Sequences of purchases in credit card data reveal lifestyles in urban populations
dc.contributor.author | Di Clemente, R | |
dc.contributor.author | Luengo-Oroz, M | |
dc.contributor.author | Travizano, M | |
dc.contributor.author | Xu, S | |
dc.contributor.author | Vaitla, B | |
dc.contributor.author | González, MC | |
dc.date.accessioned | 2020-01-29T09:38:19Z | |
dc.date.issued | 2018-08-20 | |
dc.description.abstract | Zipf-like distributions characterize a wide set of phenomena in physics, biology, economics, and social sciences. In human activities, Zipf's law describes, for example, the frequency of appearance of words in a text or the purchase types in shopping patterns. In the latter, the uneven distribution of transaction types is bound with the temporal sequences of purchases of individual choices. In this work, we define a framework using a text compression technique on the sequences of credit card purchases to detect ubiquitous patterns of collective behavior. Clustering the consumers by their similarity in purchase sequences, we detect five consumer groups. Remarkably, post checking, individuals in each group are also similar in their age, total expenditure, gender, and the diversity of their social and mobility networks extracted from their mobile phone records. By properly deconstructing transaction data with Zipf-like distributions, this method uncovers sets of significant sequences that reveal insights on collective human behavior. | en_GB |
dc.description.sponsorship | Gates Foundation | en_GB |
dc.description.sponsorship | United Nations Foundation | en_GB |
dc.description.sponsorship | Newton International Fellowship | en_GB |
dc.description.sponsorship | The Royal Society | en_GB |
dc.description.sponsorship | The British Academy | en_GB |
dc.description.sponsorship | Academy of Medical Sciences | en_GB |
dc.identifier.citation | Vol. 9, article 3330 | en_GB |
dc.identifier.doi | 10.1038/s41467-018-05690-8 | |
dc.identifier.grantnumber | OPP1141325 | en_GB |
dc.identifier.grantnumber | UNF-15-738 | en_GB |
dc.identifier.grantnumber | NF170505 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/40623 | |
dc.language.iso | en | en_GB |
dc.publisher | Nature Research | en_GB |
dc.rights | © The Author(s) 2018. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. | en_GB |
dc.title | Sequences of purchases in credit card data reveal lifestyles in urban populations | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2020-01-29T09:38:19Z | |
dc.description | This is the final version. Available from Nature Research via the DOI in this record. | en_GB |
dc.identifier.journal | Nature Communications | en_GB |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2018-07-06 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2018-07-06 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2020-01-29T09:33:48Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2020-01-29T09:38:23Z | |
refterms.panel | B | en_GB |
refterms.depositException | publishedGoldOA |
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Except where otherwise noted, this item's licence is described as © The Author(s) 2018. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.