Overcoming concerns about bicycling safety is critical to increasing the health benefits of bicycling for transportation. While exposure measures are critical for monitoring and understanding bike safety, lack of spatially and temporally detailed bike counts makes it challenging to conduct robust bicycling safety studies. Crowdsourced data from smartphone apps like Strava provide counts for nearly all individual road and trail sections with 1-min temporal resolution. Researchers have found that patterns of Strava bicyclists are similar to all bicyclists in our study area. In this paper, we develop and test a method to normalize bike safety incident hotspots using exposure estimated from Strava data for Ottawa, Canada. We mapped incident hotspots normalized by exposure at increasingly detailed temporal scales. In a dataset with more than more than 8 million Strava activities and 395 incidents (approximately 20,000 Strava activities per incident), adjusting for exposure moved incident hotspots away from protected bike lanes and multi-use paths and onto commercial streets with no bike infrastructure. Strava data are available to correct for exposure where other measures are not available. We encourage researchers, planners, and public health practitioners to consider crowdsourced data to fill exposure data gaps and provide context for interpreting incident data.

克服对骑行安全性的担忧对于提高骑行运输对健康的益处至关重要。尽管接触措施对于监控和理解自行车的安全性至关重要，但是缺乏时空详细的自行车数量使得进行稳健的自行车安全性研究具有挑战性。来自智能手机应用程序（例如Strava）的众包数据以1分钟的时间分辨率提供了几乎所有单独道路和小径路段的计数。研究人员发现Strava自行车手的模式与我们研究范围内的所有自行车手相似。在本文中，我们开发并测试了一种方法，该方法使用从Strava数据估算的加拿大渥太华的暴露量来归一化自行车安全事故热点。我们在越来越详细的时间尺度上绘制了通过曝光归一化的入射热点。在拥有超过800万次Strava活动和395次事件（每个事件大约20,000次Strava活动）的数据集中，针对暴露进行调整将事件热点从受保护的自行车道和多用途路径转移到没有自行车基础设施的商业街。在没有其他措施的情况下，Strava数据可用于校正暴露。我们鼓励研究人员，规划人员和公共卫生从业人员考虑采用众包数据来填补暴露数据的空白，并提供解释事件数据的环境。在没有其他措施的情况下，Strava数据可用于校正暴露。我们鼓励研究人员，规划人员和公共卫生从业人员考虑采用众包数据来填补暴露数据的空白，并提供解释事件数据的环境。在没有其他措施的情况下，Strava数据可用于校正暴露。我们鼓励研究人员，规划人员和公共卫生从业人员考虑采用众包数据来填补暴露数据的空白，并提供解释事件数据的环境。