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Jerome Cukier

Jerome Cukier
Related:  Data Viz Trends

Infographic Of The Day: Is "The 1%" Inevitable, Given How Networks Work? | Co.Design The viral protest meme known as "Occupy Wall Street" is still going strong, and according to some provocative research to be published in PLoS One, it may never have reason to run out of steam. Why? Because "the 1%"--#OWS-speak for the tiny subgroup of wealthy interests that exerts outsize influence over "the 99%" comprising the rest of us--may be a mathematically inevitable consequence of the way networks self-organize. The paper, entitled "The network of global corporate control", is an attempt by complex systems theorists at the Swiss Federal Institute of Technology in Zurich to "go beyond ideology to empirically identify such a network of power," according to New Scientist. The study and infographic have generated lots of press and intense debate about the researchers’ methodology and assumptions (for example, that stock ownership is a proxy for direct influence/control). [Via New Scientist]

Blog Promising difficulties At the recent VisWeek conference, Jessica Hullman and her coauthors presented ”Benefitting Infovis with Visual Difficulties (pdf)”, a paper that suggests that the charts which are read almost effortlessly are not necessarily the ones that readers understand or remember best. To answer that claim, Stephen Few wrote a rather harsh critique of this paper (pdf). As I read this I felt the original paper was not always fairly represented, but more importantly, that the views develop by both parties are not at all inreconcilable. Let me explain. What is cognitive efficiency, or “say it with bar charts” For quite some time, we were told that to better communicate with data, we had to make visuals as clear as possible. The more complicated way of saying that is talking of “cognitive efficiency”. Various charts based on the same data points. For instance: bar charts are easier to process than pie charts, because it’s easier for the human eye to compare lengths than angles. Fair enough! Agreed!

chartsnthings 19 Sketches of Quarterback Timelines On Sunday Eli Manning started his 150th consecutive game for the Giants, the highest active streak in the NFL and the third-longest streak in NFL history. (One of the other two people above him is his brother, Peyton.) The graphics department published an interactive graphic that put Eli’s streak in the context of about 2,000 streaks from about 500 pro quarterbacks. The graphic lets you explore the qbs and search for any quarterback or explore a team to go down memory lane for your team. It’s not particularly important news, but the data provided by pro-football-reference is incredibly detailed and the concept lended itself to a variety of sketches. A couple bar charts in R. And percent of games started (the people are 100% are players like Andrew Luck or RGIII who just haven’t played a lot of seasons.) Ported to a browser, just using total starts: And share of total possible starts …or all the way back to 1970

Datavisualization feltron Mapping Neighborhoods in Boston, San Francisco and New York. Hand-drawn animation of 43 years of the Sun’s weather. (via kottke) William Stone Branching Drawings (identified by wowgreat) Geometric choropleths 1895 vs 1978 Review of the Visualizing Marathon Berlin 2011 on Datavisualization Students from the greater Berlin area gathered together on Saturday morning around 10am prepared to design and code away for the next 24 hours. The team behind didn’t leave any wishes open and prepared excellent working conditions at the selected event location Urania. After a brief welcome message from GE the students learned about the data set they will try to make sense of. The data consisted of German demographics and health care statistics. The Presentations Before the student started working, they had the chance to listen to two of Germany’s best visualizers Moritz Stefaner and Gregor Aisch. Moritz had prepared a packed deck of things that would have been helpful to know beforehand. Gregor followed with the presentation of his daily routine as a freelance information visualizer. The Works Soon after the presentations, the venue was filled with sketches on paper and whiteboards, laptops running calculations and enthusiastic people bouncing ideas off each other.

Latest As I mentioned in my previous post, our collaboration with the Sabeti Lab is aimed at creating new visual exploration tools to help researchers, doctors, and clinicians discover patterns and associations in large health and epidemiological datasets. These tools will be the first step in a hypothesis-generation process, combining intuition from expert users with visualization techniques and automated algorithms, allowing users to quickly test hypothesis that are “suggested” by the data itself. Researchers and doctors have a deep familiarity with their data and often can tell immediately when a new pattern is potentially interesting or simply the result of noise. In the last post, I went into some detail about the difficulties that arise when representing pairwise associations in a dataset that contains a mixture of numerical and categorical variables. There are many online materials on information theory and Shannon entropy, starting with the obligatory Wikipedia article.

Review: Designing Data Visualizations on Datavisualization In a recent chat with Jérôme Cukier about the state of visualization related literature, he mentioned Julie Steele and Noah Iliinsky’s new book “Designing Data Visualizations” published by O’Reilly. Jérôme noted that it would be a good primer for people who are already working with data and looking for guidance about making their work more accessible. I thought of another group of people who might find themselves overwhelmed by the amount of choices they have to make while working on visualizations: designers with little knowledge about visual perception and how to apply its’ principles to their work. After reading it from cover to cover in just a few hours I can highly agree with Jérôme’s recommendation. Julie and Noah manage to introduce the basics of visualization in a very accessible and comprehensible way. What the book taught me Before starting something new, for me it is always important to know what I’m dealing with and to have a general knowledge about it.