Fine-grained tourism prediction: Impact of social and environmental features |
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| Institution: | 1. School of Information, Central University of Finance and Economics, Beijing, China;2. Department of Sociology, Tsinghua University, Beijing, China;3. School of Systems Science, Beijing Normal University, Beijing, China;4. National School of Development, Peking University, Beijing, China;1. Software and Information Systems Engineering Department, Ben-Gurion University, Beer-Sheva, Israel;2. Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, Israel;1. University of Waterloo, Canada;2. Ted Rogers School of Management, Ryerson University, Canada;3. Ontario Tech University, Canada;1. CERTH-ITI, 6th km Charilaou-Thermi Rd, 57001 Thermi, Thessaloniki, Greece;2. AUTH, Department of Electrical and Computer Engineering, 54124 Thessaloniki, Greece;1. Instituto de Sistemas e Robótica, Dept. of Electrical and Computer Engineering, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal;2. Data Science and Big Data Analytics, EURECAT (Centre Tecnológic de Catalunya) Carrer de Bilbao, 72 (Edifici A), Barcelona 08005, Spain;3. SQIG - Instituto de Telecomunicações, Department of Mathematics, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal |
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| Abstract: | Accurate predictions about future events is essential in many areas, one of them being the Tourism Industry. Usually, cities and countries invest a huge amount of money for planning and preparation in order to welcome (and profit from) tourists. The success of many businesses depends largely or totally on the state of tourism demand. Estimation of tourism demand can be helpful to business planners in reducing the risk of decisions regarding the future since tourism products are, generally speaking, perishable (gone if not used). Prior studies in this domain focus on forecasting for a whole country and not for fine-grained areas within a country (e.g., specific touristic attractions) mainly because of lack of data. Our article tackles exactly this issue. With the rapid popularity growth of social media applications, each year more people interact within online resources to plan and comment on their trips. Motivated by such observation, we here suggest that accessible data in online social networks or travel websites, in addition to environmental data, can be used to support the inference of visitation count for either indoor or outdoor touristic attractions. To test our hypothesis we analyze visitation counts, environmental features and social media data related to 27 museums and galleries in U.K as well as 76 national parks in the U.S. Our experimental results reveal high accuracy levels (above 92%) for predicting tourism demand using features from both social media and environmental data. We also show that, for outdoor attractions, environmental features have better predictive power while the opposite occurs for indoor attractions. In any case, best results, in all scenarios, are obtained when using both types of features jointly. Finally, we perform a detailed failure analysis to inspect the cases in which the prediction results are not satisfactory. |
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