How to plot interpolating data on a projected map using ggplot2 in R

Question

I want to plot some interpolating data on a projected map using ggplot2 and I have been working on this problem for a few weeks. Hope someone can help me, thanks a lot. The shapefile and data can be found at https://www.dropbox.com/s/8wfgf8207dbh79r/gpr_000b11a_e.zip?dl=0 and https://www.dropbox.com/s/9czvb35vsyf3t28/Mydata.rdata?dl=0 .

First, the shapefile is originally using "lon-lat" projection and I need to convert it to Albers Equal Area (aea) projection.

library(automap)
library(ggplot2)
library(rgdal)
load("Mydata.rdata",.GlobalEnv)
canada2<-readOGR("gpr_000b11a_e.shp", layer="gpr_000b11a_e")
g <- spTransform(canada2, CRS("+proj=aea +lat_1=50 +lat_2=70 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +datum=NAD83 +units=m +no_defs +ellps=GRS80 +towgs84=0,0,0"))
Borders=ggplot() +geom_polygon(data=g,aes(x=long,y=lat,group=group),fill='white',color = "black")
Borders

As we can see, we can plot the country correctly. Then I want to interpolate the data using Kriging method, the code is taken from Smoothing out ggplot2 map.

coordinates(Mydata)<-~longitude+latitude
proj4string(Mydata)<-CRS("+proj=longlat +datum=NAD83")
sp_mydata<-spTransform(Mydata,CRS(proj4string(g)))
Krig=autoKrige(APPT~1,sp_mydata)
interp_data = as.data.frame(Krig$krige_output)
colnames(interp_data) = c("latitude","longitude","APPT_pred","APPT_var","APPT_stdev")
interp_data=interp_data[,1:3]
ggplot(data=interp_data, aes(x=longitude, y=latitude)) + geom_tile(aes(fill=APPT_pred),color=NA)

Then we can see the interpolating data map.

Finally I want to combine these two figures and then I get the following error: Error: Don't know how to add o to a plot

ggplot(data=interp_data, aes(x=longitude, y=latitude)) + geom_tile(aes(fill=APPT_pred),color=NA)+Borders 

My question is: how can I plot the interpolating data on the map and then add grid lines (longitude and latitude). Also, I wonder how to clip the interpolating data map to fit the whole Canada map. Thanks for the help.

Answer 1

After digging a bit more, I guess you may want this:

Krig = autoKrige(APPT~1,sp_mydata)$krige_output
Krig = Krig[!is.na(over(Krig,as(g,"SpatialPolygons"))),]  # take only the points falling in poolygons
Krig_df = as.data.frame(Krig)
names(Krig_df) = c("APPT_pred","APPT_var","APPT_stdev","longitude","latitude")
g_fort = fortify(g)
Borders = ggplot() +
  geom_raster(data=Krig_df, aes(x=longitude, y=latitude,fill=APPT_pred))+
  geom_polygon(data=g_fort,aes(x=long,y=lat,group=group),
               fill='transparent',color = "black")+
  theme_bw()
Borders

which gives:

Only problem is that you still have "missing" interpolated areas in the resulting map (e.g., on the western part). This is due to the fact that, as from autokrige help:

new_data: A sp object containing the prediction locations. new_data can be a points set, a grid or a polygon. Must not contain NA’s. If this object is not provided a default is calculated. This is done by taking the convex hull of input_data and placing around 5000 gridcells in that convex hull

Therefore, if you do not provide a feasible newdata as argument, the interpolated area is limited by the convex hull of the points of the input dataset (= no extrapolation). This can be solved using spsample insp package:

library(sp)
ptsreg <- spsample(g, 4000, type = "regular")   # Define the ouput grid - 4000 points in polygons extent
Krig = autoKrige(APPT~1,sp_mydata, new_data = ptsreg)$krige_output
Krig = Krig[!is.na(over(Krig,as(g,"SpatialPolygons"))),]  # take only the points falling in poolygons
Krig_df = as.data.frame(Krig)
names(Krig_df) = c("longitude","latitude", "APPT_pred","APPT_var","APPT_stdev")
g_fort = fortify(g)
Borders = ggplot() +
  geom_raster(data=Krig_df, aes(x=longitude, y=latitude,fill=APPT_pred))+
  geom_polygon(data=g_fort,aes(x=long,y=lat,group=group),
               fill='transparent',color = "black")+
  theme_bw()
Borders

which gives:

Notice that the small "holes" that you still have near polygon boundaries can be removed by increasing the number of interpolation points in the call to spsample (Since it is a slow operation I only asked for 4000, here)

A simpler quick alternative could be to use package mapview

library(mapview)
m1 <- mapview(Krig)
m2 <- mapview(g)
m2+m1

(you may want to use a less detailed polygon boundaries shapefiles, since this is slow)

HTH !