Generating Color Palettes using Intuitive Parameters

Wijffelaars, Martijn; Vliegen, R.; Wijk, Jarke J. van; Linden, Erik-Jan van der

doi:10.1111/j.1467-8659.2008.01203.x

Cited by 69 publications

(55 citation statements)

References 11 publications

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“…The user is supported by a preset of clearly distinguishable hues to obtain a perceptually balanced map, as used in Figure 12. The preset is a pastel rainbow color map generated with PaletteView [27] and sampled at equal intervals for a given number of colors. The chosen color is associated to the highest density value of the field, and a complete color map is created by interpolating the saturation towards white, see the lower right corner of Figure 11.…”

Section: Renderingmentioning

confidence: 99%

Interactive visualization of multivariate trajectory data with density maps

Scheepens

Willems

Wetering

et al. 2011

2011 IEEE Pacific Visualization Symposium

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Figure 1: A density map of vessel traffic in front of Rotterdam during a single day. The density map is a combination of four density fields each covering a quarter of the day. The following manually defined color map is used: night is dark blue, morning is bright yellow, afternoon is dark yellow, and evening is bright blue. Furthermore, the saturation of the color represents the density field contribution and the hue is given by the period with the highest density. To discriminate daylight patterns from nighttime, the night and evening use half the kernel radius of the other periods. This figure shows that the main routes are the most used during daylight, while in the night deviations from these routes occur. ABSTRACTWe present a method to interactively explore multiple attributes in trajectory data using density maps, i.e., images that show an aggregate overview of massive amounts of data. So far, density maps have mainly been used to visualize single attributes. Density maps are created in a two-way procedure; first smoothed trajectories are aggregated in a density field, and then the density field is visualized. In our approach, the user can explore attributes along trajectories by calculating a density field for multiple subsets of the data. These density fields are then either combined into a new density field or first visualized and then combined. Using a widget, called a distribution map, the user can interactively define subsets in an effective and intuitive way, and, supported by high-end graphics hardware the user gets fast feedback for these computationally expensive density field calculations. We show the versatility of our method with use cases in the maritime domain: to distinguish between periods in the temporal aggregation, to find anomalously behaving vessels, to solve ambiguities in density maps via drill down in the data, and for risk assessments. Given the generic framework and the lack of domain-specific assumptions, we expect our concept to be applicable for trajectories in other domains as well.

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Section: Renderingmentioning

confidence: 99%

Interactive visualization of multivariate trajectory data with density maps

Scheepens

Willems

Wetering

et al. 2011

2011 IEEE Pacific Visualization Symposium

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“…To overcome the resulting overdrawing we have used alpha blending with an opacity of 0.85, which shows a subtle highlight for dense areas. Furthermore, to keep the lanes distinguishable for comparison, each lane is rendered with perceptually balanced colors generated with PaletView by Wijffelaars et al [160]. Interaction is minimalized to prevent occlusion effects; the subject may rotate the cube with the mouse around the time axis and change the angle to look into the cube.…”

Section: Space-time Cubementioning

confidence: 99%

“…The user is supported by a default setting of clearly distinguishable hues to obtain a perceptually balanced map [106], as used in Figure 5.11. The default setting is a pastel rainbow color map generated with PaletteView [160] and sampled at equal intervals for a given number of colors. The chosen color is associated to the highest density value of the field, and a complete color map is created by interpolating the saturation towards white, see the lower right corner of Figure 5.10.…”

Section: Renderingmentioning

confidence: 99%

Visualization of Vessel Traffic

Willems

Scheepens

Wetering

et al. 2013

Situation Awareness With Systems of Systems

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“…Instead, we selected the colors such that they are clearly distinguishable. At the time we designed this experiment, we did not consider guidelines for choosing color palettes (e.g., Levkowitz & Herman (1992); Rice (1991); Wijffelaars et al (2008)). However, for the third experiment, we took existing guidelines into account (cf.…”

Section: Experimental Materialsmentioning

confidence: 99%

Do background colors improve program comprehension in the #ifdef hell?

et al. 2012

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Software-product-line engineering aims at the development of variable and reusable software systems. In practice, software product lines are often implemented with preprocessors. Preprocessor directives are easy to use, and many mature tools are available for practitioners. However, preprocessor directives have been heavily criticized in academia and even referred to as "#ifdef hell", because they introduce threats to program comprehension and correctness. There are many voices that suggest to use other implementation techniques instead, but these voices ignore the fact that a transition from preprocessors to other languages and tools is tedious, erroneous, and expensive in practice. Instead, we and others propose to increase the readability of preprocessor directives by using background colors to highlight source code annotated with ifdef directives. In three controlled experiments with over 70 subjects in total, we evaluate whether and how background colors improve program comprehension in preprocessor-based implementations. Our results demonstrate that background colors have the potential to improve program This is the authors' version and intended for personal use only. Any other use may violate the copyright. comprehension, independently of size and programming language of the underlying product. Additionally, we found that subjects generally favor background colors. We integrate these and other findings in a tool called FeatureCommander, which facilitates program comprehension in practice and which can serve as a basis for further research.

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Generating Color Palettes using Intuitive Parameters

Cited by 69 publications

References 11 publications

Interactive visualization of multivariate trajectory data with density maps

Interactive visualization of multivariate trajectory data with density maps

Visualization of Vessel Traffic

Do background colors improve program comprehension in the #ifdef hell?

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