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Geo Data Science for Tourism

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 tou...

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Uloženo v:
Médium: Online
Jazyk:angličtina
Vydáno: MDPI - Multidisciplinary Digital Publishing Institute 2023
Témata:
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
thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general
thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RG Geography
On-line přístup:ONIX_20230220_9783036550299_64
Tagy: Přidat tag
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Shrnutí: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..

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