Question

I am relatively new to ggplot, so please forgive me if some of my problems are really simple or not solvable at all.

What I am trying to do is generate a "Heat Map" of a country where the filling of the shape is continous. Furthermore I have the shape of the country as .RData. I used hadley wickham's script to transform my SpatialPolygon data into a data frame. The long and lat data of my data frame now looks like this

head(my_df)
long        lat         group
6.527187    51.87055    0.1 
6.531768    51.87206    0.1
6.541202    51.87656    0.1
6.553331    51.88271    0.1

This long/lat data draws the outline of Germany. The rest of the data frame is omitted here since I think it is not needed. I also have a second data frame of values for certain long/lat points. This looks like this

my_fixed_points
long        lat         value
12.817      48.917      0.04 
8.533       52.017      0.034
8.683       50.117      0.02
7.217       49.483      0.0542

What I would like to do now, is colour each point of the map according to an average value over all the fixed points that lie within a certain distance of that point. That way I would get a (almost)continous colouring of the whole map of the country. What I have so far is the map of the country plotted with ggplot2

ggplot(my_df,aes(long,lat)) + geom_polygon(aes(group=group), fill="white") + 
geom_path(color="white",aes(group=group)) + coord_equal()

My first Idea was to generate points that lie within the map that has been drawn and then calculate the value for every generated point my_generated_point like so

value_vector <- subset(my_fixed_points, 
  spDistsN1(cbind(my_fixed_points$long, my_fixed_points$lat),  
  c(my_generated_point$long, my_generated_point$lat), longlat=TRUE) < 50, 
  select = value)
point_value <- mean(value_vector)

I havent found a way to generate these points though. And as with the whole problem, I dont even know if it is possible to solve this way. My question now is if there exists a way to generate these points and/or if there is another way to come to a solution.

Solution

Thanks to Paul I almost got what I wanted. Here is an example with sample data for the Netherlands.

library(ggplot2)
library(sp)
library(automap)
library(rgdal)
library(scales)

#get the spatial data for the Netherlands
con <- url("http://gadm.org/data/rda/NLD_adm0.RData")
print(load(con))
close(con)

#transform them into the right format for autoKrige
gadm_t <- spTransform(gadm, CRS=CRS("+proj=merc +ellps=WGS84"))

#generate some random values that serve as fixed points
value_points <- spsample(gadm_t, type="stratified", n = 200)
values <- data.frame(value = rnorm(dim(coordinates(value_points))[1], 0 ,1))
value_df <- SpatialPointsDataFrame(value_points, values)

#generate a grid that can be estimated from the fixed points
grd = spsample(gadm_t, type = "regular", n = 4000)
kr <- autoKrige(value~1, value_df, grd)
dat = as.data.frame(kr$krige_output)

#draw the generated grid with the underlying map
ggplot(gadm_t,aes(long,lat)) + geom_polygon(aes(group=group), fill="white") + geom_path(color="white",aes(group=group)) + coord_equal() + 
geom_tile(aes(x = x1, y = x2, fill = var1.pred), data = dat) + scale_fill_continuous(low = "white", high = muted("orange"), name = "value")

autoKrige Netherlands

Was it helpful?

Solution

I think what you want is something along these lines. I predict that this homebrew is going to be terribly inefficient for large datasets, but it works on a small example dataset. I would look into kernel densities and maybe the raster package. But maybe this suits you well...

The following snippet of code calculates the mean value of cadmium concentration of a grid of points overlaying the original point dataset. Only points closer than 1000 m are considered.

library(sp)
library(ggplot2)
loadMeuse()

# Generate a grid to sample on
bb = bbox(meuse)
grd = spsample(meuse, type = "regular", n = 4000)
# Come up with mean cadmium value
# of all points < 1000m.
mn_value = sapply(1:length(grd), function(pt) {
  d = spDistsN1(meuse, grd[pt,])
  return(mean(meuse[d < 1000,]$cadmium))
})

# Make a new object
dat = data.frame(coordinates(grd), mn_value)
ggplot(aes(x = x1, y = x2, fill = mn_value), data = dat) + 
   geom_tile() + 
   scale_fill_continuous(low = "white", high = muted("blue")) + 
   coord_equal()

which leads to the following image:

enter image description here

An alternative approach is to use an interpolation algorithm. One example is kriging. This is quite easy using the automap package (spot the self promotion :), I wrote the package):

library(automap)
kr = autoKrige(cadmium~1, meuse, meuse.grid)
dat = as.data.frame(kr$krige_output)

ggplot(aes(x = x, y = y, fill = var1.pred), data = dat) + 
   geom_tile() + 
   scale_fill_continuous(low = "white", high = muted("blue")) + 
   coord_equal()

which leads to the following image:

enter image description here

However, without knowledge as to what your goal is with this map, it is hard for me to see what you want exactly.

OTHER TIPS

This slideshow offers another approach--see page 18 for a description of the approach and page 21 for a view of what the results looked like for the slide-maker.

Note however that the slide-maker used the sp package and the spplot function rather than ggplot2 and its plotting functions.

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