"Heatmaps are two-dimensional graphical representations of data where the values of a variable are shown as colors. Heatmaps are compelling for two reasons. First, the intuitive nature of the color scale as it relates to temperature minimizes the amount of learning necessary to understand it. From experience, we know that yellow is warmer than green, orange is warmer than yellow, and red is hot. It is not difficult to then figure out that the amount of heat is proportional to the level of the represented variable. Second, heatmaps show the data directly over the stimulus. Because the data could not be any closer to the elements to which they pertain, little mental effort is required to read a heatmap." (Agnieszka Bojkon, "Informative or Misleading? Heatmaps Deconstructed", [in "Human-Computer Interaction: New Trends, 13th International Conference"] 2009)
"Heat maps offer a good way to systematically identify risks, but from our point of view they have one problem - they focus on risk reduction, not risk leverage. [...] The point of the inverse heat map is to highlight opportunities that might be discarded out-of-hand because they are a gamble. If something is very unlikely (the left-hand side of the heat map), it is not worth pursuing, but opportunities that are somewhat unlikely but would have a high payoff are attractive (top right portion of the heat map)." (John W Boudreau et al, "Transformative HR: How Great Companies Use Evidence-Based Change for Sustainable Advantage", 2011)
"With further similarities to small multiples, heatmaps enable us to perform rapid pattern matching to detect the order and hierarchy of different quantitative values across a matrix of categorical combinations. The use of a color scheme with decreasing saturation or increasing lightness helps create the sense of data magnitude ranking." (Andy Kirk, "Data Visualization: A successful design process", 2012)
"Heat mapping is essentially using conditional formatting, often color, to focus a reader’s attention on specific data points. Evaluators can use different colors to highlight whether output measures were met and different gradients of a single color to provide a sense of range." (Christopher Lysy, "Developments in Quantitative Data Display and Their Implications for Evaluation", 2013)
"The advantage of the calendar heat map over the line chart is that, along with seeing cycles as you scan top to bottom, it’s easy to see specific days in rows and columns, so it’s easier to reference what day of the year each value is for." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)
"The idiom of heatmaps is one of the simplest uses of the matrix alignment: each cell is fully occupied by an area mark encoding a single quantitative value attribute with color. […] The benefit of heatmaps is that visually encoding quantitative data with color using small area marks is very compact, so they are good for providing overviews with high information density. " (Tamara Munzner, "Visualization Analysis and Design", 2014)
"[...] a color-mapping visualization is effective if, by looking at the generated colors, we can easily and accurately make statements about the original scalar dataset that was color mapped." (Alexandru Telea, "Data Visualization: Principles and Practice" 2nd Ed., 2015)
"Compared to the rainbow colormap, the heat map uses a smaller set of hues, but adds luminance as a way to order colors in an intuitive manner. Compared to the two-hue colormap, the heat map uses more hues, thus allowing one to discriminate between more data values." (Alexandru Telea, "Data Visualization: Principles and Practice" 2nd Ed., 2015)
"Heat maps are effective visualizations for seeing concentrations as well as patterns. Adding time series to a heat map can also reveal seasonality that may not be obvious otherwise."
"A heatmap is a visualization where values contained in a matrix are represented as colors or color saturation. Heatmaps are great for visualizing multivariate data (data in which analysis is based on more than two variables per observation), where categorical variables are placed in the rows and columns and a numerical or categorical variable is represented as colors or color saturation." (Mario Döbler & Tim Großmann, "The Data Visualization Workshop", 2nd Ed., 2020)
"Heatmap is another representational way in which the frequencies of the various parameters of the data set is represented in different colors, much like an image captured by a thermal imaging camera in which the graph consists of varying temperatures and the temperatures are differentiated according to the colors." (Shreyans Pathak & Shashwat Pathak, "Data Visualization Techniques, Model and Taxonomy", 2020)
No comments:
Post a Comment