Title: Fuzzy clustering with spatial-time information
Authors: Pierpaolo Durso - University of Rome Sapienza (Italy)
Marta Disegna - Faculty of Management, Bournemouth University (United Kingdom) [presenting]
Riccardo Massari - Sapienza University (Italy)
Abstract: Clustering geographical units based on a set of quantitative features observed at several time occasions requires dealing with the complexity of both space and time information. In particular, one should consider (1) the spatial nature of the units to be clustered, (2) the characteristics of the space of multivariate time trajectories, and (3) the uncertainty related to the assignment of a geographical unit to a given cluster on the basis of the above complex features. Existing spatial-time clustering models can be distinguished into non-spatial time series clustering based on a spatial dissimilarity measure; spatially constrained time series clustering; density-based clustering; model-based clustering. The aim is to discuss a novel spatially constrained multivariate time series clustering for units characterised by different levels of spatial contiguity. In particular, the Fuzzy Partitioning Around Medoids algorithm with Dynamic Time Warping distance and spatial penalization terms is applied to classify multivariate time series. This clustering algorithm has been theoretically presented and discussed through different simulation examples, highlighting its main advantages. A real case study has been presented to illustrate the usefulness and effectiveness of the suggested clustering method for tourism spatial-time series, especially in the identification of the spatial tourism spill-over effect.