adjustSpatialWeights

Function to adjust the spatial weights so that they represent semantic
distances between neighbours

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

adjustSpatialWeights function

<dl><dt>data</dt>
<dd>A dataframe with numeric columns</dd>
<dt>listw</dt>
<dd>A nb object from spdep</dd>
<dt>style</dt>
<dd>A letter indicating the weighting scheme (see spdep doc)</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

Semantic adjusted spatial weights — adjustSpatialWeights

<dl>

<dt>data</dt>
<dd>A dataframe with numeric columns</dd>


<dt>listw</dt>
<dd>A nb object from spdep</dd>


<dt>style</dt>
<dd>A letter indicating the weighting scheme (see spdep doc)</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>

adjustSpatialWeights: Semantic adjusted spatial weights

Description

Usage

Value

Arguments

Examples