calcFuzzyELSA: calculate ELSA statistic for a fuzzy partition

In geocmeans: Implementing Methods for Spatial Fuzzy Unsupervised Classification

calculate ELSA statistic for a fuzzy partition

Description

Calculate ELSA statistic for a fuzzy partition. This local indicator of spatial autocorrelation can be used to identify areas where close observations tend to belong to different clusters.

Usage

calcFuzzyELSA(object, nblistw = NULL, window = NULL, matdist = NULL)

Arguments

object	A FCMres object, typically obtained from functions CMeans, GCMeans, SFCMeans, SGFCMeans. Can also be a membership matrix. Each row of this matrix must sum up to 1. Can also be a list of rasters, in which case each raster must represent the membership values for one cluster and the sum of all the rasters must be a raster filled with ones.
nblistw	A list.w object describing the neighbours typically produced by the spdep package. Required if data is a dataframe, see the parameter window if you use a list of rasters as input.
window	A binary (0,1) matrix representing the neighbours spatial weights when working with rasters. The matrix must have odd dimensions.
matdist	A matrix representing the dissimilarity between the clusters. The matrix must be squared and the diagonal must be filled with zeros.

Details

The fuzzy ELSA index is a generalization of the ELSA index \insertCitenaimi2019elsageocmeans. It can be used to measure local autocorrelation for a membership matrix. It varies between 0 and 1, 0 indicating a perfect positive spatial autocorrelation and 1 a perfect heterogeneity. It is based on the Shannon entropy index, and uses a measure of dissimilarity between categories.

Value

either a vector or a raster with the ELSA values.

Examples

data(LyonIris)
AnalysisFields <-c("Lden","NO2","PM25","VegHautPrt","Pct0_14","Pct_65","Pct_Img",
"TxChom1564","Pct_brevet","NivVieMed")
dataset <- sf::st_drop_geometry(LyonIris[AnalysisFields])
queen <- spdep::poly2nb(LyonIris,queen=TRUE)
Wqueen <- spdep::nb2listw(queen,style="W")
result <- SFCMeans(dataset, Wqueen,k = 5, m = 1.5, alpha = 1.5, standardize = TRUE)
elsa_valus <- calcFuzzyELSA(result)

geocmeans documentation built on Sept. 12, 2023, 9:06 a.m.