ColorMoves: Real-time Interactive Colormap Construction for Scientific Visualization

Samsel, Francesca; Klaassen, Sebastian; Rogers, David

doi:10.1109/mcg.2018.011461525

Cited by 26 publications

(15 citation statements)

References 4 publications

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“…How might the dark-is-more bias vary with semantic context? Efforts have been under way to design colormaps to fit different types of data (e.g., data about water vs. foliage) (Samsel, Klaassen, & Rogers, 2018;Samsel, Turton, Wolfram, & Bujack, 2017), and it is possible that the dark-is-more bias might be reduced or reversed for data about content that is inherently light (e.g., sunshine). It is also possible that the dark-is-more bias might be influenced by which properties of the data are of interest.…”

Section: Effects Of Semantic Contextmentioning

confidence: 99%

The relation between color and spatial structure for interpreting colormap data visualizations

et al. 2020

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Interpreting colormap visualizations requires determining how dimensions of color in visualizations map onto quantities in data. People have color-based biases that influence their interpretations of colormaps, such as a dark-is-more bias—darker colors map to larger quantities. Previous studies of color-based biases focused on colormaps with weak data spatial structure, but color-based biases may not generalize to colormaps with strong data spatial structure, like “hotspots” typically found in weather maps and neuroimaging brain maps. There may be a hotspot-is-more bias to infer that colors within hotspots represent larger quantities, which may override the dark-is-more bias. We tested this possibility in four experiments. Participants saw colormaps with hotspots and a legend that specified the color-quantity mapping. Their task was to indicate which side of the colormap depicted larger quantities (left/right). We varied whether the legend specified dark-more mapping or light-more mapping across trials and operationalized a dark-is-more bias as faster response time (RT) when the legend specified dark-more mapping. Experiment 1 demonstrated robust evidence for the dark-is-more bias, without evidence for a hotspot-is-more bias. Experiments 2 to 4 suggest that a hotspot-is-more bias becomes relevant when hotspots are a statistically reliable cue to “more” (i.e., the locus of larger quantities) and when hotspots are more perceptually pronounced. Yet, comparing conditions in which the hotspots were “more,” RTs were always faster for dark hotspots than light hotspots. Thus, in the presence of strong spatial cues to the locus of larger quantities, color-based biases still influenced interpretations of colormap data visualizations.

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Section: Effects Of Semantic Contextmentioning

confidence: 99%

The relation between color and spatial structure for interpreting colormap data visualizations

et al. 2020

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“…The authors thank the two eponymous Reviewers Dr. Ashok Mishra and Dr. Nadav Peleg, one anonymous Reviewer, and the Associate Editor for their constructive and detailed remarks that helped improve the original manuscript. Colormaps in figures were inspired by ColorMoves (Samsel et al., 2018). The authors have used the CoSMoS R package (Papalexiou et al., 2021) available at CRAN (R Core Team, 2021).…”

Section: Acknowledgmentsmentioning

confidence: 99%

Advancing Space‐Time Simulation of Random Fields: From Storms to Cyclones and Beyond

Papalexiou

Serinaldi

Porcu

2021

Water Resources Research

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Realistic stochastic simulation of hydro-environmental fluxes in space and time, such as rainfall, is challenging yet of paramount importance to inform environmental risk analysis and decision making under uncertainty. Here, we advance random field simulation by introducing the concepts of general velocity fields and general anisotropy transformations.This expands the capabilities of the so-called Complete Stochastic Modeling Solution (CoSMoS) framework enabling the simulation of random fields preserving: (1) any non-Gaussian marginal distributions, (2) any spatiotemporal correlation structure, (3) general advection expressed by velocity fields with locally varying speed and direction, and (4) locally varying anisotropy. We also introduce new copula-based spatiotemporal correlation structures and provide conditions guaranteeing their positive definiteness. To illustrate the potential of CoSMoS, we simulate random fields with complex patterns and motion mimicking rainfall storms moving across an area, spiraling fields resembling weather cyclones, fields converging to (or diverging from) a point, and colliding air masses. The proposed methodology is implemented in the freely available CoSMoS R package.

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“…The authors acknowledge the World Climate Research Programme (WCRP), which coordinated and promoted CMIP6 through its Working Group on Coupled Modeling, the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF. Color maps inspired by ColorBrewer v2.0 (Harrower & Brewer, 2003) and ColorMoves (Samsel et al., 2018).…”

Section: Acknowledgmentsmentioning

confidence: 99%

Probabilistic Evaluation of Drought in CMIP6 Simulations

et al. 2021

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About half of the Earth's land area is drought prone (Kogan, 1997) and an average of 55 million people around the world are affected by drought every year (Vatter et al., 2019). During 1900-2011, more than 11 million people lost their lives and two billion people were affected by drought (Spinoni et al., 2014). Droughts may further trigger other calamities such as wildfires and heatwaves (LeComte, 2011;Sivakumar, 2005). Unlike other disasters, such as floods or earthquakes, drought is a slowly developing phenomenon and often shows persistent consequences even after the drought has ended (Vogt et al., 2011). Furthermore, due to climate change and rising temperatures, droughts are expected to increase in terms of

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ColorMoves: Real-time Interactive Colormap Construction for Scientific Visualization

Cited by 26 publications

References 4 publications

The relation between color and spatial structure for interpreting colormap data visualizations

The relation between color and spatial structure for interpreting colormap data visualizations

Advancing Space‐Time Simulation of Random Fields: From Storms to Cyclones and Beyond

Probabilistic Evaluation of Drought in CMIP6 Simulations

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