interpolate_points: Interpolates point values

In whamer/papros: PAthogen PROgnosis System

Description

This function tries to interpolate points based on given parameters

Usage

interpolate_points(
  sp_points,
  aim_variable,
  outputfile,
  co_variables = FALSE,
  procedure = c("ked", "rfk", "ok", "idw")
)

Arguments

sp_points	SpatialPointsDataframe containing the aim variable and if "ked" should be applied the covariables
aim_variable	Character string with the name of the aim variable
outputfile	SpatialPointsDataframe, SpatialGridDataFrame or raster which should be filled with predictions; requires covariables for "ked" (if two values are given in a vector a raster is created with the resolution given by the values)
co_variables	default = FALSE, vector of covariables if needed
procedure	default = c("ked","rfk,"ok","idw"); vector containing the interpolation technic to be used; the first method is used and if this does not work out, the second, and so on; KED = Kriging With external Drift; RFK = Random Forest Kriging; OK = Ordinary Kriging; IDW = Inverse distance Weighting

Value

interpolated point values as sp or raster file depending on outputfile

Author(s)

Wolfgang Hamer

Examples

library(magrittr)
library(dplyr)
library(sp)
library(raster)
# Download example data
shdat <- download_statewide_hourly_station_data(state = "Schleswig-Holstein", coord = TRUE)

# Select data of specific Time / Date
da_sel <- shdat %>% dplyr::filter(DateTime == sort(unique(shdat$DateTime))[5])

# Create spatial dataset
da_sel_sp <- SpatialPointsDataFrame(da_sel[,c("lon", "lat")],
                                    da_sel,
                                    proj4string = CRS("+init=epsg:4326"))

# Transform to projected (m based!) system
da_sel_sp <- spTransform(da_sel_sp, CRS("+init=epsg:25832"))

# Manually creating points of interest
preds <- SpatialPointsDataFrame(data.frame(x=c(9.5,9.0,10.4,10.5,9.9,10.5),
                                           y=c(53.5, 54.69,54.44,53.93,53.65, 54.55)),
                                data.frame(lat=c(9.5,9.0,10.4,10.5,9.9,10.5),
                                           lon=c(53.5, 54.69,54.44,53.93,53.65, 54.55)),
                                proj4string = CRS("+init=epsg:4326"))
outputpoints <- spTransform(preds, CRS("+init=epsg:25832"))

# Application of function for point data result
myintpoints <- interpolate_points(sp_points = da_sel_sp,
                                  aim_variable = "Temperature",
                                  outputfile = outputpoints,
                                  co_variables = c("lat","lon"),
                                  procedure = c("ked","ok","idw"))

# Create raster of interest
outputraster <- raster(ncol=100, nrow=100)
extent(outputraster) <- extent(outputpoints)
crs(outputraster) <- CRS("+init=epsg:25832")
outputraster[]<- rep(1,length(outputraster$layer[]))

# Application of function for raster data result
myintraster <- interpolate_points(sp_points = da_sel_sp,
                                  aim_variable = "Temperature",
                                  outputfile = outputraster,
                                  co_variables = c("lat","lon"),
                                  procedure = c("ked","ok","idw"))

# Application of function for raster data result
myintraster2 <- interpolate_points(sp_points = da_sel_sp,
                                   aim_variable = "Temperature",
                                   outputfile = c(500,500),
                                   co_variables = c("lat","lon"),
                                   procedure = c("ked","ok","idw"))

whamer/papros documentation built on Feb. 6, 2021, 8:54 a.m.