Traditional methods of counting bicyclists are resource-intensive and generate data with sparse spatial and temporal detail. Previous research suggests big data from crowdsourced fitness apps offer a new source of bicycling data with high spatial and temporal resolution. However, crowdsourced bicycling data are biased as they oversample recreational riders. Our goals are to quantify geographical variables, which can help in correcting bias in crowdsourced, data and to develop a generalized method to correct bias in big crowdsourced data on bicycle ridership in different settings in order to generate maps for cities representative of all bicyclists at a street-level spatial resolution. We used street-level ridership data for 2016 from a crowdsourced fitness app (Strava), geographical covariate data, and official counts from 44 locations across Maricopa County, Arizona, USA (training data); and 60 locations from the city of Tempe, within Maricopa (test data). First, we quantified the relationship between Strava and official ridership data volumes. Second, we used a multi-step approach with variable selection using LASSO followed by Poisson regression to integrate geographical covariates, Strava, and training data to correct bias. Finally, we predicted bias-corrected average annual daily bicyclist counts for Tempe and evaluated the model’s accuracy using the test data. We found a correlation between the annual ridership data from Strava and official counts (R2 = 0.76) in Maricopa County for 2016. The significant variables for correcting bias were: The proportion of white population, median household income, traffic speed, distance to residential areas, and distance to green spaces. The model could correct bias in crowdsourced data from Strava in Tempe with 86% of road segments being predicted within a margin of ±100 average annual bicyclists. Our results indicate that it is possible to map ridership for cities at the street-level by correcting bias in crowdsourced bicycle ridership data, with access to adequate data from official count programs and geographical covariates at a comparable spatial and temporal resolution.

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.

Our goal is to reduce the negative impacts of bicycle theft by better understanding patterns in bicycle theft and recovery. We analyzed data from 1823 responses to a North American survey on bicycle theft conditions, recovery circumstances, and demographics. Survey recruitment was done in partnership with BikeIndex, a non-profit bicycling registration service. Most bikes were stolen from inside a shed or garage (28%) or from outdoor bicycle racks (18%) and most thefts occur overnight (41%). 15% of stolen bicycles were recovered. Key factors in recovery include police involvement, bike registration, and reporting the theft through multiple channels.