An important aspect of statistical modeling of spatial or spatiotemporal data is to determine the covariance function. It is a key part of spatial prediction (kriging). The classical geostatistical approach uses an assumption of isotropy, which yields circular isocorrelation curves. However, this is inappropriate for many applications, and several nonstationary approaches have been developed. Adding the temporal aspect, there is often interaction between time and space, requiring classes of nonseparable covariance structures. WIREs Comput Stat 2013, 5:279–287. doi: 10.1002/wics.1259
Conflict of interest: The authors have declared no conflicts of interest for this article.
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