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

Zhenlong Li, Xiao Huang, Tao Hu, Huan Ning, Xinyue Ye, Binghu Huang, Xiaoming Li

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article numbere0255259
JournalPloS one
Volume16
Issue number8 August
DOIs
StatePublished - Aug 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General

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