An Efficient Approach to Spatiotemporal Analysis and Modeling of Air Pollution Data |
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| Authors: | Georgios Tsiotas Athanassios A Argiriou |
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| Institution: | (1) Department of Economics, University of Crete, Panepistimioupolis, Rethymnon, 74100, Greece;(2) Department of Physics, Section of Applied Physics, University of Patras, 265 00 Patras, Greece |
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| Abstract: | A statistically efficient approach is adopted for modeling spatial time-series of large data sets. The estimation of the main
diagnostic tool such as the likelihood function in Gaussian spatiotemporal models is a cumbersome task when using extended
spatial time-series such as air pollution. Here, using the Innovation Algorithm, we manage to compute it for many spatiotemporal
specifications. These specifications refer to the spatial periodic-trend, the spatial autoregressive moving average, the spatial
autoregressive integrated and fractionally integrated moving average Gaussian models. Our method is applied to daily pollutants
over a large metropolitan area like Athens. In the applied part of our paper, we first diagnose temporal and spatial structures
of data using non-likelihood based criteria, such as the empirical autocorrelation and covariance functions. Second, we use
likelihood and non-likelihood based criteria to select a spatiotemporal model among various specifications. Finally, using
kriging we regionalize the resulting parameter estimates of the best-fitted model in space at any unmonitored location in
the Athens region. The results show that a specific autoregressive integrated moving average spatiotemporal model can optimally
perform in within and out of spatial sample estimation. Supplemental materials for this article are available from the journal
website. |
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