I stumbled across the most amazing set of open data maps for bike sharing cities and tracked down the creator in London to interview him for a Data Story. Oliver O’Brien is the creator of the maps, which tracks available bikes and open spaces at bike sharing stations at more than 100 cities across the world. We interviewed him about his work with open maps and his research trying to understand how people move about the city.
1. What was the genesis for creating the maps?
It started from seeing the launch of London’s system in August 2010. It was at a time when I was working with Transport for London data on a project called MapTube. Transport for London had recently created a Developer portal for their datasets. When the London bikeshare launched, their map was not great (and still isn’t) – it was just a mass of white icons – so I took advantage of the data being provided on the Developer portal to create my own version, reusing some web code from an earlier map that showed General Election voting results in a fairer and clearer way. Once London’s was created, it proved to be a hit with people, as it could be used to see areas were bikes (or free spaces) might be in short supply. I was easily able to extend the map to Montreal and Minneapolis (the latter thanks to an enthusiastic local there) and then realised there was a whole world of bikesharing systems out there waiting to be mapped.
The maps act primarily as a “front-end” to the bikesharing data that I collect, for current and potential future research into the geomorphology of cities and their changing demographics and travel patterns, based on how the population uses bikesharing systems. However i have continued to update the map as it has remained popular, adding cities whenever I discover their bikeshare datasets. After three years, I am now up to exactly 100 “live” cities, where the data is fresh to within a few minutes, plus around 50 where the data is no longer available.
2. Where did you get the information to build the maps?
Mainly from APIs provided by each city authority or bikesharing operating company, or, where this is not available (which is often the case for smaller system) from their Google Map or other online mapping page that normally has the information in the HTML.
3. What is your background?
I’m an academic researcher and software developer at UCL’s Centre for Advanced Spatial Analysis. The lab specialises in urban modelling, and my current main project, EUNOIA, is aiming to build a travel mobility model, using social media as well as transport datasets, for the major European cities of London, Barcelona and Zurich. Bikesharing systems will form a key part of the overall travel model. Previously to CASA I worked as a financial GUI technologist at one of the big City banks – before then, at university, I studied Physics.
4. What are you looking to build next?
I am looking to continue to add cities to the global map, particularly from large bikesharing systems that are appearing – I am looking forward to the San Francisco Bay Area’s system launching in August – and I’m working on creating London’s EUNOIA model, taking in the transport data and augmenting it with other geospatial information, including data from Twitter. I am also looking at more effective ways to visualise data and statistics that are emerging from the recent (2011) Census that we had in the UK – the results of which are being gradually made available.
5. What open-source maps do you think should be created next?
I am hopeful that soon, an integrated map of all social media and sensor datasets, will become easily available and widely used. Partly to increase people’s awareness of the data that now surrounds them and partly to inform decision makers and other stakeholders, in creating a better, more inclusive city landscape – the so called “smart city”.
I would add that you may be interested in some of the other maps that we have created at UCL CASA, such as the Twitter Languages maps for London and New York:
Bike sharing map in Boulder, CO
Thanks to Oliver for the interview! If you’re interested in more geo + social, check out our recent posts on Social Data Mashups Following Natural Disasters and Mapping Travel, Languages & Mobile OS Usage with Twitter Data.