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.
History
Publication title
Journal of Sustainable Tourism
Volume
28
Pagination
263-283
ISSN
0966-9582
Department/School
College Office - College of Arts, Law and Education
Publisher
Routledge
Place of publication
United Kingdom
Rights statement
Copyright 2019 Informa UK Limited, trading as Taylor & Francis Group This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of sustainable tourism on 9/10/2019, available online: http://www.tandfonline.com/10.1080/09669582.2019.1670186
Repository Status
Restricted
Socio-economic Objectives
Tourism infrastructure development; Regional planning; Other environmental management not elsewhere classified