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Int. J. Environ. Res. Public Health 2017, 14(6), 644; doi:10.3390/ijerph14060644

Examining Associations of Environmental Characteristics with Recreational Cycling Behaviour by Street-Level Strava Data

1
Urban Big Data Centre, School of Social and Political Sciences, University of Glasgow, Glasgow G12 8RZ, UK
2
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
3
Urban Studies, School of Social and Political Sciences, University of Glasgow, Glasgow G12 8RS, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Derek Clements-Croome
Received: 20 March 2017 / Revised: 5 May 2017 / Accepted: 12 June 2017 / Published: 15 June 2017
(This article belongs to the Section Health Behavior, Chronic Disease and Health Promotion)
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Abstract

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. View Full-Text
Keywords: cycling; crowdsourced geographic information; Strava; street level; big data cycling; crowdsourced geographic information; Strava; street level; big data
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Sun, Y.; Du, Y.; Wang, Y.; Zhuang, L. Examining Associations of Environmental Characteristics with Recreational Cycling Behaviour by Street-Level Strava Data. Int. J. Environ. Res. Public Health 2017, 14, 644.

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