TY - BOOK ID - 135684360 TI - Geo Data Science for Tourism AU - Marchetti, Andrea AU - Lo Duca, Angelica PY - 2022 PB - Basel MDPI Books DB - UniCat KW - green hotel KW - corporate social responsibility KW - green hotel certification KW - Chinese regional tourism KW - socioeconomic and environmental drivers KW - spatiotemporal influencing factors KW - spatiotemporal estimation mapping KW - Bayesian STVC model KW - spatiotemporal nonstationary regression KW - geographical data modeling analysis KW - sports tourism KW - spatial distribution KW - geographic detector KW - influencing factors KW - China KW - A-level scenic spots KW - spatiotemporal evolution KW - trend analysis KW - Geodetector KW - tourism economic vulnerability KW - obstacle factors KW - trend prediction KW - major tourist cities KW - tourism flow KW - cellular signaling data KW - social network analysis KW - network connection KW - node centrality KW - communities KW - relatedness between attractions KW - online tourism reviews KW - heterogeneous information network KW - embedding KW - attraction image KW - topic extraction KW - AGNES clustering KW - tourist attraction clustering KW - tourist attraction reachability space model KW - space-time deduction KW - tour route searching UR - https://www.unicat.be/uniCat?func=search&query=sysid:135684360 AB - 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.. ER -