heather

0th

Percentile

Diggle's Heather Data

The spatial mosaic of vegetation of the heather plant (Calluna vulgaris) recorded in a 10 by 20 metre sampling plot in Sweden.

Keywords
datasets, spatial
Usage
data(heather)
format

A list with three entries, representing the same data at different spatial resolutions: ll{ coarse original heather data, 100 by 200 pixels medium current heather data, 256 by 512 pixels fine finest resolution data, 778 by 1570 pixels } Each of these entries is an object of class "owin" containing a binary pixel mask.

source

Peter Diggle

History of analysis of data

The data were recorded, presented and analysed by Diggle (1983). He proposed a Boolean model consisting of discs of random size with centres generated by of a Poisson point process. Renshaw and Ford (1983) reported that spectral analysis of the data suggested the presence of strong row and column effects. However, this may have been attributable to errors in the run-length encoding of the original data.

Hall (1985) and Hall (1988, pp 301-318) took a bootstrap approach.

Ripley (1988, pp. 121-122, 131-135] used opening and closing functions to argue that a Boolean model of discs is inappropriate.

Cressie (1991, pp. 763-770) tried a more general Boolean model.

References

Cressie, N.A.C. (1991) Statistics for Spatial Data. John Wiley and Sons, New York.

Diggle, P.J. (1981) Binary mosaics and the spatial pattern of heather. Biometrics 37, 531-539.

Hall, P. (1985) Resampling a coverage pattern. Stochastic Processes and their Applications 20 231-246.

Hall, P. (1988) An introduction to the theory of coverage processes. John Wiley and Sons, New York.

Renshaw, E. and Ford, E.D. (1983) The interpretation of process from pattern using two-dimensional spectral analysis: Methods and problems of interpretation. Applied Statistics 32 51-63. Ripley, B.D. (1988) Statistical Inference for Spatial Processes. Cambridge University Press.

Aliases
  • heather
Documentation reproduced from package spatstat, version 1.19-2, License: GPL (>= 2)

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