@article{spalding2023nature,
author = {Spalding, Mark D. and Longley-Wood, Kate and McNulty, Valerie Pietsch and Constantine, Sherry and Acosta-Morel, Montserrat and Anthony, Val and Cole, Aaron D. and Hall, Giselle and Nickel, Barry A. and Schill, Steven R. and Schuhmann, Peter W. and Tanner, Darren},
title = {Nature dependent tourism – Combining big data and local knowledge},
year = {2023},
month = {April},
abstract = {The ability to quantify nature's value for tourism has significant implications for natural resource management and sustainable development policy. This is especially true in the Eastern Caribbean, where many countries are embracing the concept of the Blue Economy. The utilization of user-generated content (UGC) to understand tourist activities and preferences, including the use of artificial intelligence and machine learning approaches, remains at the early stages of development and application. This work describes a new effort which has modelled and mapped multiple nature dependent sectors of the tourism industry across five small island nations. It makes broad use of UGC, while acknowledging the challenges and strengthening the approach with substantive input, correction, and modification from local experts. Our approach to measuring the nature-dependency of tourism is practical and scalable, producing data, maps and statistics of sufficient detail and veracity to support sustainable resource management, marine spatial planning, and the wider promotion of the Blue Economy framework.},
url = {http://approjects.co.za/?big=en-us/research/publication/nature-dependent-tourism-combining-big-data-and-local-knowledge/},
journal = {Journal of Environmental Management},
}