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Tourism analytics before and after COVID-19case studies from Asia and Europe /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Tourism analytics before and after COVID-19edited by Yok Yen Nguwi.
Reminder of title:	case studies from Asia and Europe /
other author:	Nguwi, Yok Yen.
Published:	Singapore :Springer Nature Singapore :2023.
Description:	viii, 246 p. :ill. (chiefly col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	TourismData processing.
Online resource:	https://doi.org/10.1007/978-981-19-9369-5
ISBN:	9789811993695$q(electronic bk.)

Tourism analytics before and after COVID-19case studies from Asia and Europe /
Tourism analytics before and after COVID-19case studies from Asia and Europe /[electronic resource] :edited by Yok Yen Nguwi. - Singapore :Springer Nature Singapore :2023. - viii, 246 p. :ill. (chiefly col.), digital ;24 cm.

Impacts on aviation and accommodation in Europe using deep learning machine learning -- Time series model tourism forecasting, the case for Hainan, China -- Impacts on Covid on Singapore's hotel industry and pricing strategy -- Inbound tourist analysis on arrival and length of stay distribution, the case for Indonesian tourists -- Modeling tourism in Hong Kong using Ridge Linear Regression, Support Vector Machine and XGBoost approach -- Analytics on the prediction of hotel booking cancellation, the case for Portugal hotels.

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.

ISBN: 9789811993695$q(electronic bk.)

Standard No.: 10.1007/978-981-19-9369-5doiSubjects--Topical Terms:

220950
Tourism
--Data processing.

LC Class. No.: G155.A1

Dewey Class. No.: 338.47910285

Tourism analytics before and after COVID-19case studies from Asia and Europe /
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505 0 $a Impacts on aviation and accommodation in Europe using deep learning machine learning -- Time series model tourism forecasting, the case for Hainan, China -- Impacts on Covid on Singapore's hotel industry and pricing strategy -- Inbound tourist analysis on arrival and length of stay distribution, the case for Indonesian tourists -- Modeling tourism in Hong Kong using Ridge Linear Regression, Support Vector Machine and XGBoost approach -- Analytics on the prediction of hotel booking cancellation, the case for Portugal hotels.
520 $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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