New York City last week released a preliminary map showing the proposed sites for the launch of its bike sharing scheme, now named Citi Bike (with Citigroup being the lead sponsor along with Mastercard).

Citigroup’s sponsorship is crucial for the scheme, which has promised no public subsidy on at least operating costs, and is a rather convenient sponsor in terms of its name. In several other cities around the world, their bike share schemes are known as City Bikes, such as Stockholm and Vienna, so Citi Bike has a good chance of becoming the “on the street” name for the scheme, unlike the unwieldy “Barclays Cycle Hire” name we have here in London – most people here know them as the snappier, if politically incorrect “Boris Bikes”.

NYC’s scheme is clearly influenced by London’s – its of a similar size, it has a big sponsor from financial services and a mayor fully behind it, and a Boris Bike from London even appears on the front cover of the NYC DOT presentation to communities. The technology used is the same and the bikes are also the same design.

I’ve extracted the data from the official map and added it to my own set of maps for 50+ bike share cities across the world allowing for a direct comparison between the scheme and the existing ones. The initial map has 413 stands – the districts either side of Central Park are missing, as is central Brooklyn, as these areas are still undergoing consultation and will gain coverage next year. The scheme should be opening this summer and is then due to expand by 50% by Autumn 2013.

The stand sizes and descriptions are also from the official map, and I’ve simulated the empty/full status of each stand, based on the distance from Wall Street and random perturbation. This results in just under 7000 bikes, based on a roughly 1:1 empty to occupied stand ratio, which is fairly standard around the world.

New York vs London

* Announced figure. Actual figure may be less due to bikes in maintenance and temporary storage. London’s equivalent figure was 6000 bikes. ** Zone 1 only. Cost higher if travelling to Zone 2 (which has bike share bikes in it). Cost lower if only in Zone 2.

What stands out for me, when comparing New York‘s and London‘s bike share schemes, which are roughly similar in terms of number of bikes and stands, is that NY’s footprint is similar in size to London’s at launch , but with many more bikes, and the scheme is accordingly more dense. Certainly, New York will have the critical mass of stand locations, so allow the scheme to work efficiently – you’ll never have to travel very far, if your destination stand is full, to find another one.

The other thing that strikes me is that all the stands are quite big – very few of them have less than 20 docks. The biggest, on Pershing Square (by Grand Central Station) has 128 docks – this is ever so slightly larger than our own “superdock” at Waterloo Station and presumably designed with a similar purpose of satisfying the commuter “tide”. The other big commuter station in NYC, Penn Station, has three large docks surrounding it. The coverage is also fairly uniform, my only surprise is that there are only two docks in Battery City, which is surely full of people likely to use the scheme – or perhaps they just walk to work? Also there are none in Central Park – although perhaps these will be included in the Upper East/Upper West areas for next year’s expansion?

One big difference is the fee structure – at $10 a day but only $95 a year, this suggests that tourists and public-transport-based commuters are the target users, rather than local residents and errand users. This is a pity – the latter group tends have more heterogenous usage flows and help “mix” the scheme up and redistribute it organically, requiring less redistribution of bikes trucks by the operator.

$10 is four times more than the cost of the New York subway ($2.50/trip with Metrocard) so you would need to do at least four journeys a day to save money. In London, our tube in Zone 1 is £2 per journey with Oystercard) or £1 a day on the Boris Bikes. So end up often using the latter simply on cost, even for one journey. The over-30-minute journey extra cost is also significantly more – $4 compared with £1 here. Subscribers get 45 minutes free rather than 30 minutes. This gives those commuters a chance to travel further in the busy rush hour – although surely this increase the redistribution challenge even further.

NYC’s CitiBikes are thinking big, and the design of the scheme suggests that it is expected to be wildly successful at launch. Hopefully this will prove to be the case!

You can see my map here.

Photo credit: Edward Reed / NYC Mayor’s Office

I was at the third WhereCampEU “unconference” which took place in Amsterdam over the last weekend of April, following previous editions in London and Berlin which I was also at. The meeting was an ideal opportunity for me to feature CityDashboard which I unveiled at the CASA Smart Cities conference a week before, and to show a couple of the items that were popular at the exhibition that accompanied Smart Cities – namely the London Data Table and PigeonSim.

Amsterdam proved to be a challenging city (financially) to visit for the conference, as it was the weekend before Queen’s Day – which is essentially a massive party throughout central Amsterdam, resulting in expensive transport to get there and all the central hotels being booked up or extremely pricy. So it was that I ended up on the outskirts of the city, overlooking a motorway, although this did mean I got to use the very fast and efficient metro service into town each day. Pre-conference drinks were held upstairs in De Waag, the oldest non-religious building in Amsterdam and a fantastically atmospheric venue. The conference venue was a short walk from here.

To get to Amsterdam I took the Eurostar to Brussels, spent an hour and a half cycling around the city on one of the Villo bike-share bikes, and then got another high-speed train to Amsterdam. A nice way to see the countryside, but it did take six hours in total. My return was a 40-minute flight.

Unconferences have no set speaker schedule, but instead participants put a post-it note with their talk title on a grid of times and rooms, and everyone looks at the grid to determine what to go to next. The plan had been to present early on the Saturday and then just relax and enjoy the rest of the meeting, but the Saturday grid was very quickly full, and it wasn’t until Sunday lunchtime that I was able to squeeze in my talk. Although 26 minutes of my 30 minute slot was spent on CityDashboard, most of the tweeted photos were of PigeonSim (that I squeezed in the last four minutes) and my attempts at demonstrating the flying gestures…

There was as usual a wide range of geo and tech talks, one of the most unusual being a psychogeography session with Tim Waters – this unexpectedly involved a practical where we went out in groups and followed and observed pedestrians going about their business (an initial “meta” idea to follow the followers having been vetoed by Tim). I also enjoyed Jeremy Morley’s update on the OSM-GB project at Nottingham to quantify the quality of OpenStreetMap in the UK, and Peter Miller’s peek at a 2.5D rendering of OSM data. Peter also showed behind the scenes of ITO Map’s map layer scripts, these produce simple overlays highlighting particular OpenStreetMap content – these were the inspiration for similar functionality I incorporated into GEMMA. Finally, a short Geo-yoga (mimicing the shapes of countries) session was certainly an eye-opener. Parallel sessions meant I missed some more interesting talks, including one from Google on why Google can work with OSM.

Thanks to all the organisers for putting on another excellent, and free, WhereCampEU!

Here’s a map of England, overlaid on it is a choropleth map showing the modal (i.e most common) council tax band within each Census Output Area (OA) in England, based on March 2011 data released by the Office of National Statistics and listed on data.gov.uk.

I’m using a manually created colour ramp instead of a “standard” (i.e. ColorBrewer) diverging or sequential ramp, to emphasise the outliers (the big, expensive Band H houses and the small, cheap Band A ones) and try and reduce the “patchwork quilt” effect that you get when looking at such a map (which has nearly 170000 areas.) Another way to minimise this effect would have been to use larger geographies (LSOAs and MSOAs) at the smaller scales.

The map shows a swathe of light blue Band A housing across the north of England, and in Birmingham. In London, generally this doesn’t happen, and indeed a band of very large, expensive houses, protrudes from the affluant commuter belt right into the centre of London, from the south-west and north.

The map was created using UCL CASA’s MapTube, with a CSV file, descriptor file and stylesheet being the inputs. Welsh council tax bands use a different scale so are not included here. The Scotland/N.I. data is not available through the ONS website.

A gotcha when producing this map is that the file uses the new (2011) identifiers for OAs. Thankfully I found a file that maps the old to the new ones, although it took a bit of sleuthing to find it on the ONS website.

A zoomable, explorable version of the map is available..

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.

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/

The project is an output of NeISS, which is funded by JISC.

[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.]

As planned, Tower Hamlets (east London) and Shepherd’s Bush (west London) saw a big expansion of bike share docking stations, overnight last Wednesday night. There’s also been some incremental additions to the existing zone, and a build-out of Camden Town in the days leading to the “big bang” expansion.

So where are the new docks? As Diamond Geezer has noted, the all four compass points have received new bike share docking stations recently.

The map below shows (in colour) the new docking stations – those that were installed since 1 January 2012, and are currently operational. The old ones are in grey.

The numbers:

There are a few more “ghost” docking stations that appear on the map, these are old docking stations that have been decommissioned or more new ones that were recently in testing and so appeared on the official map – TfL have promised an additional 10-15 docking stations) will go in in early April.

Background map CC-By-SA OpenStreetMap contributors.

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:

The Biggest Bike Sharing Cities (March 2012)

* 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.

I’m sure there’s easier ways to do this, but here’s my technique, shown here for Minneapolis. Click the images for larger version.

1. Extract the data from OpenStreetMap. Use the Export function, and draw out the area concerned with a bounding box. Choose OpenStreetMap XML as the format. I originally tried SVG, but this presents you with screen coordinates instead of latitude/longitude pairs.

2. Open the resulting file in Quantum GIS (QGIS). I used QGIS 1.9. You need the OpenStreetMap plugin installed, this will allow the OSM file that was created in Step 1 to be read straight in (in fact you could download the file directly from the OSM servers, if you wanted to).

3. Select the feature you are interested in. My river (actually a waterbank polygon) is a “hairy feature” as it extends well beyond the extent of the data that was downloaded. Make sure you are selecting it (feature turns yellow) rather than highlighting it for feature information (feature turns red). Otherwise, the subsequent file is, rather unhelpfully, blank.

4. Do Layer > Save Selection as Vector File. Choose “KML” as the format. You probably don’t need to change the coordinate reference system (CRS) as the data will already be in WGS 84, and this (“normal GPS-style latitude/longitude) is the CRS you want.

5. Edit the resulting file, removing the XML tags, and header/footer, and replace spaces with return characters, to leave a long list of latitude/longitudes, ready for importing into your visualisation code.