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An Efficient Approach to Spatiotemporal Analysis and Modeling of Air Pollution Data.
- Source :
- Journal of Agricultural, Biological & Environmental Statistics (JABES); Sep2011, Vol. 16 Issue 3, p371-388, 18p
- Publication Year :
- 2011
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- AIR pollution
GAUSSIAN processes
KRIGING
URBAN pollution
TIME series analysis
Subjects
Details
- Language :
- English
- ISSN :
- 10857117
- Volume :
- 16
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- Journal of Agricultural, Biological & Environmental Statistics (JABES)
- Publication Type :
- Academic Journal
- Accession number :
- 65137019
- Full Text :
- https://doi.org/10.1007/s13253-011-0057-7