This research area focuses on developing and validating Big Data-driven machine learning methods to enhance the sustainability of regional tourism.
On the supply side, a monitoring system is developed by extracting data from online platforms such as Google Maps, which represent the entire regional tourism offer based on self-reported information. This enables visualisation of tourism distribution patterns on digital maps and the identification of key segments for sustainability strategies (e.g., proximity tourism, connections with culture, arts, and the creative economy, local food production).

On the demand side, geo-localised online reviews are analysed using big data and business intelligence applications to monitor tourisms. Additionally, predictive modeling methods are applied to estimate tourist mobility behavior and movement patterns between destinations based on geo-tagged feedback data from online platforms. This helps anticipate and manage visitor flows, personalise routes, and reduce congestion.

The status of projects in this research area is continuously updated.