diff --git a/R/CreateSpacetimeFolds.R b/R/CreateSpacetimeFolds.R
index de03686..2baecc7 100644
--- a/R/CreateSpacetimeFolds.R
+++ b/R/CreateSpacetimeFolds.R
@@ -12,7 +12,7 @@
 #' @param seed numeric. See ?seed
 #' @return A list that contains a list for model training and a list for
 #' model validation that can directly be used as "index" and "indexOut" in
-#' caret's trainControl function
+#' caret's trainControl function. "cluster" gives us the information to which validation fold a sample belongs.
 #' @details The function creates train and test sets by taking (spatial and/or temporal) groups into account.
 #' In contrast to \code{\link{nndm}}, it requires that the groups are already defined (e.g. spatial clusters or blocks or temporal units).
 #' Using "class" is helpful in the case that data are clustered in space
@@ -23,8 +23,9 @@
 #' @note Standard k-fold cross-validation can lead to considerable misinterpretation in spatial-temporal modelling tasks.
 #' This function can be used to prepare a Leave-Location-Out, Leave-Time-Out or Leave-Location-and-Time-Out cross-validation
 #' as target-oriented validation strategies for spatial-temporal prediction tasks.
-#' See Meyer et al. (2018) for further information. CreateSpaceTiemFolds is just a evry simple approach and the suitability depends on the choice of the groups.
+#' See Meyer et al. (2018) for further information. CreateSpaceTimeFolds is just a very simple approach and the suitability depends on the choice of the groups.
 #' You may check the suitability with \code{\link{geodist}}. Consider \code{\link{nndm}} or \code{\link{knndm}} as alternatives or other approaches such as Spatial Blocks.
+#' For spatial visualization of fold affiliation see examples.
 #' @author Hanna Meyer
 #' @seealso \code{\link[caret]{trainControl}},\code{\link{ffs}}, \code{\link{nndm}}, \code{\link{geodist}}
 #' @references
@@ -36,12 +37,24 @@
 #' indices <- CreateSpacetimeFolds(cookfarm,"SOURCEID","Date")
 #' str(indices)
 #' ### Prepare for 10-fold Leave-Location-Out cross validation
-#' indices <- CreateSpacetimeFolds(dat,spacevar="SOURCEID")
+#' indices <- CreateSpacetimeFolds(cookfarm,spacevar="SOURCEID")
 #' str(indices)
 #' ### Prepare for leave-One-Location-Out cross validation
-#' indices <- CreateSpacetimeFolds(dat,spacevar="SOURCEID",
-#'     k=length(unique(dat$SOURCEID)))
+#' indices <- CreateSpacetimeFolds(cookfarm,spacevar="SOURCEID",
+#'     k=length(unique(cookfarm$SOURCEID)))
 #' str(indices)
+#'
+#' ### example from splotopen and visualization
+#' data(splotdata)
+#' indices <- CreateSpacetimeFolds(splotdata,spacevar="Country")
+#' ggplot() +
+#' geom_sf(data = splotdata, aes(col = factor(indices$cluster)))
+#' ## is this representative?
+#' data(splotdata)
+#' studyArea <- rnaturalearth::ne_countries(continent = "South America", returnclass = "sf")
+#' dist <- geodist(splotdata, studyArea,cvfolds=indices$cluster)
+#' plot(dist)+ scale_x_log10(labels=round)
+#'
 #' }
 #' @export CreateSpacetimeFolds
 #' @aliases CreateSpacetimeFolds
@@ -104,5 +117,13 @@ CreateSpacetimeFolds <- function(x,spacevar=NA,timevar=NA,
     }
   }
 
-  return(list("index"=cvindices_train,"indexOut"=cvindices_test))
+  ## summarize folds:
+  result <- list("index"=cvindices_train,"indexOut"=cvindices_test)
+  cluster <- do.call(rbind, lapply(seq_along(result$indexOut), function(i) {
+    data.frame(Number = result$indexOut[[i]], List = i)
+  }))
+  x$Number <- seq_len(nrow(x))
+  df <- merge(x, cluster, by = "Number", all.x = TRUE)
+  result$cluster <- df$List
+  return(result)
 }
diff --git a/R/knndm.R b/R/knndm.R
index 7861189..518a4a9 100644
--- a/R/knndm.R
+++ b/R/knndm.R
@@ -70,6 +70,8 @@
 #' In the case of any categorical features, Gower distances will be used to calculate the Nearest Neighbour distances [Experimental]. If categorical
 #' features are present, and `clustering` = "kmeans", K-Prototype clustering will be performed instead.
 #'
+#' @note
+#' For spatial visualization of fold affiliation see examples.
 #' @references
 #' \itemize{
 #' \item Linnenbrink, J., Milà, C., Ludwig, M., and Meyer, H.: kNNDM: k-fold Nearest Neighbour Distance Matching Cross-Validation for map accuracy estimation, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-1308, 2023.
diff --git a/man/CreateSpacetimeFolds.Rd b/man/CreateSpacetimeFolds.Rd
index 36c7094..ac8033c 100644
--- a/man/CreateSpacetimeFolds.Rd
+++ b/man/CreateSpacetimeFolds.Rd
@@ -33,7 +33,7 @@ set k to the number of unique spatial or temporal units.}
 \value{
 A list that contains a list for model training and a list for
 model validation that can directly be used as "index" and "indexOut" in
-caret's trainControl function
+caret's trainControl function. "cluster" gives us the information to which validation fold a sample belongs.
 }
 \description{
 Create spatial, temporal or spatio-temporal Folds for cross validation based on pre-defined groups
@@ -51,8 +51,9 @@ should be similar in each fold (class="LUC").
 Standard k-fold cross-validation can lead to considerable misinterpretation in spatial-temporal modelling tasks.
 This function can be used to prepare a Leave-Location-Out, Leave-Time-Out or Leave-Location-and-Time-Out cross-validation
 as target-oriented validation strategies for spatial-temporal prediction tasks.
-See Meyer et al. (2018) for further information. CreateSpaceTiemFolds is just a evry simple approach and the suitability depends on the choice of the groups.
+See Meyer et al. (2018) for further information. CreateSpaceTimeFolds is just a very simple approach and the suitability depends on the choice of the groups.
 You may check the suitability with \code{\link{geodist}}. Consider \code{\link{nndm}} or \code{\link{knndm}} as alternatives or other approaches such as Spatial Blocks.
+For spatial visualization of fold affiliation see examples.
 }
 \examples{
 \dontrun{
@@ -61,12 +62,24 @@ data(cookfarm)
 indices <- CreateSpacetimeFolds(cookfarm,"SOURCEID","Date")
 str(indices)
 ### Prepare for 10-fold Leave-Location-Out cross validation
-indices <- CreateSpacetimeFolds(dat,spacevar="SOURCEID")
+indices <- CreateSpacetimeFolds(cookfarm,spacevar="SOURCEID")
 str(indices)
 ### Prepare for leave-One-Location-Out cross validation
-indices <- CreateSpacetimeFolds(dat,spacevar="SOURCEID",
-    k=length(unique(dat$SOURCEID)))
+indices <- CreateSpacetimeFolds(cookfarm,spacevar="SOURCEID",
+    k=length(unique(cookfarm$SOURCEID)))
 str(indices)
+
+### example from splotopen and visualization
+data(splotdata)
+indices <- CreateSpacetimeFolds(splotdata,spacevar="Country")
+ggplot() +
+geom_sf(data = splotdata, aes(col = factor(indices$cluster)))
+## is this representative?
+data(splotdata)
+studyArea <- rnaturalearth::ne_countries(continent = "South America", returnclass = "sf")
+dist <- geodist(splotdata, studyArea,cvfolds=indices$cluster)
+plot(dist)+ scale_x_log10(labels=round)
+
 }
 }
 \references{
diff --git a/man/knndm.Rd b/man/knndm.Rd
index 5e9d356..a27baf3 100644
--- a/man/knndm.Rd
+++ b/man/knndm.Rd
@@ -103,6 +103,9 @@ In this case, nearest neighbour distances are calculated in n-dimensional featur
 In the case of any categorical features, Gower distances will be used to calculate the Nearest Neighbour distances [Experimental]. If categorical
 features are present, and `clustering` = "kmeans", K-Prototype clustering will be performed instead.
 }
+\note{
+For spatial visualization of fold affiliation see examples.
+}
 \examples{
 ########################################################################
 # Example 1: Simulated data - Randomly-distributed training points