Return a map to visualize membership matrix
uncertaintyMap(
geodata,
belongmatrix,
njit = 150,
radius = NULL,
colors = NULL,
pt_size = 0.05
)An object of class feature collection from sf ordered like the original data used for the clustering.
A membership matrix
The number of points to map on each feature.
When mapping points, the radius indicates how far random points will be plotted around the original features.
A vector of colors to use for the groups.
A float giving the size of the random points on the final map (default is 0.05)
a map created with tmap
This function maps the membership matrix by plotting random points in polygons, along lines or around points representing the original observations. Each cluster is associated with a color and each random point has a probability to be of that color equal to the membership value of the feature it belongs itself. Thus, it is possible to visualize regions with uncertainty and to identify the strongest clusters.
if (FALSE) {
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)
uncertaintyMap(LyonIris, result$Belongings)
}