Buried in the London Datastore are the population estimates for each of the London Boroughs between 2001 – 2030. They predict a declining population for most boroughs with the exception of a few to the east. I was surprised by this general decline and also the numbers involved- I expected larger changes from one year to the next. I think this is because my perception of migration is of the volume of people moving rather than the net effects on the baseline population of these movements. I don’t envy the GLA for making predictions so far into the future, but can understand why they have to do it (think how long it took initiate Crossrail!). Last year I produced a simple animation showing past changes in London’s population density (data) and it provides a nice comparison to the above. In total I have squeezed 40 maps on this page!

Technical Stuff

These maps were all produced to demonstrate the mapping capabilities of R. The first uses ggplot2 (plus classInt + RColorBrewer) and is based on some code (see below) written by Mark Bulling. If you follow the code below you will end up with this map, not the one I have produced above. I will stick my code in a formal tutorial soon. The animation uses the standard plot functions (plus spatial packages) in R as per this example.

Data: all_date, Chart ID: MotionChart_2011-01-10-10-16-25

R version 2.12.1 (2010-12-16),

Google Terms of Use

Data: all_date, Chart ID: MotionChart_2011-01-10-10-10-46

R version 2.12.1 (2010-12-16),

Google Terms of Use

It was a bit fiddly to get the data formatted correctly and I couldn’t manage to get the complete dataset in one plot because my browser kept crashing (Chrome is best). Even with these teething problems it is a great way to get people creating better visualizations with their data. If you want to see Hans Rosling demonstrating these plots with his trademark enthusiasm I thoroughly recommend “The Joy of Stats” a program produced for the BBC. You can watch it here.

For those who want to create their own plots, I’m not proud of the code I used to format the data above so to get you started try this example (provided with the package).

library(googleVis)

data(Fruits)

M1 <- gvisMotionChart(Fruits, idvar=”Fruit”, timevar=”Year”)

plot(M1)

Thanks to the Recology blog for promoting this.

Cities are one of the many phenomena that follow a long-tailed distribution. In simple terms there are a few big cities and lots of small ones. The classic way of showing a long tailed distribution (and the method from which the name is taken) is to produce as plot such as that below:

The infographic at the top of the post by chinfographics.com demonstrates the distribution in a more engaging and constructive way.

One method I have used in the past to demonstrate data with a long tailed distribution is the excellent Wordle tool. I have created a Wordle (below) for the same data (downloaded from Chinfographics). Whilst it does not compete with the Chinfographics infographic in terms of quality,  I still think Wordles provide a very simple, and effective, method of displaying data with a “long tail”.

Many of us felt it would actually serve as an example of poor visualization for reasons that I think are worth mentioning here. Aside from the fact that it is missing a North Arrow and Scale, the map is misleading. The highest category represents areas where between 6.4% to 60.6% of the population are non-white. This range of values grouped together is too large in this context. Additional categories in the data would highlight the exceptional areas and prevent areas with a population of only 7% non-white, for example, being grouped with areas characterised by a population that is over 60% non-white.  In addition the spatial units (Administrative Districts) used to plot the data are too course. The Admin. Districts create an impression that the non-white ethnic groups are fairly evenly spread across Central and Southern England, and, aside from London, not clustered in major urban centres as the associated text suggests (ONS, 2004). Reproducing this map with smaller spatial units (such as Super Output Areas) and a greater number of categories for the data would produce a much more accurate picture of the spatial distribution of the non-white population in the UK today. This example demonstrates the importance of a critical eye when viewing all visualizations, no matter how official the publication may be!