JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. Clark University is collaborating with JSTOR to digitize, preserve and extend access to Economic Geography. Spatial filtering consists of those operations on two-dimensional data whose purpose is the detection of a known or hypothesized spatial pattern which is obscured by other information, that is, the detection of a spatial signal in the presence of noise. These operations are generally space-invariant in that their effect on a data point does not depend upon its coordinates in the data array. They may be applied locally, to only immediately adjacent values, or to a considerably larger set. This paper considers some procedures and applications of spatial filtering which are potentially useful in geographical analysis. These have been drawn largely from literature in geophysics, electrical engineering, computer science, pattern recognition, and applied physics. This is not to say that no work has been done by geographers in this field. Curry [8] and Casetti [6] have pointed out that much geographic data are acquired in a way that imposes a filter on reality which may significantly bias subsequent analyses. Discussions of spatial sampling problems such as that by Berry and Baker [3] are concerned with the filtering effect of sampling strategies. Tobler [33] has experimented with spatial filtering of digitized terrain data.However, spatial filtering is certainly not considered among the standard set of tools of quantitative geography, and is probably considered by many to be somewhat irrelevant even as a specialized technique. The premises of this paper are that the technique is indeed very relevant and useful, and that it has not been more widely applied simply because most geographers have not heard of it; those who have encounter substantial data and computing requirements and, more important, find they