Bike Share Map shows the locations of docking stations associated with bicycle sharing systems from 400+ cities around the world. Each docking station is represented by a circle, its size and colour depending on the size and number of bicycles currently in it. The maps generally update every few minutes. There is a version that replays the last 48 hours of colour and size changes. In many cities, an ebb and flow of cycle commuters can be seen. Bike Share Map is complemented by a UK bikeshare news blog, Bikesharp.
See it Live
- Bike Share Map across the World (Live)
- Bike Share Map for London (Live)
- Bike Share Map for London (Timeline – last 48 hours)
About the Map
Bike Share Map was born out of wanting to visualise the shiny new London Barclays Cycle Hire bikesharing system (now known as Santander Cycles), which appeared in July 2010, shortly after I joined UCL CASA. The operator’s own map wasn’t very useful (it’s improved but still not great). I reused some code from an election visualisation (where circles were used to provide a balanced view across multiple constituencies) and got the system up running in a few days. Someone found out about it who lived in Minneapolis, and wanted to do the same for the NiceRideMN system there. I realised quite quickly that the two systems were built with the same technology, so the same data was available. Washington DC was Number 3, and it then went on from there.
There were data access issues with some systems (e.g. Paris and Brussels) but these days, such issues have been largely overcome, with the recent unveiling of the JCDecaux developer portal and Barcelona City Council making their feed available. B-cycle, who run many small systems in the US, have also been very helpful. I would still love to get more information on Chinese systems though…
I’ve continued to tweak and evolve the look of the site, adding a “Timeline” view in September 2010 that replays the last 48 hours of docking station changes, and most recently (June 2013) launched a global view of all the systems I am monitoring, which is has gradually increased from just under 100 to over 300 – there are more than 1500 systems in the world though, so this remains a snapshot.
The data is normally collected, and the visualisation updated, every two minutes. Some systems, which require requesting the status of each docking station individually, or appear to be on servers that struggle with repeated requests, are collected every ten minutes or even less frequently than that.
Bike usage numbers, where quoted, are simultaneous usage and normally include cycle redistribution. Actual total usage across the day will be much higher. Total bikes available doesn’t include bikes in use (obviously) but also doesn’t include bikes that are broken (if this information is available) or are being repaired or being redistributed. This is why the number showing is often lower – sometimes much lower – that the operator’s official statistic on the size of the system.
The distribution imbalance graph shows the number of cycles that would need to be moved to a different stand, in order for all stands to be the same % full. Higher numbers indicate a more unbalanced distribution, e.g. many bikes in the centre, few on the edge. It’s an interesting metric for understanding how “stressed” the system is.
While the website itself is cloud-hosted, the observations database behind the website is run on an academic development server and so is subject to occasional interruption. Please don’t rely on it for finding bikes or spaces!
Data: Generally the operator’s or city authority’s website, or via their official API where provided, particularly if the operator provides an industry standard GBFS-format feed. For a small number of cities I use a third party – most notably citybik.es, an excellent third-party data collector using PyBikes.
Background map: Data is © OpenStreetMap contributors. For some cities I use HERE maps as a background. For the OSM mapping, I use Mapnik, and the website itself makes heavy use of OpenLayers (amazing web API for drawing those circles).
Why’s my city missing?
There are 1500+ cities and other places with bikesharing systems, but the global map only lists ~300. Here’s why a particular city might not be there:
- The system is not a “third/fourth generation bikesharing system” – that is, one with automated, computer controlled docking stations (or dockless bikes) and designed for sharing of bikes, i.e. encouraging short uses of typically an hour or less. Example: University long-rental systems, coin-slot systems.
- The system doesn’t make available the necessary information on the web. The information needs to include an API, or a map with vector points (typically a Google Map) showing the locations of the docking stations, plus information on how many bikes are currently in each docking station. Example: Most Asian systems, and most modern app-based systems, particularly those run by mainly-escootershare-providers.
- The system doesn’t display the exact number of bikes at each docking station. Some older Nextbike systems in particular don’t have this information.
- The information is slowed or rate limited to stop repeated requests. Example: Lime has a quite strict rate limitation.
- The information available doesn’t update more than once a day.
- The system is too small for interesting spatial analysis research. I generally don’t including systems which have less than six active docking stations, unless they are in the UK.
- The system doesn’t currently have any bikes in it that are available for use, e.g. it hasn’t launched yet.
- The data is extremely unreliable, although I might persevere if it’s a very large system.
- The operator has been particularly proactive at stopping third-party reuse of the information, such as a third-party map like this. This has happened a lot for many systems in China.
- I don’t know about the system. If your city it doesn’t fall into any of the above categories, then let me know about it in the comments box below!
For a comprehensive world map of bikeshare systems, see The Meddin Bike-sharing World Map crated by R. Meddin and maintained by a global team of contributors including P. De Maio (co-founder) and myself (European editor).
Why’s my data on here?
Please email me at o.obrien (@) outlook.com to let me know your concerns. I am happy to stop collecting your data if desired, although only publicly accessible webpages and APIs are being used.
I would love to support this project!
Feel free to buy me a coffee or two at Ko-Fi.
Is the code open source?
Why do you do this?
The visualisation can be considered to be a auxiliary benefit of collecting data on bikesharing systems, which was used for research at UCL, by myself and some colleagues. It was useful for me, to check that the data coming in is good. Subsequently, the map has proven popular in its own right, so I have continued to maintain and update it as a general service to system users and the bikesharing community in general. Please note it is self-supported and hosted on an external server, and so liable to disappear at any time.
What about individual journeys?
Some operators or cities make available files containing individual journey data, normally every few months. These datasets aren’t used in this website, but I still collect them for use in current and future research.
You can contact me via Twitter at @oobr or by emailing me at o.obrien (@) outlook.com – or leave a comment below.