Skeleton-Based Edge Bundling for Graph Visualization

Ersoy, Ozan; Hurter, Christophe; Paulovich, Fernando V.; Cantareiro, G.; Telea, Alexandru

doi:10.1109/tvcg.2011.233

Cited by 148 publications

(183 citation statements)

References 25 publications

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“…Such approaches have gained considerable research attention recently, with a number of following techniques based on the same idea. Latest work, such as [10,12,27], provides a comprehensive list of such methods. A recent paper by Riche et al [28] includes a detailed discussion on the design space of curved edges in node-link diagrams.…”

Section: Graph Visualization With Curved Edgesmentioning

confidence: 99%

A User Study on Curved Edges in Graph Visualization

Rooney

Passmore

et al. 2012

IEEE Trans. Visual. Comput. Graphics

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Abstract-Recently there has been increasing research interest in displaying graphs with curved edges to produce more readable visualizations. While there are several automatic techniques, little has been done to evaluate their effectiveness empirically. In this paper we present two experiments studying the impact of edge curvature on graph readability. The goal is to understand the advantages and disadvantages of using curved edges for common graph tasks compared to straight line segments, which are the conventional choice for showing edges in node-link diagrams. We included several edge variations: straight edges, edges with different curvature levels, and mixed straight and curved edges. During the experiments, participants were asked to complete network tasks including determination of connectivity, shortest path, node degree, and common neighbors. We also asked the participants to provide subjective ratings of the aesthetics of different edge types. The results show significant performance differences between the straight and curved edges and clear distinctions between variations of curved edges.

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Section: Graph Visualization With Curved Edgesmentioning

confidence: 99%

A User Study on Curved Edges in Graph Visualization

Rooney

Passmore

et al. 2012

IEEE Trans. Visual. Comput. Graphics

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“…Another approach is visual simplification by edge bundling (e.g., [25]), i.e., merging of spatially close flows and representing them by branching lines. On a geographic map, this works well only for showing flows from one or two locations or in special cases, e.g., when radial flows from/to one location prevail over all others, as the flights between Paris and other cities in France [25].…”

Section: Linking Origins To Destinationsmentioning

confidence: 99%

Visual Analytics of Mobility and Transportation: State of the Art and Further Research Directions

Andrienko

Chen

Maciejewski

et al. 2017

IEEE Trans. Intell. Transport. Syst.

118

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This is the accepted version of the paper.This version of the publication may differ from the final published version. City Research Online  Abstract-Many cities and countries are now striving to create intelligent transportation systems that utilize the current abundance of multisource and multiform data related to the functionality and use of transportation infrastructure to better support human mobility, interests, and lifestyles. Such intelligent transportation systems aim to provide novel services that can enable transportation consumers and managers to be better informed and make safer and more efficient use of the infrastructure. However, the transportation domain is characterized by both complex data and complex problems, which calls for visual analytics approaches. The science of visual analytics is continuing to develop principles, methods, and tools to enable synergistic work between humans and computers through interactive visual interfaces. Such interfaces support the unique capabilities of humans (such as the flexible application of prior knowledge and experiences, creative thinking, and insight) and couple these abilities with machines' computational strengths, enabling the generation of new knowledge from large and complex data. In this paper, we describe recent developments in visual analytics that are related to the study of movement and transportation systems and discuss how visual analytics can enable and improve the intelligent transportation systems of the future. We provide a survey of literature from the visual analytics domain and organize the survey with respect to different types of transportation data, movement and its relationship to infrastructure and behavior, and modeling and planning. We conclude with lessons learned and future directions including social transportation, recommender systems, and policy implications. Permanent repository link

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“…Obviously, these approaches may hide possibly relevant information. Another approach is edge bundling, i.e., merging or grouping spatially close flows [16,22,33,54]. The edge bundling approaches work well only for showing flows from one origin or to one destination, or in special cases, such as prevalence of radial movements from and to one central location [22].…”

Section: Related Workmentioning

confidence: 99%

MobilityGraphs: Visual Analysis of Mass Mobility Dynamics via Spatio-Temporal Graphs and Clustering

Landesberger

Brodkorb

Roskosch

et al. 2016

IEEE Trans. Visual. Comput. Graphics

188

115

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Fig. 1. Hourly flows of residential Twitter users in Greater London area over a week are spatially and temporally simplified to gain an overview of mobility dynamics. A calendar view shows the temporal cluster distribution over a week. Each map displays movements between spatial aggregates in a temporal cluster. Movement direction is represented by a color gradient from dark to light blue.Abstract-Learning more about people mobility is an important task for official decision makers and urban planners. Mobility data sets characterize the variation of the presence of people in different places over time as well as movements (or flows) of people between the places. The analysis of mobility data is challenging due to the need to analyze and compare spatial situations (i.e., presence and flows of people at certain time intervals) and to gain an understanding of the spatio-temporal changes (variations of situations over time). Traditional flow visualizations usually fail due to massive clutter. Modern approaches offer limited support for investigating the complex variation of the movements over longer time periods. We propose a visual analytics methodology that solves these issues by combined spatial and temporal simplifications. We have developed a graph-based method, called MobilityGraphs, which reveals movement patterns that were occluded in flow maps. Our method enables the visual representation of the spatio-temporal variation of movements for long time series of spatial situations originally containing a large number of intersecting flows. The interactive system supports data exploration from various perspectives and at various levels of detail by interactive setting of clustering parameters. The feasibility our approach was tested on aggregated mobility data derived from a set of geolocated Twitter posts within the Greater London city area and mobile phone call data records in Abidjan, Ivory Coast. We could show that MobilityGraphs support the identification of regular daily and weekly movement patterns of resident population.

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Skeleton-Based Edge Bundling for Graph Visualization

Cited by 148 publications

References 25 publications

A User Study on Curved Edges in Graph Visualization

A User Study on Curved Edges in Graph Visualization

Visual Analytics of Mobility and Transportation: State of the Art and Further Research Directions

MobilityGraphs: Visual Analysis of Mass Mobility Dynamics via Spatio-Temporal Graphs and Clustering

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