The optimal choice of sites to make spatial prediction is critical for a better understanding of really spatio-temporal data. It is important to obtain the essential spatio-temporal variability of the process in determining optimal design, because these data tend to exhibit both spatial and temporal variability. Two new methods of prediction for spatially correlated functional data are considered. The first method models spatial dependency by fitting variogram to empirical variogram, similar to ordinary kriging (univariate approach). The second method models spatial dependency by linear model co-regionalization (multivariate approach). The variance of prediction method was chosen as the optimization design criterion. An application to CO concentration forecasting was conducted to examine possible differences between the design and the optimal design without considering temporal structure.
Rasekhi, Mahdi; Jamshidi, B.; and Rivaz, F.
"Optimal Location Design for Prediction of Spatial Correlated Environmental Functional Data,"
Journal of Modern Applied Statistical Methods:
2, Article 26.
Available at: http://digitalcommons.wayne.edu/jmasm/vol13/iss2/26