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This tutorial was first published in “Geocomputation a Practical Primer“.

Here is a more complex example showing how to produce a map of 18th Century Shipping flows. The data have been obtained from the CLIWOC project and they represent a sample of digitised ships’ logs from the 18th Century. We are using a very small sample (link below) of the the full dataset, which is available from here. The example has been chosen to demonstrate a range of capabilities within ggplot2 and the ways in which they can be applied to produce high-quality maps with only a few lines of code.

As always, the first step is to load in the required packages and datasets. Here we are using the png package to load in a series of map annotations. These have been created in image editing software and will add a historic feel to the map. We are also loading in a World boundary shapefile and the shipping data itself. These need to be in your working directory.

library(rgdal)
library(ggplot2)
library(png)
## OGR data source with driver: ESRI Shapefile
## with 177 features
## It has 63 fields
bdata <- read.csv("british_shipping_example.csv")

If you look at the first few lines in the bdata object you will see there are 7 columns with each row representing a single point on the ships course. The year of the journey and the nationality of the ship are also included. The final 3 columns are identifiers that are used later to group the coordinate points together into the paths that ggplot2 plots.

We first specify some plot parameters that remove the axis labels.

xquiet <- scale_x_continuous("", breaks=NULL)
yquiet <- scale_y_continuous("", breaks=NULL)
quiet <- list(xquiet, yquiet)

The next step is to fortify the World coastlines and create the base plot. This sets the extents of the plot window and provides the blank canvas on which we will build up the layers. The first layer created is the wrld object; the code is wrapped in c() to prevent it from executing by simply storing it as the plot’s parameters.

wrld.f <- fortify(wrld, region="sov_a3")
base <- ggplot(wrld.f, aes(x = long, y = lat))
wrld <- c(geom_polygon(aes(group=group), size = 0.1, colour= "black", fill="#D6BF86", data=wrld.f, alpha=1))

To see the result of this simply type:

base + wrld

The code snipped below creates the plot layer containing the the shipping routes. The geom_path() function is used to string together the coordinates into the routes. You can see within the aes() component we have specified long and lat plus pasted together the trp and group.regroup variables to identify the unique paths.

route <- c(geom_path(aes(long,lat,group = paste(bdata$trp, bdata$group.regroup, sep = ".")), colour="#0F3B5F", size = 0.2, data= bdata, alpha = 0.5, lineend = "round"))

We now have all we need to generate the final plot by building the layers together with the + sign as shown in the code below. The first 3 arguments are the plot layers, and the parameters within theme() are changing the background colour to sea blue. annotation_raster() plots the png map adornments loaded in earlier- this requires the bounding box of each image to be specified. In this case we use latitude and longitude (in WGS84) and we can use these paramrters to change the png’s position and also its size. The final two arguments fix the aspect ratio of the plot and remove the axis labels.

base + route + wrld + theme(panel.background = element_rect(fill='#BAC4B9',colour='black')) +
annotation_raster(btitle, xmin = 30, xmax = 140, ymin = 51, ymax = 87) +
annotation_raster(compass, xmin = 65, xmax = 105, ymin = 25, ymax = 65) + coord_equal() + quiet

In the plot example we have chosen the colours carefully to give the appearance of a historic map. An alternative approach could be to use a satellite image as a base map. It is possible to use the readPNG function to import NASA’s “Blue Marble” image for this purpose. Given that the route information is the same projection as the image it is very straightforward to set the image extent to span -180 to 180 degrees and -90 to 90 degrees and have it align with the shipping data. Producing the plot is accomplished using the code below. This offers a good example of where functionality designed without spatial data in mind can be harnessed for the purposes of producing interesting maps. Once you have produced the plot, alter the code to recolour the shipping routes to make them appear more clearly against the blue marble background.

earth <- readPNG("earth_raster.png")

base + annotation_raster(earth, xmin = -180, xmax = 180, ymin = -90, ymax = 90) + route + theme(panel.background = element_rect(fill='#BAC4B9',colour='black')) + annotation_raster(btitle, xmin = 30, xmax = 140, ymin = 51, ymax = 87) + annotation_raster(compass, xmin = 65, xmax = 105, ymin = 25, ymax = 65) + coord_equal() + quiet