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Inferring modes of transportation using mobile phone data
(2018)
Eduardo Graells-Garrido
;
Diego Caro
;
Denis Parra
Scopus© Citations 39
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Toward finding latent cities with non-negative matrix factorization
(2018)
Graells-Garrido, Eduardo
;
Diego Caro
;
Parra, Denis
1
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Evaluation of home detection algorithms on mobile phone data using individual-level ground truth
(2021)
Luca Pappalardo
;
FERRES, LEONARDO ADRIÁN
;
Manuel Sacasa
;
Ciro Cattuto
;
BRAVO CELEDÓN, MARÍA LORETO
Inferring mobile phone users’ home location, i.e., assigning a location in space to a user based on data generated by the mobile phone network, is a central task in leveraging mobile phone data to study social and urban phenomena. Despite its widespread use, home detection relies on assumptions that are difficult to check without ground truth, i.e., where the individual who owns the device resides. In this paper, we present a dataset that comprises the mobile phone activity of sixty-five participants for whom the geographical coordinates of their residence location are known. The mobile phone activity refers to Call Detail Records (CDRs), eXtended Detail Records (XDRs), and Control Plane Records (CPRs), which vary in their temporal granularity and differ in the data generation mechanism. We provide an unprecedented evaluation of the accuracy of home detection algorithms and quantify the amount of data needed for each stream to carry out successful home detection for each stream. Our work is useful for researchers and practitioners to minimize data requests and maximize the accuracy of the home antenna location.
Scopus© Citations 35 3
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Modalflow: Cross-Origin Flow Data Visualization for Urban Mobility
(2020)
Ignacio Pérez-Messina
;
Eduardo Graells-Garrido
;
María Jesús Lobo
;
Christophe Hurter
Pervasive data have become a key source of information for mobility and transportation analyses. However, as a secondary source, it has a different methodological origin than travel survey data, usually relying on unsupervised algorithms, and so it requires to be assessed as a dataset. This assessment is challenging, because, in general, there is not a benchmark dataset or a ground truth scenario available, as travel surveys only represent a partial view of the phenomenon and suffer from their own biases. For this critical task, which involves urban planners and data scientists, we study the design space of the visualization of cross-origin, multivariate flow datasets. For this purpose, we introduce the Modalflow system, which incorporates and adapts different visualization techniques in a notebook-like setting, presenting novel visual encodings and interactions for flows with modal partition into scatterplots, flow maps, origin-destination matrices, and ternary plots. Using this system, we extract general insights on visual analysis of pervasive and survey data for urban mobility and assess a mobile phone network dataset for one metropolitan area.
5Scopus© Citations 5

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