README.md

In truemoid/declustr: Analysis of Spatial Sample Clustering and Declustering

declustr

declustr is a package for analysing the spatial patterns of point data in 2D and 3D. For spatially clustered data there are various approaches implimented for calculating declustering weights.

The current implimentation has functions for analysing and declustering 2D point patterns using Voronoi tesselations (polygonal declustering) and functions from the spatstat point pattern analysis package (Baddeley, et al. 2015).

Installation

declustr is not available on CRAN but can be installed from Github using the devtools package.

# install.packages("devtools")
devtools::install_github("truemoid/declustr")

Functions

polydeclust2d calcuates decluster weights for 2D point sample data using Voronoi tesselations of the point data. Weights are calculated as the area of influence of each sample divided by the total area of the domain and then normalized so that the sum of all weights is 1. This normalization is the default but can be turned off with the normalize argument.

The sample domain is defined by an input pixel mask, which is a regular grid of x and y coordinates that define the surface area of the domain. This pixel mask can optionally be expanded by a set number of grid units. By default the expansion is 0; however, occassionally samples on the edge of the domain cause errors in the tessellation. In these cases, setting the expansion to a small number (e.g., 1 m) can resolve errors.

If the domain mask is extrapolated too too far beyond the limits of the sample data the weights assigned to edge samples can be too high. To counter this issue you could adjust the mask. Alternatively, the function argument estdomain when set to TRUE will estimate a more compact domain using the Ripley-Rasson (1977) method.

plotable_tess returns a dataframe of points defining the edges of the tessellation constructed by polydeclust2d. These points can be used to visualize the tessellations.

point_spacing_2d computes the avererage distance to the four nearest samples in the mask domain. The number of nearest samples can be adjusted. The returned object is a dataframe of regular grid points (as defined by the input point pattern object) with column of distances.

Referneces

Ripley, B.D. and Rasson, J.P. (1977) Finding the edge of a Poisson forest. Journal of Applied Probability, 14, 483 – 491.

A. Baddeley, E. Rubak and R.Turner. Spatial Point Patterns: Methodology and Applications with R. Chapman and Hall/CRC Press, 2015.

truemoid/declustr documentation built on May 20, 2019, 9:57 a.m.