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Human Mobility Characterization from Cellular Network Data
Richard Becker, Ramon Caceres, Karrie Hanson, Sibren Isaacman, Ji Loh, Margaret Martonosi, James Rowland, Simon Urbanek, Alexander Varshavsky, Christopher Volinsky
Communications of the ACM,
2013.
[PDF]
[BIB]
ACM Copyright
(c) ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Communications of the ACM , Volume 56, Issue 1, 2013-01-01.
{Characterizing human mobility patterns is critical to a deeper understanding of the effects of people’s travel on society and the environment. Location data from cellular telephone networks can shed light on human movements cheaply, frequently, and on a large scale. We have developed techniques for analyzing anonymized cellphone locations to explore various aspects of human mobility, in particular for hundreds of thousands of people in each of the Los Angeles, San Francisco, and New York metropolitan areas. Our results include measures of how far people travel every day, estimates of carbon footprints due to home-to-work commutes, maps of the residential areas that contribute workers to a city, and relative traffic volumes on commuting routes. We have validated the accuracy of our techniques through comparisons against ground truth provided by volunteers and against independent sources such as the US Census Bureau. Throughout our work, we have taken measures to preserve the privacy of cellphone users. This article presents an overview of our methodologies and findings.}
Human Mobility Modeling at Metropolitan Scales
Sibren Isaacman, Richard Becker, Ramon Caceres, Margaret Martonosi, James Rowland, Alexander Varshavsky, Walter Willinger
10th ACM International Conference on Mobile Systems, Applications and Services (MobiSys 2012),
2012.
[PDF]
[BIB]
ACM Copyright
(c) ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in [10th ACM International Conference on Mobile Systems, Applications and Services (MobiSys 2012)] , 2012-06-26.
{Models of human mobility have broad applicability in fields such as mobile computing, urban planning, and ecology. This paper proposes and evaluates WHERE, a novel approach to modeling how large populations move within different metropolitan areas. WHERE takes as input spatial and temporal probability distributions drawn from empirical data, such as Call Detail Records (CDRs) from a cellular telephone network, and produces synthetic CDRs for a synthetic population. We have validated WHERE against billions of anonymous location samples for hundreds of thousands of phones in the New York and Los Angeles metropolitan areas. We found that WHERE offers significantly higher fidelity than other modeling approaches. For example, daily range of travel statistics fall within one mile of their true values, an improvement of more than 14 times over a Weighted Random Waypoint model. Our modeling techniques and synthetic CDRs can be applied to a wide range of problems while avoiding many of the privacy concerns surround- ing real CDRs.}
Exploring the Use of Urban Greenspace through Cellular Network Activity
Ramon Caceres, James Rowland, Christopher Small, Simon Urbanek
2nd Workshop on Pervasive Urban Applications (PURBA),
2012.
[PDF]
[BIB]
Springer Copyright
The definitive version was published in 2012 , 2012-06-19
{Knowing when and where people use greenspace is key to our understanding of urban ecology. The number of cellular phones active in a geographic area can serve as a proxy for human density in that area. We are using anonymous records of cellular network activity to study the spatiotemporal patterns of human density in an urban area. This paper presents the vision and some early results of this effort. First, we describe our dataset of six months of activity in the New York metropolitan area. Second, we present a novel technique for estimating network coverage areas. Third, we describe our approach to analyzing changes in activity volumes within those areas. Finally, we present preliminary results regarding changes in human density around Central Park. From winter to summer, we find that density increases in greenspace areas and decreases in residential areas.}
The Connected States of America: Quantifying Social Radii of Influence
Francesco Calabrese, Dominik Dahlem, Alexandre Gerber, Deirdre Paul, Xiaoji Chen, James Rowland, Christopher Rath, Carlo Ratti
in Proc. of IEEE International Conference on Social Computing (SocialCom),
2011.
[PDF]
[BIB]
IEEE Copyright
This version of the work is reprinted here with permission of IEEE for your personal use. Not for redistribution. The definitive version was published in in Proc. of IEEE International Conference on Social Computing (SocialCom). , 2011-10-09
{Human dynamics are inextricably intertwined with
the social, geographical and economic environment. The continuous
flux of people communicating as well as migrating,
commuting, and traveling inevitably spans acquaintances across
geographic space that is far from random and exhibits regular
patterns. For instance, it has been shown that the probability of
being acquainted with someone is closely related to the inverse
distance between them. In this paper we investigate aggregated
mobile phone call detail records from a large US cellular operator
and map them into space to characterize the social radius of
influence at two different scales: communication and mobility.
We discover that scaling properties with respect to population
agglomeration are similar to those discovered for other indicators
of cities. We also discover spatial community structures that
are divorced from administrative boundaries, and use them to
quantify the different social radii of influence discovered from
the data.}
Modeling and Characterization of Large-Scale Wi-Fi Traffic in Public Hot-Spots
Amitabha Ghosh, Rittwik Jana, Vaidyanathan Ramaswami, James Rowland, N Shankaranarayanan
Infocom 2011,
2011.
[BIB]
{Server side measurements from several Wi-Fi hotspots
deployed in a nationwide network over different types of
venues from small coffee shops to large enterprises are used to
highlight differences in traffic volumes and patterns. We develop
a common modeling framework for the number of simultaneously
present customers. Our approach has many novel elements: (a)
We combine statistical clustering with Poisson regression from
Generalized Linear Models to fit a non-stationary Poisson process
to the arrival counts and demonstrate its remarkable accuracy;
(b) We model the heavy tailed distribution of connection durations
through fitting a Phase Type distribution to its logarithm
so that not only the tail but also the overall distribution is
well matched; (c) We obtain the distribution of the number
of simultaneously present customers from an Mt/G/infty queuing
model using a novel regenerative argument that is transparent and
avoids the customarily made assumption of the queue starting
empty at an infinite past; (d) Most importantly, we validate
our models by comparison of their predictions and confidence
intervals against test data that is not used in fitting the models.}
