One of the great things about (software) code is that people build on it and out from it… Which means that as well as producing ever more complex bits of software, tools also get produced over time that make it easier to do things that were once hard to do, or required expensive commercial software tools.
Producing maps is a fine example of this. Not so very long ago, producing your own annotated maps was a hard thing to do. Then in June, 2005, or so, the Google Maps API came along and suddenly you could create your own maps (or at least, put markers on to a map if you had latitude and longitude co-ordinates available). Since then, things have just got easier. If you want to put markers on a map just given their addresses, it’s easy (see for example Mapping the New Year Honours List – Where Did the Honours Go?). You can make use of Ordnance Survey maps if you want to, or recolour and style maps so they look just the way you want.
Sometimes, though, when using maps to visualise numerical data sets, just putting markers onto a map, even when they are symbols sized proportionally in accordance with your data, doesn’t quite achieve the effect you want. Sometimes you just have to have a thematic, choropleth map:
The example above is taken from an Ordnance Survey OpenSpace tutorial, which walks you through the creation of thematic maps using the OS API.
But what do you do if the boundaries/shapes you want to plot aren’t supported by the OS API?
One of the common ways of publishing boundary data is in the form of shapefiles (suffix .shp, though they are often combined with several other files in a .zip package). So here’s a quick first attempt at plotting shapefiles and colouring them according to an appropriately defined data set.
The example is based on a couple of data sets – shapefiles of the English Government Office Regions (GORs), and a dataset from the Ministry of Justice relating to insolvencies that, amongst other things, describes numbers of insolvencies per time period by GOR.
The language I’m using is R, within the RStudio environment. Here’s the code:
#Download English Government Office Network Regions (GOR) from: #http://www.sharegeo.ac.uk/handle/10672/50 ##tmpdir/share geo loader courtesy of http://stackoverflow.com/users/1033808/paul-hiemstra tmp_dir = tempdir() url_data = "http://www.sharegeo.ac.uk/download/10672/50/English%20Government%20Office%20Network%20Regions%20(GOR).zip" zip_file = sprintf("%s/shpfile.zip", tmp_dir) download.file(url_data, zip_file) unzip(zip_file, exdir = tmp_dir) library(maptools) #Load in the data file (could this be done from the downloaded zip file directly? gor=readShapeSpatial(sprintf('%s/Regions.shp', tmp_dir)) #I can plot the shapefile okay... plot(gor)
Here’s what it looks like:
#I can use these commands to get a feel for the data contained in the shapefile... summary(gor) attributes(gor@data) gor@data$NAME # North East North West # Greater London Authority West Midlands # Yorkshire and The Humber South West # East Midlands South East # East of England #9 Levels: East Midlands East of England ... Yorkshire and The Humber #download data from http://www.justice.gov.uk/downloads/publications/statistics-and-data/courts-and-sentencing/csq-q3-2011-insolvency-tables.csv insolvency<- read.csv("http://www.justice.gov.uk/downloads/publications/statistics-and-data/courts-and-sentencing/csq-q3-2011-insolvency-tables.csv") #Grab a subset of the data, specifically to Q3 2011 and numbers that are aggregated by GOR insolvencygor.2011Q3=subset(insolvency,Time.Period=='2011 Q3' & Geography.Type=='Government office region') #tidy the data - you may need to download and install the gdata package first #The subsetting step doesn't remove extraneous original factor levels, so I will. require(gdata) insolvencygor.2011Q3=drop.levels(insolvencygor.2011Q3) names(insolvencygor.2011Q3) # "Time.Period" "Geography" # "Geography.Type" "Company.Winding.up.Petition" # "Creditors.Petition" "Debtors.Petition" levels(insolvencygor.2011Q3$Geography) # "East" "East Midlands" # "London" "North East" # "North West" "South East" # "South West" "Wales" # "West Midlands" "Yorkshire and the Humber" #Note that these names for the GORs don't quite match the ones used in the shapefile, though how they relate one to another is obvious to us... #So what next? [That was the original question...!] #Here's the answer I came up with... #Convert factors to numeric [ http://stackoverflow.com/questions/4798343/convert-factor-to-integer ] #There's probably a much better formulaic way of doing this/automating this? insolvencygor.2011Q3$Creditors.Petition=as.numeric(levels(insolvencygor.2011Q3$Creditors.Petition))[insolvencygor.2011Q3$Creditors.Petition] insolvencygor.2011Q3$Company.Winding.up.Petition=as.numeric(levels(insolvencygor.2011Q3$Company.Winding.up.Petition))[insolvencygor.2011Q3$Company.Winding.up.Petition] insolvencygor.2011Q3$Debtors.Petition=as.numeric(levels(insolvencygor.2011Q3$Debtors.Petition))[insolvencygor.2011Q3$Debtors.Petition] #Tweak the levels so they match exactly (really should do this via a lookup table of some sort?) i2=insolvencygor.2011Q3 i2c=c('East of England','East Midlands','Greater London Authority','North East','North West','South East','South West','Wales','West Midlands','Yorkshire and The Humber') i2$Geography=factor(i2$Geography,labels=i2c) #Merge the data with the shapefile gor@data=merge(gor@data,i2,by.x='NAME',by.y='Geography') #Plot the data using a greyscale plot(gor,col=gray(gor@data$Creditors.Petition/max(gor@data$Creditors.Petition)))
And here’s the result:
Okay – so it’s maybe not the most informative of maps, it needs a scale, the London data is skewed, etc etc… But it shows that the recipe seems to work..
(Here’s a glimpse of how I worked my way to this example using a question to Stack Overflow: Plotting Thematic Maps in R Using Shapefiles and Data Files from DIfferent Sources (note: better solutions may have since been posted to that question, and which may improve on the recipe provided in this post…)
PS If the R thing is just too scary, here’s a recipe for plotting data using shapefiles in Google Fusion Tables [PDF] (alternative example) that makes use of the ShpEscape service for importing shapefiles into Fusion Tables (note that shpescape can be a bit slow converting an uploaded file and may appear to be doing nothing much at all for 10-20 minutes…). See also: Quantum GIS