New Visualizations coming to metaLayer

For those of you wondering why there are so few charts and visualizations in our product, it’s because we’re building our own visualization libraries.  So that the data you’re mashing up and analyzing can be quickly summarized in unique ways that are also nice to look at. Here is a sneak peak at things to come.

The stacked area graph is a common chart in many programs.  The concept is simple, the columns represent periods of time, while the colors represent one measure of value, and the area of the stacked shapes represents another.

Each stack (column) could be thought of as a phase of a project, while the horizontal stripes are some value being measured in that time.

One that harkens back to my days as an audio engineer (because it looks like a sine wave). Using the same concepts illustrated above, this densely packed visualization can allow you to look at a set of information, a subset of that information, and compare it against a completely disparate type of information (the line in the background). It’s best when used with excessive datasets to spot trends over time.
The Venn diagram is common as a static graphic, but by scaling the area of the common and uncommon space along with the position of the circles, we aim to make this dynamic. An example, if you hav a real-time dataset of two keywords, Circle 1 might represent one dataset, Circle 2 might represent the other, and the overlap would represent anything in the stream containing both keywords.  What’s being measured would be a variable.
This one is based on an infographic I created back in 2009.  Statisticians hate it, because its been established that they hate circles.  However, if you just want a quick comparison this is, in my opinion, almost as effective as a pie chart and more interesting visually.

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About me

metaLayer is making the world's information easier to understand, visualize and share with products that change the way normal people think of data. We remove the barriers that exist around big data tools - such as machine learning and sentiment analysis - allowing anyone to drag and drop their way to a visual understanding of information.