As well as the blue and red house silhouettes, assembled in QGIS, I’ve added in GeoJSON files of the River Thames (from Ordnance Survey Vector Map District, like the buildings) and of tube/DLR/Overground stations – the location/name/network data is from this GitHub file and I’ve applied a custom styling in OpenLayers 2, with station name styling inspired by the NYC Subway signs. The positional information comes from an OpenLayers control – I’m using a utility function to modify the output to use degrees, minutes and seconds. Finally, the naming popup is a set of UTFGrid JSON files (with 2-pixel resolution) based on OpenStreetMap data for polygons. Where the polygon has a building, leisure or waterway tag, I’m extracting a name, if available, and showing it. The coverage here is therefore only as good as building naming is in OpenStreetMap. I could potentially add in street names in the future.
Cross-posted from Mapping London, edited slightly.
This is a map of geolocated Tweets for the whole world – I’ve zoomed into London here. The map was created by Eric Fischer of Mapbox, who collected the tweets over several years. The place where each tweet is posted from is shown by a green dot. There are millions and millions of tweets on the global map – in fact, over 6.3 billion. The map is zoomable and the volume of tweets means that popular locations stand out even at a high zoom level. The dots are in fact vectors, so retain their clarity when you zoom right in. The map is interactive – pan around to explore it.
If you think this looks familiar, you’d be right. Mapping London has featured this kind of social media ‘dot-density mapping’ a few times before, including with Foursquare and Flickr (also Eric’s work), as well as colouring by language. The key difference with this latest map is the sheer volume of data. By collecting data on geolocated tweets over the course of several years, globally, Eric has assembled the most comprehensive map yet. He has also taken time to ensure the map looks good at multiple zoom levels, by varying the dot size and dot density. He’s also eliminated multiple tweets that happen at the exact same location, and reduced some of the artefacts and data quality issues (e.g. straight lines of constant latitude or longitude) to produce perhaps the cleanest Twitter dot-density map yet. Zooming out makes the map appear somewhat similar to the classic night-time satellite photos of the world, with the cities glowing brightly – here, London, Paris and Madrid are prominent:
However it should still be borne in mind that while maps of tweets bear some relationship to a regular population density map at small scales, at large scales they will show a bias towards places where Twitter users (who may be more likely to be affluent and younger than the general population) live, work and socialise. The popularity of the social network also varies considerably on a country-by-country basis. Some countries will block Twitter usage altogether. And in other countries, the use of geolocated tweets is much less popular, either due to popularity of applications that do not record location by default or a greater cultural awareness of privacy issues relating to revealing your location when you tweet.
Above: Twitter activity in central Edinburgh, proving once and for all that the East End is a cooler place than the West End.
From the Mapbox blog. Found via Twitter, appropriately. Some of the background data is © OpenStreetMap contributors, and the map design and technology is © Mapbox.
This book, which features great examples of London building architecture, is itself distinctively designed and immaculately presented. It’s been out for a couple of years now, however I was recommended it when purchasing another book recently on Amazon, as an impulse purchase, it’s an excellent find.
The book was authored by Hannah Dipper and Robin Farquhar of People will always need plates and is based on their heavily stylised interpretation of the buildings featured.
Each building featured in the book – there are around 45 – gets a two page spread, always in the same format – the building shown in white with clean strokes of detail in black, and a distinctive, single tone of colour for the sky. A small inset box includes the buliding name, architects, age and 100 words. That’s it.
The book doesn’t just feature the modern Brutalist London landscape (e.g. Trellick Tower), and the latest modern skyscrapers (e.g. the Gherkin) it also includes such older gems as Butler’s Wharf and the Dulwich Picture Gallery. These two are treated to the wonderful, minimalistic sketch style, with just the two colours allowing the design detail of the building itself to take centre stage.
On Amazon: London Buildings: An Architectural Tour, currently for £9.99. Published by Batsford, an imprint of Anova Books.
Image from the London Design Guide website.
I contributed a number of graphics to LONDON: The Information Capital, a book co-written by Dr James Cheshire, also of UCL Geography. Two of my graphics that made it into the book were based on data from OpenStreetMap, a huge dataset of spatial data throughout the world. One of the graphics, featured in this post, forms one of the chapter intro pages, and colours all the roads, streets and paths in the Greater London Authority area (around 160,000 “ways” which are discrete sections of road/path) according to the person who most recently updated them. Over 1500 indivdual users helped create and refine the map, and all are featured here. I was pleased to discover I was the 21st most prolific, with 1695 ways most recently modified by myself at the time that the graphic was produced.
The more active users will typically have areas around home and work which they intensively map, plus other, smaller areas such as contributions made during a mapping party or other social event organised by/for the London OSM community. Here’s an example filtering for just one user:
Putting the users together reveals a patchwork of key authors and more minor contributors, together forming a comprehensive map of the city. Detail levels vary, partly as the fabric of the city varies from area to area, but also as some contributors will be careful to map every path and alleyway, while others will concentrate on the driveable road network.
The data was obtained from a local copy of the OpenStreetMap database, for Great Britain, that I maintain for various pieces of work including OpenOrienteeringMap. You can obtain the data files from GeoFabrik (this link is to their new London-only version). The data was captured in early February 2014. Newham borough in east London (light blue) shows up particularly prominently because it looks like it had had a bulk update of all roads there by a single user, just before the capture, to indicate which were lit by streetlights (lit=yes).
I used QGIS to assemble the data and applied the temp-c colour ramp, classifying across all the contributors – I then changed the ones which were assigned a white colour, to green. The colours used in the book are slightly different as some additional editing took place after I handed the graphic over. The colour ramp is relatively coarse, so multiple users will have the same colour assigned to them. The very long tail of OSM contributions (where only a small number of people make the great majority of edits) mean that this still means that most major contributors have a unique colour assigned to them.
- QGIS 2.6 project file (with line colours), London boundary mask and contributor lines (QPS, Shapefile and GeoJSON files, zipped, 18.5MB)
- Contributor colour key (SLD format, zipped)
- Contributor way counts (TSV format)
- List of ways and versions with contributors (CSV format, zipped)
Note that these files actually are for an area that is slightly larger than the Greater London Authority extent – a buffer from Ordnance Survey Open Data Boundary-Line is used to mask out the non-GLA areas.
If you like this thing, it’s worth noting that Eric Fischer independently produced a similar graphic last year, for the whole world. (Interactive version).
If you are a Londoner but felt that Tube Tongues passed you by, maybe because you live in south-east London or another part of the city that doesn’t have a tube station nearby, then here’s a special version of Tube Tongues for you. Like the original, it maps the most popularly spoken language after English (based on 2011 Census aggregate tables released by the ONS, via NOMIS) but instead of examining the population living near each tube station, it looks at the population of each ward in London. There are 630* of these, with a typical population of around 10000. I’ve mapped the language as a circle lying in the geographic centroid of each ward. This is a similar technique to what I used for my local election “Political Colour” maps of London.
A few new languages appear, as the “second language” (after English) in particular wards: Swedish, Albanian and Hebrew. Other languages, which were previously represented by a single tube station, become more prominent – Korean around New Malden, German-speaking people around Richmond, Nepalese speakers in Woolwich, Yiddish in the wards near Stamford Hill and Yoruba in Thamesmead. Looking at the lists of all languages spoken by >1% of people in each ward, Swahili makes it on to a list for the first time – in Loxford ward (and some others) in east London. You can see the lists as a popup, by clicking on a ward circle. As before, the area of the circles corresponds to the percentage of people speaking a language in a particular ward. The very small circles in outer south-east London don’t indicate a lack of people – rather that virtually everyone there speaks English as their primary language.
English remains the most popularly spoken language in every ward, right across London. Indeed, there are only a three wards, all in north-west London, where it doesn’t have an absolute majority (50%). London may seem very multilingual, based on a map like this, but actually it is very much still Europe’s English-speaking capital. See the graphic below, which shows the equivalent sizes the circles are for English speakers, or click the “Show/hide English” button, on the interactive map.
* I’ve ignored the tiny City of London ones except for Cripplegate, which contains the Barbican Estate.
Background map uses data which is copyright OpenStreetMap contributors. Language data from the ONS (2011 Census).
As a followup to Tube Tongues I’ve published Working Lines which is exactly the same concept, except it looks at the occupation statistics from the 2011 census, and shows the most popular occupation by tube station. Again, lots of spatial clustering of results, and some interesting trends come out – for example, the prevalence of teachers in Zones 3-4, that there is a stop on the central line in north-east London which serves a lot of taxi drivers, and that bodyguards really are a big business for serving the rich and famous around Knightsbridge.
The northern line (above) stands out as one that serves a community of artists (to the north) and less excitingly a community of business administrators (to the south). Tottenham/Seven Sisters has a predominance of cleaners, and unsurprisingly perhaps plenty of travel agents live near Heathrow. I never knew that the western branch of the central line, towards West Ruislip, was so popular with construction workers. Etc etc.
Only the actively working population is included, rather than the full population of each area. This makes the numbers included in each buffer smaller, so I’ve upped the lower limit to the greater of 3% and 30 people, to cut down on small-number noise and minimise the effect of any statistical record swapping.
I’ve extended my map of tube journeys and busy stations (previous article here) to add in an interesting metric from the 2011 census – that of the second most commonly spoken language (after English) that people who live nearby speak. To do this I’ve analysed all “output areas” which wholly or partly lie within 200m radius of the tube station centroid, and looked at the census aggregate data for the metric – which was a new one, added for the most recent census.
Each tube station has a circle coloured by, after English, the language most spoken by locals. The area of the circle is proportional to the percentage that speak this language – so a circle where 10% of local people primarily speak French will be larger (and a different colour) than a circle where 5% of people primarily speak Spanish.
Language correlates well with some ethnicities (e.g. South Asian) but not others (e.g. African), in London. So some familiar patterns appear – e.g. a popular, and uniform, second language appearing at almost all Tower Hamlets stations. Remember, the map is showing language, not origin – so many of the “Portuguese” speakers, for instance, may be of Brazilian origin.
Click on each station name to see the other languages spoken locally – where at least 1% of local speakers registered them in the census. There is a minimum of 10 people to minimise small number “noise” for tube stations in commercial/industrial areas. In some very mono-linguistic areas of London (typically in Zone 6 and beyond the GLA limits) this means there are no significant second languages, so I’ve included just the second one and no more, even where it is below 1% and/or 10 people.
This measure reveals the most linguistically diverse tube station to be Turnpike Lane on the Piccadilly Line in north-east London, which has 16 languages spoken by more than 1% of the population there, closely followed by Pudding Mill Lane with 15 (though this area has a low population so the confidence is lower). By contrast, almost 98% of people living near Theydon Bois, on the Central Line, speak English as their primary language. English is the most commonly spoken language at every tube station, although at five stations – Southall, Alperton, Wembley Central, Upton Park and East Ham – the proportion is below 50%.
A revealing map, and I will be looking at some other census aggregate tables to see if others lend themselves well to being visualised in this way.
I’ve also included DLR, Overground, Tramlink, Cable Car and the forthcoming Crossrail stations on the map. Crossrail may not be coming until 2018 but it’s very much making its mark on London, with various large station excavations around the capital.
The idea/methodology is similar to that used by Dr Cheshire for Lives on the Line. The metric was first highlighted by an interesting map, Second Languages, created by Neal Hudson. The map Twitter Tongues also gave me the idea of colour coding dots by language.
One quirk is that speakers of Chinese languages regularly appear on the map at many stations, but show as “Chinese ao” (all other) rather than Cantonese, whereas actually in practice, the Chinese community do mainly speak Cantonese (Yue) in London. This is likely a quirk of the way the question was asked and/or the aggregate data compiled. Chinese ao appears as a small percentage right across London, perhaps due to the traditional desire for Chinese restaurant owners to disperse well to serve the whole capital? [Update – See the comments below for an alternative viewpoint.]
The TfL lines (underground, DLR etc), station locations and names all come from OpenStreetMap data. I’ve put the collated, tidyed and simplified data, that appears on the map, as GeoJSON files on GitHub – see tfl_lines.json and tfl_stations.json. The files are CC-By-NC, licensing information is here.
The above graphic (click for full version) shows 12.4 million bicycle journeys taken on the Barclays Cycle Hire system in London over seven months, from 13 December 2013, when the south-west expansion to Putney and Hammersmith went live, until 19 July 2014 – the latest journey data available from Transport for London’s Open Data portal. It’s an update of a graphic I’ve made for journeys on previous phases of the system in London (& for NYC, Washington DC and Boston) – but this is the first time that data has been made available covering the current full extent of the system – from the most westerly docking station (Ravenscourt Park) to the the most easterly (East India), the shortest route is over 18km.
As before, I’ve used Routino to calculate the “ideal” routes – avoiding the busiest highways and taking cycle paths where they are nearby and add little distance to the journey. Thickness of each segment corresponds to the estimated number of bikeshare bikes passing along that segment. The busiest segment of all this time is on Tavistock Place, a very popular cycle track just south of the Euston Road in Bloomsbury. My calculations estimate that 275,842 of the 12,432,810 journeys, for which there is “good” data, travelled eastwards along this segment.
The road and path network data is from OpenStreetMap and it is a snapshot from this week. These means that Putney Bridge, which is currently closed, shows no cycles crossing it, whereas in fact it was open during the data collection period. There are a few other quirks – the closure of Upper Ground causing a big kink to appear just south of Blackfriars Bridge. The avoidance of busier routes probably doesn’t actually reflect reality – the map shows very little “Boris Bike” traffic along Euston Road or the Highway, whereas I bet there are a few brave souls who do take those routes.
My live map of the docking stations, which like the London Bikeshare itself has been going for over four years, is here.
[Update – A version of the map appears in Telegraph article. N.B. The article got a little garbled between writing it and its publication, particularly about the distinction between stats for the bikeshare and for commuter cyclists in London.]
Earlier this month, I gave a short presentation at the Big Data and Urban Informatics Workshop, which took place at UIC (University of Illinois in Chicago). My presentation was an abridged version of a paper that I prepared for the workshop. In due course, I plan to publish the full paper, possibly as a CASA working paper or in another open form. The full paper had a number of authors, including Prof Batty and Steven Gray.
Below are the slides that formed the basis of my presentation. I left out contextual information and links in the slidedeck itself, so I’ve added these in after the embedded section:
Slide 3: MapQuest map showing CASA centrally located in London.
Slides 4-5: More information.
Slide 6: More information about my Bike Share Map, live version.
Slide 7: More information.
Slide 8: More information about CityDashboard, live version.
Slide 10: Live version of CityDashboard’s map view.
Slide 11: More information about the London Periodic Table, live version.
Slide 14: More information about Prism.
Slide 15: London and Paris datastores.
Slide 16: Chicago, Washington DC, Boston data portals.
Slide 17: The London Dashboard created by the Greater London Authority. Many of its panels update very infrequently.
Slide 18: Washington DC’s Open Government Dashboard and Green Dashboard, these are rather basic dashboards, the first being simply a graph and the second having just three categories.
Slide 19: The Amsterdam Dashboard created by WAAG, a non-profit computer society based in the heart of the city.
Slide 20: The Open Data City Census (US version/UK version) created by OKFN – a great idea to measure and compare cities by the breadth and quality of their open data offerings.
Slide 21: More information.
Slide 22: More information.
Slide 23: Pigeon Sim.
Slide 24: Link to iCity, More information on DataShine, live version.
Slide 25: More information on DataShine Travel to Work Flows, live version.
Some slides contain maps, which are generally based on OpenStreetMap (OSM) or Ordnance Survey Open Data datasets.
The Diamond Geezer is, this month, climbing the highest tops in each one of London’s 33 boroughs.
To find the highest points, he’s used a number of websites which list the places. These derive the data from contour lines, perhaps supplemented with GPS or other measurements. However, another interesting – and new – datasource for calculating this kind of metric, is OS Terrain 50. Released as part of the Ordnance Survey Open Data packages, it is a gridded DEM (Digital Elevation Model). It’s right up to date, at 50m x 50m horizontal resolution, and 10cm vertical resolution, and it should correct for buildings, so showing the true ground height.
Looking at the DEM for Newham, I think it reveals a new highest point – not Wanstead Flats at 15m above sea level, as Diamond Geezer’s lists suggest, but Westfield Avenue, the new road that runs through the Olympic Park. Beside John Lewis, the road rises, to a highest point of 21.6m. It shows as purple in the graphic above. Nearby, the new “bowl” of the lower part of the Olympic Stadium can be seen, as well as the trench through which High Speed 1 runs, at Stratford International Station.
I can’t argue with the Chancery Lane/Holborn junction as being the highest ground-point in the City of London, at 21.9m. In Tower Hamlets, it’s more tricky. The old railyards between Shoreditch High Street and the lines into Liverpool Street look like they are at 21.7m, however the ground here is not publically accessible, and the DEM is quite noisy here, with only part of the railyard showing this height.
I’m looking for a way to do this programatically – calculating the highest DEM value for each borough. I’ve tried using QGIS’s Zonal Statistics plugin, with polygon shapefiles of London’s boroughs, but this only shows the mean value of the DEM for that borough.
Here’s the list I’ve created by measuring – the main issue with my dataset is that the measurements are only at the centre of each 50m x 50m cell.
|Borough||Hgt (m) 50m cn||10-digit grid ref||Description of
|Barking and Dagenham||45.3||TQ_48590_89948||Industrial area just E of northern part of Whalebone Lane North.|
|Barnet||146.1||TQ 21955 95622||Just south of the water tower to the east of Rowley Lane, near Rowley Green.|
|Bexley||81||TQ 45737 71256||Langdon Shaw, southwest side.||Yes|
|Brent||91.2||TQ 20732 88877||Junction of Wakemans Hill Avenue and The Grove.|
|Bromley||246.5||TQ 43637 56487||A233 – where Main Road changes name to Westerham Hill||Yes|
|Camden||135.6||TQ 26277 86225||Lower Terrace, just off Heath Street in Hampstead.||Yes|
|City of London||21.9||TQ 30970 81612||NW edge – junction of Holborn and Chancery Lane.|
|Croydon||175.7||TQ 34330 61827||Sanderstead Plantation, SW path crossroads.|
|Ealing||81.5||TQ 16177 84398||Horsenden Hill|
|Enfield||118.7||TQ 25632 97674||Just north of Camlet Way, Hadley Wood, opposite Calderwood Place.||Yes|
|Greenwich||131.1||TQ 43831 76583||Southern end of Eaglesfield Recreation Ground on Shooters Hill.|
|Hackney||39.8||TQ 32025 87574||In Finsbury Park, beside Green Lanes, opposite No. 330.||Yes|
|Hammersmith and Fulham||45.9||TQ 22960 82756||Harrow Road at north end of bridge over the railway line near Kensal Green station.||Yes|
|Haringey||129||TQ 28326 87479||Ground by Highgate School Chapel, just north of Highgate High Street.|
|Harrow||153.4||TQ 15288 93808||Magpie Hall Road, between The Common and Alpine Walk.||Yes|
|Havering||106||TQ 51192 93055||Churchyard of St John the Evangelist church (also Broxhill Road by the cricket pitch)|
|Hillingdon||130.5||TQ 10585 91678||Junction of South View Road and Potter Street Hill||Yes|
|Hounslow||33.6||TQ 11320 78815||Western Road – bridge over the Grand Union Canal.|
|Islington||99.9||TQ 28874 87217||Highgate Hill and Hornsey Lane junction.||Yes|
|Kensington and Chelsea||45.7||TQ 23014 82728||Kensal Green Cemetery, northern edge, beside the Harrow Road, above the railway line.||Yes|
|Kingston upon Thames||91.3||TQ 16644 60376||Telegraph Hill|
|Lambeth||110.9||TQ 33620 70729||Westow HIll and Japser Road junction.||Yes|
|Lewisham||111.2||TQ 33918 71779||Sydenham Hill and Rock Hill junction.||Yes|
|Merton||56||TQ 23627 70823||Lauriston Road and Wilberforce Way NW junction.|
|Newham||21.6||TQ 37967 84530||Westfield Avenue, outside John Lewis in Westfield Stratford City.|
|Redbridge||91.5||TQ 47945 93784||Cabin Hill|
|Richmond upon Thames||56||TQ 18779 73065||Bridleway/path junction just east of Queens Road, opposite the Pembroke Lodge car-park and to the NE of it.|
|Southwark||111.5||TQ 33926 71686||Sydenham Hill, between Chestnut Place and Bluebell Close.||Yes|
|Sutton||146.4||TQ 28383 59986||Middle of rectangle of land south-east of Corrigan Avenue and south-west of Richland Avenue.|
|Tower Hamlets||21.7||TQ 33720 82184||Railway yards between Shoreditch High Street station and the railways lines leading to Liverpool St Station.|
|Waltham Forest||92.2||TQ 38415 95010||Pole Hill (north top)|
|Wandsworth||60.7||TQ 22881 72780||Big Alp, Wimbledon Common|
|Westminster||53||TQ 26627 18386||Finchley Road and Boundary Road junction.||Yes|