5.5 Million Journeys at NYC Bike Share

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[Updated - timeperiod-split maps added] Following on from my London bikeshare journeys graphic, here is the same technique applied with the data released by NYC Bike Share (aka Citi Bike) earlier this week.

If you look carefully at the full size map you can see a thin line heading north-eastwards, initially well out of the bikeshare “zone”, representing journeys between Williamsburg and Central Park, via the Queensboro Bridge cycle path. We see a similar phenomenon for journeys between Tower Bridge and Island Gardens in London. Whether any of the riders actually take this route, of course, is open to question – they might take a longer – but more familiar – route, that stays more within the area of the bikeshare.

Below is a version of the graphic with the data split into four timeperiods – weekday rush-hour peaks (7-10am and 4-7pm starts), weekday interpeak (10am-4pm), weekday nights (7pm-7am) and finally weekends. The data is scaled so that the same thicknesses of lines across the four maps represent the same number of journeys along each street segment – but bear in mind that there are fewer weekends than weekdays. While, as would be expected, the rush-hour peaks see the most number of journeys, there is less spatial variation across the city, between the four timeperiods, than I expected. Click on the graphic for a larger version.

timesplits

The graphics were produced by creating idealised routes (near-shortest path, but weighted towards dedicated cycle routes and quieter roads) between every pair of the ~330 docking stations in the system, using Routino and OpenStreetMap data (extracted using the Overpass API). Edge weights were then built up using a Python script, a WKT file was created and then mapped in QGIS, with data-based stroke widths applied from the weights.

The routes are only as good as the OpenStreetMap data – I think the underlying data is pretty good for NYC, thanks to great community work on the ground, but there is still a possibility that it has missed obvious routes, or proposed wacky ones. It also doesn’t account for journeys starting or ending at the same place, or journeys where the prime purpose is an exploration by bike – with the user unlikely therefore to take an “obvious” A-B route.

Even with that caveat, it’s still a revealing glimpse into the major route “vectors” of bikeshare in New York City.

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A Census for Open Data in Cities

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The Open Knowledge Foundation (OKFN) have produced a census for government open data availability for countries around the world, known as the Open Data Index. Each country is assigned scores for 10 attributes on openness and accessibility for each of 10 types of data (such as election results and pollution information). Currently the United Kingdom is at the top of the table.

More recently, OKFN expanded the concept to look at open data for cities within each country, in other words data that is managed at the City Hall level. For example, there is a project page for individual cities within the UK. This time, 15 types of data are examined, again each gaining up to 10 points for openness. The project is still in its information gathering stage so, at the time of writing, only 6 cities have their data partially, or fully, entered. The census for Italian cities, for example, is looking more complete.

Such a census is of great interest when building an application like CityDashboard, which is currently available for eight cities around the UK. Although CityDashboard doesn’t only use open data sources, those which do have documented APIs, open data licences and machine readable formats greatly aid building and expanding a website such as CityDashboard. CityDashboard takes in social media and sensor data, as well as “official” data of the sort that is being categorised by the OKFN project, but some data, such as live running information for metro services, will quite likely always best come from the official sources.

As such, I will keep a close eye on this project. Cambridge and Sheffield look like two promising cities for which the necessary official data is both available and open, which would make implementing them in CityDashboard relatively straightforward.

The census is user-driven and reviewed, so it’s up to you to get information on the availability (or lack) of data for your local city catalogued in the census.

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A Changing City – OS Open Data Reveals a Dynamic London

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Since launching the data store in early 2010, the Ordnance Survey have been releasing a number of updates to an interesting dataset – VectorMap District – which is a generalisation and simplification of their MasterMap “gold standard” dataset for Great Britain. The updates have been appearing roughly every 6-12 months, and by comparing them in a GIS, you can start to see how places change – at least in the eyes of the Ordnance Survey surveyors tasked to keep the map current. Roads occasionally get built, but building footprints evolve more rapidly – as office blocks and housing developments get taken down and rebuilt with higher capacities or more glass windows.

I’ve taken three of the VectorMap District dataset releases – April 2012, September 2013 and March 2014 – combined the data together and used QGIS’s layer compositing operations to show the geographical differences.

The colours tell of the age of the building – bearing in mind that there is a lag of a few months or years between buildings appearing/disappearing in real life, and on the map. For example, the Olympic Stadium, the turquoise oval above, appears in the 2013 dataset but not the 2012 one, even though of course it was finished in 2011, for the London 2012 Olympic Games.

White Building has existed throughout the three years.
Red Building existed in 2012 only (see note below about extra detail).
Purple Building existed in 2012-2013, but has now gone.
Blue Building was new for 2013, but has now gone.
Turquoise Building was new for 2013, still present (see note below about extra detail).
Green Building is new for 2014, still present.
Yellow Building was around in 2012, disappeared in 2013, but has appeared again now.
Black No building existed in any of the three years.

Above, much of the Olympic Park can be seen – the permanent new buildings (turquoise), temporary buildings for the Games only (blue) and demolished for the games and associated planned development (red). Below, the map covering a wider part of London, zones of activity can be seen. For example, demolition associated with the Nine Elms and Deptford Creek developments (red), and major new blocks such as near the Arsenel stadium (yellow).

Important Note

Between the 2012 and 2013 datasets, the Ordnance Survey changed they way they applied the generalisation on the data, so some of the 2012-2013 changes (shown as red on the maps here for reductions, and turquoise for additions) are as a result of this. For example, narrow gaps between buildings, that always existed, are shown for the first time in 2013 in red (building reductions).

As such, my map slightly overemphasises changes between 2012 and 2013. For example, the pitch at Arsenal and the Great Court at the British Museum appear as changes, but they were always there. As a rough rule of thumb, the smaller red/turquoise patches are due to the generalisation changes, the larger areas of colour show genuine change. With this important caveat, the map remains an interesting insight into London changes, and the larger coloured regions give a good indication of parts of London which are undergoing intensive building redevelopment.

The Bigger Picture

Here is the map for central London – click on it to see a full-size version.

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37000 Old OS Maps

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The National Library of Scotland (NLS) yesterday unveiled a HUGE collection of maps that they have digitised and placed online. The maps, covering England and Wales, are historic Ordnance Survey maps that are between 60 and 170 years old and are at a high resolution. The scale is 6-inch-to-the-mile and covers the whole country. At the moment each map can be viewed by clicking on the appropriate box on an online map, they plan to undertake further work to join many of the maps together to create a single scrollable historic map of the whole country this summer.

The extract above, of the Kew Bridge area in 1899, is from this map (I’ve shifted the white balance.) Some of the maps have some rather nice colouring for water – with the blue colour being augmented by some subtle shading on the riverbanks. The same effect is see in a Snowdon map (extract below), from 1889.

I featured an earlier release of Victorian 60-inch-to-the-mile maps, for London, on Mapping London. The number of retweets and Facebook likes for this posting was unprecedented for the blog, suggesting a huge interest in high quality scans of historic maps.

Here’s their press release, includes the reason why the NLS is including maps from outside Scotland!

New map resource – OS six-inch England and Wales, 1842-1952

We are very pleased to announce the availability of a new website resource – zoomable colour images of the Ordnance Survey’s six-inch to the mile (1:10,560) mapping of England and Wales. All our map digitisation work in recent years has been externally funded, hence the recent expansion of our map images beyond Scotland.

This is the most detailed OS topographic mapping covering all of England and Wales from the 1840s to the 1950s. It was revised for the whole country twice between 1842-1893 and between 1891-1914, and then updated regularly for urban or rapidly changing areas from 1914 to the 1940s. Our holdings are made up of 37,390 sheets, including 35,124 quarter sheets, and 2,237 full sheets.

The maps are immensely valuable for local and family history, allowing most features in the landscape to be shown. The more detailed 25 inch to the mile (or 1:2,500) maps allow specific features to be seen more clearly in urban areas, as well as greater detail for buildings and railways. However, most topographic features on the 25 inch to the mile maps are in fact also shown on the six-inch to the mile maps.

The easiest way of finding sheets is through a clickable graphic index using our ‘Find by Place’ viewer: http://maps.nls.uk/openlayers.cfm?id=39&zoom=6&lat=53.39954&lon=-3.0305

This allows searching through a gazetteer of placenames, street names, postcodes and Grid References, as well as by zooming in on an area of interest with smaller-scale locational mapping as a backdrop.

The sheets are also available via county lists: http://maps.nls.uk/os/6inch-england-and-wales/counties.html

We plan to also make georeferenced mosaics available of the series by the late summer.

OS six-inch England and Wales home page: http://maps.nls.uk/os/6inch-england-and-wales/index.html

Further information: http://maps.nls.uk/os/6inch-england-and-wales/info1.html

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Talking Rabbits and Glowing Lamps – The Internet of London Things

At CASA we’ve always been keen on marrying the online with the tangible – such as the London Data Table (a real table, cut in the shape of London, showing live London data), PigeonSim (fly around a Google Earth view augmented with real-time information) and a couple of 3D printers, one of which was used to print the results of an online mapping field project in Lima, Peru, a couple of weeks ago. One of CASA’s core research projects, Tales of Things, is all about this space.

rabbitOver the last couple of days, Steve, boss Andy and I have been working further on linking online and offline London, by making use of Boris, one of the two Karotz Rabbits that have been knocking around the lab for a while (the other one is of course called Ken), plus a couple of wifi-controllable multi-colour Hue lightbulbs that we acquired more recently.

Steve has set up a couple of servers that receive instructions as simple URL requests, format them and pass them to the external company servers that are an inevitable part of most sensor products these days. (In the case of the Karotz server, this usefully turns text into audio files.) The servers then send instructions back into our network and on to the objects themselves.

A few Python scripts later, and we have the following:

  • Boris announces changes to the statuses of the various London Underground lines, when they occur. He also flashes the colour of the affected line as he speaks the message. Between announcements, Boris will pulsate the colour of lines which are not in “Good Service”. His ears also twitch appropriately – appearing fully alert when there are major problems on the network, and a more lackadaisical look when everything’s OK.
  • The first hue lamp, which sits in a spherical orb, shows the weather forecast, as calculated by CASA’s own weather station that sits on the roof of the building opposite. Steve has configured it to show a yellow glow for sunny and dry weather to follow, while a moody blue indicates rain. Disruptive weather, such as likely snowfalls or strong winds, are shown in red, while rain ceasing is green.
  • The second lamp, also in a spherical orb, polls a special Twitter list of active CASA researchers. Every time one tweets, the lamp which change to a particular colour linked to them. For instance, when I tweet about this blogpost, the lamp will turn a distinctive shade of green.

Data Sources

The rabbit, which is in the video above, sits in front of a TV showing CityDashboard, and speaks its wisdom to the office in general from time to time. The video shows him announcing that problems earlier on the Central and District lines are resolved. After the announcement, he goes back to pulsating green to indicate an ongoing District Line issue. The data comes from the tube line status panel on CityDashboard which is itself using the near-live feed from Transport for London’s Developer Area.

The lamps are in the corridor connecting CASA to the rest of the building. As such, it’s often quite a dark place, but now is bathed in an everchanging glow of light based on both sensor data (weather) and social media output (tweets) from our digital city. The Twitter data for the second lamp comes from the London Periodic Table, which accesses the data from Twitter via a proxy server that Steve built. Once a change is detected, another of Steve’s servers is used to send the message to the Hue servers, which then send it back through a special link, to the lamp. Convoluted, but, with a 10-20 second delay, it does work!

Steve has written up a blog post with more details behind the servers that make the system work.

Panos Mavros, a Ph.D student here at CASA, is also using the Hue lamps, in his research into “digital empathy”. He is bringing a whole new meaning to the phrase “mood lighting” – he only has to think and the colours change!

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