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

Spatial data aggregation is widely practiced for "scaling-up" environmental analyses and modeling from local to regional or global scales. Despite ack nowledgments of the general effects of aggregation, there is a lack of syst ematic comparison between aggregation methods. The study evaluated three me thods - averaging, central-pixel resampling, and median using simulated ima ges. Both the averaging and median methods can retain the mean and median v alues, respectively, but alter significantly the standard deviation. The ce ntral-pixel method alters both statistics. The statistical changes can be m odified by the presence of spatial autocorrelation for all three methods. S patially, the averaging method can reveal underlying spatial patterns at sc ales within the spatial autocorrelation ranges. The median method produces almost identical results because of the similarities between the averaged a nd median values of the simulated data. To a limited extent, the central-pi xel method retains contrast and spatial patterns of the original images. At scales coarser than the autocorrelation range, the averaged and median ima ges become homogeneous and do not differ significantly between these scales . The central-pixel method can induce severe spatially biased errors at coa rse scales. Understanding these trends can help select appropriate aggregat ion methods and aggregation levels for particular applications.