Spatially correlated data: structural analysis
DOI:
https://doi.org/10.33044/revem.41782Keywords:
Stochastic Process, Spatially Correlated Data, StationarityAbstract
We introduce and investigate the underlying theoretical framework for spatially correlated data in this article. More specifically, we characterise the mechanism that generates the data, investigate its various covariance structures, and characterise the many stationarity classes that are typically considered for this type of data. Furthermore, we investigate the theoretical and empirical semivariogram, which is likely the most extensively used tool for measuring spatial correlation. We believe that this work can be a valuable resource for the study of spatial data and its primary properties, which might be integrated into a modern statistics course.
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