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    Evacuation patterns and socioeconomic stratification in the context of wildfires
    (Springer Science and Business Media LLC, 2025-03-18)
    T. Naushirvanov
    ;
    E. Elejalde
    ;
    K. Kalimeri
    ;
    E. Omodei
    ;
    M. Karsai
      2
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    Scopus© Citations 3  6
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    Scopus© Citations 35  3
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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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    Scopus© Citations 38  3
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    Adoption-Driven Data Science for Transportation Planning: Methodology, Case Study, and Lessons Learned
    (2020)
    Eduardo Graells-Garrido
    ;
    Vanessa Peña-Araya
    ;
    BRAVO CELEDÓN, MARÍA LORETO  
    The rising availability of digital traces provides a fertile ground for data-driven solutions to problems in cities. However, even though a massive data set analyzed with data science methods may provide a powerful and cost-effective solution to a problem, its adoption by relevant stakeholders is not guaranteed due to adoption barriers such as lack of interpretability and interoperability. In this context, this paper proposes a methodology toward bridging two disciplines, data science and transportation, to identify, understand, and solve transportation planning problems with data-driven solutions that are suitable for adoption by urban planners and policy makers. The methodology is defined by four steps where people from both disciplines go from algorithm and model definition to the development of a potentially adoptable solution with evaluated outputs. We describe how this methodology was applied to define a model to infer commuting trips with mode of transportation from mobile phone data, and we report the lessons learned during the process.
    Scopus© Citations 6  3
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    Measuring the local complementarity of population, amenities and digital activities to identify and understand urban areas of interest
    (2022)
    Eduardo Graells-Garrido
    ;
    Rossano Schifanella
    ;
    OPITZ OPITZ, DANIELA PIERINA  
    ;
    Francisco Rowe
    Identifying and understanding areas of interest are essential for urban planning. These areas are normally defined from static features of the resident population and urban amenities. Research has emphasised the importance of human mobility activity to capture the changing nature of these areas throughout the day, and the use of digital applications to reflect the increasing integration between material and online activities. Drawing on mobile phone data, this paper develops a novel approach to identify areas of interest based on the degree of complementarity of digital activities, available amenities and population levels. As a case study, we focus on the largest urban agglomeration of Chile, Santiago, where we identify three distinctive groups of areas: those concentrating (1) high availability of amenities; (2) high diversity of amenities and digital activities; and (3) areas lacking amenities, yet, presenting high usage of digital leisure and mobility applications. These findings identify areas where digital activities and local amenities play a complementary role in association with local population levels, and provide data-driven insights into the structure of material and digital activities in urban spaces that may characterise large Latin American cities.
      3Scopus© Citations 6
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    Measuring Spatial Subdivisions in Urban Mobility with Mobile Phone Data
    (2020)
    Eduardo Graells-garrido
    ;
    Irene Meta
    ;
    Feliu Serra-Buriel
    ;
    Patricio Reyes
    ;
    Fernando M. Cucchietti
    Scopus© Citations 6  1
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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