Comparing effects of Aggregation methods on statistical and spatial properties of simulated spatial data

摘要

Spatial data aggregation is widely practiced for "scaling-up" environmental analyses and modeling from local to regional or global scales. Despite acknowledgments of the general effects of aggregation, there is a lack of systematic comparison between aggregation methods. The study evaluated three methods - averaging, central-pixel resampling, and median - using simulated images. Both the averaging and median methods can retain the mean and median values, respectively, but alter significantly the standard deviation. The central-pixel method alters both statistics. The statistical changes can be modified by the presence of spatial autocorrelation for all three methods. Spatially, the averaging method can reveal underlying spatial patterns at scales within the spatial autocorrelation ranges. The median method produces almost identical results because of the similarities between the averaged and median values of the simulated data. To a limited extent, the central-pixel method retains contrast and spatial patterns of the original images. At scales coarser than the autocorrelation range, the averaged and median images become homogeneous and do not differ significantly between these scales. The central-pixel method can induce severe spatially biased errors at coarse scales. Understanding these trends can help select appropriate aggregation methods and aggregation levels for particular applications.