Selection of geo-referenced points from the frame

Once optimal stratification has been obtained, and a new frame has been built by assigning to the units of the old one the new strata labels, it is possible to select a stratified sample from the frame. If geographical coordinates are available in the frame, in order to obtain spatially distributed selected points this function makes use of the 'lpm2_kdtree' function from the' SamplingBigData' package (Lisic-Grafstrom).

selectSampleSpatial(frame, outstrata, coord_names)

Arguments

frame

This is the (mandatory) dataframe containing the sampling frame, as it has been modified by the execution of the "updateFrame" function.

outstrata

This is the (mandatory) dataframe containing the information related to resulting stratification obtained by the execution of "optimStrata" function. It should coincide with 'solution$aggr_strata'.

coord_names

Indicates with which names the coordinates are indicated in the frame.

Value

A dataframe containing the selected sample

Author

Giulio Barcaroli

Examples

if (FALSE) {
#############################
# Example of "spatial" method
#############################
library(sp)
# locations (155 observed points)
data("meuse")
# grid of points (3103)
data("meuse.grid")
meuse.grid$id <- c(1:nrow(meuse.grid))
coordinates(meuse)<-c("x","y")
coordinates(meuse.grid)<-c("x","y")

## Kriging model
library(automap)
kriging_lead = autoKrige(log(lead) ~ dist, meuse, meuse.grid)
plot(kriging_lead,sp.layout = NULL, justPosition = TRUE)
kriging_zinc = autoKrige(log(zinc) ~ dist, meuse, meuse.grid)
plot(kriging_zinc, sp.layout = list(pts = list("sp.points", meuse)))
r2_lead <- 1 - kriging_lead$sserr/sum((meuse$lead-mean(meuse$lead))^2)
r2_lead
r2_zinc <- 1 - kriging_zinc$sserr/sum((meuse$zinc-mean(meuse$zinc))^2)
r2_zinc
df <- NULL
df$id <- meuse.grid$id
df$lead.pred <- kriging_lead$krige_output@data$var1.pred
df$lead.var <- kriging_lead$krige_output@data$var1.var
df$zinc.pred <- kriging_zinc$krige_output@data$var1.pred
df$zinc.var <- kriging_zinc$krige_output@data$var1.var
df$lon <- meuse.grid$x
df$lat <- meuse.grid$y
df$dom1 <- 1
df <- as.data.frame(df)
head(df)

## Optimization 
library(SamplingStrata)
frame <- buildFrameSpatial(df=df,
                           id="id",
                           X=c("lead.pred","zinc.pred"),
                           Y=c("lead.pred","zinc.pred"),
                           variance=c("lead.var","zinc.var"),
                           lon="lon",
                           lat="lat",
                           domainvalue = "dom1")
cv <- as.data.frame(list(DOM=rep("DOM1",1),
                         CV1=rep(0.01,1),
                         CV2=rep(0.01,1),
                         domainvalue=c(1:1) ))
set.seed(1234)
solution <- optimStrata (
  method = "spatial",
  errors=cv, 
  framesamp=frame,
  iter = 15,
  pops = 10,
  nStrata = 5,
  fitting = c(r2_lead,r2_zinc),
  range = c(kriging_lead$var_model$range[2],kriging_zinc$var_model$range[2]),
  kappa=1,
  writeFiles = FALSE,
  showPlot = TRUE,
  parallel = FALSE)
framenew <- solution$framenew
outstrata <- solution$aggr_strata

# Sample selection
samp <- selectSampleSpatial(framenew,outstrata,coord_names=c("LON","LAT"))
table(samp$STRATO)

# Plot
library(sf)
samp_sf <- st_as_sf(samp, coords = c("LON", "LAT"))
plot(samp_sf["STRATO"])

}