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Visualization is an effective means for exploring and analyzing complex data. Color coding is a fundamental technique for mapping data to visual representations. Although color coding is widely used in a large variety of visualizations, it is often provided in a limited way only or it is not used effectively. Therefore, we describe in this paper ho...
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... visualizations mainly use, on one hand, color scales that are created by linearly interpolating two colors from the RGB color space (e.g. ; cp. Figure 1). On the other, a rainbow color scale is used. ...
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... On the other hand, the design of color scales for identification is intricate because the whole range of data values is potentially relevant and must be easily identifiable. One way to facilitate lookup tasks is to extract statistical metadata from the underlying dataset and utilize them to adjust predefined color scales (see Schulze-Wollgast et al., 2005;Tominski et al., 2008). Let us take a look at three possible ways of adaptation. ...
Many different types of data are related to time. Meteorological data, financial data, census data, medical data, simulation data, news articles, photo collections, or project plans, to name only a few examples, all contain temporal information. In theory, because all these data are time-oriented, they should be representable with one and the same visualization approach. In practice, however, the data exhibit specific characteristics and hence each of the above examples requires a dedicated visualization.
... A data-dependent adaptation of color schemes based on statistical properties [19,21] would lead to incomparability of daily views. Thus, we disregard them. ...
This paper presents a novel color scheme designed to address the challenge of visualizing data series with large value ranges, where scale transformation provides limited support. We focus on meteorological data, where the presence of large value ranges is common. We apply our approach to meteorological scatterplots, as one of the most common plots used in this domain area. Our approach leverages the numerical representation of mantissa and exponent of the values to guide the design of novel "nested" color schemes, able to emphasize differences between magnitudes. Our user study evaluates the new designs, the state of the art color scales and representative color schemes used in the analysis of meteorological data: ColorCrafter, Viridis, and Rainbow. We assess accuracy, time and confidence in the context of discrimination (comparison) and interpretation (reading) tasks. Our proposed color scheme significantly outperforms the others in interpretation tasks, while showing comparable performances in discrimination tasks.
... 为了提高色彩表设计的效率, 研究人员提出 了若干色彩表自动优化方法 [5][6][7] . 如, Tominski 等 [6] 基于直方图均衡化的思想自动调整色彩表中的色 彩位置, 使含有更多数值的数据范围能够有更多 的色彩来表示; Zeng 等 [7] 基于边界模型调整色彩分 布, 使数据中隐藏的边界信息得以凸显. ...
... To achieve truly effective visualization, colormap design should be driven by the data (Zhou and Hansen, 2015). Colormap transformations can improve small feature perception (Schulze-Wollgast et al., 2005;Bertini et al., 2007;Eisemann et al., 2011;Thompson et al., 2013). The present study introduces the use of quantiles as color limits for fisheries acoustic data. ...
One of the first steps in evaluating fisheries acoustic data is to explore an echogram of the data. Echograms are a visual representation of reflected sound with range or distance from the echosounder on the y-axis, time on the x-axis, and the intensity of the reflected sound displayed through a mapped color scale. Many of the traditional color scales used in fisheries acoustic echograms have not changed in the last few decades and have been previously discussed in the literature. However, little research has been conducted on how the number of colors used, and color thresholds and limits influence our perception of patterns in acoustic data. Understanding how color maps influence out perception bias is particularly important when the analyst is unfamiliar with the dataset, the dataset has areas of dense, overlapping target aggregations, or the data are noisy. This study collected feedback on four different datasets from fisheries acoustics experts to understand how colormap settings influence perception of echograms. The datasets included a simulated, noisy echogram, an echogram with both volume and point targets, an echogram with layers of different intensities, and a high-frequency echogram affected by background noise. Colormap settings based on data quantiles, both for thresholds and color limits, were chosen by most of the experts as the echograms that best represented the data for the given task. This study recommends histogram-equalized colormaps for initial data exploration. General recommendations are provided based on results.
... The goal of color-mapping is to effectively communicate these features from visual imagery to those data that are the most prominent in hands-on tasks [4]. Color is a retinal variable which is conventionally determined by hue, saturation and brightness (HSB); all three being dimensions in perception-based applications [5]. These three perceptual dimensions combined with different choices and values, cause the diversification of colormaps. ...
... In the early 1990s, Bergman et al. explored a rule-based tool to help choose the best colormap for isomorphic, segmentation and highlighting tasks [16]. Schulze-Wollgast et al. exploited an enhanced automatic color-coding framework by encapsulating metadata extraction, colormap adaptation and color legend creation [5]. Tominski et al. developed a colorcoding function to choose color scales according to particular tasks [17]. ...
... Previous work has successfully proven that the usage of appropriate color coding plays a critical role in related data or tasks. In numerous cases, researchers generate colormap design principles for better data visualization by comparative studies through specific tasks [5,16,25]. Furthermore, integrating color-mapping and affect has been a ubiquitous research topic in the domain of affective color-coding. People combine empirical assessment with miscellaneous user studies to examine the link between colormaps and human affect, enhancing the information visualization [6,14,26]. ...
The design of colormaps can help tomography operators obtain accurate visual comprehension, thereby assisting safety-critical decisions. The research presented here is about deploying colormaps that promote the best affective responses for industrial microwave tomography (MWT). To answer the two research questions related to our study, we firstly conducted a quantitative analysis of 11 frequently-used colormaps on a segmentation task. Secondly, we presented the same colormaps within a crowdsourced study comprising two parts to verify the quantitative outcomes. The first part encoded affective responses from participants into a prevailing four-quadrant valence–arousal grid; the second part recorded participant ratings towards the accuracy of each colormap on MWT segmentation. We concluded that three colormaps are the best suited in the context of MWT tasks. We also found that the colormaps triggering emotions in the positive–exciting quadrant can facilitate more accurate visual comprehension than other affect-related quadrants. A synthetic colormap design guideline was consequently proposed.
... While the above algorithms improve colormaps independent of individual datasets, there are also algorithms that modify a colormap for each dataset in order to maximize some properties. For such data-driven improvement, the algorithm by Schulze-Wollgast et al. [SwTS05] automatically calculates a set of statistical measures of the data, associates key colors with these values, [TFS08] optimize a colormap based on data in order to support a visualization task better. For example, one algorithm generates a "histogram-equalized" colormap that maximizes the discrimination of visual objects in a particular dataset. ...
Colormapping is one of the simplest and most widely used data visualization methods within and outside the visualization community. Uniformity, order, discriminative power, and smoothness of continuous colormaps are the most important criteria for evaluating and potentially improving colormaps. We present a local and a global automatic optimization algorithm in Euclidean color spaces for each of these design rules in this work. As a foundation for our optimization algorithms, we used the CCC‐Tool colormap specification (CMS); each algorithm has been implemented in this tool. In addition to synthetic examples that demonstrate each method's effect, we show the outcome of some of the methods applied to a typhoon simulation.
... Kindlmann et al. [20] suggest a method to evaluate users' perception of luminance using a photograph of a human face. Schulze-Wollgast et analytic data [6,19,27,29,39,43,56,59,60] [4, 26, 61] statistics and maps [8,9,13,14,27,47,51,57,64] medical imaging [21,33,34,42,51,59,65] scientific measurements [14,16,22,29,30,42,43,46,64,65] scientific simulations [3,5,29,30,42,45,46,56] photographs [20,38,51,54] al. [47] focus on the task of comparing data using statistical information on maps. Tominski et al. [57] also stress that the characteristics of the data, tasks, goals, user, and output device need to be taken into account. ...
... Kindlmann et al. [20] suggest a method to evaluate users' perception of luminance using a photograph of a human face. Schulze-Wollgast et analytic data [6,19,27,29,39,43,56,59,60] [4, 26, 61] statistics and maps [8,9,13,14,27,47,51,57,64] medical imaging [21,33,34,42,51,59,65] scientific measurements [14,16,22,29,30,42,43,46,64,65] scientific simulations [3,5,29,30,42,45,46,56] photographs [20,38,51,54] al. [47] focus on the task of comparing data using statistical information on maps. Tominski et al. [57] also stress that the characteristics of the data, tasks, goals, user, and output device need to be taken into account. ...
Many computer science disciplines (e.g., combinatorial optimization, natural language processing, and information retrieval) use standard or established test suites for evaluating algorithms. In visualization, similar approaches have been adopted in some areas (e.g., volume visualization), while user testimonies and empirical studies have been the dominant means of evaluation in most other areas, such as designing colormaps. In this paper, we propose to establish a test suite for evaluating the design of colormaps. With such a suite, the users can observe the effects when different continuous colormaps are applied to planar scalar fields that may exhibit various characteristic features, such as jumps, local extrema, ridge or valley lines, different distributions of scalar values, different gradients, different signal frequencies, different levels of noise, and so on. The suite also includes an expansible collection of real-world data sets including the most popular data for colormap testing in the visualization literature. The test suite has been integrated into a web-based application for creating continuous colormaps (https://ccctool.com/), facilitating close inter-operation between design and evaluation processes. This new facility complements traditional evaluation methods such as user testimonies and empirical studies.
... Kindlmann et al. [20] suggest a method to evaluate users' perception of luminance using a photograph of a human face. Schulze-Wollgast et analytic data [6,19,27,29,39,43,56,59,60] [4, 26, 61] statistics and maps [8,9,13,14,27,47,51,57,64] medical imaging [21,33,34,42,51,59,65] scientific measurements [14,16,22,29,30,42,43,46,64,65] scientific simulations [3,5,29,30,42,45,46,56] photographs [20,38,51,54] al. [47] focus on the task of comparing data using statistical information on maps. Tominski et al. [57] also stress that the characteristics of the data, tasks, goals, user, and output device need to be taken into account. ...
... Kindlmann et al. [20] suggest a method to evaluate users' perception of luminance using a photograph of a human face. Schulze-Wollgast et analytic data [6,19,27,29,39,43,56,59,60] [4, 26, 61] statistics and maps [8,9,13,14,27,47,51,57,64] medical imaging [21,33,34,42,51,59,65] scientific measurements [14,16,22,29,30,42,43,46,64,65] scientific simulations [3,5,29,30,42,45,46,56] photographs [20,38,51,54] al. [47] focus on the task of comparing data using statistical information on maps. Tominski et al. [57] also stress that the characteristics of the data, tasks, goals, user, and output device need to be taken into account. ...
Many computer science disciplines (e.g., combinatorial optimization, natural language processing, and information retrieval) use standard or established test suites for evaluating algorithms. In visualization, similar approaches have been adopted in some areas (e.g., volume visualization), while user testimonies and empirical studies have been the dominant means of evaluation in most other areas, such as designing colormaps. In this paper, we propose to establish a test suite for evaluating the design of colormaps. With such a suite, the users can observe the effects when different continuous colormaps are applied to planar scalar fields that may exhibit various characteristic features, such as jumps, local extrema, ridge or valley lines, different distributions of scalar values, different gradients, different signal frequencies, different levels of noise, and so on. The suite also includes an expansible collection of real-world data sets including the most popular data for colormap testing in the visualization literature. The test suite has been integrated into a web-based application for creating continuous colormaps (https://ccctool.com/), facilitating close inter-operation between design and evaluation processes. This new facility complements traditional evaluation methods such as user testimonies and empirical studies.
... Schulze-Wollgast et al. [67] focus on the comparison task. They extract statistical information from the data, e.g., minimum, maximum, average, median, mode, skewness, and quartiles and adjust the colormap to gain a better color discrimination. ...
A myriad of design rules for what constitutes a “good” colormap can be found in the literature. Some common rules include order, uniformity, and high discriminative power. However, the meaning of many of these terms is often ambiguous or open to interpretation. At times, different authors may use the same term to describe different concepts or the same rule is described by varying nomenclature. These ambiguities stand in the way of collaborative work, the design of experiments to assess the characteristics of colormaps, and automated colormap generation.
... As we were interested in performance across a spectrum of tasks, we did not attempt to compare an optimized binning technique. There are a variety of data-informed optimization strategies for binning within the visualization literature [46,40,11]. Additionally, standardized binning approaches for choropleth maps from cartography [42] might also be generalizable. ...
The expressiveness principle for visualization design asserts that a visualization should encode all of the available data, and only the available data, implying that continuous data types should be visualized with a continuous encoding channel. And yet, in many domains binning continuous data is not only pervasive, but it is accepted as standard practice. Prior work provides no clear guidance for when encoding continuous data continuously is preferable to employing binning techniques or how this choice affects data interpretation and decision making. In this paper, we present a study aimed at better understanding the conditions in which the expressiveness principle can or should be violated for visualizing continuous data. We provided participants with visualizations employing either continuous or binned greyscale encodings of geospatial elevation data and compared participants' ability to complete a wide variety of tasks. For various tasks, the results indicate significant differences in decision making, confidence in responses, and task completion time between continuous and binned encodings of the data. In general, participants with continuous encodings were faster to complete many of the tasks, but never outperformed those with binned encodings, while performance accuracy with binned encodings was superior to continuous encodings in some tasks. These findings suggest that strict adherence to the expressiveness principle is not always advisable. We discuss both the implications and limitations of our results and outline various avenues for potential work needed to further improve guidelines for using continuous versus binned encodings for continuous data types.
