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...
Збережено в:
| Формат: | Online |
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| Мова: | Англійська |
| Опубліковано: |
MDPI - Multidisciplinary Digital Publishing Institute
2023
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| Предмети: | |
| Онлайн доступ: | ONIX_20230220_9783036550299_64 |
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| _version_ | 1869518813746167808 |
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| collection | Directory of Open Access Books |
| description | 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.. |
| format | Online |
| id | doab-20.500.12854ir-97461 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-974612024-03-28T03:31:34Z Geo Data Science for Tourism Marchetti, Andrea Lo Duca, Angelica 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 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.. 2023-02-20T16:45:36Z 2023-02-20T16:45:36Z 2022 book ONIX_20230220_9783036550299_64 9783036550299 9783036550305 https://directory.doabooks.org/handle/20.500.12854/97461 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/6008 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-5030-5 10.3390/books978-3-0365-5030-5 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036550299 9783036550305 188 Basel open access |
| spellingShingle | 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 Geo Data Science for Tourism |
| title | Geo Data Science for Tourism |
| title_full | Geo Data Science for Tourism |
| title_fullStr | Geo Data Science for Tourism |
| title_full_unstemmed | Geo Data Science for Tourism |
| title_short | Geo Data Science for Tourism |
| title_sort | geo data science for tourism |
| topic | 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 |
| topic_facet | 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 |
| url | ONIX_20230220_9783036550299_64 |