elsa_vector

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_vector function

<dl><dt>categories</dt>
<dd>An integer vector representing the m categories (1,2,3,..., m),
-1 is used to indicate missing values.</dd>
<dt>nblistw</dt>
<dd>A listw object from spdep representing neighbour relations</dd>
<dt>dist</dt>
<dd>A numeric matrix (m*m) representing the distances between categories</dd></dl>

Arguments

calculate ELSA spatial statistic for vector dataset — elsa_vector

<dl>

<dt>categories</dt>
<dd>An integer vector representing the m categories (1,2,3,..., m),
-1 is used to indicate missing values.</dd>


<dt>nblistw</dt>
<dd>A listw object from spdep representing neighbour relations</dd>


<dt>dist</dt>
<dd>A numeric matrix (m*m) representing the distances between categories</dd>

</dl>

elsa_vector: calculate ELSA spatial statistic for vector dataset

Description

Usage

Value

Arguments

Examples