Bicycle streetscapes: a data driven approach to mapping streets based on bicycle usage

Abstract

Cities are making infrastructure investments to make travel by bicycle safer and more attractive. A challenge for promoting bicycling is effectively using data to support decision making and ensuring that data represent all communities. However, ecologists have been addressing a similar type of question for decades and have developed an approach to stratifying landscapes based on ecozones or areas with homogenous ecology. Our goal is to classify street and path segments and map streetscape categories by applying ecological classification methods to diverse spatial data on the built environment, communities, and bicycling. Piloted in Ottawa, Canada, we use GIS data on the built environment, socioeconomics and demographics of neighborhoods, and bicycling infrastructure, behavior, and safety, and apply a k-means clustering algorithm. Each street or path, an intuitive spatial unit that reflects lived experience in cities, is assigned a streetscape category: bicycling destination; wealthy neighborhoods; urbanized; lower income neighborhoods; and central residential streets. We demonstrate how streetscape categories can be used to prioritize monitoring (counts), safety, and infrastructure interventions. With growing availability of continuous spatial data on urban settings, it is an opportune time to consider how street and path classification approaches can help guide our data collection, analysis, and monitoring. While there is no one right answer to clustering, care must be taken when selecting appropriate input variables, the number of categories, and the correct spatial unit for output. The approach used here is designed for bicycling application, yet the methods are applicable to other forms of active transportation and micromobility.