FISETIO: A FIne-grained, Structured and Enriched Tourism Dataset for Indoor and Outdoor attractions

Published: 21 Jun 2019 | Version 1 | DOI: 10.17632/t7bfhtzhxg.1

Description of this data

This data in brief paper introduces our publicly available datasets in the area of tourism demand prediction for future experiments and comparisons. Most 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 datasets available for fine-grained tourism analysis as well (level of attractions and points of interest). In this article, we present our fine-grained 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

Experiment data files

This data is associated with the following publication:

Fine-grained tourism prediction: Impact of social and environmental features

Published in: Information Processing and Management

Latest version

  • Version 1

    2019-06-21

    Published: 2019-06-21

    DOI: 10.17632/t7bfhtzhxg.1

    Cite this dataset

    Khatibi, Amir; Couto da Silva, Ana Paula; Almeida, Jussara; Gonçalves, M.A. (2019), “FISETIO: A FIne-grained, Structured and Enriched Tourism Dataset for Indoor and Outdoor attractions”, Mendeley Data, v1 http://dx.doi.org/10.17632/t7bfhtzhxg.1

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Categories

Tourism, Climate Data, Social Media Analytics

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CC BY 4.0 Learn more

The files associated with this dataset are licensed under a Creative Commons Attribution 4.0 International licence.

What does this mean?

This dataset is licensed under a Creative Commons Attribution 4.0 International licence. What does this mean? You can share, copy and modify this dataset so long as you give appropriate credit, provide a link to the CC BY license, and indicate if changes were made, but you may not do so in a way that suggests the rights holder has endorsed you or your use of the dataset. Note that further permission may be required for any content within the dataset that is identified as belonging to a third party.

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