In Subtitles of color, Robert Simmon talks about how color was first efficiently used to denote numerical values by the cartographers. He extensively talks about the following aspect of usage of color in data visualization:Basic color theory- Ways the retina detects colors : the cells in our eyes respond to…
A NASA data visualization expert, Simmon explores best practices for color, grounding his presentation in the science of visual perception and visual culture. Simmon begins by stating that the purpose of data visualization is to illuminate patterns and relationships hidden in numbers. He illustrates this with an early map of…
In Subtleties of Color, Robert Simmon discusses the importance of color to show patterns and relationships that are otherwise hidden in a mass of numbers. He emphasizes how color is a tool to make visualizations more intuitive and successful. Throughout the series, Simmon discusses several types of data (sequential, divergent,…
blog/lecture by Robert Simmon (below summary) by Suzanna SchmeelkThe OpenViz lecture by Robert Simmon was very good. I was particularly fascinated by the different color pallets available to programmers: LCH versus RGB and others. LCH has a richer transition of color choices and can quite nicely be paired against…
It is important to give color its due worth of being able to intuitively tell the stories of data. Lightness (Black, white, Grayscales) , Hue (Color) and Saturation (sometimes known as Chroma, which is the saturation of color, using either color or grays to create these colors).Connecting color to meaning…
The Series Subtleties of Color by Robert Simmon gives us an overview of how color and data are related along with providing examples of and history behind color usages in data visualization. The author went over concepts such as Basic Color Theory, Color Components, Color Palette, the connection between colors…
Exercise 1: Final projects Group critique of your work Assignment Reading #2 on the Subtleties of Color Post your response with the tag ‘R2’ before the start of class Presentations by Felix & Janice and D'hana & Daniel…
Subtleties of Colorby Robert SimmonThe use of color to display data is a solved problem, right? Just pick a palette from a drop-down menu (probably either a grayscale ramp or a rainbow), set start and end points, press “apply,” and you’re done. Although we all know it’s not…
Look at Data: What Makes Bad Figures Bad: Kieran HealyHealy discusses the importance of considering 'how human visual perception works' while visualizing data. He describes why it is necessary to get into a habit of thinking about the relationship between the structure of data and the perceptual features of graphics.…
Misleading Axis on GraphsGood data visualization seems to be utilitarian in a way, perhaps devoid of taste and style. But after reading the intro to this essay, and after our discussion today, I feel that we all come equipped with a ‘point of view’ – as a designer and a viewer.…
DVIA WEEK 2 READING 1 (R1)Healy’s Look at What makes Data Bad:“Good taste might make things look better, but what we really need is to make better use of the data we have, or get new information and plot that instead. In these cases, even with good…
This reading teaches me how to determine data graph that is effective and which one is not. Especially this graph with heavy shadow, this graph is what I usually see often in school and universities; Furthermore, it is not only that, even the graph from Microsoft Excel is what we…
AssessmentI enjoyed how Kieran Healy discussed the successfulness of a visualization in terms of visual perception. In the past, I’ve done some reading on how to determine if a visualization is “satisfactory.” However, in those conversations that I’ve come across, the deciding factor centered on if the reader…
Look at Data: What Makes Bad Figures BadFrom my point of view (based on the fact that I do not really have any knowledge or experience of the data), one reason that penetrates this text is that “good data visualization methods” should be 1) as complex as necessary, and 2)…
Look at Data: What makes bad figures badBy giving a variety of examples, author explained what make a figure bad. According to the author, good and bad are not subjective but can be explained based on how human's visual perception works. Also, when design a figure, we need to think…
Presentations Zui on Ben Fry/Fathom Brad on Tufte’s Beautiful Information Suzanna on W.E.B. Du Bois Karen on Otto Neurath Reading #1 on the various types of ‘badness’ in data visualization Exercise 1: critique and offer directions for further development Assignment Incorporate the class’s feedback on…
The Principle of Proportional Ink was originally captured in Edward Tufte’s seminal book The Visual Display of Quantitative Information. In this book, he states that the vast majority of the ink used to create a graphic should be designated to the presentation of the data itself. In other words,…
In the essay “Misleading Axes on Graphs” by Carl Bergstrom and Jevin West, they describe the various ways people will misuse the axes of graphs when presenting data that in some cases, benefits the agenda of the visualization’s creator. For instance, they cite the now infamous line graph created…
Upon reading the chapter “What makes bad figures bad?” by Kieran Healy there were three key takeaways that he asserts are the primary categories where visualizations can get into the weeds when presenting data narratives: aesthetics - which he describes as “ugly or inconsistent design choices”, substantive - problems that…
The articles from the reading #1 give a general sense of how the data visualization can be used as a tool to tell different stories, and what are some typical mistakes that we should avoid when we are trying to visualize data, as indicated in "Bar chart axes should include…
The essay examines the guideline of the "principle of proportional ink" (closely tied to misleading axes) and defined as “when a shaded region is used to represent a numerical value, the area of that shaded region should be directly proportional to the corresponding value.” This idea was derived in Edward…
Data visualization is a way to help the viewer understands a complex or simple data by representing it in a visual context. In order to deliver the right information, it is important to know what makes it successful. First, graphs can be misleading by using the wrong scale, for example,…
Healy, Look at Data: What Makes Bad Figures BadHealy in his introductory chapter from Data Visualization for Social Science seeks to outline organizing principles for effective data visualizations. He first states that he believes the relationship between data and the perceptual features of graphics are more important than esthetics, and…
Healy's Look at Data: What Makes Bad Figures BadSometimes we look at an infographic and we know it's bad right away. But why? Healy calls out the importance of distinguishing the badness of a figure into three separate but useful categories:AestheticSimplicity and the removal of superfluous aesthetic junk is…
In Look at Data: What Makes Bad Figures Bad before digging into the detail Healy introduces the reader to the Good Chart concept. Specifically Healy outlines that "a really good or really graph cannot be boiled down to a list of simple rules", this discussion is similar to that presented…
Data visualization relies substantively on authors' choices of axes arrangement, such as truncation and scaling, as well as the shapes, including figure types and relative sizes. Apparently, if the obtained data has significant biases, fake information, or simply a large number of missing data, then the visualization would not be…
This is a tale of two visualizations --- one honest and one dishonest. According to the article, the bar chart was created by the German economic development agency GTAI. The article claims that bar charts should start at zero; however, line graphs do not need to do so. These graphs…
Look At Data: What Makes Bad Figures Bad Takeaways: The author argues that there are three varieties of bad graphs: Aesthetics, Substantive and Perceptual. On aesthetics, annoyingly there is evidence that highly embellished charts - like the Monstrous Costs example in the text - are often more easily recalled than…
Having just researched and written about Edward Tufte's Beautiful Evidence I have an immediate sense of relief reading the opening paragraph of this essay. This statement, particularly, resonated with me - "The graphs you make are meant to be looked at by someone. The effectiveness of any particular graph is…
1. Misleading Axes on GraphsThis essay focuses on the message that data visualizations can not only bring out important aspects of data but also conceal or mislead. I agree that subtle choices, such as the range of the axes in a bar chart or line graph can have massive impact…