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Multi-scale data-driven analysis on human mobility and its impacts during the COVID-19 pandemic
Topics: Spatial Analysis & Modeling
, Geographic Information Science and Systems
, Health and Medical
Keywords: SafeGraph, similarity measurement, inflow, outflow, time-series Session Type: Virtual Paper Abstract Day: Saturday Session Start / End Time: 2/26/2022 11:20 AM (Eastern Time (US & Canada)) - 2/26/2022 12:40 PM (Eastern Time (US & Canada)) Room: Virtual 5
Authors:
Atsushi Nara, San Diego State University
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Abstract
Human mobility and interaction play key roles in virus transmissions and outbreak management during COVID -19. People’s everyday life has been significantly impacted by non-pharmaceutical interventions such as stay-at-home order, overnight curfew, facility closure, lockdown, and reopening decisions. Studies across world reported various effects of the association between human mobility and COVID-19 epidemic dynamics across different geographic and temporal scales. To further understand the association and scaling effects, this study uses fine-scale cell-phone-based data and COVID-19 confirmed case data and examines the relationship between human mobility and COVID-19 cases at multiple spatial and temporal scales from neighborhoods to regional-levels and from daily to monthly.
Multi-scale data-driven analysis on human mobility and its impacts during the COVID-19 pandemic
