71 research outputs found
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
Parallel Construction of Wavelet Trees on Multicore Architectures
The wavelet tree has become a very useful data structure to efficiently
represent and query large volumes of data in many different domains, from
bioinformatics to geographic information systems. One problem with wavelet
trees is their construction time. In this paper, we introduce two algorithms
that reduce the time complexity of a wavelet tree's construction by taking
advantage of nowadays ubiquitous multicore machines.
Our first algorithm constructs all the levels of the wavelet in parallel in
time and bits of working space, where
is the size of the input sequence and is the size of the alphabet. Our
second algorithm constructs the wavelet tree in a domain-decomposition fashion,
using our first algorithm in each segment, reaching time and
bits of extra space, where is the
number of available cores. Both algorithms are practical and report good
speedup for large real datasets.Comment: This research has received funding from the European Union's Horizon
2020 research and innovation programme under the Marie Sk{\l}odowska-Curie
Actions H2020-MSCA-RISE-2015 BIRDS GA No. 69094
News and the city: understanding online press consumption patterns through mobile data
The always increasing mobile connectivity affects every aspect of our daily
lives, including how and when we keep ourselves informed and consult news
media. By studying a DPI (deep packet inspection) dataset, provided by one of
the major Chilean telecommunication companies, we investigate how different
cohorts of the population of Santiago De Chile consume news media content
through their smartphones. We find that some socio-demographic attributes are
highly associated to specific news media consumption patterns. In particular,
education and age play a significant role in shaping the consumers behaviour
even in the digital context, in agreement with a large body of literature on
off-line media distribution channels
Evaluation of home detection algorithms on mobile phone data using individual-level ground truth
Inferring mobile phone usersâ home location, i.e., assigning a location in space to a user based on data generated by the mobile phone network, is a central task in leveraging mobile phone data to study social and urban phenomena. Despite its widespread use, home detection relies on assumptions that are difficult to check without ground truth, i.e., where the individual who owns the device resides. In this paper, we present a dataset that comprises the mobile phone activity of sixty-five participants for whom the geographical coordinates of their residence location are known. The mobile phone activity refers to Call Detail Records (CDRs), eXtended Detail Records (XDRs), and Control Plane Records (CPRs), which vary in their temporal granularity and differ in the data generation mechanism. We provide an unprecedented evaluation of the accuracy of home detection algorithms and quantify the amount of data needed for each stream to carry out successful home detection for each stream. Our work is useful for researchers and practitioners to minimize data requests and maximize the accuracy of the home antenna location. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1140/epjds/s13688-021-00284-9
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