# Sequences of purchases in credit card data reveal life styles in urban populations

arXiv: https://arxiv.org/abs/1703.00409 · HTML: https://mlsanity.com/paper/1703.00409/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2017.

## Built on by (most cited first, 5 of 5)

- [Identifying latent activity behaviors and lifestyles using mobility data to describe urban dynamics](https://mlsanity.com/paper/2209.12095/) (2022)
- [Neural Embeddings of Urban Big Data Reveal Emergent Structures in Cities](https://mlsanity.com/paper/2110.12371/) (2021)
- [Clustering of check-in sequences using the mixture Markov chain process](https://mlsanity.com/paper/2106.12039/) (2021)
- [From "Who Is This User?" to "What Does This Purchase Mean?": A Deployed Pipeline for Semantic User Profiling at Bank Scale](https://mlsanity.com/paper/2609.19928/) (2026)
- [Popularity Trajectories and Mixing Patterns of Urban Venues in Two European Cities](https://mlsanity.com/paper/2610.18917/) (2026)

## Abstract (arXiv)

Zipf-like distributions characterize a wide set of phenomena in physics, biology, economics and social sciences. In human activities, Zipf-laws describe for example the frequency of words appearance in a text or the purchases 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 purchases 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 by 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.