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dc.contributor.authorMeyer, Joachim
dc.contributor.authorShmueli, Erez
dc.contributor.authorBozkaya, Burçin
dc.contributor.authorDong, Xiaowen
dc.contributor.authorPentland, Alex Paul
dc.date.accessioned2018-04-17T19:22:46Z
dc.date.available2018-04-17T19:22:46Z
dc.date.issued2018-04
dc.date.submitted2017-07
dc.identifier.issn2193-1127
dc.identifier.urihttp://hdl.handle.net/1721.1/114768
dc.description.abstractSocietal unrest and similar events are important for societies, but it is often difficult to quantify their effects on individuals, hindering a timely and effective policy-making in emergencies and in particular localized social shocks such as protests. Traditionally, effects are assessed through economic indicators or surveys with relatively low temporal and spatial resolutions. In this work, we compute two behavioral indexes, based on the use of credit card transaction data, for measuring the economic effects of a series of protests on consumer actions and personal consumption. Using data from a metropolitan area in an OECD country, we show that protests affect consumers’ shopping frequency and spending, but in noticeably different ways. The effects show strong temporal and spatial patterns, vary between neighborhoods and customers of different socio-demographical characteristics as well as between merchants of different categories, and suggest interesting subtleties in purchase behavior such as displaced or delayed shopping activities. Our method can generally serve for the real-time monitoring of the effects of major social shocks or events on urban economy and consumer sentiment, providing high-resolution and cost-effective measurement tools to complement traditional economic indicators. Keywords: Social shocks; Economic effect; Consumer behavior; Spatiotemporal pattern; Credit card transactionen_US
dc.publisherSpringeren_US
dc.relation.isversionofhttps://doi.org/10.1140/epjds/s13688-018-0136-xen_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.sourceSpringer Berlin Heidelbergen_US
dc.titleMethods for quantifying effects of social unrest using credit card transaction dataen_US
dc.typeArticleen_US
dc.identifier.citationDong, Xiaowen et al. "Methods for quantifying effects of social unrest using credit card transaction data." EPJ Data Science 7 (April 2018): 8 © 2018 The Author(s)en_US
dc.contributor.departmentMassachusetts Institute of Technology. Media Laboratoryen_US
dc.contributor.mitauthorDong, Xiaowen
dc.contributor.mitauthorPentland, Alex Paul
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.updated2018-04-14T03:53:37Z
dc.language.rfc3066en
dc.rights.holderThe Author(s)
dspace.orderedauthorsDong, Xiaowen; Meyer, Joachim; Shmueli, Erez; Bozkaya, Burçin; Pentland, Alexen_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0002-1143-9786
dc.identifier.orcidhttps://orcid.org/0000-0002-8053-9983
mit.licensePUBLISHER_CCen_US


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