Comparative Eye Tracking Study on Node-Link Visualizations of Trajectories

Netzel, Rudolf; Burch, Michael; Weiskopf, Daniel

doi:10.1109/tvcg.2014.2346420

Cited by 30 publications

(20 citation statements)

References 28 publications

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“…Most often, readability metrics are evaluated in user studies on task performance subject to different graph visualizations [Pur97,HvW09,HIvWF11]. Comparative eye-tracking experiments are presented by Netzel et al [NBW14] to study the effect of different link representations on the readability and by Burch et al [BKH * 11] to investigate different node-link layout strategies for their effectiveness. As a result, particular visualizations are considered more effective than others.…”

Section: Summary Of Approachesmentioning

confidence: 99%

Quality Metrics for Information Visualization

Behrisch

Blumenschein

Kim

et al. 2018

Computer Graphics Forum

116

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The visualization community has developed to date many intuitions and understandings of how to judge the quality of views in visualizing data. The computation of a visualization's quality and usefulness ranges from measuring clutter and overlap, up to the existence and perception of specific (visual) patterns. This survey attempts to report, categorize and unify the diverse understandings and aims to establish a common vocabulary that will enable a wide audience to understand their differences and subtleties. For this purpose, we present a commonly applicable quality metric formalization that should detail and relate all constituting parts of a quality metric. We organize our corpus of reviewed research papers along the data types established in the information visualization community: multi-and high-dimensional, relational, sequential, geospatial and text data. For each data type, we select the visualization subdomains in which quality metrics are an active research field and report their findings, reason on the underlying concepts, describe goals and outline the constraints and requirements. One central goal of this survey is to provide guidance on future research opportunities for the field and outline how different visualization communities could benefit from each other by applying or transferring knowledge to their respective subdomain. Additionally, we aim to motivate the visualization community to compare computed measures to the perception of humans.

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Section: Summary Of Approachesmentioning

confidence: 99%

Quality Metrics for Information Visualization

Behrisch

Blumenschein

Kim

et al. 2018

Computer Graphics Forum

116

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“…Blascheck et al [36] proposed an approach to explore eye movements in node-link graphs with different layouts, and to generate and adjust node-link graphs with eye movements data. Netzel et al [37] used eye tracking to compare the visualization of node-link trajectories between different ways to represent link types, nodes, and depth sorting for overlaps. However, they did not test the influence of the shape, color, and size of flows.…”

Section: Related Workmentioning

confidence: 99%

Using Eye Tracking to Evaluate the Usability of Flow Maps

Dong

Wang

Chen

et al. 2018

IJGI

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Flow maps allow users to perceive not only the location where interactions take place, but also the direction and volume of events. Previous studies have proposed numerous methods to produce flow maps. However, how to evaluate the usability of flow maps has not been well documented. In this study, we combined eye-tracking and questionnaire methods to evaluate the usability of flow maps through comparisons between (a) straight lines and curves and (b) line thicknesses and color gradients. The results show that curved flows are more effective than straight flows. Maps with curved flows have more correct answers, fixations, and percentages of fixations in areas of interest. Furthermore, we find that the curved flows require longer finish times but exhibit smaller times to first fixation than straight flows. In addition, we find that using color gradients to indicate the flow volume is significantly more effective than the application of different line thicknesses, which is mainly reflected by the presence of more correct answers in the color-gradient group. These empirical studies could help improve the usability of flow maps employed to visualize geo-data.

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“…Therefore, measurements can be used to drive a realistic emulation of data. For example, a Markov chain model produces synthetic trajectories from real-world movement GPS data from birds [79] that can be used to assess different visual direction encodings of trajectories.…”

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confidence: 99%

Generative Data Models for Validation and Evaluation of Visualization Techniques

Schulz

Nocaj

El‐Assady

et al. 2016

Proceedings of the Sixth Workshop on Beyond Time and Errors on Novel Evaluation Methods for Visualization

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We argue that there is a need for substantially more research on the use of generative data models in the validation and evaluation of visualization techniques. For example, user studies will require the display of representative and unconfounded visual stimuli, while algorithms will need functional coverage and assessable benchmarks. However, data is often collected in a semi-automatic fashion or entirely hand-picked, which obscures the view of generality, impairs availability, and potentially violates privacy. There are some sub-domains of visualization that use synthetic data in the sense of generative data models, whereas others work with real-world-based data sets and simulations. Depending on the visualization domain, many generative data models are "side projects" as part of an ad-hoc validation of a techniques paper and thus neither reusable nor general-purpose. We review existing work on popular data collections and generative data models in visualization to discuss the opportunities and consequences for technique validation, evaluation, and experiment design. We distill handling and future directions, and discuss how we can engineer generative data models and how visualization research could benefit from more and better use of generative data models.

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Comparative Eye Tracking Study on Node-Link Visualizations of Trajectories

Cited by 30 publications

References 28 publications

Quality Metrics for Information Visualization

Quality Metrics for Information Visualization

Using Eye Tracking to Evaluate the Usability of Flow Maps

Generative Data Models for Validation and Evaluation of Visualization Techniques

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