Color Scala 06
By the mid-1960s cartographers had already established guidelines for the appropriate use of color in map-making. Jacques Bertin pointed out shortcomings of the rainbow palette in Sémiologie Graphique (The Semiology of Graphics), and Eduard Imhof was crafting harmonious color gradients for use in topographic maps [published in Kartographische Geländedarsellung (Cartographic Relief Presentation)].
Color Scala 06
In the 1980s and 1990s researchers in perception and visualization were investigating the efficacy of palettes, based on the ways our brains and eyes physically respond to light. These color scales were crafted to achieve the principal goals of spatial displays: to show patterns and relationships in data, and to allow a viewer to accurately read individual values. [Colin Ware (1988) Color Sequences for Univariate Maps: Theory, Experiments, and Principles; Brewer (1994) Color Use Guidelines for Mapping and Visualization; Rogowitz and Treinish (1995) How NOT to Lie with Visualization; Tufte (1997) Visual Explanations; Spence et al. (1999) Using Color to Code Quantity in Spatial Displays.]
Simultaneous contrast (a visual phenomenon that helps us interpret shapes through variations in brightness) shifts the appearance of colors and shades based on their surroundings. (After Ware (1988).)
A color palette that combines a continuous increase in lightness with a shift in hue is a good compromise that preserves both form and quantity. These three palettes show the smooth, even gradations that result from color scales calculated in perceptual color spaces. Color scales with varied hues and contrast are suitable for representing different datasets. (After Spence et al. (1999), chroma.js, and Color Brewer.)
The Fitzpatrick scale (also Fitzpatrick skin typing test; or Fitzpatrick phototyping scale) is a numerical classification schema for human skin color. It was developed in 1975 by American dermatologist Thomas B. Fitzpatrick as a way to estimate the response of different types of skin to ultraviolet (UV) light. It was initially developed on the basis of skin color to measure the correct dose of UVA for PUVA therapy, and when the initial testing based only on hair and eye colour resulted in too high UVA doses for some, it was altered to be based on the patient's reports of how their skin responds to the sun; it was also extended to a wider range of skin types. The Fitzpatrick scale remains a recognized tool for dermatological research into human skin pigmentation.
Ethnic classification does not correlate well with skin tone. As there are no neonatal skin color scales, we aimed to create and validate one of our own. After creating the scale and briefly training our staff, we conducted a prospective, observational study to assess reproducibility and correlation of each scale color with the melanin and erythema indexes and transcutaneous bilirubin. The reliability of our color scale was measured using Kappa agreement (and its 95% confidence interval) and the concordance index by comparing inter-observer classification of neonatal skin color. We also calculated inter-rater agreement with the intraclass correlation coefficient (ICC). The Kendall tau-b correlation coefficient was used to test the correlation between our color scale and the Mexameter MX 18. We obtained data from 258 newborns. Inter-observer agreement on color assignment was 83.2%. Median melanin index was significantly different among the 4 color groups, whereas erythema index and transcutaneous bilirubin were not.
Conclusions: Our proposed neonatal skin color scale correlates well with the melanin index at 24 h of life, increasing from colors 1 to 4, and the only chromophore different among our four color groups is melanin. Scale color assignment is reproducible. Therefore, it can be used to classify neonatal skin color. Further research is warranted to assess the clinical relevance of these findings.
Dr. Júlia Candel-Pau conceptualized and designed the study, designed the data collecting instruments, designed and created the new neonatal color scale, carried out the training of the staff in the use of the new scale, collected data, coordinated and supervised data collection, helped analyze the results of the study, helped draft the initial manuscript, and approved the final manuscript as submitted.
Dr. Jordi Garcia-Garcia helped in the initial study design, helped create the new neonatal color scale and collect data, reviewed and revised the manuscript, and approved the final manuscript as submitted.
Dr. Ana Maria Giménez-Arnau helped conceptualize and design the study, helped create the new neonatal color scale, reviewed and revised the manuscript, and approved the final manuscript as submitted.
Dr. María Ángeles López-Vílchez helped conceptualize and design the study, helped create the new neonatal color scale, collected data, reviewed and revised the manuscript, and approved the final manuscript as submitted.
Justification and objective of the study: Neonatal jaundice affects between 60-85% of term infants and is the most common cause of re-admission to the hospital in the neonatal period. Neonatal Units have protocols for the early detection of pathological jaundice (pathological hyperbilirubinemia). Pathological hyperbilirubinemia poses neonates at risk of developing acute bilirubin encephalopathy and kernicterus (chronic encephalopathy). Detecting pathological hyperbilirubinemia involves measuring total serum bilirubin. The non-invasive determination of transcutaneous bilirubin in neonates has been extensively validated and improved over recent years. It is cheap to use and it decreases the amount of blood samples. However, there still is some concern regarding its reliability depending on gestational age, skin color, and exposure to phototherapy (Grabenhenrich J 2014, Zecca E 2009). This study aims to validate a neonatal skin color scale of our own to later on determine the reliability of transcutaneous bilirubin determination depending on skin color, in order to improve early diagnosis of pathological hyperbilirubinemia and reduce economic costs and neonatal pain by reducing the amount of blood draws.
Visual variables are methods to translate information given in variables into many types of visualizations, including maps.Basic visual variables are color, size, and shape11.All of them can influence our perception and understanding of the presented information, therefore it is worth to understand when and how they can be used.
The use of visual variables on maps depends on two main things: (a) type of the presented variable, and (b) type of the map layer.Figure 6.1 shows examples of different visual variables.Color is the most universal visual variable.It can represent both qualitative (categorical) and quantitative (numerical) variables, and also we can color symbols, lines, or polygon fillings (sections 6.1.1 and 6.1.2).Sizes, on the other hand, should focus on quantitative variables.Small symbols could represent low values of a given variable, and the higher the value, the larger the symbol.Quantitative values of line data can be shown with the widths of the lines (section 6.2).The use of shapes usually should be limited to qualitative variables, and different shapes can represent different categories of points (section 6.3).Similarly, qualitative variables in lines can be presented by different line types.Values of polygons usually cannot be represented by either shapes or sizes, as these two features are connected to the geometries of the objects.
Colors, along with sizes and shapes, are the most often used to express values of attributes or their properties.Proper use of colors draws the attention of viewers and has a positive impact on the clarity of the presented information.On the other hand, poor decisions about colors can lead to misinterpretation of the map.Section 6.1.1 explains how colors are represented in R, how to decide which colors to use, and how to set different colors on maps.Section 6.1.2 focuses on how to specify color breaks and which types of scales styles are appropriate in different cases.
Colors in R are created based either on the color name or its hexadecimal form.R understands 657 built-in color names, such as "red", "lightblue" or "gray90", that are available using the colors() function.Hexadecimal form, on the other hand, can represent 16,777,216 unique colors.It consists of six-digits prefixed by the # (hash) symbol, where red, green, and blue values are each represented by two characters.In hexadecimal form, 00 is interpreted as 0.0 which means a lack of a particular color and FF means 1.0 and shows that the given color has maximal intensity.For example, #000000 represents black color, #FFFFFF white color, and #00FF00 green color.
Using a single color we are able to draw points, lines, polygon borders, or their areas.In that scenario, all of the elements will have the same color.However, often we want to represent different values in our data using different colors.This is a role for color palettes.A color palette is a set of colors used to distinguish the values of variables on maps.
Color palettes in R are usually stored as a vector of either color names or hexadecimal representations.For example, c("red", "green", "blue") or c("#66C2A5", "#FC8D62", "#8DA0CB").It allows every one of us to create our own color palettes.However, the decision on how to decide which colors to use is not straightforward, and usually requires thinking about several aspects.
Firstly, what kind of variable we want to show?Is it a categorical variable where each value represents a group or a numerical variable in which values have order?The variable type impacts how it should be presented on the map.For categorical variables, each color usually should receive the same perceptual weight, which is done by using colors with the same brightness, but different hue.On the other hand, for numerical variables, we should easily understand which colors represent lower and which represent higher values.This is done by manipulating colorfulness and brightness.For example, low values could be presented by a blue color with low colorfulness and high brightness, and with growing values, colorfulness increases and brightness decreases. 041b061a72