mapThis

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

mapThis function

<dl><dt>geodata</dt>
<dd>feature collections ordered like the original data used
for the clustering</dd>
<dt>belongmatrix</dt>
<dd>The membership matrix obtained at the end of the algorithm</dd>
<dt>undecided</dt>
<dd>A float between 0 and 1 giving the minimum value that an
observation must get in the membership matrix to not be considered as
uncertain (default = NULL)</dd>
<dt>geom_type</dt>
<dd>A string indicating the type of geometry (polygon, string or point)</dd></dl>

Arguments

Mapping the clusters — mapThis

<dl>

<dt>geodata</dt>
<dd>feature collections ordered like the original data used
for the clustering</dd>


<dt>belongmatrix</dt>
<dd>The membership matrix obtained at the end of the algorithm</dd>


<dt>undecided</dt>
<dd>A float between 0 and 1 giving the minimum value that an
observation must get in the membership matrix to not be considered as
uncertain (default = NULL)</dd>


<dt>geom_type</dt>
<dd>A string indicating the type of geometry (polygon, string or point)</dd>

</dl>

mapThis: Mapping the clusters

Description

Usage

Value

Arguments

Examples