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AGILE: GIScience Series Open-access proceedings of the Association of Geographic Information Laboratories in Europe
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Articles | Volume 2
AGILE GIScience Ser., 2, 14, 2021
https://doi.org/10.5194/agile-giss-2-14-2021
AGILE GIScience Ser., 2, 14, 2021
https://doi.org/10.5194/agile-giss-2-14-2021

  04 Jun 2021

04 Jun 2021

A Socially Aware Huff Model for Destination Choice in Nature-based Tourism

Meilin Shi1,2, Krzysztof Janowicz1,2, Ling Cai1,2, Gengchen Mai1,2, and Rui Zhu1,2 Meilin Shi et al.
  • 1STKO Lab, Department of Geography, University of California, Santa Barbara, USA
  • 2Center for Spatial Studies, University of California, Santa Barbara, USA

Keywords: nature-based tourism, socially aware Huff model, tourist destination choice, geotagged social media, Flickr

Abstract. Identifying determinants of tourist destination choice is an important task in the study of nature-based tourism. Traditionally, the study of tourist behavior relies on survey data and travel logs, which are labor-intensive and time-consuming. Thanks to location-based social networks, more detailed data is available at a finer grained spatio-temporal scale. This allows for better insights into travel patterns and interactions between attractions, e.g., parks. Meanwhile, such data sources also bring along a novel social influence component that has not yet been widely studied in terms of travel decisions. For example, social influencers post about certain places, which tend to influence destination choices of tourists. Therefore, in this paper, we propose a socially aware Huff model to account for this social factor in the study of destination choice. Moreover, with fine-grained social media data, interactions between attractions (i.e., the neighboring effects) can be better quantified and thus integrated into models as another factor. In our experiment, we calibrate a model by using trip sequences extracted from geotagged Flickr photos within two national parks in the United States. Our results demonstrate that the socially aware Huff model better simulates tourist travel preferences. In addition, we explore the significance of each factor and summarize the spatial-temporal travel pattern for each attraction. The socially aware Huff model and the calibration method can be applied to other fields such as promotional marketing.

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