Using innovations to understand tourist mobility in national parks

Technological advances have had profound impacts upon tourists’ mobility. However, until recently, there has been a gap between technological advances and their integration into tourism research methods. This paper addresses this gap by presenting a research method that utilised an application (app) equipped with a synthesised demographic survey and Global Navigation Satellite System (GNSS) technology. This enabled automated tracking of tourists’ behaviour for their entire stay within the island state of Tasmania, Australia. This paper focuses on tourists’ movement within the wellknown Freycinet National Park. The highly detailed granular data was assessed in three phases, revealing segments of tourists more likely to use the walking tracks, those more and less likely to visit during peak crowding times and finally, the development of an automated spatio-temporal dependence model via a machine-based learning environment, designed to operate in real time. The paper details the implications that innovative methodologies such as this may offer natural resource managers and tourism authorities, particularly in terms of locating, assessing and ultimately alleviating crowding and overtourism.