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Measuring sustainable tourism with online platform data.
Felix J Hoffmann1, Fabian Braesemann2,3, Timm Teubner1
1Trust in Digital Services, Technische Universität Berlin, 10623 Berlin, Germany.
EPJ Data Science
|July 25, 2022
Summary
This study explores using online platform data for sustainable tourism statistics. Machine learning accurately predicts accommodation sustainability, offering a cost-effective global tracking method.
Area of Science:
- Environmental Science
- Data Science
- Tourism Studies
Background:
- Sustainable tourism is crucial for the UN Sustainable Development Goals.
- Balancing tourism's economic, environmental, and social impacts requires robust data.
- Current sustainable tourism statistics may lack sufficient detail or timeliness.
Purpose of the Study:
- To investigate the utility of online platform data for sustainable tourism statistics.
- To assess the feasibility of using web-scraped data to measure accommodation sustainability.
- To explore machine learning applications in generating tourism sustainability metrics.
Main Methods:
- Web scraping data from a major online tourism platform.
- Development and application of machine learning models.
- Algorithmic prediction of sustainability labels for accommodations.
Main Results:
- Machine learning techniques can predict accommodation sustainability labels with reasonable accuracy.
- Online platform data serves as a viable alternative data source for tourism statistics.
- The approach enables cost-effective and accurate tracking of tourism sustainability.
Conclusions:
- Online data and machine learning offer a promising solution for enhancing sustainable tourism statistics.
- This method provides high spatial and temporal granularity for monitoring global tourism sustainability.
- The findings support the integration of big data into sustainable development initiatives.
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