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Geo Data Science for Tourism
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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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Keywords
- A-level scenic spots
- AGNES clustering
- attraction image
- Bayesian STVC model
- cellular signaling data
- China
- Chinese regional tourism
- Communities
- Corporate social responsibility
- embedding
- Geodetector
- geographic detector
- geographical data modeling analysis
- Geography
- green hotel
- green hotel certification
- heterogeneous information network
- influencing factors
- major tourist cities
- network connection
- node centrality
- obstacle factors
- online tourism reviews
- Reference, information & interdisciplinary subjects
- relatedness between attractions
- Research & information: general
- Social network analysis
- socioeconomic and environmental drivers
- space-time deduction
- spatial distribution
- spatiotemporal estimation mapping
- spatiotemporal evolution
- spatiotemporal influencing factors
- spatiotemporal nonstationary regression
- sports tourism
- topic extraction
- tour route searching
- tourism economic vulnerability
- tourism flow
- tourist attraction clustering
- tourist attraction reachability space model
- Trend Analysis
- trend prediction
Links
DOI: 10.3390/books978-3-0365-5030-5Editions