5,706 research outputs found
Estimating Attendance From Cellular Network Data
We present a methodology to estimate the number of attendees to events
happening in the city from cellular network data. In this work we used
anonymized Call Detail Records (CDRs) comprising data on where and when users
access the cellular network. Our approach is based on two key ideas: (1) we
identify the network cells associated to the event location. (2) We verify the
attendance of each user, as a measure of whether (s)he generates CDRs during
the event, but not during other times. We evaluate our approach to estimate the
number of attendees to a number of events ranging from football matches in
stadiums to concerts and festivals in open squares. Comparing our results with
the best groundtruth data available, our estimates provide a median error of
less than 15% of the actual number of attendees
Generating demand responsive bus routes from social network data analysis
Acknowledgment The research reflected in this paper has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 770115.Peer reviewedPostprin
Inferring Unusual Crowd Events From Mobile Phone Call Detail Records
The pervasiveness and availability of mobile phone data offer the opportunity
of discovering usable knowledge about crowd behaviors in urban environments.
Cities can leverage such knowledge in order to provide better services (e.g.,
public transport planning, optimized resource allocation) and safer cities.
Call Detail Record (CDR) data represents a practical data source to detect and
monitor unusual events considering the high level of mobile phone penetration,
compared with GPS equipped and open devices. In this paper, we provide a
methodology that is able to detect unusual events from CDR data that typically
has low accuracy in terms of space and time resolution. Moreover, we introduce
a concept of unusual event that involves a large amount of people who expose an
unusual mobility behavior. Our careful consideration of the issues that come
from coarse-grained CDR data ultimately leads to a completely general framework
that can detect unusual crowd events from CDR data effectively and efficiently.
Through extensive experiments on real-world CDR data for a large city in
Africa, we demonstrate that our method can detect unusual events with 16%
higher recall and over 10 times higher precision, compared to state-of-the-art
methods. We implement a visual analytics prototype system to help end users
analyze detected unusual crowd events to best suit different application
scenarios. To the best of our knowledge, this is the first work on the
detection of unusual events from CDR data with considerations of its temporal
and spatial sparseness and distinction between user unusual activities and
daily routines.Comment: 18 pages, 6 figure
Quantifying crowd size with mobile phone and Twitter data
Being able to infer the number of people in a specific area is of extreme importance for the avoidance of crowd disasters and to facilitate emergency evacuations. Here, using a football stadium and an airport as case studies, we present evidence of a strong relationship between the number of people in restricted areas and activity recorded by mobile phone providers and the online service Twitter. Our findings suggest that data generated through our interactions with mobile phone networks and the Internet may allow us to gain valuable measurements of the current state of society
Quantifying crowd size with mobile phone and Twitter data
This is the final published version, also available from The Royal Society via the DOI in this record.Being able to infer the number of people in a specific area is of extreme importance for the avoidance of crowd disasters and to facilitate emergency evacuations. Here, using a football stadium and an airport as case studies, we present evidence of a strong relationship between the number of people in restricted areas and activity recorded by mobile phone providers and the online service Twitter. Our findings suggest that data generated through our interactions with mobile phone networks and the Internet may allow us to gain valuable measurements of the current state of society.Engineering and Physical Sciences Research Council (EPSRC
Insights on the large-scale deployment of a curated Web-of-Trust: the Debian project’s cryptographic keyring
The Debian project is one of the largest free software undertakings worldwide. It is geographically distributed, and participation in the project is done on a voluntary basis, without a single formal employee or directly funded person. As we will explain, due to the nature of the project, its authentication needs are very strict - User/password schemes are way surpassed, and centralized trust management schemes such as PKI are not compatible with its distributed and flat organization; fully decentralized schemes such as the OpenPGP Web of Trust are insufficient by themselves. The Debian project has solved this need by using what we termed a “curated Web of Trust”.
We will explain some lessons learned from a massive key migration process that was triggered in 2014. We will present the social insight we have found from examining the relationships expressed as signatures in this curated Web of Trust, as well as a statistical study and forecast on aging, refreshment and survival of project participants stemming from an analysis on their key’s activity within the keyring
Can co-location be used as a proxy for face-to-face contacts?
Technological advances have led to a strong increase in the number of data
collection efforts aimed at measuring co-presence of individuals at different
spatial resolutions. It is however unclear how much co-presence data can inform
us on actual face-to-face contacts, of particular interest to study the
structure of a population in social groups or for use in data-driven models of
information or epidemic spreading processes. Here, we address this issue by
leveraging data sets containing high resolution face-to-face contacts as well
as a coarser spatial localisation of individuals, both temporally resolved, in
various contexts. The co-presence and the face-to-face contact temporal
networks share a number of structural and statistical features, but the former
is (by definition) much denser than the latter. We thus consider several
down-sampling methods that generate surrogate contact networks from the
co-presence signal and compare them with the real face-to-face data. We show
that these surrogate networks reproduce some features of the real data but are
only partially able to identify the most central nodes of the face-to-face
network. We then address the issue of using such down-sampled co-presence data
in data-driven simulations of epidemic processes, and in identifying efficient
containment strategies. We show that the performance of the various sampling
methods strongly varies depending on context. We discuss the consequences of
our results with respect to data collection strategies and methodologies
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