Academic journal article Geographical Analysis

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

Academic journal article Geographical Analysis

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

Article excerpt

Introduction

Understanding land use patterns can provide insight into the spatial processes that created these patterns. Fu and Chen (2000) researched the spatial pattern of agriculture in northern China in an attempt to determine the required landscape diversity for controlling soil erosion. Their results prompted an increase in vegetation land covers to aid in erosion control. Ripple, Bradshaw, and Spies (1991) used spatial indices of patch size, shape, abundance, and spacing to quantify the landscape of managed forest areas in the western Cascades of Oregon. They suggested that such a quantification of the spatial distribution of forest stands is important when evaluating and managing wildlife habitats.

Insight into characteristics of pattern, as the above examples illustrate, help answer questions related to spatial scale, spatial heterogeneity, boundaries, and incomplete data. Understanding and quantifying pattern also lends insight into spatial processes and changes in these processes that may have led to the existing patterns. Pattern analysis is commonly performed with a metric that describes the spatial distribution of a nonspatial variable of interest (e.g., an attribute) using the terminology clustered, random, or dispersed (also called regular). Some researchers use a combination of indices, such as patch size, shape, fragmentation, and fractals to quantify patterns on the landscape, but the metrics are evaluated separately (Ripple, Bradshaw, and Spies 1991; Remmel and Csillag 2003). No known approach exists that evaluates whether similar geographic sets from area-class maps--similar with respect to attribute type and several geometric properties--exhibit particular spatial patterns.

Humans can discern patterns based on visual structures, such as the arrangement of different shapes. Picture a quilt. Shapes, such as squares and triangles, are combined in different ways thus creating a visual pattern. Also involved in the creation of quilts is the orientation of the shapes. The quilt may contain pieces of various sizes, adding to the complexity of the overall pattern. These pieces also range in color, a characteristic geographically referred to as the attribute or type (e.g., land use type, soil type, geology type). The unique combination of quilt pieces with varying sizes, shapes, orientations, and colors generates different patterns.

The objective of this research is to develop and assess a pattern analysis approach for area-class maps that considers the distribution of orientation, size, shape, and attribute type. Area-class maps represent data-driven patterns, such as those created by soil type or land cover mapping and unlike those created by census tracts or political boundaries (Bunge 1966; Mark and Csillag 1989; Chrisman 2002). The assumption for area-class maps is that homogenously defined areas arise because of a stationary process across the study area (Boots 2003). Frequently, the data are represented as an irregular tessellation with categorical attributes, such as vegetation cover. Other times, the map is composed of discrete objects, such as lakes. The method could be applied to determine if lakes with similar geometric configurations tend to form a pattern, which could suggest a similar process occurred in their creation (e.g., the Finger Lakes in upstate New York that were formed due to continental glaciers).

We examine whether similar geographic areas--similar with respect to attribute type and geometric properties--exhibit a spatial pattern (e.g., clustered, random, or dispersed). Li and Reynolds (1993, 1994, 1995), Gustafson (1998), Remmel and Csillag (2003), and Boots (2003) defined pattern with two components: composition (e.g., the categories in a landscape) and their configuration (e.g., how the categories are distributed). We expand on this conceptually by considering orientation, size, and shape as additional components to composition of pattern. …

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