59 research outputs found
Improved Bounds on Information Dissemination by Manhattan Random Waypoint Model
With the popularity of portable wireless devices it is important to model and
predict how information or contagions spread by natural human mobility -- for
understanding the spreading of deadly infectious diseases and for improving
delay tolerant communication schemes. Formally, we model this problem by
considering moving agents, where each agent initially carries a
\emph{distinct} bit of information. When two agents are at the same location or
in close proximity to one another, they share all their information with each
other. We would like to know the time it takes until all bits of information
reach all agents, called the \textit{flood time}, and how it depends on the way
agents move, the size and shape of the network and the number of agents moving
in the network.
We provide rigorous analysis for the \MRWP model (which takes paths with
minimum number of turns), a convenient model used previously to analyze mobile
agents, and find that with high probability the flood time is bounded by
, where agents move on an
grid. In addition to extensive simulations, we use a data set of
taxi trajectories to show that our method can successfully predict flood times
in both experimental settings and the real world.Comment: 10 pages, ACM SIGSPATIAL 2018, Seattle, U
From mobile phone data to the spatial structure of cities
Pervasive infrastructures, such as cell phone networks, enable to capture
large amounts of human behavioral data but also provide information about the
structure of cities and their dynamical properties. In this article, we focus
on these last aspects by studying phone data recorded during 55 days in 31
Spanish metropolitan areas. We first define an urban dilatation index which
measures how the average distance between individuals evolves during the day,
allowing us to highlight different types of city structure. We then focus on
hotspots, the most crowded places in the city. We propose a parameter free
method to detect them and to test the robustness of our results. The number of
these hotspots scales sublinearly with the population size, a result in
agreement with previous theoretical arguments and measures on employment
datasets. We study the lifetime of these hotspots and show in particular that
the hierarchy of permanent ones, which constitute the "heart" of the city, is
very stable whatever the size of the city. The spatial structure of these
hotspots is also of interest and allows us to distinguish different categories
of cities, from monocentric and "segregated" where the spatial distribution is
very dependent on land use, to polycentric where the spatial mixing between
land uses is much more important. These results point towards the possibility
of a new, quantitative classification of cities using high resolution
spatio-temporal data.Comment: 14 pages, 15 figure
Uncovering the spatial structure of mobility networks
The extraction of a clear and simple footprint of the structure of large,
weighted and directed networks is a general problem that has many applications.
An important example is given by origin-destination matrices which contain the
complete information on commuting flows, but are difficult to analyze and
compare. We propose here a versatile method which extracts a coarse-grained
signature of mobility networks, under the form of a matrix that
separates the flows into four categories. We apply this method to
origin-destination matrices extracted from mobile phone data recorded in
thirty-one Spanish cities. We show that these cities essentially differ by
their proportion of two types of flows: integrated (between residential and
employment hotspots) and random flows, whose importance increases with city
size. Finally the method allows to determine categories of networks, and in the
mobility case to classify cities according to their commuting structure.Comment: 10 pages, 5 figures +Supplementary informatio
Flooding through the lens of mobile phone activity
Natural disasters affect hundreds of millions of people worldwide every year.
Emergency response efforts depend upon the availability of timely information,
such as information concerning the movements of affected populations. The
analysis of aggregated and anonymized Call Detail Records (CDR) captured from
the mobile phone infrastructure provides new possibilities to characterize
human behavior during critical events. In this work, we investigate the
viability of using CDR data combined with other sources of information to
characterize the floods that occurred in Tabasco, Mexico in 2009. An impact map
has been reconstructed using Landsat-7 images to identify the floods. Within
this frame, the underlying communication activity signals in the CDR data have
been analyzed and compared against rainfall levels extracted from data of the
NASA-TRMM project. The variations in the number of active phones connected to
each cell tower reveal abnormal activity patterns in the most affected
locations during and after the floods that could be used as signatures of the
floods - both in terms of infrastructure impact assessment and population
information awareness. The representativeness of the analysis has been assessed
using census data and civil protection records. While a more extensive
validation is required, these early results suggest high potential in using
cell tower activity information to improve early warning and emergency
management mechanisms.Comment: Submitted to IEEE Global Humanitarian Technologies Conference (GHTC)
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