Morisita Index Plot
Displays the Morisita Index Plot of a spatial point pattern.
miplot(X, ...)
X |
A point pattern (object of class |
... |
Optional arguments to control the appearance of the plot. |
Morisita (1959) defined an index of spatial aggregation for a spatial point pattern based on quadrat counts. The spatial domain of the point pattern is first divided into Q subsets (quadrats) of equal size and shape. The numbers of points falling in each quadrat are counted. Then the Morisita Index is computed as
MI = Q * sum(n[i] (n[i]-1))/(N(N-1))
where n[i] is the number of points falling in the i-th
quadrat, and N is the total number of points.
If the pattern is completely random, MI should be approximately
equal to 1. Values of MI greater than 1 suggest clustering.
The Morisita Index plot is a plot of the Morisita Index
MI against the linear dimension of the quadrats.
The point pattern dataset is divided into 2 * 2
quadrats, then 3 * 3 quadrats, etc, and the
Morisita Index is computed each time. This plot is an attempt to
discern different scales of dependence in the point pattern data.
None.
Adrian Baddeley Adrian.Baddeley@curtin.edu.au
and Rolf Turner r.turner@auckland.ac.nz
M. Morisita (1959) Measuring of the dispersion of individuals and analysis of the distributional patterns. Memoir of the Faculty of Science, Kyushu University, Series E: Biology. 2: 215–235.
data(longleaf) miplot(longleaf) opa <- par(mfrow=c(2,3)) data(cells) data(japanesepines) data(redwood) plot(cells) plot(japanesepines) plot(redwood) miplot(cells) miplot(japanesepines) miplot(redwood) par(opa)
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