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.

The above map (and this one) was produced using R and ggplot2 and serve to demonstrate just how sophisticated R visualisations can be. We are used to seeing similar maps produced with conventional GIS platforms or software such as Processing but I hadn’t yet seen one from the R community (feel free to suggest some in the comments). The map contains three layers: buildings, water and the journey segments. The most challenging aspect was to change the standard line ends in geom_segment from “butt” to “round” in order that the lines appeared continuous and not with “cracks” in, see below.

I am grateful to Hadley and the rest of the ggplot2 Google Group for the solution. You can see it here. From this point I layered the plots using the geom_polygon() command for the buildings and water bodies and my new function geom_segment2() for the journey segments- these were simply the start and end latitudes and longitudes for each node in the road network and the number of times a cyclist passed between them. I have included the code below
 


#Code supplied by james cheshire Feb 2012
#load packages and enter development mode
library('devtools')
dev_mode()
library(ggplot2)
library(proto)

plon2


If I said a country was 1594719800 metres squared it would mean a lot less to you than if I said it was about the size of Greater London (so long as you know about how big Greater London is). For this reason the media tend to report the extent of a flood in relation to the size of the Isle of Wight or Icebergs in relation to the size of Wales (or Luxembourg) so that we can imagine the extent and scale of a disaster or news story. Despite plenty of comment on how ridiculous such comparisons are, and a great website that will convert standard measurements into the fractions or multiples of the size of Wales, I am yet to see a mapped representation of our increasingly standard units of area. The one I produced above is not meant to be definitive, just a starting point to what I hope will be a new system to replace the frankly inadequate* metric measures we are used to.

A much more effective alternative to simply stating an area in terms of its relative size to another area is of course to produce a map.  Geographically correct maps contain most of this information in the first place but they aren’t much good if you want to compare two things at either ends of the World or even the Solar System. With loads of mapping data online it is now easy to start shifting things around and laying them on top of each other in the same way the BBC’s How Big Really? website does.

This is fine if you want to compare a couple of things, but the map gets messy if you want to do more than that. For more complex comparisons you need to start with a fresh map (be careful of the projection) and shifitng everything around to fit on a single page. Doing this can have a big impact as Kai Krause did with his “True Size of Africa” map.

…sparsely populated areas (UK Cities on top of the Highland region of Scotland by Alasdair Rae)…

…and their transport systems such as subways (by Neil Freeman)

and cycle hire schemes (by Oliver O’Brien).

I think they offer a new perspective on the world and use maps as more abstract forms of information visualisation, so lets hope we see them more often to accompany the usual descriptive “relative to Wales” statements.

*I don’t seriously mean this.

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.

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.

I would hate to think how long the map below took. Each standard station name has been re-written as an anagram.

My final favourite is a German version of the map. I like this because it made me really think about literal translations of the station names and how odd some of them really are.

Click here for the full collection.