Geo Data Science for Tourism

doi:10.3390/books978-3-0365-5030-5

Download Url(s)

https://mdpi.com/books/pdfview/book/6008

Contributor(s)

Marchetti, Andrea (editor)

Lo Duca, Angelica (editor)

Language

English

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Abstract

This reprint describes the recent challenges in tourism seen from the point of view of data science. Thanks to the use of the most popular Data Science concepts, you can easily recognise trends and patterns in tourism, detect the impact of tourism on the environment, and predict future trends in tourism. This reprint starts by describing how to analyse data related to the past, then it moves on to detecting behaviours in the present, and, finally, it describes some techniques to predict future trends. By the end of the reprint, you will be able to use data science to help tourism businesses make better use of data and improve their decision making and operations..

URI

https://directory.doabooks.org/handle/20.500.12854/97461

Keywords

green hotel; corporate social responsibility; green hotel certification; Chinese regional tourism; socioeconomic and environmental drivers; spatiotemporal influencing factors; spatiotemporal estimation mapping; Bayesian STVC model; spatiotemporal nonstationary regression; geographical data modeling analysis; sports tourism; spatial distribution; geographic detector; influencing factors; China; A-level scenic spots; spatiotemporal evolution; trend analysis; Geodetector; tourism economic vulnerability; obstacle factors; trend prediction; major tourist cities; tourism flow; cellular signaling data; social network analysis; network connection; node centrality; communities; relatedness between attractions; online tourism reviews; heterogeneous information network; embedding; attraction image; topic extraction; AGNES clustering; tourist attraction clustering; tourist attraction reachability space model; space-time deduction; tour route searching