| Abstract: |
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. |