Bundled Visualization of DynamicGraph and Trail Data

Hurter, Christophe; Ersoy, Ozan; Fabrikant, Sara Irina; Klein, Tijmen; Telea, Alexandru

doi:10.1109/tvcg.2013.246

Cited by 70 publications

(88 citation statements)

References 54 publications

(102 reference statements)

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“…Here, δ is a distance metric between R d curves, e.g., Hausdorff distance [19]; and κ reflects the dissimilarity of two paths γ i and γ j , which always considers the distance δ between paths, but can also consider path type, weights, timestamps, or directions [31,63]. Simply put, Eqn.…”

Section: Notations and Related Workmentioning

confidence: 99%

Functional Decomposition for Bundled Simplification of Trail Sets

Hurter

Puechmorel

Nicol

et al. 2018

IEEE Trans. Visual. Comput. Graphics

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Abstract-Bundling visually aggregates curves to reduce clutter and help finding important patterns in trail-sets or graph drawings. We propose a new approach to bundling based on functional decomposition of the underling dataset. We recover the functional nature of the curves by representing them as linear combinations of piecewise-polynomial basis functions with associated expansion coefficients. Next, we express all curves in a given cluster in terms of a centroid curve and a complementary term, via a set of so-called principal component functions. Based on the above, we propose a two-fold contribution: First, we use cluster centroids to design a new bundling method for 2D and 3D curve-sets. Secondly, we deform the cluster centroids and generate new curves along them, which enables us to modify the underlying data in a statistically-controlled way via its simplified (bundled) view. We demonstrate our method by applications on real-world 2D and 3D datasets for graph bundling, trajectory analysis, and vector field and tensor field visualization.

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Section: Notations and Related Workmentioning

confidence: 99%

Functional Decomposition for Bundled Simplification of Trail Sets

Hurter

Puechmorel

Nicol

et al. 2018

IEEE Trans. Visual. Comput. Graphics

Self Cite

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“…This problem is especially severe when many time moments or large/dense graphs need to be analyzed. Approaches mitigating this problem include reducing the number of time steps and highlighting of changes between consecutive graphs [9], usage of smooth edge bundling [34], drawing of clustered graphs or filtered graphs [1,24]. These approaches still suffer from animation perception problems.…”

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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“…Using bundling algorithm, flow lines which are close to each other, are rerouted to form a bundle (Fig. 3.) (Boyandin 2013) (Hurter et al 2014); • Representations, for example, animation of movements over time (Fig. 4.…”

Section: Data Complexity and Visual Cluttermentioning

confidence: 99%

Facilitating insights with a user adaptable dashboard, illustrated by airport connectivity data

Dobraja

Kraak

Engelhardt

2018

Proc. Int. Cartogr. Assoc.

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Since the movement data exist, there have been approaches to collect and analyze them to get insights. This kind of data is often heterogeneous, multiscale and multi-temporal. Those interested in spatio-temporal patterns of movement data do not gain insights from textual descriptions. Therefore, visualization is required. As spatio-temporal movement data can be complex because size and characteristics, it is even challenging to create an overview of it. Plotting all the data on the screen will not be the solution as it likely will result into cluttered images where no data exploration is possible. To ensure that users will receive the information they are interested in, it is important to provide a graphical data representation environment where exploration to gain insights are possible not only in the overall level but at sub-levels as well. A dashboard would be a solution the representation of heterogeneous spatio-temporal data. It provides an overview and helps to unravel the complexity of data by splitting data in multiple data representation views. The adaptability of dashboard will help to reveal the information which cannot be seen in the overview.

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Bundled Visualization of DynamicGraph and Trail Data

Cited by 70 publications

References 54 publications

Functional Decomposition for Bundled Simplification of Trail Sets

Functional Decomposition for Bundled Simplification of Trail Sets

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

Facilitating insights with a user adaptable dashboard, illustrated by airport connectivity data

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