for Communication & Journalism, Center for Public Relations, University of Southern California, Watt Way 3502, 90089, Los Angeles, CA, USA

    Ulrike Gretzel

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Ferrer-Rosell, B., Coenders, G., Martin-Fuentes, E. (2022). Compositional Data Analysis in E-Tourism Research. In: Xiang, Z., Fuchs, M., Gretzel, U., Höpken, W. (eds) Handbook of e-Tourism. Springer, Cham. https://doi.org/10.1007/978-3-030-05324-6_136-1

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  • DOI: https://doi.org/10.1007/978-3-030-05324-6_136-1

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