The simulated patterns were created for testing the Adapted Pair Correlation Function presented in Nuske et al. (2009).
Format
A set of WKBs
of class wk_wkb containing the study area and three simulated patterns.
| Dataset name | Description | sim_area |
| study area | sim_pat_reg | simulated regular pattern |
sim_pat_rand | simulated random pattern | sim_pat_clust |
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.
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)