copper {spatstat}R Documentation

Berman-Huntington points and lines data

Description

These data come from an intensive geological survey of a 70 x 158 km region in central Queensland, Australia. They consist of 67 points representing copper ore deposits, and 146 line segments representing geological `lineaments'. Lineaments are linear features, visible on a satellite image, that are believed to consist largely of geological faults (Berman, 1986, p. 55). It would be of great interest to predict the occurrence of copper deposits from the lineament pattern, since the latter can easily be observed on satellite images.

These data were introduced and analysed by Berman (1986). They have also been studied by Berman and Turner (1992), Baddeley and Turner (2000) and Foxall and Baddeley (2002).

Many analyses have been performed on the southern half of the data only. This subset is also provided.

Usage

data(copper)

Format

copper is a list with the following entries:

points
an object of class "ppp" representing the full point pattern of copper deposits. See ppp.object for details of the format.
lines
the coordinates of the lineaments in the full dataset, given as a data frame with 4 columns (x1, y1, x2, y2).
SouthWindow
the window delineating the southern half of the study region. An object of class "owin".
SouthPoints
the point pattern of copper deposits in the southern half of the study region. An object of class "ppp".
SouthLines
the coordinates of the lineaments in the southern half of the study region. A data frame with 4 columns in format (x1, y1, x2, y2).
SouthDistance
A function with no arguments. The return value is a pixel image with greyscale value equal to the distance to the nearest lineament. Computed only inside the southern half of the dataset. An object of class "im".

Source

Dr J. Huntington. Coordinates kindly provided by Dr. Mark Berman and Dr. A. Green, CSIRO, Sydney, Australia.

References

Baddeley, A. and Turner, R. (2000) Practical maximum pseudolikelihood for spatial point patterns. Australian and New Zealand Journal of Statistics 42, 283–322.

Berman, M. (1986). Testing for spatial association between a point process and another stochastic process. Applied Statistics 35, 54–62.

Berman, M. and Turner, T.R. (1992) Approximating point process likelihoods with GLIM. Applied Statistics 41, 31–38.

Foxall, R. and Baddeley, A. (2002) Nonparametric measures of association between a spatial point process and a random set, with geological applications. Applied Statistics 51, 165–182.

Examples


  data(copper)

  # Plot full dataset

  plot(copper$points)
  cl <- copper$lines
  segments(cl[,1], cl[,2], cl[,3], cl[,4])

  # Plot southern half of data
  plot(copper$SouthPoints)
  cl <- copper$SouthLines
  segments(cl[,1], cl[,2], cl[,3], cl[,4])

  ## Not run: 
    Z <- copper$SouthDistance()
    plot(Z)
    X <- copper$SouthPoints
    ppm(X, ~D, covariates=list(D=Z))
  
## End(Not run)

[Package spatstat version 1.6-8 Index]