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Sequences of purchases in credit card data reveal lifestyles in urban populations

Author

Listed:
  • Riccardo Di Clemente

    (Massachusetts Institute of Technology
    University College London)

  • Miguel Luengo-Oroz

    (United Nations Global Pulse)

  • Matias Travizano

    (GranData)

  • Sharon Xu

    (Massachusetts Institute of Technology)

  • Bapu Vaitla

    (Harvard University)

  • Marta C. González

    (Massachusetts Institute of Technology
    Department of City and Regional Planning
    Lawrence Berkeley National Laboratory)

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.

Suggested Citation

  • Riccardo Di Clemente & Miguel Luengo-Oroz & Matias Travizano & Sharon Xu & Bapu Vaitla & Marta C. González, 2018. "Sequences of purchases in credit card data reveal lifestyles in urban populations," Nature Communications, Nature, vol. 9(1), pages 1-8, December.
  • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-05690-8
    DOI: 10.1038/s41467-018-05690-8
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    Cited by:

    1. Saiz, Albert & Salazar-Miranda, Arianna, 2023. "Understanding Urban Economies, Land Use, and Social Dynamics in the City: Big Data and Measurement," IZA Discussion Papers 16501, Institute of Labor Economics (IZA).
    2. Clodomir Santana & Federico Botta & Hugo Barbosa & Filippo Privitera & Ronaldo Menezes & Riccardo Di Clemente, 2023. "COVID-19 is linked to changes in the time–space dimension of human mobility," Nature Human Behaviour, Nature, vol. 7(10), pages 1729-1739, October.
    3. Chao Fan & Yang Yang & Ali Mostafavi, 2024. "Neural embeddings of urban big data reveal spatial structures in cities," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-15, December.
    4. Junwei Ma & Bo Li & Ali Mostafavi, 2024. "Characterizing urban lifestyle signatures using motif properties in network of places," Environment and Planning B, , vol. 51(4), pages 889-903, May.
    5. Kaixin Zhu & Zhifeng Cheng & Jianghao Wang, 2024. "Measuring Chinese mobility behaviour during COVID-19 using geotagged social media data," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.
    6. Wang, Mingyan & Zeng, An & Cui, Xiaohua, 2022. "Collective user switching behavior reveals the influence of TV channels and their hidden community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).

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