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

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.

SUBMITTER: Di Clemente R

PROVIDER: S-EPMC6102281 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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

Di Clemente Riccardo R Luengo-Oroz Miguel M Travizano Matias M Xu Sharon S Vaitla Bapu B González Marta C MC

Nature communications 20180820 1

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 pur ...[more]

PMID: 30127416

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Project description:Human lifestyle and its relationship with the human microbiome has been a line of research widely studied. This is because, throughout human history, civilizations have experienced different environments and lifestyles that could have promoted changes in the human microbiome. The comparison between industrialized and non-industrialized human populations in several studies has allowed to observe variation in the microbiome structure due to the population lifestyle. Nevertheless, the lifestyle of human populations is a gradient where several subcategories can be described. Yet, it is not known how these different lifestyles of human populations affect the microbiome structure on a large scale. Therefore, the main goal of this work was the collection and comparison of 16S data from the gut microbiome of populations that have different lifestyles around the world. With the data obtained from 14 studies, it was possible to compare the gut microbiome of 568 individuals that represent populations of hunter-gatherers, agricultural, agropastoral, pastoral, and urban populations. Results showed that industrialized populations present less diversity than those from non-industrialized populations, as has been described before. However, by separating traditional populations into different categories, we were able to observe patterns that cannot be appreciated by encompassing the different traditional lifestyles in a single category. In this sense, we could confirm that different lifestyles exhibit distinct alpha and beta diversity. In particular, the gut microbiome of pastoral and agropastoral populations seems to be more similar to those of urban populations according to beta diversity analysis. Beyond that, beta diversity analyses revealed that bacterial composition reflects the different lifestyles, representing a transition from hunters-gatherers to industrialized populations. Also, we found that certain groups such as Bacteoidaceae, Lanchospiraceae, and Rickenellaceae have been favored in the transition to modern societies, being differentially abundant in urban populations. Thus, we could hypothesize that due to adaptive/ecological processes; multifunctional bacterial groups (e.g., Bacteroidaceae) could be replacing some functions lost in the transition to modern lifestyle.

Project description:BackgroundTo date, the evaluation of diet has mostly been based on questionnaires and diaries that have their limitations in terms of being time and resource intensive, and a tendency toward social desirability. Loyalty card data obtained in retailing provides timely and objective information on diet-related behaviors. In Finland, the market is highly concentrated, which provides a unique opportunity to investigate diet through grocery purchases.ObjectiveThe aims of this study were as follows: (1) to investigate and quantify the selection bias in large-scale (n=47,066) loyalty card (LoCard) data and correct the bias by developing weighting schemes and (2) to investigate how the degree of loyalty relates to food purchases.MethodsMembers of a loyalty card program from a large retailer in Finland were contacted via email and invited to take part in the study, which involved consenting to the release of their grocery purchase data for research purposes. Participants' sociodemographic background was obtained through a web-based questionnaire and was compared to that of the general Finnish adult population obtained via Statistics Finland. To match the distributions of sociodemographic variables, poststratification weights were constructed by using the raking method. The degree of loyalty was self-estimated on a 5-point rating scale.ResultsOn comparing our study sample with the general Finnish adult population, in our sample, there were more women (65.25%, 30,696/47,045 vs 51.12%, 2,273,139/4,446,869), individuals with higher education (56.91%, 20,684/36,348 vs 32.21%, 1,432,276/4,446,869), and employed individuals (60.53%, 22,086/36,487 vs 52.35%, 2,327,730/4,446,869). Additionally, in our sample, there was underrepresentation of individuals aged under 30 years (14.44%, 6,791/47,045 vs 18.04%, 802,295/4,446,869) and over 70 years (7.94%, 3,735/47,045 vs 18.20%, 809,317/4,446,869), as well as retired individuals (23.51%, 8,578/36,487 vs 31.82%, 1,414,785/4,446,869). Food purchases differed by the degree of loyalty, with higher shares of vegetable, red meat & processed meat, and fat spread purchases in the higher loyalty groups.ConclusionsIndividuals who consented to the use of their loyalty card data for research purposes tended to diverge from the general Finnish adult population. However, the high volume of data enabled the inclusion of sociodemographically diverse subgroups and successful correction of the differences found in the distributions of sociodemographic variables. In addition, it seems that food purchases differ according to the degree of loyalty, which should be taken into account when researching loyalty card data. Despite the limitations, loyalty card data provide a cost-effective approach to reach large groups of people, including hard-to-reach population subgroups.

Dataset Information

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

Publications

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

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