Designing for Ambiguity: Visual Analytics in Avalanche Forecasting

Nowak, S; Bartram, Lyn; Haegeli, Pascal

doi:10.1109/vis47514.2020.00023

Cited by 7 publications

(9 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…62% of survey respondents had used text to warn their viewers of the potential for uncertainty in results" [32]. Other approaches use visual approaches to communicate qualitative uncertainty, such as the use of perceptually imprecise visual encoding channels like sketchiness [7] or glyphs [51]. A different approach taken in both the visualization and machine learning communities is to explicitly expose information about the data collection process, providing analysts with contextual information that allows them to incorporate personal knowledge about potential shortcomings of the data during their interpretations [2,26,54].…”

Section: Qualitative Uncertaintymentioning

confidence: 99%

“…Just like in this example, we frequently find that interactions with a visualization tool trigger experts to express knowledge about problems with data, but that these tools leave few options for them to communicate that knowledge. This knowledge is personal and not available to others, a phenomenon reported in other design studies [47,51,54]. However, what if experts could record their hunches directly in a visualization tool in a way that allowed others to interpret the data alongside their hunches?…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we address these questions by proposing a new concept, data hunches, to describe the knowledge that analysts bring to their data analysis process that augments and complements their perception of the data, and in turn becomes part of their interpretations as demonstrated in Figure 1. Data hunches are personal knowledge about caveats to the data, such as knowledge about implicit errors [47] or ambiguities [51]. We argue that conceptualizing data hunches provides a new perspective on how to design visual analysis tools for expert, collaborative settings, allowing for the sharing of knowledge about the ways that data is imperfect, partial, and uncertain.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Data Hunches: Incorporating Personal Knowledge into Visualizations

Lin¹,

Akbaba²,

Meyer³

et al. 2022

Preprint

View full text Add to dashboard Cite

The trouble with data is that it frequently provides only an imperfect representation of a phenomenon of interest. Experts who are familiar with their datasets will often make implicit, mental corrections when analyzing a dataset, or will be cautious not to be overly confident about their findings if caveats are present. However, personal knowledge about the caveats of a dataset is typically not incorporated in a structured way, which is problematic if others who lack that knowledge interpret the data. In this work, we define such analysts' knowledge about datasets as data hunches. We differentiate data hunches from uncertainty and discuss types of hunches. We then explore ways of recording data hunches, and, based on a prototypical design, develop recommendations for designing visualizations that support data hunches. We conclude by discussing various challenges associated with data hunches, including the potential for harm and challenges for trust and privacy. We envision that data hunches will empower analysts to externalize their knowledge, facilitate collaboration and communication, and support the ability to learn from others' data hunches.

show abstract

Section: Qualitative Uncertaintymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Data Hunches: Incorporating Personal Knowledge into Visualizations

Lin¹,

Akbaba²,

Meyer³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Franke et al [54] use the term confidence instead of uncertainty, as the data in humanities projects are judged and rated by experts, and the confidence that experts have in the data will impact the steps in the analysis. Ambiguity is another term used to describe qualitative uncertainty, as introduced in Nowak et al's [10] study, where multiple interpretations were possible based on the same data. Additionally, some works include cognitive aspects as a source of uncertainty.…”

Section: How Do Data Hunches Relate To Existing Concepts?mentioning

confidence: 99%

“…In this example, an expert had specific knowledge about imperfections in the dataset, and the expert was able to provide an estimate of what the data could be. This knowledge, however, was implicit and specific to an individual expert and not available to others, a phenomenon reported in other design studies [9,10] as well as studies of tools for casual users [11].…”

mentioning

confidence: 99%

Data Hunches: Incorporating Personal Knowledge into Visualizations

Lin¹,

Akbaba²,

Meyer³

et al. 2021

Preprint

View full text Add to dashboard Cite

The trouble with data is that often it provides only an imperfect representation of the phenomenon of interest. When reading and interpreting data, personal knowledge about the data plays an important role. Data visualization, however, has neither a concept defining personal knowledge about datasets, nor the methods or tools to robustly integrate them into an analysis process, thus hampering analysts' ability to express their personal knowledge about datasets, and others to learn from such knowledge. In this work, we define such personal knowledge about datasets as data hunches and elevate this knowledge to another form of data that can be externalized, visualized, and used for collaboration. We establish the implications of data hunches and provide a design space for externalizing and communicating data hunches through visualization techniques. We envision such a design space will empower users to externalize their personal knowledge and support the ability to learn from others' data hunches.

show abstract

Interpretable, non-mechanistic forecasting using empirical dynamic modeling and interactive visualization

Mason

González

García‐Closas

et al. 2022

Preprint

View full text Add to dashboard Cite

Forecasting methods are notoriously difficult to interpret, particularly when the relationship between the data and the resulting forecasts is not obvious. Interpretability is an important property of a forecasting method because it allows the user to complement the forecasts with their own knowledge, a process which leads to more applicable results. In general, mechanistic methods are more interpretable than non-mechanistic methods, but they require explicit knowledge of the underlying dynamics. In this paper, we introduce a tool which performs interpretable, non-mechanistic forecasts using interactive visualization and a simple, data-focused forecasting technique. To ensure the work is FAIR and privacy is ensured, we have released the tool as an entirely in-browser web-application.

show abstract

Designing for Ambiguity: Visual Analytics in Avalanche Forecasting

Cited by 7 publications

References 28 publications

Data Hunches: Incorporating Personal Knowledge into Visualizations

Data Hunches: Incorporating Personal Knowledge into Visualizations

Data Hunches: Incorporating Personal Knowledge into Visualizations

Interpretable, non-mechanistic forecasting using empirical dynamic modeling and interactive visualization

Contact Info

Product

Resources

About