Task-Based Effectiveness of Basic Visualizations

Saket, Bahador; Endert, Alex; Demiralp, Çağatay

doi:10.1109/tvcg.2018.2829750

Cited by 143 publications

(141 citation statements)

References 31 publications

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“…Spatially upward trends are conventionally associated with increasing values, even when the axes are reverse labelled [33]. Bar graphs facilitate finding clusters, line graphs facilitate finding correlations and scatter plots facilitate finding outliers [41,50]. Visual marks, such as rectangular bars, lines or dots, can support different inferences about data relations based on their geometric properties.…”

Section: Related Workmentioning

confidence: 99%

Illusion of Causality in Visualized Data

Xiong¹,

Shapiro

Hullman³

et al. 2020

IEEE Trans. Visual. Comput. Graphics

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When students eat breakfast very often (more than 4 times a week), their GPA is around 3.5; while when students eat breakfast not very often (less than 4 times a week), their GPA is around 3.0.Number of breakfasts eaten per week GPA Number of breakfasts eaten per week GPA Number of breakfasts eaten per week GPA When students eat breakfast very often (more than 4 times a week), their GPA is around 3.5; while when students eat breakfast not very often (less than 4 times a week), their GPA is around 3.0. Fig. 1. The same data showing the relation between eating breakfast and GPA presented via text, bar graph, line graph or scatter plot. Which depiction makes eating breakfast causing higher GPA seem more plausible to you?Abstract-Students who eat breakfast more frequently tend to have a higher grade point average. From this data, many people might confidently state that a before-school breakfast program would lead to higher grades. This is a reasoning error, because correlation does not necessarily indicate causation -X and Y can be correlated without one directly causing the other. While this error is pervasive, its prevalence might be amplified or mitigated by the way that the data is presented to a viewer. Across three crowdsourced experiments, we examined whether how simple data relations are presented would mitigate this reasoning error. The first experiment tested examples similar to the breakfast-GPA relation, varying in the plausibility of the causal link. We asked participants to rate their level of agreement that the relation was correlated, which they rated appropriately as high. However, participants also expressed high agreement with a causal interpretation of the data. Levels of support for the causal interpretation were not equally strong across visualization types: causality ratings were highest for text descriptions and bar graphs, but weaker for scatter plots. But is this effect driven by bar graphs aggregating data into two groups or by the visual encoding type? We isolated data aggregation versus visual encoding type and examined their individual effect on perceived causality. Overall, different visualization designs afford different cognitive reasoning affordances across the same data. High levels of data aggregation by graphs tend to be associated with higher perceived causality in data. Participants perceived line and dot visual encodings as more causal than bar encodings. Our results demonstrate how some visualization designs trigger stronger causal links while choosing others can help mitigate unwarranted perceptions of causality.

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Section: Related Workmentioning

confidence: 99%

Illusion of Causality in Visualized Data

Xiong¹,

Shapiro

Hullman³

et al. 2020

IEEE Trans. Visual. Comput. Graphics

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show abstract

“…First, when the number of time-series increases the typical superpositioning of lines becomes problematic. Second, choosing an inappropriate aspect ratio, i.e., height to width ratio, influences the orientations of the line segments, thus affecting the visual perception of trends or the accuracy of value judgments [Pal99,SED17].…”

Section: Why Do We Need Quality Metrics For Line Charts?mentioning

confidence: 99%

“…Accordingly, their related analysis tasks can be categorized into discrete comparison and trend assessment [ZT99]. More specifically, Saket et al [SED17] mention (derived) value retrieval, filtering, finding extrema, sorting, distribution characterization, anomaly detection, finding clusters, and spotting of correlations as the typical analysis tasks for Line Charts. Whenever multiple time series are depicted, Javed et al add slope differentiation and discrimination tasks to the list [JME10].…”

Section: Typical Analysis Tasks For Line Chartsmentioning

confidence: 99%

“…Interestingly, we found in our study that most of the heuristic QMs presented for Line Charts put their exclusive focus on the task of trend analysis. For many other tasks, Saket et al [SED17] mentions a set of five statements guiding under which circumstances either Line Charts, Scatter Plots, Tables, Bar Charts, or Pie Charts should be preferred. The guidelines were derived from a crowdsourcing study with 203 participants measuring effectiveness and efficiency concerns.…”

Section: Summary Of Approachesmentioning

confidence: 99%

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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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“…A recent trend is the study of visual preference in achieving a broad range of analysis tasks. For example, Saket et al [32] conducted a crowdsourced experiment to evaluate the effectiveness of five types of visualization across the ten low-level tasks. Recently, Kim and Heer [24] accessed the effectiveness of visual encodings based on analysis tasks being performed.…”

Section: Task-driven Evaluationmentioning

confidence: 99%

Task-Oriented Optimal Sequencing of Visualization Charts

Shi

et al. 2019

2019 IEEE Visualization in Data Science (VDS)

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Fig. 1. A case study based on the optimal task-oriented charts sequencing technique. This figure illustrates the results of an expert user exploring the Cars dataset starting from a bar chart (the first chart on the left) to find out the clusters of cars having the same number of cylinders. During the analysis, our technique recommends proper actions to guide the exploration process to approach the analysis goal. After a series of operations, the user finally found that cars are clustered in a subspace as shown in the last view, which indicates that fewer cylinders corresponds to smaller displacement and the lower horsepower.Abstract-A chart sequence is used to describe a series of visualization charts generated in the exploratory analysis by data analysts. It provides information details in each chart as well as a logical relationship among charts. While existing research targets on generating chart sequences that match human's perceptions, little attention has been paid to formulate task-oriented connections between charts in a chart design space. We present a novel chart sequencing method based on reinforcement learning to capture the connections between charts in the context of three major analysis tasks, including correlation analysis, anomaly detection, and cluster analysis. The proposed method formulates a chart sequencing procedure as an optimization problem, which seeks an optimal policy to sequencing charts for the specific analysis task. In our method, a novel reward function is introduced, which takes both the analysis task and the factor of human cognition into consideration. We conducted one case study and two user studies to evaluate the effectiveness of our method under the application scenarios of visualization demonstration, sequencing charts for reasoning analysis results, and making a chart design choice. The study results showed the power of our method.

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Task-Based Effectiveness of Basic Visualizations

Cited by 143 publications

References 31 publications

Illusion of Causality in Visualized Data

Illusion of Causality in Visualized Data

Quality Metrics for Information Visualization

Task-Oriented Optimal Sequencing of Visualization Charts

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