This paper aims to introduce our publicly available datasets in the area of tourism demand prediction for future experiments and comparisons. Most of the previous works in the area of tourism demand forecasting are based on coarse-grained analysis (level of countries or regions) and there are very few works and consequently datasets available for fine-grained tourism analysis (level of attractions and points of interest). In this article, we present our fine-grained enriched datasets for two types of attractions - (I) indoor attractions (27 Museums and Galleries in U.K.) and (II) outdoor attractions (76 U.S. National Parks) enriched with official number of visits, social media reviews and environmental data for each of them. In addition, the complete analysis of prediction results, methodology and exploited models, features' performance analysis, anomalies, etc, are available in our original paper, "Fine-grained tourism prediction: Impact of social and environmental features"[2].
Keywords: Climate features; Indoor attractions; Outdoor attractions; Tourism prediction baselines; Trip advisor social media; U.K national museums and galleries; U.S national parks.
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