Sequences of purchases in credit card data reveal lifestyles in urban populations
Open accessAbstract
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.
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Clustering anonymized credit card payments makes it possible to model different communities and lifestyles, whose sustainability can be assessed.
“For example, clustering anonymized credit card payments makes it possible to model different communities and lifestyles-of which the sustainability can be assessed [181].”
From Tackling climate change with machine learning (2023) · cites this paper as [181]
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- Authors
- Di Clemente, R., Luengo-Oroz, M., Travizano, M., Xu, S., Vaitla, B., & González, M. C.
- Affiliations
- United Nations (US)
- Massachusetts Institute of Technology (US)
- Harvard University (US)
- Lawrence Berkeley National Laboratory (US)
- Corresponding author
- González, Marta C.
- Venue
- Nature Communications
- Published
- 2018-08-20
- Volume
- 9
- Issue
- 1
- Pages
- 3330-3330
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC6102281
- OpenAlex
- W2594767424
- Cited by
- 105
- References
- 39
- Citations by year
- 2026: 7 · 2025: 11 · 2024: 12 · 2023: 15 · 2022: 16 · 2021: 20 · 2020: 10 · 2019: 10 · 2018: 4
- FWCI
- 13.61
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Open Research Exeter (University of Exeter) (submittedVersion, cc-by)
- Funders
- Royal Society (NF170505)
- Bill and Melinda Gates Foundation (OPP1141325)
- United Nations Population Fund (UNF-15-738)
- Topics
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Complex Systems and Time Series Analysis
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Keywords
- credit card transactions, Zipf's law, consumer lifestyle, consumer clustering, collective behavior, text compression, social networks, mobility networks, urban population
- MeSH terms
- Consumer Behavior, Humans, Life Style, Semantics, Urban Population, Cell Phone
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