Mapping bikeability

The built environment has been shown to influence active transportation. Although spatial data for the built environment is increasingly available, there has been little effort to use existing data and knowledge to define and map ‘bikeability’ as an approach to promoting travel by bicycle. Goal of this study was to build a tool to identify areas that are more conducive and less conducive to cycling. The authors used empirical research to develop a bikeability index and geographic information systems to map the index across the Metro Vancouver region. Results of an opinion survey, travel behaviour studies, and focus groups were used to identify the components of the index and their relative importance. Pertinent geospatial data layers were scored and combined using a flexible weighting scheme to create a composite map highlighting both high and low bikeability areas. The bikeability index was comprised of five factors shown to consistently influence cycling: bicycle facility availability; bicycle facility quality; street connectivity; topography; and land use. For mapping purposes, the authors created corresponding metrics: density of bicycle facilities; separation from motor vehicle traffic; connectivity of bicycle-friendly roads (local streets, bicycle routes, and off-street paths); slope; and density of destination locations. Using empirical evidence to combine data layers for these metrics they generated a high-resolution (10 m) bikeability surface for the region, depicting bicycle-friendly areas and areas where cycling conditions need to be improved. Built environment interventions for specific locations are informed by evaluating scores for the five individual component layers. Mapping bikeability provides a powerful visual aid to identify zones where changes are needed to support sustainable travel. This evidence-based tool presents data in a userfriendly way for planners and policy makers. The overall bikeability score and its five component scores can guide local action to stimulate changes in cycling rates. It uses widely available data types, thus facilitating easy application in other cities. Furthermore, the flexible parameters and weighting scheme enable users elsewhere to tailor it to evidence about local preferences and conditions.