Junk Charts
This post is part 2 of an appreciation of the chart project by Google Newslab, advised by Alberto Cairo, on the gender and racial diversity of the newsroom. Part 1 can be read here. In the previous discussion, I left out the following scatter bubble plot. This plot is available in two versions, one for gender and one for race.
What Makes A Good Data Visualization?
Hi there. I’m David McCandless, creator of this site and author of two infographic mega-tomes, Information is Beautiful (2009) and Knowledge is Beautiful (2014). I’ve created a lot of data and information visualizations.
Best of the visualization web
At the end of each month I pull together a collection of links to some of the most relevant, interesting or thought-provoking web content I've come across during the previous month. Here's the latest collection from January 2018. Visualisations & Infographics
Visual Thinking
What is Visual Thinking? Visual thinking is a way to organize your thoughts and improve your ability to think and communicate. It’s a great way to convey complex or potentially confusing information. It’s also about using tools — like pen and paper, index cards and software tools — to externalize your internal thinking processes, making them more clear, explicit and actionable. Why is Visual Thinking important?
Visual Business Intelligence
We typically think of quantitative scales as linear, with equal quantities from one labeled value to the next. For example, a quantitative scale ranging from 0 to 1000 might be subdivided into equal intervals of 100 each. Linear scales seem natural to us. If we took a car trip of 1000 miles, we might imagine that distance as subdivided into ten 100 mile segments.
AnalyticsZone Blog
Guest post by Noah Iliinsky, IBM visualization luminary. This is a continuation of a series of posts covering the Four Pillars of Visualization. If you haven't done so already, please read the introductory post and the post, "Purpose: the bedrock of an effective visualization." Now that we have determined our purpose (the why of this visualization) we can start thinking about what we want to visualize. Our task is to include the relevant data (that which we know is useful) and to leave the rest out.
Data Underload
Most Common Occupation by Age As we get older, job options shift — along with experience, education, and wear on our bodies. Waiting For a Table
AnalyticsZone Blog
Guest post by Noah Iliinsky, IBM visualization luminary. This is a continuation of a series of posts covering the Four Pillars of Visualization. Please read my previous article , which describes these pillars as: purpose, content, structure and formatting. This post focuses on the first of these.
AnalyticsZone Blog
Guest post from Noah Iliinsky, Advanced Visualization Expert, IBM Center for Advanced Visualization This is the first of a series of five posts discussing the four pillars of successful visualizations. In this article I’ll introduce the four pillars and discuss why they’re in the order they’re in; and then in subsequent posts I’ll examine each pillar in depth and explain how to think about and use the concepts when building an effective data visualization. A successful visualization: > has a clear purpose and focus
AnalyticsZone Presents: Creating Effective Visualization Series with Noah Iliinsky
The world produces more than 2.5 exabytes of data every day. Visualization is one key approach to gaining insight from this flood of big data. Visualization makes data accessible, and is one of the best ways to analyze and understand the huge volumes that we're accumulating.
“Suppose there’s some connection”: Visualizing Character Interactions in Ulysses for Bloomsday 2013
For this year’s Bloomsday, Rhonda Armstrong, Regina Higgins, Steven Hoelscher, Pamela Andrews and I collaborated digitally to extend the Ulysses dataset and visualization work begun at THATCamp Prime 2012 (aka Bloomsday 2012). Rhonda, Regina, Steven, and Pamela each thoroughly scoured ten pages of the book to add to our knowledge about the network of character relationships in the novel, and I extended last year’s “Wandering Rocks” visualization (off of the data created by Chad Rutkowski and me in 2012), adding in weights showing the “depth” of each character interaction. A huge thank-you to Rhonda, Regina, Steven, and Pamela for their time and effort expanding the public dataset of Ulysses character interactions! So! Related: You can check out last year’s Bloomsday visualization work, or read the tutorials (1, 2) for making basic Gephi (infoviz software) visualizations that I created as part of my ACH Microgrant work.
