Category: Data Graphics

Steve, here in the lab at CASA, has adapted his popular Tweet-O-Meter display of Twitter activity in cities around the world, for my bike hire maps, to create Bike-O-Meter. Now, on a single screen, you can see lots of Google-powered gauges, showing how busy the bike share schemes around the world are right now. Some show massive spikes during their rush hours, while others are more popular at weekends. Most dials will move every two minutes, a few (the Velib ones) update every 10 or 20 minutes.

At the time of writing, the bike share schemes of the Spanish cities, particularly Barcelona, Girona and Valencia, are the ones being most actively used. Spanish rush-hours at lunchtimes seem generally to be as big as the morning/evening ones! Biking home for the siesta?

There’s a second mode, accessed here, that shows how unbalanced the schemes are – high values indicate that a lot of the bikes are concentrated in one part of the city, and there’s a lot of empty docking stations in another part. The metric is the percentage of bikes that would need to be moved to balance out the docking stations across the city.

Thanks Steve for making this awesome visualisation!

I’ve made a couple of enhancements to my live London cycle hire map – you can now choose from several colour sets. A couple of the sets also change the circle sizes, so that these correspond to the number of bikes (or spaces) rather than the dock size. This means the circles grow or shrink as the bikes get used, rather than remaining static as before.

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.

[A final word on my cycle hire visualisation – which you can see here.]

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]

I’ve created a visualisation of the results from the UK general election this month. By default, it shows the most significant swings between parties, for each constituency. By using the options on the right, you can change it to show simple vote counts, overall results, or swings between any two significant parties. I’m using a circle to represent each constituency. The area of the circle is directly proportional to the metric being shown (number of votes, or percentage point swing) with a dynamic key on the right to help out. Click on a circle to see the vote results.

Some Notes:

By choosing “Winning Party” from the “Other” drop down, you see the simplest map – each circle is the same size, and it simply shows the winning results – with the previous winner shown as the colour on the edge of the circle. I think this kind of view is a “best of both worlds” approach – as long as the circles don’t overlap, it has the “fairness” of cartograms (aka “proportional” maps in the BBC’s coverage) while retaining the geographical familiarity of the choropleth (“normal”) map of the UK.

Only Great Britain is included, not Northern Ireland, as the OS Open Data release, which provides the parliamentary boundaries from which the centroids were calculated for the circle locations, does not at the moment include Northern Ireland.

The definition of swing is tricky – swing can only be described as being from one party to another. Choosing which two parties to use for the “headline” swing was tricky. In the end, I’ve chosen to show the swing between the losing and winning party where the seat changed hands, and where it didn’t, the swing between the two parties with the highest number of votes. This means “interesting” swings like in Bethnal Green & Bow are still included. It does however mean that the largest swing is not necessarily shown.

There were a large number of parties contesting seats – in the end I’ve only included parties that got at least 30,000 votes in total, to prevent the lists becoming too long. I’ve also excluded swings where either party in the swing had less than 5% of the vote (i.e. losing their deposit) as such a swing is probably not very meaningful.

In general, a swing between a “small” party and a “large” party is not meaningful anyway, because any voting changes that a large party suffers/gains from are probably to/from another large party, rather than to/from the small party. So, bear this in mind when viewing the swings between (for example) Labour and UKIP. UKIP appears to have gained almost everywhere but actually it was probably another “large” party (Conservatives or Liberal Democrats) that actually got the floating votes.

The background imagery was custom-rendered to show the constituency boundaries and be entirely grey – so that the only colours are those representing the party results. Because the boundaries generally run to the low-water mark and estuary mid-lines, whereas OpenStreetMap boundaries generally run to the high-water mark and consider estuaries to be sea rather than river, there are some odd looking estuarine areas (e.g. the Bristol Channel and the Thames Estuary.)

Said another way, this is because of a mismatch between where OpenStreetMap and the parliamentary boundaries commission consider the rivers to stop and the coast to start. I have manually edited two boundaries – NW Bristol sticks far out into the Bristol Channel, and the Medway boundaries also stick far out. NW Bristol’s centroid was also manually moved. There are other, lesser quirks with the data, and being OSM data, not every village is on there yet. I consider the background imagery overall to be “good enough” for the visualisation at hand.

No fancy AJAX was used – the data (about 120KB) is simply loaded into the browser at the start – the visualisation being done entirely on your browser using the OpenLayers API.

It’s not very easy to draw circles in Javascript, so I’ve let OpenLayers do it for me. The two circles that make up the key are actually miniature OpenLayers maps themselves, with a single point feature at the origin of the map. They update in sync with the main map.

The visualisation was ready to go last week but the way Internet Explorer draws the vector graphics (using VML rather than SVG) was causing considerable performance problems, when trying to show all 650-odd constituencies at once. As a work around, the map starts by being zoomed in if you visit the site in IE. Zoom out at your peril!

If you spot any bugs (apart from the IE chronic slowness!) please let me know.