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基于大规模 GPS 数据的步行步伐观察方法的开发。

Development of a method for walking step observation based on large-scale GPS data.

机构信息

Graduate School of Environmental Studies, Tohoku University, 468-1 Aoba, Aramaki, Aoba-ku, Sendai, 980-0845, Japan.

Department of Traffic and Medical Informatics in Disaster (Endowed Research Division), Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.

出版信息

Int J Health Geogr. 2022 Sep 7;21(1):10. doi: 10.1186/s12942-022-00312-5.

Abstract

BACKGROUND

Widespread use of smartphones has enabled the continuous monitoring of people's movements and physical activity. Linking global positioning systems (GPS) data obtained via smartphone applications to physical activity data may allow for large-scale and retrospective evaluation of where and how much physical activity has increased or decreased due to environmental, social, or individual changes caused by policy interventions, disasters, and infectious disease outbreaks. However, little attention has been paid to the use of large-scale commercial GPS data for physical activity research due to limitations in data specifications, including limited personal attribute and physical activity information. Using GPS logs with step counts measured by a smartphone application, we developed a simple method for daily walking step estimation based on large-scale GPS data.

METHODS

The samples of this study were users whose GPS logs were obtained in Sendai City, Miyagi Prefecture, Japan, during October 2019 (37,460 users, 36,059,000 logs), and some logs included information on daily step counts (731 users, 450,307 logs). The relationship between land use exposure and daily step counts in the activity space was modeled using the small-scale GPS logs with daily step counts. Furthermore, we visualized the geographic distribution of estimated step counts using a large set of GPS logs with no step count information.

RESULTS

The estimated model showed positive relationships between visiting high-rise buildings, parks and public spaces, and railway areas and step counts, and negative relationships between low-rise buildings and factory areas and daily step counts. The estimated daily step counts tended to be higher in urban areas than in suburban areas. Decreased step counts were mitigated in areas close to train stations. In addition, a clear temporal drop in step counts was observed in the suburbs during heavy rainfall.

CONCLUSIONS

The relationship between land use exposure and step counts observed in this study was consistent with previous findings, suggesting that the assessment of walking steps based on large-scale GPS logs is feasible. The methodology of this study can contribute to future policy interventions and public health measures by enabling the retrospective and large-scale observation of physical activity by walking.

摘要

背景

智能手机的广泛使用使得人们的运动和身体活动得以持续监测。通过智能手机应用程序获取的全球定位系统 (GPS) 数据与身体活动数据相链接,可能会使我们能够大规模、回溯性地评估由于政策干预、灾害和传染病爆发等原因导致的环境、社会或个人变化对身体活动增加或减少的影响。然而,由于数据规格的限制,包括个人属性和身体活动信息有限,因此很少有人关注利用大规模商业 GPS 数据进行身体活动研究。本研究使用 GPS 日志和智能手机应用程序测量的步数,开发了一种基于大规模 GPS 数据的简单日常步行步数估计方法。

方法

本研究的样本为 2019 年 10 月在日本宫城县仙台市获取 GPS 日志的用户(37460 名用户,36059000 条日志),其中一些日志包含每日步数信息(731 名用户,450307 条日志)。使用包含每日步数的小规模 GPS 日志对活动空间内的土地利用暴露与每日步数之间的关系进行建模。此外,我们使用大量没有步数信息的 GPS 日志可视化估计的步数的地理分布。

结果

估计模型显示,访问高层建筑、公园和公共场所、铁路区域与步数之间存在正相关关系,而与低层建筑和工厂区域与每日步数之间存在负相关关系。城市地区的估计每日步数往往高于郊区。靠近火车站的地区,每日步数减少的情况有所缓解。此外,在强降雨期间,郊区的步数明显下降。

结论

本研究中观察到的土地利用暴露与步数之间的关系与以往的研究结果一致,表明基于大规模 GPS 日志评估步行步数是可行的。本研究的方法可以通过对步行等身体活动的回溯性和大规模观察,为未来的政策干预和公共卫生措施提供支持。

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