Category Archives: London

Big Data Here


The Consumer Data Research Centre (CDRC) at UCL is organising a short pop-up exhibition on hyperlocal data: Big Data Here. The exhibition is taking place in North Lodge, the small building right beside UCL’s main entrance. The exhibition materials are supplied by the Centre for Advanced Spatial Analysis (CASA).

Inside, a big projection shows local digital information. What the screen shows will change daily between now and Friday, when the exhibition closes. Today it is showing a live to-the-second feed of bus arrivals at the bus stop outside the North Lodge, and tube train arrivals at Euston Square station just up the road. Watch the buses zip by as they flash up “Due” in big letters on the feed. Both of these are powered by Transport for London’s Unified Push API, and we are planning on publishing the visualisation online next week. Tomorrow will be showing a different local data feed, and then a final one on Friday.


Opposite the projection is the iPad Wall. This was created by CASA a few years back by mounting a bank of iPads to a solid panel (above photo shows them in test mode) and allowing remote configuration and display. The wall has been adapted to show a number of metrics across its 12 panels. Four of these showcase footfall data collected by one of our data partners, and being used currently in CDRC Ph.D. research. The other panels show a mixture of air quality/pollutant measures, tube train numbers and trends, and traffic camera videos.

We hope that passersby will enjoy the exhibition visuals and use them to connect the real world with the digital space, a transposition of a digital data view onto the physical street space outside.

The exhibition runs 24 hours a day until Friday evening, with the doors open from noon until 3:30pm each day. The rest of the time, the visualisations will be visible through the North Lodge’s four windows. The exhibition is best viewed at night, where the data shines out of the window, spilling out onto the pavement and public space beyond:


Big Data Here is taking place during Big Data Week 2016. Visit the exhibition website or just pop by UCL before Friday evening.



Visit the new Shop
High quality lithographic prints of London data, designed by Oliver O'Brien

Busiest Tube Station Times


Here are the busiest Tube station quarter-hour periods, based on the Transport for London 2015 RODS data (modelled, based on typical autumn weekday), used in Tube Heartbeat, adding together entries, exits and interchange stats and excluding Kensington Olympia which does not have a frequent Tube service.

The main pattern shows that stations further out (map) from London’s main work areas (The West End, the City and Canary Wharf) have an earlier morning peak (or later evening peak), due to the journey taking longer and the tendency for many people to arrive at their work-end station at about the same time – just before 9am. A secondary effect is that stations which just act as simple commuter home and work portals, we would expect the usage to peak in the morning rush hour, rather than than evening one, as the morning rush hour is shorter and so the simple commuter flow is more concentrated. Therefore, stations which show a peak in the evening are often due to a combination of this simple commuter flow and an evening “going out” destination.

Station Peaks by Time of Day

7:15am-7:30am: Chesham [Zone 9]

7:30am-7:45am: Chalfont & Latimer [8], Epping [6]

7:45am-8:00am: Amersham [9], Chorleywood [7], Debden [6], Elm Park [6], Hillingdon [6], Hornchurch [6], Theydon Bois [6], Cockfosters [5], Pinner [5], South Ruislip [5], Stanmore [5], Mill Hill East [4], Chigwell [4], Grange Hill [4], Perivale [4],Kew Gardens [3/4], Wimbledon Park [3], Holland Park [2]

8:00am-8:15am: Alperton, Arnos Grove, Balham, Barking, Barkingside, Becontree, Buckhurst Hill, Canons Park, Chiswick Park, Clapham South, Colindale, Colliers Wood, Croxley, Dagenham East, Dagenham Heathway, Eastcote, East Putney, Edgware, Fairlop, Finchley Central, Gants Hill, Hainault, Harlesden, Harrow-on-the-Hill, Hatton Cross, High Barnet, Hounslow Central, Hounslow East, Hounslow West, Ickenham, Kenton, Kingsbury, Loughton, Moor Park, Morden, Neasden, Newbury Park, Northfields, North Harrow, Northolt, Northwick Park, Northwood, Northwood Hills, Oakwood, Osterley, Parsons Green, Preston Road, Ravenscourt Park, Rayners Lane, Redbridge, Rickmansworth, Roding Valley, Ruislip, Ruislip Gardens, Ruislip Manor, Seven Sisters, Snaresbrook, South Ealing, Southfields, Southgate, South Harrow, South Kenton, South Wimbledon, Stamford Brook, Sudbury Hill, Sudbury Town, Totteridge & Whetstone, Turnham Green, Upminster Bridge, Upney, Wanstead, Watford, West Acton, West Harrow, West Ruislip, Wimbledon, Woodford, Woodside Park

8:15am-8:30am: Acton Town, Archway, Arsenal, Blackhorse Road, Boston Manor, Bounds Green, Bow Road, Brent Cross, Brixton, Bromley-by-Bow, Burnt Oak, Canada Water, Canning Town, Dollis Hill, Ealing Broadway, Ealing Common, East Acton, East Finchley, Finchley Road, Finsbury Park, Fulham Broadway, Golders Green, Goldhawk Road, Hammersmith (H&C), Harrow & Wealdstone, Hendon Central, Highgate, Kensal Green, Kilburn, Kilburn Park, Leytonstone, Maida Vale, Manor House, North Acton, North Wembley, Park Royal, Plaistow, Putney Bridge, Queen’s Park, Shepherd’s Bush Market, St. John’s Wood, South Woodford, Swiss Cottage, Tooting Bec, Tooting Broadway, Tottenham Hale, Tufnell Park, Upton Park, Walthamstow Central, Warwick Avenue, Wembley Park, West Brompton, West Finchley, West Hampstead, Willesden Green, Wood Green

8:30am-8:45am: Baker Street, Bank/Monument, Barons Court, Belsize Park, Bermondsey, Caledonian Road, Canary Wharf, Chalk Farm, Earl’s Court, Edgware Road, Elephant & Castle, Euston, Hammersmith, Hampstead, Highbury & Islington, Holloway Road, Kennington, Kentish Town, Ladbroke Grove, Lancaster Gate, London Bridge, Marylebone, Mile End, Moorgate, Notting Hill Gate, Oval, Paddington, Pimlico, Richmond, Royal Oak, Stepney Green, Stockwell, Uxbridge, Vauxhall, Victoria, Westbourne Park, West Kensington, Westminster, Whitechapel

8:45am-9:00am: Barbican, Aldgate East, Blackfriars, Borough, Cannon Street, Chancery Lane, Edgware Road (Bakerloo), Euston Square, Farringdon, Great Portland Street, Latimer Road, Mansion House, Old Street, Regent’s Park, Southwark, St. James’s Park, St. Paul’s, Warren Street

3:30pm-3:45pm: North Ealing

5:00pm-5:15pm: Heathrow Terminal 5

5:15pm-5:30pm: Willesden Junction

5:30pm-5:45pm: Aldgate, Russell Square, South Kensington, West Ham, Heathrow Terminals 1 2 3, Heathrow Terminal 4

5:45pm-6:00pm: Bond Street, Embankment, Goodge Street, Green Park, Gunnersbury, Hanger Lane, Wood Lane, Holborn, King’s Cross St. Pancras, Knightsbridge, Lambeth North, Liverpool Street, Mornington Crescent, North Greenwich, Oxford Circus, Stonebridge Park, Charing Cross, Stratford, Temple, Tower Hill, Turnpike Lane, Upminster, Waterloo, White City

6:00pm-6:15pm: Angel, Camden Town, Covent Garden, East Ham, Gloucester Road, Greenford, High Street Kensington, Hyde Park Corner, Leicester Square, Leyton, Marble Arch, Piccadilly Circus, Queensway, Shepherd’s Bush, Sloane Square, Tottenham Court Road

6:15pm-6:30pm: Bayswater [1], Bethnal Green [2], Clapham Common [2], Clapham North [2], Queensbury [4], Wembley Central [4]

You can explore graphs of the flows, in detail, at Tube Heartbeat – just choose the station of your choice on the drop-down on the top right, or click on it on the map.

Six Rush Hours?

Interestingly, if you look at the flows between stations, you can actually see SIX rush hours each weekday (you can see five of them below by looking across these sample segment graphs):


These are:

  • A early morning peak, 7-8am. This is distinct from the main morning peak, and can be seen certain segments in east London, particularly on the District line near Plaistow, where the two morning peaks are an hour apart, with a noticeable dip in flow between the two. This may reflect the workforce for some traditional industries with 8am-4pm historical or shift-based working hours.
  • The main morning rush hour that almost all stations and line segments see – 7:30am-9am. Some of the more outlying stations (Zones 5-9) see their peak for this rush hour earlier than 8am, as it takes a while to get into the centre of London. You can see this is not the 7-8am peak above, by “tracing” the ripple through the network towards central London.
  • School home-time at roughly 3-4pm. Mainly affects some smaller, outer London stations, particularly in the north-west, for example Moor Park.
  • A corresponding 4-5pm peak for shift workers who started at 8am. Only a few links show this, such as Wembley Central in north-west London. The evening rush hours are less “compressed” than the morning ones so it is generally harder to distinguish between this one and the next one.
  • The main evening rush hour, 5-7pm.
  • Theatreland end-of-show rush hour, 10-11pm. Noticeable around Leicester Square, Covent Garden and Holborn. Some other areas, with established night-time economies, may also see a slight peak around this time.

You can also see 3+ rush hours in some of the stations, such as Wembley Central, which shows all six:


Visit the new Shop
High quality lithographic prints of London data, designed by Oliver O'Brien

Tube Heartbeat


Tube Heartbeat is a interactive map that I recently built as part of a commission by HERE, using the HERE JavaScript API. It visualises a fascinating dataset that TfL makes available sporadically – the RODS (Rolling Origin Destination Survey) – which reveals the movements of people on the London Underground network in amazing detail.

The data includes, in fifteen-minute intervals throughout a weekday, the volume of tube passengers moving between every adjacent pair of stations on the entire tube network – 762 links across the 11 lines. It also includes numbers entering, exiting and transferring within each of the 268* tube stations, again at a 15 minute interval from 5am in the morning, right through to 2am. It has an origin/destination matrix too, again at fine-grained time intervals. The data is modelled, based on samples of how and where passengers are travelling, during a specimen week in the autumn – a period not affected either by summer holidays or Christmas shopping. The size of the sample, and the careful processing applied, means that we can be confident that the data is an accurate representation of how the system is used. The data is published every few years – as well as the most recent dataset, I have included an older one from 2012, to allow for an easy comparison.

As well as the animation of the data, showing the heartbeat of London as the the lines pulse with passengers squeezing along them, I’ve including graphs for each station and each link. These show all sorts of interesting stats. For example, Leicester Square has a huge evening peak, when the theatre-goers head for home:


Or Croxley, in suburban north-west London, with a very curious set of peaks, possibly relating to the condensed school day:


Walthamstow (along with some other east London stations) has two morning rush-hours with a slight lull between them:


Check the later panels in the Story Map, the intro which appears when first viewing Tube Heartbeat, for more examples of local quirks.

This is my first interactive web map produced using the HERE JavaScript API – in the past, I have extensively used the OpenLayers, as well as, a long while back, Google Maps API. The API was quick to pick up, thanks to good examples and documentation, and while it isn’t quite as full-featured as OpenLayers in terms of the cartography, it does include a number of extra features, such as being quickly able to implement direction arrows along lines, and access to a wide variety of HERE map image tiles. I’m using two of these – a subdued gray/green background map for the daytime, and an equivalent darker one for the evening data. You’ll see the map transition between the two in the early evening, when you “play” the animation or scrub the slider forwards.

Additionally, I’ve overlayed a translucent light grey rectangle across the map, which acts to further diffuse the background map and highlight the tube data on top. The “killer” feature of HERE JavaScript API, for me, is that it’s super fast – much faster than OpenLayers for displaying complex vector-based data on a map, on both computer and smartphone. Being part of the HERE infrastructure makes access to the wide range of HERE map tiles, with their distinctive design, easy, and gives the maps a distinctive look. I have previously used HERE mapping for some cities in the Bike Share Map (& another example), initially where the OpenStreetMap base data was low in detail for certain cities, but now for all new cities I “onboard” to the map. The attractive cartography works well at providing context for the bikeshare station data there, and the tube flow data here.

There is some further information about the project on the HERE 360 blog, and I am looking to publish a more deatiled blogpost soon about some of the technical aspects of putting together Tube Heartbeat.


Number of stations Number of lines Number of line links between stations
268* 11 762

Highest flows of people in 15 minutes, for the four peaks:

Between stations (all are on Central line)
Morning 8208 0830-0845 Bethnal Green to Liverpool Street
Lunchtime 2570 1230-1245 Chancery Lane to Holborn
Afternoon 7166 1745-1800 Bank/Monument to Liverpool Street
Evening 2365 2230-2245 St Paul’s to Bank/Monument
Station entries
Morning 7715 0830-0845 Waterloo
Lunchtime 1798 1130-1145 Victoria
Afternoon 5825 1730-1745 Bank/Monument
Evening 2095 1015-1030 Leicester Square
Station interchanges
Morning 5881 0830-0845 Oxford Circus
Lunchtime 2060 1330-1345 Oxford Circus
Afternoon 5043 1745-1800 Oxford Circus
Evening 1109** 2215-2230 Green Park
Station exits
Morning 6923 0845-0900 Bank/Monument
Lunchtime 2357 1145-1200 Oxford Circus
Afternoon 7013 1745-1800 Waterloo
Evening 1203 1015-1030 Waterloo

* Bank/Monument treated as one station, as are the two Paddington stations.
** Other stations have higher flows at this time but as a decline from previous peak.

I’m hoping to also, as time permits, extend Tube Heartbeat to other cities which make similar datasets available. At the time of writing, I have found no other city urban transport authority that publishes data quite as detailed as London does, but San Francisco’s BART system is publishes origin/destination data on an hourly basis, there is turnstyle entry/exit data from New York’s MET subway, although only at a four-hour granularity, and Washington DC’s metro also publishes a range of usage data. I’ve not found an equivalent dataset elsewhere in Europe, or in Asia, if you know of one please do let me know below.


The data represented in Tube Heartbeat is Crown copyright & database right, Transport for London 2016. Background mapping imagery is copyright HERE.

London Panopticon

Panopticon Animation

The London Panopticon utilises the traffic camera feed from the TfL API, which recently (announcement here) added ~6-second-long video clips from the traffic cameras on TfL “red route” main roads, to show the current state of traffic near you. The site loads the latest videos from the nearest camera in each compass direction to you. The images are nearly-live – generally they are up-to-date to within 10-15 minutes. If the camera is “in use” (e.g. being panned/zoomed or otherwise operated by an official to temporarily reprogramme the traffic lights, see an incident etc) then it will blank out. The site is basically just JavaScript, when you view it, your browser is loading the videos directly from TfL’s Amazon cloud-based repository.

The Panopticon continuously loops the video clips, and updates with the latest feed from the cameras every two minutes, the same frequency as the underlying source. If you are not in London or not sharing your location, it will default to Trafalgar Square. I’ve added a special “Blackfriars” one which is where the under-construction Cycle Superhighway North/South and East/West routes converge – during rush hour you can already see bursts of cyclists using the new lanes.

Try it at and note that it only works on desktop web browsers (I’ve tested it on Chrome, Firefox and Safari). It didn’t work on Internet Explorer “Edge” when I tested it on a PC. It also does not work on Chrome on Android and by extension probably mobile in general. It possibly uses a lot of bandwidth, so this is perhaps just as well.

I’ve named it after the Panopticon, a concept postulated by Jeremy Bentham, co-founder of University College London, where I work, in the 1800s for easy management of prisons. The Panopticon encourages good behavior, because you can’t see the watcher, so you never know if you are being watched. Kind of like the traffic cameras.

The concept evolved from a special “cameras” version (no longer working) of the London Periodic Table, which was itself a follow-on from CityDashboard, both of which I created at CASA. The source is on GitHub.

p.s. If you made it this far, you might be interested in a hidden feature, where you can specify a custom location. Just add ?lat=X&lon=Y to the URL, where the X/Y is your desired latitude/longitude respectively, in decimal coordinates. Example:

London’s Bikeshare Needs A Redistribution of Stations


Here’s an interesting graph, which combines data on total journeys per day on London’s bicycle sharing system (currently called “Santander Cycles”) from the London Data Store, with counts of available bicycles per day to hire, from my own research database. The system launched in summer 2010 and I started tracking the numbers almost from the start.

You can see the two big expansions of the system as jumps in the numbers of available bikes – to all of Tower Hamlets in early 2012, and to Putney and Fulham in late 2013. Since then, the system has somewhat stagnated in terms of its area of availability, although encouragingly at least the numbers of available bikes has remained constant at around 9500, suggesting that at least the operator is on top of being able to maintain and repair the bikes (or regularly source new ones). Some of the individual bikes have had 4000 trips on them. There is a small expansion due in the Olympic Park in spring 2016, but the 8 new docking stations represents only a 1% increase in the number of docking stations across the system, so I doubt it will have a significant impact on the numbers of available bikes for use.

There is a general downward trend in the numbers of uses of each bike per day, since the halycon Olympic days of Summer 2012, over and above the normal seasonal variation, which concerns me. The one-year moving average recently dipped below 3 uses of each bike per day, this summer, and I am not confident it will pick up any time soon. (The occasional spikes in uses/bike, by the way, generally correspond to sunny summer bank holidays, tube strikes and Christmas Day).

To rejuvenate the system and draw in more users, rather than relying on the established commuter and tourist flows which likely dominate the current usage, I am convinced that the system needs to expand – not necessarily in terms of the number of bikes or docking stations, but in its footprint. I think the system would be much improved by dropping the constraining rule on density (which approximates to always having one docking station every 300m) and instead redistributing some of the poorly performing docking stations themselves further out. It’s crazy that, five years on, there are no docking stations in central Hackney, Highbury, or Brixton, three areas with an established cycling culture and easily cycle-able into the centre of London. Conversely, Putney and Tower Hamlets simply don’t need the high density of docking stations that they currently have, except in specific areas (such as around the train/tube stations in Putney, and Canary Wharf).

Ideally we would have a good density of docking stations throughout cycleable London but, as docking stations (and bikes) are very expensive, I would suggest that TfL instead adopts the model used in Bordeaux (below). Here, the city retains a high-dense core serving tourists, commuters and other centrally-based workers, but adopts a much lower density in the suburbs, so that, while tourists can still “run into” docking stations they don’t know about in the centre thanks to the high density, local users can benefit from the facility in their neighbourhood too, even if it requires a little longer walk to get to it.


Technical note: Before November 2011, the London numbers included bicycles that were in a docking station but not available to hire (i.e. marked as broken). This exaggerates the number of available bikes (and correspondingly reduces the number of hires/bike/day from the true value) in this period by a small amount – typically around 3-5%, an effect I am not considering significant for this analysis.

Living Somewhere Nice, Cheap and Close In – Pick Two!


Skip straight to the 3D graph!

When people decide to move to London, one very simple model of desired location might be to work out how important staying somewhere nice, cheap, and well located for the centre of the city is – and the relative importance of these three factors. Unfortunately, like most places, you can’t get all three of these in London. Somewhere nice and central will typically cost more, for those reasons; while a cheaper area will either be not so nice, or poorly connected (or, if you are really unlucky, both). Similarly, there’s some nice and cheap, places, but you’ll spend half your life getting to somewhere interesting so might miss out on the London “experience”. Ultimately, you have to pick your favoured two out of the three!

Is it really true that there is no magic place in London where all three factors score well? To see the possible correlations between these three factors, I’ve calculated the ward* averages for these, and have created a 3D plot, using High Charts. Have a look at the plot here. The “sweet” spot is point 0,0,0 (£0/house, 0 score for deprivation, 0 minutes to central) on the graph – this is at the bottom left as you first load it in.

Use your mouse to spin around the graph – this allows you to spot outliers more easily, and also collapse down one of the variables, so that you can compare the other two directly on a 2D graph. Unfortunately, you can’t spin the graph using touch (i.e. on a phone/tablet) however you can still see the tooltip popups when clicking/hovering on a ward. Click/touch on the borough names, to hide/show the boroughs concerned. Details on data sources and method used are on the graph’s page.

The curve away from the sweet spot shows that there is a reasonably good inverse correlation between house prices and deprivation, and house prices and nearness to the city centre. However, it also shows there is no correlation between deprivation and nearness. Newington is cheap and close in, but deprived. Havering Park is cheap and a nice area, but it takes ages to get in from there. The City of London is nice and close by – but very expensive. Other outliers include Merton Village which is very nice – but expensive and a long way out, while Norwood Green (Ealing) is deprived and far out (but cheap). Finally, Bishop’s in Lambeth is expensive and deprived – but at least it’s a short walk into the centre of London.

Try out the interactive graph and find the area you are destined to live in.


p.s. If you are not sure where your ward is, try clicking on the blobs within your borough here.

* Wards are a good way to split up London – there are around 600 of them, which is a nice amount of granularity, and importantly they have real-world names, unlike the “purer” equivalent Middle Super Output Areas (MSOAs). Using postcode “outcodes” would be even better, as these are the most familiar “coded” way of distinguishing areas by non-statisticians, but statistical data isn’t often aggregated in this way.

OpenStreetMap: London Building Coverage


OpenStreetMap is still surprisingly incomplete when it comes to showing buildings for the London area, this is a real contrast to other places (e.g. Birmingham, New York City, Paris) when it comes to completeness of buildings, this is despite some good datasets (e.g Ordnance Survey OpenMap Local) including building outlines. It’s one reason why I used Ordnance Survey data (the Vector Map District product) rather than OpenStreetMap data for my North/South print.

The map below (click to view a larger version with readable labels and crisper detail, you may need to click it twice if your browser resizes it), and the extract above, show OpenStreetMap buildings in white, overlaid on OS OpenMap Local buildings, from the recent (March 2015) release, in red. The Greater London boundary is in blue. I’ve included the Multipolygon buildings (stored as relations in the OSM database), extracting them direct from OpenStreetMap using Overpass Turbo. The rest of the OSM buildings come via the QGIS OpenStreetMap plugin. The labels also come from OS OpenMap Local, which slightly concerningly for our National Mapping Agency, misspells Hampstead.

The spotty nature of the OSM coverage reveals individual contributions. For example, Swanley in the far south east of the map is comprehensively mapped, thanks presumably due to an enthusiastic local. West Clapham is also well mapped (it looks like a small-area bulk import here from OpenMap) but east Clapham is looking sparse. Sometimes, OpenStreetMap is better – often, the detail of the buildings that are mapped exceeds OpenMap’s. There are also a few cases where OSM correctly doesn’t map buildings which have been recently knocked down but the destruction hasn’t made it through to OpenMap yet, which typically can have a lag of a year. For example, the Heygate Estate in Elephant & Castle is now gone.

The relative lack of completeness of building data in OpenStreetMap, for London, where the project began in 2004, is – in fact – likely due to it being where the project began. London has always an active community, and it drew many of the capital’s roads and quite a few key buildings, long before most other cities were nearly as complete. As a result, when the Bing aerial imagery and official open datasets of building outlines became more recently available, mainly around 2010, there was a reluctance to use these newer tools to go over areas that had already been mapped. Bulk importing such data is a no-no if it means disturbing someone’s prior manual work, and updating and correcting an already mapped area (where the roads, at least, are drawn) is a lot less glamorous than adding in features to a blank canvas. As a result, London is only slowly gaining its buildings on OSM while other cities jumped ahead. Its size doesn’t help either – the city is a low density city and it has huge expanses of low, not particularly glamorous buildings.

An couple of OpenStreetMap indoor tracing parties might be all that’s needed to fix this and get London into shape. Then the OpenStreetMap jigsaw will look even more awesome.


Click for a larger version. Data Copyright OpenStreetMap contributors (ODbL) and Crown Copyright and Database Right Ordnance Survey (OGL).

The City of London Commute

Here’s a graphic I’ve made by taking a number of screenshots of DataShine Commute graphics, showing the different methods of travelling to work in the City of London, that is, the Square Mile area at the heart of London where hundreds of thousands and financial and other employees work.

All the maps are to the same scale and the thickness of the commuting blue lines, which represent the volume of commuters travelling between each home area and the City, are directly comparable across the maps (allowing for the fact that the translucent lines are superimposed on each other in many areas). I have superimposed the outline of the Greater London Authority area, of which the City of London is just a small part at the centre.


There’s lots of interesting patterns. Commuter rail dominates, followed by driving. Car passenger commutes are negligible. The biggest single flow in by train is not from another area of London, but from part of Brentwood in Essex. Taxi flows into the City mainly come from the west of Zone 1 (Mayfair, etc). Cyclists come from all directions, but particularly from the north/north-east. Motorbikes and mopeds, however, mainly come from the south-west (Fulham). The tube flow is from North London mainly, but that’s because that’s where the tubes are. Finally, the bus/coach graphic shows both good use throughout inner-city London (Zones 1-3) but also special commuter coaches that serve the Medway towns in Kent, as well as in Harlow and Oxford. “Other” shows a strong flow from the east – likely commuters getting into work by using the Thames Clipper services from Greenwich and the Isle of Dogs.

Try it out for your own area – click on a dot to see the flows. There is also a Scotland version although only for between local authorities, for now.

Click on the graphic above for a larger version. DataShine is part of the ESRC-funded BODMAS project at UCL. I’ll be talking about this map at the UKDS Census Applications conference tomorrow in Manchester.

Tube Line Closure Map


[Updated] The Tube Line Closure Map accesses Transport for London’s REST API for line disruption information (both live and planned) and uses the information there to animate a geographical vector map of the network, showing closed sections as lines flashing dots, with solid lines for unaffected parts. The idea is similar to TfL’s official disruption map, however the official one just colours in the disrupted links while greying out the working lines (or vice versa) which I think is less intuitive. My solution preserves the familiar line colours for both working and closed sections.

My inspiration was the New York City MTA’s Weekender disruptions map, because this also blinks things to alert the viewer to problems – in this case it blinks stations which are specially closed. Conversely the MTA’s Weekender maps is actually a Beck-style (or actually Vignelli) schematic whereas the regular MTA map is pseudo-geographical. I’ve gone the other way, my idea being that using a geographical map rather than an abstract schematic allows people to see walking routes and other alternatives, if their regular line is closed.

Technical details: I extended my OpenStreetMap-based network map, breaking it up so that every link between stations is treated separately, this allows the links to be referenced using the official station codes. Sequences of codes are supplied by the TfL API to indicate closed sections, and by comparing these sequences with the link codes, I can create a map that dynamically changes its look with the supplied data. The distruption data is pulled in via JQuery AJAX, and OpenLayers 3 is used to restyle the lines appropriately.

Unfortunately TfL’s feed doesn’t include station closure information – or rather, it does, but is not granular enough (i.e. it’s not on a line-by-line basis) or incorrect (Tufnell Park is shown only as “Part Closed” in the API, whereas it is properly closed for the next few months) – so I’m only showing line closures, not station closures. (I am now showing these, by doing free-text search in the description field for “is closed” and “be closed”.) One other interesting benefit of the map is it allows me to see that there are quite a lot of mistakes in TfL’s own feed – generally the map shows sections open that they are reporting as closed. There’s also a few quirks, e.g. the Waterloo & City Line is always shown as disrupted on Sundays (it has no Sunday service anyway) whereas the “Rominster” Line in the far eastern part of the network, which also has no Sunday service, is always shown as available. [Update – another quirk is the Goblin Line closure is not included, so I’ve had to add that in manually.]

Try it out

Street Trees of Southwark

Above is an excerpt of a large, coloured-dot based graphic showing the locations of street trees in Rotherhithe, part of the London Borough of Southwark in London, as released by them to the OpenStreetMap database back in 2010. You can download the full version (12MB PDF). Street trees are trees on public land managed by LB Southwark, and generally include lines of trees on the pavements of residential streets, as well as in council housing estates and public parks. By mapping just the trees, the street network and park locations are revealed, due to their linear pattern or clumping of many types of trees in a small area, respectively. Trees of the same genus have the same colour, on this graphic.

southwarktrees_thinWhy did I choose Southwark for this graphic? Well, it was at the time (and still is) the only London borough that had donated its street tree data in this way. It is also quite a green borough, with a high density of street trees, second only to Islington (which ironically has the smallest proportion of green space of any London borough). There are street tree databases for all the boroughs, but the data generally has some commercial value, and can also be quite sensitive (tree location data can useful for building planning and design, and the exact locations of trees can also be important for neighbourly disputes and other damage claims. It would of course be lovely to have a map of the whole of London – one exists, although it is not freely available. There are street tree maps of other cities, including this very pretty one of New York City by Jill Hubley. There’s also a not-so-nice but still worthy one for Washington DC.

Also well as a PDF version, you can download a zip-file containing a three files: a GeoJSON-format file of the 56000-odd street trees with their species and some other metadata, a QGIS style file for linking the species to the colours, and a QGIS project file if you just want to load it up straight away. You may alternatively prefer to get the data directly from OpenStreetMap itself, using a mechanism like Overpass Turbo.

A version of this map appears in London: The Information Capital, by James Cheshire and Oliver Urberti (who added an attractive colour key using the leaf shapes of each tree genus). You can see most of it below. I previously talked about another contribution I made to the same book, OpenStreetMappers of London, where I also detailed the process and released the data, so think of this post as a continuation of a very small series where I make available the data from my contributions to the book.

The data is Copyright OpenStreetMap contributors, 2015, under the Open Database Licence, and the origin of most of the data is a bulk-import supplied by Southwark Council. This data is dated from 2010. There are also some trees that were added manually before, and have been added manually since, by other OpenStreetMap contributors. These likely include some private trees (i.e. ones which are not “street” trees or otherwise appear on private land.) Many of these, and some of the council-data trees, don’t have information their genus/species, so appear as “Other” on the map – orange in the above extract.