Identification of points of attraction and network analysis for GPS tracking data

Global positional system data provide accurate information on units’ movements from both the temporal and the spatial perspective. Several aspects of these movements can be analyzed according to the aim of interest. In this study, we focus on statistical methods for the identification of points of interest and for the analysis of the network of movements for GPS data. A density-based cluster algorithm is applied to summarize the vast amount of information and to find the most relevant points of attraction. A directed network synthesizes the individual unit’s path by using the latter information. Finally, we aggregate the units’ paths in a weighted directed network which is studied through network analysis. The proposed approach is applied to two case studies related to cruise passengers’ movements in urban contexts.