First author: East Malling Research, New Road, East Malling, Kent ME19 6BJ, UK; and second author: Department of Plant Pathology, Ohio State University, Wooster 44691
Go to article:
Accepted for publication 30 March 2005.
The SADIE (spatial analysis by distance indices) methodology for data analysis is a useful approach for quantifying the patterns of organisms (in terms of patches and gaps) and testing for randomness of the patterns. We investigated the interrelationship among key SADIE indices: index for distance to regularity for a data set (Ia), a global measure of aggregation or clustering; the local clustering indices (vi and vj), scaled distances to regularity for each individual sampling unit; and the averages of vi and vj across all sampling units, which are additional global measures of aggregation. We demonstrated that vi and vj are mathematically related to Ia and showed conditions when Ia and mean local clustering indices give very similar results. Overall differences in average vi and Ivj I values, and between Ia and these averages, decreased with increasing size of the sampling grid in a simulation study. This was because one component of vi and vj (iY)—a measure of the distance to regularity under randomness for a given location (not a given count)—was found generally to vary little with location, except for locations near corners of the sampling grid. Nevertheless, because distance to regularity for individual observed counts was location-dependent, and this location effect varied with the observed counts value as well, a new-scaled index for each count × location combination may be warranted. The implications of these findings on epidemiological research are discussed.
index to regularity
© 2005 The American Phytopathological Society