uncertaintyMap: Uncertainty map

In geocmeans: Implementing Methods for Spatial Fuzzy Unsupervised Classification

Uncertainty map

Description

Return a map to visualize membership matrix

Usage

uncertaintyMap(
  geodata,
  belongmatrix,
  njit = 150,
  radius = NULL,
  colors = NULL,
  pt_size = 0.05
)

Arguments

geodata	An object of class feature collection from sf ordered like the original data used for the clustering.
belongmatrix	A membership matrix
njit	The number of points to map on each feature.
radius	When mapping points, the radius indicates how far random points will be plotted around the original features.
colors	A vector of colors to use for the groups.
pt_size	A float giving the size of the random points on the final map (default is 0.05)

Details

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.

Value

a map created with tmap

Examples

## Not run: 
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)

## End(Not run)

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