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|a 10.1007/978-981-19-9369-5
|2 doi
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|a HCDD
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| 245 |
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|a Tourism analytics before and after COVID-19 :
|b case studies from Asia and Europe /
|c Yok Yen Nguwi, editor.
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|a Singapore :
|b Springer,
|c [2023]
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|c ©2023
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| 300 |
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|a 1 online resource (viii, 246 pages) :
|b illustrations (chiefly color)
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| 336 |
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 504 |
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|a Includes bibliographical references.
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|a This book is compilation of different analytics and machine learning techniques focusing on the tourism industry, particularly in measuring the impact of COVID-19 as well as forging a path ahead toward recovery. It includes case studies on COVID-19's effects on tourism in Europe, Hong Kong, China, and Singapore with the objective of looking at the issues through a data analytical lens and uncovering potential solutions. It adopts descriptive analytics, predictive analytics, machine learning predictive models, and some simulation models to provide holistic understanding. There are three ways in which readers will benefit from reading this work. Firstly, readers gain an insightful understanding of how tourism is impacted by different factors, its intermingled relationship with macro and business data, and how different analytics approaches can be used to visualize the issues, scenarios, and resolutions. Secondly, readers learn to pick up data analytics skills from the illustrated examples. Thirdly, readers learn the basics of Python programming to work with the different kinds of datasets that may be applicable to the tourism industry.
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|a Online resource; title from PDF title page (SpringerLink, viewed March 17, 2023).
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|a Intro -- Contents -- Hong Kong Tourism Under COVID-19 -- Data Preparation -- Modeling and Results Comparison -- Feature Importance -- Business Analysis -- Impact on Airlines: Case Study on Cathay Pacific and Dragon Air -- Conclusion and Future Studies -- References -- Tourism Analytics, the Case for Hainan China -- Impacts on Tourism Industry -- Analytics Methodology -- Model Selection -- Conclusions -- Reference -- Impacts of COVID-19 on Food, Aviation, and Accommodation in Europe -- Dataset and Analysis -- Methodology and Experimental Results -- Recommendation and Conclusion -- References
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| 505 |
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|a Tourism Rebounds Analysis-Lessons from Baltics Countries -- Business Understanding and Approach -- Data Model Analysis -- Tourism Income Baseline Growth Trajectory 2020-2021, Without COVID -- XGBoost -- Model Evaluation -- Prediction of International Arrivals in 2020 and 2021-an Outlook Without COVID-19 -- The Case of Travel Bubble in Estonia -- Business Case Analysis -- Policies Effectiveness Quantitative Analysis -- Qualitative Analysis of Other Measures for Consideration -- Conclusion -- References -- Compare and Contrast the Impact of COVID-19 from Small to Large Country
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| 505 |
8 |
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|a Tourism in Singapore -- Tourism in China -- Tourism Analytics-The Case for South Africa -- References -- Hotel Booking Cancellation Analytics on Imbalanced Data -- Data Preparation -- Data Visualization -- Machine Learning -- Business Insights and Solutions -- Conclusion -- References -- Tourism Prediction Analytics -- Dataset and Analysis -- Current Situation of COVID-19 -- Prediction of COVID-19 -- Development of Tourism/Hotel Industry -- Seasonality of Arrivals -- Age of Visitors -- Purpose of Trips -- Places of Interest -- Hotel Industry -- Impact of COVID-19 on Singapore's hotel industry
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| 505 |
8 |
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|a Descriptive Analysis -- Time Series Prediction -- Recommendation -- Conclusion -- References -- Marketing Segmentation and Targeted Marketing for Tourism -- Visualization with Descriptive Analytics -- Business Solutions Using Machine Learning -- Conclusion -- References -- Machine Learning for Tourism -- Visualization-Based Analysis -- Time Series Analysis -- Machine Learning Analysis -- Recommendation -- Data Visualization on Tourism -- Data Sources -- Data Visualization and Analysis -- Recommendation -- Conclusion -- References -- Sustaining Tourism Sector Through Domestic Tourism and Analytics
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| 505 |
8 |
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|a Dataset and Analysis -- Proposed Solution: Analytics-Enabled Domestic Tourism Model -- References -- Tourism Analytics with Price and Room Booking Simulation -- Analytics Approach on Tourism -- Price, Room Booking and Revenue Simulation -- Scenario 1 -- Scenario 2 -- Scenario 3 -- Conclusion -- Recommendation -- References -- Tourism Arrival Prediction -- Proposed Solutions -- Fiscal Stimulus -- Domestic Tourism -- Travel Bubble -- Reshape the Travel Activities -- References
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| 650 |
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|a Tourism
|x Data processing.
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| 650 |
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|a COVID-19 Pandemic, 2020-
|x Economic aspects
|v Case studies.
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| 650 |
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|a COVID-19 Pandemic, 2020-
|x Social aspects
|v Case studies.
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| 650 |
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|a Tourism
|x Data processing
|2 fast
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| 647 |
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|a COVID-19 Pandemic
|d (2020-)
|2 fast
|1 https://id.oclc.org/worldcat/entity/E39Qhp4vB9ppxymcDb8984mKfy
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| 648 |
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|a Since 2020
|2 fast
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|a Case studies
|2 fast
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|a Case studies.
|2 lcgft
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|a Études de cas.
|2 rvmgf
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| 700 |
1 |
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|a Nguwi, Yok Yen,
|e editor.
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| 776 |
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|i Print version:
|a Nguwi, Yok Yen
|t Tourism Analytics Before and after COVID-19
|d Singapore : Springer,c2023
|z 9789811993688
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| 856 |
4 |
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|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-981-19-9369-5
|y Click for online access
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|a SPRING-ALL2023
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| 994 |
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|a 92
|b HCD
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