As spatial autocorrelation latent in georeferenced data increases, the amount of duplicate information contained in these data also increases. This property suggests the research question asking what the number of independent observations, ay n*, is that is equivalent o the sample size, n, of a data set. This is the notion of effective sample size. Intuitively speaking, when zero spatial autoco...

The spatial autocorrelation of allelic frequencies and geographical distances were studied at a natural population of Codium fragile in Korea. Within the C. fragile population, a strong spatial structure was observed for allozyme markers, indicating a very low amount of migration among subpopulations. The species showed a significant positive spatial autocorrelation as measured by Moran's I. Ge...

The computation of Moran’s index of spatial autocorrelation requires the definition of a spatial weighting matrix. The eigendecomposition of this doubly centered matrix (i.e., one that forces the sums of all rows and columns to equal zero) has interesting properties that have been exploited in various contexts: distribution properties of the Moran coefficient (MC), spatial filtering in linear m...

Spatial autocorrelation occurs when values of a variable sampled at nearby locations are more similar than those sampled at locations more distant from each other. Spatial autocorrelation can occur at multiple spatial scales or vary with spatial orientation (cardinal direction). It is common in ecological data. In landscape ecology research, scientists often are interested in testing the statis...

Consider a linear model for a spatial system of n areal units, (1) , ~ (0,) Y X u u N V    where both  and V are unknown. The simplest procedure for specifying the covariance matrix, V , is to start by assuming that (2) 2 n V I   , so that  can be estimated by OLS. Given this estimate, one can then test to see whether there is sufficient spatial autocorrelation in the residuals to warran...