Polygon vector map distortion for increasing the readability of one-to-many flow maps

AbstractAbstractCartographers have long been interested in the representation of various movements such as migration, commercial exchanges and transportation. There are several techniques for visualizing this information; this paper focuses on flow mapping. A flow map shows a set of movements through line symbols connecting an origin to a destination. Each link is associated with a value that corresponds to the volume of the movement. However, once data reach a certain volume, the maps quickly become cluttered and can be difficult to read and understand. Moreover, the values of the movements must be correctly represented to avoid inducing biased interpretations. The objective of this paper is to create flow maps displaying flows of highly variable thicknesses so that the associated values are correctly represented. The technique used to create the flow paths does not create crossings between flows. In order to remove any visual clutter, such as overlaps between flows and geographic features, some areas of the map are distorted. In other words, our method of map distortion adapts the polygon vector base map to the flows, the central information of the visualization, and not the other way around.Keywords: Geovisualizationone-to-many flow mapmap distortiongeospatial networkautomated cartography Data and codes availability statementThe data and codes that allowed us to obtain the results presented in this paper are available in figshare at the permanent link https://doi.org/10.6084/m9.figshare.21501999.Additional informationFundingThis study was partially funded by EU [grant 874850 MOOD] and is catalogued as MOOD 065. The contents of this publication are the sole responsibility of the authors and not necessarily reflect the views of the European Commission. This work has also been supported by the STIC-AmSud program, code: 21-STIC-06.Notes on contributorsLaëtitia ViauLaëtitia Viau is a PhD student at the Université de Montpellier, France. She is a member of the data mining and visualization research team (ADVANSE) at the Laboratory of Computer Science, Robotics and Microelectronics of Montpellier (LIRMM). Her research interests include geospatial data visualization and visual analytics.Arnaud SallaberryArnaud Sallaberry is an Assistant Professor at the Université Paul Valéry Montpellier 3, France. He is the head of the applied mathematics and computer science research team (AMIS). He is also a member of the data mining and visualization research team (ADVANSE) at the Laboratory of Computer Science, Robotics and Microelectronics of Montpellier (LIRMM). His research interests include information visualization and visual analytics. He received his PhD in computer science from the Université de Bordeaux, France.Nancy RodriguezNancy Rodriguez is an Assistant Professor at the Université de Montpellier, France. She is a member of the data mining and visualization research team (ADVANSE) at the Laboratory of Computer Science, Robotics and Microelectronics of Montpellier (LIRMM). Her research interests include virtual and augmented/mixed reality, interaction, accessibility and visualization. She received her PhD in Computer Science from the Université de Toulouse, France.Jean-François GirresJean-François Girres is an Assistant Professor at the Université Paul Valéry Montpellier 3, France. He is a member of the applied mathematics and computer science research team (AMIS) and is also a member of the ESPACE-DEV research unit. His research interests include spatial data quality, spatial analysis and geovisualization. He received his PhD in Geographic Information Science from the Université Paris-Est, France.Pascal PonceletPascal Poncelet is a Full Professor at the Université de Montpellier, France, and head of the data mining and visualization research team (ADVANSE) at the Laboratory of Computer Science, Robotics and Microelectronics of Montpellier (LIRMM). His research interests include advanced data analysis techniques for emerging applications, data mining, and visual analytics. He received his PhD in Computer Science from the Université de Nice-Sophia Antipolis, France.Notes1 We set ϵ=0.2. Empirically, this value was the most effective in moderating the distortion and avoiding the creation of non-simple polygons.2 The Computational Geometry Algorithms Library (CGAL): https://www.cgal.org (accessed: 2022-09)3 When using the FMD method, we recommend performing this last step after the first distortion, just before drawing the curves, because these nodes, which are not significant in terms of appearance, can be significant for the first distortion step. On the other hand, if the objective is just to create a one-to-many flow map without distortion, then this last step can take place in the order described before drawing the curves and thicknesses of the flows like in Figure 5(d).4 https://www.fao.org/faostat/en/#data/TM (accessed: 2022-11)5 The times indicated are the average of 10 different runs.