13:57 PM

2016 Detroit Jazz Fest Moods Network

One of the great events of the summer in Detroit is the annual Detroit Jazz Festival, an epic event in the jazz world, with many of the world's foremost musicians convening in Detroit for an entirely free set of performances. It is in fact the world's largest free jazz festival, and may well be the best jazz weekend regardless of price.

For 2016, the festival plays host to the likes of the legendary bassist Ron Carter, Brad Mehldau, John Scofield, Randy Weston, Billy Harper, and a host of other musicians both international and local. So I thought it fitting to blend my love of jazz with my affection for network graphs, by using user tags from the All Music website. These tags are labels given to each musician based on listener perceptions of their work, and provide interesting information to use in building a graph.

The initial graph creation was done in Gephi, followed by deployment using sigma.js, which allows us use the web to probe and explore the graph to find interesting patterns in the data. The Force Atlas 2 algorithm was used to create the layout, with the nodes colored based on their modularity class, a form of clustering based on similar characteristics. When clustering works very well, nodes of the same color will stand apart from other color groups; in this instance, we are partially successful in this regard. We'll learn more about this shortly.

For those who want to interact with the network and draw your own conclusions, here you are:


Let's start with a view of the entire network:

Jazz Moods Network

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14:45 PM

Visualizing CEPA Education Data, Part 1

In this post, we'll begin walking through the massive educational achievement dataset provided through the CEPA project at Stanford University (Sean F. Reardon, Demetra Kalogrides, Andrew Ho, Ben Shear, Kenneth Shores, Erin Fahle. (2016). Stanford Education Data Archive. http://purl.stanford.edu/db586ns4974). This archive provides a wealth of data on educational achievement across grade levels and academic years, and is supported with a vast array of socioeconomic indicators that can be used for deeper analysis.

Our initial steps to analyze and visualize the data will begin with Microsoft Excel for data prep (use your tool of choice) before moving on to Exploratory and Trelliscope for visual analysis of the data. Each of these powerful tools are based on R, the powerful open source statistical framework that will facilitate multiple analytical paths. Exploratory has its own GUI that uses many of R's most powerful analytic packages, while Trelliscope will be employed from within RStudio.

Here's a link to Exploratory:

and Trelliscope:

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