Utilizing Crowdsourced Data for Studies of Cycling and Air Pollution Exposure: A Case Study Using Strava Data

Abstract

With the development of information and communications technology, user-generated content and crowdsourced data are playing a large role in studies of transport and public health. Recently, Strava, a popular website and mobile app dedicated to tracking athletic activity (cycling and running), began offering a data service called Strava Metro, designed to help transportation researchers and urban planners to improve infrastructure for cyclists and pedestrians. Strava Metro data has the potential to promote studies of cycling and health by indicating where commuting and non-commuting cycling activities are at a large spatial scale (street level and intersection level). The assessment of spatially varying effects of air pollution during active travel (cycling or walking) might benefit from Strava Metro data, as a variation in air pollution levels within a city would be expected. In this paper, to explore the potential of Strava Metro data in research of active travel and health, we investigate spatial patterns of non-commuting cycling activities and associations between cycling purpose (commuting and non-commuting) and air pollution exposure at a large scale. Additionally, we attempt to estimate the number of non-commuting cycling trips according to environmental characteristics that may help identify cycling behavior. Researchers who are undertaking studies relating to cycling purpose could benefit from this approach in their use of cycling trip data sets that lack trip purpose. We use the Strava Metro Nodes data from Glasgow, United Kingdom in an empirical study. Empirical results reveal some findings that (1) when compared with commuting cycling activities, non-commuting cycling activities are more likely to be located in outskirts of the city; (2) spatially speaking, cyclists riding for recreation and other purposes are more likely to be exposed to relatively low levels of air pollution than cyclists riding for commuting; and (3) the method for estimating of the number of non-commuting cycling activities works well in this study. The results highlight: (1) a need for policymakers to consider how to improve cycling infrastructure and road safety in outskirts of cities; and (2) a possible way of estimating the number of non-commuting cycling activities when the trip purpose of cycling data is unknown.

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

“…Analysis of social media data has also been used to assess air pollution exposure. For example, Sun et al () estimated the inhaled dose of pollutant (PM2.5) during a single cycling or pedestrian trip using Strava Metro data and GIS technologies in Glasgow, United Kingdom, demonstrating the potential of using such data for the assessment of average air pollution exposure during active travel, and Sun and Mobasheri () investigated associations between cycling purpose and air pollution exposure at a large scale.…”

“…When working with GPS traces, other types of data processing methods are needed. Using cycling data from Strava, a website and mobile app that citizens use to upload their cycling and running routes, Sun and Mobasheri () examined exposure to air pollution on cycling journeys in Glasgow. Using a spatial clustering algorithm (A Multidirectional Optimum Ecotope‐Based Algorithm) for displaying hotspots of cycle journeys in combination with calculations of instantaneous exposure to particulate matter (PM2.5 and PM10), they were able to show that cycle journeys for noncommuting purposes had less exposure to harmful pollutants than those used for commuting.…”

Investigating Impacts of Environmental Factors on the Cycling Behavior of Bicycle-Sharing Users

“…Third, comparing the cycling behavior of BSS riders and private bicycle riders is also interesting. Traffic count data [50] or crowdsourced data from online social networking websites for sports, such as Strava [51,52], could be used as cycling data for private bicycle riders.…”

Visualization of Pedestrian Density Dynamics Using Data Extracted from Public Webcams

“…Geolocated tweets and images have been used recently to study, e.g., urban parks visitation and access [11,12] and to gain insight about the paths of tourists through cities [13,14]. Strava-a network for tracking athletic activity-provides even more geospatially rich data which has been used to investigate cycling behavior [15], cycling infrastructure [16], and air pollution exposure of commuting cyclists [17].One of the increasingly popular methods for public space monitoring uses closed-circuit-television (CCTV) footage and webcams to capture and interpret images for a variety of purposes including security, weather monitoring, and pedestrian, bicycle, and motor vehicle traffic [18,19]. Although webcams do not provide individual mobility data, they represent a rich source of spatio-temporal data about people and their environment, and have been used in the past to study air pollution [20], phenology [21], and beach usage [22].…”