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Activity Number: 171 - New Nonparametric Methods for Correlated Data
Type: Contributed
Date/Time: Monday, July 30, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract #330258 Presentation
Title: Spatial Clustering Using Spatio-Temporal Network Data
Author(s): Ashwini Venkatasubramaniam* and Ludger Evers and Konstantinos Ampountolas
Companies: University of Glasgow and University of Glasgow and University of Glasgow
Keywords: clustering; spatial; network; temporal
Abstract:

We propose a flexible Bayesian nonparametric clustering model for spatio-temporal data recorded using a network of sensors to identify spatially contiguous clusters. This method employs a distance dependent Chinese restaurant process (ddCRP) to accommodate the geographical constraints imposed by the network and account for the presence of limited connectivity between components of the undirected graph. We modify the non-sequential ddCRP to reduce the number of singletons by controlling the number of redundant and self-links and also utilise a Metropolis within Gibbs sampler to fully explore the space of potential cluster structures in the presence of spatial and network dependencies. This model is motivated by an application to a grid-style urban road network in downtown San Francisco, where a unique observation is assumed to be present for every space and time combination and we illustrate this model with an application to both real and simulated data.


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