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ODT 流：提取、分析和共享多源多尺度的人类流动。

ODT FLOW: Extracting, analyzing, and sharing multi-source multi-scale human mobility.

机构信息

Geoinformation and Big Data Research Lab, Department of Geography, University of South Carolina, Columbia, South Carolina, United States of America.

Department of Geosciences, University of Arkansas, Fayetteville, Arkansas, United States of America.

出版信息

PLoS One. 2021 Aug 5;16(8):e0255259. doi: 10.1371/journal.pone.0255259. eCollection 2021.

DOI:10.1371/journal.pone.0255259
PMID:34351973
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8341631/
Abstract

In response to the soaring needs of human mobility data, especially during disaster events such as the COVID-19 pandemic, and the associated big data challenges, we develop a scalable online platform for extracting, analyzing, and sharing multi-source multi-scale human mobility flows. Within the platform, an origin-destination-time (ODT) data model is proposed to work with scalable query engines to handle heterogenous mobility data in large volumes with extensive spatial coverage, which allows for efficient extraction, query, and aggregation of billion-level origin-destination (OD) flows in parallel at the server-side. An interactive spatial web portal, ODT Flow Explorer, is developed to allow users to explore multi-source mobility datasets with user-defined spatiotemporal scales. To promote reproducibility and replicability, we further develop ODT Flow REST APIs that provide researchers with the flexibility to access the data programmatically via workflows, codes, and programs. Demonstrations are provided to illustrate the potential of the APIs integrating with scientific workflows and with the Jupyter Notebook environment. We believe the platform coupled with the derived multi-scale mobility data can assist human mobility monitoring and analysis during disaster events such as the ongoing COVID-19 pandemic and benefit both scientific communities and the general public in understanding human mobility dynamics.

摘要

针对人类流动数据的急剧增长需求，特别是在 COVID-19 等灾害事件期间，以及相关的大数据挑战，我们开发了一个可扩展的在线平台，用于提取、分析和共享多源多尺度的人类流动流。在该平台中，提出了一个起止时间（ODT）数据模型，与可扩展的查询引擎配合使用，以处理具有广泛空间覆盖范围的大量异构流动数据，从而允许在服务器端高效地提取、查询和聚合数十亿级别的起止（OD）流。开发了一个交互式空间网络门户，ODT Flow Explorer，允许用户使用用户定义的时空尺度探索多源流动数据集。为了促进可重复性和可复制性，我们进一步开发了 ODT Flow REST API，为研究人员提供了通过工作流程、代码和程序以编程方式访问数据的灵活性。提供了演示，以说明与科学工作流程和 Jupyter Notebook 环境集成的 API 的潜力。我们相信，该平台结合衍生的多尺度流动数据，可以帮助在 COVID-19 等持续灾害事件期间进行人类流动监测和分析，并使科学界和公众都受益于对人类流动动态的理解。

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