55,588 research outputs found
An analytical framework to nowcast well-being using mobile phone data
An intriguing open question is whether measurements made on Big Data
recording human activities can yield us high-fidelity proxies of socio-economic
development and well-being. Can we monitor and predict the socio-economic
development of a territory just by observing the behavior of its inhabitants
through the lens of Big Data? In this paper, we design a data-driven analytical
framework that uses mobility measures and social measures extracted from mobile
phone data to estimate indicators for socio-economic development and
well-being. We discover that the diversity of mobility, defined in terms of
entropy of the individual users' trajectories, exhibits (i) significant
correlation with two different socio-economic indicators and (ii) the highest
importance in predictive models built to predict the socio-economic indicators.
Our analytical framework opens an interesting perspective to study human
behavior through the lens of Big Data by means of new statistical indicators
that quantify and possibly "nowcast" the well-being and the socio-economic
development of a territory
The Effect of Pok\'emon Go on The Pulse of the City: A Natural Experiment
Pok\'emon Go, a location-based game that uses augmented reality techniques,
received unprecedented media coverage due to claims that it allowed for greater
access to public spaces, increasing the number of people out on the streets,
and generally improving health, social, and security indices. However, the true
impact of Pok\'emon Go on people's mobility patterns in a city is still largely
unknown. In this paper, we perform a natural experiment using data from mobile
phone networks to evaluate the effect of Pok\'emon Go on the pulse of a big
city: Santiago, capital of Chile. We found significant effects of the game on
the floating population of Santiago compared to movement prior to the game's
release in August 2016: in the following week, up to 13.8\% more people spent
time outside at certain times of the day, even if they do not seem to go out of
their usual way. These effects were found by performing regressions using count
models over the states of the cellphone network during each day under study.
The models used controlled for land use, daily patterns, and points of interest
in the city.
Our results indicate that, on business days, there are more people on the
street at commuting times, meaning that people did not change their daily
routines but slightly adapted them to play the game. Conversely, on Saturday
and Sunday night, people indeed went out to play, but favored places close to
where they live.
Even if the statistical effects of the game do not reflect the massive change
in mobility behavior portrayed by the media, at least in terms of expanse, they
do show how "the street" may become a new place of leisure. This change should
have an impact on long-term infrastructure investment by city officials, and on
the drafting of public policies aimed at stimulating pedestrian traffic.Comment: 23 pages, 7 figures. Published at EPJ Data Scienc
SensibleSleep: A Bayesian Model for Learning Sleep Patterns from Smartphone Events
We propose a Bayesian model for extracting sleep patterns from smartphone
events. Our method is able to identify individuals' daily sleep periods and
their evolution over time, and provides an estimation of the probability of
sleep and wake transitions. The model is fitted to more than 400 participants
from two different datasets, and we verify the results against ground truth
from dedicated armband sleep trackers. We show that the model is able to
produce reliable sleep estimates with an accuracy of 0.89, both at the
individual and at the collective level. Moreover the Bayesian model is able to
quantify uncertainty and encode prior knowledge about sleep patterns. Compared
with existing smartphone-based systems, our method requires only screen on/off
events, and is therefore much less intrusive in terms of privacy and more
battery-efficient
Autonomous Accident Monitoring Using Cellular Network Data
Mobile communication networks constitute large-scale sensor networks that generate huge amounts of data that can be refined into collective mobility patterns. In this paper we propose a method for using these patterns to autonomously monitor and detect accidents and other critical events. The approach is to identify a measure that is approximately time-invariant on short time-scales under regular conditions, estimate the short and long-term dynamics of this measure using Bayesian inference, and identify sudden shifts in mobility patterns by monitoring the divergence between the short and long-term estimates. By estimating long-term dynamics, the method is also able to adapt to long-term trends in data. As a proof-of-concept, we apply this approach in a vehicular traffic scenario, where we demonstrate that the method can detect traffic accidents and distinguish these from regular events, such as traffic congestions
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
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