I presented on the Mapping London blog, at the Society of Cartographers’ 48th Annual Conference which was at UCL this year, showing a general outline of the blogs and some maps featured on it, plus some work done by James and I. My presentation is here (6MB PDF). Note that the attribution for the many maps featured on the presentation is at the end.
There’s a new, temporary panel on the London CityDashboard which shows Twitter activity at the London 2012 venues. The panel is using data from new Twitter collector tools in the Big Data Toolkit, which being developed by my colleague Steven James Gray as part of his PhD.
For each venue, the collectors count the number of Tweets in the last hour that have latitude/longitude information stamped on them, that are located within an area radiating around the centre of each stadium or arena. Unfortunately this excludes the majority of relevant tweets, as most mobile Twitter applications don’t include this information by default – stadium designs can also interfere with the accuracy of the GPS on mobile phones – when I was in the Velodrome for a test event, my iPhone was convinced I was in, ironically, Beijing, and nothing could be done to convince it otherwise.
Nonetheless, the tweets that the collectors do manage to capture still give an indication of how lively and busy each venue is. A collector covering the whole Olympic Park is also included – this includes the venues within the park and also the various promenades and green areas. Most people, before or after visiting the venue they have tickets for, are remaining in the wider park.
On the way we discovered an obscure Twitter bug: including a search radius that spreads across the Prime Meridian (0 degrees longitude) causes an error to appear from Twitter – fixing the centre of the search point on the Meridian itself works around this bug. Until we spotted the but, the Greenwich Park collector was always reporting zero, as the Meridian line goes through the park.
After the Olympics, we hope to reuse the collectors to give an indication of Twitter activity in certain key London hotspots, such as Shoreditch and Covent Garden. Potentially, we would be able to include a similar panel for the other seven UK cities on CityDashboard.
Over at the Big Data Toolkit blog, Steve talks in detail about the Twitter collectors.
The London Data Table was one of my personal favourites from the exhibition accompanying the CASA “Smart Cities” conference which took place at the University of London last Friday. The concept was thought up by Steven Gray and it consists of a wooden table, cut by programmable lathe into the outline of London. A special “short throw” projector with a fish-eye lens was purchased. It was mounted vertically on a converted basketball hoop stand, pointing downwards and outwards, allowing the content to be approached and examined without the projector getting in the way. Steven has blogged about the construction process.
I created a generic dark grey background map (from Ordnance Survey OpenData) with a blue River Thames as the main identifying feature. This was used by several authors, including myself, to create either Processing “sketches” in Java, or pre-recorded videos, which were displayed on the table during the exhibition. A simple Javascript script running on Node.JS was written to automatically cycle through the animations.
By ensuring that the background map and accompanying sketches/videos where “pixel perfect”, we were able to take advantage of having control of every individual pixel, producing the quite pleasing pixellated effect as seen in the below closeup of one of the sketches (a photo taken of a part of the table) – it is showing a bike share station animation that I created, based on the same data that powers the equivalent website.
The photo above shows the table running another Processing sketch, showing point information from CityDashboard and similar to the map view on the website, except that points are randomly and automatically selected to be displayed, as people stand beside and watch the table.
The most interesting sketch presented on the table (and shown on the right – photo by Helen) was built by Steven Gray and connected to a airplane sensor box, that picked up near-real-time broadcasts of location, speed and aircraft ID, of planes flying over London. The sketch stored recently received information, and so was able to project little images of plans, orientated correctly and with trails showing their recent path. Attached to each plane image was a a readout of height and speed, and most innovatively of all, a QR code was programmatically generated and rendered behind each plane, allowing smartphone users to scan it. QR codes are normally encoded URLs, and these ones were set to point to a flight information website, with the aircraft’s details preloaded, showing a photo, and the origin and destination at a glance.
The QR codes were able to be made very small – using a single projector pixel per QR code pixel and little error correction. Various smoothing and blurring digital effects having been switched off, and a digital connection between computer and projector used, to allow the sharpest possible representation. As a result, my iPhone was able to tell me more about the planes I was seeing fly, in near real time, around the table.
Here are the colour ramps I am using for numeric measures in the recently launched CityDashboard (which by the way now has a new URL – http://citydashboard.org/):
The colours have been designed to be clearly distinguishable from the white text that is on top of them.
Here is the PHP code that I’m using to choose the appropriate colour for each measure, and which I also used to produce the above ramps – the reverse colour and bad value handling is only implemented where I currently needed, ideally these would be implemented for all the ramps:
$na_rgb = 128;
function getGreyRedHex($val, $min, $max, $reverse=false, $processing=false)
{
$val_0_255 = getNormalisedVal($val, $min, $max);
$r = 128 + 0.5*intval($val_0_255);
$g = 128 - 0.5*intval($val_0_255);
$b = 128 - 0.5*intval($val_0_255);
return getHex($r, $g, $b, $processing);
}
function getGreyBlueHex($val, $min, $max, $reverse=false, $processing=false)
{
$val_0_255 = getNormalisedVal($val, $min, $max);
$r = 128 - 0.5*intval($val_0_255);
$g = 128 - 0.5*intval($val_0_255);
$b = 128 + 0.5*intval($val_0_255);
return getHex($r, $g, $b, $processing);
}
function getColdWarmHex($val, $min, $max, $reverse=false, $processing=false)
{
$val_0_255 = getNormalisedVal($val, $min, $max);
$r = intval($val_0_255);
$g = 255 - 2*abs(127.5 - $r);
$b = 255 - $r;
if ($reverse)
{
$r_temp = $r;
$r = $b;
$b = $r_temp;
}
return getHex(0.8*$r, 0.8*$g, 0.8*$b, $processing);
}
function getGreenYellowRedHex($val, $min, $max, $reverse=false, $processing=false)
{
global $na_rgb;
if ($val === "n/a") { return getHex($na_rgb, $na_rgb, $na_rgb, $processing); }
if ($val === "?") { return getHex($na_rgb, $na_rgb, $na_rgb, $processing); }
$val_0_255 = getNormalisedVal($val, $min, $max);
$r = intval($val_0_255);
$g = 255 - intval($val_0_255);
if ($g > 128) { $g = 128; }
$b = 0;
return getHex($r, $g, $b, $processing);
}
function getRedGreyGreenHex($val, $min, $max, $reverse=false, $processing=false)
{
global $na_rgb;
if ($val === "n/a") { return getHex($na_rgb, $na_rgb, $na_rgb, $processing); }
$val_0_255 = getNormalisedVal($val, $min, $max);
$r = 255 - intval($val_0_255);
$g = intval($val_0_255);
if ($g > 128) { $g = 128; }
$b = 128 - abs(127.5 - $val_0_255);
return getHex($r, $g, $b, $processing);
}
function getNormalisedVal($val, $min, $max)
{
if ($val < $min) { $val = $min; }
if ($val > $max) { $val = $max; }
$range = $max - $min;
return ($val - $min)*(255/$range);
}
function getHex($r, $g, $b, $processing)
{
$hex = str_pad(dechex($r), 2, "0", STR_PAD_LEFT)
. str_pad(dechex($g), 2, "0", STR_PAD_LEFT)
. str_pad(dechex($b), 2, "0", STR_PAD_LEFT);
if ($processing) { return "FF" . $hex; }
else { return "#" . $hex; }
}
I’ll be presenting CityDashboard at the forthcoming Wherecamp EU unconference which is taking place in Amsterdam this weekend.
CityDashboard is the main project that I have been working on for the last few months. It aims to summarise quantitative data (both officially provided and crowd-sourced) for the major UK cities, in a single screen. Point data is also shown in an alternate map view.
It was launched at the CASA Smart Cities conference last Friday, for eight cities – London, Cardiff, Edinburgh, Glasgow, Manchester, Leeds, Birmingham and Newcastle. London has the most dashboard “modules” at present, with a number of London-specific modules from Transport for London, the Port of London Authority, and CASA’s own sensors. Other cities have several more generic modules (such as weather and Twitter trends) and more city-specific modules will be added to these in due course. I am also looking at improving the overall look and feel of the website, possibly by using the BBC Glow API that was suggested to me at the conference (but just now took me half an hour to find on the web!)
CityDashboard features specially curated Twitter lists. For each city, there is a general news list, featuring tweets from local newspapers, local correspondents for the BBC and other TV and radio channels, tourist organisations and the official accounts for the relevant local authorities. There is also a universities list, with the official Twitter accounts for the main universities in each city, as well as their student unions. It is hoped that this latter list with detail the latest university research outputs, coming out of that city. The account that manages the lists is CityDB and the lists take the form of, for example, http://twitter.com/citydb/london and http://twitter.com/citydb/london-uni. Anyone can subscribe to these lists, you don’t have to only view them through CityDashboard.
You can visit CityDashboard live, right now, at http://citydashboard.org/
[Updated x2] Just a note to say that I will be presenting some of my work, at the CASA Smart Cities free one-day conference. Over 200 tickets have already gone, but there are, at the time of writing, a few left.
There will be an exhibition at the conference, some people in the department have been building some very cool things which will be unveiled there. Unfortunately I’m not allowed to talk about the very coolest one of all, but I have been allowed to post the above graphic which has got something to do with it…
(If you want to have a guess at what it is, leave a comment!)
[Update 3/4 – Tickets are sold out, however I think an extra batch will be available soon.]
[Update 13/4 – A few more tickets now available.]
Part Two in an extremely rare series of ornate metal maps on features, is this map, which is on the gate to a student block in Tottenham Hale. The map is only in the correct orientation (i.e. eastwards to the right) when viewed from inside, so I’ve flipped the photo around so that it makes sense.
The gate/map shows the Walthamstow Reservoirs, as well as the surprisingly large amount of green space surrounding the area (which is part of the Lea Valley). The block itself is coloured in black. The dotted pattern appears to have no significance other than to add structure to the built-up areas where the road network doesn’t help.
The Barclays Cycle Hire bikesharing system (map) in London is due for a major expansion on 8 March. Overnight on the 7th, operators will be working flat out to add 2300 1700 1900 new bikes into 4800 3000 3400 new stands, clustered in the 200 150 new docking stations that have been tested over the last few weeks, across the East End of London (Tower Hamlets, Shoreditch/Hackney Road and Canary Wharf). Also going live the same day will be a much smaller expansion west to the area round the Westfield shopping centre in Shepherd’s Bush in West London. Another small expansion around Camden Town has just been completed, adding several new stands to the northern tip of the system, including handily around Camden Town tube station and Camden Road train station, allowing commuters from the north and the Overground network (like me!) to avoid the expensive Zone 1 fares and Boris Bike the last few kilometres to work.
The expansion will move London up the league table of bike share cities from 7th to 5th – in a top 20 dominated by China. It will remain the second largest system outside of China, after Paris, although New York’s planned system will be even larger:
| City | Country | Bikes Mar 2012 |
Bikes Nov 2011 (If Different) |
|
|---|---|---|---|---|
| 1 | Wuhan | China | |
|
| Bejing (planned) | China | 50000 | ||
| 2 | Hangzhou | China | |
|
| 3 | Paris | France | 18000 | |
| New York (planned) | United States | 10000 | ||
| 4 | Taizhou | China | 10000 | |
| 5 | London (8 March+) | Great Britain | 7200 | 5000 |
| 6 | Yantai | China | 6000 | |
| 7 | Shanghai | China | 5700 | |
| Chicago (planned) | United States | 5000 | ||
| 8 | Guangzhou | China | 5000 | |
| 9 | Barcelona | Spain | 4700 | 4400 |
| 10 | Kaohsiung | China | 4500 | |
| 11 | Montreal | Canada | 4220* | |
| 12 | Foshan | China | 4000 | |
| 13 | Lyon | France | 3400 | 3060 |
| 14 | Zhangjiagang | China | 3200 | |
| 15 | Munich | Germany | 3000 | |
| 16 | Wuham/Qinshan | China | 3000 | |
| 17 | Toulouse | France | 2500 | |
| 18 | Brussels | Belgium | 2180 | 2060 |
| 19 | Seville | Spain | 1950 | |
| 20 | Changshu | China | 1700 | 1440 |
* Currently closed for winter.
Data sources: Scheme operator websites (through my live map), http://publicbike.net/, http://citybik.es/, http://bike-sharing.blogspot.com/, press releases/newspaper articles on new schemes, Wikipedia articles. If you are aware of any mistakes, please let me know and I will correct. Photo: CC-By-NC IanVisits.
[Update January 2013 – Scottish SIMD 2012 map added, more details.]
I’ve created a new visualisation, a dasymetric map of housing demographics which you can see here, which attempts to improve on the common thematic (a.k.a. choropleth) maps – a traditional example is shown below – where areas across the country are colour-coded according to some attribute. My visualisation clips the colour-coding to the building outlines in each area, leaving open ground, parks etc uncoloured.
The shortcoming of choropleth maps is that each area is coloured uniformly. If the attribute being measured is a property of the houses in that area, such as much of the census data, then choropleth maps not only colour the houses in each area, but also the parks, rivers and mountains that might also be contained within the area, even though the data being displayed arguably only applies to the houses. This means that geodemographic classification results that predominate in rural areas tend to overwhelm a map at smaller scales – as can be seen in the map on the right – where the green represents a countryside geodemographic.
An alternative to choropleth maps is to use cartograms. These distort the area, elastically, to tessellating hexagonal groups or to circles (Dorling cartograms), to match typically population rather than geographic extent, so that the colours are represented more fairly, but cartograms are very difficult for most people to interpret and relate to familiar physical features. They can look very “alien”. One further alternative is dot distribution maps – these assign dots of colour, randomly within each area. This reduces the colour density correctly in sparsely populated areas, but distributes the dots evenly across empty parks and rows of houses, if both are in a single area, and imply single points of population.
My visualisation attempts be the best of both worlds, by retaining the familiar geographic shape of the UK and its towns and cities, but not swamping the map with colours in all areas, and indeed ensuring that unpopulated areas have no colour. This is possible because Ordnance Survey Open Data includes Vector Map District. The second release of this dataset improved the quality of building outlines considerably, allowing distinct rows of buildings on streets to be seen and even individual detached houses. Unfortunately building classifications are not included, so the process necessarily colours all buildings, rather than just the residential ones that formed part of the census data. This is why, for example, the Millennium Dome in Greenwich appears, even though no one (hopefully!) lives there.
The major shortcoming of doing this is that it falsely implies a higher level of precision within each Output Area, by often showing and colouring individual buildings, whereas the colour is representative as an average of the properties in the area concerned, rather than telling you something about that particular building itself. That is, the technique is showing no new or more detailed data than can be seen in the traditional choropleth maps, but tends to mislead the viewer otherwise. This is balanced by making the map seem more realistic, by not unformly covering everything in the area with a giant blob of a single colour.
The map can be considered to be a dasymetric map, albeit one where the spatial qualifier, population density, is one of two values – high (in a building) or zero (not in a building).
An inspiration for this kind of map is the Charles Booth Poverty Map of 1898-9, although my example is considerably less sophisticated. For this map, Booth (and his assistants) visited every house, to determine the demographic of the house, and then painstakingly coloured in the houses, along the streets. His map therefore did not suffer from the falsely implied accuracy – his map really was as accurate as it looks. The Museum of London, incidentally, has a “walk in” Booth poverty map, I featured it on Mapping London blog last year.
The photo above compares Booth’s map (from a photo of the map in the Museum exhibition, including a friend’s hand) with my map, for the Hackney area in London.
My main geodemographic map is showing the OAC (Output Area Classification), which was created by Dan Vickers in Sheffield in 2005, and is based on data from the 2001 census. The areas used are Output Areas, there are around 210,000 of them in the UK, each one with a population of roughly 250 people in 2001.
The OAC map is not particularly illuminating for London – the capital is considerably more ethnically diverse than most other parts of the country, but because the clustering process used to create OAC is run across the whole country uniformly, only one Supergroup appears to show such ethnically diverse areas – “7” (Multicultural), rather than showing the variety within this group that extends across the capital. With this in mind I have created an alternative map, which colours the housing according to the IMD (Index of Multiple Deprivation) rankings. This covers England only, and the data is only available at larger spatial units, called LSOAs (Lower Super Output Areas) but is more up-to-date, being from 2010, and shows considerable more variety across London. Use the link at the bottom of the visualisation to switch between the two.
You can view the map here. It uses geolocation to attempt to zoom to your local area, if you allow it to – it will probably ask you to allow this when you visit the site.
[If you are looking for my London Tube Stats interactive map, it’s now here.]
Transport for London (TfL) take their colours extremely seriously – the London Underground, in particularly, uses colour extensively to brand each line, and the maps and liveries are very well known.
The organisation has a colour guide to ensure that, when referencing the tube lines, the correct colour is used. Somewhat surprisingly, the guide includes hexadecimal (i.e. web) colours for only a “safe” palette – i.e. colours which would definitely work in very old web browsers. They don’t list the “true” hexadecimal for the colours, even though, confusingly, the colour shown is the true one. I couldn’t find anywhere on the web that did this either, all in one place, so here below is a summary. I’ve also included the safe colours so you can see the difference – but don’t use these unless you have to.
| Line | True Hexadecimal | Web Safe Hexadecimal |
|---|---|---|
| Bakerloo | #B36305 | #996633 |
| Central | #E32017 | #CC3333 |
| Circle | #FFD300 | #FFCC00 |
| District | #00782A | #006633 |
| Hammersmith and City | #F3A9BB | #CC9999 |
| Jubilee | #A0A5A9 | #868F98 |
| Metropolitan | #9B0056 | #660066 |
| Northern | #000000 | #000000 |
| Piccadilly | #003688 | #000099 |
| Victoria | #0098D4 | #0099CC |
| Waterloo and City | #95CDBA | #66CCCC |
| DLR | #00A4A7 | #009999 |
| Overground | #EE7C0E | #FF6600 |
| Tramlink | #84B817 | #66CC00 |
| Cable Car | #E21836 | |
| Crossrail | #7156A5 |
All the colours above can be found on my new Electric Tube print.