Geographical Filters and their Inverses*

Science proceeds by detecting structures embedded in observational data. It is no simple matter to separate these general structures from the specific details. Many analytical techniques have been designed to do just this; to separate the important from the unimportant. Among the best known of these are statistical methods of accounting for the variability in the observations. The popularity of techniques which yield ordered results (stepwise regression, principal components analysis, or clustering methods, for example) thus becomes clear.