R/doc_patterns.R

In apcf: Adapted Pair Correlation Function

#' Simulated Patterns (sample data)
#'
#' The simulated patterns were created for testing the Adapted Pair Correlation
#' Function presented in Nuske et al. (2009).
#'
#' @format A set of [WKBs](https://en.wikipedia.org/wiki/Well-known_text_representation_of_geometry)
#'   of class \code{\link[wk:is_wk_wkb]{wk_wkb}} containing the study area and three simulated patterns.
#'
#' \tabular{lll}{
#'   \strong{Dataset name} \tab \strong{Description}\cr
#'   `sim_area`      \tab study area\cr
#'   `sim_pat_reg`   \tab simulated regular pattern\cr
#'   `sim_pat_rand`  \tab simulated random pattern\cr
#'   `sim_pat_clust` \tab simulated clustered pattern\cr
#' }
#'
#' The study area is a square of 100 m x 100 m. A set of n = 100 objects were
#' created and latter placed according to the designated spatial distribution.
#' The size distribution and shapes of the objects are inspired by measurements
#' of canopy gaps. The areas of the objects range from 1.6 m2 to 57.7 m2 with
#' an arithmetic mean of 9.7 m2 and a median of 5.5 m2. The total area of all
#' objects is 969.7 m2, meaning 9.7% of the study area is covered by objects.
#'
#' For the `sim_pat_reg` dataset, the objects were arranged in a strict
#' regular manner. A centric systematic grid was constructed, and the objects
#' of the set were then randomly rotated and randomly placed by locating the
#' centroids of the objects exactly on the matching randomly numbered grid
#' points, resulting in a regular arrangement of objects with a constant
#' distance of the centroids of 10 m.
#'
#' For the `sim_pat_rand` dataset with randomly distributed objects, we
#' generated a realisation of the Binomial process with intensity 0.01 m^-2,
#' meaning one point per 100 m2. The objects were again randomly rotated and
#' numbered and objects put on matching points with their centroid as close to
#' the point as possible without overlapping other objects.
#'
#' The `sim_pat_clust` dataset represents a clustered configuration. Again,
#' we first created a point pattern with 100 points and then put the randomly
#' numbered objects on the points. The point pattern was a realisation of
#' Matern’s cluster process with w = 0.0006 m^-2 or 6 cluster centres per ha,
#' a dispersion radius of R = 10 m and on average y = 16.6 points per cluster.
#'
#' We used the R-package spatstat (Baddeley et al. 2015) for simulating
#' the Binomial process and Matern’s cluster process.
#'
#' @source  Nuske et al. 2009
#'
#' @references
#' Baddeley A., Rubak E. and Turner, R. (2015): Spatial Point Patterns:
#' Methodology and Applications with R. Chapman and Hall/CRC, London.
#' https://doi.org/10.1201/b19708
#'
#' Nuske, R.S., Sprauer, S. and Saborowski, J. (2009): Adapting the
#' pair-correlation function for analysing the spatial distribution of canopy
#' gaps. Forest Ecology and Management, 259(1): 107–116.
#' https://doi.org/10.1016/j.foreco.2009.09.050
#'
#' @examples
#' ds <- pat2dists(area=sim_area, pattern=sim_pat_reg, max_dist=25, n_sim=3)
#'
#' @name sim_patterns
#' @keywords datasets
NULL

#' @rdname sim_patterns
#' @format NULL
"sim_area"

#' @rdname sim_patterns
#' @format NULL
"sim_pat_clust"

#' @rdname sim_patterns
#' @format NULL
"sim_pat_rand"

#' @rdname sim_patterns
#' @format NULL
"sim_pat_reg"