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Modalflow: Cross-Origin Flow Data Visualization for Urban Mobility

Data Science Institute, Universidad del Desarrollo, 7610658 Las Condes, Chile
Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain
LASTIG, Univ Gustave Eiffel, ENSG, IGN, 94165 Saint-Mande, France
ENAC, University of Toulouse, 31062 Toulouse, France
Author to whom correspondence should be addressed.
Current address: Av. Plaza 680, 7610658 Las Condes, Chile.
Algorithms 2020, 13(11), 298;
Received: 14 October 2020 / Revised: 10 November 2020 / Accepted: 11 November 2020 / Published: 15 November 2020
(This article belongs to the Special Issue Graph Drawing and Information Visualization)
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. View Full-Text
Keywords: information visualization; flow data; urban mobility; mobile phone data; pervasive data information visualization; flow data; urban mobility; mobile phone data; pervasive data
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MDPI and ACS Style

Pérez-Messina, I.; Graells-Garrido, E.; Lobo, M.J.; Hurter, C. Modalflow: Cross-Origin Flow Data Visualization for Urban Mobility. Algorithms 2020, 13, 298.

AMA Style

Pérez-Messina I, Graells-Garrido E, Lobo MJ, Hurter C. Modalflow: Cross-Origin Flow Data Visualization for Urban Mobility. Algorithms. 2020; 13(11):298.

Chicago/Turabian Style

Pérez-Messina, Ignacio, Eduardo Graells-Garrido, María J. Lobo, and Christophe Hurter. 2020. "Modalflow: Cross-Origin Flow Data Visualization for Urban Mobility" Algorithms 13, no. 11: 298.

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