Abstract

This study derived the equations for computing the spatial variability in the aggregation of original maps of continuous attributes. The derivation of the equations is based on traditional statistical and geostatistical principles. The derived equations can be used to compute the variance, covariance, and spatial (auto-/cross-) covariance of the aggregated pixels and sub-areas in a given study area. Using the derived equations, the total uncertainty within a study area will not change after aggregation. For a case study, it has been shown that aggregation will reduce the values of variance/covariance and spatial covariance of the aggregated individual pixels. It was also verified that the original semivariogram models should not be used for the aggregated maps to compute spatial covariances. It is suggested to use the original scales in geostatistical analyses to produce maps and then produce courser scaled maps through aggregation.

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Apr 24th, 3:10 PM

SPATIAL VARIABILITY IN AGGREGATION BASED ON GEOSTATISTICAL ANALYSIS

This study derived the equations for computing the spatial variability in the aggregation of original maps of continuous attributes. The derivation of the equations is based on traditional statistical and geostatistical principles. The derived equations can be used to compute the variance, covariance, and spatial (auto-/cross-) covariance of the aggregated pixels and sub-areas in a given study area. Using the derived equations, the total uncertainty within a study area will not change after aggregation. For a case study, it has been shown that aggregation will reduce the values of variance/covariance and spatial covariance of the aggregated individual pixels. It was also verified that the original semivariogram models should not be used for the aggregated maps to compute spatial covariances. It is suggested to use the original scales in geostatistical analyses to produce maps and then produce courser scaled maps through aggregation.