Description Usage Arguments Value Note Author(s) References Examples

Calculates the NNI as a measure of clustering or dispersal

1 | ```
nni(x, win = "hull")
``` |

`x` |
An sp point object |

`win` |
Type of window 'hull' or 'extent' |

list object contanint NNI = nearest neighbor index, z.score = Z Score value, p = p value, expected.mean.distance = Expected meand distance, observed.mean.distance = Observed meand distance.

The nearest neighbour index is expressed as the ratio of the observed distance divided by the expected distance. The expected distance is the average distance between neighbours 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 Neighbour Index is calculated as: Mean Nearest Neighbour Distance (observed) D(nn) = sum(min(Dij)/N) Mean Random Distance (expected) D(e) = 0.5 SQRT(A/N) Nearest Neighbour Index NNI = D(nn)/D(e) Where; D=neighbour distance, A=Area

Depends: sp, spatstat

Jeffrey S. Evans <[email protected]>

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.

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