Modalflow: Cross-Origin Flow Data Visualization for Urban Mobility

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dc.contributor.authorPérez Messina, Ignacio
dc.contributor.authorGraells-Garrido, Eduardo
dc.contributor.authorLobo, Maria
dc.contributor.authorHurter, Christophe
dc.date.accessioned2021-08-19T20:10:53Z
dc.date.available2021-08-19T20:10:53Z
dc.date.issued2020
dc.description.abstractPervasive 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.es
dc.identifier.citationAlgorithms, MDPI, 2020, 13 (11), pp.298es
dc.identifier.urihttps://dx.doi.org/10.3390/a13110298es
dc.identifier.urihttp://hdl.handle.net/11447/4370
dc.language.isoenes
dc.subjectInformation visualizationes
dc.subjectFlow dataes
dc.subjectUrban mobilityes
dc.subjectMobile phone dataes
dc.subjectPervasive dataes
dc.titleModalflow: Cross-Origin Flow Data Visualization for Urban Mobilityes
dc.typeArticlees

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