(In my last post I introduced the idea of regularly posting research material in this blog as a way to bridge the gap between researchers and practitioners. Some people kindly replied to my call for feedback and the general feeling seems to be like: “cool go on! rock it! Even if I am definitely not a veteran of infovis research (far from it) I started reading my first papers around the year 2000 and since then I’ve never stopped. come from the very early days of infovisare foundationalare cited over and overI like a lot Of course this doesn’t mean these are the only ones you should read if you want to dig into this matter. Advice: in order to really appreciate them you have to think they have all been written during the ’90s (some even in the ’80s!). Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods. Please don’t tell me you don’t know this one! What’s in it? The Structure of the Information Visualization Design Space. What’s in it?
Related: Visual information resources
Data StoriesI have a goal in life: To rid the world of bad PowerPoint slides. We’ve all sat through meetings, struggling to stay awake during presentations filled with cheesy stock images, confusing bar graphs, and pie chart after pie chart. This needn’t be so. At Google, I teach everyone from marketers to engineers some basic principles of data visualization that help them turn numbers into compelling visual stories. Presenting data creatively can make numbers seem more human and turn statistics into stories. Here are a few of the most resonant lessons that I teach in ‘Data Visualization 101’ at Google. Don’t be Misleading Context will have an impact on how people interpret the information you’re providing. Don’t be a Data Fashion Victim Just because your software has plenty of bells and whistles doesn’t mean they all have to be used. “Just because your software has plenty of bells and whistles doesn’t mean they all have to be used.” Highlight what’s Important Simple Beats Sexy Use Color Strategically
An Illustrated Tour of the Pie Chart Study Results – eagereyesIn two papers, Drew Skau and I recently showed that our idea of how we read pie charts is wrong, that donut charts are no worse than pie charts, and a few more things. Here is a detailed walk-through of the results of the three studies we conducted for this purpose. Let’s go on a little journey through some real data and do a little science together! For my talk at Information+, I redid the figures we had used in the EuroVis pie chart papers, both for the papers themselves and for the presentations. The result is much clearer, I think. I figured I’d share them here since they give a nice walk-through of the study results using the real data, but without too much detail. How the Charts Work What I’m about to show are the results of three studies, each of which had about 80–100 participants who each answered about 60 questions (for details see the papers). The charts are all based on the difference between what people thought they were seeing and what we were showing them – called error.
Stephen Few: Information Visualization Research Projects that Would Benefit PractitionersIn a previous blog post titled “Potential Information Visualization Research Projects,” I announced that I would prepare a list of potential research projects that would address actual problems and needs that are faced by data visualization practitioners. So far I’ve prepared an initial 33-project list to seed an ongoing effort, which I’ll do my best to maintain as new ideas emerge and old ideas are actually addressed by researchers. These projects do not appear in any particular order. My intention is to help practitioners by making researchers aware of ways that they can address real needs. Some of the projects that appear in the Effectiveness and Efficiency Tests section have been the subject matter of past projects. Please feel free to respond to this blog post or to me directly at any time with suggestions for additional research projects or with information about any projects on this list that are actually in process or already completed. Effectiveness and Efficiency Tests
Fell in Love with Data — Data Visualization EvangelismThe Golden Age of Statistical GraphicsMichael Friendly. The Golden Age of Statistical Graphics. Ststistical Science, vol. 23, no. 4, pp. 502–535, 2008. Download: from web | via doi Statistical graphics and data visualization have long histories, but their modern forms began only in the early 1800s. Keywords: data visualization; statistics; history; smoothing; thematic cartography; Francis Galton; Charles Joseph Minard; Florence Nightingale; Francis Walker;Information Visualization Research as Pseudo-Science - Perceptual Edge Discussion ForumDespite the unnecessarily aggressive tone, critiques like this are important for the advancement of science. I am looking forward to the authors' responses and I hope this critique will start an insightful and constructive debate. Although I haven't had the chance to read the paper yet, the part on statistical unreliability seems exaggerated irrespective of the paper's content. The proper choice of sample size does depend on several factors. A few are already mentioned, but the most important of all is effect size. It is possible to show strong evidence for a large effect with very few participants. Sample representativeness is also important to consider, but no researcher tries to collect a truly random sample of the entire world's population, including in cognitive sciences where "the scientific method" is used. Power analysis can improve study design, but it is hard to put in practice and is not without problems. __________________Pierre Dragicevic
Color Theory Quick Reference PosterIt’s always good to be able to articulate design choices to your clients; why you put something where, why you chose the color scheme you did, etc. This knowledge is one of the biggest differences between a designer and a non-designer. But there is a lot to remember when it comes to the realm of graphic design – so much so that it’s pretty much impossible to remember everything from all the theories of graphic design, to web design best practices to Photoshop keyboard shortcuts. With that in mind, I decided it would be useful to have all of the basics of color theory contained in one place – specifically, a cool infographic-esque poster. This way, I can quickly reference things that may have slipped to the back of my mind since design school. *Edit: we now have an Elements of Design Quick Reference Poster as well as a Principles of Design Quick Reference poster too! The idea is that this graphic can be either printed out or used as a desktop wallpaper. The graphic contains info on:
UW Interactive Data Lab | PapersWe present Vega-Lite, a high-level grammar that enables rapid specification of interactive data visualizations. Vega-Lite combines a traditional grammar of graphics, providing visual encoding rules and a composition algebra for layered and multi-view displays, with a novel grammar of interaction. Users specify interactive semantics by composing selections. In Vega-Lite, a selection is an abstraction that defines input event processing, points of interest, and a predicate function for inclusion testing. Selections parameterize visual encodings by serving as input data, defining scale extents, or by driving conditional logic. Software release expected in Fall 2016.
Software Engineering for Big Data Systems Guest Editors" Introduction | April 2016 Theme - IEEECSGuest Editors' Introduction • Ian Gorton, Ayse Basar Bener, and Audris Mockus • April 2016 We edited a special issue on “Software Engineering for Big Data Systems” for the March/April 2016 issue of IEEE Software magazine. The issue focused on big data’s implications for software engineering and five categories of design requirements for building such systems: pervasive distribution; write-heavy workloads; variable request workloads; computation-intensive analytics; and high availability. Designed as pervasive distributed systems, big data systems must consider quality attributes such as reliability, scalability, transparency, and performance. They should be designed for fault tolerance, high consistency, high availability, and ability to embrace contextual changes. As an extension of that special issue, we present the April 2016 theme for Computing Now. In this Issue Andrea Rosa, Lydia Y. Conclusion Guest Editors