From Quantification to Visualization: A Taxonomy of Uncertainty Visualization Approaches

Potter, Kristin; Rosen, Paul; Johnson, Chris R.

doi:10.1007/978-3-642-32677-6_15

Cited by 158 publications

(102 citation statements)

References 75 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This uncertainty may arise from a random effect (random error), or a systematic effect (systematic error). Random errors can be measured by all statistically valid methods, but systematic errors are only measured by scientific judgment that uses available relevant information and statistical reasoning (Potter et al, 2012).…”

Section: Environmental Uncertaintymentioning

confidence: 46%

“…Two categories of uncertainty are identifiable by nature: 1. Cognitive uncertainty which is related to the decision maker and is caused by the lack of knowledge and limitations of data and information (Potter, Rosen, & Johnson, 2012), or complexity (plurality of elements and their high interdependency). This type of uncertainty is reduced by creating knowledge, research and learning.…”

Section: Environmental Uncertaintymentioning

confidence: 47%

See 1 more Smart Citation

Managing Environmental Uncertainty: From Conceptual Review to Strategic Management Point of View

Samsami¹,

Hosseini²,

Kordnaeij³

et al. 2015

IJBM

View full text Add to dashboard Cite

Uncertainty surrounds all aspects of organizations' lives. We rarely find studies in the field of organization and management not mentioned the phenomenon of environmental uncertainty. Despite the ubiquity of studies around the topic, there are many incompatibilities and inconsistencies in the definition and application of the concept. In this paper we try to classify the main issues and controversies around the topic and review some of the outstanding studies in this regard. As the ultimate goal of studying uncertainty in organization is to face it in the choice of appropriate strategies, we also have a brief review on the impacts of environmental uncertainty on organizational strategic decisions and managers' actions. Finally, we refer to some of the frameworks and approaches that are mainly presented in the field of strategic management for dealing with uncertainty, and resolving the strategy paradox between uncertainty and commitment to strategies.

show abstract

Section: Environmental Uncertaintymentioning

confidence: 46%

Section: Environmental Uncertaintymentioning

confidence: 47%

Managing Environmental Uncertainty: From Conceptual Review to Strategic Management Point of View

Samsami¹,

Hosseini²,

Kordnaeij³

et al. 2015

IJBM

View full text Add to dashboard Cite

show abstract

“…The importance of uncertainty visualization has been acknowledged more than a decade ago [35], and since then a number of overviews and taxonomies of uncertainty visualization techniques have been published [22,33,50,10,42,16]. The uncertainty information in ensemble fields has often been exposed by visualizing quantities such as mean and standard deviation via color maps, opacity, texture, animation, and glyphs [55,4,44,32].…”

Section: Related Worksupporting

confidence: 38%

Multi-Charts for Comparative 3D Ensemble Visualization

Demir

Dick

Westermann

2014

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

Fig. 1. Multi-chart visualization of a temperature ensemble forecast from the ECMWF Ensemble Prediction System (EPS), ECMWF's operational ensemble weather forecast system [30]. The ensemble consists of 51 members of resolution 256 × 128 × 64 each. Each bar in the multi-chart is associated with a distinct 3D subdomain, and encodes the distribution of the ensemble members in this subdomain by means of a histogram. In addition, a few user-selected ensemble members are depicted using polylines. By means of brushing in the multi-chart view (indicated by yellow background color), the user has selected regions where the range over the ensemble members and thus the uncertainty is high. The selected regions are instantly emphasized in the 3D view.Abstract-A comparative visualization of multiple volume data sets is challenging due to the inherent occlusion effects, yet it is important to effectively reveal uncertainties, correlations and reliable trends in 3D ensemble fields. In this paper we present bidirectional linking of multi-charts and volume visualization as a means to analyze visually 3D scalar ensemble fields at the data level. Multi-charts are an extension of conventional bar and line charts: They linearize the 3D data points along a space-filling curve and draw them as multiple charts in the same plot area. The bar charts encode statistical information on ensemble members, such as histograms and probability densities, and line charts are overlayed to allow comparing members against the ensemble. Alternative linearizations based on histogram similarities or ensemble variation allow clustering of spatial locations depending on data distribution. Multi-charts organize the data at multiple scales to quickly provide overviews and enable users to select regions exhibiting interesting behavior interactively. They are further put into a spatial context by allowing the user to brush or query value intervals and specific distributions, and to simultaneously visualize the corresponding spatial points via volume rendering. By providing a picking mechanism in 3D and instantly highlighting the corresponding data points in the chart, the user can go back and forth between the abstract and the 3D view to focus the analysis.

show abstract

“…The visualization of uncertainty is an important current area of visualization research [26]. In uncertainty visualization [3,32] a general stochastic uncertainty in the data is assumed. This uncertainty may be based in inexactness of the measurements or of the model.…”

Section: Introductionsupporting

confidence: 41%

Multi-Granular Trend Detection for Time-Series Analysis

Van

Staals

Löffler

et al. 2017

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract-Time series (such as stock prices) and ensembles (such as model runs for weather forecasts) are two important types of one-dimensional time-varying data. Such data is readily available in large quantities but visual analysis of the raw data quickly becomes infeasible, even for moderately sized data sets. Trend detection is an effective way to simplify time-varying data and to summarize salient information for visual display and interactive analysis. We propose a geometric model for trend-detection in onedimensional time-varying data, inspired by topological grouping structures for moving objects in two-or higher-dimensional space. Our model gives provable guarantees on the trends detected and uses three natural parameters: granularity, support-size, and duration. These parameters can be changed on-demand. Our system also supports a variety of selection brushes and a time-sweep to facilitate refined searches and interactive visualization of (sub-)trends. We explore different visual styles and interactions through which trends, their persistence, and evolution can be explored. Permanent repository link

show abstract

From Quantification to Visualization: A Taxonomy of Uncertainty Visualization Approaches

Cited by 158 publications

References 75 publications

Managing Environmental Uncertainty: From Conceptual Review to Strategic Management Point of View

Managing Environmental Uncertainty: From Conceptual Review to Strategic Management Point of View

Multi-Charts for Comparative 3D Ensemble Visualization

Multi-Granular Trend Detection for Time-Series Analysis

Contact Info

Product

Resources

About