COVID-19 EnsembleVis: Visual Analysis of County-Level Ensemble Forecast Models

Srabanti, Sanjana; Marai, G. Elisabeta; Miranda, Fábio

doi:10.1109/vahc53616.2021.00005

Cited by 6 publications

(1 citation statement)

References 23 publications

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“…Cognizant of the fact that data sets published by governments and private entities are generally aggregated into geographic units (e.g., neighborhoods, counties, states) and rarely available in higher resolution. While the shortcomings of choropleth maps have been previously pointed out [18], they are still popular in several domains, including public health [19][20][21]. We also report the results of a user study that compared these techniques.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Methods for the Visualization of Probability Distributions of Geographical Data

Srabanti

Veiga

Silva

et al. 2022

MTI

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Probability distributions are omnipresent in data analysis. They are often used to model the natural uncertainty present in real phenomena or to describe the properties of a data set. Designing efficient visual metaphors to convey probability distributions is, however, a difficult problem. This fact is especially true for geographical data, where conveying the spatial context constrains the design space. While many different alternatives have been proposed to solve this problem, they focus on representing data variability. However, they are not designed to support spatial analytical tasks involving probability quantification. The present work aims to adapt recent non-spatial approaches to the geographical context, in order to support probability quantification tasks. We also present a user study that compares the efficiency of these approaches in terms of both accuracy and usability.

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Section: Introductionmentioning

confidence: 99%

A Comparative Study of Methods for the Visualization of Probability Distributions of Geographical Data

Srabanti

Veiga

Silva

et al. 2022

MTI

Self Cite

View full text Add to dashboard Cite

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User-centered visual explorer of in-process comparison in spatiotemporal space

Dong

Ian

Liu

et al. 2022

J Vis

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We propose a user-centered visual explorer (UcVE) for progressive comparing multiple visualization units in spatiotemporal space. We create unique unit visualization with the customizable aggregated view based on the visual metaphor of flower bursts. Each visualization unit is encoded with the abstraction of spatiotemporal properties. To reduce user cognition load, UcVE allows users to visualize, save, and track in-the-process exploration results. In coordination of storage sequence and block tracking views, UcVE can facilitate comparison with multiple visualization units concurrently, selected from historical and current exploration results. UcVE offers a flexible geo-based layout, with aggregation functions and temporal views of the timeline with categorized events, to maximize the user’s exploration capabilities. Finally, we demonstrate the usefulness by using COVID-19 datasets, case studies with different user scenarios, and expert feedback. Graphical abstract

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