| Abstract: | Abstract Taking a clue from classic time-series decomposition, this article demonstrates a spatial filtering and search technique that permits the partitioning of a measure of marginality, here measured as the percent of the population living at less than 50 percent of the poverty level, into macro, meso, and micro components. This approach supports theory that has argued for scale-specific explanations of spatial marginality. The technique also offers promise for many other types of investigations such as disease incidence, microclimate dynamics, and consumer preferences. |
