The Bike Share Map shows the locations of docking stations associated with bicycle sharing systems from 100+ 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 24 hours of colour and size changes. In many cities, an ebb and flow of cycle commuters can be seen.
See it Live
- Bike Share Map for across the World (Live)
- Bike Share Map for London (Live)
- Bike Share Map for London (Timeline – last 48 hours)
About the Map
The Bike Share Map was born out of wanting to visualise the shiny new London Barclays Cycle Hire bikesharing system, which appeared in July 2010, shortly after I joined UCL CASA. The operator’s own map wasn’t amazing (and still isn’t). 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, at the time of writing, just less than 100.
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.
This 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. For some cities I use a third party – most notably citybik.es, an excellent third-party data collector using PyBikes.
Background map: Data is Copyright OpenStreetMap contributors. For some cities I use the “default” OpenStreetMap map, which is CC-By-SA OpenStreetMap. The website is powered by Mapnik (for the background images) and OpenLayers (amazing web API for drawing those circles).
Why’s my city missing?
There are other 500 cities and other urban areas with bikesharing systems, but the global map only lists about 100. Here’s why a particular city might not be there.
- The system is not a “third-generation bikesharing system” – that is, one with automated, computer controlled docking stations 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: Hangzhou.
- The system doesn’t display the exact number of bikes at each docking station. Example: Most of the German systems.
- The information is slowed or rate limited to stop repeated requests. Example: Antwerp, Shanghai.
- 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. Example: Pavia.
- The system doesn’t currently have any bikes in it that are available for use, e.g. it hasn’t launched yet. Example: Athens Keratsini.
- The data is extremely unreliable, although I might persevere if it’s a very large system. Example: Mexico City.
- The operator has been particularly proactive at stopping third-party reuse of the information, such as a third-party map like this. Example: Stockholm, Bicincitta systems in Italy, 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!
Why’s my data on here?
Please email me at o.obrien (@) ucl.ac.uk 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 like a specially enhanced version for my city (e.g. for redistribution)
Please email me at o.obrien (@) ucl.ac.uk.
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 is used for current and future research at UCL CASA, my academic lab, by myself and some colleagues. It is useful for me, to check that the data coming in is good, and the map has proven popular in its own right, so I have continued to maintain and update it.
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 or by emailing me at: o.obrien (@) ucl.ac.uk on Twitter at @oobr – or leave a comment below.