Dynamic model-based clustering for spatio-temporal data
| Autor: | Francesco Finazzi, Lucia Paci |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Statistics and Probability
010504 meteorology & atmospheric sciences Bayesian probability Bayesian analysis Inference Context (language use) computer.software_genre 01 natural sciences Theoretical Computer Science 010104 statistics & probability symbols.namesake Finite mixture models Markov chain Monte Carlo State-space modeling Cluster (physics) Statistics Probability and Uncertainty Computational Theory and Mathematics 0101 mathematics Cluster analysis 0105 earth and related environmental sciences Mathematics Statistics Mixture model Temporal database Settore SECS-S/01 - STATISTICA symbols Probability and Uncertainty Data mining Settore SECS-S/02 - Statistica per La Ricerca Sperimentale e Tecnologica computer |
| Popis: | In many research fields, scientific questions are investigated by analyzing data collected over space and time, usually at fixed spatial locations and time steps and resulting in geo-referenced time series. In this context, it is of interest to identify potential partitions of the space and study their evolution over time. A finite space-time mixture model is proposed to identify level-based clusters in spatio-temporal data and study their temporal evolution along the time frame. We anticipate space-time dependence by introducing spatio-temporally varying mixing weights to allocate observations at nearby locations and consecutive time points with similar cluster’s membership probabilities. As a result, a clustering varying over time and space is accomplished. Conditionally on the cluster’s membership, a state-space model is deployed to describe the temporal evolution of the sites belonging to each group. Fully posterior inference is provided under a Bayesian framework through Monte Carlo Markov chain algorithms. Also, a strategy to select the suitable number of clusters based upon the posterior temporal patterns of the clusters is offered. We evaluate our approach through simulation experiments, and we illustrate using air quality data collected across Europe from 2001 to 2012, showing the benefit of borrowing strength of information across space and time. |
| Databáze: | OpenAIRE |
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