Modeling Spatio-Temporal Movements Using Finite Markov Chains

This paper presents a novel method for modeling the spatio-temporal movements of tourists at the macro level using Markov Chains methodology. Markov Chains are used extensively in modeling random phenomena which results in a sequence of events linked together under the assumption of first-order dependence. In this paper, we utilize Markov Chains to analyze 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 1 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.