Community mobility MAUP-ing: A socio-spatial investigation of bikeshare demand in Chicago

Abstract

The expansion and evolution of bikesharing systems is a global phenomenon, which has motivated research to characterize “best practices” in both system operations and policy transferability across regions. Little is known, however, about the pros and cons of different approaches to define scale and zoning schemes in bikesharing evaluation. This research begins to address this challenge by juxtaposing station-level and community-level approaches to model and estimate the Annual Average Daily Bicyclist (AADB). We use the demand information from 459 Divvy stations in Chicago between June 1, 2015 and May 31, 2016 to assess the aggregation approaches concerning variable impacts, model specification, and prediction accuracy. Elasticity calculations, prediction error comparisons, and influence analysis reveal the importance of both built environment and sociodemographic variables in bikeshare modeling and the need to develop context-sensitive interventions. The detailed comparison of different levels of aggregation for analysis of bikeshare demand and user impact highlights that each level contributes insights to planners and policymakers. While the disaggregate approach provides the most information for planners in terms of improving bikeshare systems, there is value in adopting an aggregated approach for transport policy that accounts for potential neighborhood effects. In addition, the control for socio-demographic factors around stations highlights the variation in socio-spatial effects that planners need to account for when measuring outcomes and equity impacts.