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This is an exercise I use in an upper-division geomorphology course to introduce students to nearest-neighbor analysis, a basic technique in spatial statistics. Nearest-neighbor analysis is a method of comparing the observed average distance between points and their nearest neighbor to the expected average nearest-neighbor distance in a random pattern of points. The pattern of points on a map or 2-D graph can be classified into three categories: CLUSTERED, RANDOM, REGULAR. Nearest-neighbor analysis provides an objective method for distinguishing among these possible spatial distributions. The technique also produces a population statistic, the nearest-neighbor index, which can be compared from area to area. In general, nearest-neighbor analysis can be applied to any geoscience phenomenon or feature whose spatial distribution can be categorized as a point pattern. The basic distance data can come from topographic maps, aerial photographs, or field measurements. The exercise presented here applies this technique to the study of karst landforms on topographic maps, specifically the spatial distribution of sinkholes. The advantages of introducing nearest-neighbor analysis in an undergraduate lab is that: (1) it reinforces important concepts related to data collection (e.g significant figures), map use (e.g. scale and the UTM grid), and basic statistics (e.g. hypothesis testing); (2) the necessary calculations are easily handled by most students; and (3) once learned, the technique can be widely applied in geoscience problem-solving. Designed for a geomorphology course Addresses student fear of quantitative aspect and/or inadequate quantitative skills
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