Mining Flickr to Better Understand Tourist Behavior

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Author(s): Maria Giovanna Brandano (Gran Sasso Science Institute, Italy), Ludovico Iovino (Gran Sasso Science Institute, Italy)and Daniele Mantegazzi (University of Groningen, The Netherlands)
Copyright: 2022
Pages: 22
Source title: Handbook of Research on Advanced Research Methodologies for a Digital Society
Source Author(s)/Editor(s): Gabriella Punziano (University of Naples Federico II, Italy)and Angela Delli Paoli (University of Salerno, Italy)
DOI: 10.4018/978-1-7998-8473-6.ch038

Keywords: Information Science Reference / Library & Information Science / Research Methods

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Abstract

The aim of this chapter is to present an automated instrument collecting the enormous amount of information available online allowing urban planners, public administrations, tourism services suppliers, and researchers to easily understand the spatial and temporal distribution of tourist behaviors towards tourist attractions in a specific area. Geo-located photos provided by Flickr are used to identify points of interest (POIs). The developed application has been tested with data automatically retrieved and collected in L'Aquila province (Italy) during the years 2005-2018. Given the richness of information, these data are able to show how POIs changed over time and how tourists reacted to the 2009 earthquake. Results demonstrate the importance of using analytics and big data in tourism research. Moreover, by using the province of L'Aquila as pilot study, it emerges that tourist behaviors change over time and space, varying among different typologies of tourists: residents, domestic, and international visitors.

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Mining Flickr to Better Understand Tourist Behavior

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