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dc.contributor.authorRoy, Kamol Chandra
dc.contributor.authorCebrian, Manuel
dc.contributor.authorHasan, Samiul
dc.date.accessioned2020-07-30T01:40:35Z
dc.date.available2020-07-30T01:40:35Z
dc.date.issued2019-05
dc.date.submitted2018-06
dc.identifier.issn2193-1127
dc.identifier.urihttps://hdl.handle.net/1721.1/126443
dc.description.abstractMobility is one of the fundamental requirements of human life with significant societal impacts including productivity, economy, social wellbeing, adaptation to a changing climate, and so on. Although human movements follow specific patterns during normal periods, there are limited studies on how such patterns change due to extreme events. To quantify the impacts of an extreme event to human movements, we introduce the concept of mobility resilience which is defined as the ability of a mobility system to manage shocks and return to a steady state in response to an extreme event. We present a method to detect extreme events from geo-located movement data and to measure mobility resilience and transient loss of resilience due to those events. Applying this method, we measure resilience metrics from geo-located social media data for multiple types of disasters occurred all over the world. Quantifying mobility resilience may help us to assess the higher-order socio-economic impacts of extreme events and guide policies towards developing resilient infrastructures as well as a nation’s overall disaster resilience strategies.en_US
dc.publisherSpringer Science and Business Media LLCen_US
dc.relation.isversionofhttp://dx.doi.org/10.1140/epjds/s13688-019-0196-6en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceSpringer Berlin Heidelbergen_US
dc.titleQuantifying human mobility resilience to extreme events using geo-located social media dataen_US
dc.typeArticleen_US
dc.identifier.citationRoy, Kamol Chandra et al. "Quantifying human mobility resilience to extreme events using geo-located social media data." EPJ Data Science 8, 1 (May 2019): 18 © 2019 Springer Natureen_US
dc.contributor.departmentMassachusetts Institute of Technology. Media Laboratoryen_US
dc.relation.journalEPJ Data Scienceen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2020-06-26T13:27:57Z
dc.language.rfc3066en
dc.rights.holderThe Author(s)
dspace.embargo.termsN
dspace.date.submission2020-06-26T13:27:57Z
mit.journal.volume8en_US
mit.journal.issue1en_US
mit.licensePUBLISHER_CC


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