Using Big Data to Measure Cultural Tourism in Europe with Unprecedented Precision
SSRN Electronic Journal
2024
- 379Usage
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
International tourism statistics are notorious for being over-aggregated, lacking information about the tourist, available with a lag, and often provided only at the annual level. In response to this, we suggest a unique complementary approach that is computer-science driven and relies on big data collected from a leading travel portal. The novel approach enables us to obtain a systematic, consistent, and reliable approximation for tourism flows, and this with unparalleled precision, frequency, and depth of information. Our approach delivers also an unprecedented list of all tourist attractions in a country, along with data on the popularity and quality of these attractions. We provide validity tests of the approach pursued and present one application of the data by illuminating the patterns and changes in travel flows in selected European destinations during and after the Covid-19 pandemic. This project opens a range of new research questions and possibilities for cultural economics, in particular related to cultural heritage and tourism.
Bibliographic Details
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know