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

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

doi:10.1371/journal.pone.0255259

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.

Highlights

Reliable monitoring of human mobility, referring to the movement of human beings in space and time, plays a fundamental role in a variety of fields, such as tourist management [1, 2], migration [3, 4], urban planning [5,6,7], demand forecasting [8, 9], disaster management [10, 11], and epidemic modelling [12,13,14], to name a few
The daily OD flows in 2019 and 2020 were extracted using worldwide geotagged tweets collected with Twitter public API and U.S SafeGraph social distancing metrics data downloaded from SafeGraph website (Table 1)
For SafeGraph data, over 160 million social distancing metrics records were used in the flow extraction computation, resulting in over 11 billion entity level daily OD flows

Summary

Introduction

Reliable monitoring of human mobility, referring to the movement of human beings (individuals as well as groups) in space and time, plays a fundamental role in a variety of fields, such as tourist management [1, 2], migration [3, 4], urban planning [5,6,7], demand forecasting [8, 9], disaster management [10, 11], and epidemic modelling [12,13,14], to name a few. The ongoing COVID-19 pandemic (as of the time of writing) uniquely highlights the necessity of human mobility monitoring in a rapid and comprehensive manner. Extracting, analyzing, and sharing multi-source multi-scale human mobility. Geotagged tweets were collected using Twitter’s public Streaming API from the public domain following Twitter’s Developer Agreement. Following Twitter’s policy on "Redistribution of Twitter content" (https:// developer.twitter.com/en/developer-terms/moreon-restricted-use-cases), the geotagged tweet IDs used in this analysis can be shared upon reasonable request. SafeGraph Social Distancing Metrics data are obtained from SafeGraph, Inc. (https://docs.safegraph.com/docs/socialdistancing-metrics)

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Conclusion