Visualising Spatial Data UCL Geography R Spatial Workshop Tuesday 9 th November 2010 rspatialtips.org.uk

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1 Visualising Spatial Data UCL Geography R Spatial Workshop Tuesday 9 th November 2010 james.cheshire@ucl.ac.uk rspatialtips.org.uk The previous workshop served as an in depth introduction to R's spatial data handling capabilities. In this workshop we will produce a gallery of maps using many of the plotting tools introduced in the second workshop in the course. The maps produced today may not be that meaningful- the focus here is on sound visualisation not sound analysis (I know one is useless without the other!). Good quality spatial analysis will come in the next couple of weeks. Whilst the instructions are step by step you are encouraged to start deviating from them (trying different colours for example) to get a better understanding of what we are doing. #Load Packages: library(maptools) library(ggplot2) library(rcolorbrewer) library(classint) library(sm) #Set your working directory (this should include all the data provided for this session). The data can be downloaded from The "poly_coords_function.r" file is incorrect. Please replace it with the file downloadable from setwd( XX/XX ) Mapping Surfaces Last week you did this with the UK temperature data. This conveniently came clipped' to within the confines of the UK coastline. In R it is possible to create a surface from point data. The resulting outputs often fill the bounding box of the spatial data (and are therefore square/rectangular) so need to be clipped or cookie- cut to the irregular shape your study area is likely to have. In this example we will produce a hot-spot surface of bike hire availability in London based on the locations, and capacity, of docking stations and then clip the surface to our area of interest- the City of London. ## Load the cycle hire locations. Cycle<- read.csv("london_cycle_hire_locs.csv", header=t) ## Inspect column headings to check the data have loaded OK. head(cycle) ## Plot the XY coordinates (do not close the plot window). plot(cycle$x, Cycle$Y)

2 ##Load the City of London Boundary shapefile. This contains the geometry information for the City of London Borough (from OS Opendata). lon_city<- readshapepoly("lond_city.shp") ## Plot lon to see what it looks like in the context of cycle hire points. plot(lon_city, add=t, lwd=2) ## The boundary may look a bit distorted. This is because we have not plotted the correct aspect ratio. This can be achieved with asp=t. plot(cycle$x, Cycle$Y, asp=t) plot(lon_city, add=t, lwd=2) ## Create a density surface based on the locations of the points. It is commonly called a hotspot map. This uses the sm.density function in the sm package. For those interested we are using a technique called weighted Kernel Density Estimation (KDE). Four inputs are required here. The XY coordinates, the weights- in this case we are using the capacity of the cycle stations, the display how R plots the output, and the grid resolution- 100 by 100 in this case. Cycle_dens<- sm.density(data.frame(cycle$x, Cycle$Y), weights=cycle$capacity, display= "image", ngrid=100) ## Change the display type. You can also use slice and rgl ## add the points and City of London boundary for context. points(cycle$x, Cycle$Y) plot(lon_city, add=t, lwd=2) ## We can convert the Cycle_dens output into a SpatialPixelsDataFrame frame for further spatial analysis. In this step we can define a spatial reference and also save the surface for use in other software. temp=spatialpoints(expand.grid(x=cycle_dens$eval.points[,1], y=cycle_dens$eval.points[,2]), proj4string= CRS("+init=epsg:27700")) temp = SpatialPixelsDataFrame(temp, data.frame(kde = array(cycle_dens$estimate, length(cycle_dens$estimate)))) ## Now cookie-cut (clip) the surface to only include the City of London. The first step overlays the boundary data and identifies the grid cells that fall within it. The second step selects these cells to create the clipped_grid object. sel=!is.na(overlay(temp, lon_city))

3 clipped_grid= temp[sel,] ## Plot the final map. image(clipped_grid) plot(lon_city, add=t, lwd=2, asp=t) points(cycle$x, Cycle$Y) title("density of London Cycle Hire Bikes in the City of London") Mapping Discrete Data The key difference here is that we want to fill irregular polygons with solid colour rather than grid cells. As with all plots in R, there are multiple ways we can do this. The basic plot() function requires no data preparation but additional effort in colour selection/ adding the key etc. qplot() and ggplot()require some additional steps to format the spatial data but select colours add keys etc automatically. ## load the shapefile sport<- readshapepoly("london_sport.shp") ## All shapefiles have an attribute table. This is loaded with readshapepoly and can be treated in a similar way to a data.frame. R hides the geometry etc unless you print the object. Look at the headings of sport. names(sport) ##The following steps will create a map to show the percentage of the population in each London Borough who regularly participate in sports activities. The first job is to select an initial colour palette and the number of colours (5 in this case) you wish to display. For this we use a Color Brewer Palette developed my Cynthia Brewer. Many of the palettes have been provided in the RColorBrewer or you can manually enter the rgb values from the Color Brewer website: colours<- brewer.pal(5, "Blues") ## Specifying the range of values that each colour represents can be done manually brks<-c(10, 15, 20, 25) ## or preferably automatically with the classintervals function from the classint package. Look at the help for this package to see the options available. brks<-classintervals(sport$partic_per, n=5, style="quantile") ##It is possible to plot distribution of the data and view the colours assigned to each point. Here is a good point to play around with some of the other classintervals options. You can of course change the number of breaks. If you do this you should remember to create a new colour palette.

4 Consult the RColorBrewer help to see the maximum numbers of breaks/intervals allowed for each palette. plot(brks, pal=colours) ## Look at the brks object. summary(brks) ## You can see that there are two variables var and brks. We are only interested in brks from now on. brks<- brks$brks ## We have now done all the data preparation required to produce the map. plot(sport, col=colours[findinterval(sport$partic_per, brks)], axes=f, asp=t) ## use the help to find out what findinterval does. ##The map is still missing important information, such as a key, scale bar and additional information (such as copyright, author etc). Adding these is a bit labour intensive as you need to specify their locations as coordinates within the plot. So it takes a bit of trial and error to get it right. This information can be added to the bike hire surface map we produced above. You may wish to try this. ## North arrow: SpatialPolygonsRescale(layout.north.arrow(1), offset= c(505100,160000), scale = 6000, plot.grid=f) ##Scale bar: SpatialPolygonsRescale(layout.scale.bar(), offset= c(503800,154800), scale= 10000, fill= c("transparent", "black"), plot.grid= F) ## Legend legend(x=548500, y=164800, legend=leglabs(brks), fill=colours, bty="n") ## The functions above may be a bit black-box so it is worth using the help files and altering the variables to see what is happening. Finish the map with a border, title and some labels. box() title(paste ("London Sports Participation")) text(509000, , "10KM", cex= 1)

5 text(534000,152000, "Boundary Data Crown Copyright Ordnance Survey 2009.", cex= 1) text(556500, , "% Participation", cex= 1) Producing maps with ggplot2 As I said above ggplot produces very good maps with less effort than above, it just requires one additional step before plotting. ## Check that you still have the sport object, if not reload it. ## Last week the idea of slots within the SpatialPolygonsDataFrame were introduced. The polygons slot contains the geometry of the polygons in the form of the XY coordinates required for the polygon outline. The generic plot function can work out what to do with these, ggplot2 cannot. We therefore need to extract them as a dataframe. Theoretically this can be done using the fortify function written specifically for this purpose. sport_geom<- fortify(sport) ##I have found this to be a little unreliable, especially when using a Mac and 64bit installation of R. I have therefore written a function that does a similar job which we will use here. I don't think it works if your polygons have holes (whereas fortify does). It is worth playing around with both. ## load my function: source("poly_coords_function.r") ##It requires an ID field in the SpatialPolygonsDataFrame, we will rename the "ons_label" field for this. names(sport)<- c("id", "name", "Partic_Per", "Pop_2001") ## Call the function to get the coordinates sport_geom<- poly_coords(sport) ## have a look at the sport_geom object to see its contents. head(sport_geom) ## It is now straightforward to produce a map using all the built in tools (such as setting the breaks in the data) that ggplot2 has to offer. coord_equal() is the equivalent of asp=t: map<- qplot(polycoordsy, PolyCoordsX, data=sport_geom, group=poly_name, fill= Partic_Per, geom="polygon", main="london Sports Participation", xlab="easting (m)", ylab="northing (m)") + coord_equal() map # The default colours are really nice but we may wish to produce the map in black and white:

6 map + scale_fill_gradient(low="white", high="black") # Or use another colour scheme such as one recommended by Color Brewer or introduce more breaks in the data. This requires the creation of a small function called new_fill. new_fill<- function(pal, lowerlim, upperlim){ scale_fill_gradient(colours= pal, limits=c(lowerlim, upperlim)) } # The new_fill function requires the name of the colour palette and any parameters required for it as well as the upper and lower limits of the data you wish to plot. map+new_fill(brewer.pal(7, "Blues"), 0, 30) Extension Task Using the London Population shapefile produce a map of population density for a selected year. Try to produce a map with different colours to the ones above. Also experiment with the number of breaks/ intervals shown in the map. me a PNG file (surname.png) so I can show it next week. I will also share my suggested code.