nni
Average Nearest Neighbor Index (NNI)
Calculates the NNI as a measure of clustering or dispersal
The nearest neighbor index is expressed as the ratio of the observed distance divided by the expected distance. The expected distance is the average distance between neighbors in a hypothetical random distribution. If the index is less than 1, the pattern exhibits clustering; if the index is greater than 1, the trend is toward dispersion or competition. The Nearest Neighbor Index is calculated as:
Mean Nearest Neighbor Distance (observed) D(nn) = sum(min(Dij)/N)
Mean Random Distance (expected) D(e) = 0.5 SQRT(A/N)
Nearest Neighbor Index NNI = D(nn)/D(e) Where; D=neighbor distance, A=Area
Usage
nni(x, win = "hull")Arguments
- x
An sp point object
- win
Type of window 'hull' or 'extent'
Value
list object containing NNI = nearest neighbor index, z.score = Z Score value, p = p value, expected.mean.distance = Expected mean distance, observed.mean.distance = Observed meand distance.
References
Clark, P.J., and F.C. Evans (1954) Distance to nearest neighbour as a measure of spatial relationships in populations. Ecology 35:445-453
Cressie, N (1991) Statistics for spatial data. Wiley & Sons, New York.
Examples
# NOT RUN {
require(sp)
data(meuse)
coordinates(meuse) <- ~x+y
nni(meuse)
# }