Connected bikeability in London: which localities are better connected by bike and does this matter?


Bikeability, the extent to which a settlement, area or route network enables cycling for everyday travel, is a frequently-cited theme for increasing and diversifying cycling uptake and therefore one that attracts much research attention. Indexes designed to quantify bikeability typically generate a single bikeability value for a single locality. Important to transport planners making infrastructure decisions, however, is how well-connected by bike are pairs of localities. For this it is necessary to estimate the bikeability of plausible routes connecting different parts of a city. We approximate routes for all origin-destination journey pairs cycled in the London Cycle Hire Scheme for 2018 and estimate the bikeability of each route, linking to the newly-released London Cycle Infrastructure Database. We then divide the area of inner London covered by the bikeshare scheme into ‘villages’ and profile how bikeability varies for trips connecting those villages – we call this connected bikeability. Our bikeability scores vary geographically with certain localities in London better connected by bike than others. The highest levels of bikeability coincide with villages that are connected by dedicated cycling infrastructure, whilst lower levels of bikeability are between villages that require crossing the river Thames or navigating central parts of London with dense road networks and limited space for dedicated infrastructure. We demonstrate the usefulness of the index through a data analysis that relates inequalities in connected bikeability to London’s labour market geography. Focussing on potentially cyclable commutes to job-rich villages in London, we evaluate differences in connected bikeability against demand and identify key commutes made by lower-wage non- professional workers that have comparatively low levels of bikeability and that may warrant attention from transport planners.

OSF Pre-print
Roger Beecham
Roger Beecham
Associate Professor in Visual Data Science

My research interests include data visualization, applied data science and computational statistics.