RT Book, Section
T1 New Data and Computational Methods Opportunities to Enhance the Knowledge Base of Tourism
A1 Romanillos Arroyo, Gustavo
A1 Moya Gómez, Borja
A2 Bertolini, Eleonora
A2 Fontana, Matteo
A2 Gabrielli, Lorenzo
A2 Signorelli, Serena
A2 Vespe, Michele
AB Tourism is becoming increasingly relevant at different levels, intensifying its impact on the environmental, the economic and the social spheres. For this reason, the study of this rapidly evolving sector is important for many disciplines and requires to be quickly updated. This chapter provides an overview and general guidelines on the potential use of new data and computational methods to enhance tourism’s knowledge base, encourage their institutional adoption and, ultimately, foster a more sustainable tourism. First, the chapter delivers a brief review of the literature on new data sources and innovative computational methods that can significantly improve our understanding of tourism, addressing the big data revolution and the emergence of new analytic tools, such as artificial intelligence (AI) or machine learning (ML). Then, the chapter provides some guidelines and applications of these new datasets and methods, articulated around three topics: (1) measuring the environmental impacts of tourism, (2) assessing the socio-economic resilience of the tourism sector and (3) uncovering new tourists’ preferences, facilitating the digital transition and fostering innovation in the tourism sector.
PB Springer
SN 978-3-031-16623-5
SN 978-3-031-16624-2
YR 2023
FD 2023
LK https://hdl.handle.net/20.500.14352/94762
UL https://hdl.handle.net/20.500.14352/94762
LA eng
NO Romanillos, G., Moya-Gómez, B. (2023). New Data and Computational Methods Opportunities to Enhance the Knowledge Base of Tourism. In: Bertoni, E., Fontana, M., Gabrielli, L., Signorelli, S., Vespe, M. (eds) Handbook of Computational Social Science for Policy. Springer, Cham. https://doi.org/10.1007/978-3-031-16624-2_19
NO European Commission
DS Docta Complutense
RD 10 abr 2025