Identifying cycling-inducing neighborhoods: A latent class approach

Abstract

Understanding how spatial attributes of cities and neighborhoods induce cycling is relevant for urban planning and policy making. In this work, ordered logit and latent class models are specified and estimated to analyze how the built environment affects bicycle-commuting frequency. Data come from a survey to 1,487 people in the city of Santiago, Chile, including sociodemographic information, travel behavior patterns and place of residence and work. Using geographic information systems tools, the built environment was characterized with variables calculated for a 500-m-radius buffer around the residential and work locations of each individual. Two models are estimated, first an ordered logit model confirms that built environment variables effect on cycling is similar to what has been reported in the literature, with some new findings such as an increase in cycling when public transport accessibility is low and the role of built environment attributes at the destination. Second, a latent class ordered logit is used to identify two classes of neighborhood in term of their cycling patterns, as a function of their density, presence of cycling infrastructure and distance to the main activity center of the city. This result allows to map the class membership probabilities, potentially helping to identify neighborhoods that encourage cycling and providing relevant information for policy making and infrastructure decisions.