Weekly Data Viz Decomp: The Guardian’s Premier League Transfer Window Summer 2016

Weekly data visualization decomps to keep a look out for technique and learning.

This week’s viz: Premier League: transfer window summer 2016 – interactive

Decomposition of a Visualization:

  • What are the:
    • Variables (Data points, where they are, and how they’re represented):
      • Bubble size for size of transfer
      • Color hue denoting transfer or out of team
      • Position for date close to transfer window
    • Data Types (Quantitative, Qualitative, Categorical, Continuous, etc.):
      • Qualitative and categorial
    • Encodings (Shape, Color, Position, etc.):
      • Shape, position, size, color hue
  • What works well here?
    • Showing a small multiples type view for each team and their transfers
  • What does not work well and what would I improve?
    • Having the totals summary numbers on the side of the charts is a little unorthodox and unintuitive
    • Bubbles seem to be placed arbitrarily without thought to the y-axis, even though the x-axis has meaning
    • Not immediately clear why some players are featured and noted in tooltips versus those that are not
  • What is the data source?  Do I see any problems with how it’s cited/used
    • Seems to be original Guardian data collected about the English Premier League, but not as clearly stated as I’d like to expect
  • Any other comments about what I learned?
    • Example of something pleasing to the eye in terms of color hue and perhaps some flash factor, but perhaps not that functional to explore upon closer examination.
      • Certainly sense for the purposes of the Guardian though in putting out this story and is a technique I’d borrow if I had a use case
      • Good for showing a bigger picture view
    • Probably not worth it in terms of the work it would be taken incrementally as filters are difficult to work and can be computationally expensive, but the nerd in me would have liked to search for the player

Weekly Data Viz Decomp: Your City’s Kickstarter Scene Visualized

Starting off with these weekly to keep a look out for technique and learning.

This week’s viz: Your City’s Kickstarter Scene Visualized

Decomposition of a Visualization:

  • What are the:
    • Variables (Data points, where they are, and how they’re represented):
      • Cities and founding for Kickstarter projected, shown in packed bubbles
      • Barchart breakout breakout with heat using color hue categorized in a data table
    • Data Types (Quantitative, Qualitative, Categorical, Continuous, etc.):
      • Quantitative
    • Encodings (Shape, Color, Position, etc.):
      • Shape, position, size, color hue
  • What works well here?
    • Showing size of funding relative to each other in a large scope, used with the size of the bubbles for individual projects in each city and the packed bubble sizes comparing city to city
  • What does not work well and what would I improve?
    • Would want to be able to filter more, by number of backers for instance to improve explorability and hide the bubbles
  • What is the data source?  Do I see any problems with how it’s cited/used?
    • Kickstarter data, straight from the source
  • Any other comments about what I learned?
    • Good example of a lot of data put into one place to be compared with relative ease
    • The analysis the goes with it is critical because most viewers will not spend enough time to do the analysis themselves to glean the ton of insight here