Interactive maps of Crime data in Greater London

In the previous post we looked at ways to perform some introductory point pattern analysis of open data downloaded from Police.uk. As you remember we subset the dataset of crimes in the Greater London area, extracting only the drug related ones. Subsequently, we looked at ways to use those data with the package spatstat and perform basic statistics.
In this post I will briefly discuss ways to create interactive plots of the results of the point pattern analysis using the Google Maps API and Leaflet from R.

Number of Crimes by Borough
In the previous post we looped through the GreaterLondonUTM shapefile to extract the area of each borough and then counted the number of crimes within its border. To show the results we used a simple barplot. Here I would like to use the same method I presented in my post Interactive Maps for the Web to plot these results on Google Maps.

This post is intended to be a continuation of the previous, so I will not present again the methods and objects we used in the previous experiment. To make this code work you can just copy and paste it below the code you created before and it should work just fine.

First of all, let's create a new object including only the names of the boroughs from the GreaterLondonUTM shapefile. We need to do this because otherwise when we will click on a polygons on the map it will show us a long list of useless data.

GreaterLondon.Google <- GreaterLondonUTM[,"name"]

The new object has only one column with the name of each borough.
Now we can create a loop to iterate through these names and calculate the intensity of the crimes:

Borough <- GreaterLondonUTM[,"name"]
 
for(i in unique(GreaterLondonUTM$name)){
sub.name <- Local.Intensity[Local.Intensity[,1]==i,2]
 
Borough[Borough$name==i,"Intensity"] <- sub.name
 
Borough[Borough$name==i,"Intensity.Area"] <- round(sub.name/(GreaterLondonUTM[GreaterLondonUTM$name==i,]@polygons[[1]]@area/10000),4)
}

As you can see this loop selects one name at the time, then subset the object Local.Intensity (which we created in the previous post) to extract the number of crimes for each borough. The next line attach this intensity to the object Borough as a new column named Intensity. However, the code does not stop here. We also create another column named Intensity.Area in which we calculate the amount of crimes per unit area. Since the area from the shapefile is in square meters and the number were very high, I though about dividing it by 10'000 in order to have a unit area of 10 square km. So this column shows the amount of crime per 10 square km in each borough. This should correct the fact that certain borough have a relatively high number of crimes only because their area is larger than others.

Now we can use again the package plotGoogleMaps to create a beautiful visualization of our results and save it in HTML so that we can upload it to our website or blog.
The code for doing that is very simple and it is presented below:

plotGoogleMaps(Borough,zcol="Intensity",filename="Crimes_Boroughs.html",layerName="Number of Crimes", fillOpacity=0.4,strokeWeight=0,mapTypeId="ROADMAP")

I decided to plot the polygons on top of the roadmap and not on top of the satellite image, which is the default for the function. Thus I added the option mapTypeId="ROADMAP".
The result is the map shown below and at this link: Crimes on GoogleMaps



In the post Interactive Maps for the Web in R I received a comment from Gerardo Celis, whom I thank for it, telling me that now in R is also available the package leafletR, that allows us to create interactive maps based on Leaflet. So for this new experiment I decided to try it out!

I started from the sample of code presented here: https://github.com/chgrl/leafletR and I adapted with very few changes to my data.
The function leaflet does not work directly with Spatial data, we first need to transform them into GeoJSON with another function in leafletR:

Borough.Leaflet <- toGeoJSON(Borough)

Extremely simple!!

Now we need to set the style to use for plotting the polygons using the function styleGrad, which is used to create a list of colors based on a particular attribute:

map.style <- styleGrad(pro="Intensity",breaks=seq(min(Borough$Intensity),max(Borough$Intensity)+15,by=20),style.val=cm.colors(10),leg="Number of Crimes", fill.alpha=0.4, lwd=0)

In this function we need to set several options:
pro = is the name of the attribute (as the column name) to use for setting the colors
breaks = this option is used to create the ranges of values for each colors. In this case, as in the example, I just created a sequence of values from the minimum to the maximum. As you can see from the code I added 15 to the maximum value. This is because the number of breaks needs to have 1 more element compared to the number of colors. For example, if we set 10 breaks we would need to set 9 colors. For this reason if the sequence of breaks ends before the maximum, the polygons with the maximum number of crimes would be presented in grey.
This is important!!

style.val = this option takes the color scale to be used to present the polygons. We can select among one of the default scales or we can create a new one with the function color.scale in the package plotrix, which I already discussed here: Downloading and Visualizing Seismic Events from USGS

leg = this is simply the title of the legend
fill.alpha = is the opacity of the colors in the map (ranges from 0 to 1, where 1 is the maximum)
lwd =  is the width of the line between polygons

After we set the style we can simply call the function leaflet to create the map:

leaflet(Borough.Leaflet,popup=c("name","Intensity","Intensity.Area"),style=map.style)

In this function we need to input the name of the GeoJSON object we created before, the style of the map and the names of the columns to use for the popups.
The result is the map shown below and available at this link: Leaflet Map

I must say this function is very neat. First of all the function plotGoogleMaps, if you do not set the name of the HTML file, creates a series of temporary files stored in your temp folder, which is not great. Then even if you set the name of the file the legend is saved into different image files every time you call the function, which you may do many times until you are fully satisfied the result.
The package leafletR on the other hand creates a new folder inside the working directory where it stores both the GeoJSON and the HTML file, and every time you modify the visualization the function overlays the same file.
However, I noticed that I cannot see the map if I open the HTML files from my PC. I had to upload the file to my website every time I changed it to actually see these changes and how they affected the plot. This may be something related to my PC, however.


Density of Crimes in raster format
As you may remember from the previous post, one of the steps included in a point pattern analysis is the computation of the spatial density of the events. One of the techniques to do that is the kernel density, which basically calculates the density continuously across the study area, thus creating a raster.
We already looked at the kernel density in the previous post so I will not go into details here, the code for computing the density and transform it into a raster is the following:

Density <- density.ppp(Drugs.ppp, sigma = 500,edge=T,W=as.mask(window,eps=c(100,100)))
Density.raster <- raster(Density)
projection(Density.raster)=projection(GreaterLondonUTM)

The first lines is basically the same we used in the previous post. The only difference is that here I added the option W to set the resolution of the map with eps at 100x100 m.
Then I simply transformed the first object into a raster and assign to it the same UTM projection of the object GreaterLondonUTM.
Now we can create the map. As far as I know (and for what I tested) leafletR is not yet able to plot raster objects, so the only way we have of doing it is again to use the function plotGoogleMaps:

plotGoogleMaps(Density.raster,filename="Crimes_Density.html",layerName="Number of Crimes", fillOpacity=0.4,strokeWeight=0,colPalette=rev(heat.colors(10)))

When we use this function to plot a raster we clearly do not need to specify the zcol option. Moreover, here I changed the default color scale using the function colPalette to a reverse heat.colors, which I think is more appropriate for such a map. The result is the map below and at this link: Crime Density

Density of Crimes as contour lines
The raster presented above can also be represented as contour lines. The advantage of this type of visualization is that it is less intrusive, compared to a raster, and can also be better suited to pinpoint problematic locations.
Doing this in R is extremely simple, since there is a dedicated function in the package raster:

Contour <- rasterToContour(Density.raster,maxpixels=100000,nlevels=10)

This function transforms the raster above into a series of 10 contour lines (we can change the number of lines by changing the option nlevels).

Now we can plot these lines to an interactive web map. I first tested again the use of plotGoogleMaps but I was surprised to see that for contour lines it does not seem to do a good job. I do not fully know the reason, but if I use the object Contour with this function it does not plot all the lines on the Google map and therefore the visualization is useless.
For this reason I will present below the lines to plot contour lines using leafletR:

Contour.Leaflet <- toGeoJSON(Contour)
 
colour.scale <- color.scale(1:(length(Contour$level)-1),color.spec="rgb",extremes=c("red","blue"))
map.style <- styleGrad(pro="level",breaks=Contour$level,style.val=colour.scale,leg="Number of Crimes", lwd=2)
leaflet(Contour.Leaflet,style=map.style,base.map="tls")

As mentioned, the first thing to do to use leafletR is to transform our Spatial object into a GeoJSON; the object Contour belongs to the class SpatialLinesDataFrame, so it is supported in the function toGeoJSON.
The next step is again to set the style of the map and then plot it. In this code I changed a few things just to show some more options. The first thing is the custom color scale I created using the function color.scale in the package plotrix. The only thing that the function styleGrad needs to set the colors in the option style.val is a vector of colors, which must be long one unit less than the vector used for the breaks. In this case the object Contour has only one property, namely "level", which is a vector of class factor. The function styleGrad can use it to create the breaks but the function color.scale cannot use it to create the list of colors. We can work around this problem by setting the length of the color.scale vector using another vector: 1:(length(Contour$level)-1, which basically creates a vector of integers from 1 to the length of Contours minus one. The result of this function is a vector of colors ranging from red to blue, which we can plug in in the following function.
In the function leaflet the only thing I changed is the base.map option, in which I use "tls". From the help page of the function we can see that the following options are available:

"One or a list of "osm" (OpenStreetMap standard map), "tls" (Thunderforest Landscape), "mqosm" (MapQuest OSM), "mqsat" (MapQuest Open Aerial),"water" (Stamen Watercolor), "toner" (Stamen Toner), "tonerbg" (Stamen Toner background), "tonerlite" (Stamen Toner lite), "positron" (CartoDB Positron) or "darkmatter" (CartoDB Dark matter). "

These lines create the following image, available as a webpage here: Contour

R code snippets created by Pretty R at inside-R.org

Interactive maps for the web in R

Static Maps
In the last post I showed how to download economic data from the World Bank's website and create choropleth maps in R (Global Economic Maps).
In this post I want to focus more on how to visualize those maps.

Sp Package
Probably the simplest way of plotting choropleth maps in R is the one I showed in the previous post, using the function ssplot(). For example with a call like the following:

library(sp)
spplot(polygons,"CO2",main=paste("CO2 Emissions - Year:",CO2.year),sub="Metric Tons per capita")

This function takes the object polygons, which is a SpatialPolygonsDataFrame, and in quotations marks the name of the column where to find the values to assign, as colors, to each polygon. This are two basic mandatory elements to call the function. However, we can increase the information in the plot by adding additional elements, such as a title, option main, and a sub title, option sub.

This function creates the following plot:

The color scale is selected automatically and can be changed with a couple of standard functions or using customized color scales. For more information please refer to this page: http://rstudio-pubs-static.s3.amazonaws.com/7202_3145df2a90a44e6e86a0637bc4264f9f.html


Standard Plot
Another way of plotting maps is by using the standard plot() function, which allows us to increase the flexibility of the plot, for example by customizing the position of the color legend.
The flip side is that, since it is the most basic plotting function available and it does not have many built-in options, this function require more lines of code to achieve a good result.
Let's take a look at the code below:

library(plotrix)
CO2.dat <- na.omit(polygons$CO2)
colorScale <- color.scale(CO2.dat,color.spec="rgb",extremes=c("red","blue"),alpha=0.8)
 
colors.DF <- data.frame(CO2.dat,colorScale)
colors.DF <- colors.DF[with(colors.DF, order(colors.DF[,1])), ]
colors.DF$ID <- 1:nrow(colors.DF)
breaks <- seq(1,nrow(colors.DF),length.out=10)
 
 
jpeg("CO2_Emissions.jpg",7000,5000,res=300)
plot(polygons,col=colorScale)
title("CO2 Emissions",cex.main=3)
 
legend.pos <- list(x=-28.52392,y=-20.59119)
legendg(legend.pos,legend=c(round(colors.DF[colors.DF$ID %in% round(breaks,0),1],2)),fill=paste(colors.DF[colors.DF$ID %in% round(breaks,0),2]),bty="n",bg=c("white"),y.intersp=0.75,title="Metric tons per capita",cex=0.8) 
 
dev.off()

By simpling calling the plot function with the object polygons, R is going to create an image of the country borders with no filling. If we want to add colors to the plot we first need to create a color scale using our data. To do so we can use the function color.scale() in the package plotrix, which I used also in the post regarding the visualization seismic events from USGS (Downloading and Visualizing Seismic Events from USGS ). This function takes a vector, plus the color of the extremes of the color scale, in this case red and blue, and creates a vector of intermediate colors to assign to each element of the data vector.
In this example I first created a vector named CO2.dat, with the values of CO2 for each polygon, excluding NAs. Then I feed it to the color.scale() function.

The next step is the creation of the legend. The first step is the creation of the breaks we are going to need to present the full spectrum of colors used in the plot. For this I first created a data.frame with values and colors and then I subset it into 10 elements, which is the length of the legend.
Now we can submit the rest of the code to create a plot and the legend and save it into the jpeg file below:

Interactive Maps
The maps we created thus far are good for showing our data on papers and allow the reader to have a good understanding of what we are trying to show with them. Clearly these are not the only two methods available to create maps in R, many more are available. In particular ggplot2 now features ways of creating beautiful static maps. For more information please refer to these website and blog posts:
Maps in R
Making Maps in R
Introduction to Spatial Data and ggplot2
Plot maps like a boss
Making Maps with R

In this post however, I would like to focus on ways to move away from static maps and embrace the fact that we are now connected to the web all the times. This allow us to create maps specifically design for the web, which can also be much more easy to read by the general public that is used to them.
These sort of maps are the interactive maps that we see all over the web, for example from Google. These are created in javascript and are extremely powerful. The problem is, we know R and we work on it all the time, but we do not necessarily know how to code in javascript. So how can we create beautiful interactive maps for the web if we cannot code in javascript and HTML?

Luckily for us developers have create packages that allow us to create maps using standard R code but n the form of HTML page that we can upload directly on our website. I will now examine the packages I know and use regularly for plotting choropleth maps.


googleVis
This package is extremely simple to use and yet capable of creating beautiful maps that can be uploaded easily to our website.
Let's look at the code below:

data.poly <- as.data.frame(polygons)
data.poly <- data.poly[,c(5,12)]
names(data.poly) <- c("Country Name","CO2 emissions (metric tons per capita)")
 
map <- gvisGeoMap(data=data.poly, locationvar = "Country Name", numvar='CO2 emissions (metric tons per capita)',options=list(width='800px',heigth='500px',colors="['0x0000ff', '0xff0000']"))
plot(map)
 
print(map,file="Map.html")
 
#http://www.javascripter.net/faq/rgbtohex.htm
#To find HEX codes for RGB colors

The first thing to do is clearly to load the package googleVis. Then we have to transform the SpatialPolygonsDataFrame into a standard data.frame. Since we are interested in plotting only the data related to the CO2 emissions for each country (as far as I know with this package we can plot only one variable for each map), we can subset the data.frame, keeping only the column with the names of each country and the one with the CO2 emissions. Then we need to change the names of these two columns so that the user can readily understand what he is looking at.
Then we can simply use the function gvisGeoMap() to create a choropleth maps using the Google Visualisation API. This function does not read the coordinates from the object, but we need to provide the names of the geo locations to use with the option locationvar, in this case the Google Visualisation API will take the names of the polygons and match them to the geometry of the country. Then we have the option numvar, which takes the name of the column where to find the data for each country. Then we have options, where we can define various customizations available in the Google Visualisation API and provided at this link: GoogleVis API Options
In this case I specified the width and height of the map, plus the two color extremes to use for the color scale. 
The result is the plot below:

This is an interactive plot, meaning that if I hover the mouse over a country the map will tell me the name and the amount of CO2 emitted. This map is generated directly from R but it all written in HTML and javascript. We can use the function print(), presented in the snippet above, to save the map into an HTML file that can upload as is on the web.
The map above is accessible from this link:  GoogleVis Map


plotGoogleMaps
This is another great package that harness the power of Google's APIs to create intuitive and fully interactive web maps. The difference between this and the previous package is that here we are going to create interactive maps using the Google Maps API, which is basically the one you use when you look up a place on Google Maps.
Again this API uses javascript to create maps and overlays, such as markers and polygons. However, with this package we can use very simple R code and create stunning HTML pages that we can just upload to our websites and share with friends and colleagues.

Let's look at the following code:

library(plotGoogleMaps)
 
polygons.plot <- polygons[,c("CO2","GDP.capita","NAME")]
polygons.plot <- polygons.plot[polygons.plot$NAME!="Antarctica",]
names(polygons.plot) <- c("CO2 emissions (metric tons per capita)","GDP per capita (current US$)","Country Name")
 
#Full Page Map
map <- plotGoogleMaps(polygons.plot,zoom=4,fitBounds=F,filename="Map_GoogleMaps.html",layerName="Economic Data")
 
 
#To add this to an existing HTML page
map <- plotGoogleMaps(polygons.plot,zoom=2,fitBounds=F,filename="Map_GoogleMaps_small.html",layerName="Economic Data",map="GoogleMap",mapCanvas="Map",map.width="800px",map.height="600px",control.width="200px",control.height="600px")

Again there is a bit of data preparation to make. Again we need to subset the polygons dataset and keep only the variable we would need for the plot (with this package this is not mandatory, but it is maybe good to avoid large objects). Then we have to exclude Antarctica, otherwise the interactive map will have some problems (you can try and leave it to see what it does and maybe figure out a way to solve it). Then again we change the names of the columns in order to be more informative.

At this point we can use the function plotGoogleMaps() to create web maps in javascript. This function is extremely simple to use, it just takes one argument, which is the data.frame and creates a web map (R opens the browser to show the output). There are clearly ways to customize the output, for example by choosing a level of zoom (in this case the fiBounds option needs to be set to FALSE). We can also set a layerName to show in the legend, which is automatically created by the function.
Finally, because we want to create an HTML file to upload to our website, we can use the option filename to save it.
The result is a full screen map like the one below:

This map is available here: GoogleMaps FullScreen


With this function we also have ways to customize not only the map itself but also the HTML page so that we can later add information to it. In the last line of the code snippet above you can see that I added the following options to the function plotGoogleMaps():

mapCanvas="Map",map.width="800px",map.height="600px",control.width="200px",control.height="600px"

These options are intended to modify the aspect of the map on the web page, for example its width and height, and the aspect of the legend and controls, with controls.width and controls.height. We can also add the id of the HTML <div> element that will contain the final map.
If we have some basic experience with HTML we can then open the file and tweak a bit, for example by shifting map and legend to the center and adding a title and some more info.

This map is available here: GoogleMaps Small

The full code to replicate this experiment is presented below:

 #Methods to Plot Choropleth Maps in R  
 load(url("http://www.fabioveronesi.net/Blog/polygons.RData"))  
   
 #Standard method  
 #SP PACKAGE  
 library(sp)  
 spplot(polygons,"CO2",main=paste("CO2 Emissions - Year:",CO2.year),sub="Metric Tons per capita")  
   
   
 #PLOT METHOD  
 library(plotrix)  
 CO2.dat <- na.omit(polygons$CO2)  
 colorScale <- color.scale(CO2.dat,color.spec="rgb",extremes=c("red","blue"),alpha=0.8)  
   
 colors.DF <- data.frame(CO2.dat,colorScale)  
 colors.DF <- colors.DF[with(colors.DF, order(colors.DF[,1])), ]  
 colors.DF$ID <- 1:nrow(colors.DF)  
 breaks <- seq(1,nrow(colors.DF),length.out=10)  
   
   
 jpeg("CO2_Emissions.jpg",7000,5000,res=300)  
 plot(polygons,col=colorScale)  
 title("CO2 Emissions",cex.main=3)  
   
 legend.pos <- list(x=-28.52392,y=-20.59119)  
 legendg(legend.pos,legend=c(round(colors.DF[colors.DF$ID %in% round(breaks,0),1],2)),fill=paste(colors.DF[colors.DF$ID %in% round(breaks,0),2]),bty="n",bg=c("white"),y.intersp=0.75,title="Metric tons per capita",cex=0.8)   
   
 dev.off()  
   
   
   
   
   
 #INTERACTIVE MAPS  
 #googleVis PACKAGE  
 library(googleVis)  
   
 data.poly <- as.data.frame(polygons)  
 data.poly <- data.poly[,c(5,12)]  
 names(data.poly) <- c("Country Name","CO2 emissions (metric tons per capita)")  
   
 map <- gvisGeoMap(data=data.poly, locationvar = "Country Name", numvar='CO2 emissions (metric tons per capita)',options=list(width='800px',heigth='500px',colors="['0x0000ff', '0xff0000']"))  
 plot(map)  
   
 print(map,file="Map.html")  
   
 #http://www.javascripter.net/faq/rgbtohex.htm  
 #To find HEX codes for RGB colors  
   
   
   
   
   
 #plotGoogleMaps  
 library(plotGoogleMaps)  
   
 polygons.plot <- polygons[,c("CO2","GDP.capita","NAME")]  
 polygons.plot <- polygons.plot[polygons.plot$NAME!="Antarctica",]  
 names(polygons.plot) <- c("CO2 emissions (metric tons per capita)","GDP per capita (current US$)","Country Name")  
   
 #Full Page Map  
 map <- plotGoogleMaps(polygons.plot,zoom=4,fitBounds=F,filename="Map_GoogleMaps.html",layerName="Economic Data")  
   
   
 #To add this to an existing HTML page  
 map <- plotGoogleMaps(polygons.plot,zoom=2,fitBounds=F,filename="Map_GoogleMaps_small.html",layerName="Economic Data",map="GoogleMap",mapCanvas="Map",map.width="800px",map.height="600px",control.width="200px",control.height="600px")  
   
   

R code snippets created by Pretty R at inside-R.org