Multiscale dynamic human mobility flow dataset in the U.S. during the COVID-19 epidemic

Yuhao Kang,Jinmeng Rao,Song Gao,Mingxiao Li,Jake Kruse,Yunlei Liang

doi:10.1038/s41597-020-00734-5

Abstract

Understanding dynamic human mobility changes and spatial interaction patterns at different geographic scales is crucial for assessing the impacts of non-pharmaceutical interventions (such as stay-at-home orders) during the COVID-19 pandemic. In this data descriptor, we introduce a regularly-updated multiscale dynamic human mobility flow dataset across the United States, with data starting from March 1st, 2020. By analysing millions of anonymous mobile phone users’ visits to various places provided by SafeGraph, the daily and weekly dynamic origin-to-destination (O-D) population flows are computed, aggregated, and inferred at three geographic scales: census tract, county, and state. There is high correlation between our mobility flow dataset and openly available data sources, which shows the reliability of the produced data. Such a high spatiotemporal resolution human mobility flow dataset at different geographic scales over time may help monitor epidemic spreading dynamics, inform public health policy, and deepen our understanding of human behaviour changes under the unprecedented public health crisis. This up-to-date O-D flow open data can support many other social sensing and transportation applications.

Highlights

Background & SummaryThe outbreak of the novel coronavirus disease SARS-CoV-2 in December 2019 has become a global threat to public health and human societies
Several recent works have employed human movement flow matrices in understanding spatial interaction changes and social impact, and enabling network-based epidemic models to project the numbers of COVID-19 infected population in different countries such as China, Japan, Italy, France, Chile, and UK12–22, which requires up-to-date inbound and outbound human movement flow information
To address the limitations of existing mobility databases, we introduce an openly available dataset that provides an estimation of dynamic population flows at multiple spatial scales and temporal resolutions across the U.S during the COVID-19 pandemic considering the findability, accessibility, interoperability, and reusability of data[44]

Summary

Introduction

Background & SummaryThe outbreak of the novel coronavirus disease SARS-CoV-2 ( known as COVID-19) in December 2019 has become a global threat to public health and human societies. More than 25 million people have been infected by the virus with more than eight hundred thousand death cases globally[1]. Tracking dynamic human mobility changes and spatial interaction patterns is a prerequisite for measuring the effects of human mobility and interventions on predicting the virus spread[10,11]. Several recent works have employed human movement flow matrices in understanding spatial interaction changes and social impact, and enabling network-based epidemic models to project the numbers of COVID-19 infected population in different countries such as China, Japan, Italy, France, Chile, and UK12–22, which requires up-to-date inbound and outbound human movement flow information. There is no such openly and timely updated human movement origin-to-destination (O-D) flow matrix data at a fine spatiotemporal resolution available in many other countries where researchers can only use historical O-D survey data and other proxies as a compromise[23]

Objectives

Methods