These data represent the locations of 195 seedlings and saplings of California Giant Redwood (Sequoiadendron giganteum) in a square sampling region.
They were described and analysed by Strauss (1975).
  This is the ``full'' dataset; most writers have
  analysed a subset extracted by Ripley (1977)
  which is available as redwood.
Strauss (1975) divided the sampling region into two subregions I and II demarcated by a diagonal line. The spatial pattern appears to be slightly regular in region I and strongly clustered in region II.
Strauss (1975) writes: “It was felt that the seedlings would be scattered fairly randomly, except that a number of tight clusters would form around some of the redwood tree stumps present in the plot. A discontinuity in the soil, very roughly demarked by the diagonal line in the figure, was expected to cause a difference in clustering behaviour between regions I and II. Moreover, almost all the redwood stumps were situated in region II.”
The dataset redwoodfull contains the full point pattern
  of 195 trees. 
  The window has been rescaled to the unit square.
  Its physical size is approximately 130 feet across.
The auxiliary information about the subregions is contained in 
  redwoodfull.extra, which is a list with entries
| rdiag | The coordinates of the diagonal boundary | 
| between regions I and II | |
| regionI | Region I as a window object | 
| regionII | Region II as a window object | 
| regionR | Ripley's subrectangle (approximate) | 
| plotit | Function to plot the full data and auxiliary markings | 
Ripley (1977) extracted a subset of these data, containing 62 points,
  lying within a square subregion which overlaps regions I and II.
  He rescaled that subset to the unit square. 
  This subset has been re-analysed many times,
  and is the dataset usually known as
  ``the redwood data'' in the spatial statistics literature.
  The exact dataset used by Ripley is supplied in the spatstat
  library as redwood.
The approximate position of the square chosen by Ripley
  within the redwoodfull pattern
  is indicated by the window redwoodfull.extra$regionR.
  There are some minor inconsistencies with
  redwood since it originates from a different digitisation.
data(redwoodfull)The dataset redwoodfull is an object of class "ppp"
  representing the point pattern of tree locations.
  See ppp.object for details of the format of a
  point pattern object.
  The window has been rescaled to the unit square.
  Its physical size is approximately 128 feet across.
The dataset redwoodfull.extra is a list with entries
| rdiag | coordinates of endpoints of a line, | 
| in format list(x=numeric(2),y=numeric(2)) | |
| regionI | a window object | 
| regionII | a window object | 
| regionR | a window object | 
| plotit | Function with no arguments | 
Diggle, P.J. (1983) Statistical analysis of spatial point patterns. Academic Press.
Ripley, B.D. (1977) Modelling spatial patterns (with discussion). Journal of the Royal Statistical Society, Series B 39, 172--212.
Strauss, D.J. (1975) A model for clustering. Biometrika 62, 467--475.
redwood
       data(redwoodfull)
  if(require(spatstat.geom)) {
       plot(redwoodfull)
       redwoodfull.extra$plotit()
       # extract the pattern in region II 
       redwoodII <- redwoodfull[, redwoodfull.extra$regionII]
   }
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