elsa_raster

calculate ELSA spatial statistic for vector dataset

internal

Provides functions to apply spatial fuzzy unsupervised classification, visualize and interpret results. This method is well suited when the user wants to analyze data with a fuzzy clustering algorithm and to account for the spatial dimension of the dataset. In addition, indexes for estimating the spatial consistency and classification quality are proposed.
The methods were originally proposed in the field of brain imagery (seed Cai and al. 2007 <doi:10.1016/j.patcog.2006.07.011> and Zaho and al. 2013 <doi:10.1016/j.dsp.2012.09.016>) and recently applied in geography (see Gelb and Apparicio <doi:10.4000/cybergeo.36414>).

Jeremy Gelb

geocmeans

Implementing Methods for Spatial Fuzzy Unsupervised
Classification

Philippe Apparicio

elsa_raster function

<dl><dt>rast</dt>
<dd>An integer raster or matrix representing the m categories (0,1,2,..., m)</dd>
<dt>window</dt>
<dd>A binary (0,1) matrix representing the neighbours spatial weights when working
with rasters. The matrix must have odd dimensions.</dd>
<dt>matdist</dt>
<dd>A matrix representing the dissimilarity between the clusters. The matrix must
be squared and the diagonal must be filled with zeros.</dd></dl>

Arguments

calculate ELSA spatial statistic for raster dataset — elsa_raster

<dl>

<dt>rast</dt>
<dd>An integer raster or matrix representing the m categories (0,1,2,..., m)</dd>


<dt>window</dt>
<dd>A binary (0,1) matrix representing the neighbours spatial weights when working
with rasters. The matrix must have odd dimensions.</dd>


<dt>matdist</dt>
<dd>A matrix representing the dissimilarity between the clusters. The matrix must
be squared and the diagonal must be filled with zeros.</dd>

</dl>

elsa_raster: calculate ELSA spatial statistic for raster dataset

Description

Usage

Value

Arguments

Examples