Blood is everywhere when it comes to describing cities. We have arterial roads, pulsing transport flows, and cities with different metabolisms. Thanks to great new datasets and visualisation software the analogy of cities with pulsing flows is being increasingly utilised for explanatory mapping. For example the work of UCL CASA’s Jon Reades above depicts the London Underground network as a set of arteries that thicken as passenger volumes passing through the network increase, whilst Pedro Miguel Cruz has taken it one step further to depict Lisbon’s roads as “blood vessels” (complete with their own clots).

I think these visualisations offer a neat conceptualisation of city flows provided the metaphor isn’t stretched too far. As the slime mould (below) shows us, we have a lot to learn from comparing our cities to biological processes- just so long as it doesn’t get too gory!

Thanks to an Xbox Kinect, Google Earth and some programming wizardry from UCL CASA researcher George MacKerron it is now possible to fly over London. The video below shows “Pigeon Sim” which has been developed to offer a fresh way of interacting with London’s urban data. Using Peter-Pan like arm gestures (above) users can fly over London’s landmarks passing various data feeds (such as real-time tweets or travel information) as they go. Pigeon Sim is one of the most fun methods I have seen for interacting with spatial data in a long time and I can’t wait to see more of CASA’s research outputs integrated into the system. Best viewed full screen.

I recently had the pleasure of presenting at the first Data Visualisation London Meetup event where I spoke about some of work we do at UCL CASA. A fair chunk of the slides were movies so I thought it best to stick them in a blog post. If you like what you see you can sign up for the CASA masters course or check out our other blogs.

First up was my interactive surname map of London.  I used this to demonstrate that “Big Data” (the general theme of the meetup) is nothing new (we have collected large- scale population data for over a century) and that we can use visualisation to demonstrate complex data.

Next, was the now famous animation of London’s transport flows produced by Joan Serras.

I then went on to say that we can begin to build more sophisticated maps of public transport by utilising routing algorithms. We took this approach to map the 114 thousand or so bus trips in London each day.

I then showed a couple of top-secret visualisations produced by Jon Reades and others at CASA. Stay tuned for when these are released. Twitter data featured in all talks and my chosen animation was produced by Anders Johansson in collaboration with Steven Gray and Fabian Neuhaus.

Next up were a couple of visualisations of cycle hire data in London (animation by Martin Zaltz-Austwick),

and other cities (below) to see how people utilize the schemes. You can see Oliver O’Brien’s live map here.

The final slide demonstrates how we are bringing all these themes together with the “City Dashboard” project. Click here (or image below) to take a real-time look at your city.

Last year Eric Fischer produced a great map (see below) visualising the language communities of Twitter. The map, perhaps unsurprisingly, closely matches the geographic extents of the world’s major linguistic groups. On seeing these broad patterns I wondered how well they applied to the international communities living in London. The graphic above shows the spatial distribution of about 470,000 geo-located tweets (collected and georeferenced by Steven Gray) grouped by the language stated in their user’s profile information*. Unsurprisingly, English is by far the most popular. More surprising, perhaps, is the very similar distributions of most of the other languages- with higher densities in central areas and a gradual spreading to the outskirts (I expected greater concentrations in particular areas of the city). Arabic (and Farsi) tweets are much more concentrated around the Hyde Park, Marble Arch and Edgware Road areas whilst the Russian tweeters tend to stick to the West End. Polish and Hungarian tweets appear the most evenly spread throughout London.

Even though the maps represent close to half a million tweets they are still based on a selective sample- they only include people who have a good location (either through GPS or a specific address) and those who are connected to the internet. I expect the latter requirement will exclude many short term visitors to London, and may explain why there aren’t so many hotspots around London’s landmarks (as is the case with Flickr where people can upload georeferenced images when they get home). In spite of this, I think the information in these maps is useful as a basis for comparison to other cities and it helps to reveal some of the finer patterns within the broad regions mapped by Fischer.

*this is slightly different to Eric Fischer’s method. He used Google’s translation tools to determine the language of each tweet whereas I have taken the stated language of each user because I am more interested in what users feel their preferred language is. I often see English tweeters post in French for example. Google also hasn’t quite mastered the slang or abbreviations that often crop up in Londoner’s tweets.

As a cyclist in London you can do your best to avoid left turning buses and dozy pedestrians. One thing you can’t really avoid though is pollution (although I accept cyclists probably aren’t much worse off than pedestrians and drivers in this respect). To illustrate this I have taken data for 3.2 million journeys from the Barclays Cycle Hire scheme and combined it with GLA pollution data for particulate matter. Unsurprisingly, pollution is worse at junctions and where there is lots of static traffic, with the popular cycling routes around Waterloo Bridge and the Strand particularly affected. Most of the journeys are subject to relatively low (by London standards) levels because cyclists try and avoid the busiest routes, such as Euston Road. The loop around Hyde Park is really popular with Boris Bikers and fortunately one of the least polluted but clearly more could be done to sort out the pollution hotspots around the west end.

The routes have been guessed using routing algorithms and OpenStreetMap data and optimised for cyclists (ie we assumed that people would prefer cycle lanes over roads etc). Thanks to Ollie O’Brien for this analysis. You can see more of his work here. I produced this map using the R software package and blog about how I did it here.

Transport for London have just released their performance data (link here) for the London Underground network. It is in the form of a really detailed file that contains, amongst other things, the “Peak Operated Kilometres” and “Peak Passenger Journeys” for the past 6 years or so. If you total the distances covered by the Tube rolling stock at peak times, this map shows how far they travel each day. The Central Line wins with over 13,000km – the equivalent of almost reaching Australia! In humble last place is the Waterloo and City Line that just passes Dublin with a little over 500km travelled. There are many more cool things to be done with the data, but I thought this is a neat way of showing the gargantuan task of shifting London commuters!

This week I am giving a talk on some of the London maps that we produce in CASA. The hours of work I put in to such maps is minuscule compared to the amount of effort and time that the OpenStreetMap community have invested in producing a truly open (and often more accurate) map of London (and the rest of the world). To give an idea of just how far the project has come over the past five years or so, ITO World have produced a great visualisation (above).

As a bonus ITO World have also produced a similar visualisation for Berlin, Germany.

Thanks to Patrick Webber for the tip off about these.

*This post has been cross-posted from the Mapping London blog.*

A few months back I had the honour of being asked to approve the use of a couple of excerpts from my London Surname Map in The Times Atlas of London. The wait was finally over last week when I received my copy in the post. It is a great book and an essential guide to the city. The Atlas begins with the first mapped representation of London in 1250 and covers a detailed history of London mapping that provides interesting context to both the development of the city and its mapping culture.

The Atlas includes detailed information on each of the London boroughs interspersed with great photos and fascinating insights into London life covering everything from its inhabitants (below) to football,

and public transport (below) through to London’s World Heritage Sites.

As you would expect from an atlas there are may detailed maps (see below) to break up the extensive commentary associated with the topics I mentioned above. I think the Atlas is brilliant book (not just because I’m in it!) and should make a great addition to the coffee table of fellow London map addicts.

]]> http://spatialanalysis.co.uk/2011/10/the-times-atlas-of-london/feed/ 0 http://spatialanalysis.co.uk/2011/08/mapping-gcse-scores/ http://spatialanalysis.co.uk/2011/08/mapping-gcse-scores/#comments Tue, 16 Aug 2011 15:34:59 +0000 James http://spatialanalysis.co.uk/?p=2815 In the UK, August is exam results month for 16-18 year olds. Every year, photos of leaping teenagers clutching their results are accompanied by reports of record attainment rates, debates around how challenging modern exams are and, more so recently than ever, concerns for the number of sixth form and university places. Back in March the full list of the 2010 GCSE results (exams taken by UK 16 year olds [except in Scotland]) were released and I mapped them but never got round to sharing them with anyone. Now seems a good time to do this so here goes…

The map below uses the increasingly popular cartogram method to show the success of students in each Local Authority (LA) across England. The non cartogram version is also shown alongside.

This is quite a coarse map as England is only split into the 152 LAs and we know there is much greater variation between schools at a local level and even sometimes within individual schools. Moreover, schools on authority borders often serve communities from the areas on either side, limiting the application of LA data to their populations only. Independent (fee-charging) schools are also included in these broad LA results, which is significant when we take into account the predictably higher results of fee-paying pupils and the fact that these schools have not been established with regard for even distribution across the country.  The size of the LA (in school-age population terms) does not seem to have a strong link to the results of its pupils. There must be other factors at play. Concerning known evidence indicates that a pupil’s level of deprivation has a stark impact on his/her attainment. This is supported by the plot for London below that shows the relationship between a borough’s national deprivation rank (known as the index of multiple deprivation or IMD).

Another way to show represent this information is by mapping the 2010 GCSE scores for each of the London Boroughs and resizing the borough so that it represents the levels of child poverty (measured by number of under 16s receiving means-tested benefits).

Again, the map above is not perfect as it is still quite generalised and shows only one of the many measures of child poverty that are used. Both maps also show only one measure of attainment the “GCSE or Equivalent” score. The “or Equivalent” bit is important here as it covers a wide range of more vocational qualifications (called NVQs) that are often perceived as less academically challenging and can be a way for students to get the equivalent of 5 A* to C grades including English and maths (a key educational benchmark) without having to be proficient in these core subjects. This is important as schools in England are often ranked by the proportion of their students achieving this benchmark resulting in a possible bias towards the schools offering more vocational subjects and against those offering more challenging ones such as modern languages. It is interesting to consider whether the nature of equivalent qualifications makes them more likely to be used by certain types of school and to explore this further I have produced the plots below. The codes are as follows: AC= Academy, CTC= City Tech. College, CY= Community School, CYS= Community Special School, FD= Foundation School, FDS= Foundation Special School, IND= Registered Independent School, INDSS= Independent Special School, NMSS=Non-Maintained Special School, VA= Voluntary Aided School, VC= Voluntary Controlled School (if you are as baffled about these as I was see here or here).

The plot shows 9 regions of England. Each point represents a school in that region and is coloured by its type. On the x-axis is the inverse (higher= better) regional ranking of the school based on its GCSE scores only and on the y-axis is the regional ranking if “equivalents” are included. If the inclusion/ exclusion of  equivalents made no difference to the rankings then the points would follow the grey lines perfectly. In reality we get schools falling either side of this line with those under it benefitting if equivalents are counted and those above benefitting if they are excluded. For example, broadly speaking independent schools (light blue) look worse when GCSE equivalents are used in the ranking criteria and therefore would benefit if such qualifications were excluded. This also seems to be the case for the voluntary controlled schools in pink. Academy Schools (orange) however do much better with the inclusion of equivalent qualifications perhaps reflecting a more vocational emphasis to their curriculum. There are also some interesting regional distinctions with independent schools, for example, in the South West and South East appearing to do well whatever the ranking criteria whilst the East/ West Midlands and the North East present a more mixed picture. I think a lot more can be said about these plots so I would welcome comments!

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.