## Abstract

We show how to create cartograms with R by illustrating the population and age-distribution of the planning regions of Berlin by static plots and animations.

## Introduction

Every good lecture on sophisticated statistical modelling starts with underlining the importance of data visualization as the first step of an analysis. Choropleth maps are a common choice for visualizing the spatial distribution of a feature recorded in administrative regions, e.g., population density or the incidence rate of a disease. Here, each region is shaded with a color selected in accordance with the feature variable, e.g., higher hue if the feature value is higher. Choosing the right palette for such visualizations is a science of its own, see e.g. Zeileis, Hornik, and Murrell (2009) or the ColorBrewer project, which is available in R through the RColorBrewer package. A nice way to further spice up your spatial visualizations are area cartograms, where the boundary shape of each region is warped such that its area becomes proportional to the value of the feature variable you want to illustrate. The difficult part here is to preserve the arrangement of the regions, see for example Gastner and Newman (2004) for the methodological challenges of this task.

In this post we show how such area cartograms can easily be created with R using the packages Rcartogram and getcartr together with the powerful packages sp, rgeos and rgdal for the spatial data wrangling. Both Rcartogram and getcartr are only available from github, because the license of the underlying Cart C fragment implementing the method of Gastner and Newman (2004) does not appear to be GPL (or the like) compatible.

# The Data

We use population numbers for the 447 planning regions of Berlin (Lebensweltlich orientierte Räume (LOR)). The boundaries of these regions are available as ESRI Shapefile through the open data portal of Berlin under the CC BY license. The 2015 population data of the LORs are available as CSV file through the same data portal.

tmpfile <- paste0(tempfile(),".zip")
unzip(tmpfile,exdir=file.path(filePath,"RBS_OD_LOR_2015_12"))
download.file("https://www.statistik-berlin-brandenburg.de/opendata/EWR201512E_Matrix.csv",destfile=file.path(filePath,"EWR201512E_Matrix.csv"))

With the help of the rgdal, sp and the rgeos CRAN packages, R can be used as a geographic information system (GIS). This allows for easy merging of these two data sources together with a spatial aggregation to the Prognoseräume level, which is a slightly higher level of aggregation than the LORs (60 regions instead of 447). The output of these data wrangling steps will be a SpatialPolygonsDataFrame object pgrs - see GitHub code for details.

library(rgdal)
library(sp)
library(rgeos)
##Read shapefile
lor <- readOGR(dsn=file.path(filePath,"RBS_OD_LOR_2015_12"),layer="RBS_OD_LOR_2015_12")
## OGR data source with driver: ESRI Shapefile
## Source: "/Users/hoehle/Sandbox/Blog/figure/source/2016-10-10-cartograms//RBS_OD_LOR_2015_12", layer: "RBS_OD_LOR_2015_12"
## with 447 features
## It has 8 fields
proj4string(lor)
## [1] "+proj=utm +zone=33 +ellps=GRS80 +units=m +no_defs"
##Compute area of each LOR in km^2 area (unit: meters -> convert to square km)
lor$area <- gArea(lor, byid=TRUE) / (1e6) ##Read population pop <- readr::read_csv2(file=file.path(filePath, "EWR201512E_Matrix.csv")) ##Merge SpatialPolygonsDataFrame with population information lor_pop <- merge(lor, pop, by.x="PLR",by.y="RAUMID") Plotting the result of the pgrs object as an instance of SpatialPolygonsDataFrame can be done using the standard Spatial* plotting routines documented extensively in, e.g, Bivand, Pebesma, and Gómez-Rubio (2008) and its comprehensive webpage. ###################################################################### ## Plotting the result, see nice tutorial by ## http://www.nickeubank.com/wp-content/uploads/2015/10/RGIS3_MakingMaps_part1_mappingVectorData.html ## or the Bivand et al. (2008) book - a must read! ## Note: there is a 2nd edition available nowadays. ###################################################################### library(RColorBrewer) my.palette <- brewer.pal(n = 6, name = "Purples") ##Helper function for making labels for each entry sp.label <- function(x, label) {list("sp.text", coordinates(x), label,cex=0.5)} borderCol <- "white" #Plot choropleth map spplot(pgrs, "density", col.regions = my.palette, cuts = length(my.palette)-1, col = borderCol,main="Choropleth map of Population Density",sp.layout=sp.label(pgrs, pgrs$EXTPGRNAME))
require(grid)
grid.text(expression("Population density (Persons / "~km^2~")"), x=unit(0.95, "npc"), y=unit(0.50, "npc"), rot=-90)

## Installing the Cartogram R packages

Two packages Rcartogram and getcartr make the functionality of the Gastner and Newman (2004) procedure available for working with objects of class Spatial*. Installing Rcartogram requires the fftw library to be installed. How to best do that depends on your system, for Mac OS X the homebrew package system makes this installation easy.

##On command line in OS/X with homebrew. Wrapped in FALSE statement to not run system() unintentionally
if (FALSE) {
system("brew install fftw")
}
##Install the R implementation of Cart by Gastner and Newman (2004)
devtools::install_github("omegahat/Rcartogram")
devtools::install_github('chrisbrunsdon/getcartr',subdir='getcartr')

We are now ready to compute our first cartogram using the getcartr::quick.carto function.

library(Rcartogram)
library(getcartr)

##Make a cartogram
pgrs_carto <- quick.carto(spdf=pgrs,v=pgrs$E_E,res=256) ##Display it using sp functionality spplot(pgrs_carto, "area", col.regions = my.palette, cuts = length(my.palette)-1, col = borderCol,main="Population Cartogram as Choropleth of Area",sp.layout=sp.label(pgrs_carto, label=pgrs_carto$EXTPGRNAME))
grid::grid.text(expression("Area (km"^2*")"), x=unit(0.95, "npc"), y=unit(0.50, "npc"), rot=-90)

With the cartogram functionality now being directly available through R allows one to embedd cartogram making in a full R pipeline. We illustrate this by generating a sequence of cartograms into an animated GIF file using the animation package. The animation below shows a cartogram for the population size for each of the 32 age groups in the Berlin data set. One observes that the 25-45 year old tend to live in the city centre, while the 95-110 year old seem to concentrate in the wealthy regions in the south west.

# Outlook

While writing this posts some other useRs have posted on how to create interactive cartograms.

# References

Bivand, R. S., E. J. Pebesma, and V. Gómez-Rubio. 2008. Applied Spatial Data Analysis with r. Springer-Verlag.
Gastner, Michael T., and M. E. J. Newman. 2004. “Diffusion-Based Method for Producing Density-Equalizing Maps.” Proceedings of the National Academy of Sciences of the United States of America 101 (20): 7499–7504. https://doi.org/10.1073/pnas.0400280101.
Zeileis, Achim, Kurt Hornik, and Paul Murrell. 2009. “Escaping RGBland: Selecting Colors for Statistical Graphics.” Computational Statistics & Data Analysis 53 (9): 3259–70. https://doi.org/http://dx.doi.org/10.1016/j.csda.2008.11.033.