Understanding Intra-Destination Tourist Behaviors as a Tentative Strategy to Mitigate Overtourism in Florence

Abstract

Tourism is not only a movement of people, but also a key source of income for local and national governments. However, when visitor flows are uncontrolled, they can threaten the sustainability of cities, as is happening in many tourist destinations around
the world, and particularly in Italy. In this case, the term “overtourism” has become increasingly relevant to describe the negative impact of tourism on society and the environment.
To address the challenges posed by overtourism, policy-makers need to adopt information-based strategies driven by data analysis. In this contribution, we show how statistical tools can help uncover valuable insights from data on tourist behavior, such as those collected automatically through museum pass systems in cultural cities. In particular, we analyze data from Florence, focusing on the sequences of museum visits recorded via the FirenzeCard, the pass that grants access to the city’s museums and exhibitions.
We apply a latent class item response model to identify homogeneous groups of tourists with similar museum preferences. This approach provides policy-makers with a practical tool to gain a deeper understanding of tourists’ decision-making processes and to design targeted promotional strategies tailored to specific tourist profiles.