Abstract
This study leverages big data from the Strava Metro app, integrated with official count data from the Orange County Transportation Authority, to analyze spatial patterns of bicycling ridership in Orange County, California. By applying bias correction techniques to crowdsourced data and incorporating land use and socioeconomic covariates, the study generate a comprehensive map of ridership volumes across the region. The study’s analysis reveals significant spatial autocorrelation in cycling activity, with distinct patterns between coastal and inland areas. Coastal regions exhibit strong High-High clusters, indicating concentrated cycling activity, while inland areas show a more varied pattern with Low-High clusters and isolated High-High pockets. These findings demonstrate the potential of bias-corrected crowdsourced data to inform targeted infrastructure planning in both urban and suburban contexts. By identifying areas of high cycling demand and potential growth, this methodology provides valuable insights for policymakers and urban planners to enhance cycling infrastructure and promote sustainable transportation in diverse geographic settings, from coastal cities to inland communities.