Modelling spatio-temporal movement of tourists using finite Markov chains

This paper presents a novel method for modelling the spatio-temporal movements of tourists at the macro-level using Markov chains methodology. Markov chains are used extensively in modelling random phenomena which results in a sequence of events linked together under the assumption of first-order dependence. In this paper, we utilise Markov chains to analyse the outcome and trend of events associated with spatio-temporal movement patterns. A case study was conducted on Phillip Island, which is situated in the state of Victoria, Australia, to test whether a stationary discrete absorbing Markov chain could be effectively used to model the spatio-temporal movements of tourists. The results obtained showed that this methodology can indeed be effectively used to provide information on tourist movement patterns. One significant outcome of this research is that it will assist park managers in developing better packages for tourists, and will also assist in tracking tourists' movements using simulation based on the model used.

[1]  R. J. Kuo,et al.  Application of ant K-means on clustering analysis , 2005 .

[2]  John G. Kemeny,et al.  Finite Markov chains , 1960 .

[3]  Dino Zanon,et al.  Monitoring visitor patterns of use in natural tourist destinations , 2005 .

[4]  Robert E. Manning,et al.  Computer Simulation Modeling for Recreation Management: A Study on Carriage Road Use in Acadia National Park, Maine, USA , 1999, Environmental management.

[5]  H. Gulinck,et al.  Estimation of off-track visits in a nature reserve: a case study in central Belgium , 2005 .

[6]  B. Ashtakala,et al.  Sequential models to determine intercity Commodity Transportation Demand , 1993 .

[7]  Jay L. Devore,et al.  Probability and statistics for engineering and the sciences , 1982 .

[8]  Yida Wang,et al.  Efficient mining of group patterns from user movement data , 2006, Data Knowl. Eng..

[9]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[10]  Larry S. Davis,et al.  W/sup 4/: Who? When? Where? What? A real time system for detecting and tracking people , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[11]  Andre Zerger,et al.  Geo-temporal tracking and analysis of tourist movement , 2005, Math. Comput. Simul..

[12]  Max J. Egenhofer,et al.  Modeling Moving Objects over Multiple Granularities , 2002, Annals of Mathematics and Artificial Intelligence.

[13]  Chris Ryan,et al.  Taking tourism to the limits : issues, concepts and managerial perspectives , 2005 .

[14]  Michael Batty,et al.  Predicting where we walk , 1997, Nature.

[15]  Jianhong Xia,et al.  Managing scale issues in spatio-temporal movement of tourists modelling , 2005 .

[16]  William J. Stewart,et al.  Introduction to the numerical solution of Markov Chains , 1994 .

[17]  Sabine Timpf,et al.  Map cube model - a model for multi-scale data , 1998 .

[18]  Dennis V. Lindley,et al.  Principles of Random Walk. , 1965 .

[19]  Marius Iosifescu,et al.  Finite Markov Processes and Their Applications , 1981 .

[20]  D. Fennell.  A tourist space-time budget in the Shetland Islands , 1996 .

[21]  Peter A. Rogerson,et al.  Space-Time Intelligence Systems: Technology, applications and methods , 2005, J. Geogr. Syst..