Sun Yeran, Mobasheri Amin
Urban Big Data Centre, School of Social and Political Sciences, University of Glasgow, Glasgow G12 8RZ, UK.
GIScience Research Group, Institute of Geography, Heidelberg University, D-69120 Heidelberg, Germany.
Int J Environ Res Public Health. 2017 Mar 8;14(3):274. doi: 10.3390/ijerph14030274.
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.
随着信息通信技术的发展，用户生成内容和众包数据在交通与公共卫生研究中发挥着重要作用。最近，Strava（一个致力于追踪体育活动（骑行和跑步）的热门网站及移动应用程序）开始提供一项名为Strava Metro的数据服务，旨在帮助交通研究人员和城市规划者改善自行车道和行人基础设施。Strava Metro数据有可能通过在大空间尺度（街道层面和交叉路口层面）显示通勤和非通勤骑行活动的地点，来促进骑行与健康方面的研究。在主动出行（骑行或步行）过程中对空气污染的空间变化影响进行评估可能会受益于Strava Metro数据，因为城市内空气污染水平会存在差异。在本文中，为了探索Strava Metro数据在主动出行与健康研究中的潜力，我们大规模调查了非通勤骑行活动的空间模式以及骑行目的（通勤和非通勤）与空气污染暴露之间的关联。此外，我们尝试根据可能有助于识别骑行行为的环境特征来估算非通勤骑行次数。从事与骑行目的相关研究的人员在使用缺乏出行目的的骑行行程数据集时，可能会从这种方法中受益。我们在一项实证研究中使用了来自英国格拉斯哥的Strava Metro节点数据。实证结果揭示了一些发现：（1）与通勤骑行活动相比，非通勤骑行活动更有可能位于城市郊区；（2）从空间角度而言，与通勤骑行者相比，为休闲及其他目的骑行的人更有可能暴露于相对较低水平的空气污染中；（3）估算非通勤骑行活动次数的方法在本研究中效果良好。研究结果凸显了：（1）政策制定者需要考虑如何改善城市郊区的自行车道基础设施和道路安全；（2）当骑行数据的出行目的未知时，一种估算非通勤骑行活动次数的可能方法。
