adj_spconsist_arr_window_globstd

Adjusted spatial inconsistency index for rasters

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

adj_spconsist_arr_window_globstd function

<dl><dt>data</dt>
<dd>an arma cube of dimension nr,nc,ns</dd>
<dt>memberships</dt>
<dd>an arma cube of dimension nr, nc, ks</dd>
<dt>window</dt>
<dd>a matrix representing the neighbouring of each pixel</dd>
<dt>mindist</dt>
<dd>A minimum value for distance between two observations. If two
neighbours have exactly the same values, then the euclidean distance
between them is 0, leading to an infinite spatial weight. In that case,
the minimum distance is used instead of 0.</dd></dl>

Arguments

Adjusted spatial inconsistency index for rasters — adj_spconsist_arr_window_globstd

<dl>

<dt>data</dt>
<dd>an arma cube of dimension nr,nc,ns</dd>


<dt>memberships</dt>
<dd>an arma cube of dimension nr, nc, ks</dd>


<dt>window</dt>
<dd>a matrix representing the neighbouring of each pixel</dd>


<dt>mindist</dt>
<dd>A minimum value for distance between two observations. If two
neighbours have exactly the same values, then the euclidean distance
between them is 0, leading to an infinite spatial weight. In that case,
the minimum distance is used instead of 0.</dd>

</dl>

adj_spconsist_arr_window_globstd: Adjusted spatial inconsistency index for rasters

Description

Usage

Value

Arguments