PDF(Pubmed)
Abstract：
COVID-19 prompted a bike boom and cities around the world responded to increased demand for space to ride with street reallocations. Evaluating these interventions has been limited by a lack of spatially-temporally continuous ridership data. Our paper aims to address this gap using crowdsourced data on bicycle ridership. We evaluate changes in spatial patterns of bicycling during the first wave of the COVID-19 pandemic (Apr - Oct 2020) in Vancouver, Canada using Strava data and a local indicator of spatial autocorrelation. We map statistically significant change in ridership and reference clusters of change to a high-resolution base map. Amongst streets where bicycling increased, we measured the proportion of increase occurring on pre-existing bicycle facilities or street reallocations compared to streets without. In all our analyses, we evaluate patterns across subsets of Strava data representing recreation, commuting, and ridership generated by women and older adults (55 + ). We found consistent and unique patterns by trip purpose and demographics: samples generated by women and older adults showed increases near green and blue spaces and on street reallocations that increased access to parks, and these patterns were also mirrored in the recreation sample. Commute ridership highlighted distinct patterns of increase around the hospital district. Across all subsets most increases occurred on bicycle facilities (pre-existing or provisional), with a strong preference for high-comfort facilities. We demonstrate that changes in spatial patterns of bicycle ridership can be monitored using Strava data, and that nuanced patterns can be identified using trip and demographic labels in the data.
摘要：
新冠肺炎引发了自行车热潮，世界各地的城市对街道重新分配带来的空间需求增加做出了反应。由于缺乏时空连续的乘车数据，因此评估这些干预措施受到限制。我们的论文旨在使用自行车乘车的众包数据来解决这一差距。我们评估了温哥华第一波COVID-19大流行(2020年4月-10月)期间自行车空间格局的变化，加拿大使用Strava数据和空间自相关的本地指标。我们将乘客量的统计显着变化和参考变化聚类映射到高分辨率基础图。在骑自行车增加的街道上，我们测量了与没有自行车设施的街道相比，现有自行车设施或街道重新分配增加的比例。在我们所有的分析中，我们评估代表娱乐的Strava数据子集的模式，通勤,以及女性和老年人(55+)产生的乘客量。我们根据旅行目的和人口统计发现了一致而独特的模式：妇女和老年人产生的样本显示，在绿色和蓝色空间附近以及增加进入公园的街道重新分配中，这些模式也反映在娱乐样本中。通勤乘客量突出了医院区周围不同的增长模式。在所有子集中，大多数增加发生在自行车设施(预先存在或临时)上，强烈偏爱高舒适度设施。我们证明，可以使用Strava数据监测自行车乘客的空间格局变化，并且可以使用数据中的旅行和人口统计标签来识别细微差别的模式。
