Chicago Taxis

This weeks data was all about Chicago taxi usage… and more importantly the decline. No comparative data to Uber or Lyft, but one can only assume that the rise of the Ride Share ecosystem has negatively effected the taxis in large metro areas. This data set was large, so I really focused in on the […]

Chicago Taxis

G7 Employment Share and Growth

Another simple dataset this week focused on the “G7” and their share employment by county.  Mapping was my first thought but I thought that would be to simple. At work I have been utilizing Dan Montgomery’s published trick using MIN(1) to allow for separate formatting of elements. I still included a mapping element, but it […]

G7 Employment Share and Growth

Trump Tweets

I struggled with this one… I was ready to give up on my New Year’s Resolution because I despised the data set and subject.  I found a reason to Viz; learning. I’ve been meaning to do something with polygons and custom maps but had never found the right opportunity. I wanted to say something political, […]

Trump Tweets

Annual iPhone Sales

Week 2 of #MakeoverMonday gave us iPhone sales. The story line was that 2016 was the first decline in total sales. I added the quarter in to the data and you will see that Q1 sales were up slightly, so early adopters continued to grow. The mid to late adopters were weaker, and I would […]

Annual iPhone Sales

Australian Wage Gap

My 2017 resolution… complete all 52 #MakeOverMondays Week 1 – The Australian Wage Gap So may angles to look at this… but at the level of detail the data sat at, the sampling per occupation skewed the wage gaps. So I limited the data to occupations that had more than 8000 people in this positions, […]

Australian Wage Gap