Bringing forecasting into the future: Using Google to predict visitation in U.S. national parks

Clark, Matt; Wilkins, Emily J.; Dagan, Dani T.; Powell, Robert B.; Sharp, Ryan L.; Hillis, Vicken

doi:10.1016/j.jenvman.2019.05.006

Cited by 26 publications

(15 citation statements)

References 25 publications

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“…In the context of forecasting using search engine data, Google Trends have been used to predict tourist demand both at the country level (Park et al, 2017) and at the tourist destination level, such as tourist arrivals to five London museums (Volchek et al, 2019) and US National Parks (Clark et al, 2019). Besides Google Trends, several studies with forecasting context in China have utilized the Baidu index (Huang et al, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic: a multisource Internet data approach

Andariesta

Wasesa

2022

JTF

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PurposeThis research presents machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic using multisource Internet data.Design/methodology/approachTo develop the prediction models, this research utilizes multisource Internet data from TripAdvisor travel forum and Google Trends. Temporal factors, posts and comments, search queries index and previous tourist arrivals records are set as predictors. Four sets of predictors and three distinct data compositions were utilized for training the machine learning models, namely artificial neural networks (ANNs), support vector regression (SVR) and random forest (RF). To evaluate the models, this research uses three accuracy metrics, namely root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE).FindingsPrediction models trained using multisource Internet data predictors have better accuracy than those trained using single-source Internet data or other predictors. In addition, using more training sets that cover the phenomenon of interest, such as COVID-19, will enhance the prediction model's learning process and accuracy. The experiments show that the RF models have better prediction accuracy than the ANN and SVR models.Originality/valueFirst, this study pioneers the practice of a multisource Internet data approach in predicting tourist arrivals amid the unprecedented COVID-19 pandemic. Second, the use of multisource Internet data to improve prediction performance is validated with real empirical data. Finally, this is one of the few papers to provide perspectives on the current dynamics of Indonesia's tourism demand.

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Section: Literature Reviewmentioning

confidence: 99%

Machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic: a multisource Internet data approach

Andariesta

Wasesa

2022

JTF

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“…Law et al (2019) forecast monthly Macau tourist arrivals to demonstrate that the deep learning approach significantly outperforms vector regression and artificial neural network (ANN) models. Clark et al (2019) emphasize the advantage of Google Trends in forecasting visitation for most national parks in the United States. Li and Law (2020) examine whether decomposed search engine data can be used to improve accuracy in forecasting tourism demand to predict monthly tourist arrivals from nine countries to Hong Kong.…”

Section: Introductionmentioning

confidence: 99%

Forecasting tourist arrivals: Google Trends meets mixed-frequency data

Havránek

Zeynalov

2019

Tourism Economics

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In this article, we examine the usefulness of Google Trends data in predicting monthly tourist arrivals and overnight stays in Prague during the period between January 2010 and December 2016. We offer two contributions. First, we analyze whether Google Trends provides significant forecasting improvements over models without search data. Second, we assess whether a high-frequency variable (weekly Google Trends) is more useful for accurate forecasting than a low-frequency variable (monthly tourist arrivals) using mixed-data sampling (MIDAS). Our results suggest the potential of Google Trends to offer more accurate predictions in the context of tourism: we find that Google Trends information, both 2 months and 1 week ahead of arrivals, is useful for predicting the actual number of tourist arrivals. The MIDAS forecasting model employing weekly Google Trends data outperforms models using monthly Google Trends data and models without Google Trends data.

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“…De DigitaleGracht 2022 , Di Baldassarre, 2013 , Dushkova, 2021 , Folke et al, 2021 , Graczyk, 2010 , International Tourism and Covid-19 | Tourism Dashboard 2022 , Klenert, 2020 , Loh, 2021 , March, 2021 , McGinlay et al, 2020 , Merel et al, 2013 , Miller-Rushing et al, 2021 , Bates et al, 2020 , Parashar and Hait, 2021 , Pouso et al, 2021 , Publieksvoorlichting, 2021 , Rabinowitz et al, 2018 , Salesa and Cerdà, 2020 , Brownscombe, 2017 , Sanjari et al 2009 , Seelen et al, 2019 , Seelen et al, 2022 , Soga et al, 2021 , Templeton et al, 2021 , Trudeau, 2016 , Tscherning et al, 2012 , Wetzel, 2021 , Wickham et al, 2021 , wilcox.test: Wilcoxon Rank Sum and Signed Rank Tests 2022 , Zinsstag, 2012 , Chu, 2021 , Clark et al, 2019 , COVID-19 pandemic in the Netherlands, 2021…”

Section: Uncited Referencesmentioning

confidence: 99%

Making waves: Lessons learned from the COVID-19 anthropause in the Netherlands on urban aquatic ecosystem services provisioning and management

Armstrong¹,

Bahçeci²,

Donk³

et al. 2022

Water Research

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Bringing forecasting into the future: Using Google to predict visitation in U.S. national parks

Cited by 26 publications

References 25 publications

Machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic: a multisource Internet data approach

Machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic: a multisource Internet data approach

Forecasting tourist arrivals: Google Trends meets mixed-frequency data

Making waves: Lessons learned from the COVID-19 anthropause in the Netherlands on urban aquatic ecosystem services provisioning and management

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