Advances of Spatio-Temporal Clustering For Evaluating The Interaction Between Tourism And Environment

Nowadays, there is a growing interest to assess sustainability, which is assuming a key role in various socio-economic areas. In this context, the tourism sector, which is an important factor in terms of economic and social development, has a significant impact on the environment and contributes to climate change. For this reason, there is more and more attention on any forms of sustainable and green tourism and consequently on studies that combine the two underlying domains, that is the tourism growth and the environment safeguard. The aim of this work is to develop a methodological framework for a spatio-temporal clustering model to investigate the in- teraction between tourism and environmental factors in Italy, as well as the pattern recognition in order to reduce the intrinsic heterogeneity of Italian territory. In particular, an innovative boos- trap spatio-temporal clustering is proposed, where the Ward hierarchical clustering algorithm that includes spatial and temporal constraints is included. The space-time component is built as a combination of a spatial distance matrix and a temporal distance matrix based on the con- cept of a space-time metric. This new unsupervised algorithm can be performed for time series that may dier in length. The results obtained by using the proposed procedure are illustrated through a real data set characterized by several tourism and environmental indicators, taken during a five-year period for the administrative regions (NUTS2 level) of Italy. The proposed methodology aims to guide and support the development of regionally tailored environmental policies.