Predicting Tourism Demand in Indonesia Using Google Trends Data

TL;DR

This study demonstrates that Google Trends data can effectively predict and analyze tourism demand patterns in Indonesia, correlating well with official statistics and providing timely insights for policy making.

Contribution

The paper introduces a method to use Google Trends data for predicting tourism demand in Indonesia, comparing it with official statistics and evaluating various time series models.

Findings

Google Trends data correlates with official tourism statistics.

Search queries reflect tourism demand patterns during disasters.

Google Trends can provide timely tourism demand predictions.

Abstract

Tourism data is one of the strategic data in Indonesia. In addition, tourism is one of the ten priority programs of national development planning in Indonesia. BPS-Statistics Indonesia has collected data related to tourism demand in Indonesia, but these data have different time period. Several data can be provided monthly, while the other data can be provided annually. However, accurate and real time tourism data are needed for effective policy making. In this era, all of information about tourism destination or accommodation can be gotten easily through internet, especially information from Google search engine, such as information about tourism places, flights, hotels, and ticket for tourism attractions. Since 2004, Google has provided the information of user behavior through Google Trends tool. This paper aims to analyze and compare the patterns of tourism demand in Indonesia from Google Trends data with tourism statistics from BPS-Statistics Indonesia. In order to understand tourism demand in Indonesia, we used Google Trends data on a set of queries related to tourism. This paper shows that the search intensity of related queries provides the pattern of predicted tourism demand in Indonesia. We evaluated the prediction result by comparing several time series models. Furthermore, we compared and correlated the Google Trends data with official data. The result shows that Google Trends data and tourism statistics have similar pattern when there were disasters. The result also shows that Google Trends data has correlation with official data and produced accurate prediction of tourism demand in Indonesia. Therefore, Google Trends data can be used to predict and understand the pattern of tourism demand in Indonesia.