21,973 research outputs found
Sequences of purchases in credit card data reveal life styles in urban populations
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.Comment: 30 pages, 26 figure
Classifying Spending Behavior using Socio-Mobile Data
Human spending behavior is essentially social. This work motivates and grounds the use of mobile phone based social interaction features for classifying spending behavior. Using a data set involving 52 adults (26 couples) living in a community for over a year, we find that social behavior measured via face-to-face interaction, call, and SMS logs, can be used to predict the spending behavior for couples in terms of their propensity to explore diverse businesses, become loyal customers, and overspend. Our results show that mobile phone based social interaction patterns can provide more predictive power on spending behavior than personality based features. Interestingly, we find that more social couples also tend to overspend. Obtaining such insights about couple level spending behavior via novel social-computing frameworks can be of vital importance to economists, marketing professionals, and policy makers.European Commission (PERSI project inside the Marie Curie Cofund 7th framework
Mining large-scale human mobility data for long-term crime prediction
Traditional crime prediction models based on census data are limited, as they
fail to capture the complexity and dynamics of human activity. With the rise of
ubiquitous computing, there is the opportunity to improve such models with data
that make for better proxies of human presence in cities. In this paper, we
leverage large human mobility data to craft an extensive set of features for
crime prediction, as informed by theories in criminology and urban studies. We
employ averaging and boosting ensemble techniques from machine learning, to
investigate their power in predicting yearly counts for different types of
crimes occurring in New York City at census tract level. Our study shows that
spatial and spatio-temporal features derived from Foursquare venues and
checkins, subway rides, and taxi rides, improve the baseline models relying on
census and POI data. The proposed models achieve absolute R^2 metrics of up to
65% (on a geographical out-of-sample test set) and up to 89% (on a temporal
out-of-sample test set). This proves that, next to the residential population
of an area, the ambient population there is strongly predictive of the area's
crime levels. We deep-dive into the main crime categories, and find that the
predictive gain of the human dynamics features varies across crime types: such
features bring the biggest boost in case of grand larcenies, whereas assaults
are already well predicted by the census features. Furthermore, we identify and
discuss top predictive features for the main crime categories. These results
offer valuable insights for those responsible for urban policy or law
enforcement
Mobile Money: Understanding and Predicting its Adoption and Use in a Developing Economy
Access to financial institutions is difficult in developing economies and
especially for the poor. However, the widespread adoption of mobile phones has
enabled the development of mobile money systems that deliver financial services
through the mobile phone network. Despite the success of mobile money, there is
a lack of quantitative studies that unveil which factors contribute to the
adoption and sustained usage of such services. In this paper, we describe the
results of a quantitative study that analyzes data from the world's leading
mobile money service, M-Pesa. We analyzed millions of anonymized mobile phone
communications and M-Pesa transactions in an African country. Our contributions
are threefold: (1) we analyze the customers' usage of M-Pesa and report
large-scale patterns of behavior; (2) we present the results of applying
machine learning models to predict mobile money adoption (AUC=0.691), and
mobile money spending (AUC=0.619) using multiple data sources: mobile phone
data, M-Pesa agent information, the number of M-Pesa friends in the user's
social network, and the characterization of the user's geographic location; (3)
we discuss the most predictive features in both models and draw key
implications for the design of mobile money services in a developing country.
We find that the most predictive features are related to mobile phone activity,
to the presence of M-Pesa users in a customer's ego-network and to mobility. We
believe that our work will contribute to the understanding of the factors
playing a role in the adoption and sustained usage of mobile money services in
developing economies.Comment: Accepted for publication in ACM IMWUT (Ubicomp) 201
Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits
Research has proven that stress reduces quality of life and causes many
diseases. For this reason, several researchers devised stress detection systems
based on physiological parameters. However, these systems require that
obtrusive sensors are continuously carried by the user. In our paper, we
propose an alternative approach providing evidence that daily stress can be
reliably recognized based on behavioral metrics, derived from the user's mobile
phone activity and from additional indicators, such as the weather conditions
(data pertaining to transitory properties of the environment) and the
personality traits (data concerning permanent dispositions of individuals). Our
multifactorial statistical model, which is person-independent, obtains the
accuracy score of 72.28% for a 2-class daily stress recognition problem. The
model is efficient to implement for most of multimedia applications due to
highly reduced low-dimensional feature space (32d). Moreover, we identify and
discuss the indicators which have strong predictive power.Comment: ACM Multimedia 2014, November 3-7, 2014, Orlando, Florida, US
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