spatstat (version 1.11-4)

copper: 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, 2005), Foxall and Baddeley (2002) and Baddeley et al (2005). Many analyses have been performed on the southern half of the data only. This subset is also provided.

Usage

data(copper)

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. Baddeley, A., Turner, R., Moller, J. and Hazelton, M. (2005) Residual analysis for spatial point processes. Journal of the Royal Statistical Society, Series B 67, 617--666.

Baddeley, A. and Turner, R. (2005) Modelling spatial point patterns in R. In: A. Baddeley, P. Gregori, J. Mateu, R. Stoica, and D. Stoyan, editors, Case Studies in Spatial Point Pattern Modelling, Lecture Notes in Statistics number 185. Pages 23--74. Springer-Verlag, New York, 2006. ISBN: 0-387-28311-0.

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$Points)
  plot(copper$Lines, add=TRUE)

  # Plot southern half of data
  plot(copper$SouthPoints)
  plot(copper$SouthLines, add=TRUE)

  Z <- distmap(copper$SouthLines)
    plot(Z)
    X <- copper$SouthPoints
    ppm(X, ~D, covariates=list(D=Z))