@article{9ff114635000441f8d4221e4928b6a1c,
title = "Investigating patterns of tourist movement using multiple data sources",
abstract = "The investigation of tourism movement is fundamental for effective attraction and destination management. In particular, understanding patterns of movement to differentiate tourists according to their consumption of space and time has marketing and managerial implications. This paper uses a combination of mobile data, information about points of interest, and geographical data to investigate the movement of 2.95 million international visitors in Tuscany. The findings identify four types of international visitors according to their movement patterns and explore the differences between them and highlighting the consequential theoretical and practical importance of these differences for destination management and marketing. The paper also adds to the growing discussion on the use of mobile data, particularly its combined use with other data sources in the study of spatiotemporal behavior of tourists.",
keywords = "Big data, tourist movement, mobile data, tourist mobility, spatial behavior, signaling data",
author = "{Abreu Novais}, Margarida and {Del Papa}, Bruno and Qiwei Han and Kaushik Mohan and Orsolya V{\'a}s{\'a}rhelyi and Yanbing Wang and Leid Zejnilovic",
note = "Copyright: {\textcopyright} The Author(s) 2025, Article Reuse Guidelines. Funding information: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Orsolya V\u00E1s\u00E1rhelyi was funded by the European Union under Horizon EU project LearnData, 101086712. European Union under Horizon EU project LearnData (grant number 101086712). Qiwei Han and Leid Zejnilovic were funded by by Funda\u00E7\u00E3o para a Ci\u00EAncia e a Tecnologia (UIDB/00124/2020, UIDP/00124/2020, UID/00124, Nova School of Business and Economics and Social Sciences DataLab - PINFRA/22209/2016), POR Lisboa and POR Norte (Social Sciences DataLab, PINFRA/22209/2016). In addition, we acknowledge the support from Toscana Promozzione Turistica in terms of research question specification and professional advice. Also, we acknowledge the support by Vodafone Italy for sharing the data within the context of the Data Science for Social Good Summer Fellowship at Nova School of Business and Economics.",
year = "2025",
month = feb,
day = "26",
doi = "https://doi.org/10.1177/135676672412977",
language = "English",
journal = "Journal of Vacation Marketing",
issn = "1479-1870",
publisher = "SAGE Publications",
}