Categorization of tourist attractions and the modeling of tourist cities: Based on the co-plot method of multivariate analysis

Noam Shoval*, Adi Raveh

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

91 Scopus citations

Abstract

This paper examines the relation between the trip characteristics of tourists and the attractions that they visit. This analysis was made possible by means of a new method of multivariate analysis - co-plot - that enables the simultaneous analysis of observations and variables and the graphic presentation of the interrelations among them. Jerusalem was seen as an "ideal" city for the demonstration of the co-plot method of data analysis due to the heterogeneity of its tourism. The research was conducted between September 1998 and March 1999. The results of the statistical analysis show that Jerusalem's tourist attractions can be categorized into four distinct groups and that there is a tendency of spatial concentration among sights belonging to the same group. Based on these results, a spatial model of tourism consumption in large cities was developed.

Original languageEnglish
Pages (from-to)741-750
Number of pages10
JournalTourism Management
Volume25
Issue number6
DOIs
StatePublished - Dec 2004

Bibliographical note

Funding Information:
I wish to thank Professor Arie Shachar from the Department of Geography at the Hebrew University of Jerusalem for his assistance and advice, I also wish to thank Mrs. Tamar Sofer, the chief cartographer of the Department of Geography at the Hebrew University of Jerusalem, for drawing the maps in this paper and finally, to the Israel Foundation trustees, the Israeli Ministry of Tourism, the Recanati Foundation and to the Municipality of Jerusalem, for supporting this research.

Keywords

  • Co-plot
  • Jerusalem
  • Multivariate analysis
  • Tourist attractions
  • Urban tourism

Fingerprint

Dive into the research topics of 'Categorization of tourist attractions and the modeling of tourist cities: Based on the co-plot method of multivariate analysis'. Together they form a unique fingerprint.

Cite this