Time-space Kriging to address the spatiotemporal misalignment in the large datasets.

ABSTRACT: This paper presents a Bayesian hierarchical spatiotemporal method of interpolation, termed as Markov Cube Kriging (MCK). The classical Kriging methods become computationally prohibitive, especially for large datasets due to the O(n³) matrix decomposition. MCK offers novel and computationally efficient solutions to address spatiotemporal misalignment, mismatch in the spatiotemporal scales and missing values across space and time in large spatiotemporal datasets. MCK is flexible in that it allows for non-separable spatiotemporal structure and nonstationary covariance at the hierarchical spatiotemporal scales. Employing MCK we developed estimates of daily concentration of fine particulates matter ≤2.5 μm in aerodynamic diameter (PM_2.5) at 2.5 km spatial grid for the Clev

SUBMITTER: Liang D 

PROVIDER: S-EPMC3768020 | biostudies-literature | 2013 Jun

REPOSITORIES: biostudies-literature