Examining associations of environmental characteristics with recreational cycling behaviour by street-level strava data

Sun, Y., Du, Y., Wang, Y. and Zhuang, L. (2017) Examining associations of environmental characteristics with recreational cycling behaviour by street-level strava data. International Journal of Environmental Research and Public Health, 14 (6). ISSN 1660-4601

Full content URL: https://doi.org/10.3390/ijerph14060644

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Item Type:Article
Item Status:Live Archive


Policymakers pay much attention to effectively increasing frequency of people’s cycling in the context of developing sustainable and green cities. Investigating associations of environmental characteristics and cycling behaviour could offer implications for changing urban infrastructure aiming at encouraging active travel. However, earlier examinations of associations between environmental characteristics and active travel behaviour are limited by low spatial granularity and coverage of traditional data. Crowdsourced geographic information offers an opportunity to determine the fine-grained travel patterns of people. Particularly, Strava Metro data offer a good opportunity for studies of recreational cycling behaviour as they can offer hourly, daily or annual cycling volumes with different purposes (commuting or recreational) in each street across a city. Therefore, in this study, we utilised Strava Metro data for investigating associations between environmental characteristics and recreational cycling behaviour at a large spatial scale (street level). In this study, we took account of population density, employment density, road length, road connectivity, proximity to public transit services, land use mix, proximity to green space, volume of motor vehicles and traffic accidents in an empirical investigation over Glasgow. Empirical results reveal that Strava cyclists are more likely to cycle for recreation on streets with short length, large connectivity or low volume of motor vehicles or on streets surrounded by residential land.

Additional Information:cited By 39
Keywords:crowdsourced geographic information, Big data analytics, Bicycling
Subjects:F Physical Sciences > F891 Geographical Information Systems
L Social studies > L700 Human and Social Geography
Divisions:College of Science > School of Geography
ID Code:49393
Deposited On:19 May 2022 09:33

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