Great Britain’s administrative geography is rather complicated, particularly for England – some English areas are “three tier”, made up of counties which are subdivided into districts, and others are “two tier” consisting of unitary authorities. Then there’s London’s boroughs which are in a special category of their own as part of an authority.

The Ordnance Survey Open Data release (easy download page here) includes BoundaryLine, which includes the geography data file for the counties, and a separate for the districts, UAs and boroughs. The latter is complete (and also includes the Scottish and Welsh regions), but the former looks rather strange on a map, with “islands” of counties separated by a “sea”.

I received a request by someone who was interested in having a unified file, at county level for the non-GLA counties, but including the UAs and London boroughs to “fill in” the map. I’ve made such a file by doing a dissolve in Quantum GIS (the districts having the county name as an attribute), and it can be downloaded here (15MB zipped shapefile.) The data is derived from and therefore covered by the OS Open Data licence which requires simply that the original source must be attributed when using it – that is, the data contains Ordnance Survey data © Crown copyright and database right 2010.

The image above is showing the merged data, with the unmerged district data (dotted lines) superimposed.

Using value-based colour ramps and/or circle size changes, rather than the standard hue-based colour ramp, are are a more “correct” way to show quantitative data graphics such as the hire map, as the data values aren’t distorted by “colour bias” (where a particular hue has more of an impact to the viewer).

I’ve also added a couple of panels to show how busy the hire scheme currently is, and how this compares to the same time 24 hours ago, and added a ticker which lists changes as they happen (e.g. docks becoming full or emptying quickly), in the style of the old BBC Grandstand vidi-printer.

Very few people have been using the bikes to commute home this evening (and yesterday evening) as it’s been raining a lot here in London! We have a weather station here at CASA, with historical data, so it should be possible to quantify the relationship between how hard it’s raining and what proportion of people decide to try another way to get home.

James has posted a video showing how the colours (i.e. bike usage patterns) changed during Wednesday – a typical day with good weather (so high usage) and sharply defined rush hours. The video shows one hour every second and starts at midnight (so look out for the main changes at 9s and 18s in.)

Another quirk is a characteristic move from red to purple of several stations overnight (i.e. in the first 5s of the video) in the northern edge of the zone, i.e. around Angel, travelling from east to west. A redistribution vehicle at work?

Today’s evening rush hour is showing quite a different pattern – a much less pronounced spike in usage, spread out over a longer time interval. This is probably because of the rain showers this afternoon and correspondingly damp roads, but possibly because Thursdays are traditionally team drinks nights in the City for many people, and so people will either be delaying the journey home, or deciding not to take the bike at all after a few drinks (not a bad idea really.) Certainly I’ve noticed a large difference in the numbers of people spilling out of the traditional City drinking dens on Thursday (and to a lesser extent Friday) evenings, compared with Monday-Wednesday.

Aidan’s sparklines, showing yesterday’s data as grey lines and today’s in orange, show this lag effect strikingly.

Neal Lathia, a research fellow here at UCL alerted me to a study carried out on usage patterns of a very similar scheme in Barcelona – even the dock numbers and scheme shape match London – clustering and categorising docking stations based on their usage patterns. Their method of data capture is also very similar to what I’m doing and the resulting dataset should lend itself to an equivalent categorisation in London. Things will only get more interesting when “casual” (i.e. non-registered) users get access to the scheme, which may happen next month, and new user types, such as foreign tourists, get involved, and the seasons (and weather) will also probably play a part, as different user types have different levels of willingness to use the system based on daily conditions.

The BBC’s Tom Edwards has an interview with the operators of the scheme, which includes at one point a screenshot of the internal (Google-maps based) map used by them to see what docking points are on their way to becoming full or empty.

I’ve created a visualisation of how the TFL Cycle Hire scheme in London is being used – the so-called “Boris Bikes”. Around 4000 bikes have been placed in 400 cycle parking stands in the centre of the city, and people have been using them to get from A-B.

Some distinctive if not entirely surprising patterns have appeared already – with heavy usage (~10% of total bikes out on the streets) during the rush-hours, which occur in a strikingly small time interval – a narrow, sharp dip appearing only between 5:30pm to 6pm. Usage is much less in rainy weather, such as has happened today, and weekend use is both lower, and quite different in “shape”. During weekday days, the City tends to have a lot of the bikes, while in the evening, the bikes end up at the cycle parking stands near the big terminal train stations and in Pimlico in the south-west of the area – probably the biggest residential area covered by the scheme, and also a popular place for city workers to live…

10am Tuesday: Straight after a sunny morning rush-hour, before redistribution kicks in – many of the central stands are now completely full of bikes (red with yellow borders.)

8pm Tuesday: A typical evening pattern – the bikes are on the edge, and at the terminal stations, particularly around Waterloo and King’s Cross, while the centre is short of bikes…

The visualisation consists of coloured dots, which change from blue to red as each stand fills up with docked bikes. A purple dot indicates a half-full stand. The size of the dots corresponds to the total capacity of the stand.

You can click on a stand’s dot to see information about its current status, as well as its use over the last 24 hours, represented as a minimalistic graph. A graph of overall usage can also be viewed. Both get updated as the new data comes in.

The data comes from TFL’s own map of the stands in central London, and is updated at source typically every six minutes – my own visualisation updates every two minutes, so you should never be more than ten minutes out of date, looking at the map.

The background is a bespoke render of central London, from OpenStreetMap data.

See it here.

Here’s how the total number of available bikes has fluctuated, since Friday morning (click for larger version):

[Update: Some articles about the visualisation - Telegraph, Londonist, Road.cc, Real Cycling, Bikeradar]

Additional notes: Slide 6 is a comparison of OSM, Google and Bing (or Yahoo). In Slide 10, the link is to here (20MB MPG). Slide 18 refers to OpenOrienteeringMap which can be found here. Slide 19 relates to two other visualisations I’ve made, see them here and here – OSM is being used for the background. Slide 20′s screenshots of BestOfOSM show Bern, Gaza City and Berlin Zoo.

As part of my induction, I was asked to present a summary of my work at UCL so far. Here are the slides for that presentation.

The presentation includes various screenshots of mapping data, including data from the OpenStreetMap, EDINA UKBORDERS and OS Open Data projects. Attributions can be found on the respective websites.

[Update: I've also added a split-screen view for side-by-side comparison of the maps.]

Transport for London have published the 2009 data for numbers of people entering/leaving the stations on the tube network. I’ve updated my visualisation/map with the new data.

Some interesting trends have emerged. Blackfriars sees the biggest decrease (i.e. biggest red circle) – no surprise, as the station has been closed throughout 2009. The other big decreases are at Canary Wharf, Finsbury Park and Wimbledon. The former is on the Jubilee Line, and many of the stations on the line have seen a drop in usage – presumably something to do with various sections of the line being closed most weekends throughout last year. The station for the O2 is doing well though. Many of the Victoria and Metropolitan line stations have also seen a big drop. Indeed in general, the network has seen a drop in usage, the map being predominately red.

The biggest increases are Temple and Mansion House, presumably spillover from the Blackfriars closure, Euston, maybe due to increased use of the Eurostar services new higher-speed services to NW England, and Barking – lots of new build flats here?

Within the overall pattern, there’s a cluster of decreases (red) in West London Zone 2 (Shepherd’s Bush), and a a cluster of increases (blue) around East London Zone 1/2 (Aldgate) and North London Zone 2 (Camden).

The map does not include DLR, rail or London Overground usage.

See it here.

Following on from The Political Colour of Great Britain, and reusing the same code, I have produced a map for London, showing graphically the results of the voting for the local elections in Greater London’s 600-odd wards, for the May 2010 elections and back in 2006.

The “colour” map assigns every vote to one of the three RGB primary colours – red for Labour, blue for Conservative and green for all other parties. It so happens that the three groupings have roughly the same number of votes across the whole of London. These are scaled by the total number of votes for each ward, and then resulting proportions are converted to the hexadecimal “web” colours you see on the dots. An “enhance” function is used to increase the value of the colours away from the mean, to prevent the map from looking muddy.

The advantage of using colour in this way to represent each constituency is every person’s vote counts towards the final colour, rather than just those that elected the three winning councillors in each ward. Use of a single colour is the simplest way to summarise each result. The disadvantage is that it is difficult for human eyes to quantitatively perceive the colour and translate it to a result – although we are quite good at spotting differences in colours, it is more difficult to interpret these.

The voting data is from the London Datastore, the ward and borough boundaries from Ordnance Survey Open Data, and the background map from OpenStreetMap data, rendered using Mapnik. The ward centroids were calculated in ArcGIS and the map is displayed with the OpenLayers framework. Performance is very poor in Internet Explorer because the VML renderer it uses is extremely slow – SVG is used instead in Firefox and the other standards-based browsers and is vastly better.

See it here.

For a different take on the same technique of using colour to show the vote composition for each ward, see the article on Spatial Analysis.

Following on my UK General Elections 2010 Swings map, where circles represent each constituency, with the sizes and colour describing the metrics, I have used a technique that James has been studying, to combine the vote proportions together to produce a single coloured dot for each constituency. The more blue the dot, the higher the proportion of the vote was Conservative. Similarly, red for Labour and Green for the Lib Dems and the other parties and independents.

A purple constituency represents a Labour/Conservative marginal, as blue+red = purple. There are many of these in the Midlands. Similarly, orange areas indicate likely Labour/Lib Dem (or SNP/PC etc) marginals, such as in South Wales, and turquoise areas indicate Conservative/Lib Dem (& other) margins, there are many of these in SW England. Grey dots show three-way marginals, e.g. Hampstead in London.

Because I am using just three colours, to represent three political groupings, the visualisation does not show the variation in results between the Liberal Democrats and the nationalist parties – the SNP and PC in Scotland and Wales respectively.

Some interesting patterns are revealed – the ring of blue/purple around the red/orange centres of London and Birmingham – with no such corresponding ring around Manchester or Newcastle. Scotland’s lack of blue. The straightforward Labour/Conservative split in NW England. East London isn’t quite as safe-Labour as you might think – with the exception of East Ham standing out in bright red.

The benefit of this visualisation is that every vote is included in it – on a regular election map, if one party just fails to win, then an election map won’t show them at all on it. Here, every vote influences the colour of the map. Each circle represents roughly the same number of people – the populations of constituencies are fairly even, with some notable exceptions such as the Isle of Wight (very populated) and the Scottish Islands (very few people.)

You can see the colour map by going to the election visualisation and choosing “Constituency Colour” from the “Other graphs” drop-down. James has produced a regular choropleth map version, which shows the green (SNP/LD) of northern Scotland and blue (Conservative) of southern England strikingly well.