Small Multiples : Part 2 : Dendrogram Multiples

As promised, in this article I will demonstrate small multiples using dendrograms.  Dendrogram is derived from the Greek word “dendron” (which means “tree”) and “gamma” (which means “drawing”).  It literally means tree-drawing.

I’m not sure why we feel the need to use such fancy words for things.   At any rate, lets take a look at some small multiples of some tree-drawings.


If you remember from my previous blog entry, the presidential dataset depicts the relationship between political party, home state and president.  This dataset, when visualized with Dendogram Multiples in Dex, looks like:

Click on the image to view a live version


In contrast to the Chord Multiples in the previous blog entry, hierarchical relationships are much easier to see at the cost of a little extra space.  It’s not quite as colorful, nor as impactful on the eye as the Chord Multiples.  However, once the dust of the glamour and glitz of the data visualization settles, it’s all about effectively communicating information.  This works nicely in my opinion.  It’s also PowerPoint friendly.

It’s been my experience that most of the world-shaking decisions in an enterprise are being made by folks with a low tolerance for complexity.  They aren’t sifting through data in three dimensions with WebGL.  They aren’t interacting with a web page.   Often, they aren’t even looking at PowerPoint.  They have people to print such things out for them.  They most often looking at hard-copy in a board room, on a plane, or in a bar while waiting for a plane.

One of the nice aspects of multiples, is that it segregates complexity in a way that typically translates nicely to such static media.

College Football

Our college football dataset mapped college football teams to opponents and locations where the games were played in the 2012 season.  It is a much larger dataset, but effectively represented with this visual nonetheless.

Click on the image to view a live version


Wrapping Up

Next I will implement and explore an alternate small multiple.  I haven’t coded anything yet and haven’t decided where to go next.  I am considering:

  • Parallel Coordinates
  • Force Multiples
  • Line Chart Multiples
  • Area Chart Multiples
  • Pie Chart Multiples
  • Bar Chart Multiples
  • Column Chart Multiples
  • Steamgraph Multiples
  • Clustered Force Multiples
  • Node Link Multiples
  • Radar Multiples
  • Heatmap multiples
  • Map Multiples

Drop me a note and your vote will essentially drive my direction.  Anyway…that’s it for now.


About patmartin

I am a coder and Data Visualization/Machine Learning enthusiast.
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