Sequences of purchases in credit card data reveal lifestyles in urban populations
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Author(s)
Di Clemente, Riccardo
Xu, Sharon
González, Marta C.
Date Issued
August 20, 2018
Journal
Nature communications
Publisher
Springer Nature
Citation
Di Clemente, Riccardo, et al., "Sequences of purchases in credit card data reveal lifestyles in urban populations." Nature communications 9, 1 (Aug. 2018): no. 3330 doi 10.1038/s41467-018-05690-8 ©2018 Author(s)
Version
Final published version
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. ©2018
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
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Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1038/s41467-018-05690-8
