Spatiotemporal clustering using Gaussian processes embedded in a mixture model

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http://hdl.handle.net/10138/335461

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Vanhatalo , J , Foster , S D & Hosack , G R 2021 , ' Spatiotemporal clustering using Gaussian processes embedded in a mixture model ' , Environmetrics , vol. 32 , no. 7 . https://doi.org/10.1002/env.2681

Title: Spatiotemporal clustering using Gaussian processes embedded in a mixture model
Author: Vanhatalo, Jarno; Foster, Scott D.; Hosack, Geoffrey R.
Contributor: University of Helsinki, Department of Mathematics and Statistics
Date: 2021-11
Language: eng
Number of pages: 19
Belongs to series: Environmetrics
ISSN: 1180-4009
URI: http://hdl.handle.net/10138/335461
Abstract: The categorization of multidimensional data into clusters is a common task in statistics. Many applications of clustering, including the majority of tasks in ecology, use data that is inherently spatial and is often also temporal. However, spatiotemporal dependence is typically ignored when clustering multivariate data. We present a finite mixture model for spatial and spatiotemporal clustering that incorporates spatial and spatiotemporal autocorrelation by including appropriate Gaussian processes (GP) into a model for the mixing proportions. We also allow for flexible and semiparametric dependence on environmental covariates, once again using GPs. We propose to use Bayesian inference through three tiers of approximate methods: a Laplace approximation that allows efficient analysis of large datasets, and both partial and full Markov chain Monte Carlo (MCMC) approaches that improve accuracy at the cost of increased computational time. Comparison of the methods shows that the Laplace approximation is a useful alternative to the MCMC methods. A decadal analysis of 253 species of teleost fish from 854 samples collected along the biodiverse northwestern continental shelf of Australia between 1986 and 1997 shows the added clarity provided by accounting for spatial autocorrelation. For these data, the temporal dependence is comparatively small, which is an important finding given the changing human pressures over this time.
Subject: clustering
community ecology
Gaussian process
Laplace approximation
mixture
regions of common profiles
spatial
spatiotemporal
DEMERSAL FISH
SPATIAL DATA
CLASSIFICATION
INFERENCE
SELECTION
111 Mathematics
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