Geo Data Science for Tourism

• Beste egile batzuk: Marchetti, Andrea (Argitaratzailea), Lo Duca, Angelica (Argitaratzailea)
• Formatua: Baliabide elektronikoa Liburu kapitulua
• Hizkuntza: ingelesa
• Argitaratua: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
• Gaiak: Research & information: general; Geography; 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
• Sarrera elektronikoa: DOAB: download the publication; DOAB: description of the publication

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...