Pattern Analysis Based on Type, Orientation, Size, and Shape

Quantifying the pattern of geographically defined areas has proven useful in analyzing and understanding geographic processes. Pattern analyses most commonly used by geographers measure the spatial distribution of nonspatial variables at each point or in each area. The objective of this research is to develop and assess a pattern analysis for area-class maps that considers more than the nonspatial attributes when describing a spatial pattern. We examine whether similar geographic areas—similar with respect to attribute type and several geometric properties—exhibit particular spatial patterns (e.g., clustered, random, or dispersed). The additional attributes we include are orientation, size, and shape. We refer to the method as TOSS, reflecting the T ype, O rientation, S ize, and S hape of areas. The goal of considering these additional attributes is to capture the geometric aspects of pattern that other approaches to pattern analysis do not. Sets or groups of similar areas are created using cluster analysis, based on a variable standardization score using the Gower coefficient of similarity. The spatial distributions for each set of geographic objects are then calculated using nearest neighbor analysis. To test the effectiveness of the TOSS method, results from pattern analysis and the TOSS method are qualitatively and quantitatively compared. The TOSS method offers a novel approach to pattern analysis.