Velo-City Preview
[Updated] I’ll be presenting at Velo-City in Vancouver later this week. Velo-City is the “world’s premier cycling planning conference”. It is likely to have a significant bike-sharing flavour – the lead sponsor being PBSC which designed the 6000-odd “Boris Bikes” (aka Barclays Cycle Hire bikes) that are a distinctive sight in central London, as well as equivalent systems in Montreal, Washington DC, Minneapolis, Boston and (shortly) New York City – known generically as Bixi bikes. Vancouver does not have a bike-sharing system of its own, but PBSC have imported a whole load of their Montreal bikes for delegates to borrow for the week, although a recent collar-bone break means I unfortunately won’t be taking up the offer. I did however spot a PBSC/Bixi bike “in the wild” in Vancouver’s beautiful Stanley Park – see above.
I’ll be talking about some new insights into bike-sharing cities worldwide that have been revealed by my Bike Share Map, as part of a three-part presentation on looking at bike-sharing cities at different scales – my co-presenters being the author of the Bike Sharing World Map, and the software developer behind the B-Cycle bike sharing systems.
My presentation is on Wednesday morning (Pacific time) and I’ll write/tweet about it on the day, wifi-access permitting.
To prepare for the presentation, I’ve added a few new cities to the Bike Share Map: Suzhou, Zhongshan, Wujiang, Shaoxing and Heihe in China; and Kanazawa in Japan. One early insight coming from these new maps could be that the Chinese really do work hard (if you excuse the gross overgeneralisation) – typically 11 hours between morning and evening commuter peaks, and seven days a week!
Hehei is shown below – it’s right on the Russian border, opposite a much larger Russian city – hence the Cyrillic (although no bridges across the river near there!)
Note that, in the maps of the Chinese systems, the docking station locations are slightly misaligned with the background maps because of location obfuscation carried out by that country – I’m using OpenLayers rather than the Chinese-based map service that corrects for the errors. The resulting offset is typically only 1-400m though so you can still get a good idea of the shape and size of each system.
