Analyzing tourist flows by mapping time series into networks

Time series analysis is a well-known practice in tourism, usually employed for forecasting purposes. However, a time series is also a measurable representation of the dynamic characteristics of a system. In tourism, overnight stays assume an important role as they can be seen as a determinant of destination demand influenced by the perceived characteristics of the destination and, rather obviously, strongly related to tourists’ expenditures (Sainaghi, 2012). A deep investigation in the general dynamics of their time evolution is therefore quite important in order to better understand the whole phenomenon (Barros & Machado, 2010) Given its inherent complexity, though, studying tourism by using traditional techniques can be quite challenging. The application of different complexity science methods, well known in physics, mathematics sociology and economics, but not widely used in the tourism literature, has provided already a good array of insights into the structure and the dynamic behavior of a tourism destination. The general complexity characteristics have been explored by using non-linear time series analysis techniques and by applying complex network analysis methods (Baggio, 2008; Baggio & Sainaghi, 2011; Baggio et al., 2010).