234 research outputs found
Information filtering in complex weighted networks
Many systems in nature, society and technology can be described as networks,
where the vertices are the system's elements and edges between vertices
indicate the interactions between the corresponding elements. Edges may be
weighted if the interaction strength is measurable. However, the full network
information is often redundant because tools and techniques from network
analysis do not work or become very inefficient if the network is too dense and
some weights may just reflect measurement errors, and shall be discarded.
Moreover, since weight distributions in many complex weighted networks are
broad, most of the weight is concentrated among a small fraction of all edges.
It is then crucial to properly detect relevant edges. Simple thresholding would
leave only the largest weights, disrupting the multiscale structure of the
system, which is at the basis of the structure of complex networks, and ought
to be kept. In this paper we propose a weight filtering technique based on a
global null model (GloSS filter), keeping both the weight distribution and the
full topological structure of the network. The method correctly quantifies the
statistical significance of weights assigned independently to the edges from a
given distribution. Applications to real networks reveal that the GloSS filter
is indeed able to identify relevantconnections between vertices.Comment: 9 pages, 7 figures, 1 Table. The GloSS filter is implemented in a
freely downloadable software (http://filrad.homelinux.org/resources
Systematic comparison of trip distribution laws and models
Trip distribution laws are basic for the travel demand characterization
needed in transport and urban planning. Several approaches have been considered
in the last years. One of them is the so-called gravity law, in which the
number of trips is assumed to be related to the population at origin and
destination and to decrease with the distance. The mathematical expression of
this law resembles Newton's law of gravity, which explains its name. Another
popular approach is inspired by the theory of intervening opportunities which
argues that the distance has no effect on the destination choice, playing only
the role of a surrogate for the number of intervening opportunities between
them. In this paper, we perform a thorough comparison between these two
approaches in their ability at estimating commuting flows by testing them
against empirical trip data at different scales and coming from different
countries. Different versions of the gravity and the intervening opportunities
laws, including the recently proposed radiation law, are used to estimate the
probability that an individual has to commute from one unit to another, called
trip distribution law. Based on these probability distribution laws, the
commuting networks are simulated with different trip distribution models. We
show that the gravity law performs better than the intervening opportunities
laws to estimate the commuting flows, to preserve the structure of the network
and to fit the commuting distance distribution although it fails at predicting
commuting flows at large distances. Finally, we show that the different
approaches can be used in the absence of detailed data for calibration since
their only parameter depends only on the scale of the geographic unit.Comment: 15 pages, 10 figure
Data-driven modeling of systemic delay propagation under severe meteorological conditions
The upsetting consequences of weather conditions are well known to any person
involved in air transportation. Still the quantification of how these
disturbances affect delay propagation and the effectiveness of managers and
pilots interventions to prevent possible large-scale system failures needs
further attention. In this work, we employ an agent-based data-driven model
developed using real flight performance registers for the entire US airport
network and focus on the events occurring on October 27 2010 in the United
States. A major storm complex that was later called the 2010 Superstorm took
place that day. Our model correctly reproduces the evolution of the
delay-spreading dynamics. By considering different intervention measures, we
can even improve the model predictions getting closer to the real delay data.
Our model can thus be of help to managers as a tool to assess different
intervention measures in order to diminish the impact of disruptive conditions
in the air transport system.Comment: 9 pages, 5 figures. Tenth USA/Europe Air Traffic Management Research
and Development Seminar (ATM2013
Tweets on the road
The pervasiveness of mobile devices, which is increasing daily, is generating
a vast amount of geo-located data allowing us to gain further insights into
human behaviors. In particular, this new technology enables users to
communicate through mobile social media applications, such as Twitter, anytime
and anywhere. Thus, geo-located tweets offer the possibility to carry out
in-depth studies on human mobility. In this paper, we study the use of Twitter
in transportation by identifying tweets posted from roads and rails in Europe
between September 2012 and November 2013. We compute the percentage of highway
and railway segments covered by tweets in 39 countries. The coverages are very
different from country to country and their variability can be partially
explained by differences in Twitter penetration rates. Still, some of these
differences might be related to cultural factors regarding mobility habits and
interacting socially online. Analyzing particular road sectors, our results
show a positive correlation between the number of tweets on the road and the
Average Annual Daily Traffic on highways in France and in the UK. Transport
modality can be studied with these data as well, for which we discover very
heterogeneous usage patterns across the continent.Comment: 15 pages, 17 figure
Is spatial information in ICT data reliable?
An increasing number of human activities are studied using data produced by
individuals' ICT devices. In particular, when ICT data contain spatial
information, they represent an invaluable source for analyzing urban dynamics.
However, there have been relatively few contributions investigating the
robustness of this type of results against fluctuations of data
characteristics. Here, we present a stability analysis of higher-level
information extracted from mobile phone data passively produced during an
entire year by 9 million individuals in Senegal. We focus on two
information-retrieval tasks: (a) the identification of land use in the region
of Dakar from the temporal rhythms of the communication activity; (b) the
identification of home and work locations of anonymized individuals, which
enable to construct Origin-Destination (OD) matrices of commuting flows. Our
analysis reveal that the uncertainty of results highly depends on the sample
size, the scale and the period of the year at which the data were gathered.
Nevertheless, the spatial distributions of land use computed for different
samples are remarkably robust: on average, we observe more than 75% of shared
surface area between the different spatial partitions when considering activity
of at least 100,000 users whatever the scale. The OD matrix is less stable and
depends on the scale with a share of at least 75% of commuters in common when
considering all types of flows constructed from the home-work locations of
100,000 users. For both tasks, better results can be obtained at larger levels
of aggregation or by considering more users. These results confirm that ICT
data are very useful sources for the spatial analysis of urban systems, but
that their reliability should in general be tested more thoroughly.Comment: 11 pages, 9 figures + Appendix, Extended version of the conference
paper published in the proceedings of the 2016 Spatial Accuracy Conference, p
9-17, Montpellier, Franc
Transport on weighted Networks: when correlations are independent of degree
Most real-world networks are weighted graphs with the weight of the edges
reflecting the relative importance of the connections. In this work, we study
non degree dependent correlations between edge weights, generalizing thus the
correlations beyond the degree dependent case. We propose a simple method to
introduce weight-weight correlations in topologically uncorrelated graphs. This
allows us to test different measures to discriminate between the different
correlation types and to quantify their intensity. We also discuss here the
effect of weight correlations on the transport properties of the networks,
showing that positive correlations dramatically improve transport. Finally, we
give two examples of real-world networks (social and transport graphs) in which
weight-weight correlations are present.Comment: 8 pages, 8 figure
Mapping the Americanization of English in Space and Time
As global political preeminence gradually shifted from the United Kingdom to
the United States, so did the capacity to culturally influence the rest of the
world. In this work, we analyze how the world-wide varieties of written English
are evolving. We study both the spatial and temporal variations of vocabulary
and spelling of English using a large corpus of geolocated tweets and the
Google Books datasets corresponding to books published in the US and the UK.
The advantage of our approach is that we can address both standard written
language (Google Books) and the more colloquial forms of microblogging messages
(Twitter). We find that American English is the dominant form of English
outside the UK and that its influence is felt even within the UK borders.
Finally, we analyze how this trend has evolved over time and the impact that
some cultural events have had in shaping it.Comment: 16 pages, 6 figures, 2 tables. Published versio
Crowdsourcing the Robin Hood effect in cities
Socioeconomic inequalities in cities are embedded in space and result in
neighborhood effects, whose harmful consequences have proved very hard to
counterbalance efficiently by planning policies alone. Considering
redistribution of money flows as a first step toward improved spatial equity,
we study a bottom-up approach that would rely on a slight evolution of shopping
mobility practices. Building on a database of anonymized credit card
transactions in Madrid and Barcelona, we quantify the mobility effort required
to reach a reference situation where commercial income is evenly shared among
neighborhoods. The redirections of shopping trips preserve key properties of
human mobility, including travel distances. Surprisingly, for both cities only
a small fraction () of trips need to be altered to reach equity
situations, improving even other sustainability indicators. The method could be
implemented in mobile applications that would assist individuals in reshaping
their shopping practices, to promote the spatial redistribution of
opportunities in the city.Comment: 9 pages, 4 figures + Appendi
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