B0405
Title: Leveraging covariates in Bayesian nonparametric clustering: An application to transportation networks
Authors: Valentina Ghidini - Bocconi University (Italy)
Sirio Legramanti - University of Bergamo (Italy) [presenting]
Raffaele Argiento - Università degli Studi di Bergamo (Italy)
Abstract: In clustering, observed individual data are often accompanied by covariates that can assist the clustering process itself. This is the case, for example, of transportation networks, where each node has spatial coordinates, and it is often desirable that clusters of nodes are spatially cohesive. In fact, the obtained clusters may be used to inform public policy decisions, and it may be preferable that such policies are uniform over neighbouring areas. Naturally, depending on the application, different notions of closeness can be used to define such neighbourhoods, thus potentially requiring proper transformations of the spatial covariates. Motivated by real-world data about the monthly subscriptions to the public transportation system of the Bergamo province (Italy), it is shown how to incorporate properly-transformed spatial covariates into a state-of-the-art stochastic block model, while allowing to weight the contribution of each covariate.
