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.
Britain’s “top” primary roads – the A1, A2, A3… to A9 – are arranged in a particular pattern, with the A1-A6 radiating out clockwise from London and the A7 to A9 similarly radiating around Edinburgh.
I used Gemma, an old UCL CASA project that Steve and I worked on back in 2011, to draw, from OpenStreetMap, the routes of the A1-A6 as they leave London. The A5 has a gap between Edgware and Harpenden, and the A6 only starts at Luton – both of these changes likely due to the building of the M1 motorway which effectively replaced those sections. Co-numbered roads are not included in the map due to a conflict with the way OpenStreetMap and Gemma separate information. Key for the maps: Red = A1, Orange = A2, Green = A3, Blue = A4, Purple = A5, Black = A6.
Also of interest is that the only two roads that “touch” in London are the A2 and A3, at Borough. The other roads may at one time have converged at junctions, but their starts have been shortened slightly over the years. The big junction at Bank certainly looks like a place where the A1, A3 and A4 could have started from. (Outside of London, the A7 touches the A1 at its northern end and the A6 at its southern end.) Diamond Geezer walked the first mile of the A1-A5 a few years ago.
Gemma still partially works, despite not having seen much love for the last few years and having never made it out of beta (it was a short project). It is recommended you use the OpenStreetMap (or Marker) layers only, to avoid bugs, and watch out if removing layers. You can see the live A1-A6 map here or have a go at building your own.
Key for the maps: Red = A1, Orange = A2, Green = A3, Blue = A4, Purple = A5, Black = A6.
The coloured road lines are Copyright OpenStreetMap contributors and the greyscale background map is Copyright Google.
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).
Cross-posted from the Datashine Blog.
DataShine Census has two new features – local area rescaling and data download. The features were launched at the UK Data Service‘s Census Research User Conference, last week at the Royal Statistical Society.
Local Area Rescaling
This helps draw out demographic versions in the current view. You may be in a region where a particular demographic has very low (or high) values compared to the national average, but because the colour breakout is based on the national average, local variation may not be shown clearly. Clicking on the “Rescale for current view” button on the key, will recolour for the current view.
For example, the popularity of London’s underground network with its large population, means that, for other cities with metros or trams, their usage is harder to pick out. So, in Birmingham, the Midland Metro can be hard to spot (interactive version):
Upon rescaling, just the local results are used when calculating the average and standard deviation, allowing usage variations along the line to be more clearly seen:
As another example, rescaling can help “smooth” the colours for measures which have a nationally very small count, but locally high numbers – it can remove the “speckle” effect caused by single counts, and help focus on genuinely high values within a small area.
Hebrew speakers in Stamford Hill, north-east London (interactive version):
Upon rescaling, a truer indication of the shape of the core Hebrew-speaking community there can be seen:
Occasionally, the local average/standard deviation values will mean that the colour breakout (or “binning”) adopts a different strategy. This may actually make the local view worse, not better – so click “Reset” to restore the normal colour breakout. Planning/zooming the map will retain the current colour breakout. PDFs created of the current view also include the rescaled colours.
On clicking the new “Data” button on the bottom toolbar, you can now download a CSV file containing the census data used in the current view. Like the local area rescaling functionality, this data download includes all output areas (or wards, if zoomed out) in your current view. This file includes geography codes, so can be combined with the relevant geographical shapefiles to recreate views in GIS software such as QGIS.
Next on the DataShine project, we are looking to integrate further datasets – either aggregating certain census ones or including non-census ones such as IMD and IDACI deprivation measures, or pollution.
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 GIST.
I was in Vienna for most of last week, presenting at a satellite workshop of the GIScience conference, before joining the main event for the latter part of the week.
GIScience is a biennial international academic conference, alternating between America and Europe. At the intersection between geography, GIS and information visualisation. It is very much academically focused, which contrasts strongly with FOSS4G (GIS technology), WhereCamp (GIS community) and the AGI (GIS business).
My highlights for this year’s conference:
- Jason Dykes (City) gave a keynote on balancing geovisualisation and information visualisation. As ever with presentations from City’s GICentre unit, the graphics were presented by way of various live demos and compellingly explained.
- UCL Geography/CEGE had a strong presence of the conference and various of my colleagues gave presentations, a number focusing on using geolocated social media, both as a tool for research (e.g. population synthesis) and for research itself. There was also an unveiling of LOAC (UCL/Liverpool), a classification specially built for London, further details on this to follow soon as LOAC is signed off and rolled out.
- Another UCL Geography presentation on comparing surname clustering and genotype clustering in the UK
- A interesting presentation from TU Eindhoven on automatically creating and simplifying network diagrams using circular arcs.
- Automatic Itinerary Reconstruction from Texts (LIUPPA/Pau) – showed how a fairly accurate map can be made simply by scanning prose, and otherwise unknown locations of places can be roughly determined by their textual relations to other, known places.
Many of the talks appear in an LNCS proceedings book.
Outside of the conference, much Wiener Schnitzel and Gelato was consumed, and historic old Vienna was explored. A highlight was conference drinks in the huge barrelled halls underneath the very grand city hall.