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Modalflow: Cross-Origin Flow Data Visualization for Urban Mobility

Abstract : 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.
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Soumis le : samedi 28 novembre 2020 - 10:47:49
Dernière modification le : vendredi 14 janvier 2022 - 03:42:10


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Ignacio Pérez-Messina, Eduardo Graells-Garrido, Maria Lobo, Christophe Hurter. Modalflow: Cross-Origin Flow Data Visualization for Urban Mobility. Algorithms, 2020, 13 (11), pp.298. ⟨10.3390/a13110298⟩. ⟨hal-03029305⟩



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