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

  title={Sequences of purchases in credit card data reveal lifestyles in urban populations},
  author={Riccardo Di Clemente and Miguel Luengo-Oroz and Mat{\'i}as Travizano and Sharon C. Xu and Bapu Vaitla and Marta C. Gonz{\'a}lez},
  journal={Nature Communications},
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… 

Inferring psychological traits from spending categories and dynamic consumption patterns

It is found that Materialism and Self-Control could be inferred with relatively high levels of accuracy, while the accuracy obtained for the Big Five traits was lower, with only Extraversion and Neuroticism reaching reasonable classification performances.

Discovering temporal regularities in retail customers’ shopping behavior

A framework for extracting from personal retail data a temporal purchasing profile able to summarize whether and when a customer makes her distinctive purchases is introduced and how the profiles can be exploited both by customers to enable personalized services, and by the retail market chain for providing tailored discounts based on temporal purchasing regularity.

Modeling Human Dynamics and Lifestyle Using Digital Traces

A multivariate, periodic Hawkes process (MPHP) model is proposed that captures -- at the individual level -- the temporal clustering of human activity, the interdependence structure and co-excitation of different activities, and the periodic effects of weekly rhythms.

Characterizing Urban Lifestyle Signatures Using Motif Properties in Network of Places

The results show that people’s lifestyles in urban environments can be well depicted and quantified based on distribution and attributes of motifs in networks of places and show stability in quantity and distance as well as periodicity on weekends and weekdays indicating the stability of lifestyle patterns in cities.

Manifold Learning to Identify Consumer Profiles in Real Consumption Data

A new approach to compute and analyze consumer profiles based on millions of purchase transactions collected by a personal financial manager is proposed, demonstrating how these techniques can cluster consumers in more meaningful groups than demographics alone.

The wisdom of the few: Predicting collective success from individual behavior

This study combines individuals' credit-card purchasing history with their social and mobility traits across an entire nation to find that the purchasing history alone enables the detection of small sets of ``discoverers" whose early purchases offer reliable success predictions for the brick-and-mortar stores they visit.

Mining urban lifestyles: urban computing, human behavior and recommender systems

The study of the shopping and mobility patterns of individual consumers has the potential to give deeper insight into the lifestyles and infrastructure of the region.

Identifying latent activity behaviors and lifestyles using mobility data to describe urban dynamics

It is found that lifestyles can be automatically decomposed into only 12 latent interpretable activity behaviors on how people combine shopping, eating, working, or using their free time, and that city dwellers’ behavior is a mixture of those behaviors.

City consumption profile: a city perspective on the spending behavior of citizens

This work proposes a methodology to extract the consumption behaviors of a large sample of customers through credit card transaction data, and shows that cities, even geographically close, exhibit different profiles which makes them unique.

A Mobility Model for Return and Repeated Migration Based on Network Motifs

A network-based model of city-to-city migration is constructed, where the movement of individuals is modelled using the frequency of distinct network motifs and shows that the majority of people do not move, but that there is a small group which exhibits frequent migration, particularly the young and male.



Cities through the Prism of People’s Spending Behavior

This paper exploits a relatively unexplored source of data–anonymized records of bank card transactions collected in Spain by a big European bank, and proposes a new classification scheme of cities based on the economic behavior of their residents, which exhibits a substantial stability over different city definitions and connects with a meaningful socioeconomic interpretation.

Social Bridges in Urban Purchase Behavior

This article argues that people who live in different communities but work at close-by locations could act as “social bridges” between the respective communities and that they are correlated with similarity in community purchase behavior, and shows that the number of social bridges between communities is a much stronger indicator of similarity in their purchase behavior.

Influence of sociodemographics on human mobility

Credit-card records from Barcelona and Madrid are analyzed and by examining the geolocated credit-card transactions of individuals living in the two provinces, it is found that the mobility patterns vary according to gender, age and occupation.

Coupling human mobility and social ties

It is found that the composition of a user's ego network in terms of the type of contacts they keep is correlated with mobility behaviour, and a popular mobility model is extended to include movement choices based on social contacts and compared with two additional models of mobility.

Urban association rules: Uncovering linked trips for shopping behavior

The method of urban association rules and its uses for extracting frequently appearing combinations of stores that are visited together to characterize shoppers’ behaviors are introduced and can complement conventional research methods.

Predicting poverty and wealth from mobile phone metadata

It is shown that an individual’s past history of mobile phone use can be used to infer his or her socioeconomic status and that the predicted attributes of millions of individuals can accurately reconstruct the distribution of wealth of an entire nation or to infer the asset distribution of microregions composed of just a few households.

From mobile phone data to the spatial structure of cities

An urban dilatation index is defined which measures how the average distance between individuals evolves during the day, allowing us to highlight different types of city structure, and a parameter free method to detect hotspots, the most crowded places in the city is proposed.

Limits of Predictability in Human Mobility

Analysis of the trajectories of people carrying cell phones reveals that human mobility patterns are highly predictable, and a remarkable lack of variability in predictability is found, which is largely independent of the distance users cover on a regular basis.

Returners and explorers dichotomy in human mobility

It is shown that returners and explorers play a distinct quantifiable role in spreading phenomena and that a correlation exists between their mobility patterns and social interactions.

Using big data to study the link between human mobility and socio-economic development

This work studies the relations between human mobility patterns and socioeconomic development using nation-wide mobile phone data and investigates the correlations with external socio-economic indicators independently surveyed by an official statistics institute to find three main results.