Abstract
In this paper, an overview-based interactive visualization for temporally long dynamic data sequences is described. To reach this goal, each data object at a certain time point can be mapped to a number value based on a given property. Among others, a property is application-dependent and can be number of vertices, number of edges, average degree, density, number of self-loops, degree (maximum and total), or edge weight (minimum, maximum, and total) for dynamic graph data, but it can as well be the number of ball contacts in a football match, or the time-dependent visual attention paid to a stimulus in an eye tracking study. To achieve an overview over time, an aggregation strategy based on either the mean, minimum, or maximum of two values is applied. This temporal value aggregation generates a triangular shape with an overview of the entire data sequence as the peak. The color coding can be adjusted, forming visual patterns that can be rapidly explored for certain data features over time, supporting comparison tasks between the properties. The usefulness of the approach is illustrated by means of applying it to dynamic graphs generated from US domestic flight data as well as to dynamic Covid-19 infections on country levels.Graphic abstract
Highlights
Exploring dynamically changing data is a challenging task due to the fact that the data can consist of many time steps, and each data object at a certain time point can be too complex to be visualized as several timevarying variations
The usefulness of the approach is illustrated by means of applying it to dynamic graphs generated from US domestic flight data as well as to dynamic Covid-19 infections on country levels
Extended data model We extended our data model to fit to general dynamic data which is transformed into time-dependent numbers that represent each data object over time (Sect. 3.1)
Summary
Exploring dynamically changing data is a challenging task due to the fact that the data can consist of many time steps, and each data object at a certain time point can be too complex to be visualized as several timevarying variations. Getting an overview about temporal patterns in such a temporally long data sequence is difficult by traditional visualization techniques, typically trying to show the data structures in its entirety. Visualization techniques can be a more effective and appropriate way to represent the data objects and their changes over time than given numbers that summarize those properties. There is no suitable alternative that shows temporally long data sequences while providing at the same time an overview of the dynamic properties in any of the subsequences. In particular for the field of dynamic graph visualization, there is a list of approaches (Beck et al 2017) that do not provide a more aggregated perspective on those subsequences
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