Viz: Amanda Cox and countrymen chart the Facebook I.P.O.

Nice peek into the nyt process.

Amanda Cox and countrymen chart the Facebook I.P.O.

On Thursday Facebook had the third-largest I.P.O. ever. In the week leading up it, my colleague Amanda Cox spent some time thinking how to best explain and contextualize this offering to readers. What follows is a series of sketches from Amanda, who shared her project folder with me for this post, and Matt Ericson, who edited the piece.

The universe of initial public offerings is seemingly simple: about 2,400 tech companies since 1980, compiled by Jay Ritter, a professor of finance at the University of Florida.

As a first step, Amanda charted the companies by I.P.O. date (x-axis) and value at I.P.O. (y-axis), colored them by their 3-year return. (The key’s not included in her sketch, but for these purposes, know that red is bad and green is good.)


This chart’s not bad (even if, like me, you have low standards), but it doesn’t say much other than that there was a dot-com boom, that most of those companies didn’t do so well, and that Facebook is worth a ton of money.

Spotted: visualization for the analysis of gameplay data

A spatiotemporal visualization approach for the analysis of gameplay data

Günter Wallner, Simone Kriglstein

Contemporary video games are highly complex systems with many interacting variables. To make sure that a game provides a satisfying experience, a meaningful analysis of gameplay data is crucial, particularly because the quality of a game directly relates to the experience a user gains from playing it. Automatic instrumentation techniques are increasingly used to record data during playtests. However, the evaluation of the data requires strong analytical skills and experience. The visualization of such gameplay data is essentially an information visualization problem, where a large number of variables have to be displayed in a comprehensible way in order to be able to make global judgments.

Spotted: A new technique for comparing averages in visualizations

Comparing averages in time series data

Michael Correll, Danielle Albers, Steven Franconeri, Michael Gleicher

Visualizations often seek to aid viewers in assessing the big picture in the data, that is, to make judgments about aggregate properties of the data. In this paper, we present an empirical study of a representative aggregate judgment task: finding regions of maximum average in a series. We show how a theory of perceptual averaging suggests a visual design other than the typically-used line graph. We describe an experiment that assesses participants' ability to estimate averages and make judgments based on these averages. The experiment confirms that this color encoding significantly outperforms the standard practice.

Competition: International Science & Engineering Visualization Challenge

Help people understand science with apps, games, or visuals.