43 research outputs found
Detecting Home Locations from CDR Data: Introducing Spatial Uncertainty to the State-of-the-Art
Non-continuous location traces inferred from Call Detail Records (CDR) at population scale are increasingly becoming available for research and show great potential for automated detection of meaningful places. Yet, a majority of Home Detection Algorithms (HDAs) suffer from âblindâ deployment of criteria to define homes and from limited possibilities for validation. In this paper, we investigate the performance and capabilities of five popular criteria for home detection based on a very large mobile phone dataset from France (~18 million users, 6 months). Furthermore, we construct a data-driven framework to assess the spatial uncertainty related to the application of HDAs. Our findings appropriate spatial uncertainty in HDA and, in extension, for detection of meaningful places. We show how spatial uncertainties on the individualsâ level can be assessed in absence of ground truth annotation, how they relate to traditional, high-level validation practices and how they can be used to improve results for, e.g., nation-wide population estimation
The anatomy of urban social networks and its implications in the searchability problem
The appearance of large geolocated communication datasets has recently
increased our understanding of how social networks relate to their physical
space. However, many recurrently reported properties, such as the spatial
clustering of network communities, have not yet been systematically tested at
different scales. In this work we analyze the social network structure of over
25 million phone users from three countries at three different scales: country,
provinces and cities. We consistently find that this last urban scenario
presents significant differences to common knowledge about social networks.
First, the emergence of a giant component in the network seems to be controlled
by whether or not the network spans over the entire urban border, almost
independently of the population or geographic extension of the city. Second,
urban communities are much less geographically clustered than expected. These
two findings shed new light on the widely-studied searchability in
self-organized networks. By exhaustive simulation of decentralized search
strategies we conclude that urban networks are searchable not through
geographical proximity as their country-wide counterparts, but through an
homophily-driven community structure
A comparison of spatial-based targeted disease mitigation strategies using mobile phone data
Epidemic outbreaks are an important healthcare challenge, especially in developing countries where they represent one of the major causes of mortality. Approaches that can rapidly target subpopulations for surveillance and control are critical for enhancing containment and mitigation processes during epidemics.
Using a real-world dataset from Ivory Coast, this work presents an attempt to unveil the socio-geographical heterogeneity of disease transmission dynamics. By employing a spatially explicit meta-population epidemic model derived from mobile phone Call Detail Records (CDRs), we investigate how the differences in mobility patterns may affect the course of a hypothetical infectious disease outbreak. We consider different existing measures of the spatial dimension of human mobility and interactions, and we analyse their relevance in identifying the highest risk sub-population of individuals, as the best candidates for isolation countermeasures. The approaches presented in this paper provide further evidence that mobile phone data can be effectively exploited to facilitate our understanding of individualsâ spatial behaviour and its relationship with the risk of infectious diseasesâ contagion. In particular, we show that CDRs-based indicators of individualsâ spatial activities and interactions hold promise for gaining insight of contagion heterogeneity and thus for developing mitigation strategies to support decision-making during country-level epidemics
Closer to the total? Long-distance travel of French mobile phone users
Analyzing long-distance travel demand has become increasingly relevant because the share of traffic induced by
journeys related to remote activities which are not part of daily life is growing. In todayâs mobile world, such
journeys are responsible for almost 50 percent of all traffic. Traditionally, surveys have been used to gather data
needed to analyze travel demand. Due to the high response burden and memory issues, respondents are known
to underreport their number of long-distance journeys. The question of the actual number of long-distance
journeys therefore remains unanswered without additional data sources. This paper is the first to quantify the
underreporting of long-distance tour frequencies in travel diaries. We took a sample of mobile phone billing data
covering five months and compared the observed long-distance travel with the results of a national travel survey
covering the same period and the same country. The comparison shows that most of the estimates of the number
of missing tours by researchers have thus been too low. Our work suggests that the actual number of longdistance journeys is twice as high as that reported in surveys. Two different causes of underreporting were
identified. Firstly, soft refusers travelled long distances but reported no long-distance tours. Secondly, respondents underestimated their number of long-distance tours. Consequently, there is a need to use alternative
data sources in order to gain better estimates of long-distance travel demand
Assessing the quality of home detection from mobile phone data for official statistics
Mobile phone data are an interesting new data source for official statistics. However, multiple problems and uncertainties need to be solved before these data can inform, support or even become an integral part of statistical production processes. In this paper, we focus on arguably the most important problem hindering the application of mobile phone data in official statistics: detecting home locations. We argue that current efforts to detect home locations suffer from a blind deployment of criteria to define a place of residence and from limited validation possibilities. We support our argument by analysing the performance of five home detection algorithms (HDAs) that have been applied to a large, French, Call Detailed Record (CDR) dataset (~18 million users, 5 months). Our results show that criteria choice in HDAs influences the detection of home locations for up to about 40% of users, that HDAs perform poorly when compared with a validation dataset (the 35{\deg}-gap), and that their performance is sensitive to the time period and the duration of observation. Based on our findings and experiences, we offer several recommendations for official statistics. If adopted, our recommendations would help in ensuring a more reliable use of mobile phone data vis-\`a-vis official statistics
Comparing Regional Patterns of Individual Movement Using Corrected Mobility Entropy
In this paper, we propose a correction of the Mobility Entropy indicator (ME) used to describe the diversity of individual movement patterns as can be captured by data from mobile phones. We argue that a correction is necessary because standard calculations of ME show a structural dependency on the geographical density of observation points, rendering results biased and comparisons between regions incorrect. As a solution, we propose the Corrected Mobility Entropy (CME). We apply our solution to a French mobile phone dataset with âŒ18.5 million users. Results show CME to be less correlated to cell-tower density (râ=ââ0.17 instead of â0.59 for ME). As a spatial pattern of mobility diversity, we find CME values to be higher in suburban regions compared to their related urban centers, while both decrease considerably with lowering urban center sizes. Based on regression models, we find mobility diversity to relate to factors like income and employment. Additionally, using CME reveals the role of car use in relation to land use, which was not recognized when using ME values. Our solution enables a better description of individual mobility at a large scale, which has applications in official statistics, urban planning and policy, and mobility research
Detecting modules in dense weighted networks with the Potts method
We address the problem of multiresolution module detection in dense weighted
networks, where the modular structure is encoded in the weights rather than
topology. We discuss a weighted version of the q-state Potts method, which was
originally introduced by Reichardt and Bornholdt. This weighted method can be
directly applied to dense networks. We discuss the dependence of the resolution
of the method on its tuning parameter and network properties, using sparse and
dense weighted networks with built-in modules as example cases. Finally, we
apply the method to data on stock price correlations, and show that the
resulting modules correspond well to known structural properties of this
correlation network.Comment: 14 pages, 6 figures. v2: 1 figure added, 1 reference added, minor
changes. v3: 3 references added, minor change
Mass Media Influence Spreading in Social Networks with Community Structure
We study an extension of Axelrod's model for social influence, in which
cultural drift is represented as random perturbations, while mass media are
introduced by means of an external field. In this scenario, we investigate how
the modular structure of social networks affects the propagation of mass media
messages across the society. The community structure of social networks is
represented by coupled random networks, in which two random graphs are
connected by intercommunity links. Considering inhomogeneous mass media fields,
we study the conditions for successful message spreading and find a novel phase
diagram in the multidimensional parameter space. These findings show that
social modularity effects are of paramount importance in order to design
successful, cost-effective advertising campaigns.Comment: 21 pages, 9 figures. To appear in JSTA