One of the great things about working at Boulder Insight is our enthusiasm for participating in the Tableau practice community and pushing ourselves to learn more about this tool everyday.  This year’s IronViz was a great chance to do both. We had a lot of fun making entries ourselves and learning from our peer group.  Here are a few short notes on my entries:

Chinese Tattoo Generator

The topic of the Iron Viz came up on a Friday afternoon.  Almost on a dare, we challenged ourselves to see who could make *something* the fastest.  Browsing through Google Fusion tables, I found a set of data about Chinese characters.  With a curiosity to understand how Tableau handled other character sets and remembering other Chinese Tattoo Generators online and images of “Chinese Tattoo Fail”, I put this together as a fun test in a few minutes.

Chinese_Tattoo_Generator

 

 Spurious Relationship Creator

After a little Friday afternoon fun, I tried to dig deeper into what was possible with Wikipedia data.  After reviewing hundreds of data sets easily accessible via Google Fusion Tables, I settled on a series of tables that dealt with U.S. State and Territory level data.  With a little manual work, I pulled in about 28 tables, applied some basic cleanup and transforms and imported in Tableau.  The result was a large data set with about 50 dimensions and 125 measures across 28 topics.  I wanted to be able to quickly cursor through all of the combinations to see if any relationships jumped out at me.  That’s where the “Spurious Relationship Creator” came in.  I laid out some basic vizzes and made one version that allowed a user to examine any dimension against any measure and check for obvious relationships, spurious or not.  The same was done to allow any two measures to be bumped up against each other.  Here’s a snapshot of the first:

Spurious_Relationship_Creator

 

Using this tool, it was fairly easy to find some relationships worthy of further investigation.  A few other dashboards were made, but the most interesting relationships were between the Median Household Income by State and Education, Obesity and Life Expectancy metrics.

SRC_Impact_of_Income

 

Those were my entries into the competition.  Another one called, the 50ish States of Wikipedia, didn’t make the cut, but it still provided a little education about the location and types of territories as well as a nice way to look at the different time zones in use by Americans:

50ish States of Wikipedia

50ish_States_of_Wikipedia

Chris and I collaborated on a “Profiles of States” dashboard.  This pulled in several different data sets about Flags, State Names, State Quarters as well as a few geographic elements like elevation and record temperature.  See if you can spot the Easter Egg.

Profiles of American States and Territories

State_Profiles

 

Thanks for reading,

William Aubrey

 

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