andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-816 knowledge-graph by maker-knowledge-mining
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Introduction: By now you all must be tired of my one-sided presentations of the differences between infovis and statgraphics (for example, this article with Antony Unwin). Today is something different. Courtesy of Martin Theus, editor of the Statistical Computing and Graphics Newsletter, we have two short articles offering competing perspectives: Robert Kosara writes from an Infovis view: Information visualization is a field that has had trouble defining its boundaries, and that consequently is often misunderstood. It doesn’t help that InfoVis, as it is also known, produces pretty pictures that people like to look at and link to or send around. But InfoVis is more than pretty pictures, and it is more than statistical graphics. The key to understanding InfoVis is to ignore the images for a moment and focus on the part that is often lost: interaction. When we use visualization tools, we don’t just create one image or one kind of visualization. In fact, most people would argue that there is
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1 By now you all must be tired of my one-sided presentations of the differences between infovis and statgraphics (for example, this article with Antony Unwin). [sent-1, score-0.648]
2 It doesn’t help that InfoVis, as it is also known, produces pretty pictures that people like to look at and link to or send around. [sent-4, score-0.297]
3 The key to understanding InfoVis is to ignore the images for a moment and focus on the part that is often lost: interaction. [sent-6, score-0.134]
4 When we use visualization tools, we don’t just create one image or one kind of visualization. [sent-7, score-0.358]
5 In fact, most people would argue that there is not just one perfect visualization configuration that will answer a question. [sent-8, score-0.389]
6 The process of examining data requires trying out different visualization techniques . [sent-9, score-0.378]
7 Antony Unwin and I give a statisticians’ view: Quantitative graphics, like statistics itself, is a young and immature field. [sent-12, score-0.269]
8 Methods as fundamental as histograms and scatterplots are common now, but that was not always the case. [sent-13, score-0.193]
9 More recent developments like parallel coordinate plots are still establishing themselves. [sent-14, score-0.408]
10 Within academic statistics (and statistically-inclined applied fields such as economics, sociology, and epidemiology), graphical methods tend to be seen as diversions from more “serious” analytical techniques. [sent-15, score-0.48]
11 Statistics journals rarely cover graphical methods, and Howard Wainer has reported that, even in the Journal of Computational and Graphical Statistics, 80% of the articles are about computation, only 20% about graphics. [sent-16, score-0.431]
12 Outside of statistics, though, infographics and data visualization are more important. [sent-17, score-0.379]
13 Graphics give a sense of the size of big numbers . [sent-18, score-0.089]
14 ) Both articles are in the current issue of the newsletter (pages 5-8 for Kosara and 9-12 for Antony and me). [sent-22, score-0.328]
15 I’ll give my reactions to Kosara’s article in a future post. [sent-23, score-0.152]
16 For now, I wanted to link to this discussion, which I think should interest many of you. [sent-24, score-0.072]
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same-blog 1 0.99999994 816 andrew gelman stats-2011-07-22-“Information visualization” vs. “Statistical graphics”
Introduction: By now you all must be tired of my one-sided presentations of the differences between infovis and statgraphics (for example, this article with Antony Unwin). Today is something different. Courtesy of Martin Theus, editor of the Statistical Computing and Graphics Newsletter, we have two short articles offering competing perspectives: Robert Kosara writes from an Infovis view: Information visualization is a field that has had trouble defining its boundaries, and that consequently is often misunderstood. It doesn’t help that InfoVis, as it is also known, produces pretty pictures that people like to look at and link to or send around. But InfoVis is more than pretty pictures, and it is more than statistical graphics. The key to understanding InfoVis is to ignore the images for a moment and focus on the part that is often lost: interaction. When we use visualization tools, we don’t just create one image or one kind of visualization. In fact, most people would argue that there is
Introduction: Our discussion on data visualization continues. One one side are three statisticians–Antony Unwin, Kaiser Fung, and myself. We have been writing about the different goals served by information visualization and statistical graphics. On the other side are graphics experts (sorry for the imprecision, I don’t know exactly what these people do in their day jobs or how they are trained, and I don’t want to mislabel them) such as Robert Kosara and Jen Lowe , who seem a bit annoyed at how my colleagues and myself seem to follow the Tufte strategy of criticizing what we don’t understand. And on the third side are many (most?) academic statisticians, econometricians, etc., who don’t understand or respect graphs and seem to think of visualization as a toy that is unrelated to serious science or statistics. I’m not so interested in the third group right now–I tried to communicate with them in my big articles from 2003 and 2004 )–but I am concerned that our dialogue with the graphic
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