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

Clemente, Riccardo Di; Luengo-Oroz, Miguel; Travizano, Matías; Xu, Sharon; Vaitla, Bapu; González, Marta C.

doi:10.1038/s41467-018-05690-8

Cited by 81 publications

(61 citation statements)

References 46 publications

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“…Our spending habits reflect our lifestyles, capturing an essential aspect of our behavior. Within the computational social science community, the question remains whether pervasive trends exist among disparate groups at urban scale 15 . In this chapter, we use latent Dirichlet allocation (LDA) 26 to identify topics (behavioral patterns) among individuals, representing each individual's spending lifestyle as a finite mixture of an underlying set of behaviors.…”

Section: Discovering Shopping Patternsmentioning

confidence: 99%

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

Clemente

González

2019

Big Data Recommender Systems - Volume 2: Application Paradigms

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In the last decade, the digital age has sharply redefined the way we study human behavior. With the advancement of data storage and sensing technologies, electronic records now encompass a diverse spectrum of human activity, ranging from location data 1, 2 , phone 3, 4 and email communication 5 to Twitter activity 6 and open-source contributions on Wikipedia and OpenStreetMap 7, 8 . In particular, 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. Credit card records (CCRs) provide detailed insight into purchase behavior and have been found to have inherent regularity in consumer shopping patterns 9 ; call detail records (CDRs) present new opportunities to understand human mobility 10 , analyze wealth 11 , and model social network dynamics 12 .Regarding the analysis of CDR data, there exists a wide body of work characterizing human mobility patterns. As a notable example, 10 describes the temporal and spatial regularity of human trajectories, showing that each individual can be described by a time independent travel distance and a high probability of returning to a small number of locations. Further, the authors are able to model individual travel patterns using a single spatial probability distribution. There has also been work at the intersection of similar datasets, such as the inference of friendships from mobile phone data 13 , or the analysis such data in relation to metrics on spending behavior such as diversity, engagement, and loyalty 14 . Recent work 15 uses the Jaccard distance as a similarity measure on motifs among spending categories, then applies community detection algorithms to find clusters of users. These studies propose models for either mobility or spending behavior, but not in conjunction.The only known paper that incorporates both aspects 16 frames its analysis only on an aggregate scale of city regions. However, the coupled collaborative filtering methods (also known as collective matrix factorization) used in 16 have been successfully applied in a variety of urban computing applications for data fusion and prediction [17][18][19] , from location-based activity recommendations 20, 21 to travel speed estimation on road segments 22 . Recent work includes methods that use Laplacian regularization 23 to leverage social network information, and use geometric deep learning matrix completion methods to model nonlinearities 24 .In this chapter, we jointly model the lifestyles of individuals, a more challenging problem with higher variability when compared to the aggregated behavior of city regions. Using collective matrix factorization, we propose a unified dual view of lifestyles. Understanding these lifestyles will not only inform commercial opportunities, but also help policymakers and nonprofit organizations understand the characteristics and needs of the entire region, as well as of the individuals within that region. The applications of this range from targeted advertisements a...

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Section: Discovering Shopping Patternsmentioning

confidence: 99%

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

Clemente

González

2019

Big Data Recommender Systems - Volume 2: Application Paradigms

Self Cite

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“…Examples include mobile operator's data [46,47], social media data [6,48], geolocated transactions [49,50] and multi-modal datasets that couple the social-spatial and economic activities (e.g. spending behaviour) of the populations [30,51]. As the measurement of segregation concerns not only the behaviours of people but also their socio-economic characteristics, there will be privacy concerns regarding accessing and especially merging these kinds of datasets.…”

Section: Discussionmentioning

confidence: 99%

Quantifying segregation in an integrated urban physical-social space

Belyi

Santi

et al. 2019

J. R. Soc. Interface.

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Our knowledge of how cities bring together different social classes is still limited. Much effort has been devoted to investigating residential segregation, mostly over well-defined social groups (e.g. race). Little is known of how mobility and human communications affect urban social integration. The dynamics of spatial and social-network segregation and individual variations along these two dimensions are largely untapped. In this article, we put forward a computational framework based on coupling large-scale information on human mobility, social-network connections and people’s socio-economic status (SES), to provide a breakthrough in our understanding of the dynamics of spatio-temporal and social-network segregation in cities. Building on top of a social similarity measure, the framework can be used to depict segregation dynamics down to the individual level, and also provide aggregate measurements at the scale of places and cities, and their evolution over time. By applying the methodology in Singapore using large-scale mobile phone and socio-economic datasets, we find a relatively higher level of segregation among relatively wealthier classes, a finding that holds for both social and physical space. We also highlight the interplay between the effect of distance decay and homophily as forces that determine communication intensity, defining a notion of characteristic ‘homophily distance’ that can be used to measure social segregation across cities. The time-resolved analysis reveals the changing landscape of urban segregation and the time-varying roles of places. Segregations in physical and social space are weakly correlated at the individual level but highly correlated when grouped across at least hundreds of individuals. The methodology and analysis presented in this paper enable a deeper understanding of the dynamics of human segregation in social and physical space, which can assist social scientists, planners and city authorities in the design of more integrated cities.

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“…Ali et al (2017) found that there were fewer male cardholders than female ones in Saudi Arabia. Moreover, Di Clemente et al (2018) found that males outnumbered females in credit card usage at petrol stations, restaurants, on computer networks and/or information services and at miscellaneous food stores.…”

Section: Influence Of Gender On Attitude Subjective Norm and Perceivmentioning

confidence: 99%

Sharia Compliance-Credit Card Exposure and Utilization in the Growing Digital Economy

Arsyianti

Adelia

2019

JIMF

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Many cashless payment tools have been introduced to Indonesia market in the form of cards to electronic money. Sharia compliance-credit card is one of traditional electronic payment tools that are provided by only two Islamic finance institutions among many in Indonesia. The increase of Sharia compliance-credit card transactions from time to time may lead banks to be exposed to non-performing risk. Customers’ behaviors are varying in utilizing Sharia compliance-credit card, and their behavior may cause unpredicted results on card’s performance. Therefore, the purposes of this study are: 1) To identify the behavior of Sharia compliance-credit card’s users and 2) To analyze the factors influencing the behavior of Sharia compliance-credit card’s users. Primary data is gathered through distributing questionnaires with a total sample of 170 respondents. The data is analyzed by employing Structural Equation Modeling. This research shows that gender, age, and education influence perceived behavior control; and, expenditure influences subjective norms. Customers’ attitudes, subjective norms, and perceived behavior control are proven to influence customer intentions, where customers’ intentions, perceived behavior control, and income influence customers’ behavior towards using Sharia compliance-credit card. For further sustainable inclusive growth, the issuers should pay attention on education for specific audiences.

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

Cited by 81 publications

References 46 publications

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

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

Quantifying segregation in an integrated urban physical-social space

Sharia Compliance-Credit Card Exposure and Utilization in the Growing Digital Economy

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